Designing a Sustainable Framework for Thailand’s Future Emissions Trading System

Abstract

This study proposes a comprehensive framework for establishing an Emissions Trading System (ETS) in Thailand, addressing three core design elements: scope, cap setting, and allowance allocation. Using a mixed-methods approach that combines quantitative data analysis with qualitative insights from expert and stakeholder consultations, the research identifies a practical and strategic pathway for implementation. The proposed framework recommends a phased approach, with the initial phase covering 222 high-emitting facilities across seven key sub-industrial sectors. This scope, defined by a 25,000 tCO₂e threshold, is estimated to cover approximately 42.64% of the country’s total greenhouse gas (GHG) emissions. The ETS cap for the first phase is set at 20 MtCO₂e, aligning with national climate targets outlined in Thailand’s draft NDC 3.0. For allowance allocation, free allocation via output-based benchmarking is identified as the most suitable method for initial implementation, given its feasibility and effectiveness in incentivizing efficiency improvements. Furthermore, the standard cost model (SCM) was applied to assess compliance costs, indicating an annual administrative burden of 21,534 h and THB 42.18 million. These insights provide policymakers with a baseline for streamlining monitoring, reporting, and verification (MRV) requirements. The findings suggest that the proposed framework is a robust and strategic model, tailored to the unique economic and regulatory context of Thailand, providing a clear path to achieving the nation’s ambitious sustainable climate goals.

1. Introduction

Climate change stands as one of the most significant global challenges of the 21st century, demanding urgent action across all sectors. As a developing country and a major global emitter [1], Thailand has demonstrated a clear commitment to addressing this issue by setting ambitious goals to achieve carbon neutrality by 2050 and net-zero emissions by 2065 [2,3]. However, achieving these targets requires a portfolio of diverse and effective policy instruments, including the adoption of market-based mechanisms such as an ETS. The ETS is internationally recognized as a key tool for creating economic incentives that drive cost-effective and efficient GHG emission mitigations across various sectors [4,5,6]. The design and implementation of an ETS in the Thai context therefore necessitates careful consideration of the nation’s economic structure, legal framework, and the readiness of relevant stakeholders [7,8]. This study aims to analyze the key factors and considerations for establishing a suitable ETS for Thailand and to propose a structured framework to guide future policy and sustainable implementation efforts.

2. Literature Reviews

This section compiles and analyzes relevant concepts, theories, and research on the development of an ETS to build a deep understanding and serve as a foundation for designing an ETS suitable for the Thai context.

2.1. Fundamental Concepts and Theories of the ETS

An ETS, commonly known as “cap and trade,” is a widely recognized market-based mechanism effective for reducing GHG emissions at a macro-level [9,10,11,12,13]. The fundamental concept involves setting a collective emissions cap for target sectors and then allocating a fixed number of tradable allowances to participating businesses [14]. This mechanism allows companies to buy and sell these allowances in a free market, ensuring that emission mitigations occur where the cost is lowest as illustrated in Figure 1. This leads to the most efficient achievement of the overall mitigation target [15]. The essential components of an ETS include: (1) Cap setting, which gradually decreases over time to drive emissions down; (2) Allowance allocation, which can be conducted through various methods such as free allocation or auctioning; and (3) A robust monitoring, reporting, and verification (MRV) system to ensure accurate data and prevent fraud [16,17].

