39

Analysis of CO2 Emission Pathways of Thailand to
Achieve Carbon Neutrality 2050 Using AIM Model
Salony RAJBHANDARI, Achiraya CHAICHALOEMPREECHA,
Bijay B. PRADHAN and Bundit LIMMEECHOKCHAI*
Sirindhorn International Institute of Technology, Thammasat University
99 Moo 18, Km. 41 on Paholyothin Highway, Khlong Luang, Pathum Thani 12120, Thailand*E-mail: bundit@siit.tu.ac.th, bundit.lim@gmail.com

Abstract
The trend of rising greenhouse gas (GHG) emissions in Thailand is a matter of concern, demanding
ambitious mitigation efforts beyond 2030 and even before then to contribute towards meeting the long-termgoal of the Paris Agreement of staying within a 1.5°C temperature rise. Carbon dioxide (CO2) emissions form
the major part of the total GHG emissions in Thailand. This study aims to explore the energy, environmental
and macroeconomic impacts of limiting CO2 emissions during 2010–2050 for the underlying target ofachieving carbon neutrality by 2050. This study has developed a recursive dynamic Asia-Pacific Integrated
Model/Computable General Equilibrium (AIM/CGE) model for Thailand which is soft-linked with the
AIM/Enduse model. In addition to a business-as-usual (BAU) scenario, the study has formulated two different
CO2-mitigation scenarios, each indicating a carbon-neutrality pathway towards 2050. Results indicate that
Thailand should put more effort into mitigation actions to achieve carbon neutrality by 2050. Expansion ofrenewable-energy-based technologies, improvement of end-use energy efficiency, fuel-switching anddeployment of carbon capture and storage (CCS) technologies in both the power and industrial sectors are
identified as important mitigation measures for Thailand in curbing CO2 emissions by 2050. The results showthat the introduction of such mitigation measures would provide CO2 emission reductions, but at the expense of
economic losses. The price of CO2 mitigation was found to vary from 220 to 332 US dollars per ton of CO2(tCO2) in 2050 under the two carbon-neutrality scenarios.
Key words: AIM/CGE, AIM/Enduse, energy efficiency, net zero emissions, renewable energy, Thailand

1. Introduction
The trend of increasing global anthropogenic
greenhouse (GHG) emissions and subsequent negative
impacts of climate change have spurred both developed
and developing countries to formulate and implement
plans and actions aimed at putting them on a path toward
low-carbon development. Developing countries are more
vulnerable to the impacts of climate change. Althoughdeveloping countries are not obliged to reduce GHG
emissions in absolute terms at present, studies have shownthat their participation is a must for any substantial
reduction of GHG emissions that would stabilize
long-term GHG concentrations by the end of this century.
Energy security and rising GHG emissions are thetwo major problems that Thailand has been facing interms of energy use. In 2020, Thailand’s updated
nationally determined contribution (NDC) put forward
Global Environmental Research
26/2022 039−047
printed in Japan

stringent targets to reduce GHG emissions from theenergy sector, bringing its GHG emissions down by 20%–
25% by 2030. This is to be accomplished mainly bypromoting renewable energy resources and energy
efficiency improvements (ONEP, 2020a). However,Thailand’s updated NDC relies on a GHG emission
reduction from its reference level that would allow its
2030 GHG emissions to reach more than 1.5 times its
2005 emissions. Despite such mitigation targets, the trendof continuously rising GHG emissions in Thailand is amatter of concern and will require ambitious mitigation
efforts beyond 2030 or even before then to contributetowards meeting the long-term goal of the Paris
Agreement of staying within a 2°C temperature rise.
There is also a need for Thailand to review its pledges
regularly and set realistic and attainable longer-term
mitigation goals.
As carbon dioxide (CO2) emissions form a significant©2022 AIRIES40

S. RAJBHANDARI et al.

portion in Thailand’s total GHG emissions, this study
aims at exploring the energy, environmental andmacroeconomic impacts of limiting CO2 emissions during
2010–2050 in Thailand. It also aims at achieving carbon
neutrality by 2050 to contribute towards meeting thelong-term goal of the Paris Agreement of staying within a
1.5°C temperature rise. In doing so, a recursive dynamic
Asia-Pacific Integrated Model/Computable General
Equilibrium (AIM/CGE) model has been developed
which is soft-linked with Thailand’s long-term energy
system AIM/Enduse model.

