Greenhouse Gas Mitigation in Small and Medium Scale Industries in Asia - AIT, Thailand

8/9/2019 Greenhouse Gas Mitigation in Small and Medium Scale Industries in Asia - AIT, Thailand

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Greenhouse Gas Mitigationin Small and Medium Scale Industries of Asia

S. Kumar

C. Visvanathan

Sizhen Peng

R. Rudramoorthy

Alice B. Herrera

Gamini SenanayakeLy Dinh Son


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GREENHOUSE GAS MITIGATION

IN SMALL AND MEDIUM SCALE INDUSTRIES OF ASIA

PUBLISHED BY

School of Environment, Resources and DevelopmentAsian Institute of TechnologyPO Box 4, Klong LuangPathumthani 12120Thailand

Fax: (66) 2 524 5439Email: [email protected] or [email protected]

DISCLAIMER

Neither the Swedish International Development Cooperation Agency (Sida) nor the AsianInstitute of Technology (AIT) and its partners, the National Research Institutes of thestudy countries, make any warranty, expressed or implied, or assume any legal liability forthe accuracy or completeness of any information, apparatus, products, or represents thatits use would not infringe privately owned rights. Reference herein to any trademark ormanufacturers or otherwise does not constitute or imply endorsement, recommendation,or favoring by Sida or AIT.

ISBN 974 8208 59 1

600 copies

Asian Institute of Technology, 2005

Printed in Thailand.


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Principal Investigators

Dr S. Kumar, Professor, Energy Field of Study, School of Environment, Resourcesand Development, Asian Institute of Technology, Thailand ([email protected])

Dr C. Visvanathan, Professor, Environmental Engineering and Management Field ofStudy, School of Environment, Resources and Development, Asian Institute ofTechnology, Thailand ([email protected])

National Research Institute (NRI) Team Leaders

Dr Sizhen Peng, Director, Center for Environmentally Sound Technology Transfer,

Administrative Center for Chinas Agenda 21, Beijing, China ([email protected])

Dr R. Rudramoorthy, Professor, Energy Engineering Department, PSG College ofTechnology and Industrial Institute, Coimbatore, India ([email protected])

Dr Alice B. Herrera, Fuel and Energy Division, Industrial Technology DevelopmentInstitute, Department of Science and Technology, Metro Manila, The Philippines([email protected])

Mr Gamini Senanayake, Director, Industrial Services Bureau of North Western Province,

Kurunegala, Sri Lanka ([email protected])

Mr Ly Dinh Son, Director, Consulting Center for Cooperation and Capacity Building,Hanoi, Vietnam ([email protected])

Research Staff

Mr Aruna Manipura (March 2002 to December 2003)

Ms Priya Ambashankar (December 2003 to August 2004)

Mr Prajapati Shapkota (September 2003 to November 2004)

Mr Prantik Bordoloi (Since May 2004)

Research Fellows

Mr S. Sivasubramaniam (June 2004)

Mr Do Nam Trung (June 2004)

Mr R. Kannan (March to May 2005)

Project Team


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The Asian Regional Research Programme on

Energy, Environment and Climate(ARRPEEC) funded by the SwedishInternational Development CooperationAgency (Sida) conducts research on energy,environment and climate change relevant toAsia. The Small and Medium Scale Industriesin Asia project (SMI in Asia) is one of theprojects under ARRPEEC and was aimed at(i) greenhouse gas emission estimation, (ii)review of barriers inhibiting adoption of

energy efficient and environmentally soundtechnologies (E3STs) and (iii) techno-economic assessment of E3STs. The SMI in Asia project is coordinated by the AsianInstitute of Technology (AIT), Thailand andinvolves the following research institutions:the Center for Environmentally SoundTechnology Transfer, China; PSG College ofTechnology and Industrial Institute, India;

Industrial Services Bureau of North WesternProvince, Sri Lanka; Industrial TechnologyDevelopment Institute, Department ofScience and Technology, Philippines; andConsulting Center for Cooperation andCapacity Building, Vietnam.

This report highlights the research carried outto estimate Greenhouse Gas (GHG)emissions and present strategies formitigation in selected sectors of small andmedium scale industries (SMIs). It describesin brief the background to GHG emissionsin the SMIs with a review of the indicatorsand reporting systems usingIntergovernmental Panel on Climate Change(IPCC) guidelines. The present policiesregarding energy efficiency and GHGmitigations in the study countries are

described and the methodology for estimation

and analysis of sector estimation of GHG

emissions is presented. It includes significantissues related to energy use and GHGemissions in the current scenario vis--vis themove towards reducing emissions in each ofthese countries as a follow up to the KyotoProtocol, CDM.

