EPA/600/R-10/182 | September 2010 | www.epa.gov/ord

San Luis Basin Sustainability 

Metrics Project: A Methodology 

for Evaluating Regional 

Sustainability

Office of Research and Development

National Risk Management Research Laboratory
Front Cover Photo Credits

Top - Medano Creek; Photo M. Hopton; Intermittent flow of Medano Creek in the Great Sand Dunes National Park and Preserve, 

San Luis Valley, Colorado. 

Bottom Left - solar panel close-up; Photo by A. Karunanithi; Solar panel at a solar farm under construction in the San Luis Valley. 

Bottom Right - SLV Potato Harvest; Photo by Stephen Ausmus; Potatoes being harvested in the San Luis Valley of south-central 

Colorado. Rotating potatoes with cover crops provides many benefits, including nitrogen management, improved soil and water 

quality, and bigger potatoes and higher yields. http://www.ars.usda. 
Office of Research and Development

National Risk Management Research Laboratory

EPA/600/R-10/182

September 2010

San Luis Basin Sustainability

Metrics Project: A Methodology

for Evaluating Regional Sustainability

Edited by

Matthew T. Heberling

Matthew E. Hopton

Authors

Heriberto Cabezas

Daniel Campbell

Tarsha Eason

Ahjond S. Garmestani

Matthew T. Heberling

Matthew E. Hopton

Joshua Templeton

Denis White

Marie Zanowick

National Risk Management Research Laboratory

Office of Research and Development

U.S. Environmental Protection Agency

and

Richard T. Sparks

U.S. Department of Agriculture
ii

Notice

The views expressed herein are strictly the opinions of the authors and in no manner represent or 

reflect current or planned policy by the federal agencies. Mention of trade names or commercial 

products does not constitute endorsement or recommendation for use. The information and data 

presented in this product were obtained from sources that are believed to be reliable. However, in 

many cases the quality of the information or data was not documented by those sources; therefore, 

no claim is made regarding their quality.
iii

Table of Contents

Notice ...................................................................................................................................................ii

List of Tables ........................................................................................................................................iv

List of Figures ......................................................................................................................................x

List of Abbreviations ..........................................................................................................................xv

Foreword ............................................................................................................................................xvi

Authors, Contributors, and Reviewers ..............................................................................................xvii

Acknowledgements .............................................................................................................................xx

Abstract ..............................................................................................................................................xxi

Executive Summary ..........................................................................................................................xxii

ES.1 Introduction .......................................................................................................................xxii

ES.2 Sustainability Metrics .......................................................................................................xxii

ES.3 Ecological Footprint ........................................................................................................xxiii

ES.4 Green Net Regional Product ............................................................................................xxiv

ES.5 Emergy .............................................................................................................................xxiv

ES.6 Fisher Information and Order .......................................................................................... xxvi

ES.7 The San Luis Basin Sustainability Metrics Project: Results, Conclusions, and 

Recommendations ....................................................................................................................xxvii

ES.8 References ........................................................................................................................xxix

1.0 Introduction .................................................................................................................................... 1

1.1 Concept of Sustainability ........................................................................................................ 1

1.2 The San Luis Basin Sustainability Metrics Project ................................................................ 2

1.2.1 Measuring Sustainability ............................................................................................... 2

1.2.2 Choice of Region .......................................................................................................... 3

1.2.2.1 Description of the San Luis Basin ................................................................... 4

1.2.3 Community Outreach ................................................................................................... 4

1.3 Outline of Report .................................................................................................................... 6

1.4 References ............................................................................................................................... 6

2.0 Sustainability Metrics .................................................................................................................... 9

2.1 Introduction ............................................................................................................................. 9
iv

2.2 Literature Review of Ecological, Economic, Energy, and System Order Metrics .................. 9

2.2.1 Ecological Sustainability .............................................................................................. 9

2.2.2 Economic Metrics........................................................................................................ 10

2.2.3 Energy.......................................................................................................................... 12

2.2.3.1 Exergy Analysis............................................................................................. 12

2.2.3.2 Emergy Analysis............................................................................................ 12

2.2.4 System Order .............................................................................................................. 13

2.3 Sustainability Studies That Use Multiple Metrics................................................................. 14

2.4 Choice of Metrics for San Luis Basin Sustainability Metrics Project................................... 15

2.4.1 Components Basic to Environmental Systems ........................................................... 16

2.5 References.............................................................................................................................. 17

3.0 Ecological Footprint..................................................................................................................... 25

3.1 Introduction............................................................................................................................ 25

3.2 Methods ................................................................................................................................. 26

3.3 Data, Variables, and Sources.................................................................................................. 26

3.4 Results.................................................................................................................................... 27

3.5 Discussion.............................................................................................................................. 28

3.5.1 Biocapacity (supply).................................................................................................... 28

3.5.2 Ecological Footprint (demand).................................................................................... 28

3.5.3 Global Hectares........................................................................................................... 28

3.5.4 Examination of Sustainability ..................................................................................... 29

3.5.5 Strengths and Weaknesses of Methodology ................................................................ 29

3.6 References.............................................................................................................................. 30

Appendix 3-A: Estimation of Energy Use and Determination to Use EIA State Level Data..... 39

Appendix 3-B: Ecological Footprint Accounting Ledger for 1980 in the San Luis Basin......... 42

Appendix 3-C: Twenty-six Years (1980-2005) of Data for the San Luis Basin, Colorado ......... 44

Appendix 3-D: Ecological Footprint Analysis for the United States, 1980-2005 ...................... 51

4.0 Green Net Regional Product ........................................................................................................ 53

4.1 Introduction............................................................................................................................ 53

4.2 Model..................................................................................................................................... 53

4.3 Data and Sources ................................................................................................................... 53

4.4 Calculation Methodology ..................................................................................................... 55

4.5 Results ................................................................................................................................... 56

4.6 Discussion.............................................................................................................................. 57

4.7 Conclusion ............................................................................................................................. 59
v

4.8 References.............................................................................................................................. 59

Appendix 4-A: Wind Erosion Calculations................................................................................ 67

5.0 Emergy......................................................................................................................................... 77

5.1 Introduction............................................................................................................................ 77

5.2. Methods ................................................................................................................................ 78

5.2.1 Boundaries................................................................................................................... 78

5.2.2. Energy Systems Models ............................................................................................. 78

5.2.3 Emergy Analysis ......................................................................................................... 78

5.2.4. Data and Sources........................................................................................................ 79

5.2.5 Summary Tables and Indices....................................................................................... 80

5.3 Results ................................................................................................................................... 80

5.3.1 The Detailed Energy Systems Model of the San Luis Basin....................................... 80

5.3.2 Aggregate Diagram...................................................................................................... 80

5.3.3 Total Emergy and Percent Renewable Use.................................................................. 80

5.3.4 Summary of Renewable and Non-renewable Emergy ............................................... 81

5.3.5 Summary of Import-Export Exchange ........................................................................ 81

5.3.6 Imported Emergy......................................................................................................... 81

5.3.7 Exported Emergy......................................................................................................... 81

5.3.8 Population and per Capita Indices............................................................................... 82

5.4 Discussion.............................................................................................................................. 82

5.4.1 Spatial Boundaries....................................................................................................... 82

5.4.2 Data Availability.......................................................................................................... 82

5.4.3 Renewable Inputs to the SLB...................................................................................... 83

5.4.4 Temporal Patterns of Development and the Water Cycle............................................ 83

5.4.5 Nonrenewable Resources ............................................................................................ 83

5.4.6 Patterns of Import and the Emergy Budget of the SLB............................................... 83

5.4.7 Patterns of Export........................................................................................................ 84

5.4.8 Export-Import Exchange ............................................................................................. 84

5.4.9 Patterns of Population Growth and Consumption....................................................... 84

5.4.10 Analysis of the Emergy Indices ................................................................................ 85

5.4.11 Effects of Missing Data, Unknown Information, and Uncertainty............................ 85

5.4.12 Strengths and Limitations of the Method .................................................................. 86

5.5 Conclusions............................................................................................................................ 86

5.6 References.............................................................................................................................. 87

Appendix 5-A: Detailed Steps for Emergy Analysis................................................................ 113

Appendix 5-B: Additional Indices Calculated for the Emergy Analysis of the 

San Luis Basin ........................................................................................................................... 114

Appendix 5-C: Calculating Emergy per Unit Factors ............................................................... 118

Appendix 5-D: Further Description of the San Luis Basin Regional Model ............................ 139
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Appendix 5-E: Emergy Analysis Tables For Renewable Inflows, Renewable Production, 

Nonrenewable Productions And Use, Imports And Exports, And Additional Figures Not 

Included In Body of Report ...................................................................................................... 141

Appendix 5-F: Data Quality of the Purchased Data .................................................................. 149

Appendix 5-G: Miscellaneous Indices and Results ................................................................... 150

6.0 Fisher Information and Order .................................................................................................... 163

6.1 Introduction .......................................................................................................................... 163

6.1.1 Capturing Dynamic Shifts with Fisher Information .................................................. 163

6.2 Methods ............................................................................................................................... 164

6.2.1 Theory ........................................................................................................................ 164

6.2.2 Calculation Methodology .......................................................................................... 164

6.2.3 Interpreting Fisher Information ................................................................................. 166

6.2.4 Computing Fisher Information: A Simple Example ................................................. 167

6.3 Computing FI for the San Luis Basin: Data and Sources .................................................... 168

6.4 Calculation Methodology .................................................................................................... 168

6.5 Results .................................................................................................................................. 168

6.6 Discussion ............................................................................................................................ 169

6.7 Strengths and Weaknesses ................................................................................................... 169

6.8 References ............................................................................................................................ 170

Appendix 6-A: Derivation of Fisher Information .................................................................... 183

Appendix 6-B: Sensitivity Analysis on Integration Window Parameters .................................. 186

Appendix 6-C: San Luis Basin Fisher Code User Manual – Utilizing MATLAB Files 

(Nfisher And CalcMeanFisher) to Calculate Fisher Information .............................................. 192

Appendix 6-D: Using MATLAB to Compute FI for the Simple Example ............................... 196

Appendix 6-E: Fisher Information MATLAB Code .................................................................. 198

7.0 The San Luis Basin Sustainability Metrics Project: Results, Conclusions, and 

Recommendations ............................................................................................................................. 223

7.1 Introduction .......................................................................................................................... 223

7.2 Summary of Calculations .................................................................................................... 223

7.3 Holistic Results for San Luis Basin .................................................................................... 223

7.4 Strengths of Methodology .................................................................................................. 225

7.5 Limitations of Methodology ............................................................................................... 226

7.6 Recommendations for Future Research on Regional Sustainability Metrics ...................... 228

7.7 Conclusions .......................................................................................................................... 228

7.8 References ............................................................................................................................ 229
vii

List of Tables

Table 2.1 – Broad categorical list of data collected on key variables, showing which metric used the 

variable, the source of the data, the scale at which the data were collected, and the available years of 

data reported ....................................................................................................................................... 23

Table 3.1 – Scale and source of data for the variables used to calculate ecological footprint for the 

SLB..................................................................................................................................................... 33

Table 3.2 – Ecological footprint and biocapacity in the San Luis Basin, Colorado, 1980-2005. 

Twelve percent of the biocapacity area was subtracted from the total biocapacity to protect 

biodiversity ......................................................................................................................................... 35

Table 3-B.1 – Ecological footprint accounting ledger for food consumption for 1980 in the San Luis 

Basin. Local Yield and Production are italicized because they were not needed to compute apparent 

consumption. Global yields are from Chambers et al. (2000)........................................................... 42

Table 3-B.2 – Ecological footprint accounting ledger for energy consumption for 1980 in the San 

Luis Basin. Global average energy to land ratio was used to convert joules to hectares (Chambers et 

al. 2000) .............................................................................................................................................. 43

Table 3-B.3 – Ecological footprint accounting ledger for supply and demand of bioproductive land 

for 1980 in the San Luis Basin. Equivalence factors are from McIntyre et al. (2007) and Chambers 

et al. (2000) and were treated as constants for the 26 years ............................................................... 43

Table 3-C.1 – Twenty-six years (1980-2005) of data for the San Luis Basin, Colorado. Italicized 

values were estimated using linear interpolation................................................................................ 44

Table 4.1 – Green Net Regional Product (GNRP): Variables, Purpose, and Data Sources............... 61

Table 4.2 – Estimates of all components of Green Net Regional Product (GNRP) ........................... 64

Table 4-A.1 – Approximate distribution of crops between the two soil types (Based on Soil 

Erodibility Index, I) ............................................................................................................................ 69

Table 4-A.2 – Crop rotations included in soil erosion calculation...................................................... 69

Table 4-A.3 – Total acreage by crop rotation and Soil Erodibility Index........................................... 70

Table 4-A.4 – Average annual wind erosion by crop rotation: practicing mulch or noninversion 

tillage................................................................................................................................................... 72

Table 4-A.5 – Average annual wind erosion by crop rotation: conventional, moldboard plow ......... 72

Table 4-A.6 – Total Erosion (tons/year) for the SLB and Tons per Acre per Year ............................. 73

Table 5.1 – Tabular format for an emergy analysis, providing a template for the calculation of the 

emergy values ..................................................................................................................................... 89

Table 5.2 – Definition of pathway flows for the systems model of the San Luis Basin (seven county 

area), Colorado shown in Fig. 5.1....................................................................................................... 89
viii

Table 5.3 – Summary of annual emergy flows in the San Luis Basin, 1995-2005 ............................. 92

Table 5.4 – Emergy indices and indicators for the San Luis Basin, 1995-2005. Our focus for this 

study was on Item 106 ........................................................................................................................ 98

Table 5-B.1 – Some emergy indicators and indices used in the analysis of the sustainability of 

regional environmental systems. Symbols and expressions refer to Fig. 5-B.1 .............................. 116

Table 5-C.1 – Emergy per Unit Factors (solar transformities and specific emergies) used in the San 

Luis Basin study. The units for the emergy per unit factors are solar emjoules per joule (sej/j) for 

transformities unless otherwise noted ............................................................................................... 119

Table 5-C.2 – Specific emergies for imports (sej/g). All commodity specific emergies are without 

services and relative to the 9.26E24 sej/y baseline. Values of the emergy per unit factors for the 

commodity classes in this table were determined from the data given in Table 5-C.1 and other data 

given in the calculations shown in Table 5-C.3. The note numbers connect to Table 5-C.3. 

Wwt = wet weight; otherwise numbers are dwt = dry weight .......................................................... 123

Table 5-C.3 – Determination of the specific emergies for imported commodity classes in 

Table 5-C.2. All commodity specific emergies are without services and relative to the 

9.26E24 sej/y baseline ...................................................................................................................... 126

Table 5-C.4 – Specific emergies for exports. All commodity specific emergies are without services 

and relative to the 9.26E24 sej/y baseline. Values of the emergy per unit factors for the commodity 

classes in this table were determined from the data given in Tables 5-C.1 and 5-C.3, as well as 

data provided in Table 5-C.5. The note numbers connect to Table 5-C.5. Wwt = wet weight, 

otherwise numbers are dwt = dry weight. Any commodity class that is the same as that found in 

the calculations for imports was determined in the same manner as shown in Table 5-C3 and the 

calculation is not repeated here ......................................................................................................... 133

Table 5-C.5 – Specific emergies for imports. All commodity specific emergies are without services 

and relative to the 9.26E24 sej/y baseline ......................................................................................... 135

Table 5-E.1 – Renewable emergy inflows to the San Luis Basin ..................................................... 141

Table 5-E.2 – Production in the San Luis Basin that is based primarily on renewable resources .... 142

Table 5-E.3 – Nonrenewable emergy inflows in the San Luis Basin ................................................ 143

Table 5-E.4 – Imports into the San Luis Basin ................................................................................. 144

Table 5-E.5 – Exports from the San Luis Basin ................................................................................ 145

Table 5-G.1 – Example calculation of emergy and economic value, assumptions, and data sources 

used in the emergy evaluation of the San Luis Basin and Upper Rio Grande River Basin for the 

year 1995 ........................................................................................................................................... 151

Table 6.1 – Data were divided into a sequence of overlapping time windows. The eighth window 

was removed because there were not enough data in the time series to place in it. Therefore, all 

data were accounted for within seven windows. t = time (i.e., 1980-2005), x1

 = Population and x2

= Personal income (in thousands). Point 1 (pt1) was defined by (x1(t1), x2(t1)) and is highlighted as 

310352, 38469................................................................................................................................... 172
ix

Table 6.2 – Absolute difference of variables x1 (population) and x2 (personal income) in each 

window using the first point as the center. For example, the absolute difference of x1(t1) and x1(t2)

(i.e., |x1(t1) – x1(t2)| is 4562 as highlighted here in blue and the absolute difference of point 2 from 

point 1 (i.e., 2-1) is 4562 and 511) .................................................................................................... 173

Table 6.3 – Amplitude (q(s)) for each window. These values were computed by first counting the 

number of points in each state within each window, computing a probability for each state in each 

window (p(s)) and calculating the amplitude, q(s) = √p(s) for each state in each window .............. 174

Table 6.4 – Variables used to calculate FI for the San Luis Basin. These variables were selected from 

data used to compute EFA, GNRP, and EmA. The data and their sources are discussed in Chapters 

3, 4, and 5, respectively. The variables include demographic, energy, land use, food and forest 

consumption, environmental and agricultural production categories for the system ....................... 175

Table 6.5 – Three-point mean Fisher information (<FI>) for the overall system and the variables 

grouped by the categories defined in Table 6.4 ................................................................................. 176

Table 6-B.1 – Design matrix for the sensitivity analysis of the simple example data: Determining 

the impact of controllable factors, hwin and winspace on the response variables, average value of FI 

(avgFI) and the number of FI results (Npts) for computation .......................................................... 187

Table 6-B.2 – Analysis of Variance (ANOVA) for the average value of FI (avgFI). The results reveal 

that both hwin (F (3, 9) = 77.56, P<<0.05) and winspace (F (3, 9) = 4.64, P< 0.05) were significant 

factors affecting avgFI. Moreover, as an indicator of the amount of variability that is accounted for 

by factor effects (e.g., hwin or winspace) and random error (i.e., residual), the MS values reflect that 

more of the variability was determined by hwin ............................................................................... 187

Table 6-B.3 – ANOVA for the resolution of Fisher information results (Npts). Like the ANOVA for 

avgFI, both hwin (F (3,9) = 8.33, P <0.05) and winspace (F(3,9) = 519, P <<0.05) were statistically 

significant factors affecting Npts. However, the mean square (MS) values indicate that majority of 

the variability in Npts was due to winspace ...................................................................................... 188

Table 6-B.4 – Criteria for determining the optimal value for hwin and winspace for the sample 

exercise. The goal of the numerical optimization was to minimize the deviation in avgFI and 

maximize Npts, subject to (a) minimizing the standard deviation of FI, the standard error of the 

mean value of FI and standard error of the Npts and (b) maximize Npts. The highest importance was 

placed on maximizing Npts and minimizing the standard error of avgFI and Npts. The optimization 

was performed in Design Expert 8 (StatEase, Inc.) .......................................................................... 188

Table 6-D.1 – Excel file containing the time series of the variables characterizing the system for 

the simple example (SLBExdata.xls). In this exercise, there are two variables (x1 and x2) such that 

each column contains the time series data for one variable. As described in Appendix 6-C.1, the 

data file is displayed as an m × n matrix with m = the number of time steps and n = the number of 

variables ............................................................................................................................................ 196

Table 6-D.2 – Excel file containing time data for simple example (SLBExtime.xls). In this exercise, the 

time period was from 1980 to 2005 and represents 26 time steps. Accordingly, as described in Appendix 

6-C.1, the time file is displayed as an m × 1 matrix with m = the number of time steps ....................... 197

Table 6-D.3 – Excel file containing the size of state (sost) data for simple example (SLBExsost.xls). In 

this exercise, there were two variables (x1 and x2) and the size of states was computed for each variable 

(∆x1

 and ∆x2). The values are exhibited as a 1 × n matrix with n = the number of variables ................ 197

Table 7.1 – Summary of four metrics calculated for the San Luis Basin, Colorado......................... 230
x

List of Figures 

Figure 1.1 – Seven county region referred to by the investigators as the San Luis Basin (SLB), in 

south-central Colorado .......................................................................................................................... 8

Figure 3.1 – Biocapacity compared to ecological footprint for 26 years in the San Luis Basin. 

Twelve percent of the biocapacity area was subtracted from the total BC and bc to protect 

biodiversity. a – Per capita biocapacity (bc) compared to per capita ecological footprint (ef). 

b - Biocapacity for the population (BC) compared to total ecological footprint (EF) ........................ 36

Figure 3.2 – Available biocapacity (bc) for the five footprint land categories over a 26-year period in 

the San Luis Basin, Colorado. Twelve percent of the biocapacity area was subtracted from the total 

bc to protect biodiversity. Note that built-up land has no biocapacity and is not included in 

the figure ............................................................................................................................................. 37

Figure 3.3 – Ecological footprint (ef) and the demand placed on each of the six land categories over 

a 26-year period in the San Luis Basin, Colorado .............................................................................. 37

Figure 3.4 – Energy consumption (hectares per person) by major energy source over a 26-year 

period in the San Luis Basin, Colorado .............................................................................................. 38

Figure 3.5 – Ecological balance (ha/ca) of the different land categories in the SLB. Remainder 

equals biocapacity per capita minus the ecological footprint for the SLB region. Positive values 

represent a surplus or remainder of biocapacity and negative values represent a deficit of biocapacity. 

Twelve percent of the biocapacity area was subtracted from the total bc to protect biodiversity ...... 38

Figure 3-D.1 – Biocapacity (bc) compared to ecological footprint (ef) per capita for 26 years in the US. 

Twelve percent of the biocapacity area was subtracted from the total bc to protect biodiversity ............ 52

Figure 3-D.2 – Total ecological footprint (EF) and biocapacity (BC) for the US population. Twelve 

percent of the biocapacity area was subtracted from the total BCto protect biodiversity .................. 52

Figure 4.1 – Net Regional Product, Green Net Regional Product, and the change in Green Net 

Regional Product ................................................................................................................................. 65

Figure 4.2 – Components of the Depreciation of Natural Capital ...................................................... 65

Figure 4.3 – Sensitivity Analysis Using Upper and Lower Bounds on the Prices for Natural 

Capital Depreciation ........................................................................................................................... 66

Figure 4-A.1 – General approach to estimating wind erosion in the San Luis Basin ......................... 75

Figure 5.1 – An Energy Systems Diagram of the San Luis Basin showing the major features of this 

environmental system ....................................................................................................................... 102

Figure 5.2 – An aggregated model used to calculate summary variables and indices for the San Luis 

Basin system from 1995 to 2005. Year 2000 is presented here as an example ................................ 103
xi

Figure 5.3 – Total emergy used and renewable emergy used in the San Luis Basin from 1995 

to 2005 .............................................................................................................................................. 103

Figure 5.4 – The total emergy used per person in the San Luis Basin from 1995 to 2005 ............... 104

Figure 5.5 – Emergy inflows from renewable sources, imported fuels, electricity, goods and services, 

emergy outflows in exported materials, the services in material products, services, and nonrenewable 

resources exported directly in the San Luis Basin, 1995-2005 ......................................................... 104

Figure 5.6 – A comparison of the total emergy used by the system with the renewable, local 

nonrenewable and imported emergy that contribute to it in the San Luis Basin, 1995-2005 ........... 105

Figure 5.7 – Fraction of renewable emergy to total emergy use in the San Luis Basin from 1995 

to 2005 .............................................................................................................................................. 105

Figure 5.8 – The renewable emergy used per person in the SLB region from 1980 to 2005 ........... 106

Figure 5.9 – The renewable emergy inputs to the San Luis Basin, 1980-2005 ................................ 106

Figure 5.10 – The renewable emergy base for the San Luis Basin, 1980-2005 ............................... 107

Figure 5.11 – Agricultural production in the San Luis Basin supported primarily by renewable 

resources, 1980-2005 ........................................................................................................................ 107

Figure 5.12 – Major categories of nonrenewable resources used and/or produced in the seven 

counties of the San Luis Basin, 1980-2005 ...................................................................................... 108

Figure 5.13 – The emergy exported from and imported to the San Luis Basin from 1995 

to 2005 .............................................................................................................................................. 108

Figure 5.14 – Major categories of emergy imported into the San Luis Basin, 1980-2005 ............... 109

Figure 5.15 – The categories of material goods other than fuels imported to the SLB from 1995 

to 2005 .............................................................................................................................................. 109

Figure 5.16 – Emergy exported from the San Luis Basin, 1980-2005: totals and aggregate categories 

Total exports include total materials and total services exported. Total materials exported includes 

agricultural and forest products, livestock minerals, and all other materials. Services in materials 

exported includes services in services in minerals, all other materials, agricultural and forest 

products. Export of pure services was not evaluated and emigration is shown, but was not included 

in the total ......................................................................................................................................... 110

Figure 5.17 – Selected categories of agricultural, forest product, and mineral exports from the San 

Luis Basin, 1980-2005. The All Other Materials and the services in that category are shown to 

facilitate further discussion ............................................................................................................... 110

Figure 5.18 – The six largest classes of exports contained in All Other Materials exported from the 

San Luis Basin, 1995-2005 ............................................................................................................... 111

Figure 5.19 – The emergy of fuels and electricity used per person in the San Luis Basin from 1980 

to 2005 .............................................................................................................................................. 111
xii

Figure 5.20 – Renewable emergy sources of the San Luis Basin (1980-2005) that are being used in a 

nonrenewable manner ....................................................................................................................... 112

Figure 5.21 – The fraction of total emergy use that comes from local renewable emergy and the 

fraction of use from home sources in the San Luis Basin, 1995-2005 ............................................. 112

Figure 5-B.1 – An Energy Systems Diagram used to define the emergy indices (Table 5-B.1) to 

assess regional sustainability ............................................................................................................ 116

Figure 5-B.2 – The Emergy Sustainability Index (ESI = EYR/ELR) and its components the emergy 

yield ratio (EYR) and the Environmental Loading Ratio (ELR) in the San Luis Basin from 1995 

to 2005 .............................................................................................................................................. 117

Figure 5-E.1 – Renewable production compared to the emergy of fuels and electricity, and the 

renewable emergy base of the SLB................................................................................................... 148

Figure 5-E.2 – Minor components of the emergy input to the San Luis Basin 1980-2005 .............. 148

Figure 5-G.1 – The emergy of processed non-metallic minerals (i.e., perlite and scoria) imported into 

the San Luis Basin and the emergy exported in that category .......................................................... 160

Figure 5-G.2 – The change in annual empower from net immigration or emigration from 1985 

to 2005 .............................................................................................................................................. 160

Figure 5-G.3 – The emergy of fuels and electricity used per year in the San Luis Basin from 1980 

to 2005 .............................................................................................................................................. 161

Figure 5-G.4 – The difference between the total emergy outflows (B+P2

E+N2) and total emergy 

inflows (R+F+G+P2I). ....................................................................................................................... 161

Figure 5-G.5 – Digital elevation model of the Upper Rio Grande River Basin showing the 

boundaries of the closed and open basins ......................................................................................... 162

Figure 6.1 – Graphical representation of the sample settings for the Fisher information (FI) 

computation indicating the size of the integration window (hwin) and window increment (winspace) 

in time steps used to move through the data. In this example, hwin =10 and winspace = 5. The red 

star indicates the center point of the window denoting the placement of FI .................................... 177

Figure 6.2 – Flow diagram of the binning algorithm in the Fisher information computation. 

According to Equation 6.4, if each variable |xi

(t

i

) – xi(t

j

)| is less than the size of states (∆xi), then the 

two points at time i and j are indistinguishable and are grouped (binned) in the same state ............ 177

Figure 6.3 – Population (x1, green triangles) and Personal Income (x2, blue diamonds) values for 

the San Luis Basin from 1980 to 2005. These variables were used as data for the example FI 

computation exercise provided in Section 6.2.4 ............................................................................... 178
xiii

Figure 6.4 – Bulls-eye plot of binning points in state 1 for the simple example. Point 1 (pt1: (x1(t1), 

x2(t1)) = (310352, 38469)) is the center of this figure and the absolute difference from pt1 are plotted 

on the corresponding axis, e.g., absolute difference 2-1 = (4562, 511). The values plotted are from 

Table 6.2. As described in Section 6.2.2, the level of uncertainty (i.e., size of states (∆xi)) was 

computed for each variable such that in this plot ∆x1 and ∆x2 correspond to the red and blue line, 

respectively. The points are binned in a state when the difference is less than the level of uncertainty 

(i.e., size of states)............................................................................................................................. 178

Figure 6.5 – Probability density (p(s)) for each window in the simple example (Section 6.2.4). These 

values were computed by first counting the number of points in each state within each window and 

then computing a probability for each state in each window. In window 3, four points were binned 

in both state 1 and state 2; therefore, the probability was 50% for each state .................................. 179

Figure 6.6 – Calculating gradients of the amplitude (q(s)) in window 1 of the simple example 

(Section 6.2.4). Gradients were used as a convention for mimicking a discrete density function and 

were computed as qi-qi+1, where i is the state. These values used in Equation 6.3 to compute FI... 179

Figure 6.7 – Fisher information for each window from the simple example: (A) Fisher information 

(FI) and (B) Smoothed FI using three-point mean (<FI>), i.e., <FI>1999 = (FI2002+FI1999+FI1996)/3 and 

<FI>1990 = (FI1993+FI1990+FI1987)/3....................................................................................................... 180

Figure 6.8 – Fisher information calculated for the San Luis Basin over a 26-year period (1980-2006). 

Each point represents the FI computed in each window and is reported in the center of that period 

(i.e., FI; 1984 = 1980-1987, 1985 = 1981-1988, 1986 = 1982-1989, etc.)....................................... 180

Figure 6.9 – Mean Fisher information (<FI>) for the San Luis Basin. <FI> was computed as a threepoint average of the FI (Fig. 6.8) in order to smooth out short-term fluctuations and highlight trends, 

rather than fluctuations. The <FI> is reported in the center of the three points (i.e., <FI>; 2001 = 

2002-2000, 1998 = 1999-1997, etc.)................................................................................................. 181

Figure 6.10 – Mean Fisher information of the San Luis Basin for the overall system and by category. 

Using this plot, we compared changes in system mean FI (<FI>) to the <FI> the variables grouped 

in categories. The <FI> is reported in the center of the three points (e.g., <FI>2001 = (FI2000 + FI2001 + 

FI2002) ∕ 3)........................................................................................................................................... 181

Figure 6.11 – Percent change in the San Luis Basin demographic variables. The percent change in 

population corresponded to the changes in dynamic order of the demographic category. The change 

in population decreased initially, peaked in 1995, and decreased thereafter which corresponds to the 

changes in dynamic order for the demographic category ................................................................. 182

Figure 6-A.1 – Fisher information is proportional to the probability of system states (Pawlowski and 

Cabezas 2008). (a) Zero I results when a system has an equal probability of being in one state as any 

other state, resulting in a uniform probability density function (PDF), such that dp / ds → 0 

and I→0. (b) High I occurs when repeated measurements of the system result in the same state over 

time (i.e., the system is more predictable resulting in high predictability), a PDF with a steep 

slope, dp / ds → ∞ and Fisher information approaching infinity (i.e., I→∞). (c) However, real 

systems typically function between these two extremes and exhibit medium I ............................... 185
xiv

Figure 6-B.1 – The effect of changing hwin on the FI result. The value of hwin changes from panel 

to panel (A-D), while winspace varies within each panel. Therefore, each panel reflects the Fisher 

information result over time with hwin constant and a different winspace for each computation: (A) 

hwin =8, winspace =1 to 4, (B) hwin =9, winspace =1 to 4, (C) hwin =10, winspace =1 to 4, (D) hwin

=10, winspace =1 to 4. The average value (avgFI) and standard deviation (SD) reported of FI in 

each panel were computed from all the FI results in each panel as a summary statistic. For example, 

avgFI and SD in panel A are the mean and standard deviation of the FI values given hwin = 8 and 

winspace = 1 to 4. Note that avgFI decreases as hwin increases and small winspace values result in 

more fluctuations (e.g., winspace =1 is represented by the blue line).............................................. 189

Figure 6-B.2 – Interaction plots for the average value of FI (avgFI). Plotting the value of avgFI 

as a function of hwin and winspace affords the ability to evaluate the impact of both parameters 

on avgFI. (A) Each hwin value is represented by a different color plot. With winspace varying 

along the x-axis, there is little variability in avgFI as winspace increases. Conversely, note that as 

hwin increases (e.g., A1: hwin = 8, A4: hwin = 11), the value of avgFI decreases. (B) The strong 

relationship between hwin and avgFI is also shown as hwin increases along the x-axis.................. 190

Figure 6-B.3 – Interaction plots for the number of FI results (Npts). Plotting the value of Npts as a 

function of hwin and winspace affords the ability to evaluate the impact of both parameters on Npts. 

(A) Each computation with a particular hwin value is represented by a different color plot. With 

winspace varying along the x-axis, there was a great deal of variability in Npts. (B) However, as 

hwin increases along the x-axis, there are minor changes in Npts. Further, there is a greater decrease 

in Npts as winspace goes from 1 to 2, than from 2 to 3 or 3 to 4 ..................................................... 190

Figure 6-B.4 – Results of the numerical optimization of hwin and winspace using data from the 

simple example. Based on the optimization criteria of maximizing Npts and minimizing the 

deviation in avgFI, the optimal solution was found (using Design Expert 8) with integration window 

parameter settings of hwin = 8 and winspace = 1 ............................................................................. 191

Figure 6-B.5 – Final Fisher information (FI) result from the simple example. (A) FI result from 

the optimal parameter settings (hwin = 8 and winspace =1). (B) Smoothed result: <FI>, three-point 

mean Fisher information................................................................................................................... 191

Figure 6-C.1 – GUI interface developed to compute FI from system data. The GUI is used for (1) 

importing pre-formatted data files, (2) choosing parameter settings (3) computing and plotting FI, (4) 

saving the FI results and (5) further study of regime shifts. The results plotted in this figure are from 

the simple example in Appendix 6-D................................................................................................ 194

Figure 6-C.2 – The Advanced GUI was developed to study the pervasiveness and intensity of 

ecosystem regime shifts by (1) evaluating and (2) plotting the FI at various tightening levels. (3) 

The perfect order tightening level and pervasiveness are computed and shown in the text boxes. 

The results plotted are from the simple example as computed in Appendix 6-D. Using this simple 

example, note that the pervasiveness is 100% and there is one regime shift between two stable 

regimes.............................................................................................................................................. 195

Figure 7.1 – Graphs of Ecological Balance, Green Net Regional Product (GNRP), Fraction of 

Locally Renewable Emergy, and mean Fisher Information for 1980-2005...................................... 231
xv

List of Abbreviations

aai Area Appropriated Per capita 

ARS Agricultural Research Station

bc Biocapacity Per Capita 

BC Biocapacity for the Population

BEA Bureau of Economic Analysis

BLM Bureau of Land Management 

C County

CDSS Colorado’s Decision Support System 

CDWR Colorado Division of 

 Water Resource

CFC Consumption of Fixed Capital

Ci

 Consumption Item 

CO2

 Carbon Dioxide 

CON Consumption 

CPP Community Partnership Program

CSERGE Center for Social and Economic 

 Research on Global Environment

CWCB Colorado Water Conservation Board

DEM Demographic

Dwt Dry weight

EBT Estimated by Team

EF Ecological Footprint for population

ef Ecological Footprint per person

EFA Ecological Footprint Analysis

EGY Energy 

EIA Energy Information Administration 

ELR Environmental Loading Ratio 

EmA Emergy Analysis

ENV Environmental

ESI Emergy Sustainability Index 

EST Energy Systems Theory

ET Evapotranspiration 

ExA Exergy Analysis

EYR Emergy Yield Ratio 

FS Forest Service

FI Fisher information

<FI> mean Fisher information

GDI Gross Domestic Income

GDP Gross Domestic Product

gha Global Hectares 

GI Global Insight

GNDP Green Net Domestic Product

GNRP Green Net Regional Product

GRP Gross Regional Product

GS Genuine Savings

GSA General Service Administration 

GUI Graphical User Interface

I Fisher Information (historical usage)

ha Hectares

LEI Local Effect of Investment

MBF Million Board Feet

Misc Miscellaneous

MLRA Major Land Resource Area

MSY Maximum Sustainable Yield

NAICS North American Industry 

 Classification System

NASS National Agricultural Statistics 

 Service

NDP Net Domestic Product

NGO Non-governmental organization

NRCS Natural Resource Conservation 

 Service 

NRI National Resource Information

NRP Net Regional Product

ORD Office of Research and Development

PI Personal Income

PCA Principal Components Analysis

PROD Production

R Region 

S State

sej Solar Emjoule

SIC Standard Industrial Classification 

SLB San Luis Basin

SLV San Luis Valley

U Emergy Use

USDA United States Department of 

 Agriculture

USEPA United States Environmental 

 Protection Agency

UN United Nations

WEQ Wind Erosion Equation

WERU Wind Erosion Research Unit

Wwt Wet weight
xvi

Foreword

The U.S. Environmental Protection Agency (USEPA) is charged by Congress with protecting 

the Nation’s land, air, and water resources. Under a mandate of national environmental laws, the 

Agency strives to formulate and implement actions leading to a compatible balance between human 

activities and the ability of natural systems to support and nurture life. To meet this mandate, 

USEPA’s research program is providing data and technical support for solving environmental 

problems today and building a science knowledge base necessary to manage our ecological resources 

wisely, understand how pollutants affect our health, and prevent or reduce environmental risks in the 

future.

The National Risk Management Research Laboratory (NRMRL) is the Agency’s center for 

investigation of technological and management approaches for preventing and reducing risks 

from pollution that threaten human health and the environment. The focus of the Laboratory’s 

research program is on methods and their cost-effectiveness for prevention and control of pollution 

to air, land, water, and subsurface resources; protection of water quality in public water systems; 

remediation of contaminated sites, sediments and ground water; prevention and control of indoor 

air pollution; and restoration of ecosystems. NRMRL collaborates with both public and private 

sector partners to foster technologies that reduce the cost of compliance and to anticipate emerging 

problems. NRMRL’s research provides solutions to environmental problems by: developing 

and promoting technologies that protect and improve the environment; advancing scientific and 

engineering information to support regulatory and policy decisions; and providing the technical 

support and information transfer to ensure implementation of environmental regulations and 

strategies at the national, state, and community levels.

This publication has been produced as part of the Laboratory’s strategic long-term research plan. It 

is published and made available by USEPA’s Office of Research and Development to assist the user 

community and to link researchers with their clients.

Sally Gutierrez, Director

National Risk Management Research Laboratory
xvii

The National Risk Management Research Laboratory (NRMRL), within U.S. EPA’s Office of 

Research and Development, was responsible for preparing this document. Authors are listed 

alphabetically and do not necessarily represent level of contribution. Matthew T. Heberling and 

Matthew E. Hopton were the document editors.

Chapter Authors

Chapter 1.

Heriberto Cabezas, Tarsha Eason, Ahjond S. Garmestani, Matthew T. 

Heberling, Matthew E. Hopton, and Joshua Templeton

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Daniel E. Campbell

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Narragansett, RI

Denis White

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Corvallis, OR

Marie Zanowick

Region 8

U.S. Environmental Protection Agency, Denver, CO

Chapter 2.

Heriberto Cabezas, Tarsha Eason, Ahjond S. Garmestani, Matthew T. 

Heberling, Matthew E. Hopton, and Joshua Templeton

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Daniel E. Campbell

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Narragansett, RI

Denis White

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Corvallis, OR

Chapter 3.

Matthew E. Hopton

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Denis White

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Corvallis, OR

Chapter 4.

Matthew T. Heberling and Joshua Templeton

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Authors, Contributors, and Reviewers
xviii

App. 4-A. 

Matthew T. Heberling

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Richard T. Sparks

Natural Resources Conservation Service

U.S. Department of Agriculture, Center, CO

Chapter 5.

Daniel E. Campbell

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Narragansett, RI

Ahjond S. Garmestani and Matthew E. Hopton

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Chapter 6.

Heriberto Cabezas and Tarsha Eason

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Chapter 7.

Heriberto Cabezas, Tarsha Eason, Ahjond S. Garmestani, Matthew T. 

Heberling, Matthew E. Hopton, and Joshua Templeton

National Risk Management Research Laboratory

U.S. Environmental Protection Agency, Cincinnati, OH

Daniel E. Campbell

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Narragansett, RI

Denis White

National Health and Environmental Effects Research Laboratory

U.S. Environmental Protection Agency, Corvallis, OR

Marie Zanowick

Region 8

U.S. Environmental Protection Agency, Denver, CO
xix

External Reviewers

This report was externally peer-reviewed by the following professionals with experience in 

sustainability research. We acknowledge the effort and insightful comments provided by the 

reviewers and accept all remaining errors as our own.

Bhavik R. Bakshi

Department of Chemical & Biomolecular Engineeering

The Ohio State University, Columbus, OH

Matt Luck

ISciences, L.L.C.

Burlington, VT

David I. Stern

Arndt-Corden Division of Economics

Australian National University, Canberra ACT

David R. Tilley

Department of Environmental Science & Technology

University of Maryland, College Park, MD
xx

Acknowledgements

The authors would like to thank Mike Britten, Fred Bunch, Art Hutchinson, Richard Sparks, John 

Stump, Andrew Valdez, and Steve Vandiver for directing us to or collecting data for the metric 

calculations. We thank several people for providing data, including Kathy Worthington (Xcel 

Energy), JoAn Waugh (Rural Electric Co-op), Bruce Short (USDA-FS), and Pete Magee (San Luis 

Valley GIS/GPS Authority). The authors wish to acknowledge Fred Bunch for his enthusiasm, his 

assistance, and the many helpful discussions throughout the three years of this project. The authors 

thank Troy Fannon for his support in preparing part of the front matter. We recognize Brent Frakes 

for his data management skills. 

Matthew T. Heberling acknowledges Ray Bennett and Colorado’s Decision Support Systems for 

estimating the water balance model for the Rio Grande River Basin and Richard Sparks for his help 

estimating wind erosion for the SLB. We thank Rafael Weston for his helpful comments on the 

GNRP calculation and information about the region. 

Heriberto Cabezas and Tarsha Eason acknowledge the seminal contributions to Fisher information 

theory and computations from Arunprakash Karunanithi, Christopher Pawlowski, Brian Fath, and B. 

Roy Frieden.

Many individuals contributed to, and assisted with this project; we regret the possibility that we do 

not acknowledge everyone. 
xxi

Abstract

Sustainability relates to finding and maintaining conditions that can support social and economic 

development without adversely altering the environment to too great an extent. Moreover, in order 

to satisfy the definition of sustainability, the environmental, social, and economic characteristics 

of the system must effectively meet the needs of current and future generations, indefinitely. Our 

definition of sustainability depends on the extent to which the environment can be altered and 

still maintain a high quality of life for people, without jeopardizing the quality of life for future 

generations. Consequently, to assess sustainability, information is needed to understand the 

requirements for human well-being and the linkages and demands of human activity (e.g., society, 

economy, government, industry, etc.) on environmental systems. One way to collect needed 

information is through metrics that quantify the environmental, social, and economic characteristics 

of a system. We used a collaborative, interdisciplinary approach to investigate this complex 

problem. Specifically, we set out to: 1) determine the applicability of using existing datasets to 

estimate metrics of sustainability at a regional scale, 2) calculate the metrics through time (1980-

2005), and 3) compare and contrast the results to determine if a regional system is moving toward or 

away from sustainability. This information can help decision makers determine if their region is on a 

sustainable path (i.e., moving toward sustainability).

As a starting point, we identified and tested a set of four metrics that capture some of the most basic 

properties of an environmental system. The metrics represent: 1) ecological impacts of human 

activity to produce the resources consumed and to assimilate the wastes generated using Ecological 

Footprint Analysis; 2) economic well-being or welfare with Green Net Regional Product; 3) flow of 

available energy through the system using Emergy Analysis; and 4) overall system order with Fisher 

information (i.e., a well-functioning system is orderly and departure from such a state can lead to 

decreased function). Each of these metrics is most sensitive at capturing some particular aspect of 

the system, although there is some overlap or redundancy (e.g., consider a physician using multiple 

tests to examine a patient’s health). We tested the methodology on the San Luis Basin (SLB) in 

south-central Colorado. The SLB is a rural, agricultural region with a limited population. This 

seven county region contains the Upper Rio Grande River Basin, the San Luis Valley, and the Great 

Sand Dunes National Park and Preserve. 

Even though data for some of the variables were not available at the county level, we were able 

to calculate the metrics through time. From our analyses, Ecological Footprint Analysis indicated 

the SLB was moving away from sustainability. Green Net Regional Product appears to have an 

upward trend over the 26-year period providing no indication that the SLB was moving away from 

sustainability (this one-sided test can only tell if a system is moving away from sustainability). Due 

to data unavailability, a complete Emergy Analysis was estimated only for an 11-year period (1995-

2005). One emergy index (fraction of renewable emergy used) that captures sustainability within 

the system suggested a gradual movement away from sustainability, but a second index (Emergy 

Sustainability Index) revealed the region improved its relationship with the larger system, (i.e., other 

states and US) from 2003 until the end of the period. Finally, the results of the Fisher information 

assessment revealed that, although the system was relatively stable during the 26 years, there was an 

indication of slight movement away from sustainability near the end of the study period. 

We consider the entire system moving away from sustainability if any one metric reveals movement 

away from sustainability. Therefore, the weight of the evidence from our results indicate that the 

broad trend of the SLB was moving away from sustainability over the period examined. Because 

this was a pilot study, we offer a number of recommendations for future research based on what we 

learned while developing and using this approach.
xxii

Executive Summary

ES.1 Introduction

There are several established, scientifically supported metrics of sustainability. Many of these 

metrics require extensive data collection and detailed computations. Moreover, it is difficult for 

one metric to capture all aspects of a system that are relevant to sustainability. A pilot project 

was initiated to test an approach to measure, monitor, and maintain prosperity and environmental 

quality in the context of sustainability for a regional system. Sustainability relates to finding and 

maintaining conditions that can support social and economic development without adversely altering 

the environment to too great an extent. The goal of this study was to produce a straightforward, 

relatively inexpensive methodology that was simple to use and interpret. This approach required 

historical data to be readily accessible, that metrics be applicable to the relevant scale, and that 

results meet the needs of decision makers. 

Because sustainability is a multidimensional concept, the research group consisted of an 

interdisciplinary team in which the members represented expertise associated with several 

fundamental components of an environmental system. This project utilized available environmental, 

economic, and social data to calculate four sustainability metrics: 1) environmental footprint as 

characterized by Ecological Footprint Analysis (EFA), 2) economic well-being as ascertained from 

Green Net Regional Product (GNRP), 3) energy and emergy flow through the system as computed 

from an Emergy Analysis (EmA), and 4) dynamic order estimated from the computation of Fisher 

information (FI). 

The San Luis Basin (SLB), a region in south-central Colorado, was selected as the pilot study area 

because of its natural hydrological boundaries, limited population, large amount of publicly owned 

land, and interest expressed in the study by USEPA Region 8 and other government officials. We 

define the SLB as the following seven counties: Alamosa, Conejos, Costilla, Hinsdale, Mineral, Rio 

Grande, and Saguache. This region contains the Upper Rio Grande River Basin, the San Luis Valley, 

and the Great Sand Dunes National Park and Preserve. 

ES.2 Sustainability Metrics

We reviewed the literature to summarize different metrics that quantify the major components of 

an environmental system. This resulted in a review of potential metrics for this pilot study. We 

reviewed studies that examined sustainability using multiple metrics and studies that measured 

sustainability over time. Both are important attributes of the San Luis Basin Sustainability Metrics 

Project. 

No single metric should be considered a perfect measure of sustainability because of the multiple 

definitions and interpretations of sustainability and the difficulties in measuring it. The four 

sustainability metrics we selected provide a holistic view of what we considered to be the 

fundamental aspects of an environmental system. 

At a basic level, we recognized that an environmental system has some inherent order, whether the 

factors responsible for maintaining that order are clearly identified or understood. Furthermore, 

it takes available energy (i.e., energy with the potential to do work) to maintain order in an 

environmental system. Finally, sustainability is inherently an anthropocentric issue and how well 

off humans are may dictate their influences on the system. For example, an individual or population 

that is meeting its needs for existence has the luxury of being concerned about the effect they have 

on their environment, whereas a population that is barely surviving may not be intensely concerned 

with sustainability.

We selected metrics that capture each of these fundamental aspects; they also represent both strong 

and weak measures of sustainability. Weak sustainability assumes ecological functions and/or 

materials can be substituted by technological or man-made surrogates whereas strong sustainability 

assumes ecological functions are not replaceable or natural capital cannot be substituted with other 

forms of capital (e.g., Dietz and Neumayer 2007, Mayer 2008, Mayer et al. 2004, Neumayer 2010, 

Pearce 2002).
xxiii

EFA was selected to capture the impact a population has on environmental resources resulting from 

consumption and waste production. We chose EmA because it measures the flow of available energy 

through a system, by converting all types of energy to a common unit (i.e., solar emjoule). Both 

of these metrics are considered measures of strong sustainability. GNRP was chosen to capture 

the overall economic well-being of the population and FI was selected to measure dynamic order 

of the overall system (both measures of weak sustainability). Out of the four metrics, using FI to 

characterize sustainability is a relatively new metric (Mayer 2008). Application of the other metrics 

is relatively common and they are established as metrics of sustainability in the scientific literature 

(e.g., Mayer 2008). The next four sections summarize each of the metrics.

ES.3 Ecological Footprint

This chapter describes the data sources and methodology used to estimate a simplified Ecological 

Footprint Analysis (EFA) at a regional scale. EFA captures the human impact on the environmental 

system by identifying the amount of biologically productive land necessary to support a person’s 

level of consumption and waste generation. EFA is a commonly used metric of sustainability 

because it is easy to conceptualize and the calculation is relatively straightforward. A typical EFA 

requires estimation of the supply of biologically productive land in the geographic region of interest 

(called the biocapacity) and the demand placed on the biologically productive land to support the 

population (called the ecological footprint). The supply is estimated by identifying the number 

of hectares in each of six land categories (arable, pasture, energy, forest, sea, and built-up land). 

Consumption levels for food, energy, and other consumables, are assigned to a land category and 

the amount of land necessary to meet the level of consumption is calculated as the demand. An 

ecological balance is calculated by subtracting the ecological footprint from the biocapacity. If the 

ecological footprint is less than the available biocapacity, the system has an ecological remainder. 

If the ecological footprint is greater than the available biocapacity, the system is said to possess an 

ecological deficit. If the ecological balance is decreasing, resulting from an increasing ecological 

footprint and/or a decreasing biocapacity, the system is said to be moving away from sustainability. 

Utilizing free, readily available data, we calculated an EFA for the SLB. Gathering existing data at 

a regional scale can be difficult because data are often collected at national or state levels. The lack 

of data is further confounded by the fact that data are often collected at intervals greater than one 

year. Variables that were missing data for certain years were estimated using linear interpolation. 

Other data were from state or national level data and were scaled to the region. For example, energy 

consumption data were reported for the State of Colorado. A per capita consumption was calculated 

by dividing the annual consumption by the population of the state for the year of interest. This per 

capita value was assumed to represent the level of consumption for the region under investigation. 

Food consumption was reported as average per capita for the US. This value was assumed to 

represent the level of consumption for individuals in the region. 

Thirty-five variables from 1980-2005 (26 years) were collected and used to calculate a timedependent EFA and the resulting trend was visually examined. The available biocapacity in the 

region did not decrease during the period, but per capita biocapacity is decreasing due to population 

growth. Per capita biocapacity was at a period high of nearly 36 hectares per person (ha/ca) in 

1980 and steadily decreased to a low of just over 27 ha/ca in 2005. Ecological footprint remained 

relatively constant over the 26-year period, with a low of 5.10 ha/ca in 1997 to a high of 5.5 ha/ca 

in 1985. A steady ecological footprint combined with a decreasing per capita biocapacity, implies 

the ecological balance (i.e., remainder) is decreasing and thus the region is moving away from 

sustainability. Although per capita consumption did not increase substantially during the 26 years, 

more people are drawing on a set quantity of resources. Sustainability, as defined by EFA, requires 

that people reduce their footprint to free up resources for a growing population or to increase 

biocapacity. Lastly, it is important to remember the region is part of a larger system and provides 

much of its natural resources to support the larger system.

Our methodology is a simplified approach to EFA and does not follow standards that are currently 

being established. Adhering to the suggested standards would require obtaining data sets that 

consist entirely of national data. The national-level data are replaced with data specific to the 

geographic area under examination when they are available. Although national data may represent 
xxiv

the sub-national region under study, the idea requires further investigation, especially in large, 

geographically and culturally varied nations such as the US. 

ES.4 Green Net Regional Product

This chapter describes the data sources and methodology used to estimate Green Net Regional 

Product (GNRP). GNRP is equal to aggregate consumption minus the depreciation of man-made 

and natural capital. It captures the economic well-being, or welfare, measured in US dollars. 

Welfare, as defined by economists, incorporates benefits obtained outside of market transactions 

such as environmental amenities, but allows for the possibility of trade-offs between market and 

non-market goods. GNRP is a measure of weak sustainability that incorporates the assumption that 

human and man-made capital can be substituted for natural capital. We measure the movement 

toward or away from sustainability by examining the change in GNRP over time.

Any attempt at green accounting requires both economic and natural capital data. The economic 

data used in our study came from the Bureau of Economic Analysis (BEA). We collected natural 

capital data from the Energy Information Administration, Agricultural Statistics for Colorado, The 

Colorado Water Conservation Board and the Colorado Division of Water Resources as well as 

professional contacts in the SLB.

Our approach to estimating GNRP required transforming BEA economic data at the national, 

state, and county levels to the level of the SLB. Nationally, Gross Domestic Product (GDP) and 

Consumption of Fixed Capital for the US were required. At the state level, the approach required 

Personal Income and GDP for Colorado and New Mexico. For the seven counties in the SLB, we 

used Personal Income from the BEA.

Given the contribution of agribusiness to the SLB, we included the depletion of both groundwater 

and soil as components in the depreciation of natural capital. In addition, we captured the effect 

of the consumption of energy on future generations through carbon dioxide (CO2) emissions. We 

utilized the Upper Rio Grande database from Colorado’s Decision Support System to estimate the 

depletion of groundwater in the SLB. We averaged per capita CO2 emissions for Colorado and 

New Mexico and applied this to the population estimates of the SLB. Wind is the main cause of soil 

erosion in the SLB. Finding reliable, county-level soil erosion data was challenging and required 

us to make many assumptions, consult with experts, and spend many hours doing the calculations. 

Future studies are likely to find this to be one of the more difficult aspects of applying our approach. 

After collecting data for changes in natural capital, we needed to estimate the value of the change 

in the natural capital. The shadow price of groundwater for irrigation, the economic damages from 

wind erosion, and the social cost of carbon emissions were obtained from the literature and applied 

using benefit transfer. 

Aggregating Net Regional Product (NRP; i.e., Gross Regional Product adjusted for the depreciation 

of man-made capital), Groundwater Storage Depreciation, Soil Erosion Damage, and CO2 Damage 

Costs, we estimated GNRP for the SLB from 1980-2005. GNRP had a slight upward trend (based on 

a visual examination). Although there are peaks and troughs, the upward trend suggested that there 

is no definitive evidence of moving away from sustainability. 

We have some concerns about our approach for estimating values for natural capital depreciation. 

A sensitivity analysis of the shadow prices and marginal damages indicated trends in GNRP did not 

differ for either low or high estimates. We developed and used an approach for estimating GNRP 

using publicly available data. Although the natural capital depreciation estimates will differ by 

region, the general approach can be applied to other regions.

ES.5 Emergy

Emergy is an unfamiliar term to many people, yet it is a quantity of great importance for the accurate 

evaluation of the condition of systems on all scales from the chemical reactions of molecules to the 

birth and death of stars and galaxies. Several aspects of system sustainability can be measured with 

emergy indices. The potential for sustaining the condition of the present system is indicated by the 

percent of renewable emergy used to support current system structure. The overall sustainability of 

the relationship between a system and its next larger system is given by the Emergy Sustainability 
xxv

Index (ESI; Brown and Ulgiati 1998). This index is the ratio of the emergy yield to the larger 

system, the Emergy Yield Ratio (EYR), divided by the potential environmental damage done to the 

local system, Environmental Loading Ratio (ELR). Greater emergy yield for less environmental 

damage is preferable.

In this summary, our emergy evaluation of the San Luis Basin (SLB) region of Colorado, we focus 

on explaining emergy and the results of our analyses. Emergy Analysis (EmA) is another way 

to estimate the value that the environment contributes to society. EmA uses nature’s accounting 

system (that is the flow of available energy2) to determine where a system stands and to judge its 

current condition. Nature does not and cannot accept money as payment for the real work that it 

does to support society in the SLB and elsewhere, yet emergy analysis shows that almost 50% of 

the work that is done in the region is done by the natural systems of the environment. Furthermore, 

the greatest amount of nature’s work done annually is contributed by the snowpack that stands 

on the high mountains surrounding the valley. The existence of stored water at high elevations 

allows all of the geopotential energy of this water to be released in a short period of time and in the 

process, it recharges the groundwater and maintains unique geological and ecological features of 

the valley like the Great Sand Dunes and wetlands. One consequence of this fact is that the natural 

and agricultural systems of the region are vulnerable to climate changes that affect the snowpack. 

One of the largest natural assets of the region, as shown by EmA, is the stored available energy in 

groundwater. Everyone that lives in the region knows this and the history of increasing groundwater 

use that was a major factor in the rapid growth of agriculture from the 1880’s to 1980 (Emery 1996). 

The advantage that EmA gives managers is it allows them to evaluate all aspects of the system in 

common biophysical terms (i.e., the amount of solar energy required for environmental, economic, 

and social products and services of the system). In this manner, all aspects of the system for which 

they are responsible in equal terms, that is disparate quantities (like environmental damage and 

economic production) are put on the same biophysical basis so that changes in each are directly 

comparable. Economic value serves this purpose in economics, but a different perspective of value 

is used and the scaling of relative values is often different in the two approaches. Information on 

the emergy value of aspects of a system compared with economic values for the same aspects has 

the potential to lead to better and fairer decisions on contentious issues faced by land managers. An 

explanation of emergy in more technical language is given in the following paragraph.

EmA assesses the condition of a system based on the flow of available energy required to develop 

and operate that system. The quality of energy flows is normalized by expressing all kinds of 

available energy in a common unit, the solar equivalent joule (sej), by converting the quantities of 

energy in joules or materials in grams used to make a product or service. The product is converted to 

emergy by multiplying the original units (J or g) by an emergy per unit factor, called a transformity 

(sej/J) or specific emergy (sej/g), to obtain emergy. For example, a bushel of wheat weighs about 60 

lbs and a gram of wheat contains about 14,200 J of available energy or 3.87 X108

 J/bu. If we sum 

all the inputs required for the production of the annual wheat crop in Minnesota (Campbell and Ohrt 

2009), including the evapotranspiration of water, topsoil erosion, fuel, potash, phosphate, nitrogen, 

pesticide, herbicide, electricity, and groundwater we find that 2.89X1013 sej were required to produce 

the bushel of wheat. Thus, the transformity of wheat, without human services (labor), included was 

7.47 104 sej/J. This factor can be applied to wheat production in a similar area (i.e., the SLB) to 

estimate the emergy of the crop given that the harvest in bushels is known.

The emergy of any environmental, economic, or social product and service can be determined in 

this manner as long as the production process for these items is known. Once converted to emergy 

the results are directly comparable so the relative advantages and disadvantages of any change 

due to alternative policies can be readily determined by direct inspection of the values. Relative 

advantage is judged by movement toward greater emergy flow through the system network, that is in 

the direction of greater competitiveness, and therefore toward a higher probability that the system’s 

condition will be sustained given no change in the set of inputs available to the system. According 

to Emergy Systems Theory, maximizing empower is hypothesized to be the variable deciding the 

outcome in evolutionary competition among alternatives (Odum 1996). If this is true, then system 

2

 Available energy is energy with the potential to do work. For example water in the mountains can do work, such as drive a 

turbine as it flows to a lower elevation.
xxvi

empower is a master variable that serves as a reference point to characterize system operations 

(i.e., increasing system empower flow moves toward greater overall well-being, whereas a decline 

indicates a less healthy system that will not compete as successfully with other systems). From this 

perspective, every manager should know the projected effect of alternative policies on the empower 

(emergy/time) of their systems as well as the effect on system dollar flows, before making a decision.

In this study, information was gathered via government agencies, literature surveys, interviews 

with experts, the purchase of a dataset, and meetings with knowledgeable people from the region. 

EmA requires data on all aspects of a system; and therefore, data on several important variables 

were shared with EFA and GNRP data sets. The data needs to construct an EmA of a region can 

be categorized as follows: 1) data on renewable energy inputs to the system and information on 

renewable production carried out in the system; 2) data on nonrenewable energy inputs, production, 

and consumption, including any renewable energy sources used in a nonrenewable manner; 3) data 

on imports to the system including raw and finished materials and services; and 4) data on exports 

from the system including raw and finished materials and services. We performed an evaluation 

of the annual emergy flows of the SLB region synthesizing data for the seven counties and the 

associated Upper Rio Grande River Basin. Out of the numerous indices calculated during a typical 

EmA, we selected the fraction of renewable emergy used to total emergy used as the index on which 

to focus because it would provide the best indication if the SLB was moving toward or away from 

sustainability. The ESI is also used to examine the sustainability of the relationship between the 

SLB and its next larger system.

The limiting factor for determining a time series of emergy flows for the SLB was the need for 

complete and accurate data on imports and exports. This required the purchase of relevant data from 

a private economic data service. The firm compiled freight movement by county and these data were 

available for the seven-county region for the years 1995 to 2005. Therefore, the complete EmA of 

the region was limited to these 11 years. 

More often than not, the SLB exported more emergy than it imported. Thus, the SLB serves as 

a hinterland supplying raw products and resources to the larger system of Colorado and the US. 

Although there was some variability during the 11-year period, the fraction of renewable emergy 

to total emergy used had a slight decline over the period. Thus, we can infer that during this time 

the SLB was gradually moving away from sustainability. These trends culminated in 2002 when 

a large groundwater withdrawal compensating for decreased precipitation in that year brought the 

fraction of renewable emergy to a low point. In 2003, renewable emergy inflow recovered, and 

exports began to recover, while imports remained about the same level. This led to an increase in 

this emergy index of sustainability, which reversed its general downward trend, resulting in upward 

movement from 2003 to 2005. However, the total emergy used in the SLB, which is an indicator of 

well-being of the local system, recovered much more slowly with a relatively modest increase (6%) 

in total emergy flow over this time. The total renewable emergy used per person in the region also 

declined for the study period due to population growth. In summary, although both indices show 

a decline and resurgence in later years, overall, they indicate an underlying trend for the region of 

gradually moving away from sustainability for the period examined.

ES.6 Fisher Information and Order

This chapter describes the theory, data sources, and methodology for using Fisher information to 

assess the order and stability of the SLB regional system. Fisher information (FI) was developed 

as a measure of order in data and is an important method in information theory. The method is 

based on the amount of information available in a time series of data and is focused on determining 

patterns of behavior. Because the presence of patterns implies a level of organization, FI can be seen 

as a measure of dynamic order. In the context of system sustainability, order relates to the ability of 

a system to maintain a desirable steady state (i.e. regime) even in the presence of natural fluctuations. 

Assessing dynamic order enhances environmental management in its effort to monitor and manage 

the impact of human activity on ecosystems. Through this approach, changes in complex system 

behavior may be observed over time with the aim of maintaining desired regimes and avoiding 

catastrophic shifts. 
xxvii

Our adaptation of FI provides a means of monitoring variables of a system to characterize its 

dynamic order and self organization, and, accordingly, its regimes and regime shifts. FI over 

time denotes changes in dynamic order such that decreasing FI indicates movement away from 

sustainability, no net change in FI denotes the system is maintaining its current state and increasing 

FI indicates that system is becoming more orderly and possibly moving toward a new dynamic 

regime that is different from the present regime. 

In this chapter, we provide the basic theory on the method, describe the computation approach, and 

list the variables used in the assessment. To enhance understanding of the method, we provided 

a pedagogic exercise to “walk through” the steps for computing Fisher information. Further, we 

compute FI for the SLB data and discuss the results, as well as, the strengths and weaknesses of the 

approach. 

One of the main goals of this project was to use readily available data to assess regional 

sustainability. Based on the three pillars of sustainability, it is paramount that data used to compute 

FI encompass the environmental, social, and economic components of the system. Because data 

compiled to calculate EFA, GNRP, and EmA incorporate the pertinent aspects of activity in the 

region, key primary variables were selected from these data sets. These variables which capture 

the consumption of food and energy, agricultural production, environmental characteristics, 

demographic properties, and changes in land use for the SLB system from 1980-2005 are listed in 

the chapter. However, the source and details of the data are provided in the corresponding metric 

chapters.

Our analysis revealed that there was no indication of a regime shift. The system exhibited small 

changes in dynamic order during the study period, with a slight decreasing trend near the end of the 

period (i.e., 1998-2001). Accordingly, we deduce that although conditions in the SLB are relatively 

steady, there was an indication of slight movement away from sustainability. 

A primary benefit of using this information theory approach is the ability to collapse data from 

intricate, multivariate systems into a metric that can be computed over time. This is particularly 

important considering the complex, multi-disciplinary nature of sustainability. Further, the method 

is robust in that it assesses dynamic behavior of both model and real systems. One key output 

of this project is the MATLAB code and graphical user interfaces (GUI) developed to simplify 

the computation and evaluation procedure. Key considerations surrounding the computation of 

FI include establishing the integration window parameters (hwin and winspace), the need for 

determining a measure of uncertainty for the state variables (which is used to set the size of states 

for each variable), and the availability and quality of data characterizing the state of the system. 

Accordingly, we used a simple example to perform sensitivity analysis on hwin and winspace to 

determine the impact of those parameters on the result of FI. In addition, we provided guidance for 

determining the size of states parameter.

ES.7 The San Luis Basin Sustainability Metrics Project: Results, 

Conclusions, and Recommendations

This chapter examines the sustainability metrics holistically, using the results of the previous four 

chapters, to decipher whether the SLB is on a sustainable path (i.e., moving toward sustainability). 

Although each of the four metrics captures different aspects of the system, they appear to have 

similar results based on the concept of weak and strong sustainability. EFA and EmA, measures 

of strong sustainability, indicate movement away from sustainability whereas GNRP provided 

no definitive evidence of movement away from sustainability, and FI suggests relative stability, 

both metrics are measures of weak sustainability. Overall, the weight of evidence led us to the 

assessment that the region is moving slowly away from sustainability. The growth of population 

from 1980 to 2005 and the increased pressure on environmental resources that is the inevitable 

result of human use of the environment also supports our conclusion. However, we recognize the 

estimates are not perfect and have identified the limitations of each metric in their respective chapter. 

For example, the best we can do for a trend analysis is a visual examination without statistical tests 

given the lack of uncertainty measurements for the variables.
xxviii

The majority of the limitations for the methodology relate to data needs and data manipulation. 

Because much of the data needed for these metrics are typically not collected at the county level, we 

needed to rescale or convert state or US data to the county or regional level. Other challenges are 

related to quantitatively assessing trends for the metrics and using the methodology in other regions 

besides the SLB.

We believe that our methodology adequately characterized and evaluated sustainability of the SLB, 

in part, because our results are plausible and our observations and conclusions have been validated 

to a certain extent in public meetings with stakeholders in the SLB and by anecdotal evidence. The 

methodology demonstrated that this framework was not entirely straightforward or simple, nor was it 

inexpensive, but it suggests the multidisciplinary approach does a sufficient job of assessing whether 

a region is moving toward or away from sustainability. We do not contend that this methodology 

is definitive; however, we do argue that each metric identified changes to major components of the 

system. We propose that together, these metrics adequately represent the complexity of a regional 

system.

We offer the following recommendations for future research based on the results of our study: 

1) Examine previous management decisions in the SLB to see if and how the metrics changed. 

For example, the Great Sand Dunes National Park and Preserve was created in 2000 

and increased the National Monument land area by almost four times (NPS 2007). This 

recommendation would help to determine if any of the changes in the metrics could be linked 

back to the management decision to increase the public land area.

2) Continue calculating the metrics in the SLB for subsequent years to examine how sensitive the 

metrics are to management decisions. 

3) Use stakeholder meetings to determine major sustainability issues in the region and better link 

the metrics on those issues and concerns. Determine if there are other established metrics that 

may better assess sustainability of a region by addressing known issues. This relationship with 

stakeholders will help ensure the metrics are capable of capturing changes pertinent to issues 

of public concern. The meetings can be used to gauge whether stakeholders and decision 

makers can understand the results of the metrics. More research can focus on simplifying 

the interpretation of results for various groups of people: managers, regulators, landowners, 

educators, policy makers, etc.

4) Develop trend analyses and/or approaches for estimating confidence intervals for individual 

metrics. Although a trend may appear obvious, recognizing and identifying the variation in the 

data and resulting metric will better identify significant trends. One example might be using 

econometric methods (e.g., Greene 1993, Hay et al. 2002) or sensitivity analyses.

5) Develop models of alternative future scenarios and estimate multiple metrics (Baker et al. 

2004, Kok and van Delden 2009, Yeh and Chu 2004) that are sensitive to relevant changes in a 

system or region. For example, how would the installation of solar farms affect the metrics?

6) Test these multiple metrics in other regions and determine if the metrics are indeed capturing 

the trend in sustainability of regional systems.

7) Examine the correlation among alternative metrics to determine whether certain metrics could 

be dropped from analysis (i.e., one metric moves in a similar or opposite pattern to another 

over time) in order to reduce the resources needed (e.g., Bastianoni et al. 2008 used Principal 

Components Analysis).

8) Consider the use of other scientific approaches, rather than limiting analysis to visual 

examination of graphs, for deciphering the holistic results of multiple metrics, such as 

multicriteria methods (e.g., Shmelev and Rodríguez-Labajos 2009); data envelopment analysis 

(e.g., Despotis 2005, Kortelainen 2008); mathematical programming (e.g., Zhou et al. 2007); or 

other integrated sustainability metrics (e.g., Mayer 2008).
xxix

ES.8 References

Baker, J.P., Hulse, D.W., Gregory, S.V., White, D., Van Sickle, J., Berger, P.A., Dole, D., Schumaker, 

N.H. 2004. Alternative Futures for the Willamette River Basin, Oregon. Ecological Applications 

14, 313-324.

Bastianoni, S., Pulselli, F., Focardi, S., Tiezzi, E., Gramatica, P. 2008. Correlations and complementarities in data and methods through principal components analysis (PCA) applied to the results of 

the SPIn-Eco project. Journal of Environmental Management 86, 419-426.

Brown, M.T. Ulgiati, S. 1997. Emergy-based indicators and ratios to evaluate sustainability: monitoring, economies and technology toward environmentally sound innovation. Ecological Engineering 9, 51-69.

Campbell, D.E., Ohrt, A. 2009. Environmental Accounting Using Emergy: Evaluation of Minnesota. Office of Research and Development, National Health and Environmental Effects Research 

Laboratory, Atlantic Ecology Division. EPA/600/R-09/002. 138 p.

Despotis, D.K. 2005. A reassessment of the human development index via data envelopment analysis. Journal of the Operational Research Society 56, 969-980.

Dietz, S., Neumayer, E. 2007. Weak and strong sustainability in the SEEA: Concepts and measurement. Ecological Economics 61, 617-626.

Emery, P.A. 1996. Hydrology of the San Luis Valley, Colorado, an Overview--and a Look at the Future, in: Thompson, R.A., Hudson, M.R., Pillmore, C.L. (Eds.), Geologic Excursions to the Rocky 

Mountains and Beyond. Field Trip Guidebook for the 1996 Annual Meeting, Geologic Society of 

America; Oct. 28 1996-Oct. 31 1996, Denver, CO. Colorado Geological Survey, Department of 

Natural Resources, Denver, CO. Available online at http://www.nps.gov/archive/grsa/resources/

docs/Trip2023.pdf. Last accessed June 23, 2009.

Greene, W. 1993. Econometric Analysis. Prentice Hall, New Jersey.

Kok, K., van Delden, H. 2009. Combining two approaches of integrated scenario development to 

combat desertification in the Guadalentin watershed, Spain. Environment and Planning B: Planning and Design 36, 49-66.

Kortelainen, M. 2008. Dynamic environmental performance analysis: a Malmquist index approach. 

Ecological Economics 64, 701-715.

Mayer, A. 2008. Strengths and weaknesses of common sustainability indices for multidimensional 

systems. Environment International 34, 277-291.

Mayer, A., Thurston, H., Pawlowski, C. 2004. The multidisciplinary influence of common sustainability indices. Frontiers in Ecology and Environment 2, 419-426.

NPS (National Park Service). 2007. Final Summary of General Management Plan/Wilderness 

Study/Environmental Impact Statement. US Department of the Interior. Mosca, CO.

Neumayer, E. 2010. Weak versus Strong Sustainability: Exploring the Limits of Two Opposing 

Paradigms. Edward Elgar, Northampton, MA.

Pearce, D. 2002. An intellectual history of environmental economics. Annual Review of Energy 

and the Environment 27, 57-81.

Shmelev, S., Rodríguez-Labajos, B. 2009. Dynamic multidimensional assessment of sustainability 

at the macro level: the case of Austria. Ecological Economics 68, 2560-2573.

Yeh, S.C., Chu, Y.P. 2004. Economic-environmental scenario simulations to measure sustainable 

development in Taiwan. Advances in Architecture Series 18, 483-492.

Zhou, P., Ang, B.W., Poh, K.L. 2007. A mathematical programming approach to constructing composite indicators. Ecological Economics 62, 291-297.
1

1.0 

Introduction

1.1 Concept of Sustainability 

The concept of sustainability and the efforts to 

implement sustainability are, at their core, an 

undertaking to insure the needs of current and future 

generations are met. This fundamental concept is 

embodied in one form or another in a number of public 

pronouncements on the subject of sustainability. One 

frequently cited quote from the World Commission 

on Environment and Development (1987: 43) defines 

sustainability as “development that meets the needs of 

the present without compromising the ability of future 

generations to meet their own needs.” It is important 

to note the associated economic, social, and supporting 

environmental systems must work in concert to maintain 

a desired level of functioning and these are sometimes 

referred to as the pillars that support sustainable 

development (e.g., UN 2002). Sustainability, however, 

is an anthropocentric issue that speaks to the need for 

maintaining those interrelated systems over the long term 

for the benefit of humanity.

The concept of sustainability is very broad, 

complicated, and challenging and it can lead to different 

interpretations (e.g., see Prugh et al. 1999). For 

example, in the political arena, sustainability is often 

associated with ideas from social science including 

social justice and equity of wealth distribution (Prugh 

et al. 1999). In an economic sense, Pezzey and Toman 

(2002: 3-4) state a sustainable economic development 

path occurs when “expected well-being per capita 

(broadly defined to include more than just material or 

market goods) rises over the long term.” Stavins et 

al. (2003) provide another economic perspective that 

requires both dynamic efficiency and intergenerational 

equity for their definition of sustainability. In a strict 

ecological sense, sustainability is concerned with 

the conservation of ecosystem processes, including 

aspects such as biological diversity (Callicott and 

Mumford 1997, Aarts and Nienhuis 1999). Engineers 

focus on the efficiency of energy and material use in 

industrial processes, and in reducing waste with some 

consideration of social and economic factors (Mihelic et 

al. 2003, Sikdar et al. 2004).

Although there is not a consensus on defining 

sustainability, there is general recognition the current 

level of human activity appears to damage the systems 

that support us. Sustainability relates to finding and 

maintaining a set of system conditions that can support 

the social and economic development of a human 

population. To be sustainable, development must not 

have major adverse effects on the environmental systems 

that support the population. Consequently, metrics are 

needed to help us understand the linkages and demands 

of human activity (e.g., society, economy, government, 

industry, etc.) on environmental systems (Cabezas et al. 

2005a).

When choosing sustainability metrics, researchers should 

be aware of relevant issues such as the paradigms of 

weak and strong sustainability, which relate to how much 

man-made and other capital can substitute for natural 

capital (described in Chapter 2 of this report, Neumayer 

2010). In addition, there is some amount of uncertainty 

and unreliability in measures of sustainability and 

most measures have many simplifying assumptions 

that complicate this type of research. Given these 

complexities, a collaborative, interdisciplinary approach 

could alleviate some of the conflict and ambiguities in 

these interpretations and approaches.

The Federal government’s sustainability research 

strategy recognizes that an integration of economic, 

social, and environmental policies is required to achieve 

sustainability (USEPA 2007). Therefore, the strategy 

states (USEPA 2007: 23):

EPA and its partners will develop integrating 

decision support tools (models, methodologies, 

and technologies) and supporting data and 

analysis that will guide decision makers toward 

environmental sustainability and sustainable 

development.

To translate the aforementioned mandate from the 

USEPA strategy into a practical plan of action requires 

a working definition of sustainability. Thus, we define 

sustainability as identifying and maintaining a set of 

conditions that support environmental, social, and 

economic systems that meet the needs of both current 

and future generations, thereby addressing the three 

pillars of sustainability. Moreover, in order to satisfy 

the definition of sustainability, the environmental, 

social, and economic systems must effectively meet the 

needs of current and future generations, indefinitely. To 

assess sustainability, information is needed to evaluate 

human well-being, how the different components of a 

system are linked, and the stresses of human activity 

(e.g., society, economy, government, industry, etc.) on 
2

environmental systems. One way to approach this and 

guide decision makers toward sustainability is through 

metrics that quantify changes in environmental, social, 

and economic systems.

The research and results presented in this report will 

provide decision support through the estimation of 

metrics that relate to sustainability. These metrics 

will help decision makers understand the movement 

of their system toward or away from environmental 

sustainability at the regional level. The San Luis 

Basin (SLB), in south-central Colorado, was used as a 

pilot study to develop and test the applicability of the 

methodology.

