Modelling Renewable Energy and Resource Interactions Using CLEWs to Support Thailand’s 2050 Carbon Neutrality Goal

Abstract

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29.

1. Introduction

Climate change is a pressing issue that requires immediate attention and action. The Intergovernmental Panel on Climate Change (IPCC) report confirms that rising global temperatures have severe and widespread effects on the environment, the economy, and human society [1]. In recent decades, global awareness of issues such as climate change has increased, leading to international agreements like the Paris Agreement. This agreement aims to limit global temperature increases to below 2 °C and 1.5 °C compared to pre-industrial levels. At international conferences such as the Conference of the Parties (COP), nations deliberate and formulate solutions, including the reduction in greenhouse gas emissions, the promotion of renewable energy, and the support of underdeveloped countries in adaptation efforts [2,3]. As a significant energy consumer in Southeast Asia, Thailand is undertaking a substantial energy transition to fulfil national sustainability goals and global climate obligations. Thailand is committed to achieving zero greenhouse gas emissions by 2070, and the National Energy Plan (NEP) plays a significant role in this effort. The NEP states that improving energy efficiency, utilising electric vehicles, planting trees, and utilising renewable energy are all methods to enhance carbon sequestration capabilities. The CLEW framework and OSeMOSYS are essential for Thailand, as its land, water, energy, and climate systems are all interconnected. This method enables a comprehensive strategic planning process that aligns with national legislation and international climate commitments. Developed countries have agreed to provide additional financial support to developing countries to help them reduce greenhouse gas emissions and mitigate the effects of climate change. The new financial goal is higher than the former one, which was USD 100 billion per year [4]. There is an agreement to increase the amount of renewable energy in the world’s energy grid, aiming to generate three times as much by 2030 [5]. Thus, the COP29 conference is a critical platform for member states to collaborate in the development of strategies and actions to realise the net-zero emissions objective. The conference will emphasise the importance of fostering international cooperation, reducing greenhouse gas emissions, and supporting finance and technology in order to effectively and sustainably confront the climate change challenge.

Thailand ranks among the largest energy consumers in Southeast Asia. In response to global climate issues, the nation is experiencing a pivotal phase of energy transition. The National Energy Plan (NEP) serves as the primary framework for enhancing the sustainability of Thailand’s energy system. The country has officially stated that it aims to achieve net-zero greenhouse gas emissions by 2070 [6]. With twin objectives of improving energy security and lowering reliance on fossil fuels, the NEP describes thorough plans to raise the share of renewable energy, especially solar energy, wind energy, biomass, and hydropower [7]. To help lower emissions in the transportation sector, it also underlines the evolution of clean energy technologies, including energy storage, smart grid infrastructure, and extensive deployment of electric vehicles (EVs) [8]. Reducing energy intensity and supporting innovations like carbon capture and storage (CCS) will help the NEP improve energy efficiency across all significant sectors, especially industry, transportation, and buildings [9]. The scheme also supports forest conservation and reforestation as natural means of increasing Thailand’s capacity to act as a carbon sink. The geographical features of Thailand present great possibilities for renewable energy. The nation benefits from high levels of solar irradiation year-round [10], significant biomass from agriculture and byproducts, and, in some areas, suitable wind paths [11]. Nevertheless, numerous technological and financial obstacles are impeding progress. The high costs of installing solar PV and wind turbines remain a significant issue, particularly in rural and community-based areas [12]. While the long-term expenses of renewable energy operations are reduced, the acquisition of the necessary funds to implement them on a large scale remains a substantial obstacle [13]. STEEP analysis has been employed to develop two policy-driven scenarios: the Classic scenario, which emphasises technology-driven paths such as renewable energy, EVs, and CCUS, and the Orchestra scenario, which emphasises decentralisation, community empowerment, and market-based mechanisms in order to facilitate the transition. Both scenarios demonstrate the necessity of cross-sectoral collaboration to ensure sustainability and reduce emissions [14]. The CLEWs framework and the OSeMOSYS energy modelling program work together to provide a detailed analysis that considers the interconnectedness of Thailand’s energy, land, water, and climate systems. It enables planning for multiple scenarios by combining changing policies with limited resources. This strategy demonstrates how Thailand can achieve its NEP and COP29 goals by reducing its reliance on fossil fuels, increasing investments in renewables, and planting trees to absorb CO₂. This will enable the people of Thailand to make informed decisions regarding energy in the long term [15,16].

