The Pennsylvania State University

The Graduate School

The Department of Ecosystem Science and Management

LIFE CYCLE ASSESSMENT AND EMERGY ANALYSIS

IN BIOMASS CHP ENVIRONMENTAL ACCOUNTING

A Dissertation in

Forest Resources

by

Li Ma

2013 Li Ma

Submitted in Partial Fulfillment of the Requirements

for the Degree of

Doctor of Philosophy

December 2013

The dissertation of Li Ma was reviewed and approved* by the following: Charles D. Ray Associate Professor of Wood Products Operation Dissertation advisor Chair of Committee Judd H. Michael Professor of Wood Products Business Management Michael G. Messina Professor of Forest Science Head of Department of Ecosystem Science and Management Richard C. Stehouwer Professor of Environmental Soil Science *Signatures are on file in the Graduate School

iii

ABSTRACT

This study discusses the similarities, differences, and incompatibilities between two types

of environmental accounting tools: Life Cycle Assessment (LCA) and Emergy Analysis (EMA),

both of which are used to provide environmental assessment of products and processes. LCA

methodology provides emission-focused environmental accounting by expressing all the resource

uses (material and energy) across a product's entire life as categorized environmental impacts. In

contrast, EMA methodology presents a single unit measured, energy-focused environmental

accounting by expressing all the resource consumption (material, energy and labor) in a solar

energy equivalent or solar emjoule (sej).

A significant, albeit simplified, case study – that of a wood biomass Combined Heat and

Power (CHP) system - is used to compare the results and analytically assess merits of LCA and

EMA as well as to consider possible integration of the methods. Woodchips production,

transportation, facility construction, industrial conversion to energy and disposal of wastes are

included in the analysis. A Monte Carlo simulation model is developed by taking into account

factors that have inherent uncertainty in a biomass CHP system. The results obtained from the

two methods are compared by means of uncertainty analysis, sensitivity analysis and correlation

analysis.

This research provides three key contributions.

The findings suggest that information provided by the two methods is complementary

rather than competing. Each of these two methods displays its own unique "optimal field of

application":

1) LCA is a useful assessment method to evaluate local and global environmental

impacts of the system. Its usefulness is very limited to the assessment of a specific

system. However, LCA may be and is commonly used within clearly-stated

iv

assumptions, to compare two similar processes and thereby provides environmental

sciences a continuous benchmarking tool.

2) EMA provides a more robust assessment of interconnection between an industrial

process, its recognized environmental dynamics and its economic potential. Its

capability to account for externalities expands its usefulness over a broader spectrum

of cases, but also limits its use for improvement of a specific process. The crucial

benefit of EMA is that it provides an approach aimed for maximizing utilization

efficiency of local environmental resources in supporting industrial process and

economy. Simply speaking, EMA answers the question "What is the most efficient

product or process?" while LCA answers "How can we improve environmental

efficiency of a specific product or process?"

Secondly, LCA and EMA indicators are characterized by different degrees of uncertainty:

1) Uncertainty is inherent in the current LCA approach, and cannot be overcome even if

practitioners strictly follow the procedures described in the LCA standards. Therefore,

uncertainty and sensitivity analysis should always be reported in the LCA final

results.

2) EMA indicators are subject to free environmental service and human labor associated

with the system, which are not accounted in LCA. This uncertainty analysis of EMA

adds value to the extant literature.

Given the large degree of uncertainty of the LCA results, using LCA independently as the

sole tool for decision-making in energy policy will, in some cases, cause decisions resulting in

more environmental damage and poorer economical performance than expected and understood.

Therefore, LCA can be a useful tool for a company's internal decision-making, but should not be

solely trusted to guide public policy. EMA can quantify the contribution of natural capital for

v

sustaining economic activity. The results become more accurate as the scale of environmental

area gets larger, which makes well-executed EMA a better tool for environmentally and

economically conscious policy-making.

Finally, correlation analysis reveals no significant correlation between Global Warming

Potential (GWP), the most commonly referred-to indicator in LCA, and any EMA indicators. On

the other hand, varying correlations are found among EMA indicators, suggesting the number of

EMA indicators could be reduced as they lead to similar findings. Surprisingly, biogenic CO2

emission from LCA and Transformity (Tr) in EMA are strongly correlated. This relationship

suggests that using some LCA components and methodology could possibly increase the

applicability and long-term value of EMA in environmental decision-making as it complements

the assessment perspective.

vi

TABLE OF CONTENTS

LIST OF FIGURES ........................................................................................................................ ix

LIST OF TABLES........................................................................................................................... x

ACKNOWLEDGEMENTS............................................................................................................xi

Chapter 1: PROBLEM STATEMENT AND JUSTIFICATION ..............................................1

Problem statement....................................................................................................................1 Research framework.................................................................................................................2 Research Objectives.................................................................................................................3 Research questions and hypotheses..........................................................................................4 Value of work...........................................................................................................................5

Chapter 2: LITERATURE REVIEW ........................................................................................8

Introduction..............................................................................................................................8 Life cycle assessment...............................................................................................................8

Uncertainty of LCA ........................................................................................................12 Correlation of LCA results .............................................................................................15

Emergy analysis .....................................................................................................................16 Important concepts in EMA............................................................................................17 A review of EMA studies ................................................................................................19

Joint use of LAC and EMA....................................................................................................24 Biomass combined heat and power (CHP) ............................................................................26

Biomass CHP system factors..........................................................................................27

Chapter 3: RESEARCH METHODOLOGY...........................................................................37

Introduction............................................................................................................................37 Study scope ............................................................................................................................37

System boundary ............................................................................................................37 LCA and EMA indicators ...............................................................................................37 Functional unit ...............................................................................................................38

Overview of modeling procedure...........................................................................................38 Modeling steps................................................................................................................38 Data collection in LCA...................................................................................................39 Data collection in EMA..................................................................................................40

Processes of biomass CHP system.........................................................................................41 Woodchips production....................................................................................................41 Woodchips transportation ..............................................................................................42 Plant construction ..........................................................................................................43 Plant operation/emission................................................................................................43 Chemicals use in plant ...................................................................................................44 Waste disposal................................................................................................................44 Disposal ash ...................................................................................................................44

vii

Biomass CHP system factors .................................................................................................44 Emission control.............................................................................................................45 Moisture content.............................................................................................................45 Transportation distance .................................................................................................46 Power to heat ratio.........................................................................................................46

Data analysis ..........................................................................................................................47 Uncertainty and sensitivity analysis...............................................................................47 Correlation analysis .......................................................................................................48

Chapter 4: COMPARING UNCERTAINTY AND SENSITVITY OF LCA AND EMA RESULTS IN A BIOMASS CHP SYSTEM.................................................................................58

Abstract ..................................................................................................................................59 Introduction............................................................................................................................61 Literature review....................................................................................................................62

LCA.................................................................................................................................62 EMA................................................................................................................................63 Biomass CHP factors .....................................................................................................64

Methods..................................................................................................................................66 System boundary ............................................................................................................66 System functional unit ....................................................................................................66 Modeling Procedure.......................................................................................................66 Data Analysis .................................................................................................................70

Results....................................................................................................................................70 Uncertainty of LCA ........................................................................................................70 Sensitivity Analysis of LCA.............................................................................................71 Uncertainty and sensitivity analysis of EMA..................................................................72

Discussions and conclusions ..................................................................................................76

Chapter 5: CORRELATION ANALYSIS OF CARBON FOOTPRINTING AND EMERGY INDICATORS FOR BIOMASS CHP SYSTEM ..........................................................................92

Abstract ..................................................................................................................................93 Introduction............................................................................................................................94 Literature review....................................................................................................................95

LCA.................................................................................................................................95 EMA................................................................................................................................96 Relation of LCA and EMA indictors...............................................................................98

Methods..................................................................................................................................99 System boundary ............................................................................................................99 System functional unit ....................................................................................................99 Modeling Procedure.....................................................................................................100 Statistical analysis........................................................................................................103

Results..................................................................................................................................103 Correlation analysis of LCA and EMA results.............................................................103 Correlation between GWP/Biogenic Carbon Emission and Tr....................................104

Discussion and conclusions..................................................................................................105

Chapter 6: CONCLUSIONS AND RECOMMENDATIONS...............................................117

viii

Appendix A: List of the acronyms used in dissertation ...............................................................122

Appendix B: Global Warming Potentials of substances relative to CO2 .....................................123

Appendix C: LCA calculation worksheet for biomass CHP system............................................126

Appendix D: EMA calculation worksheet for biomass CHP system...........................................127

Appendix E: Histogram of uncertain biomass CHP system factors in Monte Carlo simulation .128

Appendix F: Classification of Emergy flows for biomass CHP system ......................................129

Appendix G: A timeline of major research events.......................................................................130

Literature Cited ............................................................................................................................131

ix

LIST OF FIGURES

Figure 1-1 Research framework.......................................................................................................7

Figure 2-1 Four distinct phases of Life Cycle Assessment according to International Organization

of Standardization........................................................................................................30

Figure 2-2 LCA results uncertainty sources...................................................................................31

Figure 2-3 Representation of biosphere in the natural environment..............................................32

Figure 2-4 System diagram of Emergy flows for EMA indicators calculation .............................33

Figure 3-1 Comparison of system boundary and accounting scope between LCA and EMA in

analyzing production chain of biomass CHP system...................................................49

Figure 3-2 Monte Carlo simulation for LCA and EMA using biomass CHP system factors ........50

Figure 3-3 Lower Heating Value as a function of moisture content (wet basis) for woodchips....51

Figure 3-4 Linear relationship between power-to-heat ratio and energy efficiency for biomass

CHP plant ....................................................................................................................52

Figure 4-1 Comparison of system boundary and accounting scope between LCA and EMA in

analyzing production chain of biomass CHP system...................................................78

Figure 4-2 Box plot of GHG emission of life cycle process..........................................................79

Figure 4-3 Scatterplots for GWP against biomass CHP system factors ........................................80

Figure 4-4 Emergy inputs by life cycle process and by inputs category........................................81

Figure 4-5 Environmental decision-making tools on scale of boundary and theoretical accuracy 82

Figure 5-1 Comparison of system boundary and accounting scope between LCA and EMA in

analyzing production chain of biomass CHP system.................................................108

Figure 5-2 Correlation between GWP and EMA indicators ........................................................109

Figure 5-3 Scatterplots of transformity against LCA-based GWP ..............................................110

Figure 5-4 Scatterplots of Tr against Biogenic CO2 emission.....................................................111

x

LIST OF TABLES

Table 2-1 Key benefits and challenges of bioenergy .....................................................................34

Table 2-2 Main indicators of EMA analysis ..................................................................................35

Table 2-3 Summary of key differences in LCA, Emergy and economic accounting methods......36

Table 3-1 Main indicators of EMA................................................................................................53

Table 3-2 Emergy analysis table for production of energy from biomass CHP system ................54

Table 3-3 Summary of life cycle processes assumptions in LCA and EMA.................................55

Table 3-4 Transformity and Emergy per unit mass of used chemical ...........................................56

Table 3-5 Disposal cost of waste ...................................................................................................57

Table 4-1 EMA indicators abbreviation and formula ....................................................................83

Table 4-2 Summary of life cycle processes assumptions in LCA and EMA.................................84

Table 4-3 Transformity and Emergy per unit mass of used chemical ...........................................85

Table 4-4 Disposal cost of waste ...................................................................................................86

Table 4-5 Regression analysis of total GWP and system factors...................................................87

Table 4-6 Descriptive statistics for GWP of life cycle process......................................................88

Table 4-7 Standardized regression coefficients between life cycle processes and factors ............89

Table 4-8 Comparative EMA results from different energy systems ............................................90

Table 4-9 Descriptive statistics of Emergy indicators ...................................................................91

Table 5-1 EMA indicators abbreviation and formula ..................................................................112

Table 5-2 Summary of life cycle processes assumptions in LCA and EMA...............................113

Table 5-3 Transformity and Emergy per unit mass of used chemical .........................................114

Table 5-4 Disposal cost of waste .................................................................................................115

Table 5-5 Correlation of determination (R2) between GWP and EMA indicators ......................116

xi

ACKNOWLEDGEMENTS

I would first like to thank my advisor, Dr. Charles D. Ray for all his contributions in the

conceptualization and development of this project at Penn State. I would also like to thank the

members of my committee, Dr. Judd Michael, Dr. Michael Messina, and Dr. Richard Stehouwer

for their thoughtful advices and guidance on all aspects of my Ph.D. research. I am also grateful

to Department of Ecosystem Science and Management (formerly known as School of Forest

Resources) at Penn State University for providing financial support until I have finished the study.

Finally, thanks to my parents, my wife, and friends who helped and motivated me through the

entire course of my graduation.

1

Chapter 1: PROBLEM STATEMENT AND JUSTIFICATION

Problem statement

To evaluate environmental benefits and challenges of a biomass combined heat and power

(CHP) system, a number of accounting methods have been proposed. Life Cycle Assessment

(LCA) and Emergy Analysis (EMA) are two environmental accounting methods used to guide

bioenergy decision making (Tonon et al., 2006). LCA and EMA are widely different in many

aspects including definition, purpose, problem addressed, accounting scope, system boundary,

measurement unit, and conversion factor, as summarized in Table 2-3.

Despite the growing use of LCA to measure the sustainability of products, McElroy

(2011) pointed out that LCA has less to do with sustainability than most people think. By design,

LCA provides a way of quantifying the environmental impacts of products and services from

cradle to grave of a manufactured product. But LCA does not really measure the sustainability of

products and services, per se, or report sustainability in any other authentic sense of the term

(Bakshi, 2002). Moreover, decision making based on LCA may result in perverse decisions that

encourage reliance on deteriorating ecosystem (Zhang et al., 2010). Decisions concerning energy

use and investments in energy technology require that decision-makers have the ability to

holistically compare net yields, environmental impact, and sustainability (Zhang and Long, 2010).

Therefore, joint use of LCA with some other environmental accounting tool or tools that measure

environmental sustainability becomes necessary to better understand environmental impacts and

long-term sustainability of any production system.

Within the LCA community, researchers have pointed out the technique can yield a wide

range of results due to the different assumptions of different analysts and/or modelers (Cherubini

et al., 2009). Even for apparently similar bioenergy chains, different LCA outcomes can be

observed (Cherubini and Strømman, 2011). How does a change of assumption impact LCA

2

results, namely, what is the uncertainty of LCA results? This is the question that environmental

accounting scientists need to answer in the future. Like LCA, the results of EMA vary with

different assumption of system parameters. Although a few studies have tried to estimate EMA

indicators for biomass CHP (Al-Sulaiman et al., 2010; Sha and Hurme, 2011; Buonocore et al.,

2012), the uncertainty of the results have not yet been addressed.

Given the large number of indicators developed by different environmental accounting

methods, researchers have started to simplify indictors that could serve as proxies for

environmental performance of a system (Laurent et al., 2012). A search of LCA and EMA

literature suggests a few studies of this type have been done on LCA, but none on EMA

indicators. In the correlation studies of LCA indicators, for instance, the ecological footprint and

the cumulative energy demand from LCA were found to show significant correlation with other

environmental impact indicators (Huijbregts et al., 2006; Huijbregts et al., 2012). Another study

by Berger and Finkbeiner (2011) observed that Primary Energy Demand (PED) and Abiotic

Depletion Potential (ADP) are strongly correlated, and moderate correlations were found between

GWP and PED as well as ADP. Taking into account the significant correlations between LCA

indicators, it is suggested that the number of indicators can be reduced as they lead to similar

findings (Berger and Finkbeiner, 2011). Therefore, the correlation between LCA and EMA

indicators could help to simplify indicators for assessing environmental sustainability of the

system, develop better integration solution and improve the quality of the ultimate environmental

evaluation.

Research framework

This study contains two parts: 1) it aims to examine the uncertainties and sensitivities of

LCA and EMA outcomes due to different biomass CHP system factors; 2) it explores the

correlation between LCA-based GWP and EMA indicators, as well as the relationship between

3

CO2 equivalents and solar equivalents, which could help to reduce indicators that lead to similar

findings, and to improve the consistency and accuracy of biomass energy transformity and ease

the process of EMA for biomass energy users. The research framework for this study is displayed

below (Figure1-1).

Research Objectives

A significant, albeit simplified, case - a 6.4 MWth (1.6 MWe) wood biomass Combined

Heat and Power (CHP) system from SimaPro 7.3 database is used to compare the results and

analytically assess merits of LCA and EMA as well as possible integration of the two. The

outcomes analyzed are LCA-based GWP and six EMA indicators. Given the previous problem

statement, this study has its objectives as following:

1) The first objective is to investigate the similarities and incompatibilities between LCA

and EMA based on a wood biomass CHP system, and to discover the "optimal field of

application" for each method;

2) The second objective is to examine the uncertainties and sensitivities of LCA-based

Global Warming Potential (GWP) and EMA outcomes due to four different selected CHP system

factors, including emission control (EC), feedstock moisture content (MC), transportation

distance (TD), and power to heat ratio (PHR);

3) The third objective is to investigate the relationship between LCA-based GWP and the

selected EMA indicators to determine if there is correlation between LCA and EMA results, and

if the number of EMA indicators can be reduced.

4) The fourth objective is to explore the relationship between LCA-based GWP and

EMA-based Transformity (Tr) to see if LCA database and framework can be used in EMA for

further integration.

4

Research questions and hypotheses

Based on preceding discussion of existing literature and problems regarding these two

methods, this research intends to investigate the following specific questions:

Question 1

Are the identified biomass CHP system factors significant to the environmental

performance of biomass CHP system, in terms of GWP measured by LCA and EMA indicators?

Hypothesis: At least one of the following factors: Emission control (EC), moisture

content (MC), transportation distance (TD), and power to heat ratio (PHR) has statistically

significant impact on the environmental performance of biomass CHP system, in terms of GWP

and EMA indicators.

1) te+++++= PHR TD MC EC GWP 43210 βββββ

4 3, 2, 1, j j, oneleast at for 0:H

0:H

a

43210

=≠====

jβββββ

2) te

Tr

ESI

EIR

ELR

EYR

PR

+++++=

= PHRTDMCECindicators EMA 43210 βββββ

4 3, 2, 1, j j, oneleast at for 0:H

0:H

a

43210

=≠====

jβββββ

Question 2

What are the uncertainties and sensitivities of LCA and EMA results due to different

assumptions of biomass CHP system factors? In other words, does the change of factors impact

5

GWP and Emergy indictors of biomass CHP system differently? And to what extent do the

impacts differ?

For this research question, coefficient of variation is used to compare the difference

between LCA and EMA results

Question 3

Is there a significant linear correlation between GWP and each of EMA indicators?

indicator EMAof one as i ,0 :H

0:H

a

0

≠=

i

i

r

r

Question 4

Is there a significant linear correlation between any of the six EMA indicators?

indicators EMAare j and i ,0 :H

0:H

a

0

≠

=

ij

ij

r

r

Value of work

This study jointly uses LCA and EMA to evaluate a dynamic biomass CHP system. It is

different from previous studies in the following ways:

1) It provides a complementary view by first jointly applying two environmental

accounting methods (i.e., LCA and EMA) to evaluate the biomass CHP system. Through

the examination of the shortcomings and strengths of each methods, it provides insight

on the best use of each method;

2) Correlations between GWP and the six Emergy indictors are explored, which may help

to simplify potential indicators for assessing environmental sustainability of the system,

and to develop a better integration solution for ultimate environmental evaluation;

3) It compares the uncertainties and sensitivities of LCA and EMA case study results

related to biomass CHP system factors. This helps differentiate the uncertainty sources in

6

LCA and EMA and provides guidance for system engineers, process designers, and

policy decision-makers to seek improvements on system environmental performance;

4) The relationship between LCA-based biogenic carbon emission and EMA-based

transformity can help develop accurate and consistent methods for calculating

transformity of biomass CHP, and increase the applicability of EMA on environmental

decision making by using LCA database and framework.

7

Figure 1-1 Research framework

Relationship

Relationship

Part I: Examine the uncertainties and sensitivities of LCA and EMA indicators due to different biomass CHP system factors

Impact

Six EMA Indicators

Six EMA Indicators

Emission control

Moisture content

Transportation distance

Power to heat ratio

Biomass CHP System Factors Environmental Evaluation Results

LCA indicator � Global Warming Potential (GWP)

EMA Indicators � Percent renewability (PR) � Emergy yield ratio (EYR) � Environmental loading ratio (ELR) � Emergy investment ratio (EIR) � Emergy sustainability index (ESI) � Transformity (Tr)

Part II: Relationship exploration 1) Explore correlation between GWP and EMA indicators 2) Explore correlation among six EMA indicators

LCA (GWP)

Six EMA Indicators

8

Chapter 2: LITERATURE REVIEW

Introduction

The environmental performance of products, services and processes is gaining increased

attention in today’s world, and it is important to examine ways in which both positive and

negative effects on the environment are assessed (Zhang et al., 2010). Improving environmental

performance of an individual process does not necessarily mean less environmental impact since

the impact could be simply shifted out of the analysis boundary. This recognition urges the

environmental evaluation method to expand the analysis boundary to include the entire life cycle

of a product (Zhang et al., 2010).