1.2 The San Luis Basin Sustainability 

Metrics Project 

Over the long-term, the maintenance of intact and 

sustainable ecosystems depends on the ability of all 

landowners to manage and utilize their lands in a 

sustainable manner. Because the Federal Government 

owns more than 265.9 million hectares (GSA 2004), 

the US has a vested interest in managing these lands 

in a sustainable manner. For example, the US Forest 

Service Mission is to “Sustain the health, diversity, 

and productivity of the Nation’s forests and grasslands 

to meet the needs of present and future generations” 

(USDA 2007: 2). Understanding the effects of 

facility operations and management decisions on the 

environment at the regional scale is essential to the 

work of several programs within the USEPA. This is 

especially relevant in the west, where the majority of 

the US public lands are located. In fact, over thirtythree percent of the land within USEPA Region 8, where 

the SLB is located, is publicly owned (USEPA 2009a). 

Thus, choices made daily by land managers are an 

important component of how well the environment in the 

Region’s six states (Colorado, Montana, North Dakota, 

South Dakota, Utah, and Wyoming) is protected.

Public land managers make decisions daily that 

can affect the quality of the land for which they are 

responsible. These decisions are based on several 

factors, including the mission of the agency they serve; 

the directives of the current political administration; and 

the wishes of the surrounding population. To assist land 

managers and the surrounding communities in making 

sustainable choices, USEPA needs to develop integrated, 

system-based tools that provide feedback on how these 

collective decisions are affecting ecosystems and their 

services. To fulfill this need, the USEPA Office of 

Research and Development initiated the San Luis Basin 

Sustainability Metrics Project in 2006. The results of 

this project will provide a set of sustainability metrics for 

use on the regional scale that are scientifically credible 

and assist land managers. Land managers can use the 

methodology to identify trends as the system progresses 

through time, and provide early warning of abnormal 

conditions. This will help them better protect and 

manage healthy ecosystems.

This report presents an overview of the San Luis Basin 

Sustainability Metrics Project, a five-year research 

program. The purpose of the report is to present the 

methodology, discuss approaches used to estimate the 

individual metrics, and examine results that could inform 

decision makers in the region. The goal of this study 

was to produce a straightforward, relatively inexpensive 

methodology that was simple to use and interpret.3

 

This required historical data to be readily accessible, 

that metrics be applicable to the relevant scale, and 

that results meet the needs of decision makers. The 

objectives were: 1) determine the applicability of using 

existing datasets to estimate metrics of sustainability at 

a regional scale, 2) calculate the metrics through time 

(initial proposal was 1980-2005), and 3) compare and 

contrast the results to determine if the system is moving 

toward or away from sustainability.

1.2.1 Measuring Sustainability

In general, identifying major properties (e.g., climatic, 

soil, biological diversity, etc.) and cycles (e.g., water, 

carbon, phosphorus, oxygen, nitrogen, etc.) of a system 

may seem relatively simple and straightforward, but 

it is difficult to list and quantify many of the specific 

details of the properties and cycles critical to the needs 

of humans and, therefore, to sustainability. As a starting 

point, we identified and tested a set of four metrics 

that capture some of the most basic properties of a 

system. The four metrics included Ecological Footprint 

Analysis (EFA), Green Net Regional Product (GNRP), 

Emergy Analysis (EmA), and Fisher information (FI; 

more discussion on the choice of metrics can be found 

in Chapter 2). These metrics, respectively, represent 

human burden on the environment, economic wellbeing, flow of available energy through the system, and 

overall system order. Each metric captures particular 

aspects of the system, but may not account for every 

aspect pertinent to sustainability. There is some overlap 

or redundancy (e.g., consider a physician using multiple 

tests to examine a patient’s health) in the variables 

used and, therefore, the pillars they quantify and how 

sensitive each metric is to the pillars of sustainability. In 

3

 We believe the method is relatively inexpensive because we have 

identified the majority of data sources for these types of calculations 

leading to reduced time and resources necessary for others to compute 

the metrics. However, whether we met the goal and objectives would 

be based on anecdotal evidence and discussions with stakeholders in 

the region.
3

fact, this issue of sensitivity is one of the main reasons 

we initially used multiple metrics.

Note that for this report, we use metric and index 

interchangeably; however, we acknowledge a difference 

between indicator and either of the terms we use. 

Mayer (2008) defines an indicator as measuring one 

characteristic (e.g., CO2 emissions or biodiversity), 

but an index (or metric) combines many indicators (or 

variables) through aggregation (e.g., GNRP). Therefore, 

indices or metrics can provide a multidimensional view 

of sustainability.

The four metrics we utilized are capable of measuring 

sustainability in an absolute sense. For example, if 

the ecological footprint (demand) is larger than the 

available biological capacity (supply), then the system is 

considered unsustainable following the assumptions and 

methods of that approach. It is, however, conceptually 

and practically difficult to compute values for these 

metrics with absolute accuracy. The conceptual 

difficulty arises because each of these metrics attempts 

to convert many disparate variables or indicators into 

one common measure. For example, consider that EFA 

converts everything into land area, EmA converts all 

quantities into solar emjoules, GNRP requires that all 

quantities be expressed monetarily, and FI converts 

system variables into a measure of dynamic order. This 

conversion exercise is relatively simple and accurate as 

long as the quantity is closely related to the common 

measure of the metric (e.g., converting agricultural 

production into land area). This is more difficult and less 

accurate when converting a quantity that is not closely 

related to the common measure (e.g., converting human 

transportation needs into information). The practical 

difficulty comes from the impossibility of measuring 

or estimating values for the innumerable variables or 

indicators needed to characterize a system well enough 

to compute accurate values for the metrics. Hence, it 

is likely that even under the most extraordinary efforts 

inaccurate values for the metrics would result because of 

measurement errors, errors in the conversion of unrelated 

variables into a common measure, and/or missing 

variables.

The aforementioned reasons have led us to adopt the 

strategy of focusing not on computing absolute values 

for the metrics but on identifying changes in the value 

of the metrics through time.4

 We believe the benefits 

of this strategy are: 1) errors incurred in complicated 

calculations due to either conceptual or practical issues 

often tend to cancel out when differences are taken, 2) 

4

 Without a measure of uncertainty for any of the metrics, it is not possible to determine if any change over time is statistically significant. 

We address this in detail in Chapter 7.

it is easier to formulate scientifically defensible criteria 

for changes than for absolute values (but see Alonso 

1968), 3) components that do not change over time can 

be dropped or simply approximated, and 4) trends in the 

metrics rather than absolute values are most likely to 

indicate movement toward or away from sustainability. 

Knowing how the system is evolving over time rather 

than its state at a point in time is, perhaps, the most 

critical issue. There is a body of scientific theory that 

tells us what changes in these metrics mean in terms of 

sustainability (e.g., Campbell 2001, Heal and Kristöm 

2005, Mayer 2008). We established criteria for each 

metric to determine if a system is moving toward 

sustainability. For EFA, ecological balance (biocapacity 

- ecological footprint) is stable or increasing with time. 

For EmA, the fraction of renewable emergy used to 

total emergy used is trending to one. FI and GNRP 

are one-sided tests of weak sustainability and they 

cannot technically show if the system is moving toward 

sustainability. Criteria for these one-sided tests indicate 

if the region is moving away from sustainability when 

FI is steadily decreasing with time (Karunanithi et al. 

2008) and GNRP is decreasing with time (i.e., Pezzey 

et al. 2006). Note these criteria do not presuppose any 

particular version of the system; rather they address 

the maintenance and preservation of basic properties 

and processes that are necessary for continued human 

existence. An unsustainable path is defined as one where 

any of the following are observed - human welfare (as 

defined by GNRP), ecological balance, emergy flows 

into the system, or Fisher information - are in violation 

of the aforementioned criteria. We considered a 

system that is moving away from sustainability, for that 

particular aspect of the system, to be on an unsustainable 

path, when one or more of the four criteria are not 

satisfied and/or a one-sided test is satisfied.

1.2.2 Choice of Region 

The SLB in Colorado (Fig. 1.1) was selected as the 

study area because of its distinct natural hydrological 

boundaries, limited population, large amount of publicly 

owned land, and interest expressed in the study by 

USEPA Region 8 and other government officials. 

Surrounded by the Sangre de Cristo Mountains to 

the east and the San Juan Mountains to the west, 

approximately seventy-eight percent of this region 

is publicly owned, making it an optimal system for 

our study. This study has fostered a partnership for 

sustainability research between USEPA Region 8 and 

the USEPA’s Office of Research and Development. 

Moreover, it will expand existing partnerships between 

land management agencies within USEPA Region 8 such 

as the National Park Service, USDA Forest Service, 
4

US Fish and Wildlife Service, and Bureau of Land 

Management (BLM). Some 202342 hectares (ha) on 

the borders of the valley (adjacent to National Forest 

lands) are managed by BLM and much of this land is 

leased to neighboring ranches for grazing. The Great 

Sand Dunes National Park and Preserve is the most well 

known feature of the valley. Directly west of the Sangre 

de Cristo Mountains, the dunes (the largest in North 

America) rise to a height of over 213 m, and are under 

the protection of the Park and Preserve.

1.2.2.1 Description of the San Luis Basin

We define the SLB as the following seven counties: 

Alamosa, Conejos, Costilla, Hinsdale, Mineral, Rio 

Grande, and Saguache. This region contains the Upper 

Rio Grande River Basin, the San Luis Valley, and the 

Great Sand Dunes national Park and Preserve. The 

Upper Rio Grande River Basin is nearly 21000 km2

 in 

size (USDA 1978) and the San Luis Valley, a primary 

feature of the basin, is a high desert, sitting at an average 

altitude of approximately 2300 m (CWCB 2000).

The SLB is home to approximately 50,000 people (US 

Census Bureau 2002). Although the study area is in 

the State of Colorado, the region borders New Mexico 

to the south and, based on conversations with local 

stakeholders, is closely related to that state culturally 

and economically. This relationship is not surprising 

considering the geographic features that define the 

region to the north, east, and west. The economy is 

predominantly based on agriculture (San Luis Valley 

Development Resources Group 2007). The agriculture 

is dominated by potatoes, small grains, and alfalfa 

and is dependent on irrigation (approximately 85% of 

the water consumption in the valley is for agriculture; 

CWCB 2000, Wurster et al. 2003, NASS 2009) and 

future demand for irrigation water could increase onehundred percent (HRS Water Consultants 1987). Most 

of the irrigation water comes from surface water that 

is collected and channeled through a series of historic 

canals (San Luis Valley Development Resources Group 

2007).

The SLB sits atop the Rio Grande Rift and is drained to 

the south by the Rio Grande River (Mayo et al. 2007). 

The northern section does not drain into the Rio Grande 

River and is referred to as the Closed Basin (CWCB 

2000). The Rio Grande River begins to flow just south 

of the Closed Basin (Mayo et al. 2007). The San Luis 

Valley has an annual average precipitation in the basin 

ranging from 17 cm at Alamosa to 114 cm at Wolf Creek 

Pass (CWCB 2000), but much of the surface water 

begins as snowmelt coming out of the mountains (Emery 

1979).

Given the structure of the region, the groundwater 

system is complex. According to Mayo et al. (2007), 

there are three groundwater sources within 1200 m of 

the surface: 1) an unconfined aquifer, 2) an upper active 

confined aquifer, and 3) a lower active confined aquifer. 

The unconfined aquifer is the closest to the surface 

compared to the confined aquifer (Mayo et al. 2007) 

and it provides water to valley residents and farmers 

and maintains the levels of the San Luis Lake and other 

wetlands just to the west of the dunes (Wurster et al. 

2003). The confined aquifer occurs between about 305 

m to 1829 m below the surface (HRS Water Consultants 

1987). Although layers of clay separate the different 

aquifers, there is some leakage between the unconfined 

and confined aquifers (Mayo et al. 2007). In addition 

to aquifer leakage, recharge occurs through stream 

infiltration, mountain underflow, overland infiltration, 

and canal and irrigation infiltration (Mayo et al. 2007).

The combined use of ground and surface water has been, 

and will continue to be, of major importance to proper 

management of the valley’s water resources (Emery 

1979). The Conejos River has lost some of its flow from 

withdrawal from the confined aquifer and increased 

consumptive use near the river (Emery 1979). This 

loss of water has caused problems with meeting the Rio 

Grande Compact, which requires Colorado to deliver 

a certain amount of water to New Mexico and Texas 

(Emery 1979, CWCB 2000). In order to meet the water 

requirements of the Compact, the Closed Basin Project 

pumps water from the Closed Basin using wells and 

canals and discharges the water into the river (CWCB 

2000).

1.2.3 Community Outreach 

In order to produce a useful product, the USEPA needed 

to bring the local residents into the project to let them 

know what we were doing, why we were doing it, and 

what the potential outcomes might be. The purpose 

of the outreach is that once the project is complete, 

the metric tools would be accepted and used by the 

community. In addition, USEPA needed to advertise the 

project well enough so as we collected information in the 

SLB, local residents could contact us with questions.

The outreach approach developed and implemented by 

USEPA Region 8 focused on involving people who were 

identified as community representatives and leaders:

• Federal Land Managers – Because 69% of the land 

is federally owned and managed (San Luis Valley 

Development Resources Group 2007), federal 

land managers from the National Park Service, 

USDA Forest Service, BLM, and US Fish and 

Wildlife Service played a significant role in the 
5

outreach efforts.5 These managers were engaged 

in developing the metric tool for their own use 

and are seen as leaders within the SLB community 

and its members. Our connection with the federal 

land managers in the valley served to identify key 

community leaders. Because these individuals were 

well known and respected within the community, 

their support and introduction of ORD scientists 

to the community was vital to being accepted by 

community leaders. 

• Community Organizers and Non-profit groups – We 

had a list of community organizers and interested 

parties from previous work the USEPA performed 

in the San Luis Valley on drinking water (USEPA 

2009b). 

• Adams State College (ASC) – ASC, a prominent 

institution in the community, has a stated goal of 

reaching out as a partner in the community and has a 

well-established Community Partnerships Program 

(CPP) whose mission is to connect ASC resources 

with the community in order to increase the quality 

of life for all residents of the SLB. ASC CPP was 

instrumental in helping USEPA scientists connect to 

the community. 

• National and State representatives – The Secretary 

of the Interior (Kenneth Salazar) and Colorado 

Congressman (John Salazar) are residents, 

landowners, and farmers in the community. These 

local representatives, as well as US Senators 

Michael Bennet and Mark Udall, were informed of 

the project and engaged in the community outreach 

efforts.

Once we established contact with local leaders, a USEPA 

Region 8 representative spent a week in the community 

meeting with these leaders. Desired outcomes included 

informing the individuals about the project; receiving 

feedback on how to inform the community about the 

project; integrating their concerns into the project 

design; receiving feedback on the use of the tool within 

the community; and developing an expanded list of 

community leaders that we should contact. More than 30 

such meetings were held and the results were shared with 

the project team. Community leaders collectively asked 

for a formal project overview in order to understand 

better the usefulness and application of the metrics to 

the local community. Working with ASC and CPP, the 

USEPA representative scheduled a project overview 

meeting and held a question and answer event. Seventy5

 The San Luis Valley Development Resources Group defines the Valley as the following six counties: Alamosa, Conejos, Costilla, Mineral, 

Rio Grande, and Saguache. This differs from our definition of the SLB 

by not including Hinsdale County and accounts for the difference in the 

proportion of land publicly owned (~78%) stated previously.

five letters of invitation were sent to community leaders 

who were identified during the first phase of the outreach 

effort. Over 40 people attended the project overview 

meeting and USEPA received positive feedback and an 

indication the community welcomed the project focus.

CPP notified the USEPA of an opportunity to develop 

and present a challenge to an upper level advertising 

class (BUS 345) in the School of Business as their 

class project. The USEPA representative presented an 

overview of the project and challenged the students 

to develop an outreach campaign directed to the SLB 

community. The campaign would inform the community 

about the project and provide information on who to 

contact within the project to get questions answered or 

provide feedback from the general community about the 

project. The students selected the challenge as the class 

project and developed a work plan that included a public 

meeting, press releases, and identification of potential 

media connections for additional outreach efforts.

The class research identified five areas of outreach 

opportunity and/or concern: 1) the SLB residents were 

unaware of the project, 2) the most effective form of 

advertising in the SLB is through the local newspaper, 

3) the primary concern the residents had with the project 

was USEPA’s purpose for conducting the research, 4) 

public meetings are an effective form of informing the 

SLB residents, and 5) SLB residents initially viewed 

the project as having a negative impact on the region. 

Based on these findings, the class requested a conference 

meeting with a USEPA representative to establish 

firm goals and objectives for the outreach activities 

and brainstorm ideas for an advertising project. The 

approach developed by the class included an organized 

town meeting to inform the community about the 

project. The class advertised the town meeting in the 

newspaper and placed notification of the meeting on the 

Chamber of Commerce web page. This effort revealed 

the community was concerned the project could result in 

new regulations, more government control of land use 

decisions in the region, and concern that USEPA would 

acquire land from farmers and residents. Based on these 

findings, we sought opportunities to meet with the public 

and hand out information describing the overall project. 

The information on these postcards included a contact 

name and phone number for the Region 8 representative 

and had the following text (see footnote 3 above for the 

six counties of the Valley):

This is to let you know that the Environmental 

Protection Agency is working in your community 

to gather information for a research project. The 

project will help build a scientific foundation 

for sound environmental decision-making. This 
6

is a research project only. Our goal is to provide 

the San Luis Valley with useful tools for future 

planning. This research project will not result in 

any action by the USEPA in the San Luis Valley.

These postcards were distributed at public events 

identified by the community leaders as places where the 

community gathered and would be receptive to hearing 

about the project. Additional outreach was conducted 

by CPP and included seminars open to the public. For 

example, USEPA received invitations to speak at public 

events. One such event was Focus the Nation, a national 

effort to have colleges and universities focus on one 

significant issue for one day. Another public event was 

the annual meeting of the San Luis Valley Resource 

Conservation and Development Board, a USDA 

program designed to assist communities to accelerate 

the conservation, development, and utilization of natural 

resources in order to improve their economic activity. 

USEPA held its annual team meetings in the region 

and in 2007 and 2008 invited community leaders to 

participate and provide valuable community perspectives 

to the project.

This community outreach approach was effective in 

meeting our goal of informing the community in order to 

increase the probability the community would accept and 

use the tools. We presented preliminary results from the 

project to the community and they were well received 

by the community and the attendees provided valuable 

feedback and comments to the research group.

1.3 Outline of Report 

Having introduced some of the background for this 

pilot project, the report proceeds as follows: first, we 

present a literature review of sustainability metrics in 

Chapter 2. Because of the abundance of literature on 

sustainability metrics and classification systems, we only 

reference studies that report lists of metrics. The chapter 

focuses on metrics that fit within categories of ecology, 

economics, energy, and system order. In addition, this 

chapter summarizes studies that have looked at multiple 

metrics over time. Chapters 3 through 6 present the 

four metrics used in the SLB pilot project: EFA, GNRP, 

EmA, and FI, respectively. Each of these chapters 

presents an explanation of the methodology, the data and 

their sources, results, and discussion. The remaining 

chapter summarizes the results of all the metrics and 

discusses the strengths and limitations of estimating four 

sustainability metrics over time using publicly available 

data. Seventeen appendices cover additional results or 

methodologies (e.g., estimates for wind erosion) used in 

estimating the metrics.

1.4 References

Aarts, B.G.W., Nienhuis, P.H. 1999. Ecological 

Sustainability and Biodiversity. International Journal 

of Sustainable Development and World Ecology 6, 

89-102.

Alonso, W. 1968. Predicting best with imperfect data. 

Journal of the American Institute of Planners 34, 248-

255.

Callicott, J.B., Mumford, K. 1997. Ecological 

Sustainability as a Conservation Concept. 

Conservation Biology 11, 32-40.

Campbell, D. 2001. Proposal for Including What 

Is Valuable to Ecosystems in Environmental 

Assessments. Environmental Science and Technology 

35, 2867-2873.

CWCB (Colorado Water Conservation Board). 2000. 

Rio Grande Decision Support System: An Overview. 

Section 2.0 Rio Grande Basin Conditions. CWCB, 

State of Colorado. Available online at http://cdss.state.

co.us/DNN/RioGrande/tabid/57/Default.aspx. Last 

Accessed July 21, 2009.

Emery, P. 1979. Geohydrology of the San Luis Valley, 

Colorado, USA. Proceedings of the Canberra 

Symposium 1979. IAHS-AISH Publication no. 128, 

297-305. 

GSA (General Services Administration). 2004. Federal 

Real Property Profile for FY2004. US General 

Services Administration, Office of Governmentwide 

Policy. Available online at http://www.gsa.gov/

realpropertyprofile. Last accessed July 21, 2009.

Heal, G., Kristöm, B. 2005. National income 

and the environment, in: Mäler, K., Vincent, J. 

(Eds.), Handbook of Environmental Economics, 

Economywide and International Environmental Issues, 

Vol. 3. Elsevier, North-Holland, Amsterdam.

HRS Water Consultants. 1987. San Luis Valley 

Confined Aquifer Study Final Report (Phase I). 

Consulting report, 87001-17, Lakewood, CO.

Mayer, A. 2008. Strengths and weaknesses of common 

sustainability indices for multidimensional systems. 

Environment International 34, 277-291.

Mayo, A.L., Davey, A., Christiansen D. 2007. 

Groundwater flow patterns in the San Luis Valley, 

Colorado, USA revisited: an evaluation of solute and 

isotopic data. Hydrogeology Journal 15, 383-408.
7

Mihelic, J.R., Crittenden, J.C., Small, M.J., Shonnard, 

D.R., Hokanson, D.R., Zhang, Q., Chen, H., Sorby, 

S.A., James, V.U., Sutherland, J.W., Schnoor, J.L. 

2003. Sustainability Science and Engineering: The 

Emergence of a New Metadiscipline. Environmental 

Science and Technology 37, 5314-5324.

NASS (National Agricultural Statistics Service, United 

States Department of Commerce, United States 

Department of Agriculture). 2009. United States 

Census of Agriculture: 2002. Available online 

at http://www.nass.usda.gov/Statistics_by_State/

Colorado/index.asp. Last accessed June 26, 2009.

Pezzey, J., Hanley, N., Turner, K., Tinch, D. 2006. 

Comparing augmented sustainability measures for 

Scotland: Is there a mismatch? Ecological Economics 

57, 60-74.

Pezzey, J., Toman, M. 2002. Making Sense of 

“Sustainability.” Issue Brief 02–25. Resources for the 

Future. Washington, DC.

Prugh, T., Costanza, R., Daly, H. 1999. The local 

politics of global sustainability. Island Press, 

Washington, DC.

San Luis Valley Development Resources Group. 2007. 

Comprehensive economic development strategy. 

Available online at http://www.slvdrg.org/ceds.php. 

Last accessed June 17, 2009.

Sikdar, S.K., Glavic, P., Jain, R. (Eds.). 2004. 

Technological Choices for Sustainability. SpringerVerlag, Berlin, Germany. 

Stavins, R., Wagner, A., Wagner, G. 2003. Interpreting 

sustainability in economic terms: dynamic efficiency 

plus intergenerational equity. Economic Letters 79, 

339–343.

UN (United Nations). 2002. Report of the World 

Summit on Sustainable Development. Johannesburg, 

South Africa, 26 August-4 September 2002. A/

CONF.199/20. ISBN 92-1-104521-5. United Nations, 

New York, NY.

US Census Bureau. 2002. 2000 Census of Population 

and Housing, Summary Population and Housing 

Characteristics, PHC-1-7, Colorado. Washington, DC. 

USDA (United States Department of Agriculture). 1978. 

Water and Related Land Resources, Rio Grande Basin 

Colorado. A Cooperative Study with the Colorado 

Water Conservation Board. U. S. Government 

Printing Office. Volumes 1 and 2.

USDA (United States Department of Agriculture). 2007. 

USDA Forest Service Strategic Plan FY2007-2012. 

FS-880. Forest Service. Washington, DC.

USEPA (US Environmental Protection Agency). 2007. 

Sustainability Research Strategy. EPA 600/S-07/001. 

Office of Research and Development. Washington, 

DC.

USEPA (US Environmental Protection Agency). 2009a. 

About EPA Region 8. Region 8. Denver, CO. 

Available online at http://www.epa.gov/region8/about/

index.html. Last accessed July 21, 2009.

USEPA (US Environmental Protection Agency). 2009b. 

San Luis Valley Drinking Water Well Project. Region 

8. Denver, CO. Available online at http://www.epa.

gov/region08/ej/ejinitiatives.html#slv. Last accessed 

August 19, 2009.

World Commission on Environment and Development. 

1987. Our Common Future. Oxford University Press, 

Oxford, UK

Wurster, F.C., Cooper, D.J., Sanford, W.E. 2003. 

Stream/aquifer interactions at Great Sand dunes 

National Monument, Colorado: influences on 

interdunal wetland disappearance. Journal of 

Hydrology 271, 77-100. 
8

Figure 1.1 – Seven county region referred to by the investigators as the San Luis Basin (SLB), in south-central 

Colorado.
9

2.0 

Sustainability Metrics

2.1 Introduction 

In this chapter, we review the literature of different 

measures of sustainability for the four categories 

described in Chapter 1: ecology, economics, energy, 

and system order. Once we provide background on our 

selected metrics, we review the literature of studies that 

have examined sustainability using multiple metrics, 

and those studies that examined sustainability over 

time. Both are important attributes of the San Luis Basin 

Sustainability Metrics Project. The last section justifies 

our choice of the four metrics from within the four broad 

categories.

2.2 Literature Review of Ecological, 

Economic, Energy, and System Order 

Metrics

Articles such as Böhringer and Jochem (2007), 

Gasparatos et al. (2007), Hák et al. (2007), Mayer et al. 

(2004), Mayer (2008), Ness et al. (2007), Singh et al. 

(2009), UN (2007), and Wilson et al. (2007) summarize 

different sustainability metrics and indicators. Many of 

these papers group the indices and indicators into broad 

categories and then summarize the individual measures 

in those groups. Neumayer (2010), for example, 

presents the two opposing paradigms of weak and strong 

sustainability along with metrics that fall under these 

paradigms. Weak sustainability assumes ecological 

functions and/or resources (i.e., natural capital) can 

be replaced by technological or man-made surrogates, 

whereas strong sustainability assumes ecological 

functions are not replaceable and natural capital cannot 

be substituted for with other forms of capital (e.g., Dietz 

and Neumayer 2007, Mayer et al. 2004, Mayer 2008, 

Neumayer 2010, Pearce 2002). For additional detail on 

how the specific indices mentioned in this report relate 

to sustainability, we suggest examining some of the 

papers listed above. Rather than repeating the extensive 

efforts above, we present a subset within the group of 

ecological, economic, energy, and system order metrics 

of sustainability. 

2.2.1 Ecological Sustainability 

A number of indices have been proposed that measure 

environmental or ecological sustainability and are based 

on population biology. Specifically, the indices are based 

on the concept of carrying capacity and they attempt 

to assess the number of individuals an ecosystem can 

maintain by quantifying the load a system can support 

and the impact organisms have on the system (Caughley 

1976). In other words, carrying capacity is the number 

of individuals of a given species that can be supported 

by the available resources in an ecosystem (Sharkey 

1970). Three such metrics based on carrying capacity 

that have been suggested as measures of sustainability 

are maximum sustainable yield (MSY), IPAT, and 

Ecological Footprint Analysis (EFA).

MSY attempts to identify the resilience of a population 

to harvest or extraction (e.g., Fry 1947, Ricker 1958, 

Schaeffer 1954, but see Pitcher and Hart 1982). To be 

sustainable, resources must be harvested at a rate that 

allows a population to replace or renew the extracted 

biomass to avoid depleting the supply. Implementing 

this management strategy has been difficult (Larkin 

1977) due to extreme variability in stock recruitment 

for some species, which was noted as a possible flaw by 

Russell (1931).

Another such metric adapted from carrying capacity 

by Ehrlich and Holdren (1971) is IPAT. IPAT proposes 

the Impact a population has on its environment is 

directly related to the product of Population size, 

level of Affluence, and the Technology employed by 

the population (e.g., York et al. 2003). Ehrlich and 

Holdren (1971) assumed an increase in any of the 

parameters would result in higher overall impact on 

the environment. A number of modifications have 

been made to the metric to examine if each parameter 

should have equal contribution (e.g., Sutton 2003, 

Waggoner and Ausubel 2002, York et al. 2002 and 

2003) or if additional factors should be included (e.g., 

Fan et al. 2006, Schulze 2002). For example, Dietz and 

Rosa (1994) converted IPAT to STIRPAT, a non-linear 

regression equation that allows random errors (i.e., 

stochasticity) in the estimation of the parameters and 

permits hypothesis testing (e.g., Dietz et al. 2007, Mayer 

2008). Interestingly, Dietz et al. (2007) use ecological 

footprint (see below) as their measure of anthropogenic 

stressors in their STRIPAT approach.

Rees (1992) and Wackernagel and Rees (1996) 

applied the concept of carrying capacity to humans 

by inverting the number of individuals a system can 

support and measuring the amount of biologically 

productive (bioproductive) land that is required to 

support a population of a given size. This area of land 

necessary for the production and maintenance of goods 

and services consumed by the population was termed 
10

the ecological footprint (the metric is named after this 

component of the overall analysis). Ecological footprint 

is based on the concept of strong sustainability and 

it attempts to capture ecological impacts of human 

activity from resources consumed and wastes generated 

(Neumayer 2004). Specifically, ecological footprint 

includes consumption of crops, meat, seafood, wood 

and fiber, energy, and living space, which are converted 

to one of six biologically productive land types (i.e., 

arable, pasture, sea, forest, energy, and built lands, 

respectively). Comparing the “supply” of bioproductive 

land to the “demand” of resource consumption and waste 

generation can reveal if the system is sustainable at the 

current levels of human impact. Although there has been 

a number of modifications and adaptations to improve 

the ecological footprint methodology since its inception 

(e.g., Lenzen and Murray 2001, Luck et al. 2001, 

Monfreda et al. 2004, Medved 2006, Venetoulis and 

Talberth 2005, 2008, Wackernagel 2009), there has been 

a push for standardizing the methods (e.g., Wackernagel 

2009). Nonetheless, new metrics have been proposed 

that combine existing indexes with ecological footprint 

such as emergy (e.g., Hu et al. 2009, Siche et al. 2010a, 

2010b, Zhao et al. 2005), exergy (e.g., Hau and Bakshi 

2004), and net primary production (e.g., Siche et al. 

2010b, Venetoulis and Talberth 2005 and 2008), for 

example.

Each of these metrics relates, perhaps indirectly, to 

a fourth sustainability metric, resilience. Resilience 

refers to the amount of perturbation a system can absorb 

before shifting to an alternate dynamic regime (Grimm 

and Wissel 1997, Holling 1973). Human activities can 

increase or decrease the resilience of a regime, or set of 

system conditions, that provides desirable ecosystem 

services (Mayer et al. 2004) and is beneficial to humans 

(Carpenter et al. 2001, Jackson et al. 2001, Orr 2002), 

thereby increasing or decreasing the carrying capacity.

2.2.2 Economic Metrics

Economists define sustainability as non-declining 

welfare or well-being over generations (Neumayer 2010, 

Pearce and Atkinson 1995, Tietenberg 2000). Welfare, 

as defined by economists, incorporates benefits obtained 

outside of market transactions such as environmental 

amenities, but allows for the possibility of trade-offs 

between market and non-market goods. In this sense, the 

field of economics can offer useful metrics that are based 

on the concept of weak sustainability.

Augmented economic accounts, often referred to as 

green accounting, is a common approach to estimating 

welfare, with the trends in augmented accounts 

measuring sustainability (Adamowicz 2003). Green Net 

Domestic Product (GNDP) and Genuine Savings are two 

of the most important economic metrics that account for 

depreciation of natural resources (e.g., Dasgupta 2001, 

Pearce and Atkinson 1993, 1995, Pezzey et al. 2006). 

We define both GNDP and Genuine Savings in this 

Section.

Gross Domestic Product (GDP) is one measure that 

captures the market value of a country’s output (and 

therefore, its aggregate consumption). However, it 

does not measure welfare because it excludes the value 

of leisure time and services produced by nature (van 

den Bergh 2009). Changes in GDP over time are often 

assumed in casual discourse to approximate changes 

in welfare over time. However, such a measure is 

unsatisfactory for purposes of measuring sustainability.

An economy that eats the proverbial seed corn or allows 

natural capital to depreciate faster than it is replaced, 

destroys the region’s capacity to maintain the current 

standard of living into the future and is, by definition, 

unsustainable. The productive capacity of the economy 

will be reduced in future years, eventually resulting in a 

decline in human welfare. However, such an economy 

is likely to show positive GDP growth for some period 

of time. GDP measures the consumption and production 

of market goods, while ignoring depreciation, pollution, 

and the services produced by nature. Consumption of 

seed corn and unsustainable natural resource extraction 

contribute to GDP by increasing current consumption, 

while the costs of reduced productive capacity in the 

future are not reflected in current GDP. Therefore, trends 

in GDP do not indicate the sustainability of an economy.

For the economic accounts to measure sustainability, the 

depreciation of all capital stocks should be included in 

the calculation. Net Domestic Product (NDP) is a step 

in the right direction. It modifies GDP by subtracting 

the depreciation of man-made capital. However, it does 

not incorporate all types of capital such as human, social, 

and natural capital; therefore, it is not an ideal measure 

of sustainability (Azqueta and Sotelsek 2007, Hartwick 

1990, Weitzman 1976). Natural capital stock can include 

both non-renewable and renewable resources, which use 

different approaches for their estimation (see Hartwick 

1990, Vincent 2000). Hartwick (1990) shows how 

pollution control costs could be incorporated into GNDP 

and how the stock of pollution, when it enters the utility 

function (i.e., pollution is a capital bad rather than a 

capital good), changes the calculation of GNDP.

GNDP explicitly incorporates the depreciation of manmade, human, and natural capital. Following Weitzman 

(1976, 2000) and Vincent (2000) who provide a good 

summary, we define GNDP as 

 

(2.1)

( )

() () () () () () () m h n

m h n

GNDP t

Ct tK t tK t tK t l ll

=

+ ++  
11

For this equation, C(t) is aggregate consumption through 

time, t; λ(t) are the shadow prices (i.e., the value of 

having an additional unit) of the different capital stocks; 

total capital stock includes man-made capital (Km), 

human capital (Kh), and natural capital (Kn); and the 

stock of man-made, human, and natural capital changes 

over time is represented with the dot notation. The 

first two terms on the right side of Equation 2.1 are 

the standard definition of net domestic product (NDP) 

which includes the depreciation of man-made capital. 

The last two terms transform NDP into GNDP (Cairns 

2002, Pezzey et al. 2006). Although it is not included in 

Equation 2.1, Dasgupta (2001) includes the changes in 

the stock of knowledge in GNDP.

A single year measurement of GNDP does not 

demonstrate sustainability (Heal and Kristöm 2005). 

Only trends in GNDP can give an indication of 

whether an economy is moving toward or away from 

sustainability. Dasgupta and Mäler (2000), and later 

corrected by Asheim and Weitzman (2001), test to see if 

 ΔGNDP> 0 (2.2)

GNDP measured in real prices (adjusted for inflation) 

must be increasing over time for a region to be moving 

toward sustainability. Using this type of intertemporal 

welfare analysis will allow stocks of capital to be 

dropped from the analysis if they are not changing over 

time (Usher 1981 in Ahlroth 2003 and Hartwick 1990). 

Some arguments against using the change in GNDP 

as a measure of sustainability and issues related to 

estimating the depreciation of natural capital can be 

found in Azqueta and Sotelsek (2007), Dasgupta (2001), 

Hanley et al. (1999), Mayer (2008), and Nordhaus and 

Kokkelenberg (1999). For example, Dasgupta (2001) 

concludes that GNDP could increase over time while 

the country becomes poorer and well-being decreases. 

Rather than Equation 2.2 representing sustainability, 

Dasgupta (2001: 151) states

social well-being increases during a brief interval 

of time, if, and only if, the value of changes in 

consumption services plus the change in the value 

of net changes in capital assets is positive.

Genuine Savings (GS), which is based on the Hartwick 

Rule, was introduced by Pearce and Atkinson (1993). 

As stated above, a constant capital stock is necessary in 

order to achieve a sustainable consumption flow, which 

is the basis for GS. It is defined as 

 S ΨK GS = –– - —— (2.3) Υ Υ

For this equation, S is savings, Y is income, ψ is 

depreciation on all capital stock, and K is total capital 

stock that is defined as above in Equation 2.1 but with 

different depreciation rates for each stock (Pearce and 

Atkinson 1993, 1995). To move toward sustainability, 

a region’s GS would have to be 0 or positive. This 

condition for sustainability does not have any restrictions 

for substitution between man-made and natural capital. 

As long as GS remains constant or increasing, natural 

capital could be degraded (Pearce and Atkinson 1995). 

Hamilton and Clemens (1999) have published GS results 

for developing countries through the World Bank. An 

argument against GS can be found in Pillarisetti (2005) 

and criticisms related to GS are described in Dietz and 

Neumayer (2004).

Before we discuss whether GNDP or GS are appropriate 

for measuring sustainability, we recognize there is a 

relationship between GNDP and GS such that GNDP 

is equal to consumption plus GS (Dasgupta 2001). 

Put another way, GS is measured as GNDP minus 

consumption (Hamilton et al. 1997, Pearce and Atkinson 

1995). Pezzey et al. (2006) define augmented Green Net 

National Product6

 equal to consumption expenditures 

plus augmented GS, where the augmentation term relates 

to exogenous technical progress.

Recent papers have stated that examining the trend of 

GNDP or GS actually is not an appropriate measure for 

sustainability. Some describe it as a one-sided measure 

and it only reveals when the region is moving away from 

sustainability (e.g., Pezzey 2004, Pezzey et al. 2006). 

Hanley et al. (2002: 6) summarizes this discussion:

The academic consensus at the moment is that 

both green NNP [what we call GNDP] and GS 

are one-sided indicators of weak sustainability 

in that negative GS or falling green NNP signal 

non-sustainability. However, the measurement of 

a positive genuine savings rate/rising green NNP 

at a given point in time is not sufficient to lead to 

the conclusion that the economy is on a sustainable 

path and further evidence must be sought before 

any firm judgement can be made.

Apart from the one-sidedness identified by Hanley 

above, several factors may influence the relationship 

between GNDP (and GS) and sustainability. Exogenous 

technological progress changing world interest rates 

or terms of trade may compensate for changes in net 

investment. Some of these factors are reflected in trends 

in GNDP, but Pezzey (2004) addresses exactly how this 

6 Gross Domestic Product and Gross National Product both measure 

the market value of goods and services but they differ in terms of who 

produces the goods and services (US residents regardless of location 

counts towards Gross National Product) and where the goods and 

services are produced (within the US regardless of nationality counts 

towards Gross Domestic Product).
12

should be accounted for on a theoretical level. Like 

most practical estimates of GNDP, we do not address 

these issues in our estimates. For instance, the business 

cycle may lead to deceptive trends in GNDP, or a 

recession may lead to declining GNDP due to a drop in 

consumption that reflects the business cycle rather than 

moving away from sustainability.

Whereas some studies have attempted to estimate GS or 

GNDP for countries (e.g., Hamilton and Clemens 1999, 

Hanley et al. 1999), other studies have focused on a 

particular sector of an economy. For example, Matero 

and Saastamoinen (2007) examined forest ecosystem 

services in Finland’s forestry accounts and Seroa da 

Motta and Ferraz do Amaral (2000) estimated the 

depreciation from timber harvest in Brazil. Hrubovcak 

et al. (2000) developed a green accounting framework 

for US agriculture.

Other indices, such as the Index of Sustainable 

Economic Welfare (Daly and Cobb 1989) and the 

Genuine Progress Indicator (Cobb et al. 1995), could 

be described in more detail in this section, although 

Hanley et al. (1999) describe these more as sociopolitical measures. The Index of Sustainable Economic 

Welfare and the Genuine Progress Indicator focus on 

expenditures (e.g., defensive expenditures that mitigate 

pollution damage) that reduce sustainability because 

they do not increase welfare (Cobb et al. 1995, Daly and 

Cobb 1989, Mayer 2008).

2.2.3 Energy

The transformation of available energy potentials 

underlies all action in the observable universe and, as a 

result, all known processes and systems can be analyzed 

using energy-based methods. Although all processes 

have an energetic aspect, most energy-based methods 

limit their scope of application. There are several 

energetic approaches that might be used to determine if 

a given system regime is sustainable, but two energybased methods are examined in this section; Exergy 

Analysis (ExA) and Emergy Analysis (EmA). ExA 

was first developed to enhance scientific understanding 

of thermal and chemical processes and to identify the 

thermodynamically most-efficient manner in which a 

process can be carried out within its environment. EmA 

recognizes that Energy Systems Theory (Odum 1994), 

conceptually, can be applied to analyze all known 

systems, but that boundaries must be arbitrarily set, 

based on the particular problem or research question 

under consideration. In general, the boundaries set for 

an EmA tend to be more broad and more comprehensive 

than those that would be set for an ExA of an equivalent 

problem (Sciubba and Ulgiati 2005).

2.2.3.1 Exergy Analysis

Preliminary work that eventually gave rise to exergy 

analysis was carried out in Europe during the latter 

half of the 19th Century. The work of Joule on energy 

conservation established the first law of thermodynamics 

and that of Clausius, Thomson, Tait, Maxwell, and 

primarily Gibbs gave rise to the idea of the maximum 

work that can be obtained from an energy potential or 

the second law of thermodynamics (Szargut et al. 1988). 

Gouy (1889) and Stodola (1898) independently showed 

that for work performed under real conditions, the 

amount of work actually obtained from a given thermal 

energy potential is always smaller than the maximum 

possible work, because of the irreversibility of thermal 

processes. Because of these different interpretations of 

the same word “energy,” Rant (1956) proposed the term 

“exergy” to describe the ability of energy to perform 

work in a transformation (energy quality), especially 

under the conditions occurring in technical processes 

(Szargut et al. 1988). In an exergy analysis, the quality 

of energy is accounted for, not just the quantity as in 

an energy analysis (Dewulf et al. 2005). The primary 

purpose for exergy analysis has been to give information 

about the possibilities for improving thermal processes, 

but it cannot say whether such improvements are 

practicable. However, exergy and economic analyses 

have been combined in an attempt to answer such 

questions (El-Sayed and Evans 1970, Evans 1961, Evans 

and Tribus 1965). 

Exergy can be used as an indicator of the renewability of 

industrial processes (Berthiaume et al. 2001), as a metric 

of sustainability for technology (Dewulf et al. 2005) and 

bioenergy systems (Dewulf et al. 2005). Further, ExA 

allows for the assessment of the efficiency of production 

chain, which has obvious potential benefits for industry 

(Dewulf et al. 2005), and has been made possible via 

Cumulative Exergy Consumption, which sums the 

exergy values of all resource inflows (Zhang et al. 2010). 

For assessing the sustainability of systems, an extended 

form of exergy analysis, (e.g., cumulative exergy 

analysis; Sciubba and Ulgiati 2005) should be employed. 

If ExA is extended to the boundaries of the biosphere, 

it effectively approaches an EmA as evidenced by the 

analysis of Ukidwe and Bakshi (2007), who defined a 

quantity “ecologically cumulative exergy,” which was 

effectively emergy. Exergy does not take into account 

ecosystem services, except some provisioning services, 

which is an obvious limitation when exergy is advocated 

as a “holistic” sustainability metric (Zhang et al. 2010).

2.2.3.2 Emergy Analysis

EmA developed from Energy Systems Theory (EST), a 

discipline derived from the union of the fields of ecology, 
13

irreversible thermodynamics, and general systems theory 

(Odum 1994). EST identifies and develops general laws 

and principles that help understand and interpret natural 

phenomena. The transformation of available energy 

is the fundamental, underlying causal factor behind all 

action in systems of many kinds, including ecological, 

social, and economic systems, all of which can be 

characterized and analyzed in terms of a network of 

energy flows.

EmA builds on the tools of the energy systems approach 

(Odum 1971, 1994, 1996) to evaluate the sustainability 

of present and possible future human activities. EmA 

focuses on the system’s use and exchange of emergy, 

which is the available energy of one kind previously 

used up, directly and indirectly, to make a service 

or product. The balance of emergy for a system is 

expressed in terms of the emergy inputs from renewable 

and nonrenewable resources of local or imported origin 

observed relative to the economic and social activities 

supported by the system. Emergy like exergy is a 

concept grounded in the second law of thermodynamics 

(i.e., based on the quantity of available energy [exergy] 

used up in an irreversible transformation). Both exergy 

and emergy must initially satisfy first law constraints 

(i.e., the conservation of energy).

Sustainability is not a unitary idea because it can have 

different meaning for different individuals or groups. 

In addition, sustainability itself is a relative term. In 

this context, emergy analyses have been conducted 

at various spatial scales; historical studies of nations 

have been performed (e.g., Rydberg and Jansen 2002, 

Tilley 2006) and nations have been evaluated over 

decades (e.g., Huang et al. 2006). Once the various 

spatial and temporal parameters of sustainability have 

been decided, different aspects of sustainability can be 

examined using the appropriate emergy indices (Brown 

and Ulgiati 1997, Lu et al. 2003, Lu and Campbell 2009, 

Ulgiati and Brown 1998). Finally, emergy and exergy 

methods have been used together to examine energy 

conversion processes (Tonon et al. 2006) and emergy has 

been combined with, and compared to, other metrics of 

sustainability such as ecological footprint (e.g., Giannetti 

et al. 2010, Hu et al. 2009, Siche et al. 2010a, 2010b, 

Zhao et al. 2005).

2.2.4 System Order 

Researchers have studied the impact of global trends on 

sustainability recognizing both positive and negative 

movement of classes of indicators affecting the transition 

to a sustainable future (Kates and Parris 2003). Such 

work underscores the importance of evaluating and 

managing patterns of change related to growth and 

development. Because human and natural systems are 

complex and integrated, no action exists in isolation. As 

a result, any action starts a chain of events propagating 

through multi-dimensional spatial and temporal scales. 

Although some systems are brittle and unstable, others 

(e.g., ecosystems) are evolutionary and adaptable. If a 

system is resilient, it typically will be able to withstand 

periodic fluctuations and still maintain function. 

However, it is possible for a system to reach a dynamic 

threshold and abruptly shift from one set of system 

conditions (i.e., regimes) to another.

Regime shifts have been demonstrated for a multitude 

of ecological and social systems, and often have 

significant ecological and economic consequences. A 

classic example of a regime shift is a shallow lake 

where a system may shift from an oligotrophic to an 

eutrophic regime due to inflow of phosphorus resulting 

in algae overgrowth, depletion of oxygen needed for 

survival of fish, a loss of biodiversity, and reduced water 

quality. Subsequently, the human-desired function 

and utility of the system is lost. In the context of 

dynamic change and regimes, sustainability is related 

to finding and maintaining a set of system conditions 

(i.e., a dynamic regime) that can support the social and 

economic development of human and environmental 

systems without major, irrecoverable environmental 

consequences (Karunanithi et al. 2008). Furthermore, 

sustainability is predicated on the human preference for 

the persistence of a particular regime over another in 

order to maintain desired system function and support 

current and future generations, (Mayer et al. 2006). 

Hence, quantifying and identifying regime shifts is 

critically important to sustainability.

There has been a great deal of research on developing 

regime shifts indicators (Biggs et al. 2009, Carpenter 

2003, Carpenter and Brock 2006, Chisholm and Filotas 

2009, Dakos et al. 2008, Scheffer et al. 2009, van 

Nes and Scheffer 2007). In real, complex systems, 

this task involves tracking multiple system variables 

simultaneously over time. Although variance, skewness, 

kurtosis, and critical slowing down are common 

indicators of regime shift, research is still needed to 

determine whether these indicators will signal shifts 

in real, complex systems (Scheffer et al. 2009). One 

method of assessing the dynamic changes in complex 

systems is information theory.

Information theory generally relates to the quantification 

of information in data and has enhanced the ability to 

assess organizational complexity even in the presence 

of imperfect observations (e.g., noisy data; Mayer 

et al. 2006). It has been used to understand various 

types of systems (Fath et al. 2003) including food 

web models (Fath and Cabezas 2004) and ecosystem 
14

functioning (Patricio et al. 2004), and is commonly 

used for monitoring ecosystem complexity (Anand 

and Orlóci 2000, Svirezhev 2000) and stability (Fath 

and Cabezas 2004, Ulanowicz 1997). Key methods 

within information theory include Shannon information 

(Shannon and Weaver 1949), Gini-Simpson information 

(Colubi 1996), and Fisher information (Cabezas et al. 

2005a). Both Shannon and Gini-Simpson information 

have been used, for example, as estimates of biological 

diversity as they use number of species and species 

abundance to calculate system information. One feature 

of these measures is that given a different ordering of 

the same species probabilities, both indices result in the 

same level of information. However, changes in order 

are crucial for evaluating the dynamic behavior and 

regimes in complex systems (Cabezas et al. 2005a, Fath 

et al. 2003).

Unlike other measures of system information, Fisher 

information (FI) provides a means of monitoring 

system variables to characterize a system’s dynamic 

order including its regimes and regime shifts (Cabezas 

et al. 2003). The ability to detect regimes and regime 

shifts permits the identification of fundamental changes 

occurring in the system and provides insight into what 

can be done to abate negative consequences. In practice, 

FI has been applied to deriving fundamental equations 

of physics, thermodynamics, and population genetics 

(Frieden 1998, Frieden 2001). It has been used as a 

measure of dynamic order in complex systems (Fath et 

al. 2003, Mayer et al. 2006). It has been proposed as a 

quantitative index for the detection and assessment of 

ecosystem regime shifts (Karunanithi et al. 2008, Mayer 

et al. 2006) and as a sustainability metric (Cabezas 

and Fath 2002). Moreover, FI has been used to study 

model systems including the stability of a multiple 

compartment food web (Cabezas et al. 2005a, Cabezas 

et al. 2005b, Fath and Cabezas 2004) and to optimize 

control of dynamic model systems for sustainable 

environmental management (Shastri et al. 2008a, Shastri 

et al. 2008b). 

2.3 Sustainability Studies That Use 

Multiple Metrics

No single metric should be considered a perfect measure 

of sustainability because there are multiple definitions of 

sustainability and few, if any, metrics encapsulate all the 

definitions; most metrics also are difficult to calculate 

(Hanley et al. 1999). Neumayer (2010) finds that no 

conclusion can be made about which sustainability 

paradigm (i.e., weak vs. strong sustainability) is most 

appropriate (but see Dietz and Neumayer 2007). 

This would suggest using various ways to measure 

sustainability in order to cover both paradigms.

Several papers suggest there are three dimensions, or 

pillars, of sustainability: economic, environmental, 

and social (Adamowicz 2003, Böhringer and Jochem 

2007, Hanley et al. 1999, Tanzil and Beloff 2006, UN 

2002). It seems clear that different types of indices will 

be required to assess the three pillars of sustainability. 

To help select metrics Böhringer and Jochem (2007) 

summarize five criteria for choosing the right 

sustainability metrics: 1) a connection to the definition 

of sustainability, 2) indicators from holistic fields, 3) 

good data that are available for many years, 4) processoriented selection, and 5) the ability to derive political 

objectives.

Whereas some studies have focused on particular 

metrics, a number have applied multiple metrics in an 

attempt to address sustainability. Although some of 

these studies discussed major differences in comparing 

measures of sustainability, others focused on capturing 

multiple aspects of sustainability. For example, Hanley 

et al. (1999) used a time series analysis to compare 

seven sustainability measures for Scotland. The seven 

measures included: (green) Net National Product, GS, 

EFA, Environmental Space, Net Primary Productivity, 

Index of Sustainable Economic Welfare, and Genuine 

Progress Indicator. These measures were used to 

examine Scotland from 1980-1993. Overall, they found 

Scotland to be unsustainable; however, the individual 

measures provided results for different aspects of the 

system under study.

Pezzey et al. (2006) compared Green Net National 

Product and the interest on augmented GS for Scotland 

during 1992-1999. The theory suggests that both the 

augmented Green Net National Product and the interest 

on augmented GS should be equal. The authors found 

that both measures were positive, suggesting that 

Scotland was sustainable over the time period, but the 

two measures differed in magnitude. They believe 

the mismatch was related to assumptions used in their 

approach.

In a special issue of the Journal of Environmental 

Management, Pulselli et al. (2008) and Tiezzi 

and Bastianoni (2008) presented a summary of a 

large sustainability project for the Siena Province 

using ecological metrics. The authors proposed a 

“sustainability diagnosis” where the diagnosis is 

accomplished using a systemic approach, in order to 

develop sustainable policies. The methods included 

Emergy Evaluation (i.e., EmA), EFA, Greenhouse Gas 

Inventory, Life Cycle Assessment, and Extended Exergy 

Analysis. The results from these measures were then 

analyzed using Principal Component Analysis (PCA) to 

examine similarities and differences among the methods.
15

Bastianoni et al. (2008) discussed the comparisons 

of the different metrics using PCA for the territory. 

PCA examines the correlation among many metrics 

and creates a smaller set of uncorrelated variables, or 

principal components (Jolliffe 2002). The authors found 

some interesting results when examining correlations 

related to particular territories. For example, the largest 

urban areas have similarities with population, population 

density, total income, CO2, and purchased non-local 

resources. In addition, Bastianoni et al. (2008) were 

surprised to see a low correlation between Emergy and 

EFA. They suggested the low correlation could be 

caused by EFA not calculating the extraction of nonrenewable materials explicitly and only focusing on 

household consumption. Because Emergy did include 

extraction and incorporated industrial consumption, the 

result was a low correlation between the two metrics.

Wilson et al. (2007) compared six sustainable 

development indices for 132 nations. The metrics 

included EFA, Surplus Biocapacity Measure, the 

Environmental Sustainability Index, the Well-being 

Index, Human Development Index, and GDP. Using 

the Pearson product-moment correlation, they found the 

environmental sustainability index and well-being index 

were positively correlated, as were the well-being index, 

the human development index, and GDP. Negative 

correlations were identified among EFA, the Well-being 

Index, the Human Development Index and GDP.

Eight metrics were estimated for 1990-2000 to examine 

the sustainability pattern of France (Nourry 2008). 

The eight metrics were (green) Net National Product, 

GS, EFA, Indicator of Sustainable Economic Welfare, 

Genuine Progress Indicator, Pollution-sensitive 

Human Development Indicator, Sustainable Human 

Development Indicator, and French Dashboard on 

Sustainable Development. Nourry (2008: 10) defines 

this last indicator as a “non-monetary measure composed 

of non-aggregated indicators.”7

 Some of the 15 

indicators used were life expectancy without disability, 

overfishing, GS, waste production and population, 

and public debt (Nourry 2008). The results suggest 

that France satisfies weak sustainability but not strong 

sustainability. 

Lee and Huang (2007) examined 51 indicators of 

sustainability for Taipei from 1994-2004. Those 

indicators were categorized into four dimensions and 

reduced to estimate a composite sustainability index. 

For example, the environmental dimension included 

indicators such as per capita CO2 emissions, reservoir 

7 There is not a consensus on the use of the terms indicator and metric. 

Nourry’s (2008) use of indicator is equivalent to our use of metric in 

this report.

water quality, and tap water quality. Social indicators 

included urban population density, the wealth gap, and 

crime rate among others. The economic dimension 

included average personal income, female/male 

unemployment rate, and percentage of households 

with internet connection. The institutional dimension 

incorporated enforcement of local environmental plans, 

joint international cooperation related to sustainable 

development, and the ratio of the environmental and 

ecological budget to total budget in its calculation. 

The four different dimensions were aggregated into 

one measure (i.e., metric) using equal weighting and 

suggested that Taipei was moving towards sustainability. 

However, only the environmental and social dimensions 

indicated this movement. The economic dimension 

only started increasing after 2002 and the institutional 

dimension was inconsistent.

Graymore et al. (2008) focused on South East 

Queensland as a regional case study. The metrics 

examined included EFA, Well-being Assessment, 

Ecosystem Health Assessment, Quality of Life, and 

Natural Resource Availability. The authors determined 

that calculating these metrics at the regional scale was 

not informative for a few reasons. For example, data 

limitations at the regional scale caused problems with 

calculating the metrics, especially when attempting 

to examine trends. In addition, the metrics examined 

were not always easy to understand or did not reveal 

information about sustainability. Graymore et al. (2008) 

conclude that a new approach is necessary given the 

limitations of their approach for calculating regional 

sustainability metrics.

Similar to Hanley et al. (1999), the special issue of the 

Journal of Environmental Management (2008), and 

Nourry (2008), we propose an approach that includes 

multiple metrics in order to measure the sustainability 

of a region. Rather than comparing the metrics for 

different areas (e.g., Wilson et al. 2007), we think the 

sustainability measures we chose will capture various 

attributes of the SLB region, providing a holistic view 

similar to that of the Pulselli et al. (2008) “sustainability 

diagnosis.”

2.4 Choice of Metrics for San Luis Basin 

Sustainability Metrics Project

This project utilized available environmental, economic, 

and social data to calculate four indices of sustainability. 

The metrics or indices included: 1) environmental 

footprint as characterized by EFA, 2) economic wellbeing as ascertained from Green Net Regional Product 

(GNRP; similar to GNDP but defined on a smaller spatial 

scale), 3) energy flow through the system as computed 
16

from an EmA, and 4) dynamic order estimated from 

FI. We chose several metrics for a few reasons. First, 

we suspect that no single metric will provide sufficient 

information for planners to assess adequately different 

aspects of the system. We also think that multiple 

metrics are necessary because no metric is perfect in 

capturing a specific aspect of the system. In addition, 

these four metrics cover both paradigms of weak and 

strong sustainability (as described above). GNRP is a 

measure of weak sustainability (e.g., Neumayer 2010, 

Pezzey et al. 2006) and we believe FI and the associated 

Sustainable Regimes Hypothesis are measures of weak 

sustainability. Although the approach is not directly 

indicative of resource substitutability and focuses on 

the functionality of a system, it is possible for resources 

(e.g., species) to be replaced as long as the system does 

not become disorderly (i.e., it continues to function). 

EFA is based on the concept of strong sustainability 

(e.g., Ferguson 2002, Mayer 2008, Neumayer 2010, Rees 

2002, but see Dietz and Neumayer 2007, McManus and 

Haughton 2006) and so is EmA (e.g., Giannetti et al. 

2010).

We note these multiple metrics do use similar 

variables, and therefore, there is some redundancy in 

the metrics. This redundancy in variables could result 

in similar behavior in the metrics over time, under 

some circumstances, depending on the influence of 

the particular variables. Table 2.1 summarizes the 

different metrics and the data needed for estimation. It 

presents the spatial scale of the data and the years the 

data were available. EmA is the most data intensive 

with FI following. However, EmA has specific data 

requirements (e.g., energy inflows and outflows), 

whereas FI has no requirements other than data that 

adequately represent the system behavior. Of the broad 

categories of variables presented, EFA and GNRP are the 

least data intensive. The only variable that all metrics 

include is population, but each uses it in different ways. 

For example, GNRP uses it to estimate CO2

 emissions 

for the SLB based on per capita estimates for Colorado 

and New Mexico. EFA calculates the land required for 

each land category to support both an individual and 

entire population of the region. It may be one of the 

easiest of the sustainability metrics for decision makers 

to understand and conceptualize and may be one of 

the most commonly computed. Although most of the 

variables are required by at least two of the metrics, each 

metric plays a unique and important role in measuring 

sustainability for the SLB as we describe below. 

This suite of metrics will give public land managers, 

the local community, and the USEPA a methodology 

for monitoring the effect of land management and 

decision-making on environmental quality and economic 

sustainability. We believe the sustainability metrics 

selected will assist with the review of public land 

development and management decisions and promoting 

environmental quality.

2.4.1 Components Basic to Environmental 

Systems 

The four metrics we selected capture what we 

determined to be fundamental aspects of an 

environmental system and its sustainability. We built 

our methodology based on these primary aspects and 

selected metrics in an attempt to capture or quantify 

those aspects. At a basic level, we recognized that an 

environmental system has some inherent order, whether 

the factors responsible for maintaining that order are 

clearly identified or understood. Furthermore, it takes 

energy to maintain order in an environmental system. 

Finally, sustainability is inherently an anthropocentric 

issue and how well off humans are may dictate their 

influences on the system. An individual or population 

that is meeting its needs for existence has the luxury 

of being concerned about the effect it has on its 

environment, whereas a population that is barely 

surviving may not be intensely concerned with long-term 

sustainability.

We selected metrics that capture each of these 

fundamental aspects. FI was selected to measure 

dynamic order of the overall system. We chose EmA 

because it measures the quality-normalized flow of 

energy through the system. GNRP was chosen to capture 

the overall economic well-being of the population and 

EFA was selected to capture the impact a population has 

on environmental resources resulting from consumption 

and waste production. Out of the four metrics, using 

FI to characterize sustainability is a relatively new 

metric (Mayer 2008). Application of the other metrics 

is relatively common and they are established as metrics 

of sustainability in the scientific literature (e.g., Mayer 

2008).

Although the metrics were selected because they 

measure what we identified as primary or fundamental 

aspects of an environmental system, they assess 

some component of the economic, social, and 

environmental pillars of sustainability and these 

three pillars, traditionally, are considered to support 

sustainable development (Bobylev 2009, UN 2002). 

Measurements relevant to each pillar of sustainability 

will enable decision makers to assess where the regional 

environmental system is in need of management actions 

and help them make choices that can directly guide the 

path of the regional system toward sustainability.
17

Finally, although a certain suite of metrics was chosen 

for this study, it does not mean that we view these as 

providing the final word on system sustainability. The 

issue of sustainability is complex and we expect that 

other metrics exist, or may be developed, that will 

provide improvements in the methods of calculating 

indices in the future. In the next four chapters, we 

present the methodology and results of EFA, GNRP, 

EmA, and FI, respectively.

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Table 2.1 – Broad categorical list of data collected on key variables, showing which metric used the variable, the 

source of the data, the scale at which the data were collected, and the available years of data reported. 

Variable Metric1 Source2 Scale3 Years

Population EmA EFA GNRP FI BEA C, S 1980-2005

Personal income EmA GNRP FI BEA C, S 1980-2005

Land area EmA EFA FI NASS C 1980-2005

Precipitation EmA FI PRISM Group C 1980-2005

Solar and Wind EmA FI NASS C 1980-2005

Food consumption EFA FI USDA-ARS N 1980-2005

Food production EmA FI NASS C 1980-2005

Imports EmA GI C 1995-2005

Exports EmA GI C 1995-2005

Forest harvest EmA EFA FI USDA-FS C 1980-2005

Energy consumption EmA EFA FI EIA S 1980-2005

CO2

 emissions GNRP FI EIA S 1980-2005

Water balance EmA GNRP FI CDSS R 1980-2005

Wind erosion EmA GNRP FI Multiple sources C 1980-2005

1

EmA = Emergy Analysis; EFA = Ecological Footprint Analysis; GNRP = Green Net Regional Product; FI = Fisher information. 2 BEA = Bureau of 

Economic Analysis; NASS = National Agricultural Statistics Service; PRISM Group = Parameter-elevation Regressions on Independent Slopes

Model Group; USDA-ARS = United States Department of Agriculture, Agricultural Research Station; USDA-FS = United States Department of

Agriculture, Forest Service; CDSS = Colorado's Decision Support Systems; GI = Global Insight, Inc.; EIA = Energy Information Administration.

3

C = county; R = region; S = state; N = national. 
24
25

3.0 

Ecological Footprint

3.1 Introduction

In this chapter, we introduce data sources and the 

methodology employed to perform an Ecological 

Footprint Analysis (EFA) for the San Luis Basin (SLB). 

We present and discuss the results, interpret what they 

mean, and discuss methodological issues in an attempt to 

make the approach more useful to decision makers. 

EFA, more appropriately called environmental footprint 

(Eaton et al. 2007) analysis, is a commonly used 

metric of sustainability because it is straightforward 

in theory and easy to conceptualize. EFA attempts to 

capture human impacts on the regenerative capacity 

of an environmental system (Chambers et al. 2000) by 

identifying the amount of bioproductive land required 

to support a person’s average annual consumption 

and waste production (Wackernagel and Rees 1996). 

Since Wackernagel and Rees (1996) introduced it, 

EFA methodology has been continually evolving as 

researchers have attempted to improve the methodology 

(e.g., Lenzen and Murray 2001, Luck et al. 2001, 

Monfreda et al. 2004, Medved 2006, Wackernagel 2009). 

In general, EFA categorizes bioproductive land into one 

of six types: energy, arable, forest, pasture, built, and 

sea. Land that does not meet one of these six categories 

is deemed non-productive and is not included in EFA 

accounting (Chambers et al. 2000). Furthermore, in 

the accounting twelve percent of the land is set aside 

for biodiversity (Chambers et al. 2000, but see Noss 

and Cooperrider 1994 for discussion of the appropriate 

percentage). EFA calculates the supply (biocapacity) of 

land available to support a population and the demand 

(ecological footprint) a population places on these six 

land categories by estimating how much land is being 

used to support the population. The biocapacity in a 

given region is compared to the ecological footprint 

of the population by subtracting demand from supply. 

Examining this balance sheet over time will reveal 

if there is an ecological reserve or deficit and if a 

population is moving toward or away from sustainability 

as defined by the EFA method.

This metric has been applied to numerous systems. For 

instance, EFA accounting has been conducted at global 

(e.g., WWF 2008), national (e.g., Erb 2004, Lammers 

et al. 2008, Lenzen and Murray 2001, Medved 2006, 

Wackernagel and Yount 1998, Wackernagel et al. 1999, 

Wackernagel et al. 2004a, WWF 2008), regional (e.g., 

Graymore et al. 2008, Huang et al. 2007, Pulselli et al. 

2008, Zhao et al. 2008), and municipal (e.g., Collins 

et al. 2006, Scotti et al. 2009, Wood and Garnett 2009) 

spatial scales. EFA has been conducted for industry 

or product level (e.g., wine – Niccolucci et al. 2008, 

university campus – Li et al. 2008, tourism - Patterson 

et al. 2007), and individual or household levels (e.g., 

Chambers et al. 2000) as well. Some researchers have 

conducted EFA analysis by comparing and contrasting 

multiple spatial scales (e.g., Lenzen and Murray 2003, 

Luck et al. 2001).

We conducted conventional EFA accounting using the 

compound approach as introduced by Wackernagel and 

Rees (1996) and expanded by Chambers et al. (2000) 

because it is more inclusive and robust compared to the 

component-based approach (Chambers et al. 2000). In 

general, an EFA calculation requires four components 

or sets of variables: 1) the size of the population in the 

region of interest, 2) the amount of consumables in 

various categories used per individual, 3) the amount 

of energy of various types consumed per individual, 

and 4) the amount of biologically productive land, as 

defined by EFA, available in the region of interest. The 

consumption component includes the biotic resources 

such as meat, dairy, fruits, and vegetables. Consumption 

is usually calculated by adding the quantity of imported 

consumables to the amount produced in the system 

under study and subtracting the amount exported from 

the system. The remaining quantity is assumed to be 

consumed by the population (Chambers et al. 2000, 

Wackernagel and Rees 1996). The energy balance 

component considers both locally generated energy and, 

if known, energy embodied in traded goods (Chambers 

et al. 2000, Wackernagel and Rees 1996). Primary fuel 

consumption is adjusted for carbon content and used to 

derive the amount of forested land necessary to sequester 

the resulting CO2

 emissions (Chambers et al. 2000). 

Lastly, the area (in hectares) of each of the six land 

categories of biologically productive land is measured to 

calculate the biocapacity. Each of these components is 

used in one of three accounting type ledgers to determine 

the supply (biocapacity) and demand (ecological 

footprint) of the environmental system (Chambers et al. 

2000, Wackernagel 1996).

Note that we deviate slightly from the naming 

convention commonly employed in an EFA (e.g., 

Chambers et al. 2000, Wackernagel 1996) in an attempt 

to make the text easier to follow. It is typical for the 
26

population’s ecological footprint to be denoted with EF

and the per capita ecological footprint identified with 

ef. To be consistent with this format, we have deviated 

from the convention by representing the population’s 

biocapacity (typically denoted as BCp) as BC and the 

per capita biocapacity as bc (compared to the frequently 

used BCc).

The amount of land area appropriated per capita (aai) for 

each major consumption item (ci) is estimated as: 

 Ci aai

 = — (3.1) N

where N is the population of the region. The average 

demand or ecological footprint per capita (ef) is 

computed by summing all the ecosystem areas 

appropriated (aai) by all purchased items in the annual 

consumption of goods and services.

 

 ef = Σaai (3.2)

Each item is converted into a footprint value, represented 

as hectares per capita (ha/ca). Often these areas are 

adjusted to express a world average and are converted 

to global hectares. An ecological footprint (EF) for the 

entire system or region is calculated by multiplying ef by 

the population size (N).

 EF = N(ef) (3.3)

The biocapacity for the population (BC) is the number of 

hectares of each of the six bioproductive land categories 

in the area under study.

BC = ΣLi (3.4)

where Li indexes each of the six categories of land. A 

per capita biocapacity (bc) is calculated by dividing the 

region’s BC by the population of the region.

 Σ Li bc = —— (3.5) N

An ecological balance is calculated by subtracting ef

from bc to determine if there is an ecological deficit 

or reserve. If ef exceeds bc, the system is considered 

unsustainable at the individual level, and if EF exceeds 

BC, an ecological deficit exists and the system is 

considered unsustainable regionally. Conversely, 

the system is considered sustainable if there is an 

ecological reserve (ef < bc or EF < BC). However, 

because we question the accuracy of identifying a 

system as sustainable (see Chapter 1), we identify a 

system’s movement toward or away from sustainability 

by examining the ecological balance. A system is 

considered moving away from sustainability if the 

ecological reserve is decreasing or, if there is an 

ecological deficit, the deficit is increasing through time.

3.2 Methods

The data needs of traditional EFA accounting are 

relatively straightforward, although they can be intensive. 

EFA requires food consumables such as meat, dairy, fish, 

fruits and vegetables, animal feed, roots and tubers, and 

pulses (i.e., legumes), and per capita energy consumption. 

This level of detail creates difficulty when analyzing subnational regions because it is often difficult to obtain data 

for local areas, especially parts of states. To overcome 

this quandary, researchers often approximate values for 

many of the variables or use values from disparate years 

(e.g., Bagliani et al. 2008). This occurs because data 

on consumption patterns are more likely to be collected 

for national and even state level rather than for smaller 

political units. Thus, depending on the data required, we 

were limited by the availability of data sources due to 

the regional nature of our study and the requirement for 

26 consecutive yearly (1980-2005) values. Rather than 

calculating a national EFA and scaling it to the region by 

multiplying by the population of the region, we decided 

to reduce the number of variables to those that were 

necessary to calculate the EFA for the region. Because 

our goal was to calculate a simplified ecological footprint 

for the region and not necessarily a more detailed 

ecological footprint as calculated in Wackernagel et al. 

(2005), we attempted to restrict our variables to data 

specific to the region (i.e., county-level data). However, 

because data for some essential components of a footprint 

analysis were not available at the individual or county 

level, we had to utilize data that were not specific to the 

region. When necessary, data that were available only 

at the state or national level were scaled to the region 

of study based on per capita rates in the larger systems 

(Table 3.1). Any data that were reported less frequently 

than annually were linearly interpolated in order to 

calculate EFA for all 26 years.

3.3 Data, Variables, and Sources

We selected variables easily obtained and freely 

available, thereby enabling a calculation that could 

be undertaken by virtually any entity interested in 

conducting its own EFA. Most variables were recorded 

as reported from the original data source (Table 3.1). 

However, biocapacity was calculated for some land 

categories. Specifically, arable land was reported for 

each county in the National Agricultural Statistics Service 

(NASS) report at 5-year intervals (NASS 1984, 1989, 

1994, 2009a, 2009b). Pasture was estimated using the 

NASS reports by subtracting the hectares of arable land 

from total farmland. Forest land was estimated from 

United States Department of Agriculture (USDA) Forest 

Service data and subtracting the amount of harvested 
27

forest for each county. Built land was obtained from the 

US Census Bureau from the land area reported for each 

Census-designated place in each county. Because the 

region is land-locked, there was no bioproductive area 

for the sea category. Lastly, energy land is the amount 

of forested land required to sequester CO2 produced 

from energy consumption. This value was zero hectares 

(ha) on the supply side of the equation because it is not 

a supply, per se. Energy land is obtained by subtracting 

from forest land the area necessary to sequester CO2 and, 

therefore, does not exist before CO2 is produced.

Energy consumption data for the State of Colorado 

were obtained from the Department of Energy’s 

Energy Information Administration (EIA; EIA 2006). 

We assumed that EIA data calculated on a per capita 

usage and multiplied by the population of the SLB 

was sufficient to estimate the energy consumption. 

We justified this assumption by comparing coal, 

natural gas, and propane consumption in the SLB to 

their consumptive use for the State of Colorado. We 

decided the more readily available EIA data adequately 

represented consumption for these sources (see Appendix 

3-A). Using EIA data for Colorado, consumption was 

calculated as per capita for the state and included coal, 

natural gas, petroleum, nuclear, hydroelectric, and wood 

and waste categories. An added benefit of using EIA 

data, the petroleum category includes motor fuel, thereby 

capturing energy consumption for transportation in the 

SLB. Energy consumption was converted to area of 

forest and the corresponding energy land required for 

CO2

 absorption, following Chambers et al. (2000).

Per capita food consumption for the US, from USDA 

Economic Research Service (USDA-ERS 2009), was 

used to estimate food consumption for the region under 

study. Because data were already in quantity consumed, 

it was not necessary to subtract exports from imports 

and production to estimate consumption. Each food 

item was assigned to an appropriate land category and 

a per capita footprint was calculated for each food item 

by dividing the kg consumed by the global yield for that 

kind of land and dividing the result by the population 

of the region. The footprint for each land category 

was summed to produce the amount of land required to 

support levels of consumption of the population of the 

region.

Global yields were used because it is unlikely that 

most items consumed in the region were produced in 

the region. Global yields were taken from Chambers 

et al. (2000: 70-73) and were constant for all years. 

Ideally, global yields and conversion factors (see below) 

would be calculated for each year and would result in 

a better estimate of the EFA for a region. However, 

more accurate annual values are unlikely to have a large 

influence on the results. Another compromise in our 

analysis is that many items are not included in this EFA 

because every consumable is not reported or tracked at 

the regional level and, thus, it is not currently possible to 

include every item consumed.

Many studies suggest converting footprints into global 

hectares (gha) in order to allow comparison of EFAs 

between systems (e.g., Wackernagel et al. 2004b, 

Wiedmann and Lenzen 2007). A global hectare is 

“normalized to the area-weighted average productivity of 

biologically productive land and water in a given year” 

(Global Footprint Network 2008). In other words, a 

global hectare is the global average productivity (i.e., kg 

produced) per one hectare of land or water and facilitates 

comparison between EFAs for different nations. 

Equivalence factors and yield factors are necessary to 

convert actual ha into gha and they can be obtained 

from the literature for certain years (e.g., Chambers et 

al. 2000, Wackernagel and Yount 1998), but a complete 

set is difficult to obtain without additional expense. To 

overcome this lack of factors, we recorded values from 

Chambers et al. (2000) and McIntyre et al. (2007) for 

alternative calculations using gha. However, our goal 

was not to produce a mechanism by which regions can 

be compared but rather to enable a region to analyze 

their movement toward or away from sustainability and 

manage accordingly. Moreover, because the purpose of 

our study was practical, rather than academic in nature, 

we decided to use local hectares as the working unit.

3.4 Results

We were able to assemble 26 years (1980-2005) of data 

consisting of 35 variables of consumption (Table 3.1). 

We also obtained data for agricultural production of the 

eight major products in the region but these values were 

not necessary to estimate consumption because national 

consumption values were used. Appendix 3-B contains a 

sample accounting ledger used in EFA and Appendix 3-C 

contains the complete data worksheet.

Biocapacity shows a decreasing trend for the 26-year 

period, peaking at nearly 36 hectares per capita (ha/ca) in 

1980 and decreasing to less than 28 ha/ca in 2005 (Fig. 

3.1a; Table 3.2). Ecological footprint was relatively 

flat, with a low of 5.1 ha/ca in 1997 to a high of 5.5 ha/

ca in 1985 (Fig. 3.1a; Table 3.2). Subtracting ef from bc

provides the ecological balance and reveals the SLB had 

an ecological reserve, although it was declining, during 

the period examined. The reserve was at a period high 

of 30.54 ha/ca in 1980 and declined steadily though year 

2005 when the ecological remainder was 22.34 ha/ca 

(Table 3.2).
28

3.5 Discussion

3.5.1 Biocapacity (supply)

Although the SLB appears to have an abundance of 

biocapacity, this is a result of low population density and 

large area of pasture and arable lands. These two land 

types typically increase the human carrying capacity 

of a system and are heavily weighted in EFA as is 

evident in the value the methodology places on these 

(human-centric) biologically productive land categories. 

Moreover, an abundance of forest is available to 

sequester CO2 and provide renewable wood products. 

However, it must be remembered the SLB is part of a 

larger system and resources produced in the region help 

support that larger system, including the rest of the US 

and world.

The decrease in bc seems to be due, primarily, to 

population growth in the region. An increasing 

population draws on a relatively fixed supply of 

bioproductive land in the SLB, resulting in a decreasing 

amount of bioproductive land per person. Specifically, 

biocapacity of forest declined from 22.93 ha/ca to 18.56 

ha/ca and pasture declined from 12.13 ha/ca to 7.17 

ha/ca (Fig. 3.2). Arable land was variable during the 

26-year period, starting at 5.67 ha/ca in 1980, peaking 

in 1990 at 5.96 ha/ca, declining to 5.13 ha/ca in 2000, 

and rising to 5.33 ha/ca in 2005 (Fig. 3.2). During this 

period, built land increased slightly during the 26 years 

(from 0.12 ha/ca to 0.15 ha/ca; Fig. 3.2). In absolute 

terms, the number of hectares of arable land increased, 

the hectares of forest remained constant, and the hectares 

of pasture decreased in the SLB region during the 26 

years. The results suggest the overall decline in BC

for the region was minimal, especially when compared 

to the decline in bc (Fig. 3.1a and b). Hence, as the 

population size increased, more people were drawing 

on the same number of hectares of resources, resulting 

in fewer resources available per person. This indicates 

the decrease in biocapacity of forest and pasture land 

categories were due to population growth and the bc 

available from these land categories. Conversely, 

although biocapacity of arable land was variable, there 

was an overall decrease (Fig. 3.2), indicating an increase 

in the supply of total hectares of arable land in the region 

that did not quite keep pace with population growth. 