The National Energy Plan (NEP) presents a significant gap in research, as it still fails to account for the interconnectedness of energy, water, land, and climate in national development plans. Most energy modelling studies in Thailand employ outdated approaches, such as LEAP (Long-range Energy Alternatives Planning System) or MARKAL/TIMES (Market Allocation/The Integrated MARKAL-EFOM System). These are often utilised for long-term energy planning; however, they predominantly concentrate on the temporal progression of energy systems and fail to sufficiently integrate contributions from other sectors, such as water and land use [17]. LEAP offers intuitive interfaces for demand forecasting; however, it cannot model water resources or spatial dynamics [18]. Although MARKAL and TIMES effectively optimise techno-economic energy systems, they do not account for climate change-induced water scarcity and land use constraints [19]. The CLEWs’ OSeMOSYS-integrated design facilitates a clear illustration of the interconnections between land, energy, water, and climate systems. The interconnectedness of agricultural water demand, land use changes, and renewable energy growth facilitates the analysis of integrated development pathways in resource-constrained countries, such as Thailand. The energy sector primarily concentrates on cost optimisation and supply–demand equilibrium, often ignoring the intersectoral linkages and feedback effects typical of complex systems [20]. This constraint jeopardises the accurate assessment of essential trade-offs, including the water intensity of energy production in drought periods and the competition for land between biomass cultivation and food production. This restriction makes it more challenging to accurately evaluate important trade-offs, such as the amount of water required to produce energy during dry spells and the competition for land between growing biomass and food crops. The CLEWs (Climate, Land, Energy, and Water systems) framework addresses this issue by providing a comprehensive framework that encompasses all linkages between sectors, facilitating a more unified evaluation of resource allocation, environmental impacts, and policy effectiveness [21,22,23]. When used with OSeMOSYS, CLEWs models make scenario development and policy analysis more straightforward, flexible, and easy to reproduce in different countries. This makes it easier to develop long-term scenarios that have numerous policy implications [24,25]. The CLEWs–OSeMOSYS hybrid is far better than traditional models because it links natural resource limits to energy trajectories. This is particularly important for countries like Thailand, which are vulnerable to climate change and seek to reduce their carbon emissions.

The unique aspect of this work is that it utilises a modified version of the integrated CLEW–OSeMOSYS model, specifically designed to accommodate Thailand’s social, environmental, and regulatory contexts. This method utilises data crucial to the entire country, including the potential for renewable energy, water availability, and land use characteristics, in conjunction with international modelling criteria, to make predictions about policy scenarios up to 2025. This study examines broader trade-offs and co-benefits at the system level, such as reduced water consumption and fewer land use conflicts, rather than focusing on energy measurements as previous research has done with different decarbonisation methodologies. This provides Thailand’s National Energy Policy and its international commitments under COP29 with a comprehensive, multidisciplinary decision support tool to help it achieve its goal of carbon neutrality. By using a customised CLEWs–OSeMOSYS modelling framework, this research significantly improves Thailand’s integrated resource and energy plan. This study differs from Thailand’s typical energy modelling approaches, including LEAP and MARKAL/TIMES, which primarily focus on finding the optimal balance between energy costs and supply and demand. It clearly illustrates how climate, land, energy, and water systems interact with one another across various sectors. The new aspect of CLEWs–OSeMOSYS is that it has been carefully calibrated and contextualised for Thailand’s unique sociopolitical and policy environment. They include datasets specific to Thailand, such as full renewable energy potentials, water availability, and complex land use profiles. The objective of this work is to develop a national-scale CLEWs–OSeMOSYS model specifically designed for Thailand to investigate long-term carbon neutrality methods up to 2050. The aims are threefold: (1) to build a baseline energy trajectory, (2) to simulate a carbon neutral pathway with integrated CLEWs modelling, and (3) to evaluate the ramifications of renewable integration, energy imports, and governmental limitations. The subsequent sections of the paper are organised as follows: Section 2 delineates the methodology and model architecture, Section 3 articulates the scenario framework and principal assumptions, Section 4 presents the results and discussion, and Section 5 draws conclusions and covers policy implications.

2. Materials and Methods

2.1. CLEWs–OSeMOSYS Modelling Framework

This study uses an integrated modelling framework that combines the Climate, Land, Energy, and Water systems (CLEWs) approach with the Open Source Energy Modelling System (OSeMOSYS) to look at how Thailand is moving toward a low-carbon and sustainable energy system. The CLEWs framework helps us understand how energy, land, water, and climate sectors are all connected at the system level. OSeMOSYS, on the other hand, is a flexible and strong platform for analysing and improving energy systems over the long term [20,21]. There are four main steps in the modelling process:

Step 1: Model initialisation and data acquisition. Energy supply and demand, renewable resource potentials (including solar, wind, and biomass), investment and operational expenditures, greenhouse gas emissions, and land and water utilisation were the subjects of input data collection. The OSeMOSYS Starter Data Kit (SDK) served as a baseline for Thailand, which was customised [26]. To ensure that the model accurately reflects national contexts while maintaining international comparability, supplementary data were obtained from international organisations, including the FAO (2024), OECD (2024), and IEA (2024).

Step 2: System linkages and scenario development. The CLEWs framework offers a methodical approach to integrating energy, land, water, and climate systems, thereby facilitating the evaluation of the impact of changes in one sector on the others. This investigation examined renewable energy technologies, such as solar, wind, and biomass, in various future scenarios, illustrating the varying levels of technology implementation and policy decisions. This encompasses the implementation of energy storage systems, electric vehicles (EVs), and carbon capture and storage (CCS) technologies [22,25]. The scenarios were formulated to investigate strategies that may help Thailand achieve carbon neutrality by 2050 [3]. This methodology facilitates a comprehensive evaluation of trade-offs and advantages, aiding in the identification of viable and sustainable solutions for emission reduction while maintaining energy security and optimising the utilisation of natural resources.

