Low Carbon Economy, 2011, 2, 165-172
doi:10.4236/lce.2011.23021 Published Online September 2011 (http://www.SciRP.org/journal/lce)
Copyright © 2011 SciRes. LCE
Energy and Carbon Modeling with Multi-Criteria
Decision-Making towards Sustainable Industrial
Sector Development in Thailand
Aumnad Phdungsilp, Teeradej Wuttipornpun
Department of Industrial Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand.
Email: aumnad@gmail.com
Received July 11th, 2011; revised August 4th, 2011; accepted August 10th, 2011.
This paper develops some policy options for Thailand’s industrial sector. The energy simulation model, the Long-range
Energy Alternatives Planning (LEAP) system, has been used to simulate how energy might develop from 2005-2030.
Five policy interventions are selected, and how these would change energy development is examined, and compared to
a reference case. Further, the industrial policy options are assessed using a multi-criteria decision-making framework.
Results of this study can increase the knowledge and understanding to make an explicit consideration of the transition
from high carbon intensive energy system to one which is substantially decarbonized. The most significant energy-sav-
ings are improvement of energy efficiency and process integration. These policy options also have the large potential to
reduce CO2 emissions.
Keywords: CO2 Emissions, Energy Modeling, Industrial Sector, Multi-Criteria Decision-Making
1. Introduction
Climate change and energy issues are demanding new
efforts to addressing the challenges. Significant changes
have taken place on the world energy scene, which have
important implications for energy planning. Globally, the
industrial sector accounts for 40% of primary energy
demand and approximately the same share for CO2 emis-
sions. Mitigation can be substantially cut in this sector
through policies, initiatives and efficient energy tech-
nologies. Thailand’s industrial sector consists of manu-
facturing, mining and construction industries. The manu-
facturing industry accounts for more than 90% of total
energy consumption in the industrial sector. Besides, the
manufacturing industry contributes about 95% of CO2
emissions from industrial sector, while construction and
mining industries contribute the rest [1]. Therefore, pre-
paring appropriate actions can significantly reduce en-
ergy demand and associated CO2 emissions.
There ar e few studies of energy scenario s in Thailand,
particularly in the industrial secto r, make an explicit con-
sideration of the transition from high carbon intensive
energy system to one which is substantially decarbonizes.
This paper is motivated by the need to help decision-
makers to prepare scientific-based policy, and to analyze
industrial energy demand and associated emissions in a
sustainable way. The paper would provide an in-depth
understanding of the complex dynamics of energy and
CO2 issues related to structure of industrial energy use
patterns. In addition, it can produce a scientific knowl-
edge to aid decision-makers in preparing with pathway
towards low-car bon economy. Planning for a low-carbon
society requires processes of analysis and decision-
making about what resources and technologies to be used
and pathways to achieve.
This paper aims to develop energy and carbon model-
ing of Thai industrial sector and some policy options. It
outlines and assesses the current status and future devel-
opment related to energy consumption and CO2 emis-
sions over the twenty-five years from 2005-2030. The
simulation model, the Long-range Energy Alternative
Planning (LEAP) system, is used to simulate what might
happen to energy demand and carbon emissions in the
future in a business-as-usual (BAU) case and with alter-
nate scenario. The BAU scenario provides a baseline
vision of how energy consumption and CO2 emissions
are likely to evolve, while alternate scenario explores a
range of policy interventions. The study goes further in
applying a decision support tool to evaluate different
Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development
166 in Thailand
policy options based on multi-criteria decision-making
(MCDM) approach. The scope of this paper is focused
on only manufacturing industries, including food and
beverages, textiles, wood and furniture, paper, chemical,
non-metallic, basic metal, fabricated metal, and others
2. Background Information of Thailand’s
Industrial Sector
The industrial sector is extremely diverse and includes a
wide range of activities. Thai industry has grown signifi-
cantly over the past two decades. Industrial value-added
has increased from 255.5 billion Thai Baht in 1981 to
1,043.2 billio n Thai Baht in 20 00 at co n stan t 198 8 prices.
Manufacturing industry has a dominant share and its im-
portance has increased from about 80% in 1981 to above
91% in 2000. The growth rate of manufacturing value-
added accelerated from 5% to 15% per year between the
first and second half of the 1980s, and continued to grow
rapidly by 11% per year in the first half of the 1990s.
