Technical Report

NREL/TP-6A2-44428

Revised March 2009

Life Cycle Assessment of the 

Use of Jatropha Biodiesel in 

Indian Locomotives

Michael Whitaker

Symbiotic Engineering

Garvin Heath

National Renewable Energy Laboratory
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-44428

Revised March 2009

Life Cycle Assessment of the 

Use of Jatropha Biodiesel in 

Indian Locomotives

Michael Whitaker

Symbiotic Engineering

Garvin Heath

National Renewable Energy Laboratory

Prepared under Task No. BBO7.9610
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any 

warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not 

infringe privately owned rights. Reference herein to any specific commercial product, process, or service by 

trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, 

recommendation, or favoring by the United States government or any agency thereof. The views and 

opinions of authors expressed herein do not necessarily state or reflect those of the United States 

government or any agency thereof.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/ordering.htm

Printed on paper containing at least 50% wastepaper, including 20% postconsumer waste
ERRATA SHEET

NREL REPORT NUMBER: TP-6A2-44428 

TITLE: Life Cycle Assessment of the Use of Jatropha Biodiesel in Indian Locomotives

AUTHOR(S): Michael Whitaker, Symbiotic Engineering; Garvin Heath, NREL

ORIGINAL PUBLICATION DATE: December 2008

DATE OF CORRECTIONS: March 2009 

The following corrections were made to this report:

Table 21, pages 47-49 – entire table replaced, due to an editing error

Tables 20-22, pages 43-52 – explanatory note added 
iii

Acknowledgments

The authors would first like to thank our local partners in this work, the Indian Oil Corporation,

Ltd. (IOC). Rakesh Sarin, chief research manager of the IOC, provided timely and helpful 

guidance and data to focus our modeling on the large-scale Jatropha biodiesel systems that India 

and the IOC envision. Margaret Mann and Anelia Milbrandt, both of NREL, provided their 

expertise in life cycle assessment (LCA) modeling and Jatropha production. Guido Reinhardt of 

the Institute for Energy and Environmental Research Heidelberg GmbH provided an early 

release version of an updated report summarizing valuable Jatropha production data.
iv

List of Acronyms

B5 5% biodiesel blend with diesel 

B10 10% biodiesel blend with diesel 

B20 20% biodiesel blend with diesel 

B100 100% biodiesel (neat biodiesel)

CH Switzerland

CH4 Methane

CO2 Carbon dioxide

CO2e Carbon dioxide equivalent in terms of global warming potential

MtCO2e Metric ton carbon dioxide equivalent 

DOE United States Department of Energy

EPA United States Environmental Protection Agency

GB United Kingdom

GHG Greenhouse gas

GJ Giga joules

GTK Gross tonne-kilometer

GWP Global warming potential

ha Hectare

HFCs Hydrofluorocarbons 

IOC Indian Oil Corporation, Ltd.

IPCC Intergovernmental Panel on Climate Change

IR Indian Railways

ISO International Organization for Standardization

KOH Potassium hydroxide

LCA Life cycle assessment

LCI Life cycle inventory

LCIA Life cycle impact assessment

N2O Nitrous oxide

NaOH Sodium hydroxide 

NEG Net energy gain

NER Net energy ratio

NG Nigeria

NREL National Renewable Energy Laboratory

OCE Oceanic

PFCs Perfluorocarbons

RER Europe

RME Middle East

S SimaPro System process

SF6 Sulfur hexafluoride 

T&D Transmission and distribution of electricity

Tonne Metric ton

UCPTE Union for the Coordination of Production and Transmission of Electricity 

(predecessor to UCTE)

UCTE Union for the Coordination of Transmission of Electricity

VIZAG Visakhapatnam Oil Refinery
v

Executive Summary

India’s transportation sector relies heavily on petroleum-based fuels, which account for more 

than 95% of the transportation sector’s energy use. In 2003, the Planning Commission of India 

recommended increasing the use of biodiesel blended with petroleum diesel in transportation 

fuels with the goal of reducing greenhouse gas (GHG) emissions and petroleum consumption. 

The Planning Commission of India selected Jatropha curcas L. (Jatropha) as the most suitable 

plant for the production of biodiesel in India due to its high oil yielding seeds and ability to grow 

in a variety of agro-climatic conditions. Also in 2003, Indian Oil Corporation, Ltd. (IOC) signed 

a memorandum of understanding with Indian Railways (IR) to explore the use of biodiesel in the 

Indian rail system (Ministry of Railways 2003). IR offered land to the IOC for the cultivation of 

Jatropha trees to produce Jatropha oil, which can be extracted and transesterified into biodiesel 

for use in IR locomotives. Based on recommendations from the Planning Commission of India 

and the IOC-IR agreement, Jatropha-based biodiesel is poised to take an increasingly large role 

in the Indian transportation sector. Initially, IR plans to use 5% biodiesel blends with diesel (B5) 

in its locomotive fleet. The Indian government passed a new standard in 2008 that also requires 

the use of 20% biodiesel blends (B20) in Indian diesel-based transport fuels by 2017 (Padma 

2008). 

To help evaluate the potential for Jatropha-based biodiesel in achieving sustainability and energy 

security goals, this analysis estimates the life cycle impacts of substituting petroleum diesel with 

biodiesel blends in Indian locomotives. In addition, this study identifies the parameters that have 

the greatest impact on the sustainability of the system. 

The life cycle of Jatropha biodiesel examined here includes Jatropha cultivation, Jatropha oil 

extraction, base-catalyzed Jatropha oil transesterification to biodiesel, and combustion of blends 

of 5% (B5), 10% (B10), and 20% (B20) biodiesel in Indian locomotives. B100 (100% biodiesel) 

results are also presented for reference. The petroleum diesel reference system, to which the 

results for Jatropha biodiesel are compared, includes crude oil extraction and transportation, 

crude oil refining to diesel fuel, and diesel fuel combustion in Indian locomotives. Jatropha 

biodiesel is compared to petroleum diesel based on three metrics: net changes in life cycle GHG 

emissions, petroleum consumption, and the net energy ratio (ratio of energy produced by the 

system to the net energy demand of the system). 

The results of the base case analysis in this study are summarized in Table ES-1. GHG emissions 

and petroleum displacement results are presented per gross-tonne kilometer (GTK) of train 

transport to mitigate the influence of many system boundary assumptions of this study. The 

results suggest that substituting petroleum diesel with biodiesel blends yields reductions in both 

net GHG emissions and petroleum consumption. Under base case conditions, this study finds 

that substituting petroleum diesel with B5 could reduce net GHG emissions per GTK by 3%, by 

12% for B20, and, for reference, by 62% for B100. The life cycle stages that made the greatest 

contribution to net GHG emissions for biodiesel blends were locomotive operation, petroleum 

diesel production and distribution, and Jatropha cultivation. In terms of petroleum consumption 

by IR locomotives, B5 could displace 4% of petroleum use, B20 17%, and B100 86%, under the 

base case conditions evaluated in this study. The base case net energy ratio for Jatropha biodiesel 

(B100) is estimated in this study to be nearly 2, meaning nearly two units of energy are produced 
vi

for every one unit of energy consumed in the production process. When blended with petroleum 

diesel, the net energy ratio is less than 1 for blends of 20% biodiesel and lower, owing to the 

high proportion of diesel whose net energy ratio is estimated to be approximately 0.8. 

Table ES-1. Net Life Cycle GHG Emission Intensity, Net Energy Ratio, and Petroleum 

Displacement Intensity for the Base Case

Metric Units Diesel B5 B10 B20 B100

GHG Emission Intensity g CO2e / GTK 13 13 12 12 5.1

 Change from Diesel % -- -3% -6% -12% -62%

Net Energy Ratio Energy out / energy in 0.79 0.81 0.84 0.89 1.9

 Change from Diesel % -- 3% 6% 13% 140%

Net Petroleum Displacement 

Intensity g crude oil / GTK -- 0.17 0.34 0.69 3.6

 Change from Diesel % -- -4% -8% -17% -86%

Note: Assumes a 20 year system lifetime with 2 billion GTK transported over that time. Uses values for global 

warming potential (GWP) in carbon dioxide equivalents (CO2e) for each of the six Kyoto GHGs based on the IPCC’s 

1997 guidance for consistency with the LCA literature. Results are rounded to two significant figures as an indication 

of their uncertainty. Petroleum consumption for the diesel reference system was estimated to be 4.2 g crude oil/GTK. 

Percent change values are rounded to the nearest integer. 

Sensitivity analyses on selected parameters show that, per unit change in the value of an 

individual parameter, the tree planting density, seed yield, locomotive efficiency, and seed oil 

content are the parameters that have the greatest influence on all three results. The sensitivity 

analysis also evaluated alternative scenarios for the value of individual parameters. In some 

cases, these alternative scenarios are plausible and in others they tested the extreme best/worst 

case boundaries. Considering just the most plausible alternative scenarios evaluated, one robust 

conclusion of this study appears to be that the net energy ratio of Jatropha biodiesel will most 

likely be greater than 1. The net GHG emissions could vary more considerably—up to 50-100% 

—depending on the value of certain key parameters such as number of years of irrigation 

required, whether co-products are used, the nitrous oxide (N2O) volatilization rate, as well as 

other factors listed above. And the estimation of petroleum consumption could vary even more, 

depending on the value of three parameters in particular: the amount of fertilizer required, the 

fuel economy of the locomotive, and the seed yield.

Given the embryonic state of Jatropha research, there is considerable uncertainty in modeling 

Jatropha biodiesel production systems. Therefore, it is advisable to interpret the findings of this 

report as indicative of the direction and scale of impacts relative to the diesel reference system 

rather than accurate point estimates of the magnitude of impacts. Because the focus of this study 

is on large-scale Indian plantations and biodiesel production processes, as well as on the use of 

biodiesel in rail transport, the results presented here are not necessarily broadly applicable to 

other locations, production processes, or uses. For instance, other locations might require more 

fertilizer or irrigation, other production processes may have different oil yields and energy input 

requirements, and motor vehicles have different engine efficiencies. Finally, two potentially 

important factors were not included in the analysis of this study: the impacts of direct and 
vii

indirect land use change. However, this study’s results—along with the results of other reviewed 

studies—suggest that, under plausible growing conditions and production scenarios, Jatrophabased biodiesel shows promise for helping India and possibly other nations achieve their GHG 

emission reduction and petroleum-displacement goals.

While this study was designed to evaluate Jatropha biodiesel production systems under Indian 

conditions to the greatest degree possible, the lessons learned from this study also will benefit 

other countries, including the United States. Petroleum-based transportation fuels are traded on 

an international market. Reduction in consumption anywhere in the world, but particularly in 

countries with growing demand like India, will reduce pressure on world petroleum demand and, 

ultimately, prices. While countries like India are at the leading edge of research on alternative 

transportation fuel feedstocks such as Jatropha, the plant also may be a suitable biofuel feedstock 

for the United States (e.g., in Florida) (Layden 2008), and lessons learned from the Indian 

context can be applied to the United States. Finally, to the extent that this study can help 

optimize the Jatropha biodiesel production process and reduce greenhouse gas emissions, there 

are worldwide benefits for climate change mitigation.
viii

Table of Contents

Acknowledgments..........................................................................................................................iii

List of Acronyms ........................................................................................................................... iv

Executive Summary........................................................................................................................ v

Table of Contents.........................................................................................................................viii

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

List of Tables .................................................................................................................................. x

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

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

1.2 India Rail Sector and Existing Fuels..................................................................................... 2

1.3 Related Literature.................................................................................................................. 3

1.4 System Description ............................................................................................................... 4

1.4.1 General Jatropha Biodiesel Description......................................................................... 4

1.4.2 Analysis Approach......................................................................................................... 5

1.4.3 Jatropha Biodiesel Base Case Scenario Description...................................................... 6

1.4.4 Reference System – Petroleum Diesel Production and Distribution ............................. 8

2.0 Methods................................................................................................................................... 10

2.1 Life Cycle Assessment........................................................................................................ 10

2.2 Goal of the Study ................................................................................................................ 10

2.3 Scope of the Study .............................................................................................................. 11

2.4 System Boundaries.............................................................................................................. 12

2.5 Allocation Procedures......................................................................................................... 13

2.6 Impact Categories ............................................................................................................... 14

2.7 Data Requirements.............................................................................................................. 14

2.8 Model.................................................................................................................................. 15

2.9 Uncertainty.......................................................................................................................... 15

2.10 Sensitivity Analysis Approach.......................................................................................... 17

3.0 Base Case Assumptions.......................................................................................................... 18

4.0 Base Case Scenario................................................................................................................. 21

4.1 Petroleum Diesel Production and Distribution (Reference System)................................... 21

4.2 Jatropha Cultivation............................................................................................................ 23

4.3 Jatropha Oil Extraction ....................................................................................................... 27

4.4 Biodiesel Production via Jatropha Oil Transesterification ................................................. 29
ix

4.5 Locomotive Operation ........................................................................................................ 31

4.6 Supporting Processes.......................................................................................................... 33

5.0 Base Case Results................................................................................................................... 36

5.1 Net Greenhouse Gas Emissions.......................................................................................... 36

5.2 Net Energy Ratio................................................................................................................. 39

5.3 Net Petroleum Displacement .............................................................................................. 40

6.0 Sensitivity Analyses................................................................................................................ 42

7.0 Discussion ............................................................................................................................... 55 

7.1 Study Limitations................................................................................................................ 55

7.1.1 Uncertainty Analysis.................................................................................................... 55

7.1.2 Technological and Geographic Scope ......................................................................... 55

7.1.3 Modeling Approach ..................................................................................................... 56

7.1.4 Data Availability and Certainty ................................................................................... 56

7.1.5 Metrics Evaluated ........................................................................................................ 56

7.1.6 State of the Science...................................................................................................... 56

7.2 Generalizability of Results.................................................................................................. 57

7.3 Interpretation of Sensitivity Analyses................................................................................. 57

7.4 Displaced Petroleum and Net GHG Emission Reduction Projections from Utilization of 

Jatropha Biodiesel in Indian Railways...................................................................................... 57

7.5 Comparison of Results to Other Studies............................................................................. 58

8.0 Conclusions............................................................................................................................. 59

8.1 Research Recommendations............................................................................................... 60

References..................................................................................................................................... 62

Appendix A – Details About the SimaPro Model and Base Case Model Parameter Values ....... 67

Appendix B – Sensitivity Analysis Scenario Details.................................................................... 83

Appendix C – Best- and Worst-Case Scenario Details ................................................................ 86
x

List of Figures

Figure 1. Jatropha cultivation zones. .............................................................................................. 1

Figure 2. Shatabdi Express used in biodiesel trial .......................................................................... 2

Figure 3. Jatropha curcas trees ...................................................................................................... 4

Figure 4. Jatropha fruit .................................................................................................................. 5

Figure 5. Jatropha seeds ................................................................................................................. 5

Figure 6. Chhattisgarh state, India ................................................................................................ 6

Figure 7. Schematic for continuous solvent oil extraction ............................................................ 7

Figure 8. Visakhapatnam (VIZAG) oil refinery . ........................................................................... 8

Figure 9. Petroleum diesel life cycle process map........................................................................ 11

Figure 10. Blended biodiesel life cycle process map.................................................................... 12

Figure 11. Modeling schematic for petroleum diesel production and distribution processes ...... 21

Figure 12. Modeling schematic for Jatropha cultivation processes.............................................. 24

Figure 13. Modeling schematic for Jatropha oil extraction processes.......................................... 28

Figure 14. Modeling schematic for biodiesel production via Jatropha oil transesterification 

processes. ...................................................................................................................................... 29

Figure 15. Modeling schematic for locomotive operation............................................................ 31

Figure 16. Modeling schematic for Indian electricity................................................................... 34

List of Tables

Table ES-1. Net Life Cycle GHG Emission Intensity, Net Energy Ratio, and Petroleum 

Displacement Intensity for the Base Case ..................................................................................... vi

Table 1. Global Warming Potentials (Relative to CO2)................................................................ 14

Table 2. Simapro Module Descriptions for Petroleum Diesel Production and Distribution 

Processes....................................................................................................................................... 22

Table 3. Base Case Data Inputs for Petroleum Diesel Production and Distribution .................... 23

Table 4. SimaPro Module Descriptions for Jatropha Cultivation................................................. 25

Table 5. Base Case Data Inputs for Jatropha Cultivation ............................................................. 26

Table 6. SimaPro Module Descriptions for Jatropha Oil Extraction............................................ 28

Table 7. Base Case Data Inputs for Jatropha Oil Extraction ........................................................ 29

Table 8. SimaPro Module Descriptions for Biodiesel Production via Jatropha Oil 

Transesterification......................................................................................................................... 30

Table 9. Base Case Data Inputs for Biodiesel Transesterification via Jatropha Oil 

Transesterification......................................................................................................................... 31

Table 10. SimaPro Module Descriptions for Locomotive Operation........................................... 32

Table 11. Base Case Data Inputs for Locomotive Operation ....................................................... 33

Table 12. SimaPro Module Descriptions for Indian Electricity ................................................... 34

Table 13. Base Case Data Inputs for Indian Electricity................................................................ 35

Table 14. SimaPro Module Descriptions for Transportation........................................................ 35

Table 15. Net Life Cycle Greenhouse Gas Emissions.................................................................. 36

Table 16. Life Cycle Greenhouse Gas Process Contributions...................................................... 38

Table 17. Percent Contribution by Greenhouse Gas to Total Global Warming Potential............ 39
xi

Table 18. Net Energy Ratio (Energy Out / Energy In) ................................................................. 40

Table 19. Life Cycle Net Petroleum Consumption and Displacement Intensity.......................... 41

Table 20. Global Warming Potential (GWP) Sensitivity Analyses.............................................. 43

Table 21. Net Energy Ratio Sensitivity Analyses......................................................................... 47

Table 22. Petroleum Consumption Sensitivity Analyses.............................................................. 50

Table 23, Local Sensitivity of Diesel Model Output .................................................................... 53

Table 24. Best/Worst Case Scenario Analysis.............................................................................. 54

Table A-1. List of Input Parameters that Have Assigned Values................................................. 67

Table A-2. List of Calculated Input Parameters that Have Values Determined by Formulas Based 

on Other Parameters...................................................................................................................... 71

Table A-3. India Electricity Generation Custom Module............................................................. 73

Table A-4. Indian Electricity Delivered Custom Module............................................................. 74

Table A-5. Jatropha Seedling for Planting Custom Module......................................................... 74

Table A-6. Jatropha Plantation, Planted, India Custom Module .................................................. 75

Table A-7. Jatropha Seeds Harvested from Plantation Custom Module ...................................... 76

Table A-8. Jatropha Oil, at Solvent Extraction Facility, India Custom Module .......................... 77

Table A-9. Biodiesel Production, Base-catalyzed Transesterification, India, at Plant Custom 

Module .......................................................................................................................................... 78

Table A-10. Biodiesel Blending, India, at Processing Custom Module ....................................... 79

Table A-11. Indian Freight Train Operation, Biodiesel Blend Custom Module .......................... 80

Table A-12. Crude Oil at Indian Coastal Terminal Custom Module............................................ 81

Table A-13. Diesel at Railyard Custom Module .......................................................................... 82

Table A-14. Indian Freight Train Transport, Diesel Custom Module .......................................... 82

Table B-1. Sensitivity Analysis Scenario Details......................................................................... 83

Table C-1. Parameter Values Used To Model Best and Worst Case Sensitivity Scenarios......... 86
1

1.0 Introduction

At the behest of the Department of Energy’s Office of Energy Efficiency and Renewable Energy,

and in cooperation with Indian Oil Corporation, Ltd. (IOC), the National Renewable Energy 

Laboratory (NREL) has performed a life cycle assessment (LCA) on the biodiesel made from the 

Jatropha plant seeds grown in India and used in India’s existing rail transport system. In 2003, 

the IOC signed a memorandum of understanding with Indian Railways (IR) to explore the 

potential use of biodiesel in the Indian rail system (Ministry of Railways 2003). The initial 

agreement resulted in IR offering 500 hectares (ha) of land to the IOC for plantation and 

cultivation of Jatropha curcas L. (Jatropha) to yield oil that will be extracted and transesterified 

to produce biodiesel for use in IR locomotives. IR’s stated intention is to initially use 5% 

biodiesel blends with diesel (B5), later increasing to10-20% biodiesel blends (B10-B20) as more 

biodiesel becomes available and tests establishing the good performance of locomotives using 

higher blends are completed (Ministry of Railways 2003).

1.1 Background

Oil provides more than 95% of the energy 

required for India’s transportation sector 

while only 22% of future projected demand 

can be met by domestic supplies (Planning 

Commission of India 2003). In 2003, the 

Planning Commission of India established 

the Committee on Development of Bio-Fuels 

to explore how India can use ethanol and 

biodiesel blended with motor spirit (gasoline) 

and diesel, respectively, to reduce vehicle 

emissions and to decrease the country’s 

reliance on petroleum-based fuels. Based on 

its ability to thrive in a variety of agroclimatic conditions, a low gestation period,

and high seed yield relative to other plants 

with oil-bearing seeds, Jatropha was selected 

as the most suitable for the production of 

biodiesel in India. In September 2008, the 

Indian government passed a new standard 

requiring that by 2017, all transport fuels in 

India must contain at least 20% biofuels 

(Padma 2008). It has been determined that 

biodiesel can be used in diesel engines at a 

blend of up to 20% (B20) without substantial 

engine modifications and generally results in 

reductions of hydrocarbon, carbon monoxide, 

particulate matter, and sulfur dioxide emissions (Planning Commission of India 2003). Figure 1 

shows the preferred regions in India for Jatropha growth. Green areas are most likely to be 

targeted for Jatropha cultivation.

Figure 1. Jatropha cultivation zones. Green 

areas indicate good Jatropha cultivation 

potential in India; red areas are fertile 

agricultural lands unlikely to be used for 

Jatropha cultivation; pink areas are deserts with 

poor growing conditions

Source: Lele 2008b
2

1.2 India Rail Sector and Existing Fuels

The rail sector in India, operated by IR, is vital to the domestic transport of both passengers and 

freight. Approximately two billion liters of diesel fuel (representing more than 3% of total Indian 

diesel fuel consumption) are consumed annually in the operation of almost 4,000 freight and 

passenger locomotives at a cost of almost $1.3 billion per year (Kathpal 2008). IR adds an 

average of 100-120 locomotives to its fleet every year and is beginning to test and apply

biodiesel blends with its fleet to reduce its reliance on petroleum diesel. In laboratory tests, IR 

compared the engine power, brake specific fuel consumption, and emissions of locomotives 

operating on conventional diesel to B100 and to biodiesel blends of B10, B20, and B50 (Kathpal 

2008). The study found negligible penalties in maximum engine power and fuel consumption for 

B10 and B20 compared with petroleum diesel. 

On December 31, 2002, IR used B5 in a test run of the 

prestigious Shatabdi Express (Figure 2) (Kathpal 

2008). The test revealed no adverse effect on hauling 

capacity, no unusual deposits on the filters, and no 

deterioration of the condition of the fuel injection 

system. In May 2004, following the positive results of 

the initial laboratory and field trials, IR ran a test using 

B10 to operate the Jan-Shatabadi express for five days. 

IR observed no adverse effects in terms of hauling

capacity, lube oil consumption, engine performance, or 

filter deposits. Additionally, fuel injection pumps and 

injector nozzles were found in satisfactory condition 

and free from any gum or resinous deposits (Kathpal 

2008).

Based on the early positive experimental results with biodiesel blends, IR decided to expand the 

use of biodiesel in its system. In 2003, IR signed a 15-year memorandum of understanding with 

the IOC to provide about 500 hectares of land to the IOC for the plantation of Jatropha to 

generate oil for processing into biodiesel (Ministry of Railways 2003). The IOC will direct the 

plantation and cultivation of Jatropha, oil extraction, and transesterification aspects of the 

project. One of the pilot project’s goals is to develop better baseline data on plant growth, seed 

yield, oil yield, and biodiesel production and performance. In total, IR seeks to have Jatropha 

planted on about 43,000 hectares of its land and will begin operating on blends of 1-5% with 

expansion up to 20% depending on field experience (Kathpal 2008). 

In addition to the IOC agreement, IR is also considering establishing a set of transesterification 

plants to produce biodiesel for use by its zonal railways. The Southern Railway, headquartered in 

Chennai, owns a transesterification plant and operates two diesel locomotives on B5. The 

Northern Railway, headquartered in New Delhi, recently set up two transesterification plants and 

will begin field trials with 3,000 kiloliters of B10. The Southeastern and Northeast Frontier 

Railways are also operating trains on B10. The Railway Board is requesting proposals for the 

supply of 50,000 kiloliters of biodiesel (Kathpal 2008). If biodiesel blending is fully integrated 

into IR operations in the future, diesel fuel savings could range from 100 million liters per year 

for B5 to 400 million liters per year for B20 (Kathpal 2008).

