U.S. DEPARTMENT OF COMMERCE

Technology Administration

National Institute of Standards and Technology

Office of Applied Economics

Building and Fire Research Laboratory

Gaithersburg, Maryland 20899

Benefits and Costs of Research:

A Case Study of Cybernetic

Building Systems

Robert E. Chapman

NISTIR 6303
U.S. DEPARTMENT OF COMMERCE

Technology Administration

National Institute of Standards and Technology

NISTIR 6303

Benefits and Costs of Research:

A Case Study of Cybernetic

Building Systems

Robert E. Chapman

Office of Applied Economics

Building and Fire Research Laboratory

Gaithersburg, Maryland 20899

March 1999

Office of Applied Economics

Building and Fire Research Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899

U.S. DEPARTMENT OF COMMERCE

William M. Daley, Secretary

Technology Administration

Gary R. Bachula, Acting Under Secretary for Technology

National Institute of Standards and Technology

Raymond G. Kammer, Director
iii

Abstract

The National Institute of Standards and Technology (NIST) is improving its resource

allocation process by doing “microstudies” of its research impacts on society. This report

is one of a series of microstudies prepared by NIST’s Building and Fire Research

Laboratory (BFRL).

This report focuses on a critical analysis of the economic impacts of past, ongoing, and

planned BFRL research for developing and deploying cybernetic building systems

(CBSs) in office buildings. Building systems targeted for incorporation into CBS

products and services include energy management, fire and security, fault detection and

diagnostics, the real-time purchase of electricity, and the aggregation of building stock

for multi-facility operations. A CBS is defined as a multi-system configuration that is

able to communicate information and control functions simultaneously and seamlessly at

multiple levels. Pressure to increase building systems performance and reduce costs has

created a potential market for CBS products and services. BFRL is collaborating with

industry on the development of CBS products and services and is providing a forum for

conducting interoperability testing.

This case study of BFRL’s CBS-related research, development, and deployment effort

illustrates how to apply in practice a series of standardized methods to evaluate and

compare the economic impacts of alternative research investments. It is presented in

sufficient detail to understand the basis for the economic impact assessment and to

reproduce the results. It is based on past, ongoing, and planned research efforts. Thus, it

includes CBS-related investment costs that have already occurred along with estimates of

future investment costs and cost savings due to the use of CBS products and services.

The results of this study demonstrate that the use of CBS products and services will

generate substantial cost savings to the owners, managers, and occupants of office

buildings across the nation. The present value of cost savings nationwide expected from

the use of CBS products and services in office buildings exceeds $1.1 billion ($1,176

million in 1997 dollars). Furthermore, because of BFRL’s role as a facilitator and

developer of key CBS enabling technologies, CBS products and services are expected to

become available commercially in 2003. Without BFRL’s participation, the commercial

introduction of CBS products and services would likely be delayed until 2010.

Consequently, potential cost savings accruing to the owners, managers, and occupants of

office buildings over the period 2003 until 2010 would have been foregone. These cost

savings are $90.7 million in 1997 dollars. These cost savings measure the return on

BFRL’s CBS-related investment costs of approximately $11.5 million.

Keywords

BACnet; benefit-cost analysis; building economics; cybernetic building systems;

economic analysis; energy conservation; fire panels; fire safety; impact evaluation; lifecycle costing; sensors; research impacts
v

Preface

This study was conducted by the Office of Applied Economics in the Building and Fire

Research Laboratory (BFRL) at the National Institute of Standards and Technology

(NIST). The study is designed to estimate the economic impacts resulting from BFRL

research and to estimate the return on BFRL’s research investment dollars. The intended

audience is the National Institute of Standards and Technology as well as other

government and private research groups that are concerned with evaluating how

efficiently they allocated their past, present, and future research budgets.

The measurement of economic impacts of research is a major interest of BFRL and of

NIST. Managers need to know the impact of their research programs in order to achieve

the maximum social benefits from their limited budgets. The standardized methods for

measuring economic impacts employed in this study are essential to support BFRL=s

effort to evaluate the cost effectiveness of completed and ongoing research projects. As

additional experience is gained with the application of these standardized methods, their

use will enable BFRL to select the Abest@ among competing research programs for future

funding, to evaluate how cost effective are existing research programs, and to defend or

terminate programs on the basis of their economic impact. This need for measurement

methods exists across programs in BFRL, in NIST, and in other research laboratories.
vii

Acknowledgments

The author wishes to thank all those who contributed so many excellent ideas and

suggestions for this report. They include Dr. George E. Kelly of the Building

Environment Division at NIST=s Building and Fire Research Laboratory (BFRL),

manager of BFRL’s integrated cybernetic building system (CBS) product development

effort, for his technical guidance, suggestions, and support. Special appreciation is

extended to Dr. Harold E. Marshall and Ms. Barbara C. Lippiatt of BFRL’s Office of

Applied Economics for the thoroughness of their reviews and for their many insights and

to Ms. Sandra L. Kelley for her assistance in preparing the manuscript for review and

publication. Special appreciation is extended to Ms. Amy S. Boyles for her technical

assistance throughout this research effort. Special appreciation is also extended to Ms.

Christine Izzo of Hood College and Ms. Julia D. Rhoten of Virginia Polytechnic Institute

and State University, formerly with BFRL’s Office of Applied Economics, who helped

immensely with the data collection and analysis effort during their internships. Thanks

are due to the entire BFRL CBS product development team for their assistance in

formulating this research effort. And special thanks are due each of the CBS project

leaders—Mr. Steven T. Bushby, Dr. John M. House, Ms. Jeanne M. DeGreef, Dr.

William Grosshandler, and Dr. Walter W. Jones—for their assistance in identifying CBSrelated benefits and costs and for their helpful comments on an earlier draft of this report.

The report has also benefitted from the reviews and technical comments provided by Dr.

Christoph Witzgall of NIST’s Information Technology Laboratory and Ms. Geraldine S.

Cheok of BFRL’s Structures Division.
ix

Table of Contents

Abstract .........................................................................................................................iii

Preface ............................................................................................................................ v

Acknowledgments......................................................................................................... vii

List of Tables................................................................................................................ xii

List of Figures.............................................................................................................. xiv

List of Exhibits.............................................................................................................. xv

List of Acronyms ......................................................................................................... xvi

Executive Summary ..................................................................................................... xix

1. Introduction................................................................................................................ 1

1.1 Background.......................................................................................................... 1

1.2 Purpose ................................................................................................................ 2

1.3 Scope and Approach............................................................................................. 3

2. A Methodology for Analyzing Economic Impacts...................................................... 5

2.1 Types of Analysis................................................................................................. 5

2.1.1 Baseline Analysis........................................................................................... 5

2.1.2 Sensitivity Analysis ....................................................................................... 5

2.2 Overview of Evaluation Methods ......................................................................... 6

2.2.1 Present Value of Net Benefits and Present Value of Net Savings.................... 7

2.2.2 Benefit-to-Cost Ratio and Savings-to-Investment Ratio ................................. 8

2.2.3 Adjusted Internal Rate of Return.................................................................... 9

2.2.4 Summary of Methods................................................................................... 10

2.3 Presentation and Analysis of the Results of an Economic Impact Assessment..... 12

2.3.1 Significance of Research Effort.................................................................... 12

2.3.2 Analysis Strategy......................................................................................... 14

2.3.3 Calculation of Benefits, Costs, and Additional Measures.............................. 14

3. Building and Fire Research Laboratory’s (BFRL’s) Research on Cybernetic Building

Systems......................................................................................................................... 17

3.1 Cybernetic Building Systems: What They Are and What They Will Do.............. 17

3.2 Key Components of BFRL’s Research on CBS .................................................. 20
x

3.2.1 Building Automation and Control Networks (BACnet)................................ 20

3.2.2 Smart Multi-Function Sensors...................................................................... 22

3.2.3 Advanced Fire Detection and Alarm Panels ................................................. 23

3.2.4 Fault Detection and Diagnostic Systems with Hierarchical Controls............. 24

3.2.5 Virtual Cybernetic Building Testbed............................................................ 25

3.2.6 Economic Support for Cybernetic Building Systems.................................... 27

4. Market for CBS Products and Services..................................................................... 29

4.1 Value of Construction Put in Place ..................................................................... 30

4.2 Overview of the Commercial/Institutional Sector ............................................... 35

4.3 Characteristics of Office Buildings..................................................................... 40

5. A Strategy for Identifying, Collecting, and Measuring CBS-Related Benefits and

Costs............................................................................................................................. 49

5.1 Identification of Key Stakeholders...................................................................... 49

5.2 CBS-Related Benefits and Cost Savings............................................................. 50

5.3 CBS-Related Costs and Benefit Reductions........................................................ 56

5.4 Relationships Between Benefits, Costs, and Stakeholders................................... 58

6. Data and Assumptions for the CBS Economic Impact Assessment ........................... 63

6.1 Data Sources ...................................................................................................... 63

6.1.1 Baseline Measures of Construction Industry Practices.................................. 63

6.1.2 Energy Consumption Survey of Commercial Buildings................................ 64

6.1.3 Other Data Sources...................................................................................... 76

6.2 Defining the Base Case and the CBS Alternative ................................................ 78

6.3 Estimating Significant Benefits and Costs .......................................................... 79

6.3.1 Benefits and Cost Savings............................................................................ 80

6.3.1.1 Lower Energy Costs................................................................................. 80

6.3.1.2 Lower Operations and Maintenance Costs................................................ 81

6.3.1.3 Lower Repair and Replacement Costs...................................................... 82

6.3.1.4 Increased Occupant Productivity.............................................................. 83

6.3.2 Costs and Benefit Reductions....................................................................... 84

6.3.2.1 Higher Costs to Building Owners and Managers...................................... 84

6.3.2.2 Increased Research and Development Costs............................................. 85

6.4 Key Assumptions and Analysis Issues................................................................ 86

6.4.1 Base Year for Computing Benefits and Costs............................................... 87

6.4.2 Length of the Study Period........................................................................... 87

6.4.3 Discount Rate .............................................................................................. 88

6.4.4 Diffusion Process......................................................................................... 88

6.4.5 Dealing with Uncertainty ............................................................................. 95

6.4.6 Measuring BFRL’s Contribution.................................................................. 96

7. Baseline Analysis of Economic Impacts................................................................... 99

7.1 BFRL Summary Impact Statement ..................................................................... 99

7.2 Cost Savings Nationwide.................................................................................. 102
xi

7.3 Measuring the Value of BFRL’s Contribution and the Return on BFRL’s CBSRelated Investments......................................................................................... 109

8. Sensitivity Analysis of Economic Impacts.............................................................. 115

8.1 Methodology .................................................................................................... 115

8.2 Key Variables................................................................................................... 117

8.3 Sensitivity Results............................................................................................ 119

8.3.1 Changing One Input................................................................................... 119

8.3.2 Changing All Nine Inputs in Combination ................................................. 126

9 Summary and Suggestions for Further Research....................................................... 141

9.1 Summary.......................................................................................................... 141

9.2 Suggestions for Further Research...................................................................... 143

 9.2.1 The Development of a Standard Classification of Research Benefits and

Costs ............................................................................................................... 144

 9.2.2 Factors Affecting the Diffusion of New Technologies............................... 144

 9.2.3 Conducting Ex Ante Evaluations with Scheduled Follow-ups ................... 145

9.2.4 Evaluations Based on Multiattribute Decision Analysis............................. 146

References .................................................................................................................. 147

Disclaimer:

Certain trade names and company products are mentioned in the text in order to

adequately specify the technical procedures and equipment used. In no case does such

identification imply recommendation or endorsement by the National Institute of

Standards and Technology, nor does it imply that the products are necessarily the best

available for the purpose.
xii

List of Tables

Table 2-1. Summary of Appropriateness of Each Standardized Evaluation Method for

Each Decision Type............................................................................................... 11

Table 4-1. Value of Construction Put in Place in Millions of Constant 1992 Dollars..... 32

Table 4-2. Value of Construction Put in Place in Millions of Current Dollars............... 33

Table 4-3. Value of Construction Put in Place: Sector Totals and Sum Total in

Millions of Constant 1992 Dollars......................................................................... 34

Table 4-4. Value of Construction Put in Place: Sector Totals and Sum Total in

Millions of Current Dollars.................................................................................... 35

Table 5-1. Hierarchy of CBS Stakeholders: CBS Stakeholder Groups and Classes of

Individual CBS Stakeholders................................................................................. 51

Table 5-2. Assignment of Classes of Individual CBS Stakeholders to CBS Stakeholder

Groups .................................................................................................................. 52

Table 5-3. CBS-Related Benefits and Cost Savings...................................................... 54

Table 5-4. CBS-Related Costs and Benefit Reductions................................................. 57

Table 5-5. Types of CBS-Related Benefits and Cost Savings Classified by Stakeholder

Group.................................................................................................................... 60

Table 5-6. Types of CBS-Related Costs and Benefit Reductions Classified by

Stakeholder Group................................................................................................. 62

Table 6-1. Projected Fuel Price Indices (excluding general inflation) by Fuel Type ...... 82

Table 6-2. BFRL Investment Costs by Fiscal Year........................................................ 86

Table 6-3. Percent of Total Office Floorspace Covered by Selected Energy

Management Practices........................................................................................... 92

Table 6-4. Baseline Case of P0(t) and ΔP0(t) (α = 6.0, β = 0.6, η = 0.1754) ................. 93

Table 7-1. Baseline CBS Installations by Year: 1991-2015.......................................... 103

Table 7-2. Baseline Energy Cost Savings by Year: 1997-2015 .................................... 105
xiii

Table 7-3. Baseline Cost Savings by Category and in Total in Millions of 1997 .......... 106

Table 7-4. Baseline Computation of Present Value Cost Savings Nationwide in.......... 108

Table 7-5. Summary of BFRL Research Investments: 1991-2015................................ 110

Table 7-6. Estimated Cost Savings in Millions of 1997 Dollars Attributable................ 112

Table 8-1. Baseline and Extreme Values of the Nine Input Variables Used in the

Sensitivity Analysis............................................................................................. 118

Table 8-2. Summary Statistics Due to Changes in the Input Variable Alpha ............... 120

Table 8-3. Summary Statistics Due to Changes in the Input Variable Beta.................. 121

Table 8-4. Summary Statistics Due to Changes in the Input Variable Eta ................... 122

Table 8-5. Summary Statistics Due to Changes in the Input Variable Time of First

Use...................................................................................................................... 122

Table 8-6. Summary Statistics Due to Changes in the Input Variable Length of

Delay................................................................................................................... 123

Table 8-7. Summary Statistics Due to Changes in the Input Variable Energy Cost

Savings Per Square Meter (Per Square Foot) ....................................................... 123

Table 8-8. Summary Statistics Due to Changes in the Input Variable Maintenance

Cost Savings Per Square Meter (Per Square Foot) ............................................... 124

Table 8-9. Summary Statistics Due to Changes in the Input Variable Productivity

Cost Savings Per Square Meter (Per Square Foot) ............................................... 125

Table 8-10. Summary Statistics Due to Changes in the Input Variable Discount

Rate..................................................................................................................... 125

Table 8-11. Summary Statistics Due to Changes in All of the Input Variables............ 126

Table 8-12. Percentiles for Statistical Measures Due to Changes in All of the Input ... 129
xiv

List of Figures

Figure 3-1. Flowchart of CBS Research, Development, and Deployment Effort............ 19

Figure 4-1. 1997 Breakdown of $618B Construction Market......................................... 36

Figure 4-2. 1997 Breakdown of $195B Commercial/Institutional Sector....................... 38

Figure 4-3. Total Number of Office Buildings by Size Category: 1995.......................... 42

Figure 4-4. Total Office Floorspace by Building Size Category: 1995 ........................... 42

Figure 4-5. Total Office Floorspace by Building Size Category and Number of Floors:

1995 ...................................................................................................................... 43

Figure 4-6. Total Number of Office Buildings by Year of Construction: 1995............... 45

Figure 4-7. Total Office Floorspace by Year of Construction: 1995............................... 45

Figure 4-8. Total Office Floorspace by Year of Construction and Number of Floors:

1995 ...................................................................................................................... 46

Figure 6-1. Sum of Major Fuel Expenditures by Office Building Size Category:

1995 ...................................................................................................................... 66

Figure 6-2. Office Building Heating Equipment by Building Size Category .................. 68

Figure 6-3. Office Building Cooling Equipment by Building Size Category .................. 69

Figure 6-4. Office Building Lighting Equipment by Building Size Category ................. 70

Figure 6-5. Office Building Shell Conservation Features by Building Size Category ..... 71

Figure 6-6. Office Building HVAC Conservation Feaatures by Building Size

Category................................................................................................................ 72

Figure 6-7. Office Building Lighting Conservation Features by Building Size

Category................................................................................................................ 73

Figure 6-8. Use of Energy Management Practices in Office Buildings: Part A............... 74

Figure 6-8. Use of Energy Management Practices in Office Buildings: Part B............... 75

Figure 6-9. Baseline Case of Pη(t) and ΔPη(t) by t(year)............................................... 94
xv

Figure 8-1. Present Value of Cost Savings Nationwide in Millions of 1997 Dollars.... 128

Figure 8-2. Present Value of Cost Savings Attributable to BFRL in Millions of 1997

Dollars................................................................................................................. 133

Figure 8-3. Present Value of BFRL’s Investment Costs in Millions of 1997 Dollars.... 134

Figure 8-4. Present Value of Net Savings Attributable to BFRL in Millions of 1997

Dollars................................................................................................................. 138

Figure 8-5. Savings to Investment Ratio on BFRL’s Research and Development

Investment........................................................................................................... 139

Figure 8-6. Adjusted Internal Rate of Return on BFRL’s Research and Development

Investment........................................................................................................... 140

List of Exhibits

Exhibit 2-1. Format for Summarizing the Economic Impacts of BFRL Research

Efforts................................................................................................................. 13

Exhibit 7-1. Summary of Economic Impacts of BFRL Research on Cybernetic Building

Systems for Office Buildings........................................................................... 100
xvi

List of Acronyms

Acronym Definition

AIRR Adjusted Internal Rate of Return

ANSI American National Standards Institute

ASHRAE American Society of Heating, Refrigerating, and Air-Conditioning Engineers

ASTM American Society for Testing and Materials

ATM Asynchronous Transfer Mode

BACnet Building Automation and Control Networks

BCR Benefit-to-Cost Ratio

BFRL Building and Fire Research Laboratory

BOMA Building Owners and Managers Association 

CBECS Commercial Building Energy Consumption Survey

CBS Cybernetic Building System

CDF Cumulative Distribution Function

CORBA Common Object Request Broker Architecture

CPI Consumer Price Index

DDC Direct Digital Control

DDE Dynamic Data Exchange

DOE Department of Energy

FDD Fault Detection and Diagnostic

GSA General Services Administration

HVAC Heating, Ventilation, and Air-Conditioning 

IAQ Indoor Air Quality

IFMA International Facilities Management Association

IREM Institute of Real Estate Management

LCC Life-Cycle Cost

MARR Minimum Attractive Rate of Return

MEL Manufacturing Engineering Laboratory

NEMA National Electrical Manufacturers Association

NFPA National Fire Protection Association

NIST National Institute of Standards and Technology

NSTC National Science and Technology Council

OAE Office of Applied Economics

OMB Office of Management and Budget

PVB Present Value of Benefits

PVC Present Value of Non-Investment Costs

PVC Present Value of Combined Costs

PVI Present Value of Investment Costs

PVNB Present Value of Net Benefits

PVNS Present Value of Net Savings

PVS Present Value of Savings
xvii

SIC Standard Industrial Classification

SIR Savings-to-Investment Ratio

TV Terminal Value

VAV Variable Air Volume

VCBT Virtual Cybernetic Building Testbed

VRML Virtual Reality Modeling Language

VTS Visual Test Shell
xix

Executive Summary

This report is the third in a series of microstudies prepared by NIST’s Building and Fire

Research Laboratory (BFRL).

i, ii It focuses on a critical analysis of the economic impacts

of past, ongoing, and planned BFRL research for developing and deploying cybernetic

building systems (CBSs) in office buildings. Pressure to increase building systems

performance and reduce costs has created a potential market for CBS products and

services. BFRL is collaborating with industry on the development of CBS products and

services and is providing a forum for conducting interoperability testing. A CBS is

defined as a multi-system configuration that is able to communicate information and

control functions simultaneously and seamlessly at multiple levels.

This case study of BFRL’s CBS-related research, development, and deployment effort

illustrates how to apply in practice a series of standardized methods to evaluate and

compare the economic impacts of alternative research investments. It is presented in

sufficient detail to understand the basis for the economic impact assessment and to

reproduce the results. It is based on past, ongoing, and planned research efforts. Thus, it

includes CBS-related investment costs that have already occurred along with estimates of

future investment costs and cost savings due to the use of CBS products and services in

office buildings.

Chapter 2 presents the five economic evaluation methods (i.e., economic measures) that

are most appropriate for measuring the benefits (cost savings) impacts of research

programs: (1) present value of net benefits (PVNB); (2) present value of net savings

(PVNS); (3) benefit-to-cost ratio (BCR); (4) savings-to-investment ratio (SIR); and (5)

adjusted internal rate of return (AIRR). The PVNB (PVNS) measures the overall

magnitude of the benefits (cost savings) net of the costs of undertaking the research. The

BCR (SIR) measures the benefits (cost savings) per unit cost of the research. The AIRR

is the annual percentage yield from a project over the study period, taking into account

the reinvestment of interim receipts. All five methods apply to Accept/Reject decisions.

Both PVNB and PVNS are appropriate for Design/Size decisions (selecting one among

mutually exclusive alternatives). BCR, SIR, and AIRR are appropriate for ranking

alternatives under a budget constraint. A format for summarizing economic impacts of

research investments is presented in Exhibit 2.1.

Chapter 3 describes BFRL’s CBS-related research, development, and deployment effort

and each of its six key areas of research. The CBS efforts within BFRL are aimed at

producing a suite of products and services that integrate a wide variety of building

 

i

 The first report focuses on two building technology applications: (1) ASHRAE Standard 90-75 for

residential energy conservation; and (2) 235 shingles, an improved asphalt shingle for sloped roofing. See

Chapman, Robert E., and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two Case Studies in

Building Technology. NISTIR 5840. Gaithersburg, MD: National Institute of Standards and Technology.

ii The second report focuses on a fire technology application: the Fire Safety Evaluation System. See

Chapman, Robert E., and Stephen F. Weber. 1996. Benefits and Costs of Research: A Case Study of the

Fire Safety Evaluation System. NISTIR 5863. Gaithersburg, MD: National Institute of Standards and

Technology.
xx

systems. Building systems targeted for incorporation into CBS products and services

include energy management (e.g., heating, ventilation, and air-conditioning (HVAC) and

lighting), fire and security, fault detection and diagnostics, the real-time purchase of

electricity, and the aggregation of building stock for multi-facility operations. How these

systems communicate, interact, share information, make decisions, and perform in a

“synergistic” and reliable manner is at the heart of BFRL’s CBS program.

Chapter 4 provides a snapshot of the US construction industry. As such, it provides the

context within which the scope and size of the market for CBS products and services is

defined. Information is first presented on the value of construction put in place to show

the size of the construction industry and each of its four sectors. The four sectors are

residential, commercial/institutional, industrial, and public works. Information on the

commercial/institutional sector is then presented to focus on its importance within the

overall construction industry and to define its key components. Office buildings are

shown to be a key component of the commercial/institutional sector and are considered

the most likely market for CBS products and services. Special emphasis is then placed

on detailing the key characteristics of office buildings (e.g., building floorspace and year

of construction) to define the scope and size of the market for CBS products and services

in office buildings. Detailing the key characteristics of office buildings is crucial,

because investments in CBS products and services affect additions, alterations, and

maintenance and repair activities as well as new construction activities.

A strategy for identifying, collecting, and measuring CBS-related benefits and costs is

presented in Chapter 5. The strategy identifies key stakeholders (e.g., building owners

and managers), presents comprehensive lists of CBS-related benefits and costs, and

documents the relationships between benefits, costs, and stakeholders. The strategy was

developed through an iterative process. First, information was solicited from all of the

members of the BFRL CBS team. A brainstorming session was used to develop

candidate lists of key stakeholder classes and general types of CBS-related benefits and

costs. Second, the lists were refined and organized into a suite of “classification”

hierarchies. Third, the classification hierarchies were distributed to each of the BFRL

CBS project leaders and, upon their review of the classification hierarchies, critiqued in a

series of meetings. The meetings with the BFRL CBS project leaders also sought to

identify subject matter experts for follow-on discussions. Finally, subject matter experts

from industry and government were interviewed. These interviews were used to finalize

the analysis strategy and the classification hierarchies as well as to collect information on

current industry practices and to identify additional data sources.

Chapter 6 describes the data and assumptions used to evaluate the economic impacts of

installing CBS products and services in office buildings. The goal of Chapter 6 is

fourfold. First, it establishes the sources and validity of the data used in the CBS

economic impact assessment. Second, it defines the base case and the CBS alternative.

Third, it produces estimated values for key sets of benefits and costs. Fourth, it

documents the process by which key assumptions were established, including how the

values of key parameters were set. For example, the study period over which costs and

savings are measured consists of the 25 years from 1991 through 2015. The base year is
xxi

1997, and all dollar amounts are calculated in present value 1997 dollars. The discount

rate is 7 percent (real), which is the OMB discount rate in effect for government projects

in 1997.

The CBS economic impact assessment was carried out in two stages. In the first stage, a

baseline analysis was performed. In the baseline analysis, all input variables used to

calculate the economic measures are set at their likely values. It is important to recognize

that the term baseline analysis is used to denote a complete analysis in all respects but

one; it does not address the effects of uncertainty. In the second stage, nine input

variables were varied both singly and in combination according to an experimental

design. Monte Carlo simulations are employed to evaluate how changing the value of

these variables affects the calculated values of the economic measures.

In Chapter 7 (see Exhibit 7-1), the results of the baseline analysis demonstrate that the

use of CBS products and services will generate substantial cost savings to the owners,

managers, and occupants of office buildings across the nation. The present value of cost

savings nationwide expected from the use of CBS products and services in office

buildings exceeds $1.1 billion ($1,176 million in 1997 dollars). Furthermore, because of

BFRL’s role as a facilitator and developer of key CBS enabling technologies, CBS

products and services are expected to become available commercially in 2003. Without

BFRL’s participation, the commercial introduction of CBS products and services would

likely be delayed until 2010, and potential cost savings accruing to the owners, managers,

and occupants of office buildings over the period 2003 until 2010 would have been

foregone. These cost savings are $90.7 million in 1997 dollars. These cost savings

measure the return on BFRL’s CBS-related investment costs of approximately $11.5

million. Stated in present value terms, every public dollar invested in BFRL’s CBSrelated research, development, and deployment effort is expected to generate $7.90 in

cost savings to the public. The estimated annual percentage yield from BFRL’s CBSrelated investments over the 25-year study period is 16.2 percent.

Chapter 8 covers the sensitivity analysis. The objective of the sensitivity analysis was to

evaluate how uncertainty in the values of each of the nine input variables, both singly and

in combination, translates into changes in each of the six economic measures. The six

economic measures evaluated in the sensitivity analysis are: (1) the present value of

savings nationwide, PVSALL; (2) the present value of savings due to BFRL, PVSBFRL; (3)

the present value of BFRL’s CBS-related investment costs, PVCBFRL; (4) the present

value of net savings due to BFRL, PVNSBFRL; (5) the savings-to-investment ratio on

BFRL’s CBS-related investments, SIRBFRL; and (6) the adjusted internal rate of return on

BFRL’s CBS-related investments, AIRRBFRL. The major advantage of the sensitivity

analysis is that it produces results that can be tied to probabilistic levels of significance

for each economic measure (e.g., the probability that PVNSBFRL is greater than or equal

to zero, SIRBFRL is greater than or equal to 1.0, or AIRRBFRL is greater than or equal to the

discount rate, each of which would indicate that BFRL’s CBS-related investments were

cost effective).
xxii

The results of the sensitivity analysis serve to validate the results of the baseline analysis.

For example, the Monte Carlo simulation in which all nine of the input variables were

varied in combination produced 1,000 observations for each of the six economic

measures. The median value for each economic measure was almost identical to the

value calculated in the baseline analysis for that measure. Note, however, that results

from this Monte Carlo simulation reveal that the present value of net savings due to

BFRL, PVNSBFRL, can be negative. This implies that there is some non-zero probability

that BFRL’s CBS-related investments are not cost effective. On the opposite extreme,

however, PVNSBFRL may reach nearly $1.4 billion in 1997 dollars.

The fact that the range of values for an economic measure is so wide prompted an indepth examination of the results of this Monte Carlo simulation for three of the six

economic measures. These measures are particularly helpful in understanding BFRL’s

contribution, since each measure provides a different perspective. The first, the present

value of net savings due to BFRL, is a magnitude measure; it shows a dollar value to the

public net of BFRL’s CBS-related investments. The second, the savings-to-investment

ratio on BFRL’s CBS-related investments, is a multiplier; it shows, in present value

terms, how many dollars the public receives for each public dollar spent. The third, the

adjusted internal rate of return on BFRL’s CBS-related investments, is a rate of return; it

shows the return on the public monies going into the development of CBS products and

services throughout the 25-year study period.

For each of the three economic measures, less than 60 observations out of 1,000 were

responsible for the observed “uneconomical” outcome. Stated another way, there is at

least a 94 percent chance that BFRL’s CBS-related investments are cost effective. This

underscores the importance of using multiple measures that ensure consistency in

decision making.

Chapter 9 discusses additional areas of research that might be of value to government

agencies and other institutions that are concerned with an efficient allocation of their

research budgets. These areas of research are concerned with: (1) the development of a

standard classification of research benefits and costs; (2) factors affecting the diffusion of

new technologies; (3) conducting prospective evaluations with scheduled follow-ups; and

(4) evaluations based on multiattribute decision analysis.
1

1. Introduction

1.1 Background

The pressures of competing in the global marketplace are affecting nearly every U.S.

business. Now more than ever, U.S. businesses are finding that they must continuously

improve their products and services if they are to survive and prosper. Research, with its

potential for incremental and breakthrough improvement is of central importance to most

businesses’ continuous improvement efforts. A key component of the competitiveness

problem is the “inability of American companies (or, more accurately, the U.S.-based

portions of what are fast becoming global technology firms) to transform discoveries

quickly into high-quality products and into processes for designing, manufacturing,

marketing, and distributing such products.”1

Increasingly, the winners in the competitiveness race are those businesses that most

rapidly make use of the fruits of research (e.g., new data, insights, inventions, and

prototypes). Efforts underway at the National Institute of Standards and Technology

(NIST) and elsewhere in the U.S. focus on speeding up the commercial application of

basic and applied research results. The purpose of this report is to respond to the

follwing question: “how do we measure the results of our investments in technology

development and application?”2 A case study approach is used to illustrate how

standardized evaluation methods may be used to measure the economic impacts of such

investments.

NIST’s research laboratories serve all sectors of U.S. industry through focused research

programs. Each laboratory has strong working relationships with industrial, trade, and

professional organizations in its areas of technology concentration. The program of

NIST’s Building and Fire Research Laboratory (BFRL) is guided by a prioritized

research agenda developed by experts from the building and fire communities. Its

performance prediction and measurement technologies enhance the competitiveness of

U.S. industry and public safety. Specifically, BFRL is dedicated to improving the lifecycle quality and economy of constructed facilities. BFRL studies structural, mechanical,

and environmental engineering, fire science and fire safety engineering, building

materials, and computer integrated construction practices.

To further strengthen its ties to industry, BFRL is participating in the Subcommittee on

Construction and Building of the National Science and Technology Council (NSTC).

The NSTC, a cabinet-level group charged with setting federal technology policy,

coordinates research strategies across a broad cross-section of public and private

interests. The Subcommittee on Construction and Building coordinates and defines

 

1Reich, Robert W. 1989. “The Quiet Path to Technological Preeminence.” Scientific American (October):

pp. 41-47.

2Good, Mary, and Arati Prabhakar. 1994. “Foreword.” In Mark Bello and Michael Baum, Setting

Priorities and Measuring Results at the National Institute of Standards and Technology. Gaithersburg,

MD: National Institute of Standards and Technology.
2

priorities for federal research, development, and deployment related to the industries that

produce, operate, and maintain constructed facilities, including, buildings and

infrastructure.3

BFRL has long recognized the value of measuring the impacts of its research program.

Previous studies have shown that even modest research efforts within BFRL are capable

of producing significant impacts.4 One reason for such outcomes is the unique mix of

research facilities and skills possessed by BFRL and its staff. Through many years of

active collaboration with its various user communities, BFRL’s research findings are

highly regarded when new construction, building, and disaster mitigation technologies

are considered for introduction into the U.S. market.

1.2 Purpose

This report is the third in a series of impact studies prepared by BFRL. It focuses on the

research, development, deployment, and adoption and use of Cybernetic Building

Systems (CBSs) in office buildings. The first report focuses on two building technology

applications: (1) ASHRAE Standard 90-75 for residential energy conservation; and (2)

235 shingles, an improved asphalt shingle for sloped roofing.5 The second report focuses

on a fire technology application: the Fire Safety Evaluation System for health care

facilities.6

The CBS research and development effort within BFRL is aimed at producing a suite of

products that integrate a wide variety of building systems. How these systems

communicate, interact, share information, make decisions, and perform in a “synergistic”

and reliable manner is at the heart of BFRL’s CBS research and development effort.

This report employs standardized methods to evaluate the expected economic impacts of

the adoption and use of CBS products and services in office buildings. This “case study”

approach illustrates how to apply in practice standardized methods to evaluate and

compare the economic impacts of research investments. The standardized methods for

measuring economic impacts employed in this study are essential to support BFRL’s

effort to evaluate the cost effectiveness of planned, on going, and completed research

projects. This need for measurement methods exists across programs in BFRL, in NIST,

and in other research laboratories.

 

3

Seven goals to enhance the competitiveness of the U.S. construction industry are explicit in the mission of

the Subcommittee. For a detailed description of these goals and how the Subcommittee on Construction

and Building will approach them, see Wright, Richard N., Arthur H. Rosenfeld, and Andrew J. Fowell.

1995. Construction and Building: Federal Research and Development in Support of the U.S. Construction

Industry. Washington, DC: National Science and Technology Council.

4Marshall, Harold E., and Rosalie T. Ruegg. 1979. Efficient Allocation of Research Funds: Economic

Evaluation Methods with Case Studies in Building Technology. NBS Special Publication 558.

Gaithersburg, MD: National Bureau of Standards.

5Chapman, Robert E., and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two Case Studies in

Building Technology. NISTIR 5840. Gaithersburg, MD: National Institute of Standards and Technology.

6 Chapman, Robert E., and Stephen F. Weber. 1996. Benefits and Costs of Research: A Case Study of the

Fire Safety Evaluation System. NISTIR 5863. Gaithersburg, MD: National Institute of Standards and

Technology.
3

1.3 Scope and Approach

This report takes a long-run view of research planning and evaluation. The focus is on

specific benefits and costs of research investments, with little attention being given to

institutional considerations and other constraining factors. Examples of such

constraining factors which research managers are likely to find important are the

compatibility of research projects with the organization’s mission and the ability to

perform those projects within budget constraints.

This report has eight chapters in addition to the Introduction. The body of this report,

Chapters 3 through 8, consists of a case study of CBSs in office buildings. The approach

taken in this report is to present all CBS-related information in sufficient detail both to

understand the basis for the economic impact assessment and to reproduce the results of

the economic impact assessment. The CBS case study is ex ante in that it is based on

both past and on going and planned research efforts.

The CBS case study provides estimates of the economic impacts from BFRL research

efforts aimed at the development and introduction of a suite of CBS products and services

for office buildings. The methodology and the standardized methods employed in the

study to measure the CBS’s economic impacts are described in Chapter 2. Standardized

methods are used to define the key measures of the economic impacts of research

investments. A format for summarizing the economic impacts of research investments is

also presented. Chapter 3 describes BFRL’s CBS-related research and development

effort. Both the overall CBS research and development effort and the six key areas of

research, which are its constituent parts, are described. Chapter 4 provides an overview

of the construction industry. The overview provides the context within which the market

for CBS products and services is defined. A strategy for measuring CBS-related benefits

and costs is presented in Chapter 5. The strategy identifies key stakeholders (e.g.,

building owners and managers), presents comprehensive lists of CBS-related benefits and

costs, and documents the relationships between benefits, costs, and stakeholders.

Assumptions about those years over which costs and savings are tabulated, the

appropriate discount rate, and the rate and level of adoption of CBS products and services

in office buildings are necessary to measure the economic impacts of CBSs. These

assumptions, and the supporting data upon which these assumptions are based, are

described in Chapter 6. In addition, Chapter 6 develops estimates of the key benefits and

costs that are the focus of the ex ante impact assessment. These “significant few”

benefits and costs are well-defined subsets of the comprehensive lists presented in

Chapter 5. Estimates of the cost savings from using CBS products and services in office

buildings are the focus of Chapter 7. In addition, that part of dollar savings that appears

attributable specifically to BFRL’s research and development effort is estimated. A twopage summary of the CBS case study is given in Section 7.1. Chapter 8 includes a

sensitivity analysis to provide the reader with additional background and perspective on

the economic impacts of BFRL’s CBS-related research and development effort. The

purpose of the sensitivity analysis is to evaluate the impact of changing the values of a
4

number of key variables whose values are uncertain. Monte Carlo techniques are

employed to evaluate how changing the values of these key variables in combination

affects the calculated values of the key measures of the economic impacts of CBSs.