2. Methodology
2.1 The AIM/CGE Model
This study has considered a multi-sector, recursive
dynamic AIM/CGE model to analyze the carbon
neutrality of Thailand by 2050. The AIM/CGE model issolved in one-year steps toward 2050 using theMathematical Programming System for General
Equilibrium Analysis (MPSGE) as the modelling
language embedded within the General Algebraic
Modelling System (GAMS) (Rutherford, 1999). The
AIM/CGE model is composed of a set of simultaneous
equations with no objective functions and uses mixed
complementary problems for solution. The designed
equations portray the behavior of various activities andsectors within an economy. The behavior of different
sectors is captured using fixed coefficients while those of
production and consumption activities are captured using
non-linear, first-order optimality conditions. The
formulated equations include a set of constraints that need
to be satisfied by the system and are known as themacroeconomic balance and balance of payment(Fujimori et al., 2012). The mathematical description ofFig. 1the AIM/CGE model considered in this study is based on
Dai and Masui (2017) and Fujimori et al. (2012).
The AIM/CGE model consists of a production block,government and household income and expenditure
blocks, plus a foreign trade (market) block in which both
domestic and international transactions are considered(Fig. 1). The sectoral activity is represented by a nested
constant elasticity of substitution (CES) production
function, in which inputs are classified into energy
commodities, labor, capital, materials and non-energy
intermediate inputs. More information on the AIM/CGE
model of Thailand can be found in Rajbhandari et al.
(2019).
As energy supply and demand are technologically
represented in better detail in an energy system model
than that in an economic model, a soft linkage has been
established between the bottom-up AIM/Enduse modeland the top-down AIM/CGE model by using the sector
specific GHG emission and techno-specific data
generated by the energy system model as an input to the
AIM/CGE model. To assess the economic andenvironmental impact of energy use in Thailand’s
economy, a techno-specific data linkage was establishedin terms of technology efficiency improvements,
technology penetration rates and variations in energy
share in each scenario.
2.2 Structure of the Thailand AIM/CGE Model
The Thailand AIM/CGE model was developed byusing the 2010 input-output (I/O) table obtained from the
Office of the National Economic and Social Development
Council (NESDC) to calibrate the model (NESDC, 2016).
The I/O table considered in this study is disaggregatedinto 31 production sectors of which five are energy
sectors (Table 1).

AIM/CGE Model structure for Thailand.

Analysis of CO2 Emission Pathways of Thailand

41

Table 1 Sectoral classification in the Thailand-CGE model.
Non-energy sectors

Agriculture& forestry
Transport

Crops, livestock, forestry, fisheries

Railways, road transport,
water transport, air transport,
Other transport services
Service
Water supply system,
communication, trade,
Other services
Industries
Metal and non-metal ore,
non-metallic products, basic metal,
fabricated metal products, machinery,
Food, beverages & tobacco products;
textiles; paper & printing; chemicals;
rubber & plastic products,
Construction,
Other manufacturing products
OthersOther sectors
Energy sectors
Coal & lignite, crude oil, petroleum products, gas,
electricity (including renewable & non-renewable)

2.3 The AIM/Enduse Model
The AIM/Enduse model is a technology-rich,bottom-up recursive dynamic energy system model, theobjective function of which is to minimize the total
system cost subject to numerous constraints. It is a partial
equilibrium model that can simulate calculations fornumerous years under several case studies, including
policy countermeasures for near- and long-term
mitigation actions to examine the effect of energy saving
and GHG emission abatement (NIES, 2021). Thailand’s
AIM/Enduse model includes a detailed technology
selection framework based on a linear optimization
structure.