2.2. Critical Discussion of International Case Studies

Lessons learned from existing ETS programs worldwide provide valuable insights for Thailand’s system design. The European Union’s ETS (EU ETS) is the world’s largest and oldest carbon market, primarily utilizing an auction-based allocation mechanism and a continuously declining cap [18]. These features have been instrumental in driving carbon prices upward, despite some volatility. However, the literature also documents early-phase overallocation, windfall profits in the power sector, and sharp price collapses (e.g., 2007/2012), which later prompted structural reforms such as “backloading” and the market stability reserve (MSR) to absorb surplus allowances and stabilize prices. The German national ETS (nEHS) provides a valuable lesson in expanding carbon pricing to sectors not covered by the EU ETS, such as heating and road transport, and demonstrates a flexible approach by using a fixed-price mechanism in its initial phase [19]. Yet upstream fuel coverage raises unresolved issues of potential double counting with EU ETS installations, heterogeneous emission factors (including biogenic blends), and distributional effects from pass-through to households and small and medium enterprises (SMEs), underscoring the need for robust MRV guidance and social safeguards. In contrast, the Korean ETS (K-ETS), launched in 2015, has proven its ability to reduce GHG emissions but faced initial challenges, particularly regarding insufficient allowance allocation for certain industries [20]. Evidence also points to thin liquidity and a high share of free allocation for energy-intensive and trade-exposed industry (EITE) sectors (carbon leakage factor up to 1.0), which can dampen price signals and complicate competitiveness assessments. Beyond these, other notable case studies offer distinct lessons. For example, the systems in California and China demonstrate how jurisdictions have tailored their ETS designs to fit their unique economic and political contexts [21,22,23,24,25,26]. In California, distributional and competitiveness concerns are managed through utility consignment and industry assistance, yet risks of resource shuffling/leakage persist and are tracked by the Independent Emissions Market Advisory Committee (IEMAC). In China’s national ETS, allowance prices have shown episodes of volatility amid evolving coverage and benchmarking rules, with liquidity still developing—raising questions about the strength and clarity of abatement price signals. The New Zealand ETS (NZ ETS) stands out for its broad scope, covering a wide range of sectors and GHGs, and its early linkage to international units [27,28]. Nonetheless, price-control settings (e.g., cost-containment reserves) and forestry dominance have, at times, weakened price credibility, illustrating the importance of rule stability for investor expectations. In North America, Canada has implemented provincial systems, such as Quebec’s, which is linked with California’s market, showing successful sub-national collaboration [18]. Even so, the governance of linked markets—shock transmission, alignment of market-stability instruments, and legal risk from asymmetric policy changes—remains underexplored. Switzerland’s system is notable for its linkage with the EU ETS, creating a larger, more liquid market [29]. Yet linking entails governance costs and alignment risks, as partners must continually harmonize design elements and market-stability instruments to avoid importing shocks. Similarly, the Tokyo and Saitama ETS in Japan provide a strong case study for a city-level or sub-national approach, demonstrating the effectiveness of an ETS in a dense urban environment by focusing on reducing emissions from commercial and industrial buildings [30,31,32,33,34,35]. Because these programs regulate facility-level energy use (including purchased electricity), methodological issues arise around grid emission factors and baseline construction that limit comparability with product-based industrial ETS. Furthermore, emerging economies such as Mexico and Indonesia have introduced pilot or foundational carbon pricing schemes, offering valuable insights for developing nations on how to gradually implement a market mechanism [36,37]. Nonetheless, capacity constraints in MRV, data availability, and enforcement—together with political economy risks—limit external validity and call for context-sensitive implementation research. These diverse examples underscore the fact that no one-size-fits-all model exists, and that a successful ETS must be meticulously designed to align with a country’s specific economic and regulatory landscape.

Synthesizing these findings, key research gaps include: (i) how cap-stringency and market-stability instruments jointly affect dynamic efficiency; (ii) benchmark design for developing-country subsectors (cement, iron and steel, chemical, glass, and paper); (iii) distributional and competitiveness impacts under different allocation rules; (iv) registry interoperability and Article 6 corresponding adjustments to prevent double counting; and (v) offset/removals quality and permanence within compliance ETS. For Thailand, the critical takeaways are: adopt product-level benchmarks with output-based allocation for EITE sectors (to align with carbon border adjustment mechanism (CBAM)-relevant practice); pair a declining cap with a rules-based stability device (e.g., price corridor/reserve) to avoid EU-style surplus accumulation; build strong MRV and registry governance before any linking (to avert leakage and imported shocks); design targeted recycling to address distributional burdens (California lesson); and avoid over-reliance on intensity-only metrics or large, persistent free allocation that could weaken price signals (as seen in parts of K-ETS/China).