Figure 2. Shatabdi Express used in 

biodiesel trial

Source: Kathpal 2008
3

1.3 Related Literature

As Jatropha’s potential as a feedstock for significant biofuel production has become apparent in 

recent years, studies examining potential impacts have emerged. Reinhardt and colleagues 

(2007) conducted a screening LCA examining the advantages and disadvantages of Indian 

Jatropha biodiesel compared with conventional diesel. Biodiesel blends were apparently not 

evaluated. The study evaluated the environmental impact categories of consumption of energy 

resources, greenhouse gas (GHG) emissions, acidification, eutrophication, summer smog 

formation potential, and nitrous oxide ozone depletion potential. As a functional unit, the authors 

selected the use of Jatropha fruit harvested from one hectare of land in one year (i.e., land use 

efficiency). The diesel reference system was not clearly specified as to whether it represents 

diesel as sold in India or a world average stock. Data on many of the upstream processes were 

taken from the Institute for Energy and Environmental Research Heidelberg GmbH internal 

database and is therefore not accessible for comparison to other databases. Consideration of 

impacts from the manufacture, construction, and maintenance of infrastructure was not included 

in this study. Jatropha-specific cultivation data was compiled largely through primary research 

from field and laboratory measurements or from expert judgments and was recently published 

(Reinhardt et al. 2008). The study evaluated Jatropha-based biodiesel production in the province 

of Bhavnagar, where poor soil conditions were assumed. The published cultivation data are 

among the most extensive and complete available data at this time and are used to define many 

of the base case parameters in this report. The screening LCA evaluated biodiesel use to replace 

diesel in passenger cars and concluded that Jatropha biodiesel generally shows an energy balance 

savings of about 50% (8 GJ of primary energy per hectare year) and also has a small greenhouse 

gas emission advantage of about 10% (approximately 100 kg CO2e per hectare year). The study 

also determined that Jatropha cultivation and processing made the greatest contribution to net 

GHG emissions, while the contribution of material transportation was minimal. 

Prueksakorn and Gheewala (2008) conducted an LCA to evaluate the net energy gain (NEG) and 

net energy ratio (NER) of biodiesel produced from Jatropha cultivated in Thailand. This study 

excluded the impacts of facilities construction (i.e., infrastructure). NEG was defined as the 

difference between total energy outputs and total energy inputs while NER was calculated as the 

ratio of total energy outputs (including co-products energy content) to total energy inputs 

(including offsets such as avoided fertilizer production energy). The functional unit for this study 

was the production of one hectare of land over 20 years, and data was gathered from 14 research 

sites and 10 practical sites in Thailand. For average Jatropha cultivation and biodiesel production 

conditions, the authors calculated an NEG of 4,720 GJ/ha and an NER of 6.03 for biodiesel, plus 

co-products and a NER of 1.42 for biodiesel without consideration of co-products. The study was 

not designed to evaluate GHG emissions or to make comparisons to conventional diesel.

Achten et al. (2008) published a detailed literature review of available studies regarding Jatropha 

biodiesel production and use. The article gives an overview of the published data on Jatropha 

biodiesel production processes ranging from cultivation and oil extraction to biodiesel 

conversion and use. Data reported in the article serve to define many parameter values for the 

sensitivity analyses of this report. Based on the literature reviewed, the article concludes that the 

energy balance for Jatropha biodiesel is likely positive with the extent of the positive balance 

dependent on the efficiency of co-product use and on fertilization and irrigation requirements. 

The authors also confirm the reduction in GHG emissions that result from the substitution of 
4

biodiesel for conventional diesel while identifying irrigation, fertilization, and transesterification 

as the processes with the greatest influence on net GHG emissions. The authors determined that 

the overall global warming potential for the production and use of 100% Jatropha biodiesel 

(B100) is 77% less than for conventional diesel based on another study by Prueksakorn and 

Gheewala (2006) and a life cycle assessment by Tobin and Fulford (2005). However, neither of 

the two reviewed studies considered N2O release from nitrogen fertilizer during cultivation,

thereby underestimating, likely considerably, the GWP of Jatropha biodiesel production.

Finally, two rich resources for India-specific data are Satish Lele’s books on Jatropha cultivation 

(2008b) and biodiesel (2008a). They provide primary data for developing study parameters 

related to typical conditions encountered in India. The books also contain excellent discussions 

of where data is lacking, particularly regarding fertilization, chemical application, and irrigation 

requirements.

1.4 System Description

1.4.1 General Jatropha Biodiesel Description

Biodiesel production begins with the cultivation of 

Jatropha (Figure 3), a small tree or large shrub that 

grows to an average height of 3-5 meters (with heights 

exceeding 7 meters in optimal conditions) and bears 

fruit containing seeds rich in non-edible oil suitable for 

conversion to biodiesel. Jatropha grow in a variety of 

environmental conditions, including poor soils, but 

generally prefer heat of the tropics and subtropics. 

Jatropha grow without irrigation in a wide range of 

rainfall conditions, from 300-3,000 mm per year 

(Achten et al. 2008). It grows in the wild throughout

India with a 50-year life expectancy. 

For commercial production, Jatropha is grown in 

plantations with tree densities ranging from 1,100 to 2,500 trees per hectare (Achten et al. 2008 

and Lele 2008b). Jatropha is often established through the planting of seedlings grown at 

nurseries in plastic bags (Achten et al. 2008). Irrigation and fertilization requirements are highly 

dependent on location-specific conditions. Even under adequate rainfall, irrigation may be 

required for the first three years to help plant establishment (Reinhardt et al. 2008). If 

fertilization is required, nitrogen and phosphorus tend to be the nutrients of greatest need 

(Achten et al. 2008). Jatropha fruit (Figure 4) can be harvested at least once per year, often using 

human labor (Lele 2008b).

Once harvested, the Jatropha fruit is de-husked to isolate the oil bearing seeds (Figure 5) through 

use of either a mechanical decorticator or manual labor. The husks can be collected as a coproduct and used to generate energy (heat or electricity) by combustion. Chemicals in the seed 

render the oil toxic to humans and animals but appropriate for conversion into biodiesel. The 

yield of Jatropha trees is highly uncertain. According to Achten et al. (2008), reliable data on the 

anticipated dry Jatropha seed yield per hectare per year for a given set of environmental 

Figure 3. Jatropha curcas trees

Source: JatrophaWorld 2008
5

conditions and inputs does not exist. However, Achten and colleagues suggest 4-5 metric tons 

(tonnes) of dry seed per hectare per year as a reasonable yield estimate for a well-managed 

plantation with good environmental conditions.

Jatropha seeds contain an average of 25-40% oil by mass 

which can be extracted using either mechanical systems such 

as a screw press or chemical-based processes such as solvent 

extraction. Solvent extraction is more efficient (90-99% oil 

extraction) but also more expensive and is only economical for 

commercial-scale processing. Hexane is the primary solvent 

being used for commercial extraction at this time (Adriaans 

2006). Seed cake remaining after oil extraction is rich with 

nutrients and can be returned to the field as fertilizer with an 

average nitrogen : phosphorous : potassium NPK ratio of 

40:20:10 (Prueksakorn and Gheewala 2008). 

To produce a usable biofuel, Jatropha oil is transesterified to 

biodiesel and glycerine using methanol as the alcohol and 

either sodium hydroxide (NaOH) or potassium hydroxide 

(KOH) as a base catalyst. Glycerine is a marketable coproduct whose value depends on the quantity available from 

alternative suppliers, its purity, and other attributes like its 

odor (Glycerine produced as a co-product of biodiesel 

production is known to have a strong odor, which can affect 

its marketability). The India Oil Company suggests that the 

market is robust in India for glycerine obtained from biodiesel 

production, though that may not be the case in other countries 

(Sarin 2008c).

Biodiesel can be used in both heavy-duty road vehicles such as buses and in locomotives. 

Biodiesel is initially being used in India in blends of B5, B10, and B20 and has shown no 

adverse impacts on engine performance in blends of up to B10 in Indian Railways trials (Kathpal 

2008). Biodiesel can be blended with diesel at regional storage or at the point of fueling.

1.4.2 Analysis Approach

Several options were available for developing a comparative analysis of Jatropha biodiesel and 

conventional diesel in India based on scenarios for cultivation, extraction, processing, and use in 

locomotives. One option was to form a base case scenario from parameters that were 

independently averaged based on point estimates and ranges reported in the literature. A 

motivation for using this approach is to attempt to make the scenario under consideration as 

generalizable as possible. However, because many of the parameters are causally related, a 

scenario comprised of averages formed independently could be implausible. For instance, 

because the amount of irrigation and annual rainfall should sum to the total water requirement of 

the site considered, if averaged independently, and depending on the conditions of the site, this 

sum might actually provide more or less than the total water requirement for Jatropha cultivation 

Figure 5. Jatropha seeds

Source: Lele 2008b

Figure 4. Jatropha fruit 

Source: Lele 2008b
6

Therefore, the authors chose an alternative approach. A narrative was developed coherently 

linking all key parameters into a base case scenario. The robustness of this base case scenario 

was tested by a thorough sensitivity analysis that examines reasonable alternative values of each 

parameter independently. Because so many key parameters, especially for Jatropha cultivation, 

are dependent on site conditions, the authors, following recommendations from the IOC, selected 

a specific region of India for consideration, matching certain agronomic parameters to the typical 

conditions of that location. The following sections describe the most important aspects of the 

base case scenario for both Jatropha biodiesel and conventional diesel production and use in 

Indian locomotives. 

1.4.3 Jatropha Biodiesel Base Case Scenario Description

1.4.3.1 Jatropha Cultivation

As Jatropha is capable of being grown 

throughout India (Figure 1) and numerous 

production pathways are possible, a base case 

narrative was developed to guide the analysis. 

The base case narrative is based on guidance 

from the IOC regarding likely future 

development scenarios (Sarin 2008a,b,c,d). 

This analysis assumes that the Jatropha trees 

are grown on a 50,000 hectare plantation for a 

period of 20 years in the Raipur area of the 

Chhattisgarh state of India where the average 

annual rainfall is 1,385 mm per year. The state 

of Chhattisgarh is identified in red in Figure 6 

and falls within the prime Jatropha cultivation 

zones of India displayed in Figure 1. To 

correspond to available plantation 

management data, the plantation is modeled 

as five separate 10,000 hectare units. Seeds 

are harvested at the plantation manually and 

are transported via truck to a hypothetical oil 

extraction facility in Raipur. Raipur, the capital of Chhattisgarh, is well connected to the region 

via road and rail and is one of India’s fastest growing industrial cities.

1.4.3.2 Summary of Jatropha Cultivation and Processing Plant Assumptions

The base case Jatropha cultivation and biodiesel processing characteristics are taken from the 

Planning Commission of India’s (2003) assumptions used in its biofuels assessment report. The 

following is a list of some of the most important base case assumptions.

Base Case Assumptions

• Jatropha cultivation via nursery

• 2,500 trees per hectare density at planting

• Average quality soils and agro-climatic conditions (e.g., temperature, rainfall) at the 

plantation site 

Figure 6. The Chhattisgarh state of India is 

highlighted in brown. The approximate location of 

the city of Raipur is indicated with the arrow.

Source: Chhattisgarh Online 2008

Raipur
7

• Jatropha plants reach maturity within three years from planting when full seed yield is 

expected 

• 1,500 grams of seed harvested per tree at full yield, or 3.75 tonnes of seed per hectare 

year

• Seed oil content is 35% by weight

• Solvent extraction efficiency is 91%

• Jatropha oil recovery efficiency is 32% (i.e., 35% oil content multiplied by 91% recovery 

efficiency)

• Based on above conditions, 3.125 kg seed is required to produce 1 kg Jatropha oil

• Based on assumed oil recovery efficiency, 1.2 tonnes of Jatropha oil is expected / hectare 

– yr

• Anticipated Jatropha oil recovery for the full plantation is approximately 60,000 tonnes

per year.

1.4.3.3 Jatropha Oil Extraction

The analysis assumes that the oil extraction facility has a capacity of 200 tonnes oil per day, 

giving it the ability to process up to 625 tonnes of seeds per day in a continuous solvent 

extraction process with 91% extraction efficiency. According to Adriaans (2006), continuous 

solvent extraction of Jatropha oil requires processing at least 200 tonnes of seed per day to be 

economical. Solvent extraction plants can process up to 4,000 tonnes per day making the base 

assumption of a 625 tonnes per day capacity well within the current technology range. Hexane is 

currently the only solvent used commercially on a large scale (Adriaans 2006). Based on the size 

of the facility, the extraction is assumed to be continuous (Figure 7). Not all of the individual 

Figure 7. Schematic for continuous solvent oil extraction

Adapted from: Adriaans 2006
8

processes shown in Figure 7 are specifically modeled in this analysis, though their impacts and 

results are included in summary fashion.

1.4.3.4 Biodiesel Production via Jatropha Oil Transesterification

The Jatropha oil extraction facility is co-located with the biodiesel transesterification plant for 

logistical reasons. This study assumes both are located in Raipur. The transesterification unit 

capacity is assumed to be 100,000 tonnes of biodiesel / yr with a 95% conversion efficiency of 

Jatropha oil to biodiesel. If the 200 tonnes / day oil extraction unit operated continuously 

throughout the year, it would generate 73,000 tonnes of Jatropha oil per year. The excess plant 

capacity could be used to process Jatropha oil generated by other extraction units in the area. The 

transesterification process is assumed to be base-catalyzed using potassium hydroxide with 

methanol as the alcohol.

1.4.3.5 Locomotive Operation

Based on discussions with the IOC (Sarin 2008b) and statements by IR (Kathpal 2008), the base 

case compares blends of B5, B10, and B20 to conventional diesel. The base case assumes that 

the locomotives are fueled 20 kilometers from Raipur in Bhilai, and that the biodiesel blending 

with petroleum diesel occurs at the IOC’s Bhilai petroleum depots. The base case analyzes the 

fuel requirements for 100,000 gross tonne-kilometers of locomotive travel per year. An IR study 

found a negligible negative effect for volumetric fuel consumption for locomotives operated on 

B5, B10, or B20 (Kathpal 2008). The base case assumes no negative fuel consumption impacts, 

while the sensitivity analysis tests for the impacts of fuel economy reductions with increases in 

biodiesel percentages. This study assumes that the combustion of biodiesel results in no net 

carbon dioxide emissions; carbon sequestered from the atmosphere during the growth of biomass 

offsets the carbon dioxide emissions from combustion of the same biomass. This concept is 

described further in the base case assumptions section of this report.

1.4.4 Reference System – Petroleum Diesel Production and Distribution

As biodiesel is used primarily in 

blended applications with conventional, 

petroleum-based diesel (diesel), the 

diesel life cycle serves a dual purpose in 

this analysis. First, the 100% diesel 

scenario serves as a benchmark against 

which the biodiesel blends (B5, B10, 

and B20) are compared. Second, the 

entire diesel life cycle is contained 

within the blended biodiesel life cycle

as 80% of the fuel in the highest 

blending scenario (B20) comes via the 

diesel pathway. 

Crude oil used in India is of both 

domestic and foreign origin. For the 

base case, foreign oil is assumed to be 

extracted from Saudi Arabia and 

transported to the Visakhapatnam Oil 

VIZAG

Figure 8. Visakhapatnam (VIZAG) oil refinery is located 

near the Bay of Bengal in eastern India

Source: Maps of India 2008
9

Refinery (VIZAG) on the east coast of India near the Bay of Bengal (Figure 8). Domestic oil is 

assumed to be extracted from the Bombay High oil field off the west coast of India near Mumbai 

and transported via oil tanker to VIZAG. Refined diesel is transported from VIZAG to the oil 

depots in Bhilai near Raipur via rail for fueling IR locomotives at the Bhilai depots. A complete 

set of data regarding the operations of the VIZAG refinery was not made available in time for 

completion for this study; therefore, a pre-established diesel fuel refining module based on 

Western European average refining impacts from the Ecoinvent 2.0 database (Swiss Centre for 

Life Cycle Inventories 2008) was used as a substitute. If the Western European refinery operates 

more efficiently than the Indian refinery, results may be biased in favor of the diesel system. If, 

however, tighter environmental regulations results in the Western European refinery using more 

energy for fuel processing, results may be biased in favor of the biodiesel system. Also, the 

Ecoinvent diesel refining module contains multiple transport steps. It is unclear whether these 

transport steps might double count the rail transport modeled (in a custom module) between 

VIZAG and the Bhilai depots. However, any overestimate of impacts determined in this report 

owing to potential double counting of the transport of refined diesel to the regional storage site at 

Bhilai is expected to be small. 
10

2.0 Methods

2.1 Life Cycle Assessment

Life cycle assessment (LCA) is an analysis method designed to evaluate the potential 

environmental impacts of a product or process starting from raw material extraction, through 

production and use, to an end-of-life scenario. As defined by the International Organization for

Standardization (ISO) 14044:2006 standard (ISO 2006), LCA studies are comprised of four 

phases:

1. Goal and scope definition requires defining the system boundary and level of detail 

required for input data. Depth and breadth of LCA studies can vary greatly depending on 

the target subject, quality of data, available time and budget, and intended use of the 

results.

2. Inventory analysis includes the collection and processing of all input/output data 

required to define and analyze the system. Data can be collected from established life 

cycle inventory (LCI) databases, primary documentation, academic studies, or other high 

quality, verifiable sources. Connections must also be formed between components of the 

study system to determine how changes in one process are propagated upstream or 

downstream through the LCA. Results are reported using many units of measure, 

depending on what materials and metrics are tracked in the LCA. For instance, mass of 

individual greenhouse gases emitted, area of land occupied or transformed, and volume 

of crude oil consumed could all be tracked and reported as the results of the LCI.

3. Impact assessment is designed to translate the environmental significance of a system’s 

impacts. The life cycle impact assessment (LCIA) phase takes raw input and output data 

(such as emissions of carbon dioxide and methane) and converts them to common metrics 

such as global warming potential (GWP). Impacts can be assessed at either the midpoint, 

where flows are characterized based on their expected environmental impacts such as 

GWP, or at their endpoint where actual environmental changes such as temperature 

increase, are predicted. 

4. Interpretation is an ongoing process of analyzing the results of the LCI and LCIA as a 

basis for conclusions, recommendations, and discussions.

This study focuses on inventory and midpoint impact assessment analysis, as endpoint analysis 

introduces additional uncertainty and assumptions. LCI results are classified according to their 

relevance for petroleum displacement and cumulative energy demand. Midpoint impact 

assessment characterizes inventory greenhouse gas emissions according to their GWPs. The 

following sections outline the assumptions and methodologies used to conduct this study in 

accordance with ISO 14044:2006 requirements.

2.2 Goal of the Study

The purpose of this study is to compare the environmental impacts of using Jatropha biodiesel

for rail transportation to the conventional diesel baseline. The methodology used in this study is 

consistent with that described by ISO 14044:2006 standards for LCA, and particularly those 

standards that cover inventory analysis (ISO 2006). The study is intended to lend guidance 

regarding the potential impacts of a significant increase in biodiesel production from Jatropha 
11

plants to offset a portion of the diesel fuel currently being used in the Indian rail system. LCA is 

used to evaluate the relative impacts throughout the life cycle phases including resource 

extraction, crop cultivation, processing, and use in order to develop as complete a picture as 

possible of the likely impacts. The study also seeks to identify which key parameters and 

uncertainties are most likely to influence the ultimate conclusions of the study. The intended 

audience includes policy-makers, industry executives, academics, and any interested members of 

the public.

2.3 Scope of the Study

The scope of this study is the evaluation of the production of conventional petroleum diesel and 

the production of biodiesel from Jatropha for use in the operation of IR locomotives. The 

functional unit for the study is GTK of rail transportation. GTK includes both the weight of the 

train and the weight of any passengers or cargo on board. The functional unit assumes that the 

primary goal of the Indian rail system is to move passengers or cargo and that diesel and 

biodiesel should be evaluated in terms of their ability to provide that function. An overview of 

the processes included in the system evaluations are detailed in Figures 9 and 10. The diesel and 

biodiesel production pathways are evaluated beginning with resource extraction, through 

transportation and processing, to use in the locomotives. 

The diesel life cycle is required for analyzing both the combustion of conventional petroleum 

diesel and blended biodiesel in locomotives.

Figure 9. Petroleum diesel life cycle process map
12

The entire petroleum diesel production and distribution life cycle outlined in Figure 9 is 

contained in the blended biodiesel life cycle shown in Figure 10. Supporting processes include 

Indian transportation systems and electricity generation, transmission, and distribution.

Figure 10. Blended biodiesel life cycle process map. The entire petroleum diesel production and 

distribution life cycle (Figure 9) is contained within the blended biodiesel life cycle. Supporting 

processes include Indian transportation systems and electricity generation, transmission, and 

distribution.

2.4 System Boundaries

The study analyzes both petroleum-diesel and biodiesel production and use pathways in India 

and identifies resource consumption, energy use, and emissions for the following life cycle 

stages and sub-processes:

• Petroleum diesel production and distribution (reference system)

o Foreign and domestic crude oil extraction

o Crude oil transport 

o Crude oil refining into diesel fuel

o Diesel fuel distribution to rail depots for use and for blending with biodiesel

• Jatropha cultivation

o Seedling production and planting 

o Plantation operation and management 

o Seed harvesting and transport to extraction facility

• Jatropha oil extraction

o Solvent-based extraction of oil

o De-oiled seed cake use as fertilizer substitute

• Biodiesel production via Jatropha oil transesterification

o Base-catalyzed transesterification to biodiesel

o Transport of biodiesel to railways for blending with petroleum diesel

• Locomotive operation

o Locomotive operation on conventional petroleum diesel

o Locomotive operation on blended biodiesel
13

• Supporting processes 

o Indian transportation vehicles and infrastructure

o Indian electricity and transmission and distribution infrastructure

o Local generation of steam for use in Jatropha oil extraction and biodiesel 

transesterification processes

While the amortized impacts from manufacturing, assembling, and/or constructing infrastructure 

related to most processes are included in the analysis, railroad construction and equipment 

infrastructure are omitted because the existing rail system is assumed to be utilized. Operation of 

the locomotives is included as the use phase of the study for both the petroleum-diesel and 

biodiesel pathways. 

The geographic boundary for the study is India, with a focus on biodiesel and diesel use by IR, 

except where resources are extracted and transported to India from other countries. 

The system vintage boundary for the study is set for present day technologies and systems. No 

efforts are made to project future technology advances. The most recent, quality data is used 

whenever possible.

The impacts are evaluated over a 20-year timeframe. The selection of timescale should not 

significantly impact results as it is consistent between the diesel and biodiesel systems and the 

results are normalized by GTK. The 20-year time frame is consistent with analyses conducted in 

other studies (Reinhardt et al. 2007, Prueksakorn and Gheewala 2008). 

2.5 Allocation Procedures

Many options are available within LCA for the allocation of impacts (Scientific Applications 

International Corporation 2006). Allocation is necessary when a process produces more than one 

valuable product. For example, the extraction of Jatropha oil from the seeds produces two 

valuable products: Jatropha oil and the seed cake. The Jatropha oil is processed into biodiesel 

while the seed cake can be used in the fields as fertilizer, offsetting some chemical fertilizer 

requirements. It would be inaccurate to assign all of the impacts associated with the energy and 

materials required to extract the oil to the Jatropha oil when the seed cake is also a valuable 

product. Allocation procedures typically divide process impacts among co-products using mass, 

energy, or economic value as the metric.

While each of these allocation procedures has its merits, the preferred methodology for LCAs is 

boundary expansion where all process impacts are included but credits are taken for the impacts 

that are avoided by the production and use of the co-product (ISO 2006). Using the oil extraction 

example, the full process impacts are included in the analysis; however, credit is taken for 

avoided impacts such as when chemical fertilizer production is offset by the application of the 

nutrient-rich, de-oiled seed cake to the fields. Other co-product boundary expansions considered 

in this study include biomass from pruning Jatropha plants being used to generate electricity,

offsetting the Indian grid electricity, and glycerine produced during transesterification offsetting 

synthetic glycerine production.
14

2.6 Impact Categories

Table 1. Global Warming Potentials (Relative to CO2) of a Subset of the GHGs Evaluated 

in this Study

Greenhouse Gas GWP (mtCO2e)*

CO2 1

CH4 21

N2O 310

HFC-23 11,700

HFC-32 650

HFC-125 2,800

HFC-134a 1,300

HFC-143a 3,800

HFC-152a 140

HFC-227ea 2,900

HFC-236fa 6,300

HFC-4310mee 1,300

CF4 6,500

C2F6 9,200

C3F8 7,000

C4F10 7,000

C6F14 7,400

SF6 23,900

*mtCO2e = metric tons CO2 equivalent

Source: IPCC 1995

The study focuses on three primary impact categories: 

1. Greenhouse gas emissions from all GHGs identified and characterized by the 

Intergovernmental Panel on Climate Change (IPCC) are considered cumulatively and 

weighted according to their 100-year global warming potential relative to CO2, in metric 

tonnes of carbon dioxide equivalent (mtCO2e). The GWPs of the GHG emissions are 

calculated according to the IPCC’s Second Assessment Report (1995), detailed in Table 

1. GWPs revised in more recent IPCC reports were not used in order to maintain 

consistency with previous LCAs that have used the earlier factors.

2. Net energy ratio is evaluated by dividing the energy output of the system in the form of 

fuel energy delivered to the locomotive by the cumulative energy demand of the system. 

Energy credits for the use of biodiesel system offsets are accounted for in the cumulative 

energy demand calculation.

3. Net petroleum displacement is tracked in terms of reduced crude oil consumption for 

the analyzed biodiesel blend life cycle relative to the reference conventional petroleum 

diesel system. 

Efforts were made to gather information on criteria pollutants, toxic air pollutants, and water 

consumption, but not enough India-specific, quality data were available throughout the life cycle 

processes to provide a consistent, complete analysis of these impacts.