3. Description of Scenarios
In addition to a business-as-usual (BAU) scenario,
this study has formulated two different carbon-neutrality
scenarios. The following section describes the formulated
scenarios in detail.
3.1 BAU Scenario
The BAU scenario in this study considers thecontinuation of the existing pattern of energy supply anduse considering full availability of technologies without
taking into account any climate policy intervention.
3.2 Carbon-neutrality Scenarios
Two carbon-neutrality scenarios, namely SSP1NZC2050 and SSP4-NZC2050, for net zero CO2emissions in 2050 (NZC2050) were formulated for
Thailand considering the global CO2 emission trajectories
obtained from six different integrated assessment models(IAMs) under two different Shared Socioeconomic
Pathways (SSPs), namely SSP1 and SSP4 for the
representative concentration pathway (RCP) to reach 1.9
watts per square meter (W/m2), called RCP1.9 in eachFig. 2Net CO2 emission trajectories of different scenarios.

case. SSP1 is also known as the sustainability scenario
and the SSP4 is termed the inequality scenario, which is
close to the current socioeconomic situation in Thailand.
Both carbon-neutrality scenarios assume aggressive
efforts to reduce CO2 emissions in line with the global
temperature stabilization target of limiting the temperaturerise to 1.5°C.
The country specific CO2 emission allowances for
Thailand aligned with the 1.5°C stabilization target werecomputed using an equal per capita burden-sharing
scheme based on a convergence approach, considering the
global CO2 emission trajectory with 2050 as theconvergence year. The contraction and convergence
regime considered in this study is based on the principlethat “every adult on the planet has an equal right to emit
GHGs” (Bows & Anderson, 2008). The global
CO2-emission trajectory for the SSP1 and SSP4 scenarios
for RCP1.9 are taken from the SSP Database (SSP
Database, 2018). The average value of the total CO2
emissions obtained from the six different integrated
assessment models for each scenario, SSP1 and SSP4, for
RCP1.9 were used for representing the CO2 emission
allowances allocated to Thailand to attain the carbon
neutrality target by 2050 in this study.
The total net CO2 emissions including those ofland-use, land-use change and forestry (LULUCF) in the
BAU scenario are estimated to increase from 200 MtCO2
to 477 MtCO2 during 2010–2050 (Fig. 2). The historical
net CO2 emissions during 2010–2016 were taken from theThailand Third Biennial Update Report (ONEP, 2020b).
The energy-sector-related CO2 emissions generated fromfossil fuel combustion in the power, agriculture,commercial, residential, industrial and transport sectors
during 2020–2050 were obtained from the Thailand’s
AIM/Enduse modelling analysis. The future estimationsof CO2 emissions from the industrial processes andproduct use (IPPU), waste, agriculture (non-energy related
emissions from liming and urea fertilization), and
LULUCF sectors were estimated based on the“Mid-century, Long-term Low Greenhouse Gas Emission
Development Strategy” study of Thailand (ONEP, 2021).

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S. RAJBHANDARI et al.

CO2 removal by the LULUCF sector is estimated toincrease from 62 MtCO2 in 2010 to 100 MtCO2 in 2050,
growing at a compound annual growth rate (CAGR) of
1.2% (ONEP, 2020b, 2021).
By 2050, the average net CO2 emissions are
estimated to reach 22 MtCO2 and 31 MtCO2 respectively
in the SSP1-NZC2050 and SSP4-NZ2050 scenarios. The
SSP1-NZC2050 scenario indicates a need for the peakemissions to occur in 2025 whereas the SSP4-NZC2050
scenario shows a need for peak emissions in 2020 toachieve carbon neutrality by 2050 and attain the 1.5°C
climate goal. As the CO2 emission allowances for
Thailand are computed using the global CO2 emission
trajectories for the SSP1 and SSP2 scenarios, thedifferences in the emission pathways of SSP1-NZC2050
and SSP4-NZ2050 scenarios result mainly from thevariations in global CO2 emission pathways obtained fromdifferent IAMs.