Lastly, the amount of built land increased from 4572 

ha in 1980 to an estimated 7350 ha in 2005. This is 

an increase of more than 60% and represents land that 

typically, according to EFA accounting, was agricultural 

land. In summary, the overall decrease in biocapacity in 

the SLB in these calculations is largely due to population 

increase. If the region were a self-reliant, closed system, 

the carrying capacity of the system would be far from 

reached and many more individuals could be supported. 

However, because the SLB provides resources to support 

the larger system, and draws on resources from outside 

the region (i.e., it is not a closed system), the excess 

biocapacity should be evaluated with that in mind.

3.5.2 Ecological Footprint (demand)

Unlike the US (e.g., Wackernagel and Yount 1998, WWF 

2008), the average ecological footprint of individuals in 

the SLB decreased slightly (with some variation) during 

the time analyzed (Fig 3.1a). Ecological footprint (ef)

was 5.41 ha/ca in 1980 and finished the period at 5.13 

ha/ca in 2005 (Fig 3.1a). However, the high of 5.5 ha/ca 

was in 1985 and the low of 5.10 ha/ca occurred in 1997 

(Fig 3.1a). Although ef showed a general decrease, the 

EF for the SLB increased slightly (Fig. 3.1b), likely due 

to an increase in population in the SLB.

Examining the individual land categories and the energy 

footprint can help identify components responsible for 

trends (Fig. 3.3). Overall, demand placed on each of the 

land categories remained relatively constant during the 

26-year period, except for forest (Fig. 3.3). Per capita 

demand on pasture, arable, built, sea, and energy lands 

have remained stable, as consumption did not increase 

dramatically during the period. However, during the 

same period the area of forest remained constant while 

forest harvest increased from 23973 million board feet 

(MBF) in 1980 to 35251 MBF in 1989 and has been 

declining since to 4444 MBF in 2005. This coupled with 

the increase in population resulted in a decrease in area 

of forest available per capita in the region.

Focusing on energy land, there was a general increase 

in total energy consumption from fossil fuels for the 

region. However, although there was some variability, 

per capita energy footprint revealed a slight decline for 

coal, natural gas, and petroleum consumption (slope = 

-0.0021, -0.002, and -0.004, respectively) in the 26-

year period. Energy consumption from nuclear and 

hydroelectric was nonexistent and is a source that could 

be expanded to benefit the region. Wood and waste 

usage, however, increased during the first 6 years of 

the period analyzed and has been decreasing both per 

capita and for the population since 1986 (Fig. 3.4). The 

US Census Bureau provides a category of number of 

homes using wood for heating and this value for the SLB 

decreased from 1990 to 2000 census from 3750 to 2327 

homes (US Census Bureau 1990, 2000), suggesting that 

such a reduction was realistic.

3.5.3 Global Hectares

Converting ha into gha increased ef by less than 10%, 

or about 0.5 ha and available bc nearly doubled. For 

example, in 1980 the ef was 5.41 ha (5.94 gha) and bc
29

(minus 12% for biodiversity) was 35.95 ha (64.39 gha). 

The ecological reserve was 30.54 ha (58.46 gha). In 

2005 EF was 5.13 ha (5.51 gha), bc was 27.47 ha (49.31 

gha), and the remainder was 22.34 ha (43.80 gha). Of 

course, the conversion from ha to gha would be better 

using year specific yield and equivalence factors, but 

the difference likely would be minor. For example, 

Lammers et al. (2008) report an equivalence factor for 

energy land of 1.36 in 1980 and 1.38 in 2003 in their 

time-series analysis of Ireland. However, our interest 

was in trends (i.e., was the system moving toward or 

away from sustainability), not necessarily the actual 

value and may not be worth the added expense or 

difficulty. However, we think using ha is acceptable if 

one is not comparing between different regions.

3.5.4 Examination of Sustainability

Whether using ha or gha, our analysis suggests the 

SLB was moving away from sustainability, on both a 

per capita basis (Fig. 3.1a) and a population basis (Fig. 

3.1b). Although the ef remained relatively flat during 

the 26 years (which by itself would suggest the SLB 

was not moving away from sustainability), available 

bc decreased because of increasing population size. 

These trends reveal a decreasing ecological reserve 

(the space between the two lines is decreasing; Fig. 3.1 

and Table 3.2). The decrease in the ecological reserve 

over the 26 years was amplified on a population level 

examination. The EF was increasing and BC was 

decreasing through time (Figs. 3.1 and 3.5). This was 

evident in the individual land categories as well. For 

example, per capita food consumption (e.g., meat, 

cereal, fruit and vegetable, and fish) increased during 

the period, requiring more bioproductive land to satisfy 

the consumption and the amount of available arable and 

pasture land per capita decreased. The overall result was 

a decreasing surplus of bioproductive land (Fig. 3.1a) 

that was available to support the population in the SLB. 

If the trend continues, ef will eventually overshoot bc, 

resulting in an ecological deficit.

Although our analysis suggests the ef is relatively low, 

one potential flaw was the large number of missing 

variables that represent embodied energy in goods and 

services. For example, Utah Vital Signs reported over 

2 gha/ca were required to meet the consumptive levels 

for Utah in 1990 and about 1.5 gha/ca in 2003 (McIntyre 

et al. 2007). When compared to the food and energy 

consumption for Utah in 1990 (5.2631 gha/ca) and 2003 

(5.8239 gha/ca), it is not an inconsequential quantity. 

However, these variables do not represent such a large 

part of EFA that they alter the trends captured by food 

and energy consumption. It is important to note that a 

portion of data in that report came from both US and 

Utah consumption rates and may or may not accurately 

represent consumptive levels for the Utah population.

The trends in the SLB were not unexpected. Because of 

the economic, environmental, and social characteristics 

of the SLB, we did not expect a substantial amount of 

change to occur during the 26 years examined. The SLB 

is an agricultural-based region and has many traits that 

accompany this type of society (e.g., San Luis Valley 

Development Resources Group 2007). Moreover, the 

SLB functions as a resource exporting region, providing 

much of its bioproductive land and resources to support 

larger systems, which include those of Colorado, the US, 

and the world.

3.5.5 Strengths and Weaknesses of 

Methodology

The simplified EFA we conducted has captured a 

trend indicating the SLB was moving away from 

sustainability. There are a number of strengths and 

weaknesses to the methodology we employed. The 

greatest strength of our methodology is the use of 

readily available data that makes the methodology 

available to any entity interested in computing their 

ecological footprint to determine if their system is 

moving toward or away from sustainability. The 

greatest weakness of our methodology may be the 

exclusion of potentially important variables. The lack 

of data at sub-national scales resulted in the exclusion 

of well over 100 variables that are typically included 

in a traditional footprint account. All of the variables 

usually included in the embodied energy component, 

as well as some other variables, were excluded. In 

addition, the footprint methodology in general does 

not take into account exports of natural resources that 

may lower the biocapacity. Another limitation is the 

lack of data for each year of our study. For example, 

we had data at five year intervals for agricultural 

production from the Census of Agriculture and ten year 

intervals for population and housing variables from 

the Decennial Census of Population and the census 

years did not align (i.e., decennial censuses were 1980, 

1990, and 2000 whereas the agricultural censuses were 

1978, 1982, 1987, 1992, 1997, and 2002). We used 

linear interpolation to estimate the values for missing 

years. Because of these imputations, we were limited 

in performing statistical analysis on trends in the results 

(e.g., a Mann-Kendall test for trends or time-series 

analysis). Such interpolation would bias the trend 

especially if a measure of uncertainty was available and 

we could statistically test the trend. Nonetheless, after 

calculating the ecological footprint for the US using the 

same variables employed in our study, we are confident 

the methodology does an acceptable job capturing 
30

trends in an EFA for a region, which was a central goal 

of this project (see Appendix 3-D for comparison with 

US). Although not unimportant, the embodied energy 

component of a typical ecological footprint account 

generally accounts for less than the food and energy 

consumption components. For instance, Lammers et al. 

(2008) found that less than 1 ha per capita was from the 

embodied energy component. These weaknesses, then, 

need to be balanced with the availability of the method 

and its relative ease of use. However, there is enough 

detail in the account that stakeholders can identify areas 

of the system on which to focus attention to improve 

sustainability of the system.

Although there were several methodologies used early 

on in EFA accounting, recently there has been a push 

to standardize the methods (e.g., Wackernagel 2009). 

Specifically, a couple of groups have proposed their 

methodology as a standard (e.g., Venetoulis and Talberth 

2008 - Redefining Progress; Kitzes et al. 2009 - Global 

Footprint Network, Best Foot Forward). Unfortunately, 

these groups do not publish their complete 

methodologies or data, in part because they provide 

a commercial EFA calculating service. Because the 

groups do not provide the information free of charge or 

make it readily available, it is difficult to conduct an EFA 

with limited budgets which is one of our goals for our 

methodology. Moreover, this standardization has some 

drawbacks that make the calculation more difficult than 

it need be. First, the standard recommends starting with 

national level data. However, a subsystem or region may 

not reflect a national average, or levels of consumption 

may not be typical and this could be concealed using 

national data. Thus, scaling these variables to the 

population of the subsystem may not adequately 

represent the subsystem. Second, these groups do not 

make the factors that are necessary for conversion to a 

common normalized unit (global hectare, gha) readily 

available. Our goal in this project was not to create a 

“better” or more accurate EFA; rather it was to make the 

methodology more accessible and usable for the end user 

while still being adequate for sustainability analysis.

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and biocapacity accounts 2005: the underlying 

calculation method. Global Footprint Network, 

Oakland. 33 pp.

Wackernagel, M., Onisto, L., Bello, P., Linares, 

A.C., López Falfán, I.S., Méndez García, J., Suárez 

Guerrero, A. I., Suárez Guerrero, M.G. 1999. 

National natural capital accounting with the ecological 

footprint concept. Ecological Economics 29,375-390.

Wackernagel, M., Rees, W. 1996. Our ecological 

footprint: reducing human impact on the earth. New 

Society Publishers, Gabriola Island, BC. 160 pp.

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between local and global hectares in Ecological 

Footprint analysis. Ecological Economics 60, 673-

677.

Wood, R., Garnett, S. 2009. An assessment of 

environmental sustainability in Northern Australia 

using the ecological footprint and with reference to 

Indigenous populations and remoteness. Ecological 

Economics 68, 1375-1384.

WWF. 2008. Living Planet Report 2008. Published in 

October 2008 by WWF–World Wide Fund For Nature 

(formerly World Wildlife Fund), Gland, Switzerland. 

Available online at http://assets.panda.org/downloads/

living_planet_report_2008.pdf. Last accessed July 23, 

2009.

Zhao, S., Hong, H., Zhang, L. 2008. Linking the 

concept of ecological footprint and valuation of 

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448-456.
33

Table 3.1 – Scale and source of data for the variables used to calculate ecological footprint for the SLB.

Scale Variable Purpose/Notes Source Website Specific data link

National

Consumption of 

Animal Products 

(lbs / capita)

Includes bovine, 

buffalo, sheep, goat, 

non-bovine, milk, 

cheese, butter, eggs, 

and fish categories

USDA Economic 

Research Service

www.ers.usda.gov www.ers.usda.gov/Data/

FoodConsumption/

FoodAvailQueriable.aspx

Cereal Consumption 

(lbs / capita)

Includes cereals, 

wheat, and maize 

categories

USDA Economic 

Research Service

www.ers.usda.gov www.ers.usda.gov/Data/

FoodConsumption/

FoodAvailQueriable.aspx

Fruit & Vegetable 

Consumption 

(lbs / capita)

Includes vegetables, 

fruit, animal feed, 

roots, tubers, and 

pulses consumption

USDA Economic 

Research Service

www.ers.usda.gov www.ers.usda.gov/Data/

FoodConsumption/

FoodAvailQueriable.aspx

State

Fossil Fuel (Gj / year) Includes coal, natural 

gas, and petroleum 

consumption

Energy Information 

Administration

www.eia.doe.gov www.eia.doe.gov/

emeu/states/state.

html?q_state_a=co&q_

state=COLORADO

Nuclear Electric 

Consumption 

(million kWh)

Energy Information 

Administration

www.eia.doe.gov www.eia.doe.gov/

emeu/states/state.

html?q_state_a=co&q_

state=COLORADO

Hydro-electric 

Consumption 

(million kWh)

Energy Information 

Administration

www.eia.doe.gov www.eia.doe.gov/

emeu/states/state.

html?q_state_a=co&q_

state=COLORADO

Wood and waste 

Consumption 

(trillion BTU)

Energy Information 

Administration

www.eia.doe.gov www.eia.doe.gov/

emeu/states/state.

html?q_state_a=co&q_

state=COLORADO

County

Population (persons) Bureau of Economic 

Analysis

www.bea.gov www.bea.gov/

regional/reis/default.

cfm?selTable=CA1-

3§ion=2

Land Area (ha) US Census Bureau www.census.gov quickfacts.census.gov/

qfd/states/08000.html 

and for US

Built land (ha) Census designated 

places based on 1990 

and 2000 data

US Census Bureau www.census.gov www.census.gov/geo/

cob/bdy/pl/pl90e00/

pl08_d90_e00.zip

Arable land (ha) 1982, 1987, 1992, 

1997, 2002, 2007

USDA Agricultural 

Statistics

www.agcensus.

usda.gov

http://www.agcensus.

usda.gov

Forest including 

deforestation (ha)

1987, 1997, 2002 and 

treated as a constant

USDA Forest Service www.fia.fs.fed.us www.fia.fs.fed.us/toolsdata/spatial
34

Scale Variable Purpose/Notes Source Website Specific data link

Calculated

Pasture (ha) total farmland minus 

arable land; values 

reported as “D” in 

report are listed as 

values from adjacent 

survey

USDA Agricultural 

Statistics

www.agcensus.

usda.gov

Sea (ha) No sea category in 

biocapacity in SLB

No sea category in 

biocapacity in SLB

Forest harvest (MBF) 1. USDA Forest 

Service 

2. USDA Natural 

Resources and 

Conservation Service

1. Data generated 

by Bruce F. 

Short, Biological 

Resources Staff 

Officer, San Luis 

Valley Public 

Lands Center 

2. National 

Resources 

Inventory 2001 

Annual NRI, 

Urbanization and 

Development of 

Rural Land, July 

2003 (and see 

www.nrcs.usda.

gov/technical/

land)

1. Bruce F. Short, 

Biological Resources 

Staff Officer, San Luis 

Valley Public Lands 

Center, 1803 West 

Highway 160, Monte 

Vista, CO 81144, 

bshort@fs.fed.us, phone 

(719) 852-6225

2. USDA, Natural 

Resources and 

Conservation Service, 

National Resources 

Inventory 2001 Annual 

NRI, Urbanization and 

Development of Rural 

Land, July 2003 (and 

see www.nrcs.usda.gov/

technical/land)

Energy land (ha) Based on CO production 2

and subtracted from 

forest land. Thus, 

biocapacity is zero.

Non-productive land 

(ha)

Total land area minus 

the sum of EF land 

area

Total land area minus the 

sum of EF land area
35

Table 3.2 – Ecological footprint and biocapacity in the San Luis Basin, Colorado, 1980-2005. Twelve percent of the 

biocapacity area was subtracted from the total biocapacity to protect biodiversity.

Year

Ecological Footprint 

(hectares per capita)

Biocapacity (hectares 

per capita)

Ecological Balance 

(hectares per capita)

1980 5.41 35.95 30.54

1981 5.39 34.91 29.52

1982 5.39 33.84 28.45

1983 5.31 33.23 27.92

1984 5.45 32.85 27.39

1985 5.50 32.88 27.37

1986 5.49 32.46 26.97

1987 5.37 32.15 26.78

1988 5.45 32.06 26.61

1989 5.37 32.42 27.04

1990 5.42 32.65 27.23

1991 5.31 32.23 26.92

1992 5.30 32.41 27.11

1993 5.31 32.10 26.78

1994 5.30 31.22 25.92

1995 5.20 30.30 25.10

1996 5.22 29.73 24.51

1997 5.10 29.21 24.11

1998 5.14 28.77 23.64

1999 5.15 28.43 23.29

2000 5.26 27.97 22.71

2001 5.26 28.04 22.78

2002 5.14 27.74 22.60

2003 5.15 27.65 22.50

2004 5.22 27.34 22.12

2005 5.13 27.47 22.34

 
36

Figure 3.1 – Biocapacity compared to ecological footprint for 26 years in the San Luis Basin. Twelve percent of 

the biocapacity area was subtracted from the total BC and bc to protect biodiversity. a – Per capita biocapacity (bc) 

compared to per capita ecological footprint (ef). b - Biocapacity for the population (BC) compared to total ecological 

footprint (EF).

a

b

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

1980 1983 1986 1989 1992 1995 1998 2001 2004

hectares / capita

year

ef bc

0

200

400

600

800

1,000

1,200

1,400

1,600

1980 1983 1986 1989 1992 1995 1998 2001 2004

1000 hectares

year

EF BC
37

year

Figure 3.2 – Available biocapacity (bc) for the five footprint land categories over a 26-year period in the San Luis 

Basin, Colorado. Twelve percent of the biocapacity area was subtracted from the total bc to protect biodiversity. Note 

that built-up land has no biocapacity and is not included in the figure.

Figure 3.3 – Ecological footprint (ef) and the demand placed on each of the six land categories over a 26-year period 

in the San Luis Basin, Colorado.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1980 1983 1986 1989 1992 1995 1998 2001 2004

Ecological footprint (ha / ca)

year

Forest Built-up Sea Energy Pasture Arable

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

1980 1983 1986 1989 1992 1995 1998 2001 2004

Available biocapacity (ha / ca)

Forest Sea Energy Pasture Arable
38

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

1980 1983 1986 1989 1992 1995 1998 2001 2004

Ecological balance (ha / ca)

year

Arable Pasture Energy Sea Built-up Forest

Figure 3.4 – Energy consumption (hectares per person) by major energy source over a 26-year period in the San Luis 

Basin, Colorado.

Figure 3.5 – Ecological balance (ha/ca) of the different land categories in the SLB. Remainder equals biocapacity 

per capita minus the ecological footprint for the SLB region. Positive values represent a surplus or remainder of 

biocapacity and negative values represent a deficit of biocapacity. Twelve percent of the biocapacity area was 

subtracted from the total bc to protect biodiversity.

 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

1980 1983 1986 1989 1992 1995 1998 2001 2004

Energy consumption (ha / ca)

year

Hydro-electric Nuclear Wood and waste

Natural Gas Petroleum Coal
39

Appendix 3-A: Estimation of Energy 

Use and Determination to Use EIA State 

Level Data

For exposition purposes, this appendix demonstrates 

the applicability of using state level data as an estimate 

of individual energy consumption in the San Luis 

Basin. State level data are easier to obtain, but may not 

accurately represent regional level energy consumption. 

Energy consumption data for the State of Colorado 

were obtained from the Department of Energy’s 

Energy Information Administration (EIA; EIA 2006). 

We assumed that EIA data calculated on a per capita 

usage and multiplied by the population of the SLB 

was sufficient to estimate the energy consumption. We 

justified this assumption by comparing coal, natural 

gas, and propane consumption in the SLB to their 

consumptive use for the State of Colorado. 

Xcel Energy and San Luis Valley Rural Electric 

Co-Op, the primary energy suppliers in the SLB, 

provided usage rates for their customers. We estimated 

coal consumption by starting with actual electricity 

consumption for 2006. The amount of electricity 

produced from coal is highly dependent on the type of 

coal used (EIA 2009). We assumed it takes 0.521 short 

tons (472.56 kg) of coal to produce 1 MWh of electricity 

(Heede 2005), which works out to approximately 1.024 

lbs per kWh. In 2006, the population of the SLB was 

nearly 1% of Colorado’s population. Therefore, the SLB 

would use about 1% of the energy in the state. Using 

data provided by the energy suppliers indicates that 

residents in the SLB used 236895 short tons (1.18% of 

2006 state estimates) of coal and 1.5 billion cubic feet 

(0.33% of 2006 state estimates) of natural gas. 

We attempted to use the same strategy for propane 

consumption, but were unable to get consumption 

rates of propane in the SLB from suppliers. Therefore, 

a crude estimate of propane use was generated by 

examining the relationship between heating degree days 

(HDD) and quantity of propane used in a household. We 

averaged the number of HDD from 20 weather stations 

located in and around the SLB (NOAA Western Regional 

Climate Center, http://www.wrcc.dri.edu/). Based on 

an estimate of 1200 gallons of propane used per year 

for a 2600 ft2

 home in Montrose, CO (USDA 2006) and 

6360 mean number of HDD reported at the Montrose 2 

weather station, we estimated 0.189 gallons per HDD. 

However, the majority (US Census Bureau 2000) of 

homes in the SLB were built before 1979 and the median 

house size of new construction in the US in 1972 was 

around 1,400 ft2

 (US Bureau of the Census 1976). 

Therefore, we used the mean of 0.102 gallons per HDD 

(consumption levels are dependent on numerous factors 

including house size, lifestyle, energy efficiency of 

appliances, how well a structure is insulated, etc.). The 

mean annual number of HDD in the SLB was 9006. The 

result, based on HDD, is an estimated average of 914.98 

gallons (21.78 barrels) of propane used per household 

(approximately 29%, or 5200, of the 17,687 households 

are heated with propane; US Census Bureau 2000). 

Next, we estimated the per household propane 

consumption for Colorado. According to EIA data, 

around 40.6% (2632.504 K barrels) of propane was used 

by the residential sector in Colorado (EIA 2006) in 2000. 

There were 1,808,037 households in the state in 2000 

and 6.2% (112098 households) of them used propane 

to heat their home (US Census Bureau 2000). Of the 

6484 thousand barrels of propane consumed in the state, 

40.6% (2632.504 thousand barrels) were used by the 

residential sector (EIA 2006). Thus, the average propane 

use for Colorado was 986.32 gallons per household. 

Based on HDD (0.102 gallons / HDD / household), 

the SLB would consume 113,283 barrels of propane 

in a typical year. This is 4.30% of Colorado’s total 

residential propane use. If we use state level data (986.3 

gallons / household), the SLB would consume 122,116 

barrels of propane in a typical year. This is 4.64% of 

Colorado’s total for residential propane use. Propane 

consumption is relatively similar for either method and 

would not adversely affect the energy consumption in 

the SLB. Of course, this only accounts for residential 

consumption, but may adequately represent total 

consumption as well.
40

A summary of these calculations is below:

Data sources (based on 2000 census data, US Census Bureau)

EIA data for Colorado,

 1,808,037 households

 112,098 use propane (≈6.2%)

 6,484,000 barrels of propane consumed

 40.6% of propane consumed for residential use

San Luis Basin data from energy suppliers:

 17,687 households

 5200 use propane

 9006 average heating degree days (HDD)

 1400 sq ft average house size

Montrose, CO (in SLB) special study data:

 2600 sq ft house

 1200 gallons propane per year

 6360 HDD (at Montrose 2 weather station)

SLB propane energy use proportioned from state EIA data:

 0.406 residential proportion * 6,484,000 barrels = 

 2632504 barrels of propane for residential use in CO

 2,632,504 barrels / 112,098 households = 

 23.48395 barrels of propane per household in CO

 23.48395 barrels/household * 5200 households in SLB = 

 122,116 barrels consumed in SLB per year = 4.64% of CO’s propane use (122,116 barrels / 2,632,504 barrels * 100)

SLB propane energy use based on SLB HDD data:

 1200 gallons / 42 gallons per barrel = 28.57143 barrels used in Montrose

 1400 sq ft SLB / 2600 sq ft Montrose = 0.5384615 SLB proportion of Montrose use

 9006 HDD SLB / 6360 HDD Montrose = 1.416038 SLB fraction of Montrose HDD

 28.57143 * 0.5384615 * 1.416038 = 21.78520 barrels per household in SLB

 21.78520 barrels/household * 5200 households in SLB = 

 113,283 barrels consumed in SLB per year = 4.30% of CO’s propane use (121,375 barrels / 2,632,504 barrels * 100)

Percent difference: (EIA – HDD) / HDD = 7.8%

Because of the ease of getting energy consumption from EIA and because state level data are a consistent source of 

energy consumption values, we think they are an adequate surrogate for actual consumption energy data in the region. 

Using EIA data for Colorado, consumption was calculated as per capita for the state and included coal, natural gas, 

petroleum, nuclear, hydroelectric, and wood and waste categories. 
41

References

Chambers, N., Simmons, C., Wackernagel, M. 2000. 

Sharing nature’s interest: ecological footprints as an 

indicator of sustainability. Earthscan Publications, 

Ltd., London. 185 pp.

EIA (Energy Information Administration). 2006. State 

Energy Data 2006: Consumption. State Energy Data 

System. http://www.eia.doe.gov/emeu/states/_seds.

html. Last accessed March 17, 2008.

EIA (Energy Information Administration). 2009. 

Energy Kid’s Page: coal a fossil fuel. http://www.eia.

doe.gov/kids/energyfacts/sources/non-renewable/coal.

html. Last accessed August 31, 2009.

Heede, R. 2005. Aspen emissions inventory: electricity 

carbon factor. Climate Mitigation Services. 

http://aspenglobalwarming.com/pdf/02Carbon&Notes.

pdf. Last accessed 23 July 2009.

NOAA (National Oceanic and Atmospheric 

Administration) Western Regional Climate Center. 

http://www.wrcc.dri.edu/. Last accessed April 23, 

2008. 

US Bureau of the Census. 1976. Construction Report—

Series C25 Characteristics of New Housing; 1975. 

US Department of Commerce, Washington, DC.

US Census Bureau. 2000. 2000 Census, Summary 

File 1, Table DP-4. Profile of Selected Housing 

Characteristics, Colorado. US Department of 

Commerce, Washington, DC.

USDA. 2006. Rural Development Electric Programs 

Success Stories, Colorado: Delta-Montrose Electric 

Association Home Energy Makeover Contest. May. 

www.usda.gov/rus/electric/success/co-success.htm. 

Last accessed July 23, 2009.
42

Appendix 3-B: Ecological Footprint 

Accounting Ledger for 1980 in the 

San Luis Basin 

This appendix contains the basic tables used in 

ecological footprint accounting. Tables list the main 

resources produced and consumed and the supply 

(biocapacity) and demand (ecological footprint) of land 

area in the San Luis Basin region.

Table 3-B.1 – Ecological footprint accounting ledger for food consumption for 1980 in the San Luis Basin. Local 

Yield and Production are italicized because they were not needed to compute apparent consumption. Global yields are 

from Chambers et al. (2000).

Resource Global yield Local yield Production Apparent 

consumption

Footprint 

component Land category

Unit (kg / ha) (kg / ha) (kg) (kg) (ha / capita)

MEAT

.meat (fresh) 199.243 92,990,880 2,919,258 

..bovine, buffalo 32 188.715 88,077,314 1,280,774 1.0404 pasture

..sheep, goat 72 7.379 3,443,739 17,449 0.0063 pasture

..non-bovine, goat, mutton, buffalo 764 3.149 1,469,828 1,621,035 0.0552 pasture

DAIRY

..milk 458 0.000 4,140,803 0.2350

..cheese 46 0.000 305,362 0.1726

..butter 22 0.000 0.0000

..eggs 573 0.000 1,364,454 0.0619

FISH sea

.marine, inland fish 35 0.000 218,465 0.1623 sea

GRAINS 2752 1229.170 268,326,225 4,590,896 0.0434 arable

.cereals 2752 879.013 191,887,425 579,315 0.0055 arable

..wheat 350.157 76,438,800 3,964,468 

..maize 0.000 225,095 

FRUITS & VEGETABLES 8136 0.000 10,617,864 0.0339 arable

.vegetable & fruit 0.000 10,617,864 

..vegetable etc. 0.000 5,896,099 

..fresh fruit 0.000 4,721,765 

ANIMAL FEED 0.000

..animal feed 2752 1847.935 403,401,978 0.0000 arable

ROOTS & TUBERS 0.000

.roots & tubers 12814 2278.070 497,299,806 1,280,984 0.0026 arable

PULSES 0.000

.pulses 802 0.000 93,642 0.0030 arable
43

Table 3-B.2 – Ecological footprint accounting ledger for energy consumption for 1980 in the San Luis Basin. Global 

average energy to land ratio was used to convert joules to hectares (Chambers et al. 2000).

Primary energy use * Global average 

energy to land ratio Energy use Footprint component Land type

Unit (Gj / ha/ yr) (Gj / SLB / yr) (ha / capita / yr)

Coal consumption (short tons = 2000 lbs) 55 3880385.04 0.5468 energy land

Natural Gas Consumption (billion cubic feet) 93 12625.97 168.0439 energy land

Petroleum Consumption (thousand barrels) 71 4965333.44 0.4273 energy land

Total fossil fuel consumption 340771652986

Nuclear energy consumption 71 31755.94 66.8133 energy land

Hydro-electric consumption 1000 81746.54 25.9548 energy land

Table 3-B.3 – Ecological footprint accounting ledger for supply and demand of bioproductive land for 1980 in the 

San Luis Basin. Equivalence factors are from McIntyre et al. (2007) and Chambers et al. (2000) and were treated as 

constants for the 26 years.

Demand - Footprint (per capita)

Category Total Equivalence factor Equivalence total

Unit (ha / capita) (gha / capita)

Fossil energy 2.88 1.17 3.37

Built-up land 0.67 2.22 1.48

Arable land 0.09 2.22 0.20

Pasture 1.57 0.49 0.77

Forest including deforestation 0.05 1.35 0.07

Sea 0.16 0.36 0.06

Total used 5.41 5.94

Supply - Existing regional capital (per capita)

Category Yield factor Regional area Yield adjusted 

equivalent area

Unit (ha / capita) (gha / capita)

Fossil energy

Built-up land 1.42 0.12 0.17

Arable land 1.42 5. 39 7.65

Pasture 1.63 12.18 19.85

Forest including deforestation 1.97 22.93 45.17

Sea 1.28 0.00 0.00

Other 14.30

Total existing 40.61 72.84

Total available (minus 12% for biodiversity) 35.74 64.10

ECOLOGICAL REMAINDER 30.32 58.16
44

Appendix 3-C: Twenty-six Years (1980-2005) of Data for the San Luis Basin, 

Colorado.

Table 3-C.1 – Twenty-six years (1980-2005) of data for the San Luis Basin, Colorado. Italicized values were 

estimated using linear interpolation

Variable name 1980 1981 1982 1983

Population (persons) 38469 38980 39467 40112

Coal consumption (short tons) 158449.0558 176724.9259 178864.4571 166457.3616

Natural Gas Consumption (billion cubic feet) 3.385607069 2.775030315 2.90050471 2.739301399

Petroleum Consumption (thousand barrels) 811.3289941 742.0195447 750.66351 767.7980211

Nuclear Electric Consumption (million kWh) 8.821093419 9.804234462 7.335054132 9.574754423

Hydro-electric Consumption (million kWh) 22.70737241 18.31258213 21.27036787 23.94968653

Wood and waste Consumption (trillion BTU) 0.141507795 0.184565695 0.188210528 0.200967439

Land Area (hectares) 2121718.26 2121718.26 2121718.26 2121718.26

Population density (individuals per hectare) 0.018131059 0.018371902 0.018601433 0.018905432

Built-up land (ha) 4572.30285 4646.10285 4719.90285 4793.70285

Arable land (ha) 207209.4897 214383.1902 221556.8907 225385.0547

Pasture (ha) 468443.1367 438884.2499 409325.3631 402236.8031

Forest including deforestation (ha) 882085.2711 882085.2711 882085.2711 882085.2711

everything else (ha) 559408.0596 581719.4459 604030.8322 607217.4282

mean precipitation (cm) 50.8 52.2 54.4 55.5

Meat Consumption (lbs/capita) 179.82 179.69 174.6 175.5

meat (fresh; lbs/capita) 167.3 167 162.1 162.1

bovine, buffalo (lbs/capita) 73.4 74.1 73.9 75.5

sheep, goat (lbs/capita) 1 1 1.1 1.1

non-bovine (lbs/capita) 92.9 91.9 87.1 90.1

milk (gal/capita) 27.6 27.1 26.4 26.3

cheese (lbs/capita) 17.5 18.2 19.9 20.6

eggs (lbs/capita) 271.1 264.4 264.1 260.2

fish (lbs/capita) 12.52 12.69 12.5 13.4

Cereal Consumption (lbs/capita) 263.1 262.3 262.8 264

cereals (lbs/capita) 33.2 33.98 32.32 32.17

wheat (lbs/capita) 227.2 225.7 227.7 229

maize (lbs/capita) 12.9 13.3 13.8 14.7

Fruit & Vegetable Consumption (lbs/capita) 608.5 587.3 615.1 603.4

vegetables & fruits (lbs/capita) 608.5 587.3 615.1 603.4

vegetables (lbs/capita) 337.9 333.9 336 339.3

fruit (lbs/capita) 270.6 253.4 279.1 264.1

roots and tubers (lbs/capita) 73.41199886 71.47119517 71.35533997 74.32447044

pulses (lbs/capita) 5.366536979 5.419061948 6.488707427 6.526480216

coffee and tea (lbs/capita) 11.08 10.77 10.64 10.84

cocoa (lbs/capita) 3.4 3.6 3.7 4

oil seed (lbs/capita) 1.506196043 1.391510049 1.610763691 1.557785299

fats (lbs/capita) 60.1 60.2 60.8 62.8

sweetener consumption (lbs/capita) 120.2 119.8 117.7 119.3

forest harvest (MBF) 23,973.14 14,124.07 24,001.56 25,115.40

meat production (fresh; kg) 92,990,880 99,130,808 96,894,691 102,167,967

bovine, buffalo production (kg) 88,077,314 94,061,842 92,394,147 99,536,388

sheep, goat (kg) 3,443,739 3,353,902 3,000,000 2,631,579

non-bovine, goat, mutton, buffalo (pork; kg) 1,469,828 1,715,064 1,500,544 -

grain production (kg) 268,326,225 306,234,575 312,838,180 347,138,625

cereal production (kg) 191,887,425 208,865,375 227,228,900 221,039,425

wheat production (kg) 76,438,800 97,369,200 85,609,280 126,099,200

animal feed production (kg) 403,401,978 299,723,304 295,581,960 304,422,567

roots and tubers production (kg) 497,299,806 524,182,716 581,850,528 631,668,984
45

Variable name 1984 1985 1986 1987

Population (persons) 40490 40369 40804 41104

Coal consumption (short tons) 188272.4105 191747.1074 189421.8273 189189.4125

Natural Gas Consumption (billion cubic feet) 2.937764886 2.755240242 2.495543403 2.647416314

Petroleum Consumption (thousand barrels) 767.9700599 759.9430665 774.1604165 774.6466201

Nuclear Electric Consumption (million kWh) 0.702508994 0.402592181 0.655395237 2.193573517

Hydro-electric Consumption (million kWh) 27.70440016 29.65343036 28.53490033 22.91906123

Wood and waste Consumption (trillion BTU) 0.210752698 0.212618996 0.252075091 0.166409025

Land Area (hectares) 2121718.26 2121718.26 2121718.26 2121718.26

Population density (individuals per hectare) 0.019083589 0.01902656 0.019231583 0.019372977

Built-up land (ha) 4867.50285 4941.30285 5015.10285 5088.90285

Arable land (ha) 229213.2187 233041.3827 236869.5467 240697.7122

Pasture (ha) 395148.2431 388059.6831 380971.1231 373882.5899

Forest land (ha) 882085.2711 882085.2711 882085.2711 882085.2711

everything else (ha) 610404.0242 613590.6202 616777.2162 619963.7839

mean precipitation (cm) 56.4 59.9 58.4 47.4

Meat Consumption (lbs/capita) 180.9 185 184.8 184.58

meat (fresh; lbs/capita) 166.7 169.9 169.3 168.38

bovine, buffalo (lbs/capita) 75.3 76.1 76 70.8

sheep, goat (lbs/capita) 1.1 1.1 1 0.98

non-bovine (lbs/capita) 91.2 93.2 92.3 96.6

milk (gal/capita) 26.4 26.7 26.5 26.1

cheese (lbs/capita) 21.5 22.5 23.1 24.1

eggs (lbs/capita) 260.1 255.2 253.5 253.8

fish (lbs/capita) 14.2 15.1 15.5 16.2

Cereal Consumption (lbs/capita) 268.7 283.6 289.5 301.2

cereals (lbs/capita) 34.45 39.34 43.83 48.77

wheat (lbs/capita) 231.1 241.1 242.3 249

maize (lbs/capita) 16 17.2 19.4 21.7

Fruit & Vegetable Consumption (lbs/capita) 626.5 631.6 637.4 641.1

vegetables & fruits (lbs/capita) 626.5 631.6 637.4 641.1

vegetables (lbs/capita) 356.5 359.6 360 366

fruit (lbs/capita) 270 272 277.4 275.1

roots and tubers (lbs/capita) 75.20384805 74.2373237 77.13000354 76.89765767

pulses (lbs/capita) 5.101417896 7.117753361 6.600845207 5.157145681

coffee and tea (lbs/capita) 10.96 11.25 11.26 10.94

cocoa (lbs/capita) 4.3 4.6 4.8 4.8

oil seed (lbs/capita) 1.709343849 1.572551223 1.678779644 1.301461261

fats (lbs/capita) 64.6 68.2 68.6 66.9

sweetener consumption (lbs/capita) 121.8 126.2 124.3 128.8

forest harvest (MBF) 30,777.54 30,784.99 34,714.50 32,798.34

meat production (fresh; kg) 106,428,675 - - 104790311.1

bovine, buffalo production (kg) 103,965,880 102,649,819 104,776,134 104582695.1

sheep, goat (kg) 2,462,795 100,652,087 102,909,528 207616.0228

non-bovine, goat, mutton, buffalo (pork; kg) - 1,997,731 1,866,606 -

grain production (kg) 356,523,415 483,574,415 449,167,880 313,170,000

cereal production (kg) 247,711,175 240,060,065 211,729,155 161,090,200

wheat production (kg) 108,812,240 243,514,350 237,438,725 152,079,800

animal feed production (kg) 342,193,593 114,993,440 84,188,080 84,673,600

roots and tubers production (kg) 781,827,618 441,830,724 437,380,938 511,067,769
46

Variable name 1988 1989 1990 1991

Population (persons) 41289 40903 40682 41283

Coal consumption (short tons) 200731.8624 204688.8725 210345.8029 202397.8189

Natural Gas Consumption (billion cubic feet) 2.885678728 3.084130514 3.037972946 3.266446794

Petroleum Consumption (thousand barrels) 784.1199118 743.9996627 741.978769 748.0894453

Nuclear Electric Consumption (million kWh) 8.353280528 6.605283571 0 0

Hydro-electric Consumption (million kWh) 22.08556746 21.87609984 17.46526957 21.86569235

Wood and waste Consumption (trillion BTU) 0.178456448 0.141095849 0.134064393 0.151134105

Land Area (hectares) 2121718.26 2121718.26 2121718.26 2121718.26

Population density (individuals per hectare) 0.019460171 0.019278243 0.019174082 0.019457343

Built-up land (ha) 5162.70285 5236.50285 5310 5446

Arable land (ha) 241324.6522 241951.5922 242578.5322 243205.4722

Pasture (ha) 375721.2299 377559.8699 379398.5099 381237.1499

Forest land (ha) 882085.2711 882085.2711 882085.2711 882085.2711

everything else (ha) 617424.4039 614885.0239 612345.9467 609744.3667

mean precipitation (cm) 47.5 37.3 63.8 51.0

Meat Consumption (lbs/capita) 186.7 184.69 183.39 184.51

meat (fresh; lbs/capita) 171.5 169.09 168.39 169.62

bovine, buffalo (lbs/capita) 69.7 66.09 64.79 63.72

sheep, goat (lbs/capita) 1 1 1 1

non-bovine (lbs/capita) 100.8 102 102.6 104.9

milk (gal/capita) 26.1 26 25.7 25.5

cheese (lbs/capita) 23.7 23.8 24.6 25

eggs (lbs/capita) 246.6 237 234.1 232.9

fish (lbs/capita) 15.2 15.6 15 14.89

Cereal Consumption (lbs/capita) 307.5 304.7 316.9 318.8

cereals (lbs/capita) 50.12 51.35 53.5 53.93

wheat (lbs/capita) 254.2 250.3 260.4 262

maize (lbs/capita) 21.7 21.8 21.4 21.7

Fruit & Vegetable Consumption (lbs/capita) 644.3 641.8 659.3 651.2

vegetables & fruits (lbs/capita) 644.3 641.8 659.3 651.2

vegetables (lbs/capita) 365.2 380.1 385.1 395.2

fruit (lbs/capita) 279.1 261.6 274.2 256

roots and tubers (lbs/capita) 76.16900377 78.46913146 74.99830509 80.97654572

pulses (lbs/capita) 6.935747548 5.710816178 6.716221499 7.347342826

coffee and tea (lbs/capita) 10.54 10.83 11.03 11.09

cocoa (lbs/capita) 4.8 4.9 5.4 5.7

oil seed (lbs/capita) 1.29784794 1.26545431 1.163385732 1.266307156

fats (lbs/capita) 67.7 64.7 64.9 67.1

sweetener consumption (lbs/capita) 130.2 128.5 132.4 132.9

forest harvest (MBF) 33,139.45 35,251.90 23,871.55 23,286.68

meat production (fresh; kg) 104531691.7 104273072.3 104014452.9 103808938.3

bovine, buffalo production (kg) 104389255.9 104195816.7 104002377.5 103808938.3

sheep, goat (kg) 142435.831 77255.6391 12075.44724

non-bovine, goat, mutton, buffalo (pork; kg) - - - -

grain production (kg) 308,737,340 378,839,230 370,231,930 356,537,105

cereal production (kg) 163,127,120 206,442,855 185,472,205 175,487,080

wheat production (kg) 145,610,220 172,396,375 184,759,725 181,050,025

animal feed production (kg) 88,291,200 121,690,080 65,962,720 61,183,680

roots and tubers production (kg) 513,245,030 400,367,411 452,367,770 444,429,827
47

Variable name 1992 1993 1994 1995

Population (persons) 41120 41466 42564 43793

Coal consumption (short tons) 200910.4621 200253.9844 204375.7016 198328.0663

Natural Gas Consumption (billion cubic feet) 3.05817693 3.350570905 3.188727254 3.318819344

Petroleum Consumption (thousand barrels) 740.125866 778.5602277 790.3129064 811.3139992

Nuclear Electric Consumption (million kWh) 0 0 0 0

Hydro-electric Consumption (million kWh) 17.63156623 21.9393547 17.64657663 24.38760008

Wood and waste Consumption (trillion BTU) 0.135265518 0.127367593 0.121148777 0.12245299

Land Area (hectares) 2121718.26 2121718.26 2121718.26 2121718.26

Population density (individuals per hectare) 0.019380519 0.019543594 0.020061099 0.020640346

Built-up land (ha) 5582 5718 5854 5990

Arable land (ha) 243832.4072 241884.4072 239936.4072 237988.4072

Pasture (ha) 383075.8336 382653.3536 382230.8736 381808.3936

Forest land (ha) 882085.2711 882085.2711 882085.2711 882085.2711

everything else (ha) 607142.748 609377.228 611611.708 613846.188

mean precipitation (cm) 49.2 53.8 53.7 52.6

Meat Consumption (lbs/capita) 188.31 188.01 190.99 190.05

meat (fresh; lbs/capita) 173.72 173.21 176.11 175.67

bovine, buffalo (lbs/capita) 63.23 61.75 63.65 64.32

sheep, goat (lbs/capita) 0.99 0.96 0.86 0.85

non-bovine (lbs/capita) 109.5 110.5 111.6 110.5

milk (gal/capita) 25.1 24.4 24.3 23.9

cheese (lbs/capita) 25.8 26 26.6 26.9

eggs (lbs/capita) 233.6 233.7 235.1 232.3

fish (lbs/capita) 14.59 14.8 14.88 14.38

Cereal Consumption (lbs/capita) 322.1 331.5 333.7 329.3

cereals (lbs/capita) 56.61 57.63 57.99 58.38

wheat (lbs/capita) 262.9 270.8 272.2 266.9

maize (lbs/capita) 22.1 23.1 24 24.9

Fruit & Vegetable Consumption (lbs/capita) 677.5 688 694.8 690.9

vegetables & fruits (lbs/capita) 677.5 688 694.8 690.9

vegetables (lbs/capita) 394.2 406.7 412.3 406.2

fruit (lbs/capita) 283.4 281.2 282.6 284.7

roots and tubers (lbs/capita) 78.46850247 82.22013295 82.47743694 82.41809971

pulses (lbs/capita) 7.816088605 7.230088179 7.707390748 7.507275821

coffee and tea (lbs/capita) 10.86 9.89 8.97 8.74

cocoa (lbs/capita) 5.7 5.3 4.8 4.5

oil seed (lbs/capita) 1.428604794 1.732915794 1.617091058 1.627044122

fats (lbs/capita) 69.2 71.6 69.4 67.2

sweetener consumption (lbs/capita) 136.1 139.1 141.6 144.1

forest harvest (MBF) 25,041.38 26,320.28 36,621.15 21,581.56

meat production (fresh; kg) 103615499.1 103422059.9 103228620.7 103035181.5

bovine, buffalo production (kg) 103615499.1 103422059.9 103228620.7 103035181.5

sheep, goat (kg) - - - -

non-bovine, goat, mutton, buffalo (pork; kg) - - - -

grain production (kg) 312,335,335 263,941,090 270,419,775 339,636,530

cereal production (kg) 158,970,960 132,075,390 136,866,050 165,980,305

wheat production (kg) 153,364,375 131,865,700 133,553,725 173,656,225

animal feed production (kg) 79,350,560 59,811,440 74,820,400 55,621,280

roots and tubers production (kg) 479,678,842 529,211,650 556,813,021 523,813,843
48

Variable name 1996 1997 1998 1999

Population (persons) 44566 45289 45902 46377

Coal consumption (short tons) 199934.5087 206220.1121 205489.9538 203823.0838

Natural Gas Consumption (billion cubic feet) 3.581222009 3.550272466 3.679618252 3.654395462

Petroleum Consumption (thousand barrels) 839.2337976 789.8510935 835.9981164 853.5482589

Nuclear Electric Consumption (million kWh) 0 0 0 0

Hydro-electric Consumption (million kWh) 20.69150494 22.90207509 16.30182389 17.14163877

Wood and waste Consumption (trillion BTU) 0.12392165 0.132994334 0.118193798 0.124008014

Land Area (hectares) 2121718.26 2121718.26 2121718.26 2121718.26

Population density (individuals per hectare) 0.021004674 0.021345435 0.021634352 0.021858227

Built-up land (ha) 6126 6262 6398 6534

Arable land (ha) 236040.4072 234092.0285 236491.2485 238890.4685

Pasture (ha) 381385.9136 380963.3746 375949.7946 370936.2146

Forest land (ha) 882085.2711 882085.2711 882085.2711 882085.2711

everything else (ha) 616080.668 618315.5858 620793.9458 623272.3058

mean precipitation (cm) 46.2 60.3 50.7 51.9

Meat Consumption (lbs/capita) 188.56 186.81 192.19 197.17

meat (fresh; lbs/capita) 174.07 172.54 177.62 182.42

bovine, buffalo (lbs/capita) 64.96 63.44 64.26 64.88

sheep, goat (lbs/capita) 0.81 0.8 0.86 0.84

non-bovine (lbs/capita) 108.3 108.3 112.5 116.7

milk (gal/capita) 23.8 23.4 23 22.9

cheese (lbs/capita) 27.3 27.5 27.8 29

eggs (lbs/capita) 233.4 234.3 239.2 249.7

fish (lbs/capita) 14.49 14.27 14.57 14.75

Cereal Consumption (lbs/capita) 342.9 343.5 337.4 339.9

cereals (lbs/capita) 59 57.25 57.39 56.93

wheat (lbs/capita) 279.3 281.3 274.6 277.3

maize (lbs/capita) 25.9 26.5 27.2 27.8

Fruit & Vegetable Consumption (lbs/capita) 703.3 709.7 696.2 705.2

vegetables & fruits (lbs/capita) 703.3 709.7 696.2 705.2

vegetables (lbs/capita) 416.9 415.1 411 414.3

fruit (lbs/capita) 286.4 294.6 285.2 291

roots and tubers (lbs/capita) 85.54335133 81.4107545 80.90923623 81.77681111

pulses (lbs/capita) 7.432451946 7.394777992 7.261340726 7.805818174

coffee and tea (lbs/capita) 9.51 9.87 10.18 10.64

cocoa (lbs/capita) 5.2 5 5.4 5.6

oil seed (lbs/capita) 1.337575603 1.089728557 1.320826467 1.544245332

fats (lbs/capita) 65.6 65.1 65.5 68.2

sweetener consumption (lbs/capita) 144.4 147.7 148.9 151.2

forest harvest (MBF) 11,056.57 3,871.70 4,352.81 6,737.07

meat production (fresh; kg) 102841742.3 102648303.1 102454863.9 102261424.7

bovine, buffalo production (kg) 102841742.3 102648303.1 102454863.9 102261424.7

sheep, goat (kg) - - - -

non-bovine, goat, mutton, buffalo (pork; kg) - - - -

grain production (kg) 375,290,000 383,370,840 367,873,510 354,264,550

cereal production (kg) 200,765,300 204,269,240 191,675,035 186,655,875

wheat production (kg) 174,524,700 179,101,600 176,198,475 167,608,675

animal feed production (kg) 119,122,400 97,997,520 81,382,400 96,247,200

roots and tubers production (kg) 461,094,984 668,035,017 641,613,001 680,508,942
49

Variable name 2000 2001 2002 2003

Population (persons) 47074 46863 47304 47376

Coal consumption (short tons) 213776.4764 215523.3959 208941.3594 210025.8599

Natural Gas Consumption (billion cubic feet) 4.003141834 4.910043486 4.824877192 4.543803648

Petroleum Consumption (thousand barrels) 908.0605101 919.4162247 875.4892085 903.9772299

Nuclear Electric Consumption (million kWh) 0 0 0 0

Hydro-electric Consumption (million kWh) 15.81676148 15.82007546 12.70866345 13.15201882

Wood and waste Consumption (trillion BTU) 0.125098182 0.071957534 0.067274976 0.069824506

Land Area (hectares) 2121718.26 2121718.26 2121718.26 2121718.26

Population density (individuals per hectare) 0.022186735 0.022087287 0.022295137 0.022329072

Built-up land (ha) 6670 6806 6942 7078

Arable land (ha) 241289.6885 243688.9085 246088.125 248487.345

Pasture (ha) 365922.6346 360909.0546 355895.5272 350881.9472

Forest land (ha) 882085.2711 882085.2711 882085.2711 882085.2711

everything else (ha) 625750.6658 628229.0258 630707.3367 633185.6967

mean precipitation (cm) 46.7 46.9 30.6 43.6

Meat Consumption (lbs/capita) 196.79 193.94 200.49 199.04

meat (fresh; lbs/capita) 181.58 179.14 184.85 182.81

bovine, buffalo (lbs/capita) 65.05 63.59 64.98 62.38

sheep, goat (lbs/capita) 0.83 0.85 0.87 0.83

non-bovine (lbs/capita) 115.7 114.7 119 119.6

milk (gal/capita) 22.5 22 21.9 21.6

cheese (lbs/capita) 29.8 30 30.5 30.5

eggs (lbs/capita) 251 252.4 254.6 254.3

fish (lbs/capita) 15.21 14.8 15.64 16.23

Cereal Consumption (lbs/capita) 345.8 336 328.8 329.9

cereals (lbs/capita) 59.61 60.29 61.18 61.09

wheat (lbs/capita) 280 269 260.6 261.4

maize (lbs/capita) 28.4 29 29.7 30.3

Fruit & Vegetable Consumption (lbs/capita) 711.2 684.2 684.9 702

vegetables & fruits (lbs/capita) 711.2 684.2 684.9 702

vegetables (lbs/capita) 422 411.9 411.1 420.1

fruit (lbs/capita) 289.2 272.3 273.8 281.8

roots and tubers (lbs/capita) 81.17866908 81.14909138 77.07850497 80.8407184

pulses (lbs/capita) 7.641999969 6.939987162 6.744101795 6.639901043

coffee and tea (lbs/capita) 11.14 10.37 10.03 10.3

cocoa (lbs/capita) 5.9 5.6 4.8 5.3

oil seed (lbs/capita) 1.499920327 1.49992465 1.499942751 1.499793877

fats (lbs/capita) 84.3 85.3 90.1 89.2

sweetener consumption (lbs/capita) 148.8 147 146.1 141.3

forest harvest (MBF) 5,506.58 3,223.76 2,679.38 3,897.25

meat production (fresh; kg) 102067985.5 - - -

bovine, buffalo production (kg) 102067985.5 90,209,619 79,211,661 64,341,425

sheep, goat (kg) - 90,209,619 79,211,661 64,341,425

non-bovine, goat, mutton, buffalo (pork; kg) - - - -

grain production (kg) 468,344,800 340,367,365 254,515,900 259,156,120

cereal production (kg) 236,823,800 178,770,515 121,562,200 118,279,070

wheat production (kg) 231,521,000 161,596,850 132,953,700 140,877,050

animal feed production (kg) 87,971,600 75,844,480 49,007,600 31,955,920

roots and tubers production (kg) 679,397,630 789,961,911 561,666,515 567,427,189
50

Variable name 2004 2005

Population (persons) 47831 47530

Coal consumption (short tons) 205594.4562 198190.5176

Natural Gas Consumption (billion cubic feet) 4.576624544 4.790411072

Petroleum Consumption (thousand barrels) 974.5921966 940.204808

Nuclear Electric Consumption (million kWh) 0 0

Hydro-electric Consumption (million kWh) 12.4296962 14.42219504

Wood and waste Consumption (trillion BTU) 0.076970504 0.070327311

Land Area (hectares) 2121718.26 2121718.26

Population density (individuals per hectare) 0.022543521 0.022401655

Built-up land (ha) 7214 7350

Arable land (ha) 250886.565 253285.785

Pasture (ha) 345868.3672 340854.7872

Forest land (ha) 882085.2711 882085.2711

everything else (ha) 635664.0567 638142.4167

mean precipitation (cm) 49.1 47.0

Meat Consumption (lbs/capita) 201.45 199.76

meat (fresh; lbs/capita) 184.85 183.56

bovine, buffalo (lbs/capita) 63.31 62.78

sheep, goat (lbs/capita) 0.83 0.78

non-bovine (lbs/capita) 120.4 120

milk (gal/capita) 21.2 21

cheese (lbs/capita) 31.2 31.4

eggs (lbs/capita) 256 253.9

fish (lbs/capita) 16.6 16.2

Cereal Consumption (lbs/capita) 326.6 326.3

cereals (lbs/capita) 60.58 61.89

wheat (lbs/capita) 258.1 256.4

maize (lbs/capita) 30.9 31.4

Fruit & Vegetable Consumption (lbs/capita) 693.8 688.6

vegetables & fruits (lbs/capita) 693.8 688.6

vegetables (lbs/capita) 421.7 415.4

fruit (lbs/capita) 272.1 273.2

roots and tubers (lbs/capita) 79.35153158 74.98827017

pulses (lbs/capita) 5.966688317 6.148759756

coffee and tea (lbs/capita) 10.45 10.35

cocoa (lbs/capita) 6 6.5

oil seed (lbs/capita) 1.488811125 1.478475521

fats (lbs/capita) 88.4 87.2

sweetener consumption (lbs/capita) 141.6 141.6

forest harvest (MBF) 6,280.86 4,444.48

meat production (fresh; kg) - -

bovine, buffalo production (kg) 69,523,593 68,018,376

sheep, goat (kg) 69,523,593 68,018,376

non-bovine, goat, mutton, buffalo (pork; kg) - -

grain production (kg) 265,175,950 241,908,150

cereal production (kg) 114,660,030 110,377,850

wheat production (kg) 150,515,920 131,530,300

animal feed production (kg) 27,140,160 31,252,800

roots and tubers production (kg) 569,717,854 655,502,132
51

Appendix 3-D: Ecological Footprint 

Analysis for the United States, 

1980-2005

This appendix provides results of an Ecological 

Footprint Analysis (EFA) for the United States from 

1980 to 2005 for comparative purposes with the San 

Luis Basin EFA. 

Because EFAs usually reveal patterns for the US where 

ecological deficit (ef > bc or EF > BC) is the rule, as 

demonstrated by the analysis on Utah (McIntyre et al. 

2007), and the analysis for the SLB reveals an ecological 

reserve, we decided to conduct an EFA for the US to 

test the methodology employed for SLB, using the 

same variables and data sources (see Table 3.1). For 

comparison purposes, the Global Footprint Network 

(2009) graph for the US shows a general increase in ef

for the US from 1980 (~6 gha) to 2005 (~5 gha) and a 

general decrease in bc from 1980 (~7.5 gha) to 2005 

(~9 gha). These results indicate the ecological balance 

for the US is a deficit and US was moving away from 

sustainability for the period (an increasing ecological 

deficit from ~1.5 to 4 gha). Our methodology reveals 

similar trends for the US, revealing the US had an 

ecological deficit because EF had already exceeded BC

by 1980 (whether reported ha or gha or per capita or for 

the entire population; Figs. 3-C.1 and 3-C.2). EFAs for 

the US usually identify an ecological deficit occurring 

around 1970 (e.g., Global Footprint Network 2009). The 

differences in the results between US and SLB increased 

our confidence that our methodology captured trends in 

the SLB, rather than merely duplicating that of the larger 

system. 

References

Global Footprint Network. 2009. Footprint for Nations. 

http://www.footprintnetwork.org/en/index.php/GFN/

page/trends/us/. Last accessed July 27, 2009.

McIntyre, S., Peters, H.M., Christensen, M.H., Emmi, 

P.C., Martinson, W., Mielke, M., Senbel, M., Stark, 

D.O. 2007. The ecological footprint of Utah: a 

sustainability indicators project of the Utah Population 

and Environment Coalition. Utah Vital Signs, 26 June. 

59 pp. Available online at http://www.utahpop.org/

vitalsigns/research/report_2007.htm. Last accessed 

July 13, 2009.
52

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1980 1983 1986 1989 1992 1995 1998 2001 2004

hectares/ capita

year

ef bc

Figure 3-D.1 – Biocapacity (bc) compared to ecological footprint (ef) per capita for 26 years in the US. Twelve 

percent of the biocapacity area was subtracted from the total bc to protect biodiversity. 

Figure 3-D.2 – Total ecological footprint (EF) and biocapacity (BC) for the US population. Twelve percent of the 

biocapacity area was subtracted from the total BCto protect biodiversity. 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

1000 hectares

year

EF BC
53

4.0 

Green Net Regional Product

4.1 Introduction

This chapter describes the data sources and methodology 

used to estimate Green Net Regional Product (GNRP) 

for the San Luis Basin (SLB). GNRP is similar to Green 

Net Domestic Product (GNDP; defined in Chapter 2), 

but applied to a region on a smaller spatial scale. The 

boundary of this metric was delineated by the seven 

counties that we define as the SLB (Fig. 1.1). The 

chapter is outlined as follows. First, we describe the 

basic model of estimating GNRP and the components 

of the depreciation of natural capital. Next, we 

highlight the public data sources used to estimate Net 

Regional Product (NRP), and the three components 

of natural capital depreciation. Then we present the 

methodology used to estimate GNRP, followed by the 

estimates themselves. Finally, we point out some of our 

assumptions when calculating GNRP with public data 

sources and our conclusion.

4.2 Model

Using the information describing GNDP in Section 2.2.2, 

the model for the SLB GNRP is 

GNRP(t) = NRP(t) + l (t)K n

n (t) (4.1)

Net Regional Product (NRP) is equal to Gross Regional 

Product (GRP) minus the depreciation of human-made 

capital. It does not include the depreciation of natural 

capital, which is represented by the second term on the 

right of Equation 4.1, where λn(t) is the shadow price of 

natural capital K nn(tt))( and is the change in natural capital 

over time, t. This component still has to be estimated in 

order to estimate GNRP. 

Natural capital, when incorporated in NRP calculations, 

is typically included as the extraction of natural resource 

stocks. For example, a resource stock that might be 

important is forests. Although timber is considered a 

valued output, GNRP captures the depreciation of natural 

capital through the value of decreases in the forest stock 

(Seroa da Motta and Ferraz do Amaral 2000). Given the 

contribution of agribusiness to the SLB, we included the 

depletion of both groundwater and soil as components 

in the depreciation of natural capital. In addition, we 

captured the effect of the consumption of energy on 

future generations through carbon dioxide emissions 

(CO2

). When stocks of capital are not changing over 

time or the change is minimal, an advantage of using 

the change in GNRP is that those stocks of capital 

can be dropped as explained in Chapter 2. Based on 

our assumptions, mining activities and forestry were 

considered constant. The assumption for forestry was 

consistent with Ecological Footprint Analysis. The 

decision to consider mining constant could be debated 

given the export data used in Emergy Analysis (Chapter 

5; e.g., sand and gravel was decreasing, broken stone 

or rip rap was marginally decreasing). We did not 

explicitly include the depreciation of human capital in 

our estimate of GNRP. Aggregate consumption in NRP 

(Eq. 4.1) already incorporates education expenditures. 

If these expenditures are the best proxy for investment 

in human capital, the expenditures are already included 

in the equation (see Pezzey et al. 2006 for discussion). 

We are aware, however, that education expenditures 

are typically considered a poor measurement of the 

depreciation of human capital (e.g., Ferreira and Vincent 

2005). Because sustainability relates to the potential 

for future increases or decreases in welfare rather than 

any absolute level of welfare, the absolute estimates of 

GNRP do not relate to sustainability. Sustainability is 

measured by changes in GNRP over time.

4.3 Data and Sources

One approach to calculate NRP, Equation 4.1, would 

begin with collecting data to estimate GRP and then 

adjust GRP by the depreciation of man-made capital 

stock (i.e., top-down approach). This nominal estimate 

is then converted into real NRP using the Gross 

Domestic Product (GDP) deflator (Banzhaf and Smith 

2002, Landefeld et al. 2008). Unfortunately, the lack of 

economic data for the counties in the SLB hampers the 

implementation of this approach. 

The data we used to estimate real NRP, described 

in the next section, are available from the Bureau of 

Economic Analysis (BEA) in the US Department of 

Commerce. The BEA is responsible for calculating 

the economic accounts in order to understand how the 

economy is performing (BEA 2006a). This Agency 

provides economic accounts data for the US, states, 

and regions, including counties (BEA 2006a). Our 

approach for estimating NRP included data from all 

three scales. The economic measures required include 

GDP and Consumption of Fixed Capital (CFC) for the 

US. Personal Income and GDP for Colorado and New 

Mexico were needed and available from the BEA. For 

the seven counties in the SLB, we used Personal Income 

from the BEA (Table 4.1).
54

For each component of the depreciation of natural 

capital, we needed an estimate of the change in the 

stock of the capital and a “price.” Goods and services 

provided by the environment and not traded in markets 

are not considered in the definition of natural capital 

depreciation above. A number of articles, however, 

have been published on how to proceed with calculating 

GNRP when some of the goods and services cannot be 

valued with existing prices (e.g., Banzhaf and Smith 

2002, Cairns 2001). Banzhaf and Smith (2002) stated 

that incorporating the depreciation of nonmarket goods 

and services is difficult because of the lack of data and 

because these goods and services are not measured in 

standard, well-defined units like market goods. 

Cairns (2001) suggested using shadow prices of the 

local amenities that do not have market prices. For 

environmental spillovers or externalities that extend 

beyond the local economy (e.g., reducing CO2

emissions), he recognized the global shadow value 

overstates the benefits to the local economy. However, 

he pointed out the inadequacy (for green accounting 

purposes) of imposing local value (as measured by 

willingness to pay) as the price of benefits that extend 

globally. His proposed solution was to count the 

difference between the shadow value and the domestic 

willingness to pay for mitigation of the spillover 

pollution as an export from the pollution emitter to 

the recipient of pollution. He provided a theoretical 

approach for incorporating these nonmarket values, but 

did not implement his approach. 

In the case of carbon emissions, we have opted to 

follow the example of Hamilton and Clemens (1999) 

who assumed the property right to a clean environment 

lies with the victim of pollution and thus charge global 

damages to emitting countries.8

 Carbon emissions are 

counted as an environmental cost equal to the global 

social cost of carbon.

Next, we describe the data needed to calculate the 

change in the stock of groundwater, soil erosion, and 

CO2

 emissions and the sources of these data. Additional 

details of the methodology are described in Section 

4.4. The Colorado Water Conservation Board and the 

Colorado Division of Water Resources have created 

Colorado’s Decision Support Systems that provide 

information about the water balance of many of its river 

basins (CWCB and CDWR 2008). Its purpose is to 

8

 Although we focus on determining whether the region is moving towards or away from sustainability, another possibility would have been 

to assess the region’s contribution towards global sustainability. If this 

would have been our objective, global emissions of carbon would have 

been the correct estimate. The approach used in this report is similar to 

that of Pezzey et al. (2006).

help water managers make better decisions related to 

water use. The Upper Rio Grande is one of the basins 

that is available. Colorado Division of Water Resources 

provided the updated calculations for the groundwater 

basin in the Upper Rio Grande and data required to 

include the years 1970-2005 (personal communication 

with R. Bennett, February 2008).

Wind erosion is the main cause of soil erosion in the 

SLB (personal communication, R. Sparks, USDA, 

May 2008). Finding reliable, county-level soil-erosion 

data is challenging. No estimates have been attempted 

for the SLB. The National Resource Information 

(NRI) data on wind erosion at first appeared to be a 

promising data source (e.g., NRCS 2007); however, 

NRI data are not reliable at the county level (personal 

communication, R. Sparks, USDA, May 2008). 

Following the recommended approach described by 

Richard Sparks, an agronomist in the USDA Natural 

Resources Conservation Service (NRCS) stationed in the 

region (see Appendix 4-A), a number of data sets were 

required. First, the Agricultural Statistics for Colorado 

provides annual estimates of planted acres of different 

crops by county. The US Census of Agriculture (NASS 

1984, 1989, 1994, 2009a, 2009b) provides data on total 

crop land and total harvested land. We used reports 

beginning in 1982 and continuing every five years until 

2002. Through numerous communications, R. Sparks 

provided information about crop rotations, soil type, and 

residue management by year (personal communication, 

May-November 2008). The Wind Erosion Equation 

Technology from 1980-2005 (Woodruff and Siddoway 

1965) was then applied to this calculation of acreage by 

crop rotation, soil type, and residue management. 

Carbon dioxide emission estimates do not exist at the 

county level. Therefore, we needed to find an alternative 

approach. Required data included state-level data 

for Colorado and New Mexico and the population of 

each state for the same years (1980-2005). State-level 

emissions can be found at the Energy Information 

Agency (EIA 2007). In addition, we needed the SLB 

population for the same years. Population data were 

acquired through the Bureau of Economic Analysis 

(BEA 2007). We used the average per capita CO2

emissions for Colorado and New Mexico (EIA 2008) and 

applied this to the SLB population estimates. Table 4.1 

presents the sources and the Websites where these data 

can be accessed. 
55

4.4 Calculation Methodology 

Due to the lack of data for the top-down approach, 

the alternative, bottom-up approach adjusts Personal 

Income, which is available for all counties in the SLB 

(e.g., BEA [2008] presents the relationship between 

Personal Income and GDP). Personal Income is defined 

as (BEA 2007: XI-6):

income received by persons from 

participation in production, plus transfer 

receipts from government and business, plus 

government interest (which is treated like a 

transfer receipt). 

Personal Income for the region was converted to 

Net Regional Product as follows. We estimated Net 

Domestic Product by state (NDPstate) using Gross 

Domestic Product by state (GDPstate) and the ratio of 

Consumption of Fixed Capital (CFCUS) to GDP for 

the US (GDPUS) (4.2). This was proposed because 

BEA does not estimate CFC for states (personal 

communication C. Woodruff, 25 January 2008). It is 

somewhat similar to the approach used by Pezzey et al. 

(2006) where United Kingdom estimates were used to 

convert Scottish data. Once NDPstate was calculated, we 

estimated the ratio of NDPstate to Personal Income by 

state. We used this process for both Colorado and New 

Mexico (i.e., represented by subscript state in Equations 

4.2 and 4.3) across time and multiplied by the Personal 

Income for the counties in the SLB, as presented in 

Equation 4.3. The estimate of NRP for the SLB was 

the average of both the Colorado and New Mexico 

calculations because we believed the GDPstate and 

Personal Income for Colorado and New Mexico bound 

the calculations for the SLB. 

  CFC  NDPstate ≈ GDP US 

 state − 

 

GDP

GDP state

 US  (4.2) 

 (4.3)

Without data to support this approach, the alternative 

would have been to use the ratio of Net Domestic 

Product for the US to Personal Income for the US, 

but we do not believe the Net Domestic Product is 

representative of the economy of the SLB. We are 

aware of another complication described by BEA related 

to GDP by state time series. Because BEA switched 

from Standard Industrial Classification (SIC) to North 

American Industry Classification System (NAICS)-based 

GDP in 1997, the time series results in a discontinuity. 

A SIC-based GDP is closer to Gross Domestic Income 

(GDI) and a NAICS-based GDP is closer to US GDP 

(BEA 2006b). Although BEA strongly cautions against 

combining these two series to have a complete time 

series, we appended the two by adjusting the SIC-based 

GDP by state (prior to 1997) using the ratio GDPUS/

GDIUS to make years relatively consistent with post1997. The estimate for real NRP, which has been 

adjusted using the GDP deflator, is in the second column 

of Table 4.2.

As stated in the previous section, we used data to 

estimate the change in the stock of natural capital and 

existing studies to estimate the shadow price. The 

previous section described the change in the stock of 

natural capital and data needed to estimate NRP. We 

provide more detail here on how we combined shadow 

price and the change in natural capital stock. In order to 

estimate the value of the change in the natural capital, we 

used benefit transfer, an economic valuation approach. 

An alternative to collecting primary economic value 

data via observation or survey is to use benefit transfer 

based on secondary data. Benefit transfer applies 

economic value (e.g., willingness to pay for a change 

in environmental quality) from one study to another 

location or context (Desvousges et al. 1992). The 

accuracy of benefit transfer depends on the existence and 

quality of applicable studies. The advantages of benefit 

transfer approaches include saving the time and cost of 

developing and implementing new studies. This is a 

standard approach for the USEPA because of limitations 

on primary data collection using surveys (see The Paper 

Reduction Act of 1995). Of course, the disadvantages 

are obvious. Benefit transfer is not usually as accurate 

as analysis done with primary data. This means the 

values estimated for the policy site will depend on 

differences in the resources being valued and the relevant 

populations. 

According to Wilson and Hoehn (2006: 336), “few 

benefit transfer practitioners seem fully satisfied with 

the state of the science.” However, certain steps need 

to be taken to ensure a high quality transfer. A few 

authors have listed these steps. For example, the USEPA 

(2000: 86-87) describes the steps as: 1) understanding 

the characteristics and consequences of the policy 

case along with the effected population; 2) identifying 

existing studies or values that are relevant for the policy 

case through a literature search; 3) reviewing available 

studies for quality and applicability and to determine 

their transferability; 4) transferring the benefit estimates; 

and 5) addressing uncertainty because judgments and 

assumptions are involved. 

We used a water balance model from the Colorado’s 

Decision Support Systems for the depletion of 

groundwater. This model was used to estimate the water 
56

balance for many of Colorado’s basins, including the Rio 

Grande River Basin (CWCB and CDWR 2008). Over 

the 26 years of this study, the model showed, on average, 

that groundwater storage has been declining in the region 

(outflow has been greater than inflow; Table 4.2, column 

3). We used the shadow price of water for agriculture 

from Hurd and Coonrod (2007), which was based on a 

hydro-economic model for the Rio Grande watershed. 

The purpose of their study was to examine the impact of 

climate change on water supplies in the arid southwest. 

We used $76/acre-foot (2005$) from their baseline 

estimate in 2000. It is not reflective of the market price 

of water, but rather the marginal value of an additional 

acre-foot of water for agricultural purposes. The 

authors pointed out that this estimate was relatively high 

compared to the estimate for New Mexico because of 

the Rio Grande Compact (CWCB 2000), which requires 

Colorado to deliver a certain amount of water to New 

Mexico and Texas, and because of the high marginal 

value of agricultural production in the SLB (Hurd and 

Coonrod 2007). The depreciation of groundwater is 

presented in column 4 of Table 4.2.

Soil erosion in the SLB is primarily from wind, and it 

had not been estimated over time prior to this study. 

To produce a credible estimate, we needed data on soil 

erodibility, crop rotations, tillage management, crop 

growth stages, and winterkill. Because agriculture is not 

a major activity in all of the SLB, we focus on Alamosa, 

Conejos, Costilla, Rio Grande, and Saguache counties. 

We collaborated with R. Sparks, who is familiar with the 

farming practices in the SLB (more details of this effort 

are described in Appendix 4-A). The estimate for wind 

erosion, in tons/year, is in column 5 of Table 4.2. The 

damage estimate was based on Huszar and Piper (1986) 

who calculated off-site damages due to wind erosion in 

New Mexico. Ringquist et al. (1995) revised the Huszar 

and Piper estimate to focus on agricultural sources only. 

The value we used was $2.42/ton (2005$) and the total 

estimate is in column 6 of Table 4.2. Therefore, rather 

than capturing the quality-adjusted stock of agricultural 

land as described by Hrubovcak et al. (2000), we present 

the off-site damages caused by wind erosion. Other 

papers have derived estimates from Huszar and Piper 

(1986). For example, Bunn (1998) used one particular 

Major Land Resource Area (MLRA) that is part of the 

study area and determined the off-site cost per ton of 

eroded soil per acre was approximately $10.12 (2005$). 

Williams and Young (1999) used Huszar and Piper to 

estimate off-site costs in South Australia. Additional 

studies, such as a hedonic analysis, would be necessary 

in order to determine the foregone production rents due 

to the soil erosion on farmland values (Hrubovcak et 

al. 2000). We are currently attempting to determine the 

feasibility of such a study in the SLB.

Per capita CO2 emissions for the SLB were calculated 

as the average of the EIA estimates for Colorado and 

New Mexico. This per capita emission estimate was 

multiplied by the population of the SLB to calculate total 

emissions (Table 4.2, column 7). Although we estimated 

carbon flows (i.e. emissions) to estimate the annual 

social cost of carbon emissions, global temperature is 

influenced by the absolute concentration of CO2 in the 

atmosphere. CO2 emissions remain in the atmosphere 

for many years. Pezzey et al. (2006) noted the social 

cost of carbon emission estimates should incorporate the 

atmospheric lifetime of emissions. Following Tol (2007) 

who calculated the social cost of carbon based on a 

meta-analysis of 125 peer-reviewed estimates, we used a 

value approximately equal to $24/ton CO2 (2005$). The 

damage cost caused by these emissions is presented in 

column 8 of Table 4.2. Lifetime costs of emissions were 

included in this estimate. 

Because we did not know the right prices and incorrectly 

estimating the prices could lead to a misinterpretation 

of the results, we conducted a sensitivity analysis. We 

calculated low and high estimates of the prices and 

marginal damages of natural capital depreciation. For 

the depreciation of groundwater, the low value was 

$66.05/acre-foot and the high value was approximately 

$85/acre-foot. These values were based on the 

variability of the 30 years of simulation (personal 

communication B. Hurd, May 2010). The low and 

high estimates of damages caused by wind erosion 

were $0.27/ton and $9.57/ton, respectively (Huszar 

and Piper 1986). These costs were based on specific 

regions (MLRA 48/51 and 77) rather than for the 

entire state. The social cost of carbon ranged from $0/

ton CO2

 to $77.29/ton CO2. Tol (2007) calculated the 

95-percentile for peer-reviewed studies. The estimate 

means the author is 95% confident the expected social 

cost of carbon is equal to or less than $77.29/ton CO2

. 

Tol (2007) did not provide a lower bound estimate, so we 

chose $0. 

4.5 Results 

Following the methodology presented in the previous 

section and using the 26 years of data consisting of the 

variables described in Table 4.1, we estimated the SLB 

GNRP. Aggregating the results from NRP, Groundwater 

Storage Depreciation, Soil Erosion Damage, and CO2

Damage Costs, we estimated GNRP in column 9 of 

Table 4.2. The final column shows the change in GNRP 

from one year to the next in between the rows. The 
57

NRP, GNRP, and the change in GNRP are all presented 

in Figure 4.1; components of natural capital depreciation 

are presented in Figure 4.2.

Although there were peaks and troughs, NRP and GNRP 

both had upward trends suggesting the components 

of natural capital depreciation have a minimal effect. 

Natural capital depreciation ranges from -8.6 to 0.11 

percent of NRP. However, caution should be taken 

in interpreting the actual values in those two columns 

because we dropped the components that do not vary. 

Through the 26 years of data, CO2 damages and soil 

erosion changes have remained fairly constant; however, 

groundwater storage fluctuated over this time (Fig. 

4.2). Overall, it appears that natural capital stocks have 

declined. 

If we strictly follow Pezzey et al. (2006) and Hanley 

et al. (2002), originally summarized in Chapter 2, who 

suggested the change in GNRP is a one-sided test of 

weak sustainability (e.g., when man-made capital can be 

substituted for natural capital), the calculation revealed 

the SLB was moving away from sustainability during the 

time period 1985-86 to 1987-88. This appears to be the 

longest negative range. Based on the results, there are 

other periods of negative change, but not since 1999-

2000. As we discuss below, this strict interpretation, 

based on the theoretical literature, may have some 

problems. 