Step 3: Transformation and output analysis. OSeMOSYS simulations span the timeframe from 2020 to 2050, enabling the examination of historical data alongside future forecasts. This temporal framework enables the model to analyse long-term mitigation trajectories consistent with Thailand’s carbon neutrality objectives. Despite the availability of actual investment data for 2020–2023, standardised assumptions are utilised across all scenarios for uniformity [21]. The CLEWs approach breaks this process into four phases: input resources, transformation, output, and end-user (Figure 1). This method helps to evaluate system-wide interactions, including the effect of land competition on bioenergy expansion and the application of solar and wind energy to water saving.

Step 4: Policy-relevant realisations and consequences for sustainability. The integrated framework enables the evaluation of results across multiple sectors, including energy security, emissions reduction, investment distribution, and natural resource sustainability. Quantitative data that allow one to compare development paths under different policy environments help guide well-informed policymaking [24,27]. By providing robust and replicable tools for strategy development through international frameworks [16], the framework helps strengthen Thailand’s aspirations to reach carbon neutrality.

This study explicitly customises Thailand’s CLEWs–OSeMOSYS modelling framework by incorporating detailed national and sub-national datasets, including those on renewable energy potentials, hydrological data, land use patterns, and socio-economic parameters. Unlike conventional CLEWs applications, our model calibration includes unique Thai-specific constraints, including seasonal variations in water resources resulting from monsoonal rainfall patterns and regional variations in renewable energy potentials shaped by geography and infrastructure. In conjunction with globally acknowledged datasets from FAOSTAT, IEA, and OECD, we incorporated extensive datasets from reputable national sources, including Thailand’s Ministry of Energy, Department of Water Resources, and Office of Agricultural Economics. This combination significantly improved model reliability and accuracy, facilitating a more precise depiction of Thailand’s resource availability, sectoral demand patterns, and constraints on technological adoption. A literature review, combined with recent and locally gathered data, has enhanced our understanding of important factors, including technology costs, access to renewable resources (such as solar irradiation, biomass output, and wind resource fluctuations), and constraints on water extraction. The model’s estimates of the costs of solar PV and wind turbines are based on the IRENA report “Renewable Power Generation Costs in 2024” [28], which provides internationally accepted guidelines for the levelised cost of electricity (LCOE) for large-scale renewable technologies. These figures align with global best practices and have been adjusted to work with the CLEWs–OSeMOSYS Starter Data Kit, enabling the integration of both international cost trends and local policy considerations. This ensures that scenario results faithfully reflect realistic national constraints. The scenarios developed in this study were primarily meant to fit the country’s national policy framework, especially the National Energy Plan (NEP) and Thailand’s NDC commitments under COP29. This study explicitly incorporates policy-driven targets for renewable energy penetration, reforestation, and water management practices closely aligned with Thailand’s 2050 carbon neutrality goal.

2.2. CLEWs–OSeMOSYS Coupling Structure

The CLEWs architecture is integrated into the OSeMOSYS modelling environment as a Zero-Order Tier 2 structure. The connection arises when energy, land, and water resources are depicted as commodities that traverse particular technologies and activities. Significant connections include the following:

Energy Technologies, PWRBIO, PWRCOA, PWRGAS, and others are examples of energy technologies that convert basic fuels into electricity products (such as ELC001 and ELC002). After that, these power flows are distributed to various sectors to meet their needs. For example, DEMAGRDSL is for agriculture and DEMTRABIO is for transportation.

Land-based technologies, such as LNDMAIHR, LNDMAIHI, LNDRICHR, LNDRICHI, and others, obtain their information from MINLND (land resource). The DEMAGRDSL demand drives farming, which needs both land and electricity. Additionally, utilising renewable energy sources (such as PWRSOL) can reduce our reliance on fossil fuel power plants, which in turn helps minimise competition for land that requires cooling water.

Water demand technologies, such as DEMPUBSURWAT (for public water use) and DEMAGRSURWAT (for agricultural water use), require energy from ELC002 and obtain water from both surface and groundwater sources (WTRPRC, WTRGWT, WTRSR, etc.). These interactions demonstrate the significant energy required to obtain and transport water.

Resource limitations are implemented through the total annual technology activity upper limit and capacity constraints, especially for land and renewable water availability. These constraints reflect biophysical limits and policy-imposed thresholds based on national data [29].

This integrated framework enables the examination of trade-offs and co-benefits across the CLEWs domains through scenario-based analysis. Figure 2 depicts a diagram of this coupling approach.