4. Input Data and Assumptions
4.1 Socioeconomic Data
Population projections up to 2040 are taken from thenational statistics of Thailand, which assume declining
fertility rates (NESDC, 2019). Assuming this trend
continues, the population is estimated to decline at a
CAGR of −0.5% during 2040 to 2050. The GDP
projections considered in this study are based on theestimated long-term average GDP growth rates of “Power
Development Plan 2018 (PDP2018)” and “Energy
Efficiency Plan 2018 (EEP2018)” (MOE, 2020a, 2020b).
The projected GDP is estimated to rise at an average
growth rate of 3.37% during 2010–2050. It should be
noted that the GDP growth rates are provided as abenchmark for the model. However, the model itself
calculates the GDP based on the I/O table, elasticities andproductivities of labor, capital, non-energy inputs andenergy. This study considers the real GDP with the priceof 2010 as a constant benchmark.
4.2 Assumptions
All the formulated scenarios consider common
assumptions in terms of the socioeconomic data andproductivities of capital, labor, energy and non-energy
inputs. Capital is classified into existing stock and newinvestment in the CGE model. Investment (fixed capitalformation), depreciation and economic growth form thebasis for updating the capital stock in the model. A 5%
depreciation rate per year for existing capital and a 10%
rate per year for households’ energy equipment are
assumed in this study. The installed capital is assumed to
be immobilized, meaning that it cannot be transferred toother sectors, whereas new investments can be made inany sector. Capital stock and capital endowment (income)are assumed to maintain a linear relationship. Full
mobility of labor is assumed across sectors within

Thailand.
The CGE model in this study assumes fixedtechnological coefficients, no constraint on resources, andefficient employment of all local resources. The model
considers both renewable (solar, wind, hydro andbiomass) and non-renewable (coal, oil and natural gas)
options for electricity generation. However, following theThai government plan, the model excludes considerationof the nuclear power option. Two different sets oftechnology options, namely, existing technology with anenergy productivity of 10% and efficient technologies
with varying energy productivities of 20%–30% areconsidered for each sector in the BAU scenario, while
improvement in energy productivities in the net zero CO2emission scenarios is considered to be 20%–70%. Theenergy productivities in the latter scenarios are estimatedbased on the cost-effectiveness of technology selection in
the energy sector under both of the carbon- neutrality
scenarios obtained from the technology-rich AIM/Enduse
model of Thailand. The economy-wide energy
productivity is measured as the ratio of GDP per unit ofprimary energy consumed (A2EP, 2022).

5. Results and Discussion
The energy, environmental and economy-wide
implications of achieving the carbon-neutrality targets by
2050 are discussed in this section.
5.1 Energy-related CO2 Emissions in the Carbonneutrality Scenarios
The attainment of carbon neutrality by 2050 will
require substantial changes in Thailand’s energy systemcompared to the BAU scenario. Based on the present
scenario of socioeconomic conditions, the total final
energy consumption (FEC) of Thailand would undergo
more than a two-fold increase during 2010–2050 in the
BAU scenario (Fig 3(a)). Petroleum products would
account for 47% of FEC in 2050 in the BAU scenario.
Electricity and biomass would play vital roles in cutting
down CO2 emissions in both the SSP1-NZC2050 and
SSP4-NZC2050 scenarios.
The rising usage of electricity on the demand side
would directly affect the electricity generation mix on the
supply side. In the BAU scenario, natural-gas-fired
combined-cycle power plants continuously dominate
electricity generation, accounting for more than a 44%
share of total electricity generation during 2030 to 2050.
Coal, natural gas and oil-fired thermal power plants would
occupy a 30% share of electricity generation in 2050 in
the BAU scenario. Renewable energy-based electricity
generation from solar and wind would account for 5%,
while those from biomass power plants would account for
4% of total electricity generation in 2050 in the BAU
scenario (Fig 3(b)). To achieve the carbon-neutrality
target, the share of renewable energy-based generation