Of the five participating countries, Chinaand India represent the most populouscountries in the world with a large number

of SMIs dominating the industrial scene. Forthe Philippines, Sri Lanka and Vietnam, thestudy focuses on specific SMI sectors whichaccount for the majority of the industrialsector.

This study presents the research findings anddiscusses sector approaches for GHGemission reductions with sensitivity analysis

of stepwise emission reduction scenarios thatcan be used as mitigation measures forsustainability. Several recommendations aremade for continuing the research andimplementing the findings for specificcountries for the selected sectors. GHGemission reduction and mitigation strategiescould be developed for other sectors basedon this approach. Recommendations alsoencompass new directions and strategies thatcan be adopted to help the study countriesattain optimum GHG mitigation andreduction for a sustainable environment anddevelopment of the industrial sector.

We express our sincere appreciation to thefollowing experts for critically reviewing thisreport and for their valuable suggestions priorto publication:

Preface


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Dr Ajith de Alwis, University of Moratuwa,

Sri Lanka

Dr P. Balachandra, Indian Institute ofScience, Bangalore, India

Mr Liu Bin, Beijing Economic and TradingUniversity, China

Mr Le Nguyen Tuong, Institute ofMeteorology and Hydrology, Hanoi, Vietnam

Ms Clarissa C. Cabacang, Preferred EnergyInc., Philippines

On behalf of the participating institutions, we take this opportunity to thank theSwedish International DevelopmentCooperation Agency for facilitating this phaseof an important piece of research and to AIT

and the management of the participatingnational institutions for the congenialatmosphere they provided for carrying outthis study. We look forward to the adoptionof the methodology for GHG emissionreductions in the study countries and in otherAsian countries.

S. KumarC. VisvanathanSizhen PengR. RudramoorthyAlice B. HerreraGamini SenanayakeLy Dinh Son

April 2005


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Executive Summary

This report presents the results of the studycarried out to estimate the greenhouse gas(GHG) emissions from selected small andmedium scale industries (SMI) in China,India, Sri Lanka, the Philippines andVietnam. This is one outcome of the projectentitled Small and Medium Scale Industriesin Asia: Energy, Environment and ClimateInterrelations, under the Asian RegionalResearch Programme on Energy,Environment and Climate (ARRPEEC)

Phase III.

In Asia, SMIs account for over 85% of thetotal manufacturing establishments andcontribute significantly to nationaleconomies and industrial development. Dueto lack of capital, skilled personnel andawareness about the existing Energy Efficientand Environmentally Sound Technologies

(E3STs), the SMI sector consumes excessiveenergy and generates increasing pollutionloads, of which CO

2emissions is the most

significant. This study addresses these issueswith the following objectives:

To develop a GHG emission estimationmethodology to quantify GHG emissionsfrom SMI sectors

To estimate GHG emissions from selectedSMI sectors in the participating countries,specifically: foundries, brick, tiles andceramic manufacturers, desiccatedcoconut, tea and textiles

To conduct scenario studies to estimateGHG mitigation potential from theselected SMI sectors

For estimation of SMI emissions by sector,an extrapolation methodology was usedbased on a weighted average specificemissions factor (SEF) that was establishedthrough surveys, energy-environment auditsand literature surveys, and SMI sectorproduction figures. This extrapolationmethodology aimed for a ballpark estimatethat is far better than a guesstimate of SMIsector emissions at a macro level.

The CO2emissions from selected sectors wereestimated and each estimate includes ameasure of uncertainty. Among the selectedSMIs in the study countries, the highestcontributors of CO

2emissions are the brick

sector in China and India and the tea sectorin Sri Lanka.

To meet the challenge of GHG emissions

reduction, four instruments were selectedand mitigation scenarios were studied. Thesewere: enhanced operation and maintenancepractices, adoption of E3STs, fuel switchingand policy intervention.

In the brick and foundry sectors in China,about 4-20% of the sector emissions can bereduced, which is over 10 million tonnes ofCO

2

per year. In the brick sector in Vietnam,there is potential for CO

2emissions

reductions of 5-42%. In the brick sector inIndia, 10-20% of the emissions could bemitigated through adoption of E3STs andswitching to cleaner fuels, while the textilesector has potential for about 5-25%reduction. There is potential for a reductionof more than 13 million t-CO

2emissions from

the selected SMI sector in India.


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The Philippines metal casting sector has areduction potential of about 0.13 million t-CO

2which is about 50% of the total emissions

from the sector. Sri Lanka has a potential forof up to 11% in the brick sector and 7.5% inthe desiccated coconut sector.