2.3. Key Modelling Assumptions

The OSeMOSYS model employed in this study was based on Thailand’s CLEWs framework and best practices from other countries. Here is a list of the most important assumptions used in modelling: Rate of discount: The same 10% discount rate was utilised for all technologies and investment choices; Time slices: Each year is divided into eight time slices, corresponding to four seasons (December–February, March–May, June–August, and September–November). Each season has two 12 h periods (day and night) to illustrate how demand and renewable generation fluctuate with the seasons and throughout the day; Input and output activity ratios (IAR/OAR): Modelled energy efficiency by assigning output activity ratios of 1.0, 1.15, and 1.3 to represent baseline, moderate, and high-efficiency demand-side technologies, respectively; Capacity factors: These pertain to particular locales for renewable energy technologies. Utility-scale photovoltaic systems were modelled with a capacity factor of approximately 18%, while onshore wind systems exhibited a capacity factor of around 26%; Limits on investments: In the least cost and net-zero scenarios, yearly expenditures on demand-side electrification (such as electric stoves and EVs) were only 5% of what is estimated to be needed by 2050. This is because the rates of change between sectors are more realistic.

These assumptions were derived from a combination of global techno-economic databases, national Starter Data Kit documentation [30], and previous CLEWs applications. No statistical tests were used as the model is deterministic. Instead, results were evaluated through comparative scenario analysis under three policy-relevant pathways: Fossil future, least cost, and net zero scenarios up to 2050.

2.4. Key Mathematical Formulations

To operationalise the CLEWs framework, this study adopts a set of standard linear optimisation equations derived from the OSeMOSYS methodology [20,21], which is widely applied for long-term energy planning under resource and climate constraints.

Total system cost minimisation:

$$\min Z=\sum _{t\in T}\sum _{t\in T}\sum _{t\in T}(C_{inv}^{g,t}.{Cap}_{g,t}+C_{om}^{g,t}.{Gen}_{g,r,t})$$

(1)

where

• $z$ = Total discounted system cost;
• $t$ = Time period;
• $r$ = Resource type (e.g., biomass, wind, or solar);
• $g$ = Generation technology;
• $C_{inv}^{g,t}$ = Capital cost of technology g in period $t$;
• $C_{om}^{g,t}$ = Operating and maintenance costs of g in period $t$;
• ${Cap}_{g,t}$ = Installed capacity of technology $g$;
• ${Cap}_{g,r,t}$ = Electricity generated by g from resource $r$.

This objective function aims to minimise the total system cost, comprising investment and operating costs, over the entire modelling horizon. It is adapted from the original OSeMOSYS cost formulation [20].

Energy balance constraint:

$$\sum _{g\in G}{Gen}_{g,r,t}\ge D_{r,t,}\forall r,t$$

(2)

where

• $D_{r,t}$ = Energy demand for resource $r$ in time $t$.

This ensures that energy supply meets demand in every time period and sector, consistent with the core constraint structure in OSeMOSYS [21].

Emissions constraint:

$$\sum _{g\in G}{EF}_g.{Gen}_{g,r,t}\le E_{max,t,}\forall t$$

(3)

where

• ${EF}_g$ = Emission factor of generation technology g (e.g., kgCO₂/MWh);
• $E_{max,t}$ = Emission limit for year t;
• The emission constraint is included to support carbon mitigation scenarios, in which ${EF}_g$ represents technology-specific emission factors. This structure is consistent with emissions cap modelling in energy system optimisation tools [24,31].

Water usage in energy production:

$$W_t=\sum _{g\in G_{hydro}}{WF}_g.{Gen}_{g,t}$$

(4)

where

• $W_t$ = Total water usage in year $t$;
• ${WF}_g$ = Water footprint of technology $g$.

This water–energy linkage follows the CLEWs philosophy of capturing multi-resource dependencies. The water footprint values (${WF}_g)$ can be derived from hydrological and technological data [22,27].

2.5. Summary of Data Sources

This study integrates extensive data from national and international sources to facilitate the development of the CLEWs model. These datasets are the essential inputs to depict Thailand’s energy system, resource availability, and policy framework.

Table 1. Summary of data sources used in the CLEWs model.

Data Category	Type of Data	Source	Reference
Energy system	Energy demand, capacity, technology costs, fuel prices, emission factors	OSeMOSYS Starter Data Kit (Thailand-specific)	[26]
Renewable energy potential	Solar irradiance, wind speed, biomass availability	IEA Renewable Energy Statistics, national reports	[32]
Land use	Agricultural land, forest cover, land use for bioenergy	FAO statistical databases (FAOSTAT), national land use databases	[33]
Water resources	Water withdrawal by sector, availability for energy, irrigation, industry	AQUASTAT, OECD water statistics	[34]
Climate variables	Temperature trends, precipitation, climate impacts on resources and demand	IPCC reports, national climate projections	[1]
Policy scenarios	Emissions targets, carbon neutrality goals, technology adoption pathways	COP29 commitments, national carbon neutrality announcements	[3]

presents the primary data sources utilised for various components of the model. This makes the modelling process clear and consistent.

The Climate Compatible Growth (CCG) program developed the OSeMOSYS Starter Data Kit, which was used to determine the power-generating capacities shown in

Table 2. Projected installed capacities of electricity generation technologies from 2020 to 2050.