This was primarily due to the growth of manufacturing
exports. As a result, manufacturing’s contribution to
overall gross domestic product (GDP) increased rapidly
from 23% to 31% between 1980 and 1995. This per-
formance came to an abrupt halt in 1996, when manu-
facturing exports declined. In the period 1996-2000,
manufacturing value-added and exports grew by an av-
erage of 3% and 2% per year.
Thai manufacturing industry has undergone some
structural changes during 1981-2000. Food and beverage
was among the largest contributors to the manufacturing
sector value-added in the early to mid-1980s. The share
of the export-oriented textile industries peaked during
late 1980s. Two groups of industries have gained in
shares: other industries and chemical industries. The in-
dustrial sector has begun the largest energy consuming
sector in Thailand since 2005 [2]. By fuel types, oil is the
largest share of total final energy consumption. The
gradual substitution of fuel-oil with natural gas and the
slow growth in diesel demand result in the decline in the
share of oil. Natural gas is expected to grow faster rate.
Thai government has policies to improve energy effi-
ciency and the shift of the industrial structure from en-
ergy intensive to non-energy intensive industries. Within
manufacturing sector, CO2 emissions from food and non-
metallic industries dominate over others. Food industry
accounted for about 49% of CO2 emissions in 1981 but
its share has fallen to 29% in 2000. Shares of non-metal-
lic industry and chemical industry in CO2 emissions have
increased from about 24% and 5% in 1981 to 27.5% and
13% in 2000, respectively. Thus, food, non-metallic and
chemical industries account for about 70% of CO2 emis-
sions [1].
3. Methodology
3.1. LEAP Model
Energy modeling is an important part of the methodology.
Various energy models are available to develop policy
cases quantitatively and to provide a consistent frame-
work of their analysis. LEAP is a simulation model for
energy planning tool. It is not a model of a particular
energy system, but rather a tool that can be used to create
models of different energy systems. The central concept
of LEAP is an end-use driven model in which users cre-
ate quantitative descriptions of current and future energy
demand and supply and environmental scenarios. The
model includes the Technology and Environmental Da-
tabase (TED), which provides extensive information on
the technical characteristics and environmental impacts
of energy technologies [3].
This paper looks into the development pathways of
energy consumption and energy-related CO2 emissions,
and the potentials of reducing energy consumption and
emissions in Thai manufacturing industries. The model is
disaggregated in a hierarchical tree structure of four lev-
els: sector, sub-sector, end-use, and device. The main
driver in energy consumption is the production of com-
modities. The disaggregation introduces physical energy
intensities in terms of energy use per ton of industrial
product produced for a portion of the industrial sector.
The energy demand is formulated as a function of GDP,
proportion of energy utilization, device efficiency, and
useful energy intensity. The useful energy intensity is
estimated as follows:
ij ij
where UEIj represents the energy intensity in industrial
sub-sector j (ktoe/106 Baht), EnUi,j is the energy type i
utilized in sub-sector j (ktoe), ηi,j is the efficiency of
equipment using fuel type i utilized in sub-sector j, and
GDP is gross domestic products of industrial sub-sector j
(106 Baht).
3.2. Multi-Criteria Decision-Making
A framework of the MCDM method used in this study
can be seen as two main phases. In the first phase, deci-
sion team decides on the criteria they want to use and
determine their relative importance. In this paper, the
selection of main criteria and sub-criteria are followed
the study conducted by [4]. In the second phase, the de-
cision group applies the MCDM method to judge the
relative merits of the alternatives. This is done by deter-
Copyright © 2011 SciRes. LCE
Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development
in Thailand
Copyright © 2011 SciRes. LCE
mining scores for each alternative for each criterion us-
ing the measuring scales defined in the first phase.
The application of MCDM was carried out in a labo-
ratory setting. Five people with a background in energy,
engineering, economic, environment, and political were
contacted and communicated. They were asked to imag-
ine themselves to be the decision-makers in charge of
making decisions. One of the authors was assigned as the
resident MCDM expert, that responsible for organizing
the information and following other processes. Thus,
other members do not need to become familiar with the
mechanics of aggregating the information and running
MCDM calculation.