The sensitivity analysis, when using the lower bound 

estimates, revealed that GNRP is about 3% to 6% higher 

than our GNRP estimate in Table 4.2 (see Fig. 4.3). 

When we calculated GNRP with the high values, our 

estimates ranged from 7% to 14% lower than GNRP in 

Table 4.2. 

4.6 Discussion

The previous section presented the results of estimating 

GNRP for the SLB over a 26-year period. The 

methodology satisfies the objective of estimating 

GNRP using an approach that others can follow using 

publicly available data. With that said, the data were 

not perfect. This section discusses the assumptions 

needed in order to estimate GNRP of the SLB and some 

of the limitations we found while developing the GNRP 

methodology.

Before we present some of the specific assumptions 

for our calculations, we want to state that there is 

uncertainty surrounding the application of this method. 

Although the calculation is theoretically founded, the 

approach requires many assumptions (e.g., Stern 1997). 

Although these assumptions are very important, we 

briefly mention them because they can be found in the 

literature (e.g., Dasgupta 2001, Neumayer 2010, Stern 

1997). Assumptions related to weak sustainability and 

using the right prices (i.e., shadow prices) are important 

and do lead to uncertainty in the calculations. It is not 

our intent to minimize this first group of assumptions 

underlying the theory, but this section focuses on those 

assumptions related to our specific calculations and 

interpretation.

Calculating the NRP of the SLB required a number of 

simplifying assumptions. We already stated the type of 

data necessary to use a top-down approach would have 

been prohibitive; therefore, we used US and state data to 

convert Personal Income in the SLB to NRP. To do so, 

we assumed the ratio of US GDP to US CFC was similar 

to that in both Colorado and New Mexico. Taking the 

Net Domestic Product by state and estimating the ratio to 

Personal Income to the state required another assumption 

of similarity. If neither of these are sound assumptions, 

we may have over or underestimated NRP in the SLB.

Because of a change in the BEA approach to estimating 

state GDP, we assumed the ratio of US GDI to US GDP 

is similar to that of Colorado and New Mexico prior 

to 1997. This is necessary because of the switch from 

the SIC to the NAICS-based GDP. The BEA cautions 

against completing the time series by combining the two; 

it is not clear that adjusting the SIC-based GDP with the 

US ratio is acceptable either. For studies that may follow 

this methodology, estimating GNRP after 1997 avoids 

the need for this assumption and transformation.

Based on existing literature, preliminary examination 

of data, and the methodology for the other metrics, 

human capital and certain components of natural capital 

depreciation were assumed constant (e.g., forestry). The 

decision to do this removed the need to include those 

data in the calculations. Although we were interested 

in the change in GNRP, no additional information can 

be gleaned from the calculations because the annual 

calculations do not capture these particular components.

Business cycles complicate the use of GNRP to analyze 

sustainability. Sustainability is concerned with the 

long-term and thus short-term business cycles may 

obscure the interpretation of the metric. The theoretical 

literature on GNRP assumes the economy is achieving 

its potential output. GNRP calculated with actual data 

reflects recessions that prevent GNRP from reaching its 

potential output. Temporary dips in GNRP due to the 

business cycle may not represent movement away from 

sustainability if potential GNRP is steadily increasing. 

BEA (2003) determined that recessions occurred in the 

area from 1981-82 (most severe), 1990-91, and in 2001. 

GNRP follows the decline in 1981-82, but not 1990-91 
58

(although it slows). There was a fall from 1999 to 2001 

in Real NRP, but not GNRP. This introduced some 

doubt as to whether the decline in GNRP 1981-1982 

indicates movement away from sustainability or simply 

reflects the temporary economic downturn. There is no 

generally accepted method of controlling for the business 

cycle in sustainability analysis, but Pezzey et al. (2006: 

71) attempted “an ad hoc allowance for the Scottish 

business cycle during 1992-1999.”

There is another reason to be cautious of concluding the 

region was moving away from sustainability based on 

periodic decreases in GNRP interspersed throughout the 

overall growth in GNRP during the study period. GNRP 

does not explicitly consider technological progress, 

but the overall growth or decline in GNRP is largely 

determined by a competition between technological 

progress that allows GNRP to rise and natural capital 

depreciation that pulls down GNRP (Weitzman and 

Löfgren 1997). Technological progress does not proceed 

smoothly, but rather comes in fits and starts. Because 

consumers typically wish to smooth their consumption, 

consuming capital (resulting in negative growth in 

GNRP) during years of little technology growth is 

rational behavior. 

Over the 26-year period, it appears that technological 

progress was greater than net depreciation for the region, 

since GNRP rose over the entire period.9

 The years of 

declining GNRP when GNRP was moving away from 

sustainability should provoke legitimate concern the 

region might have been consuming its capital at too rapid 

a rate and technology was not keeping up. However, the 

ultimate growth in GNRP suggests that isolated declines 

in GNRP were only signs the region was moving away 

from sustainability, rather than a definitive judgment as 

the theoretical literature would suggest.

We used a number of approaches to estimate the change 

in natural capital stock for CO2

 emissions, soil erosion, 

and groundwater. Colorado’s Decision Support Systems, 

which is used to estimate the change in groundwater 

stock, has its own set of assumptions, but given their 

approach, we feel the calculation was accurate (e.g., 

CWCB and CDWR 2008). Soil erosion was developed 

with R. Sparks and required a number of simplifying 

assumptions in order to calculate crop rotations by 

soil type and wind erosion based on the Wind Erosion 

Equation. More details on the approach can be found in 

Appendix 4-A. Finally, we assumed that Colorado and 

New Mexico per capita CO2 emissions bound the SLB 

per capita CO2 emissions. If the residents of the SLB 

9

 Error in our estimate of human capital may lead to a misleading 

result, but we think that our interpretation regarding technological 

progress was likely correct.

had different habits in terms of energy consumption 

that differs from the two states, the results may not be 

accurate. However, the research for ecological footprint 

determined that per capita energy consumption in the 

SLB was not significantly different than per capita state 

consumption (see Appendix 3-A).

We did not know the right prices for the different capital 

stocks and we used existing literature for those estimates. 

Because we used benefit transfer to estimate the shadow 

price and marginal damage costs for natural capital 

depreciation, we need to consider the quality of the 

studies, the relevant populations, and the uncertainty of 

the results. We have the most confidence in estimating 

the depreciation of water because the study of Hurd 

and Coonrod (2005) included the Upper Rio Grande 

River Basin, which is part of the SLB. They discussed 

the value of agriculture and the Rio Grande Compact, 

which both play an important role in the shadow price 

of groundwater in the SLB. We were fairly confident in 

the marginal damage cost of carbon emissions that was 

based on Tol (2005, 2007). This confidence was based 

on their use of 125 peer-reviewed studies. Nonetheless, 

there were many different statistical distributions that 

could have been used and many studies in the gray 

literature that were not included. The studies used in 

the meta-analysis may not be representative, but we 

believe it is the best information available. We had the 

least confidence in our estimate of damages due to wind 

erosion. The study we used was for New Mexico, but it 

had a very low response rate for the survey suggesting 

that it may not even be representative of New Mexico, 

let alone the SLB. We only used off-site costs of 

wind erosion and not on-site agricultural damages. In 

addition, the results were presented in total dollars 

and we revised the estimates to dollars per ton. The 

assumptions required to use the value from the Huszar 

and Piper study (1986) were quite simplifying. 

Natural capital was mostly declining (Fig. 4.2) and, if 

the weak sustainability assumption was incorrect or if we 

underestimated the prices for natural capital, the results 

could reveal a movement away from sustainability. 

Based on the sensitivity analysis, we believe other 

components, such as NRP, are relatively more important 

than the shadow prices and damage costs (see Fig. 4.3). 

Because the trends in GNRP did not differ substantially 

throughout the range of the sensitivity analysis, the 

interpretation remains consistent with the results in Table 

4.2. That is, given the results of the weak sustainability 

metric, the system appears not to be moving away from 

sustainability.
59

4.7 Conclusion

We estimated GNRP, a metric based on the concept of 

weak sustainability, from 1980-2005 for the SLB. The 

results show that GNRP had an increasing trend over 

the 26-year study period with a few negative periods. 

Given this fairly short time horizon, we conclude that 

there was no definitive evidence of movement away 

from sustainability. However, if the assumptions behind 

GNRP, like weak sustainability, are wrong, along with 

the decline in natural capital stocks, then the results 

could indicate problems in the longer term.

4.8 References

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Brief 02-09. Resources for the Future, Washington, 

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BEA (Bureau of Economic Analyses). 2003. Gross 

State Product by Industry for 2001: U.S. Economic 

Slowdown was Widespread. News Release. US 

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gdp_state/2003/pdf/gsp0503.pdf. Last accessed June 

26, 2009.

BEA (Bureau of Economic Analyses). 2006a. Final 

Strategic Plan for FY 2006–FY 2010. US Department 

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Domestic Product by State Estimation Methodology. 

Regional Economic Accounts, US Department of 

Commerce, Washington, DC. October 2006.

BEA (Bureau of Economic Analyses). 2007. Local 

Area Personal Income and Employment Methodology. 

Regional Economic Accounts, US Department of 

Commerce, Washington, DC.

BEA (Bureau of Economic Analyses). 2008. National 

Income and Products Accounts Tables. Table 1.7.5 

Relation of Gross Domestic Product, Gross National 

Product, Net National Product, National Income, and 

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Bunn, J. 1998. Government Policy, Wind Erosion, and 

Economic Viability in Semi-Arid Agriculture: The 

Case of the Southern Texas High Plains. Journal of 

Agricultural and Applied Economics 30, 339–351.

Cairns, R. 2001. Seeing the trees as a forest: What 

counts in green accounting. Ecological Economics 36, 

61-69.

CWCB and CDWR (Colorado Water Conservation 

Board and the Colorado Division of Water Resources). 

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Grande Decision Support System. Available online at 

http://cdss.state.co.us/DNN/Home/tabid/36/Default.

aspx. Last accessed June 26, 2009.

CWCB (Colorado Water Conservation Board). 2000. 

Rio Grande Decision Support System: An Overview. 

Section 4.0 Water Administration Issues. CWCB, 

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accessed June 26, 2009.

Dasgupta, P. 2001. Human Well-Being and the Natural 

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Benefit transfer: Conceptual problems in estimating 

water quality benefits using existing studies. Water 

Resources Research 28, 675-683.

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Years, 1960-2004, Colorado, DOE/EIA-0214(2004), 

US Department of Commerce, Washington, DC, p. 55.

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Energy Related Carbon Dioxide Emissions Estimates. 

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Ferreira, S., Vincent, J. 2005. Genuine savings: Leading 

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rates in developing countries. World Bank Economic 

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2002. Greening the National Accounts for Scotland. 

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1-904365-03-5. University of Stirling, Stirling.

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Agriculture, natural resources, and environmental 

accounting. Environmental and Resource Economics 

17, 145-162.

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Implications for New Mexico’s Water Resources and 

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States Department of Commerce, Bureau of the 

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US Government Printing Office. 

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States Department of Commerce, Bureau of the 

Census). 1989. United States Census of Agriculture: 

1987. Volume I; Geographic Area Series; Part 6 

Colorado State and County Data. Washington, DC: 

US Government Printing Office.

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States Department of Commerce, Bureau of the 

Census). 1994. United States Census of Agriculture: 

1992. Volume I; Geographic Area Series; Part 6 

Colorado State and County Data. Washington, DC: 

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NRCS (Natural Resources Conservation Service). 

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environmental effects of agricultural policy: the soil 

bank, the CRP and airborne particulate concentrations. 

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Estimating timber depreciation in the Brazilian 

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Parable Journal of Environmental Economics and 

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much does wind erosion cost the people of South 

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29, 602–608.
61

Table 4.1 – Green Net Regional Product (GNRP): Variables, Purpose, and Data Sources.

Scale Variable name Purpose Source* Home Website Specific Data Link

US

Gross Domestic 

Product (GDP)

BEA http://www.bea.gov/ http://www.bea.gov/national/nipaweb/

TableView.asp?SelectedTable=43&V

iewSeries=NO&Java=no&Request3P

lace=N&3Place=N&FromView=YES

&Freq=Year&FirstYear=1980&LastY

ear=2005&3Place=N&Update=Updat

e&JavaBox=no

Consumption of 

Fixed Capital 

(CFC)

BEA http://www.bea.gov/ http://www.bea.gov/national/nipaweb/

TableView.asp?SelectedTable=43&V

iewSeries=NO&Java=no&Request3P

lace=N&3Place=N&FromView=YES

&Freq=Year&FirstYear=1980&LastY

ear=2005&3Place=N&Update=Updat

e&JavaBox=no

Ratio: CFC/GDP Ratio needed to 

convert GDP by state 

to NDP by state

EBT

Gross Domestic 

Income (GDI)

BEA http://www.bea.gov/ http://www.bea.gov/national/nipaweb/

TableView.asp?SelectedTable=43&V

iewSeries=NO&Java=no&Request3P

lace=N&3Place=N&FromView=YES

&Freq=Year&FirstYear=1980&LastY

ear=2005&3Place=N&Update=Updat

e&JavaBox=no

Ratio: GDP/GDI For years prior to 

1997, GDP by state 

was calculated 

differently. This ratio 

makes GDP by state 

relatively consistent.

EBT
62

Scale Variable name Purpose Source* Home Website Specific Data Link

State

Personal Income 

(PI) for Colorado 

(thousands of 

dollars)

BEA http://www.bea.gov/ http://www.bea.gov/regional/spi/

default.cfm?selTable=summary

GDP for CO 

(thousands of 

dollars)

BEA http://www.bea.gov/ http://www.bea.gov/regional/gsp

Net Domestic 

Product (NDP) for 

CO (thousands of 

dollars)

EBT

Ratio: NDP/PI 

for CO

Ratio multiplied by 

PI for SLB provides 

estimate of Net 

Regional Product 

(NRP)

EBT

CO emissions for 2

Colorado

EIA http://www.eia.doe.gov/ http://www.eia.doe.gov/oiaf/1605/

state/state_emissions.html

Population for 

Colorado

Used to estimate per 

capita CO emissions 2

for CO and total 

emissions for SLB

BEA http://www.bea.gov/ http://www.bea.gov/regional/spi/

default.cfm?selTable=summary

Personal 

Income (PI) for 

New Mexico 

(thousands of 

dollars)

BEA http://www.bea.gov/ http://www.bea.gov/regional/spi/

default.cfm?selTable=summary

GDP for NM 

(thousands of 

dollars)

BEA http://www.bea.gov/ http://www.bea.gov/regional/gsp

Net Domestic 

Product (NDP) for 

NM (thousands of 

dollars)

EBT

Ratio: NDP/PI for 

NM

Ratio multiplied by 

PI for SLB provides 

estimate of Net Regional 

Product (NRP)

EBT

CO emissions for 2

New Mexico

EIA http://www.eia.doe.gov/ http://www.eia.doe.gov/oiaf/1605/state/

state_emissions.html

Population for New 

Mexico

Used to estimate per 

capita CO emissions for 2

NM and total emissions 

for SLB

BEA http://www.bea.gov/ http://www.bea.gov/regional/spi/default.

cfm?selTable=summary
63

Scale Variable name Purpose Source* Home Website Specific Data Link

SLB

Personal Income 

(PI) for Colorado 

counties

Used to estimate SLB 

Net Regional Product

BEA http://www.bea.gov/ http://www.bea.gov/regional/reis/default.

cfm?selTable=CA1-3§ion=2

Water Budget for 

the Upper Rio 

Grande River

Change in groundwater 

levels

CDSS http://cdss.state.co.us/

DNN/default.aspx

http://cdss.state.co.us/DNN/RioGrande/

tabid/57/Default.aspx

Crop acres planted Used to estimate total 

crop rotation acres 

and tillage/residue 

management practices 

needed for wind erosion

NASS http://www.nass.usda.gov/

index.asp

http://www.nass.usda.gov/Statistics_by_

State/Colorado/index.asp

Total crop land, 

Total harvested land

Used to estimate total 

crop rotation acres 

and tillage/residue 

management practices 

needed for wind erosion

NASS http://www.agcensus.

usda.gov/

http://www.agcensus.usda.gov/

Publications/2007/Full_Report/Census_

by_State/Colorado/index.asp

Crop rotation acres, 

Soil type, Tillage/

residue management

Used to estimate total 

crop rotation acres 

and tillage/residue 

management practices 

needed for wind erosion

EBT

Wind Erosion 

(tons/year)

WEQ, 

EBT

http://www.weru.ksu.edu/

nrcs/weq.html

Population Used for CO2

 emissions BEA http://www.bea.gov/ http://www.bea.gov/regional/reis/default.

cfm?selTable=CA1-3§ion=2

*

BEA-Bureau of Economic Analysis

 EBT-Estimated by Team

 EIA-Energy Information Agency

 CDSS-Colorado’s Decision Support Systems

 NASS-National Agricultural Statistics Service

 WEQ-Wind Erosion Equation
64

Table 4.2 – Estimates of all components of Green Net Regional Product (GNRP).

NRP*

Groundwater 

Storage

(acre-feet)

Groundwater 

Storage 

Dep.*

Total

Erosion 

(tons/year)

Soil

Erosion 

Damage*

CO2

Emissions

(metric 

tons/year)

Damage 

Cost* GNRP*

Change 

in 

GNRP*

1980 $775,613 81,972 $6,190 4506372 $10,913 1045868 $24,845 $746,045 -$76,390

-$27,686

$101,549

-$8,729

$24,531

-$35,554

-$32,440

-$44,664

$52,988

$56,913

$301

$8,414

$115,305

$38,218

$52,177

-$36,680

$103,902

-$21,134

$101,840

-$86,683

$19,579

$16,292

$59,925

$10,191

$24,787

1981 $727,258 -287,123 -$21,681 4858200 $11,765 1016955 $24,158 $669,655

1982 $661,019 222,041 $16,766 4703520 $11,390 1028255 $24,426 $641,969

1983 $761,240 250,925 $18,947 4739397 $11,477 1060526 $25,193 $743,518

1984 $759,773 157,077 $11,861 4838600 $11,717 1057820 $25,128 $734,789

1985 $771,614 306,292 $23,128 4480889 $10,851 1034373 $24,571 $759,320

1986 $737,433 258,278 $19,503 4114138 $9,963 976931 $23,207 $723,766

1987 $719,223 63,079 $4,763 3443462 $8,339 1023873 $24,322 $691,326

1988 $702,787 -300,027 -$22,655 3402277 $8,239 1062133 $25,231 $646,662

1989 $754,919 -278,902 -$21,060 3457701 $8,373 1087591 $25,836 $699,650

1990 $793,954 -39,154 -$2,957 3254087 $7,880 1117838 $26,554 $756,563

1991 $786,673 42,696 $3,224 3216740 $7,790 1062657 $25,243 $756,864

1992 $794,303 50,215 $3,792 3082521 $7,465 1067248 $25,352 $765,278

1993 $894,289 256,157 $19,342 3047803 $7,381 1080506 $25,667 $880,583

1994 $953,794 -28,618 -$2,161 2965568 $7,182 1079803 $25,651 $918,801

1995 $969,925 445,193 $33,616 2984107 $7,226 1066590 $25,337 $970,978

1996 $1,001,422 -435,869 -$32,912 3239852 $7,846 1109930 $26,366 $934,298

1997 $1,057,552 210,103 $15,865 3228373 $7,818 1153430 $27,400 $1,038,199

1998 $1,061,384 -110,554 -$8,348 3498540 $8,472 1157612 $27,499 $1,017,065

1999 $1,135,535 252,837 $19,092 3236475 $7,838 1173798 $27,884 $1,118,905

2000 $1,106,954 -504,537 -$38,098 3091684 $7,487 1227015 $29,148 $1,032,222

2001 $1,086,389 24,917 $1,881 2869825 $6,950 1242678 $29,520 $1,051,801

2002 $1,168,512 -860,888 -$65,006 2907283 $7,040 1194397 $28,373 $1,068,093

2003 $1,186,687 -302,288 -$22,826 3037519 $7,356 1199228 $28,488 $1,128,018

2004 $1,176,482 -21,781 -$1,645 3051094 $7,389 1230885 $29,240 $1,138,209

2005 $1,190,061 125,141 $9,449 3028607 $7,334 1228372 $29,180 $1,162,997

*In thousands of 2005 dollars
65

Figure 4.1 – Net Regional Product, Green Net Regional Product, and the change in Green Net Regional Product.

Figure 4.2 – Components of the Depreciation of Natural Capital.

-200,000

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

US thousand dollars (2005$)

Year

Real NRP Real GNRP Change in GNRP

-120,000

-100,000

-80,000

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

US thousand dollars (2005$)

Year

Damage Cost (CO2 emissions) Ground-water Quantity Depreciation Soil erosion damage
66

Figure 4.3 – Sensitivity Analysis Using Upper and Lower Bounds on the Prices for Natural Capital Depreciation.

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

$1,200,000

$1,400,000

19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005

Year

US thousand dollars (2005$)

Lower Bound Upper Bound Real GNRP
67

Appendix 4-A: Wind Erosion 

Calculations

Green Net Regional Product, Emergy Analysis, and 

Fisher information all included wind erosion in the 

calculations. This appendix describes details of the 

approach for estimating wind erosion in the San Luis 

Basin (SLB). Agriculture was not a major activity in all 

SLB counties, so we only focused on Alamosa, Conejos, 

Costilla, Rio Grande, and Saguache counties. The 

estimates were based primarily on the following data: 1) 

National Agricultural Statistics Service (Colorado profile 

and Census of Agriculture); 2) farming practices and 

trends in the SLB based on the knowledge, experience, 

and expert opinion of Richard Sparks, an agronomist 

and irrigation specialist in the US Department of 

Agriculture (USDA), Natural Resource Conservation 

Service (NRCS), Center, CO; and 3) Wind Erosion 

Equation (Woodruff and Siddoway 1965). Each plays an 

important role in the general approach described below. 

This work had never been attempted for the SLB over 

the 26-year study period. Figure 4-A.1 identifies the 

basic steps we used in the process. 

The general approach for collecting initial data and 

estimating wind erosion was as follows:

1. We first needed the individual county acreages of 

spring barley, spring wheat, winter wheat, potatoes, 

alfalfa, oats, and other hay (only for Conejos, 

Costilla, and Saguache counties). The National 

Agricultural Statistics Service (NASS) provides 

annual estimates of planted acres of different crops 

by county and data on total crop land and total 

harvested land beginning in 1982 and continuing 

every five years until 2002 through the Census of 

Agriculture (NASS 1984, 1989, 1994, 2009a, 2009b). 

2. The soil erodibility index was estimated for county 

acreage for two major soil types (soil erodibility 

index I=134 tons per acre per year and I=86 tons per 

acre per year) based on expert opinion and Web Soil 

Survey (Soil Survey Staff 2009). Index I represents 

potential for soil loss measured in tons per acre per 

year. It assumes the field is isolated, level, smooth, 

unsheltered, bare, wide, and loose (NRCS 2002). 

Table 4-A.1 presents the approximate distribution of 

crops between the two soil types. 

3. We estimated the acres of different crop rotations by 

the two soil types (based on the soil erodibility index) 

for each county. Seven crop rotations for the counties 

were included in the calculation (Table 4-A.2). A 

crop rotation is a series of different crops in the same 

field in succeeding seasons. It could occur over a 

couple of years to a much longer period. There are 

many benefits of rotating crops including preventing 

diseases, reducing soil erosion, controlling pests, and 

improving the soil fertility (NRCS 2002). 

Some rotations were included to factor in the 

drought and aquifer decline years (2001 - 2007). 

For example, cropland acreages were idled or 

planted to Sorghum /Sudan cover crops instead of 

spring grain. Other trends were included such as 

introducing Winter Rye cover crops between years 

of back-to-back potatoes from 1980 through 2000 

and introducing Winter Wheat into the Potato - Grain 

rotations around the late 1990s depending on the 

county.

Total acres of irrigated cropland were based on 

Agricultural Statistics for Colorado and NASS. 

Because NASS reports census data every five years, 

the estimates for the other years were based on 

trends and expert opinion (total acres of planted 

crops gleaned from the agricultural statistics for the 

counties provided the initial minimum total acres of 

irrigated cropland). Total irrigated cropland acres 

minus the total crops planted (based on Agricultural 

Statistics for Colorado) equaled unreported crops 

(e.g., canola, sorghum, and vegetables) and unplanted 

acres. We assumed that vegetables and unplanted 

acres had similar wind erosion rates (therefore, we 

combined them into one rotation). Because of some 

large numbers of unaccounted acres, we included 

Colorado estimates for Other Hay in Conejos, 

Costilla, and Saguache counties. 

The first step was to estimate continuous potatoes. 

Alamosa and Rio Grande counties planted about 

20% of their total potatoes as continuous in the 

1980’s and grew to about 40% in the 1990’s. The 

number of acres of continuous potatoes decreased 

around 2000 to about 20%, which corresponded to 

the decline of the aquifer and an increase in potato 

pathogens. Once we estimated continuous potatoes, 

we combined the remaining potatoes with small 

grains. The Potato-Small Grain rotation was a 

two-year rotation. We estimated the Alfalfa – Small 

Grain rotation next and the remaining small grains 

were assumed to be planted as continuous grains. 

We assumed the Alfalfa – Small Grain rotation was 

a 7-year rotation. For each county and every year, 

we compared total irrigated cropland acres based 

on Agricultural Statistics for Colorado and NASS 

to our estimate of total irrigated cropland acres 

based on crop rotation acres to make sure the data 

and the approach were consistent. We accounted 

for every acre of crops reported in the Agricultural 

Statistics for Colorado. Once this set approach was 
68

completed, each year was examined for accuracy 

and the time series was examined for trends. Minor 

revisions were made depending on the year and crop 

rotation based on known trends. For example, certain 

years had very high estimates of continuous small 

grains and the values were not realistic with known 

quantities; therefore, we adjusted that year to have 

more Potato - Small Grain acres in order to reduce 

continuous small grains. This required reducing the 

percent of continuous potato acres as well.

4. After all of the calculations for the individual counties 

were reviewed and determined to be accurate, a total 

estimate of crop acreage for each rotation in the SLB 

was then calculated. Table 4-A.3 displays total crop 

acreage for the SLB for 1980-2005. Rows 2-7 are 

based on the Agricultural Statistics for Colorado.

5. We accounted for the general trend of improving 

residue management and changes in tillage practices 

with the different crop rotations. The two practices 

were moldboard plow and noninversion tillage 

and we divided the rotation acreage between 

the two practices based on the trends for residue 

management. Using expert opinion, we assumed 

that 10% of continuous small grain acres were 

noninversion tillage in 1980 for soil type I=134. That 

percent grew to a high of 80% in 1997 and continued 

through 2005. 

6. We used the Wind Erosion Equation (WEQ) to 

estimate average annual wind erosion for each of 

the different crop rotations and different tillage 

practices on the two soil types. WEQ is based on 

Woodruff and Siddoway (1965) where Erosion (tons/

acre/year) is a function of soil erodibility index, soil 

ridge roughness factor, climatic factor, field length 

along the prevailing wind erosion direction, and 

equivalent quantity of vegetative cover. WEQ is 

used by the NRCS to estimate average wind erosion. 

The Wind Erosion Research Unit (WERU) of the 

NRCS has developed the “Excel Spreadsheet and 

Guidance Document” on which the wind erosion 

estimates are based (NRCS 2003). Table 4-A.4 

presents the average annual wind erosion (tons/acre) 

for noninversion tillage and Table 4-A.5 presents 

the average annual wind erosion for conventional, 

moldboard plow. Continuous potatoes show no 

difference in average annual wind erosion, but other 

crop rotations reveal significant changes in the 

amount of erosion with the noninversion tillage.

7. Combining crop rotation acres and soil types with 

the WEQ average annual wind erosion, we estimated 

total erosion for the SLB in tons/year (Table 4-A.6). 

We divided this estimate by total acres of irrigated 

cropland to estimate tons/acre/year, which allows the 

NRCS to examine the trends of erosion management. 

From 1980 to 2005, there was a general trend of tons/

acre/year decreasing, revealing an improvement in 

practices to reduce wind erosion (Table 4-A.6).

8. The final step was to have experts from the region 

review the results for wind erosion to assess if the 

results seem reasonable. 

References

NASS (National Agricultural Statistics Service, United 

States Department of Commerce, Bureau of the 

Census). 1984. United States Census of Agriculture: 

1982. Volume I; Geographic Area Series; Part 6 

Colorado State and County Data. Washington, DC: 

US Government Printing Office. 

NASS (National Agricultural Statistics Service, United 

States Department of Commerce, Bureau of the 

Census). 1989. United States Census of Agriculture: 

1987. Volume I; Geographic Area Series; Part 6 

Colorado State and County Data. Washington, DC: 

US Government Printing Office.

NASS (National Agricultural Statistics Service, United 

States Department of Commerce, Bureau of the 

Census). 1994. United States Census of Agriculture: 

1992. Volume I; Geographic Area Series; Part 6 

Colorado State and County Data. Washington, DC: 

US Government Printing Office.

NASS (National Agricultural Statistics Service, United 

States Department of Agriculture). 2009a. United 

States Census of Agriculture: 1997. Available online 

at http://www.nass.usda.gov/Statistics_by_State/

Colorado/index.asp. Last accessed June 26, 2009.

NASS (National Agricultural Statistics Service, United 

States Department of Agriculture). 2009b. United 

States Census of Agriculture: 2002. Available online 

at http://www.nass.usda.gov/Statistics_by_State/

Colorado/index.asp. Last accessed June 26, 2009.

NRCS (Natural Resources Conservation Service). 

2002. National Agronomy Manual. United States 

Department of Agriculture, Natural Resources 

Conservation Service. October 2002. Accessed at: 

http://www.weru.ksu.edu/nrcs/nam/M_190.pdf.

NRCS (Natural Resources Conservation Service). 2003. 

Wind Erosion Equation Guidance Document. USDA, 

NRCS. Accessed at: http://www.weru.ksu.edu/nrcs/

weqguidance062503.doc.
69

Soil Survey Staff. 2009. Natural Resources 

Conservation Service, United States Department of 

Agriculture. Web Soil Survey. Accessed at http://

websoilsurvey.nrcs.usda.gov/ 

Woodruff, N., Siddoway, F. 1965. A wind erosion 

equation. Soil Science Society American Proceedings

29, 602–608.

Table 4-A.1 – Approximate distribution of crops between the two soil types (Based on Soil Erodibility Index, I).

County I=134 I=86

Alamosa 50% 50%

Conejos 0% 100%

Costilla 80% 20%

Rio Grande 10% 90%

Saguache 30% 70%

Table 4-A.2 – Crop rotations included in soil erosion calculation.

Crop Rotation Description

Continuous Potatoes, Vegetables, and Unplanted Acres Potatoes planted every season, vegetables and unplanted acres assumed to have the 

same erosion rate as continuous potatoes

Potatoes-Winter Wheat 2-year rotation: one year potatoes, one year winter wheat 

Potatoes-Winter Rye 1-year rotation with rye planted after potato harvest 

Potatoes-Small Grain 2-year rotation: one year potatoes, one year small grain (e.g., barley, spring wheat)

Potatoes-Canola and/or Sorghum Cover 2-year rotation: one year potato, one year sorghum sudan

Alfalfa-Small Grain 7-year rotation: six years alfalfa, one year small grain (e.g., barley, spring wheat)

Continuous Small Grain Small grain planted every season
70

Table 4-A.3 – Total acreage by crop rotation and Soil Erodibility Index.

Variable name 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

Potatoes All (Planted) 37000 40500 45500 47000 53500 56500 57000 61000 60000 62000 65500 71000

Wheat Other Spring (Planted) 36000 43000 38000 50000 49000 50500 40000 38000 41000 55000 29000 24900

Barley All (Planted) 107000 118000 114000 100000 117000 115000 120000 77000 65000 79000 84000 89500

Hay Alfalfa (Dry) (Harvested) 122900 99800 92800 89700 108600 129600 110000 118000 115000 105000 105000 105000

Wheat Winter All (Planted) 0 0 0 0 0 0 0 0 0 0 0 0

Oats 16000 17000 24000 39000 32000 26000 15000 14000 24000 15500 16700 11800

Unreported crops (Sorghum, carrots/lettuce/canola) + unplanted acres 56550 57150 61150 53537 39755 28055 47908 62208 70574 65910 82210 80999

Vegetables & unplanted acres (100% to 30% of unreported crops) 11842 13468 15468 14555 6700 800 4834 8123 10923 7450 11413 11688

Canola and sorghum cover ( 0% to 70% of unreported crops) 0 0 0 0 0 0 0 0 0 0 0 0

Other Hay (Wild dry hay, other haylage, etc.) 44708 43682 45682 38982 33055 27255 43074 54085 59651 58460 70797 69311

Total irrigated cropland acres 375450 375450 375450 379237 399855 405655 389908 370208 375574 382410 382410 383199

134: Potatoes 8090 9310 11530 11100 14100 14680 17440 18040 17660 17930 19685 20870

86: Potatoes 28910 31190 33970 35900 39400 41820 39560 42960 42340 44070 45815 50130

134: Continuous Potatoes + Vegetables and unplanted acres 5492 7796 5612 4946 4720 3298 5760 5246 6775 7563 7051 7411

86: Continuous Potatoes + Vegetables and unplanted acres 12630 15682 20646 20710 13880 9862 12314 16837 18168 14157 18329 16764

134: Potatoes-Winter Wheat 0 0 0 0 0 0 0 0 0 0 0 0

86: Potatoes-Winter Wheat 0 0 0 0 0 0 0 0 0 0 0 0

134: Potatoes-Winter Rye (only potato acreage) 0 0 0 0 0 0 0 0 0 0 669 3482

86: Potatoes-Winter Rye (only potato acreage) 0 0 0 0 0 0 0 0 0 0 669 3645

134: Potatoes-Small Grain (1:1 acreage) 13604 14318 18326 17520 22860 23864 26948 28220 27292 27826 30409 30771

86: Potatoes-Small Grain (1:1 acreage) 47836 46662 51094 54280 60340 64416 60572 65860 64668 67634 69981 71999

134: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 0 0 0 0 0 0 0 0 0

86: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 0 0 0 0 0 0 0 0 0

134: Alfalfa-small grain (7 year rotation) 24547 19460 18468 18597 21187 25737 21467 23217 22108 19833 19833 19717

86: Alfalfa-small grain (7 year rotation) 118837 96973 89798 86053 105513 125463 106867 114450 112058 102667 102667 102783

Remaining Small Grain 107797 130877 125823 138150 138300 125760 112907 62293 64853 84270 62005 57315

134: Continuous Small Grain 26735 31158 31795 34287 34990 32605 27876 14003 15342 18967 13722 12861

86: Continuous Small Grain 81062 99719 94028 103863 103310 93155 85031 48290 49511 65303 48283 44454
71

Variable name 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Potatoes All (Planted) 66500 72500 74000 77000 78000 77000 75800 77200 75800 68100 71600 66300 64083 58883

Wheat Other Spring (Planted) 32000 23500 30500 24000 43300 34500 25000 34000 32200 25000 14500 11000 15067 15067

Barley All (Planted) 77500 66000 58000 71000 61000 63500 60000 65000 77000 59000 49000 53000 52000 38000

Hay Alfalfa (Dry) (Harvested) 115000 129000 135000 140000 130000 130000 135000 160000 165000 167000 135000 130000 132833 147000

Wheat Winter All (Planted) 0 0 0 0 1500 5000 3000 3000 5000 5000 4000 4000 4150 4150

Oats 13200 17900 18000 24000 21000 21000 15000 17000 30000 18000 17000 20617 20617 20617

Unreported crops (Sorghum, carrots/lettuce/canola) + unplanted acres 87166 78529 73929 64168 71101 101506 113768 80088 51688 75495 80293 99406 95573 100606

Vegetables & unplanted acres (100% to 30% of unreported crops) 13569 18186 13648 6105 11019 15191 31873 12918 1920 8282 13888 22924 22747 27462

Canola and sorghum cover ( 0% to 70% of unreported crops) 0 0 0 540 4824 6312 9276 7083 2343 6317 13735 12819 13387 16370

Other Hay (Wild dry hay, other haylage, etc.) 73597 60343 60281 57523 55258 80003 72619 60087 47425 60897 52670 63664 59439 56774

Total irrigated cropland acres 391366 387429 389429 400168 405901 432506 427568 436288 436688 417595 371393 384323 384323 384323

134: Potatoes 19680 22510 22860 23490 25140 24660 24740 24720 24010 21630 23110 20980 20310 18190

86: Potatoes 46820 49990 51140 53510 52860 52340 51060 52480 51790 46470 48490 45320 43773 40693

134: Continuous Potatoes + Vegetables and unplanted acres 5746 6280 4736 3202 4512 4749 10556 4981 1637 2840 4332 8060 8880 10216

86: Continuous Potatoes + Vegetables and unplanted acres 18084 16682 13616 7694 11289 15105 25863 12517 3807 8576 12359 17457 16368 19376

134: Potatoes-Winter Wheat 0 0 0 0 300 3760 2320 2300 4600 4040 3400 3120 3220 3220

86: Potatoes-Winter Wheat 0 0 0 0 2700 6240 3680 3700 5400 5960 4600 4880 5080 5080

134: Potatoes-Winter Rye (only potato acreage) 4019 6147 5905 5820 6287 6074 6089 5879 4505 4101 3391 3045 3215 2813

86: Potatoes-Winter Rye (only potato acreage) 5750 12656 12486 13083 12727 12245 11921 12093 9255 8369 7686 7126 7102 6512

134: Potatoes-Small Grain (1:1 acreage) 27680 29740 31018 32387 31251 26996 26094 27608 30715 24948 23481 21911 18945 13142

86: Potatoes-Small Grain (1:1 acreage) 65258 68104 70796 73147 64509 58415 55842 61525 71633 57409 56491 51522 48515 40675

134: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 156 3170 3483 5995 4960 1562 4189 11025 9452 10698 13397

86: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 924 6478 9141 12557 9205 3123 8444 16445 16186 16075 19342

134: Alfalfa-small grain (7 year rotation) 21058 23275 24267 26133 22750 23158 24150 30683 31033 31442 25608 21642 21039 22750

86: Alfalfa-small grain (7 year rotation) 113108 127225 133233 137200 128917 128508 133350 155983 161467 163392 131892 130025 133933 148750

Remaining Small Grain 57064 36978 33093 42900 55753 54627 36532 44767 60526 32988 18014 26233 31815 22275134: Continuous Small Grain 12206 6193 6371 8476 10126 9271 4619 5675 8855 5435 2164 4906 6802 5693

86: Continuous Small Grain 44858 30785 26722 34423 45628 45357 31913 39092 51671 27553 15850 21327 25013 16582
72

Table 4-A.4 – Average annual wind erosion by crop rotation: practicing mulch or noninversion tillage.

Soil type: Crop rotation Average Annual Wind 

Erosion (t/ac)

134: Continuous Potatoes + Vegetables and unplanted acres 39.8

86: Continuous Potatoes + Vegetables and unplanted acres 16.1

134: Potatoes-Winter Wheat 7.6

86: Potatoes-Winter Wheat 2.4

134: Potatoes-Winter Rye (only potato acreage) 10.7

86: Potatoes-Winter Rye (only potato acreage) 3.4

134: Potatoes-Small Grain 19.5

86: Potatoes-Small Grain 7.4

134: Potatoes-Canola and/or sorghum cover (1:1 acreage) 20.9

86: Potatoes-Canola and/or sorghum cover (1:1 acreage) 8.5

134: Alfalfa-small grain (7 year rotation) 5.7

86: Alfalfa-small grain (7 year rotation) 2.2

134: Continuous Small Grain 9.8

86: Continuous Small Grain 2.9

Table 4-A.5 – Average annual wind erosion by crop rotation: conventional, moldboard plow.

Soil type: Crop rotation Average Annual Wind 

Erosion (t/ac)

134: Continuous Potatoes + Vegetables and unplanted acres 39.8

86: Continuous Potatoes + Vegetables and unplanted acres 16.1

134: Potatoes-Winter Wheat 24.9

86: Potatoes-Winter Wheat 10.1

134: Potatoes-Winter Rye (only potato acreage) 15.5

86: Potatoes-Winter Rye (only potato acreage) 5.2

134: Potatoes-Small Grain 33.2

86: Potatoes-Small Grain 13.3

134: Potatoes-Canola and/or sorghum cover (1:1 acreage) 44.2

86: Potatoes-Canola and/or sorghum cover (1:1 acreage) 18.9

134: Alfalfa-small grain (7 year rotation) 12.5

86: Alfalfa-small grain (7 year rotation) 5.4

134: Continuous Small Grain 35.6

86: Continuous Small Grain 14.6
73

Table 4-A.6 – Total Erosion (tons/year) for the SLB and Tons per Acre per Year.

Variable name 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

134: Continuous Potatoes + Vegetables and unplanted acres 218471 310112 223236 196724 187754 131189 229104 208670 269491 300858 280494 294794

86: Continuous Potatoes + Vegetables and unplanted acres 203640 252854 332892 333916 223798 159013 198550 271475 292941 228262 295533 270302 3 pt134: Potatoes-Winter Wheat 0 0 0 0 0 0 0 0 0 0 0 0

86: Potatoes-Winter Wheat 0 0 0 0 0 0 0 0 0 0 0 0

134: Potatoes-Winter Rye (only potato acreage) 0 0 0 0 0 0 0 0 0 0 7969 41480

86: Potatoes-Winter Rye (only potato acreage) 0 0 0 0 0 0 0 0 0 0 3193 17400

134: Potatoes-Small Grain (1:1 acreage) 442140 435858 520125 485222 617422 628156 672335 665325 624711 598729 654307 640972

86: Potatoes-Small Grain (1:1 acreage) 637745 622093 666132 691682 751132 782899 718340 761654 728822 742330 726867 747827

134: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 0 0 0 0 0 0 0 0 0

86: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 0 0 0 0 0 0 0 0 0

134: Alfalfa-small grain (7 year rotation) 297658 229443 205352 200536 221352 251610 195453 203592 193873 173923 167265 166281

86: Alfalfa-small grain (7 year rotation) 639097 521517 468873 435848 534410 615813 524535 543840 532475 471776 455704 440132

134: Continuous Small Grain 883472 1029634 968494 1000054 975332 824536 632875 281713 268980 332527 240576 208853

86: Continuous Small Grain 1184147 1456687 1318415 1395415 1327400 1087672 942946 507194 490984 609294 422178 388698

Total Erosion (tons/year) 4506372 4858200 4703520 4739397 4838600 4480889 4114138 3443462 3402277 3457701 3254087 3216740

Tons/Acre/Year 12.00 12.94 12.53 12.50 12.10 11.05 10.55 9.30 9.06 9.04 8.51 8.39
74

Variable name 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

134: Continuous Potatoes + Vegetables and unplanted 

acres

228583 249800 188374 127358 179485 188911 419905 198149 65099 112982 172315 320596 353231 406374

86: Continuous Potatoes + Vegetables and unplanted 

acres

291580 268972 219536 124050 182021 243547 417007 201815 61389 138273 199268 281477 263911 312412

134: Potatoes-Winter Wheat 0 0 0 0 3320 41613 25676 23465 46929 41216 31745 29131 30065 30065

86: Potatoes-Winter Wheat 0 0 0 0 16808 38846 22909 21614 29474 30245 21579 22893 21882 19934

134: Potatoes-Winter Rye (only potato acreage) 47876 73225 70334 69321 74894 72353 72526 70025 53658 48852 39590 35550 37535 32845

86: Potatoes-Winter Rye (only potato acreage) 26917 58076 56144 57625 54883 52806 51410 51035 39058 35321 31727 29418 29316 26884

134: Potatoes-Small Grain (1:1 acreage) 576581 619505 646113 674643 650968 562347 525628 556126 618725 502549 472996 441383 381617 264728

86: Potatoes-Small Grain (1:1 acreage) 658593 687309 693632 716660 613033 537920 497778 548432 638538 511741 503559 459270 432459 362580

134: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 5072 103054 113238 194903 161255 48967 126431 319921 263253 285510 341945

86: Potatoes-Canola and/or sorghum cover (1:1 acreage) 0 0 0 14561 102089 144052 197885 145068 49222 133074 250631 238298 228330 264704

134: Alfalfa-small grain (7 year rotation) 177596 196291 204654 211624 184226 187533 195563 238168 240885 244055 190179 160721 156244 168952

86: Alfalfa-small grain (7 year rotation) 484345 524878 549666 544552 511675 510055 529272 594686 615591 622930 502836 495720 510620 567109

134: Continuous Small Grain 198214 100567 103465 137649 164431 138560 69031 84821 132341 81233 32345 73332 101664 85087

86: Continuous Small Grain 392235 269180 233650 300992 398963 396594 279047 341815 451807 240923 138591 186477 218709 144989

Total Erosion (tons/year) 3082521 3047803 2965568 2984107 3239852 3228373 3498540 3236475 3091684 2869825 2907283 3037519 3051094 3028607Tons/Acre/Year 7.88 7.87 7.62 7.46 7.98 7.46 8.18 7.42 7.08 6.87 7.83 7.90 7.94 7.88
75

Figure 4-A.1 – General approach to estimating wind erosion in the San Luis Basin.

Agriculture Data by 

County:

Colorado Agricultural 

Statistics, Census of 

Agriculture

Knowledge of Farming Practices 

by County: crop rotations by 

year, soil type, and tillage/residue 

management

Richard Sparks, USDA, NRCS

Estimate total acreage of crop 

rotations by soil type and 

tillage practices

Wind Erosion Equation 

(WEQ): tons/acre for 

different soil types and crop 

rotations for SLB

Wind Erosion: total wind 

erosion in tons/year for SLB
76
77

5.0 

Emergy

5.1 Introduction

The effects of changes in system states on sustainability 

can be quantified using environmental accounting 

methods based on the transformations of available 

energy (Odum 1996). Environmental accounting 

using emergy and Emergy Analysis (EmA) provide an 

alternative to economic methods, which decision makers 

can use to assess the effects of their policies on their 

system. 

When the available energy transformed from one form to 

another is traced through a network of interactions, the 

amount of energy required to create any item (e.g., an 

energy flow or storage) in the system can be determined. 

Tracing the energy flows required to support each 

network storage or flow and converting those flows to 

energy of a common unit (e.g., solar joules), enables the 

determination of the value of any item in the network 

(Campbell 2001) in terms of the resources required for 

that item to exist within the system. This value can then 

be expressed as the solar emjoules (sej) required for its 

production. 

EmA is a method for assessing the available energy 

required for the production of a product or service 

in a system in units of equivalent quality (i.e., solar 

equivalent joules; Brown 2003, Campbell 2001; see 

Odum 1996 for a detailed description). The emergy 

evaluation of systems with its emphasis on the role 

of renewable and nonrenewable resources used in 

system support and its characterization of import and 

export exchange is a useful framework for the analysis 

of sustainable system operations (see Campbell et 

al. (2005) and Odum (1996) for detailed methods for 

conducting EmA). 

Any quantity of energy can be converted into emergy 

by multiplying the original units (J or g) by an emergy 

per unit factor, the transformity (sej/J) or the specific 

emergy (sej/g), to obtain the emergy equivalent of the 

quantity in solar emjoules (sej). Other quantities (e.g., 

information or money) are converted into emergy using 

the appropriate emergy per unit factors. The emergy 

of environmental, economic, and social products and 

services is then directly comparable so the relative 

advantages and disadvantages of any changes can be 

readily determined by direct inspection of the values. 

Relative advantage is judged by movement toward 

greater emergy flow (empower) through a system 

network, because maximizing empower in a system 

network is the decision variable used to predict the 

outcome of evolutionary competition between alternative 

systems designs (Lotka 1922a, 1922b, Odum 1996). 

Movement toward sustainability, from an energy systems 

perspective, is measured by the degree to which a system 

depends upon renewable resources for its operation. As 

a system decreases its use of non-renewable emergy 

and increases the fraction of renewable emergy in its 

total emergy use, the system moves toward a more 

sustainable state. The concept of sustainability requires 

that we specify what system state is to be sustained and 

over what time we expect to sustain it (Campbell 1998). 

The system structure and function maintained through 

greater use of renewable emergy must be equivalent to 

that supported by nonrenewable emergy on a per capita 

basis, for there to be no net degradation in the quality 

of life. Increasing empower, or the amount of emergy 

moving through the system may lead toward greater 

health and vitality; whereas, decreasing emergy flow 

may lead toward a less robust system with lower levels 

of overall function. Therefore, the evolution of networks 

toward higher empower designs is the overriding process 

governing the success of any condition or state of a 

system. This process may occur at the expense of other 

systems, depending on the emergy sources available 

to support its organization (Campbell et al. 1998). 

Ultimately, what is sustainable for a system and for how 

long depends on the available energy in the suite of 

external forcing functions. Often the operation of the 

maximum power principle appears to lead to pulsing 

designs, in which resources accumulate slowly over 

time and are then consumed rapidly once a threshold is 

exceeded (Odum 1996, Campbell 2001). Often under 

such scenarios, the only pattern that is truly sustainable 

is the pulsing cycle of change (Odum et al. 1995). 

EmA provides managers with a comprehensive suite 

of indices to assess the ecological and economic wellbeing of their system (Odum 1996). It gives managers 

a comprehensive decision variable (emergy flow in 

the system network) against which they can judge the 

efficacy of their policies. From this comprehensive suite 

of indices and body of theory, we have abstracted a few 

indices (see below) that apply to system sustainability 

and used them to characterize conditions in the SLB. 

EmA has been applied broadly to analyze nations 

(Brown 2003, Lefroy and Rydberg 2003, Ulgiati et 

al. 1994), states (Campbell 1998, Campbell et al. 
78

2005, Campbell and Ohrt 2009, Tilley 1999) and 

counties (Lambert 1999), but the application of the 

method to regions has been less common (Tiezzi and 

Bastianoni 2008). The San Luis Basin Sustainability 

Metrics Project is a regional study that presented us 

with a unique set of challenges in data gathering and 

interpretation. The standard EmA method calculates a 

number of indices showing various aspects of system 

functions and the relationship of the system to its next 

larger system. However, because our focus was on 

regional sustainability, we thought it would be more 

straightforward, if we focused initially on a single index 

of emergy that relates strongly to sustainability. We 

identified the fraction of renewable emergy to total 

emergy used as the best index to assess, the condition of 

the regional system and to determine if the region was 

moving toward or away from sustainability. We report 

all the indices used in a standard EmA in this Chapter as 

well, and discuss aspects of system structure, function, 

and sustainability that are revealed by our analysis of 

these measures. Among these additional indices, we 

focused on the total emergy use as a measure of overall 

well-being of the region and the Emergy Sustainability 

Index, ESI, as a measure of the sustainability of the 

relationship between the SLB and its next larger system 

(i.e., the State of Colorado and the Nation). Before 

calculating the indices, we had to address several 

challenges, including setting the regional boundaries 

and assembling time-series data relevant to the region. 

In this study, we focused on developing emergy indices 

of sustainability over a period of 26 years from 1980 to 

2005. However, data availability limited the complete 

emergy evaluation to the eleven-year period of 1995 to 

2005. 

5.2. Methods

The methods used in the emergy portion of this study 

needed to address two challenges. The first was a 

technical question of how to set boundaries for a regional 

study where more than one criterion could be logically 

used. The second challenge was to find and assemble 

relevant data on the region for a 26-year period. Both of 

these challenges have been seldom addressed in emergy 

analyses, where the considerable data requirements of 

the method often limit the analysis to systems with wellestablished boundaries (e.g., a nation, state, or county 

examined for one or two years). Detailed methods used 

to carry out an EmA are available in Campbell et al. 

(2005) and Campbell and Ohrt (2009). We will outline 

the methods in the following sections by discussing the 

aspects of the SLB regional study that were unique to 

this analysis. The seven steps for this emergy evaluation 

are described in Appendix 5-A.

5.2.1 Boundaries

The system boundaries of a region could be based with 

strong justification on two different sets of criteria (i.e., 

hydrologic or the political boundaries of counties). Both 

sets of boundaries were used for the study. Political 

boundaries were used to quantify all human activities 

(e.g., energy use, economic activity, etc.) and most 

natural inputs and storages (e.g., solar radiation and 

forest biomass). The hydrologic system boundary was 

used to determine the water flows that support human 

activities in the Upper Rio Grande River Basin of the 

San Luis Basin (SLB; see Chapter 1 for a description 

of the SLB). Ultimately, we chose a mixture of both 

because they better represent the actual flow of emergy 

through the system and they are similar in size and areal 

coverage.

5.2.2. Energy Systems Models

Information about a region is used to draw a detailed 

energy systems diagram using the Energy Systems 

Language (ESL) to represent the system under study 

(for a detailed description of this process, see Campbell 

et al. 2005). The resulting diagram should capture the 

essence of the system and its operation. The detailed 

model is simplified by aggregating components of 

similar function, characterizing the inputs and outputs 

and reducing the complexity of internal components and 

flows to basic measures of economic activity, resource 

supply, and total emergy flow. Energy Systems Models 

aggregated in this manner provide indices of system 

operation related to sustainability and self-sufficiency, as 

well as other system properties.

We reexamined the indices used to evaluate two aspects 

of the relationship between a system and the associated 

larger system. First, the emergy yield ratio (EYR) for 

a local system, often characterized for a nation, state, 

or region as U/F (where U is total emergy flow in the 

system and F is feedback from the next larger system), 

is redefined as Y/F, where Y or yield is equivalent to 

exports. This definition more closely matches the 

original definition of EYR formulated for production 

processes (Odum 1996). Second, the ratio formed by 

the total emergy flow in the system (U) 

and the feedback 

(F) from the larger system is defined as the local effect 

of emergy investment because it shows the effect of 

feedback from the larger system on the local system’s 

empower (see Appendix 5-B). 

5.2.3 Emergy Analysis 

EmA tables provide a template for the creation of 

the accounts needed to construct an emergy income 

statement and balance sheet. The tables provide a simple 

way to demonstrate and record the calculation of the 
79

emergy values for annual flows of mass, energy, and 

information. In the emergy tables, data on the mass of 

flows are converted to energy and then to emergy to 

aid in comparisons and to provide information for the 

analysis of public policies (Table 5.1). Note that for 

many items, transformities are available in the literature 

or they can be calculated (Campbell et al. 2005, Odum 

1996; see Appendix 5-C for values used in this study 

and the other appendices 5-D through 5-G for other 

results and discussion, e.g., Table 5-G.1 provides sources 

used to obtain data for the EmA of the SLB system). 

Figure 5.1 shows a detailed Energy Systems Language 

diagram of the SLB region and Table 5.2 lists the energy, 

material, and information flows, which are linked to the 

diagram through the k coefficient values shown in the 

table and the diagram. 

The energy content of many items has been widely 

tabulated, but a similar compendium of transformities 

and other emergy per unit factors does not exist at 

present. If the production process is unknown or not 

easily documented, transformities may be determined 

using one of the ten methods given in Odum (1996) and 

Campbell and Ohrt (2009). The emergy per unit factors 

used in this study are given in Appendix 5-C. 

We estimated the transformity for each commodity class 

to determine the emergy per ton of each commodity 

imported. These transformities were approximated by 

averaging known transformities of items within the 

class (without services); however, not all items in a class 

were included in the determination of the transformity. 

In some cases, when a transformity was not known for 

any item in the class, the parent material was used as a 

surrogate for the item’s transformity. The use of parent 

materials resulted in a minimum estimate of the emergy 

imported and exported in these commodity classes. The 

information needed for EmA is most often reported as 

annual flows of energy and/or mass. Usually mass can 

be easily converted to energy as the energy content of 

many objects has been widely tabulated (e.g., USDA 

2009). The transformity (sej/J) and specific emergy 

(sej/g) have been calculated for many items and can 

be used to convert energy and mass flows to emergy 

(Appendix 5-C). In this study, emergy accounts for 

renewable and nonrenewable local inflows, renewable 

production, imports, and exports were calculated for the 

seven counties comprising the SLB.

Initially, we assumed all imports were used in the 

system. However, we discovered a situation in the SLB 

where Antonito, Colorado (Conejos County) is a railhead 

and trans-shipment point for heavy materials, such as 

unexpanded perlite and volcanic scoria (San Luis Valley 

Development Resources Group 2007). To account for 

the movement of these materials through the system, 

we removed processed nonmetallic minerals from the 

import-export balance, to avoid falsely attributing the 

emergy of these materials to use by the SLB system. 

In addition, the actual amount of local nonrenewable 

resources (e.g., crushed stone, sand and gravel) used in 

the SLB was unknown. Export vastly exceeded import 

of these materials, so we assumed the difference was 

“apparent production” and assumed that 10% of the 

“apparent production” was used in the system.

5.2.4. Data and Sources

Data on the human system, and much of the “natural” 

system, of the SLB were collected based on political 

boundaries, whereas, hydrologic data were collected 

using the basin boundary (Fig. 1.1). Water resource 

data conformed to the boundaries of our system. Data 

on economic and social activities were collected at the 

county level (Table 5-G.1). When only the state or 

national level data were available, we used average per 

capita data from the state or national level to estimate 

variables (Table 5-G.1). All data were yearly averages. 

Data needed to construct an emergy income statement 

of a region can be categorized as follows: (1) 

renewable energy inputs to the system and information 

on renewable production carried out in the system; 

(2) nonrenewable energy inputs, production and 

consumption (including any renewable energy sources 

used in a nonrenewable manner; i.e., rate of use exceeds 

rate of replenishment); (3) imports to the system 

(including raw and finished materials and services); (4) 

exports from the system (including raw and finished 

materials and services). Data on the SLB were gathered 

via literature surveys, interviews with experts, the 

purchase of a dataset, and meetings with knowledgeable 

people from the region (Table 5-G.1). 

The need for data on SLB imports and exports was the 

limiting factor in determining the period investigated 

using EmA. Commodity Flow Surveys produced by 

the US Census Bureau every five years provide such 

data at the state level, but these data did not provide 

annual estimates and they were not available to us at the 

county level. Thus, we identified a private company, 

IHS Global Insight, Inc. (GI; Global Insight 2009) 

that used data from a network of shippers, publicly 

available sources, and US Census Bureau to compile 

freight movements by US county. GI data for the seven 

counties in the SLB, from 1995 to 2005, were purchased 

for approximately $20,000. We report less detailed EmA 

results using variables for the period 1980-2005. 
80

5.2.5 Summary Tables and Indices

A summary of the variables with their definitions are 

given in Table 5.3. Summary variables were used to 

define total emergy used and fraction of renewable 

emergy (Fig. 5.2; Table 5.3). Fraction of renewable 

emergy used to total emergy used was the primary 

evaluation criterion used to assess local system condition 

and movement toward or away from sustainability. A 

number of additional indices were computed (Table 5.4), 

but these were not necessary for estimating the fraction 

of renewable emergy to total emergy that we used in this 

study (Appendix 5-B presents the definitions of other 

indices important for a complete EmA, including the 

Emergy Yield Ratio, the Environmental Loading Ratio, 

and the Emergy Sustainability Index).

5.3 Results 

In this section, we present the major results of EmA for 

the SLB. Other results and associated discussion are 

located in the appendices.

5.3.1 The Detailed Energy Systems Model of the 

San Luis Basin

We produced a detailed diagram of an Energy Systems 

Model of the SLB environmental system (Fig. 5.1; see 

Chapter 1 and Appendix 5-D for a detailed description 

of SLB). The large rectangular box represents the 

boundaries of the SLB. External forcing functions or 

energy, material, or information sources entering from 

outside the system are shown as circles. They enter the 

system through pathway lines crossing the boundaries. 

They are arranged in order of increasing transformity 

from left to right around the system boundary. Flows 

of energy and matter leave the system as shown by 

arrows crossing the boundary. All the flows on pathways 

crossing the system boundaries as well as flows of 

energy and materials moving within the system are 

defined (Table 5.2). Surface and groundwater flow out 

of the region into New Mexico. Unused solar energy 

(albedo) is reflected into space and wind blows over 

the region and passes on to the surrounding systems. A 

fully specified energy source (circles) always contains 

all the necessary parameters needed to define the source 

over the simulation time planned for the models created 

from the detailed diagram. For example, wind speed and 

direction over many annual cycles would be important 

variables to model the development and maintenance 

of the dunes in Great Sand Dunes National Park and 

Preserve. Although this detailed information about the 

wind is implied by the wind source symbol, it is not 

included in this highly aggregated depiction of the SLB.

Used energy leaves the lower boundary of the box 

delineating the SLB system. No other flows exit from 

the lower boundary and the heat sink symbol, which 

receives the energy dissipated by all components 

and flows in the system, including the background 

temperature of the environment. The inclusion of 

temperature as part of the specification of energy systems 

models allows entropy calculations to be performed.

The major physiographic systems within the SLB are 

aggregated into three classes: 1) mountains which are 

to a large extent forested, 2) the valley floor, which 

is divided into an area covered by the natural shrub 

vegetation of the region including phreatophytes and 

xerophytes (e.g., sagebrush), and 3) agricultural land, 

which is generally irrigated with surface or groundwater 

(Emery 1996). The primary economic systems of the 

region are centered around its natural resources (e.g., 

food processing and shipment, mining and processing 

ores and building materials, and recreation are important 

activities tied to natural resources). In addition, power 

companies recently have recognized the high solar power 

potential of the region (SLVDRG 2007). For example, a 

solar power plant that was under construction during the 

course of our study is now operational. However, during 

the time of this study, all the electricity used in the SLB 

was imported. 

5.3.2 Aggregate Diagram

Aggregated ESL diagrams of the SLB were used to 

evaluate the SLB each year under study. They show 

how emergy from renewable resources and local 

nonrenewable resources (including renewable resources 

like soil and ground water that were being used in a 

nonrenewable manner) interacted with imported goods 

and services to support regional economic activity 

and produce exports (Fig. 5.2, Table 5.3). Although 

the major social, economic, and environmental flows 

were evaluated, there were several gaps in the data. 

For example, we were not able to find data needed to 

document internal mineral resources (i.e., primarily 

sand and gravel and crushed stone) used in the system, 

thus this value was estimated by assuming 10% of the 

total resources remained in the region. In addition, we 

were not able to apply the method utilized by Campbell 

et al. (2005) to determine the import and export of pure 

services to and from the SLB. Thus, there is a “?” in the 

places designated by P2

I3, P2E3, and E4 on the diagram 

(Fig. 5.2). 

5.3.3 Total Emergy and Percent Renewable Use

Data used to compute total emergy used and the 

renewable emergy used were available from 1995 to 

2005. These data are the main components of our 

sustainability metric based on EmA. During this 11-

year period, the overall trend indicates little change in 
81

total emergy use and a slight decrease in the renewable 

emergy used (Fig. 5.3). Total emergy use per person 

rose 8% from 1995 to 2000 and then fell 14.7% from 

its peak in 2000 to 2005 with the largest year to year 

decrease (9.8%) occurring from 2002 to 2003 (Fig. 

5.4). Total outflows exceeded inflows until 2001, 

when the two were almost equal (Fig. 5.5). In 2000, 

outflows began to decline more rapidly than inflows 

and as a result, inflows slightly exceeded outflows in 

2003; however, in 2004 and 2005 outflows increased 

rapidly to more than 2.5 times the inflows (Fig 5.5). The 

components of total emergy inflow determine the total 

emergy used, which is defined as the total emergy inflow 

plus the emergy from local sources (Fig. 5.6). The 

increase in emergy use by the system seen from 2003 to 

2005 followed similarly timed increases in the emergy of 

renewable sources and imports. 

Both the emergy used from local renewable sources 

and the fraction of total emergy used from home 

sources (those within the SLB region) were in a slight 

declining trend from 1997 to 2001, but the fraction of 

the local renewable sources was more variable (Fig. 5.7). 

Renewable emergy available per capita declined from 

1980 to 2005 such that the renewable emergy per person 

in 2005 was only around 75% of that available in 1980 

(Fig. 5.8). Further, there was up to a 22% year-to-year 

(2001-2002) decrease in the renewable emergy per capita 

along its declining trend due to variations in rain and 

snowfall.

5.3.4 Summary of Renewable and 

Non-renewable Emergy 

The geopotential energy of snow was the largest emergy 

inflow into the system over the 26-years examined (Figs. 

5.9 and 5.10). Agricultural production is considered to 

be fundamentally renewable in most emergy analyses 

(i.e., renewable inputs of rain, wind and soil fertility 

can support agriculture without large subsides of 

nonrenewable emergy). We recognize that nonrenewable 

emergy is a major input to most agriculture in the US, 

but this study does not include a detailed analysis of 

the agricultural subsystems of the SLB. Emergy in 

livestock, primarily cattle, was the largest agricultural 

product of the SLB from 1980 until 1997 (Fig. 

5.11). Wheat accounted for the largest emergy of 

crop production from 1980 to 1985 (Fig. 5.11). Hay 

accounted for the largest emergy from crop production 

from 1986 until the conclusion of the study period, 

except in 1989, when wheat once again was briefly 

dominant (Fig. 5.11). 

Data on nonrenewable emergy production in the SLB 

(crude oil, metallic ores, broken stone or riprap, sand and 

gravel, and non-metallic minerals) were taken from the 

GI dataset, which was for the years 1995 to 2005. The 

use of nonrenewable emergy in fuels and electricity in 

the SLB remained constant over the study period (Fig. 

5.12). However, nonrenewable emergy production 

fluctuated, with emergy of sand and gravel and broken 

stone/riprap in a declining trend from 1995 to 2005, but 

showing intermittent spikes in production (Fig. 5.12). 

5.3.5 Summary of Import-Export Exchange

From 1995-2005, exported emergy exceeded imported 

emergy, with the most dramatic increase in the difference 

from 2003 to 2005 (Fig. 5.13). In addition, there was 

a dramatic spike in net immigration in 1988, 1989 and 

1990, and a smaller surge of people into the SLB in 2004 

and 2005 (Fig. 5-E.2). 

5.3.6 Imported Emergy

Data on petroleum, coal, natural gas and electricity 

imported, and the services required to deliver these 

energy inputs were collected for 1980 to 2005 (Fig. 

5.14). Data were available from 1995 to 2005 for 

material imports other than fuels, services in materials 

(other than fuels), and tourism (Fig. 5.14, see Appendix 

5-E for detailed data on Imports). “Materials other 

than Fuels” carried the largest amount of emergy into 

the SLB over the period for which we had data. The 

emergy in these materials was more than twice as large 

as the next largest input, which was the service required 

to deliver the material inputs. From 1995 to 2005, 

emergy of “Agricultural Goods,” principally fertilizers 

and agricultural chemicals, increased from about 18% 

to 38% of material imports, whereas “Construction 

Goods” were around 22% of imports from 1995 to 

1999, but then declined to 13% of total imports in 2005 

(Fig. 5.15). Emergy of “Industrial and Mining Goods” 

was about 35% of inputs from 1995 to 1998 and then 

declined to 24% in 1999, after which it rapidly rose to a 

peak of around 44% of inflows from 2000 to 2001, led 

by emergy of industrial gases and electrometallurgical 

products, respectively (Fig. 5.15). After 2001, emergy of 

“Industrial and Mining Goods” fell to 27.2% of inflows 

in 2005. Over the 11-year period, emergy of “Consumer 

Goods” remained around 25% of the emergy of material 

imports until 2000 when it fell to around 17% of inflows 

in 2001 and 2002. After 2002, the import of the emergy 

of “Consumer Goods” recovered to its former level 

before declining to 22% of the input in 2005. 

5.3.7 Exported Emergy

Data on agricultural crops and livestock were available 

from 1980-2005 and data on emigration were available 

from 1985-2005. Remaining export data were obtained 

from the GI dataset and were only available from 1995 

to 2005. If we assume that almost all agricultural 
82

production was exported, livestock was the major 

agricultural export of emergy from the region until 1996 

when hay (Table 5-E.2) became the dominant export of 

emergy (Fig. 5.16). From 1995 until 2003, “Gravel or 

Sand” was the largest emergy export from the region 

(Table 5-E.5).

The overall pattern of major exports of emergy showed 

a declining trend from 1995 to 2003, after which there 

was a dramatic resurgence of the annual empower 

exported (Fig. 5.16). The resurgence of emergy exports 

was lead by total materials and by “All Other Materials” 

(i.e., materials without agricultural crops, livestock, and 

mineral and forest products; Fig. 5.17). The emergy 

in services in material exports that were required to 

make them followed the trend of materials, but was of 

smaller magnitude, except for the services in “All Other 

Materials,” which exceeded the emergy of the materials 

during the export resurgence from 2003-2005 (Fig. 

5.17). From 2003 to 2004, there was a steep increase 

in the emergy exported in the “All Other Materials” 

category, such that it was three times greater than 

the emergy exported in sand and gravel in 2004 and 

2005 (Fig. 5.17). For the period from 1995 to 2000, 

“Miscellaneous (Misc.) Food Preparations, nec (not 

elsewhere classified)” was the largest export of emergy 

from the SLB, but in 2001 “Misc. Printed Matter” was 

the largest component briefly until in 2002 when “Misc. 

Agricultural Chemicals” became the dominant export 

(Fig. 5.18). After falling to its lowest point in 2003, 

emergy of “All Other Materials” exported had a sharp 

jump upward in 2004 lead by emergy of “Miscellaneous 

Agricultural Chemicals.” “Miscellaneous Agricultural 

Chemicals” includes such products as insecticides, 

herbicides, soda ash and sulfur, soil conditioners, 

and mulch. While all “other” materials exported 

increased from 2003 to 2005 (Fig. 5.18), the increase 

in “Miscellaneous Agricultural Chemicals” exported 

was six times greater than that of the next largest export 

(“Misc. Printed Matter”; Fig. 5.18). 

5.3.8 Population and per Capita Indices

The population of the SLB grew steadily from 1980 

to 2005, increasing nearly 15% during the period and 

there was a corresponding increase in the emergy of 

electricity and fuel use per capita during the same period 

(Fig. 5.19). The rate of growth in the use of both fuel 

and electricity use increased after 1992 (Fig. 5-G.3). Per 

capita use of emergy from fuels and electricity declined 

from 1980 to 1983 and then began to increase in two 

stages (1983-1997 and 1997-2005) with some variation 

(±3%) within a stage (Fig. 5.19). The most rapid growth 

in per capita energy use occurred between 1999 and 

2001, when it increased by approximately 10%. 

5.4 Discussion

Emergy analyses that consider how a system changes 

over many years are becoming more common. 

Historical studies of nations have been performed (e.g., 

Abel 2007, Rydberg and Jansen 2002, Sundberg et al. 

1994, Tilley 2006) and nations have been evaluated over 

decades (e.g., Cialani et al. 2005, Ferreyra and Brown 

2007, Hagstrom and Nilsson 2005, Huang et al. 2006). 

Also, Pulselli et al. (2008) studied the sustainability of 

two regional systems in Italy over 32 years using the 

Index of Sustainable Economic Welfare and emergy 

analysis; however, studies of the sustainability of 

regional environmental systems with nonstandard 

boundaries (i.e., boundaries that were not based on 

standard political or administrative reporting units) 

carried over many years appear to be rare. The SLB, as 

defined for this study, is such a nonstandard region. 

5.4.1 Spatial Boundaries

This study is unique in that the major organizing 

renewable energy flows for the region are the available 

geopotential and chemical potential energy in rain 

and snow. Although the transformity of snow was 

determined by Campbell and Ohrt (2009), this is the first 

EmA where snow was shown to account for the largest 

renewable emergy input to a system. The variation in the 

geopotential energy of snow is the primary driver behind 

the fluctuations in total geopotential energy inflow and 

the renewable emergy base of the SLB system over 

the 26-year period of the study (Fig. 5.8). The present 

human system in the SLB is largely organized around 

agriculture, which is dependent on the surface and 

ground water flows that originate in the high mountain 

snowpack (Emery 1996). Thus, as mentioned above, 

the boundaries for the analysis of this nonstandard 

region might be logically set to conform to those of the 

Upper Rio Grande River Basin, or to conform to the 

political economic reporting units based on the county 

boundaries. We chose to deal with the initial boundary 

problem by choosing those boundaries that were 

appropriate for the process under consideration (i.e., 

economic and social data were collected for counties, 

whereas water flows were determined by the Upper Rio 

Grande River Basin).

5.4.2 Data Availability

Another major obstacle was finding a complete set of 

data for the local region that we could use. Technical 

problems related to determining the inflow and outflow 

of commodities to and from the region that had been 

solved at the state level (Campbell et al. 2005, Campbell 

and Ohrt 2009) had to be revisited in the context of 

the SLB. Of all the data needed to perform an EmA 

for the region, information on the import and export 
83

of commodities was the most difficult to obtain over 

the entire period of interest. Once we found a source 

from which these data could be purchased for a subset 

of years, we found that it contained inconsistencies 

and uncertainty greater than data from the U.S. Census 

Bureau Commodity Flow Survey. 

5.4.3 Renewable Inputs to the SLB

Ordinarily, the renewable emergy base of a system is 

determined by averaging the independent inputs of 

emergy to the system over many years. This rule is 

applied because societal structures have many years 

to adjust to the emergy signature of a location (i.e., 

economic activities adjust to take advantage of the 

amounts of available energy of various kinds in the 

region and their expected availability). This study 

departs from this custom by looking at the year-to-year 

variability in the availability of renewable resources. 

For our study of the SLB, where agriculture and the 

region’s unique natural systems are highly dependent 

on the year-to-year availability of water resources, we 

think the inclusion of this level of detail is justifiable. 

Evapotranspiration (ET) appears nearly constant because 

we had only one composite annual estimate for the ET 

of each vegetation class and thus its variability depends 

on the change in land use over the 26-year period 

(Appendix 5-E). If we had annual estimates of ET, we 

would expect it to vary with the hydrological cycle in a 

manner similar to precipitation.

5.4.4 Temporal Patterns of Development 

and the Water Cycle

Until 2002, emergy of production from renewable 

sources (e.g., crops, livestock, and forests) was more 

than twice the emergy of total fossil fuel and electricity 

use including services. After 2002, this ratio fell due 

to a marked decline in the total emergy of agricultural 

production and the steady increase in the emergy 

supporting society. The salient characteristic of the 

renewable emergy base for the SLB is the geopotential 

energy expended due to snowmelt and its subsequent 

runoff, which is the largest renewable emergy input to 

the region. However, the work done by the snow occurs 

after a period of storage; thus, there is a lag between the 

time the work is delivered from the time the input enters 

the system. Snowmelt delivers high quality energy 

in a pulse that carries twice the emergy of the next 

largest input, ET. This pulse is essential to maintaining 

rare geological features like the sand dunes and for 

recharging groundwater. If climate change results 

in drier winters or in warmer winter temperatures as 

indicated by climate change predictions for the region, as 

evidenced by State of New Mexico (2005), the formation 

and duration of the snow pack may be affected. Changes 

in the long-term snowfall or winter temperatures could 

result in significant changes in the hydrological and 

geological features of this unique system and have 

effects on agricultural production.

5.4.5 Nonrenewable Resources

The use of nonrenewable resources, such as, fossil 

fuels and electricity in the SLB showed a steady growth 

trend over the period examined that was proportional to 

population growth. The construction material (sand and 

gravel and broken stone) produced in the SLB declined 

by about 40% from 1995 to 2005. Miscellaneous 

nonmetallic minerals in unprocessed form were mined 

in the SLB but apparently were not used there. Exports 

in this category precipitously increased from 2003 to 

2005, and were part of a general economic resurgence 

that occurred in the region during this time. Soil erosion 

from wind steadily declined over the period (Fig. 

5.20 and Appendix 4-A), likely due to improvement 

in cultivation methods. The use of groundwater for 

irrigation was episodic and it corresponds with periods of 

decreased precipitation in the region. Peak groundwater 

withdrawals exceeding the recharge rate occurred with 

increasing frequency over the 26-year period with the 

greatest decline occurring in 2002. During times of 

peak withdrawal, the emergy supplied by groundwater 

use was a significant contribution to the annual emergy 

used in the region, and this emergy source approached 

that supplied by fuels and electricity in 2002 (Fig. 5.12). 

Over-withdrawals of groundwater (i.e., faster than it can 

recharge), could threaten the water resource base for 

agriculture in the region and the sustainability of this 

activity in the SLB.

5.4.6 Patterns of Import and the Emergy Budget 

of the SLB

Material goods other than energy account for the largest 

part of total imports from 1995 to 2005. Processed 

nonmetallic minerals accounted for most of the 

emergy in imported material goods excluding fuels 

and electricity. However, imports in this category fell 

45% over the 11-year period examined (Appendix 

5-E). Perlite is classified as a processed nonmetallic 

mineral used for construction and agricultural purposes. 

Because large quantities of perlite entered the SLB 

(San Luis Valley Development Resources Group 

2007) and there is a defunct perlite mine in Conejos 

County, we assumed that materials moving in the 

nonmetallic minerals category would be largely perlite. 

Perlite is both imported into and exported from the 

SLB, however, according to the GI data, much more 

is imported than exported. We were puzzled by this 

difference and what might have happened to the missing 

mass or, alternatively, if the GI data were incorrect. 
84

If the difference was consumed in the SLB, it would 

greatly increase the emergy used there. The apparent 

consumption of perlite declined about 40% from 1995 

to 2005. Further investigation revealed that perlite 

was trucked into the SLB from mines in northern New 

Mexico to the railhead at Antonito, where it was mixed 

to order and placed on rail cars for shipment out of the 

region (San Luis Valley Development Resources Group 

2007). Furthermore, the mines in northern New Mexico 

supplied 80-85% of the perlite from US sources during 

the late 1990s (Barker et al. 2002), so these material 

flows pertain to the larger system and not to the SLB. A 

similar condition applies to scoria, another processed 

nonmetallic mineral, and thus, the emergy of processed 

nonmetallic minerals was removed from the emergy 

import–export balance for the SLB. 