2.3. Key Components in ETS Design

Building on the concepts and case studies above, an effective ETS design requires a comprehensive consideration of several key components: (1) Scope: defining which industries or sectors will participate and which GHGs will be covered; (2) Cap: determining an appropriate and achievable total quantity of allowances; (3) Allocation: choosing between free allocation, auctioning, or a hybrid approach, a choice often made to balance economic efficiency with political feasibility by combining free allocation with auctioning; (4) MRV system: establishing a robust and transparent system to ensure the credibility of emissions data, with a preliminary concept involving a three-pillar approach: consistent monitoring, standardized reporting, and third-party verification, all aligned with the principles of transparency, accuracy, consistency, comparability, and completeness (TACCC); (5) Market stability mechanisms: implementing measures to manage price volatility and ensure a predictable and functional carbon market. This is crucial for maintaining a stable price signal, which is vital for long-term investment decisions and requires robust safeguards to prevent price spikes or collapses that could undermine the system’s effectiveness; and (6) Governance mechanism: creating clear institutional roles, legal backing, and appropriate mechanisms for dispute resolution and penalties [14,16]. In addition, (7) External units and market linkage: clear rules are needed for the purchase and use of external units (e.g., offsets or imported allowances) and for any prospective linking with other ETSs; these rules should include strict qualitative and quantitative eligibility criteria, robust MRV and registry interoperability (with Article 6 corresponding adjustments to avoid double counting), and alignment of market-stability instruments to prevent imported volatility and regulatory arbitrage. Attention must also be paid to carbon leakage and competitiveness risks, using evidence-based measures rather than open-ended free allocation.

This research focuses specifically on the first three design components (scope, cap, and allocation), analyzing their suitability for Thailand’s economic and regulatory landscape. This prioritization rests on three academic rationales. First, scope–cap–allocation constitutes the core “scarcity-creation and distribution” kernel of an ETS: Scope defines the regulated emissions base and MRV boundary; Cap determines system-wide scarcity and alignment with national decarbonization trajectories; Allocation governs the incidence of compliance costs and competitiveness, thereby shaping political feasibility and dynamic efficiency. Second, these levers exert the greatest ex-ante causal influence on price formation and abatement outcomes, while other modules (MRV architecture, stability instruments, registry/linking) are largely derivative of—and constrained by—the chosen scope and cap. Third, data and identification considerations favor starting here: available sectoral activity/fuel data enable tractable ex-ante modeling, counterfactual cap paths, and distributional analysis of allocation rules, allowing policymakers to quantify trade-offs before locking in downstream institutional details. For Thailand in particular—where target sectors are energy-intensive and trade-exposed, and increasingly CBAM-relevant—early choices on scope–cap–allocation determine exposure, revenue-recycling envelopes, and interoperability constraints, warranting first-order analysis. Within Thailand’s context, external-credit use and any future linkage should be sequenced cautiously—limited in volume, filtered for integrity and permanence, and introduced only after domestic MRV/registry systems and price-stability tools are fully operational—to minimize leakage, protect price credibility, and preserve environmental integrity. Studying these factors within Thailand’s context is therefore a crucial first step in establishing an efficient and sustainable ETS.

2.4. Climate Change Act and Thailand’s ETS

Thailand is actively developing a robust legal and policy framework to support its ambitious climate commitments. This is primarily centered on the forthcoming Climate Change Act, which is currently in development and expected to be officially adopted soon. This act will serve as the overarching legal foundation for all climate-related policies and instruments in the country, including the establishment of a mandatory ETS. To meet its climate goals, Thailand is in the process of drafting its 2nd Nationally Determined Contribution (NDC 3.0) [38], a key component of its long-term low GHG emission development strategy (LT-LEDS) [3]. As depicted in Figure 2, the country has set a clear trajectory for GHG mitigation. The draft NDC 3.0 specifically uses 2019 as its baseline year and sets a GHG mitigation target of 49.6 MtCO₂e for the energy and Industrial Processes and Product Use (IPPU) sectors by 2035. In this context, an ETS is proposed as a crucial mechanism to help achieve the national climate goal. The system is projected to contribute approximately 50% of the total mitigation goal for the selected sectors, a figure that aligns with the established role of successful international counterparts, such as the EU-ETS [18]. Its integration within the new climate change act will provide the necessary legal and institutional framework for successful implementation, positioning it as a central pillar of Thailand’s climate strategy.