Realising the potential for CO2

emissionreduction requires effective strategies thatencourage SMIs to improve their energy andenvironmental performance. The strategy

could initially focus on enhancing O&Mpractices, also known as good housekeepingpractices, as the first step for reductions of5-10%. These initial results would help SMIsgain confidence and consider further optionslike changes to E3STs and alternative fuels, which enable reductions of up to 50% inselected SMI sectors. Policy options related toincentives can then be implemented while

those related to regulations and legislationshould be enforced at a later stage to ensurethe sustainability of implemented programmes.


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Table of Contents

Project Team ......................................................................................................... III

Preface ......................................................................................................................IVExecutive Summary ...............................................................................................VI

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

1.1. Energy-Environment Interrelations............................................................... 1

1.2. Small and Medium Scale Industries in Asia ................................................. 2

1.3. Overview of GHG Emission Mitigation in SMI Sectors ....................... 5

1.4. Objectives of the Study ................................................................................... 6

1.5. Organization of the Report ........................................................................... 6

2. GHG Emission Estimation Methodology for SMI .......................... 7

2.1. Introduction ....................................................................................................... 7

2.2. GHG Emission Estimation Methodologies ............................................... 8

2.3. Framework on GHG Emission Estimation Methodology ..................... 9

2.3.1. Data collection .............................................................................................. 10

2.3.2. Planning for a GHG inventory ................................................................ 12

2.3.3. GHG emission estimations ....................................................................... 142.3.4. Uncertainty .................................................................................................... 16

2.3.5. Options for GHG emission mitigation .................................................. 17

3. GHG Emissions from SMI Sector .................................................. 19

3.1. Introduction ...................................................................................................... 19

3.2. Data Collection ................................................................................................ 19

3.3. Emission Boundaries and Assumptions .................................................... 20

3.4. GHG Emission Estimation .......................................................................... 223.4.1. SEF at plant level ......................................................................................... 22

3.4.2. SMI sector GHG emissions ...................................................................... 24

3.5. Benchmarking Energy Use and Emissions for SMIs ............................. 31

3.6. Uncertainty Analysis ...................................................................................... 31

3.7. Summary ........................................................................................................... 34

4. GHG Emission Mitigation Scenarios ............................................. 37

4.1. Emission Reduction Scenarios ...................................................................... 374.2. Emission Reduction through Enhanced O&M ...................................... 37


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4.3. Emission Reduction through Adoption of E3STs ................................. 39

4.3.1. Tea sector ...................................................................................................... 394.3.2. Textile sector ................................................................................................. 40

4.3.3. Foundry sector ............................................................................................. 40

4.3.4. Brick sector ................................................................................................... 41

4.3.5. Scenario summary ....................................................................................... 41

4.4. Emission Reduction from Fuel Switching ................................................. 41

4.4.1. Natural gas .................................................................................................... 43

4.4.2. Renewable energy ........................................................................................ 44

4.5. Emission Reduction through Policy Intervention ................................... 444.6. Limitations of the Mitigation Scenarios Study ........................................ 46

4.7. CDM as a Tool to Mitigate GHG Emission in SMIs ............................. 48

4.8. Summary ........................................................................................................... 49

5. Conclusions and Recommendations .............................................. 51

5.1 Conclusions ........................................................................................................ 51

5.2. Recommendations .......................................................................................... 53

5.2.1. GHG emission estimations ....................................................................... 535.2.2. Emission mitigation .................................................................................... 53

5.2.3. CDM opportunities in SMI sectors ......................................................... 54

References ................................................................................................................ 55

Appendix A: Net Calorific Value of Fuels ......................................................... 60

Appendix B: Carbon Emission Factor and Carbon Oxidation

Factor of Fuels............................................................................................................61

Appendix C: Calculations of SMI Sector CO2

Emission fromIndian Textile Sector ............................................................................................. 63

Appendix D: National GHG Emission Inventory in 1994 .......................... 66


10/76X Abbreviations

CDM Clean Development MechanismCEF Carbon Emission FactorCER Certified Emission ReductionCH

4Methane

CO2

Carbon dioxideCTC Cut-tear-curl DOE United States Department of EnergyDSM Demand Side Management E3ST Energy Efficient and Environmentally Sound TechnologyEEF Electricity Emission FactorEIA Environmental Impact Assessment

FBD Fluidized Bed DrierGEF Global Environment FacilityHFC HydrofluorocarbonsIEA International Energy AgencyIPCC Intergovernmental Panel on Climate ChangeIPPC/IPC Integrated Pollution Prevention and Control/Integrated Pollution ControlIREDA Indian Renewable Energy Development AgencyJI Joint Implementationkg-CO