2.7 Data Requirements

Whenever possible, India-specific data from literature or the IOC are used for the base case 

analysis. When Indian data are not available, preference is given to regional studies from South 
15

or Southeast Asia with data gaps filled by established LCI data from Europe and North America 

when required. The sensitivity analysis is used to test the importance of using non-regional data 

in the study. 

2.8 Model

The diesel and biodiesel systems are modeled using SimaPro 7.1 LCA software from Product 

Ecology Consultants (http://www.pre.nl/simapro/default.htm). The SimaPro software allows the 

modeling of complex life cycles and the running of detailed sensitivity studies to determine the 

importance of parameter uncertainty and variability. Whenever possible, custom SimaPro 

process modules were developed to meet Indian-specific operating conditions. Modules within 

the SimaPro model are designed to define material, energy, and environmental inputs and 

outputs that are required for a specific process within the life cycle. For example, a module may 

define the electricity, steam, and water required for Jatropha oil extraction along with the 

required seeds that must be delivered from the plantation and the impacts of constructing the 

facility infrastructure.

When Indian data was unavailable or insufficient, gaps were filled using data from the Ecoinvent 

v2 LCI database (http://www.ecoinvent.org/) included with the SimaPro software. Because of the 

depth and breadth of the data modules and the consistent inclusion of infrastructure impacts, 

Ecoinvent system processes were used to maintain consistency throughout the process. For these 

and other reasons, Ecoinvent data are commonly used in LCAs from other researchers, 

improving the comparability of our results to others in the literature. Infrastructure impact data 

are lacking for Indian-specific conditions and therefore are taken from the available datasets in 

Ecoinvent. Ecoinvent data is primarily focused on European conditions but contain many 

worldwide modules with datasets ranging from energy, building materials, and transport to 

chemicals, agriculture, and waste management.

2.9 Uncertainty

As with all LCAs, this analysis encountered a great deal of uncertainty. Lloyd and Ries (2007) 

provide an excellent discussion of how uncertainty is characterized, addressed, and analyzed in 

LCA studies, along with the various types of uncertainty likely to be encountered. Uncertainty is 

particularly relevant to the outcomes of this study because the model is deterministic, using 

single value parameters to generate single point output estimates each time the model is run. A

deterministic model that produces point estimates can yield a false perception of certainty in 

results that are generated from uncertain inputs. The authors’ approaches for addressing 

uncertainty are discussed in the introduction to the sensitivity analysis section. 

The study is faced with three primary types of uncertainty:

1. Parameter uncertainty – uncertainty in the numerical value assigned to a particular data 

point.

2. Scenario uncertainty – uncertainty related to developing the analysis scenarios for the 

study, including selection of functional units, time horizons, allocation procedures, use of 

co-products, and technology trends. 

3. Model uncertainty – uncertainty in the mathematical relationships that drive the 

calculations in the model that is designed to represent real world systems. Model 

uncertainty is minimized in this study for impacts such as GHG emissions from fuel 

combustion where the mathematical relationships between fuel consumption and GHG 
16

emissions are well-established. The primary model uncertainty stems from random error 

and statistical variation related to the projected outputs of the Jatropha cultivation 

processes based on defined inputs.

Applicable to each of these three types of uncertainty, Lloyd and Reis list seven major sources of 

uncertainty and variability. The five major sources of uncertainty and variability in this study are 

highlighted below, with examples representing one or more of the three types of uncertainty.

1. Data unavailability: At a fundamental level, high-quality, consistent, and coherent data 

is not available for many of the project parameters, particularly related to the cultivation 

of Jatropha. Comprehensive data sets detailing anticipated Jatropha seed and oil 

production given a particular set of environmental conditions are not yet developed. 

Moreover, required inputs related to fertilizer, pesticides, and irrigation water are also not 

well defined, nor is the likely impact of these inputs on expected outputs well 

characterized.

2. Measurement uncertainty: Even when important parameters are identified and 

analyzed, it may be difficult to precisely and accurately estimate their value(s). For 

instance, precise and accurate measurements of N2O volatilization rates from soils after 

fertilization are extremely difficult to obtain. As N2O is a potent GHG, the uncertainty in 

the measurement of N2O volatilization rate contributes substantially to uncertainty in the 

prediction of GHG emissions from Jatropha cultivation. 

3. Inherent variability: Many of the parameters in this study are strongly influenced by 

temporal and geographical conditions which vary over time, such as rainfall and the mix 

of foreign and domestic crude oil entering India. Moreover, while some of the parameters 

have well-established relationships, such as anticipated CO2 emissions from combusting 

a given amount of diesel fuel, several of the parameters in this study lack direct 

deterministic correlations. For example, seed and oil yield are challenging to predict with 

a great degree of certainty even if all environmental and man-made inputs and conditions 

of the system are known. These particular parameters are inherently variable, with many 

other parameters exhibiting similarly variable numerical estimates. 

4. Systematic errors and subjective judgment: Scenarios evaluated in this study reflect

current practice. However, the markets for the products and co-products of Jatrophabased biodiesel production are immature in India, making it likely the scenario evaluated 

in this study will change over time. No technology advancement is assumed over the 

analysis lifetime as predictions for the likely evolution of technology are not available.

5. Expert uncertainty and disagreement: There is no “most likely” scenario that experts 

agree on for how Jatropha cultivation and transformation into biodiesel will develop in 

India. Multiple scenarios are plausible and vary greatly in terms of geographical location, 

production pathways, and co-product use even before the uncertainty of input parameters 

is included. The lack of expert agreement makes the development of a coherent analytical 

narrative challenging. As multiple competing scenarios could be proposed, the 

applicability of the results outside of the developed scenarios is uncertain.
17

2.10 Sensitivity Analysis Approach

Reasons to conduct a sensitivity analysis are at least twofold. First, sensitivity analysis can test 

the robustness of conclusions to parameter uncertainty and variability. Second, sensitivity 

analysis can determine and rank the influence a given parameter has on model outputs. This 

report attempts to achieve both of these goals in its sensitivity analysis. It focuses on a set of key 

parameters judged to be the most likely influential on model outcomes.

Sensitivity analysis is distinct from uncertainty analysis. One method of uncertainty analysis 

propagates the uncertainty and variability of parameters through model calculations to estimate 

the uncertainty (error bounds) of model results. This study does not conduct a formal uncertainty 

analysis because the uncertainty and variability of parameter values for many parameters is not 

known and the web of modeled processes is so complex that propagation is challenging. 

However, a sense of the outer boundaries of model results is determined based on analyzing a 

compilation of best- and worst-case parameter values into what this report refers to as best- and 

worst-case scenarios. These scenarios are not likely or even necessarily plausible, but inform the 

reader of the likely extremes of model outcomes. 
18

3.0 Base Case Assumptions

Developing a coherent narrative for a base case analysis is among the most difficult tasks in an 

LCA of Jatropha biodiesel in India. As Achten and colleagues noted in their literature review, a 

quality set of data identifying anticipated Jatropha yields for specific environmental conditions 

and detailed irrigation and fertilization scenarios does not exist (Achten et al. 2008). Developing 

such a coherent set of primary data inputs was outside the scope of this study. Consequently, the 

authors had to make many assumptions to define the scenarios and estimate values for all 

parameters and scenarios. In addition, LCAs require decisions about modeling approaches and 

calculation methods in order to complete the analysis. These decisions do not necessarily have a 

correct answer. It is critical for interpretation of the results produced from an LCA that the 

assumptions and decisions of the analysts be transparent to the reader. Below, 10 major base case 

assumptions and modeling decisions are outlined:

1. Fuel economy does not decrease with increasing biodiesel blends. According to initial 

IR trials of biodiesel in their locomotives, no difference in volumetric fuel consumption 

was observed for operation using B5, B10, or B20 compared to operation using 

conventional diesel (Kathpal 2008). If a fuel economy decrement is in fact experienced, 

then this study’s impacts will have been underestimated. Therefore, the sensitivity 

analysis examines the impact of assuming a small fuel economy decrement with the use 

of biodiesel based on evidence from the U.S. Environmental Protection Agency that the 

use of B20 may result in a fuel economy decrement of approximately 2% (U.S. EPA 

2008). 

2. The biodiesel fuel combusted in the locomotives is assumed to have no CO2

emissions. At some point in this biofuel LCA, the carbon sequestered by the growth of 

the Jatropha trees must be credited as a reduction in GHG emissions from the biodiesel 

system. This study incorporates that credit by assuming an emission factor of zero for all 

biofuels combustion, including the portion of the locomotive fuel composed of biodiesel 

and the Jatropha biomass. The alternative assumption would be to account for carbon 

sequestration during the plantation operation phase. Data sets are not well established to 

define the rate Jatropha plants sequester carbon. Therefore, the authors chose to credit the 

sequestration at the point and time of use. Assuming no net GHG emissions from biofuels 

combustion is based on an assumption of complete combustion—negligible emissions of 

other carbon-containing compounds such as carbon monoxide and methane.

Consequently, by first principles, carbon dioxide emissions from the complete 

combustion of the biomass (in the form of solid or liquid fuel) must equal the carbon 

dioxide sequestered from the atmosphere. This assumption should not bias results toward 

diesel or biodiesel.

3. Potential land use changes were not evaluated. The location of the hypothetical 

Jatropha plantation considered in this study was not specified in enough detail to 

ascertain its prior land use and above and below-ground carbon content. Therefore, 

determining net change in carbon content of the plantation site was not feasible. The 

carbon emissions from direct land use change could be zero, small or significant, and 

could be either positive or negative, depending on the prior land use. Utilizing lands that 

are abandoned or degraded are best from a GHG perspective. Direct land use change 
19

should be accounted for when specific plots of land are being considered as plantation 

sites. 

4. A 20-year time horizon is assumed. A time horizon of greater than one year is required 

to analyze Jatropha biomass systems in order to include upfront activities like the 

development of land into a plantation and also the time for Jatropha plants to mature. The 

life cycle length should not bias results as most results are normalized to the functional 

unit (GTK) for reporting. Twenty years is well within the lifetime of most pieces of 

infrastructure in the study and is the value used in comparable studies (Reinhardt et al. 

2007, and Prueksakorn and Gheewala 2008). However, it is likely that technological 

innovations will occur over this time period, particularly for the immature biodiesel 

production industry and are not accounted for in the study.

5. The amount of rainfall required to avoid the need for irrigation is 2,500 mm per 

year. The circumstances where irrigation is required for the establishment of Jatropha 

trees is very poorly defined in literature as Jatropha has been known to grow in places 

with rainfall ranging from approximately 300 mm per year to 3,000 mm per year. 

However, Prueksakorn and Gheewala (2008) identify 2,500 mm per year as a rainfall 

level where no irrigation is required for Jatropha grown in Thailand. This study calculates 

the required irrigation as the difference between 2,500 mm per year and the average 

rainfall at the plantation location. If the water requirement for Jatropha plants is in fact 

greater than 2500 mm per year, this study’s results will have underestimated the true 

impacts. Likewise, if Jatropha’s water requirement is less than 2500 mm per year, then 

this study’s results will have overestimated the true impacts.

6. Irrigation is assumed to be required for only the first three years of cultivation.

Reinhardt and colleagues (2008) suggest that irrigation is only required when the 

plantation is establishing itself, which they report as three years. If irrigation turns out to 

be necessary for longer than three years, the impacts estimated in this study will be 

underestimated. However, the sensitivity analysis does evaluate the impacts of requiring 

irrigation for the full life cycle.

7. Initial tree density is 2,500 trees per hectare in a 2 m x 2 m planting grid. Tree 

planting densities reported in the literature range from 1,100 to 2,500+ trees per hectare 

with the appropriate planting grid largely dependent on local conditions. The Planning 

Commission of India (2003) uses 2,500 trees per hectare as the density for its 

calculations, taken as the base case assumption for this study. This assumption will tend 

to increase both seed and biomass yields per hectare compared to cases of lower densities 

reported in the literature, benefiting the biodiesel system in comparison to the diesel 

reference system.

8. No pesticides, insecticides, or herbicides are applied to the crops. Some literature 

suggests that the use of protective chemicals on the trees may not be necessary (Reinhardt 

et al. 2008; Prueksakorn and Gheewala 2008) due, in part, to the toxic nature of the plant. 

Other studies have cited pests that do impact Jatropha crop yields (Lele 2008b). Data sets 

recommending the appropriate amounts of chemicals to apply over tree life cycles are not 
20

well developed, therefore this study assumes no protective chemicals are necessary. This 

assumption would lead to underestimated impacts for biodiesel production if protective 

chemicals are necessary.

9. Seed cake is used to offset fertilizer use on the plantation. The Jatropha seed cake has 

multiple potential uses once the oil has been extracted. This study assumes the seed cake 

is returned to the plantation to offset an amount of NPK fertilizer equal to the nutrient 

content of the seed cake. Combustion to produce useable heat or power, an alternative use 

of the seed cake, is not considered.

10. Biomass removed from the plantation is combusted to generate electricity. Biomass 

removed from the plantation via pruning and clipping is assumed to be combusted to 

generate electricity (Reinhardt et al. 2008). The electricity generated from the plantation 

biomass offsets Indian grid electricity. No CO2 emissions are assumed for the biomass 

combustion to account for the credit that should be given for CO2 sequestration during 

Jatropha cultivation. Efficiency of conversion from biomass combustion to electricity 

generation is assumed to be 25% (U.S. CCTP 2005). An alternative assumption is that the 

energy produced by combusting the biomass offsets heat required in some local industrial 

process. This study did not evaluate that alternative use since no literature reports 

Jatropha biomass used for this purpose, and it is unclear whether a local process requiring 

heat would exist where the biodiesel processing facility is located.
21

4.0 Base Case Scenario

The subsequent sections report important data inputs that define the base case scenario. Input 

data are separated into the six primary categories listed below. Additional details on the included 

data and processes are highlighted in each section:

• Petroleum diesel production and distribution (Reference System)

• Jatropha cultivation

• Jatropha oil extraction

• Biodiesel production via Jatropha oil transesterification

• Locomotive operation

• Supporting processes.

Each category is defined in SimaPro by several modules, as shown in Figures 11-16. Many of the 

modules were developed by the authors (referred to hereafter as “custom” modules). The exact 

coding of the custom modules is reported in Appendix A. Where India-specific data were not 

available, Ecoinvent 2.0 modules were used. The detailed coding of these modules is proprietary 

and cannot be reported.

4.1 Petroleum Diesel Production and Distribution (Reference System)

Both the conventional petroleum diesel reference system and the biodiesel pathways include the 

life cycle impacts of diesel fuel production and distribution. The overview of the petroleum 

diesel production and distribution pathways was shown in Figure 9. Figure 11 displays the 

modules used to model the petroleum diesel production and distribution system in SimaPro in 

more detail.

Figure 11. Modeling schematic for petroleum diesel production and distribution processes

Table 2 describes the purpose of each custom and Ecoinvent 2.0 module utilized in modeling the 

petroleum diesel production and distribution processes. 
22

Table 2. Simapro Module Descriptions for Petroleum Diesel Production and Distribution 

Processes

Module 

Name Module Purpose Comments

Onshore 

Crude Oil 

Extraction 

– Middle 

East

This Ecoinvent 2.0 module calculates the impacts of 

onshore oil production in the Middle East. As the 

largest percentage of Indian foreign crude oil 

originates in the Middle East, this module is used to 

represent the 70% of Indian crude oil that is from 

foreign sources.

Indian oil may also come from 

foreign offshore sources, Africa 

(particularly Nigeria), or other 

foreign places. Oil from these 

sources is not considered in this 

study. 

Offshore 

Crude Oil 

Extraction 

– Great 

Britain

This Ecoinvent 2.0 module calculates the impacts of

offshore oil production near Great Britain. The module 

is used as a proxy for the impacts of domestic offshore 

oil production from the Bombay High oil fields in India.

The largest share of domesticallyproduced Indian crude oil is 

produced offshore at the Bombay 

High oil fields. The Ecoinvent 

module for offshore crude oil 

production in Great Britain is used 

as a proxy for Indian production.

Ocean 

Tanker 

Transport

This Ecoinvent 2.0 module calculates the impacts of 

the transport of foreign and domestic crude oil via 

ocean tanker to the coastal terminal in India.

Crude oil is assumed to be 

delivered to the port at 

Visakhapatnam, India, adjacent to 

the VIZAG refinery. Foreign oil 

originates in Saudi Arabia, 

domestic oil from Bombay High.

Crude Oil 

at Indian 

Terminal

This custom module aggregates crude oil produced 

domestically and from foreign sources at the port at 

Visakhapatnam, India prior to refining.

Diesel at 

Refinery / 

India

This modified Ecoinvent 2.0 module quantifies the 

impacts of refining crude oil into high speed diesel. 

This module represents an average European refinery 

from year 2000.

The only aspect modified to Indiaspecific conditions in this module 

is the source of crude oil.

Diesel at 

Regional 

Storage / 

India

This modified Ecoinvent 2.0 module quantifies the 

impacts distributing diesel to regional storage via road, 

rail, and pipeline.

By using the diesel at regional 

storage module, losses that occur 

during diesel distribution and 

filling are included in the analysis. 

The only modification to Indiaspecific conditions is the source of 

crude oil to the refinery.

Diesel at 

Railyard

This custom module aggregates diesel fuel at the 

railyard prior to fueling locomotives or blending with 

biodiesel.

Key data used to develop the custom module for Indian crude oil is reported in Table 3. Seventy 

percent of Indian crude oil comes from foreign sources with the greatest percentage originating 

in the Middle East (Bureau of Energy Efficiency 2008). Domestically, the largest percentage of 

Indian oil is extracted from the offshore oil fields at Bombay High (Ministry of Petroleum and 

Natural Gas 2006). Because tracing a specific drop of oil through the Indian system is not 

possible, this study constructs a plausible base case scenario based on country averages, where 

foreign oil is extracted in Saudi Arabia and domestic oil is extracted from Bombay High with 

both locations shipping the crude oil via ocean tanker to the VIZAG refinery on the east coast of 
23

India. Refined diesel is then shipped via rail to the rail terminals and depot at Bhilai, near Raipur, 

for use in IR locomotives.

Table 3. Base Case Data Inputs for Petroleum Diesel Production and Distribution

Parameter Value Units Assumptions/Notes Source

Foreign 

Crude Oil 0.75 Mass fraction

Fraction of India’s crude oil from 

foreign sources. Sarin 2008e

Domestic 

Crude Oil 0.25 Mass fraction

Fraction of India’s crude oil from 

domestic sources. Sarin 2008e

Domestic 

Offshore 1 Mass fraction

Fraction of domestic oil from offshore 

fields. Base case assumes all oil is 

derived from the Bombay High 

offshore oil field.

Base case 

assumption

Domestic 

Onshore 0 Mass fraction

Fraction of domestic oil from onshore 

fields. Base case assumes no crude 

oil generation from onshore fields in 

locations such as Assam.

Base case 

assumption

Fraction 

Middle East 1 Mass fraction

Fraction of foreign oil from Middle 

East. Largest percentage of Indian 

foreign crude oil comes from Saudi 

Arabia. Nigeria is another potential 

source.

Base case 

assumption

Foreign 

Crude Oil 

Transport

7,000 km

Transport distance by ocean tanker 

between Middle East and VIZAG 

Refinery.

World News 

Network 2008

Domestic 

Crude Oil 

Transport

3,200 km

Transport distance by ocean tanker 

between Bombay High and VIZAG 

Refinery.

World News

Network 2008

Diesel Rail 

Transport 600 km Distance crude oil travels by rail from VIZAG oil terminal to Bhilai.

LiveIndia 

2008

4.2 Jatropha Cultivation

Modeling the cultivation of Jatropha trees and operation of the plantation requires data on 

numerous inputs and outputs including fertilizer use, irrigation water, electricity, and diesel fuel,

along with parameters such as the rate of N2O release from nitrogen fertilizer. This portion of the 

model carried the greatest uncertainty as deterministic correlations amongst cultivation 

parameters, including environmental conditions and human inputs, are not well established. 

Figure 12 outlines the processes included in the modeling of Jatropha cultivation. 
24

Figure 12. Modeling schematic for Jatropha cultivation processes

Table 4 describes the purpose of each module utilized in the analysis of Jatropha cultivation. 

Table 5 summarizes key base case input parameters for Jatropha cultivation. 

The nitrous oxide release rate in Table 5 represents the default value of 0.01 g N2O/g N in 

fertilizer reported by IPCC (2006) from a stated range of 0.003 – 0.03 g N2O / g N in fertilizer. 

The emission factor accounts for direct emissions from a variety of organic and synthetic 

nitrogen fertilizers and accounts for crop residue and mineralization of organic carbon in the soil 

due to land management and use changes. The emission factor does not include secondary or 

indirect emission sources of N2O such as leaching N entering water systems, crop residue being 

plowed into the fields for successor crops, or dung and urine from animals that feeding on the 

crops. Crutzen et al. (2008) use a top-down calculation method to suggest that the actual N2O-N 

emission factor may be three to five times larger than the default IPCC value (ranging from 0.03 

– 0.05 g N2O per gram N in fertilizer). The impact of the uncertainty in this parameter on overall 

study results is tested in the sensitivity analysis. 

The plantation electricity parameter is also uncertain as Lele (2008e) does not explicitly state 

what plantation operations are included in this estimate. Therefore, plantation electricity use is 

considered a conservative value as it may double count some of the electricity required for 

plantation processes such as irrigation and oil extraction that are modeled separately.
25

Table 4. SimaPro Module Descriptions for Jatropha Cultivation

Module 

Name Module Purpose Comments

Polyethylene 

Bag 

Production

This Ecoinvent 2.0 module calculates the impacts of 

producing and transporting the polyethylene used in the 

Jatropha seedling bags at the nursery.

Each Jatropha seedling is generally 

raised in a polyethylene bag for the 

first few months.

Tillage and 

Ploughing

This Ecoinvent 2.0 module calculates the impacts of 

clearing and preparing the required land for developing 

the Jatropha plantation.

The base case assumes that 

50,000 hectares are cleared for 

plantation land based on IOC 

guidance. 

Irrigation by 

Hectare

This Ecoinvent 2.0 module calculates the impacts of 

irrigating the plantation area during initial planting of the 

trees to aid establishment.

Irrigation data is not well 

established for Jatropha 

plantations.

Jatropha 

Seedling for 

Planting

This custom module is designed to represent the 

requirements for the cultivation of Jatropha seedlings at 

the nursery. Nursery is likely to be located at or near the 

plantation. Better data is needed to identify energy 

requirements of the nursery.

Module information is incomplete as 

only polyethylene bag requirement 

is known. However, impacts are 

very small.

Jatropha 

Plantation, 

Planted, 

India

This custom module calls the required number of 

seedlings, fertilizer, tillage and ploughing, and irrigation 

water to establish the Jatropha trees on the plantation. 

The output is a hectare of planted Jatropha plantation.

The impacts for operating and 

managing the plantation are 

separately tracked in the “Jatropha 

Seeds Harvested from Plantation” 

module.

Jatropha 

Seeds 

Harvested 

from 

Plantation

This custom module quantifies all of the impacts 

associated with operating and managing the plantation 

over the entire life cycle. Plantation management 

impacts are normalized per tonne of seeds produced. 

Jatropha fruit harvesting and de-husking are assumed 

to be done with manual labor. Jatropha husk 

combustion is used to offset Indian electricity delivered.

This module calls the required 

inputs from the fertilizer and 

irrigation modules along with 

electricity and diesel fuel for 

plantation operation and contains 

much of the model’s uncertainty.

Urea 

Production, 

Transport, & 

Application

This Ecoinvent 2.0 module quantifies the impacts of 

producing, transporting, and applying urea to provide 

the required amount of nitrogen to the plantation.

Required fertilizer levels are not 

well defined for the management of 

Jatropha plantations.

Single Super 

Phosphate 

Production, 

Transport, & 

Application

This Ecoinvent 2.0 module quantifies the impacts of 

producing, transporting, and applying single super 

phosphate to provide the required amount of phosphate 

to the plantation.

Required fertilizer levels are not 

well defined for the management of 

Jatropha plantations.

Potassium 

Chloride 

Production, 

Transport, & 

Application

This Ecoinvent 2.0 module quantifies the impacts of 

producing, transporting, and applying potassium 

chloride to provide the required amount of potassium.

Required fertilizer levels are not 

well defined for the management of 

Jatropha plantations.

Irrigation 

Water 

Required

This Ecoinvent 2.0 module accounts for the impacts of 

applying the required levels of irrigation water to the 

plantation. 

The required amount of water is 

assumed to be the rainfall deficit 

between the rainfall on the site and 

a threshold of 2,500 mm/yr for no 

irrigation.
26

Table 5. Base Case* Data Inputs for Jatropha Cultivation

Parameter Value Units Assumptions / Notes Source

Life cycle 20 Years

Defines the lifetime over which the 

study is conducted.

Reinhardt et al.

2007

Plantation 

Location

Raipur

Area

Chhattisgarh

India

Target plantation location selected by 

IOC. Sarin 2008b 

Plantation 

Rainfall 1,385 mm/yr Average rainfall for Raipur, used to calculate required irrigation.

Chhattisgarh 

Online 2008

Rainfall 

Required for 

No Irrigation

2,500 mm

Irrigation data is difficult to find. 2,500 

mm per year cited as amount of rainfall 

where no irrigation is needed in 

Thailand. This rainfall value is 

compared with site average rainfall to 

determine irrigation requirement.

Prueksakorn 

and Gheewala 

2008

Plantation Size 50,000 Hectares Based on IOC anticipated plantation 

size. Sarin 2008b

Seedling 

Survival Rate 0.8

Surviving 

seedlings per

total seedling 

planted

Represents Jatropha seedling survival 

rate of 80% under average planting 

conditions.