Figure 3 operationalizes Thailand’s NDC tracking under Article 4 on a business-as-usual (BAU) basis, with baseline emissions projected to 2030 using 2015 as the base year [2]. Mitigation is quantified from the portfolio of CO₂ countermeasures. The unconditional NDC requires 184.8 MtCO₂e (33.3%) of mitigations by 2030 to be achieved with domestic resources [39], while an additional 37.5 MtCO₂e (6.7%) constitutes the conditional component that would require international support. The bars in the figure depict only the unconditional pathway for 2021–2030—rising from 60,000 ktCO₂e (10.9%) in 2021–2022 to ≈185,000 ktCO₂e (33.3%) in 2030—and reveal a back-loaded profile with a sharp step-up after 2024, implying escalating marginal abatement costs and risks of technological lock-in if structural measures are delayed. Aligned with this context, the ETS is sequenced as a pilot in 2029–2030 and a first compliance period in 2031–2035. The pilot functions as a pre-compliance proving ground to test MRV templates, verify product and benchmark definitions, refine leakage classifications, and rehearse registry/auction operations—generating empirical inputs for parameter choices without binding surrender. The 2031–2035 phase then applies a declining cap calibrated to post-2030 national targets and the sectoral scope defined in this study, providing a predictable signal for long-lived industrial investments. In effect, the figure situates the necessity and timing of a Thailand ETS: institutional readiness is built during the 2030 peak-effort year, and the cap framework takes over in 2031–2035 to maintain momentum toward—and beyond—the unconditional NDC.

3. Methodology

This research employs a mixed-methods approach, combining both quantitative and qualitative methods to gain a comprehensive understanding of the factors involved in establishing an ETS in Thailand.

3.1. Research Approach

The study utilizes a mixed-methods approach to analyze the design of a national ETS as illustrated in Figure 4. The quantitative component provides empirical data, gathered primarily from secondary sources such as official government reports and statistical databases, to establish the current context. Simultaneously, the qualitative component, which is based on primary data from structured public hearing sessions with key stakeholders, offers an in-depth exploration of complex and nuanced issues that may not be captured through quantitative data alone. This combined approach allows for a comprehensive and well-rounded analysis, integrating both factual evidence and stakeholder perceptions.

3.2. Data Collection

Data for this study will be collected from both primary and secondary sources. The quantitative data necessary for this research will be sourced from government agencies. The primary source is the database of legally mandated designated factories, managed by the Department of Alternative Energy Development and Efficiency (DEDE). The data spans from 2018 to 2020, and a three-year average will be used for the analysis. This specific timeframe was selected because it represents the most recent complete and officially validated data. Data from 2021 to 2022 was considered abnormal due to the impact of the COVID-19 pandemic, while data for 2023 onwards is currently undergoing validation and has not yet been officially released. This includes data from 4333 out of the total 6161 designated factories, representing 70.33% of the total [40], reported through the energy management reports (EMR) annually.

The activity data for each facility will be collected on an annual basis, separated by type, and will include stationary combustion fuel consumption, electricity consumption, and production volume. A sectoral categorization of energy consumption data is provided in

Table 1. The energy consumption data categorized by industrial sectors.

Industrial Sectors	Annual Energy Consumption (TJ/y)				Share (%)
	2018	2019	2020	Average
Food and beverage	253,552.58	285,100.11	158,714.42	242,598.17	9.19
Stone, sand and clay	2915.26	3079.91	2072.88	2733.62	0.10
Textiles	41,989.77	20,070.93	45,252.86	44,844.17	1.70
Wood	14,672.83	16,506.24	13,907.58	17,033.29	0.65
Paper	66,964.04	93,703.48	75,632.80	80,123.49	3.04
Non-metallic	231,291.82	186,596.78	277,174.74	237,605.28	9.00
Basic metal	52,932.59	50,763.60	48,712.77	51,753.08	1.96
Fabricated metal	51,838.93	53,638.33	50,573.21	54,185.64	2.05
Water supply	2116.31	2438.30	2362.30	2316.15	0.09
Chemical	299,536.41	270,236.90	232,012.31	269,790.96	10.22
Gas	53,150.16	50,391.50	68,551.73	57,368.59	2.17
Other (unclassified)	36,296.78	35,397.18	28,904.01	34,669.66	1.31
Electricity	1,496,267.96	1,615,654.93	1,326,620.64	1,544,387.42	58.51
Total	2,603,525.45	2,683,578.19	2,330,492.25	2,639,409.52	100.00