Analysis of CO2 Emission Pathways of Thailand

from solar and wind would increase to 66% in the
NZC2050 scenarios. Both combined-cycle and thermal
power plants equipped with carbon capture and storage(CCS) technologies would emerge as promising options in
the NZC2050 scenarios. Biomass power plants equippedwith CCS (BECCS) would account for 4%–5% of total
electricity generation in 2050 in the NZC2050 scenarios.
Achievement of the carbon-neutrality target will
require significant reduction of CO2 emissions from theenergy sector by 2050 (Fig 3(c)). Decarbonization of theenergy sector is the primary mainstay for reducing CO2
emissions, especially from the power, industry andbuilding sectors. CO2 emissions from the power sector
need to be reduced to zero by 2050 to attain the NZC2050
scenarios. Improvements in energy efficiency, increased
penetration of renewable energy resources, anddeployment of CCS (including both fossil-fuel-based CCS
(a)

(b)

and BECCS) would have a positive effect on CO2emission reduction by 2050. The transport sector would
emerge as the largest CO2 emitter in the NZC2050
scenarios. Reducing CO2 emissions from the transport
sector will be a formidable task requiring major shifts
from gasoline and diesel vehicles to biofuel, electric andfuel cell vehicles. In addition, a significant modal shift
from private to public transport would need to beachieved. The CO2 emission reductions in the
manufacturing industries would mainly be achieved bydeploying efficient fossil- fuel-based combustion
technologies, particularly in thermal applications. In the
NZC2050 scenarios, carbon capture, usage and storage(CCUS) technologies would be deployed from 2035
onwards in the non-metallic, paper & pulp and chemicalindustries. Due to increased electrification in the
NZC2050 scenarios, the CO2 emissions would be zero incommercial buildings. The achievement of carbon
neutrality by 2050 would require use of behavioral change
techniques in clean cooking, requiring a shift from LPG tobiogas, biomass and electric cookstoves in the residential
sector.
5.2 Effects on Macroeconomic and Welfare Indicators
5.2.1 Impacts on GDP
The GDP of Thailand would undergo a four-foldincrease during the period of 2010–2050, i.e., from
US$335 billion in 2010 to US$1,358 billion in 2050 underthe BAU scenario, increasing at a CAGR of 3.6% (Fig. 4).
The shares of consumption demand in the household andgovernment sectors and the total investment demand as
the percentage of GDP would change during the study
period. Consumption demand would account for a major
share of Thailand’s national GDP, with its value as apercentage of the GDP remaining at over 64.0% during
2010–2050 in the BAU scenario. The share of investment
demand in the GDP would lie between 62.9% to 59.6%
during 2010 to 2050 in the BAU scenario. The net tradebalance, measured as the total value of exported goods
and services minus the total value of imported products,
remains positive throughout the study period in the BAU
scenario. This means that Thailand would continue to be(c)Fig. 3 Energy and emissions: (a) final energy (b) power
generation (c) CO2 emissions by sector.

43

Fig. 4GDP in all scenarios during 2010–2050.

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S. RAJBHANDARI et al.

an export-oriented economy to the same degree
throughout the study period under the BAU scenario.
However, the net trade deficit taken as a percentage of the
GDP would gradually decline during the study period, i.e.,from 6.6% in 2010 to 2.7% in 2050 under the BAU
scenario.
Thailand AIM/CGE modeling results show that theimposition of various carbon-neutrality scenarios would
cause a decline in the GDP. In cumulative terms, the GDP
loss would vary from 0.6% to 2.0% between the two
carbon-neutrality scenarios. The cumulative GDP loss
would be higher in the SSP4-NZC2050 scenario. This
study found that forcing CO2 emissions to decline on thepathway to attain the goals of the Paris Agreement would
lead to distortion of the GDP in either of the carbonneutrality scenarios. When seen in 2050, the GDP loss
would vary from 3.2%–8.8%, respectively, in the SSP1NZC2050 and SSP4-NZC2050 scenarios. Attainment of
the carbon-neutrality goal towards 2050 would encourage
the use of efficient mitigation measures including carbon
removal technologies. Together with stronger mitigation
measures, such steeper CO2 emission reductions would
cause output reductions from carbon-intensive industries,
mainly the coal & lignite, gas and petroleum industries,
thereby leading to GDP distortions. Comparatively, theoutput reductions in 2050 are much larger in the SSP4NZC2050 scenario (about 12%) and require lower
government consumption expenditures for purchases ofgoods and services. This results in a higher GDP loss in
SSP4-NZC2050 compared to SSP1-NZC2050.
5.2.2 Impacts on Household and Government
Consumption
Government consumption would increase by morethan five-fold during 2010 to 2050, i.e., from US$53
billion in 2010 to US$292 billion in 2050 in the BAU
scenario. Household consumption would undergo anincrease of almost four-fold during the same period in the
BAU scenario, i.e., from US$172 billion in 2010 to
US$608 billion in 2050. Compared to the BAU scenario,
the carbon-neutrality scenarios would demand asubstantial increase in government consumption in
cumulative terms ranging from 24.5% to 26.3% in both
NZC2050 scenarios. The increase is more significant in
the SSP1-NZC2050 scenario than in the SSP4-NZC2050
scenario. This is mainly due to a higher increase in thegovernment consumption expenditure for goods andservices in the agriculture & forestry, electricity,
industries, construction, trade, services and transport
sectors in the SPP1- NZC2050 scenario. Meanwhile, theCO2 mitigation pathways following the carbon-neutrality
scenarios would cause a severe decline in cumulative
household consumption. In cumulative terms, the decline
would be in the range of 10.0% in the SSP1-NZC2050
scenario to 12.8% in the SSP4-NZC2050 scenario. The
results show that the CO2 emission reduction targets