The emergy of “Agricultural Goods” increased 269% 

from 1995 to 2005 suggesting increased use of fertilizer 

and agricultural chemicals in the region and by extension 

increased cultivation of certain crops. During this 

period, BEA (2009) reports the total expenses for 

fertilizer and lime increased. “Industrial and Mining 

Goods” imported increased two times from 1999 to 

2002 and then declined to the 1995 level by 2005, which 

may be indicative of increased industrial activity in the 

region during this time. The rapid increase in imports 

in this category was due to industrial gases (2000-2002) 

and electrometallurgical products (2002). The import 

of “Consumer Goods” (i.e., imported commodities 

classified as primarily for use by consumers) remained 

constant from 1995 to 2000 and then declined to 67% 

of this level in 2001 and 2002 and recovered to 86% of 

the 1995-2000 plateaus by 2005. This decrease in the 

imported consumer goods occurred in spite of population 

growth, which was 10% for this period, indicating that 

consumers in the SLB may have decreased their per 

capita utilization of imported consumer goods during 

this time. 

5.4.7 Patterns of Export

Exports of most materials were in a declining trend 

from 1995 to 2003, when total materials exported 

was at a minimum for the 11-year period. From 2003 

to 2005, the emergy exported in materials increased 

markedly due to a general resurgence of the export 

economy as indicated by the fact that exports of “All 

Other Materials” increased in all categories during this 

time. These data show the category “Miscellaneous 

Agricultural Chemicals” led this resurgence (Fig. 5.18). 

However, we do not know the exact commodity or 

industrial activity that accounts for these exports. The 

miscellaneous agricultural chemicals commodity class 

includes commodities such as pesticides, herbicides, 

fungicides, defoliates, soil conditioners, mulch, sulfur 

and soda ash. From 1995 to 1998, exports had a slightly 

increasing trend in emergy due to the manufacture of 

“Misc. Food Preparations,” (i.e., processed agricultural 

products such as french fries from potatoes) which was 

the largest category of all other exports until 2003. We 

assumed that almost all the agricultural production in the 

SLB was exported so the discussion above pertaining to 

the patterns of renewable production applies to exports. 

5.4.8 Export-Import Exchange

The fact that exports exceeded imports by a considerable 

margin over the entire study period indicates the SLB 

is a hinterland supplying raw materials and primary 

products to other areas in North America as shown by 

the GI data. Exports and imports have different temporal 

patterns prior to 2003 (Fig. 5.13), but both reach a low 

point at this time. From 2000 to 2003, both decline 

together, suggesting the import-export sectors of the 

economy of the SLB were contracting during that time. 

From 2003 to 2005, exports increased precipitously to 

the highest level obtained during the 11-year period, 

while imports increased very slightly. 

5.4.9 Patterns of Population Growth and 

Consumption

Fuel use in and fuel import to the SLB increased 16% 

over the 26-year period and electricity use increased 

by 80% compared to a 25% increase in population. 

Thus, we must look for factors other than population 

growth to explain the increase in energy use. From 

1980 to 2005, the consumption of electricity per capita 

increased by 44%, which accounts for the additional 

growth in electricity consumption. Because of its high 

transformity, the use of more electricity by people may 

be indicative of an increase in their quality of life over 

this time. Per capita fuel consumption declined 7% 

over the period but the concomitant population increase 

resulted in a 16% rise in fuel consumption. From 1995 

to 2005, the population increased 9.8% as the emergy of 

the fuels and electricity used increased 20.7% or 28.8% 

(services included). Without considering services, the 

emergy of fuel consumption increased 19.2% as the 

emergy of electricity use increased 23.4% from 1995 

to 2005 (Fig. 5.19). Increased consumption of fuels 

(+8%) and electricity (+12%), along with population 

growth, may contribute to the observed increases in 

consumption. 

The SLB has a very high proportion of renewable 

emergy (31-51% of total emergy use) supporting 

the system and its people compared to renewable 

contributions of around 3% for many developed areas 

in the US (Campbell et al. 2005, Campbell and Ohrt 
85

2009). Population growth in the SLB had the effect 

of decreasing the renewable emergy available per 

person (Appendix 5-E), and of putting pressure on the 

environment from development and waste production. 

In the end, this process is self-limiting, but at the present 

rate of increase, it may be some time before the inflow 

of people is diminished by degrading natural resources. 

Indeed, other factors related to economic development 

may continue to draw more people to the region in 

the future. The overall quality of life for people in the 

SLB as indicated by the Total Emergy Used per person, 

which is high (Fig. 5.4) with values equal to those in 

Minnesota, a state with high levels of emergy use per 

capita compared to West Virginia and the U.S. as a whole 

(Campbell and Ohrt 2009). 

5.4.10 Analysis of the Emergy Indices 

The fraction of emergy use from renewable sources 

can be viewed as an indicator of movement toward or 

away from sustainability (see Fig. 5.7). If the renewable 

emergy is of local origin, it is an indicator of the selfsufficiency of the system. The fraction of energy use 

from locally renewable sources declined from 1997 to 

2002 (Figs. 5.6, 5.21). After 2002, it increased abruptly 

indicating movement toward sustainability (or perhaps 

more accurately away from unsustainability), which was 

the result of the excess ground water use that occurred 

in 2002. This increase was due both to an increase in 

precipitation and to a relatively low level of emergy 

imported. On average, renewable emergy accounted 

for 44% of total emergy use over the 11-year period 

examined, which is five times that of an average location 

in the US (Campbell and Ohrt 2009). However, this 

index showed a 4% decline over the 11-year period 

examined, indicating the region was moving away from 

sustainability. 

The fraction of emergy derived from home sources has 

a pattern similar to, but not the same as the emergy 

from renewable sources. As demonstrated in 2000 and 

2002, the principal difference between these patterns is 

determined by the years of drought when local renewable 

resource contributions decline, (along with decreased 

precipitation) and the fraction of use from home sources 

increases due to increased use of groundwater (compare 

Figs. 5.9, 5.20, and 5.21). The use of ground water in 

this manner decreases the variability of total emergy use 

in the system by compensating for declines in emergy 

inflows from the renewable resource base. The fraction 

of emergy used from home sources is an indicator of 

self-sufficiency and in the SLB home sources exceed 

40% in some years making it moderately self-sufficient. 

However, because the region has a high potential for 

developing solar, wind, and geothermal power, the 

potential self-sufficiency of the SLB using renewable 

sources alone is large. The principle of matching 

environmental emergy with appropriate purchased or 

economic inputs in the form of investment implies that 

we would expect economic investment from outside 

to be drawn into the region. Per capita income in 

the region is low, so the necessary investment capital 

probably is not available within the region itself. Thus, 

we might expect tension to arise between the desire 

of outside influence from the next larger system to 

obtain “green” energy and the desire of local people for 

self-determination of their lifestyle and energy selfsufficiency. 

One index of how well the local system is doing is the 

emergy used annually by the system. The emergy used 

in the SLB remained fairly constant over the entire 

period (the coefficient of variation was 0.05) despite 

variability in the emergy inputs. Because the use of 

nonrenewable emergy from inside the system can 

be used to compensate for declines in the renewable 

emergy inflows in most years, the pattern of total 

emergy use in the system appears to follow closely 

the third component, imported emergy. Systems at all 

levels of organization compete for the available energy 

in resources. Emergy matching of a high with a low 

transformity resource develops the most emergy flow. 

Thus, imports and exports are continually adjusting to 

meet the need to increase emergy flow at all levels of 

organization under varying circumstances. In theory, the 

entire hierarchical, coupled ecological-economic system 

continually seeks win-win relationships that increase 

system empower. This concept is somewhat parallel 

to the general equilibrium model in economics where 

supply and demand are brought into relationship through 

a set of equilibrium prices. The objective function is 

different and the method of calculation is not determined 

in terms of emergy matching, but the two ideas are 

theoretically similar. Lu and Campbell (2009), Lu et al. 

(2006), and Lu et al. (2007) discussed additional indices 

that may be useful for answering particular questions 

about the relationship between emergy and economic 

flows between a local system and its larger system.

5.4.11 Effects of Missing Data, Unknown 

Information, and Uncertainty

The commodity flow data taken by the US Census 

Bureau met the data quality objectives for measuring 

total commodity movements for the studies of West 

Virginia and Minnesota (Campbell et al. 2005, Campbell 

and Ohrt 2009). In these studies, the estimated 

movements of individual commodities had higher 

uncertainties than the average of all commodities. The 

GI data were critical in allowing us to apply the revised 
86

method (Campbell et al. 2005) for calculating emergy 

import and export to and from a region. The methods 

reported by GI appear to be logical and deliberate, even 

though there is considerable uncertainty associated with 

some of their measurements. Principally, the export of 

“nonmetallic minerals, processed” appears to be grossly 

underestimated (see Appendix 5-F) and others were 

questionable. 

The transformities and specific emergies by which 

the energy or mass flows are multiplied, respectively, 

to obtain emergy are critical numbers in the analysis 

(Appendix 5-C) and they are important to understand 

the general level of uncertainty associated with these 

numbers. Campbell (2003) analyzed five global water 

budgets, and determined the transformities of global 

hydrological flows, such as rain, evapotranspiration, 

and river flow, within an average standard deviation 

of 5.9% of the mean value. Odum (1996) determined 

the transformity of coal from its relative efficiency 

in producing electricity and its geological production 

process. The former method gave an estimate of 4.3E+4 

sej/J and the latter 3.4 E+4 sej/J. The two values are 

within 12% of the mean value, which may be a rough 

estimate of the model uncertainty in determining 

transformities. 

5.4.12 Strengths and Limitations of the Method

Emergy methods are comprehensive and based on 

thermodynamic laws and principles governing the order 

and organization of all systems; therefore, they have a 

broad capability to address many problems. In addition, 

Energy Systems Theory is a meta-theory in the sense 

that theory and methods from all other disciplines that 

are translated into Energy Systems Language and further 

analyzed within the context of the thermodynamic 

constraints that govern them. This is true because all 

real systems have an underlying energy basis. However, 

EmA is not a substitute for other approaches but a 

complement to them. Perhaps the greatest strength of 

EmA is that it can transform disparate quantities into 

similar quantities with the same units that are then 

directly comparable for decision-making.

The strengths of EmA are also its weaknesses. Because 

it is comprehensive, it often requires more data and 

information than other approaches. In addition, the 

more aspects of a system that one considers, the more 

complicated the emergy work can become, because it is 

forced by the imperative to be complete to incorporate 

all relevant data and theory into the emergy assessment. 

In addition, the uncertainty of the analysis grows with 

the complexity of data and models used. One of the 

greatest challenges in an emergy assessment is to set 

the proper boundaries for the study so that a particular 

set of research questions are answerable. Perhaps the 

greatest weakness of emergy is that it is an abstract 

concept that can be difficult to communicate to scientists 

and managers. To use it with facility requires years of 

study and a broad background in the natural and social 

sciences. 

5.5 Conclusions

The process of assembling information on the SLB 

and diagramming the system demonstrated that it is 

a fully developed agricultural system with crop and 

livestock production, food processing, and animal waste 

processing components. This system is a high desert 

and may be under additional pressure due to climate 

change if drier, warmer years become more frequent in 

the future, which could threaten unique natural features 

like the sand dunes, local wetlands, and wildlife refuges. 

Farming in the region may come under pressure and 

the area irrigated may have to contract to meet the 

diminished capacity of the annual water budget. The 

alternative of drawing excess water for agriculture from 

groundwater storage may be a short-term solution to deal 

with shortages. Long term, however, such a solution 

poses a potential recipe for disaster, not only for the 

agricultural system, but also for the SLB as a whole. 

Examination of Fraction of Renewable Emergy to 

Total Emergy Used revealed movement away from 

sustainability. Renewable emergy use declined 4% over 

the 11-year study period, and only about half of the 

present population could be supported at their standard 

of living on renewable emergy alone, indicating that the 

region as a whole is moving away from sustainability. 

Social and economic systems in the SLB have been 

growing over the period examined in this study and 

we expect that this trend will continue in the future as 

the region becomes a center for the development of 

renewable energy. The EmA showed there are pulses 

within the general growth trend of the economy and the 

emergy exported experienced declines followed by rapid 

increases that make the overall trend irregular. Even 

though the SLB receives 31-51% of its emergy base 

from renewable sources, it depends on the larger system 

for the purchase of the majority of the emergy used; and 

therefore, it is subject to larger national trends. Perhaps, 

the national and global trend that is most likely to affect 

the SLB in the near future is the current movement 

toward developing renewable energy sources. The SLB 

has a rich potential for future development of solar, 

wind, and geothermal energy and could rely more on 

renewable sources of power in the future.
87

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Available online at http://www.slvdrg.org/ceds.php . 

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Siena Province through ecodynamic indicators. 

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89

Table 5.1 – Tabular format for an emergy analysis, providing a template for the calculation of the emergy values.

Column 1 Column 2 Column 3 Column 4 Column 5

Note Item Data Solar Emergy/Unit Solar Emergy

 J, g, $ sej/J, sej/g, sej/$ sej, sej/y

Table 5.2 – Definition of pathway flows for the systems model of the San Luis Basin (seven county area), Colorado 

shown in Fig. 5.1.

Pathway Definition of Flow

R0 Albedo 

R1 Wind passing through the Region

k0 Solar radiation absorbed by the Region

k1 Solar radiation absorbed by agricultural crops

k2 Solar radiation absorbed by natural vegetation (shrubs)

k3 Solar radiation absorbed by forests

k4 Wind energy absorbed by agricultural crops

k5 Wind energy absorbed by natural vegetation (shrubs)

k6 Wind energy absorbed by forests

k7 Wind energy absorbed by sand flats and dunes

k8 Rain and snow falling on agricultural crops

k9 Rain and snow falling on natural vegetation (shrubs)

k10 Rain and snow falling on forests

k11 Snow falling in the mountains

k12 River water flowing out of the SLB

k13 Runoff to rivers from the forests

k14 Infiltration from forests to groundwater

k15 Infiltration from rivers in forestland to groundwater

k16 Groundwater uptake by forest vegetation

k17 Nutrient and rainwater uptake by the forest

k18 Evapotranspiration by the forest 

k19 Snow melt feeding mountain rivers

k20 Sublimation of snow

k21 Groundwater loss to deeper levels

k22 Evapotranspiration from rivers in mountains

k23 Water use for mining and processing ores

k24 Forest biomass growth

k25 Timber harvest

k26 River flow crucial to dunes formation

k27 Erosion of mountains

k28 Wind erosion of dunes

k29 Groundwater flow from mountains to the valley

k30 River flow from mountains to rivers in valley Shrub land

k31 River flow from mountains to agricultural land

k32 Runoff to rivers from the shrub land

k33 Infiltration from shrub land to groundwater

k34 Infiltration from lakes and rivers in shrub land to groundwater

k35 Groundwater uptake by shrub land vegetation

k36 Nutrient and rainwater uptake by the shrub vegetation

k37 Evapotranspiration by the shrub vegetation

k38 Shrub biomass growth

k39 Evapotranspiration from rivers and lakes in Shrub land

k40 Groundwater loss to deeper levels

k41 Groundwater flow from agricultural areas to the closed basin
90

Pathway Definition of Flow

k42 Surface water use in the region

k43 Water use by production manufacturing

k44 Runoff to rivers from the agricultural land

k45 Infiltration from agricultural land to groundwater

k46 Infiltration from rivers in farmland to groundwater

k47 Groundwater and river water used to irrigate crops

k48 Groundwater loss to deeper levels

k49 Groundwater flow to New Mexico

k50 Water and nutrients taken up by crops

k51 Crop biomass growth

k52 Evapotranspiration by crops

k53 Evapotranspiration of water in rivers

k54 Livestock biomass growth

k55 Crops eaten by livestock

k56 Water used by livestock

k57 Waste produced by livestock

k58 Waste produced by crops

k59 Livestock processed or shipped

k60 Crops processed or shipped

k61 Geologic processes building landform and mineral deposits

k62 Sand mined and processed

k63 Crushed rock mined and processed

k64 Minerals mined and processed

k65 Electricity and fuels used by the mining industry

k66 Goods and services used by the mining industry 

k67 Government control of mining

k68 Mining industry inputs to manufacturing and construction

k69 Waste produced by mining and processing ores

k70 Knowledge and labor used in the mining industry

k71 Water use by food processing 

k72 Water use by service and commerce

k73 Water use by manufacturing and construction

k74 Water use by the recreational systems

k75 Fuels and electricity input to the recreational systems

k76 Goods and services input to recreational systems

k77 Government regulation of recreational systems

k78 Human knowledge and labor used by recreational systems

k79 Transport of fuels and electricity into the State

k80 Transport of goods and services into the State

k81 Government regulation of transportation

k82 Human knowledge and labor used in the transportation sector

k83 Goods and services input to the transportation sector

k84 Fuels and electricity input to the transportation sector

k85 Fuels and electricity used by the government sector

k86 Goods and services input to the government sector

k87 Human knowledge and labor used in the government sector

k88 Federal government regulations 

k89 Federal taxes

k90 Federal outlays

k91 Money spent on fuels

k92 Money spent on goods and services

k93 Solar electricity generated in SLB joins the regional grid

k94 Fuels and electricity input to the power distribution system
91

Pathway Definition of Flow k95 Goods and services input to power distribution system k96 Government regulation of power distribution system k97 Human knowledge and labor used by power distribution system k98 Fuels and electricity input to education systems k99 Goods and services input to education systems k100 Government regulation of education k101 Human knowledge and labor used in the schools k102 Teaching k103 Learning k104 Increase in human knowledge and skills k105 Loss of information (knowledge and skills) k106 Gain of knowledge and skills with immigrants k107 Loss of knowledge and skills with emigrants k108 Government regulation of people k109 Goods and services used by people and households k110 Fuels and electricity used by people and households k111 Water used by people and households k112 Waste produced by people and households k113 Immigration k114 Emigration k115 Raw and processed ores exported k116 Manufactured products exported k117 Raw and processed food exported k118 Fuels and electricity used by production and manufacturing k119 Goods and services used by production and manufacturing k120 Government regulation of industry k121 Human knowledge and labor used in manufacturing k122 Waste produced by industry k123 Fuels and electricity used by food processing k124 Goods and services used by food processing k125 Government regulation of food processing industry k126 Human knowledge and labor used in food processing k127 Food processing inputs to manufacturing k128 Waste produced by food processing k129 Production and manufacturing inputs to service and commerce k130 Food processing inputs to service and commerce k131 Fuels and electricity used by service and commerce k132 Goods and services used by service and commerce k133 Government regulation of service and commerce k134 Human knowledge and labor used in service and commerce k135 Service and commerce used by tourists k136 Exports from the service and commerce sector k137 Tourists entering the State k138 Tourists leaving the State k139 Money gained from the sale of products and services k140 Money spent by tourists k141 Effects of wastes on forests k142 Effects of wastes on shrub land k143 Effects of wastes on agricultural lands k144 Wastes leaving the Region in water or air k145 Residents using recreation and cultural resources
92

Table 5.3 – Summary of annual emergy flows in the San Luis Basin, 1995-2005. 

Note Symbol Item

1995 1996

Emergy

 (E+20 sej)

Dollars

 (E+6 $)

2000 Em$

(E+6 Em$)

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$

(E+6 Em$)

63 RA Renewable sources used 32.4 1379.8 28.8 1227.1

R1 Renewable Electric Produced 0.0 0.0 0.0 0.0

64 N Nonrenewable source flows 12.6 535.6 16.7 710.0

N′ Internal fuels and minerals extracted 47.6 2025.2 43.9 1869.6

65 N0 Dispersed Rural Source 1.2 52.6 5.3 227.1

66 N1 Concentrated use, minerals, hydroelectric) 11.4 483.1 11.3 482.967 N2 Exported without Use 43.2 1836.7 39.8 1695.768 F Imported Fuels, Minerals+U, Electric 6.9 294.6 7.3 309.0

69 F1 Fuels and minerals used (F+N1-Renewable&Electric) 11.4 483.1 11.3 482.9

70 F2 In region minerals used (F1- F) 4.4 188.5 4.1 173.9

71 G Imported Goods (materials) 13.3 566.7 13.9 591.1

72 I Dollars Paid for all Imports 312.5 304.9

73 I1 Dollars Paid for Service in Fuels, Minerals, Electric 71.8 76.874 I2 Dollars Paid for Service in Goods 240.7 228.1

75 I3 Dollars Paid for Services 0.0 0.076 I4 Dollars Spent by Tourists 55.3 57.4

77 I5 Federal Transfer Payments 23.5 25.1

78 P2I Imported Services Total 8.1 346.0 7.9 336.9

79 P2I1 Imported Services in Fuels, Minerals, Electric 1.9 79.5 2.0 84.8

80 P2I2 Imported Services in Goods without fuels 6.3 266.5 5.9 252.181 P2I3 Imported Services 0.0 0.0 0.0 0.082 P1I4 Emergy Purchased by Tourists 1.4 61.2 1.5 63.483 P1I5 Net Emergy Purchased by Federal $ 0.0 0.0 0.0 0.084 B Exported Products without minerals 27.4 1164.0 31.5 1342.3

86 E Dollars Paid for All Exports 826.8 861.3

87 E1 Dollars Paid for Goods without minerals 736.8 777.7

88 E2 Dollars Paid for Minerals Exported 90.1 83.6

89 E3 Dollars Paid for Exported Services 0.0 0.0

90 E4 Federal Taxes Paid 0.0 0.091 P E2 Exported Services Total 20.7 21.8

92 P E2 1 Exported Services in Goods other than minerals 18.4 0.8 19.6 0.8

93 P E2 2 Exported Services in Minerals 2.3 0.1 2.2 0.194 P E3 2 Exported services 0.0 0.0 0.0 0.0

95 X Gross Regional Product 1161.8 1229.6

96 P2 Emergy $ ratio for the US in 2000 sej/$ 2.4E+12 2.4E+12
93

Note Symbol Item

1997 1998

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$ 

(E+6 Em$)

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$ 

(E+6 Em$)

63 RA Renewable sources used 36.5 1555.0 32.1 1367.0

R1 Renewable Electric Produced 0.0 0.0 0.0 0.0

64 N Nonrenewable source flows 12.8 544.2 14.3 607.4

N’ Internal fuels and minerals extracted 47.0 2001.6 48.6 2069.6

65 N0 Dispersed Rural Source 1.3 56.9 2.5 104.7

66 N1 Concentrated use, minerals, hydroelectric) 11.5 487.3 11.8 502.6

67 N2 Exported without Use 42.7 1815.7 44.1 1877.5

68 F Imported Fuels, Minerals+U, Electric 7.1 301.5 7.3 310.6

69 F1 Fuels and minerals used (F+N -Renewable&Electric) 1 11.5 487.3 11.8 502.6

70 F2 In region minerals used (F - F) 1 4.4 185.8 4.5 192.0

71 G Imported Goods (materials) 14.6 620.1 14.8 630.1

72 I Dollars Paid for all Imports 315.8 323.0

73 I1 Dollars Paid for Service in Fuels, Minerals, Electric 76.4 72.9

74 I2 Dollars Paid for Service in Goods 239.5 250.2

75 I3 Dollars Paid for Services 0.0 0.076 I4 Dollars Spent by Tourists 59.3 61.8

77 I5 Federal Transfer Payments 24.5 24.078 P I2 Imported Services Total 8.1 343.8 8.0 339.179 P I2 1 Imported Services in Fuels, Minerals, Electric 2.0 83.1 1.8 76.5

80 P I2 2 Imported Services in Goods without fuels 6.1 260.6 6.2 262.6

81 P I2 3 Imported Services 0.0 0.0 0.0 0.0

82 P I1 4 Emergy Purchased by Tourists 1.5 64.5 1.5 64.9

83 P I1 5 Net Emergy Purchased by Federal $ 0.0 0.0 0.0 0.0

84 B Exported Products without minerals 31.3 1331.0 30.8 1309.4

86 E Dollars Paid for All Exports 937.9 3536.1

87 E1 Dollars Paid for Goods without minerals 847.0 3420.2

88 E2 Dollars Paid for Minerals Exported 90.9 116.0

89 E3 Dollars Paid for Exported Services 0.0 0.090 E4 Federal Taxes Paid 0.0 0.091 P E2 Exported Services Total 23.2 23.4

92 P E2 1 Exported Services in Goods other than minerals 20.8 0.9 21.0 0.9

93 P E2 2 Exported Services in Minerals 2.3 0.1 2.3 0.1

94 P E2 3 Exported services 0.0 0.0 0.0 0.095 X Gross Regional Product 1354.1 1385.7

96 P2 Emergy $ ratio for the US in 2000 sej/$ 2.4E+12 2.4E+12
94

Note Symbol Item

1999 2000

Emergy

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$ 

(E+6 Em$)

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$ 

(E+6 Em$)

63

RA Renewable sources used 32.2 1370.5 29.6 1261.6

R1 Renewable Electric Produced 0.0 0.0 0.0 0.0

64

N Nonrenewable source flows 11.8 501.3 16.9 718.5

N′ Internal fuels and minerals extracted 34.6 1472.0 35.8 1524.9

65

N0 Dispersed Rural Source 1.3 57.0 5.9 251.2

66

N1 Concentrated use, minerals, hydroelectric) 10.4 444.3 11.0 467.2

67

N2 Exported without Use 31.5 1339.9 32.6 1386.7

68 F Imported Fuels, Minerals+U, Electric 7.3 312.2 7.7 328.969 F1 Fuels and minerals used (F+N -Renewable&Electric) 1 10.4 444.3 11.0 467.2

70 F2 In region minerals used (F1- F) 3.1 132.1 3.2 138.3

71

G Imported Goods (materials) 18.2 776.3 21.3 905.3

72 I Dollars Paid for all Imports 357.5 387.873 I

1 Dollars Paid for Service in Fuels, Minerals, Electric 77.6 96.0

74 I

2 Dollars Paid for Service in Goods 279.9 291.8

75 I

3 Dollars Paid for Services 0.0 0.0

76 I

4 Dollars Spent by Tourists 63.8 66.0

77 I

5 Federal Transfer Payments 24.2 25.4

78 P I2 Imported Services Total 8.2 351.0 9.1 387.5

79 P I2 1 Imported Services in Fuels, Minerals, Electric 1.8 76.2 2.3 95.980 P I2 2 Imported Services in Goods without fuels 6.5 274.8 6.9 291.6

81 P I2 3 Imported Services 0.0 0.0 0.0 0.0

82 P I1 4 Emergy Purchased by Tourists 1.5 62.6 1.5 66.083 P I1 5 Net Emergy Purchased by Federal $ 0.0 0.0 0.0 0.084

B Exported Products without minerals 27.3 1160.3 27.2 1155.6

86

E Dollars Paid for All Exports 962.1 876.5

87

E1 Dollars Paid for Goods without minerals 867.1 808.7

88

E2 Dollars Paid for Minerals Exported 94.9 67.9

89

E3 Dollars Paid for Exported Services 0.0 0.0

90

E4 Federal Taxes Paid 0.0 0.0

91 P E2 Exported Services Total 20.2 19.5

92 P E2 1 Exported Services in Goods other than minerals 18.7 0.8 17.7 0.8

93 P E2 2 Exported Services in Minerals 1.6 0.1 1.8 0.194 P E3 2 Exported services 0.0 0.0 0.0 0.095

X Gross Regional Product 1486.0 1590.9

96 P2 Emergy $ ratio for the US in 2000 sej/$ 2.4E+12 2.4E+12
95

Note Symbol Item

2001 2002

Emergy

(E+20 sej)

Dollars

(E+6 $)

2000 Em$

(E+6 Em$)

Emergy

(E+20 sej)

Dollars

(E+6 $)

2000 Em$

(E+6 Em$)

63 RA Renewable sources used 30.3 1289.4 22.5 956.9

R1 Renewable Electric Produced 0.0 0.0 0.0 0.0

64 N Nonrenewable source flows 13.0 551.3 19.9 845.5

N′ Internal fuels and minerals extracted 41.3 1758.6 37.4 1593.6

65 N0 Dispersed Rural Source 1.2 50.5 9.1 387.0

66 N1 Concentrated use, minerals, hydroelectric) 11.8 500.7 10.8 458.6

67 N2 Exported without Use 37.7 1602.4 34.7 1476.5

68 F Imported Fuels, Minerals+U, Electric 8.1 344.6 8.0 341.569 F1 Fuels and minerals used (F+N -Ren.&Elec.) 1 11.8 500.7 10.8 458.6

70 F2 In region minerals used (F1- F) 3.7 156.2 2.8 117.1

71 G Imported Goods (materials) 21.3 905.3 22.0 937.072 I Dollars Paid for all Imports 402.2 394.3

73 I1 Dollars Paid for Service in Fuels, Minerals, Electric 106.3 93.4

74 I2 Dollars Paid for Service in Goods 295.9 300.9

75 I3 Dollars Paid for Services 0.0 0.076 I4 Dollars Spent by Tourists 69.7 67.077 I5 Federal Transfer Payments 26.6 27.178 P I2 Imported Services Total 8.8 374.4 8.2 346.9

79 P I2 1 Imported Services in Fuels, Mineral, Electric 2.3 99.0 1.9 82.280 P I2 2 Imported Services in Goods without fuels 6.5 275.4 6.2 264.7

81 P I2 3 Imported Services 0.0 0.0 0.0 0.0

82 P I1 4 Emergy Purchased by Tourists 1.5 64.9 1.4 58.983 P I1 5 Net Emergy Purchased by Federal $ 0.0 0.0 0.0 0.084 B Exported Products without minerals 19.9 845.8 17.7 753.686 E Dollars Paid for All Exports 654.0 718.9

87 E1 Dollars Paid for Goods without minerals 566.1 648.588 E2 Dollars Paid for Minerals Exported 88.0 70.489 E3 Dollars Paid for Exported Services 0.0 0.090 E4 Federal Taxes Paid 0.0 0.091 P E2 Exported Services Total 12.1 14.9

92 P E2 1 Exported Services in Goods other than minerals 10.1 0.4 13.4 0.6

93 P E2 2 Exported Services in Minerals 1.9 0.1 1.5 0.194 P E3 2 Exported services 0.0 0.0 0.0 0.095 X Gross Regional Product 1600.3 1630.2

96 P2 Emergy $ ratio for the US in 2000 sej/$ 2E+12 2E+12
96

Note Symbol

2003 2004

Item

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$

(E+6 Em$)

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$

(E+6 Em$)

63 RA Renewable sources used 28.5 1211.7 31.0 1318.7

R1 Renewable Electric Produced 0.0 0.0 0.0 0.0

64 N Nonrenewable source flows 14.3 610.0 12.3 523.7

N′ Internal fuels and minerals extracted 23.7 1007.6 31.3 1331.3

65 N0 Dispersed Rural Source 4.0 171.4 1.5 62.2

66 N1 Concentrated use, minerals, hydroelectric) 10.3 438.6 10.8 461.4

67 N2 Exported without Use 21.4 911.3 28.8 1225.0

68 F Imported Fuels, Minerals+U, Electric 8.0 342.2 8.3 355.2

69 F1 Fuels and minerals used (F+N -Renewable&Electric) 1 10.3 438.6 10.8 461.4

70 F2 In region minerals used (F1- F) 2.3 96.4 2.5 106.3

71 G Imported Goods (materials) 15.3 650.3 16.5 704.2

72 I Dollars Paid for all Imports 371.5 379.0

73 I1 Dollars Paid for Service in Fuels, Minerals, Electric 105.9 127.4

74 I2 Dollars Paid for Service in Goods 265.6 251.675 I3 Dollars Paid for Services 0.0 0.076 I4 Dollars Spent by Tourists 71.3 73.6

77 I5 Federal Transfer Payments 29.8 0.0

78 P I2 Imported Services Total 7.4 316.9 7.7 327.7

79 P I2 1 Imported Services in Fuels, Minerals, Electric 2.1 90.3 2.6 110.280 P I2 2 Imported Services in Goods without fuels 5.3 226.5 5.1 217.5

81 P I2 3 Imported Services 0.0 0.0 0.0 0.0

82 P I1 4 Emergy Purchased by Tourists 1.4 60.8 1.5 63.683 P I1 5 Net Emergy Purchased by Federal $ 0.0 0.0 0.0 0.084 B Exported Products without minerals 18.5 786.2 62.3 2652.8

86 E Dollars Paid for All Exports 933.3 3536.187 E1 Dollars Paid for Goods without minerals 885.9 3420.2

88 E2 Dollars Paid for Minerals Exported 47.4 116.089 E3 Dollars Paid for Exported Services 0.0 0.0

90 E4 Federal Taxes Paid 0.0 0.091 P E2 Exported Services Total 18.7 71.8

92 P E2 1 Exported Services in Goods other than minerals 17.8 0.8 69.5 3.0

93 P E2 2 Exported Services in Minerals 1.0 0.0 2.4 0.1

94 P E3 2 Exported services 0.0 0.0 0.0 0.095 X Gross Regional Product 1709.5 1849.8

96 P2 Emergy $ ratio for the US in 2000 sej/$ 2E+12 2E+12
97

Note Symbol Item

2005

Emergy 

(E+20 sej)

Dollars 

(E+6 $)

2000 Em$

(E+6 Em$)

63 RA Renewable sources used 29.9 1272.5

R1 Renewable Electric Produced 0.0 0.0

64 N Nonrenewable source flows 12.0 511.6

N′ Internal fuels and minerals extracted 31.1 1325.0

65 N0 Dispersed Rural Source 1.3 53.3

66 N1 Concentrated use, minerals, hydroelectric) 10.8 458.267 N2 Exported without Use 28.7 1221.068 F Imported Fuels, Minerals+U, Electric 8.3 354.369 F1 Fuels and minerals used (F+N -Renewable&Electric) 1 10.8 458.2

70 F2 In region minerals used (F - F) 1 2.4 103.971 G Imported Goods (materials) 17.0 721.8

72 I Dollars Paid for all Imports 416.3

73 I1 Dollars Paid for Service in Fuels, Minerals, Electric 155.2

74 I2 Dollars Paid for Service in Goods 261.175 I3 Dollars Paid for Services 0.076 I4 Dollars Spent by Tourists 73.1

77 I5 Federal Transfer Payments 0.0

78 P I2 Imported Services Total 8.0 338.4

79 P I2 1 Imported Services in Fuels, Minerals, Electric 3.0 126.180 P I2 2 Imported Services in Goods without fuels 5.0 212.281 P I2 3 Imported Services 0.0 0.082 P I1 4 Emergy Purchased by Tourists 1.4 59.483 P I1 5 Net Emergy Purchased by Federal $ 0.0 0.084 B Exported Products without minerals 67.2 2861.686 E Dollars Paid for All Exports 3788.4

87 E1 Dollars Paid for Goods without minerals 3667.0

88 E2 Dollars Paid for Minerals Exported 121.4

89 E3 Dollars Paid for Exported Services 0.0

90 E4 Federal Taxes Paid 0.0

91 P E2 Exported Services Total 72.4

92 P E2 1 Exported Services in Goods other than minerals 70.0 3.0

93 P E2 2 Exported Services in Minerals 2.3 0.194 P E3 2 Exported services 0.0 0.095 X Gross Regional Product 1975.7

96 P2 Emergy $ ratio for the US in 2000 sej/$ 2E+12
98

Table 5.4 – Emergy indices and indicators for the San Luis Basin, 1995-2005. Our focus for this study was on Item 106. 

Item Name of Index Expression Units†

1995

(x E+20)*

1996

(x E+20)*

1997

(x E+20)*

97 Renewable Use RA sej y-1 32.43 28.84 36.54

98 In Region Non-renewable Use N0+N1 sej y-1 12.59 16.68 12.79

99 Imported Emergy F+G+P2I sej y-1 28.37 29.07 29.73

100 Total Emergy Inflows R+F+G+P2I sej y-1 60.80 57.91 66.28

101 Total Emergy Used U=(RA+N0+F1+G+P2I) sej y-1 66.46 67.33 71.98

102 Total Exported Emergy B+P2E+N2 sej y-1 91.25 93.21 97.12

103 Emergy used from Home Sources (N0+F2+R)/U 0.57 0.57 0.59

104 Imports-Exports (F+G+P2I)- (B+P2E+N2) sej y-1 -62.87 -64.14 -67.38

105 Ratio of Exports to Imports (B+P1E+N2)/ (F+G+P2I) 3.22 3.21 3.27

106 Fraction Used, Locally Renewable R/U 0.49 0.43 0.51

107 Fraction of Use Purchased Outside (F+G+P2I)/U 0.43 0.43 0.41

108 Fraction Used, Imported Services P2I/U 0.12 0.12 0.11109 Fraction of Use that is Free (R+N0)/U 0.51 0.51 0.53

110 Ratio of Purchased to Free (F1+G+P2I)/(R+N0) 0.97 0.97 0.90

111 Environmental Loading Ratio (F1+N0+G+P2I)/R 1.05 1.33 0.97

112 Investment Ratio (F+G+P2I)/(R+N0+F2) 0.74 0.76 0.70

113 Use per Unit Area U/Area sej/m2 2.76E+11 2.79E+11 2.99E+11114 Use per Person U/Population sej/pers. 1.52E+17 1.51E+17 1.59E+17

115 Renewable Carrying Capacity (R/U)*Population people 21366 19088 22992

116 Developed Carrying Capacity 8*(R/U)*Population people 170926 152703 183940

117 SLB Gross Regional Product GRP 1 $/yr 1.16E+09 1.23E+09 1.35E+09

118 Ratio of SLB Emergy Use to GRP U/GRP sej/$ 5.72E+12 5.48E+12 5.32E+12119 Ratio of U.S. Emergy Use to GDP U/GDP sej/$ 2.60E+12 2.60E+12 2.56E+12120 Ratio of Electricity Use /Emergy Use El/U J/sej 0.037 0.038 0.037

121 Fuel Use per Person F2/Population sej/pers. 1.00E+16 1.02E+16 9.73E+15122 Population Population people 43793 44566 45289

123 Area Area m2 2.41E+10

124 Renewable empower density RA/Area sej m-2 1.34E+11 1.20E+11 1.52E+11

125 Emergy Yield Ratio (B+P1E+N2)/ (F+G+P2I) 3.22 3.21 3.27

126 Emergy Index of Sustainability EYR/ELR 3.06 2.40 3.37

127 Local Effect of Investment U/(F+G+P2I) 2.34 2.32 2.42

* Units in 1023 except as noted. † Where the “Units” column is blank, the indicators are dimensionless. 

1 See Appendix 5-G for estimation of GRP.
99

Item Name of Index Expression Units†

1998

(x E+20)*

1999

(x E+20)*

2000

(x E+20)*

97 Renewable Use RA sej y-1 32.12 32.21 29.65

98 In Region Non-renewable Use N0+N1 sej y-1 14.27 11.78 16.88

99 Imported Emergy F+G+P2I sej y-1 30.07 33.83 38.11

100 Total Emergy Inflows R+F+G+P2I sej y-1 62.20 66.03 67.76

101 Total Emergy Used U=(RA+N0+F1+G+P2I) sej y-1 69.17 70.48 76.91102 Total Exported Emergy B+P2E+N2 sej y-1 98.25 78.98 79.24103 Emergy used from Home Sources (N0+F2+R)/U 0.57 0.52 0.50104 Imports-Exports (F+G+P2I)- (B+P2E+N2) sej y-1 -68.18 -45.16 -41.12

105 Ratio of Exports to Imports (B+P1E+N2)/ (F+G+P2I) 3.27 2.33 2.08106 Fraction Used, Locally Renewable R/U 0.46 0.46 0.39107 Fraction of Use Purchased Outside (F+G+P2I)/U 0.43 0.48 0.50108 Fraction Used, Imported Services P2I/U 0.12 0.12 0.12109 Fraction of Use that is Free (R+N0)/U 0.50 0.48 0.46

110 Ratio of Purchased to Free (F1+G+P2I)/(R+N0) 1.00 1.10 1.16

111 Environmental Loading Ratio (F1+N0+G+P2I)/R 1.15 1.19 1.59

112 Investment Ratio (F+G+P2I)/(R+N0+F2) 0.77 0.92 0.98113 Use per Unit Area U/Area sej/m2 2.87E+11 2.92E+11 3.19E+11

114 Use per Person U/Population sej/pers. 1.51E+17 1.52E+17 1.63E+17

115 Renewable Carrying Capacity (R/U)*Population people 21317 21193 18154

116 Developed Carrying Capacity 8*(R/U)*Population people 170536 169544 145234

117 SLB Gross Regional Product 1 GRP $/yr 1.39E+09 1.49E+09 1.59E+09

118 Ratio of SLB Emergy Use to GRP U/GRP sej/$ 4.99E+12 4.74E+12 4.83E+12

119 Ratio of U.S. Emergy Use to GDP U/GDP sej/$ 2.47E+12 2.31E+12 2.35E+12

120 Ratio of Electricity Use /Emergy Use El/U J/sej 0.039 0.039 0.037121 Fuel Use per Person F2/Population sej/pers. 9.94E+15 9.90E+15 1.03E+16

122 Population Population people 45902 46377 47097123 Area Area m2

124 Renewable empower density RA/Area sej m-2 1.33E+11 1.34E+11 1.23E+11

125 Emergy Yield Ratio (B+P1E+N2)/ (F+G+P2I) 3.27 2.33 2.08126 Emergy Index of Sustainability EYR/ELR 2.83 1.96 1.30127 Local Effect of Investment U/(F+G+P2I) 2.30 2.08 2.02

* Units in 1023 except as noted. † Where the “Units” column is blank, the indicators are dimensionless.

1 See Appendix 5-G for estimation of GRP.
100

Item Name of Index Expression Units†

2001

(x E+20)*

2002

(x E+20)*

2003

(x E+20)*

97 Renewable Use RA sej y-1 30.30 22.49 28.47

98 In Region Non-renewable Use N0+N1 sej y-1 12.96 19.87 14.33

99 Imported Emergy F+G+P2I sej y-1 38.17 38.20 30.77

100 Total Emergy Inflows R+F+G+P2I sej y-1 68.47 60.68 59.24

101 Total Emergy Used U=(RA+N0+F1+G+P2I) sej y-1 73.33 72.53 65.54

102 Total Exported Emergy B+P2E+N2 sej y-1 69.59 67.27 58.60

103 Emergy used from Home Sources (N0+F2+R)/U 0.48 0.47 0.53

104 Imports-Exports (F+G+P2I)- (B+P2E+N2) sej y-1 -31.42 -29.07 -27.83

105 Ratio of Exports to Imports (B+P1E+N2)/ (F+G+P2I) 1.82 1.76 1.90

106 Fraction Used, Locally Renewable R/U 0.41 0.31 0.43

107 Fraction of Use Purchased Outside (F+G+P2I)/U 0.52 0.53 0.47

108 Fraction Used, Imported Services P2I/U 0.12 0.11 0.11109 Fraction of Use that is Free (R+N0)/U 0.43 0.44 0.50

110 Ratio of Purchased to Free (F1+G+P2I)/(R+N0) 1.33 1.30 1.02

111 Environmental Loading Ratio (F1+N0+G+P2I)/R 1.42 2.23 1.30

112 Investment Ratio (F+G+P2I)/(R+N0+F2) 1.09 1.11 0.89

113 Use per Unit Area U/Area sej/m2 3.04E+11 3.01E+11 2.72E+11114 Use per Person U/Population sej/pers. 1.56E+17 1.53E+17 1.38E+17

115 Renewable Carrying Capacity (R/U)*Population people 19383 14698 20681

116 Developed Carrying Capacity 8*(R/U)*Population people 155064 117581 165445

117 SLB Gross Regional Product GRP 1 $/yr 1.60E+09 1.63E+09 1.71E+09

118 Ratio of SLB Emergy Use to GRP U/GRP sej/$ 4.58E+12 4.45E+12 3.83E+12

119 Ratio of U.S. Emergy Use to GDP U/GDP sej/$ 2.19E+12 2.07E+12 2.00E+12

120 Ratio of Electricity Use /Emergy Use El/U J/sej 0.039 0.041 0.046

121 Fuel Use per Person F2/Population sej/pers. 1.11E+16 1.06E+16 1.06E+16

122 Population Population people 46907 47404 47598

123 Area Area m2

124 Renewable empower density RA/Area sej m-2 1.26E+11 9.33E+10 1.18E+11125 Emergy Yield Ratio (B+P1E+N2)/ (F+G+P2I) 1.82 1.76 1.90

126 Emergy Index of Sustainability EYR/ELR 1.28 0.79 1.46

127 Local Effect of Investment U/(F+G+P2I) 1.92 1.90 2.13

* Units in 1023 except as noted. † Where the “Units” column is blank, the indicators are dimensionless.

1 See Appendix 5-G for estimation of GRP.
101

Item Name of Index Expression Units†

2004

(x E+20)*

2005

(x E+20)*

97 Renewable Use RA sej y-1 30.99 29.9098 In Region Non-renewable Use N0+N1 sej y-1 12.31 12.0299 Imported Emergy F+G+P2I sej y-1 32.60 33.24100 Total Emergy Inflows R+F+G+P2I sej y-1 63.58 63.14101 Total Emergy Used U=(RA+N0+F1+G+P2I) sej y-1 67.54 66.84102 Total Exported Emergy B+P2E+N2 sej y-1 162.97 168.30103 Emergy used from Home Sources (N0+F2+R)/U 0.52 0.50104 Imports-Exports (F+G+P2I)- (B+P2E+N2) sej y-1 -130.37 -135.06

105 Ratio of Exports to Imports (B+P1E+N2)/ (F+G+P2I) 5.00 5.06

106 Fraction Used, Locally Renewable R/U 0.46 0.45107 Fraction of Use Purchased Outside (F+G+P2I)/U 0.48 0.50108 Fraction Used, Imported Services P2I/U 0.11 0.12

109 Fraction of Use that is Free (R+N0)/U 0.48 0.47110 Ratio of Purchased to Free (F1+G+P2I)/(R+N0) 1.08 1.15111 Environmental Loading Ratio (F1+N0+G+P2I)/R 1.18 1.24112 Investment Ratio (F+G+P2I)/(R+N0+F2) 0.93 0.99113 Use per Unit Area U/Area sej/m2 2.80E+11 2.77E+11

114 Use per Person U/Population sej/pers. 1.40E+17 1.39E+17

115 Renewable Carrying Capacity (R/U)*Population people 22117 21520

116 Developed Carrying Capacity 8*(R/U)*Population people 176938 172157

117 SLB Gross Regional Product GRP 1 $/yr 1.85E+09 1.98E+09

118 Ratio of SLB Emergy Use to GRP U/GRP sej/$ 3.65E+12 3.38E+12

119 Ratio of U.S. Emergy Use to GDP U/GDP sej/$ 2.03E+12 1.91E+12

120 Ratio of Electricity Use /Emergy Use El/U J/sej 0.044 0.046

121 Fuel Use per Person F2/Population sej/pers. 1.10E+16 1.09E+16

122 Population Population people 48207 48101

123 Area Area m2

124 Renewable empower density RA/Area sej m-2 1.29E+11 1.24E+11

125 Emergy Yield Ratio (B+P1E+N2)/ (F+G+P2I) 5.00 5.06

126 Emergy Index of Sustainability EYR/ELR 4.24 4.10127 Local Effect of Investment U/(F+G+P2I) 2.07 2.01

* Units in 1023 except as noted. † Where the “Units” column is blank, the indicators are dimensionless.

1 See Appendix 5-G for estimation of GRP.
102

Figure 5.1 – An Energy Systems Diagram of the San Luis Basin showing the major features of this environmental system.

x x

Mountains

Valley

Wind

State & 

Federal 

Gov.

Markets

People

Forest

Manufacturing

Construction

Solar

People & 

Households

Electricity

Distribution

(Solar Power Plant)

Geologic

Processes

San Luis Basin

Tourists

Mining &

Processing

Service & 

Commerce

Transportation

Goods & 

Services

Education 

Systems

Waste

GRP

Soil

Shrub Land

W

Lakes & 

Rivers

Soil

ET

Rivers

Food Processing 

and Shipment

Fuels

Electricity

Government

Knowledge

W

W

W

S

S

S

k0

k

k

1

3

k

k5

6

R0

R1

k12

k8

k9

k13

k11

k10

k15

k17

k18

k14

k4

Rain &

Snow

k19

k20

k

k

24

25

k27

k26

k31

k34

k32

k40

k44

k43

k46

k47

k51

k57 k60

k71

k72

k52

k53

k65

k86

k66

k68

k67

k69

k70

k81

k82 k83 k84

k85

k79

k80

k87

k88

k89

k90

k91

k92

k93

k142

k97

k107

k98

k99

k100

k101

k102

k103

k104

k105

k106

k108

k109

k110

k111 k114

k113

k115

k112

k116

k117

k120

k118 k119

k121

k122

k123 k124 k125

k126

k127

k128 k129

k130

k131 k132 k133 k134

k135

k136

k139

k138

k137

k140

k

k

143

141

k

k

144

145

Snow

Sand 

Flats

Minerals Rock

Dunes

Groundwater

Biomass

Soil

Mountains

Valley, Natural

Valley, Agriculture

Rivers

Groundwater

Groundwater

Biomass

Livestock

Crops

To New Mexico

P

P

G. S. Dunes

National Park

Other Recreation

k2

k7

k16

Biomass

k21

k23

k22 - k28

k29

k30

k33 k35 k36

k37

k38

k39

k41

k42

k43

k45

k48 k49

k50

To New Mexico

Biomass

k54

k55

k56

k58

k59

k61

k62 k63

k64

k94

TR

TR

R

R

k96

k95

k73

k74

k75

k76

k77 k78

T

T

T
103

Figure 5.2 – An aggregated model used to calculate summary variables and indices for the San Luis Basin system from 

1995 to 2005. Year 2000 is presented here as an example. 

Fuels Goods

Sun 

Wind 

Rivers 

Rain Environment

Soil

Groundwater

Minerals 

Used Nonrenewably

E20 sej/yr 

E6 $/yr

GRP

Markets

Tourists

Services

Federal 

Gov’t

Colorado, U.S. 

Economies

X

RA

N0

N1'

F1

F G

P2I

B

P2E3

Economy

San Luis Basin, 2000

29.7

1590.9

5.9

35.9

7.7 21.3

I

387.8

32.6

N2

11.0

I1

96.0

I2

291.8

I3

4.2

I4

66

I5 25.4 

9.1

P2I3

?

P2I2

6.85

P2I1 2.3

27.2

E1

808.7

P2E2 1.8

?

E2

67.9

E3

E4

?

17.7

P2E1

Figure 5.3 – Total emergy used and renewable emergy used in the San Luis Basin from 1995 to 2005.
104

Figure 5.4 – The total emergy used per person in the San Luis Basin from 1995 to 2005.

Figure 5.5 – Emergy inflows from renewable sources, imported fuels, electricity, goods and services, emergy outflows 

in exported materials, the services in material products, services, and nonrenewable resources exported directly in the 

San Luis Basin, 1995-2005. 
105

Figure 5.6 – A comparison of the total emergy used by the system with the renewable, local nonrenewable and 

imported emergy that contribute to it in the San Luis Basin, 1995-2005.

Figure 5.7 – Fraction of renewable emergy to total emergy use in the San Luis Basin from 1995 to 2005.

0

1E+21

2E+21

3E+21

4E+21

5E+21

6E+21

7E+21

8E+21

9E+21

1995 1997 1999 2001 2003 2005

Empower (sej y-1)

Renewable Emergy

Imported Emergy

Emergy Used

Nonrenewable Use from within the System

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1995 1997 1999 2001 2003 2005

Fraction

Fraction Used Locally Renewable
106

Figure 5.8 – The renewable emergy used per person in the SLB region from 1980 to 2005.

Figure 5.9 – The renewable emergy inputs to the San Luis Basin, 1980-2005. 
107

Figure 5.10 – The renewable emergy base for the San Luis Basin, 1980-2005. 

Figure 5.11 – Agricultural production in the San Luis Basin supported primarily by renewable resources, 1980-2005.
108

Figure 5.12 – Major categories of nonrenewable resources used and/or produced in the seven counties of the San Luis 

Basin, 1980-2005. 

Figure 5.13 – The emergy exported from and imported to the San Luis Basin from 1995 to 2005.
109

Figure 5.14 – Major categories of emergy imported into the San Luis Basin, 1980-2005.

Figure 5.15 – The categories of material goods other than fuels imported to the SLB from 1995 to 2005. 
110

Figure 5.16 – Emergy exported from the San Luis Basin, 1980-2005: totals and aggregate categories Total exports 

include total materials and total services exported. Total materials exported includes agricultural and forest products, 

livestock minerals, and all other materials. Services in materials exported includes services in services in minerals, all 

other materials, agricultural and forest products. Export of pure services was not evaluated and emigration is shown, 

but was not included in the total.

Figure 5.17 – Selected categories of agricultural, forest product, and mineral exports from the San Luis Basin, 1980-

2005. The “All Other Materials” and the services in that category are shown to facilitate further discussion.

0.0E+00

1.0E+21

2.0E+21

3.0E+21

4.0E+21

5.0E+21

6.0E+21

7.0E+21

1980 1985 1990 1995 2000 2005

Empower (sej y-1)

Potatoes Grain , Wheat, Barley, Oats

Livestock Broken Stone Or Riprap

Gravel Or Sand Misc Nonmetallic Minerals, Nec

Forest Products Total All Other Materials

Services in All Other Materials

0

2E+21

4E+21

6E+21

8E+21

1E+22

1.2E+22

1.4E+22

1.6E+22

1.8E+22

1980 1985 1990 1995 2000 2005

mpower (sej y-1)

Total Materials Exported Services in Material Exports

Total Exports Agricultural Crops 

Livestock Minerals Total

Forest Products Total All Other Materials

Services in Agricultural Products Services in Minerals

Services in Forest Products Services in All Other Materials

Emigration
111

Figure 5.18 – The six largest classes of exports contained in “All Other Materials” exported from the San Luis Basin, 

1995-2005.

Figure 5.19 – The emergy of fuels and electricity used per person in the San Luis Basin from 1980 to 2005.

1.45E+16

1.5E+16

1.55E+16

1.6E+16

1.65E+16

1.7E+16

1.75E+16

1980 1985 1990 1995 2000 2005

Emergy Used per Person (sej y - 1 capita- 1)

Fuels and Electricity Used per Person

0.0E+00

5.0E+20

1.0E+21

1.5E+21

2.0E+21

2.5E+21

3.0E+21

3.5E+21

1995 1997 1999 2001 2003 2005

Empower (sej y-1)

Misc Agricultural Chemicals Misc Food Preparations, Nec

Newspapers Misc Printed Matter

Meat, Fresh Or Chilled Animal By-prod,inedible
112

Figure 5.20 – Renewable emergy sources of the San Luis Basin (1980-2005) that are being used in a nonrenewable 

manner.

Figure 5.21 – The fraction of total emergy use that comes from local renewable emergy and the fraction of use from 

home sources in the San Luis Basin, 1995-2005. 
113

Appendix 5-A: Detailed Steps for 

Emergy Analysis

An emergy evaluation of the SLB was performed in 

seven steps. 

1. The spatial and temporal boundaries of the system 

under analysis were specified. System boundaries 

were chosen based on the research question to be 

answered, considering the issue to be studied. 

2. A diagram of a detailed Emergy Systems Model was 

created using the Energy Systems Language (ESL; 

Odum 1971). The diagram represented the main 

features of the system components and flows that 

exist within the specified boundaries. Performance of 

the second step required prior study of the system and 

data gathering from the literature and from meetings 

with experts on the SLB. 

3. Translated this knowledge of the system into an 

aggregated ESL diagram addressing the specific 

questions to be answered with the simplest possible 

model formulation. Simplification of the detailed 

model was done by aggregating components or 

flows of similar function, thus information was not 

discarded in this process. 

4. Emergy analysis tables were set-up and descriptions 

of the system’s sources, components, and pathways 

in the aggregated diagram were transferred to the 

tables where the calculations needed to evaluate these 

pathways quantitatively were compiled. 

5. Gathered and assembled the raw data needed to 

complete the emergy analysis tables along with the 

conversion factors (energy contents, transformities, 

specific emergies, etc.) needed to convert the raw data 

into emergy. 

6. After the raw data were converted into emergy, 

summary variables were defined and calculated using 

the aggregate diagram as a guide (Lu et al. 2007, 

Odum 1996). 

7. Calculated emergy indices representing various 

aspects of system structure and function and 

interpret the meaning of these indices relative to our 

knowledge of their values in other systems. The 

methods used to carry out an emergy analysis of West 

Virginia and Minnesota are presented in detail in two 

recent EPA publications, Campbell et al. (2005) and 

Campbell and Ohrt (2009). 

References

Campbell, D. E., Brandt-Williams, S., Meisch, M. 2005. 

Environmental accounting using emergy: evaluation of 

the state of West Virginia. EPA600/R-05/006. United 

States Environmental Protection Agency, Narragansett, 

Rhode Island.

Campbell, D.E., Ohrt, A. 2009. Environmental 

accounting using emergy: evaluation of Minnesota. 

USEPA Project Report, EPA/600/R-09/002, 138 pp.

Lu, H.F., Campbell, D.E., Chen, J., Qin, P., Ren, H. 

2007. Conservation and economic vitality of nature 

reserves: an emergy evaluation of the Yancheng 

Biosphere Reserve. Biological Conservation 139, 

415-438. 

Odum, H.T. 1971. An energy circuit language for 

ecological and social systems, in: Patten, B. (Ed.), 

Systems Analysis and Simulation in Ecology, Vol. 2, 

Academic Press, New York, pp. 139-211. 

Odum, H.T. 1996. Environmental Accounting: Emergy 

and Environmental Decision Making. John Wiley & 

Sons, New York.
114

Appendix 5-B: Additional Indices 

Calculated for the Emergy Analysis of 

the San Luis Basin

We present revised definitions for two aspects of the 

relationship between a socioeconomic system and its 

associated larger systems. First, the Emergy Yield Ratio 

(EYR) for a local system, often characterized as U/F, 

is redefined as Y/F (see original definition in Odum, 

1996), where Y or yield is equivalent to exports of the 

socioeconomic system. Second, the ratio formed by the 

total emergy flow in the system (U) and the feedback 

(F) from the larger system is defined as the Local 

Effect of Investment (LEI) or the effect of feedback 

from the larger system on the local system’s empower. 

This ratio is different from existing energy investment 

ratios , which look at the matching between purchased 

feedbacks and local renewable and nonrenewable 

resources. Table 5-B.1 and Fig. 5-B.1 present some 

emergy indicators and indices used in the analysis of the 

sustainability of regional environmental systems.

The EYR for the SLB was defined to be the emergy 

value of the exports divided by the feedback from the 

larger system, which in this case is represented by 

imports. This definition is more consistent with the 

original conception of the EYR (Odum 1996), which 

was used to characterize a production process, rather 

than with the commonly used definition of the EYR 

for a state nation, or region as the ratio of total system 

emergy use (U) divided by the feedback from the larger 

system. In this case, exports are the productive yield to 

the larger system received from the local system, thus 

the ratio of exports to imports is more appropriately 

termed the EYR of a regional system. The ratio of U 

to the feedback from the larger system is redefined as 

the local effect of investment, LEI, which represents the 

local changes in empower that result from investments 

by the larger system in the local system. It captures the 

local empower return on investment and as such it is a 

measure of the change in well-being of the local system 

as a function of emergy invested by the larger system. 

The redefined indices more accurately characterize 

both indices based on their function and lead to logical 

inferences about the welfare of both local and larger 

systems (Campbell 2008).

The Emergy Sustainability Index (ESI; Brown and 

Ulgiati, 1997) of the SLB is determined according 

to the variation of its two components, the EYR and 

the Environmental Loading Ratio (ELR; Fig. 5-B.2). 

Trends in these indices show that a slow increase in 

environmental loading combined with a slow decline in 

the EYR resulted in declining sustainability from 1997 

until 2002. After 2002, the ELR falls rapidly as the EYR 

increased rapidly resulting in a 5-fold increase in the ESI 

from 0.79 in 2002 to 4.1 in 2005. 

The balance of total emergy inflow and outflow is 

a new index, which we calculated for this study. It 

characterizes the macroscopic emergy balance by 

including all inflows and outflows across the system 

borders including the renewable emergy. It gives an 

overall balance of inflows and outflows, which one 

might expect to be approximately balanced for a system 

in a dynamic steady state. Although this flow is not large 

compared to the others evaluated in this study, the index 

in its current form does not include the emergy of water 

flowing out of the system as an outflow of renewable 

emergy. The pattern of this index shows that outflows 

exceed inflows in most years except in 2001 and 2003 

when the two are nearly in balance. This situation is in 

contrast to the export-import balance for which outflows 

always exceed inflows by a considerable margin. 

Internal resources used are not included in the inflows 

to the system, but can show up in outflow; thus, when 

outflow markedly exceeds inflow, it may indicate that 

capital storages within the system are being depleted. 

The ESI characterizes sustainability as the ratio of the 

EYR to the ELR, thus it has high values when the larger 

system receives larger yields from its investment in the 

local system, and when the intensity of nonrenewable 

emergy use to attain those yields is less. The ESI was 

above 2 in eight of the eleven years evaluated, which 

indicates the SLB would be classified as a sustainable 

system within the context established by Brown and 

Ulgiati (1997) and subsequent studies. However, the fact 

that this index falls below 2 from 2001 to 2003 raises 

concern the system may become less sustainable in the 

future. The ELR increased to a maximum in 2002 as the 

EYR declined to a minimum in the same year resulting 

in minimum sustainability in that year. The year 2002 

was a drought year in which the system was supported 

by a large, unsustainable use of groundwater, and after 

this time the ESI increased rapidly as a result of a large 

increase in EYR (i.e., during this period, for almost the 

same inputs of emergy from the larger system, there 

was a marked increase in the emergy of exports to the 

larger system). This increase could only come either 

from the system’s internal resources or from additional 

value added to imports by the improved application of 

local labor. Over the course of the 11 years examined, 

this index showed the system was moving toward 

sustainability, but this trend was determined entirely 

by the upsurge of exports in the final three years of the 

study.
115

A final index of the relative sustainability of the SLB 

is the renewable carrying capacity of the system (Table 

5.4). This index shows the system had the potential 

to support 21520 people out of the current (2005) 

population of 48101 at their present standard of living 

using renewable emergy inflows alone. This assumes 

that these inflows have not been further upgraded to 

support socio-economic activities (e.g., by generating 

electricity from solar or wind power). During the 11-

year period examined, there was a 4% decline in the 

renewable carrying capacity of the SLB from 48.7% to 

44.7% of the current population, but in 2002 only 31% 

of the people in the SLB could have been supported by 

the renewable emergy that was available in that year. 

This index shows the SLB was moving away from 

sustainability over the period examined and although 

the decline was moderate, it could become worse, if the 

natural precipitation regime is altered by regional climate 

change in a similar manner to that predicted for the State 

of New Mexico (2005). The percent renewable emergy 

used is the most important factor determining this index 

and thus the two indices give related results. 

Another look at system well-being is shown by the 

relationship between EYR, ESI, and LEI (Fig. 5-B.2). 

EYR is in an overall declining trend from 1998 until 

2002, and ESI and LEI are in a declining trend from 

1997 until 2002. Between 2002 and 2005, the EYR 

increased 2.87 times, the ESI increased 5.18 times, 

and the LEI increased 1.06 times. Although the EYR 

and ESI recover strongly after 2002, the total emergy 

use in the local system increases only slightly. The 

ESI, EYR, and LEI reflect the relative advantages and 

disadvantages that may exist in the relationship between 

the SLB and its larger system trading partners (imports). 

The pattern of the EYR and the ESI shows large benefits 

accruing to the larger system in the economic recovery 

that took place in the region from 2002 to 2005, but the 

LEI indicates that this increase resulted in only a slight 

increase (6%) in the efficacy of external investments in 

the region in increasing well-being in the SLB. 

The EYR shows the benefit to the larger system and 

ranged from a low of 1.76 times the emergy invested 

in 2002 to a high of 5.06 in 2005. From 1995 to 2005, 

the LEI shows that about 2 times the invested emergy 

flow was stimulated in the SLB by each unit of emergy 

invested from outside. This multiplier remained positive 

(1.90 to 2.42) over the 11-year time period and thus 

investment from the larger system has improved local 

well-being over the entire time. 

An examination of other emergy indices of sustainability 

indicated there was a movement away from 

sustainability in the SLB over the period from 1995 to 

2005. Renewable emergy use declined 4% over the 

11-year study period and approximately half of the 

present population could be supported at their current 

standard of living on renewable emergy alone. The 

ESI, which increased 34% from 1995 to 2005, provided 

perspective on the relationship between the SLB and its 

larger systems. From our results, it appears the SLB and 

its larger systems go through cycles of improving and 

declining sustainability and that pulsing apparently plays 

a role in this variability. 

References

Brown, M.T. Ulgiati, S. 1997. Emergy-based indices 

and ratios to evaluate sustainability: monitoring 

economics and technology toward environmentally 

sound innovation. Ecological Engineering 9, 51-69.

Campbell, E.T. 2008. Environmental accounting of 

natural capital and environmental services of the 

National Forest system. Master’s Thesis, Graduate 

School of the University of Florida, Gainesville, 

Florida, 227 pp. 

Odum, H.T. 1996. Environmental Accounting: Emergy 

and Environmental Decision Making. John Wiley & 

Sons, New York.

State of New Mexico. 2005. Potential Effects of 

Climate change on New Mexico. Agency Technical 

Work Group, State of New Mexico. Available online 

at http://www.nmenv.state.nm.us/aqb/cc/Potential_

Effects_Climate_Change_NM.pdf. Last accessed June 

11, 2010
116

Table 5-B.1 – Some emergy indicators and indices used in the analysis of the sustainability of regional environmental 

systems. Symbols and expressions refer to Fig. 5-B.1.

Name of Index Symbol or Expression Definition

Total Emergy Flow U Empower (emergy/time) of the regional network. A measure of the 

local system’s well-being.

Renewable Emergy Inflow Absorbed RA Renewable emergy inputs to the system without double counting.

Percent renewable RA/U A measure of the potential for sustainability of the present system. 

Feedback or investment from the larger 

system

F = M + S Feedback is the sum of material, M, and service, S, feedbacks.

Local Effect of Investment (LEI) U/F Effect of external investment on system empower.

Yield Y For a regional system Y is equivalent to exports, i.e., yield to larger 

systems.

Emergy Yield Ratio (EYR) Y/F Emergy return to the larger system on its investments. Y is equal to 

exports in the case of a local system and F is equivalent to imports. 

Nonrenewable Emergy Inflow N, N0 N, nonrenewable, i.e. fossil fuels, N0

, renewable sources being used in 

a nonrenewable manner, e.g., soil or groundwater used faster than their 

replacement rate.

Environmental Loading Ratio (ELR) (F+N+N0

)/R Potential effect of nonrenewable emergy use on the environment

Emergy Sustainability Index (ESI) EYR/ELR In this index sustainability increases with greater returns to the larger 

system and decreases with greater potential for environmental damage 

to the local system.

Figure 5-B.1 – An Energy Systems Diagram used to define the emergy indices (Table 5-B.1) to assess regional 

sustainability.
117

Figure 5-B.2 – The Emergy Sustainability Index (ESI = EYR/ELR) and its components the emergy yield ratio (EYR) 

and the Environmental Loading Ratio (ELR) in the San Luis Basin from 1995 to 2005. 
118

Appendix 5-C: Calculating Emergy 

per Unit Factors

To calculate emergy units, data are converted first to 

energy or mass units and then into emergy units by 

multiplying the values by the appropriate coefficients 

(the emergy per unit values). When only the 

transformity (emergy per unit available energy) of 

a quantity was available and the variable was given 

in units of mass, it was first converted to energy by 

multiplying by the energy per unit mass. Then it 

was converted into emergy by multiplying by the 

transformity. Quantities were aggregated by similar 

function into composite variables considered most 

important for defining key interactions. An example 

of the calculation process follows: crop production is 

reported as bushels of grain harvested. The bushels of 

a particular crop are converted to mass by multiplying 

by the average dry weight of a bushel of that type of 

grain. The mass of grain is converted to energy by 

multiplying by the energy content in joules per gram dry 

weight (gdwt). Next, the energy content of the grain is 

converted to emergy by multiplying by the transformity. 

Finally, the emergy of all grain crops are summed to 

obtain the total emergy of grain production. 

Accounting units of mass and volume are most often 

used to record the physical quantity of items used 

by society. Other physical units may be used for 

environmental data (e.g., velocity for wind, energy flux 

for solar radiation, heat flux for geothermal energy, 

etc.). In almost all cases, these original units can be 

converted to energy or mass using readily available 

formulae and/or average conversion factors (Campbell 

and Ohrt 2009). The mass of a bushel of wheat and 

the energy content of a gram of wheat are examples 

of conversion factors that can easily be found in the 

literature, the transformity of a joule of wheat, although 

available in the emergy literature, might be harder to 

find for the average investigator. The energy content 

of many items has been widely tabulated, but a similar 

compendium of transformities and other emergy per unit 

factors does not exist at the present time. Therefore, in 

any emergy evaluation, the investigator will invariably 

have to calculate transformities for certain items. The 

first choice is to determine the transformity in the 

context of the production system of the item used in the 

system under study. If this information is not available, 

the emergy per unit values may be determined by 

analyzing a fast and efficient production process (i.e., 

one operating at the optimum efficiency for maximum 

power; see Odum and Pinkerton 1955) for the item. 

If the production process is unknown or not easily 

documented, transformities may be determined using one 

of the ten methods given in Odum (1996). The emergy 

per unit factors used in this study are given in Tables 

5-C.1 to 5-C.3. This appendix contains the derivation 

and assumptions used for new and revised emergy per 

unit calculations that were needed to complete this study.
119

Table 5-C.1 – Emergy per Unit Factors (solar transformities and specific emergies) used in the San Luis Basin study. 

The units for the emergy per unit factors are solar emjoules per joule (sej/j) for transformities unless otherwise noted.

Item Emergy/ unit#

Adjustment 

Ratio†

Adjusted 

Transformity 

Renewable Emergy Inflows

Solar energy reaching the surface of the earth by definition 1 1 1

Solar energy absorbed 1.21 1.21 1.21

Kinetic Energy of Wind (Odum 1996) 1496 0.9809 1467

Earth Cycle Energy (Odum 1996) 34377 0.9809 33720

Rain, Chemical Potential (Odum 1996, Campbell 2003a) 18200 NA 18100

Chem. Potential Energy of Evapotranspiration (Campbell 2003a) NA NA 28100

Rain, Geo-Potential on Land (Odum 1996 Campbell 2003a) 10488 0.9809 10100

Snow, Geo-Potential on Land (Campbell and Ohrt 2009) 1.03E+05 0.9809 1.01E+05

Rain, Geo-Potential Runoff (streams) (Odum 1996) 27764 0.9809 27200

Snow, Geo-Potential Runoff (Campbell and Ohrt 2009) 1.03E+05 0.9809 1.01E+05

Wave Energy (Lake Superior) (Campbell and Ohrt 2009) 30550 0.9809 30000

Rivers, Chemical Potential (Odum 1996, Campbell 2003a) 48459 NA 50100

Rivers, Geo-potential (Odum 1996) 27764 0.9809 27200

Nitrogen Deposition ammonia (Odum 1996) sej/g 3.9E+09 0.9809 3.8E+09

Adjusted from calculations made using the 15.83 baseline

NH3 (Campbell 2003b) sej/g 2.4E+09

NOx (Campbell 2003b) sej/g 1.2E+10

Ammonia (Campbell 2003b) sej/g NA NA 1.4E+09

NO, NO2

, NO3 (Campbell 2003b) sej/g NA NA 6.8E+09

Sulfur Deposition S transformity (Campbell and Ohrt 2009) sej/g NA NA 1.58E+11

Chloride Ion, Cl-

, Deposition (Campbell and Ohrt 2009) sej/g NA NA 1.31E+10

Renewable Products 

Agricultural Crops (without services, listed below)

Hay (Campbell et al. 2005); see also Brandt-Williams (2002) sej/g NA NA 7.39E+08

Wheat (Campbell and Ohrt 2009) sej/g NA NA 2.88E+09

Barley (average of Oats and Wheat) sej/g NA NA 3.46E+09

Oats (Campbell et al. 2005; see also Brandt-Williams 2002) sej/g NA NA 4.05E+09

Potatoes, (Campbell et al. 2005; see also Brandt-Williams 2002) sej/g NA NA 2.32E+08

Livestock (Odum et al. 1998) 2.00E+06 0.9809 1961800

Beef (Brandt-Williams 2002) 5.64E+05 0.9809 553228

Fish Production (Odum et al. 1998) 2.00E+05 0.9809 1961800

Hydroelectricity (Odum 1996) 1.23E+05 0.9809 120258

Net Timber Growth (Tilley 1999) 2.10E+04 0.9809 20599

Timber Harvest with Service (Tilley 1999) 7.00E+04 0.9809 68663

Ground Water (Odum et al. 1998) 1.62E+05 0.9809 159068

# - Emergy per unit values calculated on the 15.83 E24 sej/y baselines can be converted to the 9.26 line by multiplying by 0.585. † - The adjustment 

ratio converts values calculated using the 9.44 E24 sej/y planetary baseline to their values on the 9.26 E24 sej/y baseline used in this study and 

recommended by Campbell et al. (2005). 
120

Item Emergy/ unit#

Adjustment 

Ratio†

Adjusted 

Transformity 

Solid Waste Production (Brown & Buranakarn 2000) sej/g 6.40E+09 0.9809 6.28E+09

Cement with fly ash 1.40E+10

Concrete recycled aggregate 4.82E+09

Recycled steel 3.09E+09

Recycled aluminum 1.20E+10

Recycled lumber 3.22E+09

Lumber from recycled plastic 5.58E+09

Tile from glass 2.16E+09

Average for Solid Waste 6.40E+09

Nonrenewable Inflows

Energy

Coal (Odum 1996, Campbell and Ohrt 2009) 4.00E+04 NA 3.78E+04

Geologic processes. 9.44E24 sej/y baseline 3.40E+04

Relative efficiency in electricity generation 4.30E+04

Average 3.85E+04

Rounded value commonly used. 4.00E+04

Natural Gas (Odum 1996, Bastianoni et al. 2005) 4.80E+04 NA 4.35E+04

Geologic processes. 9.26 baseline 4.00E+04

Relative efficiency. 9.44 baseline 4.80E+04

Average on the 9.26 baseline 4.35E+04

Crude Oil (Odum 1996, Bastianoni et al. 2005) 5.40E+04 NA 5.42E+04

Geologic processes. 9.26 baseline 5.54E+04

Relative efficiency. 9.44 baseline 5.40E+04

Average on the 9.26 baseline 5.42E+04

Electricity from coal (Campbell et al. 2005, Odum 1996) 1.74E+05 0.9809 1.70E+05

Minerals

Iron Ore (Odum 1996) 6.20E+07 0.9809 6.08E+07

Sand and Gravel (Campbell et al. 2005) sej/g NA NA 1.31E+09

Limestone (Odum 1996) sej/g 1.00E+09 0.9809 9.81E+08

Dolomite (Campbell and Ohrt 2009) NA NA 1.98E+07

Dolomite (Campbell and Ohrt 2009) sej/g NA NA 1.08E+10

Peat (Odum 1996) sej/g 3.60E+08 0.9809 3.53E+08

Granite (Odum 1996) sej/g 5.00E+08 0.9809 4.90E+08

Clay (Odum 1996) sej/g 2.00E+09 0.9809 1.96E+09

Sandstone (Odum 1996) sej/g 1.00E+09 0.9809 9.81E+08

Alumina/Bauxite (Odum 1996) 1.50E+07 0.9809 14713500

Soil Erosion, top soil, (Odum 1996) 74000 0.9809 72600

Tourism (dollars) (Campbell and Lu 2009) NA NA 2.60E+12
121

Adjustment Adjusted 

Item Emergy/ unit# Ratio† Transformity 

Tourism (virtual) (calculated this study) NA NA 4.60E+12

Uranium (Cohen et al. 2007) sej/g 1.6 E+11 0.585 9.36E+10

Electricity from Uranium (Campbell and Ohrt 2009) NA NA 4.81E+04

Imported Goods

Petroleum (refined) (Odum 1996, Bastianoni et al. 2009) 6.60E+04 NA 6.58E+04

Fuels (services for 2000) (Campbell and Lu 2009) NA NA 2.35E+12

Electricity (services 2000) (Campbell and Lu 2009) NA NA 2.35E+12

Services (2000) (Campbell and Lu 2009) NA NA 2.35E+12

Federal Government (spent in US) (Campbell and Lu 2009) NA NA 2.35E+12

Goods w/o iron ore and fuels (materials) NA NA Variable

Iron ore as taconite (Campbell and Ohrt 2009) sej/g NA NA 3.61E+09

Steel (Brown and Buranakarn 2000) sej/g 3.45E+09 0.9809 3.38E+09

Wood and Wood Products NA NA

Lumber and Wood Products, (Tilley 1999) NA NA 7.90E+04

Furniture and Fixtures NA NA

Paper Products, (Campbell and Cai 2007) NA NA 2.22E+05

N fertilizer, (Odum 1996) sej/g 3.80E+09 0.9809 3.73E+09

P fertilizer, (Odum 1996) sej/g 3.90E+09 0.9809 3.83E+09

Potash, (Odum 1996) sej/g 1.10E+09 0.9809 1.08E+09

Storages

Forests (Tilley 1999) NA NA 28234

Water, lakes (Campbell and Ohrt 2009). NA NA 1.81E+04

Soil (Top soil) (Odum 1996) 74000 0.9809 72600

Iron ore (Campbell and Ohrt 2009) 6.20E+07 0.9809 6.08E+07

Iron (specific emergy) sej/g 1.20E+10 0.9809 3.51E+09

Peat (Odum 1996) 1.90E+04 0.9809 1.86E+04

Sand & Gravel (Campbell et al. 2005) sej/g NA NA 1.31E+09

Limestone (Odum 1996) 1.00E+09 0.9809 9.81E+08

Dolomite (Campbell and Ohrt 2009) sej/g NA NA 1.08E+10

Nickel (Campbell and Ohrt 2009) sej/g 2.00E+11 0.9809 2.55E+10

Copper (Campbell and Ohrt 2009) sej/g 9.80E+10 0.9809 1.14E+11

Platinum (Campbell and Ohrt 2009) sej/g 3.70E+11 0.9809 1.13E+11

People (Odum 1996) sej/individual NA NA NA

Preschool sej/individual 3.40E+16 0.9809 3.34E+16

School sej/individual 9.40E+16 0.9809 9.22E+16

College Grad sej/individual 2.80E+17 0.9809 2.75E+17

Post-College sej/individual 1.31E+18 0.9809 1.28E+18

Elderly (70+) (Campbell et al. 2005) sej/individual NA NA 1.69E+17
122

Item Emergy/ unit#

Adjustment 

Ratio†

Adjusted 

Transformity 

Public Status sej/individual 3.93E+18 0.9809 3.85E+18

Legacy sej/individual 7.85E+18 0.9809 7.70E+18

Emergy to Money Ratio for the United States (Campbell and Lu 2009) sej/$ Year NA

1980 5.5421E+12

1981 4.9734E+12

1982 4.3017E+12

1983 3.7936E+12

1984 3.7571E+12

1985 3.5117E+12

1986 3.1773E+12

1987 3.1736E+12

1988 3.1685E+12

1989 2.9942E+12

1990 2.8753E+12

1991 2.6836E+12

1992 2.5672E+12
123

Table 5-C.2 – Specific emergies for imports (sej/g). All commodity specific emergies are without services and relative 

to the 9.26E24 sej/y baseline. Values of the emergy per unit factors for the commodity classes in this table were 

determined from the data given in Table 5-C.1 and other data given in the calculations shown in Table 5-C.3. The note 

numbers connect to Table 5-C.3. Wwt = wet weight; otherwise numbers are dwt = dry weight.

