This section provides the methodology, including the conceptual framework of the AIM/Enduse model and the assumptions used in analyses. Moreover, the scenario description is elucidated.
Conceptual framework of the AIM/Enduse model
The Asia–Pacific Integrated Assessment model/Enduse (AIM/Enduse) is a bottom-up model that relies on a framework of linear programming based on the General Algebraic Modeling System (GAMS) which is a high-level modeling system for mathematical programming problems . The AIM/Enduse model is a partial equilibrium model which selects the combination of technological frameworks inside an energy and environment system for medium to long-term assessment . It was developed by the National Institute for Environmental Studies (NIES), Kyoto University, the Mizuho Information & Research Institute, and several research institutes in the Asia–Pacific region . The selected technologies are assessed by using a linear optimization framework. The objective function of the model is to minimize the total system cost. In addition, several constraints can be included in the optimization process for satisfying service demand growth, analyzing maximum share of technology diffusion, preparing energy resources, reducing pollutant emission, ensuring equipment stock and so on [31,32,33]. The AIM/Enduse model is a recursive dynamic model used to solve the problem for multiple years. It can analyze the time-series optimized results under various scenarios, including policy packages in each sector [34, 35]. The model considers the balance between the supply and demand sides. The supply side and demand side interact by using the equation of total service demand and supply balance in the AIM/Enduse model. The total service demand must not exceed the total service output supplied (see the equations in Additional file 1).
The model structure primarily consists of three components including “Energy”, “Energy Technology”, and “Energy Service” (as shown in Fig. 1) . “Energy Technology” refers to a device that provides useful “Energy Services” by consuming “Energy”. The “Energy” information consists of fuel types and fuel prices as well as designated emission factors to determine the emissions. “Energy Technology” considers the details of the technologies such as capital cost, fixed and variable cost, efficiency or energy consumption per output unit, and lifetime. The energy service demands are determined exogenously from external sources. The AIM/Enduse model calculates energy consumption from the amount of specific energy consumed by each technology and the combination of technologies. Emissions are determined from energy consumption and emission factors of fuel types. In addition, the model can analyze impacts of fuel switching, energy savings, emission mitigation, and fuel diversification.
Thailand’s AIM/Enduse model
The structure of the AIM/Enduse model covers both the demand and supply side which can be connected by the concept of internal services and energy sources in the model. The energy service of the power sector in the supply side can be linked to the energy component of devices in the demand sector . The linkage between the supply side or the power sector and the demand side is illustrated in Fig. 2.
The structure of energy supply for power generation particularly relies on fossil fuel, for example, natural gas, coal, and oil. Renewable energy resources in the energy supply include biomass, biogas, hydropower, solar and wind. The conventional generating technologies in the baseline model include the combined cycle and thermal power plants. The cogeneration capacity will be increasing in the future plan [36,37,38]. The current policies have included promotion of capital-intensive renewable energies for power generation such as solar, wind and biomass; they are integrated into the model along with the continuing development of existing hydro-power projects [37,38,39]. To promote low-carbon electricity generation, the integrated gasification combine cycle (IGCC) and supercritical power plants are required. Similar to the clean technologies, carbon capture storage (CCS) is the alternative technology to mitigate CO2 emissions.
Electricity production from various generation technologies should be integrated with other energy sources to provide a sufficient final energy supply for service appliances in the demand-side sectors (see Fig. 2). The demand-side sectors include the residential and commercial building, industrial, and transportation sectors. These sectors have various final energy consumption types such as liquefied petroleum gas (LPG) in the building sector, natural gas and coal in the industrial sector, and gasoline and diesel in the transportation sector. Furthermore, renewable energies such as biomass, charcoal, and agricultural wastes are consumed in the demand-side sector. For example, fuel wood, charcoal and paddy husk are supplied for traditional stoves in Thailand’s rural households . In the development of low-carbon Thailand, energy efficiency improvement and renewable energy are the important measures to reduce energy consumption and mitigate emissions. Several types of services are categorized in terms of quantitative demand for various appliances in the sector such as space cooling, lighting, heating, etc.
SSPs of Thailand
Based on the characteristics of an SSP, a number of possible elements have been concluded . The socio-economic information, a combination of social and economic concepts, is the key factor used in evaluation of the SSPs situation in Thailand.
Thailand’s population increased from 59.46 million in 1995 to 66.19 million in 2017 with an average annual growth rate (AAGR) of 0.49% . The Office of the National Economic and Social Development Council (NESDC)  projected the population for Thailand during 2010–2040 to increase at an AAGR of 0.08%. The population will increase until 2028 and then decrease until 2040 .