2Kilogram of CO

2

kWh Kilowatt-hourLPG Liquefied Petroleum GasMJ Mega JouleMNES Ministry of Non-conventional Energy Sources, IndiaN

2O Nitrous Oxide

NCV Net Calorific ValueNRI National Research InstitutePFCs Perfluoro compoundsppm Parts Per MillionSEC Specific Energy ConsumptionSEF Specific Emission FactorSF

6Sulphur Hexaflouride

SME/SMI Small and Medium Scale Enterprise or IndustryTCE Tonnes of Carbon Equivalent t-CO

2Tonne of CO

2

TJ Terra JouleUNFCCC United Nations Framework Convention on Climate ChangeWBCSD World Business Council for Sustainable Development

List of Abbreviations


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Introduction

1

Chapter 1

Introduction

1.1. Energy-Environment

Interrelations

Over 85% of the worlds primary energy

supply is met from fossil fuels (EIA, 2004).

Burning fossil fuels and other humanactivities are now accepted as the primary

cause of changes in atmospheric carbon

dioxide (CO2) concentration and other heat-

trapping gases like methane (CH4) and nitrous

oxide (N2O). An atmospheric concentration

of CO2

of 368 ppm in the year 2000 is a

significant increase compared with 280 ppm

during the period 1000-1750. This human-

made greenhouse effect has the potential to

change the earths climate dramatically in arelatively short span of time. There is new

and stronger evidence that most of the

warming has occurred over the last 50 years

and is attributable to anthropogenic activities.

The global average surface temperature has

increased by 0.6 0.2C in the 20th century. At the current emission rates, global

atmospheric CO2concentrations are expected

to double by the middle of the 21st

century.This will result in the warming of earths

atmosphere by 1.5-4.5C and cause global

mean sea level to rise by 0.25-0.50 meters

(IPCC, 2001). The consequences of these

effects will be serious. Currently, climate

change is the centrepiece of the worlds

environmental agenda. The goal of

environmental sustainability is incredibly

complex.

There is strong growth in energy

consumption among the developing nations

but the fastest growth is projected for thenations of developing Asia, including China

and India (EIA, 2004). The five Asian

countries selected for this study, China, India,

Philippines, Sri Lanka and Vietnam, account

for about 57% of the total CO2

emissions

from Asia and Oceania, of which China and

India contribute a major amount (EIA,

2004a). The share of CO2

emissions from

fossil fuel use in the different regions of the

world and the participating countries of thisstudy are illustrated in Figure 1.1.

Global warming is expected to worsen unless

concrete measures are taken to reduce the

trend of increasing emissions. The Kyoto

Protocol of the United Nations Framework

Convention on Climate Change (UNFCCC)

began to address the issues related to climate

change (UNFCCC, 1997). Under theProtocol, industrialized countries agree to

meet quantitative targets for reducing or

limiting their GHG emissions. Studies and

actions pertaining to GHG emissions are

carried out at the national level in many

sectors of the economy, including large-scale

industries. However, few measures are

undertaken in small and medium scale

industries even though they form a major

share of the manufacturing sector.


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Greenhouse Gas Mitigation

2

Domestic Product (GDP) in the selectedcountries (ADB, 2003) and their growth ratesare even stronger than the national GDP

growth rates shown in Figure 1.2. This rapidindustrial growth has led to significantincreases in energy use and GHG emissions.

Figure 1.1 Share of CO2

emissions by region (left) and in the study countries (2002)

Figure 1.2 National GDP growth

rates in the selected countries

1.2. Small and Medium Scale

Industries in Asia

There is a substantial economic growth in theselected five countries (Figure 1.2). Alongwith economic development, environmental

pollution has also increased, especially GHGemissions from industry. The industrial sectoraccounts for about 27-52% of National Gross

Source: (EIA, 2004a)


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Introduction

5

1.3. Overview of GHG Emission

Mitigation in SMI Sectors

To mitigate GHG emissions, governmentshave initiated many activities and formulatedlaws and regulations. These includeimproving energy efficiency, promotingrenewable energy technologies andregulations on emissions and pollutants.These are aimed at reducing energyconsumption and pollution from industriesin general. A comparison of the energy andenvironmental policies of the study countriesis given in Table 1.3. So far, laws and plans

do not specifically target SMIs but coverthem under the general category ofindustry.Almost all study countries have formulatedenvironmental protection laws, air emissionstandards, wastewater discharge standardsand requirements for Environmental Impact Assessment (EIA) for new industries.However, only a few countries haveimplemented and carried out efforts to

provide financial incentives for pollution

prevention, adoption of the polluter pay prin-ciple, and policies covering pollution mitiga-

tion in SMIs.