Note New Commodity Classes Specific Emergy w/o services Units

1 Fresh and Leafy Vegetables 3.58E+08 sej/g wwt

2 Fresh Fish or Whale Products 1.58E+10 sej/g wwt

3 Manganese Ores 3.50E+11 sej/g

4 Bituminous Coal 1.11E+09 sej/g

5 Natural Gasoline 2.92E+09 sej/g

6 Dimension Stone, quarry 8.17E+08 sej/g

7 Broken Stone or Riprap 4.90E+08 sej/g

8 Gravel, Sand 1.31E+09 sej/g

9 Clay and ceramics 1.96E+09 sej/g

10 Chemical or Fertilizer Mineral Crude 2.88E+09 sej/g

11 Meat, Fresh or Chilled 1.83E+10 sej/g wwt

12 Meat, Fresh Frozen 2.11E+10 sej/g wwt

13 Meat Products 2.28E+10 sej/g wwt

14 Animal By-product, inedible 9.87E+10 sej/g wwt

15 Poultry, fresh, frozen and processed and eggs 9.95E+08 sej/g wwt

16 Milk & Milk Products 3.19E+09 sej/g wwt

17 Canned or Cured Sea Foods 1.62E+10 sej/g

18 Fruit and Vegetables processed 3.19E+09 sej/g wwt

19 Frozen Specialties 1.22E+10 sej/g wwt

20 Flour or Other Grain Mill Products 3.36E+09 sej/g

21 Prepared or Canned Feed 5.07E+09 sej/g

22 Flour and meal 6.19E+09 sej/g

23 Dog, cat or Other Pet Food 5.24E+09 sej/g

24 Sugar and candy 1.33E+09 sej/g

25 Malt Liquors 1.65E+08 sej/g

26 Malt 3.46E+09 sej/g

27 Wine, brandy or Brandy Spirit 1.18E+09 sej/g

28 Distilled or Blended Liquors 5.68E+08 sej/g

29 Soft Drinks or Mineral Water 1.20E+08 sej/g

30 Misc Flavoring Extracts 7.10E+08 sej/g

31 Soybean Oil or By-products 3.11E+10 sej/g

32 Marine Fats or Oils 1.37E+11 sej/g

33 Macaroni, spaghetti, etc. 5.22E+09 sej/g

34 Miscellaneous Food Preparations 7.84E+09 sej/g

35 Men’s or Boy’s Clothing 1.41E+10 sej/g

36 Canvas Products 1.41E+10 sej/g
124

Note New Commodity Classes Specific Emergy w/o services Units

37 Primary Forest Materials 3.77E+08 sej/g

38 Lumber + Sawmill 5.18E+08 sej/g

39 Millwork or Cabinetwork 7.78E+08 sej/g

40 Plywood or Veneer 2.11E+09 sej/g

41 Prefabricated & Structural Wood Products. 1.97E+09 sej/g

42 Treated Wood Products 2.02E+09 sej/g

43 Miscellaneous Wood Products 1.72E+09 sej/g

44 Mattresses Furniture 2.89E+09 sej/g

45 Pulp or Pulp Mill Products 7.28E+08 sej/g

46 Paper and Paper Products 4.26E+09 sej/g

47 Paper or Building Board 2.15E+09 sej/g

48 Newspaper and Printed matter 4.26E+09 sej/g

49 Potassium or Sodium Compound 7.45E+09 sej/g

50 Industrial Gases 9.63E+10 sej/g

51 Misc Industrial Inorganic Chemicals 2.75E+09 sej/g

52 Misc Indus Organic Chemicals 5.94E+09 sej/g

53 Plastic Matter or Synthetic Fibers 3.09E+09 sej/g

54 Fertilizers 2.88E+09 sej/g

55 Miscellaneous Agricultural Chemicals 1.45E+10 sej/g

56 Adhesives 3.73E+08 sej/g

57 Explosives 3.73E+09 sej/g

58 Chemical Preparations 7.45E+09 sej/g

59 Petroleum Refining Products 2.64E+09 sej/g

60 Liquefied Gases, Coal or Petroleum 3.11E+09 sej/g

61 Asphalt 2.64E+09 sej/g

62 Tires, Rubber Products 4.22E+09 sej/g

63 Plastic 4.13E+09 sej/g

64 Portland Cement 1.93E+09 sej/g

65 Brick, ceramics 2.15E+09 sej/g

66 Concrete Products 1.32E+09 sej/g

67 Ready mix concrete, wet 1.62E+09 sej/g

68 Lime or Gypsum Products 1.61E+09 sej/g

69 Non-metallic Minerals Processed 3.69E+09 sej/g

70 Blast Furnace Or Coke 2.12E+09 sej/g

71 Primary Iron or Steel Products 3.31E+09 sej/g

72 Electrometallurgical Products 6.64E+10 sej/g

73 Iron or Steel Shapes 3.31E+09 sej/g

74 Primary Aluminum Smelter Products 1.27E+10 sej/g

75 Copper or Alloy Basic Shapes 5.03E+10 sej/g
125

Note New Commodity Classes Specific Emergy w/o services Units

76 Aluminum Or Alloy Basic Shapes 1.27E+10 sej/g

77 Miscellaneous Nonferrous Basic Shapes, Wire 3.15E+10 sej/g

78 Metal products 4.02E+09 sej/g

79 Industrial Machinery and motors 7.76E+09 sej/g

80 Carbon Products For Electric Uses 1.45E+11 sej/g

81 Household Machinery & Components 4.02E+09 sej/g

82 Electronics, precision instruments 1.91E+10 sej/g

83 Toys, Games, Sports, Household 5.43E+09 sej/g

84 Matches 8.68E+09 sej/g

85 Rubber or Plastic Scrap 4.17E+09 sej/g

86 Warehouse & Distribution Center NA

87 Rail International Drayage NA
126

Table 5-C.3 – Determination of the specific emergies for imported commodity classes in Table 5-C.2. All commodity 

specific emergies are without services and relative to the 9.26E24 sej/y baseline. 

Notes Commodity Class, Calculations Source, Assumptions Values Units

1 Average for Vegetables given below

(Campbell and Ohrt 2009, BrandtWilliams 2002) 3.58E+08 sej/g

Average of 6 vegetables. Cabbages 2.29E+08 sej/g

Tomatoes 3.25E+08 sej/g

Cucumbers 2.59E+07 sej/g

Green beans 9.68E+08 sej/g

Bell Peppers 2.85E+08 sej/g

Sweet corn 3.13E+08 sej/g

2 Fresh Fish Or Whale Products

Average of 3 species Tilapia, (Brown et al. 1992) 5310 J/g

Salmon, (Odum 2000) 6410 J/g

Catfish, (Odum et al. 1998) 5650 J/g

Avg. heat content fish 5790 J/g

Tilapia 5.53E+05 sej/J

Salmon 5.67E+06 sej/J

Catfish 1.96E+06 sej/J

Average specific emergy of 3 fish species 2.73E+06 sej/J

3 Manganese Ores (Cohen et al. 2007) 1.58E+10 sej/g

4 Bituminous Coal (Campbell and Ohrt 2009) 3.78E+04 sej/J

Coal heat content 29400 J/g

Specific Emergy 1.11E+09 sej/g

5 Natural Gasoline (Bastianoni et al. 2009) 2.92E+09 sej/g

6 Dimension Stone 8.17E+08 sej/g

Limestone (Odum 1996) 9.81E+08 sej/g

7 Granite (Odum 1996) 4.90E+08 sej/g

8 Sandstone (Campbell et al. 2005) 9.81E+08 sej/g

Average of 3 stones Average 8.17E+08 sej/g

9 Clay (Odum 1996) 1.96E+9 sej/g

10 Fertilizer 2.87E+09 sej/g

N fertilizer (Odum 1996) 3.73E+09 sej/g

P fertilizer (Odum 1996) 3.83E+09 sej/g

Potash (Odum 1996) 1.08E+09 sej/g

Average 2.87E+09 sej/g
127

Notes Commodity Class, Calculations Source, Assumptions Values Units

11 Meat, Fresh or Chilled

Assume, average of beef and pork plus energy 

used in slaughter and cutting. Beef, (Brandt-Williams 2002) 1.33E+10 sej/g

Pork 2.01E+10 sej/g

Hogs, (Cavalett et al. 2006) 1.28E+06 sej/J

Hogs 15730 J/g

Average 1.67E+10 sej/g

Slaughter and cutting 1.54E+09 sej/g

Estimate for Meat, Fresh or Chilled

(Carlsson-Kanayama and Faist 2000)

http://www.infra.kth.se/fms/pdf/

energyuse.pdf 1.83E+10 sej/g

Meat Processing Energy Use for Meat Processing

MJ/90 g (megajoules per 90 g)

slaughter, cutting 0.815 9056 J/g

grinding, freezing 0.14 1556 J/g

storage 1.375 15278 J/g

frying or cooking 0.895 9944 J/g

Total 3.225

Estimate Energy required for meat processing 

Assume electricity is used for processing 

energy. 35833 J/g

12 Meat, Fresh Frozen

Add grinding, freezing and storage to 11. 2.87E+09 sej/g

13 Meat Products

add cooking and frying to above 1.69E+09 sej/g

2.28E+10 sej/g

14 Animal By-product, inedible Assume Beef carcass is 19% bone 0.185

Pig carcass is 18% bone and skin Specific emergy 9.87E+10 sej/g

15 Poultry, fresh, frozen and processed and eggs (Odum et al. 1998) 9.95E+08 sej/g wwt

16 Milk & Milk Products Milking, cheese making 0.24 MJ/15 g

(Carlsson-Kanayama & Faist 2000) Storage 0.04 MJ/15 g

Total 0.28 MJ/15 g

Processing 3.18E+09 (sej/g wwt)

Cheese 4.78E+09 (sej/g wwt)

Milk 1.60E+09 (sej/g wwt)

Average Milk and Cheese 3.19E+09 (sej/g wwt)

17 Canned Or Cured Sea Foods Pickling 2297 J/g

fish plus pickling 3.91E+08 sej/g
128

Notes Commodity Class, Calculations Source, Assumptions Values Units

18 Fruit and Vegetables processed Oranges 1.37E+08 sej/g

Oranges + freeze drying and pickling Freeze drying 3.35E+04 J/g

(Carlsson-Kanayama and Faist 2000) Pickling 2.30E+03 J/g

Average 1.79E+04 J/g

Emergy 3.19E+09 (sej/g wwt)

19 Frozen Specialties 1.22E+10 sej/g wwt

Average of Frozen Meat and Fruit and 

Vegetables Processed

20 Flour or Other Grain Mill Products Wheat 2.88E+09 sej/g

Wheat + energy of milling Milling 2.84E+03 J/g

(Carlsson-Kanayama and Faist 2000) 3.36E+09 sej/g

21 Prepared or Canned Feed Corn, (Brandt-Williams 2002) 8.53E+09 sej/g

Soybean, (Brandt-Williams 2002) 6.19E+09 sej/g

Sorghum, (Brandt-Williams 2002) 1.49E+09 sej/g

Oats, (Brandt-Williams 2002) 4.05E+09 sej/g

Average 5.07E+09 sej/g

22 Flour and meal

Average corn and wheat plus milling Wheat 2.88E+09 sej/g

Corn 8.53E+09 sej/g

Milling 2838 J/g

6.19E+09 sej/g

23 Dog, cat or other pet food Corn, (Brandt-Williams 2002) 8.53E+09 sej/g

Soybean, (Brandt-Williams 2002) 6.19E+09 sej/g

Chicken 9.95E+08 sej/g

5.24E+09 sej/g

24 Sugar and candy (Odum et al. 1986) 16190 J/g

Transformity 8.24E+04 sej/J

Specific Emergy 1.33E+09 sej/g

25 Malt Liquors Rain water sej/g (Campbell 2003a) 85740 sej/g

Use fraction water, barley, and alcohol in beer Beer composition (content) 0.9196

Alcohol content 0.06

Barley content 0.02

http://bioenergy.ornl.gov/papers/misc/energy_

conv.html Ethanol heat content 2.67E+04 J/g

Transformity ethanol (Odum 1996) 58860 sej/J

Specific emergy ethanol 1.57E+09 sej/g

Emergy 1.65E+08
129

Notes Commodity Class, Calculations Source, Assumptions Values Units

26 Malt Use barley

27 Wine, brandy or brandy Spirit (Bastianoni et al. 2001) 3.41E+05 sej/J

Wine 1.18E+09 sej/g

Grapes 9.72E+08 sej/g

Wine

Alternate estimate for wine Water content 0.8658

Alcohol content 0.104

Grape essence 0.0302

factor (weight of grapes per weight of 

wine) 1.68

Water (fraction x specific emergy water) 7.42E+04 sej/g

Alcohol (fraction x specific emergy 

alcohol) 1.64E+08 sej/g

Grapes (fraction x factor* specific emergy 

grapes) 1.64E+09 sej/g

Total Wine 1.80E+09 sej/g

28 Distilled or Blended Liquors 86 proof whisky (water content) 0.639

Alcohol by weight 0.361

Emergy 5.68E+08 sej/g

29 Soft Drinks or Mineral Water water 0.9031

Fraction times specific emergy of ingredients sugar 0.0897

Emergy 1.20E+08 sej/g

30 Miscellaneous Flavoring Extracts Vanilla extract

Water 0.5258

Alcohol by weight 0.344

Sugar 0.1265

Emergy 7.10E+08 sej/g

31 Soybean Oil or By-products Soybeans (Brandt-Williams 2002) 6.19E+09 sej/g

http://www.ces.purdue.edu/extmedia/GQ/GQ39.html Fat content 0.1994

specific emergy soybean oil 3.11E+10 sej/g

32 Marine Fats or Oils

Oily fish avg. of herring, mackerel, 

Chinook salmon

Fraction oil 0.1155

Fish oil (energy content) 7710 sej/J

specific emergy fish oil 1.37E+11 sej/g
130

Notes Commodity Class, Calculations Source, Assumptions Values Units

33 Macaroni, spaghetti, etc. Bread making

Assume noodles are similar to bread. Milling 0.21 mJ/74 g

Wheat + emergy to process Baking 0.725 mJ/74 g

(Carlsson-Kanayama and Faist 2000) Storage 0.08 mJ/74 g

Milling 2838 J/g

Baking 9797 J/g

Storage 1081 J/g

Total 13716 J/g

Specific Emergy for processing 2.34E+09 sej/g

5.22E+09 sej/g

34 Miscellaneous Food Preparations

Corn, soy, wheat, oats + emergy to process Average 5.41E+09 sej/g

Processing emergy as fraction of total 144.81%

Specific Emergy 7.84E+09 sej/g

35 Men’s or Boy’s Clothing Cotton (Odum 1996) 1.41E+10 sej/g

36 Canvas Products Cotton (Odum 1996) 1.41E+10 sej/g

37 Primary Forest Materials

Logs avg. soft and hardwood (Tilley 

1999) 19620 sej/J

Energy per gram dry weight. 19200 J/g

Specific Emergy 3.77E+08 sej/g

38 Lumber + Sawmill Lumber (Tilley 1999) 27000 sej/J

Specific emergy 5.18E+08 sej/g

39 Millwork or Cabinetwork Specific emergy 7.78E+08 sej/g

Assume 50% increase over lumber due to 

energy and materials used in processing

40 Plywood or Veneer Plywood (Tilley 1999) 110000 sej/J

Specific emergy 2.11E+09 sej/g

41 Prefabricated & Structural Wood Products Average lumber and plywood 1.32E+09 sej/g

Assume 50% increase due to energy and 

materials used in processing plywood and 

lumber

Specific emergy of Prefabricated wood 

products 1.97E+09 sej/g

42 Treated Wood Products Paint (Buranakarn 1998) 1.50E+10 sej/g

Lumber plus 0.1 paint Specific Emergy 2.02E+09 sej/g

43 Miscellaneous Wood Products

Average value of 4 preceding wood 

products 1.72E+09 sej/g

44 Mattresses Furniture (Buranakarn 1998) 2.89E+09 sej/g

45 Pulp or Pulp Mill Products

Wood pulp (Campbell and Cai 2007, 

Tilley 1999) 37900 sej/J

Specific Emergy 7.28E+08 sej/g
131

Notes Commodity Class, Calculations Source, Assumptions Values Units

46 Paper and Paper Products

Paper (Campbell and Cai 2007, Tilley 

1999) 222000 sej/J

Specific Emergy 4.26E+09 sej/g

47 Paper or Building Board

Paper board (Campbell and Cai 2007, 

Tilley 1999) 112000 sej/J

2.15E+09 sej/g

48 Newspaper and Printed matter Assume paper 4.26E+09 sej/g

49 Potassium Or Sodium Compound Caustic soda (Pritchard 2000) 7.45E+09 sej/g

50 Industrial Gases Nitrogen gas (Campbell 2003b) 9.63E+10 sej/g

51 Miscellaneous Industrial Inorganic Chemicals

Average of hydrated lime, caustic soda, 

diatomite, sulfuric acid (Odum et al. 

2000) 2.75E+09 sej/g

52 Miscellaneous Industrial Organic Chemicals pesticides 1.45E+10 sej/g

glue 3.73E+08 sej/g

Average pesticides, glue, petroleum products. Specific emergy 5.94E+09 sej/g

53 Plastic Matter or Synthetic Fibers plastics (Buranakarn 1998) 3.09E+09 sej/g

54 Fertilizers Average N, P, K (Odum 1996) 2.88E+09 sej/g

55 Miscellaneous Agricultural Chemicals

Assume pesticides (Brown and Arding 

1991) 1.45E+10 sej/g

56 Adhesives Assume glue (Buranakarn 1998) 3.73E+08 sej/g

57 Explosives Assume similar to ammonium fertilizer 3.73E+09 sej/g

58 Chemical Preparations Assume average of glue and pesticides 7.45E+09 sej/g

59 Petroleum Refining Products (Bastianoni et al. 2009) 2.64E+09 sej/g

60 Liquefied Gases, Coal or Petroleum LPG (Bastianoni et al. 2009) 3.11E+09 sej/g

61 Asphalt (Bastianoni et al. 2009) 2.64E+09 sej/g

62 Tires, Rubber Products Rubber (Buranakarn 1998) 4.22E+09 sej/g

63 Plastic (Buranakarn 1998) 

Polyethylene 5.17E+09 sej/g

Plastics 3.09E+09 sej/g

Average 4.13E+09 sej/g

64 Portland Cement (Buranakarn 1998) 1.93E+09 sej/g

65 Brick ceramics (Buranakarn 1998) 2.15E+09 sej/g

66 Concrete Products (Haukoos 1995) 1.32E+09 sej/g

67 Ready mix concrete, Wet Average sand and gravel and cement 1.62E+09 sej/g

68 Lime or Gypsum Products (Pritchard 2000) 1.61E+09 sej/g
132

Notes Commodity Class, Calculations Source, Assumptions Values Units

69 Non-metallic Minerals Processed (Buranakarn 1998, Haukoos 1995) 

Average of glass and ceramics Glass 4.61E+09 sej/g

Flat glass 1.57E+09 sej/g

Float glass 7.53E+09 sej/g

Average 4.57E+09 sej/g

Ceramics 2.81E+09 sej/g

Average Specific Emergy 3.69E+09 sej/g

70 Blast Furnace or Coke (Bastianoni et al. 2009) 2.12E+09 sej/g

71 Primary Iron or Steel Products (Buranakarn 1998, Haukoos 1995) 

Average iron and steel Pig Iron 2.60E+09 sej/g

Steel Conventional 4.02E+09 sej/g

Average 3.31E+09 sej/g

72 Electrometallurgical Products (Odum et al. 1987) 6.64E+10 sej/g

73 Iron or Steel Shapes Use iron and steel 3.31E+09 sej/g

74 Primary Aluminum Smelter Products (Odum 2002) Al sheet and recycle 1.27E+10 sej/g

75 Copper or Alloy Basic Shapes (Buranakarn 1998) 5.03E+10 sej/g

76 Aluminum or Alloy Basic Shapes (Odum 1996) 1.27E+10 sej/g

77 Miscellaneous Nonferrous Basic Shapes, Wire Average copper and aluminum 3.15E+10 sej/g

78 Metal products Assume steel 4.02E+09 sej/g

79 Industrial Machinery and motors (Odum 1996) 7.76E+09 sej/g

80 Carbon Products For Electric Uses Specific Emergy of Graphite

use graphite @ 10% of world coal World Coal 7.55E+17 grams

World Graphite 2.9E+14 grams

Graphite/coal ratio 0.000384106

Heat Content Graphite 32805 J/g

Transformity of Graphite 9.83E+07 sej/J

Specific Emergy Graphite 2.89E+12

1.45E+11 sej/g

81 Household Machinery & Components Assume steel 4.02E+09 sej/g

82 Electronics, precision instruments Use average Steel, plastics, copper 1.91E+10 sej/g

83 Toys, Games, Sports, Household Use average plastic, wood, paper, Al 5.43E+09 sej/g

84 Matches Use average of lumber and phosphorus 8.68E+09 sej/g

Phosphorus (Odum 1996) 1.74E+10 sej/g

85 Rubber or Plastic Scrap Use average of rubber and plastic 4.17E+09 sej/g
133

Table 5-C.4 – Specific emergies for exports. All commodity specific emergies are without services and relative to the 

9.26E24 sej/y baseline. Values of the emergy per unit factors for the commodity classes in this table were determined 

from the data given in Tables 5-C.1 and 5-C.3, as well as data provided in Table 5-C.5. The note numbers connect 

to Table 5-C.5. Wwt = wet weight, otherwise numbers are dwt = dry weight. Any commodity class that is the same 

as that found in the calculations for imports was determined in the same manner as shown in Table 5-C3 and the 

calculation is not repeated here. 

Note New Commodity Classes Specific Emergy w/o 

services Units

1 Field Crops 2.32E+08 sej/g wwt

2 Grain 2.94E+09 sej/g

3 Oil Kernels, Nuts or Seeds 6.23E+09 sej/g

4 Miscellaneous Field Crops 2.32E+08 sej/g

5 Citrus Fruits 1.37E+08 sej/g wwt

6 Fresh Vegetables 3.58E+08 sej/g

7 Horticultural Specialties 1.23E+09 sej/ g wwt

8 Metallic Ores 1.77E+11 sej/g

9 Iron Ores 3.51E+09 sej/g

10 Manganese Ores 3.50E+11 sej/g

11 Crude Petroleum 2.32E+09 sej/g

12 Broken Stone or Riprap 4.90E+08 sej/g

13 Gravel or Sand 1.31E+09 sej/g

14 Miscellaneous Nonmetallic Minerals 1.96E+09 sej/g

15 Meat, Fresh or Chilled 1.83E+10 sej/ g wwt

16 Meat, Fresh Frozen and Meat Products 2.11E+10 sej/g wwt

17 Animal By-products, inedible 9.87E+10 sej/g wwt

18 Flour or Other Grain Mill Products 3.36E+09 sej/g

19 Prepared or Canned Feed 5.07E+09 sej/g

20 Rice, Corn flour and Cereal preparations 5.69E+09 sej/g

21 Dog, cat or Other Pet Food 5.24E+09 sej/g

22 Soybean Oil or By-products 3.11E+10 sej/g

23 Miscellaneous Food Preparations 7.84E+09 sej/g

24 Primary Forest Materials 3.77E+08 sej/g

25 Lumber or Dimension Stock 5.18E+08 sej/g

26 Treated Wood Products 2.02E+09 sej/g

27 Miscellaneous Wood Products 1.72E+09 sej/g

28 Newspapers 4.26E+09 sej/g

29 Miscellaneous Printed Matter 4.26E+09 sej/g

30 Industrial Chemicals 0.00E+00 sej/g

31 Potassium or Sodium Compound 7.45E+09 sej/g

32 Industrial Gases 9.63E+10 sej/g

33 Miscellaneous Industrial Organic Chemicals 5.94E+09 sej/g
134

Note New Commodity Classes Specific Emergy w/o 

services Units

34 Miscellaneous Industrial Inorganic Chemicals 2.75E+09 sej/g

35 Miscellaneous Agricultural Chemicals 1.45E+10 sej/g

36 Miscellaneous Glassware, Blown or Pressed 4.57E+09 sej/g

37 Concrete Products 1.32E+09 sej/g

38 Ready-mix Concrete, Wet 1.62E+09 sej/g

39 Nonmetal Minerals, Processed etc. 3.69E+09 sej/g

40 Steel and Wire Products 4.02E+09 sej/g

41 Total Machinery - Motor V 7.76E+09 sej/g

42 Motor Vehicles - Transportation Equipment 7.76E+09 sej/g

43 Metal Scrap or Tailings 4.02E+09 sej/g

44 Textile and paper waste 8.78E+09 sej/g

45 Chemical or Petroleum Waste 5.67E+09 sej/g

46 Rubber or Plastic Scrap 4.17E+09 sej/g

47 Warehouse & Distribution Center NA

48 Rail and Air Drayage NA
135

Table 5-C.5 – Specific emergies for imports. All commodity specific emergies are without services and relative to the 

9.26E24 sej/y baseline.

Notes Commodity Class, Calculations Source, Assumptions Values Units

1 Field Crops Assume potatoes 2.32E+08 sej/g wwt

2 Grain Wheat 2.88E+09 sej/g

Rice 1.89E+09 sej/g

Oats 4.05E+09 sej/g

Average 2.94E+09 sej/g

3 Oil Kernels, Nuts or Seeds

Assume pecans (Brandt-Williams 2002) 6.23E+09 sej/g

4 Miscellaneous Field Crops Assume potatoes 2.32E+08 sej/ g wwt

5 Citrus Fruits Assume oranges 1.37E+08 sej/ g wwt

6 Fresh Vegetables Same as imports 3.58E+08 sej/ g wwt

7 Horticultural Specialties

Average 3 arbitrary plants Cabbages 2.29E+08 sej/ g wwt

Lettuce 3.90E+08 sej/ g wwt

Sorghum 1.19E+08 sej/ g wwt

Average 2.46E+08 sej/ g wwt

Assume average plant sold is 5 years old 1.23E+09 sej/ g wwt

8 Metallic Ores Average Iron and MN 1.77E+11 sej/g

9 Iron Ores (Cohen et al. 2007) 3.51E+09 sej/g

10 Manganese Ores (MN) (Cohen et al. 2007) 3.50E+11 sej/g

11 Crude Petroleum (Bastianoni et al. 2005) 2.32E+09 sej/g

12 Broken Stone or Riprap Same as Imports 4.90E+08 sej/g

13 Gravel or Sand Same as Imports 1.31E+09 sej/g

14 Miscellaneous Nonmetallic Minerals Assume clay, (Odum 1996) 1.96E+09 sej/g

15 Meat, Fresh or Chilled Same as Imports 1.83E+10 sej/ g wwt

16 Meat, Fresh Frozen and Meat Products Same as Imports 2.11E+10 sej/ g wwt

17 Animal By-products, inedible Same as Imports 9.87E+10 sej/ g wwt

18 Flour or Other Grain Mill Products Same as Imports 3.36E+09 sej/g

19 Prepared or Canned Feed Same as Imports 5.07E+09 sej/g

20 Rice, Corn flour and Cereal preparations 5.69E+09 sej/g

Average (Rice + Corn) + Milling energy

21 Dog, cat or Other Pet Food Same as Imports 5.24E+09 sej/g

22 Soybean Oil or By-products Same as Imports 3.11E+10 sej/g

23 Miscellaneous Food Preparations Same as Imports 7.84E+09 sej/g

24 Primary Forest Materials Same as Imports 3.77E+08 sej/g

25 Lumber or Dimension Stock Same as Imports 5.18E+08 sej/g

26 Treated Wood Products Same as Imports 2.02E+09 sej/g

27 Miscellaneous Wood Products Same as Imports 1.72E+09 sej/g

28 Newspapers Same as Imports 4.26E+09 sej/g
136

Notes Commodity Class, Calculations Source, Assumptions Values Units

29 Miscellaneous Printed Matter 4.26E+09 sej/g

30 Industrial Chemicals (Campbell and Ohrt 2009) 2.68E+09 sej/g

Caustic soda (Pritchard 2000) 7.31E+09 sej/g

Hydrated lime (Pritchard 2000) 1.31E+09 sej/g

Diatomite (Pritchard 2000) 1.99E+09 sej/g

Sulfuric acid (Pritchard 2000) 8.96E+07 sej/g

Average 2.68E+09 sej/g

31 Potassium or Sodium Compound Same as Imports 7.45E+09 sej/g

32 Industrial Gases Same as Imports 9.63E+10 sej/g

33 Miscellaneous Industrial Organic Chemicals Same as Imports 5.94E+09 sej/g

34 Miscellaneous Industrial Inorganic Chemicals Same as Imports 2.75E+09 sej/g

35 Miscellaneous Agricultural Chemicals Same as Imports 1.45E+10 sej/g

36 Miscellaneous Glassware, Blown or Pressed 4.57E+09 sej/g

37 Concrete Products Same as Imports 1.32E+09 sej/g

38 Ready-mix Concrete, Wet Same as Imports 1.62E+09 sej/g

39 Nonmetal Minerals, Processed etc. Same as Imports 3.69E+09 sej/g

40 Steel and Wire Products Assume Steel 4.02E+09 sej/g

41 Total Machinery - Motor V Same as Imports 7.76E+09 sej/g

42 Motor Vehicles + Transportation Equipment Assume machinery 7.76E+09 sej/g

43 Metal Scrap or Tailings Assume Steel 4.02E+09 sej/g

44 Textile and Paper Waste Average 8.78E+09 sej/g

Cotton (Odum 1996) 1.33E+10 sej/g

Paper, same as imports 4.26E+09 sej/g

45 Chemical or Petroleum Waste Average coke, pesticides, glue 5.67E+09 sej/g

46 Rubber or Plastic Scrap Same as Imports 4.17E+09 sej/g
137

References 

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E. 2001. Sustainability assessment of a farm in the 

Chianti area (Italy). Journal of Cleaner Production 9, 

365-373.

Bastianoni, S., Campbell, D., Susani, L., Tiezzi, E. 

2005. The solar transformity of oil and petroleum 

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F.M. 2009. The solar transformity of petroleum fuels. 

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Brandt-Williams, S.L. 2002. Handbook of Emergy 

Evaluation. Folio #4. Emergy of Florida Agriculture. 

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Brown, M.T., Arding, J. 1991. Transformative Working 

Paper. Center for Wetlands, University of Florida, 

Gainesville, Florida.

Brown, M.T., Green, P., Gonzalez, A., Venegas, J. 1992. 

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and Development Guidelines for the Coastal Zone 

of Nayarit, Mexico Volume 2: Emergy Analysis and 

Public Policy Options. Center for Wetlands and Water 

Resources, University of Florida. 

Brown, M.T., Buranakarn, V. 2000. Emergy evaluation 

of material cycles and recycle options, in: Brown, 

M.T., Brandt-Williams, S.L., Tilley, D.R., Ulgiati, S. 

(Eds.), Emergy Synthesis, Proceedings of the First 

Biennial Emergy Analysis Research Conference. 

The Center for Environmental Policy, Department of 

Environmental Engineering Sciences, University of 

Florida, Gainesville, Florida, pp. 141-154.

Buranakarn, V. 1998. Evaluation of recycling and 

reuse of building materials using the emergy analysis 

method. Ph.D. Dissertation, University of Florida, 

UMI Dissertation Services, Ann Arbor, Michigan. 

Campbell, D.E. 2003a. A note on the uncertainty in 

estimates of transformities based on global water 

budgets, in: Brown, M.T., Odum, H.T., Tilley, D.R., 

Ulgiati, S. (Eds.), Emergy Synthesis 2, Proceedings 

of the Second Biennial Emergy Analysis Conference. 

Center for Environmental Policy, University of 

Florida, Gainesville, Florida, pp. 349-353.

Campbell, D.E. 2003b. Emergy Analysis of the 

prehistoric global nitrogen cycle, in: Brown, 

M.T., Odum, H.T., Tilley, D.R., Ulgiati, S. (Eds.), 

Emergy Synthesis 2, Proceedings of the Second 

Biennial Emergy Analysis Conference. Center 

for Environmental Policy, University of Florida, 

Gainesville, Florida, pp. 221-239.

Campbell, D.E., Brandt-Williams, S.L., Meisch, M.E.A. 

2005. Environmental accounting using emergy: 

evaluation of the State of West Virginia. USEPA 

Research Report, EPA/600/R-05/006.

Campbell, D.E., Cai, T.T. 2007. Emergy and economic 

value, in: Brown, M.T., Bardi, E., Campbell, D., 

Comar, V., Huang, S.L., Rydberg, T., Tilley, D., 

Ulgiati, S. (Eds.), Emergy Synthesis 4: Theory and 

Application of the Emergy Methodology, Proceedings 

of the 4th Biennial Emergy Conference, Gainesville, 

Florida, pp. 24.1-24.16.

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ratio of the United States from 1900 to 2007. in: 

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S.L., Ortega, E, Rydberg, T., Tilley, D., Ulgiati, S. 

(Eds.), Emergy Synthesis 5, Theory and Applications 

of the Emergy Methodology, Proceedings of the 

5th Emergy Research Conference. The Center for 

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culture. Paper presented at the International Institute 

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Oregon. Available online at http://oregonstate.edu/

dept/iifet/2000/papers/odum.pdf. Last accessed on 

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Taylor and Francis, London, New York, pp. 37-71.

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in Arkansas. Center for Environmental Policy, 

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Environment: Using Wetlands for their Removal. 

Lewis Publishers, Boca Raton, Florida, pp.261-268.

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139

Appendix 5-D: Further Description of 

the San Luis Basin Regional Model

The major physiographic systems within the SLB are 

aggregated into three classes: 1) mountains which are 

to a large extent forested, 2) the valley floor, which 

is divided into an area covered by the natural shrub 

vegetation of the region including phreatophytes and 

xerophytes (e.g., sagebrush), and 3) agricultural land, 

which is generally irrigated with surface or groundwater. 

Cottonwood and other trees grow along riparian habitat 

and are included in the symbol for shrub lands vegetation 

(Fig. 5.1). Each vegetation subsystem includes surface 

and groundwater with flows appropriate for the location 

(i.e., considering drainage). Groundwater storages 

include a specified temperature, because geothermal 

energy is a potentially important energy source in 

the region. Much of the valley floor is occupied by 

agriculture, growing mainly grains and potatoes. 

Specialized agricultural crops (e.g., quinoa) that require 

high altitude can be grown in the SLB, which has an 

average elevation of approximately 2300 m. Some 

livestock, mostly cattle, are raised in the valley and are 

included in the agricultural subsystem. 

The high elevation valley is surrounded by still higher 

mountains with peaks that rise more than 1800 m above 

the valley floor (San Luis Valley Development Resources 

Group 2007). The work of geological processes deep 

in the earth has given rise to many of the present salient 

features of the SLB. The SLB itself was originally 

formed as a rift valley (Chapin and Cather 1994). Also 

in the past, various periods of orogeny gave rise to the 

mountains with stores of rock and concentrated mineral 

deposits (e.g., perlite) suitable for mining. Erosion of 

the mountains over millions of years filled the deep rift 

valley with sediments, which are deeply bedded with 

layers of ground water present throughout the sediment 

deposit (CWCB 2000, Mayo et al. 2006). In recent 

times, sandy plains and the unique wind regime of the 

basin along with the dogleg shape of the Sangre de 

Cristo range on the eastern border of the valley have 

given rise to a dune field containing the highest sand 

dunes in North America (San Luis Valley Development 

Resources Group 2007). Seasonal river flow from the 

Sangre de Cristo Mountains plays an important part in 

the maintenance of these dunes (Madole et al. 2008) as 

indicated by the interaction symbol in the model (Fig. 

5.1) describing dune production as an interaction of the 

sand supply on the valley floor with wind and water 

flows. Finally, the high mountains surrounding the 

valley ensure that a large part of the water that falls on 

the basin comes down as snow (Emery 1979). 

The primary economic systems of the region are 

centered on its natural resources (e.g., food processing 

and shipment, mining and processing ores and building 

materials, and recreation). Service and commerce, 

education, transportation, and government are connected 

within the system network (Fig. 5.1) as noted by the 

pathways defined in Table 5.2. In addition, people are 

an important storage of capital within the system and we 

documented the effects of immigration and emigration 

on the annual emergy inflows to and outflows from the 

SLB. For example, in 1989 the emergy brought into the 

SLB by immigrants exceeded the emergy of petroleum 

used in that year. Net movement of people appeared 

to follow economic opportunity, so managers might 

indirectly work to increase the movement of people to 

the SLB by zoning land for industrial and commercial 

use. Also, elected officials might alter the tax structure 

to attract more or less business to the region. Tourists 

enter and leave the system and are apparently attracted 

by the extraordinary features in the natural environment 

of the region (e.g., Great Sand Dunes National Park and 

Preserve, various wildlife preserves with rare species, 

hot springs, etc.). 

References

Chapin, C.E., Cather, S.M. 1994. Tectonic setting of 

the axial basins of the northern and central Rio Grande 

rift, in: Keller, G.R., Cather, S.M. (Eds.), Basins of the 

Rio Grande Rift: Structure, Stratigraphy, and Tectonic 

Setting, Geological Society of America, Special Paper 

291, Boulder, CO, pp. 5-25.

Colorado Water Conservation Board (CWCB). 2000. 

Rio Grande Decision Support System: An Overview. 

Section 2.0 Rio Grande Basin Conditions. CWCB, 

State of Colorado. Available online at http://cdss.state.

co.us/DNN/RioGrande/tabid/57/Default.aspx. Last 

Accessed July 21, 2009.

Emery, P. 1979. Geohydrology of the San Luis Valley, 

Colorado, USA. Proceedings of the Canberra 

Symposium 1979. IAHS-AISH Publication no. 128, 

297-305. 

Emery, P.A. 1996. Hydrology of the San Luis Valley, 

Colorado, an overview and a look at the future, in: 

Thompson, R.A., Hudson, M.R., Pillmore, C.L. (Eds.), 

Geologic Excursions to the Rocky Mountains and 

Beyond. Field Trip Guidebook for the 1996 Annual 

Meeting, Geologic Society of America, Oct. 28 1996-

Oct. 31 1996, Denver, Colorado. Colorado Geological 

Survey, Department of Natural Resources, Denver, 

CO. Available online at http://www.nps.gov/archive/

grsa/resources/docs/Trip2023.pdf. Last accessed 

August 4, 2010.
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Madole, R.F., Romig, J.H., Aleinikoff, J.N., VanSistine, 

D.P., Yacob, E.Y. 2008. On the origin and age of the 

Great Sand Dunes, Colorado. Geomorphology 99, 

99-119.

Mayo, A.L., Davey, A., Christiansen, D. 2007. 

Groundwater flow patterns in the San Luis Valley, 

Colorado USA revisited: an evaluation of solute and 

isotopic data. Hydrogeology Journal 15, 383-408.

San Luis Valley Development Resources Group. 2007. 

Comprehensive economic development strategy. 

Available online at http://www.slvdrg.org/ceds.php. 

Last accessed June 17, 2009.
141

Appendix 5-E: Emergy Analysis Tables For Renewable Inflows, Renewable Production, Nonrenewable Productions And Use, Imports And Exports, And Additional Figures Not Included In Body Of Report Table 5-E.1 – Renewable emergy inflows to the San Luis Basin.

Year

Solar Energy 

Absorbed

Wind Energy 

Absorbed Earth Cycle

Rain 

Chemical 

Potential

Rain 

Geopotential 

on the Land

Snow 

Geopotential 

on Land

Rain 

Geopotential 

as Runoff

Snow 

Geopotential 

as Runoff

Total 

Geopotential 

as Runoff

Evapotranspiration

Renewable* 

Emergy 

Base

(sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y)

1980 1.26E+20 6.98E+20 2.30E+21 1.02E+21 1.73E+21 1.73E+22 2.35E+20 1.97E+21 2.20E+21 1.00E+21 3.20E+21

1981 1.31E+20 1.05E+21 2.30E+21 1.05E+21 1.77E+21 1.77E+22 2.44E+20 2.05E+21 2.29E+21 9.78E+20 3.27E+21

1982 1.23E+20 1.05E+21 2.30E+21 1.09E+21 1.84E+21 1.84E+22 2.56E+20 2.14E+21 2.40E+21 9.50E+20 3.35E+21

1983 1.21E+20 1.32E+21 2.30E+21 1.11E+21 1.86E+21 1.86E+22 2.54E+20 2.13E+21 2.38E+21 9.46E+20 3.33E+21

1984 1.19E+20 1.05E+21 2.30E+21 1.13E+21 1.92E+21 1.92E+22 2.59E+20 2.17E+21 2.43E+21 9.43E+20 3.37E+21

1985 1.25E+20 1.05E+21 2.30E+21 1.21E+21 2.05E+21 2.05E+22 2.87E+20 2.41E+21 2.69E+21 9.40E+20 3.63E+21

1986 1.23E+20 9.75E+20 2.30E+21 1.17E+21 1.97E+21 1.97E+22 2.75E+20 2.31E+21 2.58E+21 9.36E+20 3.52E+21

1987 1.22E+20 9.75E+20 2.30E+21 9.50E+20 1.60E+21 1.60E+22 2.25E+20 1.89E+21 2.11E+21 9.33E+20 3.04E+21

1988 1.26E+20 6.98E+20 2.30E+21 9.50E+20 1.60E+21 1.61E+22 2.23E+20 1.87E+21 2.09E+21 9.35E+20 3.03E+21

1989 1.27E+20 8.29E+20 2.30E+21 7.50E+20 1.27E+21 1.27E+22 1.75E+20 1.47E+21 1.65E+21 9.38E+20 2.58E+21

1990 1.30E+20 8.29E+20 2.30E+21 1.29E+21 2.16E+21 2.17E+22 3.08E+20 2.58E+21 2.89E+21 9.40E+20 3.83E+21

1991 1.21E+20 6.68E+20 2.30E+21 1.02E+21 1.72E+21 1.72E+22 2.46E+20 2.06E+21 2.30E+21 9.43E+20 3.24E+21

1992 1.16E+20 6.38E+20 2.30E+21 9.86E+20 1.65E+21 1.66E+22 2.36E+20 1.98E+21 2.21E+21 9.45E+20 3.16E+21

1993 1.09E+20 3.12E+20 2.30E+21 1.08E+21 1.81E+21 1.81E+22 2.58E+20 2.16E+21 2.42E+21 9.43E+20 3.36E+21

1994 1.04E+20 4.33E+20 2.30E+21 1.08E+21 1.81E+21 1.81E+22 2.57E+20 2.16E+21 2.41E+21 9.41E+20 3.35E+21

1995 1.10E+20 2.78E+20 2.30E+21 1.05E+21 1.77E+21 1.78E+22 2.46E+20 2.06E+21 2.30E+21 9.38E+20 3.24E+21

1996 1.19E+20 5.29E+20 2.30E+21 9.16E+20 1.55E+21 1.55E+22 2.08E+20 1.74E+21 1.95E+21 9.36E+20 2.88E+21

1997 1.31E+20 7.62E+20 2.30E+21 1.21E+21 2.04E+21 2.04E+22 2.90E+20 2.43E+21 2.72E+21 9.34E+20 3.65E+21

1998 1.31E+20 4.80E+20 2.30E+21 1.02E+21 1.72E+21 1.72E+22 2.43E+20 2.04E+21 2.28E+21 9.31E+20 3.21E+21

1999 1.42E+20 6.38E+20 2.30E+21 1.04E+21 1.76E+21 1.76E+22 2.44E+20 2.05E+21 2.29E+21 9.29E+20 3.22E+21

2000 1.41E+20 6.98E+20 2.30E+21 9.38E+20 1.59E+21 1.59E+22 2.17E+20 1.82E+21 2.04E+21 9.26E+20 2.96E+21

2001 1.41E+20 6.38E+20 2.30E+21 9.45E+20 1.59E+21 1.59E+22 2.25E+20 1.88E+21 2.11E+21 9.23E+20 3.03E+21

2002 1.39E+20 4.80E+20 2.30E+21 6.12E+20 1.03E+21 1.03E+22 1.42E+20 1.19E+21 1.33E+21 9.21E+20 2.25E+21

2003 1.42E+20 6.98E+20 2.30E+21 8.74E+20 1.47E+21 1.47E+22 2.06E+20 1.72E+21 1.93E+21 9.18E+20 2.85E+21

2004 1.39E+20 6.38E+20 2.30E+21 9.86E+20 1.66E+21 1.67E+22 2.33E+20 1.95E+21 2.18E+21 9.15E+20 3.10E+21

2005 1.42E+20 5.82E+20 2.30E+21 9.42E+20 1.59E+21 1.59E+22 2.22E+20 1.86E+21 2.08E+21 9.13E+20 2.99E+21

* The Renewable emergy base for the systems consists of the sum of the geopotential of runoff from rain and snow and the evapotranspiration.
142

Table 5-E.2 – Production in the San Luis Basin that is based primarily on renewable resources.

Year

Agricultural 

Crops (Total) Hay Barley Oats Wheat Potatoes

Livestock 

(Cattle) Fish Timber Harvest

(sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y)

1980 1.32E+21 4.76E+20 2.30E+20 1.13E+20 3.85E+20 1.16E+20 6.52E+20 0.00E+00 7.43E+19

1981 1.36E+21 3.52E+20 2.41E+20 1.37E+20 5.05E+20 1.22E+20 7.03E+20 0.00E+00 4.38E+19

1982 1.41E+21 3.57E+20 3.03E+20 1.73E+20 4.43E+20 1.35E+20 6.89E+20 0.00E+00 7.44E+19

1983 1.42E+21 1.79E+20 2.79E+20 1.81E+20 6.30E+20 1.47E+20 7.45E+20 0.00E+00 7.78E+19

1984 1.42E+21 2.03E+20 3.33E+20 1.80E+20 5.27E+20 1.82E+20 7.80E+20 0.00E+00 9.54E+19

1985 1.56E+21 2.65E+20 3.11E+20 2.21E+20 5.70E+20 1.89E+20 7.56E+20 4.73E+16 9.54E+19

1986 1.70E+21 5.28E+20 4.30E+20 1.35E+20 4.07E+20 1.99E+20 7.47E+20 3.07E+17 1.08E+20

1987 1.55E+21 6.04E+20 2.14E+20 9.71E+19 4.34E+20 2.06E+20 7.43E+20 3.07E+17 1.02E+20

1988 1.54E+21 6.06E+20 1.87E+20 1.43E+20 4.05E+20 2.01E+20 7.39E+20 3.31E+17 1.03E+20

1989 1.66E+21 4.79E+20 2.20E+20 1.69E+20 5.71E+20 2.17E+20 7.34E+20 3.31E+17 1.09E+20

1990 1.55E+21 5.48E+20 2.75E+20 1.62E+20 3.20E+20 2.40E+20 7.30E+20 3.31E+17 7.40E+19

1991 1.42E+21 5.25E+20 2.50E+20 1.19E+20 2.76E+20 2.51E+20 7.25E+20 7.09E+17 7.22E+19

1992 1.50E+21 5.74E+20 2.07E+20 1.04E+20 3.78E+20 2.33E+20 7.21E+20 8.99E+17 7.76E+19

1993 1.43E+21 6.46E+20 1.44E+20 9.41E+19 2.77E+20 2.67E+20 7.16E+20 8.99E+17 8.16E+19

1994 1.54E+21 6.87E+20 1.52E+20 8.28E+19 3.49E+20 2.72E+20 7.11E+20 8.99E+17 1.13E+20

1995 1.48E+21 6.23E+20 2.08E+20 1.38E+20 2.56E+20 2.51E+20 7.06E+20 8.99E+17 6.69E+19

1996 1.73E+21 5.62E+20 1.60E+20 1.48E+20 5.56E+20 3.08E+20 7.02E+20 8.99E+17 3.43E+19

1997 1.90E+21 8.40E+20 2.34E+20 1.32E+20 4.33E+20 2.64E+20 6.96E+20 1.37E+18 1.20E+19

1998 1.79E+21 8.00E+20 1.96E+20 1.50E+20 3.72E+20 2.68E+20 6.91E+20 1.49E+18 1.35E+19

1999 1.87E+21 8.07E+20 1.95E+20 1.32E+20 4.60E+20 2.72E+20 6.86E+20 1.23E+18 2.09E+19

2000 2.00E+21 8.15E+20 2.72E+20 2.18E+20 4.04E+20 2.95E+20 6.81E+20 1.51E+18 1.71E+19

2001 1.86E+21 9.63E+20 2.01E+20 1.36E+20 3.32E+20 2.25E+20 6.81E+20 1.75E+18 9.99E+18

2002 1.42E+21 6.73E+20 1.66E+20 7.31E+19 2.18E+20 2.94E+20 5.74E+20 1.80E+18 8.30E+18

2003 1.35E+21 7.10E+20 1.87E+20 8.83E+19 1.19E+20 2.50E+20 5.59E+20 1.84E+18 1.21E+19

2004 1.29E+21 7.32E+20 1.89E+20 4.36E+19 9.39E+19 2.27E+20 4.66E+20 2.13E+18 1.95E+19

2005 1.49E+21 8.98E+20 1.96E+20 3.54E+19 1.36E+20 2.22E+20 4.98E+20 2.06E+18 1.38E+19
143

Table 5-E.3 – Nonrenewable emergy inflows in the San Luis Basin.

Year Coal Used

Natural Gas 

Used

Petroleum 

Fuels Used

Electricity 

Used

Crude Oil 

Produced*

Metallic 

Ores*

Broken 

Stone, Rip 

Rap*#

Sand and 

Gravel*#

Nonmetallic 

Minerals* Soil Erosion

Groundwater

(sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y)

1980 2.28E+19 1.36E+20 2.91E+20 1.69E+20 1.87E+20 0.00E+00

1981 2.09E+19 1.19E+20 2.66E+20 1.91E+20 2.01E+20 2.63E+20

1982 1.48E+19 1.30E+20 2.70E+20 1.87E+20 1.95E+20 0.00E+00

1983 9.62E+18 1.23E+20 2.76E+20 1.90E+20 1.96E+20 0.00E+00

1984 1.16E+19 1.32E+20 2.76E+20 2.01E+20 2.00E+20 0.00E+00

1985 1.10E+19 1.24E+20 2.73E+20 2.06E+20 1.85E+20 0.00E+00

1986 1.03E+19 1.12E+20 2.78E+20 2.10E+20 1.70E+20 0.00E+00

1987 9.68E+18 1.17E+20 2.78E+20 2.17E+20 1.43E+20 0.00E+00

1988 9.07E+18 1.28E+20 2.82E+20 2.28E+20 1.41E+20 2.75E+20

1989 8.18E+18 1.34E+20 2.67E+20 2.31E+20 1.43E+20 2.56E+20

1990 8.97E+18 1.32E+20 2.66E+20 2.32E+20 1.35E+20 3.59E+19

1991 9.06E+18 1.43E+20 2.69E+20 2.35E+20 1.33E+20 0.00E+00

1992 8.65E+18 1.34E+20 2.66E+20 2.30E+20 1.28E+20 0.00E+00

1993 8.70E+18 1.47E+20 2.80E+20 2.32E+20 1.26E+20 0.00E+00

1994 9.31E+18 1.36E+20 2.84E+20 2.42E+20 1.23E+20 2.62E+19

1995 7.94E+18 1.40E+20 2.91E+20 2.48E+20 7.28E+15 1.49E+20 1.24E+21 3.63E+21 1.82E+20 1.24E+20 0.00E+00

1996 4.01E+18 1.51E+20 3.01E+20 2.59E+20 1.34E+18 1.38E+20 1.14E+21 3.35E+21 1.68E+20 1.34E+20 4.00E+20

1997 8.27E+18 1.49E+20 2.84E+20 2.63E+20 2.44E+18 1.54E+20 1.22E+21 3.58E+21 1.80E+20 1.34E+20 0.00E+00

1998 4.23E+18 1.52E+20 3.00E+20 2.71E+20 5.62E+18 1.46E+20 1.29E+21 3.67E+21 1.99E+20 1.45E+20 1.01E+20

1999 5.40E+18 1.47E+20 3.06E+20 2.73E+20 2.33E+18 1.78E+20 6.77E+20 2.74E+21 1.75E+20 1.34E+20 0.00E+00

2000 5.11E+18 1.52E+20 3.26E+20 2.87E+20 6.62E+17 1.06E+20 8.27E+20 2.75E+21 2.27E+20 1.28E+20 4.62E+20

2001 5.91E+18 1.84E+20 3.30E+20 2.87E+20 7.88E+17 2.31E+20 9.50E+20 3.09E+21 2.31E+20 1.19E+20 0.00E+00

2002 4.21E+18 1.84E+20 3.15E+20 2.97E+20 5.33E+17 7.64E+20 5.06E+20 2.52E+21 2.29E+20 1.20E+20 7.89E+20

2003 5.40E+18 1.72E+20 3.26E+20 2.99E+20 8.70E+17 3.94E+19 5.43E+20 1.95E+21 6.31E+19 1.26E+20 2.77E+20

2004 5.00E+18 1.72E+20 3.53E+20 3.00E+20 1.10E+18 3.66E+19 7.93E+20 1.95E+21 5.94E+20 1.26E+20 2.00E+19

2005 4.13E+18 1.78E+20 3.42E+20 3.06E+20 2.27E+18 4.80E+19 8.26E+20 1.86E+21 6.21E+20 1.25E+20 0.00E+00

* Assumed from Global Insight Inc. data to be produced in the Basin. # Ten percent was added for local consumption.
144

Table 5-E.4 – Imports into the San Luis Basin.

Year Coal Petroleum

Natural 

Gas Electricity

Total 

Fuels and 

Electricity

Materials 

other than 

fuels

Services in 

materials 

other than 

fuels

Services in 

Fuels

Services in 

Electricity

Total 

Goods and 

Services Immigration Tourism

Federal 

and State 

Outlays

(sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y)

1980 2.28E+19 1.36E+20 2.91E+20 1.69E+20 6.19E+20 2.37E+20 6.76E+19

1981 2.09E+19 1.19E+20 2.66E+20 1.91E+20 5.97E+20 2.27E+20 7.99E+19

1982 1.48E+19 1.30E+20 2.70E+20 1.87E+20 6.01E+20 2.07E+20 7.93E+19

1983 9.62E+18 1.23E+20 2.76E+20 1.90E+20 5.98E+20 1.74E+20 7.11E+19

1984 1.16E+19 1.32E+20 2.76E+20 2.01E+20 6.21E+20 1.72E+20 7.38E+19

1985 1.10E+19 1.24E+20 2.73E+20 2.06E+20 6.13E+20 1.57E+20 7.19E+19 1.23E+20 4.89E+19

1986 1.03E+19 1.12E+20 2.78E+20 2.10E+20 6.10E+20 1.14E+20 6.57E+19 0.00E+00 5.09E+19

1987 9.68E+18 1.17E+20 2.78E+20 2.17E+20 6.22E+20 1.19E+20 6.56E+19 3.21E+19 5.31E+19

1988 9.07E+18 1.28E+20 2.82E+20 2.28E+20 6.46E+20 1.17E+20 7.02E+19 1.80E+20 5.48E+19

1989 8.18E+18 1.34E+20 2.67E+20 2.31E+20 6.40E+20 1.17E+20 6.72E+19 3.80E+20 5.12E+19

1990 8.97E+18 1.32E+20 2.66E+20 2.32E+20 6.40E+20 1.23E+20 6.43E+19 2.93E+20 4.98E+19

1991 9.06E+18 1.43E+20 2.69E+20 2.35E+20 6.56E+20 1.15E+20 6.14E+19 1.63E+19 4.77E+19

1992 8.65E+18 1.34E+20 2.66E+20 2.30E+20 6.38E+20 1.08E+20 5.80E+19 7.39E+19 4.85E+19

1993 8.70E+18 1.47E+20 2.80E+20 2.32E+20 6.68E+20 1.14E+20 5.86E+19 0.00E+00 5.24E+19

1994 9.31E+18 1.36E+20 2.84E+20 2.42E+20 6.71E+20 1.18E+20 6.23E+19 0.00E+00 5.86E+19

1995 7.94E+18 1.40E+20 2.91E+20 2.48E+20 6.87E+20 1.33E+21 6.26E+20 1.22E+20 6.46E+19 2.83E+21 0.00E+00 1.44E+20 6.12E+19

1996 4.01E+18 1.51E+20 3.01E+20 2.59E+20 7.15E+20 1.39E+21 5.92E+20 1.33E+20 6.65E+19 2.90E+21 0.00E+00 1.49E+20 6.52E+19

1997 8.27E+18 1.49E+20 2.84E+20 2.63E+20 7.04E+20 1.46E+21 6.13E+20 1.30E+20 6.55E+19 2.97E+21 0.00E+00 1.52E+20 6.26E+19

1998 4.23E+18 1.52E+20 3.00E+20 2.71E+20 7.27E+20 1.48E+21 6.17E+20 1.15E+20 6.51E+19 3.00E+21 0.00E+00 1.52E+20 5.91E+19

1999 5.40E+18 1.47E+20 3.06E+20 2.73E+20 7.32E+20 1.82E+21 6.46E+20 1.18E+20 6.13E+19 3.38E+21 0.00E+00 1.47E+20 5.59E+19

2000 5.11E+18 1.52E+20 3.26E+20 2.87E+20 7.71E+20 1.83E+21 6.85E+20 1.61E+20 6.48E+19 3.51E+21 0.00E+00 1.55E+20 5.96E+19

2001 5.91E+18 1.84E+20 3.30E+20 2.87E+20 8.08E+20 2.13E+21 6.47E+20 1.71E+20 6.19E+19 3.81E+21 0.00E+00 1.52E+20 5.82E+19

2002 4.21E+18 1.84E+20 3.15E+20 2.97E+20 8.00E+20 2.20E+21 6.22E+20 1.33E+20 6.03E+19 3.82E+21 0.00E+00 1.39E+20 5.60E+19

2003 5.40E+18 1.72E+20 3.26E+20 2.99E+20 8.02E+20 1.53E+21 5.32E+20 1.46E+20 6.62E+19 3.07E+21 0.00E+00 1.43E+20 5.97E+19

2004 5.00E+18 1.72E+20 3.53E+20 3.00E+20 8.30E+20 1.65E+21 5.11E+20 1.89E+20 6.94E+19 3.25E+21 9.16E+19 1.50E+20

2005 4.13E+18 1.78E+20 3.42E+20 3.06E+20 8.30E+20 1.70E+21 4.99E+20 2.23E+20 7.30E+19 3.32E+21 1.45E+20 1.40E+20
145

Table 5-E.5 – Exports from the San Luis Basin.

Year

Total 

Materials 

Exported

Services in 

material 

exports

Total 

Exports

Agricultural 

Crops Potatoes

Grain, 

Wheat, 

Barley, Oats

Vegetables, 

Fruit, Nuts, 

Seeds

Horticulture 

Specialties Livestock

Minerals 

Total

Metallic 

Ores

(sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y) (sej/y)

1980 7.72E+20 1.16E+20 6.57E+20 6.52E+20

1981 9.25E+20 1.22E+20 8.03E+20 7.03E+201982 9.53E+20 1.35E+20 8.18E+20 6.89E+201983 1.14E+21 1.47E+20 9.91E+20 7.45E+201984 1.11E+21 1.82E+20 9.32E+20 7.80E+201985 1.17E+21 1.89E+20 9.85E+20 7.56E+201986 1.06E+21 1.99E+20 8.60E+20 7.47E+201987 8.87E+20 2.06E+20 6.81E+20 7.43E+201988 8.64E+20 2.01E+20 6.63E+20 7.39E+201989 1.09E+21 2.17E+20 8.74E+20 7.34E+201990 9.04E+20 2.40E+20 6.64E+20 7.30E+201991 8.19E+20 2.51E+20 5.68E+20 7.25E+201992 8.58E+20 2.33E+20 6.24E+20 7.21E+201993 7.30E+20 2.67E+20 4.63E+20 7.16E+201994 8.07E+20 2.72E+20 5.34E+20 7.11E+201995 7.49E+21 2.07E+21 9.57E+21 7.88E+20 2.51E+20 5.27E+20 1.08E+18 8.63E+18 7.06E+20 4.76E+21 1.49E+20

1996 7.55E+21 2.18E+21 9.73E+21 1.11E+21 3.08E+20 7.95E+20 1.13E+18 8.18E+18 7.02E+20 4.39E+21 1.38E+20

1997 7.83E+21 2.32E+21 1.01E+22 9.95E+20 2.64E+20 7.21E+20 1.53E+18 9.19E+18 6.96E+20 4.70E+21 1.54E+20

1998 7.94E+21 2.34E+21 1.03E+22 9.22E+20 2.68E+20 6.42E+20 1.34E+18 1.08E+19 6.91E+20 4.86E+21 1.46E+20

1999 6.19E+21 2.02E+21 8.21E+21 1.00E+21 2.72E+20 7.16E+20 1.52E+18 1.20E+19 6.86E+20 3.46E+21 1.78E+20

2000 6.30E+21 1.95E+21 8.25E+21 1.10E+21 2.95E+20 7.85E+20 1.46E+18 1.44E+19 6.81E+20 3.58E+21 1.06E+20

2001 6.12E+21 1.21E+21 7.33E+21 8.35E+20 2.25E+20 5.95E+20 1.78E+18 1.29E+19 6.81E+20 4.13E+21 2.31E+20

2002 5.52E+21 1.49E+21 7.00E+21 7.24E+20 2.94E+20 4.08E+20 1.24E+19 9.47E+18 5.74E+20 3.74E+21 7.64E+20

2003 4.22E+21 1.87E+21 6.09E+21 6.10E+20 2.50E+20 3.38E+20 1.39E+19 8.56E+18 5.59E+20 2.37E+21 3.94E+19

2004 9.36E+21 7.18E+21 1.65E+22 5.31E+20 2.27E+20 2.82E+20 1.32E+19 8.41E+18 4.66E+20 3.13E+21 3.66E+19

2005 9.84E+21 7.24E+21 1.71E+22 5.72E+20 2.22E+20 3.24E+20 1.36E+19 1.20E+19 4.98E+20 3.11E+21 4.80E+19
146

Year

Crude 

Petroleum

(sej/y)

Broken Stone 

Or Riprap

(sej/y)

Gravel Or Sand

(sej/y)

Misc 

Nonmetallic 

Minerals, Nec

(sej/y)

Forest Products 

Total

(sej/y)

Primary Forest 

Products, Logs

(sej/y)

Lumber and 

dimension stock

(sej/y)

Wood Products

(sej/y)

1980198119821983198419851986198719881989199019911992199319941995 7.19E+15 1.13E+21 3.30E+21 1.82E+20 1.222E+20 1.11439E+19 5.434E+19 5.6669E+19

1996 1.33E+18 1.04E+21 3.05E+21 1.68E+20 1.324E+20 1.20767E+19 5.89E+19 6.1418E+19

1997 2.41E+18 1.11E+21 3.26E+21 1.80E+20 1.416E+20 1.29163E+19 6.299E+19 6.5687E+19

1998 5.55E+18 1.18E+21 3.34E+21 1.99E+20 1.377E+20 1.31497E+19 5.826E+19 6.6308E+19

1999 2.30E+18 6.16E+20 2.49E+21 1.75E+20 1.818E+20 2.31487E+19 6.752E+19 9.1163E+19

2000 6.54E+17 7.51E+20 2.50E+21 2.27E+20 1.737E+20 2.12302E+19 7.06E+19 8.1919E+19

2001 7.78E+17 8.64E+20 2.81E+21 2.31E+20 4.672E+19 8.4775E+18 3.478E+18 3.4765E+19

2002 5.26E+17 4.60E+20 2.29E+21 2.29E+20 2.7E+19 4.98659E+18 3.397E+18 1.8621E+19

2003 8.59E+17 4.94E+20 1.77E+21 6.31E+19 2.749E+19 5.28069E+18 2.06E+18 2.0149E+19

2004 1.08E+18 7.21E+20 1.78E+21 5.94E+20 5.397E+19 1.21393E+19 9.845E+18 3.1991E+19

2005 2.25E+18 7.51E+20 1.69E+21 6.21E+20 5.682E+19 1.29705E+19 1.045E+19 3.3402E+19
147

Year All other materials

(sej/y)

Services in 

Agricultural 

Products

(sej/y)

Services in 

Minerals

(sej/y)

Services in Forest 

Products

(sej/y)

Services in All 

Other materials

(sej/y)

Emigration by age

(sej/y)

198019811982198319841985 01986 7.032E+191987 01988 01989 01990 01991 01992 01993 4.9303E+191994 6.7808E+191995 1.119E+21 2.296E+20 2.344E+20 6.7331E+20 9.35722E+20 1.2821E+20

1996 1.209E+21 2.253E+20 2.17E+20 7.2813E+20 1.01145E+21 6.4372E+19

1997 1.294E+21 2.509E+20 2.325E+20 7.6716E+20 1.06632E+21 4.1769E+19

1998 1.326E+21 2.757E+20 2.342E+20 6.9573E+20 1.13029E+21 3.7275E+19

1999 8.571E+20 3.034E+20 1.566E+20 7.9454E+20 7.68159E+20 6.0803E+19

2000 7.651E+20 2.661E+20 1.828E+20 8.192E+20 6.81173E+20 6.2125E+19

2001 4.247E+20 2.85E+20 1.925E+20 9.5246E+19 6.33211E+20 2.8154E+19

2002 4.464E+20 6.722E+20 1.456E+20 6.1747E+19 6.06636E+20 6.6619E+19

2003 6.514E+20 6.598E+20 9.502E+19 5.1814E+19 1.06415E+21 4.9303E+19

2004 5.184E+21 5.802E+20 2.356E+20 1.4459E+20 6.22359E+21 0

2005 5.598E+21 5.682E+20 2.319E+20 1.4389E+20 6.29179E+21 0
148

Figure 5-E.1 – Renewable production compared to the emergy of fuels and electricity, and the renewable emergy base 

of the SLB.

Figure 5-E.2 – Minor components of the emergy input to the San Luis Basin 1980-2005.
149

Appendix 5-F: Data Quality of the 

Purchased Data

Global Insight, Inc. (GI) data were important in that 

they allowed us to apply the revised method (Campbell 

et al. 2005) for calculating the import and export of 

emergy to and from a region. The commodity flow 

data collected by the Bureau of Transportation Statistics 

(1993, 1997, 2002) met the data quality objectives for 

measuring commodity movements for the studies of 

West Virginia and Minnesota (Campbell et al. 2005, 

Campbell and Ohrt 2009). GI uses their proprietary 

program, TRANSEARCH, to estimate domestic freight 

flows by county, commodity, and mode of transportation. 

They used a variety of public data sources to derive these 

flows (Global Insight 2006). To verify the accuracy 

of TRANSEARCH data, GI benchmarks the flows 

against reported freight volume data using two primary 

sources: 1) private carrier information that they acquire 

as part of a data exchange program with railroads and 

truck carriers; and 2) truck count information released 

by the state departments of transportation. However, 

GI does not perform any statistical quantification of 

this process to estimate the uncertainty inherent in 

the TRANSEARCH data (Rich Fullenbaum, personal 

communication). Even though no statistical estimates 

were made to ascertain this uncertainty, we believe 

that it is reasonable to assume their methods carry 

uncertainty comparable to that in the US Census Bureau 

estimates, because both are based on similar sources. 

Furthermore, GI crosschecks their estimates with 

independent information (Rich Fullenbaum, personal 

communication). Despite the fact the methods reported 

by GI appear to be logical and deliberate, there is 

considerable uncertainty associated with some of their 

measurements. Principally, the export of “nonmetallic 

minerals, processed” appears to be underestimated, 

based on conversations with officials of two perlite 

companies. Both Dicaperl and Harborlite are doing 

business in Antonito, CO, and stated that almost all of 

the material that arrives at Antonito is shipped out by 

rail. At this time we are unable to determine the reason 

for this apparent error; however, this is a moot point 

because this commodity class passed through the SLB 

without local use and therefore we did not include it in 

our system’s import-export balance. 

References

Bureau of Transportation Statistics. 1993. Commodity 

Flow Survey. U.S. Department of Transportation, 

Washington, DC.

Bureau of Transportation Statistics. 1997. Commodity 

Flow Survey. U.S. Department of Transportation, 

Washington, DC.

Bureau of Transportation Statistics. 2002. Commodity 

Flow Survey. U.S. Department of Transportation, 

Washington, DC.

Campbell, D. E., Brandt-Williams, S., Meisch M. 2005. 

Environmental accounting using emergy: evaluation of 

the state of West Virginia. EPA600/R-05/006. United 

States Environmental Protection Agency, Narragansett, 

Rhode Island.

Campbell, D.E., Ohrt, A. 2009. Environmental 

accounting using emergy: evaluation of Minnesota. 

USEPA Project Report, EPA/600/R-09/002, 138 pp.

Global Insight, Inc. 2009. Development of the 

TRANSEARCH Database. Global Insight, Lexington, 

Massachusetts.
150

Appendix 5-G: Miscellaneous Indices 

and Results

A thorough emergy analysis often consists of several 

analyses and corresponding results. In an effort to be 

concise, many of the aspects related to the emergy of 

this study were not included in the body of the report. 

Below are the reference notes and tables that contain 

and the methods and assumptions used for calculating 

the energy and mass variables used in the emergy 

evaluation of the SLB. Various figures represent the 

reference notes and additional aspects of the San Luis 

Basin. This information makes available the core data 

needed to calculate the emergy flows of the region for 

1995. Information for other years sufficient to reproduce 

the calculations for that year using the online spreadsheet 

for 1995 is available at http://www.epa.gov/aed/research/

desupp5.html.
151

Table 5-G.1 – Example calculation of emergy and economic value, assumptions, and data sources used in the emergy 

evaluation of the San Luis Basin and Upper Rio Grande River Basin for the year 1995. 

Note Variables Values Values Sources

Areas (mi2

) (m2)

1 Alamosa 722.74 1.87E+09 http://quickfacts.census.gov/qfd/states/08/08003.html 

Conejos 1287.22 3.33E+09 http://quickfacts.census.gov/qfd/states/08/08021.html

Costilla 1227.1 3.18E+09 http://quickfacts.census.gov/qfd/states/08/08023.html

Hinsdale 1117.68 2.89E+09 http://quickfacts.census.gov/qfd/states/08/08053.html

Mineral 875.72 2.27E+09 http://quickfacts.census.gov/qfd/states/08/08079.html

Rio Grande 911.6 2.36E+09 http://quickfacts.census.gov/qfd/states/08/08105.html

Saguache 3168.44 8.21E+09 http://quickfacts.census.gov/qfd/states/08/08109.html

Area of 7 counties 9310.5 2.41E+10 Sum of 7 counties.

Area counties not in watershed 3.65E+09 By difference between counties

Area counties in region 8.29E+09 The region consists of 7 counties.

Area counties in mountains 1.58E+10 All area outside the watershed is mountainous.

Area of Upper Rio Grande watershed 7900 2.05E+10 http://www.nps.gov/archive/grsa/resources/docs/Trip2023.pdf

Area of valley Floor 3200 8.29E+09 http://www.nps.gov/archive/grsa/resources/docs/Trip2023.pdf

Area of mountains 4700 1.22E+10 http://www.nps.gov/archive/grsa/resources/docs/Trip2023.pdf

Closed Basin watershed (see Figure 5-G.5 1

) 8.05E+09 This study, GIS estimate

Rio Grande watershed excluding the closed 

basin (see Figure 1

) 1.15E+10 This study, GIS estimate

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

Renewable Energy Sources

2 Solar Energy

Solar radiation absorbed was 

determined from weather data for two 

Received, watershed 1.2378E+20 J y-1

stations from the National Renewable 

Energy Laboratory (NREL)

Dataset, Alamosa in the valley and 

Wolf creek in the mountains.

Absorbed, watershed 9.3062E+19 J y-1

Average precipitation and albedo were 

prorated by area.

Received, 7 

counties

1.46E+20 J y-1

Absorbed, 7 

counties

1.10E+20 J y-1

Solar insolation for 

watershed

NREL Data (1 station, n=46) 

Avg. Insolation 

(Alamosa)

1.68E+07 J m-2 d-1 http://rredc.nrel.gov/solar/old_data/

nsrdb/1991-2005/siteonthefly.

cgi?id=724620 

Solar for watershed

Solar Energy 

Received

1.2378E+20 J y-1 Area weighted adjustment by solar 

fraction for mountains.

Albedo 3.07181E+19 J y-1

Solar Energy 

Absorbed

9.3062E+19 J y-1

Solar Insolation for 

Counties

Solar Energy 

Received

1.45697E+20 J y-1

Albedo 3.60751E+19 J y-1

Solar Energy 

Absorbed

1.09622E+20 J y-1
152

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

Solar Insolation (Wolf Creek/Alamosa) 0.979775281 dimen-sionless These are dimensionless numbers

Albedo Fraction 

reflected

http://eosweb.larc.nasa.gov/sse/ 

Alamosa 0.25417 dimen-sionless

Wolf Creek 0.24417 dimen-sionless

3 Kinetic Energy of Wind 

Used at the Surface

1.89E+17 J y-1 http://www4.ncdc.noaa.gov/cgiwin/wwcgi.dll?WWDI~StnSrch

(density)(drag coefficient)(geostrophic 

velocity)3(area)(seconds/year)

Calculated in Odum (1999) 

“Evaluating Landscape Use of Wind 

Kinetic Energy.”

Wind Speed, @ 10 m 2.5 m s-1

Air Density 1.3 kg m-3

Geostrophic Velocity 4.166666667 m s-1

Drag Coefficient 

(area weighted)

0.002656302

Area, 7 counties 24114195000 m2

sec / year 31400000

Drag coefficient flat 

land

0.002

Drag coefficient 

mountains

0.003

4 Earth Cycle Energy 6.81E+16 J y-1 http://smu.edu/geothermal/georesou/

resource.htm

(land area)(heat flow/area)

Area 24114195000 m2

Heat flow/area 89.53571429 mW m-2 Heatflow is the same for all years.

Heat flow per m2/year 2823598.286 J m-2 y-1

5 Rain, Chemical Potential 5.83E+16 J y-1

(area)(rainfall)(density water)(Gibbs 

Free Energy water relative to seawater)

The chemical potential of rain 

was determined by a GIS analysis 

performed for this study.

Rainfall was averaged for each county 

and the counties were summed to get 

the total for the SLB.

Detailed data on the chemical potential 

of rainfall is found in the Geo and 

Chemical Potential spread sheet 1

Area 24114195000 m2

Rainfall variable by 

county

m y-1

Gibbs Energy 4.74 J g-1

Density 1000000 g m-3
153

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

6 Rain, Geopotential

Received on the Land

1.76E+17 J/yr J y-1

Received: (area)(mean elevation)

(precipitation)(density)(gravity)

We estimated that 1/2 the precipitation 

falls as rain over a year.

Total Area 24114195000 m2

elevation variable by 

county

m

(State location of 

station)

rain/yr variable by 

county

m y-1

Density 1000 kg m-3

Gravity 9.81 m s-2

7 Rain, Geopotential Energy 

Absorbed from Runoff

9.04E+15 J/yr J y-1

Rain Geopotential used, Rain Runoff

Absorbed: (area)(mean elevationminimum elevation)(runoff)(density)

(gravity)

The geopotential energy absorbed from 

the rain runoff was determined from 

GIS calculations made for this study.

The closed basin and the Rio Grande 

drainage were handled separately, 

because the low points, average 

elevations, and rainfall of the two 

watersheds were not the same (see 

Geochemical Potential Table online). 

http://www.epa.gov/aed/research/

desupp5.html

Area

Closed Basin 8047770000 m2

Rio Grande Drainage 11502440000 m2

Average Elevation

Closed Basin 2679 m

Rio Grande Drainage 2836 m

Minimum Elevation

Closed Basin 2289 m

Rio Grande Drainage 2252 m

Rainfall

Closed Basin 0.377 m y-1

Rio Grande Drainage 0.553 m y-1

Density 1000 kg m-3

Gravity 9.81 m s-2
154

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

8 Snow, Geopotential 

Received on the Land

1.76E+17 J/yr J y-1

Received: (area)(mean elevation)

(precipitation)(density)(gravity)

We estimated that 1/2 the precipitation 

falls as rain over a year.

Total Area 24114195000 m2

elevation variable by 

county

m

(State location of 

station)

rain/yr variable by 

county

m y-1

Density 1000 kg m-3

Gravity 9.81 m s-2

9 Snow, Geopotential Energy 

Absorbed from Runoff

2.04E+16 J/yr J y-1

Snow Geopotential used, Snow Runoff

Absorbed= (area)*(mean elevationminimum elevation)*(runoff)*

(density)*(gravity)

The geopotential energy absorbed from 

the snow runoff was determined from 

GIS calculations made for this study.