would cause a decline in production output, especiallyfrom carbon-intensive industries, thus leading to a decline
in the household consumption of goods and services in the
carbon-neutrality scenarios. The government takes
essential responsibility during economic downturns andincreases spending to boost economic activities.
Government spending on welfare activities is higher
during 2030 to 2050 in both carbon-neutrality scenarios,i.e., in the period when the CO2 emission pathways
undergo steeper reductions. Thailand’s AIM/CGE
modeling results show a need for increased government
spending on welfare benefits, mainly in the forestry,electricity, construction, machineries, transport andservice sectors to achieve carbon neutrality by 2050. The
service sector including, banking & insurance, real estate,business and public services (such as public
administration, education, research & training, sanitary,
hospitals, restaurants and hotels) would comprise thelargest share of total government and household
consumption in both the BAU and carbon- neutrality
scenarios. The share of the service sector in total
consumption would increase from 43.2% in 2010 to
45.9% in 2050 in the BAU scenario. By 2050, the share of
the service sector would be much larger in the NZC2050
scenarios, ranging from 48.9% to 52.7%. The service
sector would play a critical role in Thailand’s economic
development and would account for a larger share in the
NZE2050 scenarios. The results indicate that thecountry’s economy is expected to shift from an agrarian
economy to a more service-oriented economy in the longrun. This could be a reasonable trend for an emerging andbooming economy like Thailand with a rich tourism
sector progressing towards industrialization andcommercialization.
5.2.3 Welfare Losses
Welfare losses are an important indicator of
decreased economic and social well-being as a result of
the imposition of policy shocks. The drastic decline inhousehold consumption would lead to a sharp increase in
welfare losses in the carbon-neutrality scenarios (Table 2).
Welfare losses increase in the range of 9.5% to 10.9%
among the considered carbon-neutrality scenarios during
2010–2050. In both NZC2050 scenarios, the welfare
losses tend to be higher during the period of 2030–2050.
This is because of the sharp decline in GHG emissions
during this period which would cause household
consumption to drop by 13.5% in SSP1-NZC2050 to16.2% in SSP4-NZC2050. The country’s economy would
Table 2 Welfare losses compared to the BAU scenario.