SMIs are profit oriented and not muchconcerned about the impact of their energyuse on local and regional pollution. Theyface many difficulties such as lack of capital,human resources, support and training,standards and benchmarks, awareness ofresource management and access to E3STs.

In most SMIs, the technology is old andinefficient. They are less energy efficient andthey generate a lot of pollution (Kumar etal. , 2002). Considering the potential foreconomic development and employmentcreation, it is expected that in the future they will contribute significantly to energy useand GHG emissions.


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6

Energy-environment surveys were conductedfor SMIs in the selected countries to identifytheir future significance in GHG emissionmitigation measures. The results arepresented in Table 1.4. There has been no

significant effort to address these issues.Therefore, considering their large numbers,energy inefficiency, and potential for emissionreductions, it is important to target the SMIsector for pollution prevention and GHGemission mitigation measures. Earlier SMI in Asia project studies focused on capacitybuilding, analyzing and benchmarking energy-use patterns of selected SMI sectors and

identification of E3STs for SMIs (AIT,2002a; 2002b; 2002c; 2002d).

This study aims to quantify the GHGemissions from SMI sectors to understandtheir significance in the overall national GHGemissions. Due to lack of participation ofSMIs, their large numbers and scatterednature and lack of information on theirenergy use, quantification of their GHG

emissions is fraught with challenges andbarriers.

1.4. Objectives of the Study

The study aims to achieve the following fourobjectives:

1. To customize a GHG emission

estimation methodology from existingmethodologies to quantify emissions;

2. To estimate GHG emissions from theselected SMI sectors (foundries, brick, tileand ceramic manufacturers, desiccatedcoconut, tea and textiles) in the participatingcountries;

3. To compare emission indicators at across-country level; and

4. To conduct scenario studies to estimateGHG mitigation potential through enhancedoperation and maintenance (O&M) practices,adoption of E3STs, fuel switching and policyintervention.

1.5. Organization of the Report

The report structure is outlined in Figure 1.3below.


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GHG Emission Estimation Methodology for SMI

7

Chapter 2

GHG Emission

EstimationMethodology

for SMI

2.1. Introduction

The Intergovernmental Panel on ClimateChange (1996) developed guidelines forreporting national GHG emission inventories.National inventory estimates are based onaggregate energy consumption data from various sectors of the economy such aspower, transport and industry. Using thesame methodology, GHG emissions from theSMI sector can be estimated if the aggregatedenergy consumption data of a sector isavailable. Unfortunately, such sector-specificenergy consumption data are not commonlyavailable, except in some countries likeIndonesia (Priambodo and Kumar, 2001).Therefore, it becomes necessary to seek

alternative approaches to estimate emissions.

One such approach is extrapolation of GHGemission data gathered from a sample ofSMIs. The basic input data required for suchsector estimations are national productiondata, sector contributions of SMIs in totalproduction and a reliable specific emissionsfactor (SEF)1 per unit of product output. The

national production data can be obtained orestimated from national statistics, trade

unions or manufacturers associations. TheSEF can be established through surveys,energy-environment audits from a sample ofSMIs and the existing literature. From theSEF data of sample SMIs, a national weightedaverage SEF can be established. Using this weighted average SEF and the totalproduction of that sector, the sectoremissions can be estimated. The reliabilityof an extrapolated emission estimate largelydepends on the robustness of the weightedaverage SEF, which depends on sample size.Generally, the higher the sample number, thebetter the estimate. However, manyuncertainties such as fuel mix, technology use

and production volume also need to beconsidered in determining a national weighted average SEF. These factors arehighlighted in Box 2.1.

Extrapolation allows the use of inventorydata from a particular sector directly andemploys transparent data sources for nationalinventories. Any changes in emissions from

1SEF is defined in Section 2.3.3.1


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8

2.2. GHG Emission Estimation

Methodologies

To establish the weighted average SEF, anestimation of SEF from a sample of SMIs isrequired. Although methodologies areavailable to estimate GHG emissions fromindividual and corporate industries andbusiness establishments, compatibility withthe IPCC guidelines is important for the useof these results. To estimate the GHGemissions from SMIs, two approaches canbe used, namely, direct monitoring and SEFbased calculation.

The direct monitoring approach is morecommon in process industries and electricutilities in the USA. If a direct monitoringsystem has already been established in afacility, the associated data provides a goodestimate of CO

2emissions.

In the SEF based estimation, source or

facility-specific fuel data is used. Thisapproach is more accurate and will alsofacilitate the identification of emissionreduction opportunities. If calculating fueldata at this level is not possible, a corporate-wide approach of calculating total fuel usefrom fuel purchasing data can be employed.