The closed basin and the Rio Grande 

drainage were handled separately, 

because the low points, averages 

elevation, and rainfall of the two 

watersheds were not the same (see Geo 

chemical Potential Table online).

Area

Closed Basin 8047770000 m2

Rio Grande Drainage 11502440000 m2

Average Elevation

Closed Basin 2679 m

Rio Grande Drainage 2836 m

Minimum Elevation

Closed Basin 2289 m

Rio Grande Drainage 2252 m

Rainfall

Closed Basin 0.377 m y-1

Rio Grande Drainage 0.553 m y-1

Density 1000 kg m-3

Gravity 9.81 m s-2
155

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

Assumptions for snow

Assume all snow runs off into closed 

basin except for 8% sublimation 

(Campbell and Ohrt 2009)

Assume all snow runs off into Rio 

Grande Drainage except for 8% 

sublimation and 10% infiltration 

(calculated from Emery 1996).

10 Chemical and Geopotential of Rivers 

entering form Outside

Colorado is at the topographic high 

point for the Nation; therefore, all 

rivers flow out of Colorado to the east 

or to the West.

11 Chemical Potential Energy of 

Evapotranspiration

3.34E+16 J/yr J y-1

(Area)(Evapotranspiration)(density)

(Gibbs Free Energy per gram)

Forest land Area 8820852711 m2

Avg. Forest 

Transpiration

0.30 m y-1 An average of 4 values for the ET of 

three species found in the area was 

used to

Density water 1000000 g m-3 Estimated evapotranspiration. 

Lodgepole pine (Knight et al. 1981), 

large and small, Ponderosa pine 

(Ryan et al. 2000) and cottonwood 

(Gazal et al. 2006) were used. 

Gibb’s Free Energy, 

Rainwater

4.74 J g-1

ET Forests 1.23698E+16 J y-1 See the online table for Forests, ET, 1 and GW 

Arable land Area 2379884072 m2

Avg. Crop 

Transpiration

0.69 m y-1 Average transpiration for crops was 

taken from (Campbell et al. 2005)

Density water 1000000 g m-3

Gibb’s Free Energy, 

Rainwater

4.74 J g-1

ET Crops 7.82877E+15 J y-1

Pasture land Area 3818083936 m2

Avg. Pasture 

Transpiration

0.73 m y-1 Average transpiration for pasture 

was taken from (Campbell et al. 

2005)

Density water 1000000 g m-3

Gibb’s Free Energy, 

Rainwater

4.74 J g-1

ET Pasture 1.31932E+16 J y-1
156

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

Renewable Production

12 Total Agricultural 

Crops

2.11E+12 g y-1 Data on crop production is from 

Colorado Agricultural Statistics 

(2006).

(amount sold)

(energy/unit)

Mass

Hay 8.44E+11 g y-1

Barley 6.01E+10 g y-1

Oats 3.41E+10 g y-1

Potatoes 1.08097E+12 g y-1

Wheat 8.89E+10 g y-1

13 Livestock 1.2769E+15 J y-1

(annual production 

mass)(energy/mass)

Cows

# sold 190249 (Animal production data from 

Colorado Agricultural Statistics 

(2006).

wt 551065.2 g/animal

Energy /unit 12180 J g-1 Energy content from US Dept of 

Agriculture Handbook

14 Fish Production 3.7158E+11 J y-1 Fish production was estimated by a 

survey of 3 local producers 

(mass)(energy/mass) Carried out by Fred Bunch of the 

National Parks Service.

Mass 190,000 lbs y-1

453.59 g lb-1

Energy/mass 4311.58 J g-1

15 Timber Harvest 9.7388E+14 J y-1

(vol. forest harvested)

(dry wt)(J/g)

Volume harvested 3582538.96 ft3 Estimate made in the Ecological 

Footprint section of this study, 

1.01446E+11 cm3 Using harvest records of the Rio 

Grande National Forest and other 

federal lands.

dry wt 0.5 g cm-3

Forest mass 50723103070 g/yr

J/g 19200

Nonrenewable Use

16 Coal Used 2.10E+14 J/yr J y-1

Assume that all coal imported is 

consumed.

Corrected for coal used to generate 

electricity.

8572 Short tons/yr

907200 g/short ton

27000 J g-1
157

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

17 Natural Gas Used 3.22E+15 J/yr J y-1

Assume that all natural gas imported is 

consumed.

Amount 3055602.64 Thousand ft3

Corrected for natural gas used to 

generate electricity.

1055000000 J/thousand ft3

18 Petroleum Used 4.43E+15 J/yr J y-1

Assume that all petroleum imported is 

consumed.

811314.00 barrels

5.46E+09 J/barrel http://bioenergy.ornl.gov/papers/

misc/energy_conv.html

19 Electricity Used 1.46E+15 J/yr J y-1

Amount 404174975.10 kW-hr

Renewables Used in a Nonrenewable Manner

20 Groundwater use in some years 2.57E+15 J/yr J y-1

(vol.)(density)(Gibbs free energy) Volume used 5.49E+08 m3 y-1 Based on the volume of ground 

water withdrawn as estimated in the 

GNRP section.

Density 1000000 g m3

Gibbs 4.9 J g-1

21 Soil Erosion 1.70E+15 J/yr J y-1

(area)(erosion rate)(organic fraction)

(energy)

Soil mass eroded 2.71E+12 g y-1 Appendix 4-A

Organic fraction 0.03

Energy per gram 20930 J g-1

Nonrenewable Production

Nonrenewable production is based on 

the excess of imports over

Crude Oil Produced 1.35E+11 J y-1 See Imports-exports worksheet1

 for 

calculation of the nonrenewable 

Exports for those nonrenewables 

known to be produced in the SLB.

Metallic Ores 

Produced

8.40E+08 g y-1 Materials produced based on the 

import-export balance.

Broken Stone, Rip 

Rap Produced

2.30E+12 g y-1 Assume 10% of crushed stone 

remains within the SLB. 

Sand and Gravel 

Produced

2.52E+12 g y-1 Assume 10% of sand and gravel 

remains within the SLB.

Non-metallic 

Minerals Produced

9.26E+10 g y-1

Imports

22 Fuels

All fuels and electricity used in the 

SLB were imported during the time of 

this study

Coal 2.10E+14 J y-1

Petroleum 4.43E+15 J y-1

Natural Gas 3.22E+15 J y-1

Electricity 1.46E+15 J y-1
158

Note Variable and formulae Subsets of the Value Units Sources and additional 

Variable information

23 Other goods (without nonmetallic 

minerals processed) and services

Materials other than 

fuels

2.87E+11 g y-1 See Imports-Exports worksheet 1

Services in 

materials other than 

2.41E+08 $ y-1 See Imports-Exports worksheet 1

fuels

Services in Fuels 4.56E+07 $ y-1 See Energy Consumption worksheet 

1

Services in 

Electricity

2.48E+07 $ y-1

24 People

Net Immigration 0 individuals

y-1

See the Population worksheet 1

 Tourism 5.53E+07 $ y-1 Dean Runyon Associates, Colorado 

Travel Impacts (1996-2003), 

Colorado Tourism Office

25 Government

Federal and State 

Outlays

2.35E+07 $ y-1 See Government Worksheet 1

Exports

26 Exports by Category

Agricultural Crops

Potatoes 1.08E+12 g y-1 See Imports-Exports worksheet 1

Grain, Wheat, 

Barley, Oats

1.83E+11 g y-1 See Imports-Exports worksheet 1

Vegetables, Fruit, 

Nuts, Seeds

2.18E+09 g y-1 See Imports-Exports worksheet 1

Horticultural 

Specialties

7.02E+09 g y-1 See Imports-Exports worksheet 1

Livestock 1.28E+15 J y-1 see Livestock worksheet

Minerals

Metallic Ores 8.40E+08 g y-1 See Imports-Exports worksheet 1

Crude Petroleum 3.10E+06 g y-1 See Imports-Exports worksheet 1

Broken Stone Or 

Riprap

2.30E+12 g y-1 See Imports-Exports worksheet 1

Gravel Or Sand 2.52E+12 g y-1 See Imports-Exports worksheet 1

Misc Nonmetallic 

Minerals, Nec

9.26E+10 g y-1 See Imports-Exports worksheet 1

Forest Products

Primary Forest 

Products, Logs

2.96E+10 g y-1 See Imports-Exports worksheet 1

Lumber and 

dimension stock

1.05E+11 g y-1 See Imports-Exports worksheet 1

Wood Products 2.82E+10 g y-1 See Imports-Exports worksheet 1

(without nonmetallic minerals 

processed)

All other materials 8.21E+10 g y-1 See Imports-Exports worksheet 1
159

Note Variable and formulae Subsets of the 

Variable

Value Units Sources and additional 

information

27 Services

Services in Agricultural Products 8.83E+07 $ y-1 See Imports-Exports worksheet 1

Services in Minerals 9.01E+07 $ y-1 See Imports-Exports worksheet 1

Services in Forest Products 2.59E+08 $ y-1 See Imports-Exports worksheet 1

Services in All Other materials 

(without nonmetallic minerals 

processed)

3.60E+08 $ y-1 See Imports-Exports worksheet 1

Services N.A. $ y-1 We have not yet calculated exported 

services.

Gross Regional Product $ y-1 Used data from Chapter 4 and 

estimated for this Chapter10

28 People

Emigration by age 9.70E+02 individuals y-1 See Population worksheet1

29 Government

Federal Taxes $ y-1 Data on federal and state taxes are 

only available for 2004. 

http://www.taxfoundation.org/

files/fedtaxburden

bycounty-20070321.pdf

State Taxes $ y-1

1 http://www.epa.gov/aed/research/desupp5.html 

References

Gazal, R.M., Scott, R.L., Goodrich, D.C., Williams, 

D.G. 2006. Controls on transpiration in a semiarid 

riparian cottonwood forest. Agricultural and Forest 

Meteorology 137, 56-67.

Knight, D.H., Fahey, T.J., Running, S.W., Harrison, 

A.T., Wallace, L.L. 1981. Transpiration from 100-yrold lodgepole pine forests estimated with whole-tree 

potometers. Ecology 62, 717-726.

National Agricultural Statistics Service. 2006. Colorado 

Agricultural Statistics, http://www.nass.usda.gov/

Statistics_by_State/Colorado/Publications/Annual_

Statistical_Bulletin/2006bulletin.pdf.

Ryan, M.G., Bond, B.J., Law, B.E., Hubbard, R.M., 

Woodruff, D., Cienciala, E., Kucera, J. 2000. 

Transpiration and whole-tree conductance in 

Ponderosa pine of different heights. Oecologia 124, 

553-560

10 Chapter 4 did not specifically estimate Gross Regional Product (GRP). To estimate GRP for this chapter, we used Gross Domestic Product (GDP) 

for Colorado and New Mexico and population for the two states. We multiplied population for the SLB by the average per capita GDP from both 

states to estimate GRP. This approach requires numerous assumptions. However, the main assumption depends on whether the average per capita 

GDP for both Colorado and New Mexico represents consumption in the SLB. If this assumption is not sound, we may have overestimated or underestimated GRP. 
160

Figure 5-G.1 – The emergy of processed non-metallic minerals (i.e., perlite and scoria) imported into the San Luis 

Basin and the emergy exported in that category.

Figure 5-G.2 – The change in annual empower from net immigration or emigration from 1985 to 2005.

0.0E+00

5.0E+20

1.0E+21

1.5E+21

2.0E+21

2.5E+21

3.0E+21

3.5E+21

1995 1997 1999 2001 2003 2005

Empower (sej y-1)

Imported Nonmetallic Minerals Processed Exported Nonmetallic Minerals, Processed

0.0E+00

5.0E+19

1.0E+20

1.5E+20

2.0E+20

2.5E+20

3.0E+20

3.5E+20

4.0E+20

1985 1990 1995 2000 2005

Empower (sej y-1)

Immigration Emigration
161

Figure 5-G.3 – The emergy of fuels and electricity used per year in the San Luis Basin from 1980 to 2005. 

Figure 5-G.4 – The difference between the total emergy outflows (B+P2E+N2) and total emergy inflows (R+F+G+P2I). 

-2.0E+21

0.0E+00

2.0E+21

4.0E+21

6.0E+21

8.0E+21

1.0E+22

1.2E+22

1994 1996 1998 2000 2002 2004 2006

Empower (sej y -1)

Outflows - Inflows

0.0E+00

1.0E+20

2.0E+20

3.0E+20

4.0E+20

5.0E+20

6.0E+20

1980 1985 1990 1995 2000 2005

Empower (sej y-1)

Electricity Used Fuels used
162

Figure 5-G.5 – Digital elevation model of the Upper Rio Grande River Basin showing the boundaries of the closed and 

open basins.
163

6.0

Fisher Information and Order

6.1 Introduction

This chapter presents the theory, data, and methodology 

for using Fisher information (FI) to assess the dynamic 

order and overall stability of the San Luis Basin (SLB) 

regional system. In this chapter we provide background 

on the method, details of the numerical approach, and 

include a simple example demonstrating the computation 

method. Further, we delineate the data used to compute 

FI for the SLB, present results from the FI analysis and 

discuss the strengths and weaknesses of the approach.

Fisher information is a key method used in information 

theory and provides a means of monitoring a broad range 

of system variables to characterize the dynamic order 

of a system, to include its regimes, and identify regime 

shifts (Cabezas et al. 2003). Fisher information has not 

traditionally been used as a metric of sustainability, and 

its application to sustainability is, in fact, a relatively 

new concept. However, its usefulness as a measure of 

sustainability has been documented in the peer-reviewed 

scientific literature (Cabezas and Fath 2002, Cabezas et 

al. 2003, Fath et al. 2003, Karunanithi et al. 2008, Mayer 

et al. 2007) over a wide array of applications ranging 

from real and modeled ecosystems to climate.

We have used FI as a metric in the SLB project because 

it captures a critical aspect of the system that the other 

metrics do not. Well-functioning economic, ecological, 

and social systems may be necessary to preserve the 

self-organization and resilience of an environmental 

system and FI captures these aspects. Conceptually, the 

repeatability of observations denotes order in dynamic 

systems (Karunanithi et al. 2008). Therefore, a dynamic 

system is said to be well functioning and orderly when 

its behavior over time follows characteristic, regular, and 

nearly predictable patterns. FI captures organization and 

resilience by assessing the dynamic behavior of systems.

To illustrate this point, we offer three examples. 

First, consider that biological systems have elaborate 

mechanisms that allow them to maintain a specific 

degree of organization (Kauffman, 1993). The fact 

that an ecosystem can be identified as such is itself 

evidence that it has observable properties that, although 

variable, are still sufficiently stable to define the 

system. Indeed, the properties do vary, but they do so 

in a characteristically orderly and almost predictable 

manner. Further, as a system experiences a regime shift 

(i.e., loss of characteristic properties), the variation of 

key properties generally increase (Carpenter and Brock 

2006) which causes a loss of order. Second, similar 

arguments can be made for markets that are left to 

operate freely. The observable properties of the markets 

follow regular patterns, which indicate order. Movement 

toward market equilibrium helps to predict how prices 

will change (Maurice and Phillips 1992). The existence 

of patterns in the markets (i.e., prices as rationing and 

signaling devices) allows consumers and producers to 

make decisions with the expectation of particular results 

(Mankiw 2009; Maurice and Phillips 1992). Finally, 

consider that the four seasons of the year constitute an 

orderly dynamic system because the seasons succeed 

each other in regular, although not exactly precise, 

timing year after year (Houghton 2002). Indeed, autumn 

may sometimes come late and climatic fluctuations can 

occur; however, the seasons follow a pattern. 

In summary, ecosystems and social systems follow 

very complex but real patterns of change over time. 

Many processes in ecosystems cycle and change along 

relatively orderly patterns often driven by the daily 

and yearly cycles of light, tides and seasons along with 

longer term decadal, centurial, and other temporal and 

spatial variations. These cycles, although complex, 

are sufficiently regular to be studied and understood 

(Odum 1971). Markets likewise follow complex but 

orderly patterns and they are sufficiently regular to be 

studied and understood (Maurice and Phillips 1992). 

In brief, many systems, when functioning well, show 

regular characteristic behavior, which is an indication of 

dynamic order. Accordingly, order, or more specifically 

dynamic order, is an important and very fundamental 

indicator of the state or condition of the system (Mayer 

et al. 2007). It is for this reason that FI has been 

incorporated into the suite of metrics used to assess 

sustainability in the SLB project.

6.1.1 Capturing Dynamic Shifts with 

Fisher Information

When any system, including the aforementioned ones, 

undergoes a change from one characteristic pattern 

or set of behaviors to another, the change is generally 

termed a regime shift. Because no two regimes have 

the same observable patterns, a regime shift is typically 

accompanied by corresponding changes in dynamic 

order, which can be tracked by FI. To illustrate this 

point, consider the very simple case where a system is 

represented by one variable (e.g., temperature) which 

is being measured over time. Keeping in mind that the 
164

system here is represented by its temperature only, if 

the temperature is the same (i.e., within measurement 

error) every time it is measured, then the system can be 

said to exist in one state only. This system has as much 

order as it can possibly have, and the FI associated with 

the temperature is at a maximum. On the other hand, if 

the temperature is different every time a measurement 

is made, even after accounting for measurement error, 

say the temperature is rising linearly with time, then 

the system can be said to exist in many different states 

and the FI is at a minimum. Hence, a system with a 

well-defined pattern (i.e., constant properties) has high 

dynamic order and high FI, and a system that has no 

defined patterns (i.e., constantly changing properties) 

has low order and low FI. Although these two examples 

represent extreme behavior, real systems typically 

function between these extremes. 

Further exploring this idea, consider a situation where 

the aforementioned system is cooled, allowed to remain 

at a constant low temperature, within measurement 

error, for a period of time, and then heated to a constant 

temperature with larger variability, perhaps very small 

amounts of heat are added or removed randomly. In this 

case, when the temperature is decreasing, the FI will 

be very small or zero. Once it reaches a constant low 

temperature, the FI will be very high. As the system is 

heated and the temperature increases, the FI will again 

be very small or zero (during the change), and then 

increases to a relatively steady FI. However, the final 

“steady” FI value will be less than the original due to 

the higher variability in the temperature. The system 

described here experienced a dynamic regime shift, 

as there was a noted drop in FI between two stable 

regimes, one with higher order than the other. From 

these explorations, it is evident that FI tracks the order 

and stability of the system. Although we used simple 

examples to illustrate the very basic relationship between 

system dynamics and FI, as discussed later, the FI 

method has been adapted to assess the dynamic changes 

in complex systems described by multiple and disparate 

variables. 

6.2 Methods

6.2.1 Theory

Fisher information was formally developed by 

statistician Ronald Fisher as the information obtainable 

from data when estimating the value of a parameter (Fath 

et al. 2003, Fisher 1922). It has since been adapted as 

a measure of dynamic order. Details on the derivation 

of FI from its original form are provided in Fath et al. 

(2003), Mayer et al. (2007), Karunanithi et al. (2008), 

and Appendix 6-A.1. From the derivation, we obtain a 

representation of FI (denoted here as I to preserve the 

historical context; Fisher 1922, Frieden, 1998, 2004, 

Karunanithi et al. 2008, Mayer et al. 2007) based upon 

the probability of a system p(s) being in a particular state 

(s):

 

 (6.1)

From this expression, an approach was developed that 

affords the ability to assess the dynamic order of real 

systems and is derived as follows. In order to minimize 

calculation errors from very small probability values, we 

replace p(s) in Equation 6.1 with its amplitude, which is 

defined, by q2(s) ≡ p(s), such that:

 

 

 (6.2a)

Substituting Equation 6.2a into Equation 6.1, the 

expression becomes: 

 

 (6.2b)

Equation 6.2b is adapted for use with discrete data by 

using a summation to approximate the integral and 

replacing ds and dq(s) with ∆s=si-si+1 and ∆q=qi-qi+1, such 

that:

 (6.2c)

In Equation 6.2c, m is the number of states and si is 

merely an index denoting a particular state of the system 

(e.g., s1 is state 1, s2 is state 2, etc.). Accordingly, si - si+1 

= 1 and Fisher information is then:

 (6.3)

Equation 6.3 is our working expression for computing 

the FI metric. 

6.2.2 Calculation Methodology

The basis of computing FI is assessing changes in the 

probability of observing different states of the system 

through time. Therefore, we must obtain information 

on its condition (state) over time. Borrowing from 

standard statistical mechanics approaches, we define a 

system by n measurable variables (xi

) that characterize 

the system and its state at any point in time (Mayer 

et al. 2007). Note that the correlation structure of the 

variable time series is not critical, because our goal is 

to assess changes in dynamic order and not to develop 

a predictive model of system behavior. By describing a 

dynamic system in this way, it is said to have a trajectory 
165

in a phase space representing all possible states of the 

system in n-dimensions and time. In the absence of 

measurement error, each point in phase space is defined 

by specifying a value for each of the variables at a 

point in time, pti: (x1(ti), x2(ti) x3(ti)...xn (ti)). However, 

because measurements of real system parameters 

contain error (i.e., uncertainty), the state s of the system 

is delineated by a region rather than a point. In other 

words, two points that differ from each other by less 

than the measurement error are indistinguishable and 

can be thought of as two measurements within the same 

state. Conversely, two points that differ from each other 

by more than the measurement error are, in fact, two 

legitimately different points and consequently, exist in 

two distinct states of the system. From this conceptual 

description, we have a foundation for understanding 

the process of characterizing a dynamic system and 

assessing changes in its state over time. 

In order to capture the dynamic behavior of a system, 

once the variables characterizing its state have been 

gathered, the time period is divided into time windows. 

We achieve this by first defining a parameter (hwin) 

that denotes the size (in time steps) of each window and 

then determining the amount of overlap (in time steps) 

desired for each window (winspace). The parameters 

hwin and winspace denote the integration window 

parameters used to move through the time series data by 

creating a sequence of overlapping windows such that 

hwin > winspace as shown in Fig. 6.1. This convention 

affords the ability to compensate for changes in 

dynamic behavior that may extend beyond the boundary 

of each window. The probability densities p(s) and 

corresponding FI are computed within each of these 

windows. Whereas hwin is selected based upon the 

amount of data available, we have empirically found 

that it should be at least eight time steps. The next step 

in the procedure is “binning” each point within the time 

windows into discrete states of the system. Estimating 

the uncertainty of the variables is key to defining the 

states of the system. 

As previously mentioned, inherent in any measurement 

is a level of uncertainty. Accordingly, we define a 

parameter ∆xi

 as the measurement uncertainty for each 

variable (xi) such that, if the condition: 

 

 (6.4)

is true for all variables at time ti and tj then the two points 

are indistinguishable and subsequently, grouped in the 

same state (i.e., binned together). Because multiple 

variables are used to characterize a system and each 

variable has a distinct measure of uncertainty, a state is 

represented as an n-dimensional hyper-rectangle where 

each side is defined by an uncertainty (∆xi) for each 

variable. Therefore, this ∆xi is the size of the states for 

the system.

Typically, knowledge of the measurement uncertainty 

of underlying state variables is unknown; therefore, 

we have developed approaches for estimating ∆xi. 

One tactic is to find a relatively stable time period 

within the system trajectory, calculate the variation in 

each variable in this period, and assume this to be the 

measurement uncertainty. This approach is implemented 

by calculating the standard deviation (SD) for each 

variable in the “stable” period and applying Chebyshev’s 

inequality, which is defined by:

 (6.5)

and indicates that independent of the type or form of 

the probability distribution, “The proportion of the 

observations falling within k standard deviations of the 

[population] mean is at least 1-1/k2” (Lapin 1975: 58). 

Therefore, we define the size of states parameter (∆xi) as 

a function of SD, such that Δxi = +kSDi. By setting k = 2, 

Δxi = +2SDi at least 75% of the data would occur within 

this level of uncertainty. This provides a lower bound 

for the probability of values being k standard deviations 

from the mean. However, the probability could be 

much higher (>95% at k=2) if, for instance, the data are 

normally distributed. Given that each variable has a 

unique measure of uncertainty, the size of states then is 

noted as a row vector ∆xi = [kSD1, kSD2 ....kSDn], where 

k is a scalar constant. Another method of defining the 

size of states is by locating a similar system that exhibits 

stability and using the variation within this system as a 

measure of uncertainty for the system under study. 

Once the integration window parameters (hwin and 

winspace) and size of states (∆xi) are determined, the data 

may be distributed (binned) into different states of the 

system. The binning process begins with the first point 

within the time window taken as the center of the first 

state. A hyper-rectangle with the side lengths defined 

by ∆xi

 for each variable is established around the point, 

such that all of the points falling within its boundaries 

are considered to be in the same state. Then, the next 

unbinned point in the window is assumed to be the center 

of a new state, a new hyper-rectangle is constructed 

around it, and all the points within its boundaries are 

binned into that state. The process continues until all 

of the points within the first window are binned and is 

repeated for the remaining time windows until all of 

the points in each window are binned into states of the 

system and the data are exhausted.
166

When measurement uncertainty of the underlying state 

variables is known and independent, the binning process 

alone would work well in defining states of the system. 

Unfortunately, data collected from public sources, 

as in this project, often do not report the uncertainty 

associated with their data. Accordingly, an additional 

step is implemented to mitigate the effect of data error 

by applying a tightening level parameter (Karunanithi et 

al. 2008). The tightening level (TL) adjusts the binning 

criteria such that a point can be declared to be within 

a given hyper-rectangle (particular state of the system) 

when at least a certain percentage of the variables meet 

the size of states criteria (Equation 6.4). The percentage 

itself is the tightening level. For example, if a system is 

characterized by 100 variables and 95 of the variables 

indicate that a particular point fits within the state being 

evaluated, then the two points would be binned together 

at a 95% tightening level. Therefore, points may be 

binned into a particular state of the system if the size of 

states criteria is true for all variables in particular time 

steps or the number of variables that satisfies the size of 

states criteria is greater than the product of the tightening 

level (TL) and the total number of variables (Fig. 6.2). 

When all of the points in a window are binned into states 

at a given tightening level, a probability distribution (pi

) 

is generated for each window using:

 # points in state

 Pi = ————————————— total # points in time window (6.6)

Next the amplitude, q ( ) is calculated for each 

state and FI for the time window is computed using 

Equation 6.3. The computed FI value itself is assigned 

to the middle of each time window (see example in 

Fig. 6.1). Because there are no rigorous criteria for 

setting the tightening level, FI is computed for multiple 

tightening levels between strict and relaxed tightening. 

Rather than establishing an arbitrary lower bound for 

the tightening level, relaxed tightening is set as the 

lowest tightening level at which more than one state is 

observed in the window and FI is calculated by taking 

the arithmetic average of FI between strict tightening 

(TL=100%) and lower bound (relaxed tightening level). 

Note that this results in multiple pi

 and FI values for each 

window (Karunanithi et al. 2008). The final FI reported 

in each window is again an average over the tightening 

levels from strict to relaxed tightening. Finally, in 

accordance with the Sustainable Regimes Hypothesis 

(see section 6.2.3), a regime is denoted as sustainable 

when the dynamic order does not change with time 

(i.e., ( ) where <FI> indicates a mean FI

value). The <FI> is calculated by computing the mean 

of neighboring FI values. This convention affords the 

ability to focus on trends in dynamic order and not 

fluctuations. As such, Equation 6.7 is used to compute a 

three-point mean, <FI >j for window j: 

 

 j=w-1, w-3, w-5, w-7,... (6.7)

where w is the year corresponding to the last window of 

the original FI computation. Based on our experience, 

the three-point mean tends to preserve trends and 

deemphasize short fluctuations. The <FI>j is computed 

in reverse order to ensure that the more recent period is 

not omitted when <FI> is evaluated. However, given 

that a true measurement of uncertainty is unknown for 

the metrics or underlying variables, we can visually 

inspect the trends, but we cannot test for statistical 

significance.

6.2.3 Interpreting Fisher Information

The Sustainable Regimes Hypothesis encompasses 

conceptual ideas governing the use and interpretation 

of FI as a metric for assessing sustainability (Cabezas 

and Fath 2002, Karunanithi et al. 2008). In summary, 

the hypothesis states that: 1) well functioning systems 

exist within an orderly dynamic regime with non-zero 

FI that does not change with time (i.e.,( ); 

2) steadily decreasing FI signifies a progressive loss of 

dynamic order and denotes a system that is becoming 

disorganized and losing functionality; 3) steadily 

increasing FI indicates that the system that is becoming 

more ordered (although, not necessarily more desirable 

by humans); and 4) a steep decrease in FI between two 

dynamic regimes denotes a regime shift (Karunanithi 

et al. 2008). As a note, both conditions of statement 1 

must be true for a system to be considered sustainable. 

In other words, a completely disorganized system has no 

order over time; therefore, a system with <FI> ≈ 0 is not 

sustainable even if . 

Important elements in regime shift detection are 

determining the occurrence, pervasiveness, and intensity 

of a shift (Karunanithi et al. 2008). The fourth statement 

of the Sustainable Regimes Hypothesis indicates that a 

regime shift has occurred when there is a significant drop 

in FI between two dynamic regimes. Once a regime 

shift has been detected, the intensity simply relates to 

the level of the drop in FI (e.g., a more severe shift has 

a steeper drop). The pervasiveness of the shift relates 

to the number of system variables affected by the shift 

and is characterized by varying the tightening level and 

noting the lowest tightening level at which a particular 

shift can be detected. Recall, that the tightening level 

relates to the number of variables that must meet the 

size of states criteria for binning. Accordingly, the more 

relaxed (lower) the tightening level at which the regime 

shift is recognized, the more pervasive the shift.
167

6.2.4 Computing Fisher Information: 

A Simple Example

Below a simple example is used to demonstrate the 

procedure for computing FI. From Section 6.2.2, the 

basic algorithm is as follows: (1) establish the size of the 

time windows (hwin), (2) determine the time increment 

that the window will be moved forward (winspace) to 

create overlapping windows, (3) set a tightening level 

(TL), (4) bin all of the points into states within each 

window, (5) compute the probability density for each 

state in each time window – the result at this point 

will be a sequence of probability densities pi for each 

time window, (6) calculate FI from the qi in each time 

window, (7) set a new tightening level, (8) repeat steps 

4 through 7 until all the computations have been done 

from strict tightening (TL = 100%) to relaxed tightening 

(the lowest TL at which more than one state is present 

in the window), (9) compute an average FI for each 

window over the tightening levels from strict to relaxed 

tightening and (10) calculate <FI> (mean FI) values for 

the system.

For the sake of simplicity, we used two demographic 

variables (population and personal income) from the 

SLB data (Fig. 6.3). Because the goal of this exercise 

is simply to step through the computation algorithm, 

we used the guidelines for the integration window 

parameters (see section 6.2.2) and selected values for 

hwin and winspace that met the criteria. Recall that 

the parameters should be set such that hwin ≥ 8 and 

winspace < hwin. Accordingly, we set hwin to eight 

time steps (years) and winspace to three time steps 

to ensure we ended up with a manageable number 

of windows for this computation demonstration. To 

provide insight into how the integration window 

parameter settings may affect the computation result, we 

performed a sensitivity analysis to examine the impact 

changes in these parameters have on FI (Appendix 6-B). 

However, the sensitivity analysis is not a requirement for 

setting hwin and winspace. The only specific parameter 

guidelines are provided in section 6.2.2.

Following the basic algorithm already described, we 

used the hwin and winspace values to partition the data 

into over lapping windows (Table 6.1). Next, we set the 

tightening level (TL) at 100% to include all variables in 

the computation and then binned the points into states 

of system within each window (Fig. 6.2). The purpose 

of binning is to determine the state of the system over 

time and the basis of this process is grouping points 

(e.g., pt1

=(x1(t1), x2(t1)) within an established boundary 

of uncertainty. As explained in section 6.2.2, if the level 

of uncertainty for the variables under study is unknown, 

it may be estimated. In this case, it was estimated by 

assuming a measurement uncertainty by computing the 

standard deviation (SD) of each variable over the first 

five time steps. Each SD was then multiplied by two 

in accordance with Chebyshev’s theorem (Lapin 1975: 

58), such that the level of uncertainty (∆xi) defined 

as ±kSDι, is a 1×2 vector: ∆xi = [2×SD1,

 

2×SD2] = 

[86285.39, 1639.83]. From Fig. 6.2 and Equation 6.4, 

if the tightening level multiplied by the total number of 

variables in the time series meet the size of states criteria 

(i.e., |xi(t

i

) – xi(t

j

)| < Δxi) then the two points at ti and tj are 

binned in the same state. The differences were computed 

by subtracting the value of the variables at each time step 

starting with the first point in the window (Table 6.2). 

For example, the difference between variable x1 at time 

=1 and time = 2, i.e.,|x1(t

2

) – x1(t

1

)| = |314914-310352| = 

4562. 

A “bulls-eye” (radar) plot of the differences provides 

a visual depiction of point binning (Fig. 6.4). The first 

binning pass starts with assessing the distance from 

pt1, accordingly, pt1 is the center of this figure with an 

absolute difference value of (0, 0) and the remaining 

absolute differences are plotted on the corresponding 

axis (e.g., 2-1 = (|x1(t

2

) – x1(t

1

)| , |x2(t

2

) – x2(t

1

)| ) = (4562, 

511)). The red and blue lines represent the boundary 

of uncertainty (i.e., size of states = ∆x1 and ∆x2) around 

the center for each variable. Points 1, 2, and 3 are 

binned into state 1, because pt2 and pt3 are within the 

boundary of uncertainty (i.e., less than ∆xi from pt1). 

The process then moves to the next “un-binned” point 

(pt4), establishes it as the center of state 2 and then 

bounds state 2 as ∆xi from pt4, such that points 4-8 are 

binned into state 2. The binning procedure continues 

to the next window until all points have been binned 

into a state of the system. Once all the points have 

been binned, then probability densities are computed 

for each window using Equation 6.6. In window 1, 

there were three points binned into state 1 and five in 

state 2 resulting in a 37.5% chance (p(1)=3/8) that the 

system is in state 1 and a 62.5% chance that it is in 

state 2. This process was repeated for each window, 

resulting in probability densities for each window (Fig. 

6.5). Next, the amplitude ( ) was computed 

for each state (Table 6.3). To compute FI for each time 

window, the gradients of the amplitude ( ) were 

used as in Equation 6.3 (see Fig. 6.6). At this point, 

the tightening level may be decremented (i.e., TL = 

TL-1) and the computation steps repeated as articulated 

in section 6.2.2. This produces FI values for the time 

series for TL ranging from strict to relaxed tightening. 

An average FI result is calculated by taking the average 

of all FI values computed within TL range. For this 

exercise, we only computed FI at TL = 100% and then 
168

used Equation 6.7 to calculate a three-point <FI> to 

smooth any fluctuations in the result and focus on the 

general trend of the metric (Fig. 6.7). Further details 

for using the code and graphical user interface (GUI) to 

compute FI are provided in Appendices 6-C and 6-D. 

The procedure for computing FI has been automated 

in MATLAB (Release 2009a; Mathworks, Inc.) so that 

only time series data that characterize the system, size 

of states, hwin, and winspace need to be provided by the 

user to perform the analysis. The code is provided in 

Appendix 6-E.

6.3 Computing FI for the San Luis 

Basin: Data and Sources

To assess dynamic order in the SLB, we needed data that 

characterized the state of the system. Accordingly, it was 

necessary to gather data that represent environmental, 

social, and economic aspects of the region. Because 

the variables used to compute the other metrics satisfied 

this criterion, data for this project were selected from 

the datasets used to calculate EFA, GNRP and EmA 

(Chapters 3, 4, and 5, respectively). However, any 

data that adequately characterize a system may be 

used to calculate FI. Further, we selected variables 

that contained data for the entire 26 years of the study 

(1980 - 2005). Each of the variables was assigned to 

one of six categories for computing FI and encompassed 

information on consumption (food and forest), 

environment, and demographic characteristics, as well 

as, the energy consumption, land use, and agricultural 

production aspects of the SLB (Table 6.4). Details on 

each variable are provided in the corresponding chapters 

(Chapters 3-5). 

6.4 Calculation Methodology

Following the approach described in sections 6.2.2 and 

6.2.4, as well as, the guidelines for computing FI using 

MATLAB (Appendices 6-C and 6-D), the GUI (Main_

Fisher_data_proc.m) was used to compute both FI and 

<FI>. The file containing data for the 53 variables 

over 26 years, the time file which indicates the years 

examined (i.e., 1980, 1981, 1982,... 2005), and the file 

containing the size of states were selected. Following 

the guidelines in Section 6.2.2, the size of states for the 

SLB was determined using a small number of years 

where data were relatively stable. Thus, the size of states

was defined by calculating the SD of the first five data 

points (years) for each variable and then (in accordance 

with Chebyshev’s inequality) multiplying the result by 2 

(k = 2). 

Given the amount of data and the results of the 

sensitivity analysis (Appendix 6-B), the time window 

was set to be eight time steps (hwin = 8) and the window 

increment was set to one time step (winspace = 1). 

Thus, FI was integrated over an eight-year window 

that was moved in one-year increments. The FI value 

reported represents the FI computed over a specific 

period and is reported in the center of that window (i.e., 

FI for 1984 = 1980-1987, FI for 1985 = 1981-1988, 

FI for 1986 = 1982-1989, etc). The <FI> (for each 

window j) was calculated using Equation 6.7, such that 

the <FI>j

 reported is a mean placed in the center of the 

three points used to calculate it (e.g., <FI>2001 = (FI2000 + 

FI2001 + FI2002) ∕ 3). Further, in order to explore drivers 

potentially responsible for changes in dynamic order 

of the system overall, we compared FI of the overall 

system (included all 53 variables) to that of the variables 

grouped by categories (e.g., energy consumption). 

Spearman’s rank correlation coefficient analysis was 

used to assess the relationship between the dynamic 

order of the system and the variables by category. The 

statistical significance level established was P ≤ 0.05.

6.5 Results

FI was computed for the SLB over the 26 years of 

the study and ranged in value from 2.92 in the period 

centering 2001 to 4.37 in the period centering 1996 (Fig. 

6.8). <FI> ranged from 3.22 in the period centering 

1986 to 3.89 in the period centering 1995 (Fig. 6.9; Table 

6.5). The system <FI> increased initially, peaked in 

the period centering 1995, and then decreased slightly 

thereafter. 

The minimum <FI> in the system in 1986 corresponded 

(visually) to a minimum <FI> in the consumption (food 

and forest) and agricultural production categories (Fig. 

6.10). The peak in the system <FI> in 1995 corresponds 

(visually) to a peak in the <FI> of the environmental and 

energy categories, as well as, agricultural production 

which increases up to 1992 and essentially remains 

steady until 1998 (Table 6.5; Figs. 6.9 and 6.10). 

Although the <FI> of consumption (food and forest) 

category remains relatively steady, the <FI> of the other 

categories exhibit larger changes over time.

Similar to the overall system, dynamic order of the 

agricultural production category rose from 1989 to 1992, 

was relatively steady until 1998, and decreased thereafter 

(Fig. 6.10). The opposite was true of the demographic 

variables as the <FI> peaked in 1989, decreased from 

1989 to 1995 and increased thereafter. By the end of 

the study period energy, environment, and demographic 

categories exhibited an increasing trend (Table 6.5; Fig. 

6.10). Further, Spearman’s rank analysis indicated a 

significant, negative correlation between the overall 

system and the demographic category (r = -0.88, df = 4, 

P = 0.033). 
169

6.6 Discussion

According to criteria in the Sustainable Regimes 

Hypothesis (Section 6.2.3), the results of this <FI>

assessment indicated that although there were changes 

in <FI> over time, <FI> was relatively steady during the 

period and there was no indication of an overall system 

regime shift (i.e., no sharp drop between two regimes). 

The dynamic order of the system increased up to 1995 

and exhibited a small decrease thereafter. Although 

the demographic, energy, and environmental categories 

showed an increase in dynamic order at the end of the 26 

years, the overall system, consumption (food and forest), 

land use, and agricultural production categories indicated 

a decreasing trend in <FI> which may denote some 

movement away from sustainability (i.e., decreasing 

dynamic order). There was, however, no sharp drop in 

<FI> that could indicate a regime shift. 

Spearman’s rank analysis indicated that there is an 

inverse relationship between the demographic category 

and that of the overall system. Toward the goal of policy 

setting and decision making, variables within a category 

may be explored to uncover possible drivers of dynamic 

behavior. As an illustration, a closer look at the FI of the 

demographic category revealed that changes in dynamic 

order seem to correspond to changes in the underlying 

variables (e.g., population and personal income). 

Because FI is a measure of system order, increases in 

variability of the system variables generally result in 

decreasing FI. As a simple exploration to see if we could 

identify such variables, we computed the annual percent 

change in the demographic variables as a measure 

of variability. While population generally increased 

during the period examined, the percent change varied 

during the 26 years (e.g., +1.6% from 1982 to 1983 and 

+0.9% from 1983 to 1984 graphically is a decrease). 

Further, unlike the percent change in personal income 

over time, the percent change in population decreased 

initially, peaked in 1995, and decreased thereafter. A 

visual comparison of these patterns and demographic 

FI suggests, the variation in population (as described 

by percent change) appears to be inversely proportional 

to demographic FI (i.e., low variation in demographic 

data corresponds to high FI values). For that reason, 

changes in annual percent change of population seem 

to correspond to changes in dynamic order of the 

demographic category (Fig. 6.11). From this simplified 

approach, there appears to be a relationship; however, 

other techniques (e.g., sensitivity analysis) to explore the 

effect of changes in underlying variables on FI may help 

determine which variables drive changes in dynamic 

order. 

In summary, there was no indication of a regime 

shift in the overall system. Although, the system 

exhibited small changes in dynamic order during the 

26 years, we conclude the SLB is relatively stable with 

a slight indication of possible movement away from 

sustainability near the end of the period. 

6.7 Strengths and Weaknesses

One of the key strengths of FI is the ability to collapse 

data from complex, multivariate systems into a 

fundamental metric that can be computed over time and 

used to evaluate the dynamic behavior of systems. This 

is important because the characterization of complex, 

integrated systems for sustainability (social, ecological, 

and economic systems) often requires a large number of 

disparate variables. Traditional approaches to assessing 

regime shifts and system dynamics (e.g., variance) 

have typically only been demonstrated on simple model 

systems and work must be done to determine whether 

these methods can be used to evaluate real, complex 

systems (Scheffer et al. 2009). However, the calculation 

of dynamic order using FI is insightful, theoretically 

sound, and provides a means of evaluating both model 

and real systems that are characterized by multiple, 

disparate variables. 

As is the case for many methods used to analyze 

complex systems, the computation of FI has its 

challenges. Some of the challenges include establishing 

the integration window parameters (i.e., hwin and 

winspace), the need for determining the measurement 

error for each variable (used to set the size of states), and 

the availability and quality of data to characterize the 

system. The integration window parameters are used 

to traverse through the time series data and establish 

overlapping windows for the FI computations. These 

parameters must be carefully selected based on amount 

of data available and knowledge of the system. The 

size of states is a key parameter used for binning points 

into states of the system and is based on estimating 

the amount of error present in the data. Strategic 

recommendations for determining the size of states and 

the integration window parameters were provided in 

sections 6.2.2 and Appendix 6-B, respectively. Data 

availability and quality issues are discussed in Chapter 7. 

Another challenge is that because PDFs are used to 

calculate FI, one FI value is provided in each time 

window and reported in the center of that period. 

Accordingly, there is a unique FI for each time window 

and not each time step. Hence, FI values tend to come 

several years behind the latest data point (e.g., the 

last data were for 2005, but the last FI value was for 

2001). Moreover, according to the Sustainable Regimes 
170

Hypothesis, in order to capture the trends (and not 

fluctuations) in the dynamic order, a <FI> is calculated. 

We therefore use a three-point <FI> to evaluate trends 

in dynamic order over time. Based on our experience 

with these calculations, three points seem to smooth the 

result, while maintaining the characteristic changes in 

dynamic order associated with trends.

Further, the methodology can identify if a system is 

either maintaining its current order (i.e., ( )or 

losing dynamic order ( <0) and heading toward 

or already experiencing a regime shift (i.e., a sharp 

drop in <FI> between two stable regimes). The loss of 

dynamic order, and certainly a regime shift, indicates the 

system is moving away from sustainability. However, 

an increase in <FI> indicates the system is gaining order, 

yet does not necessarily mean the system is moving 

toward a more preferable state (i.e., sustainability) in 

terms of human wants and needs (Karunanithi et al. 

2008). Hence, FI is a one-sided test of sustainability and 

care must be taken on interpreting changes in dynamic 

order with reference to human preferences.

Lastly, we acknowledge that the details of the conceptual 

underpinning of FI analysis can be quite abstract and, 

perhaps, difficult for many potential users to understand. 

However, we also believe that anyone with a good 

grounding in science or engineering can understand 

the fundamental concept sufficiently to apply the 

method and interpret the results appropriately. We note 

that practical and effective end users of methods and 

technologies in many professions are not necessarily 

experts in the theory behind the tool being used. For 

example, most physicians are not experts in Quantum 

Mechanics, yet they can effectively use Magnetic 

Resonance Imaging (MRI) and interpret the results. Use 

of MRI may involve a medical technologist who serves 

as a bridge between those who designed the MRI and the 

physicians that interpret and use the results. The point 

here is that it should be possible to make the benefits of 

otherwise abstract concepts (such as FI) available to nonexpert end users through software and/or individuals that 

act as intermediaries. To help make the concept more 

accessible, we provided an example of the computation 

and interpretation of FI using a simple example of 

dynamic order (Section 6.2.4). Further, we provided the 

MATLAB code necessary to compute FI and stand-alone 

software with a GUI to simplify calculating FI. The GUI 

improves the usability of the metric by automating the 

computation process. 

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signals for critical transitions. Nature, 461, 53-59. 
172

Table 6.1 – Data were divided into a sequence of overlapping time windows. The eighth window was removed because there were not enough data in the time series to place in it. Therefore, all data were accounted for within seven windows. t = time (i.e., 1980-2005), x1 = Population and x2 = Personal income 

(in thousands). Point 1 (pt1) was defined by (x1(t1), x2(t1)) and is highlighted as 310352, 38469. 

Window 1 Window 2 Window 3

# t x1 x2 # t x1 x2 # t x1 x2

1 1980 310352 38469 4 1983 384256 40112 7 1986 431318 40804

2 1981 314914 38980 5 1984 400888 40490 8 1987 436775 41104

3 1982 318946 39467 6 1985 424601 40369 9 1988 448738 41289

4 1983 384256 40112 7 1986 431318 40804 10 1989 504014 40903

5 1984 400888 40490 8 1987 436775 41104 11 1990 551552 40682

6 1985 424601 40369 9 1988 448738 41289 12 1991 549641 41283

7 1986 431318 40804 10 1989 504014 40903 13 1992 562981 41120

8 1987 436775 41104 11 1990 551552 40682 14 1993 625715 41466

Window 4 Window 5 Window 6

t x1 x2 t x1 x2 t x1 x2

10 1989 504014 40903 13 1992 562981 41120 16 1995 715547 43793

11 1990 551552 40682 14 1993 625715 41466 17 1996 754872 44566

12 1991 549641 41283 15 1994 661317 42564 18 1997 781542 45289

13 1992 562981 41120 16 1995 715547 43793 19 1998 839246 45902

14 1993 625715 41466 17 1996 754872 44566 20 1999 892574 46377

15 1994 661317 42564 18 1997 781542 45289 21 2000 910329 47097

16 1995 715547 43793 19 1998 839246 45902 22 2001 967522 46907

17 1996 754872 44566 20 1999 892574 46377 23 2002 1042786 47404

Window 7 Window 8

t x1 x2 t x1 x2

19 1998 839246 45902 22 2001 967522 46907

20 1999 892574 46377 23 2002 1042786 47404

21 2000 910329 47097 24 2003 1042145 47598

22 2001 967522 46907 25 2004 1048231 48207

23 2002 1042786 47404 26 2005 1097044 48101

24 2003 1042145 47598 0 0 0 0

25 2004 1048231 48207 0 0 0 0

26 2005 1097044 48101 0 0 0 0

pt1= (x1(t1), x2(t1))= (310352, 38469)
173

Table 6.2 – Absolute difference of variables x1 (population) and x2 (personal income) in each window using the first point as the center. For example, the 

absolute difference of x1(t1) and x1(t2) (i.e., |x1(t1) – x1(t2)| is 4562 as highlighted here in blue and the absolute difference of point 2 from point 1 (i.e., 2-1) is 

4562 and 511). 

Window 1 Window 2 Window 3 Window 4

points x1 x2 points x1 x2 points x1 x2 points x1 x2

2-1 4562 511 5-4 16632 378 8-7 5457 300 11-10 47538 221

3-1 8594 998 6-4 40345 257 9-7 17420 485 12-10 45627 380

4-1 73904 1643 7-4 47062 692 10-7 72696 99 13-10 58967 217

5-1 90536 2021 8-4 52519 992 11-7 120234 122 14-10 121701 563

6-1 114249 1900 9-4 64482 1177 12-7 118323 479 15-10 157303 1661

7-1 120966 2335 10-4 119758 791 13-7 131663 316 16-10 211533 2890

8-1 126423 2635 11-4 167296 570 14-7 194397 662 17-10 250858 3663

Window 5 Window 6 Window 7

points x1 x2 points x1 x2 points x1 x2

14-13 62734 346 17-16 39325 773 20-19 53328 475

15-13 98336 1444 18-16 65995 1496 21-19 71083 1195

16-13 152566 2673 19-16 123699 2109 22-19 128276 1005

17-13 191891 3446 20-16 177027 2584 23-19 203540 1502

18-13 218561 4169 21-16 194782 3304 24-19 202899 1696

19-13 276265 4782 22-16 251975 3114 25-19 208985 2305

20-13 329593 5257 23-16 327239 3611 26-19 257798 2199
174

Table 6.3 – Amplitude (q(s)) for each window. These values were computed by first counting the number of points 

in each state within each window, computing a probability for each state in each window (p(s)) and calculating the 

amplitude, for each state in each window. Window

q(s)

1 2 3 4

1 0.6124 0.7906

2 0.8660 0.5000

3 0.7071 0.7071

4 0.7071 0.5000 0.5000

5 0.5000 0.5000 0.6124 0.3536

6 0.6124 0.6124 0.5000

7 0.6124 0.7071 0.3536
175

Table 6.4 – Variables used to calculate FI for the San Luis Basin. These variables were selected from data used 

to compute EFA, GNRP, and EmA. The data and their sources are discussed in Chapters 3, 4, and 5, respectively. 

The variables include demographic, energy, land use, food and forest consumption, environmental and agricultural 

production categories for the system. 

Category Variable

Demographic

Personal income (thousands of dollars)

Population (persons)

Energy

Coal consumption (short tons)

Natural Gas Consumption (billion cubic feet)

Petroleum Consumption (thousand barrels)

Hydro-electric Consumption (million kilowatt hours; kWh)

Wood and waste Consumption (trillion British thermal units; BTU)

Solar Energy Absorbed (Joules per year; J/yr)

Rain Chemical Potential (J/yr)

Rain Geopotential on the Land (J/yr)

Snow Geopotential on Land (J/yr)

Rain Geopotential as Runoff (J/yr)

Snow Geopotential as Runoff (J/yr)

Land type

Built-up land (hectares; ha)

Arable land (ha)

Pasture (ha)

Forest including deforestation (ha)

Food and forest consumption

bovine, buffalo (pounds per capita; lbs/ca)

sheep, goat (lbs/ca)

non-bovine (lbs/ca)

milk (gal/ca)

cheese (lbs/ca)

eggs (lbs/ca)

fish (lbs/ca)

cereals (lbs/ca)

wheat (lbs/ca)

vegetables (lbs/ca)

maize (lbs/ca)

fruit (lbs/ca)

roots and tubers (lbs/ca)

pulses (lbs/ca)

coffee and tea (lbs/ca)

cocoa (lbs/ca)

oil seed (lbs/ca)

fats (lbs/ca) 

sweetener consumption (lbs/ca)

forest harvest (million board feet, MBF)
176

Category Variable

Environmental

mean precipitation (centimeters; cm)

Change in ground water storage (acre-feet)

Change in surface water storage (acre-feet)

SLB CO2

 emissions

Evapotranspiration (J/yr)

Soil Erosion (tons/year)

Food production

bovine, buffalo production (kilograms; kg)

cereal production (kg)

wheat production (kg)

animal feed production (kg) 

roots and tubers production (kg)

Potatoes All (Planted) (unit:acre)

Wheat Other Spring (Planted) (unit:acre)

Barley All (Planted) (unit:acre)

Hay Alfalfa (Dry) (Harvested) (unit:acre)

Oats (unit:acre)

Table 6.5 – Three-point mean Fisher information (<FI>) for the overall system and the variables grouped by the 

categories defined in Table 6.4. 

Year System Consumption Demographic Energy Environment Land Production

1986 3.22 3.34 5.02 4.59 3.86 4.82 4.56

1989 3.51 3.93 6.04 4.03 3.39 5.58 4.79

1992 3.64 3.66 4.14 4.63 4.61 6.32 5.99

1995 3.89 4.13 2.77 4.63 4.85 5.12 5.94

1998 3.88 4.48 2.81 3.82 4.32 6.00 6.02

2001 3.58 4.28 3.32 3.93 4.42 4.73 5.03
177

Figure 6.1 – Graphical representation of the sample settings for the Fisher information (FI) computation indicating 

the size of the integration window (hwin) and window increment (winspace) in time steps used to move through the 

data. In this example, hwin =10 and winspace = 5. The red star indicates the center point of the window denoting the 

placement of FI. 

Figure 6.2 – Flow diagram of the binning algorithm in the Fisher information computation. According to Equation 6.4, 

if each variable |xi

(t

i

)– xi(t

j

)| is less than the size of states (∆xi), then the two points at time i and j are indistinguishable 

and are grouped (binned) in the same state. 

winspace

1980 1985 1990 1995 2000 2005

hwin

Yes

No

Goto next

point

Bin in same state

Save point and use

for binning next

state
178

19801982

1984

1986

19881990

1992

1994

1996

1998

200020022004

Year

Personal Income ($Thousands)

Population

Figure 6.3 – Population (x1, green triangles) and Personal Income (x2, blue diamonds) values for the San Luis Basin 

from 1980 to 2005. These variables were used as data for the example FI computation exercise provided in Section 

6.2.4.

Figure 6.4 – Bulls-eye plot of binning points in state 1 for the simple example. Point 1 (pt1: (x1(t1), x2(t1)) = (310352, 

38469)) is the center of this figure and the absolute difference from pt1 are plotted on the corresponding axis, e.g., 

absolute difference 2-1 = (4562, 511). The values plotted are from Table 6.2. As described in Section 6.2.2, the level 

of uncertainty (i.e., size of states (∆xi)) was computed for each variable such that in this plot ∆x1 and ∆x2 correspond 

to the red and blue line, respectively. The points are binned in a state when the difference is less than the level of 

uncertainty (i.e., size of states).

0

20000

40000

60000

80000

100000

120000

140000

2-1

3-1

4-1

6-1 5-1

7-1

8-1
179

Figure 6.5 – Probability density (p(s)) for each window in the simple example (Section 6.2.4). These values were 

computed by first counting the number of points in each state within each window and then computing a probability 

for each state in each window. In window 3, four points were binned in both state 1 and state 2; therefore, the 

probability was 50% for each state. 

Figure 6.6 – Calculating gradients of the amplitude (q(s)) in window 1 of the simple example (Section 6.2.4). 

Gradients were used as a convention for mimicking a discrete density function and were computed as qi

-qi+1, where i is 

the state. These values used in Equation 6.3 to compute FI. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1234567

Window

p(s)

1

2

3

4

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Stat1e (s)

q(s)

1

2

q(1)-q(0) 

=0.61 

q(2)-q(1) 

=0.18 

q(0)-q(2) 

= -0.79 
180

Figure 6.7 – Fisher information for each window from the simple example: (A) Fisher information (FI) 

and (B) Smoothed FI using three-point mean (<FI>), i.e., <FI>1999 = (FI2002+FI1999+FI1996)/3 and <FI>1990 = 

(FI1993+FI1990+FI1987)/3. 

Figure 6.8 – Fisher information calculated for the San Luis Basin over a 26-year period (1980-2006). Each point 

represents the FI computed in each window and is reported in the center of that period (i.e., FI; 1984 = 1980-1987, 

1985 = 1981-1988, 1986 = 1982-1989, etc.).

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

2

4

6

8

Original FI

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

2

4

6

8

< FI >, Smoothed FI

A

B

San Luis Basin Fisher Information

0

1

2

3

4

5

6

7

8

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Year

Fisher Information
181

 Figure 6.9 – Mean Fisher information (<FI>) for the San Luis Basin. <FI> was computed as a three-point average of 

the FI (Fig. 6.8) in order to smooth out short-term fluctuations and highlight trends, rather than fluctuations. The <FI>

is reported in the center of the three points (i.e., <FI>; 2001 = 2002-2000, 1998 = 1999-1997, etc.).

Figure 6.10 – Mean Fisher information of the San Luis Basin for the overall system and by category. Using this plot, 

we compared changes in system mean FI (<FI>) to the <FI> the variables grouped in categories. The <FI> is reported 

in the center of the three points (e.g., <FI>2001 = (FI2000 + FI2001 + FI2002) ∕ 3). 

San Luis Basin Mean Fisher Information

0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

6.0000

7.0000

8.0000

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Year

Mean FI, <FI>

San Luis Basin Mean FI (System and by Category) 

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

1986 1989 1992 1995 1998 2001

Time

mean FI, <FI>

System Consumption Demographic Energy Environment Land Production
182

Figure 6.11 – Percent change in the San Luis Basin demographic variables. The percent change in population 

corresponded to the changes in dynamic order of the demographic category. The change in population decreased 

initially, peaked in 1995, and decreased thereafter which corresponds to the changes in dynamic order for the 

demographic category. 
183

Appendix 6-A: Derivation Of Fisher 

Information 

Fisher information was developed by the statistician 

Ronald Fisher as a statistical measure of information in 

data being used to fit a parameter (Fisher 1922). It is 

formally defined by Frieden (1998, 2004) as:

 

 

 (6-A.1)

where, I is Fisher information and p(y|θ) is the 

probability density of observing a particular measured 

value of a variable y in the presence of a parameter 

θ. From this equation, I is a measure of the amount 

of information about θ that is obtainable from the 

measurement of y (we use I here to preserve the 

historical context; Fisher 1922, Frieden, 1998, 2004, 

Karunanithi et al. 2008, Mayer et al. 2007). For 

example, if y has no information about θ, then the 

derivative in Equation 6-A.1 is zero and I is zero as well. 

The parameter θ can represent many items. With that in 

mind, in order to transform this equation into a measure 

of order for complex dynamic systems, let the parameter 

θ be the mean <y> of y over a particular period of time, 

T:

 

 (6-A.2)

where T is the integration period for all observations of 

y. By substituting Equation 6-A.2 into (6-A.1), we have:

 (6-A.3)

Next, to represent the fluctuations in y around the mean 

<y>, we define a new variable s, such that s ≡ y - <y>. 

According to elementary statistics, systems where the 

variation around the mean is independent of the value of 

the mean are said to be shift-invariant (Frieden, 2004)

 (6.A.4a)

indicating that the probability distribution does not 

depend on the value of the mean. Using the results 

of Equation 6-A.4a and incorporating the chain rule 

(6-A.4b):

 (6-A.4b)

 where we have tacitly dropped the subscript “0” on p(s), 

and Equation 6.A.1 becomes: 

 (6-A.5)

where p(s) is the probability density finding a particular 

value of s. This is Equation 6.1 found in the main text 

of Chapter 6. Now let s be a state of the system in 

a Euclidean (linear) space defined by the observable 

variables of the system and time. That is, a particular 

state s is a region in a space, (i.e., linear phase space 

where the dimensions are the observable variables of 

the system and time). Then, p(s) is the likelihood of 

observing a particular state, s. 

From Equation 6-A.5, we note that I is proportional to

dp / ds. In the context of order, systems can exist within 

two idealized extremes, perfect disorder and perfect 

order, as discussed in the main text within a different 

context. The perfect disorder case occurs when a system 

is unbiased toward any particular state. In other words, 

it has the same probability of being in one state as any 

other state of the system (s=1: n), i.e., p(s) = p(1) = p(2) 

=...p(n) and the probability density function (PDF) is flat 

(Fig. 6-A.1a) so that dp / ds →0. Accordingly, the system 

lacks order (which in some contexts can be thought of 

as predictability) and the resulting Fisher information 

approaches zero (i.e., I→0) (Fath et al. 2003). Perfect 

order occurs when repeated measurements of the system 

result in the same state over time. This more structured 

system has high order, is more predictable, and is biased 

toward a particular state or states. Accordingly, the PDF 

has a steep slope, dp / ds →∞ and Fisher information

approaches infinity (i.e., I→∞) (Fig. 6-A.1b). However, 

real systems typically function between these two system 

extremes (e.g., Fig. 6-A.1c). 

For completeness, we would like to point out that 

one reasonably elegant means of evaluating Equation 

6-A.5 is by adapting a statistical mechanics approach 

and representing the system in its phase space. Here 

the space coordinates are again the measurable system 

variables, and the probability density for observing a 

particular state (s) is proportional to the time the system 

spends in state s, i.e., p(s) ∞ Δt(s). This method provides 

us with a resulting expression that is a function of the 

velocity (R’(t)) and acceleration (R’’(t)) tangential to the 

system path in its phase space (Mayer et al. 2007). The 

expression used to compute the Fisher information under 

this theory is: 

 (6-A.6)

However, Equation 6.A-6 requires the evaluation of 

the first and second order derivatives tangential to the 

system path defined by the system variables (Cabezas et 

al. 2005, Fath et al. 2003, Mayer et al. 2006). Although 

this approach is appropriate for model systems for which 

smooth data are readily available, the challenge is that it 
184

is extremely difficult to obtain high quality second order 

derivatives from the noisy, sparse data sets characteristic 

of real systems (Karunanithi et al. 2008). Accordingly, 

the numerical approach (derived and discussed in 

Chapter 6) was developed for calculating I to assess 

the dynamic order of real systems without the need 

to compute second derivatives. Further details of the 

analytical and numerical derivation of I can be found in 

Mayer et al. (2007) and Karunanithi et al. (2008). 

References

Cabezas, H., Pawlowski, C.W., Mayer, A.L., Hoagland, 

N.T. 2005. Simulated experiments with complex 

sustainable systems: ecology and technology. 

Resources, Conservation and Recycling 44, 279-291.

Fath, B., Cabezas, H., Pawlowski C.W. 2003. Regime 

changes in ecological systems: an information theory 

approach. Journal of Theoretical Biology 222- 4, 517-

530.

Fisher, R.A. 1922. On the mathematical foundations 

of theoretical statistics. Phil. Trans. Royal Society of 

London 222, 309-368.

Frieden, B.R. 1998. Physics from Fisher Information: A 

Unification, Cambridge University Press, Cambridge, 

UK.

Frieden, B. R. 2004. Science from Fisher Information: 

A Unification. Cambridge University Press, 

Cambridge, UK.

Karunanithi A.T., Cabezas, H., Frieden, B.R., 

Pawlowski, C.W. 2008. Detection and assessment 

of ecosystem regime shifts from Fisher information, 

Ecology & Society 13(1), 22.

Mayer, A.L., Pawlowski C.W., Cabezas, H. 2006. 

Fisher Information and dynamic regime changes in 

ecological systems. Ecological Modeling 195, 72-82.

Mayer, A.L., Pawlowski, C.W., Fath, B.D., Cabezas, 

H. 2007. Applications of Fisher information to the 

management of sustainable environmental systems, in: 

Frieden, B.R., and Gatenby, R.A. (Eds.), Exploratory 

Data Analysis Using Fisher Information. London: 

Springer-Verlag, pp. 217-244.

Pawlowski, C.W., Cabezas, H. 2008. Identification 

of regime shifts in time series using neighborhood 

statistics. Ecological Complexity 5, 30-36 
185

 Figure 6-A.1 – Fisher information is proportional to the probability of system states (Pawlowski and Cabezas 2008). 

(a) Zero I results when a system has an equal probability of being in one state as any other state, resulting in a uniform 

probability density function (PDF), such that dp / ds →0 and I→0. (b) High I occurs when repeated measurements 

of the system result in the same state over time (i.e., the system is more predictable resulting in high predictability), 

a PDF with a steep slope, dp / ds →∞ and Fisher information approaching infinity (i.e., I→∞). (c) However, real 

systems typically function between these two extremes and exhibit medium I.

p

a) Zero Fisher 

information

b) High Fisher 

information

c) Medium Fisher

information

→∝ 0

ds

dp I

∞→∝

ds

dp I

s
186

Appendix 6-B: Sensitivity Analysis On 

Integration Window Parameters

In section 6.2.4, we used a simple example to 

demonstrate the FI computation steps. The FI result 

was calculated using the integration window parameters 

selected in accordance with the basic criteria (section 

6.2.2) and contained seven values (one for each time 

window) which were limited by the amount of data 

available, hwin, and winspace (Table 6.1). However, 

one of the challenges in computing FI is determining 

optimal settings for the integration window parameters. 

Accordingly, this appendix provides an exploration of 

the effect of changing the value of hwin and winspace on 

both the FI results and the resolution of results (number 

of FI results produced from a computation) for the data 

from the simple example. This exercise is not meant 

to cover every possibility; however, it is intended to 

(1) underscore the fact that care should be taken when 

selecting the integration window parameters and (2) 

provide key insights on the impact of the parameters on 

the FI result. 

The basic guidelines for setting the integration window 

parameters are hwin ≥ 8 and winspace < hwin (section 

6.2.2). For this exercise, we used the data from the 

simple example and examined four different values for 

hwin (ranging from 8 to 11 time steps) and winspace

(ranging from 1 to 4 time steps). Graphing FI computed 

from these settings provide some sense of the effect 

changes in winspace and hwin on FI (Fig. 6-B.1). Each

panel of Fig. 6-B.1 reflects the FI result with hwin

constant and a different winspace for each computation. 

For example, panel A contains the FI results given hwin

= 8 and winspace ranging from 1 to 4. The average 

value of FI (avgFI) and standard deviation (SD) of FI 

reported were computed from all the FI results in each 

panel as a summary statistic. For example, the avgFI 

and SD reported in panel A are the average value and 

standard deviation of FI given hwin = 8 and winspace

from 1 to 4. In general, smaller winspace values 

resulted in more fluctuations in FI, yet, the overall 

impact of winspace on the value of FI was relatively 

minor. However, looking at avgFI for each panel, as 

hwin increased avgFI decreased. We performed similar 

analysis on the data by holding winspace constant and 

found that when assessing the variability of avgFI, the 

standard deviation is 0.11 as winspace changes and 0.44 

as hwin changes. Therefore, it appears that the selection 

of hwin is the primary factor affecting FI. However, this 

only just begins to answer the question of the impact of 

the parameters on the FI result and provides minimal 

insight regarding optimal settings for hwin or winspace

for this system. 

One of the common approaches to assessing the 

impact of controllable (independent) variables on 

output responses includes evaluating the system one 

factor at a time (OFAT). Not only is this approach 

time consuming and costly, factor interactions are not 

considered (Anderson 2005, Montgomery 1997). In lieu 

of an OFAT approach, we opted to perform a sensitivity 

analysis by designing a factorial experiment using hwin

and winspace as controllable factors and the average 

value of FI (avgFI) and resolution of FI results (Npts) 

as output response variables. A designed experiment 

affords the ability to determine critical factors by 

assessing the impact of controlled variable changes (and 

their interactions) on output responses. Using Design 

Expert 8 (StatEase, Inc., Minneapolis, MN) we created a 

design matrix for the 42

 full factorial design showing the 

factor combinations and responses for each experiment 

(Table 6-B.1). An analysis of variance (at α = 0.05) 

of avgFI revealed that both hwin (F (3, 9) = 77.56, 

P<<0.05) and winspace (F (3, 9) = 4.64, P< 0.05) are 

statistically significant factors (Table 6-B.2). However, 

note that the sum of squares (SS) and mean squares 

(MS) summarizes the amount of variability accounted 

for by each factor and random error (Montgomery 1997; 

NIST/SEMANTECH 2006). Accordingly, the results 

indicate that more of the variation in avgFI comes from 

changes in hwin. Similarly, we found that both factors 

have a statistically significant impact on the resolution 

of the FI results (number of FI results produced from a 

computation); however, winspace is dominant (Table 

6-B.3). Further, there was a strong negative correlation 

(r = -0.9934, P = 0.0066, df = 2) between the avgFI 

and hwin (i.e., as hwin increases, avgFI decreases) 

and although hwin has little effect on the resolution of 

FI results, smaller winspace values produce a higher 

resolution of FI results (Figs. 6-B.2 and 6-B.3). From 

the analysis, it appears that hwin is the primary factor 

affecting the variability and amplitude of the avgFI 

result and winspace is the key parameter affecting the 

number of FI results produced from a computation 

(Npts). The challenge at this point is determining 

optimal settings for the integration window parameters 

such that the deviation in the avgFI result is minimized 

and the number of FI data points is maximized. In order 

to determine the optimal settings, we sought a solution 

to the multiple objective problem of (a) minimizing 

the standard deviation of FI, the standard error of the 

mean value of FI and standard error of the Npts and (b) 

maximizing Npts (Table 6-B.4). Using the optimization 

function within Design Expert 8 by StatEase, Inc., an 

optimal solution was found at hwin = 8 and winspace = 

1 (Fig. 6-B.4). Using the solution from the sensitivity 
187

analysis and numerical optimization, we calculated FI 

and the three-point mean FI result, <FI> (Fig. 6-B.5). 

These parameter settings were also used to compute FI 

for the SLB system. 

Table 6-B.1 – Design matrix for the sensitivity analysis of the simple example data: Determining the impact of 

controllable factors, hwin and winspace on the response variables, average value of FI (avgFI) and the number of FI 

results (Npts) for computation. 

References

Montgomery, D.C. 1997. Design and Analysis of 

Experiments. John Wiley and Sons, Inc., New York, 

NY.

NIST/SEMATECH. 2006. e-Handbook of Statistical 

Methods. Available online at http://www.itl.nist.

gov/div898/handbook/toolaids/pff/1-eda.pdf. Last 

Accessed August 26, 2010.

Factors Response

Run hwin winspace avgFI Npts

1 8 2 3.13511 10

2 9 2 2.74041 9

3 9 1 2.87719 18

4 11 1 2.22149 16

5 10 1 2.44636 17

6 11 2 2.13994 8

7 9 3 3.16116 6

8 8 1 3.18285 19

9 8 3 3.24848 7

10 10 2 2.3549 9

11 11 3 2.15498 6

12 9 4 2.90048 5

13 10 3 2.75425 6

14 10 4 2.44245 5

15 8 4 2.99323 5

16 11 4 2.12815 4

Table 6-B.2 – Analysis of Variance (ANOVA) for the average value of FI (avgFI). The results reveal that both hwin (F 

(3, 9) = 77.56, P<<0.05) and winspace (F (3, 9) = 4.64, P< 0.05) were significant factors affecting avgFI. Moreover, 

as an indicator of the amount of variability that is accounted for by factor effects (e.g., hwin or winspace) and random 

error (i.e., residual), the MS values reflect that more of the variability was determined by hwin. 

Source Sum of

Squares (SS)

df Mean

Square (MS)

F

value

p-value

Prob > F

Model 2.4199 6 0.4033 41.1021 < 0.0001 significant

 A-hwin 2.2833 3 0.7611 77.5644 < 0.0001 significant

 B-winspace 0.1366 3 0.0455 4.6397 0.0317 significant

Residual 0.0883 9 0.0098

Cor Total 2.5082 15
188

Table 6-B.3 – ANOVA for the resolution of Fisher information results (Npts). Like the ANOVA for avgFI, both hwin

(F (3,9) = 8.33, P <0.05) and winspace (F(3,9) = 519, P <<0.05) were statistically significant factors affecting Npts. 

However, the mean square (MS) values indicate that majority of the variability in Npts was due to winspace.

Source Sum of

Squares (SS)

df Mean

Square (MS)

F

Value

p-value

Prob > F

Model 395.5 6 65.92 263.67 < 0.0001 significant

 A-hwin 6.25 3 2.08 8.33 0.0058 significant

 B-winspace 389.25 3 129.75 519 < 0.0001 significant

Residual 2.25 9 0.25

Cor Total 397.75 15

Table 6-B.4 – Criteria for determining the optimal value for hwin and winspace for the sample exercise. The goal of 

the numerical optimization was to minimize the deviation in avgFI and maximize Npts, subject to (a) minimizing the 

standard deviation of FI, the standard error of the mean value of FI and standard error of the Npts and (b) maximize 

Npts. The highest importance was placed on maximizing Npts and minimizing the standard error of avgFI and Npts. 

The optimization was performed in Design Expert 8 (StatEase, Inc.). 

Constraints

Name Goal Lower

Limit

Upper

Limit

Importance

A:hwin is in range 8 11 3

B:winspace is in range 1 4 3

StdErr(mFI) minimize 0.065521169 0.065521169 4

Npts maximize 4 19 5

StdErr(Npts) minimize 0.330718914 0.330718914 4

std mFI minimize 0.421906393 1.178581952 3
189

Figure 6-B.1 – The effect of changing hwin on the FI result. The value of hwin changes from panel to panel (A-D), 

while winspace varies within each panel. Therefore, each panel reflects the Fisher information result over time with 

hwin constant and a different winspace for each computation: (A) hwin =8, winspace =1 to 4, (B) hwin =9, winspace

=1 to 4, (C) hwin =10, winspace =1 to 4, (D) hwin =10, winspace =1 to 4. The average value (avgFI) and standard 

deviation (SD) reported of FI in each panel were computed from all the FI results in each panel as a summary statistic. 

For example, avgFI and SD in panel A are the mean and standard deviation of the FI values given hwin = 8 and 

winspace = 1 to 4. Note that avgFI decreases as hwin increases and small winspace values result in more fluctuations 

(e.g., winspace =1 is represented by the blue line). 

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

5

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

5

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

5

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

5

winspace=1 winspace=2 winspace=3 winspace=4

D

B

A

C

avgFI = 3.14, SD = 0.11

avgFI = 2.16, SD = 0.042

avgFI = 2.50, SD = 0.175

avgFI = 2.92, SD = 0.176

11

10

9

8

hwin
190

20

A1

A2A3

A4

20

B1

Figure 6-B.2 – Interaction plots for the average value of FI (avgFI). Plotting the value of avgFI as a function of 

hwin and winspace affords the ability to evaluate the impact of both parameters on avgFI. (A) Each hwin value is 

represented by a different color plot. With winspace varying along the x-axis, there is little variability in avgFI as 

winspace increases. Conversely, note that as hwin increases (e.g., A1: hwin = 8, A4: hwin = 11), the value of avgFI 

decreases. (B) The strong relationship between hwin and avgFI is also shown as hwin increases along the x-axis. 

Figure 6-B.3 – Interaction plots for the number of FI results (Npts). Plotting the value of Npts as a function of hwin

and winspace affords the ability to evaluate the impact of both parameters on Npts. (A) Each computation with a 

particular hwin value is represented by a different color plot. With winspace varying along the x-axis, there was a 

great deal of variability in Npts. (B) However, as hwin increases along the x-axis, there are minor changes in Npts. 

Further, there is a greater decrease in Npts as winspace goes from 1 to 2, than from 2 to 3 or 3 to 4. 

A: hwin

123 4

Interaction

B: winspace

1.5

2

2.5

3

3.5

A1

A2

A3

A4

B: winspace

8 9 10 11

Interaction

A: hwin

1.5

2

2.5

3

3.5

B1

B2

B3

B4

A B

avg FI

avg FI

A B A: hwin

1 2 3 4

Interaction

B: winspace

Npts

0

5

10

15

20

A1

A2A3

A4

B: winspace

8 9 10 11

Interaction

A: hwin

Npts

0

5

10

15

20

B1

B2

B3

B4
191

Figure 6-B.4 – Results of the numerical optimization of hwin and winspace using data from the simple example. 

Based on the optimization criteria of maximizing Npts and minimizing the deviation in avgFI, the optimal solution was 

found (using Design Expert 8) with integration window parameter settings of hwin = 8 and winspace = 1. 

Figure 6-B.5 – Final Fisher information (FI) result from the simple example. (A) FI result from the optimal parameter 

settings (hwin = 8 and winspace =1). (B) Smoothed result: <FI>, three-point mean Fisher information. 

 8

 9

 10

 11

1 

2 

3 

4 

0 

0.2 

0.4 

0.6 

0.8 

 Desirability 

 B: winspace A: hwin 

0.800

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

2

4

6

8

Original FI

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

0

2

4

6

8

< FI >, Smoothed FI

A

B
192

Appendix 6-C: San Luis Basin Fisher 

Code User Manual – Utilizing MATLAB 

Files (Gfisher.m and CalcFishMean.m) 

to Calculate Fisher Information

Appendix 6-C.1: Using MATLAB Files to 

Calculate Fisher Information via Command Line: 

(GFisher.m, CalcAvgFish.m, and SmoothFI.m)

The primary MATLAB files developed for this project 

include GFisher.m, CalcAvgFish.m and Main_Fisher_

data_proc.m (GUI) and AdvancedFishGUI.m (GUI). 

Other supporting files are Nfisherpdf.m, round2.m, 

CloseGUI.m and SmoothFI.m. In this section, we 

present a general description for using MATLAB to 

execute the functions (e.g., GFisher.m, CalcAvgFish.m) 

via command line to compute FI. Using the functions 

in this way requires some knowledge of MATLAB; 

however, this manual is not intended to be a MATLAB 

tutorial. Conversely, the GUIs (i.e., Main_Fisher_data_

proc.m and Advanced FishGUI.m) are not command 

line functions and are the most user-friendly means 

of computing FI. They will be described in Appendix 

6-C.2:

The file GFisher.m contains the code developed in 

conjunction with the ongoing research within the 

US EPA’s Sustainable Environments Branch for 

implementing the numerical approach to computing FI. 