Lele 2008b,

Renewable 

Energy 2008

Tree Density 2,500 Trees per

hectare

Assumed initial Jatropha tree density 

based on Planning Commission of 

India assumptions.

Planning 

Commission of 

India 2003

Years 

Required for 

Irrigation

3 Years

Reinhardt et al.’s optimized scenario 

assumes irrigation is only required for 

the first three years of plantation 

establishment.

Reinhardt et al. 

2008

Fertilizer 

Application 2 Applications per year

Assumes one fertilizer application at 

the beginning and end of the rainy 

season.

Lele 2008b

Urea Fertilizer 

Required 81 kg per hayear

Urea fertilizer use based on the 

Optimized scenario of Reinhardt et al. 

Reinhardt assumes density of 1,667 

trees / hectare which is scaled to base 

case density.

Reinhardt et al. 

2008

P2O5 Fertilizer 

Required 31 kg per hayear

P2O5 fertilizer use based on the 

Optimized scenario of Reinhardt et al.

Reinhardt et al. 

2008

K2O Fertilizer 

Required 89 kg per hayear

K2O fertilizer use based on the 

Optimized scenario of Reinhardt et al.

Reinhardt et al. 

2008

Diesel Fuel 

Required 86 liters per hayear

Diesel fuel use based on the Optimized 

scenario of Reinhardt et al.

Reinhardt et al. 

2008

Nitrous Oxide 

Release 0.01 g N2O per g N in fertilizer

Fraction of nitrogen contained in 

fertilizer that is released to the air 

based on IPCC’s default value for N2O 

emissions from nitrogen fertilizers.

IPCC 2006

Oil Content of 

Jatropha Seed 0.35

Mass oil per

mass total 

seed

Assumed average oil content of dry 

seed on mass basis. Matches 

assumption in Indian Planning 

Commission calculations.

Achten et al. 

2008

Plantation 

Electricity 12,000 MWh per

year

Approximate electricity required to 

operate a 50,000 hectare plantation for 

one year.

Lele 2008c
27

Parameter Value Units Assumptions / Notes Source

Seed Husk 

Yield 1,429

kg sun dried 

husks per hayear

Estimated seed husk yield after seed 

extraction and assuming a water 

content of 9%. Based on the Optimized 

scenario of Reinhardt et al.

Reinhardt et al. 

2008

Seed Husk 

Energy Density 15.5 MJ per kg Gross energy content of the dry matter of Jatropha seed husks.

Reinhardt et al. 

2008

Jatropha Seed 

Yield 2,382

kg sun dried 

seeds per hayr

Estimated Jatropha seed yield 

assuming 5.8% water content. Based 

on the Optimized scenario of Reinhardt 

et al.

Reinhardt et al. 

2008

Biomass Yield, 

Year 1 2.5 kg biomass per tree

IOC supplied estimate of first year 

biomass yield from pruning. Sarin 2008c

Biomass Yield 

Year 2 4.5 kg biomass per tree

IOC supplied estimate of second year 

biomass yield from pruning. Sarin 2008c

Biomass Yield 

from Mature 

Jatropha 

Plants

8.5 kg biomass 

per tree

IOC supplied estimate of biomass yield 

from pruning mature Jatropha trees. Sarin 2008c

Mass Fraction 

Stems 0.67 Mass fraction

Based on approximate breakdown of 

dried Jatropha plant biomass. 

Remaining mass fraction is comprised 

of leaves.

Nivitchanyong 

2007

Energy Density 

of Leaves 3,624 kJ per kg Gross specific energy content of Jatropha leaves.

Nivitchanyong 

2007

Energy 

Density of 

Stems

3,932 kJ per kg Gross specific energy content of 

Jatropha stems.

Nivitchanyong 

2007

Seed 

Transportation 50 km

Assumed distance for Jatropha seeds 

to be transported by truck from 

plantation to oil extraction unit.

Calculated for 

base case 

narrative

*Base case assumes no application of pesticides, herbicides, or insecticides.

4.3 Jatropha Oil Extraction

The Jatropha oil extraction process considered in this study is a continuous solvent extraction 

process with 91% extraction efficiency. Limited data is available on the use of this solvent 

extraction process for Jatropha oil collection in India. The base case data represents Indiaspecific published data. Figure 13 highlights the processes used to model Jatropha oil extraction 

while Table 6 describes the modules.
28

Figure 13. Modeling schematic for Jatropha oil extraction processes

Table 6. SimaPro Module Descriptions for Jatropha Oil Extraction

Module 

Name Module Purpose Comments

Indian 

Electricity 

Delivered

This custom module represents the Indian electric grid 

mix supplied to the oil extraction unit.

See the section “Supporting 

Processes” for more details on 

the Indian Electricity module.

Jatropha 

Oil at 

Solvent 

Extraction 

Facility, 

India

This custom module calculates the impacts of 

continuous solvent-based Jatropha oil extraction. 

Infrastructure of an oil extraction facility in Western 

Europe is included. The process produces Jatropha oil 

that is transported to the transesterification plant and 

de-oiled seed cake that is returned to the plantation as 

a fertilizer.

This module calls the required 

inputs for electricity, steam, 

hexane, and water needed to 

operate continuously.

Steam 

Generation

This Ecoinvent 2.0 module calculates the impacts of 

generating and delivering the steam required for the 

oil extraction process.

Generic steam production is used 

due to a lack of information on 

India-specific processes for 

generating steam for oil 

extraction.

Hexane 

Production 

and 

Transport

This Ecoinvent 2.0 module quantifies the impacts of 

producing and transporting the hexane that is used as 

the solvent in the Jatropha oil extraction process.

Hexane is the only solvent used 

on a commercial scale for oil 

extraction at this time.

Tap Water 

Supply

This Ecoinvent 2.0 module quantifies the impacts of 

supplying tap water to the oil extraction facility for use 

in the oil extraction process.

The module is based on Western 

European data as India-specific 

water production and delivery 

data is unavailable.

Table 7 reports key base case data inputs used to model Jatropha oil extraction via a continuous 

solvent extraction process.
29

Table 7. Base Case Data Inputs for Jatropha Oil Extraction

4.4 Biodiesel Production via Jatropha Oil Transesterification

The study focuses on base-catalyzed transesterification of Jatropha oil to biodiesel as the process 

promoted by the Planning Commission of India (2003). Figure 14 outlines the important 

processes included in the model while Table 8 describes each module.

Figure 14. Modeling schematic for biodiesel production via Jatropha oil transesterification 

processes

Parameter Value Units Assumptions/Notes Source

Extraction 

Efficiency 91% Mass percent

Percent of Jatropha oil available in 

seeds extracted via solvent extraction.

Planning 

Commission 

of India 2003

Electricity Use 55

kWh per

tonne of 

seed input

Average electricity use for continuous 

solvent extraction per tonne of 

Jatropha seed input.

Adriaans 

2006

Hexane Use 4

kg per tonne 

of seed 

input

Average amount of hexane used in 

continuous solvent extraction; 99% is 

recycled.

Adriaans 

2006; Sarin 

2008c

Steam Use 280

kg per tonne 

of seed 

input

Average amount of steam required for 

continuous solvent extraction.

Adriaans 

2006

Water Use 12

m3 per

tonne of 

seed input

Average amount of water required for 

continuous solvent extraction 

(consumed and discharged to sewer).

Adriaans 

2006

Jatropha Oil 

Transportation 0 km

Assumes oil extraction facility is colocated with the transesterification 

plant.

Sarin 2008c
30

Table 8. SimaPro Module Descriptions for Biodiesel Production via Jatropha Oil 

Transesterification

Module Name Module Purpose Comments

Indian Electricity 

Delivered

This custom module represents the Indian 

electric grid mix supplied to the biodiesel 

production unit.

See the section “Supporting 

Processes” for more details on 

the Indian Electricity module.

Biodiesel 

Production, Basecatalyzed 

Transesterification, 

India

This custom module calculates the impacts of 

biodiesel production via base-catalyzed 

Jatropha oil transesterification. Infrastructure of 

a transesterification facility in Western Europe 

is included. The process produces biodiesel to 

be transported to railways and glycerine to 

offset synthetic glycerine production.

This module calls the required 

inputs for electricity, steam, 

potassium hydroxide, and 

methanol, and water needed to 

operate.

Steam Generation

This Ecoinvent 2.0 module calculates the 

impacts of generating and delivering the steam 

required for the oil extraction process.

Generic steam production is used 

due to a lack of information on 

India-specific processes for 

generating steam for 

transesterification.

Potassium 

Hydroxide 

Production and 

Transport

This Ecoinvent 2.0 module quantifies the 

impacts of producing and transporting the 

potassium hydroxide that is the base catalyst 

for the transesterification process.

Potassium hydroxide production 

is modeled based on Western 

European conditions due to a lack 

of data on Indian production.

Methanol 

Production and 

Transport

This Ecoinvent 2.0 module quantifies the 

impacts of producing and transporting the 

methanol that is used as the alcohol for the 

transesterification process.

Methanol production is modeled 

based on Western European 

conditions due to a lack of data on 

Indian production.

Glycerine from 

Epicholorhydrin

This Ecoinvent 2.0 module quantifies the 

impacts that are offset through the generation 

of the co-product glycerine replacing some 

synthetic glycerine production. 

The module is based on Western 

European conditions due to a lack 

of Indian data.

Table 9 reports key base case data inputs for the modeling of biodiesel transesterification.
31

Table 9. Base Case Data Inputs for Biodiesel Transesterification via Jatropha Oil 

Transesterification

Parameter Value Units Assumptions/Notes Source

Conversion 

Efficiency 95% Mass percent

Conversion efficiency of Jatropha oil to 

biodiesel Lele 2008d

Electricity Use 36

kWh per

tonne of 

biodiesel 

produced

Electricity use based on a 100,000 

tonne biodiesel / year plant

Planning 

Commission 

of India 2003

Steam Use 660

kg per tonne 

of biodiesel 

produced

Steam use based on a 100,000 tonne 

biodiesel / year plant

Planning 

Commission 

of India 2003

Water Use 55 m3

circulated

Water is circulated, not consumed, so 

the amount reported here is for initial 

loading of the system.

Planning 

Commission 

of India 2003

Base Catalyst 

Required 

(KOH)

18

kg per tonne 

of biodiesel 

produced

KOH used as base catalyst for a 

100,000 tonne biodiesel / year plant

Planning 

Commission 

of India 2003

Methanol 

Required 110

kg per tonne 

of biodiesel 

produced

Methanol use based on a 100,000 

tonne biodiesel / year plant

Planning 

Commission 

of India 2003

Glycerine

Yield 0.08 Mass fraction

Mass fraction yield of glycerine as coproduct during production of biodiesel Lele 2008a

Biodiesel 

Transportation 20 km Distance between Raipur and Bhilai depots

LiveIndia 

2008

4.5 Locomotive Operation

Both the reference petroleum diesel life cycle and the comparison blended biodiesel scenarios 

end with the fuels being combusted to operate IR locomotives. Figure 15 displays the primary 

SimaPro modules for analyzing the locomotive combustion while Table 10 describes each 

process.

Figure 15. Modeling schematic for locomotive operation
32

Table 10. SimaPro Module Descriptions for Locomotive Operation

Module 

Name Module Purpose Comments

Indian 

Electricity 

Delivered

This custom module represents the Indian electric grid 

mix supplied to the biodiesel blending unit.

See the section “Supporting 

Processes” for more details on 

the Indian Electricity module.

Biodiesel 

Blending, 

India, At 

Processing 

Facility

This custom module calls biodiesel from the 

transesterification plant and diesel fuel at the rail yard 

and blends them using Indian electricity into the 

desired mix for the analysis.

Analyzed biodiesel blends include 

0%, 5%, 10%, and 20%.

Fuel 

Transport

This Ecoinvent 2.0 module calculates the impacts of 

transporting the blended biodiesel a short distance via 

truck from the Bhilai depot to the railway fueling 

location.

Blended biodiesel transport is a 

small impact in the overall life 

cycle.

Indian 

Freight 

Train 

Operation, 

Biodiesel 

Blend

This custom module calls the required amount of 

blended biodiesel to operate the locomotive over the 

entire life cycle. Carbon dioxide emissions from the 

diesel portion of the blended biodiesel are also 

calculated in this module.

The base case analysis assumes 

an operating life time of 2 billion 

GTK.

Table 11 reports key base case data inputs for locomotive operation including CO2 emission 

factors, fuel consumption, and biodiesel efficiency.
33

Table 11. Base Case Data Inputs for Locomotive Operation

Parameter Value Units Assumptions/Notes Source

Biodiesel 

Blend

0.05, 

0.1, 0.2

Fraction 

biodiesel

Base analysis covers 5%, 10%, and 

20% biodiesel blends.

Sarin 2008a; 

Kathpal 2008

Biodiesel 

Efficiency 1

Biodiesel 

fuel 

consumption 

per diesel 

fuel 

consumption

Preliminary IR field trials showed a 

negligible negative effect in volumetric 

fuel consumption for B5, B10, and B20 

(checked with sensitivity).

Kathpal 

2008; 

Skinner et al. 

2007 

Specific 

Gravity 

Biodiesel

0.88 kg per liter Specific gravity of 100% biodiesel used 

in model conversions.

Planning 

Commission 

of India 2003; 

Gubler 2006

Specific 

Gravity Diesel 0.84 kg per liter Specific gravity of diesel fuel used in the model conversions.

Planning 

Commission 

of India 2003;

Gubler 2006

Diesel Fuel 

Consumption 

(Locomotive)

4.38

liters diesel 

per 1,000 

GTK

Average fuel consumption for Indian 

Railways passenger and freight trains. 

Gross tonnage includes both the 

weight of the train and the 

passengers/cargo.

Indian 

Railways 

2007

Lifetime Gross 

Tonne 

Kilometers 

(GTK)

2 billion

Total GTK 

analyzed 

over 

locomotive 

life cycle

GTK is the functional unit for this 

study. The lifetime of this study is 20 

years.

Indian 

Railways 

2007

Diesel CO2

Emission 

Factor

2.68

kg CO2 per

liter of diesel 

combusted

CO2 emission factor for the combustion 

of diesel fuel in the locomotives.

Climate 

Registry 

2008

Biodiesel CO2

Emission 

Factor

0

kg CO2 per

liter of 

biodiesel 

combusted

Combustion of 100% biodiesel 

assumed to emit no CO2 emissions to 

account for the carbon sequestered in 

Jatropha during cultivation.

By definition

4.6 Supporting Processes

Many of the biodiesel processes, including cultivation, oil extraction, and transesterification

require supporting processes such as electricity or transportation. An Indian specific electricity 

generation profile was created based on the national average annual proportion of electricity 

generation by fuel type. The impacts of each generating technology were calculated using 

Ecoinvent 2.0 data modules as outlined in Figure 16 and described in Table 12. The proportion 

of electricity generation by source is reported in Table 13. The Indian electric grid suffers from 

significant electricity transmission and distribution (T&D) losses. The base case assumes T&D 

losses of 32% (Indian Central Electricity Authority 2008) and includes impacts from T&D 

infrastructure in the calculations to test the potential impact of electricity infrastructure on the 

results. The inclusion of the T&D infrastructure had a negligible impact on model results.
34

Figure 16. Modeling schematic for Indian electricity

Table 12. SimaPro Module Descriptions for Indian Electricity

Module Name Module Purpose Comments

Electricity 

Generation –

Natural Gas

This Ecoinvent 2.0 module calculates the 

impacts of electricity generation from natural 

gas in Western Europe. Indian data was 

unavailable.

The amount of natural gas used in the 

Indian Electricity module is based on 

average annual electricity generation 

by fuel type in India.

Electricity 

Generation –

Coal

This Ecoinvent 2.0 module calculates the 

impacts of electricity generation from coal in 

Western Europe. Indian data was unavailable.

The amount of coal used in the Indian 

Electricity module is based on 

average annual electricity generation 

by fuel type in India.

Electricity 

Generation –

Nuclear

This Ecoinvent 2.0 module calculates the 

impacts of electricity generation from nuclear 

energy in Western Europe. Indian data was 

unavailable.

The amount of nuclear power used in 

the Indian Electricity module is based 

on average annual electricity 

generation by fuel type in India.

Electricity 

Generation –

Hydropower

This Ecoinvent 2.0 module calculates the 

impacts of electricity generation from 

hydropower in Western Europe. Indian data 

was unavailable.

The amount of hydropower used in 

the Indian Electricity module is based 

on average annual electricity 

generation by fuel type in India.

Electricity 

Generation –

Renewables

This module calls two Ecoinvent 2.0 modules 

that calculate the impacts of electricity 

generation from solar and wind energy in 

Western Europe. Indian data was unavailable.

Indian electricity generation data were 

listed with renewables as a category. 

In order to match with existing 

Ecoinvent 2.0 modules, the authors 

assumed that 50% of the renewable 

electricity came from solar and 50% 

came from wind.

Indian 

Electricity 

Generated

This custom module defines the source mix for 

Indian electricity using Ecoinvent 2.0 modules 

for each generation type.

Indian 

Electricity 

Delivered

This custom module includes impacts from 

T&D infrastructure and accounts for 

transmission and distribution losses in India.

Transmission and distribution losses 

in India can be greater than 30%.

Table 13 reports key base case data inputs for calculating the Indian electricity SimaPro module.
35

Table 13. Base Case Data Inputs for Indian Electricity

Fuel % Generation Source

Coal 70% Indian Electricity 2008

Natural Gas 12% EIA 2008

Hydroelectric 15% EIA 2008

Nuclear 2% EIA 2008

Renewable (solar, wind, etc) 1% EIA 2008

Transmission & Distribution Loss* 32% (loss) Indian Central Electricity Authority 2008

*Transmission & Distribution Loss refers to national average electricity that is lost between generation and delivery 

to end users.

Note: Impacts from electricity infrastructure is included in the analysis.

Table 14 describes the SimaPro modules that are used to represent transportation impacts 

throughout the model.

Table 14. SimaPro Module Descriptions for Transportation

Module Name Module Purpose Comments

Truck - Lorry

This Ecoinvent 2.0 module is used to calculate 

the impacts of road transport and associated 

infrastructure whenever truck transport is 

required in the model. 

Data is for Western European 

conditions; India-specific data was 

not available.

Ocean Tanker

This Ecoinvent 2.0 module is used to calculate 

the impacts of transoceanic transport and 

associated infrastructure whenever ocean tanker 

transport is required in the model. 

Data is for Western European 

conditions; India-specific data was 

not available.

Rail

This Ecoinvent 2.0 module is used to calculate 

the impacts of rail transport and associated 

infrastructure whenever railcar transport is 

required in the model. 

Data is for Western European 

conditions; India-specific data was 

not available.

Pipeline

This Ecoinvent 2.0 module is used to calculate 

the impacts of pipeline transport and associated 

infrastructure whenever pipeline transport is 

required in the model. 

Data is for Western European 

conditions; India-specific data was 

not available.
36

5.0 Base Case Results

The base case results are presented using three impact assessment metrics:

1. Net GHG emission intensity: net emissions of the IPCC-identified GHGs are calculated 

with results grouped according to the six Kyoto Protocol gas classifications (CO2, CH4, 

N2O, PFCs, HFCs, SF6) expressed in carbon dioxide equivalents (grams CO2e per GTK).

2. Net energy ratio: useful fuel energy delivered to the locomotive divided by the 

cumulative energy demand of the system (including offset credits).

3. Petroleum displacement intensity: reduction in crude oil use per GTK for the biodiesel 

analysis scenarios compared with the conventional diesel baseline.

Base case results are presented for conventional diesel and biodiesel blends of B5, B10, and B20. 

Results for B100 (neat biodiesel), although not envisioned for use in the Indian rail system, are 

shown for informational purposes and to help the reader calculate results for other blends. The 

sensitivity analysis section explores the relative influence of certain key parameters. 

5.1 Net Greenhouse Gas Emissions

Table 15 presents the net life cycle GHG emissions normalized by the functional unit of the 

study (GTK). The percent change for each biodiesel blend compared with conventional diesel is 

also reported. The results suggest that, for the case considered, life cycle GHG emissions will 

decrease by approximately 3% for B5, 6% for B10, 12% for B20, and 62% for B100 compared 

with conventional diesel emissions. Note that GHG emissions trend proportionally with 

increasing biodiesel blend percent as expected. 

Table 15. Net Life Cycle Greenhouse Gas Emissions

Units Diesel B5 B10 B20 B100

GHG Emission Intensity g CO2e per GTK 13 13 12 12 5.1

Change from Diesel % -- -3% -6% -12% -62%

Assumes a 20 year system lifetime with 2 billion gross tonne-kilometers (GTK) transported over that time. Uses IPCC 

1997 GWP values for consistency with the LCA literature. Results are rounded to two significant figures as an 

indication of their uncertainty. Percent change values are rounded to the nearest integer.

Based on the analysis of the case considered in this study, Jatropha-based biodiesel has a 

significant greenhouse gas emission benefit compared to petroleum diesel. The results indicate 

that there is not a significant negative impact on the biodiesel system’s net GHG emissions due 

to the cultivation and processing of the Jatropha crop, at least for the base case conditions 

considered here. While this benefit may be reduced if the impact of direct land use change were 

evaluated, a slight benefit remains when site-specific carbon content changes are accounted for 

in prior work (Reinhardt et al. 2008). 

The results of Table 15 demonstrate that net GHG emissions reductions compared to petroleum 

diesel are proportional to the biodiesel content of the fuel. This result is mainly a consequence of 
37

the modeling assumption that all CO2 emissions emitted during the combustion of biodiesel in 

the locomotive are offset by CO2 uptake during the growing of the plants, whereas considerable 

CO2 is emitted during combustion of diesel fuel by the locomotive. 

Beyond reporting the GHG emissions intensity and the anticipated percent change compared to 

petroleum diesel for each biodiesel blend, it is informative to identify the life cycle processes 

responsible for the greatest amount of GHG emissions. Identifying the key processes can better 

focus the sensitivity analyses to test the assumptions in those modules and to determine which 

parameters are likely to be critical in potentially changing the conclusions of the study. 

Table 16 reports the percent contribution of each stage to total GHG emissions. Each major stage 

is separated, where appropriate, into sub-processes to provide more detail on the source of GHG 

emissions. Owing to limitations of the LCA software used in this study, the impacts of two 

supporting processes—Indian electricity and steam production—are aggregated into combined 

sub-processes instead of their contributions being determined separately for each stage they 

support. For instance, Indian electricity supports Jatropha cultivation, Jatropha oil extraction, and 

base-catalyzed transesterification stages, while steam production supports Jatropha oil extraction 

and base-catalyzed transesterification stages. Negative percentages represent GHG emission 

credits resulting from co-product boundary expansion. 
38

Table 16. Life Cycle Greenhouse Gas Process Contributions

Process Diesel B5 B10 B20 B100

Locomotive Operations 89% 87% 85% 81% 0%

Crude Oil Production, 

Transport, and Refining 10% 10% 9.9% 9.4% 0%

Jatropha Cultivation -- 0.83% 1.7% 3.7% 44%

N2O Release from Fertilizer -- 0.32% 0.66% 1.4% 17%

Irrigation -- 0.53% 1.1% 2.4% 28%

Fertilizer Application and 

Offset -- <-0.1% <-0.1% <-0.1% -0.83%

Jatropha Oil Extraction -- <0.1% <0.1% <0.1% 0.93%

Hexane Production -- <0.1% <0.1% <0.1% 0.93%

Base-catalyzed 

Transesterification -- -0.42% -0.87% -1.9% -22%

Methanol Production -- 0.13% 0.26% 0.56% 6.7%

Potassium Hydroxide 

Production -- <0.1% 0.10% 0.22% 2.7%

Glycerine Offset -- -0.60% -1.2% -2.7% -32%

Supporting Processes -- 1.4% 2.8% 6.1% 72%

Indian Electricity -- 0.78% 1.6% 3.4% 40%

Truck Transport -- <0.1% 0.16% 0.35% 4.2%

Steam Production -- 0.53% 1.1% 2.4% 28%

Note: Percent contributions for each process to total GHG emissions are displayed. Negative percentages 

represent emission credits due to boundary expansion (co-product offsets). Column totals may not sum to 

100% due to rounding and contribution of minor processes throughout the life cycle. Indian electricity and 

steam production contribute to impacts for multiple life cycle stages but are aggregated separately due to 

model limitations. Results are rounded to two significant figures as an indication of their uncertainty.

The results show that life cycle GHG emissions are primarily driven by the combustion of diesel 

in the locomotive, followed by the conventional diesel production process. As a reminder, 

combustion of biodiesel in locomotives is assigned a zero emission factor to account for the 

carbon sequestered during the cultivation of the Jatropha trees. Therefore, all GHG emissions in 

the freight train transport process result from the combustion of the portion of the fuel that is 

diesel. For the diesel scenario, locomotive transport accounts for nearly 90% of total life cycle 

GHG emissions, decreasing to 81% for B20. The contribution of crude oil extraction, transport, 

and processing into diesel fuel accounts for approximately 10% of the GHG emissions for the 

conventional diesel scenario, down to 9.4% for B20. 

As the percentage of biodiesel increases, the proportionate contribution of the Jatropha 

cultivation, oil extraction, biodiesel transesterification, and supporting processes increases. 