Growing fossil fuels consumption is a substantial cause of the rapid increase in

greenhouse gas (GHG) emissions (IPCC, 2011). Biomass energy is considered one renewable

energy option to mitigate climate change and reduce fossil fuel consumption (Brown, 2003).

However, biomass energy development also faces many key challenges as summarized in Table

2-1. Life Cycle Assessment (LCA) and Emergy analysis (EMA) are two methods used to account

for different benefits and challenges.

Life cycle assessment

Life cycle thinking recognizes that all product life cycle stages including extracting and

processing of raw materials, manufacturing, transportation and distribution, use/reuse, recycling,

and waste management generate environmental impacts which need to be evaluated and then

reduced (Finnveden et al., 2009; Guinée et al., 2011; Rugani and Benetto, 2012). This perspective

has been the basis for the development and standardization of the Life Cycle Assessment (LCA)

method (ISO, 2006). The standardized LCA framework is comprised of four phases: Goal and

Scope Definition, Life Cycle Inventory, Impact Assessment and Interpretation (ISO, 2006), as

shown in Figure 2-1. The phases are often interdependent in that the results of one phase will

9

inform how other phases are completed. Compared with other environmental and economic

accounting tools, LCA exhibits large differences in term of definition, purpose, problem

addressed, accounting scope, system boundary, space boundary, time boundary, measurement

unit, conversion factors and objectiveness. Table 2-3 at the end of this chapter has been compiled

as part to this study to compare LCA, EMA and Economic Cost in different aspects. Providing

information about many emissions and consumption of resources, LCA is one of the most

accepted and used tools for the environmental evaluation of products and services, and has

become a principal factor in environmental policy and energy development (Andersson, 2000;

EPA, 2012).

Although LCA was originally developed to assess the environmental burdens associated

with industrial manufacturing (Baumann and Tillman, 2004), methodological developments in

recent years have greatly improved the capacity of LCA to adequately assess the environmental

impacts of specific bioenergy systems. The majority of these studies are in the nature of

comparative. González-García et al. (2010) assessed the environmental performance of three

poplar-based ethanol applications (E10, E85 and E100) in comparison with conventional gasoline

by means of LCA approach. The findings suggest that fuel ethanol derived from poplar biomass

may help to reduce the contributions to global warming, abiotic resources depletion and ozone

layer depletion up to 62%, 72%, and 36%, respectively. Fleming et al. (2006) compared selected

life cycle-based studies of fuel alternatives for light-duty vehicles with a focus on

lignocelluloses-derived fuels including hydrogen, Fischer-Tropsch liquids, and ethanol. The

authors identified some key issues having impact on the results that are assumptions regarding

feedstock characteristics, vehicle propulsion system efficiency, land use changes and associated

carbon sequestration, nitrous oxide emissions due to agricultural practices, co-product allocation,

energy accounting practices, and expected progress on commercial-scale fuel production

processes and associated infrastructure.

10

With respect to biodiesel systems, Huo et al. (2008) investigated the life-cycle energy and

greenhouse gas (GHG) emission impacts of four soybean-derived fuels: biodiesel fuel produced

via transesterification, two renewable diesel fuels produced from different hydrogenation

processes, and renewable gasoline produced from catalytic cracking. The relative rankings of

soybean-based fuels in terms of energy and environmental impacts were found to be different

under the different allocation approaches. Results from the five allocation approaches showed that

although the production and combustion of soybean-based fuels might increase total energy use,

they could have significant benefits in reducing fossil energy use (>52%), petroleum use (>88%),

and GHG emissions (>57%) relative to petroleum fuels. This study emphasized the importance of

the methods used to deal with co-product issues and provided a comprehensive solution for

conducting a life-cycle assessment of fuel pathways with multiple co-products.

Some LCA studies have been carried out to assess the performance of different bioenergy

technologies and different feedstock. Elsayed et al., (2003) reviewed forty-three existing studies

of different biofuel technologies and produced a set of baseline energy and carbon balances for a

range of electricity, heat, combined heat and power, and transport fuel production systems based

on biomass feedstocks including wood chips from forestry residues, wood chips from short

rotation coppice, sugar beet, and wheat. Uihlein and Schebek (2009) performed LCA of a

lignocellulose feedstock biorefinery system and compared it to conventional product alternatives.

The biorefinery was found to have the greatest environmental impacts in three categories: fossil

fuel use, respiratory effects, and carcinogenics. Authors also analyzed the various variants of the

system. They noted that the optimum variant (acid and heat recoveries) yields better than fossil

alternatives, with the total environmental impacts being approximately 41% lower than those of

the fossil counterparts. Another study of Solli et al., (2009) conducted a comparative LCA of a

wood-based heating system in Norway using two stoves, one old and another modern to evaluate

the environmental effects of wood-based household heating and also estimate the total life cycle

11

benefits associated with the change from old to new combustion technology. It was found that the

new technology contributes to a significantly improved performance (28-80%) for all types of

environmental impact studied (Solli et al., 2009).

LCA has also been used to compare bioenergy systems and fossil fuel-based systems

(Eriksson et al., 2007; Steubing et al., 2011; Steubing et al., 2012). Eriksson et al. (2007)

compared the district heating system performance based on waste incineration with combustion

of biomass or natural gas by performing LCA. Their results indicate that combustion of biofuel in

a CHP is environmentally favorable and robust with respect to the avoided type of electricity and

waste management. Steubing et al. (2011) performed LCA on wood-to-synthetic natural gas

(SNG) systems for heating, electricity generation, and transportation and compared the

environmental performance with fossil and conventional wood reference systems. It was

concluded that substituting fossil technologies with SNG systems is environmentally beneficial

with regard to Global Warming Potential and for selected technologies also with regard to

aggregated environmental impact, including eutrophication, ecotoxicity, and respiratory disease

caused by inorganics. Another study by Steubing et al. (2012) developed an energy system model

comprising 13 principal fossil technologies for the production of heat, electricity, and

transportation and 173 bioenergy conversion routes. The study calculated the net environmental

benefits of substituting fossil energy with bioenergy for all approximately 1500 combinations

based on LCA results. Authors also developed an optimization model that determines the best use

of biomass availability and fossil energy utilization based on consideration of factors like

conversion efficiencies of bioenergy technologies and the kind and quantity of fossil energy

technologies that can be substituted. They concluded that optimizations for different

environmental indicators almost always indicate that woody biomass is best used for combined

heat and power generation, if coal, oil, or fuel oil based technologies can be substituted.

12

Although the extant LCA studies claim to look at bioenergy systems from a life cycle

perspective, most are limited in scope. All LCA studies focus primarily on emissions and their

impacts while ignoring the role of ecosystem goods and human labor services that industrial

processes rely upon (Odum, 1996; Urban and Bakshi, 2009; Zhang et al., 2010). Researchers have

commented that LCA suffers shortcoming of ignoring ecosystem products and services despite its

benefits and popularity (Bakshi, 2002; Duan et al., 2011). Ignoring these inputs can lead to

significant error in the analysis and misleading conclusion, since ecological services are estimated

to be twice as valuable as the global gross national product (Costanza et al., 1997). This limitation

also makes it difficult for LCA to determine the environmental sustainability of products and

processes (Bakshi, 2002).

Uncertainty of LCA

Many of the concerns that have been expressed about the accuracy of LCA results are

linked to potentially significant sources of uncertainties (Brunn, 1995; Ross et al., 2002). They

include poor data quality, invalid or non-transparent assumptions and failure to perform

sensitivity analyses (Maurice et al., 2000; Venkatesh et al., 2010). Helton (1993) noted that there

are basically two types of uncertainty present in a LCA study: objective uncertainty and

subjective uncertainty. Figure 2-2 provides different sources that lead to LCA results uncertainty

(Cherubini and Strømman, 2011). Huijbregts (1998) proposed a framework to classify types of

uncertainty and variability in LCA results, where uncertainty includes parameter uncertainty,

model uncertainty, and uncertainty due to choices, while variability covers spatial variability,

temporal variability, and variability between objects and sources. When comparing LCA results

reported by different authors and sources, a wide range of final outcomes can be observed, even

for apparently similar bioenergy chains (Cherubini and Strømman, 2011). It is suggested that

wide variation in LCA assumption and system parameters need to be considered for reducing the

13

uncertainty of LCA results (McManus, 2010; Steubing et al., 2012). Therefore, uncertainty and

sensitivity analysis are recommended or even required in LCA given the concerns over the

accuracy of its results (Steen, 1997). The International Organization for Standardization (ISO)

encourages LCA practitioners to undertake uncertainty analysis on results and conclusions, but

such analysis is not a mandatory requirement:

"Uncertainty analysis... would help to characterize uncertainty in results using ranges

and/or probability distributions to determine uncertainty in LCA results and conclusions,

whenever feasible, such analysis should be performed to better explain and support the

conclusions" (ISO, 2010).

Given the importance of the uncertainty analysis in LCA studies, much research has been

conducted to identify the uncertainty sources and to seek solutions for reducing them. Owens

(1996) presented a technical framework to evaluate the strengths and the limitations of LCA

impact assessment categories to yield accurate, useful results which indicates that the various

uncertainties in each individual category have a number of different technical origins and the

degree of uncertainty varies significant between categories. Owen (1996) concluded that

interpretation and valuation cannot presume an equivalency of processes or merit behind

numerical value for different categories. Ross et al. (2002) examined the ISO standards to

establish how they deal with uncertainties caused by the data collection during the inventory

phase and undertook an analysis of journal articles relevant to LCA uncertainties to seek how the

problem of uncertainty is handled in practice. Their findings reveal that the significance of the

limitations on the reliability of LCA results given in the standard has not been fully appreciated

by practitioners and suggest that the standards need to be revised to ensure that LCA studies

include at least a qualitative discussion on all relevant aspects of uncertainty.

Uncertainty regarding the most important environmental impact category, greenhouse gas

(GHG) emission has been given much attention in the literature. Ventakatesh et al. (2010) argued

14

that the potential greenhouse gas (GHG) emissions reductions estimated for those policies using

LCA method are predominantly based on deterministic approaches that do not account for any

uncertainty in outcomes. They used a process-based framework to examine the uncertainty of life

cycle GHG emission associated with petroleum-based fuels consumed in the United States. In the

bioenergy field, Cherubini et al. (2009) discussed the key issues in bioenergy system LCA that

have a strong influence on the final results and the overlooked uncertainty problems in the

existing literature. They noted that the energy and GHG balance of bioenergy systems differ

depending on the type of feedstock sources, conversion technologies, end-use technologies,

system boundaries and reference energy system with which the bioenergy chain is compared.

Furthermore, they suggested that regional differences can also be significant, especially with

respect to land use, biomass production patterns and the reference energy system, and the LCA

results can change as technologies evolve. The findings of Cherubini et al. (2009) reveal that all

the key issues and methodological assumptions in LCA prevent an exact quantification of the

GHG emission savings because too many variables are involved. Some of the key parameters

such as changes in soil carbon pools and nitrous oxide emissions from soils are not well known

and uncertainties cannot be completely avoided. They concluded that the presentation of LCA

results by means of probable ranges is preferred (Cherubini et al., 2009). Another study

conducted by Mullins et al. (2010) used Monte Carlo simulation to estimate life-cycle emission

distributions from ethanol and butanol from corn or switch grass and provided a wide range of the

corn ethanol emissions. The findings suggest that potential GHG emissions reductions from

displacing fossil fuels with biofuel are difficult to forecast given the high degree of uncertainty in

life cycle emissions. Mullins et al. (2010) further explained that the uncertainty is driven by the

importance and uncertainty of indirect land use change emissions and concluded that

incorporating uncertainty in the decision-making process can illuminate the risks of policy failure

15

(e.g., increased emissions), and a calculated risk of failure due to uncertainty can be used to

inform more appropriate reduction targets in future biofuel policies.

Correlation of LCA results

It is recognized that different methods provide different perspectives and sometimes

hardly comparable results (Hau and Bakshi, 2004; Sciubba and Ulgiati, 2005), the various

indicators could contribute to the complexity of interpretation and decision making. Corporations

and authorities often aim for simplicity and thus use tools for which data are readily available.

Because this might result in environmental policies solely relying on these indictors, it calls for

guaranteeing a proper reflection of the whole environmental burden (Laurent et al., 2012). A

search of LCA literature suggests correlation analysis has often been used to check the

dependencies among different indicators and ultimately to simplify LCA indicators.

Huijbregts et al. (2006) examined the correlation between the fossil cumulative energy

demand (CED) and environmental life-cycle impacts and the results show that for all products

groups but waste treatment, the fossil CED correlates well with most impact categories, such as

global warming, resource depletion, and human toxicity. Then Huijbregts et al. (2006) concluded

that the use of fossil fuels is an important driver of several environmental impacts and it may

therefore serve as a screening indicator for environmental performance. Another study by Berger

and Finkbeiner (2011) used 100 materials from the GaBi and Ecoinvent databases to observe the

correlation between resources- and emission-oriented indicators. The findings show that Primary

Energy Demand (PED) and Abiotic Depletion Potential (ADP) are strongly correlated, and a

moderate correlation was found between GWP and PED as well as ADP. Laurent et al. (2012)

modeled and analyzed the life cycle impacts from about 4,000 different products, technologies,

and services taken from several sectors, including energy generation, transportation, material

production, infrastructure, and waste management to investigate the limitations of Carbon

16

Footprint (CFP) as indicator of environmental sustainability. By examining the correlations

between the CFP and thirteen other impact categories, the study shows that some environmental

impacts, notably those related to emissions of toxic substances, often do not co-vary with climate

change impacts. In such situation, CFP is a poor representative of the environmental burden of

products, and environmental management focused exclusively on CFP runs the risk of

inadvertently shifting the problem to other environmental impacts when products are optimized to

become more “green”.

Emergy analysis

Emergy analysis (EMA) is an energy-based environmental accounting method that

expresses all the process inputs (i.e., energy, raw material, human service, etc.) and output

products in solar energy equivalent joules (Sej). Emergy measures how much energy would be

needed to do a particular task if solar radiation were the only input. The theory is based on the

fact that earth has one principal energy input: solar energy (Odum, 1996). Besides solar energy,

the other major energy inputs are tidal energy and crustal heat sourced from moon-earth

interaction and earth core, which are both converted to solar equivalents in EMA accounting

theory (Odum, 1996). In EMA theory, all the activities on earth are driven by solar energy

(Hermann, 2006): fossil fuels formation represents millions of years of embodied energy from the

sun and geological activities (Odum, 1996); wind, rain, rivers are initiated by more recent solar

energy; plant growth depends on mineral, rain and solar insolation; even money is related to

Emergy by considering the circulation of money through the environmental-economic

interface(Odum, 1996). Therefore, every product or service is comprised of some amount of solar

energy in term of energy. EMA can be used as a method for assessing the performance of the

plant on the larger time and space scales of biosphere, thus EMA is a sustainability assessment

tool (Brown and Ulgiati, 1997).

17

EMA differs from LCA on space boundary. EMA evaluates the environmental

performance of the system from biosphere perspective, while LCA employs an atmosphere

perspective. The biosphere is the global sum of all ecosystems (CUP, 2008). From the broadest

biophysiological point of view, the biosphere is the global ecological system integrating all living

beings and their relationships, including their interaction with the elements of the lithosphere,

hydrosphere, and atmosphere (Campbell et al., 2006). Figure 2-3 below depicts the differences

between biosphere and atmosphere (Simpson and Edwards, 2013).

Important concepts in EMA

Emergy and Money

EMA evaluation classifies inputs into different categories (i.e., renewable inputs - R,

non-renewable inputs - N, and purchased inputs - F), as the system diagram shown in Figure 2-4.

Purchased resources and services (F) are included in the EMA by using investment cost and an

Emergy-money index. The Emergy-money index indicates solar Emergy per unit of money

(Sej/$), which is calculated as ratio of total solar Emergy a nation used in one year to its Gross

National Product (GNP) (Cao and Feng, 2007). Total solar Emergy used by the U.S. in one year

is estimated by use of coal, natural gas, crude oil, uranium, iron ore, aluminum ore, and wood, as

well as sunlight, rain and wind for a whole year (Odum, 1996).

EMA indicators

Based on the classes of EMA inputs as shown in Figure 2-4, EMA indicators can be

computed in order to assess the advantage and disadvantage of system alternatives (Pizzigallo et

al., 2008). Six of the most commonly used ratios or indicators extracted from existing literature to

evaluate the sustainability of different systems (Ulgiati et al., 1995; Bastianoni and Marchettini,

2000; Sha and Hurme, 2011) are given in Table 2-2.

18

1) The Percent Renewability (PR) gives degree of renewability. The higher PR value

indicates higher renewability of the process. Among the existing Emergy-based

indicators, Percent Renewable (PR) represents the first measure of system

sustainability: the lower the fraction of renewable Emergy used, the higher the pressure

on the environment (Zhang and Long, 2010). Brown and Ulgiati (Brown and Ulgiati,

1997) suggested that only processes with high values of this index are sustainable in the

long run.

2) Emergy Yield Ratio (EYR) measures how much a process will contribute to the

economy, also indicating how dependent the process is on the purchased inputs. The

higher EYR value indicates a larger amount of products obtained per unit of money

spent. This index indicates the efficiency of the system using purchased inputs (Ortega

et al., 2005).

3) Environmental Loading Ratio (ELR) is given by the ratio between non-renewable and

imported Emergy used to renewable Emergy used. It represents the pressure of a

transformation process on the environment and can be considered as a measure of

ecosystem stress due to production (Ulgiati and Brown, 1998). A higher value of ELR

indicates that environmental cycles are overloaded (Pizzigallo et al., 2008).

4) Emergy Investment Ratio (EIR) is the ratio of Emergy of purchased inputs to the

indigenous Emergy input (both renewable and non-renewable), which evaluates

whether a process is an economical user of the Emergy invested in comparison with

alternatives (Brown and Ulgiati, 1997; Zhang and Long, 2010). A high level of EIR

represents a certain fragility of the system due to its dependence on inputs from other

economic systems (Pizzigallo et al., 2008).

5) Emergy Sustainability Index (ESI) is the ratio of the Emergy Yield Ratio (EYR) to the

environmental loading ratio (ELR), which measures the potential contribution of a

19

resource or process to the economy per unit of environmental loading (Zhang and Long,

2010). It is an aggregate measure of economic performance and sustainability of the

system considering both the contribution of renewable vs. non-renewable resources and

the need of purchased inputs to drive the process (Mirandola et al., 2010). To be

sustainable in the long run, a system should have a high EYR and low ELR, producing

a high ESI value (Sha and Hurme, 2011).

6) Transformity (Tr) is the amount of solar energy required to make one unit of a given

product (Odum, 1996). The larger transformity, the greater the ecological support

required to produce that product (Baral and Bakshi, 2010). The solar transformity is

very important for any Emergy study because all input flows, including materials,

energies, and currency, must first be transferred into Emergy units using appropriate

transformities. A lower transformity indicates that less solar energy is needed to

produce a given unit of product; it is one important Emergy indicator when comparing

different systems producing the same product (Peng et al., 2008; Mirandola et al.,

2010)

A review of EMA studies

Since the 1980s, EMA has been used to evaluate systems as diverse as agricultural

systems, ecological systems, industrial systems and economic systems (Brown and Ulgiati, 1997;

Brown and Ulgiati, 2002; Brown and Ulgiati, 2004; Yang et al., 2010; Ciotola et al., 2011; Ju and

Chen, 2011). Brown and Ulgiati (2002) used EMA to evaluate six electricity production systems

regarding their relative thermodynamic and environmental efficiencies. The production systems

studied included plants using both nonrenewable energy sources (natural gas, oil, and coal

thermal plants) and the so-called renewable energy sources (geothermal, hydroelectric, and wind

plants). It was concluded that environmental loading was highest with coal-thermal plants. Using

20

an Emergy index of sustainability, the study quantitatively showed how renewable energy source

plants like wind, hydroelectric, and geothermal had higher sustainability compared to thermal

plants. Ciotola et al. (2011) assessed the relative sustainability and environmental impact of

small-scale energy production using Taiwanese model plug-flow anaerobic digesters to treat

livestock manure in Costa Rica by means of EMA. The authors reported the EMA results

including the fraction of Emergy inputs from renewable sources, Emergy Yield Ratios, Emergy

Sustainability Index, and Environmental Loading Ratio for both biogas production and electricity

generation from the biogas. The results demonstrated that the production of biogas and the

generation of electricity from biogas are environmentally sustainable processes that result in the

production of energy that is largely dependent on renewable and recycled energies. Ju and Chen

(2011) presented an ecological accounting framework based on embodied energy, EMA, and CO2

emission from the whole production chain of biodiesel made from Jatropha curcas L. (JCL) oil.

In order to shed a better light into investigated production system, the authors compared the

results with bioethanol production from wheat in China and corn-ethanol production in Italy. The

findings of Ju and Chen's study suggest that EMA considering the environmental work as input

flows to support the ecosystem and human-dominated production system is a more suitable mode

for the cost or ecological footprint analysis compared to embodied energy metrics.

On a large scale, Yang et al. (2010) used EMA to evaluate the Chinese economy. A

unified evaluation integrating various forms of energy sources and natural resources, products and

services, and imports and exports was carried out systematically at the national scale for the

booming Chinese economy from 1978 to 2005, based on the solar Emergy from EMA. It was

shown that the development of the economy is heavily dependent on the consumption of

non-renewable natural resources.