Given that m=number of time steps (e.g., years) and 

n=number of characteristic variables, the command line 

for this code is: 

[FI,midt_win] = GFisher (t,data,sost,hwin,winspace,TL,I

P) where the required inputs into the function are:

 t: the time data for study of size m × 1 

data: the time series data of variables

characterizing the state of the system

of size m × n

sost: a row vector of the size of states for

each variable, size = 1× n

hwin: the size of the time window 

winspace: the moving window increment 

TL: the tightening level (%)

IP: a toggle on or off (1 or 0) for requesting 

an automatic plot of the FI result

The function outputs are:

FI: Fisher information for each time 

window

midt_win: Midpoint of each time window 

corresponding to the FI result

Prior to executing the code, the user must gather the 

input data as described above, to include: time series 

data of the variables that characterize the state of the 

system (data), the time period (e.g., 1980-2005) that 

the data represent time (t), the integration window 

parameters (hwin and winspace) and the size of states

(sost) for each variable. Please refer to sections 6.2.2 

and 6.2.4 for information on determining and gathering 

this information.

Once the data are compiled, they may be entered into 

MATLAB directly via command line or imported from 

a saved file of a supported type (e.g., csv, Excel). The 

data entry method is not critical when executing the 

command line functions; however, data must be in a 

particular format: 

• The time series data of the variables characterizing 

the system (data) should be configured such that 

data for each variable are listed in one column 

with each row containing the variable values for a 

particular time step, producing a m× n matrix. For 

example, if there are 5 variables and 20 time steps. 

The data will be entered into a 20 × 5 matrix. 

• The time data (time, t) should be formatted such that 

one time step value is entered in each row producing 

a m ×1 column vector with each time period 

corresponding to a datum point. 

• The size of states (sost) data should be formatted 

such that there is a size of states value for each 

variable in each column, producing a 1 × n row 

vector (see 6.2.2 for computing size of states). 

For example, the 20 × 5 matrix mentioned above 

contains 5 variables and would have 5 size of states 

values, one for each variable (i.e., 1 × 5 row vector). 

All of these inputs must be loaded into the workspace 

prior to executing the function. In other words, properly 

formatted data must be loaded into the workspace and 

parameter values must be defined in the MATLAB 

command window before entering the command.

While GFisher.m provides the FI result for one specified 

tightening level (e.g., 100% = 100), CalcAvgFish.m calls 

GFisher.m to compute FI by incrementally changing the 

tightening level and calculating the average of the FI 

from the strict (TL = 100%) to relaxed tightening (see 

Section 6.2.2). The command line for this file is: 

[AvgFish,PO_TL,mY]= CalcAvgFish 

(t,data,hwin,winspace,sost)
193

The function inputs are similar to GFisher and outputs 

are:

AvgFish: Average Fisher for each time window

PO_TL: Tightening Level the system is in perfect 

 order. When there is a regime shift, it is an 

 indication of pervasiveness

mY: Mean year for each time window

The SmoothFI.m file is used to compute the mean FI 

<FI>, a m point mean which smoothes the FI results to 

focus the analysis on trends and not fluctuations. The 

command line is: [FIm]=SmoothFI(m,t,FI), where m

is the number of points to be used in calculating the 

average, t is the time vector and FI is the computed FI 

result from GFisher.m, CalcAvgFish.m or Main_Fisher_

data_proc.m GUI. The output is 2 columns containing 

the mean time and mean FI computed. In the SLB 

project, m was set to a default of three in order to provide 

a three-point mean FI. 

Appendix 6-C.2: Graphical User Interfaces 

(GUI) for Calculating Fisher Information: 

(Main_Fisher_data_proc.m and AdvFishGUI.m)

To simplify the calculation of FI and enhance userfriendliness, a GUI was developed as an extension to 

the San Luis Basin Sustainability Metrics Project (Fig. 

6-C.1). The GUI is an interface for data entry, executing 

code to compute FI, as well as, displaying and saving 

results from an FI analysis (Fig. 6-C.1). It is designed to 

simplify the procedure by allowing the user to select preformatted data files, alter pre-set parameter values and 

process data to calculate FI over numerous tightening 

levels (from strict to relaxed tightening). The computed 

FI is plotted in the GUI and the user is able to save the FI 

computations and plot for further use. 

Although MATLAB can import multiple file types, the 

code for the GUI was setup to accept data specifically 

in Excel format. Accordingly, although the data files 

are created using the data formatting guidelines (see 

Appendix 6-C.1), there are a few caveats:

• The Excel file containing the variable time series 

data should be formatted as an m×n matrix with 

a descriptive name that ends in data.xls (e.g., 

SLBdata.xls).

• The time data should be saved in a separate Excel 

file whose descriptive name ends in time.xls (e.g., 

SLBtime.xls) and contains the m ×1 time data, where 

m is the number of time steps

• The size of states data should be saved in a separate 

Excel file whose descriptive name ends in sost.xls

(e.g., SLBsost.xls) and contains the data in a 1× n 

row vector, where n is the number of variables.

• There should be no column headings in these files.

Navigating through the GUI, the [Add/Remove 

Files] menu tab contains controls that allow the user 

to select the data files for computing FI. The [Add 

Files] button is used to identify the location and add 

the pre-formatted files. Note that because there is one 

evaluation done at a time, only one time series, one size 

of states, and one time series data file (e.g., SLBdata.

xls, SLBtime.xls, SLBsost.xls) should be loaded at a 

time. The [Delete Files] button may be used to remove 

unnecessary files. Once the data, time, and size of state

files have been selected, the user inputs the hwin and 

winspace parameters, as well as, the number of points 

for the smoothing the FI results. The values for these 

parameters are preset to 8, 1, and 3 (respectively) for 

this project. The [START] button executes the function 

to perform the FI computation. Once this button is 

selected, all buttons and figures are disabled until the 

computation is complete and then a graph of the result 

(FI and smoothed FI (<FI>)) is displayed in the Plot 

Window. The [Save Plot] and [Save Calculations] 

buttons are used to save the FI plot and numerical result 

for further use. The [Save Calculations] button saves 

the FI values, date and time of calculation, as well as 

the smoothed mean FI results into an Excel file in the 

following format:

Row1 Col1: Date

Row2 Col1: Time (military)

Row3 Col1: Perfect order tightening level: PO_TL

Row4 Col2: Average FI results: [Year FI]

The smoothed <FI> results follow the average FI results

The [Reset] button allows the user to clear the figure and 

parameter settings and the [Help] button hyperlinks to a 

help file, which provides instructions on using the Main 

GUI. The [Advanced] button opens the Advanced Fisher 

GUI, which allows the user to assess the pervasiveness, 

and intensity of a regime shift, if one exists (see Section 

6.2.2 and Fig. 6-C.2). Such an examination is pertinent 

only if a regime shift has been identified. 

Given the same data and parameter settings that were 

established in the Main GUI (Main_Fisher_data_

proc.m), clicking [Study] in the Advanced Fisher 

GUI executes the computation of the FI for multiple 

tightening levels and plots the result on two axes (axis1: 

TL=100%-60% and axis2: 50%-10%) along with the 

mean FI within the GUI interface. Through these plots, 

the user is able to study the pervasiveness and intensity 

of the regime shift as described in Section 6.2.2 and 

Karunanithi et al. (2008). Pervasiveness indicates the 

number of variables affected by the shift and is estimated 

by determining the lowest level in which the drop in FI 

is still present. This is based on finding the TL value at 
194

which the system is perfectly ordered (i.e., FI = 8) for 

the entire period. The pervasiveness is calculated by 

subtracting the perfect order tightening level (POTL) 

from 100 and denotes the percentage of variables that 

are impacted by the shift. The intensity relates to the 

amplitude of the shift and is determined by how far the 

FI drops between two stable regimes. A system with a 

more intense shift would have a lower drop in FI before 

settling into a new dynamic regime.

The main benefit of the graphical interface is that it 

requires minimal user input and simplifies the evaluation 

process for FI computation. In conjunction with this 

project, MATLAB files for the GUIs were made into 

executable files using the MATLAB Compiler. This 

provides portability to locations where the MATLAB 

software is not readily available; thereby making the 

GUIs available for use on any desktop. However, these 

executable files are not modifiable. 

Figure 6-C.1 – GUI interface developed to compute FI from system data. The GUI is used for (1) importing preformatted data files, (2) choosing parameter settings (3) computing and plotting FI, (4) saving the FI results and (5) 

further study of regime shifts. The results plotted in this figure are from the simple example in Appendix 6-D.
195

Figure 6-C.2 – The Advanced GUI was developed to study the pervasiveness and intensity of ecosystem regime 

shifts by (1) evaluating and (2) plotting the FI at various tightening levels. (3) The perfect order tightening level 

and pervasiveness are computed and shown in the text boxes. The results plotted are from the simple example as 

computed in Appendix 6-D. Using this simple example, note that the pervasiveness is 100% and there is one regime 

shift between two stable regimes. 
196

Appendix 6-D: Using MATLAB to 

Compute FI for the Simple Example

In this section, we describe the steps involved in using 

the MATLAB GUI to analyze the data from the simple 

example. The first step was to save the time series, 

time, and size of states data into separate Excel files. In 

accordance with the guidelines in Appendix 6-C.2, the 

time series of the variables characterizing the system 

and the time data (i.e., 1980 to 2005) plotted in Fig. 6.3 

were saved as SLBExdata.xls and SLBExtime.xls (Tables 

6-D.1 and 6-D.2). The size of states for each variable 

as computed in section 6.2.4 (i.e., [86285.39 1639.83]) 

were saved in SLBExsost.xls (Table 6-D.3). Once the 

data files were set up, we opened the Main_data_proc 

GUI and clicked the [Add files] button to select the 

formatted data files (SLBExtime.xls, SLBExdata.xls and 

SLBExsost.xls) for computing FI. We left the default 

settings for the integration window parameters, hwin = 

8, winspace = 1 and the number of smoothing points = 

3 and pressed [START] to process the data and generate 

a plot of the FI and smoothed FI (<FI>) results (see Fig. 

6-D.1). After the data were processed, the plot and FI 

calculations were saved using the [Save Plot] and [Save 

Calculations] buttons. 

Although, the data used in this example do not fully 

characterize the sustainability aspects of the region 

(contains only two demographic variables), for the 

sake of this exercise, we assumed that these variables 

characterize the system under study. Looking at Fig. 

6-C.1, we noted a drop in FI “bookended” by two stable 

regimes indicating the existence of a regime shift. 

Thus, in order to study the pervasiveness and intensity 

of the shift, we clicked the [Advanced] button to open 

the AdvancedFish GUI (Fig. 6-C.2). After clicking 

the [Study] button, the FI (green plot), as well as FI at 

various tightening levels from strict (TL = 100%) to 

completely relaxed tightening (TL=0%) are computed 

and plotted. In addition, the perfect order tightening 

level (POTL) and pervasiveness for the system were 

shown in the textboxes. Typically, the POTL may be 

observed graphically by noting the lowest tightening 

level at which the features of the shift (drop in FI) are 

still present. Note that there are only a few plots visible 

because there are only two variables in this study. At 

any rate, because the features of the shift remain intact 

for the plots, the POTL = 0 and pervasiveness = 100%, 

both variables are impacted by the shift. 

Table 6-D.1 – Excel file containing the time series of 

the variables characterizing the system for the simple 

example (SLBExdata.xls). In this exercise, there are 

two variables (x1

 and x2) such that each column contains 

the time series data for one variable. As described in 

Appendix 6-C.1, the data file is displayed as an m × n 

matrix with m = the number of time steps and n = the 

number of variables. 

x1 x2

310352 38469

314914 38980

318946 39467

384256 40112

400888 40490

424601 40369

431318 40804

436775 41104

448738 41289

504014 40903

551552 40682

549641 41283

562981 41120

625715 41466

661317 42564

715547 43793

754872 44566

781542 45289

839246 45902

892574 46377

910329 47097

967522 46907

1042786 47404

1042145 47598

1048231 48207

1097044 48101
197

Table 6-D.2 – Excel file containing time data for simple 

example (SLBExtime.xls). In this exercise, the time 

period was from 1980 to 2005 and represents 26 time 

steps. Accordingly, as described in Appendix 6-C.1, the 

time file is displayed as an m × 1 matrix with m = the 

number of time steps. 

Year

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Table 6-D.3 – Excel file containing the size of state

(sost) data for simple example (SLBExsost.xls). In this 

exercise, there were two variables (x1

 and x2) and the size 

of states was computed for each variable (∆x1 and ∆x2). 

The values are exhibited as a 1 × n matrix with n = the 

number of variables. 

x1 x2

86285.39 1639.83
198

Appendix 6-E: Fisher Information MATLAB Code

Code is available for download in the zipped file named “Appendix 6E - Fisher Information code.zip”

Gfisher.m

function [FI,midt_win,t_win]=GFisher(t,data,sost,hwin,winspace,TL,IP) %used with GUI

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%

%This program calculates FI as a measure of dynamic order.

%It is one of the products of an on-going effort within the US 

%Environmental Protection Agency’s Sustainable 

%Environments Branch under the direction of Heriberto Cabezas. 

%Key contributors to this effort include:

%Theory, methodology and direction from Heriberto Cabezas as adapted from 

%Ronald Fisher’s work in theoretical statistics(1922); Chris Palowski for 

%the initial coding and framework: recurnumFI4.m (6/17/2004). 

%Methodology enhancements and corresponding source code 

%implementation by Arunprakash Karunanithi (3/13/2006) and additional 

%modifications by Tarsha Eason (2008-2009). 

%Further details on the methodology may be found in: 

%Karunanithi, AT. Cabezas H, Frieden BR and Pawlowski CW. 2008. Detection 

%and Assessment of ecosystem regime shifts from FI. Ecology 

%& Society. 13(1): 22. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%

%This program calculates the FI from time series data.

%Pertinent parameters include:

% FI : Fisher information

% midt_win : mid point of each time window

% t_win : time within each window

% t : time data 

% data : Input data (matrix), size (#time steps, #variables)

% sost : size of states

% hwin : window size(number of points in each calculation)

% winspace : number of time steps we move the window (<hwin)

% TL : tightening Level

% IP : Toggles the internal plots (IP=1==>on)

 %profile on

FI=[];

t_win=[];

window=0; 

for i=1:winspace:size(data,1) %start big loop to go through all the data

 %Revised Method: #pts=hwin

 lmin=min(i,size(data,1));

 lmax=min(i+hwin-1,size(data,1));

 NP=size(data(lmin:lmax,1),1);

if NP== hwin

 window=window+1;

 [pdf,neighbour]=Nfisherpdf(data,lmin,lmax,sost,TL/100); %calculate pdf for data

 %pdf

 %neighbour

 q=sqrt(pdf(:,1)); %convert to amplitude of the pdf 

%modification

 counter=0;

 Q=0;
199

 neighbourQ=[];

 for j=1:size(q,1)

 if q(j,1)~=0

 counter=counter+1;

 Q(counter,1)=q(j,1);

 neighbourQ(counter,:)=neighbour(j,:);

 end

 end

%neighbourQ

%Q

%%%%%%%

%This portion of code re-arranges the states by assessing proximity. As

%such, it calculates the Euclidean distance (zz) of the points in the state, 

%finds the smallest distance and orders the q vector by the Euclidean 

%distance. This ensures that the states are indeed ordered by distance. 

if size(neighbourQ,1)>2

minimumneighbourQ=0;

tempneighbourQ=0;

tempQ=0;

z=[];

for ii=1:(size(neighbourQ,1)-1)

 for jj=1:size(neighbourQ,1)

 if jj>ii

 z(ii,jj)=sqrt(sum(neighbourQ(ii,:)-neighbourQ(jj,:))^2);

 else

 z(ii,jj)=5000000000;

 end

 end

minimumneighbourQ=min(z(ii,:));

 for kk=2:size(neighbourQ,1)

 if kk>ii

 if z(ii,kk)==minimumneighbourQ

 tempneighbourQ=neighbourQ(ii+1,:);

 tempQ=Q(ii+1);

 neighbourQ(ii+1,:)=neighbourQ(kk,:);

 Q(ii+1)=Q(kk);

 neighbourQ(kk,:)=tempneighbourQ;

 Q(kk)=tempQ;

 end

 end

 end

end

end

QQ=[0;Q(1:end);0]; %adding points (beginning and end) for edge gradients

dq=diff(QQ)./1; %calculating dqs

 %Calculates fisher for each window and places it in the middle of the %window

if (i+(hwin-winspace))<=size(data,1)

 t_win(lmin:lmax,window)=[t(lmin:lmax)]; %time within each window

 FI(window)=[4*sum(dq.^2*1,1)]; %One fisher for each window

 midt_win(window)=ceil(mean(nonzeros(t_win(:,window))));

 %midt_win(window)= mean(nonzeros(t_win(:,window))); %for ode calc

end

end
200

end

%FI

if IP==1 %plot outputs

 

subplot(2,1,1), plot(t,data) %plots data

%subplot(2,1,1), plot(data(:,1),data(:,2)) %plots data for ode

title(‘Time series data’); 

subplot(2,1,2),plot(midt_win,FI,’-b*’) 

title(‘Fisher information for each time window’);

axis([min(t) max(t) 0 8]);

xlabel(‚Time‘); ylabel(‚Fisher information‘);

end
201

NFisherpdf.m

function [pdf,neighbour]=NFisherpdf(data,lmin,lmax,sizeofstates,TL)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%

%The methodolgy for calculating FI as a sustainability 

%indicator as a measure of dynamic order has been an on-going interative 

%effort within the US Environmental Protection Agency’s Sustainable 

%Environments Branch under the direction of Heriberto Cabezas. 

%Key contributors to this effort include:

 %Theory, methodology and direction from Heriberto Cabezas as adapted from 

%Ronald Fisher’s work in theoretical statistics(1922); Chris Palowski for 

%the conceptual framework and coding of initial Matlab program, recurnumFI4.m 

%(6/17/2004); Methodology enhancements and corresponding source code 

%implementation by Arunprakash Karunanithi (3/13/2006) and additional 

%functional and conceptual modifications by Tarsha Eason (2008-2009). 

%Further details on the methodology can be found in: Karunanithi AT. 

%Cabezas H, Frieden BR and Pawlowski CW. 2008. Detection and Assessment of 

%ecosystem regime shifts from FI. Ecology & Society. 13(1): 22. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%

 %The purpose of this program is to bin the points into states by 

%calculating the difference (dist) of each variable vector per timestep 

%and then counting the variable vector if dist<= size of the state AND the 

%percentage of variables in the vector that meets that criteria is over the 

%tightening level (TL). Further, the number of vectors that fit within a 

%state are counted(rnum)and then used to calculate the pdf (rum/sum(rum)).

fp_tol=1e-12; %correcting for floating point significant digits

% Initialize the vector which will hold the counter for points in a state

 rnum=zeros(lmax-lmin+1,1);

%Initialize array of points that will be counted in line with lmin and lmax. 

%This dummy variable is used to ensure that the rows are not double 

%counted.

 pout=zeros(lmax-lmin+1,1);

 %

 % now, find recurrence points about each in window and record their

 % instances, ignoring points that have already been counted

 for j=lmin:lmax %looping over the window in time steps

 j;

 count=0;

 if pout(abs(j-lmin)+1,1)==0 %no double counting variable vector

 pouttemp=zeros(size(pout));

 for k=lmin:lmax

 k;

 Mcount=0;

 for m=1:size(data,2) %entire column length(#of variables)

 m;

 

 dist=abs(data(j,m)-data(k,m)); %for each variable 1 by 1

 
202

 %Dealing with floating point error

 r_dist=round2(dist,fp_tol); %rounds it to certain precision

 if r_dist<=(sizeofstates(1,m))

 

 Mcount=Mcount+1;

 end

 end

 

%Ensures that the number of variables that have been counted (meet 

%the criteria is >= TL*total number of variables and this variable

%vector has not been previously counted

 

 if

Mcount>=TL*size(data,2))&(pout(abs(lmin-k)+1,1)==0)

 count=count+1;

 pouttemp(abs(lmin-k)+1,1)=1;

 end

 end

 rnum(abs(lmin-j)+1,1)=count; %number of points in state

 neighbour(abs(lmin-j)+1,:)=data(j,:);

 end

 pout=pout|pouttemp; %update no double counting using logical OR 

 end

 %Calculating pdf

 if sum(rnum,1)==0

 pdf=rnum;

 else

 pdf=rnum/sum(rnum,1);

 end

size1=size(neighbour);

pdf;
203

CalcAvgFish.m

function [AvgFish,PO_TL,mY]= CalcMeanFish(t,data,hwin,winspace,sost)

%This code was developed to calculate the mean value of the Fisher

%information by computing FI over various tightening levels to find the

%point above which perfect order is experienced. And then calculating a

%mean of the values for each time window. The program returns:

%AvgFish: Average Fisher information over TL from strict to relaxed

%PO_TL: Tightening Level the system is in perfect order. It is

% connected to pervasiveness (1-PO_TL)

%mY: Mean Year for each time window

% The code was developed by Tarsha Eason (2009)

%[AvgFish Y TL t_win]= Availdata(t,data,hwin,winspace,sost)

TL=[]; FisherMat=[];

profile on

display(‘Working....’);

for i=1:100

 Tol=100-(i-1);

[FI,midt_win]=GFisher(t,data,sost,hwin,winspace,Tol,0);

FMat(:,i)=FI;

n=size(FI,2); %# of Fisher information Calculations

%Calculates average FI for each time window by seeking the tightening 

%level at which all resulting fisher calculation for each widow is at its 

%max = 8, perfect order. The average FI is calculated

%from the FisherMat which stores all of the Fisher results from each time

%window less the windows where all of the Fisher results are 8.

 if sum(FMat(:,i))<8*n %Tightening level with all Fishers = 8, perfect order

 % display(‘Yes’);

 FisherMat(:,i)=FMat(:,i);

 TL=Tol; %Final value is lowest tightening level before all Fisher results are 8 (max FI) 

 

 %else

 % display(‘End TL Calcs’); 

 end

end %End Tol loop

if isempty(TL)==1

display(‘The system is in perfect order for this size of states at all tightening levels. Please feel free to make an 

adjustment accordingly!!’);

f = warndlg(‘The system is in perfect order for this size of states at all tightening levels. Please feel free to make an 

adjustment accordingly!!’, ‘Warning Dialog’);

 PO_TL=100;

else

PO_TL=TL-1;

end

mY=midt_win;
204

%Provides an ouput when the system is competely orderly at all tightening

%levels. This results in no collection of Fisher values before the system 

%is a maximum level. Accordingly, FisherMat is empty indicates either true

%perfect order or a need to adjust the size of states (smaller)

if isempty(FisherMat)==1;

 AvgFish=8*ones(size(FI));

else

%Calculates mean FI for each time window

AvgFish=mean(FisherMat’);

 end

%Provides an ouput when the system is competely orderly at all tighten

%levels. Indicates either true perfect order or a need to adjust the size

%of states (smaller)

 

%Transposing (aesthetic)

AvgFish=AvgFish’; 

mY=midt_win’;

%%%%%Simple Plots%%%%%%%%%%%%%%%%%

subplot(2,1,1), plot(t,data) %plots data

%subplot(2,1,1), plot(data(:,1),data(:,2)) %plots data for ode

title(‘Time series data’);

subplot(2,1,2), plot(mY,AvgFish)

axis([mY(1) mY(end) 0 9])

title(‘Fisher information over time’);

xlabel(‘Time’); ylabel(‘Fisher information’);
205

SmoothFI.m

function [FIm]=SmoothFI(n,t,FI,plot)

% Computes <FI>, a “m” point mean of FI smoothing results to focus on trends

% and not fluctuations.

% The code was developed by Tarsha Eason (2010)

data=[t FI];

A=sortrows(data,-1); FIs=A(:,2);ts=A(:,1);

window=0; I=[];T=[];A=[];B=[]; FIm=[];

for i=1:n:size(FI,1) %loop through FIs

 i

 

 %Revised Method: #pts=hwin

 lmin=min(i,size(FIs,1));

 lmax=min(i+n-1,size(FIs,1));

 NP=size(FIs(lmin:lmax,1),1);

 if NP== n

 window=window+1;

 

I(window)= mean(FIs(lmin:lmax)); %<FI>

T(window)=ceil(mean(ts(lmin:lmax)));

end

end

B=[T’ I’];

FIm=sortrows(B,1);

if plot==1

subplot(2,1,1),plot(t,FI,’-*’); %plot original FI

title(‘Original FI’); axis([t(1) t(end) 0 8]); 

subplot(2,1,2),plot(FIm(:,1),FIm(:,2),’-*’); %plot <I> 

title(‘Smoothed FI(< FI >)’); axis([t(1) t(end) 0 8]); 

end

%%
206

GUI: Main_Fisher_data_proc.m

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%

%This program provides a graphical user interface (GUI) for performing 

%Fisher information(FI)calculations. It is designed to simplify the 

%procedure by allowing the user to select formatted data files, set 

%parameter values and process the data which calculates FI over numerous 

%tightening levels (from strict to relaxed tightening) providing an average 

%FI result. The computed FI is plotted in the GUI, along with a plot of the 

%FI results that have been “smoothed” using an “m” point average (<FI>). 

%The user is able to save the FI computations and plot for further use. It %also provides access for the advanced 

study of regime shifts, if they exist.

%Procedure for use

%

%Select the formatted data files (*time.xls, *data.xls and *sost.xls; see 

%Appendix 6-B.2 for file formatting details) and press the 

%’START’ button to process the data and generate a plot of the 

%time-averaged fisher. After the data is processed, the plot and 

%calculations may be saved by using the ‘Save Plot’ and ‘Save Calculations’ 

%buttons. The ‘Advanced’ button calls the AdvancedFisher GUI for further 

% Fisher information analysis. 

%Main_Fisher_data_proc was developed by Tarsha Eason (2008-2010)as a 

%graphical extension to work done from 2004 to 2008 by Cabezas, Pawloski, 

%Karunithini and Eason to calculate FI in the Gfisher.m and 

%Nfisherpdf.m code. Please feel free to refer to the source code of those 

%files for additional references.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%

function varargout = Main_Fisher_data_proc(varargin)

gui_Singleton = 1;

gui_State = struct(‘gui_Name’, mfilename, ...

 ‘gui_Singleton’, gui_Singleton, ...

 ‘gui_OpeningFcn’, @Main_Fisher_data_proc_OpeningFcn, ...

 ‘gui_OutputFcn’, @Main_Fisher_data_proc_OutputFcn, ...

 ‘gui_LayoutFcn’, [] , ...

 ‘gui_Callback’, []);

if nargin && ischar(varargin{1})

 gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

 [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

 gui_mainfcn(gui_State, varargin{:});

end

handles.processDataCompleted = 0; %

%%

function Main_Fisher_data_proc_OpeningFcn(hObject, eventdata, handles, varargin)

 handles.output = hObject;

%set(hObject,’toolbar’,’figure’); %enables toolbar

%this variable used to prevent users from breaking the GUI

%the variable is set to 1 once the data has been processed
207

%this command asks the user to confirm closing of GUI

set(handles.figure1,’CloseRequestFcn’,’closeGUI’);

% Update handles structure

guidata(hObject, handles);

% UIWAIT makes Main_Fisher_data_proc wait for user response (see UIRESUME)

% uiwait(handles.figure1);

%%

% --- Outputs from this function are returned to the command line.

function varargout = Main_Fisher_data_proc_OutputFcn(hObject, eventdata, handles) 

% varargout cell array for returning output args (see VARARGOUT);

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure

varargout{1} = handles.output;

%%

function inputFiles_listbox_Callback(hObject, eventdata, handles)

%no code needed for this callback, the listbox is only used as a visual

 

%%

function addFiles_pushbutton_Callback(hObject, eventdata, handles)

%gets input file(s) from user

[input_file,pathname] = uigetfile( ...

 {‘*.xls’, ‘Data Files (*.xls)’; ...

 ‘*.*’, ‘All Files (*.*)’}, ...

 ‘Select files’, ...

 ‘MultiSelect’, ‘on’);

%if file selection is cancelled, pathname should be zero

%and nothing should happen

if pathname == 0

 return

end

%gets the current data file names inside the listbox

inputFileNames = get(handles.inputFiles_listbox,’String’);

%if they only select one file, then the data will not be a cell

if iscell(input_file) == 0

 

 %add the most recent data file selected to the cell containing

 %all the data file names

 inputFileNames{length(inputFileNames)+1} = fullfile(pathname,input_file);
208

%else, data will be in cell format

else

 %stores full file path into inputFileNames

 for n = 1:length(input_file)

 inputFileNames{length(inputFileNames)+1} = fullfile(pathname,input_file{n});

 end

end

 

%updates the gui to display all filenames in the listbox

set(handles.inputFiles_listbox,’String’,inputFileNames);

%make sure first file is always selected so it doesn’t go out of range

%the GUI will break if this value is out of range

set(handles.inputFiles_listbox,’Value’,1);

% Update handles structure

guidata(hObject, handles);

%%

function deleteFiles_pushbutton_Callback(hObject, eventdata, handles)

%this function allows the user to delete files they accidentally

%added to the list box

%get the current list of file names from the listbox

inputFileNames = get(handles.inputFiles_listbox,’String’);

%get the values for the selected file names

option = get(handles.inputFiles_listbox,’Value’);

%is there is nothing to delete, nothing happens

if (isempty(option) == 1 || option(1) == 0 || isempty(inputFileNames))

 return

end

%erases the contents of highlighted item in data array

inputFileNames(option) = [];

%updates the gui, erasing the selected item from the listbox

set(handles.inputFiles_listbox,’String’,inputFileNames);

%moves the highlighted item to an appropriate value or else will get error

if option(end) > length(inputFileNames)

 set(handles.inputFiles_listbox,’Value’,length(inputFileNames));

end

% Update handles structure

guidata(hObject, handles);

%%

function edit_hwin_Callback(hObject, eventdata, handles)

% hObject handle to edit_hwin (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,’String’) returns contents of inputText_hwin as text

% str2double(get(hObject,’String’)) returns contents of edit_hwin as a double
209

HW=str2num(get(hObject,’String’));

%checks to see if input is empty. If so, default input1_editText to zero

if(isempty(HW))

 set(hObject,’String’,’0’)

end

guidata(hObject,handles);

% --- Executes during object creation, after setting all properties.

function edit_hwin_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit_hwin (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

%%

function edit_winspace_Callback(hObject, eventdata, handles)

% hObject handle to edit_winspace (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,’String’) returns contents of inputText_winspace as text

% str2double(get(hObject,’String’)) returns contents of inputText_winspace as a double

WS=str2num(get(hObject,’String’));

%checks to see if input is empty. If so, default input1_editText to zero

if(isempty(WS))

 set(hObject,’String’,’0’)

end

guidata(hObject,handles);

% --- Executes during object creation, after setting all properties.

function edit_winspace_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit_winspace (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

%%

function edit_Smoothpts_Callback(hObject, eventdata, handles)

% hObject handle to edit_winspace (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)
210

% Hints: get(hObject,’String’) returns contents of inputText_winspace as text

% str2double(get(hObject,’String’)) returns contents of inputText_winspace as a double

nS=str2num(get(hObject,’String’));

%checks to see if input is empty. If so, default input1_editText to zero

if(isempty(nS))

 set(hObject,’String’,’0’)

end

guidata(hObject,handles);

% --- Executes during object creation, after setting all properties.

function edit_Smoothpts_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit_winspace (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

%%

function reset_pushbutton_Callback(hObject, eventdata, handles)

%resets the GUI by clearing all relevant fields

handles.processDataCompleted = 0;

cla(handles.axes1,’reset’);

set(handles.edit_hwin,’String’,’8’);

set(handles.edit_winspace,’String’,’1’);

set(handles.edit_Smoothpts,’String’,’3’);

%set(handles.inputFiles_listbox,’String’,’’);

%set(handles.inputFiles_listbox,’Value’,0);

guidata(hObject, handles);

%%

function start_pushbutton_Callback(hObject, eventdata, handles)

inputFileNames = get(handles.inputFiles_listbox,’String’);

%checks to see if the user selected any input files

%if not, nothing happens

if isempty(inputFileNames)

 return

end

%disables the button while data is processing

disableButtons(handles);

refresh(Main_Fisher_data_proc);

%initialize the cell structures

handles.data = {};

handles.time = {};

sost=[];

%Read input data
211

for x=1:length(inputFileNames)

 A=inputFileNames{x};

 a=size(A,2); 

 b=a-7;

 if(A(b:a)==’data.xls’)

 %display(‘Loading Variable Data’);

 data=xlsread(inputFileNames{x}); 

 %size(data)

 

 elseif(A(b:a)==’time.xls’)

 %display(‘Loading Time Data’);

 time=xlsread(inputFileNames{x}); 

 %size(time)

 elseif(A(b:a)==’sost.xls’)

 %display(‘Loading Size of states Data’);

 sost=xlsread(inputFileNames{x});

 %size(sost)

 else

 display(‘Not correct input filename. See Appendix 6-B.2’);

 end

end

%Calculate Fisher information

t=time;

handles.data=data;

handles.t=time;

Flag=isempty(sost); %test to see if *sost.xls file was uploaded

if Flag==1

%display(‘Since no size of states file was added. The size of states will be calculated from the first 5 data points’)

errordlg(‘Since no size of states file was added. The size of states will be calculated from the first 5 data points’,’File 

Error’);

sost=2*std(data(1:5,:)); 

end

handles.sost=sost;

%get hwin and winspace from input form

H=get(handles.edit_hwin,’String’);

W=get(handles.edit_winspace,’String’);

nS=get(handles.edit_Smoothpts,’String’);

hwin=str2num(H);

winspace=str2num(W);

nSpts=str2num(nS);

handles.hwin=hwin;

handles.winspace=winspace;

TL=[]; FisherMat=[];

%Using search algorithm for finding the size of states

%[s_sost,region_year,diff_test]=statesize(4,hwin,winspace,data,time);

%sost=s_sost;
212

%Calculating lowest tightening level before all FI results are 8

%(max): PO_TL

for i=1:100

 Tol=100-(i-1);

 [FI, midt_win, t_win]=GFisher(t,data,sost,hwin,winspace,Tol,0);

 %YMat(:,i)=t_win;

 %YMat(:,i)=t_win(i);

 FMat(:,i)=FI’;

n=size(FI,2); %# of FI Calculations

%Calculates FI for each time window by seeking the tightening 

%level at which all resulting fisher calculation for each widow is at its 

%max = 8, perfect order. The average fisher is calculated

%from the FisherMat which stores all of the FI results from each time

%window less the windows where all of the FI results are 8.

 if sum(FMat(:,i))<8*n %Tightening level with all FI values = 8, perfect order

 % display(‘Yes’);

 FisherMat(:,i)=FMat(:,i);

 

 TL=Tol; %Final value is lowest tightening level before all FI results are 8

 else

 %display(‘End TL Calcs’); 

 end

end %End Tol loop

if isempty(TL)==1

display(‘The system is in perfect order for this size of states at all tightening levels. Please feel free to make an 

adjustment accordingly!!’);

f = warndlg(‘The system is in perfect order for this size of states at all tightening levels. Please feel free to make an 

adjustment accordingly!!’, ‘Warning Dialog’);

 PO_TL=100;

else

PO_TL=TL-1;

end

handles.PO_TL=PO_TL;

%Provides an ouput when the system is competely orderly at all tighten

%levels. This results in no collection of FI values before the system is a

%maximum level. Accordingly, FisherMat is empty indicates either true 

%perfect order or a need to adjust the size of states (smaller)

if isempty(FisherMat)==1;

 AvgFish=8*ones(size(FI));

else

%Calculates Avg FI for each time window

AvgFish=mean(FisherMat’);

end
213

T=zeros(size(t_win));

P=T|t_win;

t_win;

 handles.AvgFish=AvgFish;

 %AvgFish

for i=1:size(AvgFish,2)

 AvgFI(:,i)=P(:,i)*AvgFish(i);

end

handles.AvgFI_win=AvgFI;

handles.PO_TL=PO_TL;

%PO_TL

%plot mean FI over time window

axes(handles.axes1)

YMat=t(1:size(handles.AvgFI_win,1)); %time period

handles.YMat=YMat;

%%%%Plot Avg FI over time window%%%%%%%%%%%%%%%%%

%plot(YMat,handles.AvgFI_win)

%title(‘Average FI vs. Time’);

%%%%%Plot average FI in center of window%%%%%%%%%%%%%%%%%

for j=1:size(t_win,2)

 R(:,j)=nonzeros(t_win(:,j));

 Z(:,j)=AvgFish(j)*ones(size(R,1),1);

Y(j)=ceil(mean(R(:,j))); %mean year for each time window

end

plot(Y,AvgFish,’*b-’)

handles.Year=Y;

title(‘Average FI over time’);

hold

n=nSpts; %n=3; %make sure to create an edit text to change this value

[FIm]=SmoothFI(n,Y’,AvgFish’,0);

handles.MnYr=FIm(:,1);

handles.MnFI=FIm(:,2);

plot(FIm(:,1),FIm(:,2),’*r-’)

legend(‘Fisher information(FI)’,’Smoothed FI (<FI>)’)

legend(handles.axes1,’boxoff’)

axis([Y(1,1) Y(end) 0 8]); xlabel(‘Year’); ylabel(‘Fisher information’);

enableButtons(handles);

handles.processDataCompleted = 1;

guidata(hObject, handles);

%%

function savePlot_pushbutton_Callback(hObject, eventdata, handles)
214

%if the data hasn’t been processed yet, 

%nothing happens when this button is pressed

if (handles.processDataCompleted == 0)

 return

end

disableButtons(handles);

refresh(Main_Fisher_data_proc);

savePlot(handles.axes1); %figure out how to save the legend?

enableButtons(handles);

guidata(hObject, handles);

%%

function export_pushbutton_Callback(hObject, eventdata, handles)

%This function saves the mean FI values, date and time of

%calculation, as well as the smoothed mean FI results into an Excel file in 

%the format:

%Row1 Col1: Date

%Row2 Col1: Time (military)

%Row3 Col1: Perfect order tightening level: PO_TL

%Row4 to Row4+size(FI) Col2: [Year FI]

%The Smoothed mean FI results follow

%if the data hasn’t been processed yet, 

%nothing happens when this button is pressed

if (handles.processDataCompleted == 0)

 return

end

calctime=clock;

%stores savepath for results

[filename, pathname] = uiputfile ({‘*.xls’, ‘Data Files (*.xls)’; ...

 ‘*.*’, ‘All Files (*.*)’}, ...

 ‘Save file as’,’default’);

 

%if user cancels save command, nothing happens

if isequal(filename,0) || isequal(pathname,0)

 return

end

%saves the data

dlmwrite(fullfile(pathname, filename),[calctime(2),calctime(3),calctime(1)],’delimiter’,’/’,’-append’);

dlmwrite(fullfile(pathname, filename),[calctime(4) calctime(5)],’delimiter’,’:’,’precision’,’%4.2d’,’-append’);

dlmwrite(fullfile(pathname, filename),handles.PO_TL,’-append’);

%dlmwrite(fullfile(pathname, filename),’Mean FI’,’-append’)

dlmwrite(fullfile(pathname, filename),[handles.Year’,handles.AvgFish’],’-append’,’delimiter’,’\t’,’roffset’,1)

dlmwrite(fullfile(pathname, filename),[handles.MnYr,handles.MnFI],’-append’,’delimiter’,’\t’,’roffset’,2) 

%%
215

function disableButtons(handles)

set(handles.figure1,’Pointer’,’watch’);

set(handles.start_pushbutton,’Enable’,’off’);

set(handles.reset_pushbutton,’Enable’,’off’);

set(handles.addFiles_pushbutton,’Enable’,’off’);

set(handles.savePlot_pushbutton,’Enable’,’off’);

set(handles.deleteFiles_pushbutton,’Enable’,’off’);

set(handles.inputFiles_listbox,’Enable’,’off’);

set(handles.export_pushbutton,’Enable’,’off’);

set(handles.Advanceduser_pushbutton,’Enable’,’off’);

set(handles.Help_pushbutton,’Enable’,’off’);

%%

function enableButtons(handles)

set(handles.figure1,’Pointer’,’arrow’);

set(handles.start_pushbutton,’Enable’,’on’);

set(handles.reset_pushbutton,’Enable’,’on’);

set(handles.addFiles_pushbutton,’Enable’,’on’);

set(handles.savePlot_pushbutton,’Enable’,’on’);

set(handles.deleteFiles_pushbutton,’Enable’,’on’);

set(handles.inputFiles_listbox,’Enable’,’on’);

set(handles.export_pushbutton,’Enable’,’on’);

set(handles.Advanceduser_pushbutton,’Enable’,’on’);

set(handles.Help_pushbutton,’Enable’,’on’);

%%

function inputFiles_listbox_CreateFcn(hObject, eventdata, handles)

% Hint: listbox controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

% --- Executes on button press in Advanceduser_pushbutton.

function Advanceduser_pushbutton_Callback(hObject, eventdata, handles)

% hObject handle to Advanceduser_pushbutton (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

AdvancedFisherGUI() %Call subGUI for further analysis

guidata(hObject, handles);

function Help_pushbutton_Callback(hObject, eventdata, handles)

% hObject handle to edit_hwin (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

%testflag=250000

HelpPath = which(‘helpMain.html’);

web(HelpPath); %opens GUI help file 

guidata(hObject,handles);
216

AdvancedFisherGUI.m

function varargout = AdvancedFisherGUI(varargin)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%

%This program is called from the Fisher_data_proc GUI and provides a 

%graphical user interface for studying the pervasiveness and intensity of

%regime shifts by plotting the Fisher information (FI) results for tightening 

%levels (TL) from 100%-10% overlaid by the average FI. Pervasiveness indicates 

%the number of variables affected by the shift and is determined by finding 

%the TL value at which the system is perfectly ordered (i.e. FI is at its 

%max (8) for the entire period. Pervasiveness is computed by subtracting 

%the perfect order tightening level (POTL) from 100) and denotes the percentage 

%of variables that were impacted. The intensity relates to the amplitude of 

%the shift and is determined by how far the FI drops between two stable regimes.

%NOTE: Data files must be selected through the Fisher_data_proc GUI and 

%processed prior to doing advanced analysis through this GUI.

%

%AdvancedFisher was developed by Tarsha Eason(2008-2010) as a graphical 

%extension to work done from 2004 to 2008 by Cabezas, Pawlowski, Karuninithi 

%and Eason to calculate FI in the Revfisher.m, fisherpdf.m code. 

%Please feel free to refer to the source code of those files for additional 

%references.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%

%Matlab GUIDE information

% ADVANCEDFISHERGUI M-file for AdvancedFisherGUI.fig

% ADVANCEDFISHERGUI, by itself, creates a new ADVANCEDFISHERGUI or raises 

% the existing singleton*.

%

% H = ADVANCEDFISHERGUI returns the handle to a new ADVANCEDFISHERGUI or 

% the handle to the existing singleton*.

%

%ADVANCEDFISHERGUI(‘CALLBACK’,hObject,eventData,handles,...) calls the 

%local function named CALLBACK in ADVANCEDFISHERGUI.M with the given input 

%arguments.

%

%ADVANCEDFISHERGUI(‘Property’,’Value’,...) creates a new ADVANCEDFISHERGUI 

%or raises the existing singleton*. Starting from the left, property value 

%pairs are applied to the GUI before AdvancedFisherGUI_OpeningFcn gets 

%called. An unrecognized property name or invalid value makes property 

%application stop. All inputs are passed to AdvancedFisherGUI_OpeningFcn 

%via varargin.

% Last Modified by GUIDE v2.5 17-Jun-2010 12:44:09

% Begin initialization code - DO NOT EDIT

gui_Singleton = 1;

gui_State = struct(‘gui_Name’, mfilename, ...

 ‘gui_Singleton’, gui_Singleton, ...
217

 ‘gui_OpeningFcn’, @AdvancedFisherGUI_OpeningFcn, ...

 ‘gui_OutputFcn’, @AdvancedFisherGUI_OutputFcn, ...

 ‘gui_LayoutFcn’, [] , ...

 ‘gui_Callback’, []);

if nargin && ischar(varargin{1})

 gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

 [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

 gui_mainfcn(gui_State, varargin{:});

end

% End initialization code - DO NOT EDIT

% --- Executes just before AdvancedFisherGUI is made visible.

function AdvancedFisherGUI_OpeningFcn(hObject, eventdata, handles, varargin)

% This function has no output args, see OutputFcn.

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% varargin command line arguments to AdvancedFisherGUI (see VARARGIN)

% Choose default command line output for AdvancedFisherGUI

handles.output = hObject;

%this command asks the user to confirm closing of GUI

set(handles.figure1,’CloseRequestFcn’,’closeGUI’);

%set(hObject,’toolbar’,’figure’); % activates toolbar

% Update handles structure

guidata(hObject, handles);

% UIWAIT makes AdvancedFisherGUI wait for user response (see UIRESUME)

% uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line.

function varargout = AdvancedFisherGUI_OutputFcn(hObject, eventdata, handles) 

% varargout cell array for returning output args (see VARARGOUT);

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure

varargout{1} = handles.output;

% --- Executes during object creation, after setting all properties.

function inputText_hwin_CreateFcn(hObject, eventdata, handles)

% hObject handle to inputText_hwin (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called
218

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

function inputText_winspace_Callback(hObject, eventdata, handles)

% hObject handle to inputText_winspace (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,’String’) returns contents of inputText_winspace as text

% str2double(get(hObject,’String’)) returns contents of inputText_winspace as a double

%WS=str2num(get(hObject,’String’));

%checks to see if input is empty. If so, default input1_editText to zero

%if(isempty(WS))

% set(hObject,’String’,’0’)

%end

guidata(hObject,handles);

% --- Executes during object creation, after setting all properties.

function inputText_winspace_CreateFcn(hObject, eventdata, handles)

% hObject handle to inputText_winspace (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

function inputText_TL_Callback(hObject, eventdata, handles)

% hObject handle to inputText_TL (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,’String’) returns contents of inputText_TL as text

% str2double(get(hObject,’String’)) returns contents of inputText_TL as a double

%Tol=str2num(get(hObject,’String’));

%checks to see if input is empty. If so, default input1_editText to zero

if(isempty(Tol))

 set(hObject,’String’,’0’)

end

guidata(hObject,handles);

% --- Executes during object creation, after setting all properties.

function inputText_TL_CreateFcn(hObject, eventdata, handles)

% hObject handle to inputText_TL (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called
219

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

%Calculates and plots FI different tightening levels

 

% --- Executes on button press in FishTL_pushbutton.

function FishTL_pushbutton_Callback(hObject, eventdata, handles)

% hObject handle to FishTL_pushbutton (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

YMat=[];FMat=[];

mainFigureHandle=Main_Fisher_data_proc; %stores the figure handle of main GUI here

mainGUIdata = guidata(mainFigureHandle); 

%Pulling data, time, AvgFish, and midyear from Main_Fisher_data_proc.m

t=mainGUIdata.t;

data=mainGUIdata.data;

AvgFish=mainGUIdata.AvgFish;

midyear=mainGUIdata.Year;

PO_TL=mainGUIdata.PO_TL;

SOST=mainGUIdata.sost;

hwin=mainGUIdata.hwin;

winspace=mainGUIdata.winspace;

 

%Studying Pervasiveness and Intensity from Main_Fisher data_proc parameter 

%settings

for i=1:10

 Tol=100-(i-1)*10;

 [FI, midt_win, t_win]=GFisher(t,data,SOST,hwin,winspace,Tol,0);

 FMat(:,i)=FI’;

 YMat(:,i)=midt_win;

end

axes(handles.axes3); plot(YMat(:,1),FMat(:,1),YMat(:,2),FMat(:,2),YMat(:,3),FMat(:,3),YMat(:,4),FMat(:,4),YMat(:,

5),FMat(:,5));

% Create legend

legend1 = legend(handles.axes3,’TL=100%’,’TL=90%’,’TL=80%’,’TL=70%’,’TL=60%’);

set(legend1,’YColor’,[1 1 1],’XColor’,[1 1 1],...

 ‘Location’,’NorthEast’,...

 ‘FontSize’,8,...

 ‘FontName’,’Arial’);
220

hold

 

plot(midyear,AvgFish,’-gd’,’LineWidth’,2);

title(‘Pervasiveness and Intensity (100%-60%)’);

axis([YMat(1,1) YMat(size(YMat,1)) 0 10]); xlabel(‘Year’); ylabel(‘Fisher information’);

 

hold off

 

axes(handles.axes4); plot(YMat(:,6),FMat(:,6),YMat(:,7),FMat(:,7),YMat(:,8),FMat(:,8),YMat(:,9),FMat(:,9),YMat(:,

10),FMat(:,10))

% Create legend

legend2 = legend(handles.axes4,’TL=50%’,’TL=40%’,’TL=30%’,’TL=20%’,’TL=10%’);

set(legend2,’YColor’,[1 1 1],’XColor’,[1 1 1],...

 ‘Location’,’NorthEast’,...

 ‘FontSize’,8,...

 ‘FontName’,’Arial’);

 hold

plot(midyear,AvgFish,’-gd’,’LineWidth’,2);

title(‘Pervasiveness and Intensity (50%-0%)’);

axis([YMat(1,1) YMat(size(YMat,1)) 0 10]); xlabel(‘Year’); ylabel(‘Fisher information’);

hold off

PVL=100-PO_TL;

set(handles.edit_POTL,’String’,num2str(PO_TL));

set(handles.edit_Pervasiveness,’String’,num2str(PVL))

%Save result of User Analysis

%dlmwrite(‘Results.xls’,[hwin,winspace],’-append’);

%dlmwrite(‘Results.xls’,[YMat(:,1) AFish’],’-append’,’delimiter’,’\t’,’precision’,’%4.4f’);

%dlmwrite(‘Results.xls’,[midyear’ AvgFish’],’-append’,’delimiter’,’\t’,’precision’,’%4.4f’);

warning off

%%%%%

guidata(hObject, handles); %updates the handles

 

function edit_Pervasiveness_Callback(hObject, eventdata, handles)

% hObject handle to edit_Pervasiveness (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,’String’) returns contents of edit_Pervasiveness as text

% str2double(get(hObject,’String’)) returns contents of edit_Pervasiveness as a double

% --- Executes during object creation, after setting all properties.

function edit_Pervasiveness_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit_Pervasiveness (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called
221

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end

% --- Executes on button press in RESET_Pushbutton.

function RESET_Pushbutton_Callback(hObject, eventdata, handles)

% hObject handle to RESET_Pushbutton (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

cla(handles.axes3,’reset’);

cla(handles.axes4,’reset’);

set(handles.inputText_TL,’String’,100)

set(handles.inputText_hwin,’String’,10)

set(handles.inputText_winspace,’String’,5)

set(handles.edit_Pervasiveness,’String’,0);

set(handles.edit_POTL,’String’,0);

%handles.buttonCounter = 0; %reset buttoncounter

guidata(hObject, handles); %updates the handles 

function edit_POTL_Callback(hObject, eventdata, handles)

% hObject handle to edit_POTL (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% Hints: get(hObject,’String’) returns contents of edit_POTL as text

% str2double(get(hObject,’String’)) returns contents of edit_POTL as a double 

% --- Executes during object creation, after setting all properties.

function edit_POTL_CreateFcn(hObject, eventdata, handles)

% hObject handle to edit_POTL (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc && isequal(get(hObject,’BackgroundColor’), get(0,’defaultUicontrolBackgroundColor’))

 set(hObject,’BackgroundColor’,’white’);

end 

% --- Executes on button press in Help_pushbutton.

function Help_pushbutton_Callback(hObject, eventdata, handles)

% hObject handle to Help_pushbutton (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

HelpPath = which(‘helpAdvGUI2.html’);

web(HelpPath); 

guidata(hObject,handles);
222

round2.m : Floating Point Correction

function z = round2(x,y)

%ROUND2 rounds number to nearest multiple of arbitrary precision.

%Z = ROUND2(X,Y) rounds X to nearest multiple of Y.

%Floating point work around designed by Robert Bemis 15 Dec 2003 and updated 

%(29 Jun 2006): http://www.mathworks.com/matlabcentral/fileexchange/4261 

%% defensive programming

error(nargchk(2,2,nargin))

ror(nargoutchk(0,1,nargout))

if prod(size(y))>1

 error(‘n must be scalar’)

end

z = round(x/y)*y;

CloseGUI.m

function closeGUI

selection = questdlg(‘Do you want to close the GUI?’,...

 ‘Close Request Function’,...

 ‘Yes’,’No’,’Yes’);

switch selection,

 case ‘Yes’,

 delete(gcf)

 case ‘No’

 return

end
223

7.0

The San Luis Basin Sustainability Metrics Project: 

Results, Conclusions, and Recommendations

7.1 Introduction

In this report, we have presented the results of a five-year 

project that estimated four metrics of sustainability for 

the San Luis Basin (SLB). The SLB is an agricultural 

region in south-central Colorado, adjacent to New 

Mexico, with a large portion of land publicly owned. 

It is a relatively uncomplicated region that has not 

experienced a substantial amount of change during the 

26 years we examined. This lack of change provided an 

opportunity to develop a methodology for calculating 

sustainability metrics and to examine a large geographic 

region with relatively little diversity in land uses (i.e., 

there are few major industries or land uses to overly 

complicate the calculation of metrics) and economic 

drivers. For example, the San Luis Valley Development 

Resources Group (SLVDRG; 2007) finds the top three 

land uses in 2002 were: 1) rangeland (43.6%); 2) 

forest land (39.6%); and 3) agricultural land (11.8%). 

Urban land is only 0.2% of the area in the six county 

Valley.11 Because individual metrics do not capture all 

aspects of a system, multiple metrics were estimated 

to examine the SLB’s movement toward or away from 

sustainability. We assembled a detailed dataset that 

met the three research objectives presented in Chapter 

1. We successfully estimated the metrics, even though 

data for some of the variables were not available at 

the county level. In the previous four chapters, we 

described the individual methodologies and presented 

the metric results. Those chapters will enable the reader 

to understand each metric in terms of what they capture 

and the meaning of the results. Even though we chose 

a less complicated region, we still faced a number of 

hurdles while developing the methodology. This chapter 

examines the sustainability metrics together, using the 

results of the previous four chapters, to decipher whether 

the SLB is moving toward or away from sustainability. 

It describes the strengths and limitations of the overall 

approach (rather than the strengths and limitations of 

the individual metric approaches, which can be found 

in Chapters 3-6). We present future research directions 

generated from this research project and review our 

research goal.

7.2 Summary of Calculations

Based on the results of Ecological Footprint Analysis 

(EFA), we found that even though there was a surplus 

in bioproductive land, the ecological remainder was 

decreasing due to population growth in the SLB (Chapter 

3). If the trend continues, the ecological remainder 

(a reserve in this region) will eventually become an 

ecological deficit. Hence, we concluded that the SLB 

is moving away from sustainability based on the EFA. 

The estimation of Green Net Regional Product (GNRP) 

provides a one-sided test of sustainability and reveals 

when a system is not sustainable (see Chapter 4). The 

results for GNRP provided no definitive evidence 

that the system was moving away from sustainability. 

Emergy Analysis (EmA) used the fraction of renewable 

emergy used to total emergy used as a metric for 

sustainability, but it was limited by data availability 

(Chapter 5). Unlike the other metrics, a complete EmA 

was estimated only for the 11-year period of 1995-2005. 

Overall, there was a general decrease in the fraction 

of renewable emergy used to total emergy used. Early 

in this period, EmA suggested the SLB was moving 

away from sustainability. More recently, there was an 

increase in the fraction of renewable emergy used to 

total emergy used, indicating the SLB has been moving 

toward sustainability since 2003. Finally, the results 

of the Fisher information (FI) assessment revealed the 

system exhibited no regime shifts for the period of the 

study (Chapter 6). Although the system exhibited small 

changes in dynamic order during the study period, it 

was relatively stable. Further, with the slight decreasing 

trend in dynamic order at the end of the study period, 

we noted the SLB may be starting to move away from 

sustainability. These results are summarized in Table 

7.1. 

7.3 Holistic Results for San Luis Basin 

In Chapter 1, we stated that for the SLB to be on a 

sustainable path, the following criteria must be satisfied: 

1) ecological balance (i.e., remainder) is increasing (or 

the ecological deficit is decreasing) with time and 2) the 

ratio of renewable emergy used to total emergy used is 

trending to one. FI and GNRP are one-sided tests and 

cannot technically show if the system is moving toward 

sustainability. Criteria for these one-sided tests indicate 

if the region is moving away from sustainability when: 

3) FI is steadily decreasing with time (Karunanithi et 

al. 2008); and 4) GNRP is decreasing with time (i.e., 

11 The San Luis Valley Development Resources Group defines the Valley as the following six counties: Alamosa, Conejos, Costilla, Mineral, 

Rio Grande, and Saguache. This differs from our definition of the SLB 

by not including Hinsdale County.
224

Pezzey et al. 2006). For this study, we define a system 

to be moving away from sustainability if ecological 

balance, GNRP (or human welfare), the ratio of 

renewable emergy used to total emergy used, or FI are 

declining over time. Because each metric captures 

different aspects of a system, and all aspects are vital to 

the maintenance of a system, losing any one component 

would result in problems with the system and, we 

would consider the system to be moving away from 

sustainability.

Interpretation of these results could be examined 

statistically (e.g., Bastianoni et al. 2008, Shmelev and 

Rodríguez-Labajos 2009) or graphically (e.g., Hanley et 

al. 1999, Lammers et al. 2008, Pezzey et al. 2006). In 

this case, given the difference in the number of years 

calculated, interpreting the results statistically is not 

likely to provide useful conclusions (although FI uses 

data for the entire 26-year period, it produces only six 

data points). Furthermore, we cannot satisfactorily 

account for variation in each of the variables used in 

any of the metrics. Hence, we lack statistical analyses 

for trends and uncertainty. To explore further, we 

graphed the results using a similar scale. The ecological 

remainder showed a downward trend whereas GNRP 

was increasing from 1980-2005 (Fig. 7.1). First, we can 

examine how EFA and GNRP might relate because these 

metrics were estimated for the entire 26-year period. It 

is not surprising that GNRP has an inverse relationship 

with EFA (as represented by ecological remainder) 

because increased economic activity often accompanies 

a corresponding increase in the human impact on the 

environment or resource use. Although GNRP attempts 

to account for environmental degradation, the effect 

on human welfare might be mitigated by increases in 

physical capital. The indirect relationship between 

GNRP and EFA is consistent with Nourry (2008) who 

examined the sustainability of France. Other studies 

have found little relationship between GNRP and 

EFA (e.g., Hanley et al. 1999, Nourry 2008). This is 

consistent with our calculation of EFA and GNRP after 

1994. 

When we only look at 1995-2005 (11 years used for 

EmA), we find that the fraction of renewable emergy 

used to total emergy used was declining early in the time 

period and began to increase again in the last couple of 

years. Ecological Balance continued to decline during 

these 11 years. Ecological Balance may be positively 

correlated to total emergy used. In addition, total 

emergy used per person, is decreasing slightly (Fig. 

5.4). Ecological footprint (ef) and total emergy used per 

person are supposed to lead to comparable results, but 

similar to Bastianoni et al. (2008), there appears to be no 

similar trend. 

FI is difficult to include in this conversation on a yearby-year basis because the window settings established 

to capture the dynamic behavior of the system does not 

result in a FI value for each year. Further, as noted in the 

Sustainable Regimes Hypothesis, the method requires 

that a mean FI (<FI>) be computed to capture the trends 

in the dynamic order. As such, only six <FI> values are 

reported for the 26 year period. From the analysis, we 

found the system FI increased slightly until it peaked 

during the period centering 1995, and decreased slightly 

thereafter. Although there were changes in the FI over 

time, it was relatively steady and there was no indication 

of a regime shift for the period of the study. Upon 

examining the relationship between the <FI> for the 

overall system and the variables by category, we found 

the peak in system’s <FI> during the period centering 

1995 occurred at the same time as <FI> for the energy 

and environmental categories. In addition, we found the 

system had a negative correlation with the demographic 

category. However, the overall system, consumption 

(food and forest), land use, and agricultural production 

categories indicated movement away from sustainability 

by the end of the study period.

Interpreting the results holistically, we find evidence that 

the SLB may be moving away from sustainability. All 

four metrics are capturing different aspects or answering 

different sustainability questions (e.g., Hanley et al. 

1999), and EFA and EmA indicate movement away 

from sustainability. EFA and EmA helped us reach this 

conclusion based on the criteria listed above and the 

graphs just described (Fig 7.1). If, as Neumayer (2010) 

points out, weak sustainability is an important step in 

the right direction, then, according to GNRP and FI, the 

SLB was moving in the right direction. According to the 

results of EFA and EmA, the region does not meet the 

requirements of strong sustainability and was moving 

in the wrong direction. If we correctly identified each 

metric as strong and weak measures of sustainability, it 

is interesting to realize the weak measures indicate the 

region is stable or moving toward sustainability, whereas 

the strong measures show the region is moving away 

from sustainability. These results support Neumayer’s 

(2010) idea that, although not a long-term solution, 

weak sustainability may be a step in the right direction. 

However, we recognize the estimates are not perfect 

and have identified the limitations of each metric in 

their respective chapter. The next sections focus on the 

strengths and limitations we identified using the SLB 

project methodology.
225

7.4 Strengths of Methodology 

Knowing if a region is trending toward or away from 

sustainability is quite useful and interest in this type of 

information is growing (e.g., Graymore et al. 2008). 

We believe that our methodology –our first attempt– 

adequately characterizes and evaluates the movement 

toward or away from sustainability of a regional 

system. The methodology suggests the multidisciplinary 

approach adequately assesses whether a region is 

moving toward or away from sustainability. Whereas 

no methodology measures every aspect of the system, 

we argue that each metric identifies changes to major 

components of the system and that together these metrics 

adequately represent the complexity of a regional 

system. 

The metrics are data intensive and the data requirements 

are often prohibitive and lead to difficulty in calculating 

and interpreting the results. However, we were able to 

overcome several of the shortcomings by collecting and 

adapting data for many of the variables at county, state, 

and national levels during much of the 26-year period, 

thereby successfully characterizing the SLB (discussed 

in previous sections). We were tasked with developing 

a methodology that used readily accessible data to 

estimate four metrics. We believe we were successful 

in completing this task. All data used in this approach 

could be found on the World Wide Web and through 

personal communication with experts and stakeholders 

in the SLB. Table 2.1 summarizes data used this project. 

It identifies which data were used and by which metrics, 

the source of the data, and the number of years the data 

were available. More detailed tables on data and their 

sources are located in Chapters 3-6 for each individual 

metric. The majority of data were available from Federal 

government agencies including the Department of 

Commerce, Bureau of Economic Analysis; Department 

of Energy, Energy Information Administration; and the 

US Department of Agriculture including the Agricultural 

Research Service, the Forest Service, and the National 

Agricultural Statistics Survey. Data from Global 

Insight, Inc., used to compute Emergy, although readily 

accessible, were not free.

Another strength of this methodology is the use of 

multiple metrics to cover multiple attributes of the 

environmental system including ecological, economic, 

energetic, and system order. Sustainability is not 

limited to one attribute of a region and multiple metrics 

are necessary to cover the three pillars (economic, 

environmental, and social aspects) of sustainability 

(e.g., Graymore et al. 2008, Hanley et al. 1999). 

Because we used a multidisciplinary approach to 

estimate the metrics, we were able to take advantage 

of the economies of scale (i.e., specialization) to 

share information, knowledge, and data to support the 

calculation of the four metrics. For example, as data 

for wind erosion were being collected and analyzed, 

the individuals calculating GNRP were able to explain 

and share the process for the other metric groups 

that used wind erosion (rather than estimating it on 

their own). Once data were available for one metric, 

they were available for all the metrics. Assigning the 

collection of specific variables to individuals provided 

an approach that limited contacting various experts, 

thereby saving time (and money) of the researchers and 

entities that possessed data. This sharing of data and 

assigning responsibilities for collecting data reduced the 

duplication of efforts and streamlined data collection 

activities.

Another objective was to determine if data were 

available for a time-series analysis, thereby examining 

trends. Our initial goal was to calculate metrics and 

examine a regional system from 1980 to 2005. This 

required 26 years of data for numerous variables. EmA 

was limited by data suggesting results are truly only 

available from 1995-2005. Because data were available 

for a number of variables over a number of years, we 

successfully calculated four metrics and were able to 

visually test for trends. This indicates that useful data 

in electronic format can be easily obtained. Alone 

this finding is a valuable conclusion to our study and 

suggests that other useful metrics may be estimated 

with relatively little direct costs to local and regional 

governments.

Finally, one reason for the apparent success of this 

project in addressing the multidisciplinary nature of 

sustainability was including stakeholders in the process 

of identifying components of this system that were 

responsible for the trends of the metrics through time. 

We recognize that future regional analyses should 

include stakeholders in the process from the beginning, 

in order to address directly aspects of sustainability that 

stakeholders identify as important. It helps identify 

appropriate metrics that can quantify those aspects which 

are most useful to interested parties (i.e., the product 

is wanted by the stakeholders). Getting participation 

from individuals who were interested in the work and 

were potential users of the information proved quite 

useful in developing and improving the methodology in 

this study. For example, interacting with stakeholders 

early on assured that results from the metric calculations 

coincided with their observations of the SLB. 
226

7.5 Limitations of Methodology 

The majority of the limitations of this methodology 

relate to data needs, data availability, and data 

manipulation. Because many of the data needed for 

these metrics typically are not collected at the county 

level, we needed to rescale or convert state or US data 

to the county or regional level. Additional difficulties 

included measuring trends and making the methodology 

transferable to other regions besides the SLB. We 

discuss these in turn.

One of the objectives of this research was to develop a 

methodology that used readily accessible data. Although 

this objective was met, not all of the data were free. 

Global Insight, Inc. data were needed to estimate some 

components of EmA. Import and export data, down to 

transportation type and quantities of different goods, 

are desirable for EmA, but difficult to estimate without 

detailed data. Imports and exports to a system can be 

estimated by alternative means with some sacrifice 

in accuracy (Odum 1996). However, the cost of the 

Global Insight, Inc. data (≈$20K for 11 years) may 

preclude other regions from calculating this metric 

with the accuracy obtained in this study. It should be 

noted the import and export data were limited by the 

years available (1995-2005; see Table 2.1). Hence, the 

difficulty we had examining the results for the 26-year 

research period. Future research could compare the 

resulting trends from excluding these type data, thereby 

eliminating the added expense.

We took a number of steps to overcome the issue of data 

intensive calculations required for these sustainability 

metrics. In addition, we addressed the lack of regionspecific data. Simplifying assumptions were needed 

in order to estimate the metrics, but they are common 

for these types of calculations. Note the assumptions 

become limitations if they somehow misrepresent 

the region of study. In this study, we assumed the 

SLB could be represented by state and/or US data 

with the appropriate rescaling (i.e., estimated on a per 

capita basis), and we tested some of these rescalings 

(e.g., Appendix 3-A). We attempted to confirm the 

assumptions were appropriate through discussions with 

stakeholders and by reviewing other data sources. For 

example, to estimate the Net Regional Product (NRP) 

for the SLB, initially we used Colorado data, but local 

stakeholders felt that both Colorado and New Mexico 

influenced the economy. Using this information, we 

averaged the NRP calculation for the SLB using both 

Colorado data and New Mexico data. In cases of 

missing data, interpolation was necessary to ensure a 

value could be computed for every year and because 

complete data were needed to proceed with calculations 

of EFA, EmA, and GNRP. Linear interpolation is a 

weakness if it was not appropriate. There are a number 

of examples described in Chapters 3-6 where we 

manipulated some state or US data in order to proceed 

with the calculations. The weakness here lies in whether 

these manipulations yielded adequately representative 

data for the SLB. It is probable that some manipulations 

better represented the SLB than others.

The team initially thought it could estimate confidence 

intervals and analyze trends for each of the metrics 

in order to understand uncertainty in the data. The 

proposed approach was to use bootstrapping, but we 

found it difficult to proceed given the state of the data 

(i.e., missing years, interpolated values, and lack of 

variation). From the bootstrap distribution, an estimate 

of bias, standard error, and confidence intervals are 

produced (Dixon 2001), thereby permitting hypothesis 

testing. Confidence intervals were difficult to estimate 

because there is some amount of error associated with 

data collection, but this error is rarely reported with the 

dataset. Furthermore, the metrics are a manipulated 

quantity rather than the individual variables. This 

manipulation has an unknown impact on the error. 

Additionally, each variable in all metrics will have some 

unknown variation for each year, especially when we 

assume that a parameter is constant across years. Using 

a bootstrap approach to resample the metric would 

miss this type of uncertainty. Therefore, we were not 

confident that an uncertainty analysis would yield useful 

results given the project methodology and our estimates 

could be criticized for lacking a measure of uncertainty. 

Although trend analysis is quite common in 

environmental studies and market studies (e.g., Burn 

and Elnur 2002, Hirsch et al. 1982, Hirsch et al. 

1991), we were only able to find a statistic of trends 

in one paper examining sustainability metrics (Sato 

and Samreth 2008), but it lacked references to address 

its legitimacy. Initially we considered using MannKendall to test for trends. Mann-Kendall test is a 

non-parametric test that concentrates on the relative 

value (i.e., positive or negative) of the observations 

rather than the magnitude of the observations and is 

frequently used for trend detection (Burn and Elnur 

2002, Hirsch et al. 1982). Although Mann-Kendall 

is non-parametric and, therefore, free of assumptions 

about the distribution of the data, it does assume data 

are not serially correlated. To calculate each metric 

on a yearly basis, we did a linear interpolation to fill 

variables for missing years. This interpolation resulted 

in a strong autocorrelation between points. Thus, we 

were not confident in the resulting p-value from the 

test (i.e., p-value overestimated the significance). The 
227

majority of published studies on sustainability metrics 

appear to examine trends visually through graphs of the 

metric calculation over time (e.g., Chen and Chen 2007, 

Haberl et al. 2001, Hanley et al. 1999, Lammers et al. 

2008, Pezzey et al. 2006, Wackernagel et al. 2004b) 

which was the approach we ultimately used for this 

project. Nonetheless, we recognize the methodological 

improvement that would result if a measure of 

uncertainty could be included and the benefit of testing 

for statistical significance in apparent trends.

Multiple metrics were estimated to cover all aspects of 

sustainability. However, in some instances, different 

metrics used several of the same variables, suggesting 

a potential correspondence. For example, wind erosion 

is used in GNRP, EmA, and FI (e.g., see Table 2.1). A 

relationship between metrics may not be a weakness per 

se, but it limits any correlation calculations. Rather than 

being independent metrics, we have estimated metrics 

that were already linked and duplicated certain aspects 

of the system, but we do not think this is a problem. For 

example, although population drives the EFA calculation, 

it plays a minor role in GNRP. 

The methodology presented in this report was general in 

structure and was useful in evaluating the sustainability 

of the SLB. However, certain approaches, variables, 

data, and calculations can only represent the SLB 

specifically. Accordingly, other regions will require in 

depth study prior to estimating the metrics. Because 

different regions have different issues in regards to their 

sustainability, the specific methodology presented here 

is not simply a generic recipe for sustainability metric 

calculations. However, it is useful in a general sense. 

For example, GNRP used soil erosion, groundwater 

level, and CO2

 emissions to document the depreciation of 

natural capital. A new region may have concerns related 

to mining, forestry, and surface water quality, requiring 

different data and calculations. A different region might 

not be able to adopt the approach used to estimate soil 

erosion, but it could examine how it was calculated and 

apply that to its own issues. The approach will have to 

be adapted to capture the new depreciation of natural 

capital and the literature and methodology presented here 

will provide a certain amount of direction. On the other 

hand, there is complete transportability in the ability to 

assess the dynamic order of other regional systems as FI 

may be computed for any system given the availability 

of pertinent data characterizing the state of the system. 

Stakeholders expressed an interest in examining how 

the metrics change using different scenarios. They saw 

a need for scenario or futures analysis in part because 

of a delay between the time that an event occurs and the 

time when data on the event are released to the public. 

For example, CO2 emissions data only become available 

three years after the year when the emissions occurred. 

In other words, the calculations are always three years 

behind the present year making the metric calculations 

somewhat limited in their ability to provide decision 

support for the present. Some stakeholders suggested 

that the lag could be managed with an alternative futures 

component to the methodology. However, an objective 

of this report was to describe an approach to examine 

sustainability over time, and not an approach to look into 

alternative futures. Kok (2009) presents a framework 

for considering the interrelationships between the types 

of variables considered in our metrics. An alternative 

futures approach would require significant modeling 

efforts that were not part of the project’s objectives. The 

information that would be created through this modeling 

would be very useful for the SLB or any regional 

system. Regional plans could be evaluated on the basis 

of their sustainability. Development scenarios could be 

created and then modeled using the metric calculations 

and used to see how the metric changes under different 

scenarios. The results could provide local decision 

makers information that results in a movement 

toward sustainability. Our results provide a different 

decision support tool that requires more of an adaptive 

management strategy. The metrics reveal a region’s 

existing movement toward or away from sustainability 

and identify components of the system that require 

attention. Continued monitoring of the system and 

quantifying the resulting trend can influence additional 

management decisions.

Our final concern relates to our goal of producing a 

straightforward, relatively inexpensive methodology 

that is simple to use and interpret. We cannot state 

with confidence that we met the goal because it would 

be difficult to test any one of these components. Some 

anecdotal evidence would suggest that the metrics are 

not as simple to use or interpret as we initially projected. 

Calculating FI was automated by developing a graphical 

user interface and spreadsheets were created for the 

remaining three metrics. However, understanding 

each metric and what variables should be included for 

different regions may prove somewhat intimidating for 

land managers and decision makers. Although we have 

scientific criteria for interpreting the metrics, it may 

not provide enough insight into what the results really 

mean. The four metrics are based on sound science, but 

the weakness may occur with the ability to succinctly 

synthesize what they tell decision makers about a region 

like the SLB. Therefore, we suggest this as one of the 

recommendations for future research activities below. 
228

7.6 Recommendations for Future 

Research on Regional Sustainability 

Metrics

We offer the following recommendations for future 

research based on the results of our study: 

1) Examine previous management decisions in the 

SLB to see if and how the metrics changed. For 

example, the Great Sand Dunes National Park and 

Preserve was created in 2000 and increased the 

National Monument land area by almost four times 

(NPS 2007). This recommendation would help 

to determine if any of the changes in the metrics 

could be linked back to the management decision 

to increase the public land area.

2) Continue calculating the metrics in the SLB for 

subsequent years to examine how sensitive the 

metrics are to management decisions. 

3) Use stakeholder meetings to determine major 

sustainability issues in the region and better 

link the metrics on those issues and concerns. 

Determine if there are other established metrics 

that may better assess sustainability of a region 

by addressing known issues. This relationship 

with stakeholders will help ensure the metrics are 

capable of capturing changes pertinent to issues 

of public concern. The meetings can be used to 

gauge whether stakeholders and decision makers 

can understand the results of the metrics. More 

research can focus on simplifying the interpretation 

of results for various groups of people: managers, 

regulators, landowners, educators, policy makers, 

etc.

4) Develop trend analyses and/or approaches for 

estimating confidence intervals for individual 

metrics. Although a trend may appear obvious, 

recognizing and identifying the variation in the 

data and resulting metric will better identify 

significant trends. One example might be using 

econometric methods (e.g., Greene 1993, Hay et 

al. 2002) or sensitivity analyses.

5) Develop models of alternative future scenarios and 

estimate multiple metrics (Baker et al. 2004, Kok 

and van Delden 2009, Yeh and Chu 2004) that are 

sensitive to relevant changes in a system or region. 

For example, how would the installation of solar 

farms affect the metrics?

6) Test these multiple metrics in other regions and 

determine if the metrics are indeed capturing the 

trend in sustainability of regional systems.

7) Examine the correlation among alternative metrics 

to determine whether certain metrics could be 

dropped from analysis (i.e., one metric moves in 

a similar or opposite pattern to another over time) 

in order to reduce the resources needed (e.g., 

Bastianoni et al. 2008 used Principal Components 

Analysis).

8) Consider the use of other scientific approaches, 

rather than limiting analysis to visual examination 

of graphs, for deciphering the holistic results of 

multiple metrics, such as multicriteria methods 

(e.g., Shmelev and Rodríguez-Labajos 2009); 

data envelopment analysis (e.g., Despotis 2005, 

Kortelainen 2008); mathematical programming 

(e.g., Zhou et al. 2007); or other integrated 

sustainability metrics (e.g., Mayer 2008).

7.7 Conclusions

Although we met the three objectives of this research 

project, at this time we cannot definitely state we met 

the overall goal of the project. Recall from Chapter 1, 

the goal was to produce a straightforward, relatively 

inexpensive methodology that was simple to use 

and interpret. We believe the method is relatively 

inexpensive because we have identified the majority 

of data sources for these types of calculations leading 

to reduced time and resources necessary for others 

to compute the metrics. How straightforward the 

methodology is would be based on anecdotal evidence 

and discussions with stakeholders in the region. We 

do think we partially achieved the goal for some of the 

metrics, but are utilizing feedback from end users to 

fine-tune the descriptions and explanations to enable 

better understanding by decision makers. We recognize 

it may require an intermediary to compute some of the 

metrics, but we want decision makers to understand 

the methodology sufficiently so they see the benefit 

and importance of calculating the metrics and they 

understand what the results mean in order to aid in future 

decision-making.

We think we made progress in this regard by producing 

a methodology that successfully characterizes whether 

a region is moving toward or away from sustainability. 

However, the next major accomplishment will be 

translating the information for decision makers. Applying 

one or more of the eight recommendations will help us 

do just that. 
229

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Table 7.1 – Summary of four metrics calculated for the San Luis Basin, Colorado.

Metric Paradigm Summary

Ecological Footprint Strong sustainability Movement away from sustainability, although gradually 

during the 26-year period

Green Net Regional Product Weak sustainability No definitive evidence to suggest movement away from 

sustainability

Emergy Strong sustainability Movement away from sustainability, although showing 

improvement recently

Fisher information Weak sustainability Relative stability, slight movement away from sustainability 

at the end of the study period
231

Figure 7.1 – Graphs of Ecological Balance, Green Net Regional Product (GNRP), Fraction of Locally Renewable 

Emergy, and mean Fisher Information for 1980-2005.
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