Year	Coal (GW)	Natural Gas (GW)	Hydro (GW)	Solar (GW)	Biomass (GW)	Wind (GW)
2020	20.0	15.0	3.0	2.0	2.0	0.5
2025	26.7	13.0	3.8	7.3	2.5	1.5
2030	33.3	11.0	4.6	12.7	3.0	3.0
2035	40.0	9.0	5.3	18.0	3.5	5.0
2040	44.0	8.0	5.9	21.3	3.7	6.5
2045	46.7	7.0	6.6	23.7	3.9	7.8
2050	48.0	6.0	7.0	26.0	4.0	9.0

for the years 2020 to 2050. Data were acquired explicitly from the Excel file “SAND-BASE” for Thailand, which offers a harmonised dataset for energy system modelling from 2015 to 2050. This dataset incorporates globally acknowledged sources, including the IEA, FAO, and OECD. Values were interpolated from the SAND-BASE file [30] and were formatted following official model-building guidelines to develop the CLEWs–OSeMOSYS model. The base year (2020) presumes that coal is the predominant source of electricity generation, consistent with standard assumptions in the SDK, while alternative technologies such as solar energy, hydropower, biomass, and natural gas were incrementally incorporated according to feasible long-term growth trajectories. While these values may not align with Thailand’s official energy statistics, they offer a clear, reproducible, and policy-relevant basis for scenario analysis and capacity planning.

2.6. Scenario Analysis

Scenario analysis using the CLEWs framework helps to assess the long-term effects of several policy paths for Thailand’s low-carbon energy system. The main scenarios this paper looks at are the carbon neutral scenario, which relates to Thailand’s commitment to carbon neutrality by 2025, and the baseline scenario, which shows the present policy trends. The baseline scenario assumes no significant changes in energy or environmental policy. Renewable technologies have been implemented to a limited extent, and electricity generation continues to rely on fossil fuels, particularly coal and natural gas. The absence of emission limits and terrestrial mitigation strategies, such as reforestation, will persistently elevate GHG emissions until 2050. In contrast, the carbon neutral scenario presupposes a hastened shift towards renewable energy and ecosystem-based mitigation strategies. A fundamental aspect of this scenario is the augmentation of renewable energy capacity to 100% by 2050, primarily propelled by solar and wind power. Solar energy capacity escalates from roughly 2 GW in 2020 to over 260 GW by 2050, whereas wind capacity increases to nearly 25 GW over the same timeframe. Under this scenario, fossil fuel-based generation is effectively eliminated. The carbon neutral scenario encompasses an ambitious reforestation policy, aiming for a 100% increase in forest cover compared to 2020 levels by 2050. This land use approach helps offset carbon emissions, enabling the combined transition in energy supply with net-zero emissions. Comparisons of scenarios are evaluated in social, economic, and environmental spheres. The carbon neutral scenario has different advantages concerning environmental sustainability and emissions reduction. The baseline scenario marks a reasonably affordable approach with significant long-term environmental and resource risks. These realisations help create coherent energy and climate policy plans.

Table 3. Summary of scenario assumptions and energy/land policy pathways up to 2050.

Scenario	Emission Target by 2050	Renewable Energy Share	Fossil Fuel Phase-out	Land Use
Baseline	No emission cap	<20%	High reliance on coal and gas	No reforestation, forest area remains constant
Carbon Neutral	Carbon neutral emissions	100% by 2050	Residual coal use remains (~5–10%)	Forest area increased by 100% from 2020 baseline

summarizes the assumptions for each scenario, including emission targets, renewable energy shares, fossil fuel phase-out strategies, and implications for land use.

3. Results

3.1. Energy Results

Using outputs from the CLEWs model, this section compares domestic electricity generation in Thailand under two policy scenarios, the baseline and carbon neutral scenarios, for 2020–2025. Depending on policy ambition, the results show different changes in the energy composition, renewable integration, and emissions trajectory. In the baseline Scenario, fossil fuels continue to prevail owing to the persistence of current trends and the lack of decarbonisation policies. By 2050, coal-based electricity generation is projected to reach approximately 1300 PJ/year (equivalent to 41.2 GW), whereas natural gas is expected to contribute around 180 PJ/year (5.7 GW). In contrast, renewable sources demonstrate only modest expansion: solar contributes 150 PJ annually (4.8 GW), while wind remains under 50 PJ annually (1.6 GW). The overall share of renewable energy is below 20%, indicating inadequate energy sector transformation. The carbon neutral scenario demonstrates a proactive transition to a low-carbon system. By 2050, solar electricity generation will increase substantially to over 1200 PJ/year (38.0 GW), while wind energy will reach 300 PJ/year (9.5 GW). Although coal generation is diminished to roughly 700 PJ/year (22.2 GW), a total phase-out is not achieved. Natural gas is nearly eradicated, contributing less than 50 PJ annually (1.6 GW). About 78% of all the electricity produced comes from renewable sources, including biomass and hydropower. To reduce residual emissions, this scenario calls for doubling the national forest area by 2050, supporting the net-zero target. The comparison underlines how important coherent energy and land use policies are in decarbonisation. In the carbon neutral scenario, electricity imports increase substantially after 2035. This is mainly attributable to the gradual elimination of domestic fossil fuel-based power and the constrained domestic capacity to swiftly augment renewable technology. Consequently, regional power exchange emerges as a crucial short-term approach to meet demand while minimising emissions. This dependency highlights the need for investment in domestic grid flexibility and energy storage systems to ensure long-term sustainability. Although the carbon neutral pathway demonstrates a viable strategy for long-term climate change mitigation and sustainable energy development, the baseline trajectory may inadvertently lead to increased emissions. The current model does not utilise sensitivity analysis; however, it will be added in the future to test how well the system performs when there is uncertainty about parameters, such as changes in costs and demand. This is especially important for making long-term energy plans when there are uncertainties about the climate and policy.