With the increasingly important role that bioenergy plays in economic development and

environmental protection, EMA is being increasingly applied to bioenergy systems. Dong et al.

21

(2008) used EMA to evaluate the environmental performance of ethanol production from wheat

and corn in the two agro-industrial systems. The study reported the EMA results for wheat-based

ethanol and corn-based ethanol including the output/input energy ratio, transformity,

Renewability, Emergy Yield Ratio, Environmental Loading Ratio, and Emergy Sustainability

Index. The comparisons show that bioethanol from food crops is not a sustainable source of fuel.

Another study by Pereira and Ortega (2010) assessed the sustainability of ethanol produced from

sugarcane and examined the environmental feasibility of a large-scale production through the use

of fossil fuel embodied energy and EMA. The findings show that the Transformity of ethanol is

about the same as those calculated for fossil fuels, and the Renewability of ethanol is 30%. It was

suggested that sugarcane and ethanol products exhibit low renewability when a large-scale system

is adopted.

Alonso-pippo et al. (2004) applied EMA to bio-oil production using sugarcane biomass

residues. Emergy ratios obtained for bio-oil production including Transformity, Emergy Yield

Ratio, Environmental Loading Ratio, Renewability, Emergy Investment Ratio, Emergy Exchange

Ratio, and Emergy Sustainability Index were reported. Further, the authors identified the

tendencies that will affect the bio-oil production from energy, environmental, economical, and

social points of view.

Cavalett and Ortega (2010) and Sheng et al. (2007) evaluated biodiesel production using

EMA. Cavalett and Ortega (2010) presented the results of an environmental impact assessment of

biodiesel production from soybean in Brazil based on EMA, embodied energy analysis and

material flow accounting. The transformity of biodiesel was found to be higher than those

calculated for fossil fuel, indicating a higher demand for direct and indirect environmental

support in order to produce the biodiesel. The findings show that when crop production and

industrial conversion to fuel are supported by fossil fuels in the form of chemicals, goods, and

process energy, the fraction of fuel that can actually be considered renewable is very low.

22

There have been several studies using EMA to evaluate combined heat and power

systems (CHP) from various feedstock. Feng et al. (2005) compared a conventional coal-fired

process with two designs of waste incineration CHP plants. Wang et al. (2005) used the Emergy

approach to analyze an eco-industrial park with three alternative types of coal-fired CHP power

plant. The park with coal-fired integrated gasification combined cycle plant was found to be more

sustainable than the coal-fired pressurized fluidized bed combustion combined cycle or the

pulverized coal-fired CHP plant. Peng et al. (2008) used Emergy to evaluate three operatioal

modes of the Jiufa coal-fired CHP plant in Shandong China in an eco-industrial park context.

Their results showed that small coal-based CHP plants have lower energy efficiency, higher

environmental loading, and lower sustainability than large fossil fuel and renewable energy-based

systems. Al-Sulaiman et al. (2011) studied an integrated organic rankine cycle (ORC) process

with a biomass combustor for combined cooling, heating, and power production as a trigeneration

system by exergy assessment. Bargigli et al. (2010) studied three natural gas CHP processes (gas

turbine, internal combustion engine and a fuel cell hybrid system), also using Emergy evaluation.

No conventional CHP boiler plants were included in the analysis.

However, Sha and Hurme (2011) were the first to apply EMA in the biomass CHP

production system. Biomass and coal-based CHP alternatives were compared with independent

production of heat and power in this study. It was found that biomass-based cogeneration is 3.3

times more Emergy-efficient than coal-based independent production, heat and power production

from biomass is 2.3 times more Emergy-efficient than that from coal in a similar process; and the

Emergy sustainability index of biomass CHP plant is 15 times higher than that of a coal CHP

plant. Despite the increasing interest of EMA for bioenergy system evaluation, studies on

biomass CHP plants using forest wood or wood waste are limited.

It is suggested that EMA is a promising tool to support environmental management

actions and public dynamics of a territorial system (Campbell, 1998; Pulselli, 2010; Pulselli et al.,

23

2008). It goes beyond the accounting scope of LCA by evaluating the environmental work needed

for natural resources formation. In this application, two key features of EMA over LCA were

shown to be:

1) By using a common unit (i.e., Sej), EMA allows all resources to be compared on a fair

basis;

2) By equating Sej value to economic cost, it allows easier understanding by non-technical

audience, and compensates for the inability of money to value non-market inputs in an

objective manner (Hau and Bakshi, 2004).

However, EMA has been criticized for sweeping generalizations that still remain

unproven, in particular, the calculation of transformity (Hau and Bakshi, 2004; Rugani and

Benetto, 2012). Transformity is the conversion factor in EMA to convert material and energy

items in Sej, which is the Emergy amount required to make one unit of a given product of service

(Odum, 1988; Odum, 1996). The calculation of Transformity is rooted on the “Baseline concept”

(Odum, 1996; Odum et al., 2000; Brown and Ulgiati, 2010; Brown et al., 2011). The transformity

of a given resource (e.g., mineral, water, biomass) is quantified by dividing the baseline to the

total annual quantity of that resource, estimated by the ratio of the stored quantity of its turnover

time (Odum et al., 2000; Rugani et al., 2011), where the Emergy baseline is the sum of solar

radiation Emergy, tids Emergy, and geothermal heat Emergy (Rugani and Benetto, 2012).

Literature studies, rather than overtly confuting the baseline concept (i.e., sun, tide, and geo) are

weighted differently (Odum, 1996; Campbell, 2000; Odum et al., 2000). Sciubba identified

several uncertainty issues behind the Emergy calculation of these three primary inputs to the

geobiosphere (Sciubba, 2010). Because transformities are calculated through a sort of pyramidal

process starting from the baseline, it is not surprising to find large inconsistencies (Campbell,

2000). Furthermore, transformity calculation is based rather crude assumptions and results are

therefore seldom reproducible (Sciubba, 2010).

24

In the EMA community, a large effort has been spent to provide a uniform approach and

to increase transformity robustness. For instance, the National Environmental Accounting

Database (NEAD) addressed a global formalization of EMA (Sweeney et al., 2007). However,

this framework includes sets of aggregated and unclear data and results are therefore only useful

for comparisons at national scale (Sweeney et al., 2007). Another effort by Rugani and Benetto

(2012) provided framework to improve EMA evaluation by using LCA. The LCA method has the

great advantage of using structured software tools and large databases that make the operational

framework more flexible. In this context, it appears that the use of detailed network models

typically considered in LCA may allow improvement of the accuracy of Emergy calculations

(Raugei et al., 2006; Ulgiati et al., 2006; Ingwersen, 2011; Rugani et al., 2011).

Joint use of LAC and EMA

Given the shortcomings and advantages of both LCA and EMA, researchers have started

to jointly use them to provide complementary evaluation of products and/or processes, and/or to

seek possible integration of these two methods to provide encompassing tools for environmental

sustainability evaluation. Emergy analyses have been used for a multitude of LCA-related

purposes, including to measure cumulative energy consumption (Federici et al., 2008), to

compare environmental performance of process alternatives (La Rosa et al., 2008), to create

indices for measuring sustainability (Brown and Ulgiati, 1997), to quantify the resource base of

ecosystems (Tilley, 2003), to measure environmental carrying capacity (Cuadra and Björklund,

2007), and for nonmarket-based evaluation (Odum and Odum, 2000).

Brown and Buranakarn (2003) noted that main drawback in LCA is that the ranking and

indicators that result are of mixed units which often make comparative analysis between products

or services difficult; accordingly, they developed an Emergy-Life-Cycle-Assessment

methodology by extending Emergy to include disposal and recycling processes as a way of

25

accounting for materials, energy, and human services of building materials and three different

recycle trajectories. Their findings suggest that recycle of wood may not be advantageous on a

large scale, but metals, plastic, and glass have very positive benefits. With respect to the recycle

systems, it was concluded that materials having large refining costs have greatest potential for

high recycle benefits and that highest benefits appear to accrue from material recycle systems,

followed by adaptive reuse systems and then by byproduct reuse systems.

Pizzigallo et al. (2008) evaluated two agro-industrial productive processes in their

entirety: one organic and one semi-industrial to compare the impacts derived from the inputs and

outputs of the system using LCA, integrated with a physical evaluation of the resources and

natural services on a common basis using EMA. They noted that the joint use of LCA and EMA

contributes important elements and information useful for the comprehension of the organization

of agricultural processes and for the use of energy flows that determine their development.

Moreover, it was concluded that the combined use of the two methods gives a comparative

thermodynamic performance evaluation between organic and semi-industrial farming.

Ingwersen (2011) proposed using EMA as an indicator of aggregate resource use for

LCA based on data from the life cycle inventory of a large mine in Peru. Ingwersen (2011)

expanded the system boundary beyond traditional LCA to include flows of energy underlying the

creation of resources used as inputs to the foreground and background processes, and extracted

the relevant Emergy data from previous Emergy analyses as well as data from LCA database.

Some challenges for a theoretically and procedurally consistent integration of EMA and LCA

were discussed, including the complexities and potential inconsistencies of integrating Emergy

into LCA on issues like allocation, and the uncertainty in unit Emergy values due to the

differences in different models used to estimate Emergy in minerals.

The incorporation of EMA in LCA could enhance the ability of LCA studies to achieve

multiple purposes such as measuring cumulative energy consumption and evaluate system

26

sustainability. However, no such integration has been applied to any type of bioenergy system

yet.

Biomass combined heat and power (CHP)

Biomass energy is essentially solar energy being converted to chemical energy stored in

plants through photosynthesis (McKendry, 2002). Biomass can be used to produce heat,

electricity or transport fuels through various bioenergy conversion routes. Despite concerns over

biomass availability, it is claimed that biomass is more flexible and reliable as an energy source to

replace fossil fuels than others, such as sunlight, wind, geothermal heat, etc (Zhang and Long,

2010). Currently, most electricity is produced in independent production, where heat is lost. The

advantages of biomass CHP include a higher total efficiency than in conventional power plants

and consequent reduction of greenhouse gas and other pollutants, provided the heat can be

utilized as a by-product. From the local point of view, the application of biomass energy can

contribute to sustainable development in multiple regards, not only from the environmental aspect

but also in social ways, and by enhancing the local economy due to the demand for biomass in the

proximity of the power plant. In general, biomass-fired CHP systems are considered to have a

great market potential (Dong et al., 2009).

Two key factors determining optimal use of biomass are the conversion efficiency of

bioenergy technologies, and the kind of fossil energy technologies that can be substituted

(Steubing et al., 2012). A biomass CHP system, cogenerating electricity and usable heat in a

single unit, can enhance the overall efficiency up to 85% compared to only 50% overall

efficiency when heat and power are generated separately (Pirouti et al., 2010). Steubing et al.

(2012) suggested biomass is best used for combined heat and power production from

environmental aspects evaluated by LCA, if heat can be used efficiently and coal, oil or fuel oil

based technologies can be replaced in the process.

27

Another recent study also made a similar recommendation for bioenergy conversion from

an economic perspective. Kalt and Kranzl (2011) outlined in the context of Austria that the most

cost-efficient bioenergy options for reducing GHG emissions using woody biomass are direct

heating and CHP, provided that heat can be used efficiently.

In contrast, EMA has rarely been applied to evaluate a biomass CHP system. Wang et al.

(2006) applied EMA to evaluate three types of coal-fired CHP systems. Sha and Hurme (2011)

were the first to utilize EMA to compare biomass CHP and a coal-fired CHP system. No previous

study is found that jointly applies LCA and Emergy to evaluate biomass CHP, let alone conduct a

sensitivity analysis of the results. There is a need for research to bridge this gap.

Biomass CHP system factors

During the LCA evaluation process, factors assumed to have significant impact on the

final results or where data was considered to be uncertain were altered in order to quantify the

impact (Kimming et al., 2011). In the following section, literature review is conducted to identify

some primary factors that could have significant impact on LCA results of biomass CHP systems.

These factors include emission control, feedstock moisture content, transportation distance, and

power to heat ratio. Since very few previous studies have utilized Emergy analysis to evaluate

biomass CHP systems, the factors having significant impact on Emergy results are still unknown.

This leads to one of our primary research questions that whether significant factors identified in

LCA may impact Emergy results differently.

Emission control

McManus (2010) examined the life cycle impacts of the production and use of three

biomass heating systems using waste wood in England and concluded that the boiler emissions

are the most significant impact associated with the life cycle.

28

Feedstock moisture content

Energy density and heating value of biomass fuel change according to fuel type, fuel

composition and moisture content which in turn lead to disturbance in system operation

performance (Pirouti et al., 2010). Pirouti (2010) examined the effect of moisture content in

biomass fuel on system performance, ranging from 15 to 30%, and found that the impact of

moisture content is significant on the operation of the system.

Transportation distance

LCA includes the entire life-cycle of the product, process or activity, encompassing

extracting and processing raw materials; manufacturing; transportation and distribution; use,

re-use; maintenance; recycling and final disposal (Consoli et al., 1993). Transport distance

occurring between nearly any two process steps of a product or process system is often of major

importance for a product/process life cycle, and in turn the LCA outcomes (Spielmann and Scholz,

2005). This has been demonstrated in evaluation of different products and/or processes. For

instance, Solli et al. (2009) performed LCA on wood-based household heating system and found

that firewood transportation distances played an important role in the life cycle. A study of LCA

on U.S. industry-average corrugated product found that transportation represented a significant

factor impacting the overall life-cycle impacts for Global Warming Potential (CPA, 2010). Pisoni

et al. (2009) conducted LCA on provincial waste management plan and found that major potential

impacts of the plan are associated with waste collection and transport, they further recommended

that neglecting the effects of transport might result in a severe underestimation of the

environmental impacts.

Power-to-heat ratio

Power-to-heat ratio (PHR) indicates the ratio of generated power to the generation of

heat/steam on the basis of the same energy unit. PHR is one important concept related to CHP

29

efficiency (EPA, 2008). Both an EPA report (2008) and the study of Van Loo (2008) indicate

overall efficiency of biomass CHP systems may decrease when PHR increases. Several previous

studies pointed out that system overall efficiency can have a strong influence on the LCA results

(Cherubini et al., 2009; Solli et al., 2009; Steubing et al., 2012).

In summary, the impacts of emission control, feedstock moisture content, transportation

distance, and power-to-heat ratio on LCA results allow an identification of opportunities for

environmental improvement of a biomass CHP system. But, the influence of these factors on

EMA outcomes remains unknown. With the ability to determine the environmental sustainability

of systems, knowledge about the influences of these factors on EMA results (i.e., the six

indicators shown in Table 2-2) could help in developing strategies to improve sustainability

performance of the biomass CHP system.

30

Figure 2-1 Four distinct phases of Life Cycle Assessment according to International Organization of Standardization

Goal and Scope Definition

Inventory Analysis

Impact Assessment

Interpretation

31

Figure 2-2 LCA results uncertainty sources

LCA results

uncertainty

Objective

source

Subjective

source

System

parameters

System

boundary

Reference

system

Functional

unit

Allocation Accounting

scope

32

Figure 2-3 Representation of biosphere in the natural environment

Source: (Simpson and Edwards, 2013)

33

Figure 2-4 System diagram of Emergy flows for EMA indicators calculation

Source: (Odum, 1996)

34

Table 2-1 Key benefits and challenges of bioenergy

Key benefits of bioenergy Key challenges of bioenergy

� Renewability – renewable energy source for power, heat and transport

� Mitigation of climate change – significantly reduce GHGs emission compared to fossil fuels

� Energy security – diversify energy mix � Rural development – develop feedstock

for new markets � Retention of local energy dollar

� Ensuring sustainability – environmental, social and economic

� Guarding food security – ensure increase demand on bioenergy does not affect the hunger

� Protecting biodiversity � Managing competition for land and water � Controlling pollution of air, water and

soil. � Lower energy density increases logistical

resources and energy costs Sources: (Hazell et al., 2006; Cherubini and Strømman, 2011)

35

Table 2-2 Main indicators of EMA analysis

Number Term Abbreviation and formula Unit 1) Percent Renewability PR= R/(R+N+F) ratio 2) Emergy Yield Ratio EYR= Y/F ratio 3) Environmental Loading Ratio ELR=(F+N)/R ratio 4) Emergy Investment Ratio EIR= F/(R+N) ratio 5) Emergy Sustainability Index ESI= EYR/ELR ratio 6) Transformity Tr=R+N+F Sej/J

36

Table 2-3 Summary of key differences in LCA, Emergy and economic accounting methods

LCA Emergy Economic cost

Definition A method for assessing the environmental burden, and material and energy consumption of a product or a process across its entire life (ISO, 2010).

The availability of energy of one kind that is used up directly and indirectly to make a product or service (Odum, 1996).

A technique to obtain the approximation of project cost (Brown, 2003).

Purpose To compare the full range of environmental performances of alternative product systems for meeting the same end-use function, from a broad, societal perspective (Norris, 2001).

To present an energetic basis for quantification of valuation of ecological goods and services, from a biosphere perspective, based on thermodynamic approach(Zhang et al., 2010).

To predict probable capital cost and operating cost of a specific project, from an individual owner or investor perspective

Problem addressed

Environmental impacts Sustainability Economic Cost and benefit

Accounting scope

Primarily consider fossil fuels and minerals; Recent focus on land, water, and other service; Ignore fish, plant, genetic resources; Completely ignore supporting services (Zhang et al., 2010).

Can account for renewable and nonrenewable material and energy resources; Also account for land use and food; Consider quality difference by conversion to solar equivalents; Supporting services are considered (Zhang et al., 2010).

Capital cost: the amount of money to build a plant/facility and includes all equipments and labor associated with installation of the equipments (Brown, 2003); Operating cost: the annual expense to keep a plant in full production. It includes cost of feedstock and fuel, labor, payment of principle and interest on loans (Brown, 2003).

System boundary

When entering economic system Before entering economic system Within economic system

Space boundary

Regional and global atmosphere Biosphere Physical boundary of a plant or facility.

Time boundary

Time boundary is mostly ignored. The timing of process, emission release or consumption rate is ignored. Some impact assessment may address on fixed time, such as IPCC 2007 addresses global warming potential on 100-year time horizon.

Very large time boundary. Traveling backward in the history of thermodynamics transformation of energy, to account for solar energy used to produce resources.

Timing is critical. Focusing on present value. Specific time zone is adopted, and any cost or benefits occurring outside that range are ignored.

Measurement unit

Midpoint: No single unit (e.g.: kg CO2 eq., H+ moles eq., kg benzene eq. etc.) Endpoint: Eco-indicator single point (Pt)

Sej (solar equivalent joules) Monetary unit (e.g. US dollar)

Conversion factor

Characterization factors: emission in equivalent of another chemical

Transformities: the ratio of Emergy to available energy, or the solar Emergy required to make 1 J of a service or product (Odum, 1996)

Monetary value: the value that a product or service will bring to someone if sold

Objectiveness Objective evaluation at Midpoint, Involving subjectivity at Endpoint

Objective evaluation Subjective evaluation due to human preference, money is also subject to inflation and exchange rate

37

Chapter 3: RESEARCH METHODOLOGY

Introduction

This study aims to investigate the similarities, differences, and incompatibilities between

LCA and EMA results. The uncertainties of LCA and EMA indicators due to different

assumptions of biomass CHP system factors were examined by means of uncertainty and

sensitivity analyses. And the relationship between LCA and EMA indicators were explored

through correlation analysis.

In this study, the uncertainty in LCA and EMA results associated with a biomass CHP

system is determined using a process-based framework and statistical modeling methods.

Probability distributions fitted to available data were used to represent uncertain parameters of

biomass CHP system in the LCA and EMA model. Where data were not readily available, a

partial least-squares (PLS) regression model based on existing data was developed. Finally, a

Monte Carlo simulation was performed to generate sample output distributions for further data

analysis.

Study scope

System boundary

The life cycle of biomass CHP system is divided into seven processes listed in Figure 3-1.

These seven processes were defined as the case study system boundary in both LCA and EMA.

The detailed accounting scope of LCA is presented on the left side of Figure 3-1 within dashed

lines; the EMA scope is detailed on the right side of the figure.

LCA and EMA indicators

Since climate change mitigation and energy independence are the main driving forces for

future bioenergy (Cherubini and Jungmeier, 2010), in this study, LCA results are presented only

38

with reference to a single environmental indicator, namely, Global Warming Potential (GWP),

measured in kgCO2/MMBtu in the IPCC 2007 climate change impact assessment method on a

100-year time horizon (Appendix B). Meanwhile, the main six indicators (Table 3-1) commonly

used by previous bioenergy EMA studies are selected.

Functional unit

The reference functional unit used in this study for GWP and Transformity (Tr) is 1

MMBtu thermal energy generation. The allocation criteria for the analysis is that 1 MMBtu of

electricity is equal to 3 MMBtu of heat when electricity is used in heat pump for heat generation

(Van Loo and Koppejan, 2008; Abusoglu and Kanoglu, 2009).