The objective of the study is to propose sustainable
energy system. The criteria for energy system assessment
reflect three main aspects, including environmental,
economic and social. Some criteria and sub-criteria are
quantifiable, while others are qualitative. The perform-
ance prediction is done based on computer simulation
(energy modeling), databases, rules of thumb, and ex-
perience or expert judgment. Qualitative values are
words or phrases that can be used to characterize how
well a scheme rates against a particular criteria. These
are quality issues, such as public acceptance or integra-
tion into an urban context. The criteria weights are de-
termined using a mathematical technique so-called Ana-
lytic Hierarchy Process (AHP). AHP enables to st ructure
a complex problem in the form of hierarchy and to evalu-
ate a large number of quantitative and qualitative factors
in a systematic manner under multiple conflicting criteria.
AHP makes use of pairwise comparisons with 9-point
ratio scaling to apply weights to attributes. Computer
software Web-HIPRE (Hierarchical PREference analysis
on the World Wide Web) is used in this process. For
more detail of Web-HIPRE see [5,6] and the AHP see
4. Scenario Definition
4.1. Scenario Description
Energy model allows the construction of policy options
in a quantitative way. The imp lication o f policy interven -
tions can be tested. In order to demonstrate the future
energy consumption and CO2 emissions, and to evaluate
the benefits of energy policies, two scenarios were de-
veloped in LEAP model under different sets of policy
options–a reference scenario or BAU case and an alter-
nate policy scenario. These scenarios are primarily gov-
erned by four factors: economic growth, proportion of
energy types, efficiency of energy devices, and energy
intensity. The policy options and assumptions are given
in Table 1.
Scenario provides a framework for exploring future
energy perspectives, including various combinations of
Table 1. Policy options and assumptions for scenario generation.
Scenario Policy options Assumptions
Business as usual Historical trends will continue and the GDP growth rate 4.5% (2005-
2010) and 5.5% (2011-2030).
Alternate policy Improvement of industrial energy efficiency (IEE) A target of 10% and 20% increasing energy efficiency by 2015 and
2030. These improvements are from compressed air, boiler and steam
systems, and lighting systems.
Switching to natural gas (ING) Thermal energy supplied by non-renewable resources such as diesel and
fuel-oil will be switched to natural gas by 2015.
Combined heat and power in design ate factories (ICHP)Combined heat and power (CHP) systems will use to produce electricity
in selected industries and the waste heat will use to replace heat from
fuel-oil fired boilers. CHP systems will replace fuel-oil by 2015 and
assume that electricity consumption will decrease 10% in each industry.
Efficient electricity end-use devices (IElect) Only electricity will be considered—availability of efficient and less
energy intensive pumps, compressors and motors for industrial proc-
esses. This policy can consider as a part of Energy Labeling Program. It
is assumed that increasing 20% of electricity efficiency by 2010.
Process integration (IPI) Process integration will apply to food and beverages, chemical and pa-
per industries. It is assumed that 20% reduction in useful energy inten-
sity by 2015.
Integrated policy (IIP) All of the above mentioned policies are considered together. This policy
option would give the cumulative effect of the different options, giving
Thai industry the lowest possible emission reductions.
Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development
168 in Thailand
technology options and their implications. The scenario
analysis timespan covers the years 2005-2030. All sce-
narios start from a common base year (2005), which is
named as the current account (CA) scenario in LEAP
model. CA scenario describes energy demand in indus-
trial sector based on proven historical data. Data for the
CA scenario were collected from different sources as
described in the following section. The CA scenario
forms the base of BAU and alternate policy scenarios.
BAU scenario assumes that past trends continue in the
future and no new policies for energy-savings and emis-
sion mitigation will be implemented. Energy demand is
predicted as a function of time. This scenario aims to
show the future through the prism of current policies and
strategies. For the BAU scenario, the current patterns in
the industrial structure will be maintained and the indus-
trial sub-sectors will be the same as in CA scenario. This
implies n o change compa red to the industr ial structure in
The alternate policy scenario is inherited from BAU
scenario. It is, thus, reflected sensitivities on the original
scenario. This considers the cumulative impact of five
industrial energy policies, including improvement of in-
dustrial energy efficiency, switching to natural gas, CHP
in designate factories, efficient electricity end-use de-
vices, and process integration. This scenario can also
consider as a mitigation scenario, which means that more
ambitious energy conservation and emission reduction
objectives and relevant policies are adopted. The policy
options form the basis for technology selection. LEAP
converts the assumptions (Table 1) into quantitative way.