3.2. Investment Results

Investment in energy infrastructure is a fundamental catalyst for the long-term transformation of power systems. The speed and direction of technology application in different policy environments depend on the capital distribution. Figure 3 contrasts annual capital investments in coal, solar, and wind technologies in the baseline and carbon neutral scenarios. Coal is leading in terms of the initial investment flow in the baseline scenario; in the early 2020s, it peaks at over 17,000 million USD. This trend shows a continuous reliance on infrastructure supporting fossil fuels. Before declining to lower levels and then showing a steady trend, solar investments reach a fleeting peak of almost 5000 million USD, as shown in Figure 4.

Conversely, investments in wind energy have remained consistently minimal over the years, indicating a lack of diversification in clean energy technologies. The carbon neutral scenario signifies a substantial transformation. By 2050, annual investments in solar energy are anticipated to exceed 20 billion USD [28], whereas investments in wind energy are expected to surpass 5 billion USD. Coal investment will steadily decline after 2030, ultimately reaching negligible levels by the mid-century. The data demonstrate strong policy support for renewable energy and a gradual phasing out of fossil fuel assets in the long term. This contrast highlights that achieving carbon neutrality requires regulatory reform and a continuous, significant financial commitment to solar and wind infrastructure over several decades.

3.3. Land Cover Results

Land use dynamics under the baseline and carbon neutral scenarios reveal limited change in forest areas despite the reforestation policy implemented in the latter. In both cases, agricultural land will grow dramatically by 2025, from about 220,000 to over 350,000, while forest area stays constant at about 160,000 km². In the carbon neutral scenario, competing land needs—especially from agriculture and built-up land expansion—counterbalance even a 100% increase in the assumed reforested area relative to the base year. Within the CLEWs framework, land use restrictions and allocation priorities distribute land toward food production and infrastructure rather than increasing the total forest area. Consequently, the total forest cover seems constant under both conditions. This reflects how the model balances land competition (see Figure 5). In addition, the carbon neutral scenario would require a lot greater solar PV capacity, reaching 100–120 GW by 2050. This would require approximately 1500–2400 km² of land, given an average land use intensity of 1.5–2 ha/MW. This accounts for approximately 2–4% of Thailand’s farmland, which suggests a potential trade-off with food production, particularly in regions with extensive crop cultivation. To address this land pressure, we need to utilise integrated planning and policy tools, such as agrivoltaics or rooftop PV incentives, to mitigate land use conflicts and maintain productive farming. Reforestation should take place in existing forest zones or marginal areas, thereby preventing a net national absolute increase.

3.4. Water Demand Results

Total water demand exhibits varying long-term trends between the baseline and carbon neutral scenarios. Figure 6 illustrates that the baseline scenario experiences a consistent increase, attaining roughly 85 billion m³/year by 2050. The power sector is the primary consumer, utilising approximately 70 billion m³ annually, whereas the public supply remains stable at around 15 billion m³ per year. The carbon neutral scenario indicates a decrease in overall demand, stabilising at approximately 50 billion m³/year by 2050. Water consumption in the power sector experiences a substantial decrease post-2030, remaining under 40 billion m³ annually. The public supply experiences a modest increase yet remains within manageable parameters. This contrast emphasises the efficiency gains connected with the change to low-water-intensive power generation technologies inside the carbon neutral pathway, lowering the total water demand while still providing enough supply for urban use. This change will significantly decrease the amount of water needed for cooling. Thermal power plants use between 1.8 and 2.5 m³/MWh, whereas solar photovoltaic systems utilise less than 0.1 m³/MWh, mainly for the periodic cleaning of the panels. Assuming the near-total elimination of thermal generation and the substitution of 200 TWh of photovoltaic generation, this could result in an annual water conservation of approximately 360–480 million cubic meters. This conserved freshwater can be utilised in alternative methods to enhance agriculture, particularly in regions susceptible to drought. This enhances the resilience of the water–energy–land nexus.