Chapter 9 concludes the report with a summary and suggestions for further research.
5

2. A Methodology for Analyzing Economic Impacts

This chapter focuses on laying out a methodology for conducting and summarizing an

economic impact assessment. The methodology is based on two types of analysis, five

measures of economic performance, and a format for summarizing the results of an

economic impact assessment. The two types of analysis are baseline analysis and

sensitivity analysis. They are described in Section 2.1. The five measures of economic

performance are present value of net benefits, present value of net savings, benefit-to-cost

ratio, savings-to-investment ratio, and adjusted internal rate of return. They are described

in Section 2.2. The format for summarizing the results of the economic impact

assessment is described in Section 2.3.

2.1 Types of Analysis

2.1.1 Baseline Analysis

The starting point for conducting an economic impact assessment is referred to as the

baseline analysis. In the baseline analysis, all data (i.e., all input variables and any

functional relationships among these variables) entering into the benefit, cost, and

savings calculations are set at their likely values. For selected types of data, the input

values are fixed (e.g., a physical constant or a value that is mandated by legislation). The

input values associated with these data types are considered to be known with certainty.

For other types of data, the likely values reflect the fact that some information associated

with these data is uncertain. Consequently, the values of any data subject to uncertainty

are set based on some measure of central tendency.7 Throughout this report, likely value

and baseline value are used interchangeably. Baseline data represent a fixed state of

analysis based on likely values. For this reason, the results and the analysis of these

results are referred to as the baseline analysis. Throughout this report, the term baseline

analysis is used to denote a complete analysis in all respects but one; it does not address

the effects of uncertainty.

2.1.2 Sensitivity Analysis

Sensitivity analysis measures the impact on project outcomes of changing the values of

one or more key input variables about which there is uncertainty. Sensitivity analysis can

be performed for any measure of economic performance (e.g., present value of net

benefits, present value of net savings, benefit-to-cost ratio, savings-to-investment ratio,

adjusted internal rate of return). Since sensitivity analysis is easy to use and understand,

it is widely used in the economic evaluation of government and private-sector

 

7

 Two common measures of central tendency are the mean (e.g., the sum of the individual values of the

items divided by the number of items in the sample) and the median (e.g., the middle value in a rank

ordering of the individual values of the items in the sample). In most cases in this report, the mean is used

as the measure of central tendency. Any cases where the median is used as the measure of central tendency

is clearly indicated in the text. Consequently, if no explicit reference is made to the measure of central

tendency, the measure used is the mean.
6

applications. Office of Management and Budget Circular A-94 recommends sensitivity

analysis to federal agencies as one technique for treating uncertainty in input variables.

Therefore, a sensitivity analysis complements the baseline analysis by evaluating the

changes in output measures when selected key sets of data vary about their likely (i.e.,

baseline) values. Readers interested in a comprehensive survey on methods for dealing

with uncertainty for use in government and private-sector applications are referred to the

study by Marshall8 and the subsequent video9 and workbook.10

2.2 Overview of Evaluation Methods

Several methods of economic evaluation are available to measure the economic

performance of a research program, a new technology, a building, a building system, or

like investment, over a specified time period. These methods include, but are not limited

to, present value of net benefits, present value of net savings, benefit-to-cost ratio,

savings-to-investment ratio, and the adjusted internal rate of return. These methods differ

in the way in which they are calculated and, to some extent, in their applicability to

particular types of investment decisions. The five methods described in this section are

based on ASTM standard practices.11 Detailed descriptions of each of the standardized

methods are given in Chapman and Fuller.12 Readers interested in an excellent, in-depth

survey covering these as well as other methods are referred to Ruegg and Marshall.13

In order to describe each of the five standardized methods, it is necessary to first

introduce and define a series of terms. These terms are used to define each of the

standardized methods. Throughout this section the following terms are used as the basis

for defining the standardized methods:

a* = the alternative under analysis;

t = a unit of time, where –ta is the earliest point (i.e., beginning of the

study period) before the base year (i.e., t=0) and T is the last point

after the base year (i.e., end of the study period);

L = the length of the study period (e.g., t

a

+ T);

Bt

a*

 = the benefits for alternative a* in year t;

 

8Marshall, Harold E. 1988. Techniques for Treating Uncertainty and Risk in the Economic Evaluation of

Building Investments. NIST Special Publication 757. Gaithersburg, MD: National Institute of Standards

and Technology.

9Marshall, Harold E. 1992. Uncertainty and Risk—Part II in the Audiovisual Series on Least-Cost Energy

Decisions for Buildings. Gaithersburg, MD: National Institute of Standards and Technology.

10Marshall, Harold E. 1993. Least-Cost Energy Decisions for Buildings—Part II: Uncertainty and Risk

Video Training Workbook. NISTIR 5178. Gaithersburg, MD: National Institute of Standards and

Technology.

11American Society for Testing and Materials (ASTM). Third Edition, 1994. ASTM Standards on Building

Economics. Philadelphia, PA: American Society for Testing and Materials.

12Chapman and Fuller, Two Case Studies in Building Technology, pp. 27-37.

13Ruegg, Rosalie T. and Harold E. Marshall. 1990. Building Economics: Theory and Practice. New York:

Chapman and Hall.
7

It

a*

 = the investment costs for alternative a* in year t;

Ct

a* 

 = the non-investment costs for alternative a* in year t;

Ct

a* 

 = the combined cost for alternative a* in year t (i.e.,

Ct

a*

 = It

a* + Cta*

 );

St

a*

 = the savings for alternative a* in year t;

d = the discount rate.

Throughout this section the prefix, PV, is used to designate dollar denominated quantities

in present value terms. The present value is derived by discounting (i.e., using the

discount rate) to adjust all benefits, costs, and savings—past, present, and future—to the

base year (i.e., t=0). The dollar denominated quantities defined above and their

associated present value terms are: the present value of benefits (PVB), the present value

of investment costs (PVI), the present value of non-investment costs (PVC), the present

value of combined costs (PVC), and the present value of savings (PVS).

2.2.1 Present Value of Net Benefits and Present Value of Net Savings

The present value of net benefits (PVNB) method is reliable, straightforward, and widely

applicable for finding the economically efficient choice among alternatives (e.g., building

systems). It measures the amount of net benefits from investing in a given alternative

instead of investing in the foregone opportunity (e.g., some other alternative or

maintenance of the status quo).

PVNB is computed by subtracting the time-adjusted costs of an investment from its timeadjusted benefits. If PVNB is positive, the investment is economic; if it is zero, the

investment is as good as the next best investment opportunity; if it is negative, the

investment is uneconomical. Emphasis is on economic efficiency because the method is

appropriate for evaluating alternatives that compete on benefits, such as revenue or other

advantages that are measured in dollars, in addition to costs.

The present value of net savings (PVNS) method is the PVNB method recast to fit the

situation where there are no significant benefits in terms of revenue or the like, but there

are reductions in future costs (e.g., reductions in the cost of ownership to consumers).14

By treating savings like revenue benefits, the PVNB method may be reformulated as the

PVNS method.

 

14If there are any benefits, say in the form of revenues or other positive cash flows; add them to the cost

savings associated with the alternative under analysis.
8

The PVNB for a given alternative, a*, may be expressed as:

( )/(1 )

* *

* * *

a t

t

T

t t

a

t

a a a

B d

PVNB PVB PV

a

= − +

= −

∑=−

C

C

2.1

If there are no important benefits in terms of revenue or the like, but there are reductions

in future costs, then, the PVNS for a given alternative, a*, may be expressed as:

( )

( )

t

T

t t

a

t

a

t

a a a

S I d

PVNS PVS PVI

a

/(1 )

* *

* * *

= − +

= −

∑=−

2.2

If the decision maker anticipates revenues from the investment, then use the PVNB

measure. If the decision maker expects costs to be reduced, then use the PVNS measure.

The PVNS measure is one of the methods used in the Cybernetic Building System (CBS)

case study (see Chapters 7 and 8).

2.2.2 Benefit-to-Cost Ratio and Savings-to-Investment Ratio

The benefit-to-cost ratio (BCR) and the savings-to-investment ratio (SIR) are numerical

ratios whose sizes indicate the economic performance of an investment. The BCR is

computed as benefits, net of future non-investment costs, divided by investment costs.

The SIR is savings divided by investment costs. The SIR is the BCR method recast to fit

the situation where the investment’s primary advantage is lower costs. SIR is to BCR as

PVNS is to PVNB.

A ratio less than 1.0 indicates an uneconomic investment; a ratio of 1.0 indicates an

investment whose benefits or savings just equal its costs; and a ratio greater than 1.0

indicates an economic project. A ratio of, say, 4.75 means that the investor (e.g., the

general public for a public-sector research program) can expect to receive $4.75 for every

$1.00 invested (e.g., public funds expended), over and above the required rate of return

imposed by the discount rate.

The BCR for a given alternative, a*, may be expressed as:

( )

( )

∑

∑

=−

=−

+

− +

=

= −

T

t t

a t

t

T

t t

a t

t

a

t

a a a a

a

a

I d

B C d

BCR PVB PVC PVI

/(1 )

/(1 )

/

*

* *

* * * *

2.3
9

The SIR for alternative a* may be expressed as:

∑

∑

=−

=−

+

+

=

=

T

t t

a t

t

T

t t

a t

t

a a a

a

a

I d

S d

SIR PVS PVI

/(1 )

/(1 )

/

*

*

* * *

2.4

As was the case for the PVNB and PVNS measures, use the BCR if the decision maker

anticipates revenues from the investment, and use the SIR if the decision maker

anticipates costs to be reduced. The SIR measure is the second method used in the CBS

case study (see Chapters 7 and 8).

2.2.3 Adjusted Internal Rate of Return

The adjusted internal rate of return (AIRR) is the annual yield from a project over the

study period, taking into account reinvestment of interim receipts. Because the AIRR

calculation explicitly includes the reinvestment of all net cash flows, it is instructive to

introduce a new term, terminal value (TV). The terminal value of an investment, a*, is

the future value (i.e., the value at the end of the study period) of reinvested net cash flows

excluding all investment costs. The terminal value for an investment a*, is denoted as

TVa*.

The reinvestment rate in the AIRR calculation is equal to the minimum attractive rate of

return (MARR), the opportunity cost of capital, which is assumed to equal the discount

rate, d, a constant. When the reinvestment rate is made explicit, all investment costs are

easily expressible as a time equivalent initial outlay (i.e., a value at the beginning of the

study period) and all non-investment cash flows (e.g., benefits, non-investment costs,

savings) as a time equivalent terminal amount. This allows a straightforward comparison

of the amount of money that comes out of the investment (i.e., the terminal value) with

the amount of money put into the investment (i.e., the time equivalent initial outlay).

The AIRR is defined as the interest rate, r*, applied to the terminal value, TVa*, which

equates (i.e., discounts) it to the time equivalent value of the initial outlay of investment

costs. It is important to note that all investment costs are discounted to a time equivalent

initial outlay (i.e., to the beginning of the study period) using the discount rate, d.

Several procedures exist for calculating the AIRR. These procedures are derived and

described in detail in the report by Chapman and Fuller.15 The most convenient

procedure for calculating the AIRR is based on its relationship to the BCR (SIR). This

 

15Chapman and Fuller, Two Case Studies in Building Technology, pp. 35-37.
10

procedure results in a closed-form solution for r*. The AIRR—expressed as a decimal—

is that value of r* for which:

(1 )( ) 1

(1 )( ) 1

1

1

*

*

*

= + −

= + −

a L

a L

d SIR

r d BCR

2.5

The AIRR measure is the third method used in the CBS case study (see Chapters 7 and

8).

2.2.4 Summary of Methods16

The methods presented in the previous sections provide the basis for evaluating the

economic performance of research investments. The equations underlying the methods

presented earlier are all based on ASTM standard practices. All of the methods are

appropriate for evaluating accept or reject type decisions. But among the methods are

several distinctions that relate to the type of investment decision the decision maker is

facing.

There are four basic types of investment decisions for which an economic analysis is

appropriate:

(1) whether to accept or reject a given project;

(2) the most efficient project size/level, system, or design;

(3) the optimal combination of interdependent projects (i.e., the right mix of sizes/levels,

systems, and designs for a group of interdependent projects); and

(4) how to prioritize or rank independent projects when the allowable budget can not

fund them all.

Each type of investment decision is important in a research environment. First, and

foremost, decision makers need to know whether or not a particular project or program

should be undertaken in the first place. Second, how should a particular research

project/program be configured? The third type of decision builds on the second and

introduces an important concept, interdependence. Many research projects/programs are

multidisciplinary and are analogous to a portfolio. In addition, there may be both

economies of scale (e.g., spreading out the use of specialized equipment) and of scope

 

16For a comprehensive treatment of how to choose among economic evaluation methods, see the

NIST/BFRL video (Marshall, Harold E. 1995. Choosing Economic Evaluation Methods—Part III in the

Audiovisual Series on Least-Cost Energy Decisions for Buildings. Gaithersburg, MD: National Institute of

Standards and Technology) and workbook (Marshall, Harold E. 1995. Least-Cost Energy Decisions for

Buildings—Part III: Choosing Economic Evaluation Methods Video Training Workbook. NISTIR 5604.

Gaithersburg, MD: National Institute of Standards and Technology).
11

(e.g., packaging of staff talents). Consequently, for a given set of skills, laboratory

facilities, candidate projects, and implied interdependencies, the problem becomes how to

choose that combination of projects which maximizes PVNB (PVNS). The fourth type of

decision introduces a budget constraint. The key here is how to get the most impact for

the given budget amount.

Table 2-1 provides a summary of when it is appropriate to use each of the evaluation

methods described earlier. Note that the PVNB (PVNS) method is appropriate in three of

the four cases. Only in the presence of a budget constraint is the use of PVNB (PVNS)

inappropriate and even in that case it plays an important role in computing the aggregate

measure of performance.

Table 2-1. Summary of Appropriateness of Each Standardized Evaluation Method

for Each Decision Type

 Decision Type

PVNB

PVNS

BCR

SIR AIRR

Accept/Reject Yes Yes Yes

Design/Size Yes No No

Combination

(Interdependent)

Yes No No

Priority/Ranking

(Independent)

No Yes Yes

In summary, there are several reasons why multiple measures of economic performance

are necessary. First and foremost, managers want to know if a particular research project

is economic. Reference to Table 2-1 shows that all of the evaluation methods address

this type of decision. Furthermore, these evaluation methods may be used ex ante for

emerging technologies as well as ex post for past research projects. Second, as issues of

design, sizing, and packaging combinations of projects become the focus of attention—as

often occurs in conjunction with budget reviews—the PVNB (PVNS) method emerges as

the principle means for evaluating a project’s or program’s merits.17 Finally, the

tightening budget picture involves setting priorities. Consequently, decision makers need

both measures of magnitude, provided by PVNB (PVNS), and of return, provided by

either the BCR (SIR) or the AIRR, to assess economic performance. Multiple measures,

when used appropriately, ensure consistency in both setting priorities and selecting

projects for funding. The results from the CBS case study presented in Chapters 7 and 8

illustrate the importance of multiple measures of economic performance.

 

17If incremental values of the BCR (SIR) or AIRR are computed, they can be used to make design/size and

packaging decisions. See Ruegg and Marshall, Building Economics, pp. 54-58 and 85-87.
12

2.3 Presentation and Analysis of the Results of an Economic Impact Assessment

The presentation and analysis of the results of an economic impact assessment are central

to understanding and accepting its findings. If the presentation is clear and concise, and

if the analysis strategy is logical, complete, and carefully spelled out, then the results will

stand up under close scrutiny. The purpose of this section is to outline a generic

framework for economic impact studies that meets the two previously cited conditions.

The generic framework is built upon the following three factors: (1) the significance of

the research effort; (2) the analysis strategy; and (3) the calculation of key benefit and

cost measures. A specific framework, tailored to BFRL, is given in Exhibit 2-1; it is also

used as the basis for summarizing the CBS case study (see Section 7.1).

The discussion that follows relates the three factors for the generic framework referenced

above to the specific framework given in Exhibit 2-1. Exposition of the generic

framework serves two purposes. First, it provides a means for organizing the way to

present material associated with an in-depth economic impact assessment. Second, it

provides a vehicle for clearly and concisely presenting the salient results of the analysis.

Such a short summary is appropriate for use by senior research managers (e.g., laboratory

directors) as the basis for statements on the benefits of the research project or program to

the public. A two-page summary of the CBS case study is provided at the beginning of

Chapter 7.

2.3.1 Significance of Research Effort

This section of an economic impact assessment sets the stage for the results that follow.

The goal at this point is to clearly describe:

(1) why the research is important and how the organization conducting the

research became involved; and

(2) why some or all of the changes brought about were due to the research

organization’s contribution.

Emphasis is placed on providing dollar estimates to define the magnitude of the problem.

If any non-financial characteristics are of key importance to senior management, list and

describe them briefly. A clear tie into the research organization’s mission or vision is

included to demonstrate why the organization conducting the research is well qualified

and well positioned to participate in the research effort. The section concludes with a

statement of the research organization’s contribution.
13

Exhibit 2-1. Format for Summarizing the Economic Impacts of BFRL Research

Efforts

1.a Significance of Research Effort:

Describe why the research is important and how BFRL

became involved.

Describe the changes brought about by the BFRL

research effort.

1.b Key Points:

Highlight two or three key points which

convey why this research effort is

important.

2. Analysis Strategy:

Describe how the present value of total benefits (savings) to the nation stemming from all

contributions to the research effort was determined.

Describe how the present value of total costs to the nation stemming from all contributors to the

research effort was determined.

Describe how the present value net benefits (savings) to the nation was determined.

Describe how the present value of total benefits (savings) attributable to BFRL’s research effort was

determined.

Describe how the present value of total costs attributable to BFRL’s research effort was determined.

Describe how the present value of net benefits (savings) attributable to BFRL’s research effort was

determined.

Describe how any additional measures were calculated and how BFRL’s contribution was

determined.

Summarize key data and assumptions: (a) Base year; (b) Length of study period; (c) Discount rate or

minimum acceptable rate of return; (d) Data; and (e) other.

3.a Calculation of Benefits, Costs, and Additional

Measures:

Total Benefits (Savings):

Report the present value of the total benefits (savings)

attributable to BFRL’s research effort.

Total Costs:

Report the present value of the total costs attributable to

BFRL’s research effort.

Net Benefits (Savings):

Report the present value of net benefits (savings)

attributable to BFRL’s research effort.

Additional Measures:

Report the values of any additional measures calculated.

3.b Key Measures:

Report the calculated value of the

Present Value of Net Benefits (PVNB)

or the Present Value of Net Savings

(PVNS) attributable to BFRL and at

least one of the following:

v Benefit-to-Cost Ratio (BCR) or

Savings-to-Investment Ratio (SIR)

v Adjusted Internal Rate of Return

(AIRR)
14

2.3.2 Analysis Strategy

This section of an economic impact assessment focuses on documenting the steps taken

to ensure that the analysis strategy is logical and complete. Particular emphasis is placed

on summarizing the key assumptions, including any constraints that limited the scope of

the study. Responses are provided for key assumptions concerning: (a) the base year for

the study; (b) the length of the study period; and (c) the discount rate or minimum

acceptable rate of return used.

Special emphasis is placed on documenting the sources and validity of any data used to

make estimates or projections of key benefit and cost measures. This section establishes

an audit trail from the raw data, through data manipulations (e.g., represented by

equations and formulae), to the results which describe how:

(1) the present value of total benefits (savings) to the nation stemming from

all contributors to the research effort under study was determined;

(2) the present value of total costs for all contributors to the research effort

under study, any users of the new technology under study, and any third

parties affected by either the research effort or the use of the new

technology was determined;

(3) the present value of net benefits (savings) to the nation stemming from all

contributors to the research effort under study, any users of the new

technology under study, and any third parties affected by either the

research effort or the use of the new technology was determined;

(4) the present value of total benefits (savings) attributable to the research

organization’s contribution was determined;

(5) the present value of total costs attributable to the research organization’s

contribution was determined;

(6) the present value of net benefits (savings) attributable to the research

organization’s contribution was determined; and

(7) any additional measures were calculated and how the research

organization’s contribution was determined.

2.3.3 Calculation of Benefits, Costs, and Additional Measures

This section of an economic impact assessment focuses on reporting the calculated values

of the key benefit and cost measures, as well as any additional measures that are deemed

appropriate. At this point, we note that it is essential to report the calculated value of the

present value of net benefits or the present value of net savings attributable to the

research organization’s contribution and at least one of the following:
15

(a) the benefit-to-cost ratio or the savings-to-investment ratio; or

(b) the adjusted internal rate of return.

Summaries (e.g., tables, graphs, comparative statistics) of the following information

should also be reported:

(1) the present value of the total benefits attributable to the research

organization’s contribution;

(2) the present value of the total costs attributable to the research

organization’s contribution;

(3) the present value of net benefits attributable to the research organization’s

contribution; and

(4) the values of any additional measures calculated.
17

3. Building and Fire Research Laboratory’s (BFRL’s) Research

on Cybernectic Building Systems

3.1 Cybernetic Building Systems: What They Are and What They Will Do

During the next ten years, building control companies, equipment and systems

manufacturers, energy providers, utilities, and design engineers will be under increasing

pressure to improve performance and reduce costs. One means of accomplishing this is

through the development, adoption, and use of cybernetic building systems (CBSs) that

integrate more and more building systems. Building systems targeted for incorporation

into CBS products and services include energy management (e.g., heating, ventilation,

and air-conditioning (HVAC) and lighting), fire (e.g., detection and fire fighting),

security, fault detection and diagnostics, optimal control, the real-time purchase of

electricity, and the aggregation of building stock for multi-facility operations. How these

systems communicate, interact, share information, make decisions, and perform in a

synergistic and reliable manner needs to be addressed on an industry wide basis if CBSs

are to be successful and the U.S. is to obtain a significant share of the potential global

market for such systems.

A CBS is defined as a multi-system configuration able to communicate information and

control functions simultaneously and seamlessly at multiple levels. The configuration

must also allow for two-way communication between the building(s) in which it is

installed, utilities, and energy and service providers. The multiple levels of

communication and control are based on the BACnet (Building Automation and Control

networks) layered protocol architecture.18

BFRL is working towards a fully operational CBS being tested and deployed by 2002.

To achieve this goal, BFRL is working with industry (e.g., equipment and systems

manufacturers, and service providers), building professionals (e.g., owners, designers,

and operators), trade associations, professional societies, standards organizations,

university researchers, and other government agencies (e.g., General Services

Administration and the Department of Energy). Strategic partnerships for the overall

CBS research, development, and deployment effort is being patterned after the NIST

BACnet Interoperability Testing Consortium. The BACnet Consortium is a cooperative

research and development agreement between equipment manufacturers, facilities

managers, and researchers aimed at developing interoperable building control equipment

communicating with the BACnet protocol (see Section 3.2.1).

The overall CBS research, development, and deployment effort is built around six key

projects (see Sections 3.2.1 through 3.2.6). In addition, the overall effort includes a fullscale demonstration of a CBS.

 

18 For an overview of BACnet, see Section 3.2.1. For a description of BACnet’s layered protocol

architecture, see Bushby (Bushby, Steven T. 1997. “BACnet: A Standard Communication Infrastructure

for Intelligent Buildings.” Automation in Construction (Vol. 6): pp. 529-540).
18

A schematic for how the six key projects fit together and how BFRL will work with

industry to develop CBS products and services is shown in Figure 3-1. Each of the six

key projects is represented by a rectangle in the figure. These activities are undertaken

and funded primarily by BFRL. Those activities undertaken by the private sector are

represented by ovals in the figure. Demonstration projects are a hybrid activity,

involving a broad cross-section of participants; they are represented by the rectangle with

rounded edges in the figure. Unidirectional arrows or bi-directional arrows (i.e.,

including a feedback mechanism) represent information flows between activities. Note

that the Virtual Cybernetic Building Testbed provides the mechanism through which

feedback between the upper tier of BFRL projects takes place. Figure 3-1 includes a

vendor tier. Because many different vendors will develop and offer commercial products

and/or services, the figure uses an ellipsis (...) to reflect the indeterminacy of the number

of vendors in the vendor tier. Figure 3-1 shows the culmination of BFRL’s efforts as the

demonstration projects. Once the demonstration projects are completed, the private

sector moves into a full-scale market adoption process. This process will evolve over a

number of years as the CBS products and services diffuse throughout the marketplace.

Prior to the deployment of fully operational CBS products and services in 2002, BFRL

will produce a series of intermediate products. These products are described briefly in

the series of bullets that follow:

• Develop standard communication protocols for the open exchange of information

among energy providers, utilities, energy management systems, fire detection/smoke

control systems, security systems, elevator controls, and service providers.

• Develop enabling technologies, such as fault detection and diagnostic methods, a

hierarchical framework for control decision making, advanced operating strategies for

aggregated buildings, and the application of real time fire modeling in buildings.

• Develop advanced measurement technologies, including smart multi-functional

sensors.

• Develop performance measures, standards, and evaluation tools for protocol

compliance testing, real time monitoring, and the evaluation and documentation of

CBS interactions.

• Construct a Virtual Cybernetic Building Testbed in the laboratory to facilitate the

development and evaluation of new products and systems by manufacturers and

external service providers.
19

Figure 3-1. Flowchart of CBS Research, Development, and Deployment Effort

 

Virtual Cybernetic Building Testbed

Fault Detection

and Diagnostics

Advanced Fire

Panel

Fault Detection

and Diagnostics

Commercial

Products and

Services

Smart

Multi-Function

Sensors

BACnet

Commercial

Products and

Services

Market

Adoption

Demonstration

Projects

Economic

Support for CBS

...
20

• Develop a Consortium of manufacturers and service providers interested in

producing, testing, demonstrating, and selling CBS products and services.

• Develop interoperability testing and certification programs to facilitate the

development and introduction of CBS products and services into the marketplace.

• Conduct a prospective economic impact assessment of BFRL’s CBS-related research,

monitor outcomes, and conduct a follow-up economic impact assessment.

• Develop and demonstrate the integration of CBS products, services, and concepts on

the system/subsystem level.

3.2 Key Components of BFRL’s Research on CBS

3.2.1 Building Automation and Control Networks (BACnet)

Today’s direct digital control systems (DDCs) employ proprietary communication

protocols that prevent systems made by different manufacturers from communicating

with each other. The problem dates from the 1980s when dropping prices and rising

capabilities for computer-based technologies spurred the controls industry to use digital

controls. To operate these controls requires exchanging data over a network, and

individual DDC manufacturers solved the communication problems in different ways.

The proliferation of proprietary systems has frustrated building owners’ efforts to

integrate innovative products from different DDC manufacturers in ways that best suit the

unique needs of their building(s). Prior to the introduction of BACnet, building owners

were forced to either forego potential cost savings due to systems integration or accept a

proprietary system from a single vendor that could severely limit future expansion

capabilities.

BACnet is a standard communications protocol for building automation and control

systems developed under the auspices of the American Society of Heating, Refrigerating,

and Air-Conditioning Engineers (ASHRAE). BACnet provides a standard

communications infrastructure interconnecting building automation and control devices

made by different manufacturers. This makes it possible for building owners to obtain

competitive upgrades to building control systems. In addition, BACnet makes possible

the integration of building systems that currently stand-alone. In June 1995 BACnet was

approved as an ASHRAE standard and, later, as an American national standard by the

American National Standards Institute (ANSI). It has been selected as a European

Community pre-standard by the European Committee for Standardization. Today, there

are over 4,000 installed systems running BACnet in at least 16 countries.

In 1996, the largest federal building west of the Mississippi River, the Phillip Burton

Federal Office Building in San Francisco, was selected by the General Services
21

Administration (GSA) for the first large-scale demonstration of BACnet among multiple

vendors.19 BFRL provided technical assistance to GSA for this project including

technical review of the control system design and specifications, laboratory testing of the

BACnet capabilities of the products to be used in the building, and on site commissioning

support. BFRL has also been collecting and analyzing network traffic data to document

how BACnet performs in large control systems. Phase II of the project, retrofit of the

control systems for the air handling units and over 1300 variable air volume (VAV) box

controllers, was completed in 1998 and the multi vendor BACnet control system is fully

operational. Phase III when underway will expand the BACnet system to a new central

plant facility and connect the control system in this building with other GSA buildings.

This will provide centralized access to energy consumption and system performance data,

and prepare GSA for aggregating utility loads in a deregulated marketplace.

BACnet work is expanding beyond the HVAC realm. BFRL is working with the

National Electrical Manufacturers Association (NEMA) and the National Fire Protection

Association (NFPA) to extend BACnet to fire protection products. NEMA has endorsed

BACnet as the industry's recommended method of integrating security and fire alarm

systems with other building control systems. The first commercial BACnet fire system

products will be introduced within the next two years. New features are being added to

the protocol that will enhance the use of BACnet in life-safety systems. For example,

some day “smart elevators” may be able to tap into HVAC control and fire detection

systems so, if there is a fire, elevators can be used to help evacuate people in a safe and

efficient manner.

To date, BFRL has entered into cooperative research and development agreements with

22 partners to develop interoperable building control equipment that communicates using

the BACnet protocol. The objective of the consortium is to assist the member companies

in developing products that conform to the BACnet standard and to develop

conformance-testing tools and procedures that can be used to establish an industry-run

certification program. BFRL has developed test methods and software testing tools and

provided facilities for member consortium companies to bring their prototype products

together for testing.

The Visual Test Shell (VTS) is a BFRL developed software tool for testing building

control products for conformance to the BACnet standard. VTS is now being used by

manufacturers who are developing BACnet products. The testing procedures

implemented in VTS have become the basis for a draft addendum to the BACnet standard

that defines a conformance test suite. A revised version of this tool, which runs in a

Windows95 or WindowsNT environment, was released in 1998. Development of the

testing tool will continue in parallel with an ASHRAE addendum to the BACnet standard

which will define conformance-testing procedures for BACnet.

 

19 Applebaum, Martin A., and Steven T. Bushby. 1998. “The 450 Golden Gate Project: The World’s First

Large-Scale Use of BACnet.” ASHRAE Journal (July): pp. 23-30.
22

3.2.2 Smart Multi-Function Sensors

The United States currently spends billions of dollars annually to install and maintain

systems in buildings to assure safety from unwanted fires. A major opportunity for cost

savings is to reduce both these expenditures and fire-related losses through the

introduction of new products. Smart multi-function sensors will permit fire and indoor

air quality (IAQ) sensor designers to demonstrate the feasibility of new concepts, to

provide the critical link between sensor input and output required for meaningful

numerical simulations, and to improve the reliability and performance of fire detection

systems.

Test protocols and certification processes have been developed to accommodate specific

fire sensor technologies. In the past, the sources used in these test methods were

optimized for a unique fire or smoke property to quantify detector response. Very little

has been done to determine the impact of test methods on the development of innovative

IAQ sensors. To improve detection sensitivity and reduce inappropriate responses, the

industry has developed new sensor designs based on the measurement of different aspects

of the fire source, or on specific combinations of sensors that can help in distinguishing a

real fire from an interfering background signal. Existing test methods are unable to

evaluate and quantify the performance of the new sensing systems needed for monitoring

and predicting the changing environment as part of a CBS.

BFRL is working with the IAQ and fire sensing industries to identify the state of the

technology, the opportunities for sharing information among fire and other building

control systems, and the advantages and barriers hindering the adoption of emerging

technologies (e.g., micro-electronic gas sensor arrays and wireless communication

sensing). Emerging sensing technologies for duct and ceiling air velocity and pressure

differences between adjacent rooms will also be examined. Efforts will be aimed at

demonstrating the advantages of multi-function sensors (e.g., using the output of an

existing CO2/IAQ sensor to help define the fire/non-fire state or the movement of fire

gases) and multi-sensor (e.g., using the output from CO, temperature, and smoke sensors

to distinguish nuisance sources from threatening fires). This will include the use of such

sensors for improving the reliability and performance of fire detection systems through

earlier detection of small fires, and the reduction in both false negatives (i.e., reported

fires that do not exist) and false positives (i.e., unreported fires). A standard means for

evaluating the response of fire and IAQ sensors, including exposure to nuisance sources,

will be developed and offered for adoption to industry. Water vapor condensation is

thought to be a major source of false negatives, but is also a useful marker for the

environmental state. A means for assessing a sensor’s response to water vapor will be

explored. Full-scale room tests will be conducted to determine the environment adjacent

to the detector, and the fire-emulator/detector-evaluator will be programmed to reproduce

those conditions.
23

3.2.3 Advanced Fire Detection and Alarm Panels

As noted earlier, the United States currently spends billions of dollars annually to install

and maintain systems in buildings to assure safety from unwanted fires. A major

opportunity for cost savings is to reduce both these expenditures and fire-related losses

through the introduction of new products. Advanced fire detection and alarm panels have

the potential to revolutionize the way building fires are detected, located, and fought by

fire-fighting personnel.

Advanced fire detection and alarm panels, when fully developed and deployed, will

isolate the location of a fire in a building and predict the short and long term behavior and

effects of fire growth and smoke spread in the building. Development and deployment of

the advanced fire detection and alarm panels will be facilitated through a strategic

coalition with the detection industry, the National Electrical Manufacturers Association,

and the National Fire Protection Association.

To date, BFRL has developed an advanced model of fire growth and smoke spread in

buildings. As sensor use in buildings becomes more widespread, it is possible to use this

information as input to the BFRL model to detect and predict the evolution of a fire in a

building. Work is currently underway to orient the BFRL model so the inputs are based

on building plans, the contents of the buildings, and sensor data. As a result, model

inputs can be specified both for current building systems and for more advanced systems

(e.g., those using the BACnet protocol). As the sensor suite becomes more extensive and

provides more information, model predictions can be refined to provide greater detail and

more reliable predictions for longer time intervals.

The advanced fire detection and alarm panels and their associated models and algorithms

will provide continuous estimates of the state of a building, enabling smart sensing to

reduce false alarms and to produce both short and long term predictions for purposes of

escape and rescue. For very large facilities, this would enable a measured response so

those incidents can be isolated and contained without general interruption of business. It

is important to verify the algorithms developed for the advanced fire detection and alarm

panels and to test them under a variety of adverse conditions to minimize false negative

reports. Elimination of both false negatives and false positives will be a high priority.

The algorithms employed will use sensor data to start the predictive models, and then

refine the prediction, as additional information becomes available. An important aspect

of this approach is the knowledge of what information sensors are reporting. For

example, analog information is available from the sensors, but a better understanding is

needed of how different sensors respond to the ambient environment. This effort will

require development of a standard test method to obtain these data for use in the sensordriven models and to quantify the response of single and multi-function building sensors

to thermal, flow, gas, and particulate loadings.
24

3.2.4 Fault Detection and Diagnostic Systems with Hierarchical Controls

Today’s building energy management systems have the capability to monitor and log

operating data for thousands of measurement and control points. These capabilities

routinely exceed the capabilities of building owners and operators to process and

understand the data. Consequently, HVAC equipment frequently operates under the

influence of faults that go undetected. The faults lead to energy waste, occupant

discomfort, and shorter equipment life. Building energy management systems need to be

equipped with intelligent fault detection and diagnostic (FDD) tools to enable building

operators to ensure that HVAC systems are operating as expected. These FDD tools will

detect problems (i.e., faults) as they occur, determine what component or system is

failing or has failed, and recommend maintenance and repair procedures. These FDD

tools can then be incorporated into either the building energy management system, the

building equipment, or into stand-alone systems dedicated to fault detection and

diagnostics.

In 1998, BFRL completed the development of a prototype FDD Test Shell. The FDD

Test Shell is a platform based on Microsoft Windows dynamic data exchange (DDE) that

facilitates the integration of FDD modules (e.g., data, reference models, and possibly

multiple FDD methods) developed in any application development environment that

supports DDE. The FDD Test Shell can accept data from an experimental rig, a

simulation model, or a file containing columns of data. Part of this data might be used to

drive reference model modules that provide expected values of variables or parameters to

the FDD Test Shell. The differences (i.e., residuals) between the data and the reference

model values are computed automatically and made available to FDD methods that

operate on the residuals and present results on their individual user interfaces. FDD

methods can also access the unprocessed data provided to the Test Shell by the data

source. The modular architecture provides a structured way for researchers to share data,

models, and methods. Annex 34 participants20 have adopted the FDD Test Shell as a

working tool, and seven countries, including 11 separate institutions, have committed to

use the FDD Test Shell or to test their FDD methods with shared data sets.

During 1999, BFRL will extend the FDD Test Shell to include a module for evaluating

and comparing the output of FDD methods. In addition, a front-end program will be

written that will allow BFRL’s HVACSIM+ program

21 to be a data source for the FDD

Test Shell. Finally, effort will begin on integrating FDD methods into a hierarchical

framework that coordinates operational information from various subsystems (e.g., VAV

boxes and air handling units). In its completed form, the FDD Test Shell is envisioned as

a platform that will accept data from various sources, allow different modules to be

written in a variety of programming languages, and synthesize information obtained from

the various FDD methods in a logical manner.

 

20 International Energy Agency Annex 34 Committee on Computer-Aided Evaluation of HVAC System

Performance: The Practical Application of Fault Detection and diagnostics Techniques in Real Buildings.