Overview of modeling procedure

Modeling steps

Monte Carlo simulation was used in this study to compare LCA and EMA results in

terms of uncertainty, sensitivity, and correlation. Monte Carlo simulation use repeated random

sampling to simulate data for a given mathematical model and evaluate the outcome. Simulated

data was routinely used in situations where resources are limited or gathering real data would be

too expensive or impractical (Hung and Ma, 2009). The relationships of biomass CHP system

factors to LCA and EMA outcomes were used to develop the simulation model, which are

illustrated in Figure 3-2. The Monte Carlo simulation in this study consists of four steps as

follows:

Step 1: Develop LCA and EMA model for a specific case

A 6.4 MWth wood biomass CHP system in Europe was extracted from SimaPro 7.3

database as the case study subject. The reasons for choosing this case are: it presents the average

biomass CHP system in Europe and its life cycle inventory data was validated. For LCA study,

39

the entire life cycle of wood biomass CHP system was divided into seven processes. For EMA

study, the same life cycle processes are applied, but the inputs were classified into three groups as

required by EMA study, i.e., R-Renewable inputs, N-Nonrenewable inputs and F-Purchased

inputs.

Step 2: Define input parameters

This step was to identify input parameters that are highly uncertain and have significant

impacts on bioenergy life cycle environmental performance, and to determine the mean, standard

deviation and distribution that are most likely to encounter for each parameter. Four input

parameters of biomass CHP system were extracted from the literature, including: Emission

control (EC), feedstock moisture content (MC), transportation distance (TD), and power to heat

ratio (PHR). The detailed assumption descriptions for these four input parameters can be seen in

the later section of "Biomass CHP system factors".

Step 3: Create random data

Based on an assumed mean, standard deviation and distribution probability for each

uncertain factor identified in step 2, a large random data set for each factor was created, one

thousand sample points for each factor.

Step 4: Simulate and analyze process output

With the simulated input data in place, LCA and EMA models were used to calculate

simulated outcomes.

Data collection in LCA

In this study, LCA results are presented only with reference to a single environmental

indicator, namely, Global Warming Potential (GWP), measured in kgCO2/MMBtu in the IPCC

2007 climate change impact assessment method on 100-year time horizon. GWP was measured

40

by the total greenhouse gas emissions associated with all seven processes of biomass CHP system

(see below) which are available in the SimaPro 7.3 database. The calculation worksheet of LCA

results are outlined in Appendix C. Each life cycle process is shown as function of biomass CHP

system factors, as described below:

( )

PHR)(MC, F ash l Disposa

(PHR) F wastel Disposa

PHR)(EC, F emission Plant

PHR)(EC, Fplant in use Chemical

(PHR) F onconstructi Plant

PHR)TD, (MC, F ation transportWoodchips

PHRMC, F production Woodchips :Where

ash) Disposal waste, Disposalemission, Plant plant, in use Chemical on,constructiPlant

ation, transportWoodchips ,production (Woodchips of Sum eq./MMBtu) CO2 (kg GWP

===

==

==

=

Data collection in EMA

The same assumptions were used in EMA as specified in the LCA. All inputs were

re-classified into three groups on the common basis of solar energy equivalents, i.e.,

R-Renewable inputs, N-Nonrenewable inputs and F-Purchased inputs, as shown in Table 3-2.

Renewable inputs, Nonrenewable inputs and Purchased inputs are shown as functions of biomass

CHP system factors shown below. EMA indicators were calculated by ratios and sums using R, N

and F follow the methodology and numerical quantities used by Odum (1996). Appendix D

outlines the calculation of Emergy inputs for the biomass CHP system.

41

++

++

++++

=

=

=

FNR

ELRERY

NRF

RNF

FFNR

FNRR

/

)/(

/)(

/)(

)/(

tyTransformi

Indexlity Sustainabi Emeryg

RatioInvestmentEmergy

Ratio LoadingtalEnvironmen

RatioldEmergy Yie

ity RenewabilPercentage

Tr

ESI

EIR

ELR

EYR

PR

indicatorsEmergy

Where:

( )( )

( ) PHRTD, MC,EC,Finputs PurchasedR

PHRTD, MC,EC,Finputs leNonrenewabN

PHRMC,Finputs RenewableR

====

==

Processes of biomass CHP system

The entire life cycle of wood biomass CHP system is divided into seven processes. The

shared assumptions for each process in LCA and EMA are summarized in italics in Table 3-3;

assumptions that differ between the methods are detailed in the shaded boxes of the same table.

The assumptions in Table 3-3 are detailed in the following text.

Woodchips production

Woodchips production was assumed to be carried out by a stationary chopper in a sawmill.

The stationary chopper has electric input of 25 kw, hourly output of 3.3 m3/h bulked chips, and a

life time output of 100,000 m3 bulked chips. In addition to electric input, the chopper also

consumes lubricant oil and steel during operation.

In this study, the life cycle of the biomass CHP was assumed to begin with feedstock

production (i.e., woodchips) and processes before this such as tree harvesting and log

transportation to sawmill were not considered. The extant studies recommend different

approaches on allocation methods, for example, partitioning environmental burdens of the

systems by different co-product based on mass (Gabrielle and Gagnaire, 2008), energy (Lardon et

42

al., 2009), or economic market values (Mortimer et al., 2003). In this study, woodchips were

assumed to have low economic market value relative to that of main high-value wood products

such as lumber. In the LCA accounting, a certain degree of cut-off is allowed, that is,

specification of level of environmental significance associated with unit processes or product

system to be excluded from the study (ISO, 2006). Therefore, all emissions from pre-processes

(i.e., tree harvesting and log transportation) were not considered. The results of these preliminary

processes were not relevant to the purpose of this study, and would not have impacted the

outcome.

Besides all the material and energy uses as specified by LCA, human labor input was

added in EMA. The way of converting human labor service to Emergy units was calculated by

summing all the money spent in a process and multiplying by an Emergy-money index (Bakshi,

2002). Emergy-money index indicates solar Emergy per unit of money (Sej/$), which is

calculated as ratio of total solar Emergy an nation used in one year to the Gross National Product

(GNP) of the nation (Cao and Feng, 2007). Total solar Emergy used by the U.S. in one year was

estimated by use of coal, natural gas, crude oil, uranium, iron ore, aluminum ore, and wood, as

well as sunlight, rain and wind for a whole year (Odum, 1996). Cost of woodchips production in

this study was assumed to be consisting of capital cost of chopper, electricity use and human

labor service involved, and estimated value was $7.58 per cubic meters.

Woodchips transportation

Transportation distance, occurring between nearly any two process steps of product or

process system, is often of major importance for the product life cycle, and in turn the LCA

outcomes (Spielmann and Scholz, 2005). Emission of woodchips transportation can be calculated

given the transportation distance and type of truck used. In this study, transportation vehicle was

assumed to be lorry, with average in-bound load of 25 ton and empty on the returning trip.

43

The following processes were taken into consideration for woodchips transportation:

operation of vehicle; production, maintenance and disposal of vehicles; construction and

maintenance and disposal of road. Diesel fuel consumption and direct airborne emission were

included in operation of vehicle. All these data were collected from SimaPro 7.3 database.

Since human labor for transportation was added for EMA, the service was converted to

Emergy units through the Emergy-money index. The overall cost for transportation service was

estimated by the driver’s hourly payment and transportation distance.

Plant construction

Biomass CHP plant construction is divided into two major parts: facility building

construction and equipments construction. The lifetime of each of these two constructions was

assumed to be twenty years. Most important materials used for construction: concrete, steel,

aluminum, copper, cast iron, gravel, wood, rock wool, paint, as well as their estimated

transportation were taken into account in LCA. Emission data were obtained from SimaPro 7.3

database.

In comparison, the capital cost of plant construction was used to estimate Emergy input in

EMA, which was estimated at $3.8 million US dollar in 2010 (Salomon et al., 2011).

Plant operation/emission

Biomass CHP plant operation is a process of woodchips combustion to generate heat and

electricity. In this study, all the emission data of combustion process was extracted from SimaPro

7.3 database.

For EMA, human labor cost involved in the plant operation was used to calculate Emergy

input. The labor requirement was estimated as 1 person per shift, with one additional manager on

the day shift, with a total of 3 shifts per day. The operation time was assumed to be 7,654 hours

44

per year (87.4%). Salary and social expenses were estimated at $64,000 US per worker annually

and $90,000 US per manager annually based on the U.S. Bureau Labor Statistics (BLS, 2011).

Chemicals use in plant

The chemicals needed for plant operation include lubricant oil, ammonia, organic

chemicals, sodium chloride, chlorine and decarbonized water. Total emissions of all these

chemical production were taken into account in LCA. In contrast, transformities of these

chemicals were used to estimate Emergy input in EMA, which are shown in table 3-4.

Waste disposal

Three types of waste were assumed to be generated in the biomass CHP plant, including

used mineral oil, municipal solid waste and sewage. These waste materials require different

disposal treatments. LCA takes into account emissions from three treatments, which are

documented in SimaPro 7.3 database.

The cost of these three waste disposals was used to estimate Emergy input in EMA. The

estimated costs for each treatment are shown in Table 3-5 below.

Disposal ash

For LCA, ash disposal is also required for biomass CHP system. The amount of ash

generated was determined by the amount of wood burned and its ash content. Ash content was

assumed to be 1.1% on dry weight basis. For EMA, the cost of wood ash disposal was estimated

as $45 US per ton (Zwahr, 2004).

Biomass CHP system factors

Factors of biomass CHP system having significant impact on LCA results were used to

conduct Monte Carlo Simulation. Monte Carlo Simulation model was developed by substituting a

range of possible values for biomass CHP system factor that has inherent uncertainty (Mooney,

45

1997). Then results were repeatedly calculated with different sets of random values from the

probability functions. Each of the four factors was assumed to have normal probability

distribution, with a mean and standard deviation. Normal distribution was chosen because it is the

most likely distribution for independent random factor whose distribution is unknown according

to central limit theorem (Ott et al., 2001). Where data were not readily available, a partial

least-squares (PLS) regression model based on existing data was developed.

This study used four biomass CHP system factors in Monte Carlo Simulation, including

Emission control (EC), feedstock moisture content (MC), transportation distance (TD) and power

to heat ratio (PHR). The detailed description of factors is given below.

Emission control

Emission control factor in this study represents whether urea treatment is implemented or

not for exhaust gases release. Urea is a type of reduction agent for noxious gas, which can reduce

the NOx pollution in exhaust gases from combustion (Trautwein, 2003). The emissions data for

scenarios of with or without urea treatment are both available in SimaPro 7.3 database.

It was assumed that two scenarios (with or without urea treatment) are equally likely to

occur.

Moisture content

Moisture content is an important measurement of the biomass fuel quality and has

significant impact on heat energy released during biomass combustion. In the biomass CHP plant,

Lower Heating Value (LHV) of biomass is generally used to calculate the energy input into the

boiler (Van Loo and Koppejan, 2008), which decreases as the increase of moisture content of

biomass fuel (Figure 3-3) (Van Loo and Koppejan, 2008). The change of moisture content

influences the amount of woodchips needed to generate a given unit of energy, thus further

46

impacting other biomass CHP system processes including woodchips production, transportation,

plant emission and ash disposal.

In this study, moisture content (wet basis) of woodchips was assumed to be normally

distributed with mean MC of 25% and standard deviation of 7.5%.

Transportation distance

The transportation distance is one parameter affecting the emission from the woodchips

transporting process, where diesel fuel consumption, lubricant oil usage and road maintenance are

involved. Energy use and labor services required for woodchips transporting process vary with

the change of transportation distance. Therefore, transportation distance was modeled to estimate

Emergy input on transportation services.

In this study, transportation distance was assumed to be normally distributed with mean

of 60 km and standard deviation of 15km based on previous studies' assumptions on

transportation distance (Hoogwijk et al., 2009; Timmons and Mejía, 2010; Brechbill et al., 2011).

Power to heat ratio

The ratio of generated power to the generation of heat/steam on the basis of the same

energy unit is called the power-to-heat ratio (PHR). PHR indirectly impact every biomass CHP

system process in two ways: energy efficiency and allocation between electricity and heat.

A qualitative study on the efficiency for biomass CHP system showed higher PHR tends

to have lower energy efficiency (Van Loo and Koppejan, 2008). EPA (2008) reported biomass

CHP system can reach 85% energy efficiency when PHR is approximately equal to 0.1. Another

biomass CHP case study by Sha and Hurme’s (2011) found that 65% energy efficiency with PHR

equal to 0.4. Therefore, PHR and energy efficiency was assumed to have this linear relationship:

with increases of PHR from 0.1-0.4, the energy efficiency at facility decreases from 85% to 65%,

as shown in Figure 3-4. Secondly, allocation was carried out to attribute shares of total

47

environmental emissions and solar energy to the different products of biomass CHP system. The

allocation criteria used in this analysis was that assuming 1 MMBtu of electricity is equal to 3

MMBtu of heat when electricity is used in heat pump for heat generation (Van Loo and Koppejan,

2008; Abusoglu and Kanoglu, 2009).

In this study, PHR was assumed having normal distribution with mean of 0.25 and

standard deviation of 0.075 as suggested by Van Loo and Koppejan (2008).

Data analysis

Uncertainty and sensitivity analysis

The Monte Carlo method repeatedly sampled one thousand random data points to

generate numerical results for the LCA and EMA models, based on the assumed distribution

probabilities for the four biomass CHP system factors (Appendix E). Uncertainty and sensitivity

analyses were performed based on the simulated data to examine the similarities and differences

between LCA and EMA.

Uncertainty and sensitivity analysis are recommended or even required as a must in LCA

given the concerns over the accuracy of its results (Steen, 1997). Uncertainty analysis examines

reliability and applicability of results, while sensitivity analysis examines the differential effects

that biomass CHP system factors have on results (Guinée et al., 2011). There are basically two

types of uncertainty present in a LCA study: objective uncertainty and subjective uncertainty

(Helton, 1993). This study only focused on selected objective uncertainty (i.e., emission control,

transportation distance, power to heat ratio and moisture content). The subjective uncertainty

were not simulated into the model because there is no consensus regarding selection of CHP

system boundary, functional unit, impact assessment method, allocation methodology and other

assumptions. Due to a lack of literature on EMA uncertainty analysis, this study firstly

48

investigated the uncertainty of the EMA results due to four system factors, then explored other

potential sources that could be linked to larger uncertainties.

Correlation analysis

This study jointly used LCA and EMA to evaluate the environmental impact (i.e., GWP)

and long-term sustainability (i.e., EMA indicators) of the biomass CHP system, aiming to

investigate the relationship between LCA and EMA indicators. Correlation analysis was

conducted to check for potential dependencies between indicators and to determine if the number

of indicators could be reduced.

Correlation analysis measures the relationship between two variables and confirms the

fact that the data moves in tandem (Ott et al., 2001). It typically gives a number result that lies

between +1 and -1, with zero signifying no correlation. The closer the number moves towards +1

or -1, the stronger the correlation is. Usually for the correlation to be considered significant, the

correlation must be 0.5 or above in either direction (Ott et al., 2001).

49

Figure 3-1 Comparison of system boundary and accounting scope between LCA and EMA in analyzing production chain of biomass CHP system

Woodchips production

Woodchips Transport

Waste disposal

Chemical uses

Construction, maintenance of chipper, and energy use

Construction, maintenance of truck, and diesel use

Energy and material consumption for waste and

ash disposal

Plant operation

Construction, maintenance of plant, and combustion

emission from stack

Account for all emissions due to production and use of materials, fuels and energy

Account for all solar energy inputs of materials, fuels, energy and labor

Global Warming Potential (GWP)

Ash disposal

Plant construct

ion

Plant growing, Chipping cost, includes energy use

and labor

Delivery cost, includes diesel use and labor

MSW, mineral oil, and ash disposal cost

Construction cost, chemicals uses and

operation labor

Six EMA indicators

50

Figure 3-2 Monte Carlo simulation for LCA and EMA using biomass CHP system factors

Emission Control Moisture Content

Transportation Distance Power to Heat Ratio

R - Renewable inputs N - Nonrenewable inputs F - Purchased inputs

Life Cycle Processes of biomass CHP system

Emissions

LCA outcome EMA outcomes

51

y = -16.465x + 13.979

R2 = 0.9937

0

5

10

15

20

0% 10% 20% 30% 40% 50% 60%

Moisture Content (wet basis)

Low

er H

eatin

g V

alue

(M

MB

tu/t

on)

Figure 3-3 Lower Heating Value as a function of moisture content (wet basis) for woodchips (Adapted from Van Loo and Koppejan 2008)

52

y = -0.6214x + 0.8954

R2 = 0.9794

0%

20%

40%

60%

80%

100%

0 0.1 0.2 0.3 0.4 0.5

Power to heat ratio

Ene

rgy

Effi

cien

cy

Figure 3-4 Linear relationship between power-to-heat ratio and energy efficiency for biomass CHP plant Source: Van Loo and Koppejan 2008

53

Table 3-1 Main indicators of EMA

Number Term Abbreviation and formula Unit 1) Percent Renewability PR= R/(R+N+F) ratio 2) Emergy Yield Ratio EYR= Y/F ratio 3) Environmental Loading Ratio ELR=(F+N)/R ratio 4) Emergy Investment Ratio EIR= F/(R+N) ratio 5) Emergy Sustainability Index ESI= EYR/ELR ratio 6) Transformity Tr=R+N+F Sej/MMBtu

54

Table 3-2 Emergy analysis table for production of energy from biomass CHP system

(Adapted from Odum 1996)

Item Unit Transformity (sej/unit) Units/year R: Renewable inputs

Oxygen in air g 5.16E+07 Woodchips g 2.57E+07

Water g 6.64E+05 N: Non-renewable inputs

Lubricant oil g 2.82E+09 Ammonia g 3.80E+09 Chemicals g 1.60E+09

Chlorine g 1.60E+09 Sodium Chloride g 1.00E+09

Emission control: Urea g 2.15E+09 F: Purchased inputs

Construction cost of facility building and equipments

$ 1.37E+12

Chipping cost* $ 1.37E+12 Transportation cost* $ 1.37E+12 Plant operation labor $ 1.37E+12

Ash disposal cost $ 1.37E+12 Waste disposal cost $ 1.37E+12

Products Heat output MMBtu Electricity output MMBtu * Estimated as estimated 125% of labor cost (WE, 2002), the labor costs are based on U.S. Bureau Labor Statistics (BLS, 2011)

55

Table 3-3 Summary of life cycle processes assumptions in LCA and EMA

Life Cycle Process LCA EMA

Equipment: stationary chopper Electric input: 25kw Hourly output: 3.3m3/hour Life time: 100,00 m3

Woodchips production

Material and energy use Additional human labor cost:

$7.58 per m3 Equipment: lorry Load capacity: 25 ton, and empty on returning trip

Transportation

Fuels and necessary maintenance Additional human labor cost: $25/hour Life time: 20 years Facility

construction All material and energy use for construction

Capital cost: $3.8 million US dollar in 2010

Operation hour: 7,654 hours/year (87.4%)

Plant operation Emissions from combustion process

Labor cost: Three shifts per day

1 person per shift with one additional manager on the day shift

Labor expense: $64,000 per worker, 90,000 per manager

Lubricant oil, ammonia, organic chemicals, sodium chloride, chlorine and decarbonized water

Chemical uses

Emissions Transformity Mineral oil Municipal solid waste Sewage

Waste disposal

Emissions due to disposal and treatments of waste

Disposal costs

Ash content: 1.1% on dry weight basis Ash disposal

Emissions due to disposal Disposal cost: $45 per ton

56

Table 3-4 Transformity and Emergy per unit mass of used chemical

Chemicals unit Transformity (sej/unit) Source Lubricant oil g 2.82E+09 (Odum, 1996) Ammonia g 3.80E+09 (Odum, 1996) Chemicals g 1.60E+09 (Odum, 1996) Chlorine g 1.60E+09 (Odum, 1996) Sodium Chloride g 1.00E+09 (Odum, 1996) Water g 6.64E+05 (Odum, 1996)

57

Table 3-5 Disposal cost of waste

Disposal cost Disposal cost ($/ton) Source Mineral oil 75 (SCGOV, 2012) MSW (municipal solid waste) 57 (SCGOV, 2012) Sewage 45 (SCGOV, 2012)

58

Chapter 4: COMPARING UNCERTAINTY AND SENSITVITY OF LCA

AND EMA RESULTS IN A BIOMASS CHP SYSTEM

This paper, co-authored by Li Ma and Charles D. Ray, was written for submission to " Ecological Modeling".

59

Abstract

With the increasing concern over environmental sustainability of different products and

processes, the accuracy of different environmental accounting methods have gained growing

attention. Many of the concerns that have been expressed about the accuracy of environmental

evaluation results are linked to potentially significant sources of uncertainty. Life Cycle

Assessment (LCA) and Emergy Analysis (EMA) are two environmental accounting methods used

to guide bioenergy decision-making. The uncertainty of LCA and EMA results contributes to

complexity of decision-making in regards to improvements. Therefore, uncertainty and sensitivity

analyses are carried out in this study to check if uncertainty of LCA and EMA attribute to the

same factors and how sources linking to uncertainty differ between these two methods.

This study uses a biomass combined heat and power (CHP) production system as an

example to jointly conduct LCA and EMA. To examine the uncertainty and sensitivities of LCA

and EMA results, a Monte Carlo simulation model is developed using four system factors having

inherent uncertainty in the biomass CHP system. The findings suggest that the LCA possesses a

larger degree of uncertainty on all four system factors than EMA, and EMA is subject to

additional uncertainties associated with free environmental service and human labor inputs that

are not accounted in LCA.