. For the purpose of calculating emissions, specific conversion factors will be used: the global warming potential (GWP) over a 100-year period values from the fifth assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) [41], the emission factor (EF) values from the IPCC guidelines [42] and the Thailand Greenhouse Gas Management Organization (TGO) [43], and the heat unit conversion factors from the DEDE [44].

In addition to the above, the emissions mitigation target (cap) for this study will be informed by the draft of Thailand’s 2nd Nationally Determined Contribution (NDC 3.0), which is being developed by the Department of Climate Change and Environment (DCCE) [38]. The qualitative data, which was collected directly by the authors, was obtained through stakeholder discussions conducted during a public hearing meeting. This information will be used to develop the sector selection criteria, which is a crucial step in defining the scope of the ETS.

3.3. GHG Emissions

The calculation methodology is based on established international standards, including the IPCC Guidelines [42], ISO 14064-1 [45], and the carbon footprint for organization (CFO) standard from TGO [43]. The emissions are categorized by source into different scopes. These are defined as follows:

• Scope 1: Direct GHG emissions from sources owned or controlled by the factory;
• Scope 2: Indirect GHG emissions from the generation of imported energy (e.g., electricity) consumed by the factory;
• Scope 3: Other indirect GHG emissions, which are not considered in this research as they are typically not within the direct control or reporting boundary of the factory.

The methodology is specifically applied to the 13 industrial sectors as defined by the DEDE [40]. The calculation of total GHG emissions focuses on three primary greenhouse gases: carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). The process combines emissions from both the energy and IPPU sectors, as detailed in equation:

GHG Emissions = AD × EF × GWP₁₀₀AR5

(1)

where AD is activity data representing fuel and electricity consumption, or industrial production, measured in the relevant units (e.g., kgs, liters, kWh, etc.). EF is the specific emission factor of the activity data, expressed in kilograms of GHG per unit of activity (kgGHG/units). GWP₁₀₀AR5 refers to the global warming potential of each GHG over a 100-year period, as defined in the AR5 of the IPCC. This value is used to convert the mass of a specific GHG into its carbon dioxide equivalent (kgCO₂e/kgGHG).

A key consideration in the calculation is the exclusion of specific emissions to avoid double-counting and to align with TGO standards. Specifically, Scope 1 and 2 emissions from electricity generation are not included, as these are accounted for at the power plant level. Furthermore, biogenic CO₂ emissions from the combustion of biofuels are excluded from the CO₂ calculation in accordance with Annex 13 of the CFO standard [43]. The final calculated emissions are reported in units of tonnes of carbon dioxide equivalent (tCO₂e). The results of the GHG emissions calculation are presented in

Table 2. The GHG emissions data categorized by industry sectors.

Industrial Sectors	GHG Emissions in Scope 1 Only (MtCO2e/y)		GHG Emissions in Scope 1 and 2 (MtCO2e/y)
	Average Year 2018–2020	Share (%)	Average Year 2018–2020	Share (%)
Food and beverage	4.31	2.47	9.92	4.71
Stone, sand and clay	0.58	0.33	0.66	0.31
Textiles	1.76	1.01	3.69	1.75
Wood	0.03	0.02	0.83	0.40
Paper	6.73	3.86	7.86	3.73
Non-metallic	34.20	19.64	41.31	19.59
Basic metal	2.32	1.33	5.60	2.66
Fabricated metal	0.70	0.40	6.69	3.17
Water supply	0.00	0.00	0.32	0.15
Chemical	25.48	14.63	31.93	15.15
Gas	3.02	1.74	3.34	1.58
Other (unclassified)	0.58	0.34	3.95	1.87
Electricity	94.44	54.23	94.74	44.93
Total	174.16	100.00	210.84	100.00

.