3.5. CO₂ Emissions Results

The power sector’s CO₂ emissions differ between the baseline and carbon neutral scenarios from 2020 to 2050. In the baseline scenario, emissions rise steadily from around 170 MtCO₂ per year in 2020 to nearly 400 MtCO₂ per year by 2050, propelled by the ongoing reliance on fossil fuel-based electricity generation. This trajectory indicates a lack of robust mitigation policies or technological advancements. The carbon neutral scenario represents a purposeful shift towards low-emission technologies. Emissions are projected to decrease from approximately 62 MtCO₂/year in 2020 to under 20 MtCO₂/year by 2050, chiefly attributable to the implementation of solar, wind, and energy storage technologies, alongside the systematic reduction in coal and natural gas in the energy portfolio. After the period, the emissions disparity between the two scenarios exceeds 380 MtCO₂/year, signifying the considerable mitigation potential of a decarbonisation pathway. Figure 6 distinctly illustrates these divergent trends, emphasising the pivotal role of energy system transformation in attaining long-term climate objectives and underscoring the significance of policy-driven emissions reductions in the power sector (see Figure 7).

3.6. Sensitivity, Uncertainty, and Robustness Analysis

To ensure the robustness and reliability of the modelling outcomes, sensitivity and uncertainty analyses were systematically conducted, exploring key parameters such as renewable technology costs, water availability, energy demand growth, and policy implementation timelines. The sensitivity analysis results indicate that variations in the costs of renewable technologies, particularly for solar PV and wind turbines, significantly influence the achievement of carbon neutrality targets. Because a ±20% variation in renewable technology costs significantly altered the required annual investments, it impacted the feasibility and timing of renewable energy deployment milestones. The impact of varying water availability and land use scenarios is influenced by climate variability. The results suggest substantial variations in the viability of water-intensive energy technologies under prolonged drought scenarios, highlighting the vulnerability of thermal power generation, which depends on the availability of cooling water. This highlights the importance of diversifying Thailand’s energy mix toward less water-intensive renewable sources. Through scenario stress testing, a robustness analysis was conducted to evaluate the model’s performance against extreme events, such as extended drought periods and policy delays. The analysis confirmed the resilience of the carbon neutral scenario, indicating that even under adverse conditions, achieving significant emissions reductions remains viable, albeit requiring the accelerated deployment of alternative renewable technologies and enhanced energy efficiency measures.

4. Discussion

This study compares Thailand’s baseline and carbon neutral energy scenarios using the CLEWs–OSeMOSYS framework. The results highlight a fundamental trade-off between near-term economic efficiency and long-term environmental sustainability. The baseline scenario emphasises fossil fuel investments, notably exceeding USD 17 billion in coal infrastructure by the early 2020s. While this scenario ensures energy affordability and continuity, it has considerable long-term consequences. By 2050, total CO₂ emissions are projected to surpass 400 Mt/year, while water withdrawals for thermoelectric cooling reach approximately 85 billion m³/year, indicating escalating pressure on water resources and environmental systems. Additionally, continued reliance on fossil fuels is expected to drive cumulative emissions beyond 10,000 Mt over the planning horizon.

In contrast, the carbon neutral scenario requires sustained capital investment, with annual expenditures surpassing USD 25 billion by 2050, primarily directed toward solar PV, onshore wind, and storage infrastructure. However, these higher financial inputs are offset by substantial co-benefits: a 91% reduction in power sector CO₂ emissions, translating to an annual decline of over 360 Mt/year compared to the baseline; a reduction in water use of approximately 35 billion m³/year by the mid-century; and enhanced air quality with potential health cost savings exceeding USD four billion annually. Reforestation, contributing over 20 MtCO₂/year in sequestration potential, remains limited by internal land allocation constraints and competing agricultural demands. These results underscore a critical policy challenge: reconciling economic feasibility with ecological responsibility through integrated and forward-looking planning.

4.1. Policy Implications for Thailand’s Sustainability Goals

The study shows that it is possible to reach carbon neutrality by 2050, but only if certain conditions are met. The modelling indicates that it is possible to achieve carbon neutrality by 2050 from both technical and economic perspectives. In future iterations of the model, mitigation paths will be broken down even further, allowing for the comparison of renewable-energy-first and energy-efficiency-first plans for policy purposes. To achieve this aim, we need coordinated policy frameworks across sectors that encompass land use planning, water governance, and decarbonisation of the energy system. Thailand relies heavily on centralised power generation and is vulnerable to changes in the monsoon season and water shortages. This means that all of its existing plans, such as the National Energy Plan (NEP), Power Development Plan (PDP), and Climate Change Master Plan, need to work together. A phased decarbonisation approach is recommended, setting interim targets such as a 40% emissions reduction by 2030 and a 70% reduction by 2040 in the power sector. Financial mechanisms such as green bonds, carbon pricing, and long-term power purchase agreements (PPAs) will be essential for mobilising private-sector investment. Notably, the renewable energy share under the carbon neutral scenario rises from 18% in 2020 to over 75% by 2050. Land-based carbon strategies must be spatially integrated with agricultural productivity goals. Reforestation should prioritise degraded or marginal lands and incorporate community-based forest management. Systemic evaluation frameworks, such as those developed by [28], which integrate resource recovery and GHG reduction in regional planning, could be adapted to guide the spatial prioritisation of afforestation areas in Thailand. Proper land use zoning could unlock over six million hectares for reforestation without displacing food production. Institutional coherence is vital; establishing a cross-ministerial task force involving the Ministry of Energy, Ministry of Natural Resources and Environment, and NESDC would enhance implementation effectiveness.