21 Park, Cheol, Daniel R. Clark, and George E. Kelly. 1985. “An Overview of HVACSIM+

, A Dynamic

Building/HVAC/Control Systems Program.” Proceedings of the 1st Annual Building Energy Simulation

Conference: pp.175-185.
25

By 2002, the knowledge gained by integrating and synthesizing multiple FDD methods in

the FDD Test Shell will be used to develop a hierarchical architecture for CBSs that will

allow various CBS functions (e.g., energy management, fire detection, elevator control,

system optimization, building aggregation, and the real time purchase of electricity) to

work together. Both expert systems and fuzzy logic will be studied to address the

problem of “command fusion” (i.e., command coordination). The latter approach

converts command fusion into a logic of graded preferences with each control function

portrayed as an agent expressing “preferences” that suggest which command to apply.

Fuzzy operators are used to combine the various preferences and to generate a single

control choice based on multiple trade-offs. Fuzzy logic can also be used to develop

“meta-rules” to describe and implement strategies for high-level control arbitration.

BFRL is initiating work with industrial partners and other control manufacturers to verify

the proper performance of prototype FDD products. In conjunction with this testing,

BFRL will begin an FDD demonstration project at the Phillip Burton Federal Office

Building or other suitable site. By 2001, FDD methods are expected to begin to be

implemented in energy management and control system products. By 2002, some of

these methods are projected to begin to be implemented in other building control

products.

3.2.5 Virtual Cybernetic Building Testbed

With the increasing pressure to integrate more and more building control systems and

services, there is a need to be able to test and evaluate the complex interactions that are

likely under both normal and adverse (e.g., emergency) operating situations. In addition,

there is a need to assist control manufacturers and service providers in the development,

testing, and certification of new products. Due to the complexity of the systems involved

and the need to maintain a comfortable and safe building occupant environment at all

times, these tasks can not be accomplished using real buildings. However, these tasks

can be done through simulation/emulation. The establishment of a Virtual Cybernetic

Building Testbed (VCBT) will enable manufacturers to bring the actual control products

under development, obtain assistance in testing and evaluating their performance, and

perform interoperability tests with other manufacturers.

When fully deployed in 2001, the VCBT will consist of a variety of simulation models

emulating the performance of a typical CBS. The simulation models will be interfaced

with real state-of-the-art and prototype BACnet compliant control systems to provide a

hybrid software/hardware testbed. The testbed will be used by NIST researchers, control

manufacturers, service companies, and software developers to develop and evaluate

control strategies and control products that use the BACnet communication protocol.

The VCBT is designed to emulate the performance of both fault free and fault containing

building heating and cooling equipment, different HVAC systems, and the building shell.

In addition, lighting, vertical transport systems, and other services would be emulated.

An advanced graphical user interface is being developed along with remote accessibility
26

to the VCBT through various communication interfaces, including telephone and the

Internet.

The VCBT will combine BFRL's extensive experience with the modeling and simulation

of buildings, HVAC systems, controls, and fires with the Manufacturing Engineering

Laboratory's (MEL’s) expertise in the area of systems integration, object oriented

programming, the use of a Common Object Request Broker Architecture (CORBA),

advanced information models and data bases, and the Virtual Reality Modeling Language

(VRML). It will make use of MEL's ATM (asynchronous transfer mode) network to

exchange information among the various VCBT components in real time and will allow

for both on-site and remote use of the VCBT by NIST customers.

The development and deployment of the VCBT is divided into four phases. Phase I,

which was completed in 1998, involves the development of an HVAC emulator to

simulate the performance of a VAV air handling unit, three VAV boxes, and three

building zones using BFRL's HVACSIM+ program. Phase II, which will be completed in

September 1999, involves development of a building shell emulator, a fire emulator, a

more complex HVAC emulator, a building/HVAC Product Model, and a VRML based

interactive VCBT display. The various VCBT components, which will be in different

NIST locations, will use the CORBA paradigm to provide a real time, distributed

emulation environment based on message passing between objects and client-server

programming. The last two phases will involve the enhancement of the VCBT front-end

and the expansion of the VCBT to include the emulation of additional building services,

fault containing systems, and other services likely to be provided by outside service

companies.

The VCBT will be used by researchers at NIST and other organizations to study the

complex interactions that occur as a result of integrating different building services and

systems. Of particular interest is the impact of integrating fire detection, smoke control,

transportation, HVAC, and energy management systems on life safety. The extension of

the BACnet protocol to cover lighting, fire detection, transportation, and other services

will be facilitated by the availability of the VCBT as a testbed for testing and evaluating

changes to the BACnet standard. The VCBT will also be used by manufacturers of

building controls and future CBS products to develop and test algorithms, evaluate the

performance of new products, and perform interoperability testing with other

manufacturers. The existence and use of the VCBT by newly developing service

companies will facilitate the development of new building services, such as fault

detection and diagnosis, automated commissioning, building system optimization, and

predictive maintenance.

The VCBT will allow BFRL and ASHRAE to more quickly and reliably extend the

BACnet standard to cover non-HVAC services. By helping building researchers better

understand the interaction between different building control systems, the VCBT will

facilitate the development of new national and international standards for integrating

these systems in a manner that will enhance life safety, increase reliability, and result in

more efficient operation and enhanced building system performance.
27

3.2.6 Economic Support for Cybernetic Building Systems

CBS products and services are one means to improve the performance of building

systems and to reduce the costs of these systems. But investments in and the use of CBS

products and services will be forthcoming only if industry perceives that the economic

benefits outweigh the costs of using such products and services. Being able to

demonstrate net economic savings from using CBS products and services will encourage

their acceptance and use. Economic support for the overall CBS effort addresses the

need for information on the economic consequences of investing in CBS products and

services in two distinct ways.

First, the Office of Applied Economics (OAE) will conduct an ex ante (i.e., prospective)

economic impact assessment of BFRL’s CBS-related research, monitor outcomes, and

conduct a follow-up economic impact assessment. The subject of this report is the ex

ante economic impact assessment. OAE will also design and create a database for

compiling information on CBS-related impacts. Once the database is in place, OAE will

monitor outcomes and compile information on CBS-related impacts in preparation for the

follow-up economic impact assessment.

Second, OAE will develop user-friendly, decision-support software to facilitate the

economic evaluation of CBS products and services and the identification of cost-effective

levels of investment in these products and services. To make cost-effective choices for

investments in CBS products and services, decision makers must have data on benefits

and costs associated with these products and services, information on who bears the costs

and reaps the benefits, and tools (methods and software) for measuring those benefits and

costs. Having a package of economic tools that helps users and stakeholders identify and

measure the benefits and costs of choosing between CBS products and services and

traditional products and services will accelerate the introduction and acceptance of CBS

products and services in the U.S. and abroad. Thus, OAE will produce an integrated

software package providing life-cycle cost (LCC) measurement capabilities for

evaluating CBS products and services. To assure industry acceptance of the software

package, it will be made consistent with ASTM’s LCC standard practice, E 917. Once

the software package has been finalized, OAE will seek out a private-sector collaborator

to market, distribute, and maintain the decision support software package.
28
29

4. Market for CBS Products and Services

The construction industry is a key component of the US economy and is vital to its

continued growth. Investment in plant and facilities, in the form of construction activity,

provides the basis for the production of products and the delivery of services. Investment

in infrastructure promotes the smooth flow of goods and services and the movement of

individuals. Investment in housing accommodates new households and allows existing

households to expand or improve their housing. Clearly, construction activities affect

nearly every aspect of the US economy.22

This chapter provides a snapshot of the US construction industry. As such, it provides

the context within which the scope and size of the market for CBS products and services

is defined. The chapter contains three sections. Each section deals with a particular

topic. The topics progress from general in nature to very specific. This progression is

described below.

Section 4.1 presents information on the value of construction put in place to show the size

of the construction industry and each of its four sectors. The four sectors, which taken

together define the construction industry, are residential, commercial/institutional,

industrial, and public works. Data from the past five years (i.e., 1993 through 1997) are

used to highlight the magnitude of construction-related investments in each sector. Data

from 1997 are then used to establish the relative shares of construction-related

investments for each sector.

Section 4.2 uses information on the commercial/institutional sector both to focus on its

importance within the overall construction industry and to define its key components.

Information on investment activity, the number of commercial/institutional buildings, and

the amount of commercial/institutional floorspace is used to identify both those

characteristics that are changing and those that are remaining constant. Office buildings

are a key component of the commercial/institutional sector. Information showing the

relative share of construction-related investments in office buildings vis-à-vis the other

components of the commercial/institutional sector is also presented.

Section 4.3 places special emphasis on identifying and detailing the key characteristics of

office buildings. Office buildings are considered the most likely market for CBS

products and services. Consequently, information detailing key characteristics (e.g.,

building floorspace and year of construction) is needed to define the scope and size of the

market for CBS products and services in office buildings. Detailing the key

characteristics of office buildings is crucial, because investments in CBS products and

services affect not only new construction activities but additions and alterations and

maintenance and repair activities as well. Ways in which these key characteristics affect

the calculation of CBS-related benefits and costs are discussed in Chapter 6.

 

22 Readers interested in learning more about construction statistics, their sources and interpretation, are

referred to the document by Rogers (Rogers, R. Mark. 1994. Handbook of Key Economic Indicators. Burr

Ridge, IL: Irwin Professional Publishing).
30

4.1 Value of Construction Put in Place

This section provides information on a key indicator of construction activity; the value of

construction put in place. Data published by the US Bureau of the Census are used to

establish the composition of construction expenditures by type of construction/function

(e.g., non-residential/office building). These expenditures are then assigned to the four

key construction industry sectors. The reference document used throughout this section

is the Current Construction Reports series C30 publication Value of Construction Put in

Place.23 A brief description of the “C30 report” follows. Special attention is given to the

organization of the data in the C30 report and how these data map into the four key

construction industry sectors. The section concludes with tabular and graphical

summaries of the value of construction put in place.

Construction expenditures data are published monthly in the Current Construction

Reports series C30 publication Value of Construction Put in Place. Construction

expenditures refer to actual construction rather than planned or just initiated activity. It is

noteworthy that the C30 report covers both private residential and non-residential

construction activities and public sector construction activities.

The value of construction put in place is a measure of the value of construction installed

or erected at a site during a given period. For an individual project, this includes: (1) cost

of materials installed or erected; (2) cost of labor and a proportionate share of

construction equipment rental; (3) contractor’s profit; (4) cost of architectural and

engineering work; (5) miscellaneous overhead and office costs chargeable to the project

on the owner’s books; and (6) interest and taxes paid during construction. Expenses do

not include the cost of land nor do they include maintenance and repairs to existing

structures or service facilities.

The C30 data are compiled via survey and through indirect estimation. In the context of

the C30 survey, construction includes the following: (1) new buildings and structures; (2)

additions, alterations, conversions, expansions, reconstruction, renovations,

rehabilitations, and major replacements (e.g., the complete replacement of a roof or a

heating system); (3) mechanical and electrical installations (e.g., plumbing, heating,

electrical work, and other similar building services); (4) site preparation and outside

construction of fixed structures or facilities (e.g., sidewalks, highways and streets, water

supply lines, sewers, and similar facilities which are built into or fixed to the land); (5)

installation of boilers, overhead hoists and cranes, and blast furnaces; (6) fixed, largely

site-fabricated equipment not housed in a building (e.g., petroleum refineries and

chemical plants); and (7) cost and installation of construction materials placed inside a

building and used to support production machinery (e.g., concrete platforms, overhead

steel girders, and pipes).

 

23 US Department of Commerce. 1998. Current Construction Reports: Value of Construction Put in

Place. C30. Washington, DC: US Bureau of the Census.
31

The data presented in the C30 report are summarized in Tables 4-1 and 4-2. To facilitate

comparisons between this report and the C30 report, Tables 4-1 and 4-2 use the same row

and column headings as are used in the C30 report.

Tables 4-1 and 4-2 record annual values for the years 1993 through 1997. Table 4-1

records annual values in millions of constant 1992 dollars. Table 4-2 records annual

values in millions of current dollars.24 Reference to Table 4-1 reveals that total

construction expenditures in real terms have increased modestly over the five-year period

(i.e., from $461.1 billion to $520.1 billion). When the effects of inflation are included,

the rate of increase appears more pronounced. Table 4-2 shows total construction

expenditures in current dollars.

Tables 4-1 and 4-2 are organized to allow for in-depth analyses of the

components/subcomponents of total construction expenditures. To facilitate such

analyses, the data presented in Tables 4-1 and 4-2 are initially divided into two parts: (1)

private construction; and (2) public construction.

Private construction contains two major components--residential buildings and nonresidential buildings--plus a number or subcomponents. Both the two major components

and the various subcomponents are shown as headings in the first column of Tables 4-1

and 4-2.

The residential buildings component includes new private housing and improvements.

New private housing includes new houses, apartments, condominiums, and town houses.

New private housing units are classified as “1 unit” or “2 or more units.” The value of

improvements put in place are a direct measure of the value of residential additions and

alterations activities.

The non-residential buildings component includes industrial, office buildings, hotels and

motels, and “other commercial” (e.g., shopping centers, banks, service stations,

warehouses, and other categories). Also falling under the non-residential buildings

component are religious, educational, hospital and institutional, and “miscellaneous” nonresidential buildings.

 

24 Inflation reduces the purchasing power of the dollar over time; deflation increases it. When amounts are

stated in actual prices as of the year in which they occur, they are said to be in current dollars. Current

dollars are dollars of any one year’s purchasing power, inclusive of inflation/deflation. That is, they reflect

changes in purchasing power of the dollar from year to year. In contrast, constant dollars are dollars of

uniform purchasing power, exclusive of inflation/deflation. Constant dollars indicate what the same good

or service would cost at different times if there were no change in the general price level to change the

purchasing power of the dollar. For additional information on conducting economic analyses using either

constant dollars or current dollars, see Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle

Costing Manual for the Federal Energy Management Program. NIST Handbook 135. Gaithersburg, MD:

National Institute of Standards and Technology.
32

Table 4-1. Value of Construction Put in Place in Millions of Constant 1992 Dollars

1993 1994 1995 1996 1997

Total construction 461,078 480,620 478,069 506,655 520,117

Private construction 347,851 367,247 360,040 385,967 395,321

Residential buildings 200,502 218,005 201,677 220,017 221,546

New housing units 137,243 153,250 142,413 153,966 156,038

1 unit 126,960 140,416 126,773 136,516 137,156

2 or more units 10,283 12,833 15,640 17,450 18,882

Improvements 63,259 64,755 59,264 66,052 65,508

Nonresidential buildings 106,729 111,416 120,627 131,188 139,067

Industrial 25,554 26,803 29,043 28,503 26,440

Office 20,197 20,553 22,891 24,329 27,631

Hotels, motels 4,405 4,308 6,351 9,521 10,741

Other commercial 31,292 34,756 38,098 42,042 42,748

Religious 3,748 3,584 3,864 3,955 4,951

Educational 4,484 4,471 4,908 5,880 7,101

Hospital and institutional 12,050 11,377 10,051 10,280 11,576

Miscellaneous 5,000 5,565 5,421 6,677 7,880

Farm nonresidential 3,271 2,990 2,692 3,319 3,329

Public utilities 34,120 32,074 32,401 29,286 29,448

Telecommunications 9,468 9,785 10,073 10,245 9,918

Other public utilities 24,652 22,289 22,328 19,041 19,529

Railroads 3,056 3,186 3,201 3,894 4,321

Electric light and power 15,096 13,877 12,656 9,914 10,545

Gas 5,536 4,308 5,637 4,330 3,820

Petroleum pipelines 965 918 834 903 843

All other private 3,229 2,763 2,643 2,156 1,931

Public construction 113,227 113,373 118,029 120,688 124,796

Buildings 46,813 45,728 49,683 51,119 53,515

Housing and redevelopment 3,833 3,495 3,928 3,958 4,055

Industrial 1,658 1,358 1,348 1,214 842

Educational 18,465 18,838 20,800 21,035 22,786

Hospital 3,579 3,663 3,871 4,050 4,247

Other 19,279 18,373 19,737 20,863 21,585

Highways and streets 34,164 36,219 35,303 36,483 38,605

Military facilities 2,405 2,196 2,728 2,317 2,223

Conservation and development 5,771 5,996 5,779 5,335 4,841

Sewer systems 8,622 8,199 8,557 9,260 8,951

Water supply facilities 4,868 4,237 4,695 5,187 5,393

Miscellaneous public 10,583 10,799 11,284 10,987 11,267

Constant (1992) Dollars

VALUE OF CONSTRUCTION PUT IN PLACE (SERIES C30)

Type of Construction
33

Table 4-2. Value of Construction Put in Place in Millions of Current Dollars

1993 1994 1995 1996 1997

Total construction 478,648 519,539 538,134 583,638 618,217

Private construction 362,688 399,346 407,477 446,306 471,159

Residential buildings 210,455 238,874 230,688 256,460 265,610

New housing units 144,071 167,919 162,898 179,448 187,075

1 unit 133,282 153,838 145,009 159,124 164,444

2 or more units 10,788 14,081 17,889 20,324 22,631

Improvements 66,384 70,955 67,790 77,012 78,535

Nonresidential buildings 110,635 120,285 135,022 150,350 165,146

Industrial 26,482 28,947 32,505 32,657 31,394

Office 20,920 22,178 25,613 27,886 32,816

Hotels, motels 4,565 4,648 7,112 10,912 12,752

Other commercial 32,453 37,551 42,654 48,188 50,763

Religious 3,887 3,869 4,326 4,534 5,885

Educational 4,649 4,822 5,493 6,742 8,437

Hospital and institutional 12,492 12,268 11,248 11,780 13,741

Miscellaneous 5,188 6,002 6,071 7,650 9,358

Farm nonresidential 3,392 3,226 3,014 3,804 3,956

Public utilities 34,925 34,071 35,859 33,261 34,188

Telecommunications 9,619 10,121 11,093 11,772 11,626

Other public utilities 25,306 23,950 24,766 21,489 22,562

Railroads 3,108 3,340 3,509 4,398 5,059

Electric light and power 15,567 14,918 14,049 11,211 12,144

Gas 5,645 4,694 6,279 4,865 4,390

Petroleum pipelines 986 998 929 1,015 969

All other private 3,281 2,890 2,893 2,431 2,258

Public construction 115,960 120,193 130,657 137,333 147,058

Buildings 48,559 49,446 55,700 58,659 63,603

Housing and redevelopment 4,011 3,835 4,491 4,614 4,861

Industrial 1,718 1,465 1,508 1,389 998

Educational 19,129 20,361 23,278 24,112 27,065

Hospital 3,710 3,951 4,332 4,638 5,042

Other 19,991 19,834 22,089 23,907 25,637

Highways and streets 34,299 37,419 38,498 41,243 45,197

Military facilities 2,453 2,318 3,011 2,634 2,620

Conservation and development 5,937 6,363 6,368 6,011 5,658

Sewer systems 8,863 8,700 9,435 10,433 10,463

Water supply facilities 5,085 4,647 5,283 5,964 6,339

Miscellaneous public 10,765 11,301 12,362 12,388 13,177

Current Dollars in Millions

VALUE OF CONSTRUCTION PUT IN PLACE (SERIES C30)

Type of Construction
34

Rounding out the private construction component are farm non-residential, public

utilities, and “all other private.” These are generally of a non-residential nature but are

not part of non-residential buildings. Farm non-residential construction includes

structures such as barns, storage houses, and fences. Land improvements such as

leveling, terracing, ponds, and roads are also a part of this subcomponent. Privately

owned public utilities construction is categorized by industry rather than function of the

building or structure. This subcomponent includes expenditures made by utilities for

telecommunications, railroads, petroleum pipelines, electric light and power, and natural

gas. “All other private” includes privately owned streets and bridges, sewer and water

facilities, airfields, and similar construction.

For public construction, there are two major components--building and non-building.

Both the two major components and the various subcomponents are shown as headings in

the first column of Tables 4-1 and 4-2. The building component contains subcomponents

similar to those for private construction, with educational buildings being the largest

subcomponent. Expenditures for the non-building component overwhelmingly consist of

outlays for highways and streets, with sewer systems being a distant second

subcomponent.

To get the sector totals, each subcomponent was assigned to a sector and summed. The

sector assignments are identical to those used in Chapman and Rennison.25 The sector

totals and the overall total are recorded in Tables 4-3 and 4-4. Reference to the tables

reveals that sector totals vary considerably, with residential being the largest and

industrial the smallest.

Table 4-3. Value of Construction Put in Place: Sector Totals and Sum Total in

Millions of Constant 1992 Dollars26

 

25 Chapman, Robert E., and Roderick Rennison. 1998. An Approach for Measuring Reductions in

Operations, Maintenance, and Energy Costs: Baseline Measures of Construction Industry Practices for the

National Construction Goals. NISTIR 6185. Gaithersburg, MD: National Institute of Standards and

Technology.

26 Note that due to rounding the values entered in the “Total – All Sectors” row in Table 4-3, differ slightly

from the values entered in the “Total Construction” row in Table 4-1.

1993 1994 1995 1996 1997

Residential 204,335 221,500 205,605 223,975 225,601

Commercial/Institutional 125,770 128,478 138,684 151,951 164,575

Industrial 27,212 28,161 30,391 29,717 27,282

Public Works 103,763 102,483 103,390 101,011 102,658

Total - All Sectors 461,080 480,622 478,070 506,654 520,116

Value of Construction Put in Place ($ Millions) Sector
35

Table 4-4. Value of Construction Put in Place: Sector Totals and Sum Total in

Millions of Current Dollars27

Reference to Table 4-3 reveals that the commercial/institutional sector is the only sector

to have grown consistently in real terms over the entire five-year period. In real terms,

expenditures in the commercial/institutional sector grew from $125.8 billion in 1993 to

$164.6 billion in 1997, an increase of almost 31 percent. Real expenditures for two of the

four sectors, industrial and public works, were essentially constant over the same fiveyear period. Real expenditures for the residential sector exhibited a cyclical pattern.

The data contained in Tables 4-3 and 4-4 provide the basis for calculating each sector’s

relative share of total construction expenditures. Each sector’s relative share of total

construction expenditures is shown graphically in pie chart form in Figure 4-1. It was

constructed using 1997 data from Table 4-4 (i.e., current dollar expenditures). Reference

to Figure 4-1 reveals that in 1997 the commercial/institutional sector accounted for 32

percent of total construction expenditures (i.e., 32 percent of $618.2 billion). The

commercial/institutional sector’s relative share of total construction expenditures is

exceeded only by the residential sector, which constitutes 44 percent of the total. In

addition, the commercial/institutional sector’s relative share exceeds the combined total

for the industrial and public works sectors.

4.2 Overview of the Commercial/Institutional Sector

The commercial/institutional sector, defined in economic terms, consists of

establishments that provide services. Defined in this way, the commercial/institutional

sector is extremely varied. It includes office buildings, service businesses (e.g., retail and

wholesale stores, hotels and motels, restaurants, and hospitals), as well as a wide range of

facilities that would not be considered “commercial” in a traditional sense (e.g., public

schools, correctional institutions, and religious and fraternal organizations).

 

27 Note that due to rounding the value entered in the “Total-All Sectors” row in Table 4-4 differ slightly

from the values entered in the “Total Construction” row of Table 4-2.

1993 1994 1995 1996 1997

Residential 214,466 242,709 235,179 261,074 270,471

Commercial/Institutional 130,376 138,710 155,230 174,153 195,452

Industrial 28,200 30,412 34,013 34,046 32,392

Public Works 105,608 107,709 113,709 114,365 119,900

Total - All Sectors 478,650 519,540 538,131 583,638 618,215

Value of Construction Put in Place ($ Millions) Sector
36

Figure 4-1. 1997 Breakdown of $618B Construction Market

Expenditures by establishments in the commercial/institutional sector for the built

environment include construction expenditures (e.g., new construction and additions and

alterations) as well as expenditures for facility operations, for maintenance and repair

activities, and for energy. The market for CBS products and services both affects and is

affected by each type of expenditure. Consequently, it is instructive to first define what

is included in each type of expenditure and then examine the characteristics of

commercial/institutional buildings that affect these expenditures. This approach is aimed

at producing a better understanding of the market for CBS products and services within

the commercial/institutional sector.

Construction expenditures include both new construction activities and additions and

alterations.

New construction activities include the complete original building of structures and

essential service facilities and the initial installation of integral equipment (e.g., elevators

and plumbing, heating, and air-conditioning supplies and equipment).

Additions and alterations include construction work that adds to the value or useful life

of an existing building or structure, or which adapts a building or structure to a new or

different use. Included are major replacements of building systems (e.g., installation of a

new roof or heating system).

Residential

44%

Industrial

5%

Public Works

19%

Commercial/

Institutional

32%
37

Facility operations include all non-process or end-product related activities required to

operate a building or structure (e.g., water consumption, trash removal/environmental

costs, cleaning services/janitorial, and security services/life safety costs), with the

exception of maintenance and repair activities and energy. In some cases, fixed

operations components may also be included (e.g., real estate and other taxes, insurance,

and leasing expenses).

Maintenance and repair activities include incidental construction work that keeps a

building or structure in ordinary working condition.

Energy is defined as including all non-process or end-product related energy

consumption required to operate a building or structure. Energy consumption can be

categorized by energy source (e.g., electricity, gas, and oil) and by end-use (e.g., space

heating, cooling, and lighting).

Construction expenditures in 1997 for the commercial/institutional sector were $195.5

billion in current dollars (see Table 4-4). Total expenditures include expenditures from

subcomponents listed under both the “private construction” and “public construction”

headings in Tables 4-1 and 4-2. The subcomponents included under the private

construction heading are: office, hotels and motels, other commercial, religious,

educational, hospital and institutional, miscellaneous, and farm nonresidential. The

subcomponents included under the public sector heading are: educational, hospital and

other. Because the commercial/institutional sector is so varied, it is useful to group these

subcomponents into a small number of key components. For convenience, these

subcomponents are grouped into four key components, three of which are fairly

homogeneous. The four key components are: office, educational, hospital, and other.

The relative share of the overall commercial/institutional sector’s construction

expenditures for each of the four key components is shown graphically in pie chart form

in Figure 4-2. It was constructed using 1997 data from Table 4-2 (i.e., current dollar

expenditures). Reference to Figure 4-2 reveals that in 1997 the office buildings

component accounted for 17 percent of the commercial/institutional sector’s construction

expenditures.

A recent report by Chapman and Rennison presented and analyzed information on

operations, maintenance, and energy costs.28 The commercial/institutional sector figured

prominently in these analyses. Readers interested in an in-depth discussion and analysis

of operations, maintenance, and energy costs in the commercial/institutional sector are

referred to the report by Chapman and Rennison.29 Wherever possible, Chapman and

Rennison normalized data on a per unit area basis. If the data were sufficiently detailed,

as was the case with energy data, they were classified into “bins” within which certain

characteristics were homogeneous (e.g., building type, size, and age) and then normalized

on a per unit area basis. Although there are a number of data sets which allow such indepth analyses, the data associated with the Department of Energy’s (DOE’s)

 

28 Chapman and Rennison, An Approach for Measuring Reductions in Operations, Maintenance, and

Energy Costs.

29 Ibid., pp. 115-182.
38

Commercial Building Energy Consumption Survey (CBECS) is the source of preference

for summarizing the characteristics of the commercial/institutional sector’s stock of

buildings.

Figure 4-2. 1997 Breakdown of $195B Commercial/Institutional Sector

The CBECS collects information on physical characteristics of commercial buildings,

building use and occupancy patterns, equipment use, conservation features and practices,

and types and uses of energy in buildings. The survey also collects information on the

amount of energy consumed and the costs for energy in commercial buildings. The

survey is conducted in two stages, the Building Characteristics Survey and the Energy

Suppliers Survey. The focus of this section and the next is on the Building

Characteristics Survey. Readers interested in the CBECS and its associated micro-data

files are referred to Section 6.1.2.

The most recent DOE Commercial Buildings Characteristics report30 provides detailed

information on the size, age, and other characteristics of commercial/institutional

buildings. In 1995, there were 4.58 million commercial buildings and 5.46 billion square

meters (58.78 billion square feet) of commercial floorspace in the United States. The

mean size of all commercial buildings was 1,193 square meters (12,840 square feet). The

DOE report grouped buildings into eight size categories and into eight age categories.

The vast majority of commercial buildings were found in the smallest size categories,

with more than half in the smallest category and three quarters in the two smallest

 

30 US Department of Energy. 1997. Commercial Buildings Characteristics 1995. DOE/EIA-E024695.

Washington, DC: Energy Information Administration.

Office

17%

Educational

18%

Hospital

10%

Other

55%
39

categories. Most commercial buildings, once constructed, are expected to last for

decades or longer. New buildings are constructed each year and older buildings are

demolished, but the commercial buildings stock at any point in time is dominated by

older buildings. More than 70 percent of all commercial buildings and total floorspace

were constructed prior to 1980, and more than 50 percent of buildings and floorspace

were constructed prior to 1970.

The DOE report also examined whether any changes in major characteristics had

occurred between 1989 and 1995. The report concluded that the profiles of major

characteristics of commercial buildings showed no statistically significant changes from

1989 to 1992 to 1995, the years in which the last three surveys were conducted.31

Significant changes between surveys would occur if characteristics in the newest

buildings (i.e., those constructed since the previous survey) were quite different, or if

changes were made to buildings in the existing stock. However, each three-year

increment of new buildings and floorspace was generally small compared to all buildings

and floorspace in a given category and the changes that did occur were not great enough

to be statistically significant.

The profiles of major characteristics which showed no significant changes included the

total number of buildings, the total amount of floorspace, the distribution of floorspace by

principal building activity (e.g., office buildings), the distribution of buildings by size of

building, and the distribution of floorspace by census region. The findings presented in

the DOE report and the characteristics just referenced are of particular importance in

defining the market for CBS products and services for two reasons.

First, the market for CBS products and services, or any new technology intended for use

in the built environment, is dominated by the characteristics of the current building stock.

Thus, the diffusion of new technologies in general, and CBS technologies in particular,

must include explicit reference to the current building stock and not just to new

construction. This statement is consistent with the assumption that CBS products and

services will be employed both as retrofits to existing buildings and as initial installations

in new buildings.

Second, DOE’s findings imply that both total floorspace for the entire commercial/

institutional sector and total floorspace by principal building activity have remained

constant for an extended period of time. Consequently, the total floorspace associated

with each of the major components shown in Figure 4-2 has remained constant over the

same period of time. If this trend continues, as seems likely, then total floorspace for

each key component will continue to remain constant. It is important to note that during

the period covered by the last survey the commercial/institutional sector experienced

steady growth in construction expenditures relative to the rest of the construction

industry. Even during this period of growth, total floorspace in the commercial/

institutional sector remained constant.

 

31 Ibid., p. vii. and p.10.
40

The choice to adopt a new technology is driven by a number of factors. These factors are

discussed in some detail in Section 6.4.5. However, one factor that is of particular

importance to decision makers considering investments in new technologies is the ability

of those technologies to reduce costs. Clearly, cost reductions have an immediate impact

on the bottom line and thus are reflected on the “corporate” balance sheet. In the case of

office buildings, expenditures for facility operations, for maintenance and repair

activities, and for energy are dwarfed by expenditures for employees’ wages and

salaries.32 Thus, any investments which have the potential both to reduce expenditures

for facility operations, for maintenance and repair activities, and for energy and to

increase productivity will have a doubly beneficial impact on the bottom line. As is

shown in Chapter 5, CBS products and services offer such an opportunity in the office

buildings component of the commercial/institutional sector. Although such a doubly

beneficial impact will probably result for the other key components of the commercial/

institutional sector, office buildings are considered more likely candidates for early

adoption of CBS technologies. Consequently, this impact assessment adopts a

conservative approach in defining the scope of the market for CBS products and services

to be the office buildings component of the commercial/institutional sector. The next

section defines the size of the market for CBS products and services by detailing

information on key office building characteristics.

4.3 Characteristics of Office Buildings

The previous section concluded with a market scope statement. This section

demonstrates how that market scope statement is translated into a specific statement of

market size. To better understand both the scope and size of the CBS market, it is useful

to examine in some detail the characteristics of office buildings.

In 1995, there were 705,000 office buildings in the United States. Collectively, these

705,000 office buildings had 973 million square meters (10,478 million square feet) of

floorspace. Office buildings, which include some of the largest commercial buildings in

the United States, had a mean size of 1,384 square meters (14,900 square feet).33 A

common image of an office building is the multi-story building that dominates the

skyline of the central business district. However, the key category of office buildings is

actually dominated by smaller buildings. These smaller office buildings bring the mean

size of all office buildings close to the mean size of all commercial/institutional buildings

(1,193 square meters (12,840 square feet)).

Figures 4-3 through 4-8 provide detailed snapshots of the nation’s stock of office

buildings. In each figure, information is classified along one of two major dimensions,

either by building size, measured in terms of total floorspace, or by building age,

 

32 Wright, Richard N., Arthur H. Rosenfeld, and Andrew J. Fowell. 1995. National Planning for

Construction and Building R&D. NISTIR 5759. Gaithersburg, MD: National Institute of Standards and

Technology.

33 Commercial Buildings Characteristics 1995, p. 5.
41

measured in terms of year of construction. Each set of figures (e.g., Figures 4-3, 4-4, and

4-5) uses the same bar chart format to facilitate comparisons of characteristics.

Figures 4-3, 4-4, and 4-5 record the distribution of the number of office buildings and

total office floorspace by building size. All three figures use the same eight size

categories specified in the DOE report. The DOE size categories are specified in

customary units; they range from 1,001 square feet to 5,000 square feet (93.0 square

meters to 464.5 square meters) for the smallest size category to over 500,000 square feet

(over 46,451.5 square meters) for the largest size category. The eight size categories, as

defined in this section, are used throughout this report. To facilitate reference to the DOE

report, customary units are shown on the left-hand axis and SI units on the right-hand

axis of each figure.

Figure 4-3 records the distribution of the number of office buildings by building size.

Figure 4-3 shows clearly why smaller buildings dominate the key category of office

buildings. More than half of the stock of office buildings (406,000 of 705,000) is

contained in the smallest size category, and more than three quarters (538,000 of

705,000) in the two smallest size categories. By contrast, only 16,000 buildings are

contained in the three largest size categories, and only 1,000 in the largest category.

Figure 4-4 shows a very different snapshot of the stock of office buildings than was seen

in Figure 4-3. Figure 4-4 records the distribution of total floorspace by building size.

When the total floorspace associated with each size category is tabulated, the floorspace

in the largest category, which contains 1,000 buildings, exceeds the floorspace in the

smallest category, which contains 406,000 buildings. Furthermore, the two smallest size

categories, which contain more than three quarters of the office building stock, have the

two smallest amounts of floorspace. Total floorspace in these categories is

approximately one sixth of the total floorspace of all office buildings.

Figure 4-5 introduces an additional characteristic, the number of floors in the building.

This characteristic serves to sharpen the distinctions between the buildings in each size

category. Figure 4-5 uses the same classification scheme as employed in the DOE report.

DOE grouped office buildings into one of five categories, based on the number of floors.

These categories are one floor, two floors, three floors, four to nine floors, and ten or

more floors. Each floor category is coded by shading; a legend is provided on the figure

to match the floor category to a specific bar in each of the eight size categories. Figure 4-

5 shows clearly how the distribution of total floorspace shifts as building size increases.

For example, buildings with one and two floors dominate total floorspace in the two

smaller size categories. As building size increases, the total floorspace in buildings with

four or more floors rises quickly.
42

Figure 4-3. Total Number of Office Buildings by Size Category: 1995

Figure 4-4. Total Office Floorspace by Building Size Category: 1995

Total Floorspace of Office Buildings (1995) = 10,478 Million Square Feet

 = 973 Million Square Meters

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

1,001 -

5,000

(93.0-

464.5)

5,001 -

10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001 -

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Floorspace in Square Feet (Square Meters)

Total Floorspace (Million Square 

Feet)

0

20

40

60

80

100

120

140

160

Total Floorspace (Million Square 

Meters)

Total Number of Office Buildings (1995) = 705,000

0

50

100

150

200

250

300

350

400

450

1,001 -

5,000

(93.0-

464.5)

5,001 -

10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001 -

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Floorspace in Square Feet (Square Meters)

Number of Office Buildings (Thousands)

132

 94

 35 22

 10 5 1

406
43

Figure 4-5. Total Office Floorspace by Building Size Category and Number of Floors: 1995

0

200

400

600

800

1,000

1,200

1,001 -

5,000

(93.0-

464.5)

5,001 -

10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001 -

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Total Floorspace (Million Square Feet)

0

20

40

60

80

100

Total Floorspace (Million Square Meters)

One Floor

Two Floors

Three Floors

Four to Nine Floors

Ten or More Floors
44

Figures 4-6, 4-7, and 4-8 record the distribution of the number of office buildings and

total office floorspace by year of construction. All three figures use seven year of

construction (i.e., age) categories. It is important to note that these seven year of

construction categories differ from the eight year of construction categories specified in

the DOE report. This is because the last two DOE year of construction categories have

been combined. The last two DOE year of construction categories were 1990 to 1992

and 1993 to 1995. These categories were combined to form the 1990 to 1995 year of

construction category. The year of construction categories used are 1919 or before, 1920

to 1945, 1946 to 1959, 1960 to 1969, 1970 to 1979, 1980 to 1989, and 1990 to 1995. The

seven year of construction categories, as defined in this section, are used throughout this

report.

Figure 4-6 records the distribution of the number of office buildings by year of

construction. Figure 4-6 shows that more than half of the stock of office buildings

(371,000 of 705,000) has been constructed since 1970. Approximately one fifth of the

stock was constructed prior to 1946.