3.4. Sector Selection Criteria

The scope of an ETS is one of its most critical design elements. To define a suitable scope for Thailand, a structured approach was developed based on primary data collected through discussions with experts and key stakeholders during a public hearing meeting. This methodology identified two primary categories of factors for consideration: readiness and impact [14].

Readiness assesses a sector’s preparedness to participate in an ETS, considering criteria such as MRV readiness (the capacity to accurately monitor, report, and verify GHG data), compliance with regulations (current adherence to existing environmental and energy regulations), and alignment with NDC (the sector’s strategic importance in contributing to Thailand’s national climate targets). Meanwhile, impact evaluates a sector’s potential contribution to the ETS and its economic significance, measured by its total GHG emissions, its contribution to the country’s gross domestic product (GDP), and its exposure to the CBAM, a carbon tariff on imported goods imposed by the European Union (EU).

The scoring for these criteria was based on consensus among a group of experts from relevant sectors, with each criterion being assigned a specific weighting to reflect its importance, as shown in

Table 3. The scoring of impact and readiness assessments for sector selection in the ETS.

Categories of Factor	Selection Criteria	Full Scores	Scoring Weights
Readiness (X)	MRV readiness	5	20
	Compliance with regulations	5	15
	Alignment with NDC	5	15
Impact (Y)	GHG emissions	5	25
	Contribution to GDP	5	10
	CBAM sectors	5	15
Total		30	100

. The weights for the two main criteria, readiness and impact, were determined through a structured elicitation process designed to enhance transparency and reduce subjectivity in the weighting. The group encompassed representatives from regulatory authorities, policy-planning agencies, relevant governmental departments, industrial associations, academic institutions, research organizations, and independent verification bodies. This diversity of perspectives was intended to mitigate potential biases that could arise from single-sector dominance, while still acknowledging that experts’ institutional backgrounds might influence their judgments. The results of this evaluation were then plotted on a four-quadrant matrix to visually categorize and prioritize sectors [46] as illustrated in Figure 5. Consequently, industrial sectors demonstrating both high readiness and high impact were identified as prime candidates for inclusion in the first phase of the ETS.

Thailand’s proposed ETS coverage—electricity, paper, cement, glass, iron and steel, aluminum, and chemical—aligns closely with product-/subsector-based designs in the EU ETS [18], UK ETS [18], Switzerland ETS [29], the Western Climate Initiative (California–Québec) [18], K-ETS [20], and NZ ETS [27,28], where allocation and compliance are defined at the product level (e.g., clinker, hot metal, ammonia, container glass, pulp and paper). By contrast, the Regional Greenhouse Gas Initiative (RGGI) regulates only power generation [18]; thus, only Thailand’s electricity sector is congruent with RGGI’s scope. Tokyo and Saitama’s facility-based schemes (covering purchased electricity) are not like-for-like comparators for product subsectors [30,31,32,33,34,35]. China’s national ETS currently covers power only, with industrial expansion pending [21,22,23,24,25,26]. Overall, this mapping supports adopting internationally comparable product benchmarks and output-based allocation for Thai industry, while recognizing that RGGI alignment is limited to electricity. A sensitivity analysis was performed to examine the robustness of the sectoral prioritization with respect to variations in the assigned weights. The results indicate that the overall classification of sectors remained consistent, even when alternative weighting scenarios were applied. This finding suggests that the evaluation outcomes are stable and not overly sensitive to minor changes in the weighting assumptions, thereby enhancing the reliability of the prioritization framework.

This study faces three methodological constraints. First, data quality and coverage: the facility dataset (2018–2020 averages) and default factors (EF, GWP) may not fully capture post-2020 structural shifts, while incomplete reporting introduces potential non-random missingness. Second, expert-based judgments used in the readiness/impact scoring and sectoral weighting are subject to subjectivity, notwithstanding structured rubrics and documentation from the public hearing. Third, the analysis does not employ equilibrium or dispatch modeling; economy-wide feedbacks, pass-through to prices, and network constraints are therefore assessed qualitatively rather than via computable general equilibrium (CGE)/partial-equilibrium or unit-commitment models. These limitations motivate the robustness checks and future research agenda below.