Scenarios% loss in cumulative terms
2010–2050 2020–2030 2030–2050

SSP1-NZC2050

9.5

3.7

12.9

SSP4-NZC2050

10.9

7.7

14.0

Analysis of CO2 Emission Pathways of Thailand

face severe damage from increasing welfare losses if theCO2 mitigation goals specified by the Paris Agreement’s
1.5°C targets as considered in this study are imposed withlimited technological improvements.
5.3 Economic Implications of the NZC2050 Scenarios
Carbon prices, or CO2 mitigation costs, presented inFig. 5 reflect the stringency of mitigation requirements for
Thailand to achieve carbon neutrality by 2050. Based on
the CO2 emission pathways considered in this study, thecarbon price increases with the level of mitigation efforts
across all scenarios. The price of CO2 mitigation is in therange of US$32-157/tCO2 in 2030, US$256-310/CO2 in
2040, and US$220-332/tCO2 in 2050 between the two
NZC2050 scenarios.
Values for the SSP4-NZC2050 scenario are relatively
higher than those of the SSP1-NZC2050 pathway during
2030–2040, but the difference decreases over time,
particularly beyond 2040 (Fig. 5). This is because in the
SSP1-NZC2050 scenario, there is comparatively lessmitigation activity during 2025–2040 and more mitigation
towards 2050. The low mitigation target of the CO2
mitigation pathway towards 2050 depicted by the SSP4NZC2050 scenario exhibits the lowest values of carbon
prices across the two carbon-neutrality scenarios in 2050.
The wide range of CO2 mitigation prices across the
scenarios depends on many factors, including CO2
mitigation targets, availability of technology andcharacteristics of technologies in terms of investment costs
and rate of deployment (Riahi et al., 2017; Rogelj et al.,2015). The pathways that have limited flexibility for
substituting fossil fuels with low-carbon technologies
such as in the SSP4-NZC2050 scenario in this analysis,
provide high estimates of CO2 prices during 2030–2040.
However, towards 2050, the prices of CO2 mitigation in
the SSP1-NZC2050 scenario tend to be the higher
between the two scenarios. Though the level of emission
reductions does not appear to differ much between thetwo carbon-neutrality scenarios, the differences intechnology selection, variations in government
consumption expenditure and reductions in production
output and household consumption are responsible for the

Fig. 5

Variations in carbon prices across various
carbon-neutrality scenarios.

45

variation in carbon prices. A higher carbon price implies
larger reductions in household consumption of goods andservices. Such large reductions in household consumption
could be minimized by switching towards cleaner andmore efficient energy resources and technologies.
The price of CO2 mitigation is sensitive to the limited
availability of technologies and varies according to the
non-availability of BECCS technologies (Bauer et al.,2020). The deployment of technologies and mitigation
strategies is also sensitive to varying discount rates(Rogelj et al., 2018). The result of this analysis is basedon a 10% discount rate for energy equipment and 5%
depreciation rate for the existing capital. In addition,socioeconomic conditions and policy assumptions greatly
affect the price of CO2 mitigation. Delayed mitigation
policies and measures may even result in a further
increase in carbon prices. The increased carbon prices insuch cases result mainly from a need for stronger efforts
to counterbalance the higher emissions. Finally, studies
reveal that there is no unique path for the price of CO2
mitigation and it varies considerably across studies(Rogelj et al., 2018).

6. Conclusion
This study was designed with the aim of analyzing
the energy, environmental and macroeconomic impacts ofattaining carbon neutrality in Thailand by 2050. The
introduction of low-carbon mitigation measuresconsidered in this analysis provides benefits in terms of
CO2 emission reductions, but the imposition of suchstrategies would be economically inefficient because of
reduced GDP and welfare. To achieve thecarbon-neutrality target, the energy sector would need toundergo deep decarbonization. The power sector would
need to achieve zero CO2 emissions by 2050. Renewableenergy, CCS and BECCS technologies would play key
roles in reducing CO2 emissions from the power sector.
Results suggest that expansion of renewable energy-based
technologies, improvement of end-use energy efficiency,
fuel switching and deployment of CCS and BECCS
technologies in the power and industrial sectors will be
important mitigation measures for Thailand in attaining
carbon neutrality by 2050.
However, several limitations are involved in thisstudy. The analysis of economic impacts is based on the
input-output table of 2010. The study considers national
population and GDP from governmental documents, butestimates Thailand’s CO2 emission allowances for
attaining carbon neutrality by 2050 using the global CO2
emission trajectory of the SSP database. Moreover, thisanalysis is based on underlying assumptions of fixedtechnological coefficients, constant return of scale, noconstraints on resources and efficient employment of all
local resources. Nuclear-based power generation, which
could be a potential option to abate CO2 emissions, is not