Figure 4-7 records the distribution of floorspace by year of construction. Note that more

than 60 percent of the total floorspace has been constructed since 1970. In addition,

approximately one third of all office floorspace was constructed between 1980 and 1989.

Figure 4-8 introduces information on the number of floors. Begin by comparing Figure

4-8 with Figure 4-5. Recall that Figure 4-5 showed a definite shift in the distribution of

total floorspace among the floor categories as building size increased. When year of

construction is the characteristic under investigation, a different pattern emerges.

Basically, since 1946, the distribution of floorspace among the floor categories within a

given year of construction category is U-shaped. For each year of construction category

since 1946, total floorspace for one and two floor buildings and for four or more floor

buildings tends to exceed the floorspace for buildings with three floors. Although this

outcome may seem puzzling at first, it is easily rationalized. In any extended period, say

1980 to 1989, the distribution of building sizes tends to take on the characteristics (e.g.,

shape) of the distribution of building sizes for the overall population. Thus the

characteristics of the building size distribution for the 1980 to 1989 year of construction

category would be similar to Figure 4-5, the distribution for the overall population.

Consequently, summing across building size for each floor category would produce a Ushaped distribution with a relatively lower value of total floorspace for buildings with

three floors than for the other four floor categories.
45

Figure 4-6. Total Number of Office Buildings by Year of Construction: 1995

Figure 4-7. Total Office Floorspace by Year of Construction: 1995

Total Number of Office Buildings (1995) = 705,000

0

20

40

60

80

100

120

140

160

180

1919

 or

 Before

1920-

1945

1946-

1959

1960-

1969

1970-

1979

1980-

1989

1990-

1995

Year of Construction

Number of Office Buildings (Thousands)

57

74

128

75

158 151

62

Total Floorspace of Office Buildings (1995) = 10,478 Million Square Feet

 = 973 Million Square Meters 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

1919 

or 

Before

1920-

1945

1946-

1959

1960-

1969

1970-

1979

1980-

1989

1990-

1995

Year of Construction

Total Floorspace (Million 

Square Feet)

0

50

100

150

200

250

300

350

Total Floorspace (Million 

Square Meters) 
46

Figure 4-8. Total Office Floorspace by Year of Construction and Number of Floors: 1995

0

100

200

300

400

500

600

700

800

900

1,000

1919

or

Before

1920-

1945

1946-

1959

1960-

1969

1970-

1979

1980-

1989

1990-

1995

Year of Construction

Total Floorspace (Million Square Feet)

0

10

20

30

40

50

60

70

80

90

Total Floorspace (Million Square Meters)

One Floor

Two Floors

Three Floors

Four to Nine Floors

Ten or More Floors
47

The material presented in this section demonstrated several ways in which the nation’s

973 million square meters (10,478 million square feet) of office floorspace can be

characterized. This section’s approach to characterizing the market for CBS products and

services and detailing the market in terms of total floorspace sets the stage for the impact

assessment. By focusing on total office floorspace as the potential target market, the

calculation of CBS-related costs and benefits is driven by floorspace-related

considerations. This is fortuitous because the construction industry is geared towards

processing information presented in terms of per unit costs (e.g., costs per square meter

or per square foot). As a consequence, most published information on construction

industry expenditures for facility operations, for maintenance and repair activities, and

for energy is presented on a per unit basis. By casting the economic impact assessment in

terms of total floorspace, this study will be able to denominate all key inputs in dollars

per unit of area. This approach will promote a better understanding of the findings of this

study and its implications for the construction industry.
48
49

5. A Strategy for Identifying, Collecting, and Measuring CBSRelated Benefits and Costs

The strategy outlined in this chapter was developed through an iterative process. First,

information was solicited from all of the members of the BFRL CBS team. A

brainstorming session was used to develop candidate lists of key stakeholder classes (e.g.,

building owners) and general types of CBS-related benefits and costs. Second, the lists

were refined and organized into a suite of “classification” hierarchies. Third, the

classification hierarchies were distributed to each of the BFRL CBS project leaders (see

Section 3.2 for a description of each CBS project) and, upon their review of the

classification hierarchies, critiqued in a series of meetings with the project leaders. The

meetings with the BFRL CBS project leaders also sought to identify subject matter

experts for follow-on discussions. Finally, subject matter experts from industry and

government were interviewed. These interviews were used to finalize the analysis

strategy and the classification hierarchies presented in this chapter as well as to collect

information on current industry practices and to identify additional data sources.

5.1 Identification of Key Stakeholders

Because individual stakeholders are affected in different ways by the introduction,

adoption, and use of CBS products and services, it is useful to first identify classes of

individual stakeholders and then classify them into stakeholder groups. By developing a

classification hierarchy of stakeholders, we are better able to understand and identify

both potential opportunities (i.e., real or perceived benefits and cost savings accruing to

that stakeholder) and potential barriers (i.e., real or perceived additional costs and benefit

reductions borne by that stakeholder) to the adoption of CBS products and services.

Since individual stakeholder classes evaluate the benefits and costs of CBS products and

services purely from their “stakeholder” viewpoint, it is important to reflect not only that

viewpoint, but the viewpoints of aggregations of stakeholder classes (i.e., a single

stakeholder group or a collection of stakeholder groups) and all stakeholder groups as

well. The viewpoint of the individual stakeholder is important because they make the

decision of whether or not to invest in CBS products and services. Examples of

individual stakeholder classes are building owners, engineering consultants, and trade

associations. A single stakeholder group is a special aggregation of individual

stakeholders classified according to a common theme. An example of a stakeholder

group is construction and associated support services. This stakeholder group contains

four classes of individual stakeholders: general contractors, specialty trade contractors,

wholesale/retail trade/supply, and trade associations. A collection of stakeholder groups

is important because an individual stakeholder class may be a key player in several

stakeholder groups. The overall picture (i.e., all stakeholder groups) is important because

it reflects the benefits and costs of CBS-related products and services to society. BFRL’s

assessment of CBS-related impacts is undertaken from society’s frame of reference.

Thus, it includes all benefits and costs to whomsoever they accrue.
50

Tables 5-1 and 5-2 identify the classes of individual stakeholders and the corresponding

stakeholder group(s) used in the assessment of CBS-related benefits and costs. Both

tables provide the same information, but are organized in different ways.

Table 5-1 is a hierarchy of stakeholders; it lists stakeholder groups with their

corresponding classes of individual stakeholders. It shows how the stakeholder groups

are formed. In Table 5-1, the eight stakeholder groups are listed in a bold-italics

typeface.

Table 5-2 is arranged as a checklist; it assigns each of the 36 classes of individual

stakeholders to its corresponding stakeholder group(s). Table 5-2 lists the classes of

individual stakeholders in alphabetical order to facilitate cross-referencing of individual

stakeholders and stakeholder groups. Note that an individual stakeholder class may be

associated with more than one stakeholder group. For example, trade associations are

associated with three stakeholder groups.

The analysis conducted in this report encompasses all stakeholder groups. However, if

analyses from the perspective of a single stakeholder or stakeholder group were desired,

Tables 5-1 and 5-2 could be used to structure these analyses (see Section 5.4). In such

cases, either Table 5-1 or Table 5-2 may be used to select which class (classes) of

individual stakeholders is (are) appropriate.

5.2 CBS-Related Benefits and Cost Savings

Stakeholders invest in CBS products and services because they anticipate receiving, in

present value terms, benefits or cost savings in excess of the costs or benefit reductions

associated with these investments. Table 5-3 provides a framework for one side of the

stakeholders investment decision problem. Namely, how to measure CBS-related

benefits and cost savings.

Table 5-3 is organized as a two-tiered hierarchy. Table 5-3 represents the culmination of

the Office of Applied Economics CBS project team’s efforts to produce a consensus on a

comprehensive list of CBS-related benefits and cost savings.

The first tier of the hierarchy lists generic types of CBS-related benefits and cost savings.

Although the types of benefits and cost savings appearing in the first tier are generic, the

list is considered to be exhaustive. In addition, the generic types of benefits and cost

savings listed in the first tier are considered to be self-evident. The 23 first tier elements

are listed in a bold-italics typeface. Examples of first tier benefits and costs savings are

fewer false alarms, increased occupant productivity, lower energy costs, and lower

operations and maintenance costs.
51

Table 5-1. Hierarchy of CBS Stakeholders: CBS Stakeholder Groups and Classes of Individual CBS Stakeholders

Building Owners and Managers

• Building Owners

• Building Managers

Codes, Standards, and Support Services

• Code Organizations

• Standards Organizations

• Research Organizations

• Building Owners

• Building Products Manufacturers

• Trade Associations

• Professional Societies

• Product Evaluation Services

• Product Certification Services

• Code Officials

• Building Permitting and Inspection

Manufacturing Interest Group

• Building Products Manufacturers

• Product Innovators

• Product Designers

• Research Organizations

• Testing Laboratories

• Testing Services

• Trade Associations

• Professional Societies

• Product Marketing, Sales, and Distribution

• Customer Service Operations

Construction and Associated Support Services

• General Contractors

• Specialty Trade Contractors

• Wholesale/Retail Trade/Supply

• Trade Associations

Professional and Financial Services

• Designers

• Architects

• Engineering Consultants

• Investment Banking Services

• Insurance Companies

• Warranty Companies

• Real Estate Companies

Emergency and Security Services

• Fire, Rescue, and Police Services

• Security Services

Utilities and Energy Providers

• Utilities

• Energy Distribution Network

• Energy Providers

Other

• Building Occupants

• Special Interest Groups

• Third Parties
52

Table 5-2. Assignment of Classes of Individual CBS Stakeholders to CBS Stakeholder Groups

Stakeholder Group

Individual Stakeholder Class Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Emergency

& Security

Services

Utilities &

Energy

Providers

Other

Architects  ̧

Building Managers  ̧

Building Occupants  ̧

Building Owners  ̧  ̧

Building Permitting and

Inspection

 ̧

Building Products Manufacturers  ̧  ̧

Code Officials  ̧

Code Organizations  ̧

Customer Service Operations  ̧

Designers  ̧

Energy Distribution Network  ̧

Energy Providers  ̧

Engineering Consultants  ̧

Fire, Rescue, and Police Services  ̧

General Contractors  ̧

Insurance Companies  ̧

Investment Banking Services  ̧

Professional Societies  ̧  ̧

Product Certification Services  ̧
53

Table 5-2. Assignment of Classes of Individual CBS Stakeholders to CBS Stakeholder Groups (Continued)

Stakeholder Group

Individual Stakeholder Class Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Emergency

& Security

Services

Utilities &

Energy

Providers

Other

Product Designers  ̧

Product Evaluation Services  ̧

Product Innovators  ̧

Product Marketing, Sales, and

Distribution

 ̧

Real Estate Companies  ̧

Research Organizations  ̧  ̧

Security Services  ̧

Special Interest Groups  ̧

Specialty Trade Contractors  ̧

Standards Organizations  ̧

Testing Laboratories  ̧

Testing Services  ̧

Third Parties  ̧

Trade Associations  ̧  ̧  ̧

Utilities  ̧

Warranty Companies  ̧

Wholesale/Retail Trade/Supply  ̧
54

Table 5-3. CBS-Related Benefits and Cost Savings

Better Load-Leveling Capabilities

• Better Overall Load Management

• Allows for Graceful Degradation of Services

Fewer False Alarms

Fire and Rescue Services Better Able to Deploy Resources

• Faster Response

• Improved Fire-Fighting Capabilities (e.g., room of origin, fuel load)

• Allows Elevators to be Integrated Into Fire Response System

• Improved Safety for Fire and Rescue Personnel

Helps Create an Emerging Service Industry

• Facilitates Use of Third Party Operations, Maintenance, and Repair

Services

Improved Health, Safety, and Security

• Lower Risk of Building-Related Illnesses

• Lower Risk of Injuries and Fatalities During Emergencies

• Evacuate Affected Areas vs. Entire Building

Improvements in Energy Pricing

• Optimization of Time-of-Use Pricing

• Better Able to Handle Real-Time Pricing Changes

Increased Functionality and Performance

• Better Control of Temperature, Humidity, and Lighting Levels

• Increased Potential for both Systems Integration and Systems

Optimization

Increased Occupant Productivity

• Improved Indoor Air Quality

• Fewer Lost Workdays

• Increased Occupant Comfort

• Less Down Time

Increased Sales for System Design/Integration/Optimization Services

Increased Sales of Building Products with New Features

Increased Sales of Complementary Product Lines and Services

Less Occupant Turn Over
55

Table 5-3. CBS-Related Benefits and Cost Savings (Continued)

Lower Energy Costs

• Energy Cost Savings Due to Better Controls

• Ability to Aggregate Buildings and Negotiate Lower Electricity Rates

Lower First Costs/Acquisition Costs

• More Opportunities for Input Substitution (e.g., products of different

vendors)

• Potential for Cycle Time Reduction

Lower Health Costs

• Less Building-Related Sickness

• Lower Medical Costs

• Lower Workman’s Compensation Insurance Premiums

Lower Operations and Maintenance Costs

• Better Diagnostics Saves Time in Trouble-Shooting Maintenance

Problems

• Availability of Online Information on the Building’s Characteristics

Promotes Use of Fact-Based Operations and Maintenance Programs

• Facilitates Cross-Training of Support Staff for Multi-Building Operations

• Better Management of Unoccupied Spaces

Lower Repair and Replacement Costs

• Enables Electronically-Transmitted Fixes for Many Repair Problems

• Longer Equipment Life

Promotes Code Changes Due to New Ways of Controlling Building Functions

Reduced Property Losses

• Reductions in Expected Building Losses in the Event of a Fire

• Reductions in Expected Losses of Occupant Assets in the Event of a Fire

Reductions in Insurance Costs

• Lower Premiums Due to Expected Reductions in Property Losses

• Reductions in Health-Related Claims

Reductions in Warranty Costs

Reductions in Waste and Pollution

Smoother Start-Up Operations

• Increased Potential for Automated Commissioning
56

The second tier lists specific types of benefits and cost savings associated with its

“parent” first tier element. The second tier elements are listed as a series of bullets under

the parent first tier element. An example of a second tier element for lower energy costs

is energy cost savings due to better controls. Not all generic types of benefits and cost

savings have a second tier (e.g., fewer false alarms, less occupant turnover, and

reductions in warranty costs).

It is important to recognize that the benefits and cost savings listed in Table 5-3 might

accrue to any individual stakeholder (i.e., they are aggregated according to society’s

frame of reference). Thus, Table 5-3 is structured from “society’s” frame of reference

rather than from the perspective of a single stakeholder or stakeholder group. The main

purpose of Table 5-3 is to illustrate how BFRL approaches the assessment of the

“benefits” side of CBS-related impacts. Specifically, BFRL used this table to identify the

data needed to measure these impacts. For the impact assessment presented in this report,

Table 5-3 identifies the potential “benefits” data links. However, if the focus is on an

individual stakeholder or stakeholder group, it will be necessary to develop a crosswalk

between the generic types of benefits and cost savings listed in Table 5-3 and the

stakeholder groups listed in Table 5-1. This crosswalk is the subject of Section 5.4.

The classification hierarchy presented as Table 5-3 has been limited to two tiers.

Because Tables 5-3 and 5-4 (see Section 5.3) are used to measure the “benefits” and

“costs” sides of CBS-related impacts, the end product of these classification hierarchies is

a collection of economic data. In the case of CBS-related benefits and cost savings, the

depth of the hierarchy (i.e., the number of tiers) is equal to two. In principle, the depth of

these data-related classification hierarchies could be equal to one, to two, or to some

number greater than two. The rule governing the depth of the hierarchy is how far down

in the hierarchy one must go until all lowest level elements in the hierarchy are indicative

of economic data. For CBS-related benefits and cost savings, two tiers were considered

adequate.

5.3 CBS-Related Costs and Benefit Reductions

Costs are at the heart of any investments in new products.34 For the CBS economic

impact assessment, costs are incurred at several points in the “product” life cycle.

Specifically, CBS-related costs include research costs, product development costs,

production costs, dissemination costs, and installation costs. In addition, a particular

vendor may experience benefit reductions due to reduced sales of some of its more

“traditional” products. These costs and benefit reductions are summarized in Table 5-4;

they are organized as a three-tiered hierarchy.

The first tier of the hierarchy lists generic types of CBS-related costs and benefit

reductions. The list is considered to be exhaustive and self-evident. The eight first tier

elements are listed in a bold-italics typeface. Examples of first tier costs and benefit

 

34 The word product is used generically to represent technologies, hardware (e.g., building systems,

subsystems, components, piece parts, and support equipment), software, and services.
57

reductions are higher evaluation costs, increased costs for new standards development,

and increased investments by building products manufacturers.

The second tier lists specific types of costs and benefit reductions associated with its

“parent” first tier element. The second tier elements are listed as a series of bullets under

the parent first tier element. An example of a second tier element for increased

investments by building products manufacturers is increased research and development

costs. Not all generic types of costs and benefit reductions have a second tier (e.g.,

increased costs for new standards development, increased training costs, and reduced

sales of selected product lines and services).

Table 5-4. CBS-Related Costs and Benefit Reductions

Higher Evaluation Costs

• Increased Cost for Building Code Allowances/Permits

Higher First Costs

• Longer Cycle Time Due to Building with New Equipment, Technologies,

or Processes

• Additional Building Systems Infrastructure Needed to Monitor and

Control Systems and Components

Increased Costs of Adapting New Building Products to Industry Use

Increased Costs for New Standards Development

Increased Investments by Building Products Manufacturers

• Increased Research and Development Costs

ß Increased Costs for Product Testing/Simulation

ß Increased Costs for Product Development

• New Plant and Equipment

• New Production Processes

Increased Marketing, Advertising, and Distribution Costs

Increased Training Costs

Reduced Sales of Selected Product Lines and Services
58

The third tier elements are concerned with increased research and development costs.

These costs are of central importance to both BFRL and its industry collaborators.

Consequently, it was desirable to increase the depth of the CBS-related costs and benefit

reductions classification hierarchy to three. Information on increased research and

development costs is presented and discussed in Section 6.3.2.2. Examples of increased

research and development costs are increased costs for product testing/simulation and

increased costs for product development.

5.4 Relationships Between Benefits, Costs, and Stakeholders

Recall that BFRL’s assessment of CBS-related impacts is undertaken from society’s

frame of reference. Thus, it includes all benefits and costs to whomsoever they accrue.

Although this is the traditional approach for public-sector economic impact studies, it is

too broad for most stakeholder groups. This is because most stakeholder groups want to

evaluate the pros and cons of “their” investments in CBS products and services. In

addition, the traditional approach employed in public-sector studies complicates the data

collection effort. Basically, the higher the level of abstraction, the more difficult it

becomes to define data “categories” and collect the types of data that lead to meaningful

results. Consequently, this study develops crosswalks between stakeholder groups and

CBS-related benefits and cost savings and CBS-related costs and benefit reductions. The

two crosswalks are presented as Table 5-5 and Table 5-6. Table 5-5 lists key types of

benefits and cost savings by stakeholder group; Table 5-6 lists key types of costs and

benefit reductions by stakeholder group.

The two crosswalks serve three purposes. First, they define in an unambiguous manner

all of the potential data categories from which to collect economic data. In fact, each data

category may be specified as a unique combination of stakeholder group and type of

benefit or type of cost. Second, the crosswalks promote a priority-setting process for

identifying what specific types of data to collect and where to collect them. For example,

if we know that three stakeholder groups—building owners and managers, utilities and

energy providers, and other (e.g., building occupants)—are beneficiaries of lower energy

costs (see the cells beneath the “stakeholder group” column headings in Table 5-5 that are

checked), then we can focus our “energy cost” data collection effort on these three

stakeholder groups. Thus, the data collection strategy, stated in its simplest terms, is to

limit the data collection effort to those cells of Table 5-5 and Table 5-6 with check marks

( ̧). This priority-setting approach to data collection is employed throughout the next

three chapters. Finally, the crosswalks provide the means through which an individual

stakeholder or stakeholder group may evaluate the pros and cons of investing in CBS

products and services. Thus, the crosswalks not only greatly simplify the current

economic impact assessment they also provide the framework for identifying key data

elements and for specifying a data collection strategy for individual stakeholders.
59

The third purpose of the crosswalks is best understood by considering a specific

stakeholder group, say building owners and managers. If building owners and managers

are considering investing in a specific CBS product versus a traditional product, they

need to know if the life-cycle cost over the proposed study period of the CBS product is

less than that of the traditional product.

The first step in this “decision problem” is to identify the types of benefits and the types

of costs. The “benefits” accruing to and the “costs” borne by building owners and

managers are recorded in the first “stakeholder group” column of Tables 5-5 and 5-6,

respectively. Reference to Table 5-5 shows that building owners and managers benefit

from all but four of the twenty-three types of benefits and cost savings. Examples of

specific types of benefits and cost savings are better load-leveling capabilities, fewer

false alarms, lower energy costs, and smoother start-up operations. Reference to Table

5-6 shows that building owners and managers bear four types of increased costs. They

are: higher evaluation costs, higher first costs, increased cost of adapting new building

products to industry use, and increased training costs. The second step is to compile a list

of the types of benefits and the types of costs for which data are available and are

relevant (i.e., data that allow comparisons between the products being considered). The

third step is to collect the economic data. The economic data collected in the third step

are used to support a life-cycle cost analysis of the products being considered. Finally,

evaluate the economic performance of each product being considered. This is done by

calculating the life-cycle cost for each product and selecting the one that minimizes the

life-cycle cost over the proposed study period.

The same procedure can be used for an individual stakeholder class. First, select the

individual stakeholder class. Then, refer to Table 5-2 to identify the appropriate

stakeholder group(s). Finally, follow the procedure just described to determine whether

or not that stakeholder should invest in the CBS product under consideration.
60

Table 5-5. Types of CBS-Related Benefits and Cost Savings Classified by Stakeholder Group

Stakeholder Group

Type of Benefit or

Cost Saving Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Emergency

& Security

Services

Utilities &

Energy

Providers

Other

Better Load-Leveling Capabilities  ̧  ̧  ̧

Fewer False Alarms  ̧  ̧  ̧

Fire and Rescue Services Better

Able to Deploy Resources

 ̧  ̧  ̧

Helps Create an Emerging Service

Industry

 ̧  ̧  ̧

Improved Health, Safety, and

Security

 ̧  ̧  ̧

Improvements in Energy Pricing  ̧  ̧  ̧

Increased Functionality and

Performance

 ̧  ̧  ̧  ̧  ̧

Increased Occupant Productivity  ̧  ̧

Increased Sales for System

Design/Integration/Optimization

Services

 ̧  ̧  ̧

Increased Sales of Building

Products with New Features

 ̧  ̧  ̧  ̧

Increased Sales of Complementary

Product Lines and Services

 ̧  ̧  ̧
61

Table 5-5. Types of CBS-Related Benefits and Cost Savings Classified by Stakeholder Group (Continued)

Stakeholder Group

Type of Benefit or

Cost Saving Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Emergency

& Security

Services

Utilities &

Energy

Providers

Other

Less Occupant Turn Over  ̧  ̧

Lower Energy Costs  ̧  ̧  ̧

Lower First Costs/Acquisition

Costs

 ̧  ̧

Lower Health Costs  ̧  ̧  ̧

Lower Operations and

Maintenance Costs

 ̧

Lower Repair and Replacement

Costs

 ̧

Promotes Code Changes Due to

New Ways of Controlling

Building Functions

 ̧  ̧  ̧  ̧  ̧

Reduced Property Losses  ̧  ̧  ̧  ̧

Reductions in Insurance Costs  ̧  ̧  ̧

Reductions In Warranty Costs  ̧  ̧  ̧  ̧

Reductions in Waste and Pollution  ̧  ̧  ̧  ̧  ̧

Smoother Start-Up Operations  ̧  ̧  ̧  ̧  ̧  ̧  ̧
62

Table 5-6. Types of CBS-Related Costs and Benefit Reductions Classified by Stakeholder Group

Stakeholder Group

Type of Cost or

Benefit Reduction Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Emergency

& Security

Services

Utilities &

Energy

Providers

Other

Higher Evaluation Costs  ̧  ̧  ̧  ̧  ̧  ̧  ̧  ̧

Higher First Costs  ̧  ̧

Increased Cost of Adapting New

Building Products to Industry Use

 ̧  ̧  ̧  ̧  ̧  ̧  ̧  ̧

Increased Costs for New

Standards Development

 ̧  ̧  ̧  ̧  ̧

Increased Investments by

Building Products Manufacturers

 ̧

Increased Marketing, Advertising,

and Distribution Costs

 ̧  ̧  ̧

Increased Training Costs  ̧  ̧  ̧  ̧  ̧  ̧  ̧  ̧

Reduced Sales of Selected

Product Lines and Services

 ̧  ̧  ̧  ̧
63

6. Data and Assumptions for the CBS Economic Impact

Assessment

This chapter describes the data and assumptions used to evaluate the economic impacts

associated with the adoption and use of CBS products and services in office buildings.

The goal of this chapter is fourfold. First, it establishes the sources and validity of the

data used in the CBS economic impact assessment. Second, it defines the base case and

the CBS alternative. Third, it produces estimated values for key sets of benefits and

costs. Fourth, it documents the process by which key assumptions were established,

including how the values of key parameters were set.

6.1 Data Sources

Establishing the sources and validity of the data used in the CBS economic impact

assessment is essential if readers are to be able to follow the analysis, gain insights useful

for their own applications, and reproduce our results. This section describes the three

groups of data upon which the economic impact assessment is based. The material

presented in this section is intended to establish an audit trail which readers can follow to

gain access to the same information used in the CBS economic impact assessment.

6.1.1 Baseline Measures of Construction Industry Practices

The Construction and Building Subcommittee of the National Science and Technology

Council has established seven National Construction Goals in collaboration with a broad

cross section of the construction industry.35 Data describing current practices of the US

construction industry are needed to establish baselines against which the industry can

measure its progress towards achieving the seven National Construction Goals. The

Goals are: (1) reductions in the delivery time of constructed facilities; (2) reductions in

operations, maintenance, and energy costs; (3) increases in occupant productivity and

comfort; (4) reductions in occupant-related illnesses and injuries; (5) reductions in waste

and pollution; (6) increases in the durability and flexibility of constructed facilities; and

(7) reductions in construction worker illnesses and injuries.

Baseline measures and measures of progress will be produced for each National

Construction Goal in each of the four key construction industry sectors. The four sectors

are: (1) residential; (2) commercial/institutional; (3) industrial; and (4) public works.

Industry performance in 1994 is used as the reference point from which the values of the

baseline measures are calculated.

 

35 Wright, Richard N., Arthur H. Rosenfeld, and Andrew J. Fowell. 1995. Construction and Building:

Federal Research and Development in Support of the US Construction Industry. Washington, DC:

National Science and Technology Council.
64

A recent report by Chapman and Rennison36 provides a detailed set of baseline measures

for National Construction Goal 2, reductions in operations, maintenance, and energy

costs. Goal 2 was identified as one of the highest priority National Construction Goals by

the construction industry.

Chapter 6 of the Chapman and Rennison report37 describes the commercial/institutional

sector and traces the development of the baseline measures for the commercial/

institutional sector. The baseline measures for the commercial/institutional sector are

based on data published by DOE, the International Facilities Management Association,

the Building Owners and Managers Association, Whitestone Research, the Association of

Higher Education Facilities Officers, and the Institute of Real Estate Management.

The Goal 2 baseline measures for the commercial/institutional sector were the starting

point for collecting the data and information needed to conduct the CBS economic impact

assessment. Specifically, the values of the baseline measures for operations,

maintenance, and energy costs are reference data against which the values contained in

this report can be compared. In addition, the report by Chapman and Rennison provided

extensive cross-referencing of data to sources. This enabled the current effort to quickly

and efficiently retrieve data and information focused exclusively on the office building

component of the commercial/institutional sector. The remainder of this section is

devoted to the description of these data sources and the key data sets associated with

these data sources.

6.1.2 Energy Consumption Survey of Commercial Buildings

The purpose of this subsection is to establish the sources for two key data items: (1) the

national average energy cost per square meter (per square foot); and (2) the rates of

utilization of energy conserving features and practices. The first data item is needed to

estimate per unit energy cost savings. The second data item provides a range of

utilization rates on energy conserving features and practices, many of which have

characteristics similar to those of CBS products and services.

The source of information on these data items is the DOE Commercial Buildings Energy

Consumption Survey (CBECS). CBECS is a national sample survey that collects energyrelated building characteristics and consumption and expenditure data for US commercial

buildings. CBECS was first conducted in 1979 and then triennially since 1983.

In the 1995 CBECS, there were 6,590 sampled buildings of which 5,766 were

successfully interviewed. Energy-related characteristics of the buildings are obtained in

an on-site personal interview with the building managers, owners, or tenants during the

 

36 Chapman, Robert E., and Roderick Rennison. 1998. An Approach for Measuring Reductions in

Operations, Maintenance, and Energy Costs: Baseline Measures of Construction Industry Practices for the

National Construction Goals. NISTIR 6185. Gaithersburg, MD: National Institute of Standards and

Technology.

37 Ibid., pp. 115-182.
65

Commercial Buildings Characteristics Survey. Energy consumption and expenditures

information are obtained from the energy suppliers to the responding buildings during the

Energy Suppliers Survey. The CBECS defines commercial buildings as enclosed roofed

and walled structures used predominantly for commercial purposes with floorspace

greater than 1,000 square feet (92.9 square meters). This definition includes buildings

such as schools, health care buildings, and religious institutions, as well as office

buildings and retail stores (i.e., the full range of the commercial/institution sector as

defined in Chapman and Rennison).

The survey includes characteristics of each building sampled. One of these

characteristics, principal building activity, is used to separate office buildings from other

types of commercial buildings. Readers interested in a detailed description of the 1995

CBECS are referred to the recent report by the Energy Information Administration.38

The information presented in this subsection is derived from data contained in the

CBECS Public Use Files. The Public Use Files are microdata files that contain 5,766

records, representing commercial buildings from the 50 States and the District of

Columbia. Each record corresponds to a single sampled building and contains

information for that building about the building size, year constructed, types of energy

used, energy-using equipment, energy consumption and expenditures, conservation

features and energy management practices, and energy used for nine end uses. The nine

end uses are space heating, cooling, ventilation, lighting, water heating, cooking,

refrigeration, office equipment, and other.

The CBECS sample was designed so that survey responses could be used to estimate

characteristics of the entire commercial buildings stock nationwide. In order to arrive at

national estimates from the CBECS sample, DOE calculated base-sampling weights for

each building. Therefore, a building with a base weight of 1,000 represents itself and 999

similar, but unsampled buildings in the total building stock. The base weight is further

adjusted to account for nonresponse bias. In order to obtain a weighted estimate, each

sample building’s value must be multiplied by the building’s weight.

Figures 6-1 through 6-8 summarize the information on office buildings extracted from the

CBECS microdata files. All of the figures record building size on the horizontal axis.

The same eight size categories introduced in Chapter 4 are used in each figure. Tic

marks on the horizontal axis are used to help separate the size categories. Two different

measures are recorded on the vertical axis. For Figure 6-1 the vertical axis records the

annual fuel expenditure per unit of floor area. For Figures 6-2 through 6-8 the vertical

axis records the percent of total floorspace covered by the factors (i.e., end-use equipment

and energy conservation features and practices) under analysis. It is important to

recognize that portions of a building’s floorspace may be covered by more than one

factor. Consequently, the sum total across all factors may exceed 100 percent of total

floorspace.

 

38 US Department of Energy. 1998. A Look at Commercial Buildings in 1995: Characteristics, Energy

Consumption, and Energy Expenditures. DOE/EIA-0625(95). Washington, DC: Energy Information

Administration.
66

Figure 6-1 shows how annual energy expenditures per unit of floor area vary by building

size. The figure also records the national average energy expenditure per square meter

(per square foot). Reference to the figure shows that the national average energy

expenditure is $16.25 per square meter ($1.51 per square foot). Examination of the

figure shows considerable variability about the national average across the eight building

size categories. However, the figure does not show any trend towards higher or lower

energy expenditures per unit of floor area as building size increases. The national

average energy expenditure per square meter is used as the reference point against which

cost savings per square meter are estimated (see Section 6.3.1.1).

Figure 6-1. Sum of Major Fuel Expenditures by Office Building Size Category: 1995

Figures 6-2 through 6-8 are concerned with end-use equipment and energy conservation

features and practices. Energy is used within buildings by end-use equipment. End-use

equipment refers to the specific type of equipment that is used to perform a given end

use. Types of end-use equipment include heat pumps, furnaces, packaged airconditioning units, central chillers, fluorescent light fixtures, and compact fluorescent

bulbs. Figures 6-2 through 6-4 record information on end-use equipment. Office

buildings use a variety of features and practices to conserve the use of energy by end-use

equipment. Energy conservation features include those related to the building shell,

HVAC systems, and lighting systems. Figures 6-5 through 6-7 record information on

energy conservation features. Energy management practices are conservation programs

and energy technologies designed to reduce the energy used by specific end-use

equipment. Figure 6-8 records information on energy management practices.

Fuel Expenditure National Average = $1.51 per Square Foot

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1,001 -

5,000 

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Floorspace in Square Feet (Square Meters)

Fuel Expenditures per Square Foot

0

2

4

6

8

10

12

14

16

18

20

Fuel Expenditures per Square 

Meter

 = $16.25 per Square Meter
67

For Figures 6-2 through 6-8 the factors under analysis are listed in a box beneath the

legend for the horizontal axis. The number of factors analyzed in each figure range from

a high of seven (see Figure 6-2) to a low of four (see Figure 6-6). Each factor is crossreferenced to a bar in the figure. This is done by shading each bar, and for selected bars

bolding their border. Each factor is indicated by a small shaded box and a factor name

pairing. To identify a specific factor, start at the top and read from left to right across the

shaded box/factor name pairings until the factor of interest is found. Note that each

factor is recorded for each size category in Figures 6-2 through 6-8. Due to different

building characteristics, some factors will have a zero value for some size categories. In

such cases, a gap will appear between one or more bars. The positions of each bar,

however, will be the same as indicated by the order of the shaded box/factor name

pairings.

Information on space heating equipment for office buildings is presented in Figure 6-2.

Five types of heating equipment were used extensively in office buildings: packaged

heating units, boilers, individual space heaters, district heat, and furnaces. Although all

five types were used extensively, their use in office buildings of different sizes varied

considerably. For example, boilers, individual space heaters, and district heat were used

more in larger buildings, while packaged heating units and furnaces were used more in

smaller buildings.

Information on cooling equipment for office buildings is presented in Figure 6-3.

Reference to the figure reveals that packaged air-conditioning units were by far the most

widely used type of cooling equipment. Two types of cooling equipment showed

significant differences in use by size of building. Residential-type central airconditioning units showed relatively greater use in the smallest office buildings. Central

chillers were used primarily in the largest buildings. That equipment type cooled more

than 60 percent of the combined floorspace of the three largest size categories, but less

than 5 percent of the combined floorspace of the three smallest size categories.

Information on five types of lighting equipment is summarized in Figure 6-4. The five

types of lighting equipment are: incandescent, standard fluorescent, compact fluorescent,

high-intensity discharge, and halogen. Standard fluorescent lighting fixtures were found

in nearly all office buildings (more than 90 percent of all floorspace). Incandescent

lighting was also widely used (around 60 percent of all floorspace). The three newer

kinds of lighting technology, high-intensity discharge, compact fluorescent, and halogen,

were used primarily in larger office buildings.

Figure 6-5 summarizes information on building shell conservation features. Figure 6-5

shows that most office buildings had some type of building shell conservation feature.

The feature most often found was roof or ceiling insulation; utilization of roof or ceiling

insulation ranged from 80 percent to 90 percent of total floorspace within each size

category.
68

Figure 6-2. Office Building Heating Equipment by Building Size Category

0.00

10.00

20.00

30.00

40.00

50.00

60.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Furnaces Heat Pumps Individual Space Heaters District Heat

Boilers Packaged Heating Units Other

 
69

Figure 6-3. Office Building Cooling Equipment by Building Size Category

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over 

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Residential Type - Central A/C Heat Pumps Individual A/C

District Chilled Water Central Chillers Packaged A/C Units

Other
70

Figure 6-4. Office Building Lighting Equipment by Building Size Category

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Incandescent Standard Fluorescent Compact Fluorescent

High-Intensity Discharge Halogen Other
71

Figure 6-5. Office Building Shell Conservation Features by Building Size Category

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001- 

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Roof or Ceiling Insulation Exterior or Interior Shading or Awnings

Storm or Multiple Glazing Tinted, Reflective, or Shading Glass

Wall Insulation
72

Figure 6-6. Office Building HVAC Conservation Features by Building Size Category

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001 -

5,000

(93.0-

464.5)

5,001 -

10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Variable Air-Volume System Economizer Cycle HVAC Maintenance Other
73

Figure 6-7. Office Building Lighting Conservation Features by Building Size Category

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size /Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Specular Reflectors Natural Lighting Control Sensors Occupancy Sensors

T ime Clock Manual Dimmer Swit ches Energy-Efficient Ballasts

Other
74

Figure 6-8. Use of Energy Management Practices in Office Buildings: Part A

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Electricity Load Control Energy Management and Control System

Special Rates or Incentives Energy-Efficient Motor System

Building Energy Manager
75

Figure 6-8. Use of Energy Management Practices in Office Buildings: Part B

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

1,001-

5,000

(93.0-

464.5)

5,001-

10,000

(464.6-

929.0)

10,001-

25,000

(929.1-

2,322.6)

25,001-

50,000

(2,322.7-

4,645.1)

50,001-

100,000

(4,645.2-

9,290.3)

100,001-

200,000

(9,290.4-

18,580.6)

200,001-

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Size/Floorspace in Square Feet (Square Meters)

Percent of Total Floorspace

Energy-Efficient Water Heating Equipment Interruptible Natural Gas

Thermal Energy Storage Waste-Heat Recovery

Energy Audit
76

Figure 6-6 summarizes information on HVAC conservation features. Comparison

between Figure 6-5 and Figure 6-6 shows that HVAC conservation features were, in

general, less common than building shell features. HVAC maintenance, the most widely

practiced of the HVAC conservation features, was performed in about 80 percent of the

floorspace. For the four largest size categories, HVAC maintenance was performed in 95

percent or more of the floorspace.