In the SSPs database, Thailand’s population has been estimated in various SSP situations including SSP1, SSP2, SSP3, SSP4, and SSP5 . When assessing the population in the SSP situations, the population in SPP1, SSP2, SSP4 and SSP5 will decrease with AAGRs of 0.179% 0.282%, 0.137%, and 0.253%, respectively, during 2010–2040. The population in SSP3 has increased and will continue to increase until 2040. The population pattern in SSP4 is similar to Thailand’s population projected by NESDC. However, the decreasing population projected by NESDC occurs more rapidly than the population decrease in the SSP4 situation.
The GDP is a driving socio-economic indicator for tracking economic development. Thailand’s GDP increased from 237.88 billion US$ in 1990 to 722.27 billion US$ in 2017 with an AAGR of 4.20% per year . In this paper, the GDP projection during 2018–2036 was employed, which is consistent with the GDP used by the Energy Policy and Planning Office (EPPO) . The estimated GDP will increase to 1512.92 billion US$ in 2036. The AAGR during 2017–2036 will be 3.97%. Manufacturing industries have the highest share in the GDP followed by wholesale and retail trades, financial and insurance activities, and transportation. Half of the Thailand’s GDP share is in the Greater Bangkok area followed by the Eastern area, the Northeastern area, and the Southern area. The Western area of Thailand has the smallest share of GDP of 3.00% .
The GDP of Thailand has been estimated under various SSP situations including SSP1, SSP2, SSP3, SSP4, and SSP5 . The AAGR of estimated GDP in the SSP situations are 4.91% in SSP1, 4.21% in SSP2, 3.26% in SSP3, 3.91% in SSP4 and 5.61% in SSP5, respectively, during 2015–2040. The GDP estimated by NESDC and the GDP in the SSPs increase at different rates [43, 44].
The proportion of poverty in Thailand is one of the indicators for considering SSP situations. Like many other countries in the East Asia Pacific, Thailand has been successful in reducing poverty over the last few decades. From 2007, the number of poor people in Thailand decreased from 20.0% to 8.6% in 2016, a rate of 9.0% per year . Moreover, Thailand’s household poverty diminished from 3.5 million households in 2007 to 1.7 million households in 2016 with an AAGR of 7.7% . Most of the household poverty is located in rural areas where it accounts for 60.3% of total household poverty in Thailand.
Education in Thailand is provided mainly by the Thai government through the Ministry of Education from pre-school to senior high school. In 2016, the budget for education in Thailand was about 3.9% of GDP and 20.2% of national budget . However, the education budget in 2016 was higher than the education budget in 2010 by almost 1.34 times . Equal access to education is an important problem and is a result of the income gaps . The issues that lead to discrimination include disability and education level. There are various factors affecting people’s opportunities to access education, such as the inequality of income and property, the dissatisfaction with education, gender, age and location .
The government of Thailand has established the National Health Security Office (NHSO) for covering and accessing public health with confidence. Everyone who lives in Thailand has been provided health insurance from the government since 2002. The budget for public health was 10.08% of the national budget in 2016 . The public health insurance covers 99.0% of people in Thailand .
Thailand’s research and development (R&D) budget increased from 2005 to 2016 with an AAGR of 19.06% . The R&D budgets were derived from other sources (79.41% of the budget) such as the private sector; only 20.59% of the budget came from the national budget in 2016 . Besides the development of domestic technology, the technological development needs support from developed countries, such as the Joint Crediting Mechanism (JCM) project between Japan and Thailand. The JCM facilitates diffusion of leading low-carbon technologies, products, systems, services, and infrastructure as well as implementation of mitigation actions, and contributes to sustainable development of developing countries. The JCM contributes to the ultimate objective of the UNFCCC by facilitating global actions for GHG emission reduction or removal [52, 53].
The projected socio-economic indicators are population and GDP. The SSP4 is close to the situation of Thailand’s current socio-economics. Based on the SSP analysis, Thailand’s current socio-economic situation is under the SSP4 narrative or inequality situation. It is implied that Thailand faces a medium socio-economic challenge for mitigation and a high socio-economic challenge for adaptation.
Key scenarios are considered in this study, including a business-as-usual scenario (BAU), carbon prices without CCS scenarios (CT) and carbon prices with CCS scenarios (CT_CCS). The CT and CT_CCS scenarios adopted in this study have been based on the considerations of the effect of carbon prices on energy intensive sectors (especially in the power sector and the industrial sector). Besides the BAU scenario, the CT and CT_CCS scenarios include four carbon prices pathways. The details of the scenarios are given below.
Business-as-usual scenario (BAU)
In this study, the GHG emissions in the BAU scenario follow the emission trend in the updated national GHG emissions inventory . In the BAU scenario, carbon prices and CCS technology are not considered. In the power sector, electricity generation from renewable energy is about 5% of the total electricity generation mix. In the transport sector, the technology mix shows that vehicles using biofuels will be limited to a share of 35% in 2050. Electricity and LPG are the major energy consumption in the residential and commercial sectors, accounting for 50% of energy consumption in those sectors. In the industries, the efficient technologies such as efficient motors and boilers are considered in the end-use services.