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S. RAJBHANDARI et al.

considered in this analysis. Attainment of either of the
carbon-neutrality scenarios would necessitate asubstantially higher level of production from theagriculture and forestry sectors to meet demand. The
expansion of production from the forestry sector, however,would require not only a larger landmass area, but also
technological improvements for efficient use of bioenergy
resources. In addition, afforestation would need to be
implemented on a large scale to sequester carbon
emissions. This study, however, does not consider
limitations on the availability of land, energy and water
resources. Consideration of such limitations would
certainly change the magnitude of production activity
from the forestry sector and yield more realistic results.
Still, this study provides the insight that the forestry sector
could play a significant role in fostering CO2 mitigation
opportunities for Thailand. Consideration of an increased
removal potential with reforestation could further lower
net CO2 emissions in 2050. This would most probably
lead to lower differences in economic losses in the
carbon-neutrality scenarios compared to BAU. Finally,high uncertainties and challenges remain to the wide
adoption of CCS technologies in both the electricity
generation and manufacturing industries.
Acknowledgement
The authors would like to thank the National Institutefor Environmental Studies for their support of AIM
modeling, and the Knowledge and Communications,
Strategic Management Office (SMO) at the Institute for
Global Environmental Strategies (IGES) and theThammasat University Research Unit in Sustainable
Energy and Built Environment for their support in datacollection and analyses.
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47

Salony RAJBHANDARI
Dr. Rajbhandari is working as a researcher at the
Sirindhorn International Institute of Technology,
Thammasat University, Thailand. She completed
her Doctoral degree in the Engineering andTechnology program at the same institute. She
holds a Master’s degree in Energy from the
Asian Institute of Technology, Thailand, with specialization in Energy
Economics and Planning, and has a Bachelor’s degree in ElectricalEngineering from the Institute of Engineering, Tribhuvan University,
Nepal. Her major areas of research interests include climate change
mitigation, energy economics and environmental policy modeling, energy
economics and planning, energy access, and emission inventory. She has
expertise in both energy and macroeconomic modeling using the
MARKAL, AIM/Enduse and AIM/CGE models.

Achiraya CHAICHALOEMPREECHA
Ms. Chaichaloempreecha is a Ph.D. candidate in
the Engineering and Technology program at
Sirindhorn International Institute of Technology,
Thammasat University, Thailand. She has
expertise in energy policies and management andenergy modeling, especially in the energy
sectors. Her background is as a mechanicalengineer. She is currently participating in “The revision and update ofThailand’s Long-Term Low Greenhouse Gas Emission Development
Strategy (LT-LEDS) and Thailand’s National Determined Contribution(NDC)” project. She has published an article on the topic of “Thailand’s
mid-century greenhouse gas emission pathways to achieve the 2 degrees
Celsius target.”Bijay B. PRADHAN
Bijay B. Pradhan works as a postdoctoral
researcher at the Sirindhorn International
Institute of Technology, Thammasat University,
Thailand. He completed his Doctoral degree inthe Engineering and Technology program at thesame institute. He holds a Master’s degree inEnergy from the Asian Institute of Technology,
Thailand and has a Bachelor’s degree in Mechanical Engineering from
Kathmandu University, Nepal. His major areas of research interests
include climate change, energy modeling, energy economics, energy
policies, energy efficiency and renewable energy.

Bundit LIMMEECHOKCHAI
Dr. Limmeechokchai graduated from King
Mongkut's Institute of Technology, North
Bangkok with First Class Honours in
Mechanical Engineering. From there, he gained
his Master and Doctoral degrees in Engineering
at the Asian Institute of Technology, Thailand.
His research expertise includes energy
technologies, energy efficiency, energy economics, planning and policy,
modeling of energy and environment systems, renewable energy,
low-carbon technologies, demand-side management, integrated resource
planning, and CO2 mitigation. Currently he works at the Sirindhorn
International Institute of Technology, and heads the Thammasat
University Research Unit in Sustainable Energy and Built Environment.

(Received 2 April 2022, Accepted 30 September 2022)