Reference to Figure 6-7 reveals that a significant percentage of office building floorspace

employed some type of lighting conservation feature. The most widely used lighting

conservation feature was energy-efficient ballasts. About two-thirds of office building

floorspace was covered by energy-efficient ballasts. Comparisons between Figures 6-5,

6-6, and 6-7 show that both HVAC and lighting system conservation were more often

found in larger office buildings.

The CBECS collected information on buildings that participated in, or used, a variety of

conservation programs and energy technologies, collectively referred to by DOE as

energy management practices. This information is summarized in Figure 6-8, Parts A

and B. As a whole, the level of participation/use was low for office buildings. The most

widely used energy management practice was energy management and control systems.

Use of energy management and control systems rises steadily as building size increases

(from less than 5 percent in the smallest size category to more than 80 percent in the

largest size category). The ten energy management practices shown in Figure 6-8, Parts

A and B, serve as a reference point against which the introduction of CBSs can be

measured. Specifically, the percentage of floorspace covered by these ten energy

management practices provides a range of values against which the potential for use of

CBSs can be measured. This subject is covered in considerable detail in Section 6.4.4,

where they are used to estimate the level at which the market for CBS products and

services reaches saturation.

6.1.3 Other Data Sources

In addition to the information extracted from CBECS, information on maintenance costs,

repair costs, amount of floorspace per office worker, and office worker cost per unit of

floorspace was needed. The focus of this subsection is on identifying the data sources

and presenting the values of key data items. How this information is used to develop

estimates of savings per unit of floorspace is described in Section 6.3.1.

Information on the maintenance cost and repair costs per square foot for a typical office

building are available from a variety of published sources. Four sources were particularly

useful in producing estimated values for the maintenance cost per square foot and the

repair cost per square foot. These sources are: (1) International Facilities Management

Association (IFMA); (2) Institute of Real Estate Management (IREM); (3) Building

Owners and Managers Association International (BOMA); and (4) Whitestone Research.

IFMA is an association serving the facility management profession. IFMA has carried

out a number of benchmarking studies covering both the commercial/institutional and
77

industrial sectors. IFMA’s Research Report #13,39 published in 1994 is the result of a

1993 survey of IFMA members. The report presents benchmarking data derived from

283 survey questionnaires.

IREM is an organization that educates and certifies real estate professionals and carries

out a variety of annual surveys of apartment buildings, commercial office buildings, and

most recently, open and enclosed shopping centers. The 1997 Income/Expense Analysis:

Office Buildings40 provides detailed information on operations, maintenance, and energy

costs. The 1997 edition presents 1996 data collected from over 2,900 private-sector

office buildings across the US and Canada.

BOMA is a trade association providing technical support to its membership in the

building management sector. The 1996 BOMA Experience Exchange Report: Operating

a Cost Effective Office Building41 provides published tables of operating income and

expense data for over 4,000 office buildings located throughout North America for fiscal

year 1995. The sample includes 3,657 US private-sector properties, 213 US government

buildings, 175 private properties in Canada, and 430 government buildings in Canada.

The BOMA Report provides detailed descriptions of operations, maintenance, and energy

costs for commercial office buildings in both the US and Canada for both the government

and private sector, as well as national cross tabulations, and city specific analyses.

The Whitestone Building Maintenance and Repair Cost Reference 199742 is the third of a

series of annual reports produced by Whitestone Research which presents estimates of

50-year maintenance cost profiles for 24 different building models. Building types

include fast food restaurants, motels, auto service garages, and supermarkets, as well as

office buildings. The profile for each model includes a building description, a list of

major building components, and forecasts of maintenance and repair costs at various

levels of aggregation over the service life of the building. These can be adjusted for

selected metropolitan areas, and modified to include different building components.

In this report, data from the Whitestone Building Maintenance and Repair Cost Reference

1997 have been used to produce estimated values for the annual per unit maintenance

cost and the annual per unit repair cost for a typical office building. These values are

$16.04 per square meter ($1.49 per square foot) per year for maintenance, and $6.57 per

square meter ($0.61 per square foot) per year for repairs. Note that the value for repair

costs used in this report are for mechanical and electrical systems and components only.

These values are used as the basis for computing savings due to reduced maintenance and

repair costs resulting from the installation of CBS products and services in office

buildings.

 

39 International Facilities Management Association. 1994. Benchmarks II. Research Report #13. Houston,

TX: International Facilities Management Association.

40 Institute of Real Estate Management. 1997. 1997 Income/Expense Analysis: Office Buildings. Chicago,

IL: Institute of Real Estate Management.

41 Building Owners and Managers Association International. 1996. 1996 BOMA Experience Exchange

Report: Operating a Cost Effective Office Building. Washington, DC: Building Owners and Managers

Association International.

42 Lufkin, Peter S., and Anthony J. Pepitone. 1997. Whitestone Building Maintenance and Repair Cost

Reference 1997. Seattle, WA: Whitestone Research.
78

Information on the amount of floorspace per office worker is available from both IFMA

and BOMA reports. Although this information is a useful reference point, data collected

as part of CBECS are considered more authoritative. Consequently, the estimated value

for the number of square meters per office worker used in this report is based on data

from CBECS. The estimated value used in the CBS economic impact assessment is

35.95 square meters (387 square feet) per office worker.

To calculate the annual office worker cost per square meter, it is first necessary to

estimate the average annual salary of an office worker. This information is available,

both on the national and regional levels, in the County Business Patterns.43 The

“national” average annual salary of an office worker was estimated as the total payroll

divided by the total number of employees in five Standard Industrial Classification (SIC)

Codes. These five SIC Codes are: 73, business services; 81, legal services; 871,

engineering and architectural services; 872, accounting, auditing, and bookkeeping

services; and 874, management and public relations services. Based on the County

Business Patterns data, the average annual salary of an office worker was estimated to be

$30,377 in 1997 dollars. Combining this figure with the average amount of floorspace

per office worker produces an estimated annual office worker cost per square meter of

$844.90 ($78.49 per square foot). The $844.90 per square meter figure is used as the

basis for computing savings expected due to improvements in occupant productivity

resulting from the installation of CBS products and services in office buildings.

6.2 Defining the Base Case and the CBS Alternative

The purpose of this section is to define the base case and the CBS alternative to the base

case. This “definition step” is done to draw two key distinctions between the base case

and the CBS alternative (i.e., the two configurations). These distinctions are important

because they facilitate the estimation of the benefits and costs covered in Section 6.3.

It is anticipated that CBS products and services will be installed in both new and existing

office buildings. In the case of new office buildings, CBS products and services will be

installed during construction. In the case of existing office buildings, CBS products and

services will be retrofitted into the building while the building is undergoing renovation.

Verification that the CBS products and services once installed are performing “as

stipulated” is done as part of a formal building commissioning/recommissioning. If the

CBS alternative is not chosen, the same process applies for an installation of the base

case. Thus, for new buildings, either the base case or the CBS alternative is installed

during construction. Similarly, for existing buildings, either the base case or the CBS

alternative is installed while the building is undergoing renovation.

Both the base case and the CBS alternative (i.e., both configurations) have features

against which costs, savings, and performance are measured. These features include

equipment and software required for heating, cooling, lighting, and life safety. It is

 

43 US Department of Commerce. 1995. County Business Patterns 1993. CBP-93-1. Washington, DC:

Bureau of the Census.
79

important to recognize that both configurations must meet all building-related

performance requirements. This “performance requirement” constraint is needed to

ensure that both configurations are comfort-compatible, reliable, serviceable, userfriendly, safe, and at a minimum, neutral with regard to occupant productivity and design

aesthetics.44 The performance requirement applies both to either configuration installed

during the construction of a new office building and to either configuration installed

during the renovation of an existing office building.

Throughout the remainder of this report, the term base case is used to represent the

configuration that maintains the status quo (i.e., the use of traditional heating, cooling,

lighting, and life safety technologies). The CBS alternative is that collection of products

and services (i.e., configuration) that provides equivalent or enhanced performance for all

features of the base case while satisfying the definition of a CBS given in Section 3.1.

Based on the definitions of the base case and the CBS alternative, there are two key

differences between the two configurations. First, the degree to which the building

service features are integrated, automated, and controlled is significantly higher in the

CBS alternative. The second difference is that the CBS alternative has the potential to

achieve enhanced performance for selected building service features. These differences,

although interrelated, are crucial in structuring differences in costs (e.g., due to the

installation of additional equipment and software to generate improved systems

integration, automation, and control) and savings (e.g., energy cost savings due to

improved performance of HVAC and lighting systems) between the two configurations.

Quantitative measures of these differences are developed in Section 6.3.

6.3 Estimating Significant Benefits and Costs

This section develops estimates of the key benefits and costs that are the focus of the

CBS economic impact assessment. These benefits and costs are well-defined subsets of

the comprehensive lists of benefits and costs presented in Chapter 5.

It is important to recognize that every effort has been made to capture and record any

cost-related information affecting the users of CBS products and services. Similarly,

considerable effort went into documenting or estimating BFRL’s CBS-related

investments. Relatively less effort went into estimating the full range of CBS-related

benefits and cost savings. We focused on what we judged the most substantial and

measurable benefits, which we termed the “significant few” benefits. Thus, the return on

BFRL’s CBS-related investments is expected to be very conservative (i.e., the values

presented in this report are lower bounds on the potential range of returns on BFRL’s

CBS-related investments).

 

44 For more information on how to specify performance requirements, see Chapter 2 of Fuller and Petersen

(Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle Costing Manual for the Federal Energy

Management Program. NIST Handbook 135. Gaithersburg, MD: National Institute of Standards and

Technology).
80

6.3.1 Benefits and Cost Savings

The enhanced performance of HVAC and lighting systems under the CBS alternative visà-vis the base case produces four types of benefits and cost savings. These benefits and

cost savings are: (1) lower energy costs; (2) lower operations and maintenance costs; (3)

lower repair and replacement costs; and (4) increased occupant productivity. The first

three types listed are readily classified as cost savings. While increased occupant

productivity may be thought of as a benefit, this report classifies it as a type of cost

savings since it reduces the occupant company’s cost of business for a given level of

output.

Although the CBS alternative will result in enhanced fire safety performance, no

estimates of these, potentially significant, benefits and cost savings are included in the

current CBS economic impact assessment. Although subject matter experts have reached

consensus on the generic types of benefits and cost savings due to enhanced fire safety

performance (see Chapter 5), no such consensus emerged on how to quantify these

benefits and cost savings. Similarly, estimates of the cost savings due to the ability to

respond to real-time electricity price changes and to aggregate building stock for multifacility operations are not included. Plans for incorporating such estimates in a future

economic impact assessment are described in Section 9.2. It is important to recognize

that although the benefits of these features are not included in this assessment, the costs

of installing, operating, and maintaining such features are included. This decision was

made to maintain the conservative approach of the CBS economic impact assessment.

6.3.1.1 Lower Energy Costs

In Section 6.1.2 information from the 1995 CBECS was presented on the national

average energy expenditure per square meter (per square foot). This subsection begins by

first updating the 1995 figure of $16.25 to 1997 dollars. This step is necessary because

1997 is the base year for the CBS economic impact assessment (see Section 6.4.1).

Converting the 1995 cost per square meter figure to 1997 dollars, produces a value of

$17.11 per square meter ($1.59 per square foot).

The next step is to estimate how much the use of CBS products and services will reduce

the energy cost per square meter. The estimate used in this report is based on information

provided by industry experts and facility operators and managers who were interviewed

in the fall of 1998. The consensus among the experts was a range of energy cost savings

of between 5 and 15 percent. Consequently, the baseline value used in this report is 10

percent. Estimated baseline annual energy cost savings are thus $1.71 per square meter

($0.16 per square foot). The range of values for energy cost savings (i.e., 5 to 15 percent)

is used to set the range of values for annual energy cost savings used in the sensitivity

analysis (see Chapter 8).

Because energy prices change over time, it is necessary to develop an energy price index

to apply to the per unit energy cost savings. The index is used to produce both annual per

unit cost savings and national level energy cost savings on a year-by-year basis. The

index used in this report makes use of indices contained in the annual supplement to
81

NIST Handbook 13545 and data retrieved from the 1995 CBECS. The indices contained

in the annual supplement to NIST Handbook 135 are based on official DOE projections.

The data retrieved from the 1995 CBECS were used to compute weights for each of the

three major types of energy used in office buildings (i.e., electricity, natural gas, and

distillate oil). These weights are 0.915 for electricity, 0.075 for natural gas, and 0.010 for

distillate oil. These weights are applied to the three sets of energy price indices recorded

in Table Ca-546 of the annual supplement to NIST Handbook 135. The three computed

values for each year between 1997 and 2015 (i.e., the product of each energy price index

for each year and its weight) are then summed to get a weighted time series for energy for

office buildings. Table 6-1 shows the results of the process just described. The first three

columns of the table are the annual values of the price index for each energy type taken

from Table Ca-5 of the annual supplement to NIST Handbook 135. The last column is

the weighted time series for energy for office buildings.

6.3.1.2 Lower Operations and Maintenance Costs

The use of CBS products and services will lower the costs of operating and maintaining

office buildings. How much these costs are reduced depends on a number of factors,

such as, the ability to reduce overtime expenses, to reconfigure operations and

maintenance staff functions, and to use third-party operations and maintenance

contractors. The focus of this subsection is on annual maintenance costs; it uses the value

of $16.04 per square meter ($1.49 per square foot) published by Whitestone Research

(see Section 6.1.3) as its reference point. Note that all dollar amounts are expressed in

1997 dollars.

To develop a range of estimates for annual per unit maintenance costs savings, industry

experts and facility managers and operators were interviewed during the fall of 1998.

The general consensus was that a significant proportion of overtime expenses could be

eliminated (estimates ranged up to 80 percent). In addition, many occupant complaints

related to heating and cooling problems could be handled remotely. Maintenance staff

could then follow-up during normal work hours to ensure that the problem had been

solved. The advantage of this approach is that it enabled both smaller crew sizes and the

ability to handle more client-initiated requests with a given size of maintenance staff.

Based on these inputs, cost savings were estimated to range from 5 percent to 20 percent.

This range of values is used to specify the range of values for maintenance cost savings

used in the sensitivity analysis (see Chapter 8). The baseline value for annual per unit

maintenance cost savings used in the economic impact assessment is 10 percent, or $1.60

per square meter ($0.15 per square foot).

 

45 Fuller, Sieglinde K. 1997. Energy Price Indices and Discount Factors for Life-Cycle Cost Analysis.

NISTIR 85-3273-12. Gaithersburg, MD: National Institute of Standards and Technology.

46 Ibid., pp. 36-37.
82

Table 6-1. Projected Fuel Price Indices (excluding general inflation) by Fuel Type

and Weighted Time Series for Energy

6.3.1.3 Lower Repair and Replacement Costs

The use of CBS products and services will allow systems and equipment to operate under

near optimal conditions for extended periods of time. In addition, through the use of

FDD technologies, equipment and component malfunctions can be diagnosed and

remedied before a catastrophic failure occurs. As a result, equipment life will be

extended, fewer replacements will be required, and replacement costs will decline.

Furthermore, having better diagnostic tools will enable support staff to more quickly and

effectively repair equipment and components. These improvements will result in lower

repair costs over the useful life of a major system (e.g., HVAC).

To estimate how much annual per unit repair and replacement costs will be reduced, we

begin with the long-term average per unit repair and replacement cost for mechanical and

Year

Projected 

Electricity 

Price Index

Projected 

Natural Gas 

Price Index

Projected 

Distillate Oil 

Price Index

Weighted 

Time Series 

for Energy

1997 1.00 1.00 1.00 1.000

1998 1.00 1.00 0.99 1.000

1999 1.00 1.00 1.00 1.000

2000 0.99 1.01 1.01 0.992

2001 0.98 1.00 1.02 0.982

2002 0.98 1.01 1.03 0.983

2003 0.97 1.01 1.04 0.974

2004 0.96 1.00 1.05 0.964

2005 0.95 1.00 1.06 0.955

2006 0.95 0.99 1.07 0.954

2007 0.94 0.99 1.07 0.945

2008 0.94 0.98 1.07 0.944

2009 0.94 0.97 1.08 0.944

2010 0.93 0.97 1.09 0.935

2011 0.92 0.96 1.08 0.925

2012 0.91 0.96 1.07 0.915

2013 0.89 0.96 1.07 0.897

2014 0.89 0.96 1.07 0.897

2015 0.90 0.96 1.07 0.906
83

electrical systems published by Whitestone Research (see Section 6.1.3). The resultant

figure is $6.57 per square meter ($0.61 per square foot). Note that all dollar amounts are

expressed in 1997 dollars.

It is important to recognize that this study uses mechanical and electrical systems as its

reference point rather than the entire range of building systems. This approach was taken

to ensure that the values were reflective of systems, equipment, and components likely to

be affected by the use of CBS products and services. For example, interior finishes,

exterior closures, roofing, and interior construction did not appear to be affected in any

significant way by the use of CBS products and services. Consequently, their long-term

repair and replacement costs are excluded from the reference data point (i.e., $6.57 per

square meter) used in this study. Based on these inputs, cost savings were estimated to be

10 percent. Thus, the baseline value for annual per unit repair and replacement cost

savings used in the economic impact assessment is $0.66 per square meter ($0.06 per

square foot).

6.3.1.4 Increased Occupant Productivity

The use of CBS products and services will increase occupant comfort by providing

HVAC and lighting systems with enhanced operating performance vis-à-vis the base case

configuration. Industry and government studies have shown that the annual salary costs

of the occupants of a commercial or institutional building are of the same order of

magnitude as the capital cost of the building.47 Studies48,49,50 have shown that the quality

of indoor environments also has a large impact on occupant health and productivity.

Improvement of the productivity of occupants is an important performance characteristic

for most constructed facilities. National Construction Goal 3 targets a 30 percent

increase in occupant productivity and comfort.51

Improvements in comfort and control can have major impacts on worker productivity. A

recent article by Lomonaco and Miller52 surveyed a number of studies on the effects of

improvements in comfort and control. They concluded that the physical environment

could have a measurable impact on worker productivity of about 3 to 15 percent.53

It is important to recognize that some improvements in occupant productivity are likely

under the base case configuration. Thus, the task at hand is concerned with estimating

how much productivity improvement over and above the base case configuration will be

 

47 Wright, Rosenfeld, and Fowell. Construction and Building, p. 8.

48 Fisk, William J., and Arthur H. Rosenfeld. 1977. “Estimates of Improved Productivity and Health from

Better Indoor Environments,” Indoor Air (Vol.7): pp. 158-172.

49 Lorsch, Harold G., and Ossama A. Abdou. 1994. “The Impact of Building Indoor Environment on

Occupant Productivity,” ASHRAE Transactions: pp. 895-901.

50 Kroner, Walter M., and Jean A. Stark-Martin. 1994. “Environmentally Responsive Workstations and

Office-Worker Productivity,” ASHRAE Transactions: pp. 750-755.

51 Wright, Rosenfeld, and Fowell. Construction and Building, pp. 7-9.

52 Lomonaco, Carol, and Dennis Miller. 1997. “Comfort and Control in the Workplace,” ASHRAE Journal

(September): pp. 50-56.

53 Ibid., p. 55.
84

generated through installation of the CBS alternative. Because professional opinion on

the range of values for productivity improvement is so broad, we have chosen to use a

very conservative estimate, which has a high probability of being achieved. In addition,

the range used in this study includes the possibility that no improvements in occupant

productivity result from the installation of CBS products and services (i.e., the lowest

value used is 0 percent). Similarly, the maximum improvement in occupant productivity

used in this study is 1 percent. This 0 to 1 percent range of values is used for

improvements in occupant productivity in the sensitivity analysis (see Chapter 8). The

baseline value for improvement in occupant productivity is 0.5 percent. Thus, the

baseline value for annual per unit cost savings due to improvements in occupant

productivity is $4.20 per square meter ($0.39 per square foot).

6.3.2 Costs and Benefit Reductions

Two types of costs—higher costs to building owners and managers and increased

research and development costs—are central to this economic impact assessment.

Understanding the types of costs that affect building owners and managers is necessary in

order to estimate annual values of net savings on a national level. These estimates affect

not only the present value of net savings nationwide, but the estimated return on BFRL’s

CBS-related investments as well. The second type of costs, increased research and

development costs, focuses only on BFRL’s CBS-related investments. No estimates of

the investments required to develop, test, and market CBS products and services by the

vendor tier (see Figure 3-1) are included in this subsection. Plans for incorporating these

costs in a future economic impact assessment are described in Section 9.2.

6.3.2.1 Higher Costs to Building Owners and Managers

If building owners and managers install the CBS alternative rather than the base case,

they can expect to bear four types of additional costs (see Table 5-6). These costs are: (1)

higher evaluation costs; (2) higher first costs; (3) increased costs of adapting new

building products and services to industry use; and (4) increased training costs.

With the exception of higher first costs, the three remaining costs may be classified as

new-technology introduction costs. Ehlen and Marshall54 define new-technology

introduction costs as those costs covering the activities that bring the material/product

from the research laboratory to full field implementation. New-technology introduction

costs include the extra time and labor to design, test, monitor, and use the new

technology. Ehlen’s and Marshall’s research on new-technology introduction costs is

particularly relevant for this economic impact assessment because they demonstrate that

new-technology introduction costs disappear once the designer is satisfied with the

technology’s performance and service life, the technology enters full implementation, and

its application has become routine.55

 

54 Ehlen, Mark A., and Harold E. Marshall. 1996. The Economics of New-Technology Materials: A Case

Study of FRP Bridge Decking. NISTIR 5864. Gaithersburg, MD: National Institute of Standards and

Technology.

55 Ibid., p. 15.
85

Higher first costs from installing a CBS are expected due to the increased use of sensors.

While the increased use of sensors will result in higher first costs, the use of open

systems is not expected to increase first costs. Discussions with industry experts leads to

the conclusion that any “premium” attached to the costs of open systems would be

quickly eliminated through a competitive procurement process. In addition, the increased

first cost associated with the increased use of sensors is likely to decline over time due

both to improved sensor technology and increased competition.

Finally, the establishment of the VCBT will enable manufacturers to bring actual control

products that they have under development, obtain assistance in testing and evaluating

their performance, and perform interoperability tests with other manufacturers. Thus,

both higher costs in the form of new-technology introduction costs and first costs in

general are expected to decline over time. However, in keeping with the conservative

approach employed in this economic impact assessment, these costs are held constant

throughout the study period. Specifically, an additional cost of $10.76 per square meter

($1.00 per square foot) is assigned when an office building first installs the CBS

alternative. Discussions with industry experts were used to specify a conservative value

for this additional cost. As more information becomes available, the estimated value of

$10.76 per square meter will be revised (see Section 9.2).

6.3.2.2 Increased Research and Development Costs

BFRL launched a multidisciplinary CBS research effort in Fiscal Year (FY) 1998. This

effort and the six projects that support the overall CBS effort are described in Chapter 3.

Because CBS products and services are targeted for demonstration in 2002 and

commercial availability in 2003, BFRL’s highest level of investment is for FY1998

through FY2002. Beginning in FY2003, BFRL’s CBS-related investments will decline

rapidly.

It is also important to recognize that BFRL’s research on BACnet was crucial to the

establishment of its overall CBS effort. Consequently, BFRL’s BACnet-related

investments between FY1991 and FY1997 are included as part of its CBS-related

investments. FY1991 was chosen as the starting point, since by that time BFRL’s

research on BACnet had reached a high-level of maturity.

BFRL’s CBS-related investments are summarized in Table 6-2. The first two columns of

the table record actual investments by Fiscal Year in thousands of dollars for Fiscal Years

FY1991 through FY1997. The second two columns of the table record actual

investments by Fiscal Year in thousands of dollars for Fiscal Years FY1998 and FY1999

and estimated investments for FY2000 through FY2004. Note that all values recorded in

Table 6-2 are on a Fiscal Year basis. Because the vast majority of BFRL’s investment

costs are staff-related costs, it is straightforward to convert Fiscal Year dollars to calendar

year dollars. This conversion is necessary, because the values presented in Chapters 7

and 8 are on a calendar year basis. For example, the estimated FY2000 investment is
86

$2,000,000. Of the $2,000,000 total, 25 percent, or $500,000, is allocated to calendar

year 1999, and 75 percent, or $1,500,000, is allocated to calendar year 2000.

Table 6-2. BFRL Investment Costs by Fiscal Year

a = actual investment costs

e = estimated investment costs

6.4 Key Assumptions and Analysis Issues

A clear statement of the assumed values of key sets of parameters underlying the analysis

is vital to understanding how the analysis was conducted. The assumptions covered in

this section focus on the setting of the assumed values of the following key sets of

parameters: (1) the base year; (2) the starting and ending points in the study period; (3)

the discount rate; (4) the process by which CBS products and services diffuse into the

marketplace; and (5) the process by which BFRL’s contribution is measured. The

assumed values of these five key sets of parameters figure prominently in evaluating the

economic impacts of CBS products and services. Documenting the assumptions and the

rationale behind the setting of the assumed values of these key sets of parameters is

necessary to ensure that: (1) all costs and savings are discounted to an equivalent time

basis for purpose of comparison; and (2) readers can follow the flow of the analysis, gain

insights useful for their own applications, and reproduce our results.

The base year establishes the anchor point for all cost and savings calculations. The

starting and ending points in the study period define both the scope of the study period—

those years over which costs and savings are tabulated—and the length of the study

period—a key parameter in the AIRR calculation. Because cash flows, both costs and

savings, are distributed throughout the study period, the choice of the discount rate is of

central importance to the analysis. The diffusion process is the critical link between per

unit savings (see Section 6.3.1) and cost savings nationwide (see Section 7.2). The model

Fiscal Year

1991 - 1997

BFRL Actual 

Investment 

Costs (In 

Thousands of 

Dollars)

Fiscal Year

1998 - 2004

BFRL 

Investment 

Costs (In 

Thousands of 

Dollars)

1991 300 1998 1,390a

1992 291 1999 1,610a

1993 200 2000 2,000e

1994 190 2001 2,500e

1995 190 2002 2,500e

1996 300 2003 1,000e

1997 300 2004 500e
87

of the diffusion process presented in Section 6.4.4 provides the basis for calculating yearby-year savings following the introduction of CBS products and services. Because

BFRL’s CBS-related research is expected to speed up the introduction of CBS products

and services into the commercial marketplace, a process for evaluating the “value” of

BFRL’s contribution is needed. This process is described in Section 6.4.6.

In addition to the five key sets of parameters used to make explicit the assumptions of the

economic impact assessment, there are issues linking the baseline analysis to the

sensitivity analysis. These “analysis issues” are concerned with the discount rate, the

diffusion process, measuring BFRL’s contribution, and dealing with uncertainty. The

first three analysis issues, in conjunction with per unit savings, provide the necessary

linkage between the baseline analysis and the sensitivity analysis. They are crucial in

measuring how variations about the baseline input values affect the economic outcome

measures. The last analysis issue, dealing with uncertainty, is the core concept in

structuring the sensitivity analysis. This analysis issue is discussed in Section 6.4.5.

6.4.1 Base Year for Computing Benefits and Costs

The base year for computing all CBS-related costs and savings is 1997. There are two

reasons, one primary and one secondary, why 1997 was selected as the base year.

(1) 1997 marks the year in which BFRL formed an integrated CBS project team. BFRL

is working towards a fully operational CBS being tested and deployed in a full-scale

demonstration project by 2002. Thus, by using 1997 as the base year, this economic

impact study maintains its ex ante (i.e., prospective) nature while still being rooted in

the present.

(2) 1997 is the latest year for which authoritative and comprehensive construction

industry cost data are available. Thus, cost conversions for previous years may be

accomplished through the use of a well-defined cost index to equate them to constant

1997 dollars. Similarly, estimated energy costs and associated energy cost savings

for subsequent years may be equated to constant 1997 dollars based on official

projections (i.e., energy cost indices) published by DOE.

6.4.2 Length of the Study Period

The study period begins in 1991 and ends in 2015. Thus, the length of the study period is

25 years. Any costs and/or savings that occur after 2015 are not included. Two factors

were instrumental in determining the beginning and end of the study period.

(1) The study period begins in 1991, which is when BFRL’s research on BACnet had

reached a high-level of maturity. BFRL’s research on BACnet is crucial to the

overall CBS effort, as BACnet provides the communication protocol for all major

CBS products and services. However, major investments in the overall CBS effort

did not begin until 1997, when BFRL formed an integrated CBS project team.
88

BFRL’s CBS-related investments will continue at a fairly high level until 2002, at

which point they will rapidly decline.

(2) The end of the study period is 2015. By 2002, BFRL will be completing a full-scale

demonstration project. By 2003, the first commercial applications of CBS products

and services are anticipated (i.e., other than demonstration projects). Thus, 2003

marks the point at which CBS products and services penetrate the commercial

marketplace. By 2015, the use of CBS products and services is expected to be

widespread.

6.4.3 Discount Rate

The baseline analysis for the CBS economic impact assessment uses a real rate of 7

percent to convert dollar amounts to present values. This rate is specified in Section 8.b

of OMB Circular A-9456 as the rate for all benefit-cost analyses of public investments and

regulatory programs that provide benefits or incur costs to the general public. For

purposes of this analysis, all CBS-related research costs are classified as a public

investment. The benefits that accrue to the public are in the form of cost savings,

including improvements in occupant productivity.

OMB recommends that separate analyses be used to evaluate the sensitivity of key

economic measures to variations in the discount rate.57 The sensitivity analysis presented

in Chapter 8 evaluates the implications of raising the discount rate to 10 percent or

lowering the discount rate to 4 percent. All values of the discount rate used in this report

are real rates, since constant dollar estimates of benefits and costs are used.

Readers familiar with energy conservation practices will recognize that the discount rate

used in the baseline analysis and the discount rates used in the sensitivity analysis are

higher than the DOE discount rate for 1997, which was 3.8 percent real (i.e., net of

general price inflation).58 Because energy cost savings occur in the future (i.e., once CBS

products and services become available commercially) a lower value for the discount rate

will increase the present value of these savings. Conversely, a higher value for the

discount rate will decrease the present value of these savings. Higher rates are used in

this study to ensure that the return on BFRL’s CBS-related investments calculated both in

the baseline analysis and in the sensitivity analysis are very conservative.

6.4.4 Diffusion Process

Facts and data are essential components in any rigorous analysis. Factual information on

the number of office buildings was tabulated from published sources (see Section 4.3).

These data provide the basis for estimating the likely per unit savings (i.e., annual cost

savings per square meter) associated with the use of the CBS products and services in

 

56 Executive Office of the President. 1992. OMB Circular A-94. Washington, DC: Office of Management

and Budget.

57 Ibid., p. 7.

58 Fuller, Energy Price Indices and Discount Factors for Life-Cycle Cost Analysis, p. 1.
89

office buildings (see Section 6.3.1). However, to develop realistic estimates of cost

savings nationwide, it is also necessary to generate estimated values for the following

three factors: (1) the overall rate of adoption of CBS products and services in office

buildings; (3) the annual amount of office floorspace receiving new CBS installations;

and (3) the total amount of office floorspace covered by CBS products and services. To

generate estimates of cost savings nationwide, information on per unit savings must be

coupled with a model of the diffusion process. Much of the discussion in this section and

in Section 7.2 of the next chapter is aimed at establishing an audit trail for how the values

of these three factors were established and employed in the economic impact assessment.

The focus of this section is on how the diffusion process is modeled (i.e., the form of the

model and its key parameter values). Section 7.2 focuses on how the diffusion model is

employed in the economic impact assessment.

An economy is not affected in any material way by a new technology until the use or

ownership of that technology is widespread. This spread of a new technology is a topic

usually referred to as technological diffusion. It is modeled via a diffusion process. The

underlying basis for the study of technological diffusion is to rationalize why, if a new

technology is superior, it is not taken up immediately by all potential users.

The empirical analysis of diffusion processes is a vast and complex subject. Although a

full treatment of the topic is beyond the scope of this report, four factors affecting the

diffusion process are worth noting. Readers interested in thorough treatments of this

important subject, including case studies, are referred to the books by Stoneman59 and

Mansfield.60

First, new technology and its adoption involve uncertainty. Thus, the attitude of decision

makers to uncertainty needs to be considered. The degree of uncertainty may be related

to the level of use of the new technology and to how learning proceeds.

Second, how learning proceeds affects the diffusion process in a number of ways. It can

involve learning about the existence of a new technology or learning about its true

characteristics. For example, firms might learn about how to use the new technology to

produce new or current products at lower cost. For a given initial state of knowledge, the

faster that learning occurs, the higher the rate of diffusion.

Third, during a diffusion process, how learning proceeds may not be the only factor

changing. The good itself may be improving. This improvement may have a doubleedged effect on diffusion: a direct effect, stimulating greater use; and an indirect effect,

whereby expectations of future advances may lead to the postponement of adoption.

Fourth, to a large degree the adoption decision for the firm will be related to expected

profitability, which in turn will be dependent upon a number of factors. Thus differences

 

59 Stoneman, Paul. 1983. The Economic Analysis of Technological Change. New York: Oxford University

Press.

60 Mansfield, Edwin. 1995. Innovation, Technology and the Economy: Selected Essays of Edwin Mansfield.

2 vols. Economists of the Twentieth Century Series. Aldershot, UK:Elgar.
90

between firms will be important, as may be the behavior of the industry supplying any

new goods. The market structure of the user and supplying industries (i.e., situations

involving imperfect competition) are also important.

The most widely accepted model of technology diffusion was developed by Edwin

Mansfield. Consequently, the Mansfield model is employed in the CBS economic impact

assessment. The Mansfield model estimates the proportion of potential users who have

adopted the new technology by time t. The mathematical representation of the model is

[ ]

1 ( )

( ) 1

− −

= +

t P t e

a b

where

P(t) = the proportion of potential users who have adopted the new technology by

time t,

e = Euler’s number, the base of the natural system of logarithms,

" = the location parameter, and

$ = the shape parameter ( $ > 0 ).

A plot of P(t) produces an S-shaped logistics curve, which is asymptotic to 0 as the value

of t gets small and to 1 as the value of t gets large. Because the diffusion of a new

technology may not achieve 100 percent penetration of the marketplace, P(t) must be

modified to reflect the level at which the potential market is saturated. The version of the

Mansfield model employed in this report uses a subscript 0 to designate the market

saturation level. The mathematical representation of the model is

[ ]

1 ( )

( ) 1

− −

= +

t

P t e

a b

h h

where

P0(t) = the proportion of potential users who have adopted the new technology by

time t,

0 = the market saturation level,

e = Euler’s number, the base of the natural system of logarithms,

" = the location parameter, and

$ = the shape parameter ( $ > 0 ).

An extensive review of the economics literature on the diffusion process produced

candidate values for " and $. Readers interested in case studies based on the Mansfield

model that are useful in specifying values for " and $ are referred to Mansfield’s
91

collection of articles.61 An additional factor used to specify the values of " and $ is the

length of time it takes for P0(t) to reach 50 percent of its "designated" potential market

(i.e., η/2). Due to the relationship between the Mansfield model and the logistics

distribution, the value at which P0(t) reaches 50 percent of its designated potential market

has a closed-form relationship based solely on the values of " and $. If we assume t = 1

is the time at which the technology is first introduced, then " /$ is the number of years it

takes that technology to reach 50 percent of its designated potential market. In order to

get a meaningful value of t, it is necessary to constrain " to be positive (i.e., " > 0 ).

The values of the ratio " /$ range from 8 years to 12 years in a wide range of articles

published in the economics literature (see Mansfield,62 Mansfield et al,

63 and Simon64 ).

Consequently, this report uses a value of 10 for the ratio " /$ as its baseline value. The

corresponding baseline values for " and $ are 6.0 and 0.6, respectively.

In order to produce an estimate for 0, data on the use of energy management practices

were analyzed. These data were summarized in Figure 6-8 and are recorded in a slightly

different form in Table 6-3. The estimated value for 0 was set equal to the median value

of the 10 energy management practices recorded in Table 6-3. Thus, the baseline value

for 0 is 0.1754. This means that CBS products and services will eventually be installed

in 17.54 percent of the nation’s 973 million square meters (10,478 million square feet) of

office floorspace. This estimate is considered to be conservative. For example, nearly 40

percent of office floorspace is covered by energy management and control systems.

Thus, the estimated savings nationwide and the value of BFRL’s contribution may be

considered to be lower bound estimates.

The specification of the baseline values of the diffusion model is not complete until a

time of first use is made explicit. As noted earlier, the time of first use corresponds to the

value at which t = 1. The time of first use is based on the assumption that the

demonstration project will be completed in 2002. Once the demonstration project has

been completed, CBS products and services will become available commercially. Thus,

the baseline value for the time of first use is 2003.