Given the large degree of uncertainty of the LCA results, the use of LCA as the sole tool

for decision-making in energy policy might lead to unanticipated environmental damage and

suggest actions that prove economically unsustainable. Therefore, LCA can be a useful tool for an

organization's internal use on a specific project, but is not recommended for a regional public

policy development.

EMA, on the other hand, can quantify the contribution of natural capital for sustaining

economic activity. The results become more theoretically accurate as the studied environmental

60

project scale gets larger, which makes EMA better than LCA for environmentally conscious

public policy development and decision-making.

Key words: LCA, EMA, Uncertainty analysis, Sensitivity analysis, GWP, EMA indicators

61

Introduction

Life Cycle Assessment (LCA) and Emergy Analysis (EMA) are two environmental

accounting methods that are both applied to guide bioenergy decision-making. However, there are

conflicting claims in regard to the validity of one approach versus the other (Sciubba and Ulgiati,

2005). LCA provides a way of quantifying the environmental impacts of products and services

from cradle to grave, or any subset of that process deemed useful for a particular project. The

development and standardization of LCA methodology has led LCA to become one of most used

tool for assessing environmental impacts and system sustainability (Curran, 2006; EC, 2010).

One the other hand, EMA accounts for the amount of solar energy required to support products

and services during an entire production chain. It is suggested that EMA is suitable for accounting

a wide set of natural resources, and can be used to support environmental sustainability

management (Campbell, 1998; Rugani and Benetto, 2012). This conflicting claim increases the

complexity of decision-making on method selection.

Despite that LCA and EMA studies on bioenergy system are quite rich in the extant

literature, no study has been carried out to practically examine the differences and similarities of

these two methods in the bioenergy field. This study is therefore an attempt to address the

difference and similarities of LCA and EMA with respect to results uncertainty. This is because

previous research has claimed that LCA indicators are subjective to many uncertainties, which

could greatly limit applicability of the results, especially when the indicators are presented for

decision-making in bioenergy development. Cherubini and Strømman (2011) suggested that

future research on LCA should be focused on reducing the uncertainties. Moreover, Ross et al.

(2002) noted that some of these uncertainties are common to all environmental accounting

methods, though the problem of uncertainty has not been recognized in the EMA literature. This

further warrants the need to investigate the uncertainties of LCA and EMA results. This study

62

will provide insight on differentiating respective uncertainty sources for designers and system

engineers seeking improvements of system performance.

Literature review

LCA

Life cycle thinking recognizes that all product life cycle stages including extracting and

processing of raw materials, manufacturing, transportation and distribution, use/reuse, recycling,

and/or waste management generate environmental impacts which need to be evaluated and then

reduced (Finnveden et al., 2009; Guinée et al., 2011; Rugani and Benetto, 2012). This perspective

has been the basis for the development and standardization of the Life Cycle Assessment (LCA)

method (ISO, 2006). Providing information about many emissions and consumption of resources,

LCA has become a principle factor in bioenergy development (Andersson, 2000; EPA, 2012), and

one of the best methodologies to evaluate greenhouse gases balance of a bioenergy system

(Cherubini et al., 2009). The bioenergy LCA studies are quite rich in the literature. For example,

LCA has been applied to bioethanol production (Fleming et al., 2006; Felder and Dones, 2007),

biodiesel production (Mortimer et al., 2003; Hossain and Davies, 2010), and biomass heat and

power production (Elsayed et al., 2003; Solli et al., 2009; Uihlein and Schebek, 2009). LCA has

also been used to compare bioenergy systems and fossil fuel-based systems (Eriksson et al., 2007;

Steubing et al., 2012). Among multiple measurements provided by LCA for assessing materials

and energy uses, reduction of Global Warming Potential (GWP) has been shown to be the largest

potential environmental benefit of biomass energy over other fossil fuels, and has been frequently

reported in bioenergy LCA studies (Cherubini et al., 2009; Steubing et al., 2012). Cherubini and

Strømman (2011) reviewed 94 bioenergy LCA studies published in the last fifteen years and

found that about 90% of the reviewed studies included GWP in the findings report. GWP is a

measure of the equivalent carbon dioxide that allows for the relative weightings of damaging

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greenhouse gasses (Shine, 2009), which is sometimes called Climate Change Impact or its more

recent name - Carbon Footprinting (CFP). GWP has been a widely used metric of climate change

impacts and the main focus of many sustainability policies among companies and authorities

(Laurent et al., 2012).

Although the extant LCA studies claim to look at bioenergy systems from a life cycle

perspective, most are limited in scope. Traditional LCA studies focus only on emissions and their

impacts while ignoring the role of ecosystem goods and human labor services that industrial

processes rely upon (Odum, 1996; Urban and Bakshi, 2009; Zhang et al., 2010). In addition, LCA

results are subject to great uncertainty due to use of different system assumptions, such as

feedstock sources, conversion technologies, system boundaries, and reference systems with which

bioenergy system is compared (Cherubini et al., 2009). These two shortcomings of LCA could

lead to rising concern over the sustainability of bioenergy (Dickie, 2007; Sheehan, 2009; Zhang et

al., 2010).

EMA

Emergy analysis is an energy-based environmental accounting method that expresses all

the process inputs (i.e., energy, raw material, human service, etc.) and output products in solar

energy equivalent joules (Sej). Emergy measures how much energy would be needed to do a

particular task if solar radiation were the only input. The theory is based on the fact that earth has

one principle energy input: solar energy (Odum, 1996). Every product or service is comprised of

some amount of solar energy in term of energy.

With the increasingly important role that bioenergy plays in economic development and

environmental protection, EMA has been increasingly applied to bioenergy systems: Brown and

Ulgiati (2004), Dong et al. (2008) and Pereira and Ortega (2010) used Emergy to evaluate

bioethanol production from different feedstocks; Alonso-pippo et al. (2004) applied it to bio-oil

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production using sugarcane biomass residues; Cavalett and Ortega (2009) and Sheng et al. (2007)

evaluated biodiesel production from soybean using Emergy; and Sha and Hurme (2011) were the

first to apply EMA in the biomass CHP production system. Despite the increasing interest of

Emergy for bioenergy system evaluation, studies on bioenergy system using forest wood or wood

waste are limited. Furthermore, Ross et al. (2002) noted that all environmental accounting

methods are subject to the uncertainty problem. The majority of previous EMA studies focus on

specific case of biomass CHP systems CHP (Al-Sulaiman et al., 2010; Sha and Hurme, 2011;

Buonocore et al., 2012); no study has been conducted to address the uncertainties of EMA results.

It is suggested that without addressing the uncertainty and sensitivity of results, the robustness

and usefulness of results is unknown and greatly limited.

Biomass CHP factors

Among various bioenergy conversion routes, studies have suggested that biomass is best

used for combined heat and power (CHP) production from environmental and economic

perspective. Steubing et al (2012) suggested that biomass is best used for CHP production from

environmental aspects evaluated by LCA, if heat can be used efficiently and coal, oil or fuel oil

based technologies can be substituted in the process. Kalt and Kranzl (2011) made the similar

recommendation in the context of Austria that the most cost-efficient bioenergy options for

reducing GHG emissions using woody biomass are direct heating and CHP, provided that heat

can be used efficiently.

During the LCA evaluation process, factors assumed to have significant impact on the

final results or where data was considered to be uncertain were altered in order to quantify the

impact (Kimming et al., 2011). A search of literature suggests four primary factors that have

significant impact on LCA results of biomass CHP systems.

65

1) Emission control: McManus (2010) examined the life cycle impacts of the production

and use of three biomass heating systems using waste wood in England and concluded that the

boiler emissions are the most significant impact associated with the life cycle.

2) Moisture content: Pirouti (2010) examined the effect of moisture content in biomass

fuel on system performance, ranging from 15% to 30%, and found that the impact of moisture

content is significant on the operation of the system.

3) Transportation distance: Transport distance occurring between nearly any two process

steps of a product or process system is often of major importance for a product/process life cycle,

and in turn the LCA outcomes (Spielmann and Scholz, 2005). Solli et al. (2009) performed LCA

on wood-based household heating system and found that firewood transportation distances played

an important role in the life cycle.

4) Power-to-heat ratio: PHR indicates the ratio of generated power to the generation of

heat/steam on the basis of the same energy unit. PHR is one important concept related to CHP

efficiency (EPA, 2008). Both EPA report (2008) and Van Loo (2008) indicates overall efficiency

of biomass CHP system might decrease when PHR increases. Several previous studies pointed

out that system overall efficiency can have a strong influence on the LCA results (Cherubini et al.,

2009; Solli et al., 2009; Steubing et al., 2012).

In summary, the impact of emission control, feedstock moisture content, transportation

distance, and power-to-heat ratio on LCA results allow an identification of opportunities for

environmental improvement of a biomass CHP system. But the influence of these factors on

EMA outcomes remains unknown. With ability to determine the environmental sustainability of

systems, knowledge about the influences of these factors on EMA results could help in

developing strategies to improve sustainability performance of a biomass CHP system.

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Methods

System boundary

The life cycle of biomass CHP system is divided into seven processes listed in Figure 4-1.

These seven processes were defined as the case study system boundary in both LCA and EMA.

The detailed accounting scope of LCA is presented on the left side of Figure 4-1 within dash line;

the EMA scope is detailed on the right side of the figure. Since climate change mitigation and

energy independence are the main driving forces for future bioenergy (Cherubini and Jungmeier,

2010), LCA outcome only focuses on Global Warming Potential (GWP) evaluated using IPCC

2007 on 100 years horizon (IPCC, 2007). Meanwhile, the six indicators commonly used by

previous EMA studies are selected as EMA outcomes in this study, including Percent

Renewability (PR), Emergy Yield Ratio (EYR); Environmental Loading Ratio (ELR); Emergy

Investment Ratio (EIT); Emergy Sustainability Index (ESI); and Transformity (Tr) (Table 4-1).

System functional unit

The main purpose of the functional unit is to provide a reference to which the input and

output process data are normalized and the basis on which the final results are shown (Cherubini

et al., 2009). In this study, the reference functional unit for GWP and Transformity (Tr) is 1

MMBtu thermal energy generation. The allocation criteria for the analysis is that 1 MMBtu of

electricity is equal to 3 MMBtu of heat when electricity is used in heat pump for heat generation

(Van Loo and Koppejan, 2008; Abusoglu and Kanoglu, 2009).

Modeling Procedure

The modeling methodology in LCA and EMA are slightly different. To measure GWP,

emissions from seven processes are accumulated and divided by all heat energy generated in one

year. EMA indicators are calculated through ratios among Renewable inputs (R), Non-renewable

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inputs (N) and Purchased inputs (F) (Odum, 1996). The modeling uses primary data from a

validated life cycle inventory of 6.4 MWth CHP plant in Europe available in SimaPro 7.3

database.

Life Cycle Processes of biomass CHP system

The entire life cycle of wood biomass CHP system is divided into seven processes. The

shared assumptions for each process in LCA and EMA are summarized in italics in Table 4-2;

assumptions that differ between the methods are detailed in the shaded boxes of the same table.

The assumptions in Table 4-2 are detailed in the following text.

1) Woodchips production: Woodchips production was assumed to be carried out by a

stationary chopper, which has electric input of 25 kw, hourly output of 3.3 m3/h bulked chips, and

a life time output of 100,000 m3 bulked chips. In addition of electric input, the chopper also

consumes lubricant oil and steel during operation. Besides all the material and energy uses as

specified by LCA, human labor input is added in EMA through Emergy-money index. The cost

of wood chipping process in this study consisting of capital cost of chopper, electricity use and

human labor service, is estimated to be $7.58 per cubic meters.

2) Woodchips Transportation: Woodchips were assumed to be transported by Lorry

with average load of 25 tonnes and empty on the returning trip. Besides fuel uses in this process,

the human labor service is also accounted in EMA, which was estimated by the cost of driver’s

hourly payment and transportation distance.

3) Facility Construction: The lifetime of plant facility was assumed to be 20 years in the

analysis of LCA. EMA uses capital cost of plant construction to estimate Emergy input, which

was estimated at $3.8 million US in 2010 (Salomon et al., 2011).

4) Chemical uses: The chemicals needed for plant operation include lubricant oil,

ammonia, organic chemicals, sodium chloride, chlorine and decarbonized water. LCA accounts

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for emissions of all these chemical production. In contrast, transformities of these chemicals were

used to estimate Emergy input in EMA (Odum, 1996) (Table 4-3).

5) Plant operation: LCA accounts for all the emission from combustion process which is

available in SimaPro 7.3 database. For EMA, human labor cost involved in the plant operation

was used to calculate Emergy input. The labor requirement was estimated as 1 person per shift,

with one additional manager on the day shift. Total 3 shifts per day was assumed. The operation

time was assumed to be 7,654 hours per year (87.4%). The salary and social expenses were

estimated at $64,000 US per worker annually and $90,000 US per manager annually based on the

U.S. Bureau Labor Statistics (BLS, 2011).

6) Waste disposal: Three types of waste were assumed to be generated in the biomass

CHP plant, including mineral oil, municipal solid waste, and sewage. LCA takes into account

from three treatments, which are documented in SimaPro 7.3 database. The cost of these three

waste disposals was used to estimate Emergy inputs in EMA (SCGOV, 2012). The estimated

costs for each treatment are shown in Table 4-4 below.

7) Ash disposal: For LCA, the amount of ash generated is determined by the amount of

wood burned and its ash content. Ash content was assumed to be 1.1% on dry weight basis. In

EMA, the cost of wood ash disposal was estimated as $45 US per ton (Zwahr, 2004).

Biomass CHP system factors

Factors of biomass CHP system having significant impact on LCA results were used to

conduct Monte Carlo Simulation. Monte Carlo Simulation model was developed by substituting a

range of values for biomass CHP system factor that has inherent uncertainty (Mooney, 1997).

Then results are repeatedly calculated with different sets of random values from the probability

functions. Each of the four factors was assumed to have normal probability distribution, with a

mean and standard deviation. Normal distribution was chosen because it is the most likely

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distribution for independent random factor whose distribution is unknown according to central

limit theorem (Ott et al., 2001). Where data were not readily available, a partial least-squares

(PLS) regression model based on existing data was developed.

This study used four biomass CHP system factors in Monte Carlo Simulation, including

Emission control (EC), feedstock moisture content (MC), transportation distance (TD) and power

to heat ratio (PHR). The detailed description of factors is given below.

1) Emission control factor in this study represents whether urea treatment is implemented

or not for exhaust gases release. It was assumed that two scenarios (with or without urea

treatment) are equally likely to occur.

2) Lower Heating Value of biomass is generally used to calculate the energy input into

the boiler (Van Loo and Koppejan, 2008), which decreases as the increase of moisture content of

biomass fuel (Van Loo and Koppejan, 2008). In this study, the relationship between LHV and

moisture content provided by Van Loo and Koppejan (2008) was used to develop the assumption

for feedstock moisture content, that is, moisture content (wet basis) of woodchips was assumed to

be normally distributed with mean of 25% and standard deviation of 7.5%.

3) Transportation distance was assumed to be normally distributed with mean of 60km

and standard deviation of 15km based on previous studies' assumptions on transportation distance

(Hoogwijk et al., 2009; Timmons and Mejía, 2010; Brechbill et al., 2011).

4) PHR and energy efficiency was assumed to have this linear relationship: with increases

of PHR from 0.1-0.4, the energy efficiency at facility decreases from 85% to 65% (EPA, 2008;

Sha and Hurme, 2011). PHR was assumed having normal distribution with mean of 0.25 and

standard deviation of 0.075 as suggested by Van Loo and Koppejan (2008).

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Data Analysis

The Monte Carlo method repeatedly sampled one thousand random data points to

generate numerical results for the LCA and EMA models, based on the assumed distribution

probabilities for the four biomass CHP system factors (Appendix E). Uncertainty and sensitivity

analyses were performed based on the simulated data to examine the similarities and differences

between LCA and EMA.

Uncertainty and sensitivity analysis are recommended or even required as a must in LCA

given the concerns over the accuracy of its results (Steen, 1997). Uncertainty analysis examines

reliability and applicability of results, while sensitivity analysis examines the differential effects

that biomass CHP system factors have on results (Guinée et al., 2011). There are basically two

types of uncertainty present in a LCA study: objective uncertainty and subjective uncertainty

(Helton, 1993). This study only focused on selected objective uncertainty (i.e., emission control,

transportation distance, power to heat ratio and moisture content). The subjective uncertainty

were not simulated into the model because there is no consensus regarding selection of CHP

system boundary, functional unit, impact assessment method, allocation methodology and other

assumptions. Due to a lack of literature on EMA uncertainty analysis, this study firstly

investigated the uncertainty of the EMA results due to four system factors, then explored other

potential sources that could be linked to larger uncertainties.

Results

Uncertainty of LCA

Table 4-5 shows the multiple linear regression analysis result between GWP and biomass

CHP system factors. The p-values indicate that all factors have significant impact on the total

GWP of biomass CHP system.

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Figure 4-2 and Table 4-6 present the contribution and variability of each life cycle

processes to the value of GWP. A coefficient of variation of 43.9% is found in the total GWP

which quantifies the extent of variability in relation to the mean of GWP (7.02 kg CO2/MMbtu).

This suggests that GWP of the biomass CHP system is highly uncertain ranging from 2.34 - 13.20

kg CO2/MMbtu, as shown in the last box/whisker in Figure 4-2. The coefficient variation in

combination with the share of total GWP of each life cycle process are used to compare its

uncertainty contribution to total uncertainty of GWP. Stack emission from the plant operation

process contributes the highest to total GWP uncertainty with highest share of total GWP (51%)

and coefficient variation (cv=82%), followed by feedstock transportation process (Table 4-6). It is

worth noting that the greenhouse gas (GHG) emission from chemicals use process has large

variability (cv = 75%), however, the share of this process only represents 0.9% of total GWP,

therefore it does not play significant role in determining the total GWP. In addition, the GWP

contributions of construction, waste disposal and ash disposal processes are negligible, which

account for less than 3% combined (Table 4-6).

Sensitivity Analysis of LCA

The uncertainties in transportation and stack emission are significantly high, and since

they represent 81% of the total variation in GWP, it is necessary to understand their origin and

significance. Therefore, the regression analysis between factors and these processes is called for

as a sensitivity analysis. To provide a measure of variable importance, the standardized regression

analysis is used since it can eliminate the difference in units in which factors are expressed.

Specifically, the standardized regression coefficient (SRC) refers to how much the standard

deviations of the dependent variable will change when the independent variable changes by one

standard deviation (Helton and Davis, 2002). The sensitivity analysis resulting between processes

and factors is presented and discussed based on Figure 4-3 and Table 4-7:

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1) TD and MC contribute most of the variation in feedstock transportation process, while

EC (urea application) contributes most of the variation in stack emission.

2) EC has the largest impact on total GWP variation followed by TD, MC and PHR.

Observed from the emission inventory in SimaPro, the GWP increase by using urea for flue gas

treatment is not caused by the production or transportation of urea, but by the newly generated

emission of Dinitrogen Monoxide (N2O) from the process, which is considered to have 298 times

more GWP impact than CO2 (IPCC, 2007). This explains the highest impact of EC has on the

total GWP.

3) PHR is found to have an inverse relationship with the total GWP. As the PHR

increases, the relative proportion of power generation to heat output is increased, and

consequently reduces the GWP of the whole system. In other words, when PHR is higher, more

electricity is generated per unit of wood consumed, since one unit of electricity is assumed to

generate three units of heat when heat pump is applied (Abusoglu and Kanoglu, 2009). In this

case, the overall efficiency of the CHP system is improved, thus decreasing GWP per unit of heat.

4) The impact of TD on GWP is approximately 33% larger than that of MC, when the

distance and moisture content are within the range of (45km, 90km) and (10%, 40%), respectively.

It can be further inferred that 1 km increase in transportation distance would be offset by 0.62%

lower moisture content in term of GWP.

Uncertainty and sensitivity analysis of EMA

EMA indicators

Table 4-8 presents the mean of simulated EMA results of the wood biomass CHP system

in this study. In order to better understand the meaning of EMA indicators, findings from other

EMA studies on other energy systems such as coal-based CHP system and solar PV are used for

comparison.

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Table 4-8 shows different rankings of biomass CHP systems in terms of different EMA

indicators compared to other two systems:

1) The PR of biomass CHP system is about 6 times higher than that of the coal-based

CHP, suggesting that the biomass CHP system enjoys higher degree of renewability and is more

sustainable in the long run (Brown and Ulgiati, 1997);

2) EYR measures how much a process contributes to the economy and how dependent

the process is on the purchased inputs. It indicates the efficiency of the system using purchased

inputs (Ortega et al., 2005). The comparison suggests that the biomass CHP system has slightly

lower efficient in using purchased inputs than coal-based CHP but much higher than Solar PV

system. These metrics can be explained by comparing the energy density of the three types of

fuels; solar fuel is very diffuse, while coal is more energy-dense per unit of volume than biomass.

3) ELR represents the pressure of a transformation process on the environment and can

be considered as a measure of ecosystem stress due to a production (Ulgiati and Brown, 1998).