4.2. Data Sources
This study draws on a wide variety of sources. Data were
obtained from various sources: statistical information,
government publications of official energy data, utility
statistics, journal articles, book chapters, research reports,
and others. The principle sources are Ministry of Energy
[2,10] and Ministry of Industry [11-12]. Sources of data
used on economic [13-14], energy [2,10,15-17], and
technology issue [18-21]. Data obtained from the original
source were processed to meet the input requirements in
LEAP model to develop a base year dataset. Growth in
GDP was assumed to be the same in all scenarios. We
assumed Thailand’s GDP from industrial sector will
maintain its growth with an annual growth rate set to be
4.5% from 2005 to 2010. Then the growth will grow up,
with an annual growth rate set to be 5.5% from 2010 to
2030. This assumption is based on the estimation by Na-
tional Economic and Social Development Board. The
industrial structure will remain constant during time-
5. Results
5.1. Scenario Analysis Results
5.1.1 Business-as Usual Scenario
The BAU scenario represents a base case without policy
interventions. It is a projection of what would happen in
the absence of specific energy policy and strategy. Cur-
rently, industrial sector energy consumption accounts for
around 36% of total national energy consumption, and
the highly energy intensive industries, such as food and
beverage, non-metallic and chemical, make up over 70%
of total industrial energy d emand. Detail results for BAU
case are shown in Table A-1. It shows that total indus-
trial energy demand in 2005-2030 will in crease b y 5.5 3%
annually, and in 2030, the energy demand is estimated to
be about 3.5 times of that of the year 2005.
Food and beverage, non-metallic and chemical indus-
tries are expected to grow rapidly in BAU scenario. Es-
timated renewable energy (including bagasse, biomass
and wood), coal, electricity and natural gas demands in
2030 are 2 0,112 ktoe , 26,573 kto e, 19,034 k toe and 6 222
ktoe, respectively. Shares of biomass, coal, electricity
and natural gas in energy demand of 2030 are estimated
to be 26.5%, 35%, 25% and 8.2%, respectively. Among
non-renewable energy, coal, electricity and natural gas
are primarily responsible for the majority of final energy
demand (Table A-1).
The emissions for 2030 at BAU scenario are estimated
3.7 times for CO2 compared to the base year 2005. It
should be noted that emissions from biomass are not
taking into account. Non-metallic and fabricated metal
are responsible for majority of the emissions in 2030.
Non-metallic is responsible for emitting about 29.5%,
while fabricated metal emits about 17.8% of total CO2
from industrial sector. CO2 emissions are estimated to be
68 MtCO2, 116 MtCO2 and 198 MtCO2 in 2010, 2020
and 2030, respectively. The estimations for the interme-
diate years are illustrated in Figure 1. Electricity and
coal products are responsible for the majority of emis-
5.1.2. Alternate Scenari o
The alternative policy options are compared to the BAU
case in order to assess their potentials of energy demand
and CO2 emission reductions. Indicators are selected to
compare the results from the viewpoint of avoided en-
ergy demand and avoided CO2 emissions compared to
BAU case. The reduction in final energy demand in the
industrial sector that occurs as a result of different policy
options are shown in Table 2.
It is found that energy efficiency improvement alone
ould reduce energy demand by 40% from the BAU w
Copyright © 2011 SciRes. LCE
Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development 169
in Thailand
Figure 1. Estimated CO2 emissions from industrial energy consumption in BAU case.
Table 2. Avoided energy demand compared to BAU under
various policies (ktoe).
20102015 2020 20252030
Energy efficiency improvement 12933379 5904 966115,168
Switching to natural gas 239624 816 10661393
CHP in designate factories 5051321 1725 22532943
Efficient electricity end-use 10871421 1857 24273172
Process integration 25996794 8880 11,60615,168
Integrated policy 572313,539 19,182 27,01337,844
case in 2030. The reductions of energy demand in this
policy case are higher compared to the other measures,
such as switching to natural gas, CHP in designate facto-
ries, and efficient electricity end-use devices. Process
integration policy shows high possibility to reduce en-
ergy demand during 2010-2015. It is, however, due to the
assumption in the model the potential reduction of en-
ergy demand decrease to 40% by 2030. Implementing all
of the policy options is estimated to reduce energy de-
mand by 22% in 2010, 43% in 2020, and 50% in 2030.