To meet the special needs of SMIs, thefollowing two GHG emission estimation

methodologies have been identified from theliterature:

the target sector inventory can beincorporated in the national inventory(WBCSD, 2001). Although the extrapolationmethodology may not lead to preciselyaccurate emission estimates (see Box 2.1), it

helps establish ballpark values. Suchestimates are far better than theguesstimates of sector GHG emissionfigures at the macro level.

Box 2.1Reliability of extrapolation of SMI

sectoral emissions

It is assumed that emissions from sample SMIsare representative of the whole SMI sector. In

some sectors or countries this is not alwaysthe case. Many factors affect the robustnessof the sample data including:

1. Type of finished product, e.g. differentsize of bricks, cast iron or steel

2. Variations in production processes;e.g. tea is produced from twoproduction processes, Orthodox andCut-tear-curl

3. Production volume; typically higherproduction volume lowers energy use

4. Energy use pattern of the sampleSMIs (also depends on technologyused in production process)

5. Fuel mix of energy use (depends onfuel availably in the region and type oftechnology used)

6. With electricity, indirect emissiondepends on fuel mix used to generateelectricity

Although these factors may affect the nationalweighted average SEF, a range of emissionscan be established by categorizing the dataqualitatively though inputs from experts anddata from the literature.


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9

1. GHG Indicator of the United NationsEnvironment Programme (see Box 2.2); and

2. Greenhouse Gas Protocol initiative of the World Resources Institute/World Business

Council for Sustainable Development (WRI/WBCSD) (see Box 2.3).

The UNEP GHG indicator methodologydescribes a procedure for gathering all thedata required to estimate emissions fromelectricity use and other fuel consumption.The WRI/WBCSD GHG Protocol clearlydefines the procedure for determining the

2.3. Framework on GHG Emission

Estimation Methodology

As noted in Section 2.1, the extrapolationmethodology was used to estimate sectorGHG emissions. To establish a national weighted average SEF, energy and

environmental audits were carried out insample SMIs, which are described in Chapter3. For the estimation of emissions from thesample SMIs, also referred to as plant levelemissions, the IPCC (1996) guidelines andUNEP (2000) GHG Indicator approacheswere used. The former was used to estimateemissions due to primary energy use, i.e.direct fuel consumption, while the latter was

used to estimate emissions due to secondaryenergy use such as electricity. The overallframework of the sector GHG emissionestimation and measures for mitigation ofGHG emissions is shown in Figure 2.1. Itconsists of five steps based on the Plan-Do-Check-Act cycle to deal with the dynamicbehaviour of GHG emissions in the SMIsector. It is aimed at continual improvementof estimating GHG emissions. The detailsof each step are described in the followingsub-sections.

fuel consumed in each operation. These twomethods complement one another and differonly in their magnitude or scope of study.They are briefly described in Boxes 2.2 and2.3.

Box 2.2

UNEP GHG Guidelines

UNEPs Guidelines for CalculatingGreenhouse Gas Emissions for Businessesand Non-Commercial Organizations helpsorganizations in accounting and reporting theiremissions. The guidelines provide a methodwhereby GHG emissions are calculated and

combined to give a single GHG Indicator toshow an organizations contribution to climatechange. An essential characteristic of theGHG Indicator is that it uses informationreadily accessible by the industries. Thisdata, expressed in commonly used basicunits, can be converted and aggregated tocalculate the total contribution to climatechange. The indicator is applicable at all levelsof a company and regardless of their size.The figure below shows the generic framework

of the process and the information needed toderive the GHG Indicator.

Source: Adapted from UNEP, 2000


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10

2.3.1. Data collection

Figure 2.2 illustrates the data collectionprocedure for a GHG emission estimation.The primary data collection includes energy

consumption (both primary and secondaryenergy sources such as electricity, diesel, fueloil, coal, coke and firewood) at SMI level, which requires energy and environmentalaudits at a micro or factory level. These dataare used to calculate GHG emissions at plantlevel and then extrapolated to the SMI sectorlevel. Information on sector productionprocess/flow charts can also be collected to

facilitate the next step of planning theinventory.

For the calculation of emissions at plantlevel, information and data such as fuel type,their heating values and carbon emissionfactors have to be collected. These data maybe available in national databases or fromestablished databases such as IPCC (1996)inventories or the UNEP (2002) Indicator.