The values of " and $ specify the rate of adoption of CBS products and services in office

buildings, whereas the value of 0 specifies the size of the potential market for these

products and services. Consequently, once the time of first use is made explicit, it

becomes possible to estimate the total amount of office floorspace covered by CBS

products and services. For any given year, this amount is equal to the product of P0(t) for

that year and the total amount of office floorspace (i.e., 973 million square meters). It is

also necessary to estimate the annual amount of floorspace receiving new CBS

installations. This amount is based on the annual change in value of P0(t); it is

 

61 Mansfield, Innovation, Technology and the Economy, Vol. II, pp. 3-83.

62 Ibid., pp. 63-72.

63 Mansfield, Edwin, John Rapport, Anthony Romeo, Edmond Villani, Samuel Wagner, and Frank Husic.

1977. The Production and Application of New Industrial Technology. New York: W. W. Norton &

Company, Inc.

64 Simon, P. 1975. Models of Process Diffusion and Entry in the U.S. Chemical Industry. Ph.D. dissertation,

University of Pennsylvania.
92

designated as )P0(t). The value of )P0(t) is defined as: )P0(t) = P0(t) - P0(t-1). Thus,

for any given year, the amount of new CBS installations is equal to the product of )P0(t)

for that year and the total amount of office floorspace. Table 6-4 records the values of

P0(t) and )P0(t) for values of t from 0 to 18 (i.e., from 2002 to 2020). Note that the years

shown on the table extend past the end of the study period.

Table 6-3. Percent of Total Office Floorspace Covered by Selected Energy

Management Practices

1,001 to

25,000

Square Feet

25,001 to

100,000

Square Feet

Over

100,000 

Square Feet

All Office

Buildings

Thermal Energy

 Storage 0.14 2.59 3.58 2.14

Interruptible

 Natural Gas 1.37 4.52 10.88 5.91

Waste-Heat

 Recovery 0.21 4.46 16.66 7.74

Energy-Efficient Water

Heating Equipment 6.61 17.52 21.00 15.14

Electricity Load

 Control 4.10 12.92 30.10 16.57

Special Rates or

 Incentives 6.79 13.69 32.20 18.51

Building Energy

 Manager 5.41 17.04 36.64 20.67

Energy Audit 10.29 18.12 32.02 20.84

Energy-Efficient

 Motor System 7.04 22.68 55.50 30.07

Energy Management 

and Control System 5.88 30.20 70.72 37.60

Percent of Total Floorspace per Office

 Building Size Category 

Energy Management 

Practice
93

Table 6-4. Baseline Case of P0(t) and ΔP0(t) (α = 6.0, β = 0.6, η = 0.1754)

The diffusion model, as specified above and used in the baseline analysis, is plotted in a

graphical form in Figure 6-9. The trace of P0(t) is shown as a solid line in Figure 6-9.

The trace of )P0(t) is shown as a dashed line in Figure 6-9. The trace of )P0(t) is

included to show how new CBS installations track against total installations. The figure

includes both a left and right vertical axis. The left vertical axis of Figure 6-9 records the

values of P0(t). The right vertical axis of Figure 6-9 records the values of )P0(t). The

values on both vertical axes range from 0 to 0. The horizontal axis of Figure 6-9 records

the values of t and the years for which values of P0(t) and )P0(t) are calculated. Recall

that in the baseline analysis t = 1 corresponds to the year 2003. Note that the years

shown on the horizontal axis extend past the end of the study period. This is done to

show that P0(t) does not approach the market saturation level, 0, until well after the study

period is over. Thus, substantial cost savings due to the use of CBS products and services

will continue to accrue well after the end of the study period. Once again, this leads to

the conclusion that the estimated savings nationwide are a lower-bound estimate.

Year t P(t) ΔP(t)

2002 0 0.0000 0.0000

2003 1 0.0008 0.0008

2004 2 0.0014 0.0006

2005 3 0.0026 0.0012

2006 4 0.0047 0.0021

2007 5 0.0083 0.0037

2008 6 0.0146 0.0063

2009 7 0.0249 0.0103

2010 8 0.0406 0.0157

2011 9 0.0622 0.0216

2012 10 0.0877 0.0255

2013 11 0.1132 0.0255

2014 12 0.1348 0.0216

2015 13 0.1505 0.0157

2016 14 0.1608 0.0103

2017 15 0.1671 0.0063

2018 16 0.1707 0.0037

2019 17 0.1728 0.0021

2020 18 0.1740 0.0012
94

Figure 6-9. Baseline Case of Pη(t) and ΔPη(t) by t(year)

Much of the sensitivity analysis is concerned with the diffusion model (see Chapter 8).

As such, ranges of values were specified for ", $, 0, and the time of first use. The ranges

for " and $ were selected based on values of " and $ published in the economics

literature and their implications for the values of the ratio " /$ also published in the

economics literature. The range of values for " used in the sensitivity analysis are a low

of 5 and a high of 7 (i.e., 5 # " # 7). The range of values for $ used in the sensitivity

analysis are a low of 0.5 and a high of 0.7 (i.e., 0.5 # $ # 0.7). These ranges of values for

" and $ result in a range of the ratio " /$ which is consistent with the values published in

the economics literature (i.e., 7.14 # " /$ # 14.0).

The range of values for 0 is based on the range of values for the 10 energy management

practices recorded in Table 6-3. Specifically, the estimated values of 10th and 90th

percentiles for the data recorded in Table 6-3 are used. These values range from a low of

5.53 percent to a high of 30.82 percent (i.e., 0.0553 # 0 # 0.3082).

The range of values for the time of first use are based on expected times (i.e., years) at

which CBS products and services will be available commercially. These times range

from a low of 2002 to a high of 2005. That is, the earliest date at which CBS products

and services are commercially available is in the year 2002 and the latest date is 2005.

The alternative times of first use are specified by a discrete distribution, also known as

the multinomial distribution. The discrete probabilities for each year are: 2002, 0.125;

2003, 0.5; 2004, 0.25; and 2005, 0.125.

Baseline Case of Pη(t) (α = 6.0, β = 0.6, η = 0.1754)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0(2002)1(2003)2(2004)3(2005)4(2006)5(2007)6(2008)7(2009)8(2010)9(2011)

10(2012)11(2013)12(2014)13(2015)14(2016)15(2017)16(2018)17(2019)18(2020)

t(year)

Pη(t)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Pη (t)

P (t) Δ P (t)

η η
95

6.4.5 Dealing with Uncertainty

Uncertainty enters into a benefit-cost analysis in three main ways. First, the value of cash

flows (i.e., benefits, costs, and savings) may not be known with certainty. For example, a

new technology may not be well understood by many potential users, implying that their

benefits of adopting the technology may be subject to considerable variability.

Consequently, decision makers are presented with a range of potential benefit values

(e.g., high, moderate, and low). As the technology becomes better known, this range of

values may be reduced (i.e., uncertainty, in the form of benefit variability, is being

reduced with time as new information becomes available). In addition, variations in the

discount rate affect the present value of any cash flows which do not occur in the base

year.

Second, the timing of cash flows may not be known with certainty. In the case of a new

technology, the process by which the technology diffuses to firms and households may

take many time paths.65 For example, one time path might imply slow adoption at first

followed by a period of rapid adoption. Such might be the case if, shortly after

introduction, the technology were adopted as a standard. Alternatively, the new

technology might enjoy a brief period of rapid adoption followed by a relatively long

period of slow adoption. Such might be the case if, after introducing the new technology,

there were a series of product improvements that caused many potential users to adopt a

Await and see@ attitude.

Third, the value, timing, and magnitude of cash flows may not be known with certainty.

This Acomposite@ source of uncertainty is more complex than the two cases just

discussed. It includes three issues related to the time path overlaid by variability in

benefits, costs, and savings. The three time path issues are related to the time of first use

(i.e., when the technology is introduced to the market place), the rate of adoption over the

time path, and the level of adoption that prevails when the market reaches saturation.

Although the introduction of a new technology can be expected to result in variability of

benefits, costs, and savings for users which adopt it (i.e., there is some uncertainty about

the values of these cash flows and, via the discount rate, their present values), the case at

hand is more complex. Variations in the time of first use and the rate of adoption are the

principal sources of variability in the timing of cash flows. Variations in the level of

adoption enter as factors affecting both the values and the magnitudes of cash flows.

This is because the level of adoption comes into play as a multiplicative factor applied to

any given time path. While different times of first use and rates of adoption affect the

timings of cash flows, different adoption levels affect the values (i.e., due to its being

overlaid by the variability in benefits, costs, and savings) and magnitudes (i.e., due to its

 

65 The time paths by which a new technology may diffuse have several characteristics that are important.

First, there is a time of first use (i.e., when the technology is introduced to the market place). If the time of

first use is considered fixed, then it is the same for all that technology’s time paths. Second, for each time

path, there is a rate of adoption; the rate of adoption affects the slope of the time path. It is important to

recognize that the slope of the time path need not be the same at different points on the time path. Finally,

there is a level of adoption that prevails when the market reaches saturation.
96

affect on the size of the potential market) of these cash flows. Consider the case of the

direct benefits to users from adopting a new technology. Other things being equal, higher

levels of adoption result in larger benefit streams and higher variability (i.e., a wider

range of values) of those benefit streams across all time paths than do lower levels of

adoption.

6.4.6 Measuring BFRL’s Contribution

This section describes the process used to measure the “value” of BFRL’s contribution

leading to the development and use of CBS products and services in office buildings. It

begins with a review of the nature of BFRL’s contribution.

BFRL’s contribution serves two vital roles. One is that of a facilitator, and the other is

that of a developer of key CBS enabling technologies. Both roles are crucial if

commercial products and services are to be developed in a timely manner.

BFRL’s role as facilitator has three facets. First, BFRL has formed a consortium of

manufacturers and service providers interested in producing, testing, demonstrating, and

selling CBS products and services. Second, BFRL has established the VCBT to facilitate

the development and evaluation of new products and systems by manufacturers and

external service providers. Third, BFRL is working towards a fully operational CBS

being tested and deployed in a full-scale demonstration project by 2002.

BFRL’s role as developer of key CBS enabling technologies is extensive and pervasive;

it spans all six projects in the integrated CBS project team (see Section 3.2). However,

four areas of BFRL’s research and development effort are particularly important, since

they provide platforms on which vendors can develop commercial products and services.

First, BFRL is developing and testing standard communication protocols for the open

exchange of information. Second, BFRL is developing advanced measurement

technologies. Third, BFRL is developing performance measures, standards, and

evaluation tools for protocol compliance testing, real time monitoring, and the evaluation

and documentation of CBS interactions. Fourth, BFRL is developing interoperability

testing and certification programs to facilitate the development and introduction of CBS

products and services into the marketplace.

This review of the nature of BFRL’s contribution makes it clear that BFRL is a catalyst in

the development of CBS products and services. Does this mean that CBS products and

services would not be developed without BFRL’s participation? The answer to that

question is an unequivocal “No.” Eventually, CBS products and services would become

commercially available. Would they have the same capabilities? The answer to that

question is a qualified “Probably not.” The reasoning stems from the fact that the nature

of BFRL’s dual role is one that few organizations can fill. Consider the case of an

enabling technology. Few if any vendors will invest in enabling technologies, since they

can not adequately recapture their investment. In fact, other vendors might be able to

employ the enabling technology to develop their own proprietary products. BFRL and

NIST do not have this problem, since a key part of their mission is to promote

competitiveness through the development of enabling technologies. A similar reasoning
97

holds for BFRL’s role as a facilitator. Thus, BFRL’s contribution both serves to speed up

the introduction of CBS products and services and to result in products and services with

better understood properties and, in all likelihood, better capabilities. The remainder of

this section focuses on how to measure the value of BFRL’s contribution in speeding up

the introduction of CBS products and services.

Because BFRL’s research effort is expected to result in a faster introduction of CBS

products and services into the commercial marketplace, those savings which would have

been foregone in the event of a delay are attributable to BFRL. Information from subject

matter experts and similar economic impact assessments66 was used to develop an

estimate of how much the commercial introduction of CBS products and services would

have been delayed, were it not for BFRL’s dual role as a facilitator and developer of key

CBS enabling technologies. Without BFRL’s involvement, the likely delay for the

commercial introduction of CBS products and services is estimated to be seven years

(i.e., commercial introduction in 2010 rather than in 2003). Therefore, any savings prior

to the “delayed” introduction of CBS products and services in 2010 would have been

foregone. Such an accounting framework may be handled through use of a 0-1 weighting

factor. For those years in which savings are attributable to BFRL, the weighting factor

takes on a value of 1. For all years after the “delayed” introduction of CBS products and

services in 2010, the weighting factor takes on a value of 0.

An important part of the sensitivity analysis is concerned with measuring changes in the

value of BFRL’s contribution. The value of BFRL’s contribution is measured through

the use of a 0-1 weighting factor tied to an estimated delay for the commercial

introduction of CBS products and services. In the sensitivity analysis, a range of values

for the estimated delay for the commercial introduction of CBS products and services is

specified. The range of values draws on the same information used to develop the

baseline value (i.e., the likely delay). The range of values used in the sensitivity analysis

is a low of four years and a high of ten years. To better understand how the range of

values is used in the sensitivity analysis, consider the case where the time of first use is

2003 (i.e., the baseline value). If the estimated delay is four years, then the weighting

factor takes on a value of 1 for those years up to and including 2006 and a value of 0

from 2007 until the end of the study period. If the estimated delay is ten years, then the

weighting factor takes on a value of 1 for those years up to and including 2012 and a

value of 0 from 2013 until the end of the study period.

 

66 Chapman and Weber, A Case Study of the Fire Safety Evaluation System, pp. 31-42.
98
99

7. Baseline Analysis of Economic Impacts

The baseline analysis presented in this chapter is the reference point for the CBS impact

assessment. Recall that in the baseline analysis, all data entering into the calculations are

set at their likely values (see Section 2.1.1). Throughout this report, likely value and

baseline value are used interchangeably. Thus, the baseline values represent a fixed state

of analysis. The term baseline analysis is used to denote a complete analysis in all

respects but one; it does not address the effects of uncertainty. Sensitivity analysis

measures the impact on project outcomes of changing the values of one or more key

variables about which there is uncertainty. Sensitivity analysis is the subject of Chapter 8.

The results of the baseline analysis portion of the CBS economic impact assessment are

presented for two basic cases. First are the cost savings nationwide achievable through

the use of CBS products and services in office buildings. Second are the cost savings

attributable to BFRL and the return on BFRL’s CBS-related investment costs.

Key economic measures show the present value of savings (PVS), the present value of

net savings (PVNS), the savings-to-investment ratio (SIR), and the adjusted internal rate

of return (AIRR) that are attributable to BFRL’s CBS-related research, development, and

deployment efforts (see Chapter 3). These values are derived by measuring how cost

savings nationwide would have been reduced if BFRL had not been involved in the

development of CBS products and services (see Section 6.4.6).

The results of the baseline analysis demonstrate that the use of CBS products and services

will generate substantial cost savings to owners, managers, and occupants of office

buildings. The present value of savings nationwide expected from the use of CBS

products and services is in excess of $1.1 billion. Furthermore, because of BFRL’s

timely involvement, CBS products and services are expected to be commercially

available in 2003. If BFRL had not participated in the development of CBS products and

services, the commercial introduction of CBS products and services is expected to be

delayed until 2010. Consequently, potential cost savings accruing to owners, managers,

and occupants of office buildings over the period 2003 through 2009 would have been

foregone. These cost savings are in excess of $90 million. These cost savings measure

the value of BFRL’s contribution for its CBS-related investment costs of approximately

$11.5 million. Stated in present value terms, every public dollar invested in BFRL’s

CBS-related research, development, and deployment efforts is expected to generate $7.90

in cost savings to the public (i.e., an SIR of 7.9). The estimated annual percentage yield

(AIRR) from BFRL’s CBS-related investments over the study period is 16.2 percent.

7.1 BFRL Summary Impact Statement

Exhibit 7-1 is a summary impact statement, covering the background, approach, and

results of the baseline analysis. Exhibit 7-1 utilizes the framework introduced in Chapter

2 (see Exhibit 2-1).
100

Exhibit 7-1. Summary of Economic Impacts of BFRL Research on Cybernetic

Building Systems for Office Buildings

1.a Significance of Research Effort:

Owners and managers of office buildings are pressing building

control companies, equipment and systems manufacturers, and design

engineers to improve building systems performance and reduce costs.

One means of accomplishing this is through the development, adoption,

and use of cybernetic building systems (CBSs) that integrate building

systems. A CBS is defined as a multi-system configuration able to

communicate information and control functions simultaneously and

seamlessly at multiple levels. The multiple levels of communication and

control are based on the Building Automation and Control Networks

(BACnet) layered protocol architecture.

BFRL’s focused research on BACnet, under the auspices of the

American Society of Heating, Refrigerating, and Air-Conditioning

Engineers, led to BACnet's acceptance as a national standard in 1995.

Complementary efforts in fire technology, mechanical and electrical

systems, fault detection and diagnostic systems, virtual reality modeling,

and economic analysis led BFRL to form an integrated CBS project team

in 1997. In addition, BFRL is uniquely positioned to collaborate with

industry on the development of CBS products and services and to

provide a forum for conducting interoperability testing. BFRL is

working towards a fully operational CBS being tested and deployed by

2002. To achieve this goal, BFRL is working with equipment and

systems manufacturers, service providers, facilities owners and

managers, designers, trade associations, professional societies, standards

organizations, university researchers, and other government agencies.

Without BFRL’s participation, it is likely that the introduction of CBS

products and services will be delayed for at least seven years.

1.b Key Points:

• Pressure to increase

building systems

performance and

reduce costs has

created a potential

market for CBS

products and services.

• BFRL is uniquely

positioned to

collaborate with

industry on the

development of CBS

products and services

and to provide a

forum for conducting

interoperability

testing.

• Without BFRL’s

participation, it is

likely that the

introduction of CBS

products and

services will be

delayed for at least

seven years.

2. Analysis Strategy: How Key Measures are Estimated

The objective of the study is to (1) evaluate, for the period 1991 through 2015, the net cost savings

due to the adoption and use of CBS products and services in office buildings, and (2) estimate BFRL’s

contribution to these net cost savings. The approach is to estimate in 1997 present value (PV) dollars:

Present Value Cost Savings Nationwide in office buildings that install CBS products and services.

PV cost savings nationwide are estimated for each year from 1991 to 2015 and summed.

Present Value Savings (PVS) attributable to BFRL by including the savings only for those years

that accrued due to BFRL’s participation (i.e., 1991 to 2009).

Present Value Net Savings (PVNS) attributable to BFRL by subtracting from BFRL PVS the

present value of BFRL's investment costs (PV Costs). A PVNS >0 indicates an economically

worthwhile project.

Two additional measures are also estimated:

Savings-to-Investment Ratio (SIR) attributable to BFRL by taking the ratio of BFRL PVS to

BFRL PV costs. A ratio >1 indicates an economically worthwhile project.

Adjusted Internal Rate of Return (AIRR), the annual rate of return over the study period on

BFRL’s investment. An AIRR > the discount rate indicates that the project is economically

worthwhile.
101

Exhibit 7-1. Summary of Economic Impacts of BFRL Research on Cybernetic

Building Systems for Office Buildings (continued)

2. Analysis Strategy: Data and Assumptions

• The period over which costs and savings are measured begins in 1991 and ends in 2015. Hence

the length of the study period is 25 years.

• The base year is 1997, and all amounts are calculated in PV 1997 dollars.

• The discount rate is 7 percent (real), which is the discount rate currently in effect for government

projects.

• Estimates of cost savings associated with the adoption and use of CBS products and services are

based on construction industry data and information provided by industry experts.

• The market potential for CBS products and services is based on data derived from the

Commercial Building Energy Consumption Survey.

• Without BFRL’s participation, the introduction of CBS products and services will be delayed by

seven years.

3.a Calculation of Savings, Costs, and Additional Measures

Savings and Costs

Present Value Cost Savings Nationwide:

Sum from 1991 to 2015 of present value of cost savings nationwide

by year

= $1,175.6 million

Present Value Savings (PVS) Attributable to BFRL:

Sum from 1991 to 2009 of present value of cost savings nationwide

by year

= $90.7 million

Present Value Investment Costs (PV Costs) to BFRL:

Sum from 1991 to 2015 of present value of investment cost to BFRL

by year

= $11.475 million

Present Value Net Savings (PVNS) Attributable to BFRL:

Difference between present value savings (PVS) attributable to

BFRL and present value of investment costs (PV Costs) to BFRL

 = $90.7 - $11.475 = $79.3 million

Additional Measures

SIR of BFRL Contribution:

Savings-to-Investment Ratio on BFRL investment

 = $90.7/$11.475 = 7.9

AIRR of BFRL Contribution:

Adjusted Internal Rate of Return on BFRL investment

 = (1+ 0.07) * 7.91/25 – 1 = 0.162

3.b Key Results:

1997 Dollars

($ amounts in millions)

Cost Savings Nationwide:

 $1,175.6

Savings Attributable to

BFRL:

PVS $90.7

PV Costs $11.475

PVNS $79.3

SIR 7.9

AIRR 16.2%
102

7.2 Cost Savings Nationwide

This section combines three types of information presented in Sections 6.3 and 6.4 to

generate a baseline estimate of cost savings nationwide. These three types of information

are related to: (1) the diffusion model developed in Section 6.4.4; (2) the per unit cost

savings for energy, maintenance, repairs and replacements, and occupant productivity;

and (3) the additional costs to building owners and managers for installing CBS products

and services. These three types of information are combined via four sets of calculations

to estimate “annual” cost savings to the nation. Estimates are produced for each year

from 1991 to 2015. Each year’s cost savings is then discounted to a present value and

summed to get the present value of cost savings nationwide. The present value of cost

savings nationwide is a key indicator of the merits of installing CBS products and

services in office buildings. The results of the baseline analysis show that cost savings

nationwide exceed $1.1 billion ($1,176 million in present value 1997 dollars). Each set

of calculations used to produce the estimate of cost savings nationwide is summarized

through a table and described in the text that follows. Each table presented in this section

records information based on both SI units and conventional units. To facilitate

distinctions between the two sets of units, references in the text to SI units are shown in a

regular font and references in the text to conventional units are shown within parentheses

in Italics.

Table 7-1 summarizes information derived from the diffusion model. Column 1 of the

table lists each year of the study period from 1991 through 2015. Columns 2 and 5 of

Table 7-1 record information on the calculated values of P0(t) and )P0(t) for each year.

Recall that t = 1 corresponds to the year in which CBS products and services are

expected to become commercially available (i.e., t = 1 corresponds to the year 2003).

Thus, P0(t) = 0 and )P0(t) = 0 for all values of t less than 1. Next, the diffusion model is

combined with information on office building floorspace to generate two sets of annual

estimates (i.e., estimates for each year from 1991 to 2015). These estimates are (1) an

estimate of the cumulative total CBS installations in millions of square meters (millions

of square feet) and (2) an estimate of the new CBS installations in millions of square

meters (millions of square feet). To get the cumulative total CBS installations in millions

of square meters (millions of square feet), the value of P0(t) in Column 1 is multiplied by

the total amount of office floorspace. The total amount of office floorspace is equal to

973 million square meters (10,478 million square feet). The product of the two terms

equals the cumulative total CBS installations in millions of square meters (millions of

square feet); its value is recorded in Column 3 (Column 4) of Table 7-1. Notice that

following the commercial introduction of CBS products and services in 2003, the

cumulative totals rise steadily until the end of the study period. To get the new CBS

installations in millions of square meters (millions of square feet), the value of )P0(t) in

Column 5 is multiplied by the total amount of office floorspace. The product of the two

terms equals the new CBS installations in millions of square meters (millions of square

feet); its value is recorded in Column 6 (Column 7) of Table 7-1. Notice that these values

rise over the period 2003 to 2012, then level off, and then decline. This is because P0(t)

reaches 50 percent of its designated potential market after 10 years (i.e., η/2). This point

also corresponds to a point of inflection beyond which P0(t) increases at a decreasing

rate.
103

Table 7-1. Baseline CBS Installations by Year: 1991-2015

Square Meters Square Feet Square Meters Square Feet

Col. (1) Col. (2) Col. (3) Col. (4) Col. (5) Col. (6) Col. (7)

1991 0 0 0 0 0 0

1992 0 0 0 0 0 0

1993 0 0 0 0 0 0

1994 0 0 0 0 0 0

1995 0 0 0 0 0 0

1996 0 0 0 0 0 0

1997 0 0 0 0 0 0

1998 0 0 0 0 0 0

1999 0 0 0 0 0 0

2000 0 0 0 0 0 0

2001 0 0 0 0 0 0

2002 0 0 0 0 0 0

2003 0.0008 0.8 8.3 0.0008 0.8 8.3

2004 0.0014 1.4 15.0 0.0006 0.6 6.7

2005 0.0026 2.5 27.2 0.0012 1.1 12.2

2006 0.0047 4.5 48.9 0.0021 2.0 21.7

2007 0.0083 8.1 87.2 0.0037 3.6 38.3

2008 0.0146 14.2 152.9 0.0063 6.1 65.7

2009 0.0249 24.2 260.7 0.0103 10.0 107.8

2010 0.0406 39.5 425.4 0.0157 15.3 164.7

2011 0.0622 60.5 651.2 0.0216 21.0 225.8

2012 0.0877 85.4 918.9 0.0255 24.9 267.7

2013 0.1132 110.2 1,186.6 0.0255 24.9 267.7

2014 0.1348 131.2 1,412.4 0.0216 21.0 225.8

2015 0.1505 146.5 1,577.1 0.0157 15.3 164.7

New CBS Installations in

 Millions of

Year

Proportion of Office 

Floorspace Covered by CBS 

Products and Services

Pη(t)

Cumulative Total CBS Installations in 

Millions of Proportion of Office 

Floorspace Covered by 

New CBS Installations 

ΔPη(t)
104

Table 7-2 summarizes how baseline energy cost savings are calculated. Recall that since

energy prices change over time it is necessary to develop a weighted time series for

energy. This process was described in Section 6.3.1.1; it was done through reference to

information contained in the annual supplement to NIST Handbook 135. Because 1997

is the base year for the CBS economic impact assessment, the annual supplement for

1997 was used to construct the weighted time series for energy (see Table 6-1 in Section

6.3.1.1). Thus, the weighted time series for energy begins in 1997. The years for which

the weighted time series for energy is defined are listed in Column 1 of Table 7-2. The

years run from 1997 until 2015, the end of the study period. Column 2 of Table 7-2

contains the unweighted energy cost savings in millions of 1997 dollars. These savings

are calculated for each year from 1997 until 2015. Notice that no energy cost savings

occur until 2003, the year in which CBS products and services first become commercially

available. The unweighted energy cost savings for each year is equal to the product of

energy cost savings per square meter (per square foot) of $1.71 ($0.16) and the

cumulative total CBS installations in millions of square meters (millions of square feet)

for that year (i.e., the year of the specific calculation). The latter value is contained in the

row of Column 3 (Column 4) of Table 7-1 corresponding to the year of the specific

calculation. Column 4 of Table 7-2 contains the weighted energy cost savings in millions

of 1997 dollars. These savings are calculated for each year from 1997 until 2015. The

weighted energy cost savings for each year is equal to the product of the unweighted

energy cost savings, contained in Column 2, and the weighted time series for energy,

contained in Column 3.

Table 7-3 summarizes how baseline cost savings by category and in total are calculated.

The years for which cost savings are calculated are listed in Column 1 of Table 7-3. The

years run from 1991 until 2015 (i.e., the entire study period). The table records

information on four categories of cost savings: (1) weighted energy cost savings; (2)

maintenance cost savings; (3) repair and replacement cost savings; and (4) productivity

cost savings. Annual values for each category of cost savings are recorded in Column 2

for energy, Column 3 for maintenance, Column 4 for repair and replacement, and

Column 5 for productivity. Note that no cost savings for any category occur until 2003,

the year in which CBS products and services first become commercially available. The

weighted energy cost savings for each year, recorded in Column 2, is transferred from the

respective row of Column 4 of Table 7-2. The maintenance cost savings for each year,

recorded in Column 3, is equal to the product of maintenance cost savings per square

meter (per square foot) of $1.60 ($0.15) and the cumulative total CBS installations in

millions of square meters (millions of square feet) for that year. The latter value is

contained in the row of Column 3 (Column 4) of Table 7-1 corresponding to the year of

the specific calculation. The repair and replacement cost savings for each year, recorded

in Column 4, is equal to the product of repair and replacement cost savings per square

meter (per square foot) of $0.66 ($0.06) and the cumulative total CBS installations in

millions of square meters (millions of square feet) for that year. The productivity cost

savings for each year, recorded in Column 5, is equal to the product of productivity cost

savings per square meter (per square foot) of $4.20 ($0.39) and the cumulative total CBS

installations in millions of square meters (millions of square feet) for that year.
105

Table 7-2. Baseline Energy Cost Savings by Year: 1997-2015

Year

Unweighted Energy 

Cost Savings (In 

Millions of 1997 

Dollars)

Weighted Time

Series for Energy

Weighted Energy 

Cost Savings (In 

Millions of 1997 

Dollars)

Col. (1) Col. (2) Col. (3) Col. (4) 

(2) x (3)

1997 0 1.000 0

1998 0 1.000 0

1999 0 1.000 0

2000 0 0.992 0

2001 0 0.982 0

2002 0 0.983 0

2003 1.322 0.974 1.287

2004 2.400 0.964 2.314

2005 4.344 0.955 4.148

2006 7.821 0.954 7.463

2007 13.946 0.945 13.179

2008 24.457 0.944 23.095

2009 41.712 0.944 39.361

2010 68.066 0.935 63.615

2011 104.196 0.925 96.340

2012 147.027 0.915 134.581

2013 189.858 0.897 170.312

2014 225.988 0.897 202.723

2015 252.343 0.906 228.673
106

Table 7-3. Baseline Cost Savings by Category and in Total in Millions of 1997

Dollars by Year: 1991-2015

Weighted 

Energy Cost 

Savings 

Maintenance 

Cost Savings 

Repair and 

Replacement 

Cost Savings 

Productivity 

Cost Savings 

Col. (1) Col. (2) Col. (3) Col. (4) Col. (5) Col. (6) 

(2)+(3)+(4)+(5)

1991 0 0 0 0 0

1992 0 0 0 0 0

1993 0 0 0 0 0

1994 0 0 0 0 0

1995 0 0 0 0 0

1996 0 0 0 0 0

1997 0 0 0 0 0

1998 0 0 0 0 0

1999 0 0 0 0 0

2000 0 0 0 0 0

2001 0 0 0 0 0

2002 0 0 0 0 0

2003 1.287 1.240 .496 3.223 6.245

2004 2.314 2.250 .900 5.851 11.314

2005 4.148 4.073 1.629 10.589 20.440

2006 7.463 7.332 2.933 19.064 36.791

2007 13.179 13.074 5.230 33.993 65.476

2008 23.095 22.929 9.171 59.615 114.810

2009 39.361 39.105 15.642 101.673 195.781

2010 63.615 63.812 25.525 165.912 318.864

2011 96.340 97.684 39.074 253.979 487.076

2012 134.581 137.838 55.135 358.379 685.934

2013 170.312 177.992 71.197 462.779 882.281

2014 202.723 211.864 84.746 550.846 1,050.179

2015 228.673 236.571 94.628 615.085 1,174.958

Year

Annual Cost Savings By Category

Total Cost 

Savings by 

Year 
107

The cost savings by category for each year recorded in Table 7-3 are based on the

cumulative total CBS installations up to and including that year. The reason for using

cumulative total CBS installations rather than new CBS installations is that CBS products

and services, once installed, continue to generate cost savings. Recall that CBS-related

costs savings are based on comparisons between the base case and the CBS alternative.

Thus, office buildings that installed the CBS alternative in 2003 continue to accrue cost

savings vis-à-vis the base case throughout the remainder of the study period.

In addition to cost savings by category, Table 7-3 also contains total cost savings by year.

These cost savings are recorded in Column 6. Total cost savings for each year equal the

sum of each category’s cost savings for that year. Total cost savings, denominated in

millions of 1997 dollars, increase steadily between 2003 and 2015.

Table 7-4 summarizes how the present values of cost savings nationwide by year and in

total are calculated. The table also includes information on total cost savings, additional

CBS-related installation costs, net cost savings, and the discount factor needed to

translate yearly net cost savings into yearly present value cost savings nationwide. The

years for which present values are calculated are listed in Column 1 of Table 7-4. The

years run from 1991 until 2015 (i.e., the entire study period). Column 2 of Table 7-4

contains total cost savings by year in millions of 1997 dollars. The total cost savings for

each year is transferred from the respective row of Column 6 of Table 7-3. The

additional cost to install CBS products and services for each year is recorded in Column 3

of Table 7-4. This cost equals the product of the additional cost to building owners and

managers of $10.76 per square meter ($1.00 per square foot) and the new CBS

installations for that year. The value for new CBS installations is contained in Column 6

(Column 7) of Table 7-1 for each year of the specific calculation. Note that these costs

first increase, then level off, and finally begin to decline. The difference between total

cost savings and the additional costs to install CBS products and services equals net cost

savings. Column 4 of Table 7-4 records net cost savings for each year in millions of

1997 dollars. Note that net cost savings are negative in 2003, after which they become

positive and increase steadily. The calculated value of the single compound amount

factor for each year is recorded in Column 5 of Table 7-4. All entries are calculated

using a real discount rate of 7 percent (see Section 6.4.3). Because 1997 is the base year,

the single compound amount factor takes on a value of 1.0 for that year. For years prior

to 1997, the single compound amount factor is greater than 1.0. For years following

1997, the single compound amount factor is less than 1.0. The single compound amount

factor for any given year, Y, equals (1.07)

1997-Y where 1991 # Y # 2015. The present

value of cost savings nationwide by year is recorded in Column 6 of Table 7-4. It equals

the product of the net cost savings, in Column 4, and the single compound amount factor,

in Column 5, for that year. Note that the present value of cost savings nationwide is

negative in 2003, after which it becomes positive and increases steadily.

Because the entries in Column 6 are in present value terms, they can be summed to get

total cost savings nationwide over the entire study period. Total cost savings nationwide

resulting from the four sets of baseline analysis calculations exceed $1.1 billion ($1,176

million in present value 1997 dollars); see the bottom of Column 6 in Table 7-4.
108

Table 7-4. Baseline Computation of Present Value Cost Savings Nationwide in

Millions of 1997 Dollars: 1991-2015

Year

Total Cost 

Savings by 

Year 

Additional 

Cost to Install 

CBS Products 

and Services

Net Cost 

Savings 

Single 

Compound 

Amount 

Factor by 

Year

Present Value 

of Net Cost 

Savings 

Nationwide by 

Year

Col. (1) Col. (2) Col. (3) Col. (4) 

(2) - (3) Col. (5) Col. (6) (4) x (5)

1991 0 0 0 1.501 0

1992 0 0 0 1.403 0

1993 0 0 0 1.311 0

1994 0 0 0 1.225 0

1995 0 0 0 1.145 0

1996 0 0 0 1.070 0

1997 0 0 0 1.000 0

1998 0 0 0 0.935 0

1999 0 0 0 0.873 0

2000 0 0 0 0.816 0

2001 0 0 0 0.763 0

2002 0 0 0 0.713 0

2003 6.245 8.263 -2.018 0.666 -1.345

2004 11.314 6.738 4.576 0.623 2.850

2005 20.440 12.151 8.289 0.582 4.824

2006 36.791 21.729 15.063 0.544 8.193

2007 65.476 38.280 27.196 0.508 13.825

2008 114.810 65.697 49.113 0.475 23.333

2009 195.781 107.842 87.940 0.444 39.046

2010 318.864 164.715 154.149 0.415 63.966

2011 487.076 225.813 261.264 0.388 101.323

2012 685.934 267.693 418.241 0.362 151.590

2013 882.281 267.693 614.587 0.339 208.182

2014 1,050.179 225.813 824.366 0.317 260.973

2015 1,174.958 164.715 1,010.243 0.296 298.894

TOTAL 1,175.655
109

Reference to Table 7-4 demonstrates the magnitude of the savings to the nation from

using CBS products and services in office buildings. These cost savings nationwide also

provide a basis for measuring the value of BFRL’s contribution.

7.3 Measuring the Value of BFRL’s Contribution and the Return on BFRL’s CBSRelated Investments

Measuring the value of BFRL’s contribution to the development of CBS products and

services and the return on its CBS-related investments is the focus of this section.

Information on BFRL’s CBS-related research, development, and deployment efforts—in

terms of its dollar investments—over the 25-year period from 1991 to 2015 are first

presented. These figures demonstrate not only a significant, up-front research

commitment by BFRL, but also a continued effort as CBS products and services move

into the commercial marketplace. Next, the likely delay in the commercial availability of

CBS products and services is addressed. Finally, a full array of economic measures

summarizes the importance of BFRL’s contribution to the development of CBS products

and services for use in office buildings.

Table 7-5 summarizes information on BFRL’s CBS-related investments. Column 1 of

the table records the year in which CBS-related investments were made. Column 2

records the value (in millions of current dollars) by year of investment for each year

between 1991 and 1996. For example, in 1991 the investment was $298,000 (in 1991

dollars), in 1992 the investment was $268,000 (in 1992 dollars), and in 1993 the

investment was $198,000 (in 1993 dollars). For 1997 through 2015, the entries in

Column 2 are in millions of 1997 dollars. Investments over the 1991 to 1996 time period

cover BACnet-related research. Investments beginning in 1997 include research,

development, and deployment efforts aimed at producing CBS products and services.