The selected technologies in the countermeasure scenarios, including the CT and CT_CCS scenarios, depend on a linear optimization framework where system costs are minimized under constraints. System costs include the initial costs, the operating costs of technologies, energy costs, taxes and subsidies, etc. (see Additional file 1). The reduction of technology cost is also considered in this study; the costs of wind turbines and solar PV in 2050 are assumed to be 40% of the cost in 2005. In this study, the carbon price will accelerate the adoption of low-carbon technologies.
Carbon prices without CCS in the CT scenario
Various studies selected the SSPs for their analysis [22, 55,56,57]. Oshiro et al.  considered the socio-economic conditions by considering the SSP2 scenario for attaining the net-zero emissions pathway by 2050 in Japan. The fossil-fueled development scenario or SSP5 is considered in the Nepal study . Pradhan et al.  analyzed the emission reduction target with the carbon price profiles under SSP5 during 2015–2050. Adib et al.  assessed the future rainfall pattern considering climate change in Malaysia in the SSP1-2.6, the SSP2-4.5, and the SSP5-8.5 scenarios during 2021–2080.
Out of the five SSPs, the SSP5 is the least environmentally friendly pathway. The scenario relies on the fossil fuel development. The carbon prices profile in SSP5 to achieve the RCP2.6 are high. However, SSP5 is not in-line with Thailand’s situation. The renewable energy and low-carbon technologies have revealed a tremendous progress in Thailand. Thus, SSP4 is considered to be the pathway for Thailand to achieve the 2 degrees Celsius target . Final energy demand is moderately coupled to economic activity, which results in large disparities in energy consumption because of slow income convergence .
In this study, population and GDP are considered from Thailand’s government policy/plan for envisaging the service demand in the AIM/Enduse model. The carbon price profiles in SSP4 for achieving RCP2.6 are considered to analyze the effects on Thailand’s energy system. The change of future service demands following the socio-economic information of all SSPs is a limitation in this study. Besides, the study time frame is limited to the period 2005–2050 due to the limit of the AIM/Enduse model for the long-term technology selection.
The CT scenarios simulate GHG mitigation by using the carbon prices. The scenarios have been mitigated according to the renewable energy technology, efficiency improvements, advance technologies, and fuel switching during 2020–2050. In the CT scenarios, various carbon prices are considered, including zero (T0) and four different carbon prices pathways under the SSP4 scenario for RCP2.6 .
Several studies suggest that the carbon price for achieving specific climate targets will significantly differ across models and scenarios [5, 60,61,62]. The carbon prices in this study were obtained from the database of SSPs [5, 43]. Four carbon price profiles were obtained from Asia–Pacific Integrated Model/Computable General Equilibrium (AIM/CGE), Global Change Assessment model (GCAM), Integrated Model to Assess the Global Environment (IMAGE), and World Induced Technical Change Hybrid model (WITCH). Hereafter, the CT scenarios are referred to as the AIMC, the GCAM, the IMAGE, and the WITCH scenarios. The carbon prices trajectories are shown in Fig. 3.
Carbon prices with CCS in the CT_CCS scenario
Various literature emphasizes that the deployment of CCS is necessary to achieve the targets [63,64,65,66,67]. The IPCC Fifth Assessment report (AR5) concluded that if bio-energy, CCS, and BECCS are limited, keeping warming to below 2 degrees Celsius cannot be achieved . However, the CCS technologies could be cost-competitive with other low-carbon technologies by 2030 . The CCS technologies consider both fossil fuel and bio-energy based power plants in the CT_CCS scenario with the share setting of CCS technologies ranging from 15 to 30% during 2030–2050. The carbon prices in this scenario are the same as in the CT scenarios. Table 1 summarizes the constructed scenarios for considering Thailand’s 2 degrees Celsius target.
CO2 removals by sinks
This study considers the CO2 removal by forestry areas. The calculation of CO2 sink follows the IPCC guideline. Land use, land use change and forest (LULUCF) in this study were divided into three categories: changes in forest and other wood biomass stocks, forest and grassland conversion, and abandonment of managed land [69, 70]. The Royal Forest Department (RFD) of Thailand announced that total forest areas will be 40% by 2026 [71, 72].
The RFD projected that the economic forest area will increase to 15% of the country’s total area in 2026. However, this study assumes that the 15–level will be reached in 2030. The economic forest area is estimated by using the linear interpolation from 2013 to 2030. The economic forest area will be 77,646.9 km2 in 2030. It was assumed that the area will be constant towards 2050. The estimated areas are assumed to be constant from 2030 onwards due to land limitations. Figure 4 shows the CO2 removal by sinks in this study. Total annual CO2 removal by sinks will be 187 MtCO2 in 2050. The cumulative CO2 removal by sinks will be 2.7 GtCO2, during 2010–2030 and 6.4 GtCO2 during 2010–2050, respectively.
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