The secondary data at macro level arenational annual production for the sector, which can be obtained from nationalstatistics. These production data ofteninclude production from large industries. Inthis case it is necessary to identify the shareof SMIs in national production. Nationalenergy-related data such as annual fuel

consumption of the power sector, electricitygeneration and efficiency of powertransmission and distribution are required tocalculate the country specific emission factorsfor electricity.

Box 2.3

Greenhouse Gas Protocol Initiative ofWRI/WBCSD

The Greenhouse Gas Protocol Initiative was

developed by the World Resources Institute

(WRI) and the World Business Council for

Sustainable Development (WBCSD) to promote

the use of voluntary international accounting

and reporting standards for businesses. The

inventory has three separate but linked

modules: the core inventory, reporting project-

based reductions, and accounting for GHGs

in the value chain. The first module (core

inventory) was published in 2001 while the lasttwo are currently being developed (GHG

Protocol Initiative 2004). The first module is

aimed at helping companies and organizations

develop credible inventory data underpinned by

GHG accounting and reporting principles,

account and report information from global

operations, provide internal management to

build an effective strategy to manage and

reduce GHG, and provide information that

compliments other climate initiatives, and

reporting and financial standards. This Protocol

also introduces operational boundaries that

account for direct and indirect GHG emissions

and allows the treatment of other indirect

emissions. It aims to:

account direct GHG emissions from sources

that are owned or controlled by the reporting

company

account for indirect emissions associated

with the generation of imported/purchased

electricity, heat or steam

allow treatment of other indirect emissions

that are a consequence of the activities of

the reporting company, but occur from

sources owned or controlled by another

entity (e.g. emplemployee business travel,

commuting, and outsourced activities).

Source: Adapted from WBCSD/WRI 2004


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11

Figure 2.2 Data collection procedure

Figure 2.1 Framework of SMI sector GHG emission estimation and mitigation measure


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12

2.3.2. Planning for a GHG inventory

The demarcation of sources of emissions isimportant in a GHG emission estimation.Figure 2.3 shows typical sources of emissions

from SMIs. CO2 is the main emission and ismainly due to energy use, some of whichoccurs on the SMI premises. For example,fuel consumption, like coal, oil, firewood andgas cause direct emissions. Other directemission sources include productionprocesses. Indirect emissions occur outsidethe SMI premises but these are due tosecondary energy use and wastewater

treatment. For example, electricity use causesCO2emissions depending on the source of

power generation but occurs on the utilitypremises. Other activities such as energy use

for raw material/fuel production andtransportation, travel by employees andlogistics related to finished products also

cause emissions. Therefore, before beginningthe estimate, the boundary of an emissionsource should be clearly defined. To identifythe detailed emission sources, each unitoperation and production process needs tobe studied. An energy balance will clearlyshow the amount of energy consumption byeach category. For this study, CO

2was

considered the only GHG emission. Other

gases were ignored because of theiruncertainties or their insignificantcontribution to the total GHG emissionfigure.

Figure 2.3 Potential sources of GHG emissions from an SMI


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13

Figure 2.4 Procedure for a plant level and SMI sector CO2emission estimation


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25/76Chapter 2


15

where EFab c

is the emission factor oftransportation for fuel type a, vehicle typeb and type of emissions control c. EF

abc

values are available in IPCC (1996).Activity

abcis the amount of energy consumed

or distance travelled for a given mobilesource activity.

4. The sum of the emissions from all theabove sources i.e. from fuel and electricityuse and transportation, is calculated as thetotal GHG emission of the sample SMI.From the total GHG emissions at the plantlevel, the following specific emission

indicators can be estimated and used tocompare the emission intensity among SMIsor at the country or regional level. This canbe done in terms ofproduct unit, employee,energy use and product value:

Specific emissions per unit productoutput (kg-CO

2/kg of product or

kg-CO2/piece)

Specific emissions per employee(kg-CO

2/employee)

(2.3)

where Fe

is annual fuel consumed forelectricity generation and E is annualelectricity generation in the country.

3. Emissions related to transportationactivities can be estimated using Equation2.5 (IPCC, 1996).

(2.5)

Specific emissions per energy use(kg-CO

2/kWh or kg-CO

2/MJ)

Specific emissions per product value

(kg-CO2/$)

In terms of production value, the plant levelSEF can be calculated from the total GHGemissions and total production, given byEquation 2.6.

2.3.3.2. SMIs sector level

The national weighted average SEF is calcu-lated from the sample of audited factories.From the SEF of individual SMIs, i.e. plantlevel SEF, a weighted average SEF is estab-lished using Equation 2.7.

The weighted average SEF is calculated toavoid any large variation observed amongindividual SMIs. This aggregate value has to

be subjected to an uncertainty analysis sothat the tolerance of accuracy can beestimated.