The ELR values for biomass CHP and coal-based CHP systems are 0.88 and 10.32, respectively,

suggesting that biomass CHP system using wood as feedstock has much smaller environmental

stress per unit of energy produced from the system. This is related to the fact that biomass

harvesting is essentially a process that results in a renewable resource, whereas coal mining

essentially permanently depletes the resource from the biosphere.

4) EIR evaluates whether a process is an economical user of the Emergy invested in

comparison with alternatives (Brown and Ulgiati, 1997; Zhang and Long, 2010). A high level of

EIR represents a certain fragility of the system due to its dependence on inputs from other

economic systems (Pizzigallo et al., 2008). Both biomass and coal CHP systems have ratio lower

than 1, indicating that the systems do not depend highly on inputs from society. The exceptionally

high value of EIR for Solar PV system demonstrates a high degree of fragility and long

investment payback period (or no payback).

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5) ESI measures the potential contribution of a resource or process to the economy per

unit of environmental loading (Zhang and Long, 2010) and represents long term sustainability of

the system. Biomass CHP system has a much higher value of ESI than that of coal-based CHP

systems (2.45 versus 0.26), indicating that biomass CHP system has higher sustainability in the

long run.

6) Tr expresses the production efficiency of the system (Peng et al., 2008). The average

Tr of wood biomass CHP system is about 14, 616 sej/J (or 1.54E+13 sej/MMBtu ) compared to

94,900 sej/J from coal-based CHP system. This suggests that the biosphere need work 6.5 times

more to produce a unit of energy by coal CHP than by biomass CHP.

In summary, EMA enables different kinds of energy, materials, environmental and

human service in the energy system to be evaluated on a uniform basis (i.e., sej). The set of EMA

indicators allows assessing different aspects of system functions, such as renewability, benefits to

economy, environmental loading, and investment benefits. Since economic benefit is the

fundamental driving force in global market economy, EYR and EIR are very important indices to

predict economic success, and should be taken into consideration for decision-making.

Uncertainty of EMA

Table 4-9 summarizes the statistics of six EMA indicators with mean, standard deviation

and coefficient of variation. The calculation items for EMA indicators are shown in Appendix F.

Table 4-9 shows that all EMA indicators have a small variability with coefficient of variation

ranging from 0.5% to 2.8%, which is much smaller than the observed variability of LCA-based

GWP (cv =44%).

Given the small uncertainties that four system factors accounted for EMA results, other

potential sources that could lead to uncertainty of EMA are further discussed from two

perspectives: life cycle process and system input category (i.e., R, N and F) (Figure 4-4).

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1) The life cycle process contributions of Emergy input are shown in upper graph of

Figure 4-4. It clearly shows that growing & chipping and plant operation are the most dominant

processes contributing to total Emergy input. Thus some sources like biomass growing, costs

associated with plant operation, and capital investment could lead to potential uncertainty in

EMA results. Notably, although growing biomass is the most dominant process of Emergy inputs,

it is hard to improve Emergy performance through this process given the difficulties of increasing

the basic efficiency of gross photosynthesis. Perhaps selecting genetically advanced energy crop

and high yield plantation site can serve this purpose.

2) The lower graph in Figure 4-4 shows contributions of different input categories. It

clearly shows that renewable inputs (R) and purchased inputs (F) have much larger proportion of

total Emergy input than do nonrenewable inputs (N). The sum of these inputs (R, F, and N)

results in the transformity of biomass energy as represented in this case study. This transformity

(Tr) is shown in Figure 4-4. This suggests that the improvement of Emergy performance would

be focusing on reducing renewable inputs and purchased inputs. After inspecting the components

of renewable inputs and purchased inputs as listed in Appendix F, the conclusion is the same as

described in life cycle process section: that potential factors influencing EMA include biomass

growing, operation cost, and capital investment, which are mostly associated with free

environmental service and human labor cost. Among operation cost, harvesting and processing

cost are most significant, followed by plant operation cost.

Sensitivity of EMA

Since the uncertainties of EMA indicators due to the four biomass CHP system factors

are very small (Table 4-9), there is little need to further investigate the sensitivities of the EMA

results.

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Discussions and conclusions

The purpose of this study is to examine the uncertainty and sensitivity of LCA and EMA

results from a wood biomass CHP system. Several conclusions are drawn based on the findings in

this study.

LCA and EMA are characterized by different degrees of uncertainty.

1) The higher degree of uncertainty of LCA confirms the need of reporting uncertainty in

the LCA studies suggested by previous research (Cherubini et al., 2009; ISO, 2010). The

objective and subjective uncertainty are inherent in the current LCA approach, and cannot be

overcome even if the practitioner strictly follows the procedures described in the LCA standards,

which results in the risk of making conclusions that cannot be justified strictly by the results

indicated by the many and various LCA indicators. Therefore, uncertainty and sensitivity analysis

should always be reported in the LCA final results, and should be presented with ranges that take

into account all the different assumptions and variables.

2) EMA results are subject to smaller degree of uncertainty due to different assumptions

of system factors. Free environmental service and human labor cost included in the biomass

growing, operation cost, and capital investment are observed to account for large amount of total

Emergy inputs, and could be the potential sources leading to uncertainty of EMA. However, these

vital inputs are not accounted by LCA, which would limit its usefulness for a larger picture. Thus,

using LCA to evaluate an energy system and improve its performance by lowering its

environmental impact (i.e. GWP) will not necessarily increase sustainability, and in some cases,

the effect could be opposite. In contrast, EMA expands its boundary to account for other flows in

addition to energy and material consumptions, which theoretically provides valuable insight about

environmental sustainability for systems that largely interact with environment and economy,

such as agriculture food systems and bioenergy production system.

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The contributions of life cycle processes to LCA and EMA uncertainties differ. Stack

emission from the plant operation process and feedstock transportation process in LCA are found

to have the highest contribution to GWP uncertainty, while feedstock growing & chipping, and

plant operation have the most impact on the total Emergy input in EMA. In this case study, at

least, LCA and EMA conclusions have resulted in different focuses and approaches for system

environmental performance improvement.

The results of the case study presented in the study are paradigmatic: they clearly

show that information provided by the two methods is complementary rather than

competing. The two methods display "optimal field of application" (Figure 4-5):

1) LCA is a useful assessment method to evaluate local and global environmental impacts

of a system. Its usefulness is very limited to the assessment of a system itself, but this technique

can allow for immediate comparison of similar processes. Thus LCA is better to serve as a

continuous benchmarking tool to maximize efficiency of resources use through a case-by- case

approach.

2) EMA provides better assessment of interconnection between industrial processes,

environmental dynamics and economy. Its capability to account for externalities expands its

usefulness for a broader picture, but also limits its usefulness for process improvement. The

crucial benefits of EMA is that it provides an approach to take account of local environmental

resources conditions, and aim to maximize efficiency of local environmental resources use to

support industrial process and economy.

Simply speaking, EMA answers the question "What is the most efficient product or

process?", while LCA answers "How can we improve the environmental efficiency of a specified

product or process?".

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Figure 4-1 Comparison of system boundary and accounting scope between LCA and EMA in analyzing production chain of biomass CHP system

Woodchips production

Woodchips Transport

Waste disposal

Chemical uses

Construction, maintenance of chipper, and energy use

Construction, maintenance of truck, and diesel use

Energy and material consumption for waste and

ash disposal

Plant operation

Construction, maintenance of plant, and combustion

emission from stack

Account for all emissions due to production and use of materials, fuels and energy

Account for all solar energy inputs of materials, fuels, energy and labor

Global Warming Potential (GWP)

Ash disposal

Plant construct

ion

Plant growing, Chipping cost, includes energy use

and labor

Delivery cost, includes diesel use and labor

MSW, mineral oil, and ash disposal cost

Construction cost, chemicals uses and

operation labor

Emergy indicators

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Figure 4-2 Box plot of GHG emission of life cycle process

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Figure 4-3 Scatterplots for GWP against biomass CHP system factors

81

Figure 4-4 Emergy inputs by life cycle process and by inputs category

82

Figure 4-5 Environmental decision-making tools on scale of boundary and theoretical accuracy

Small Large System Boundary

Acc

urac

y

High

Low

LCA

EMA

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Table 4-1 EMA indicators abbreviation and formula

Indicator Abbreviation and formula Unit Percent Renewability PR= R/(R+N+F) ratio Emergy Yield Ratio EYR= Y/F ratio Environmental Loading Ratio ELR=(F+N)/R ratio Emergy Investment Ratio EIR= F/(R+N) ratio Emergy Sustainability Index ESI= EYR/ELR ratio Transformity Tr=R+N+F Sej/MMBtu

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Table 4-2 Summary of life cycle processes assumptions in LCA and EMA

Life Cycle Process LCA EMA

Equipment: stationary chopper Electric input: 25kw Hourly output: 3.3m3/hour Life time: 100,00 m3

Woodchips production

Material and energy use Additional human labor cost:

$7.58 per m3 Equipment: lorry Load capacity: 25 ton, and empty on returning trip

Transportation

Fuels and necessary maintenance Additional human labor cost: $25/hour Life time: 20 years Facility

construction All material and energy use for construction

Capital cost: $3.8 million US dollar in 2010

Operation hour: 7,654 hours/year (87.4%)

Plant operation Emissions from combustion process

Labor cost: Three shifts per day

1 person per shift with one additional manager on the day shift

Labor expense: $64,000 per worker, $90,000 per manager

Lubricant oil, ammonia, organic chemicals, sodium chloride, chlorine and decarbonized water

Chemical uses

Emissions Transformity Mineral oil Municipal solid waste Sewage

Waste disposal

Emissions due to disposal and treatments of waste

Disposal costs

Ash content: 1.1% on dry weight basis Ash disposal

Emissions due to disposal Disposal cost: $45 per ton

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Table 4-3 Transformity and Emergy per unit mass of used chemical

Chemicals unit Transformity (sej/unit) Source Lubricant oil g 2.82E+09 (Odum, 1996) Ammonia g 3.80E+09 (Odum, 1996) Chemicals g 1.60E+09 (Odum, 1996) Chlorine g 1.60E+09 (Odum, 1996) Sodium Chloride g 1.00E+09 (Odum, 1996) Water g 6.64E+05 (Odum, 1996)

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Table 4-4 Disposal cost of waste

Disposal cost Disposal cost ($/ton) Source Mineral oil 75 (SCGOV, 2012) MSW (municipal solid waste) 57 (SCGOV, 2012) Sewage 45 (SCGOV, 2012)

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Table 4-5 Regression analysis of total GWP and system factors.

GWP (kg CO2/MMBtu) Independent Variable Regression Coefficients B t value p-value

(Constant) .941 21.632 .000 EC 5.998 418.207 .000 TD .034 70.253 .000

PHR -.257 -2.907 .004 MC 4.550 52.458 .000

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Table 4-6 Descriptive statistics for GWP of life cycle process

Life cycle process Mean

(kg CO2 eq./MMBtu) Standard deviation

Coefficient of variation

% of GWP

Wood chipping 1.10 0.03 3.0% 15.6%

Transportation 2.08 0.59 28.4% 29.6%

Construction 0.16 0.00 1.3% 2.3%

Stack emission 3.61 2.96 81.8% 51.4%

Chemicals use 0.06 0.04 74.8% 0.9%

Disposal waste 0.01 0.00 1.3% 0.2%

Disposal ash 0.01 0.00 3.1% 0.1% Total GWP 7.02 3.09 43.9% 100%

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Table 4-7 Standardized regression coefficients between life cycle processes and factors

Standardized regression coefficients (SRC) System factor Transportation process Stack emission process Total GWP EC 0.002 0.99 0.972 TD 0.86 -0.001 0.163

PHR -0.01 -0.004 -0.007 MC 0.46 0.033 0.122

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Table 4-8 Comparative EMA results from different energy systems

Energy production systems EMA indicators Wood biomass

CHP Coal-based

CHPa Solar PVb

Percent of Renewability (PR) 53.2% 9.0% -

Emergy yield ratio (EYR) 2.16 2.67 0.48-1.5

Environmental loading ratio (ELR) 0.88 10.32 -

Emergy investment ratio (EIR) 0.87 0.60 7809

Emergy sustainability index (ESI) 2.45 0.26 -

Transformity (Tr) 14,616 sej/J 94,900 sej/J - a Case study of 71.7 MW coal-based CHP plant (Sha and Hurme, 2011) b Data is obtained from Odum (1996)

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Table 4-9 Descriptive statistics of Emergy indicators

Emergy indicator

Formula Mean Standard deviation

Coefficient of variation

PR Percent of Renewability = N / Y 0.532 0.003 0.5%

EYR Emergy yield ratio = Y / F 2.155 0.012 0.5%

ELR Environmental loading ratio = ( F + N) / R 0.878 0.009 1.1%

EIR Emergy investment ratio = F / (R + N) 0.866 0.009 1.0%

ESI Emergy sustainability index = EYR/ELR 2.454 0.037 1.5%

Tr Transformity = Y = R + N +F 1.54E+13 4.27E+11 2.8%

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Chapter 5: CORRELATION ANALYSIS OF CARBON FOOTPRINTING

AND EMERGY INDICATORS FOR BIOMASS CHP SYSTEM

This paper, co-authored by Li Ma and Charles D. Ray, was written for submission to "Environmental Science and Technology"

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Abstract

Life Cycle Assessment (LCA) and Emergy Analysis (EMA) are two environmental

accounting methods used to guide bioenergy decision making. LCA and EMA are often

conducted independently, and each method generates numerous indicators. Consequently, the

environmental decision-making seems to depend on selections of environmental accounting

methods and indicators. This study therefore aims at analyzing LCA and EMA results to check if

different indexes lead to similar results and if the number of indicators can be further reduced.

A biomass combined heat and power (CHP) production system is used as an example to

jointly conduct LCA and EMA. The correlation among LCA and EMA indicators are examined

based on a Monte Carlo simulation model. In this study, Global Warming Potential (GWP) is

used as LCA indicator given the increasing public concern over climate change and the great

interest of bioenergy development, and six commonly used EMA indicators are included in the

analysis, including Percent Renewability (PR), Emergy Yield Ratio (EYR), Environmental

Loading Ratio (ELR), Emergy Investment Ratio (EIR), Emergy Sustainability Index (ESI), and

Transformity (Tr).

The findings suggest no correlation between LCA and EMA indictors, implying either

method alone is insufficient to measure environmental sustainability of a bioenergy system.

Surprisingly, the biogenic CO2 emission from LCA and Tr in EMA are strongly correlated. This

relationship leads to a possible integration of LCA and EMA. In addition, strong correlations are

found among five EMA indicators, suggesting that the number of EMA indicators can be reduced

as they lead to similar findings.

Key words: LCA, EMA, Correlation, GWP, Emergy indicators

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Introduction

The sustainable use of natural resources is an important step to build a sustainable society

(Berger and Finkbeiner, 2011). This idea has been extensively accepted by scientific community,

and has gradually been implemented through policy initiative in political panels such as

"International Panel for Sustainable Resources Management"(UNEP, 2009). To put to the idea

into practice, it is important to develop measurements that are able to precisely quantify resource

use, so that system with higher efficiency in resources use will be selected and promoted.

Due to the intensive use of resources during the entire production chain of bioenergy,

Life Cycle Assessment (LCA) and Emergy Analysis (EMA) have been widely applied to assess

the bioenergy system. Since these two methods are often conducted independently, the results for

bioenergy system analysis seems to depend on selections of environmental accounting methods

and indicators (Berger and Finkbeiner, 2011). Researchers have begun to consider potential

integration of LCA and EMA; however, most of those efforts have been done on the theoretical

level (Zhang et al., 2010; Ingwersen, 2011; Rugani and Benetto, 2012). The answer to the

question of "what is the relationship between LCA and EMA outcomes" is less certain.

LCA and EMA both provide multiple indicators, measuring resources use from different

perspectives. The majority of the existing bioenergy LCA studies have viewed Global Warming

Potential (GWP) impact category as the most important indicator for LCA results (Cherubini and

Strømman, 2011), while six Emergy indicators have often been presented in bioenergy EMA

studies to address different aspects of sustainability (Baral and Bakshi, 2010; Zhang and Long,

2010). Given the large amount of work and the complexity of calculation involved in performing

LCA and EMA, studies have been called for investigating the ability of simplified indicators as

proxies for environmental performances of the system. This study is, therefore, an attempt to

examine the correlation, if any, between LCA-based GWP and six EMA indicators as well as

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among EMA indicators. This will provide insight on whether different indictors lead to similar

results and whether the number of indicators can be reduced for simplification.

Literature review

LCA

Life Cycle Assessment (LCA) is the most accepted and used tool for assessing

environmental impacts of products and services (Curran, 2006; EC, 2010), and has become a vital

decision-support tool in environmental policy or voluntary actions in many fields, especially

biofuel and bioenergy (Cherubini et al., 2009). Despite the renewability and low-carbon intensity

of bioenergy, questions about the sustainability of bioenergy systems have been often raised

(Petrou and Pappis, 2009; Sheehan, 2009). When addressing the sustainability of a bioenergy

system, one must evaluate potential benefits and disadvantages from economic and environmental

perspectives (Zhang and Long, 2010). However, LCA bears some limitations in that: 1) it largely

ignores the ecosystem services and human labor service which are of significance to the

bioenergy development, and 2) it is unable to integrate environmental and economic costs (Hau

and Bakshi, 2004; Zhang et al., 2010).

Previous research has pointed out that the results of LCA studies seem to depend on the

selection of impact categories (Berger and Finkbeiner, 2011). Among multiple measurements

provided by LCA for assessing materials and energy uses, Global Warming Potential (GWP) is

the most often reported impact category in the bioenergy literature (Cherubini and Strømman,

2011). GWP is a measure of the equivalent carbon dioxide that allows for the relative weightings

of damaging greenhouse gasses (Shine, 2009), which is sometimes called Climate Change Impact

or a more recent name - Carbon Footprinting (CFP). Resting on the work of the

Intergovernmental Panel on Climate Change (IPCC), consideration and action on climate change

issues have been steered by political agendas, sometimes driven by the exclusive use of

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stand-alone GWP evaluation (ISO, 2006). GWP has been a widely used metric of climate change

impacts and the main focus of many sustainability policies among companies and authorities

(Laurent et al., 2012). However, sustainability concerns not just climate change but also other

environmental problems and economic concerns (Laurent et al., 2012). Yossapoll et al. (2002)

noted that economic value and environmental performance should be linked in order to provide

"true" criteria for sustainability. Laurent et al. (2012) suggest that more broadly encompassing

tools are needed to assess and manage environmental sustainability.

EMA

Emergy analysis (EMA) and its applications are first introduced in Howard Odum' Book

"Environmental Accounting: Emergy and the environmental decision-making" (Odum, 1996).

The concept of Emergy as put forward by Odum has been considered as one of the few

approaches that can link economy and environment, and quantify both economic and

environmental inputs (Vassallo et al., 2007; Zhang and Long, 2010). As the concept slowly

comes into broader acceptance, EMA has been increasingly applied to evaluate bioenergy

systems (Ortega et al., 2005; Zhang and Long, 2010; Sha and Hurme, 2011).

Emergy is defined as the solar energy used directly or indirectly to generate a service or a

product (Odum, 1996). It is the only methodology yet developed that enables the analyst to

express all biospheric system inputs (e.g. energy, natural resources, human labor) in single unit -

solar energy equivalents - and thereby enabling the assessment of system performance on the

larger time and space scale (Hau and Bakshi, 2004). A number of indicators for the assessment of

the environmental sustainability are deducible from the described EMA (Odum, 1996):

1) Percent Renewability (PR): PR gives the degree of renewability. The higher PR value

indicates a more renewable process. Among the existing Emergy-based indicators, PR represents

the first measure of system sustainability: the lower the fraction of renewable Emergy used, the

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higher the pressure on the environment (Zhang and Long, 2010). Brown and Ulgiati (Brown and

Ulgiati, 1997) suggested that only processes with high values of this index are sustainable in the

long run.

2) Emergy Yield Ratio (EYR): EYR measures how much a process will contribute to the

economy, also indicates how dependent the process is on the purchased inputs. The higher EYR

value indicates large amount of products is obtained per unit of money spent. This index indicates

the efficiency of the system using purchased inputs (Ortega et al., 2005).

3) Environmental Loading Ratio (ELR): ELR is the ratio between non-renewable and

imported Emergy used to renewable Emergy used. ELR represents the pressure of a

transformation process on the environment and can be considered as a measure of ecosystem

stress due to production (Ulgiati and Brown, 1998). A higher value of ELR indicates that

environmental cycles are overloaded (Pizzigallo et al., 2008).

4) Emergy Investment Ratio (EIR): EIR is the ratio of Emergy of purchased inputs to the

indigenous Emergy input (both renewable and non-renewable), and evaluates whether a process is

an economical user of the Emergy invested in comparison with alternatives (Brown and Ulgiati,

1997; Zhang and Long, 2010). A high level of EIR represents a certain fragility of the system due

to its dependence on inputs from other economic systems (Pizzigallo et al., 2008).

5) Emergy Sustainability Index (ESI): ESI is the ratio of the EYR to the ELR, which

measures the potential contribution of a resource or process to the economy per unit of

environmental loading (Zhang and Long, 2010). ESI is an aggregate measure of economic

performance and sustainability of the system considering both the contribution of renewable vs.

non-renewable resources and the need of purchased inputs to drive the process (Mirandola et al.,

2010). To be sustainable in the long run, a system should have a high EYR and low ELR, thus

resulting in a high ESI value (Sha and Hurme, 2011).