Because the values for 1991 through 1996 in Column 2 are in current dollars by year, it is

necessary to convert them to constant 1997 dollars and then convert them to present value

(i.e., time equivalent) dollars. This involves a two-step process. First, each year’s

current dollar investment is converted to a “real” investment in 1997 constant dollars

through application of the Consumer Price Index (CPI). The conversion factors, for each

year, are shown in Column 3 of Table 7-5. The constant 1997 dollar values (in millions

of dollars) are the year-by-year products of the entries in Column 2 and Column 3. These

values are shown in Column 4. The values in Column 4 are converted into present values

terms through the use of a single compound amount factor, based on a real discount rate

of 7 percent. The value of each year’s single compound amount factor is given in

Column 5. The present values in millions of 1997 dollars are recorded in Column 6; they

are the year-by-year products of the entries in Column 4 and Column 5.

Because entries in Column 6 are in present value terms, they can be summed to get the

present value of BFRL’s CBS-related investments. The present value of BFRL’s CBSrelated investments, PV Costs, totals $11.475; this value is recorded at the bottom of

Column 6.
110

Table 7-5. Summary of BFRL Research Investments: 1991-2015

a

 The dollar amounts for 1991 through 1996 are in millions of current dollars. The dollar

amounts for 1997 through 2015 are in millions of 1997 dollars.

Year

Annuala 

Dollar 

Amount (In 

Millions of 

Dollars)

Conversion 

Factor by Year 

(Current 

Dollars to 

1997 Dollars)

Investment 

Cost by 

Year (In 

Millions of 

1997 

Dollars)

Single 

Compound 

Amount 

Factor by 

Year

Present Value 

of Investment

Cost by Year

 (In Millions 

of 1997 Dollars)

Col. (1) Col. (2) Col. (3) Col. (4) 

(2) x (3) Col. (5) Col. (6) (4) x (5)

1991 .298 1.178 .351 1.501 .527

1992 .268 1.144 .307 1.403 .430

1993 .198 1.111 .219 1.311 .288

1994 .190 1.083 .206 1.225 .252

1995 .218 1.053 .229 1.145 .262

1996 .300 1.023 .307 1.070 .328

1997 .573 1.000 .573 1.000 .573

1998 1.445 1.000 1.445 0.935 1.350

1999 1.708 1.000 1.708 0.873 1.491

2000 2.125 1.000 2.125 0.816 1.735

2001 2.500 1.000 2.500 0.763 1.907

2002 2.125 1.000 2.125 0.713 1.515

2003 .875 1.000 .875 0.666 .583

2004 .375 1.000 .375 0.623 .234

2005 0 1.000 0 0.582 0

2006 0 1.000 0 0.544 0

2007 0 1.000 0 0.508 0

2008 0 1.000 0 0.475 0

2009 0 1.000 0 0.444 0

2010 0 1.000 0 0.415 0

2011 0 1.000 0 0.388 0

2012 0 1.000 0 0.362 0

2013 0 1.000 0 0.339 0

2014 0 1.000 0 0.317 0

2015 0 1.000 0 0.296 0

TOTAL 11.475
111

Table 7-6 provides the information needed to calculate the present value of savings

attributable to BFRL. The years for which present values are calculated are listed in

Column 1 of Table 7-6. The years run from 1991 until 2015 (i.e., the entire study

period). The present value of cost savings nationwide by year is recorded in Column 2 of

Table 7-6. The present value of cost savings nationwide for each year is transferred from

the respective row of Column 6 of Table 7-4. BFRL’s dual role as a facilitator and

developer of key CBS enabling technologies is expected to speed up the introduction of

CBS products and services into the commercial marketplace. Without BFRL’s

participation, the introduction of CBS products and services into the commercial

marketplace would likely have been delayed. Information from subject matter experts

and similar economic impact assessments suggest a range of values from four to 10 years

for the likely delay. The selected baseline value for the delay is seven years (see Section

6.4.6). Because BFRL’s research, development, and deployment efforts resulted in faster

introduction of CBS products and services, those savings which would have been

foregone in the event of a delay are attributable to BFRL. Therefore, any savings over

the first seven years (starting with 2003), prior to the “delayed” introduction of CBS

products and services in 2010, would have been foregone. Such an accounting

framework may be handled through use of a 0-1 weighting factor. For those years in

which savings are attributable to BFRL, the weighting factor takes on a value of 1. The

year-by-year values of the BFRL baseline weighting factor are given in Column 3 of

Table 7-6. The present value of savings attributable to BFRL is the product of each

year’s present value of cost savings nationwide in Column 2 and the value of the BFRL

baseline weighting factor in Column 3. The present value of savings attributable to

BFRL on a year-by-year basis is given in Column 4 of Table 7-6.

Because entries in Column 4 are in present value terms, they can be summed to get the

present value of savings attributable to BFRL. The present value of savings attributable

to BFRL, PVS, totals $90.7 million; this value is recorded at the bottom of Column 6.

Given the values of PV Costs and PVS attributable to BFRL, it is now possible to derive

three other economic impact measures. These measures are: (1) present value of net

savings (PVNS) attributable to BFRL; (2) the savings-to-investment ratio (SIR) on

BFRL’s CBS-related investments; and (3) the adjusted internal rate of return (AIRR) on

BFRL’s CBS-related investments.

The PVNS attributable to BFRL, expressed in millions of present value 1997 dollars and

based on the approach outlined in Section 2.1.1, is equal to:

PVNS = PVS – PV Costs

= $90.727 - $11.475

= $79.3 million
112

Table 7-6. Estimated Cost Savings in Millions of 1997 Dollars Attributable

to BFRL

Year

Present Value of 

Cost Savings 

Nationwide by Year 

BFRL Baseline 

Weighting Factor 

(7-Year Delay)

Present Value of Cost 

Savings by Year 

Attributable to BFRL 

Col. (1) Col. (2) Col. (3) Col. (4) 

(2) x (3)

1991 0 1 0

1992 0 1 0

1993 0 1 0

1994 0 1 0

1995 0 1 0

1996 0 1 0

1997 0 1 0

1998 0 1 0

1999 0 1 0

2000 0 1 0

2001 0 1 0

2002 0 1 0

2003 -1.345 1 -1.345

2004 2.850 1 2.850

2005 4.824 1 4.824

2006 8.193 1 8.193

2007 13.825 1 13.825

2008 23.333 1 23.333

2009 39.046 1 39.046

2010 63.966 0 0

2011 101.323 0 0

2012 151.590 0 0

2013 208.182 0 0

2014 260.973 0 0

2015 298.894 0 0

TOTAL 90.727
113

Utilizing the approach outlined in Section 2.1.2, the SIR on BFRL’s CBS-related

investments is equal to:

SIR = PVS / (PV Costs)

= $90.727 / $11.475

= 7.9

Utilizing the approach outlined in Section 2.1.3, the AIRR on BFRL’s CBS-related

investments is equal to:

AIRR = (1 + 0.07) * 7.91/25 - 1

= 0.162

= 16.2%

The values of the five economic impact measures derived in Chapter 7 are the baseline

values that appear in Section 3.b of Exhibit 7-1. These values also figure in the

sensitivity analysis, which is the subject of the next chapter.
114
115

8. Sensitivity Analysis of Economic Impacts

The CBS economic impact assessment described in this report was carried out in two

stages. In the first stage, a baseline analysis was performed. The data and assumptions

underlying the baseline analysis were described in Chapter 6; the results of the baseline

analysis were presented in Chapter 7.

In the second stage, nine variables were varied both singly and in combination according

to an experimental design. The sensitivity analysis uses the same data and assumptions

as the baseline analysis for its starting point. Information on how the deviations about the

baseline values for each of the nine input variables were specified and how the range of

values for each variable was determined is described and documented in Chapter 6. The

sensitivity analysis described in this chapter is based on Monte Carlo techniques. The

objective of the sensitivity analysis is to evaluate how uncertainty in the values of each of

the nine input variables, both singly and in combination, translates into changes in each

of six key economic measures. The six economic measures evaluated in the sensitivity

analysis are: (1) the present value of savings nationwide; (2) the present value of savings

due to BFRL; (3) the present value of BFRL’s CBS-related investment costs; (4) the

present value of net savings due to BFRL; (5) the savings-to-investment ratio on BFRL’s

CBS-related investments; and (6) the adjusted internal rate of return on BFRL’s CBSrelated investments. Three of these measures are particularly helpful in understanding

BFRL’s contribution, since each measure provides a different perspective. The first, the

present value of net savings due to BFRL is a magnitude measure; it show a dollar value

to the public net of BFRL’s CBS-related investments. The second, the savings-toinvestment ratio on BFRL’s CBS-related investments is a multiplier; it shows, in present

value terms, how many dollars the public receives for each public dollar spent. The third,

the adjusted internal rate of return on BFRL’s CBS-related investments is a rate of return;

it shows the return on the public monies going into the development of CBS products and

services throughout the 25-year study period.

8.1 Methodology

Because the values of many variables that enter into the CBS economic impact

assessment are not known with certainty, it is advisable to select a small set of variables

whose impact is likely to be substantial and subject them to a sensitivity analysis.

Variations in the values of these input variables translate into the value of each outcome

(e.g., the SIR) in such a manner that the impacts of uncertainty can be measured

quantitatively.

Sensitivity analysis may be divided into two polar cases: (1) deterministic; and (2)

probabilistic. Deterministic sensitivity analyses are the most straightforward. Their

advantage is that they are easy to apply and the results are easy to explain and

understand. Their disadvantage is that they do not produce results that can be tied to

probabilistic levels of significance (i.e., the probability that the SIR is less than 1.0).
116

For example, a deterministic sensitivity analysis might use as inputs a pessimistic value, a

value based on a measure of central tendency (e.g., mean or median), and an optimistic

value for the variable of interest. Then an analysis could be performed to see how each

outcome (e.g., the SIR) changes as each of the three chosen values for the selected input

is considered in turn, while all other input variables are maintained at their baseline

values. A deterministic sensitivity analysis can also be performed on different

combinations of input variables. That is, several variables are altered at once and then an

outcome measure is computed. This is the approach used in the two previous economic

impact assessments.67

In a probabilistic sensitivity analysis, a small set of key input variables is varied either

singly or in combination according to an experimental design. In most cases,

probabilistic sensitivity analyses are based on Monte Carlo techniques, or some other

form of simulation. The major advantage of probabilistic sensitivity analysis is that it

permits the effects of uncertainty to be rigorously analyzed. For example, not only the

expected value of each economic measure can be computed but also the variability of that

value. In addition, probabilistic levels of significance can be attached to the computed

values of each economic measure. The disadvantage of a probabilistic sensitivity

analysis is that it requires many calculations carried out according to an experimental

design, and is therefore practical only when used with a computer.

The approach selected for this study makes use of works by McKay, Conover, and

Beckman68 and by Harris;69 it is based on the method of model sampling. Model

sampling provides the basis for many probabilistic sensitivity analyses. Model sampling

is a procedure for sampling from a stochastic process to determine, through multiple

trials, the characteristics of a probability distribution.

The method of model sampling was implemented through application of the @RISK

software product.70 This software product is an add-in for spreadsheets. For the case at

hand, selected columns of the spreadsheet were associated with one or more of the nine

input variables. The @RISK software product allows the user to specify a unique

probability distribution for each input variable. Specification of the experimental design

involves defining which variables are to be simulated and the number of simulations.

Throughout this sensitivity analysis, 1,000 simulations were run for each input variable or

combination of input variables under analysis. The number of simulations was chosen to

ensure that values in the tails of the distribution for each input variable would be selected

for inclusion in the analysis. When the @RISK software product is executed, it randomly

 

67 See Chapman and Fuller, Two Case Studies in Building Technology, and Chapman and Weber, A Case

Study of the Fire Safety Evaluation System.

68 McKay, M. C., W. H. Conover, and R.J. Beckman. 1979. “A Comparison of Three Methods for Selecting

Values of Input Variables in the Analysis of Output from a Computer Code.” Technometrics (Vol. 21): pp.

239-245.

69 Harris, Carl M. 1984. Issues in Sensitivity and Statistical Analysis of Large-Scale, Computer-Based

Models. NBS GCR 84-466. Gaithersburg, MD: National Bureau of Standards.

70 Palisade Corporation. 1997. Guide to Using @RISK: Risk Analysis and Simulation Add-In for Microsoft

Excel or Lotus 1-2-3. Newfield, NY: Palisade Corporation.
117

samples from the parent probability distribution for each input variable of interest (i.e.,

the input variable(s) specified by the experimental design).

In reality, the exact nature of the parent probability distribution for each input variable is

unknown. Estimates of the parameters (e.g., mean and variance) of the parent probability

distribution can be made and uncertainty can be reduced by investigation and research.

However, uncertainty can never be eliminated completely. The true specification of the

parent probability distribution can only be known after CBS products and services have

been operating in the marketplace for an extended period of time. Therefore, in order to

implement the procedure without undue attention to the characterization of the parent

probability distribution, it was decided to focus on only three probability distributions.

These probability distributions are: (1) the triangular; (2) the uniform; and (3) the discrete

or multinomial. Readers interested in learning more about these probability distributions,

including variate relationships, estimation procedures, and random number generation,

are referred to Evans, Hastings, and Peacock.71

One reason for using these three probability distributions is that they are all defined over

a finite interval. Furthermore, the specification of each probability distribution is

accomplished with as few as two data points. The triangular distribution is widely used

in simulation modeling; its specification requires three data points, the minimum value,

the most likely value, and the maximum value. The triangular distribution is used

whenever the range of input values is continuous and a clustering about some central

value is expected. Discussions with subject matter experts and reference to selected

publications indicated six input variables for which clustering about a central value was

to be expected. Once the triangular distribution was selected for these six input variables,

all three values were derived through investigation and discussions with subject matter

experts. The uniform distribution is also widely used in simulation modeling; its

specification requires only two data points, the minimum value and the maximum value.

In addition, all values between the minimum and maximum are equally likely. The

uniform distribution is used whenever the range of input values is continuous but no a

priori reason can be given for expecting clustering about some central value. It was used

for one input variable (see Section 8.2). The discrete distribution is used whenever the

range of input values is discrete. It was used for two input variables (see Section 8.2).

8.2 Key Variables

Information on the nine input variables that are the focus of the sensitivity analysis is

presented in this section. The nine variables are: (1) alpha, ", the location parameter in

the diffusion model; (2) beta, $, the shape parameter in the diffusion model; (3) eta, 0,

the market saturation level in the diffusion model; (4) the time of first use; (5) the length

of the delay; (6) per unit energy cost savings; (7) per unit maintenance cost savings; (8)

per unit productivity cost savings; and (9) the discount rate.

 

71 Evans, Merran, Nicholas Hastings, and Brian Peacock. 1993. Statistical Distributions. New York, NY:

John Wiley & Sons, Inc.
118

Table 8-1 summarizes information on each of the nine input variables. The table includes

information on the type of probability distribution used to model variations about the

baseline value for each input variable, the baseline value for each input variable, and the

minimum and maximum values for each input variable.

Reference to the entries under the heading Probability Distribution shows that all but

three of the nine input variables use the triangular distribution to model variations about

the baseline value for that variable. One of these variables, ", employs the uniform

distribution. The input variable " is modeled with a uniform distribution because a

review of the economics literature on the diffusion process produced no a priori reason

for expecting a clustering of values around a value of 6.0. The economics literature was,

however, useful in specifying the range about the baseline value of ". The other two

input variables, the time of first use and the length of delay, employ the discrete

distribution. Both of these variables designate a year. For example, the time of first use

(i.e., when CBS products and services first become available commercially) either occurs

in 2003 (i.e., the year corresponding to the baseline value) or in some other year. It does

not occur in year 2003.5. Thus, the discrete distribution is the most meaningful way to

model when CBS products and services first become available commercially.

Table 8-1. Baseline and Extreme Values of the Nine Input Variables Used in the

Sensitivity Analysis

Variable Name Probability Setting and Value

Distribution Baseline Minimum Maximum

(1) Alpha Uniform 6 5 7

(2) Beta Triangular 0.6 0.5 0.7

(3) Eta Triangular 0.175 0.0553 0.3082

(4) Time of First Use Discrete 2003 2002 2005

(5) Length of Delay Discrete 7 4 10

(6) Energy Cost

Savings

Triangular $1.71 ($0.16) $0.86 ($0.08) $2.58 ($0.24)

(7) Maintenance Cost

Savings

Triangular $1.60 ($0.15)$0.81

($0.075)

$3.23 ($0.30)

(8) Productivity Cost

Savings

Triangular $4.20 ($0.39) $0.00 ($0.00) $8.39 ($0.78)

(9) Discount Rate Triangular 0.07 0.04 0.10

The next three headings record, for each input variable, its setting (i.e., baseline,

minimum, and maximum) and value. For each input variable, the baseline value is

recorded first. For example, the baseline value for the discount rate is 7 percent (real); it

is recorded in decimal form as 0.07. Two other values for the discount rate, 4 percent and

10 percent, are selected to bracket the baseline value. These values are recorded in

decimal form as 0.04 and 0.10, respectively.
119

Note that three input variables have two sets of entries. These input variables are all

associated with per unit cost savings. In Chapter 7, information was presented based both

on SI units and on conventional units. This chapter also includes information based both

on SI units and on conventional units. To facilitate distinctions between the two sets of

units, references to SI units are shown in a regular font—e.g., energy cost savings per

square meter are $1.71—and references to conventional units are shown within

parentheses in Italics—e.g., (energy cost savings per square foot are $0.16).

8.3 Sensitivity Results

The results of the sensitivity analysis are summarized in a series of tables and figures.

Two sets of results are presented. The first set covers the case where each of the nine

input variables is varied singly. The first set of results is designed to show the effect of

each input on each of the economic measures. This is done by varying each input

variable singly while holding the other eight input variables at their baseline values.

These results are summarized in Tables 8-2 through 8-10. The second set covers the case

where all nine input variables are varied in combination. The second set of results is

designed to produce a data set that facilitates an in-depth analysis of the results, and

promotes an understanding of what these results mean. These results are summarized in

Tables 8-11 and 8-12 and in Figures 8-1 through 8-6. To facilitate comparisons among

each of the Monte Carlo simulations, Tables 8-2 through 8-11 use the same presentation

format. Table 8-12 summarizes in tabular form the results plotted in Figures 8-1 through

8-6.

8.3.1 Changing One Input

Tables 8-2 through 8-11 report a series of statistical measures for each economic

measure. To facilitate comparisons among the economic measures, a shorthand notation

for each is used. The present value of savings nationwide over the entire study period is

denoted by PVSALL. The present value of savings due to BFRL is denoted by PVSBFRL.

The present value of BFRL’s CBS-related investment costs is denoted by PVCBFRL. The

present value of net savings due to BFRL is denoted by PVNSBFRL. The savings-toinvestment ratio on BFRL’s CBS-related investments is designated by SIRBFRL. The

adjusted internal rate of return on BFRL’s CBS-related investments is designated by

AIRRBFRL. The statistical measure and its corresponding value are recorded under the

heading Statistical Measure. Seven statistical measures are reported to characterize the

results of each Monte Carlo simulation. The calculation of these statistical measures is

based on a “sample of 1,000 observations” produced by each Monte Carlo simulation.

These statistical measures are: (1) the minimum; (2) the 25th percentile, denoted by 25%;

(3) the 50th percentile (i.e., the median), denoted by 50%; (4) the 75th percentile, denoted

by 75%; (5) the maximum; (6) the mean; and (7) the standard deviation. The minimum

and the maximum define the range of values for the results from each of the Monte Carlo

simulations. The 50th percentile and the mean are measures of central tendency. The 25th

and 75th percentiles define the interquartile range, a range that includes the middle 50

percent of the observations. The interquartile range is also a crude measure of central
120

tendency. The standard deviation measures the variability of the results from each of the

Monte Carlo simulations. It is important to recognize that the values reported for

PVSALL, PVSBFRL, PVCBFRL, and PVNSBFRL are all in millions of 1997 dollars.

The results presented in Tables 8-2, 8-3, and 8-4 are related to the values of the

parameters in the diffusion model (see Section 6.4.4). Each parameter, ", $, and 0 is

analyzed in turn. Table 8-2 shows how variations about the baseline value for " (i.e., " =

6.0) affect each economic measure. The parameter " was selected for evaluation because

it is the location parameter for the diffusion model. The effect of " is as follows: lower

values of " produce a thicker tail immediately following the introduction of CBS

products and services into the marketplace (i.e., higher values of P0(t), whenever t is

small), whereas higher values of " produce a thinner tail (i.e., lower values of P0(t),

whenever t is small). Reference to Table 8-2 reveals that " exerts a strong effect on all

six of the economic measures. For example, the minimum value of PVSALL is only one

third of the highest value. Although the present value of savings nationwide is strongly

affected by changes in the value of ", the measures of BFRL’s influence are affected to a

far greater degree. The reason is due to the way in which BFRL’s influence is measured.

Because those savings occurring in the first seven years are attributable to BFRL, higher

values of " reduce these savings and lower values of " increase these savings over the

value calculated in the baseline analysis. For example, the minimum value of SIRBFRL is

one-seventh the highest value, and PVNSBFRL varies by a factor of 10. Note that

PVCBFRL is unaffected by changes in the value of ". Thus, the standard deviation for

PVCBFRL is 0.0. Consequently, the standard deviation for PVSBFRL and the standard

deviation for PVNSBFRL are equal (i.e., $55.4 million in 1997 dollars).

Table 8-2. Summary Statistics Due to Changes in the Input Variable Alpha

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 624.164 894.965 1,168.150 1,486.652 1,904.940 1,197.917 362.409

PVSBFRL 33.874 57.573 89.707 140.576 235.455 103.486 55.414

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 22.399 46.098 78.232 129.101 223.980 92.011 55.414

SIRBFRL 2.952 5.017 7.818 12.250 20.519 9.018 4.829

AIRRBFRL 0.117 0.141 0.162 0.183 0.207 0.162 0.025

Table 8-3 shows how variations about the baseline value for $ (i.e., $ = 0.6) affect each

economic measure. The parameter $ was selected for evaluation because it specifies the

rate of change for the diffusion model. The effect of $ is as follows: higher values of $

produce a higher rate of adoption of CBS products and services in the marketplace

immediately following the introduction of these products and services (i.e., higher values

of P0(t), whenever t is small), whereas lower values of $ produce a lower rate of adoption

(i.e., lower values of P0(t), whenever t is small). Reference to Table 8-3 reveals that $

exerts a moderate to strong effect on all six of the economic measures. For example, the
121

range of values for PVSALL is slightly in excess of $1.0 billion in 1997 dollars. Although

the present value of savings nationwide is strongly affected by changes in the value of $,

the measures of BFRL’s influence are affected to lesser degree than for changes in ".

The reason is due to the way in which BFRL’s influence is measured. Because those

savings occurring in the first seven years are attributable to BFRL, lower values of $

reduce these savings and higher values of $ increase these savings over the value

calculated in the baseline analysis. However, these differences are less than those

associated with ", because " affects the thickness of the lower tail of P0(t), whereas $

only affects the rate of change of the slope of the tail in the period immediately following

the introduction of CBS products and services. For example, the minimum value of

SIRBFRL is slightly less than half the highest value, and PVNSBFRL varies by a factor of

slightly less than 2.5. Note that PVCBFRL is unaffected by changes in the value of $.

Thus, the standard deviation for PVCBFRL is 0.0. Consequently, the standard deviation

for PVSBFRL and the standard deviation for PVNSBFRL are equal (i.e., $15.2 million in

1997 dollars).

Table 8-3. Summary Statistics Due to Changes in the Input Variable Beta

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 676.822 1,020.123 1,189.457 1,349.001 1,704.823 1,187.159 224.237

PVSBFRL 61.709 80.999 91.635 102.775 133.418 92.574 15.244

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 50.234 69.524 80.160 91.300 121.943 81.099 15.244

SIRBFRL 5.378 7.059 7.986 8.956 11.627 8.067 1.328

AIRRBFRL 0.144 0.157 0.163 0.168 0.180 0.163 0.008

Table 8-4 shows how variations about the baseline value for 0 (i.e., 0 = 0.1754) affect

each economic measure. The parameter 0 was selected for evaluation because it

specifies the level at which the market for CBS products and services saturates. The

effect of 0 is as follows: higher values of 0 produce a higher level of adoption of CBS

products and services in the marketplace towards the latter part of the study period (i.e.,

higher values of P0(t), for all values of t, especially whenever t is large), whereas lower

values of 0 produce a lower level of adoption (i.e., lower values of P0(t)). Reference to

Table 8-4 reveals that 0 exerts a moderate to strong effect on all six of the economic

measures. For example, the range of values for PVSALL (i.e., Maximum – Minimum)

exceeds $1.5 billion in 1997 dollars. Although the present value of savings nationwide is

strongly affected by changes in the value of 0, the measures of BFRL’s influence are

affected to a lesser degree than for changes in ". The reason, once again, is due to the

way in which BFRL’s influence is measured. Because those savings occurring in the first

seven years are attributable to BFRL, lower values of 0 reduce these savings and higher

values of 0 increase these savings over the value calculated in the baseline analysis.

However, these differences are less than those associated with ", because " affects the

thickness of the lower tail of P0(t), whereas 0 affects the level at which the market

saturates. Thus, the influence of 0 on the years immediately following the introduction
122

of CBS products and services is quite modest. Consequently, the range of values for the

measures of BFRL’s influence due to variations about the baseline value for 0 tend to be

wider than for $, but narrower than for ".

Table 8-4. Summary Statistics Due to Changes in the Input Variable Eta

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 377.648 953.668 1,187.391 1,468.240 2,025.147 1,199.016 359.822

PVSBFRL 29.144 73.596 91.633 113.306 156.284 92.530 27.768

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 17.669 62.121 80.158 101.831 144.809 81.055 27.768

SIRBFRL 2.540 6.414 7.985 9.874 13.619 8.064 2.420

AIRRBFRL 0.111 0.153 0.163 0.173 0.188 0.161 0.015

Table 8-5 shows how variations about the baseline value for the time of first use (i.e., t =

1 in the year 2003) affect each economic measure. The alternative times of first use are

specified by a discrete distribution (see Table 8-1). The discrete probabilities for each

year are: 2002, 0.125; 2003, 0.5; 2004, 0.25; and 2005, 0.125. The time of first use

affects primarily the present value of savings nationwide, PVSALL, since it determines the

number of years over which cost savings can accrue. This is because the end of the study

period is fixed at 2015. Thus, if the year of first use is 2005, there are fewer years over

which savings can accrue than for the baseline value (i.e., 2003). Notice that the

measures of BFRL’s influence are only slightly affected (i.e., the mean for each is

approximately equal to the value calculated in the baseline analysis and the standard

deviation for each is quite low). This is because BFRL’s contribution is measured in

terms of the savings occurring in the first seven years. The year of first use defines when

the seven-year period begins. Thus, the differences from the value calculated in the

baseline analysis are due to the timing of the savings, which, through the discount rate,

affects the value of PVSBFRL. Variations in the value of PVSBFRL are responsible for

variations in the values of PVNSBFRL, SIRBFRL, and AIRRBFRL.

Table 8-5. Summary Statistics Due to Changes in the Input Variable Time of First

Use

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 535.567 818.262 1,175.655 1,175.655 1,600.777 1,062.731 297.979

PVSBFRL 78.744 84.564 90.727 90.727 98.652 88.754 5.455

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 67.269 73.089 79.252 79.252 87.177 77.279 5.455

SIRBFRL 6.862 7.369 7.906 7.906 8.597 7.735 0.475

AIRRBFRL 0.156 0.159 0.162 0.162 0.166 0.161 0.003

Table 8-6 shows how variations about the baseline value for the length of the delay (i.e.,

seven years) affect each economic measure. The alternative numbers of years for the
123

length of the delay are specified by a discrete distribution (see Table 8-1). The discrete

probabilities for each length of delay are: 4 years, 0.016; 5 years, 0.047; 6 years, 0.187;

7 years, 0.5; 8 years, 0.187; 9 years, 0.047; and 10 years, 0.016. The length of delay only

affects the measures of BFRL’s influence. Thus, the computed value for PVSALL is equal

to the value calculated in the baseline analysis. Reference to Table 8-6 reveals that the

minimum value for each economic measure is lower than the corresponding minimum

value for each of the four variables examined previously, and the maximum values are

higher than the corresponding maximum value for each of the four variables examined

previously. Although the range of values for each measure of BFRL’s influence is quite

wide, in every case BFRL’s contribution is positive (i.e., PVSBFRL > 0.0, SIRBFRL > 1.0,

and AIRRBFRL > 0.07).

Table 8-6. Summary Statistics Due to Changes in the Input Variable Length of

Delay

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 1,175.655 1,175.655 1,175.655 1,175.655 1,175.655 1,175.655 0.0

PVSBFRL 14.522 90.727 90.727 90.727 407.606 103.894 63.411

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 3.047 79.252 79.252 79.252 396.131 92.419 63.411

SIRBFRL 1.266 7.906 7.906 7.906 35.521 9.054 5.526

AIRRBFRL 0.080 0.162 0.162 0.162 0.234 0.162 0.026

The results presented in Tables 8-7, 8-8, and 8-9 are related to the values of cost savings

per square meter (per square foot). Table 8-7 summarizes the results of the Monte Carlo

simulation of variations about the baseline value of energy cost savings of $1.71 per

square meter ($0.16 per square foot). Reference to Table 8-7 reveals moderate variations

about the values calculated in the baseline analysis for five of the six economic measures.

For example, the value calculated in the baseline analysis for the SIRBFRL is 7.9. In Table

8-7, the minimum value for the SIRBFRL is 6.031, and the maximum value is 9.759.

Table 8-7. Summary Statistics Due to Changes in the Input Variable Energy Cost

Savings Per Square Meter (Per Square Foot)

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 1,006.142 1,126.266 1,174.601 1,227.235 1,343.108 1,174.899 71.004

PVSBFRL 69.208 84.457 90.593 97.275 111.985 90.631 9.014

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 57.733 72.982 79.118 85.800 100.510 79.156 9.014

SIRBFRL 6.031 7.360 7.895 8.477 9.759 7.898 0.785

AIRRBFRL 0.150 0.159 0.162 0.166 0.172 0.162 0.005
124

Table 8-8 summarizes the results of the Monte Carlo simulation of variations about the

baseline value of maintenance cost savings of $1.60 per square meter ($0.15 per square

foot). Table 8-8 reveals moderate variations about the values calculated in the baseline

analysis for five of the six economic measures. This pattern is similar to the one resulting

from per unit energy cost savings. For example, the minimum value for the SIRBFRL is

6.122, and the maximum is 11.527. Note that the variability in these results is higher

than for per unit energy cost savings (compare the two sets of entries under the heading

Standard Deviation). This is because the triangular distribution used to model variations

in per unit maintenance cost savings is positively skewed. Thus, the mean and the

median for five of the six economic measures are higher (compare the two sets of entries

for the mean and median values of SIRBFRL) for per unit maintenance cost savings, since

there is a small probability of maintenance cost savings exceeding $3.00 per square meter

($0.28 per square foot).

Table 8-8. Summary Statistics Due to Changes in the Input Variable Maintenance

Cost Savings Per Square Meter (Per Square Foot)

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 1,008.428 1,154.812 1,234.276 1,321.336 1,514.990 1,240.999 112.832

PVSBFRL 70.251 88.175 97.905 108.565 132.277 98.728 13.816

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL 58.776 76.700 86.430 97.090 120.802 87.253 13.816

SIRBFRL 6.122 7.684 8.532 9.461 11.527 8.604 1.204

AIRRBFRL 0.150 0.161 0.166 0.171 0.180 0.166 0.007

Table 8-9 summarizes the results of the Monte Carlo simulation of variations about the

baseline value of productivity cost savings of $4.20 per square meter ($0.39 per square

foot). The results presented in Table 8-9 differ significantly from those presented in any

of the previous tables in two important ways. First, the present value of cost savings

nationwide, PVSALL, ranges from just over $250 million to nearly $2.1 billion in 1997

dollars. Thus, productivity cost savings are a key driver in estimating PVSALL. Second,

the minimum values of three of the key measures of BFRL’s influence (i.e., PVNSBFRL,

SIRBFRL, and AIRRBFRL

72) indicate that BFRL’s CBS-related investments may not be cost

effective. To place the previous remark in context, it is important to recognize that this

outcome corresponds to a few “low-probability” events. For example, the 25th percentile

value of SIRBFRL is 4.8, well above the minimum requirement of 1.0, and of AIRRBFRL is

13.9 percent, also well above the minimum requirement of 7 percent. How likely are

“uneconomic” outcomes is a subject that will be explored more fully when all nine input

variables are allowed to vary in combination.

 

72 The value of the AIRR is only defined for cases where the computed value of the SIR is non-negative. If

the computed value of SIRBFRL is negative, then the value of AIRRBFRL is listed as n.a.
125

Table 8-9. Summary Statistics Due to Changes in the Input Variable Productivity

Cost Savings Per Square Meter (Per Square Foot)

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 258.125 884.554 1,169.202 1,446.833 2,094.076 1,172.358 380.455

PVSBFRL -21.620 55.083 89.937 123.932 203.184 90.323 46.585

PVCBFRL 11.475 11.475 11.475 11.475 11.475 11.475 0.0

PVNSBFRL -33.095 43.608 78.462 112.457 191.709 78.848 46.585

SIRBFRL -1.884 4.800 7.838 10.800 17.706 7.871 4.060

AIRRBFRL n.a. 0.139 0.162 0.177 0.200 0.120 0.275

Table 8-10 shows how variations about the baseline value of the discount rate (7 percent

(real)) affect each economic measure. The discount rate affects calculations in a number

of ways. BFRL’s CBS-related investment costs, PVCBFRL, are affected by the discount

rate. The present value of savings nationwide, PVSALL, and the present value of savings

due to BFRL, PVSBFRL, are also affected by the discount rate. Reference to Table 8-10

reveals that PVSALL is more sensitive to changes in the discount rate than are the key

measures of BFRL’s influence. This is because savings do not begin until 2003, whereas

the base year is 1997. Thus, savings occurring in the out years (e.g., 2010 and beyond)

benefit from a lower discount rate and are penalized by a higher discount rate. This

explains the wide range in computed values for PVSALL, a range that exceeds $1.0 billion

in 1997 dollars. On the other hand, BFRL’s CBS-related investments are largely

clustered around 1997, and BFRL’s savings occur between 2003 and 2009 (i.e., much

earlier than the bulk of the savings used to calculate PVSALL). This explains why the key

measures of BFRL’s influence are less sensitive to changes in the discount rate than is

PVSALL.

Table 8-10. Summary Statistics Due to Changes in the Input Variable Discount

Rate

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 772.835 1,041.206 1,173.724 1,328.731 1,811.587 1,198.829 216.187

PVSBFRL 68.005 83.472 90.625 98.669 121.988 91.626 11.278

PVCBFRL 10.952 11.313 11.473 11.648 12.128 11.490 0.246

PVNSBFRL 57.053 72.159 79.152 87.021 109.860 80.136 11.031

SIRBFRL 6.209 7.379 7.899 8.471 10.058 7.957 0.805

AIRRBFRL 0.142 0.157 0.162 0.168 0.182 0.162 0.008
126

8.3.2 Changing All Nine Inputs in Combination

Table 8-11 summarizes the results of the Monte Carlo simulation in which all nine of the

input variables were varied in combination. Reference to Table 8-11 reveals that both the

present value of savings nationwide, PVSALL, and the present value of savings due to

BFRL, PVSBFRL, can be negative. This implies that there is some non-zero probability

that BFRL’s CBS-related investments are not cost effective. However, on the opposite

extreme, PVNSBFRL may reach nearly $1.4 billion in 1997 dollars, and SIRBFRL reaches

118.6.

The table also serves to highlight one of the merits of the median as a measure of central

tendency. Comparison between the median value of each economic measure, recorded

under the heading 50%, and the corresponding value calculated in the baseline analysis

reveals that the two sets of values are remarkably close. The value computed in the

baseline analysis for each economic measure (see Exhibit 7-1) is $1,175.6 million for

PVSALL, $90.7 million for PVSBFRL, $11.475 million for PVCBFRL, $79.3 million for

PVNSBFRL, 7.9 for SIRBFRL, and 0.162 for AIRRBFRL. The mean values for PVSALL,

PVSBFRL, PVNSBFRL, and SIRBFRL tend to exceed the corresponding value calculated in

the baseline analysis. This is because a small number of very large observations are

pulling up the computed value of the mean.

The fact that the range of outcomes is so wide suggests that an in-depth examination of

the results of this Monte Carlo simulation is warranted. We now turn to this in-depth

examination.

Table 8-11. Summary Statistics Due to Changes in All of the Input Variables

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL -2.503 611.492 1,168.482 1,552.634 5,820.787 1,215.738 822.988

PVSBFRL -29.834 40.649 90.886 172.463 1,402.408 136.714 157.107

PVCBFRL 10.943 11.285 11.459 11.652 12.128 11.471 0.252

PVNSBFRL -41.424 29.058 79.484 161.109 1,390.585 125.243 157.065

SIRBFRL -2.574 3.548 7.896 15.287 118.616 11.873 13.543

AIRRBFRL n.a. 0.128 0.161 0.193 0.291 0.161 0.305

The graphical results of the sensitivity analysis where all nine input variables were varied

in combination are shown in Figures 8-1 through 8-6. The figures were constructed by

first sorting the values of each economic measure from smallest to largest. The resultant

cumulative distribution function (CDF) was then plotted. In each figure, the vertical axis

records the probability that the economic measure (e.g., SIRBFRL) is less than or equal to a

specified value. The values recorded on the horizontal axis cover the range of values

encountered during this Monte Carlo simulation.
127

The tabular results of the sensitivity analysis are recorded in Table 8-12. The table lists

each of the calculated percentiles from the resultant CDF. The range of percentiles

included in the table go from the 1st to the 99th. For purposes of this analysis, the 0th

percentile is set equal to the minimum value, and the 100th percentile is set equal to the

maximum value. This enables a close coupling of the values recorded in Table 8-12 and

the values used to plot each figure.