Reducing the net impacts of the Jatropha biodiesel production process are the three major offsets 

considered in this study: 1) glycerine production, which receives a GHG credit for offsetting 

synthetic glycerine production; 2) seed cake reused as fertilizer, which reduces the use of urea 

fertilizer; and 3) excess biomass combusted to produce electricity thereby offsetting grid 

electricity (which mainly is derived from fossil fuels). The most significant of these offsets is 

glycerine production, providing a 32% offset in the case of neat biodiesel. 
39

Two parameters that deserve particular attention are irrigation and N2O release from nitrogen 

fertilizer. These processes contribute 28% and 17% to B100 net life cycle GHG emissions, 

respectively. However, the parameter values modeled for each are highly uncertain. The base 

case scenario assumes irrigation for only the first three years of the life cycle while the plantation 

is being established, as suggested by Reinhardt and colleagues (2008). No other published source 

confirmed this assumption. Also, available literature provides little guidance on the relationship 

between rainfall and irrigation water requirements, nor the correlation amongst irrigation 

requirements and soil conditions, fertilizer usage, and other important agronomic information. 

While the best available information was used to develop the base case scenario, the impact to 

this report’s conclusions owing to changes in irrigation-related assumptions is examined through 

a sensitivity analysis. N2O, which is released after application of nitrogen fertilizers, is a potent 

GHG. Both the amount of nitrogen fertilizer required and the fraction of nitrogen in the fertilizer 

that is volatized to N2O are highly uncertain owing to challenges regarding the measurement and 

modeling of the fate and transport of N2O. The sensitivity analysis section shows how different 

assumptions regarding these parameters can significantly alter the estimate of biodiesel life cycle 

GHG emissions.

In addition to examining the contribution of each process to GHG emissions, it is also 

informative to analyze the proportionate contribution of each major GHG to net carbon dioxide 

equivalent emissions and how those contributions vary across different biodiesel blends. The 

categories of gases considered in this analysis include CO2, CH4, N2O, perfluorocarbons (PFCs), 

hydrofluorocarbons (HFCs), and sulfur hexafluoride (SF6). As shown in Table 17, CO2 has the 

greatest contribution in all scenarios, as would be expected for a life cycle that heavily relies on 

combustion of fossil fuels. CO2 contributes from 99% of life cycle net GHG emissions for diesel 

to 76% for B100. Methane and N2O emissions constitute the majority of the remainder 

depending on biodiesel blend, where methane emissions are a greater contributor to total net 

GHG emissions for diesel through B10. Table 17 highlights the increasing contribution of 

agricultural N2O emissions as the percentage of biodiesel increases. N2O contribution is 

negligible for the diesel case, increasing to 1.5% for B20 and 18% for B100. The sensitivity 

analyses show that this contribution can increase even further if different assumptions are used 

for the volatilization rate of N2O from nitrogen fertilizer during plantation operation. HFCs, 

PFCs, and SF6 combined contribute less than 0.1% to overall life cycle GHG emissions for both 

diesel and all biodiesel blends.

Table 17. Percent Contribution by Greenhouse Gas to Total Global Warming Potential

Greenhouse Gas Diesel B5 B10 B20 B100

CO2 99% 99% 98% 97% 76%

CH4 0.68% 0.78% 0.89% 1.1% 6.0%

N2O 0.61% 0.39% 0.74% 1.5% 18%

HFCs, PFCs, SF6 

(combined) <0.1% <0.1% <0.1% <0.1% <0.1%

Results are rounded to two significant figures as an indication of their uncertainty. 

5.2 Net Energy Ratio

The net energy ratio (NER) is used to compare the useful energy produced by the system to the 

net energy consumed by the system. As described in the supporting materials to Farrell et al.
40

(2006), NER is a problematic metric. Chiefly, the NER is difficult to compare between studies as 

it is often poorly defined and strongly influenced by the analyst’s method of calculation (i.e., 

whether energy offsets produced by the system are added to the energy output or subtracted from 

the energy input). Nevertheless, it is used in this study because its meaning is intuitive and for 

the sake of comparison to other studies that have used this metric. 

In this study, NER is calculated as 

Energy Out (MJ) NER

Net Energy Demand (MJ) = (1)

where, Energy Out is defined as the useful energy delivered to the locomotive to produce 

motion, and Net Energy Demand is defined as all energy consumed by the system minus energy 

saved or produced due to system offsets such as biomass combustion.

The results of the NER analysis are reported in Table 18. The diesel reference case shows an 

NER of less than one. Fossil fuel based systems have NERs less than one because energy out is 

always less than energy in due to energy consumed during processing and transport. By contrast, 

the B100 case shows an NER of greater than one which indicates a favorable energy balance. 

Owing to the greater contribution of diesel, blends up to B20 still show an NER of less than one. 

The robustness of the B100 NER base case result is tested in the sensitivity analyses.

Table 18. Net Energy Ratio 

Units Diesel B5 B10 B20 B100

Net Energy Ratio Energy out per

net energy demand 0.79 0.81 0.84 0.89 1.9

Change from Diesel % -- 3% 6% 13% 140%

Assumes a 20 year system lifetime with 2 billion gross tonne-kilometers (GTK) transported over that time. Results 

are rounded to two significant figures as an indication of their uncertainty. Percent change values are rounded to 

the nearest integer.

5.3 Net Petroleum Displacement

Biodiesel use is often cited as a means for decreasing dependence on petroleum. This issue is 

particularly important to India as approximately 70% of all crude oil used in India is imported. 

IR estimates that use of B20 for all diesel locomotive operations could save approximately 400 

million liters of diesel fuel per year (Kathpal 2008). Reduction in petroleum use is defined as the 

consumption of petroleum by the reference system (here, diesel), minus the amount of petroleum 

consumed in an alternative scenario (here, various biodiesel blends), often termed petroleum 

displacement. By examining petroleum displacement over the full life cycle, this study analyzes 

what degree the reduction in diesel fuel combustion in the locomotive is offset by petroleum 

consumption during the cultivation of Jatropha and transportation and processing of Jatropha oil 

and biodiesel. Table 19 shows petroleum consumption and displacement over the life cycle 

normalized by the functional unit, GTK. The percent decrease in petroleum consumption is also 

tabulated for each scenario examined.
41

Table 19. Life Cycle Net Petroleum Consumption and Displacement Intensity

Units Diesel B5 B10 B20 B100

Net Petroleum Consumption 

Intensity

g crude oil per

GTK 4.2 4.0 3.8 3.5 0.58

Net Petroleum Displacement 

Intensity

g crude oil per

GTK -- 0.17 0.34 0.69 3.6

Change from Diesel % -- -4% -8% -17% -86%

The study life cycle assumes two billion GTKs of train transport. Petroleum consumption is measured in terms of 

mass of crude oil consumed. Results are rounded to two significant figures as an indication of their uncertainty. 

Percent change values are rounded to the nearest integer.

If upstream life cycle phases for the diesel and biodiesel production systems were identical in 

terms of petroleum consumption, one would expect to see a percent decrease in petroleum 

consumption equal to the percent biodiesel blended in the fuel (e.g., 20% biodiesel would reduce 

diesel requirements by 20%). As Table 19 shows, the overall life cycle petroleum displacement 

is close to, but slightly less than the percent biodiesel used. B5 yields a 4% reduction, B10’s 

reduction is 8%, and B20 displaces 17% of petroleum use. These results indicate that diesel use 

during Jatropha cultivation, transport, and processing into biodiesel offsets a small percentage of 

the gains from reducing diesel use in the locomotives.
42

6.0 Sensitivity Analyses 

The sensitivity analyses focus on determining the influence of individual parameters on the 

overall study results and on establishing best and worst case bounding estimates for net GHG, 

net petroleum displacement, and net energy analyses. In this analysis, each parameter (or set of 

parameters) is tested individually, while other parameters are held at their base case values. 

This sensitivity analysis focuses on 15 parameters or sets of parameters that were selected 

because they met two criteria: 1) they were judged likely to influence one or more of the model 

outputs significantly, and 2) their values are uncertain, as determined by a review of the available 

literature. The uncertainty of values was due in some cases (such as N2O volatilization) to expert 

uncertainty and disagreement while other parameters (such as fertilizer required) were subject to 

data unavailability uncertainty due to lack of information on India-specific requirements. 

Appendix B details the parameter values used in each sensitivity case.

The sensitivity analyses are reported in Tables 20-22 for net global warming potential, net 

energy ratio, and net petroleum consumption, respectively. In addition to reporting the difference 

between the result of the sensitivity run and the base case result, Tables 20-22 also rank the 

scenarios according to how sensitive the model output is to changes in the value of individual 

parameters. The sensitivity of the model to changes in the value of individual parameters is 

evaluated using the local sensitivity coefficient, Si,j. Si,j is defined as the partial derivative of the 

model prediction with regard to a specific input parameter:

,

j

i j

i

C

S

λ

∂ = ∂ , (2)

where C is the set of model outputs, with j representing a specific output, and λ is the set of 

model input parameters, with i representing a specific input parameter. C is a function of λ. 

The finite difference approach is used to estimate the local sensitivity coefficient. This approach 

estimates the local derivative as the ratio of the difference between two model runs, where the 

two runs are distinguished by a change in the value of only one model input parameter, and the 

change in value of that one parameter:

( ) () j ji i ji

i i

CC C λλ λ

λ λ

∂ +∆ − = ∂ ∆ . (3)

Note that the local sensitivity coefficient is undefined for sensitivity cases where the value of 

more than one parameter was changed, and therefore will not be reported for a few sensitivity 

scenarios tested.

The local sensitivity coefficient can be presented unnormalized (in units of model output per 

model input), as above, or normalized (dimensionless), as below:
43

j

j j i

i j i

i

C

C C

C

λ

λ λ

λ

∂

∂ = ∂ ∂ . (4)

The normalized local sensitivity coefficient can be interpreted as the fractional change in model 

output resulting from a 100% change in model input. Local sensitivity coefficients greater than 

one in absolute value indicate input parameters with outsized influence on the model result; local 

sensitivity coefficients less than one indicate parameters that have proportionally less influence 

on model outcomes. Negative local sensitivity coefficients indicate that the model output and 

input are anti-correlated (e.g., the value of the model output decreases as the value of the model 

input increases); positive local sensitivity coefficients can be interpreted in the opposite manner. 

As LCAs are typically linear models, the local sensitivity coefficient is expected to remain 

consistent throughout the likely range of input parameter values. 

The sensitivity cases evaluated in Tables 20-22 are presented in decreasing order of the absolute

value of the normalized Sij parameter. Although Sij could be determined for diesel fuel and any 

biodiesel blend, it is only presented in Tables 20-22 for the case of neat biodiesel (B100) because 

the parameters tested in the sensitivity scenarios primarily impacted the biodiesel production 

system. The biodiesel production system used the least certain data in the study; the diesel 

reference system largely relied on peer-reviewed life cycle inventory databases produced by 

Ecoinvent (Swiss Centre for Life Cycle Inventories 2008). The only parameter tested in the 

sensitivity analyses that impacted the diesel reference system was the diesel fuel consumption 

requirement. Results from this analysis are presented separately in Table 23.

Table 20. Results of the Global Warming Potential (GWP) Sensitivity Analyses

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 

GWPB

Change from Base Case GWP 

for Given Biodiesel BlendC

(g CO2e/GTK)

B5 B10 B20 B100

Base case 

GWP 

(g CO2e/GTK)

13D 12 12 5.1

1

Tree 

planting 

density

Low tree 

density

Set to low tree 

density with a 

3m x 3m 

planting grid 

(Lele 2008b) -1.4

0.22 0.44 0.88 4.6

Medium tree 

density

Set to medium

tree density with 

a 2.5m x 2.5m 

planting grid 

(Lele 2008b)

<0.1 0.17 0.35 1.8
44

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 

GWPB

Change from Base Case GWP 

for Given Biodiesel BlendC

(g CO2e/GTK)

B5 B10 B20 B100

Base case 

GWP 

(g CO2e/GTK)

13D 12 12 5.1

2 Dry seed 

yield

High seed 

yield

Set to high end 

of future 

projections 

(Reinhardt et al. 

2008) -1.3

-0.13 -0.27 -0.53 -2.8

Low seed 

yield

Set to low end of 

future 

projections 

(Reinhardt et al. 

2008)

0.20 0.39 0.79 4.1

3

Biodiesel 

efficiency 

compared to 

diesel

Medium 

biodiesel 

efficiency 

decrement

Set to middle of 

the range of 

reported 

efficiency 

decrement for 

biodiesel blends 

(EPA 2008) 1.0

0.26 0.25 0.23 0.10

High biodiesel 

efficiency 

decrement

Set to high end 

of the range of 

reported 

efficiency 

decrement for 

biodiesel blends 

(EPA 2008)

0.64 0.62 0.58 0.25

4

Diesel fuel 

consumption 

requirement

Low diesel 

fuel 

consumption 

Set to low end of 

reported range 

(Indian Railways 

2008)

1.0

-5.1 -5.0 -4.7 -2.0

High diesel 

fuel 

consumption 

Set to double 

the base case 

locomotive 

diesel fuel 

consumption 

13 12 12 5.1

5 Seed oil 

content

High seed oil 

content

Set to high end 

of reported

range 

(Jongschaap et 

al. 2007) -0.94

<-0.1 <-0.1 -0.10 -0.52

Low seed oil 

content

Set to low end of 

reported range 

(Lele 2008b)

<0.1 0.16 0.32 1.7

6

Jatropha oil 

extraction 

efficiency 

High oil 

extraction 

efficiency

Set to high end 

of reported 

range (Achten et 

al. 2008) -0.78

<-0.1 <-0.1 <-0.1 -0.34

Medium oil 

extraction 

efficiency

Set to middle of 

reported range 

(Achten et al. 

2008)

<-0.1 <-0.1 <-0.1 -0.18
45

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 

GWPB

Change from Base Case GWP 

for Given Biodiesel BlendC

(g CO2e/GTK)

B5 B10 B20 B100

Base case 

GWP 

(g CO2e/GTK)

13D 12 12 5.1

7 Seed cake 

offset

No seed cake 

offset

Eliminates the 

benefits of seed 

cake used as 

fertilizer

-0.51 0.12 0.25 0.50 2.6

8 Fertilizer use

No fertilizer 

use

Sets the use of 

fertilizer to zero

0.44

-0.11 -0.21 -0.43 -2.2

Double 

fertilizer use

Sets the use of 

fertilizer to 

double the base 

case value

0.11 0.21 0.43 2.2

9 Rainfall No annual 

rainfalll

Assumes 

irrigation 

provides all 

water 

requirements

-0.34 <0.1 0.16 0.33 1.7

10

Glycerine 

production 

offset

No glycerine 

offset

Eliminates the 

benefits of the 

glycerine coproduct 

offsetting 

synthetic 

glycerine 

production

-0.32 <0.1 0.15 0.31 1.6

11 Irrigation Full irrigation 

years

Plantation is 

irrigated for all 

life cycle years

0.27 0.37 0.75 1.5 7.8

12

Plantation 

electricity 

consumption

Half plantation 

electricity

Sets plantation 

electricity to half 

the base case 

value assuming 

that half is 

already 

accounted for by 

other processes 0.20

<-0.1 <-0.1 -0.10 -0.51

No plantation 

electricity

Sets plantation 

electricity use to 

zero assuming 

all of it is already 

accounted for in 

other processes

<-0.1 -0.10 -0.20 -1.0

13

N2O

volatilization 

rate

Low N2O 

volatilization 

rate

Set to low end of 

reported range 

for volatilization 

rate (IPCC 

2006)

0.17

<-0.1 <-0.1 -0.12 -0.60

High N2O 

volatilization 

rate

Set to high end 

of reported 

range for 

volatilization rate 

(Crutzen et al. 

2008)

0.12 0.25 0.49 2.6
46

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 

GWPB

Change from Base Case GWP 

for Given Biodiesel BlendC

(g CO2e/GTK)

B5 B10 B20 B100

Base case 

GWP 

(g CO2e/GTK)

13D 12 12 5.1

14 All offsets No offsets

Benefits of 

offsets 

(glycerine 

production, 

biomass 

combustion, and 

seed cake used 

as fertilizer) are 

not included

- 0.20 0.40 0.81 4.2

15 Biomass 

offsets

No biomass 

offset

Eliminates the 

benefits of 

biomass 

combustion 

offsetting grid 

electricity

- < 0.1 <0.1 <0.1 <0.1

A This column ranks the absolute value of B100 Sij value from greatest to least. B Normalized Sij represents the percent change in model output per 100% percent change in an input parameter. Greater 

absolute Sij values indicated greater model sensitivity to change in the specified input parameter. C To calculate the GWP per GTK of a sensitivity scenario, sum the sensitivity “change from” value reported in these 

columns and the base case value. 

D Model results, Sij values, and sensitivity run differences are rounded to two significant figures as an indication of their 

uncertainty.

The results displayed in Table 20 identify tree planting density and dry seed yield as the two 

parameters with the greatest influence on the model’s GWP results for B100, followed by 

biodiesel efficiency and locomotive diesel fuel consumption requirement. By increasing trees per 

hectare and seed yield per tree, more seeds are produced from the same fertilizer and energy 

inputs. Even if this result accurately reflects the sensitivity of the modeled system, its relevance 

to an actual Jatropha plantation is less understood. There are no published data correlating 

plantation inputs to either plantation configuration parameters or outputs. Plantation operational 

energy consumption or fertilizer requirements may in fact increase as tree density or seed yield 

increases. Further research is needed to establish these relationships and to better define the 

Jatropha plantation processes. 

The reason the B100 scenario results are sensitive to the diesel fuel consumption requirement is 

that the fuel consumption requirement of biodiesel-fueled locomotives is calculated by 

multiplying the diesel fuel consumption requirement by the biodiesel efficiency value. Among 

the system offsets considered in this study, Table 20 shows that the GWP results are more 

sensitive to the offset that accounts for seed cake substituting for fertilizer than to the glycerine 

production. The local sensitivity coefficient for the biomass combustion offset could not be 

determined since it is characterized by multiple input parameters. However, one can easily see 

that its influence is small since the difference in GWP between base case and sensitivity model 

runs is negligible. Interestingly, N2O volatilization rate, which is subject to significant expert 
47 

uncertainty, is the least influential single parameter tested in the sensitivity analyses on GWP. 

For other biofuels, this parameter has been found to be highly influential (Farrell et al. 2006). 

The same set of sensitivity cases as tested on GWP were evaluated for affect on NER (Table 21). 

As for GWP, the Jatropha biodiesel (B100) NER is shown to be most sensitive to seed yield, the 

locomotive diesel fuel consumption requirement, biodiesel efficiency, and tree density, in that 

order. The most notable result in this table can be seen by examining the column presenting the 

difference in B100 NER from the sensitivity case to the base case. Only the high locomotive 

diesel consumption requirement scenario and the scenario that requires irrigating the plantation 

for every year in the life cycle bring the B100 NER below one. This result suggests that the 

irrigation requirement for the plantation may be critical in determining if the production of

biodiesel has a favorable net energy ratio. 

Table 21. Net Energy Ratio (NER) Sensitivity Analyses

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 NERB

Change from Base Case NER 

for Given Biodiesel BlendC

B5 B10 B20 B100

Base case 

NER 0.81D 0.84 0.89 1.9

1 Dry seed 

yield

High seed 

yield

Set to high end 

of future 

projections

(Reinhardt et 

al. 2008) 1.2

0.0088 0.019 0.044 2.0

Low seed 

yield

Set to low end 

of future 

projections

(Reinhardt et 

al. 2008)

-0.013 -0.026 -0.057 -0.82

2

Diesel fuel 

consumption 

requirement

Low diesel 

fuel 

consumption

Set to low end 

of reported 

range (Indian 

Railways 2008) 

-1.1

0.54 0.56 0.59 1.3

High diesel 

fuel 

consumption

Set to double 

the base case 

locomotive 

diesel fuel 

consumption

-0.41 -0.42 -0.45 -0.94

3

Biodiesel 

efficiency 

compared to 

diesel

Medium 

biodiesel 

efficiency 

decrement

Set to middle 

of the range of 

reported 

efficiency 

decrement for 

biodiesel 

blends

(EPA 2008) -0.97

-0.016 -0.016 -0.017 -0.037

High 

biodiesel 

efficiency 

decrement

Set to high end 

of the range of 

reported 

efficiency 

decrement for 

biodiesel 

blends

(EPA 2008)

-0.039 -0.040 -0.042 -0.090
48

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 NERB

Change from Base Case NER 

for Given Biodiesel BlendC

B5 B10 B20 B100

Base case 

NER 0.81D 0.84 0.89 1.9

4

Tree 

planting 

density

Low tree 

density

Set to low tree 

density with a 

3m x 3m 

planting grid

(Lele 2008b)

0.75

-0.013 -0.027 -0.060 -0.84

Medium tree 

density

Set to medium 

tree density 

with a 2.5m x 

2.5m planting 

grid

(Lele 2008b)

-0.0052 -0.011 -0.024 -0.45

5 Seed oil

content

High seed oil 

content

Set to high end 

of reported 

range 

(Jongschaap 

et al. 2007) 0.72

0.0015 0.0031 0.0071 0.18

Low seed oil 

content

Set to low end 

of reported 

range (Lele 

2008b)

-0.0047 -0.098 -0.022 -0.41

6

Jatropha oil 

extraction 

efficiency

High oil 

extraction 

efficiency

Set to high end 

of reported 

range (Achten 

et al. 2008) 0.69

<0.001 0.0020 0.0046 0.11

Medium oil 

extraction 

efficiency

Set to middle 

of reported 

range (Achten 

et al. 2008)

<0.001 0.0011 0.0024 0.057

7 Fertilizer use

No fertilizer 

use

Sets the use of 

fertilizer to 

zero

-0.59

0.0078 0.017 0.039 1.6

Double 

fertilizer use

Sets the use of 

fertilizer to 

double base 

case value

-0.0077 -0.016 -0.036 -0.60

8 Seed cake 

offset

No seed 

cake offset

Eliminates 

benefits of 

seed cake 

used as 

fertilizer

0.35 -0.0089 -0.019 -0.041 -0.66

9 Rainfall No annual 

rainfalll

Assumes 

irrigation 

provides water 

requirements

0.26 -0.0058 -0.012 -0.027 -0.49

10

Glycerine 

production 

offset

No glycerine 

offset

Eliminates the 

benefits of the 

glycerine coproduct 

offsetting 

synthetic 

glycerine 

production

0.24 -0.052 -0.011 -0.025 -0.45
49

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for 

B100 NERB

Change from Base Case NER 

for Given Biodiesel BlendC

B5 B10 B20 B100

Base case 

NER 0.81D 0.84 0.89 1.9

11

Plantation 

electricity 

consumption

Half 

plantation 

electricity

Sets plantation 

electricity to 

half base case 

value 

assuming half 

is accounted 

for by other 

processes -0.16

0.0012 0.0026 0.0060 0.15

No 

plantation 

electricity

Sets plantation 

electricity use 

to zero 

assuming it is 

accounted for 

in other 

processes

0.0025 0.0052 0.012 0.32

12 Irrigation Full irrigation 

years

Plantation is 

irrigated for all 

life cycle years

-0.11 -0.026 -0.053 -0.11 -1.2

13

N2O

volatilization 

rate

Low N2O 

volatilization 

rate

Set to low end 

of reported 

range for 

volatilization 

rate (IPCC 

2006) 0.0

0.0 0.0 0.0 0.0

High N2O 

volatilization 

rate

Set to high end 

of reported 

range for 

volatilization 

rate (Crutzen 

et al. 2008)

0.0 0.0 0.0 0.0

14 All offsets No offsets

Benefits of 

offsets 

(glycerine 

production, 

biomass 

combustion, 

and seed cake 

used as 

fertilizer) are 

not included

-0.014 -0.029 -0.064 -0.87

15 Biomass 

offsets

No biomass 

offset

Eliminates the 

benefits of 

biomass 

combustion 

offsetting grid 

electricity

0.0 0.0 0.0 -0.01

A This column ranks the absolute value of B100 Sij value from greatest to least. B Normalized Sij represents the percent change in model output per 100% percent change in an input parameter. Greater absolute Sij

values indicated greater model sensitivity to change in the specified input parameter.

C To calculate the NER of a sensitivity scenario, sum the sensitivity “change from” value reported in these columns and the 

base case value.

D Model results, Sij values, and sensitivity run differences are rounded to two significant figures as an indication of their uncertainty.
50

As shown in the petroleum consumption results in Table 22, this outcome is most sensitive to 

tree density, seed yield, and seed oil content, all with absolute Sij values greater than one. These 

results highlight that most of the petroleum consumption during the production of B100 occurs 

during plantation maintenance and operation activities. 