Such reductions would have potentials to reduce a large
amount of government revenues to be spent on imported
energy. The potential CO2 emission reductions in various
policies are presented in Table 3.
Table 3. Reduction of CO2 emissions in various policies
from BAU case (MtCO2).
2010 2015 2020 20252030
Energy efficiency improvement3.37 8.82 15.39 25.1839.51
Switching to natural gas 0.95 2.49 3.25 4.255.56
CHP in designate factories 2.11 5.52 7.22 9.4312.33
Efficient electricity end- u se 7.21 9.42 12.31 16.0921.03
Process integration 6.77 17.7 23.13 30.2339.51
Integrated policy 20.41 43.95 61.3 85.18117.94
5.2. Evaluation of Policy Options
There are five main criteria for this analysis, including
resource use, environmental loading, financial and eco-
nomic, social, and practical. Main findings are found that
resource use and environmental loading equal impor-
tance on both criteria. It is also found that few differ-
ences between social and practical aspects. Among sub-
criteria (see Table 4).
The annual fuel is considered to be the most important
of the resource use criteria. The annual CO2 emissions
are evaluated as the most important and the annual SO2
emissions is more importan ce than annu al NOx emissions
in the environmental loading criteria. The market matur-
ity is found the least important of the four aspects of the
Copyright © 2011 SciRes. LCE
Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development
170 in Thailand
Table 4. Evaluation results of policy options.
Main criteria &
Sub-criteria Alt1 Alt2 Alt3 Alt4 Alt5
1.Resource use
- Resource depletion 0.014 0.003 0.005 0.0060.008
- Annual electricity 0.037 0.007 0.010 0.0300.018
- Annual fuel 0.083 0.012 0.019 0.0410.038
2. Enviromental
- Annual CO2
emissions 0.084 0.013 0.023 0.0450.084
- Annual SO2
emissions 0.012 0.004 0.005 0.0110.023
- Annual NOx
emissions 0.012 0.002 0.002 0.0060.006
3. Financial &
- Construction cost 0.009 0.001 0.002 0.0040.004
- Annual operating
cost 0.023 0.004 0.005 0.0230.010
- Annual
maintenance 0.010 0.004 0.003 0.0090.012
- Market maturity 0.003 0.001 0.003 0.0010.001
4. Social
- Job creation 0.002 0.006 0.004 0.0020.002
- Public acceptance 0.015 0.003 0.004 0.0120.012
- Human health
impacts 0.013 0.002 0.001 0.0060.008
- Integration in ur
context 0.003 0.000 0.001 0.0030.001
5. Practical
- Political support
exist 0.008 0.009 0.007 0.0050.002
- Data available 0.003 0.001 0.001 0.0030.000
- Maintainability 0.026 0.005 0.012 0.0270.003
Overall 0.355 0.075 0.105 0.2340.231
financial and economic criteria. The public acceptance is
more significant than the other three social criteria and
human health is more important than job creation. In the
practical criteria, the maintainability is considered to be
the most important than the other two criterions.
The alternatives consist of five policy options: im-
provement of industrial energy efficiency (Alt1); switch-
ing to natural gas (Alt2); combined heat and power in
designate factories (Alt3); efficient electricity end-use
devices (Alt4; and process integration (Alt5). Alterna-
tives were evaluated based on their subjectively esti-
mated contribution to each criterion. The results of
evaluation are presented in Table 4. Five policy options
are prioritized with respect to the overall score of each
alternative, which is computed by multiplication of its
scores for criteria with the corresponding weights ob-
tained by AHP. Energy efficiency improvement policy
was evaluated to be the most competitive for industrial
energy policy with a score of 0.355, followed by efficient
electricity end-use policy and process integration policy
with a slightly lower score. Policy for CHP in designate
factories was form the fourth priority. Switching to natu-
ral gas policy was ranked the lowest.
6. Conclusions
This paper applied energy modeling and multi-criteria
assessment to develop, and evaluate a set of policy op-
tions which explore a range of technical, managerial and
behavioral options for Thailand’s industrial sector. The
overall research question is whether there are energy
policies to make the developmen t in Thailand’s industrial
sector more sustainable economically, socially and envi-
ronmentally. A contribution of this paper lies in combin-
ing energy model and MCDM approach to identify in-
dustrial policy options that can promote and prepare
pathway towards low-carbon economy.