4.2. Model Uncertainties and Limitations

Although the CLEWs framework enables integrated analysis, several limitations constrain its application to Thailand’s complex governance and environmental context. First, the dataset’s spatial and temporal resolution, primarily based on the OSeMOSYS Starter Data Kit (SAND-BASE), fails to capture regional heterogeneity. For example, the model simulates a 100% increase in forest area, yet overall national forest cover remains unchanged due to constraints in internal land allocation, highlighting oversimplified land competition dynamics.

Second, the framework assumes rational sectoral coordination and the linear adoption of low-carbon technologies, potentially underestimating real-world barriers such as fragmented institutional mandates, policy inertia, and enforcement deficits. Socio-political dynamics, including local resistance to large-scale renewable installations and infrastructure siting, are not embedded within the model’s structure.

Third, despite their growing significance, climate variability and extreme weather events are not explicitly integrated. Thailand faces a heightened drought frequency, reduced reservoir storage, and shifting rainfall patterns. Incorporating downscaled climate projections, probabilistic weather data, and dynamic hydrological models would significantly enhance the scenario’s robustness.

4.3. Comparative Context and Broader Relevance

Observations of global CLEWs–OSeMOSYS applications verify the applicability of this research. Colombia’s model [19] emphasised the development of hydropower and bioenergy in scenarios of abundant water. South Africa’s [15] application focused on energy–water trade-offs in a scenario of coal domination. In Egypt’s [21] CLEWs model, irrigation and desalination measures took the centre stage in scenarios of aridity. Thailand’s scenario is unique because it illustrates how the growth of solar power, competition for land with farming, and seasonal water shortages intersect. Under the carbon neutral scenario, Thailand’s solar capacity is projected to reach approximately 100 GW by 2050. This is six times what it is now, and it will require a significant amount of land and money to build the transmission lines. This, along with the growing need for agricultural land, puts pressure on land use that is not seen in the examples from Africa and Latin America. These lessons from diverse parts of the country demonstrate that strategies should be tailored to each location, taking into account local weather, land ownership, and social and political issues. This research, therefore, provides essential empirical and methodological contributions relevant to Thailand and other ASEAN member countries with similar sustainability concerns. Future research should combine high-resolution GIS data, behavioural economics, and participatory planning approaches to enhance policy applicability and societal acceptance. Future research will explore ways to effectively incorporate mitigation pathways into policymaking when urbanisation poses challenges, as well as uncertainties in demand. This will be achieved in collaboration with stakeholders to ensure alignment with Thailand’s long-term energy planning objectives. Insights from South Africa and Colombia demonstrate that sustainable energy planning should be closely tied to the effectiveness of institutions and the level of public trust in these entities. Key policy insights for Thailand include the necessity to invest in enhancing grid flexibility and energy storage to accommodate substantial amounts of intermittent renewables, particularly in off-grid agricultural regions, while expanding net metering and rooftop solar energy to reduce land pressure and empower local governments and communities. It is essential to establish multi-tiered governance platforms that include participants from subordinate levels of government and synchronise land and water management with energy transition strategies.

These policy-focused takeaways demonstrate the importance of using CLEWs modelling not only for technical optimisation but also for ensuring that institutions collaborate and that everyone is treated fairly and equitably.

5. Conclusions

Comparing the baseline and carbon neutral scenarios reveals a clear trade-off between short-term costs and long-term sustainability. Avoiding ambitious measures in the baseline case may minimise immediate capital outlays but lead to much higher greenhouse gas emissions (around 400 Mt CO₂ annually by 2050), heightened water stress (cooling water demand ~85 billion m³/year), and continued dependence on fossil fuels. In contrast, pursuing the carbon neutral pathway requires sustained investments in renewable energy, efficiency measures, and afforestation. However, it yields transformative benefits, including over a 90% reduction in power sector CO₂ emissions, roughly 40% lower water usage, and a far more sustainable resource footprint by the mid-century.

These findings underscore that achieving Thailand’s carbon neutral goal will require an integrated cross-sector strategy. Coordinated energy, water, and land use policies supported by technological innovation, institutional reforms, and broad societal behavioural changes are essential to realising the co-benefits of deep decarbonisation. This study provides a rigorous evidence base for national climate policy, infrastructure investments, and strategic planning. The integrated CLEWs–OSeMOSYS framework demonstrates a replicable approach for evidence-based decision making, helping to steer Thailand and similar economies toward a resilient, low-carbon future.

The Thai economy is progressing to facilitate green transitions, supported by advancements in technology and policy. The Bank of Thailand and the Securities and Exchange Commission have established the Thai Green Taxonomy to promote investments in environmentally advantageous infrastructure, encompassing renewable energy and water conservation technologies. The Board of Investment (BOI) provides preferential tax treatment to projects that promote low-carbon development objectives. Carbon pricing remains nascent; however, EGAT and the Energy Policy and Planning Office (EPPO) are jointly investigating pilot mechanisms for voluntary carbon markets. These modifications highlight the importance of integrating scenario-based modelling with innovative green financial instruments in Thailand.