Table 22. Petroleum Consumption Sensitivity Analyses

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for B100 

Petroleum 

ConsumptionB

Change from Base Case Petroleum 

Consumption for Given Biodiesel 

BlendC

(g crude oil/GTK)

B5 B10 B20 B100

Base case 

petroleum 

consumption 

(g crude oil/

GTK)

4.0D 3.8 3.5 0.58

1

Tree 

planting 

density

Low tree 

density

Set to low tree 

density with a 

3m x 3m 

planting grid

(Lele 2008b)

-1.7

0.032 0.065 0.13 0.68

Medium tree 

density

Set to medium 

tree density 

with a 2.5m x 

2.5m planting 

grid

(Lele 2008b)

0.013 0.025 0.051 0.27

2 Dry seed 

yield

High seed 

yield

Set to high 

end of future 

projections 

(Reinhardt et 

al. 2008) -1.5

-18 -35 -71 -370

Low seed 

yield

Set to low end 

of future 

projections 

(Reinhardt et 

al. 2008)

26 52 100 540

3 Seed oil 

content

High seed oil 

content

Set to high 

end of 

reported range 

(Jongschaap 

et al. 2007) -1.2

<0.01 <-0.01 -0.015 -0.077

Low seed oil 

content

Set to low end 

of reported 

range (Lele 

2008b)

0.012 0.024 0.048 0.25
51

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for B100 

Petroleum 

ConsumptionB

Change from Base Case Petroleum 

Consumption for Given Biodiesel 

BlendC

(g crude oil/GTK)

B5 B10 B20 B100

Base case 

petroleum 

consumption 

(g crude oil/

GTK)

4.0D 3.8 3.5 0.58

4

Biodiesel 

efficiency 

compared to 

diesel

Medium 

biodiesel 

efficiency 

decrement

Set to middle 

of the range of 

reported 

efficiency 

decrement for 

biodiesel 

blends

(EPA 2008)

1.0

72 68 61 0.012

High biodiesel 

efficiency 

decrement

Set to high 

end of the 

range of 

reported 

efficiency 

decrement for 

biodiesel 

blends

(EPA 2008)

180 170 150 0.029

5

Diesel fuel 

consumption 

requirement

Low diesel 

fuel 

consumption

Set to low end 

of reported 

range (Indian 

Railways 

2008) 1.0

-1600 -1500 -1400 -230

High diesel 

fuel 

consumption

Set to double 

the base case 

locomotive 

diesel fuel 

consumption

3900 3800 3400 570

6

Jatropha oil 

extraction 

efficiency

High oil 

extraction 

efficiency

Set to high 

end of 

reported range 

(Achten et al.

2008) -0.99

<-0.01 <-0.01 -0.010 -0.050

Medium oil 

extraction 

efficiency

Set to middle 

of reported 

range

(Achten et al.

2008)

<-0.01 <-0.01 <-0.01 -0.026

7 Seed cake 

offset

No seed cake 

offset

Eliminates 

benefits of 

seed cake 

used as 

fertilizer

-0.45 0.012 0.025 0.050 0.26

8 Fertilizer use

No fertilizer 

use

Sets use of 

fertilizer to 

zero

0.43

-12 -24 -48 -250

Double 

fertilizer use

Sets use of 

fertilizer to 

double the 

base case 

value

12 24 48 250
52

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for B100 

Petroleum 

ConsumptionB

Change from Base Case Petroleum 

Consumption for Given Biodiesel 

BlendC

(g crude oil/GTK)

B5 B10 B20 B100

Base case 

petroleum 

consumption 

(g crude oil/

GTK)

4.0D 3.8 3.5 0.58

9 Rainfall No annual 

rainfall

Assumes 

irrigation 

provides all 

water 

requirements

-0.40 0.011 0.023 0.045 0.24

10

Glycerine 

production 

offset

No glycerine 

offset

Eliminates 

benefits of 

glycerine coproduct 

offsetting 

synthetic 

glycerine 

production

-0.37 0.010 0.021 0.042 0.22

11 Irrigation Full irrigation 

years

Plantation is 

irrigated for all 

life cycle years

0.33 0.052 0.10 0.21 1.1

12

Plantation 

electricity 

consumption

Half plantation 

electricity

Sets plantation 

electricity to 

half the base 

case value 

assuming half 

is accounted 

for by other 

processes 0.02

<-0.01 <-0.01 <-0.01 -0.01

No plantation 

electricity

Sets plantation 

electricity use 

to zero 

assuming it is 

accounted for 

in other 

processes

<-0.01 <-0.01 <-0.01 -0.010

13

N2O

volatilization 

rate

Low N2O 

volatilization 

rate

Set to low end 

of range for 

volatilization 

rate (IPCC 

2006)

0.00 0.00 0.00 0.00 0.00

High N2O 

volatilization 

rate

Set to high 

end of range 

for 

volatilization 

rate (Crutzen 

et al. 2008)

-- 0.00 0.00 0.00 0.00
53

RankA Parameter Category 

Tested

Sensitivity 

Scenario 

Name

Sensitivity 

Scenario 

Description

Normalized 

Local 

Sensitivity 

Coefficient 

(Sij) for B100 

Petroleum 

ConsumptionB

Change from Base Case Petroleum 

Consumption for Given Biodiesel 

BlendC

(g crude oil/GTK)

B5 B10 B20 B100

Base case 

petroleum 

consumption 

(g crude oil/

GTK)

4.0D 3.8 3.5 0.58

14 All offsets No offsets

Benefits of 

offsets 

(glycerine 

production, 

biomass 

combustion, 

seed cake 

used as 

fertilizer) are 

not included

-- 0.023 0.046 0.092 0.48

15 Biomass 

offsets

No biomass 

offset

Eliminates 

benefits of 

biomass 

combustion 

offsetting grid 

electricity

-- < 0.01 < 0.01 < 0.01 < 0.01

A This column ranks the absolute value of B100 Sij value from greatest to least. B Normalized Sij represents the percent change in model output per 100% percent change in an input parameter. Greater absolute Sij

values indicated greater model sensitivity to change in the specified input parameter. C To calculate the petroleum consumption per GTK of a sensitivity scenario, sum the sensitivity “change from” value reported 

in these columns and the base case value.

D Model results, Sij values, and sensitivity run differences are rounded to two significant figures as an indication of their uncertainty.

The sensitivity analyses of this study primarily evaluate the biodiesel system. The sensitivity of 

the diesel base case is only tested by varying the locomotive diesel fuel consumption 

requirement. The local sensitivity coefficients for GWP, NER, and petroleum consumption are 

shown in Table 23 to yield changes in model ouputs that are approximately proportionate to the 

change in the diesel fuel consumption requirement parameter.

Table 23. Local Sensitivity of Diesel Model Output

Sensitivity Scenario Diesel Global 

Warming Potential Sij

Diesel Net 

Energy Ratio Sij

Diesel Petroleum 

Consumption Sij

Locomotive diesel fuel 

consumption 

requirement

1.0 -1.1 1.0
54

As a final sensitivity check on the model results, best and worst case sensitivity scenarios were 

developed by combining together 35 model parameters set to their optimum (best) and least 

advantageous (worst) values, respectively; the results are presented in Table 24. Details of the 

values assigned to each of the 35 parameters are reported in Appendix C. The table shows the 

base case value along with the best and worst case bounding values for each model output. Note 

that the scenarios modeled in the best/worst cases are unlikely to be seen in real world conditions 

but are presented as bounding scenarios. Results to note are that the best case scenario for B100 

NER results in net negative energy inputs as the life cycle offsets are greater than the required 

inputs. This unlikely scenario leads to the large negative number in the table. Also, the best case 

scenario for GWP and petroleum consumption yield net negative life cycle values for B100. 

Table 24. Best-/Worst-Case Scenario Analysis

Metric Units Case Diesel B5 B10 B20 B100

Global Warming 

Potential Intensity

g CO2e 

per GTK

BASE 13 12 12 12 5.1

Best 7.9 7.5 7.1 6.2 -1.3

Worst 26 47 66 100 430

Net Energy Ratio NER

BASE 0.79 0.81 0.84 0.89 1.9

Best 1.3 1.4 1.5 1.7 -8.5

Worst 0.40 0.23 0.16 0.10 0.026

Petroleum 

Consumption 

Intensity

g crude oil 

per GTK

BASE 4.2 4.0 3.8 3.5 0.58

Best 2.5 2.3 2.2 2.0 -0.15

Worst 52 67 79 100 310
55

7.0 Discussion

This section reviews several critical aspects of this study that are important to keep in mind when 

interpreting its results. This study has significant limitations that constrain the generalizability 

and certainty of its findings. 

7.1 Study Limitations 

The study faced several limitations that constrain the interpretation and certainty of the results 

presented in this report. Categories of limitations include technological and geographical scope, 

methods, data availability, metrics evaluated, and state of the science. Each category of 

limitation is discussed in further detail in the following sections.

7.1.1 Uncertainty Analysis

Data limitations prevented a thorough evaluation of uncertainty. Many important parameters 

have not been studied in enough detail to enable proper characterization of variability and 

uncertainty, to identify causal relationships between parameters, and, in some cases, to even 

establish plausible value ranges for the parameters under a given scenario. While investigating 

the sensitivity of our model to alternative input parameter values provides useful insight to the 

system studied and our model of it, plausible and internally consistent sets of alternative 

parameter estimates need to be developed to provide reasonable boundaries to the model output 

estimates. Our best and worst case scenarios are not internally consistent, and therefore only 

provide an estimate of the extreme boundary which we would not expect to occur. Due to the 

limited data available to define base case parameter values and to develop internally consistent 

modeling scenarios, considerable uncertainty remains in our estimates of parameter inputs and 

model outputs. 

7.1.2 Technological and Geographic Scope

• The study focuses on locomotive transport as the ultimate end use of the biodiesel. A 

much larger market for biodiesel exists in the road transport sector. Much of the analysis 

of this study is applicable to road transport, though adjustments for the ultimate end use 

of the product would need to be made.

• In order to produce a coherent narrative, the study’s base case focused on specific 

locations in India, with sensitivity analysis used to explore the impact of plausible, 

alternative parameter estimates. It is unclear how well the results of this study can be 

generalized to Jatropha biodiesel production in other regions of India, or of the world. 

This limitation is largely due to a paucity of data that accurately relates Jatropha seed and 

oil yield to specified environmental conditions and levels of artificial assistance through 

fertilization and irrigation.

• The scope of this study is also limited in terms of the technologies evaluated. Upon the 

advice of the IOC, this study focused on large-scale methods of Jatropha cultivation and 

biodiesel production technologies. This study did not evaluate alternative methods and 

technologies; therefore, its results should not be considered reflective of the impacts or 

benefits of alternative systems.
56

• Clearly, the results of this study are not applicable to biodiesel production systems using 

other feedstocks nor to other biofuels as cultivation and processing requirements can vary 

greatly amongst biofuels and feedstocks.

7.1.3 Modeling Approach

• This study is prospective in that it attempts to evaluate biodiesel production systems as 

they would be built in the near future in India. It is not based on data collected from an 

already established system. Consequently, it is unclear how well the narrative evaluated 

actually describes systems as they will be built.

• This analysis was conducted using secondary data collected from many disparate sources. 

No primary research was conducted to better establish site-specific data parameters. 

Thus, it is not clear how well the parameter values evaluated in this study are applicable 

to the specific site selected, nor how internally consistent they are in describing a 

biodiesel production system as it would be built. 

7.1.4 Data Availability and Certainty

• India has not yet developed a comprehensive LCI database covering the production of 

basic materials, energy and fuels, transportation, and the ultimate disposal of goods based 

on India-specific conditions. As a result, many life cycle stages required inputs from 

better established European databases, particularly to obtain infrastructure impacts. A key 

limitation of this study is that the authors were required to use non India-specific data to 

represent Indian processes where data was lacking. Specific examples of substituting 

non-Indian data included diesel refining, truck transport, and crude oil extraction. It is 

unclear how relevant the non-Indian data is to Indian processes, and also how much

influence on the final results the use of Indian-specific data would have. 

• Limited data was available for India-specific waste generation and end-of-life scenarios 

for both the biodiesel and diesel pathways. Facing this limitation, this study omitted 

impacts associated with the ultimate disposal of process waste. Inclusion of these impacts 

would likely not affect the conclusions of this study since additional GHG emissions, 

petroleum use, or energy demand for waste disposal in India is likely very small relative 

to the processes already included.

7.1.5 Metrics Evaluated

• The full extent of the environmental impacts of switching from conventional diesel to 

Jatropha biodiesel is not fully represented by the metrics evaluated in this study. Other 

impacts such as hazardous waste generated; pollutant emissions to air, water, and soil, 

land use changes; and socio-economic impacts are also important consideration in a full 

impact assessment. However, adequate data was not available to include an analysis of 

these metrics in this study.

7.1.6 State of the Science

• Biodiesel cultivation and production systems in India are still immature. This study 

represents the current state of knowledge and data availability. Changes to the systems 

and improvements in the quality and quantity of data available are expected as the 
57

technologies and procedures mature. A reassessment using new data as they become 

available may alter the findings of this study.

7.2 Generalizability of Results

The previous section discussed limits to how applicable the results of this study are to other 

locations, technologies, etc. Nevertheless, there are also reasons to believe that the results of this 

study might be indicative to how other systems would perform. For instance, while this study 

was designed to address Indian-specific conditions, the results may be generalizable to other 

locations because much of the input data either originated from studies of other regions (e.g., use 

of Ecoinvent datasets describing European systems) or are based on parameters whose value was 

not made site-specific (e.g., N2O volatilization rate). Furthermore, some parameter values reflect 

the crude state of the science more than they do a detailed reflection of site-appropriate values, 

(e.g., fertilization rates). 

Nonetheless, the relative results from this study are most easily generalized to other regions than 

the absolute results. For instance, the rankings of which parameters are most influential to the 

model outcomes are likely applicable to other situations. 

7.3 Interpretation of Sensitivity Analyses

As many of the model’s input parameters are uncertain, the sensitivity analyses are most useful 

in focusing future research on the parameters most likely to impact study results and policy 

decisions. To evaluate the net GHG impacts of proposed biodiesel production from Jatropha 

cultivation, assumptions related to tree density, dry seed yield, and seed oil content are the most 

critical. These three parameters are also some of the most influential for determining NER and 

petroleum displacement, along with diesel and biodiesel fuel efficiency . Tree density, dry seed 

yield, and seed oil content directly determine the amount of Jatropha oil that can be extracted 

from one hectare of land. As these values are improved through optimal cultivation, required 

inputs of fertilizer and irrigation water per liter of Jatropha oil extracted decrease leading to 

lower resource use impacts throughout the system. Optimal tree density varies with the quality of 

the land while dry seed yield and seed oil content are functions of not only land quality and 

location but also of human inputs such as fertilizer and irrigation water. Unfortunately, the 

current literature does not contain correlated sets of data that accurately predict dry seed yield 

and seed oil content given a set of agro-climatic conditions and a selected tree density. Further 

research is needed in this area to improve the predictive quality of models that evaluate systems 

with Jatropha cultivation.

7.4 Displaced Petroleum and Net GHG Emission Reduction Projections from

Utilization of Jatropha Biodiesel in Indian Railways

IR freight and passenger trains travel in excess of 500 billion GTK per year (Kathpal 2008). If all 

IR trains were to switch to B20 instead of petroleum diesel, the potential savings in GHG 

emissions and petroleum consumption are great. Considering the base case results produced in 

this study, a switch to B20 would reduce IR’s annual net GHG emissions by 750,000 mtCO2e per 

year and petroleum consumption by 350,000 tonnes of crude oil per year. The potential GHG 

emission reductions represent 0.04% of India’s total GHG emissions from 2005 (Pew Center on 

Global Climate Change 2008) while the potential petroleum displacement represents 0.3% of 

India’s 2006 total annual crude oil consumption and is in line with IR’s own estimate of potential 

crude oil savings per year (Kathpal 2008).
58

7.5 Comparison of Results to Other Studies

The results of LCA studies can be difficult to compare directly due to differences in selection of 

system boundaries, site-specific conditions, different functional units, and other variations in 

modeling assumptions. However, attempts were made to compare the results of this study to the 

two most comprehensive Jatropha biodiesel LCAs previously published: Reinhardt et al. (2007) 

and Prueksakorn and Gheewala (2008).

Reinhardt and colleagues evaluated biodiesel use in passenger cars as opposed to locomotives 

and expressed the results on a per hectare-year (ha-yr) basis as opposed to per gross tonnekilometer transported. Our results were converted to their functional unit by dividing the net life 

cycle GHG emissions (over the entire system lifetime) and energy consumption by the 20-year 

life cycle and the 50,000 hectare study plantation size. Their study calculated the net GHG 

emissions from the conventional diesel reference system as approximately 1,200 kg CO2e per hayr. The diesel reference system for this study estimates the net GHG emissions as 26 kg CO2e 

per ha-yr. Reinhardt calculates net GHG emissions from B100 production and use of 

approximately 1,100 kg CO2e per ha-yr, an 8.3% reduction from the diesel reference system.. In 

contrast, this study calculates B100 net GHG emissions of 10 kg CO2e per ha-yr, a 62% 

reduction from our reference system. For net energy, Reinhardt and colleagues calculated energy 

consumption of 16 GJ per ha-yr for the diesel reference system compared with 8 GJ per ha-yr for 

the B100 case, a 50% reduction. In comparison, this study calculates net energy consumption as 

0.7 GJ per ha-yr for the diesel reference system compared with 0.5 GJ per ha-yr for the B100 

case, a 32% reduction. 

It is difficult to identify the precise reasons for the discrepancy between this study’s results and 

those of Reinhardt et al. Possibilities include use of a proprietary data set by Reinhardt, 

Reinhardt’s inclusion of pre-existing vegetation stocks in the calculation, and Reinhardt’s 

assigning of a CO2 emission factor to biodiesel combustion in the vehicle as opposed to zeroing 

out that factor. Further investigation into the source(s) of the discrepancy is a high priority for 

future research on this topic. 

Prueksakorn and Gheewala reported NERs for Jatropha biodiesel production in Thailand ranging 

from 1.93 (worst case) to 11.99 (best case) with an average of 6.03 when seed cake was 

combusted as fuel stock, and 0.53 (worst case) to 2.70 (best case) with an average of 1.42 when 

co-product benefits were omitted. They cited co-product yield assumptions, fertilizer and 

irrigation requirements, and transportation distances as having the greatest impact on NER. This 

study calculated a base case NER (including offsets) for Jatropha-based B100 in India of 1.9. 

This result is slightly below the low end of Prueksakorn and Gheewala’s NER range with offsets 

but above the average of their no offset range. While closer than the comparison of this study’s 

results to Reinhardt’s, there is still a discrepancy with the results of Prueksakorn and Gheewala. 

Again, it is difficult to determine exact reasons that account for the discrepancy. 

A favorable comparison of this study’s petroleum displacement results to those made by IR was 

made in the sensitivity analyses section of this report.
59

8.0 Conclusions

With India’s transportation sector relying heavily on imported petroleum-based fuels, the 

Planning Commission of India and the Indian government recommended the increased use of 

blended biodiesel in transportation fleets, identifying Jatropha as a potentially important biomass 

feedstock. The Indian Oil Corporation and Indian Railways are collaborating to increase the use 

of biodiesel blends in Indian locomotives with blends of up to B20, aiming to reduce GHG 

emissions and decrease petroleum consumption. To help evaluate the potential for Jatrophabased biodiesel in achieving sustainability and energy security goals, this study examines the life 

cycle, net GHG emission, net energy ratio, and petroleum displacement impacts of integrating 

Jatropha-based biodiesel into locomotive operations in India. In addition, this study identifies the 

parameters that have the greatest impact on the sustainability of the system. 

This study was designed to evaluate Jatropha cultivation, biodiesel production, and biodiesel 

blend use under Indian conditions to the greatest degree possible. However, the lessons learned 

from this study also will benefit other countries, including the United States. Petroleum-based 

transportation fuels are traded on an international market. Reduction in consumption anywhere in 

the world, but particularly in countries with growing demand like India, will reduce pressure on 

world petroleum demand and, ultimately, prices. While countries like India are at the leading 

edge of research on alternative transportation fuel feedstocks such as Jatropha, the plant also may 

be a suitable biofuel feedstock for the United States (e.g., in Florida) (Layden 2008), and lessons 

learned from the Indian context can be applied to the United States. Finally, to the extent that this 

study can help optimize the Jatropha biodiesel production process and reduce greenhouse gas 

emissions, there are worldwide benefits for climate change mitigation.

For the base case considered, this study has found that a blend of B20 would reduce GHG 

emissions by 1.5 g CO2e per GTK (12%) and displace 0.69 g crude oil per GTK (17%) while 

increasing the net energy ratio by 0.10 (13%). These results suggest that the Jatropha-based 

biodiesel system under consideration could potentially achieve the identified sustainability goals 

of reducing net GHG emissions, displacing petroleum consumption, and improving the net 

energy ratio. Through the use of sensitivity analyses, this study also identified tree density, dry 

seed yield, and seed oil content as critical parameters that influence the system’s overall 

sustainability.

It is important to note, however, not only that the data used to produce the results of this study 

are uncertain, but also that the potential impacts of direct and indirect land-use change, 

particularly to net GHG emissions, were not considered. Therefore, it is advisable to interpret the 

findings of this report as indicative of the direction and scale of impacts relative to the diesel 

reference system rather than accurate point estimates of the magnitude of impacts. Because agroclimatic conditions and optimal biodiesel feedstocks vary widely throughout the world, no one 

study can definitively determine the sustainability of biofuels in all scenarios. However, this 

study’s results, along with the results of other reviewed studies, suggest that under plausible 

growing conditions and production scenarios, Jatropha-based biodiesel shows promise for 

helping India and possibly other nations achieve their GHG emission reduction and petroleumdisplacement goals. 
60

8.1 Research Recommendations 

Based on the results of this study and the influence of uncertain individual parameters as shown 

in the sensitivity analyses, the following seven research topics are recommended.

1) The accuracy of the results produced by this study was hampered by the lack of prior, 

quality research on the requirements and performance of Jatropha cultivation systems. 

When considering cultivation parameters individually, knowledge gaps exist; but more 

importantly, there are major knowledge gaps regarding how individual parameters relate 

to each other across a range of agro-climatic conditions. The following are specific, key 

parameters that should be the focus of future research efforts, though not to the exclusion 

of consideration of other parameters.

a. The individual parameters with the most influence on all three metrics evaluated 

in this study are seed yield, tree density, and seed oil content. They each also have 

considerable influence on the economic viability of Jatropha cultivation and 

biodiesel production, but more research is needed to better relate these parameters 

to site-specific, agro-climatic conditions. However, these parameters should not 

be considered in isolation because, for example, the sustainability benefits of 

increased yield are mitigated by any resulting requirements for increased

fertilization and irrigation. Once robust data sets are completed, these trade-offs 

should be evaluated on a life cycle basis.

b. The amount of irrigation has an influence on whether the substitution of diesel 

fuel with blended biodiesel results in a net increase or decrease in GHG 

emissions. Quality irrigation data are lacking in the literature. Research is needed 

to define how much irrigation is required based on site-specific, agro-climate 

conditions; how many years irrigation is required after plantation establishment;

and what level of rainfall is necessary to no longer require irrigation.

c. Fertilizer use impacts all three metrics evaluated in this study. The literature lacks 

quality data on required fertilizer levels and application rates for site-specific 

conditions and also lacks quality studies that correlate fertilizer-use levels to 

anticipated seed yields and N2O volatilization rates. Future research should target 

these gaps.

2) Future research that would produce data sets and models that could predict seed yield, oil 

content, fertilizer use, and irrigation requirements based on specific agro-climatic 

conditions of a proposed Jatropha plantation site would be especially useful. This 

information would help analysts, project developers, and decision makers accurately 

forecast the economic and environmental viability of a proposed project. Such predictive 

models should include the ability to assess and track changes in land use and carbon 

stocks on specific land parcels, which could determine the potential impacts of direct land 

use change on net GHG emissions. This research would have direct benefits to other 

countries, including the United States, in defining how and where Jatropha should be 

used to produce biodiesel. 
61

3) Viable scenarios for the use of co-products produced from all Jatropha cultivation 

activities and biodiesel production processes need to be developed for India-specific 

economic and technological conditions. Removing the offsets in this study changes GHG 

emissions from net reductions to net increases and, therefore, changes the overall 

conclusions of the study’s GHG emission analysis. Future research should aim to better 

characterize issues such as: the glycerine co-product yield and the robustness of localized 

markets; the feasibility of distributed electricity generation using seed husks and the 

potential for local heat demand to make combined heat and power (or simply combustion 

for heat production) viable alternatives; and to document the amount of synthetic 

fertilizer that Jatropha seed cake actually offsets when the practices of farmers are 

monitored. 

4) The impacts of indirect land-use change have been shown to potentially significantly alter 

the net GHG balance of biofuels (Searchinger et al. 2008). If lands previously used to 

produce useful products were to be transformed to Jatropha, it is possible (partly

depending on the elasticity of demand for those products) that new lands would be sought 

to fill the previous demand. The net GHG emissions from transforming those other lands 

should be included on the balance sheet of Jatropha biodiesel production. While the 

concept of indirect land-use change is logical, the determination of its net GHG emissions 

that are attributable to an individual decision to plant biofuel feedstock is currently both 

difficult and highly uncertain, leading to considerable controversy. The science is 

immature in this area and urgently needs attention because, for example, legislation in the 

United States (EISA 2008) already demands that regulatory decisions be based on 

consideration of this effect. 

5) The accuracy of this study’s comparison between biodiesel blends and petroleum diesel 

would be greatly improved if India-specific data were available on petroleum refining. 

Future Indian LCAs would be improved with the development of Indian-specific 

inventory modules for energy and material production to decrease the reliance on 

European data. 

6) An examination of impacts on net GHG and petroleum displacement for use of Jatrophabased biodiesel in the road sector (automobiles, trucks, and buses) would be a useful 

extension to this study. The Indian biofuels mandate applies to all transportation fuels, 

not just the rail sector, making an evaluation of the road sector an important area for 

future research.

7) Examining Jatropha biodiesel production systems, or potential ones, in the United States

(in Florida, for example) (Layden 2008) also would be a useful extension to this study. 