Results from the modeling study shows that under the
BAU scenario the industrial energy demand would be
75,945 ktoe by 2030 and the CO2 emissions are esti-
mated to be 198 MtCO2 in the same year. Among en-
ergy-related CO2 emissions from industrial sector it is
found that electricity and coal are the main sources of
emissions. In the alternate scenario, the energy efficiency
improvement and process integration have great potential
to reduce energy demand and CO2 emissions as well as
improve the local air pollutants as co-benefits relative to
the BAU scenario. These policies have the potential of
energy-savings of 30,336 ktoe and 79.02 MtCO2 emis-
sion reductions in 2030. These amounts of energy-saving
are important for Thailand, since the country depends
upon imported fuels. Nev ertheless, if all industrial policy
options are simultaneously implemented, the highest po-
tential of energy-savings in 2030 is expected to be 37,845
ktoe. There would be a reduction of 117.94 MtCO2 by
The prioritization of five industrial policy options was
carried out by AHP and Web-HIPRE. The study devel-
oped a three-level hierarchy structure, with five main
criteria, and evaluated the importance and the competi-
tiveness of each policy option. The evaluation of indus-
trial energy policies can be concluded that the energy
efficiency improvement and is ranked highly in all di-
mensions of sustainable development with a score of
0.355. The findings suggest that efficient electricity end-
use devices and process integration have priority for pol-
icy interventions. Results of modeling and evaluating
Copyright © 2011 SciRes. LCE
Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development
in Thailand
Copyright © 2011 SciRes. LCE
policies provide useful, policy-relevant information, and
can be used as a basis for priority policy planning in the
industrial sector.
7. Acknowledgements
This work was made possible by the financial support
from the Science and Technology Research Institute,
King Mongkut’s University of Technology North Bang-
kok (KMUTNB), Th ailand. This work was also benefited
from the discussions at Department of Industrial Engi-
neering, Faculty of Engineering, KMUTNB. The authors
also gratefully acknowledge the Stockholm Environ-
mental Institute—Boston Center for supporting LEAP
model, and the System Analysis Lab., Helsinki Univer-
sity of Technology, Finland for the Web-HIPRE, web-
based decisio n ana l y sis software.
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Energy and Carbon Modeling with Multi-Criteria Decision-Making towards Sustainable Industrial Sector Development
172 in Thailand
Table A-1. Energy demand of industrial sub-sec tor in BAU case (ktoe).
Coal Petroleum Product Natural Gas
20052010 2020 20302005 2010202020302005 2010 20202030
Food and Beverage 0.1 0.1 0.2 0.3 162 202 345 589 7 8 14 24
Textile - - - - 66 82 140 238 5 6 10 17
Wood and Furniture - - - - 4 6 9 16 - - - -
Paper 141 176 300 513 73 91 156 267 - - - -
Chemical 796 992 1695 289575 93 159 271 1127 1404 23984097
Non-metallic 58187251 12,38621,15618 22 38 65 425 529 904 1544
Basic Metal 41 51 87 148 60 75 128 218 - - - -
Fabricated Metal - - - - 9 11 19 33 141 176 301 514
Others 512 638 1090 1861 553 689 117720107 9 15 26
Total 73089107 15,55726,5731020 1271217137081711 2132 36426222
Renewable* Electricity Total
2005 2010 2020 2030 2005 2010202020302005 2010 20202030
Food and Beverage 5331 6644 11,349 19,385669 833 142324316169 7687 13,13122,430
Textile - - - - 979 1220208435591049 1307 22333815
Wood and Furniture 2 3 5 8 130 162 277 472 137 170 291 496
Paper - - - - 174 217 370 632 388 484 826 1412
Chemical 32 40 68 116 846 1055180230782876 3584 612110,456
Non-metallic 247 308 526 899 492 614 104817907001 8724 14,90225,455
Basic Metal - - - - 542 675 11531970643 801 13682,337
Fabricated Metal - - - - 1399 1743297750851549 1930 32975632
Others - - - - 4 5 9 15 1076 1341 22913913
Total 5613 6994 11,947
20,4085235 652311,14319,03420,887 26,028 44,46075,945
*Renewable energy is including bagasse, biomass and wood.
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