GHG emissions from the SMI sector areextrapolated by multiplying the weightedaverage SEF, total national production andthe share of SMI production, given byEquation 2.8.

(2.6)

(2.7)

(2.4)


26/76Chapter 2


16

emissionGHGsectorSMI =

SEFAverage

x National total production

x Share of SMIs production

SEFAveragex National total production

x Share of SMIs production (2.8)

A sample calculation of CO2emissions from

the sample of SMIs, weighted average SEFand sector CO

2emissions for the Indian textile

sector are shown in Appendix C.

2.3.4. Uncertainty

Calculating the range, confidence interval orother limitations of the estimated emissionsare important in assessing their uncertaintylevels. However, these statistics are notcomplete measures of quality because theremay be systematic errors (biases) associated with the emissions estimates that are not

bounded by the range or confidence intervals.In addition, uncertainty is due to manycauses, one of which is the inherent variability in the process or processes thatcause the emissions. Even if all other sourcesof uncertainty are taken into consideration,this variability remains. As some processesare more variable than others, some alwayshave larger error bounds than others. Such

estimates are not of lower quality nor do theyindicate less confidence in the ability topredict emissions at a particular point in timebut do indicate that it is possible toconfidently predict a range (EPA, 1996).

The first step towards characterizinguncertainty associated with emissions datais to understand and quantify the differentsources of variability and inaccuracies in the

data being used. Uncertainty in emission

estimates can be due to systemic errors orinherent errors or a combination of both.

Systemic uncertainty results from choicessuch as:

Use of factors that are poorly researchedand uncertain;

Use of average case factors notperfectly matched to specific andvarying circumstances (e.g. averagekm/litre, average kg CO

2/MWh

generated); Deliberate estimation to compensate

for missing data (e.g. non-reporting facilities, or missing fuelbills); and

Assumptions that simplify calculationof emissions from highly complexprocesses.

Inherent uncertainty results from randomerrors such as imprecise measurement ofemissions-producing activity, insufficient

frequency of measurement, omissions and toerrors of calculation.

The process of estimating uncertainties inGHG inventories is based on certaincharacteristics of the variable of interest(input quantity) as estimated from itscorresponding data set. The ideal informationincludes:

The arithmetic mean (mean) of the dataset

The standard deviation of the data set (thesquare root of the variance)

The standard deviation of the mean (thestandard error of the mean)

The probability distribution of the data Covariance of the input quantity with

other quantities used in the inventorycalculations


27/76Chapter 2


17

To estimate uncertainty by source categories

and gases for sector emission estimations, it

is necessary to develop information specific

to the individual industrial sector in a country

and the methodology and data sources used.

In scientific and process control literature, aconfidence limit of 95% (2) is often

regarded as appropriate for range definition.

Where there is sufficient information to

define the underlying probability distribution

for conventional statistical analysis, a 95%

confidence interval should be calculated as

a definition of the range. Uncertainty ranges

can be estimated using classical analysis or

the Monte Carlo technique. Otherwise theranges have to be assessed by local experts

in the study countries. The following

Equation (2.9) shows how to calculate the

overall uncertainty (UT ) using the individual

uncertainties of emission factors (UE ) and

socio-economic activity data (UA).

For individual uncertainties greater than 60%,

the sum of squares procedure is not valid.

Therefore, limiting values can be combined

to define an overall range, although this leads

to upper and lower limiting values which are

asymmetrical about the central estimate

(IPCC, 1996). A detailed analysis ofuncertainties for the selected SMI sector is

presented in Chapter 3.

2.3.5. Options for GHG emission mitigation

After planning, performing and checking the

GHG emission estimate, a baseline for each

sector is established. The next step is to

identify options for GHG emission mitigationand to estimate its potential. For this study,

the following four instruments were selected

to perform GHG mitigation scenarios:

1. Enhanced Operation & Maintenance

practices (O&M): SMIs generally lack good

O&M practices. Therefore, improving their

O&M could lead to energy savings and

emissions reductions.

2. Adoption of E3STs: SMIs are using

outdated technologies, which consume more

energy. Implementation of E3STs could

reduce energy use and thereby reduce

emissions.

3. Fuel switching: Changing from high

carbon intensive fuel to low carbon fuel or

use of renewable energy sources also reduces

fossil energy use and GHG emissions.

4. Policy intervention: For effective

implementation and the success of any

emission reduction measure, a supportive

policy is crucial. Appropriate policy is an

important part of any GHG emission

mitigation campaign.

(2.9)60%U,Uaslongso AE

Documents

Greenhouse Gas Mitigation in Small and Medium Scale Industries in Asia - AIT, Thailand