Potential use of co-products; origin; and manufacturing impacts of cultivation inputs, 

scale of operations, and many other attributes of the Jatropha biodiesel production system 

could differ, and perhaps significantly alter the results found here for India. Research in 

this context is justified even though results of the sensitivity analyses in this study could 

provide indications of the scale of impacts to be expected in other locations.
62

References

Achten, W.M.J; Verchot, L.; Franken, Y.J.; Mathijs, E.; Singh, V.P.; Aerts, R.; Muys, B. (2008). 

“Jatropha Bio-diesel Production and Use.” Biomass and Bioenergy. DOI: 

10.1016/j.biombioe.2008.03.003. May 2008.

Adrianns, T. (2006). “Sustainability of Solvent Extraction for Jatropha Curcas,” Ingenia 

Consultants & Engineers for FACT Foundation. November 2006. Available online at

http://www.fact-fuels.org/media_en/FACT_(2006)_-

_Suitability_of_solvent_extraction_for_jatropha_curcas Accessed July 29, 2008.

Bureau of Energy Efficiency, India. (2008). “Mathura Refinery - Indian Oil Corporation Limited 

- Mathura (Uttar Pradesh).” Available online at http://www.beeindia.nic.in/sidelinks/EC%20Award/eca05/AwardBook/Refinery.pdf. Accessed July 29, 2008.

Chhattisgarh Online. (2008). “Raipur.” Available online at

http://chhattisgarhonline.in/profile/districts/Raipur.asp Accessed September 6, 2008.

Climate Change Technology Program (CCTP). (2005). “Technology Options for the Near and 

Long Term: Thermochemical Conversion of Biomass.” Page 2.3-14. August 2005. Available 

online at http://www.climatetechnology.gov/library/2005/tech-options/tor2005-236.pdf. 

Accessed September 6, 2008.

Climate Registry. (2008). “General Reporting Protocol – Version 1.1.” May 2008. Available 

online at http://www.theclimateregistry.org/downloads/GRP.pdf. Last accessed July 29, 2008.

Crutzen, P.J.; Mosier, A.R.; Smith, K.A.; Winiwarter, W. (2008) “N2O release from agro-biofuel 

production negates global warming reduction by replacing fossil fuels.” Atmospheric Chemistry 

and Physics, Vol. 8, pp. 389-395, January 2008. Available online at http://www.atmos-chemphys.org/8/389/2008/acp-8-389-2008.pdf.

Energy Information Administration (EIA). (2008) “Country Analysis Brief – India.” Last 

updated January 2007. Available online at: http://www.eia.doe.gov/emeu/cabs/India/Full.html.

Accessed December 1, 2008.

Environmental Protection Agency (EPA). (2008). “Verified Retrofit Technologies – Biodiesel –

a Biodiesel Reduction Spreadsheet.” Available online at 

http://www.epa.gov/oms/retrofit/techlist-biodiesel.htm. Accessed July 29, 2008.

Farrell, A.; Plevin, R.; Turner, B.; Jones, A.; O’Hare, M.; Kammen, D. (2006). “Ethanol Can 

Contribute to Energy and Environmental Goals.” Science, Vol. 311, No. 5760, pp. 506-508. 

January 27, 2006.

Gubler, R. (2006). “Biodiesel.” Chemical Economics Handbook, Marketing Report, SRI 

Consulting. November 2006. http://www.sriconsulting.com/CEH/
63

Indian Central Electricity Authority. (2008). “Highlights of Power Sector.” Available online at

http://cea.nic.in/power_sec_reports/Executive_Summary/2008_04/1-2.pdf. Accessed June 19, 

2008.

Indian Electricity. (2008) “Sector Information on Generation in India: Conventional Generation.” 

Available online at http://www.indianelectricity.com/overview.htm. Accessed June 19, 2008.

Indian Railways. (2007). “Annual Report and Accounts: 2006-2007: Financial Statements and 

Operating Statistics.” Available online at http://indianrailways.gov.in/deptts/stat-eco/annual-rep0607/fin-stmt-optg-stmt.pdf. Accessed June 10, 2008.

Intergovernmental Panel on Climate Change (IPCC). (2006). “2006 IPCC Guidelines for 

National Greenhouse Gas Inventories.” Prepared by the National Greenhouse Gas Inventories 

Programme; edited by: Eggleston, H.S.; Buendia, L.; Miwa, K.; Ngara, T.; Tanabe, K. Volume 4, 

Chapter 11, Table 11.1, “N2O emissions from managed soils, and CO2 emissions from lime and 

urea application.” IGES, Hayama, Japan.

IPCC. (1995). “IPCC Second Assessment Report: Climate Change 1995.” Geneva, Switzerland. 

Available online at http://www.ipcc.ch/ipccreports/assessments-reports.htm. Accessed 

September 23, 2008.

International Organization for Standardization (ISO). (2006). “Environmental management –

Life cycle assessment – Requirements and guidelines.” ISO 14044:2006.

JatrophaWorld. (2008) “Economics: Jatropha Fuel Farming.” Available online at

http://www.jatrophabiodiesel.org/farming.php. Accessed June 19, 2008.

Jongschaap, R.E.E.; Corre, W.J.; Bindraban, P.S.; Brandenburg, W.A. (2007). “Claims and Facts 

on Jatropha curcas L.: Global Jatropha curcas evaluation, breeding and propagation.” Plant 

Research International, Report No. 158. October 2007.

Kathpal, A.K. (2008). “Use of biodiesel as a Traction fuel for Indian Railways – A Case Study.”

1st Workshop on Railways and Biofuel at UIC, Paris. Available online at

http://www.uic.asso.fr/reunion.php/19822/4_kathpal_biodiesel_roadmap_indian_railways.pps. 

Accessed July 25, 2008.

Layden, L. (2008). “New biodiesel crop Jatropha taking off in S.W. Florida.” Naples Daily 

News. April 5, 2008. Available online at http://www.naplesnews.com/news/2008/apr/05/newbiodiesel-crop-jatropha-taking-sw-florida/. Accessed December 1, 2008.

Lele, S. (2008a). “Biodiesel.” March 2008. Available online at: http://www.svlele.com. Accessed

June 19, 2008.

Lele, S. (2008b). “Jatropha Cultivation.” March 2008. Electronic book available online at

http://www.svlele.com. Accessed June 19, 2008.
64

Lele, S. (2008c). “Project Report for Jatropha Plantation.” March 2008. Excel workbook.

Available online at http://www.svlele.com.

Lele, S. (2008d). “Project Report for 1,000 Liters per day BioDiesel Plant.” March 2008. 

Available online at: http://www.svlele.com/biodiesel_1tpd.htm. Accessed June 19, 2008.

Lele, S. (2008e). “Project Report for 10,000 Liters per day BioDiesel Plant.” March 2008. 

Available online at: http://www.svlele.com/biodiesel_10tpd.htm. Accessed June 19, 2008.

LiveIndia. (2008). “Distance Calculator India.” Available online at

http://www.liveindia.com/distance/Visakhapatnam.html. Accessed November 12, 2008.

Lloyd, S.; Ries, R. (2007). “Characterizing, Propagating, and Analyzing Uncertainty in LifeCycle Assessment: A Survey of Quantitative Approaches.” Journal of Industrial Ecology, 

Volume 11, No. 1, pp. 161-17.

Maps of India. (2008). “Economy Maps of India.” Available online at 

http://www.mapsofindia.com/. Accessed December 1, 2008.

Ministry of Petroleum and Natural Gas. (2006). “Review of Crude Oil Production during the 

month of October 2005 and cumulatively for the period April-October 2005 vis-à-vis 2004.”

Annexure-I, p 1. Available online at http://petroleum.nic.in/Monthly_Production/P_Oct_05.pdf. 

Accessed December 2, 2008.

Ministry of Railways. (2003). “Indian Railways to Use Bio-diesel Fuels, Signs MOU with IOC 

for Production of bio-Diesel.” News release. February 12, 2003. Available online at

http://pib.nic.in/archieve/lreleng/lyr2003/rfeb2003/12022003/r120220033.html. Accessed 

September 6, 2008.

Nivitchanyong, S. (2007). “Zero Waste Agriculture for Jatropha Plantation.” Presented at the 4th

Biomass Asia Workshop. Shah Alam, Malaysia. November 2007.

Padma, T.V. (2008). “India Approves Biofuel Rise.” Science and Development Network. 

September 17, 2008. Available online at http://www.scidev.net/en/news/india-approves-biofuelrise.html. Accessed September 23, 2008. 

Pew Center on Global Climate Change. (2008). “Climate Change Mitigation Measures in India,” 

International Brief 2, September 2008. Available online at

http://pewclimate.org/docUploads/India-FactSheet-09-08.pdf. Accessed December 1, 2008.

Planning Commission of India. (2003). “Report of the Committee on Development of Bio-fuel.”

New Delhi, India. April 13, 2003.

Prueksakorn, K.; Gheewala, S.H. (2008). “Full Chain Energy Analysis of Biodiesel from 

Jatropha curcas L. in Thailand.” Environmental Science & Technology. Vol. 43, No. 9, pp. 

3,388-3,393.
65

Prueksakorn, K.; Gheewala, S.H. (2006). “Energy and greenhouse gas implications of biodiesel 

production from Jatropha curcas L.” Proceedings of the second joint international conference on 

“Sustainable Energy and Environments.” Bangkok, Thailand. November 21-23, 2006.

Reinhardt, G.; Becker, K; Chaudhary, D.R.; Chikara, J.; von Falkenstein, E.; Francis, G.;

Gärtner, S.; Gandhi, M.R.; Ghosh, A.; Ghosh, P.; Makkar, H.; Münch, J.; Patolia, J.S.; Reddy, 

M.P.; Rettenmaier, N.; Upadhyay, S.C. (2008). “Basic data for Jatropha production and use.” 

Updated version. Institute for Energy and Environmental Research Heidelberg GmbH, Central 

Salt & Marine Chemicals Research Institute. Bhavnagar, University of Hohenheim Institute of 

Animal Production in the Tropics and Subtropics. June 2008.

Reinhardt, G.; Gartner, S.; Rettenmaier, N.; Munch, J.; von Falkenstein, E. (2007). “Screening 

Life Cycle Assessment of Jatropha Biodiesel,” Commissioned by Daimler AG, Stuttgart, 

Prepared by the Institute for Energy and Environmental Research Heidelberg GmbH. December 

11, 2007.

Renewable Energy. (2007). “Jatropha for Biodiesel Figures: Look at the financial costs for 

Jatropha growing for Biodiesel.” November 2, 2007. Available online at 

http://www.reuk.co.uk/Jatropha-for-Biodiesel-Figures.htm. Accessed July 25, 2008.

Sarin, R. (2008a). Indian Oil Corporation (IOC). Personal communication to Garvin Heath 

(NREL), May 14, 2008.

Sarin, R. (2008b). IOC. Personal communication to Garvin Heath (NREL), June 17, 2008.

Sarin, R. (2008c). IOC. Personal communication to Garvin Heath (NREL), July 15, 2008.

Sarin, R. (2008d). IOC. Personal communication to Garvin Heath (NREL), July 28, 2008.

Sarin, R. (2008e). IOC. Personal communication to Garvin Heath (NREL), November 11, 2008.

Scientific Applications International Corporation. (2006). “Life Cycle Assessment: Principles 

and Practice.” For the U.S. Environmental Protection Agency, EPA/600/R-06/060. May 2006. 

Available online at http://www.epa.gov/nrmrl/lcaccess/pdfs/600r06060.pdf. Accessed December 

2, 2008.

Searchinger, T.; Heimlich, R.; Houghton, R.A.; Fengxia, D.; Elobeid, A.; Gabiosa, J.; Tokgoz, 

S.; Hayes, D.; Yu, T.H. (2008). “Use of U.S. croplands for biofuels increases greenhouse gases 

through emissions from land-use change.” Science. Vol. 319, No. 5867, pp. 1238-1240. February 

29, 2008.

Skinner, I.; Hill, N.; Kollamthodi, S.; Mayhew, J.; Donnelly, B. (2007). “Railways and Biofuel.” 

First International Union of Railways Report. July 2007.

Swiss Centre for Life Cycle Inventories. (2008). “Ecoinvent Centre Portal: The Ecoinvent 

Database.” Available online at http://www.ecoinvent.org/. Accessed November 24, 2008.
66

Tobin, J.; Fulford, D.J. (2005). “Life cycle assessment of the production of biodiesel from 

Jatropha.” MSc dissertation, The University of Reading. September 2005.

World News Network. (2008). “World Port Distances Calculator” on Distances.com. Available 

online at http://www.distances.com. Last accessed June 19, 2008.
67

Appendices

Appendix A – Details About the SimaPro Model and Base Case Model Parameter 

Values

Appendix A contains tables detailing the contents of the custom modules developed to construct 

the SimaPro model and the base case model parameter values. These custom modules were 

introduced in the Base Case Scenario Section of the main body of the report. Tables A-1 and A-2

list all relevant parameters in the study along with their abbreviations, values, units and 

descriptions to facilitate understanding of the custom modules. The parameters are listed in 

alphabetical order by abbreviation, with parameters that have specific values assigned listed first 

and those with values that are calculated based on formulas calling other parameters listed 

second. Tables A-3 through A-14 show how the custom modules are coded.

Table A-1. List of Input Parameters that Have Assigned Values

Input Parameters Value Description

annual_rain 1,385 mm, average rainfall in Raipur province, India, the plantation's 

assumed location

bio_blend_elec 0.00086 kWh/kg, energy required to blend ethanol with gasoline, assuming 

similar requirement for blending biodiesel with diesel

bio_elec_eff 0.25 Assumed efficiency of converting solid biofuel to electricity

bio_fuel_switch 1 Set value to "1" if analyzing blended biodiesel, set value to "0" if 

analyzing diesel operation

bio_plant_life 50 Years, lifetime of biodiesel transesterification facility in Ecoinvent 

database

bio_plant_prod 63 MT/day, daily production of biodiesel in Ecoinvent database

biodiesel_blend 0.05 Biodiesel / (biodiesel + diesel) for defining the blending ratio of 

the biodiesel mix.

biodiesel_dist 25 km, distance biodiesel must be transported from the production 

facility in Raipur to rail depots in Bhilai where blending occurs

biodiesel_eff 1

Fuel consumption of B5 compared with conventional diesel, "1" 

indicates no significant change in volumetric fuel consumption as 

indicated by Indian Railway studies

cal_val_bio 39,500 kJ/kg, calorific value of biodiesel produced from Jatropha

cal_val_diesel 42,000 kJ/kg, calorific value of diesel (IOC)

cal_val_husk 4.6 MJ/m^3, calorific value of the JCL husk if used as a feedstock for 

gasification

cal_val_kernel 30.4 MJ/kg, calorific value of the JCL kernel if it is combusted

cal_val_scake 18.2 MJ/kg, calorific value of Jatropha seed cake if combusted

cal_val_shell 19.4 MJ/kg, calorific value of the JCL shell if it is combusted

chem_per_yr 0

Assumption is that application of protective chemicals occurs this 

many times per year. We are assuming that no protective 

chemicals are applied.

CO2_bio 0

kg CO2 / liter of B100 combusted. Zero value assumes that 

carbon sequestered during growth of plant is offset by combustion 

for no net carbon emissions

CO2_dies 2.68 kg CO2 / liter of diesel combusted (from The Climate Registry 

Data)

crude_foreign 0.75 Fraction of IOC crude oil from foreign sources (Middle East and 

Nigeria)

crude_offshore 1 Mass fraction of Indian domestic crude oil produced offshore
68

Input Parameters Value Description

crude_oil_trans 600

km, approximate distance crude oil travels via rail between Vizag 

crude oil terminal on the east coast of Andhra Pradesh to the

Bhilai rail depots near Raipur

degum_elec 1.69 kWh electricity required to degum the oil extracted from 1 MT of 

Jatropha seeds 

degum_steam 16,354 kCal steam required to degum the oil extracted from 1 MT of 

Jatropha seeds 

diesel_fuel_con 4.38 Liters diesel / 1000 gross tonne kilometers transported via 

locomotive

diesel_fuel_cul 86 l/ha-yr of diesel used for cultivation

diesel_loss 0.006 Grams diesel lost at filling

domestic_offsh 1 Fraction of domestic crude from offshore field (country-wide

average is 66%)

domestic_onsh 0 Fraction of domestic crude produced onshore (country-wide 

average is 34%)

elec_gen_coal 0.7 Fraction of Indian electricity generated from coal

elec_gen_hydro 0.15 Fraction of Indian electricity generated from hydro power

elec_gen_ng 0.12 Fraction of Indian electricity generated from natural gas

elec_gen_nuclea 0.02 Fraction of Indian electricity generated from nuclear power

elec_gen_renew 0.01 Fraction of Indian electricity generated from renewables

elec_TD_loss 0.32 Fraction of Indian electricity generated that is lost due to 

transmission and distribution losses

elec_tran_dist 1,000 km, length of electricity transmission infrastructure included in 

inventory, can be checked for relevance with sensitivity

fert_app_num 2 Fertilizer applications per year, one each at the beginning and 

end of the rainy seasons

fert_switch 1 "1" uses Reinhardt numbers, "0" uses Lele, IOC numbers for 

fertilizer use. Default is using Reinhardt’s values.

foreign_advan 0.03 Fraction of foreign crude produced using advanced recovery 

techniques

foreign_offshor 0.2 Fraction of foreign crude produced in offshore fields

foreign_onshore 0.77 Fraction of foreign oil produced in onshore fields

frac_mid_east 1 Assumed fraction of foreign crude oil arriving from Middle East

frac_stem 0.67 Mass fraction of Jatropha biomass comprised of stems

fuel_econ_inrl 2.63 liters/1,000 gross tonne kilometers, average fuel econ for rail 

cargo transport in India

full_seed_yield 3 Years estimated for trees to achieve full seed yield, supplied by 

Indian Oil Corporation (IOC)

glyc_cool_wat 17 m^3 cooling water required to purify 100 kg glycerine

glyc_elec 2 kWh electricity required to purify 100 kg glycerine

glyc_energy 25.6 MJ/kg, energy content of glycerine

glyc_feed 100 kg, raw glycerine feed for glycerine purification

glyc_steam 230 kg steam required to purify 100 kg glycerine

glyc_yield 0.079 Fraction yield of glycerine during jatropha oil conversion to 

biodiesel

glycerol_yield 0.97 Fraction of glycerol produced during glycerine purification

harvest_per_yr 1 Assumes one harvest per year at given seed yield per hectare

husk_energy_con 15.5 MJ/kg energy content of husks

irr_years 3 Years, number of years irrigation is required

jat_husk_wt 0.375 Mass fraction of jatropha husk weight compared with jatropha fruit

jat_oil_grav 0.92 kg/liter, specific gravity of crude jatropha oil
69

Input Parameters Value Description

jatoil_extract 63 MT/day, daily production of extracted oil in Ecoinvent database

jatoil_life 50 Years, lifetime of oil extraction facility in Ecoinvent database

K2O_fert_req 89 kg/ha-yr requirement for K2O fertilizer

life_cycle_yr 20 Years, defines the lifetime of the study being analyzed

loco_gtk 1E+08

Gross tonne kilometers per locomotive - year (based on IR 

averaging 400 billion gtk transported each year with 4000 

locomotives)

mat_KCL_app 80 Grams KCl / tree per application for mature Jatropha plantation

mat_KNit_app 25 Grams K Nitrate per tree per application for mature Jatropha 

plantation

mat_SSP_app 100 Grams single super phospate per tree per application for mature 

Jatropha plantation

mat_urea_app 150 Grams urea per tree per application for mature Jatropha 

plantation

mature_biomass 8.5 kg biomass per tree

N_fert_req 81 kg/ha-yr requirement for N fertilizer

N2O_release 0.01 Fraction of nitrogen contained in fertilizer that is released to the 

air as N2O, fraction is on a mass basis

oil_content 0.35 Percent oil content of seed, weight oil per weight total seed

oil_domestic 3,200 km, transport distance for domestic oil between Bombay High and 

VIZAG per sea route

oil_extract_cap 200

MT oil/day, assumed capacity of the oil extraction unit based on 

IOC guidance and assumption that extraction unit will meet more 

than just the IOC plantation seed demand

oil_mid_east 7,000 km, approximate distance traveled for transport of crude oil by 

tanker from Middle East to Vizag

oil_Nigeria 13,000 km, approximate distance traveled for transport of crude oil by 

tanker from Nigeria to Salaya

oil_tanker_CO2 4.9 Grams CO2 per tonne-km transported by transoceanic oil tanker

P2O5_fert_req 31 kg/ha-yr requirement for P2O5 fertilizer

plant_manure 4.5 kg manure per planting hole (Lele only, not mentioned by IOC)

plant_MOP 16 Grams of muriate of potash (common name for KCl) per planting 

hole

plant_SSP 120 Grams single super phosphate per planting hole

plant_urea 20 Grams urea per planting hole (IOC estimate, comparable to Lele 

estimates)

plantation_elec 2,431,620 kWh electricity per yr required to operate a 10,000 hectare 

plantation

plantation_tot 50,000 Total hectares, to be split into five 10,000 hectare units for 

management

plantation_unit 10,000 Hectares, used as manageable unit based on available data

poly_bag 1 polyethylene bag per seedling

poly_eth_bag 7 Grams, weight of LLDPE bag for seedling cultivation

rail_CO2 20.7 Grams CO2 per tonne-km transported for railcar transport

reinhardt_dens 1,667 Assumed trees per hectare for Reinhardt’s calculations

required_rain 2,500 mm/yr, amount of rain required to avoid irrigation

seed_cake_K 0.1 Fraction potassium of fertilizer replaced by seed cake

seed_cake_N 0.4 Fraction Nitrogen of fertilizer replaced by seed cake

seed_cake_P 0.2 Fraction phosphorus of fertilizer replaced by seed cake
70

Input Parameters Value Description

seed_cake_rep 0.15 kg of NPK (40:20:10) fertilizer replaced by 1 kg jatropha seed 

cake

seed_drying 0 Energy used to dry seeds (assumed 0 at this point for sun drying, 

could also use an oven)

seed_husk_yield 1,429 Yield of kg sun dried husk per ha-yr

seed_survive 0.8 Fraction of planted seedlings that survive

seed_tran_dist 50 km, transport distance for Jatropha seeds to processing facility, 

seed transport via truck

seed_yield_tot 2,382 Yield kg sun dried seeds per ha-yr

seed_yield_tree 1.5 kilograms dry seed per tree, assumed seed yield based on India 

Planning Commission (IPC) estimates

solv_extract_n 0.91 Percent, extraction efficiency for solvent extraction, weight oil 

extracted per weight oil available in seed

solvent_elec 55 kWh electricity per tonne of seed required for continuous solvent 

extraction

solvent_hexane 4 kg hexane-n per tonne of seed required for continuous solvent 

extraction

solvent_recycle 0.99 Mass fraction of hexane recycled during solvent extraction

solvent_steam 280 kg steam per tonne of seed required for continuous solvent 

extraction

solvent_trans 0

Tonne-km transport for oil extracted at solvent extraction facility to 

reach biodiesel transesterification facility. Assumption is that the 

oil extraction and transesterification units are co-located

solvent_water 12 m^3 water per tonne of seed required for continuous solvent 

extraction (consumed and discharged to sewer)

spec_ener_leaf 3,624 kJ/kg specific energy of Jatropha leaf

spec_ener_stem 3,932 kJ/kg specific energy of jatropha stem

spec_grav_biod 0.88 kg/L, specific energy of biodiesel; within reasonable range, more 

specific IOC data would be useful

spec_grav_dies 0.84 kg/L, specific energy of diesel; within reasonable range, more 

specific IOC data would be useful

transest_cap 100,000 MT biodiesel produced per year

transest_eff 0.95 Conversion efficiency of jatropha oil to biodiesel

transest_elect 36 kWh electricity required per tonne of biodiesel produced

transest_KOH 18 kg KOH required per tonne of biodiesel produced

transest_meth 110 kg methanol required per tonne of biodiesel produced

transest_minacd 6 kg mineral acid required per tonne of biodiesel produced

transest_steam 660 kg steam required per tonne of biodiesel produced

transest_water 55 m^3 circulating water required (not consumed)

tree_density 2,500 Trees/hectare, initial plantation density of Jatropha trees

truck_CO2 51.2 Grams CO2 per tonne-km transported by lorry

yr_1_biomass 2.5 kg total biomass yield per tree in Year 1

yr_2_biomass 4.5 kg total biomass yield per tree in Year 2
71

Table A-2. List of Calculated Input Parameters that Have Values Determined by Formulas Based on Other Parameters

Name Formula Description

b5_fuel_cons diesel_fuel_con*biodiesel_eff Liters B5 per 1000 gross tonne kilometers transported via locomotive

bio_blend_sg

spec_grav_dies*(1-biodiesel_blend) 

+spec_grav_biod*biodiesel_blend kg per liter, specific gravity of blended fuel

bio_fuel_con biodiesel_eff*diesel_fuel_con Liters biodiesel per 1000 gross tonne kilometers

bio_plant_piece 1/bio_prod_life

Piece of a biodiesel transesterification plant allocated to each tonne of biodiesel 

produced based on Ecoinvent numbers. This value is used to represent the 

infrastructure contribution of the transesterification facility in the Indian case 

study

bio_prod_life bio_plant_life*bio_plant_prod*365 Lifetime biodiesel fuel production assumed in Ecoinvent inventory calculation