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6) Transformity (Tr): Tr is defined as Emergy input per unit of available energy output

(Brown and Ulgiati, 2004). For example, assuming 4000 solar equivalent joules from the sun are

required to generate 1 joule of wood from the forest, then the solar transformity of the wood is

4000 solar emjoules per joule (abbreviated seJ/J). When comparing two or more processes with

the same output, transformity is a measure of efficiency representing more product obtained with

a given quantity of Emergy, or less Emergy needed to produce a given amount of product (Odum,

1996).

Relation of LCA and EMA indictors

It is recognized that different methods provide different perspectives and sometimes

hardly comparable results (Hau and Bakshi, 2004; Sciubba and Ulgiati, 2005). Since LCA and

EMA differ on definition, accounting scope, system boundary, measurement unit and conversion

factor (Odum, 1996; Norris, 2001; Brown and Buranakarn, 2003; ISO, 2010; Zhang et al., 2010),

researchers have begun to seek means of potential integration of LCA and EMA in order to

provide a broadly encompassing tool to assess and manage environmental sustainability.

However, most of those efforts have been done on the theoretical level (Zhang et al., 2010;

Ingwersen, 2011; Rugani and Benetto, 2012). The relationship between LCA and EMA results

remains unknown.

Given the large number of indicators provided by different environmental accounting

methods, researchers have started to simplify indictors that could serve as proxies for

environmental performance of a system (Laurent et al., 2012). A search of LCA and EMA

literature suggests a few studies have been done on LCA, but none on EMA indicators. In the

correlation studies of LCA indicators, for instance, the ecological footprint and the cumulative

energy demand from LCA were found to show significant correlation with other environmental

impact indicators (Huijbregts et al., 2006; Huijbregts et al., 2012). Another study by Berger and

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Finkbeiner (2011) observed that Primary Energy Demand (PED) and Abiotic Depletion Potential

(ADP) are strongly correlated, and moderate correlations were found between GWP and PED as

well as ADP. Taking into account the significant correlations between some LCA indicators, it is

suggested that the number of indicators can be reduced as they lead to similar findings (Berger

and Finkbeiner, 2011). Therefore, the correlation between LCA and EMA indicators could help to

simplify indicators for assessing environmental sustainability of the system, and develop a better

integration solution to improve the quality of the ultimate environmental evaluation.

Methods

System boundary

The life cycle of biomass CHP system is divided into seven processes listed in Figure 5-1.

These seven processes were defined as the case study system boundary in both LCA and EMA.

The detailed accounting scope of LCA is presented on the left side of Figure 5-1 within dash line;

the EMA scope is detailed on the right side of the figure. Since climate change mitigation and

energy independence are the main driving forces for future bioenergy (Cherubini and Jungmeier,

2010), LCA outcome only focuses on Global Warming Potential (GWP) evaluated using IPCC

2007 on 100 years horizon (IPCC, 2007). Meanwhile, the six indicators commonly used by

previous EMA studies are selected as EMA outcomes in this study, including Percent

Renewability (PR), Emergy Yield Ratio (EYR); Environmental Loading Ratio (ELR); Emergy

Investment Ratio (EIT); Emergy Sustainability Index (ESI); and Transformity (Tr) (Table 5-1).

System functional unit

The main purpose of the functional unit is to provide a reference to which the input and

output process data are normalized and the basis on which the final results are shown (Cherubini

et al., 2009). In this study, the reference functional unit for GWP and Transformity (Tr) is 1

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MMBtu thermal energy generation. The allocation criteria for the analysis is that 1 MMBtu of

electricity is equal to 3 MMBtu of heat when electricity is used in heat pump for heat generation

(Van Loo and Koppejan, 2008; Abusoglu and Kanoglu, 2009).

Modeling Procedure

The modeling methodology in LCA and EMA are slightly different. To measure GWP,

emissions from seven processes are accumulated and divided by all heat energy generated in one

year. EMA indicators are calculated through ratios among Renewable inputs (R), Non-renewable

inputs (N) and Purchased inputs (F) (Odum, 1996). The modeling uses primary data from a

validated life cycle inventory of 6.4 MWth CHP plant in Europe available in SimaPro 7.3

database.

Life Cycle Processes of biomass CHP system

The entire life cycle of wood biomass CHP system is divided into seven processes. The

shared assumptions for each process in LCA and EMA are summarized in italics in Table 5-2;

assumptions that differ between the methods are detailed in the shaded boxes of the same table.

The assumptions in Table 5-2 are detailed in the following text.

1) Woodchips production: Woodchips production was assumed to be carried out by a

stationary chopper, which has electric input of 25 kw, hourly output of 3.3 m3/h bulked chips, and

a life time output of 100,000 m3 bulked chips. In addition of electric input, the chopper also

consumes lubricant oil and steel during operation. Besides all the material and energy uses as

specified by LCA, human labor input is added in EMA through Emergy-money index. The cost

of wood chipping process in this study consisting of capital cost of chopper, electricity use and

human labor service, is estimated to be $7.58 per cubic meters.

2) Woodchips Transportation: Woodchips were assumed to be transported by Lorry

with average load of 25 tonnes and empty on the returning trip. Besides fuel uses in this process,

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the human labor service is also accounted in EMA, which was estimated by the cost of driver’s

hourly payment and transportation distance.

3) Facility Construction: The lifetime of plant facility was assumed to be 20 years in the

analysis of LCA. EMA uses capital cost of plant construction to estimate Emergy input, which

was estimated at $3.8 million US in 2010 (Salomon et al., 2011).

4) Chemical uses: The chemicals needed for plant operation include lubricant oil,

ammonia, organic chemicals, sodium chloride, chlorine and decarbonized water. LCA accounts

for emissions of all these chemical production. In contrast, transformities of these chemicals were

used to estimate Emergy input in EMA (Odum, 1996) (Table 5-3).

5) Plant operation: LCA accounts for all the emission from combustion process which is

available in SimaPro 7.3 database. For EMA, human labor cost involved in the plant operation

was used to calculate Emergy input. The labor requirement was estimated as 1 person per shift,

with one additional manager on the day shift. Total 3 shifts per day was assumed. The operation

time was assumed to be 7,654 hours per year (87.4%). The salary and social expenses were

estimated at $64,000 US per worker annually and $90,000 US per manager annually based on the

U.S. Bureau Labor Statistics (BLS, 2011).

6) Waste disposal: Three types of waste were assumed to be generated in the biomass

CHP plant, including mineral oil, municipal solid waste, and sewage. LCA takes into account

from three treatments, which are documented in SimaPro 7.3 database. The cost of these three

waste disposals was used to estimate Emergy inputs in EMA (SCGOV, 2012). The estimated

costs for each treatment are shown in Table 5-4 below.

7) Ash disposal: For LCA, the amount of ash generated is determined by the amount of

wood burned and its ash content. Ash content was assumed to be 1.1% on dry weight basis. In

EMA, the cost of wood ash disposal was estimated as $45 US per ton (Zwahr, 2004).

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Biomass CHP system factors

Factors of biomass CHP system having significant impact on LCA results were used to

conduct Monte Carlo Simulation. Monte Carlo Simulation model was developed by substituting a

range of values for biomass CHP system factor that has inherent uncertainty (Mooney, 1997).

Then results are repeatedly calculated with different sets of random values from the probability

functions. Each of the four factors are assumed to have normal probability distribution, with a

mean and standard deviation. Normal distribution was chosen because it is the most likely

distribution for independent random factor whose distribution is unknown according to central

limit theorem (Ott et al., 2001). Where data were not readily available, a partial least-squares

(PLS) regression model based on existing data was developed.

This study used four biomass CHP system factors in Monte Carlo Simulation, including

Emission control (EC), feedstock moisture content (MC), transportation distance (TD) and power

to heat ratio (PHR). The detailed description of factors is given below.

1) Emission control factor in this study represents whether urea treatment is implemented

or not for exhaust gases release. It was assumed that two scenarios (with or without urea

treatment) are equally likely to occur.

2) Lower Heating Value of biomass is generally used to calculate the energy input into

the boiler (Van Loo and Koppejan, 2008), which decreases as the increase of moisture content of

biomass fuel (Van Loo and Koppejan, 2008). In this study, the relationship between LHV and

moisture content provided by Van Loo and Koppejan (2008) was used to develop the assumption

for feedstock moisture content, that is, moisture content (wet basis) of woodchips was assumed to

be normally distributed with mean of 25% and standard deviation of 7.5%.

3) Transportation distance was assumed to be normally distributed with mean of 60km

and standard deviation of 15km based on previous studies' assumptions on transportation distance

(Hoogwijk et al., 2009; Timmons and Mejía, 2010; Brechbill et al., 2011).

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4) PHR and energy efficiency was assumed to have this linear relationship: with increases

of PHR from 0.1-0.4, the energy efficiency at facility decreases from 85% to 65% (EPA, 2008;

Sha and Hurme, 2011). PHR was assumed having normal distribution with mean of 0.25 and

standard deviation of 0.075 as suggested by Van Loo and Koppejan (2008).

Statistical analysis

The Monte Carlo method repeatedly sampled one thousand random data points to

generate numerical results for the LCA and EMA models, based on the assumed distribution

probabilities for the four biomass CHP system factors (Appendix E). Correlation between LCA

and EMA indicator results were analyzed using the Pearson's correlation using statistical software

of Minitab 16. The correlation analysis was used to determine potential dependencies between

indicators and if the number of indicators can be reduced.

Results

Correlation analysis of LCA and EMA results

The results of the correlation analysis of GWP and Emergy indicators assessing biomass

CHP system are shown in Table 5-5 and Figure 5-2. The strong correlation coefficients are

highlighted in bold in Table 5-5.

Correlation between GWP and EMA indicators

Results from the Pearson's correlation tests show that correlation of determination range

between 0.03 and 0.15 for GWP and six EMA indicators, suggesting very poor correlation (Table

5-5). This weak correlation reveals that LCA and EMA present different aspects of environmental

performance of the system, and in order to provide more comprehensive environmental

sustainability evaluation, joint use of these two methods are needed. In addition, with regard to

the measurement of environmental consequence, the selection of indicators has a high impact on

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the results (Laurent et al., 2012). This poor correlation between LCA and EMA indicators reveals

the fundamental challenge in using single indicator to reflect the complex environmental

consequence of a product system.

Correlation among EMA indicators

Strong correlations are found between PR, ELR and ESI (R2 = 0.98, and 0.94,

respectively) (Table 5-5), suggesting that these three indicators lead to similar results. It is worth

noting that the a higher value of EIR indicates that environmental cycles are overloaded

(Pizzigallo et al., 2008). The negative correlation coefficients of ELR and PR as well as ESI

suggest that the system having high PR also rates high on ELR and low on ELR, presenting better

environmental performance from these three aspects. Therefore, the number of indicators can be

reduced to one to sufficiently represent the three aspects of sustainability of the system. For

example, the EMA practitioner could choose to use PR, which is relatively easy to understand,

as a proxy for the other two indicators, ELR and ESI.

In addition, a strong correlation (R2 =0.96) is observed between the remaining two EMA

indicators of EYR and EIR (Table 5-5), which leads to conclusion that it is sufficient to choose

either one to evaluate the system. EIR measures the fragility of the system due to its dependence

on inputs from other economy system thus a low value of EIR is favorable to the system in terms

of its sustainability. The negative correlation between EIR and EYR indicates that a system with

high score of EYR rates low on the fragility of the system relative to other systems, representing

the better sustainability of the system.

Correlation between GWP/Biogenic Carbon Emission and Tr

Figure 5-3 shows the relation between Tr and GWP for two types of emission control:

with and without urea treatment. The Pearson's correlation coefficients (R) of Tr and GWP for

two scenarios (with and without urea treatment) are 0.749 and 0.611, respectively. This suggests

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that as the system GWP emission increases, the solar energy inputs of the system also increases in

both scenarios in terms of emission control. Another observation is that the system with urea

treatment as emission control has higher GWP rating than the one without urea treatment. It is

found that the GWP increase by using urea for flue gas treatment in biomass CHP plant is not

caused by production or transportation of urea, but by the resulting emission of Dinitrogen

Monoxide (N2O), which is considered to have 298 times more GWP impact than CO2 (IPCC,

2007). This explains the large impact EC has on the total GWP, and is also a good example of

LCA indicator assessment presenting results that are in conflict with each other. The urea

treatment of emission, when performed, is done so for the human health benefits of reducing

nitrous oxide (NOx) emission, but doing so increases the GWP of the emission of the operation.

Considering that there are different emission control options available depending on emission

regulation and technologies from different regions, it is difficult to use GWP to predict

transformity for biomass CHP systems in different regions.

Surprisingly, a nearly perfect positive correlation (R2=0.98) is found between LCA-based

biogenic CO2 emission and EMA-based Tr (Figure 5-4). This relationship is the same: whether

urea treatment is used with the emission control, or not. Thus biogenic CO2 emission from LCA

could be used to predict Tr of energy of a biomass CHP system for EMA.

Discussion and conclusions

Decisions concerning energy use and investments in bioenergy development require that

decision-makers have the ability to compare holistic analyses of net yields, environmental impact,

and sustainability (Zhang and Long, 2010). A discussion of correlation analysis among LCA

indicators can be found in previous work (Berger and Finkbeiner, 2011), while this study

examines the correlation between the most important LCA indicator (GWP) and six EMA

indicators for bioenergy system.

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The findings of this study suggest no significant correlation between GWP and multiple

EMA indicators (R2=0.03~0.15). Possible explanation might be the methodological differences.

Firstly, the system inputs accounted for the indicators differ. EMA indicators represent all inputs

required to sustain the system including fossil fuel, human service, and ecosystem service, while

GWP result from LCA only takes fossil fuels consumption into account. Secondly, the expression

of the results is different. EMA indicators are expressed with respect to the system itself rather

than a chosen functional unit like GWP, which is presented as greenhouse gas per unit of product.

This advances the idea that GWP from LCA is insufficient to measure the environmental

sustainability of a bioenergy system. And the poor correlation between GWP and EMA indicators

implies that in order to provide more comprehensive environmental sustainability evaluation,

joint use of these two methods are needed.

Among EMA indicators, strong correlations are found between PR, ELR and ESI (R2 =

0.98, and 0.94, respectively), and between EYR and EIR (R2 = 0.96). These strong correlations

might also be explained in a similar way. PR, ELR and ESI are assessing environmental burden

of the system, while EYR and EIR are both representing how the system interacts with economy.

The strong correlations suggest that the number of Emergy indicators can be reduced as they lead

to similar findings.

The relationship between Tr and GWP is found to be different depending on the emission

control options. Moderate correlations are observed for two emission control scenarios: with and

without urea treatment (R2=0.56 and 0.37, respectively). Since there are many emission control

technologies available depending on emission regulation from different regions, it is difficult to

use LCA-based GWP to predict the Tr of energy from a particular biomass CHP system.

However, a nearly perfect correlation is found between biogenic CO2 emission and Tr (R2 =0.98).

This strong relationship contributes an easy and straightforward way to calculate the transformity

for biomass CHP system by using LCA-based biogenic CO2 emission. The findings provide a

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way of using standardized LCA databases and LCA framework to increase the application of

EMA concept in environmental decision-making when analyzing renewable energy systems.

Although the full integration of EMA in LCA might need more research efforts to overcome the

challenges, for example, calculating transformity for elementary resources, implementation of

EMA algebra in to LCA, and expansion of the scope of LCA, the joint use of LCA and EMA on

the biomass CHP of this study offers evidence that complementary use of the techniques may

provide better assessment perspectives towards a nature-oriented evaluation of natural resources.

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Figure 5-1 Comparison of system boundary and accounting scope between LCA and EMA in analyzing production chain of biomass CHP system

Woodchips production

Woodchips Transport

Waste disposal

Chemical uses

Construction, maintenance of chipper, and energy use

Construction, maintenance of truck, and diesel use

Energy and material consumption for waste and

ash disposal

Plant operation

Construction, maintenance of plant, and combustion

emission from stack

Account for all emissions due to production and use of materials, fuels and energy

Account for all solar energy inputs of materials, fuels, energy and labor

Global Warming Potential (GWP)

Ash disposal

Plant construct

ion

Plant growing, Chipping cost, includes energy use

and labor

Delivery cost, includes diesel use and labor

MSW, mineral oil, and ash disposal cost

Construction cost, chemicals uses and

operation labor

Emergy indicators

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Figure 5-2 Correlation between GWP and EMA indicators

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Figure 5-3 Scatterplots of transformity against LCA-based GWP

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Figure 5-4 Scatterplots of Tr against Biogenic CO2 emission

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Table 5-1 EMA indicators abbreviation and formula

Indicator Abbreviation and formula Unit Percent Renewability PR= R/(R+N+F) ratio Emergy Yield Ratio EYR= Y/F ratio Environmental Loading Ratio ELR=(F+N)/R ratio Emergy Investment Ratio EIR= F/(R+N) ratio Emergy Sustainability Index ESI= EYR/ELR ratio Transformity Tr=R+N+F Sej/MMBtu

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Table 5-2 Summary of life cycle processes assumptions in LCA and EMA

Life Cycle Process LCA EMA

Equipment: stationary chopper Electric input: 25kw Hourly output: 3.3m3/hour Life time: 100,00 m3

Woodchips production

Material and energy use Additional human labor cost:

$7.58 per m3 Equipment: lorry Load capacity: 25 ton, and empty on returning trip

Transportation

Fuels and necessary maintenance Additional human labor cost: $25/hour Life time: 20 years Facility

construction All material and energy use for construction

Capital cost: $3.8 million US dollar in 2010

Operation hour: 7,654 hours/year (87.4%)

Plant operation Emissions from combustion process

Labor cost: Three shifts per day

1 person per shift with one additional manager on the day shift

Labor expense: $64,000 per worker, $90,000 per manager

Lubricant oil, ammonia, organic chemicals, sodium chloride, chlorine and decarbonized water Chemical uses

Emissions Transformity Mineral oil Municipal solid waste Sewage Waste disposal

Emissions due to disposal and treatments of waste

Disposal costs

Ash content: 1.1% on dry weight basis Ash disposal

Emissions due to disposal Disposal cost: $45 per ton

114

Table 5-3 Transformity and Emergy per unit mass of used chemical

Chemicals unit Transformity (sej/unit) Source Lubricant oil g 2.82E+09 (Odum, 1996) Ammonia g 3.80E+09 (Odum, 1996) Chemicals g 1.60E+09 (Odum, 1996) Chlorine g 1.60E+09 (Odum, 1996) Sodium Chloride g 1.00E+09 (Odum, 1996) Water g 6.64E+05 (Odum, 1996)

115

Table 5-4 Disposal cost of waste

Disposal cost Disposal cost ($/ton) Source Mineral oil 75 (SCGOV, 2012) MSW (municipal solid waste) 57 (SCGOV, 2012) Sewage 45 (SCGOV, 2012)

116

Table 5-5 Correlation of determination (R2) between GWP and EMA indicators

GWP PR EYR ELR EIR ESI

GWP 1

PR 0.15* 1

EYR 0.10 0.53 1

ELR 0.15 0.98* 0.53* 1

EIR 0.10* 0.50* 0.96* 0.52 1

ESI 0.03* 0.94 0.76 0.94* 0.76* 1

* The sign of correlation coefficient is negative.

117

Chapter 6: CONCLUSIONS AND RECOMMENDATIONS

This chapter summarizes the conclusions based on the research objectives proposed for

the dissertation, which are restated as following:

1) The first objective is to investigate the similarities and incompatibilities between Life

Cycle Assessment (LCA) and Emergy analysis (EMA) by using a significant case study -

a wood biomass CHP system, and to discover the "optimal field of application" for each

method;

2) The second objective is to examine the uncertainties and sensitivities of LCA-based

Global Warming Potential (GWP) and EMA outcomes due to different CHP system

factors, including emission control (EC); feedstock moisture content (MC);

transportation distance (TD); and power to heat ratio (PHR);

3) The third objective is to investigate the relationship between LCA-based GWP and EMA

indicators to check if there is correlation between LCA and EMA results, and if the

number of EMA indicators can be reduced.

4) The fourth objective is to explore the relationship between LCA-based GWP and

EMA-based transformity to see if LCA database and framework can be used in EMA for

further integration.

Objective 1: to investigate the similarities, differences and incompatibilities between LCA

and EMA, and identify "optimal field of application" for each method.

The results of case study presented in the study are paradigmatic: they clearly show that

information provided by the two environmental accounting methods (i.e., LCA and EMA) is

complementary rather than competing. The two methods display "optimal field of application":

118

1) LCA is a useful assessment method to evaluate local and global environmental impacts

of the system. Its usefulness is very limited to the assessment of a specific system. However,

LCA may be and is commonly used within clearly-stated assumptions, to compare two similar

processes and thereby provides environmental sciences a continuous benchmarking tool.