Table 8-12 includes for each percentile the computed value for PVSALL, PVSBFRL,

PVCBFRL, PVNSBFRL, SIRBFRL, and AIRRBFRL. The percentiles are computed based on all

1,000 data points (i.e., observations) for each economic measure. The percentiles are

estimated by first ordering each economic measure and then applying a statistical

procedure. Readers interested in procedures for estimating percentiles are referred to the

text by Ott.73

Figure 8-1 shows how present value cost savings nationwide, PVSALL, varies when all

nine input variables are varied in combination. In analyzing Figure 8-1, it is useful to

keep in mind that the value of PVSALL resulting from the baseline analysis was $1,175.6

million. As was seen in Table 8-11, the median value of the 1,000 observations was

nearly equal to the value of PVSALL calculated in the baseline analysis. What the figure

shows clearly is the considerable degree to which PVSALL varies—both above and below

the median value.

To best understand the implications of these variations, it is useful to refer both to Figure

8-1 and the entries under the PVSALL column heading of Table 8-12. First, the lower

limit shown on Figure 8-1 is approximately $0.0. However, the 1st percentile for PVSALL

is $119.8 million. Thus, only 10 observations out of 1,000 are clustered between $0.0

and the 1st percentile ($119.8 million). Second, the CDF increases at a steady, almost

linear rate between the 3rd percentile ($228.6 million) and the 75th percentile ($1,552.6

million). Third, above the 75th percentile, the CDF increases at a decreasing rate. This is

shown by the way in which the CDF tails off as the calculated value of PVSALL gets

large. Finally, the maximum value of PVSALL is nearly $6.0 billion. However, the 99th

percentile is $3,791.3 million. Thus, only 10 observations out of 1,000 account for

approximately $2.0 billion in the total range of values for PVSALL. This implies that the

trace of the CDF for PVSALL is positively skewed.

 

73 Ott, Lyman. 1984. An Introduction to Statistical Methods and Data Analysis. Boston, MA: Duxbury

Press.
128

Figure 8-1. Present Value of Cost Savings Nationwide in Millions of 1997 Dollars

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

-500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000

Present Value of Cost Savings Nationwide in Millions of 1997 Dollars

Cumulative Probability
129

Table 8-12. Percentiles for Statistical Measures Due to Changes in All of the Input

Variables

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

1

ST 119.824 -5.622 10.995 -16.763 -0.501 n.a.

2

ND 183.241 1.006 11.013 -10.288 0.089 n.a.

3

RD 228.621 4.670 11.040 -6.482 0.419 0.031

4

TH 249.364 7.242 11.050 -4.436 0.624 0.049

5

TH 265.328 9.377 11.066 -1.945 0.828 0.065

6

TH 290.924 11.582 11.082 0.444 1.040 0.071

7

TH 307.471 12.919 11.096 1.526 1.134 0.080

8

TH 341.957 14.867 11.105 3.330 1.295 0.084

9

TH 350.749 16.641 11.123 4.999 1.432 0.088

10TH 360.897 19.252 11.133 7.587 1.647 0.092

11TH 383.994 20.474 11.151 9.158 1.810 0.097

12TH 407.786 22.311 11.167 11.010 1.985 0.100

13TH 415.719 24.015 11.181 12.716 2.128 0.104

14TH 435.314 24.852 11.189 13.510 2.194 0.108

15TH 444.027 26.539 11.197 15.109 2.319 0.110

16TH 459.366 27.603 11.205 16.334 2.436 0.112

17TH 481.689 28.875 11.212 17.412 2.540 0.114

18TH 499.720 31.340 11.221 19.772 2.748 0.115

19TH 519.309 33.203 11.234 21.778 2.893 0.117

20TH 537.852 34.394 11.244 22.839 2.984 0.118

21ST 552.953 35.106 11.250 23.769 3.121 0.120

22ND 572.247 36.597 11.262 25.291 3.212 0.122

23RD 587.972 38.047 11.275 26.590 3.325 0.124

24TH 599.569 39.165 11.278 27.938 3.429 0.125

25TH 611.492 40.649 11.285 29.058 3.548 0.128

26TH 621.346 41.785 11.294 30.432 3.634 0.131

27TH 632.027 43.900 11.301 32.464 3.788 0.133

28TH 645.426 45.391 11.308 33.777 3.940 0.134

29TH 662.137 46.956 11.314 35.633 4.083 0.135

30TH 674.850 49.329 11.322 38.056 4.327 0.136

31ST 700.585 51.820 11.330 40.413 4.546 0.137

32ND 726.674 53.082 11.336 41.729 4.667 0.139

33RD 744.583 55.060 11.341 43.824 4.753 0.140

Economic Measure

Percentile
130

Table 8-12. Percentiles for Statistical Measures Due to Changes in All of the Input

Variables (continued)

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

34TH 764.498 56.212 11.347 44.613 4.949 0.141

35TH 786.522 57.707 11.354 46.088 5.061 0.143

36TH 816.554 59.391 11.361 47.958 5.179 0.143

37TH 832.884 61.827 11.369 50.242 5.363 0.144

38TH 851.606 63.081 11.374 51.513 5.512 0.146

39TH 865.486 65.212 11.381 53.720 5.705 0.146

40TH 879.298 68.613 11.387 57.464 5.949 0.147

41ST 890.950 70.302 11.394 58.765 6.147 0.148

42ND 917.081 71.690 11.399 60.203 6.257 0.150

43RD 924.849 74.395 11.405 62.774 6.411 0.151

44TH 947.165 76.049 11.416 64.455 6.622 0.152

45TH 955.129 79.162 11.422 67.755 6.934 0.153

46TH 974.491 81.761 11.430 70.236 7.168 0.154

47TH 993.461 85.661 11.442 74.181 7.429 0.156

48TH 1,016.152 87.430 11.448 76.013 7.574 0.157

49TH 1,036.961 88.958 11.454 77.690 7.751 0.159

50TH 1,048.482 90.886 11.459 79.484 7.896 0.161

51ST 1,070.888 91.926 11.465 80.348 8.045 0.162

52ND 1,087.093 94.554 11.473 82.742 8.150 0.165

53RD 1,104.631 96.329 11.477 85.184 8.409 0.166

54TH 1,120.000 99.246 11.486 87.704 8.672 0.167

55TH 1,137.351 101.390 11.497 89.943 8.864 0.169

56TH 1,154.523 104.005 11.503 92.563 9.134 0.171

57TH 1,175.424 107.686 11.512 96.026 9.295 0.172

58TH 1,185.143 109.186 11.516 97.810 9.608 0.173

59TH 1,213.544 112.739 11.525 101.168 9.809 0.175

60TH 1,231.675 115.968 11.534 104.512 10.134 0.176

61ST 1,252.945 120.408 11.539 109.143 10.491 0.177

62ND 1,269.792 122.643 11.548 111.249 10.716 0.178

63RD 1,287.812 126.465 11.551 114.951 11.031 0.179

64TH 1,306.804 128.389 11.559 116.953 11.223 0.180

65TH 1,337.499 130.092 11.565 118.690 11.390 0.181

66TH 1,351.516 133.183 11.572 121.630 11.564 0.182

Percentile

Economic Measure
131

Table 8-12. Percentiles for Statistical Measures Due to Changes in All of the Input

Variables (continued)

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

67TH 1,370.683 135.565 11.580 124.204 11.924 0.183

68TH 1,389.332 140.734 11.589 129.166 12.282 0.185

69TH 1,414.330 143.574 11.596 132.248 12.558 0.186

70TH 1,432.018 148.869 11.608 136.973 12.980 0.187

71ST 1,447.771 154.754 11.618 143.200 13.373 0.189

72ND 1,474.278 157.668 11.626 145.912 13.694 0.190

73RD 1,509.997 161.873 11.632 150.406 14.176 0.191

74TH 1,533.073 169.291 11.643 157.847 14.740 0.192

75TH 1,552.634 172.463 11.652 161.109 15.287 0.193

76TH 1,585.860 180.111 11.661 168.519 15.514 0.195

77TH 1,618.044 183.912 11.673 172.314 16.018 0.196

78TH 1,649.848 188.075 11.680 176.429 16.541 0.197

79TH 1,676.267 193.287 11.689 181.738 16.843 0.198

80TH 1,733.399 201.952 11.696 189.959 17.446 0.199

81ST 1,801.257 209.839 11.719 198.253 18.278 0.201

82ND 1,851.337 216.075 11.728 204.375 18.744 0.204

83RD 1,899.135 226.909 11.742 215.194 19.369 0.206

84TH 1,929.266 232.870 11.752 221.234 20.188 0.207

85TH 1,988.531 248.407 11.759 237.188 21.603 0.209

86TH 2,045.326 256.050 11.763 244.457 22.376 0.212

87TH 2,123.945 266.114 11.770 254.397 23.117 0.213

88TH 2,189.905 274.141 11.786 263.011 23.876 0.216

89TH 2,262.602 289.058 11.802 277.835 25.190 0.218

90TH 2,352.084 304.969 11.815 293.024 26.128 0.221

91ST 2,433.249 328.940 11.832 317.273 28.522 0.223

92ND 2,502.007 343.558 11.849 332.349 29.688 0.227

93RD 2,605.023 362.317 11.863 350.430 31.222 0.228

94TH 2,721.486 376.727 11.891 365.176 32.887 0.234

95TH 2,867.994 419.699 11.903 408.382 37.087 0.237

96TH 2,992.995 475.669 11.920 464.117 41.140 0.242

97TH 3,207.017 513.572 11.950 502.186 43.850 0.246

98TH 3,382.433 567.612 11.987 556.228 49.858 0.253

99TH 3,791.309 799.278 12.035 787.434 67.483 0.269

Percentile

Economic Measure
132

Figure 8-2 shows how present value savings due to BFRL, PVSBFRL, varies when all nine

input variables are varied in combination. In analyzing Figure 8-2, it is useful to keep in

mind that the value of PVSBFRL resulting from the baseline analysis was $90.7 million.

As was seen in Table 8-11, the median value of the 1,000 observations was nearly equal

to the value of PVSBFRL calculated in the baseline analysis. Figure 8-2 exhibits a pattern

similar to the pattern seen in Figure 8-1. There is, however, one important difference

between the traces of the CDFs in the two figures. Figure 8-2 is much more positively

skewed than Figure 8-1 (compare the upper tails of the two CDF traces).

To best understand the implications of these variations, it is useful to refer both to Figure

8-2 and the entries under the PVSBFRL column heading of Table 8-12. First, note that the

lower limit shown on Figure 8-2 extends below $0.0. Reference to Table 8-12 reveals

that the 1st percentile for PVSBFRL is still negative (-$5.6 million). However, by the 2nd

percentile, the computed value of PVSBFRL becomes positive ($1.0 million). Thus, less

than 20 observations out of 1,000 are negative. Stated another way, there is at least a 98

percent probability that BFRL’s CBS-related efforts will produce positive and

measurable cost savings to building owners, managers, and occupants. Second, the CDF

increases at a steady, almost linear rate between the 4th percentile ($7.2 million) and the

60th percentile ($116.0 million). Third, above the 60th percentile, the CDF increases at a

decreasing rate. This is shown by the way in which the CDF tails off as the calculated

value of PVSBFRL gets large. Finally, the maximum value of PVSBFRL is approximately

$1.4 billion. However, the 99th percentile is $799.3 million. Thus, only 10 observations

out of 1,000 account for approximately $600 million in the total range of values for

PVSBFRL.

Figure 8-3 shows how the present value of BFRL’s CBS-related investment costs,

PVCBFRL, varies when all nine input variables are varied in combination. Because the

only variable that produces variations in PVCBFRL is the discount rate, the shape of Figure

8-3 differs from the shapes of all of the other figures presented in this section.

Reference to the figure and to Tables 8-11 and 8-12 reveals that the CDF for PVCBFRL is

very nearly symmetric. From Table 8-11 we see that the mean and median are nearly

equal, the standard deviation is quite small, and the differences between the maximum

and minimum values and the median are nearly equal. An examination of Figure 8-3

reveals that the trace of the CDF does not exhibit the same, sharp tailing off as was seen

in the other figures. In fact, the trace of the CDF for PVCBFRL is nearly linear between

the 20th percentile ($11.244 million) and the 80th percentile ($11.696). Below the 20th

percentile and above the 80th percentile the traces exhibit very similar patterns of nonlinearity.
133

Figure 8-2. Present Value of Cost Savings Attributable to BFRL in Millions of 1997 Dollars

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

-100 0 100 200 300 400 500 600 700 800 900 1,000 1,100 1,200 1,300 1,400 1,500

Present Value of Cost Savings Attributable to BFRL in Millions of 1997 Dollars

Cumulative Probability
134

Figure 8-3. Present Value of BFRL’s Investment Costs in Millions of 1997 Dollars

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

10.8 11.0 11.2 11.4 11.6 11.8 12.0 12.2

Present Value of BFRL's Investment Costs in Millions of 1997 Dollars

Cumulative Probability
135

Figure 8-4 shows how present value net savings due to BFRL, PVNSBFRL, varies when all

nine input variables are varied in combination. In analyzing Figure 8-4, it is useful to

keep in mind that the value of PVNSBFRL resulting from the baseline analysis was $79.3

million. As was seen in Table 8-11, the median value of the 1,000 observations was

nearly equal to the value of PVNSBFRL calculated in the baseline analysis. Also, Figure

8-4 exhibits a pattern similar to the pattern seen in Figure 8-2. Note that both Figure 8-2

and Figure 8-4 are highly, positively skewed (compare the upper tails of the two CDF

traces). In addition, both figures are defined over nearly identical ranges of values. This

similarity is to be expected since the only difference between PVSBFRL (see Figure 8-2)

and PVNSBFRL is PVCBFRL (see Figure 8-3). Recall that PVCBFRL was defined over a

very narrow range of values, and was also symmetric. Thus, throughout the range of

values over which PVSBFRL and PVNSBFRL are defined, the value of PVCBFRL acts very

much like a constant term.

As was the case for the previous figures, it is useful to refer both to Figure 8-4 and the

entries under the PVNSBFRL column heading of Table 8-12. First, note that the lower

limit shown on Figure 8-4 extends below $0.0. Reference to Table 8-12 reveals that the

1

st percentile for PVNSBFRL is still negative (-$16.8 million). However, by the 6th

percentile, the computed value of PVNSBFRL becomes positive ($0.4 million). Thus, less

than 60 observations out of 1,000 are negative. Stated another way, there is at least a 94

percent chance that BFRL’s CBS-related investments are cost effective. Second, the

CDF increases at a steady, almost linear rate between the 10th percentile ($7.6 million)

and the 50th percentile ($79.5 million). Third, above the 50th percentile, the CDF

increases at a decreasing rate. This is shown by the way in which the CDF tails off as the

calculated value of PVNSBFRL gets large. Finally, the maximum value of PVNSBFRL is

nearly $1.4 billion. However, the 99th percentile is $787.4 million. Thus, only 10

observations out of 1,000 account for approximately $600 million in the total range of

values for PVNSBFRL.

Because there are so many similarities—very low values, very high values, and the CDF

traces—between the results of the sensitivity analysis for PVSBFRL and PVNSBFRL, it is

useful to analyze the underlying characteristics of both the upper and lower tails of the

two CDF traces. This analysis was facilitated through the use of the @RISK software

product. Specifically, the @RISK software product enables the random draw for each

input variable for each of the 1,000 simulations to be stored in a spreadsheet. This

produces a one-to-one correspondence between each simulation’s input set and the

resultant values of the economic measures.

As might be expected, the factors that contribute to very low values of PVSBFRL and

PVNSBFRL differ from those that contribute to very high values of PVSBFRL and

PVNSBFRL. Consider first the very low values of PVSBFRL and PVNSBFRL. Two factors

contribute to very low values of PVSBFRL and PVNSBFRL. One factor, the level of

productivity cost savings, is always present. For this case, the level of productivity cost

savings was always less than or equal to the 10th percentile of the parent probability

distribution. The second factor was more complex; it operated in combination with the

first. The second factor involved four different possible combinations. These
136

combinations resulted from either (1) per unit energy cost savings less than or equal to

the 10th percentile, (2) maintenance cost savings less than or equal to the 10th percentile,

(3) a value of " greater than or equal to the 90th percentile, or (4) a value of $ less than or

equal to the 10th percentile. Very high values resulted from either a 10-year delay or a

combination of a delay of at least seven years and high values for productivity savings, $,

or 0, and relatively low values of ". For example, for the 50 highest values (i.e., the top

5 percent of the 1,000 observations), productivity savings exceeded their 90th percentile

14 times, $ exceeded its 90th percentile 11 times, and 0 exceeded its 90th percentile 14

times. In addition, the value of " was less than the baseline value, 6.0, 47 times.

Figure 8-5 shows how the savings-to-investment ratio on BFRL’s CBS-related

investments, SIRBFRL, varies when all nine input variables are varied in combination. In

analyzing Figure 8-5, it is useful to keep in mind that the value of SIRBFRL resulting from

the baseline analysis was 7.9. As was seen in Table 8-11, the median value of the 1,000

observations was nearly equal to the value of SIRBFRL calculated in the baseline analysis.

Also, Figure 8-5 exhibits a pattern similar to the pattern seen in Figure 8-2. Note that

both Figure 8-5 and Figure 8-2 are highly, positively skewed (compare the upper tails of

the two CDF traces). This similarity in shapes is to be expected since SIRBFRL is the ratio

of PVSBFRL to PVCBFRL. Recall that PVCBFRL was defined over a very narrow range of

values, and was also symmetric. Thus, the value of PVCBFRL acts very much like a

constant term. Although the shapes of the two distributions are similar, the ranges of

values are specified in different units.

As was the case for the previous figures, it is useful to refer both to Figure 8-5 and the

entries under the SIRBFRL column heading of Table 8-12. First, note that the lower limit

shown on Figure 8-5 extends below 0.0. Reference to Table 8-12 reveals that the 1st

percentile for SIRBFRL is negative (-0.501). However, by the 6th percentile, the computed

value of SIRBFRL exceeds 1.0 (1.040). Thus, less than 60 observations out of 1,000 are

less than 1.0. Stated another way, based on the calculated value of SIRBFRL, there is at

least a 94 percent chance that BFRL’s CBS-related investments are cost effective.

Second, the CDF increases at a steady, almost linear rate between the 10th percentile

(1.647) and the 50th percentile (7.896). Third, above the 50th percentile, the CDF

increases at a decreasing rate. This is shown by the way in which the CDF tails off as the

calculated value of SIRBFRL gets large. Finally, the maximum value of SIRBFRL is nearly

120. However, the 99th percentile is 67.483. Thus, only 10 observations out of 1,000

account for approximately 50 units in the total range of values for SIRBFRL.

Figure 8-6 shows how the adjusted internal rate of return on BFRL’s CBS-related

investments, AIRRBFRL, varies when all nine input variables are varied in combination.

In analyzing Figure 8-6, it is useful to keep in mind that the value of AIRRBFRL resulting

from the baseline analysis was 0.162. As was seen in Table 8-11, the median value of the

1,000 observations was nearly equal to the value of AIRRBFRL calculated in the baseline

analysis. Figure 8-6 exhibits a pattern different from those seen in the other figures.

Note that Figure 8-6 is negatively skewed (compare the lower and upper tails of the CDF

trace). Although the values for AIRRBFRL are a monotonic transformation of the values

for SIRBFRL, the shapes of the two CDFs are quite dissimilar. This is because the
137

AIRRBFRL is functionally related to (SIRBFRL)

1/25. This relationship is highly non-linear,

explaining why the two CDF traces are so dissimilar.

As was the case for the previous figures, it is useful to refer both to Figure 8-6 and the

entries under the AIRRBFRL column heading of Table 8-12. First, note that the lower

limit shown on Figure 8-6 is 0.0. This is because a value of AIRRBFRL less than 0.0 has

no economic meaning. In addition, to calculate AIRRBFRL for values of SIRBFRL less than

0.0 involves taking the root of a negative number, a process that is only defined when the

length of the study period in years is an odd number. Both such cases are designated by

the term n.a. in Tables 8-11 and 8-12. Reference to Table 8-12 reveals that the 1st and 2nd

percentiles for AIRRBFRL are n.a. These entries are reflected by the “step-up” in the CDF

at 0.0. By the 6th percentile, the computed value of AIRRBFRL exceeds the seven-percent

real discount rate. Thus, less than 60 observations out of 1,000 produce a value for the

AIRRBFRL less than the discount rate. Stated another way, based on the calculated value

of AIRRBFRL, there is at least a 94 percent chance that BFRL’s CBS-related investments

are cost effective. Second, the CDF increases at a steady, almost linear rate between the

15th percentile (0.110) and the 85th percentile (0.209). Third, below the 15th percentile,

the CDF increases at an increasing rate. Finally, above the 85th percentile, the CDF

increases at a decreasing rate.

Note that the values of PVNSBFRL, SIRBFRL, and AIRRBFRL all indicated the regions of the

appropriate CDF trace where BFRL’s CBS-related investments were cost effective. In

each case these economic measures defined the same break-even point in each of the

CDF traces. The break-even point corresponds to a value of each economic measure just

below the 6th percentile of its CDF. This point is noteworthy, since each measure

provides a different perspective, but produces the same end result in terms of identifying

the break-even point.
138

Figure 8-4. Present Value of Net Savings Attributable to BFRL in Millions of 1997 Dollars

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

-100 0 100 200 300 400 500 600 700 800 900 1,000 1,100 1,200 1,300 1,400

Present Value of Net Savings Attributable to BFRL in Millions of 1997 Dollars

Cumulative Probability
139

Figure 8-5. Savings to Investment Ratio on BFRL’s Research and Development Investment

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

-10 0 10 20 30 40 50 60 70 80 90 100 110 120

Savings to Investment Ratio on BFRL's Research and Development Investment

Cumulative Probability
140

Figure 8-6. Adjusted Internal Rate of Return on BFRL’s Research and Development Investment

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30

Adjusted Internal Rate of Return on BFRL's Research and Development Investment

Cumulative Probability
141

9 Summary and Suggestions for Further Research

9.1 Summary

A formal resource allocation process for funding research is needed in both the public

and private sectors. Research managers need guidelines for research planning so that

they can maximize the payoffs from their limited resources. Furthermore, quantitative

descriptions of research impacts have become a basic requirement in many organizations

for evaluating budget requests.

There are several reasons for measuring the economic impacts of a federal laboratory’s

research program. First, economic impact studies are a management tool; they help set

priorities and point to new research opportunities. Second, as federal laboratories

become more customer oriented, by revealing the “voice of the customer,” such studies

will strengthen the ties to industry and identify opportunities for leveraging federal

research investments. Finally, changing requirements, such as the Government

Performance and Results Act, will affect how federal research funds are allocated.

Increasingly, federal agencies and laboratories that fail to demonstrate that their research

efforts complement those of industry and that they are having a positive impact on

society will be at a disadvantage when competing for federal research funds.

The National Institute of Standards and Technology (NIST), a scientific research agency

of the U.S. Department of Commerce’s Technology Administration, is improving its

resource allocation process by doing “microstudies” of its research impacts on society.

This report is the third in a series of microstudies prepared by NIST’s Building and Fire

Research Laboratory (BFRL).74, 75 It focuses on a critical analysis of the economic

impacts of past, ongoing, and planned BFRL research for developing and deploying

cybernetic building systems (CBSs) in office buildings.

The CBS research, development, and deployment effort within BFRL is aimed at

producing a suite of products that integrate a wide variety of building systems. Building

systems targeted for incorporation into CBS products and services include energy

management, fire and security, fault detection and diagnostics, the real-time purchase of

electricity, and the aggregation of building stock for multi-facility operations. A CBS is

defined as a multi-system configuration that is able to communicate information and

control functions simultaneously and seamlessly at multiple levels. Pressure to increase

building systems performance and reduce costs has created a potential market for CBS

 

74 The first report focuses on two building technology applications: (1) ASHRAE Standard 90-75 for

residential energy conservation; and (2) 235 shingles, an improved asphalt shingle for sloped roofing. See

Chapman, Robert E., and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two Case Studies in

Building Technology. NSTIR 5840. Gaithersburg, MD: National Institute of Standards and Technology.

75 The second report focuses on a fire technology application: the Fire Safety Evaluation System for health

care facilities. See Chapman, Robert E., and Stephen F Weber. 1996. Benefits and Costs of Research: A

Case Study of the Fire Safety Evaluation System. NSTIR 5863. Gaithersburg, MD: National Institute of

Standards and Technology.
142

products and services. BFRL is collaborating with industry on the development of CBS

products and services and is providing a forum for conducting interoperability testing.

This case study of BFRL’s CBS-related research, development, and deployment effort

illustrates how to apply in practice a series of standardized methods, referred to as

economic measures, to evaluate and compare the economic impacts of alternative

research investments. It is presented in sufficient detail to understand the basis for the

economic impact assessment and to reproduce the results. It is based on past, ongoing,

and planned research efforts. Thus, it includes CBS-related investment costs that have

already occurred along with estimates of future investment costs and cost savings due to

the use of CBS products and services.

The CBS economic impact assessment was carried out in two stages. In the first stage, a

baseline analysis was performed. In the baseline analysis, all input variables used to

calculate the economic measures are set at their likely values. It is important to recognize

that the term baseline analysis is used to denote a complete analysis in all respects but

one; it does not address the effects of uncertainty. In the second stage, nine input

variables were varied both singly and in combination according to an experimental

design. Monte Carlo simulations are employed to evaluate how changing the value of

these variables affects the calculated values of the economic measures.

The results of the baseline analysis demonstrate that the use of CBS products and services

will generate substantial cost savings to the owners, managers, and occupants of office

buildings across the nation. The present value of cost savings nationwide expected from

the use of CBS products and services in office buildings exceeds $1.1 billion ($1,176

million in 1997 dollars). Furthermore, because of BFRL’s role as a facilitator and

developer of key CBS enabling technologies, CBS products and services are expected to

become available commercially in 2003. Without BFRL’s participation, the commercial

introduction of CBS products and services would likely be delayed until 2010, and

potential cost savings accruing to the owners, managers, and occupants of office

buildings over the period 2003 until 2010 would have been foregone. These cost savings

are $90.7 million in 1997 dollars. These cost savings measure the return on BFRL’s

CBS-related investment costs of approximately $11.5 million. Stated in present value

terms, every public dollar invested in BFRL’s CBS-related research, development, and

deployment effort generates $7.90 in cost savings to the public. The annual percentage

yield from BFRL’s CBS-related investments over the 25-year study period is 16.2

percent.

The objective of the sensitivity analysis was to evaluate how uncertainty in the values of

each of the nine input variables, both singly and in combination, translates into changes

in each of the six economic measures. The six economic measures evaluated in the

sensitivity analysis are: (1) the present value of savings nationwide, PVSALL; (2) the

present value of savings due to BFRL, PVSBFRL; (3) the present value of BFRL’s CBSrelated investment costs, PVCBFRL; (4) the present value of net savings due to BFRL,

PVNSBFRL; (5) the savings-to-investment ratio on BFRL’s CBS-related investments,

SIRBFRL; and (6) the adjusted internal rate of return on BFRL’s CBS-related investments,
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AIRRBFRL. The major advantage of the sensitivity analysis is that it produces results that

can be tied to probabilistic levels of significance for each economic measure (e.g., the

probability that PVNSBFRL is greater than or equal to zero, SIRBFRL is greater than or

equal to 1.0, or AIRRBFRL is greater than or equal to the discount rate, each of which

would indicate that BFRL’s CBS-related investments were cost effective).

The results of the sensitivity analysis serve to validate the results of the baseline analysis.

For example, the Monte Carlo simulation in which all nine of the input variables were

varied in combination produced 1,000 observations for each of the six economic

measures. The median value for each economic measure was almost identical to the

value calculated in the baseline analysis for that measure. Results from this Monte Carlo

simulation reveal that the present value of net savings due to BFRL, PVNSBFRL, can be

negative. This implies that there is some non-zero probability that BFRL’s CBS-related

investments are not cost effective. However, on the opposite extreme, PVNSBFRL may

reach nearly $1.4 billion in 1997 dollars.

The fact that the range of values for an economic measure is so wide prompted an indepth examination of the results of this Monte Carlo simulation for three of the six

economic measures. These measures are particularly helpful in understanding BFRL’s

contribution, since each measure provides a different perspective. The first, the present

value of net savings due to BFRL, is a magnitude measure; it shows a dollar value to the

public net of BFRL’s CBS-related investments. The second, the savings-to-investment

ratio on BFRL’s CBS-related investments, is a multiplier; it shows, in present value

terms, how many dollars the public receives for each public dollar spent. The third, the

adjusted internal rate of return on BFRL’s CBS-related investments, is a rate of return; it

shows the annual return on the public monies going into the development of CBS

products and services throughout the 25-year study period.

For each of the three economic measures, less than 60 observations out of 1,000 were

responsible for the observed “uneconomical” outcome. Stated another way, there is at

least a 94 percent chance that BFRL’s CBS-related investments are cost effective. This

underscores the importance of using multiple measures that ensure consistency in

decision making.

9.2 Suggestions for Further Research

The background work for this report uncovered additional areas of research that might be

of value to government agencies and other institutions that are concerned with an

efficient allocation of their research budgets. These areas of research are concerned with:

(1) the development of a standard classification of research benefits and costs; (2) factors

affecting the diffusion of new technologies; (3) conducting ex ante evaluations with

scheduled follow-ups; and (4) evaluations based on multiattribute decision analysis.
144

9.2.1 The Development of a Standard Classification of Research Benefits and Costs

A survey by the Civil Engineering Research Foundation shows that expenditures for

research and development efforts in the areas of construction, building, and disaster

mitigation technologies were over $2.1 billion in 1992.76 Private industry, trade

association, university, and government research bodies would like to know what are the

economic impacts of these investments. The standardized evaluation methods employed

in this report are appropriate for measuring these economic impacts. However, there is

no systematic and comprehensive classification of research benefits and costs to guide

analysts who must identify the benefits and costs associated with new construction,

building, and disaster mitigation technologies that are used in these standardized

evaluation methods. Such a classification, if developed, refined, and adopted as a

standardized classification, could be used in several ways.77 First, the classification will

help researchers and research managers identify potential benefits and costs associated

with candidate research projects and thereby help them choose those with maximum net

benefits (maximum net savings). Second, the classification will provide a standardized

basis for identifying benefits and costs in research proposals. Finally, the classification

will make possible a consistent treatment of benefits and costs in ex ante evaluations of

new technologies and in ex post evaluations of completed building- and fire-related

research projects.

9.2.2 Factors Affecting the Diffusion of New Technologies

Reliable estimates of the data input values for the standardized evaluation methods

cannot be made without some relatively sound basis for predicting the rate of diffusion

and the ultimate level of adoption of a new technology. The rate of diffusion and the

ultimate level of adoption of a new technology depend on many factors. Uncertainty

about how a new technology will perform affects both its rate of diffusion and its

ultimate level of adoption.

Two factors over which a research laboratory exerts some control and which have the

potential to reduce uncertainty about new technologies are: (1) the research laboratory’s

information dissemination efforts; and (2) the research laboratory’s participation in

standards-making organizations. Additional research on these two factors is warranted

for a number of reasons. First, the characteristics of information are changing

dramatically. With the advent of the World Wide Web and the increased acceptance of

electronic media, the fruits of research may be quickly and widely disseminated. The

reliance on printed reports sent to a targeted audience as the sole vehicle for

communication is being eclipsed by other means of information dissemination. This

 

76 Civil Engineering Research Foundation. 1993. A Nationwide Survey of Civil Engineering-Related R&D.

Report no. 93-5006. Washington, DC: Civil Engineering Research Foundation.

77 Although the standardized classification would be focused on identifying benefits and costs associated

with building- and fire-related research projects, it would be generic to the extent that scientific research in

general produces types of benefits and costs that are similar across technology areas. Thus the standardized

classification will be applicable to many non-building- and non-fire-related technologies as well.
145

transition needs to be studied to ensure that the information dissemination strategy that

emerges is tailored to the needs of the research laboratory’s customer base. Second,

research results in the form of technical reports often provide the basis for standards.

Consequently, information dissemination efforts may be used to leverage private-sector

activities aimed at standardization. Finally, standards are an important means for

disseminating information on expected levels of performance and for measuring key

performance characteristics (e.g., through the use of standard practices, specifications,

and test methods). For new technologies, acceptance by a standards-making organization

should lead both to higher rates of diffusion and to higher levels of adoption.

Consequently, research on how a research laboratory’s participation in standards-making

organizations (e.g., those concerned with building codes and standards) affects the rates

of diffusion and levels of adoption of new technologies will enable it to improve the

efficiency with which it allocates staff and other resources to these activities.

9.2.3 Conducting Ex Ante Evaluations with Scheduled Follow-ups

From an analysis perspective, an ex ante evaluation of a new technology poses several

challenges which are absent in an ex post evaluation of a completed research project. The

biggest challenge involves the diffusion of a new technology (i.e., predicting the rate of

diffusion and the ultimate level of adoption). Although two of the factors affecting the

diffusion of a new technology were discussed in the previous suggestion for further

research, much can be learned about the diffusion process by performing ex ante

evaluations with the understanding that scheduled follow-up evaluations will be

conducted.

The follow-up evaluation focuses on answering several key questions. These questions

are aimed at learning more about the research laboratory’s role and ability to move

research results towards the market place and about the way in which firms and

households (i.e., the intended users of the new technology) adopt and make use of the

new technology. First, did the new technology become available to the intended users

when anticipated in the ex ante evaluation? Second, is the new technology being adopted

at the rate anticipated? Third, are the users that adopt the new technology experiencing

the types of changes anticipated (e.g., cost savings, increased durability, and increased

reliability)? Finally, are the types of users that adopt the new technology the same as

anticipated? If these questions are asked and the answers are reviewed, critiqued, and fed

back to research managers, ex ante evaluations will become a key link in the research

laboratory’s continuous improvement efforts.

Because ex ante evaluations are more complex than ex post evaluations, it is not always

possible to quantify all of the relevant benefits and costs going into the evaluation. Such

was the case in this economic impact assessment. Specifically, estimates of the cost

savings due to enhanced fire safety performance and estimates of the cost savings due to

the ability to respond to real-time electricity price changes and to aggregate building

stock for multi-facility operations are not included. Similarly, estimates of the

investments required to develop, test, and market CBS products and services by the

vendor tier (see Figure 3-1) are not included. These challenges and others are being
146

addressed through the design and creation of a database for compiling information on

CBS-related impacts. This database is currently under development by BFRL’s Office of

Applied Economics (OAE). Once the database is in place, OAE will monitor outcomes

and compile information on CBS-related impacts in preparation for the follow-up CBS

economic impact assessment.

9.2.4 Evaluations Based on Multiattribute Decision Analysis

Many research investment alternatives differ in characteristics that decision makers

consider important but that are not readily expressed in monetary terms. Because the

standardized evaluation methods employed in this report consider only monetary benefits

and monetary costs associated with alternative research investments, their application

does not reflect the importance of these non-financial characteristics to the decision

maker. When non-financial characteristics are important, decision makers need a method

that accounts for these characteristics (also called attributes) when choosing among

alternative research investments. A class of methods that can accommodate nonmonetary benefits and costs is multiattribute decision analysis.78

The analytical hierarchy process (AHP) is one of a set of multiattribute decision analysis

methods that considers non-financial characteristics in addition to common economic

evaluation measures when evaluating project alternatives. The AHP has several

important strengths: (1) it is well-known and well-reviewed in the literature; (2) it

includes an efficient attribute weighting process; (3) it incorporates hierarchical

descriptions of attributes; (4) its use is facilitated by available software; and (5) it has

been accepted by ASTM as a standard practice for investments related to buildings and

building systems.79

The AHP and its associated software represent a powerful and versatile management tool.

How to apply this management tool most productively in a research environment

suggests additional research in two areas. First, what will be the relationship between the

AHP software and the standard classification proposed earlier? Second, how will the

AHP be used to assess fit to mission, to set priorities, or to evaluate performance against

some other management goal?

If research is conducted on the two topics just outlined, the AHP-based tool which

emerges will provide a format for: (1) efficiently and reliably screening and selecting

among alternative research investments (e.g., by embedding information from the

standard classification of research benefits and costs, information on fit to mission, and

on research priorities); (2) selecting research projects for in-depth analyses, either of the

ex ante or ex post type of evaluation; and (3) selecting and scheduling follow-up

evaluations.

 

78 For more information on multiattribute decision analysis, see Norris, Gregory A., and Harold E.

Marshall. 1995. Multiattribute Decision Analysis Method for Evaluating Buildings and Building Systems.

NISTIR 5663. Gaithersburg, MD: National Institute of Standards and Technology.

79 American Society for Testing and Materials. 1998. Standard Practice for Applying Analytical

Hierarchy Process (AHP) to Multiattribute Decision Analysis of Investments Related to Buildings and

Building Systems. E 1765. Philadelphia, PA: American Society for Testing and Materials.
147

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