CO2_biodiesel CO2_bio/spec_grav_biod CO2 emissions for biodiesel on a kg CO2 per kg biodiesel basis

CO2_diesel CO2_dies/spec_grav_dies CO2 emissions for diesel on a kg CO2 per kg diesel basis

crude_assam (1-crude_foreign)*(1-crude_offshore) Fraction of Indian crude oil produced onshore at Assam

crude_bombay (1-crude_foreign)*crude_offshore Fraction of total crude oil from domestic fields (Bombay High)

crude_mid_east crude_foreign*frac_mid_east Fraction of total crude oil from Middle East

crude_Nigeria crude_foreign*(1-frac_mid_east) Fraction of total crude oil from Nigeria

crude_ocean_trn

crude_Nigeria*oil_Nigeria+crude_mid_east* 

oil_mid_east+crude_bombay*oil_domestic

t-km, tonne kilometer of oil tanker transport required to deliver 1 tonne of total 

crude oil to the Indian coastal oil terminal

frac_leaf 1-frac_stem Mass fraction of biomass that is leaves

frac_nig 1-frac_mid_east Assumed fraction of foreign crude oil arriving from Nigeria

husk_tot_mass

seed_husk_yield*plantation_tot*harvest_per_yr

* life_cycle_yr kg seed husks produced per plantation lifetime

jatoil_ext_life jatoil_extract*jatoil_life*365 Lifetime oil extraction assumed in Ecoinvent inventory calculation

jatoil_plnt_pce 1/jatoil_ext_life

Piece of an oil extraction facility plant allocated to each tonne of oil extracted 

based on Ecoinvent numbers. This value is used to represent the infrastructure 

contribution of the solvent extraction facility in the Indian case study

jatoil_required 1/transest_eff kg jatropha oil required to produce 1 kg of biodiesel

life_biomass_el

(life_biomass_en+life_husk_en)*0.000278* 

bio_elec_eff kWh electricity offset over lifetime (converted from kilojoules)

life_biomass_en

life_biomass_to*(frac_stem*spec_ener_stem+ 

frac_leaf*spec_ener_leaf)/1000 MJ leaf and stem energy per plantation lifetime

life_biomass_pl

yr_1_biomass+yr_2_biomass+ 

(mature_biomass*(life_cycle_yr-2)) Total kg biomass per plant over plantation lifetime

life_biomass_to life_biomass_pl*tree_density*plantation_tot kg, total biomass produced on plantation over lifetime

life_husk_en husk_tot_mass*husk_energy_con MJ husk energy produced per plantation lifetime
72

loco_bio_fuel bio_fuel_con*bio_blend_sg kg biodiesel blend used per 1000 gross tonne kilometer transported, locomotive

loco_dies_fuel diesel_fuel_con*spec_grav_dies kg diesel used per 1000 gross tonne kilometer transported, locomotive

loco_life_gtk loco_gtk*life_cycle_yr Lifetime gross tonne kilometer analyzed in the study

locomotive_CO2

CO2_diesel*(1-biodiesel_blend)+ 

CO2_biodiesel*biodiesel_blend

(kg CO2/kg fuel) Adjusted CO2 emission factor for the locomotive based on the 

biodiesel blend being used

N2O_release_ioc N2O_release*(1-fert_switch)*urea_tot_lele kg N20 release under IOC fertilizer scenario

N2O_release_rei

N2O_release*fert_switch* 

(N_fert_req*plantation_tot*life_cycle_yr) kg N2O release in Reinhardt fertilizer scenario

N2O_volatized N2O_release_rei+N2O_release_ioc kg N2O volatized from N fertilizer over plantation lifetime

oil_per_hectare seed_yield_hect/seed_required/1000

Metric tonnes (MT) of oil yield expected per hectare calculated from anticipated 

seed and oil yields

oil_produced_yr oil_per_hectare*plantation_tot

MT of anticipated oil produced by the crops on the plantation and solvent 

extraction in an average year

oil_recov_eff oil_content*solv_extract_n Weight of oil that is available and recovered per total weight of seed

rainfall_def required_rain-annual_rain mm/yr rainfall deficit

req_irrigation rainfall_def/1000*10000 m^3 irrigation water required per hectare-yr

seed_process_dy oil_extract_cap*seed_required

MT of seeds per day processed in a continuous process if oil extraction unit is 

operating at full capacity (200 MT seed per day considered minimum for 

economically viable continuous solvent extraction of jatropha oil; top end of 

solvent extraction capacity is 4000 MT seed per day)

seed_required 1/oil_recov_eff

Amount of dry seed required in kilograms to generate 1 kg of jatropha oil when 

oil content and extraction efficiency are taken into account

seed_tran_tot

seed_tran_dist*seed_yield_hect* 

plantation_tot/1000

Tonne-km of truck transport service required to transport all Jatropha seed 

produced on the plantation in one year to the oil extraction facility

seed_yield_hect seed_yield_tree*tree_density

Kilograms dry seed expected per hectare based on tree density and seed yield 

per tree

seedling_plant tree_density/seed_survive

Total trees required to be planted to achieve desired mature tree density based 

on seedling survival rate

solvent_req (1-solvent_recycle)*solvent_hexane kg hexane required to be replenished per kg seeds processed

tot_biomass_en life_biomass_en+life_husk_en

MJ, total biomass energy available based on biomass yield and energy content 

over life of plantation

urea_tot_lele

mat_urea_app*tree_density*fert_app_num* 

life_cycle_yr*plantation_tot/1000 kg urea as N required under Lele IOC fertilizer scenario
73

Table A-3. India Electricity Generation Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Indian Electricity Generated 1 kWh

Avoided Products

Resources

Materials/Fuels

Electricity/Heat

Electricity, hard coal, at power plant/UCTE S elec_gen_coal kWh Fraction of a kWh generated by coal

Electricity, natural gas, at power plant/UCTE 

S elec_gen_ng kWh Fraction of a kWh generated by natural gas

Electricity hydropower in UCPTE S elec_gen_hydro kWh Fraction of a kWh generated by hydro 

(based on European average)

Electricity, nuclear, at power plant/UCTE S elec_gen_nuclea kWh Fraction of a kWh generated by nuclear power

Electricity, at wind power plant/RER S elec_gen_renew/2 kWh

Fraction of a kWh generated by wind power 

(fraction of renewable energy by technology not 

specified, assuming 50% of renewable wind, 50% 

solar)

Electricity, production mix photovoltaic, at 

plant/U.S. S elec_gen_renew/2 kWh

Fraction of a kWh generated by photovoltaics

(fraction of renewable energy by technology not 

specified, assuming 50% of renewable wind, 50% 

solar)

Emissions to Air

Emissions to Water

Emissions to Soil
74

Table A-4. Indian Electricity Delivered Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Indian Electricity Delivered 1 kWh

Avoided products

Resources

Materials/fuels

Electricity/heat

Indian Electricity Generated 1/(1-elec_TD_loss) kWh Amount of electricity required to be generated in 

order to deliver 1 kWh to the user on average

Infra electricity LV use UCPTE S 1 kWh

Inclusion of impacts from transmission and 

distribution infrastructure for low voltage (LV) 

electricity delivery

Emissions to Air

Emissions to Water

Emissions to Soil

Table A-5. Jatropha Seedling for Planting Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Jatropha Seedling for Planting 1 p

Avoided products

Resources

Materials/fuels

Polyethylene, LLDPE, granulate, at 

plant/RER S poly_eth_bag g

Electricity/Heat

Emissions to Air

Emissions to Water

Emissions to Soil
75

Table A-6. Jatropha Plantation, Planted, India Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Jatropha Plantation, Planted Ha, India 1 ha

Avoided Products

Resources

Materials/Fuels

Jatropha Seedling for Planting seedling_plant p

Urea, as N, at regional storehouse/RER S seedling_plant*plant_urea/2.17/1000 kg Urea required to plant one hectare

Single superphosphate, as P2O5, at 

regional storehouse/RER S seedling_plant*plant_SSP/4.76/1000 kg

Single super phosphate required to 

plant one hectare

Potassium chloride, as K2O, at regional 

storehouse/RER S seedling_plant*plant_MOP/1.67/1000 kg

Potassium chloride as potassium used 

to substitute for muriate of potash

Poultry manure, dried, at regional 

storehouse/CH S seedling_plant*plant_manure kg

Manure may be processed in the Indian 

context less than this module indicates

Irrigating/ha/CH S 1

ha

Tillage, ploughing/CH S 1

ha

Used to represent energy required to 

plough the field to clear prior to 

planting. Individual holes dug and 

planted using manpower. Fertilizer 

applied by hand to each hole in initial 

establishment

Electricity/Heat

Emissions to Air

Emissions to Water

Emissions to Soil
76

Table A-7. Jatropha Seeds Harvested from Plantation Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or Variable Units Comments

Products

Jatropha Seeds Harvested from 

Plantation

seed_yield_hect*plantation_tot*

harvest_per_yr*life_cycle_yr/1000 tonne Seeds harvested over 20 yr lifetime

Avoided Products

Indian Electricity Generated life_biomass_el kWh Indian electricity offset by combusting Jatropha 

biomass for electricity generation

Resources

Materials/Fuels

Fertilising, by broadcaster/CH S plantation_tot*fert_app_num*life_cycle_yr Ha

Energy required to distribute fertilizer to entire 

plantation given number of times per year for entire life 

cycle

Irrigating/m3/CH S plantation_tot*irr_years*req_irrigation m3

Energy required to distribute irrigation water to entire 

plantation given number of times per year for entire life 

cycle 

Jatropha Plantation, Planted Ha, India plantation_tot Ha Number of planted hectares required for the plantation.

Urea, as N, at regional storehouse/RER 

S

Fert_app_num/2*fert_switch*

(N_fert_req*plantation_tot*life_cycle_yr*

tree_density/reinhardt_dens)

kg Amount of fertilizer required for plantation lifetime

Potassium chloride, as K2O, at regional 

storehouse/RER S

Fert_app_num/2*fert_switch*

(K2O_fert_req*plantation_tot*life_cycle_yr*

tree_density/reinhardt_dens)

kg Amount of fertilizer required for plantation lifetime

Single superphosphate, as P2O5, at 

regional storehouse/RER S

Fert_app_num/2*fert_switch*

(P2O5_fert_req*plantation_tot*

life_cycle_yr*tree_density/reinhardt_dens)

kg Amount of fertilizer required for plantation lifetime

Diesel, at regional storage/RER S diesel_fuel_cul*plantation_tot*

life_cycle_yr*spec_grav_dies kg Amount of diesel combusted for plantation operations over plantation lifetime

Indian Electricity Delivered plantation_elec*life_cycle_yr*

plantation_tot/10000 kWh Amount of electricity consumed over plantation lifetime

Electricity/Heat

Emissions to Air

Dinitrogen monoxide N2O_volatized kg N2O released over plantation lifetime owing to N 

fertilizer application

Emissions to Water

Emissions to Soil
77

Table A-8. Jatropha Oil, at Extraction Facility, India Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or Variable Units Comments

Products

Jatropha Oil oil_recov_eff tonne

Jatropha Seed Cake 1-oil_recov_eff tonne

Avoided Products

Urea, as N, at regional 

storehouse/RER S

(1-oil_recov_eff)*seed_cake_rep*

seed_cake_N tonne

Avoided urea fertilizer production due to the 

generation and use of Jatropha seed cake 

Single superphosphate, as P2O5, at 

regional storehouse/RER S

(1-oil_recov_eff)*seed_cake_rep*

seed_cake_P tonne Avoided single super phosphate production due to the generation and use of Jatropha seed cake

Potassium nitrate, as K2O, at regional 

storehouse/RER S

(1-oil_recov_eff)*seed_cake_rep*

seed_cake_K tonne Avoided potassium nitrate production due to the generation and use of Jatropha seed cake

Resources

Materials/Fuels

Jatropha Seeds Harvested from 

Plantation 1 tonne

Hexane, at plant/RER S solvent_hexane kg

Oil mill/CH/I S jatoil_plnt_pce*oil_recov_eff p Fraction of oil extraction plant infrastructure 

allocated to Jatropha oil produced in this module

Electricity/Heat

Steam, for chemical processes, at 

plant/RER S solvent_steam kg

Indian Electricity Delivered solvent_elec kWh

Tap water, at user/RER S solvent_water tonne

Water quality requirements not specified, 

assumes that tap water is adequate for this 

process 

Transport, lorry 3.5-16t, fleet 

average/RER S seed_tran_dist tkm Transport of seeds to facility

Emissions to Air

Emissions to Water

Emissions to Soil
78

Table A-9. Biodiesel Production, Base-catalyzed Transesterification, India, at Plant Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or Variable Units Comments

Products

Biodiesel 1 tonne Biodiesel output

Avoided Products

Glycerine glyc_yield*jatoil_required tonne

Avoided synthetic glycerine production based on 

anticipated glycerine yield from biodiesel production

Resources

Materials/Fuels

Methanol, at regional storage/CH S transest_meth kg Methanol module includes transport distance

Sulphuric acid, liquid, at plant/RER S transest_minacd kg

Transportation distance for sulfuric acid is unknown. 

Composition of mineral acid required is unknown. 

Sulfuric acid is currently assumed in this analysis

Potassium hydroxide, at regional 

storage/RER S transest_KOH kg Potassium hydroxide module includes transport distance

Jatropha Oil jatoil_required tonne Amount of jatropha oil required to produce 1 ton 

biodiesel

Vegetable oil esterification plant/CH/I 

S bio_plant_piece p

Fraction of biodiesel transesterification plant 

infrastructure impacts attributed to each tonne 

produced

Electricity/Heat

Indian Electricity Delivered transest_elect kWh Energy data is per tonne of biodiesel generated at a 

100,000 MT per year biodiesel facility

Steam, for chemical processes, at 

plant/RER S transest_steam kg

In absence of specific Indian steam data, generic 

steam production for use in chemical processes in 

Europe is used. Amount of steam is specific to the 

Indian transesterification plant

Transport, lorry 3.5-16t, fleet 

average/RER S solvent_trans*jatoil_required tkm

Emissions to Air

Emissions to Water

Emissions to Soil
79

Table A-10. Biodiesel Blending, India, at Processing Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Blended biodiesel 1 tonne

Avoided Products

Resources

Materials/Fuels

Biodiesel biodiesel_blend tonne

Diesel, at rail yard, India 1-biodiesel_blend tonne

Transport, freight, rail/RER S biodiesel_dist*biodiesel_blen

d tkm

Rail transport assumed for biodiesel from 

biodiesel transesterification plant to blending 

facility

Electricity/Heat

Indian Electricity Delivered bio_blend_elec*1000 kWh Electricity required to blend diesel with biodiesel

Emissions to Air

Emissions to Water

Emissions to Soil
80

Table A-11. Indian Freight Train Operation, Biodiesel Blend Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Indian Freight Train Transport, Blended 

Biodiesel 1000 tkm

Avoided Products

Resources

Materials/fuels

Blended biodiesel loco_bio_fuel kg

Electricity/Heat

Emissions to Air

Carbon dioxide locomotive_CO2*loco_bio_fuel kg CO2 emissions from just the diesel portion of 

the blend

Emissions to Water

Emissions to Soil
81

Table A-12. Crude Oil at Indian Coastal Terminal Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or Variable Units Comments

Products

Crude Oil, Indian Mix 1 tonne

Avoided Products

Resources

Materials/Fuels

Crude oil, at production onshore/RME S crude_mid_east tonne Represents foreign oil extraction from the 

Middle East

Crude oil, at production/NG S crude_Nigeria tonne

Represents foreign oil extraction from the Niger 

delta of Nigeria. India's foreign oil primarily 

comes from the Middle East and Nigeria.

Crude oil, at production offshore/GB S crude_bombay tonne

Domestic oil production is assumed to occur in 

India's largest oil field, Bombay High and be 

transported to the coastal terminal to be 

combined with the foreign crude via oil tanker. 

United Kingdom (GB) production is used as 

proxy for Indian offshore production.

Transport, transoceanic tanker/OCE S crude_ocean_trn tkm Average shipping distance for foreign and 

domestic oil weighted by the proportion of each

Electricity/Heat

Emissions to Air

Emissions to Water

Emissions to Soil
82

Table A-13. Diesel at Railyard Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Diesel, at rail yard, India 1 tonne

Avoided Products

Resources

Materials/Fuels

Diesel, at regional storage/Ecoinvent-

>India S 1 tonne

Represents refining of crude oil to diesel and 

transport to rail yard for combustion in a 

locomotive

Electricity/Heat

Emissions to Air

Emissions to Water

Emissions to Soil

Table A-14. Indian Freight Train Transport, Diesel Custom Module

Module Flows

(drawn from Ecoinvent 2.0 or other 

custom modules)

Input Value or 

Variable Units Comments

Products

Indian Freight Train Transport, Diesel 1000 tkm

Avoided Products

Resources

Materials/Fuels

Diesel, at rail yard, India loco_dies_fuel kg

Amount of diesel used in locomotive when train 

exclusively using diesel

Electricity/Heat

Emissions to Air

Carbon dioxide locomotive_CO2*loco_dies_fuel kg

Emissions to Water

Emissions to Soil
83

Appendix B – Sensitivity Analysis Scenario Details

Table B-1 details the parameter values used to generate the sensitivity scenarios evaluated in Tables 20-22.

Table B-1. Sensitivity Analysis Scenario Details

Sensitivity 

Scenario 

Number

Parameter 

Category 

Tested

Parameter 

Evaluated Units Base Case Value Sensitivity Scenario A

Sensitivity 

Scenario B

1 Rainfall Plantation 

rainfall mm/year 1,385 0 --

2 Irrigation Years required 

for irrigation Years 3 20 --

3

N2O 

Volatization 

Rate

Nitrous oxide 

release

g N2O per g N in 

fertilizer 0.01 0.003 0.04

4

Plantation 

Electricity 

Consumption

Plantation 

electricity

MWh per year for 

50,000 hectare 

plantation

12,000 6,000 0

5 Tree Planting 

Density Tree density Trees per hectare 2,500 1,667 1,100
84

Sensitivity 

Scenario 

Number

Parameter 

Category 

Tested

Parameter 

Evaluated Units Base Case Value Sensitivity Scenario A

Sensitivity 

Scenario B

6

Biodiesel 

Efficiency 

Compared to 

Diesel

Biodiesel 

efficiency

Biodiesel fuel 

consumption per

diesel fuel 

consumption

1 1.02 1.05

7

Diesel Fuel 

Consumption 

Requirement

Diesel fuel 

consumption Liters per 1000 GTK 4.38 2.63 8.76

8 Fertilizer Use Fertilizer 

application Applications per year 2 0 4

9 Seed Oil 

Content

Oil content of 

Jatropha seed

Mass oil per mass 

total seed

0.35 0.4 0.25

10 Dry Seed 

Yield

Jatropha seed 

yield

kg sun dried seeds 

per ha-yr 2,382 4,446 1,413

11

Oil 

Extraction 

Efficiency 

from Seeds

Extraction 

efficiency Mass percent 0.91 0.99 0.95

12

Glycerine 

Production 

Offset

Glycerine yield Mass fraction 0.08 0 --
85

Sensitivity 

Scenario 

Number

Parameter 

Category 

Tested

Parameter 

Evaluated Units Base Case Value Sensitivity Scenario A

Sensitivity 

Scenario B

13 Seed Cake 

Offset

Seed cake 

replacement

kg of NPK fertilizer 

(40:20:10) replaced 

by 1 kg of Jatropha 

seed cake

0.15 0 --

14 Biomass 

Offsets

Biomass Yield, 

Year 1, Year 2, 

Mature; Seed 

husk yield

kg biomass per tree; 

kg sun dried husks 

per ha-yr

2.5, 4.5, 8.5; 1,429 0, 0, 0; 0 --

15 All Offsets

Combines the glycerine production offset, seed cake offset, and biomass offsets into one 

sensitivity scenario run (all used or none used)
86

Appendix C – Best- and Worst-Case Scenario Details

Table C-1 reports the parameters used to create the best and worst case scenarios tested in the sensitivity analysis. Note that these 

parameters are not necessarily internally consistent but are designed to represent the range of each individual parameter. 

See Table A-1 for more detailed descriptions of each parameter.

Table C-1. Parameter Values Used To Model Best- and Worst-Case Sensitivity Scenarios

Parameter Description Input Parameter Units Base Case Value Best Case Value Worst Case Value

Annual plantation rainfall annual_rain mm / yr 1,385 2,500 0

Years of irrigation 

required irr_years Years 3 0 20

Nitrous oxide 

volatilization rate from 

nitrogen fertilizer

N2O_release g N2O / g N 

fertilizer 0.01 0.003 0.04

Number of fertilizer 

applications / year fert_app_num Applications / year

2 0 4

Jatropha seed oil content oil_content Mass fraction 0.35 0.4 0.25

Total Jatropha seed yield seed_yield_tot kg sun dried 

seeds per ha-yr 2,382 4,436 1,418

Jatropha seed yield per 

tree seed_yield_tree kg sun dried seeds per tree 1.5 2.8 0.89

Jatropha oil extraction 

efficiency solv_extract_n Mass fraction 0.91 0.99 0.91

Jatropha seedling 

survival rate seed_survive Fractional survival rate 0.8 1 0.4

Tree density tree_density Trees / hectare 2,500 2,500 1,100

Nitrogen fertilizer 

required N_fert_req kg / ha-yr 81 48 141

P2O5 fertilizer required P2O5_fert_req kg / ha-yr 31 19 56

K2O fertilizer required K2O_fert_req kg / ha-yr 89 53 139

Diesel fuel for plantation 

operation diesel_fuel_cul L / ha-yr 86 55 141
87

Parameter Description Input Parameter Units Base Case Value Best Case Value Worst Case Value

Jatropha seed husk yield seed_husk_yield kg sun dried 

husk / ha-yr 1,429 2,136 0

Biomass harvested from 

Jatropha trees in year 1 yr_1_biomass kg biomass / tree 2.5 3 0

Biomass harvested from 

Jatropha trees in year 2 yr_2_biomass kg biomass / tree 4.5 5 0

Biomass harvested from 

Jatropha trees from year 

3 onward

mature_biomass kg biomass / 

tree 8.5 10 0

Transportation distance 

of Jatropha seeds from 

plantation to oil extraction 

facility

seed_tran_dist km 50 0 100

Jatropha oil extraction 

required electricity solvent_elec kWh / tonne seed 55 27.5 110

Jatropha oil extraction 

required hexane solvent_hexane kg hexane / tonne seed 4 2 8

Jatropha oil extraction 

required steam solvent_steam kg steam / tonne seed 280 140 560

Jatropha oil extraction 

required water solvent_water m3 / tonne seed 12 6 24

Transport distance for 

biodiesel from production 

facility to fueling

biodiesel_dist km 25 0 50

Biodiesel production 

efficiency transest_eff

Fractional 

conversion 

efficiency

0.95 0.99 0.95

Biodiesel production 

electricity required transest_elect kWh / tonne biodiesel 36 18 72

Biodiesel production 

steam required transest_steam kg / tonne biodiesel 660 330 1,320

Biodiesel production KOH 

required transest_KOH kg / tonne biodiesel 18 9 36

Biodiesel production 

methanol required transest_meth kg / tonne biodiesel 110 55 220

Biodiesel production 

glycerine yield glyc_yield Mass fraction 0.079 0.1 0
88

Parameter Description Input Parameter Units Base Case Value Best Case Value Worst Case Value

Amount of NPK fertilizer 

offset by Jatropha seed 

cake

seed_cake_rep

kg NPK 

(40:20:10) 

fertilizer 

replaced by 1 

kg seed cake

0.15 0.15 0

Fuel consumption 

increase for use of 

biodiesel blends

biodiesel_eff

Fractional fuel 

consumption 

increase

1 1 1.05

Plantation electricity 

consumption per 10,000 

hectares

plantation_elec

kWh / yr for a 

10,000 hectare 

plantation

2,431,620 0 4,863,240

Diesel fuel consumption 

in locomotives diesel_fuel_con

Liters / 1,000 

gross tonne 

kilometer

4.38 2.63 8.76
F1147-E(10/2008)

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

Revised March 2009

2. REPORT TYPE

Technical Report

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Life Cycle Assessment of the Use of Jatropha Biodiesel in Indian 

Locomotives

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

M. Whitaker and G. Heath

5d. PROJECT NUMBER

NREL/TP-6A2-44428

5e. TASK NUMBER

BBO7.9610

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/TP-6A2-44428

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

With India’s transportation sector relying heavily on imported petroleum-based fuels, the Planning Commission of 

India and the Indian government recommended the increased use of blended biodiesel in transportation fleets, 

identifying Jatropha as a potentially important biomass feedstock. The Indian Oil Corporation and Indian Railways are 

collaborating to increase the use of biodiesel blends in Indian locomotives with blends of up to B20, aiming to reduce 

GHG emissions and decrease petroleum consumption. To help evaluate the potential for Jatropha-based biodiesel in 

achieving sustainability and energy security goals, this study examines the life cycle, net GHG emission, net energy 

ratio, and petroleum displacement impacts of integrating Jatropha-based biodiesel into locomotive operations in 

India. In addition, this study identifies the parameters that have the greatest impact on the sustainability of the 

system. 

15. SUBJECT TERMS

India; Jatropha; transportation sector; biodiesel; life cycle; Indian locomotives; Indian Oil Corporation; Indian 

Railways; B20; greenhouse gas emissions; Garvin Heath

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18