2) EMA provides a more robust assessment of interconnection between an industrial

process, its recognized environmental dynamics and its economic potential. Its capability to

account for externalities expands its usefulness over a broader spectrum of cases, but also limits

its use for improvement of a specific process. The crucial benefit of EMA is that it provides an

approach aimed for maximizing utilization efficiency of local environmental resources in support

of industrial process and economy. Simply speaking, EMA answers the question "What is the

most efficient product or process?" while LCA answers "How can we improve environmental

efficiency of a specific product or process?".

Objective 2: to compare the uncertainties and sensitivities of LCA-based GWP and EMA

indicators due to uncertain CHP system factors.

1) The higher degree of uncertainty of LCA confirms the need of reporting uncertainty in

the LCA studies suggested by previous research (Cherubini et al., 2009; ISO, 2010). The

objective and subjective uncertainty are inherent in the current LCA approach, and cannot be

overcome even if the practitioner strictly follows the procedures described in the LCA standards,

which results in the risk of making conclusions that cannot be justified strictly by the results

indicated by the many and various LCA indicators. Therefore, uncertainty and sensitivity analysis

should always be reported in the LCA final results, and should be presented with ranges that take

into account all the different assumptions and variables.

2) EMA results are subject to smaller degree of uncertainty due to different assumptions

of system factors. Free environmental service and human labor cost included in the biomass

119

growing, operation cost, and capital investment are observed to account for large amount of total

Emergy inputs, and could be the potential sources leading to uncertainty of EMA. However, these

vital inputs are not accounted by LCA, which would limit its usefulness for a larger picture. Thus,

using LCA to evaluate an energy system and improve its performance by lowering its

environmental impact (i.e. GWP) will not necessarily increase sustainability, and in some cases,

the effect could be opposite. In contrast, EMA expands its boundary to account for other flows in

addition to energy and material consumptions, which theoretically provides valuable insight about

environmental sustainability for systems that largely interact with environment and economy,

such as agriculture food systems and energy production systems.

The contributions of life cycle processes to LCA and EMA uncertainties differ. Stack

emission from the plant operation process and feedstock transportation process in LCA are found

to have the highest contribution to GWP uncertainty, while feedstock growing & chipping, and

plant operation have the most impact on total Emergy input in EMA. In this case study, at least,

LCA and EMA conclusions have resulted in different focuses and approaches for system

environmental performance improvement.

Objective 3: to investigate the relationship between LCA-based GWP and EMA indicators

to check if there is correlation between LCA and EMA results, and if the number of EMA

indicators can be reduced.

The findings of this study suggest no significant correlation between GWP and multiple

EMA indicators (R2=0.03~0.15). Possible explanation might be the methodological differences.

Firstly, the system inputs accounted for the indicators differ. EMA indicators represent all inputs

required to sustain the system including fossil fuel, human service, and ecosystem service, while

GWP result from LCA only takes fossil fuels consumption into account. Secondly, the expression

of the results is different. EMA indicators are expressed with respect to the system itself rather

120

than a chosen functional unit like GWP, which is presented as greenhouse gas per unit of product.

This advances the idea that GWP from LCA is insufficient to measure the environmental

sustainability of a bioenergy system. And the poor correlation between GWP and EMA indicators

implies that in order to provide more comprehensive environmental sustainability evaluation,

joint use of these two methods are needed.

Among EMA indicators, strong correlations are found between PR, ELR and ESI (R2 =

0.98, and 0.94, respectively), and between EYR and EIR (R2 = 0.96). These strong correlations

might also be explained in a similar way. PR, ELR and ESI are assessing environmental burden

of the system, while EYR and EIR are both representing how the system interacts with economy.

The strong correlations suggest that the number of Emergy indicators can be reduced as they lead

to similar findings.

Objective 4: to explore the relationship between LCA-based GWP and EMA-based

transformity to see if LCA database and framework can be used in EMA for further

integration.

The relationship between Tr and GWP is found differ depending on the emission control

options. Moderate correlations are observed for two emission control scenarios: with and without

urea treatment (R2=0.56 and 0.37, respectively). Since there are many emission control

technologies available depending on emission regulation from different regions (Van Loo and

Koppejan, 2008), it is difficult to use LCA-based GWP to predict the Tr of energy from a

particular biomass CHP system. However, a nearly perfect correlation is found between biogenic

CO2 emission and Tr (R2 =0.98). This strong relationship contributes an easy and straightforward

way to calculate the transformity for biomass CHP system by using LCA-based biogenic CO2

emission. The findings provide a way of using standardized LCA databases and LCA framework

to increase the application of EMA concept in environmental decision-making when analyzing

121

renewable energy systems. Although the full integration of EMA in LCA might need more

research efforts to overcome the challenges, for example, calculating transformity for elementary

resources, implementation of EMA algebra in to LCA, and expansion of the scope of LCA, the

joint use of LCA and EMA on the biomass CHP system of this study offers evidence that

complementary use of the techniques may provide better assessment perspectives towards a

nature-oriented evaluation of natural resources.

122

Appendix A: List of the acronyms used in dissertation

Acronym Explanation LCA Life cycle assessment EMA Emergy analysis GWP Global warming potential GHGs Greenhouse gases CHP Combined heat and power R Renewable inputs N Non-renewable inputs F Feedback/Purchased inputs Tr Transformity PR Percent renewability EYR Emergy yield ratio ELR Environmental loading ratio EIR Emergy investment ratio ESI Emergy sustainability index EC Emission control MC Moisture content TD Transportation distance PHR Power to heat ratio Sej Solar equivalent joule Sej/$ Solar equivalent joule per dollar MMBtu Million British thermal unit w.b. Wet basis

123

Appendix B: Global Warming Potentials of substances relative to CO2

IPCC 2007 is the successor of the IPCC 2001 method, which was developed by the Intergovernmental Panel on Climate Change. IPCC 2007 contains the climate change factors of air emissions with a timeframe of 20, 100 , 500 years. IPCC characterization factors for the direct global warming potential of air emissions within 100 years timeframe are listed in the following table extracted from SimaPro 7.3 software. Global Warming Potentials of substances relative to CO2 for 100 years timeframe

Substance CAS number Factor Unit

1-Propanol, 3,3,3-trifluoro-2,2-bis(trifluoromethyl)-, HFE-7100

014117-17-0 297 kg CO2 eq / kg

Butane, 1,1,1,3,3-pentafluoro-, HFC-365mfc 000406-58-6 794 kg CO2 eq / kg Butane, perfluoro- 000355-25-9 8860 kg CO2 eq / kg Butane, perfluorocyclo-, PFC-318 000115-25-3 10300 kg CO2 eq / kg Carbon dioxide 000124-38-9 1 kg CO2 eq / kg Carbon dioxide, fossil 000124-38-9 1 kg CO2 eq / kg Carbon dioxide, land transformation 000124-38-9 1 kg CO2 eq / kg Chloroform 000067-66-3 31 kg CO2 eq / kg Dimethyl ether 000115-10-6 1 kg CO2 eq / kg Dinitrogen monoxide 010024-97-2 298 kg CO2 eq / kg Ethane, 1-chloro-1,1-difluoro-, HCFC-142b 000075-68-3 2310 kg CO2 eq / kg Ethane, 1-chloro-2,2,2-trifluoro-(difluoromethoxy)-, HCFE-235da2

026675-46-7 350 kg CO2 eq / kg

Ethane, 1,1-dichloro-1-fluoro-, HCFC-141b 001717-00-6 725 kg CO2 eq / kg Ethane, 1,1-difluoro-, HFC-152a 000075-37-6 124 kg CO2 eq / kg Ethane, 1,1,1-trichloro-, HCFC-140 000071-55-6 146 kg CO2 eq / kg Ethane, 1,1,1-trifluoro-, HFC-143a 000420-46-2 4470 kg CO2 eq / kg Ethane, 1,1,1,2-tetrafluoro-, HFC-134a 000811-97-2 1430 kg CO2 eq / kg Ethane, 1,1,2-trichloro-1,2,2-trifluoro-, CFC-113 000076-13-1 6130 kg CO2 eq / kg Ethane, 1,1,2-trifluoro-, HFC-143 000430-66-0 353 kg CO2 eq / kg Ethane, 1,1,2,2-tetrafluoro-, HFC-134 000359-35-3 1100 kg CO2 eq / kg Ethane, 1,2-dibromotetrafluoro-, Halon 2402 000124-73-2 1640 kg CO2 eq / kg Ethane, 1,2-dichloro-1,1,2,2-tetrafluoro-, CFC-114 000076-14-2 10000 kg CO2 eq / kg Ethane, 1,2-difluoro-, HFC-152 000624-72-6 53 kg CO2 eq / kg Ethane, 2-chloro-1,1,1,2-tetrafluoro-, HCFC-124 002837-89-0 609 kg CO2 eq / kg Ethane, 2,2-dichloro-1,1,1-trifluoro-, HCFC-123 000306-83-2 77 kg CO2 eq / kg Ethane, chloropentafluoro-, CFC-115 000076-15-3 7370 kg CO2 eq / kg Ethane, fluoro-, HFC-161 000353-36-6 12 kg CO2 eq / kg Ethane, hexafluoro-, HFC-116 000076-16-4 12200 kg CO2 eq / kg Ethane, pentafluoro-, HFC-125 000354-33-6 3500 kg CO2 eq / kg Ether, 1,1,1-trifluoromethyl methyl-, HFE-143a 000421-14-7 756 kg CO2 eq / kg Ether, 1,1,2,2-Tetrafluoroethyl 2,2,2-trifluoroethyl-, HFE-347mcc3

000406-78-0 575 kg CO2 eq / kg

Ether, 1,1,2,2-Tetrafluoroethyl 2,2,2-trifluoroethyl-, HFE-347mcf2

000406-78-0 374 kg CO2 eq / kg

124

Substance CAS number Factor Unit

Ether, 1,1,2,2-Tetrafluoroethyl methyl-, HFE-254cb2 000425-88-7 359 kg CO2 eq / kg Ether, 1,1,2,3,3,3-Hexafluoropropyl methyl-, HFE-356mec3 000382-34-3 101 kg CO2 eq / kg Ether, 1,1,2,3,3,3-Hexafluoropropyl methyl-, HFE-356pcc3 000382-34-3 110 kg CO2 eq / kg Ether, 1,1,2,3,3,3-Hexafluoropropyl methyl-, HFE-356pcf2 000382-34-3 265 kg CO2 eq / kg Ether, 1,1,2,3,3,3-Hexafluoropropyl methyl-, HFE-356pcf3 000382-34-3 502 kg CO2 eq / kg Ether, 1,2,2-trifluoroethyl trifluoromethyl-, HFE-236ea2 084011-06-3 989 kg CO2 eq / kg Ether, 1,2,2-trifluoroethyl trifluoromethyl-, HFE-236fa 084011-06-3 487 kg CO2 eq / kg Ether, 2,2,3,3,3-Pentafluoropropyl methyl-, HFE-365mcf3 000378-16-5 11 kg CO2 eq / kg Ether, di(difluoromethyl), HFE-134 001691-17-4 6320 kg CO2 eq / kg Ether, difluoromethyl 2,2,2-trifluoroethyl-, HFE-245cb2 001885-48-9 708 kg CO2 eq / kg Ether, difluoromethyl 2,2,2-trifluoroethyl-, HFE-245fa1 001885-48-9 286 kg CO2 eq / kg Ether, difluoromethyl 2,2,2-trifluoroethyl-, HFE-245fa2 001885-48-9 659 kg CO2 eq / kg Ether, ethyl 1,1,2,2-tetrafluoroethyl-, HFE-374pc2 000512-51-6 557 kg CO2 eq / kg Ether, pentafluoromethyl-, HFE-125 003822-68-2 14900 kg CO2 eq / kg Hexane, perfluoro- 000355-42-0 9300 kg CO2 eq / kg HFE-227EA 1540 kg CO2 eq / kg HFE-236ca12 (HG-10) 2800 kg CO2 eq / kg HFE-263fb2 11 kg CO2 eq / kg HFE-329mcc2 919 kg CO2 eq / kg HFE-338mcf2 552 kg CO2 eq / kg HFE-338pcc13 (HG-01) 1500 kg CO2 eq / kg HFE-347pcf2 580 kg CO2 eq / kg HFE-43-10pccc124 (H-Galden1040x) 1870 kg CO2 eq / kg Methane 000074-82-8 25 kg CO2 eq / kg Methane, biogenic 000074-82-8 22 kg CO2 eq / kg Methane, bromo-, Halon 1001 000074-83-9 5 kg CO2 eq / kg Methane, bromochlorodifluoro-, Halon 1211 000353-59-3 1890 kg CO2 eq / kg Methane, bromodifluoro-, Halon 1201 001511-62-2 404 kg CO2 eq / kg Methane, bromotrifluoro-, Halon 1301 000075-63-8 7140 kg CO2 eq / kg Methane, chlorodifluoro-, HCFC-22 000075-45-6 1810 kg CO2 eq / kg Methane, chlorotrifluoro-, CFC-13 000075-72-9 14400 kg CO2 eq / kg Methane, dibromo- 000074-95-3 1.54 kg CO2 eq / kg Methane, dichloro-, HCC-30 000075-09-2 8.7 kg CO2 eq / kg Methane, dichlorodifluoro-, CFC-12 000075-71-8 10900 kg CO2 eq / kg Methane, dichlorofluoro-, HCFC-21 000075-43-4 151 kg CO2 eq / kg Methane, difluoro-, HFC-32 000075-10-5 675 kg CO2 eq / kg Methane, fluoro-, HFC-41 000593-53-3 92 kg CO2 eq / kg Methane, fossil 000074-82-8 25 kg CO2 eq / kg Methane, iodotrifluoro- 002314-97-8 0.4 kg CO2 eq / kg Methane, monochloro-, R-40 000074-87-3 13 kg CO2 eq / kg Methane, tetrachloro-, CFC-10 000056-23-5 1400 kg CO2 eq / kg Methane, tetrafluoro-, CFC-14 000075-73-0 7390 kg CO2 eq / kg Methane, trichlorofluoro-, CFC-11 000075-69-4 4750 kg CO2 eq / kg Methane, trifluoro-, HFC-23 000075-46-7 14800 kg CO2 eq / kg Nitrogen fluoride 007783-54-2 17200 kg CO2 eq / kg

125

Substance CAS number Factor Unit

Pentane, 2,3-dihydroperfluoro-, HFC-4310mee 138495-42-8 1640 kg CO2 eq / kg Pentane, perfluoro- 000678-26-2 9160 kg CO2 eq / kg PFC-9-1-18 7500 kg CO2 eq / kg PFPMIE 10300 kg CO2 eq / kg Propane, 1,1,1,2,2,3-hexafluoro-, HFC-236cb 000677-56-5 1340 kg CO2 eq / kg Propane, 1,1,1,2,3,3-hexafluoro-, HFC-236ea 000431-63-0 1370 kg CO2 eq / kg Propane, 1,1,1,2,3,3,3-heptafluoro-, HFC-227ea 000431-89-0 3220 kg CO2 eq / kg Propane, 1,1,1,3,3,3-hexafluoro-, HCFC-236fa 000690-39-1 9810 kg CO2 eq / kg Propane, 1,1,2,2,3-pentafluoro-, HFC-245ca 000679-86-7 693 kg CO2 eq / kg Propane, 1,1,3,3-tetrafluoro-, HFC-245fa 004556-24-5 1030 kg CO2 eq / kg Propane, 1,3-dichloro-1,1,2,2,3-pentafluoro-, HCFC-225cb 000507-55-1 595 kg CO2 eq / kg Propane, 3,3-dichloro-1,1,1,2,2-pentafluoro-, HCFC-225ca 000422-56-0 122 kg CO2 eq / kg Propane, perfluoro- 000076-19-7 8830 kg CO2 eq / kg Propane, perfluorocyclo- 17340 kg CO2 eq / kg Sulfur hexafluoride 002551-62-4 22800 kg CO2 eq / kg Trifluoromethylsulfur pentafluoride 000373-80-8 17700 kg CO2 eq / kg

126

Appendix C: LCA calculation worksheet for biomass CHP system

System components Unit Amount required to

generate 1 functional unit of product

Associated GWP emissions

Chemical and material use in plant

Lubricant oil kg

Ammonia kg

Chemicals kg

Chlorine kg

Sodium Chloride kg

Water kg

Construction

Construction of common component for cogeneration unit

piece

Construction of building piece

Emission control: Urea kg

Woodchips use m3

Transportation distance km

Moisture content %

Power-to-heat ratio -

Electricity output kwh

Heat output MMBtu

Total energy per year MMBtu/year

Operation factor %

Functional unit 1 MMBtu

Total function unit per year

127

Appendix D: EMA calculation worksheet for biomass CHP system

System components unit Transformity

(sej/unit)

Amount per year

(units/year)

Emergy input

(sej/year)

Input category

Chemical and material use in plant

Lubricant oil g 2.82E+09 N

Ammonia g 3.80E+09 N

Chemicals g 1.60E+09 N

Chlorine g 1.60E+09 N

Sodium Chloride g 1.00E+09 N

Water g 6.64E+05 R

Construction

Capital cost $ 1.37E+12 F

Urea g 2.15E+09 N

Oxygen in air g 5.16E+07 R

Forest wood g 2.57E+07 R

Harvesting labor $ 1.37E+12 F

Transporting labor $ 1.37E+12 F

Plant labor $ 1.37E+12 F

Ash disposal cost $ 1.37E+12 F

Ash disposal cost $ 1.37E+12 F

Waste disposal cost $ 1.37E+12 F

Transportation distance km

Moisture content %

Power-to-heat ratio -

Electricity output kwh

Heat output MMBtu

Total energy per year MMBtu/year

Operation factor %

Functional unit 1 MMBtu

Total function unit per year

128

Appendix E: Histogram of uncertain biomass CHP system factors in Monte

Carlo simulation

129

Appendix F: Classification of Emergy flows for biomass CHP system

Emergy flows Unit Units/MMBtu Transformity

(sej/unit) Solar energy (sej/MMBtu)

cv % of

Emergy

Renewable inputs (R) Water g 7.97E+02 6.64E+05 5.29E+08 < 2% 0.01%

Oxygen from air g 9.28E+04 5.16E+07 4.79E+12 3.05% 31.09% Wood in forest g 1.33E+05 2.57E+07 3.41E+12 3.05% 22.14%

Nonrenewable inputs (N)

Lubricant oil g 3.32E+00 2.82E+09 9.36E+09 < 2% 0.06% Ammonia g 8.30E-03 3.80E+09 3.15E+07 < 2% 0.00% Chemicals g 5.81E+00 1.60E+09 9.30E+09 < 2% 0.06%

Chlorine g 3.32E-01 1.60E+09 5.31E+08 < 2% 0.00% Sodium Chloride g 4.15E+00 1.00E+09 4.15E+09 < 2% 0.03%

Urea g 1.37E+01 2.15E+09 2.94E+10 94.24% 0.19%

Purchased inputs (F) Capital cost $ 0.92 1.37E+12 1.26E+12 < 2% 8.18%

Harvesting and processing cost

$ 2.67 1.37E+12 3.66E+12 3.05% 23.75%

Transporting cost $ 0.23 1.37E+12 3.09E+11 19.96% 2.01% Plant operation cost $ 1.37 1.37E+12 1.88E+12 < 2% 12.17%

Ash disposal cost $ 0.03 1.37E+12 4.63E+10 3.05% 0.30% Waste disposal cost $ 0.00 1.37E+12 6.49E+08 < 2% 0.00%

130

Appendix G: A timeline of major research events

Date Event Status April 22, 2010 • Candidacy exam Completed June, 2012 • Committee approval of dissertation proposal Completed

October, 2012 • Complete the preliminary data analysis of LCA and EMA results

Completed

November 13, 2012 • Comprehensive exam Completed

December, 2013

• Change mathematical model to Monte Carlo model by replacing fixed values of CHP system factors with random values

• Complete preliminary data analysis of LCA and EMA results from newly developed model

Completed

January, 2013 • Revise research methodology • Dissertation layout

Completed

February, 2013

• Draft the Chapter 4: the uncertainty and sensitivity analysis of LCA and EMA results

• Article is targeting at "Ecological Modeling" journal

Completed

March, 2013

• Draft the Chapter 5: the correlation analysis of LCA and EMA results

• Article is targeting at "Environmental Science and Technology" journal

Completed

April, 2013 • Full review the first draft of dissertation Completed

May, 2013

• Co-review with dissertation advisor • Summarize research findings for presenting at

Forest Products Society International Convention in June, 2013

Completed

June, 2013 • Defend dissertation To be completed

131

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Vita

Li Ma

Li was born in Wenzhou, China in 1983. He earned a Bachelor of Engineering degree in

Wood Science and Technology in 2005 and a Master of Engineering degree in Automation in

Wood Products Processing in 2008 from Beijing Forestry University. During his time in school,

Li also gained working experience through internships in wood products manufacturing industry

and wood products consulting firm in China. Upon completion of his Master degree, Li decided

to pursue a doctoral degree in Wood Products program supervised by Dr. Charles D. Ray at the

Pennsylvania State University. During the first three years of study, he primarily focused on

developing a national wood-for-energy utilization database in the United States, and optimizing

feedstock supply and demand through analysis of developed database. His dissertation focused on

comparing two important environmental accounting methods for assessing wood biomass energy

system: Life Cycle Assessment and Emergy Analysis. His work provided insight on what is the

best use of each method, and how to integrate these two methods for ultimate environmentally

conscious decision-making.