4. Threshold Scenarios

4.1. Assumptions

This study simulates various potential scenarios for the future implementation of an ETS. The analysis is structured into three distinct scenarios to explore different design options:

• Scenario 1 (S1): This scenario includes only Scope 1 GHG emissions and encompasses the electricity generation sector, aligning with the design principles of several international systems, including the EU ETS [18], the China national ETS [21,22,23,24,25,26], the New Zealand ETS [27,28], the California/Québec ETS [18], and the Switzerland ETS [29];
• Scenario 2 (S2): In contrast, this scenario exclusively considers Scope 1 GHG emissions but excludes the electricity generation sector, aligning with the design principles of the German national ETS [19] and the proposed EU ETS for buildings and road transport (EU ETS 2) [18];
• Scenario 3 (S3): This scenario broadens the scope to include both Scope 1 and Scope 2 GHG emissions while also excluding the electricity generation sector, which aligns with the design principles of the Tokyo and Saitama ETS in Japan [30,31,32,33,34,35].

The rationale for isolating the electricity generation sector stems from Thailand’s current energy structure, known as the Enhanced Single Buyer (ESB) model. This model involves long-term Power Purchase Agreements (PPAs) that limit the flexibility of producers to reduce generation or improve efficiency. These contracts often include an availability payment (AP), a fee that ensures a constant capacity to supply electricity upon demand. Each scenario incorporates varying GHG emissions thresholds for industrial facilities—15,000, 20,000, and 25,000 tCO₂e. These thresholds align with current international standards, such as those used by the EU ETS [47], and allow for an examination of the impacts on industrial facilities under different conditions, as depicted in Figure 6, Figure 7 and Figure 8.

Given the ESB’s long-term PPAs and availability payments, integrating power into an ETS raises distinct political-legal and economic feasibility issues: guaranteed revenue streams heighten the risk that carbon costs are passed through into tariffs while producers retain free allocations or other rents, creating windfall profits—as observed in early EU ETS phases where opportunity costs were priced into electricity despite free allocation [48]. Moreover, carbon pricing is intended to reorder dispatch (e.g., coal-to-gas switching), but contractual must-run clauses, take-or-pay fuel contracts, or AP structures can mute merit-order responses; international evidence shows that robust carbon price signals alter fuel choice and dispatch when markets are allowed to respond. To manage pass-through and windfall risks, comparative practice suggests utility “consignment” of allowances with consumer-facing revenue recycling (as in California) and explicit price-stability instruments to avoid importing volatility [49]. Linking or using external units must also address leakage channels such as resource shuffling in electricity trade, calling for registry interoperability, corresponding adjustments, and anti-shuffling rules before any cross-border linkage [50].

Accordingly, the electricity sector warrants a dedicated legal-economic appraisal separate from the industrial thresholds simulated here. In Thailand’s context, PPAs may need targeted deregulation/revisions to (i) recognize carbon costs while preventing over-recovery (e.g., claw-back conditions, tariff true-ups), (ii) enable carbon-reflective, merit-order-compatible dispatch, and (iii) permit greater flexibility for variable renewables to enter and displace thermal generation without undue compensation claims. Complementary options include contract conversion (e.g., capacity/AP redesign), technology benchmarking for generators (heat-rate/CO₂-intensity benchmarks), and regulatory adjustments to align market-stability tools and auction/assistance rules with consumer protection. Fuel-switching enablers—such as hydrogen blending in the gas grid—should be evaluated against international technical and regulatory precedents (current blending limits and pilots), with safeguards for metering, safety, and emissions accounting before inclusion in compliance pathways. These measures would reduce pass-through to households/SMEs, mitigate windfall-profit risks, and ensure that any future linkage or external-unit use does not compromise environmental integrity or dispatch efficiency.

4.2. Number of Factories and Sectors

The hypotheses established under different scenarios in Section 4.1 directly influence the number of industrial facilities included in the cap and trade system and, consequently, the total annual GHG emissions.

Table 4. Number of factories and GHG emissions covered under each scenario.

Scenarios	No. of Factories (Places)		Annual Emissions (ktCO2e/y)
		(% of Total)		(% of Total)
Scenario 1 (S1)	4522	100.00	174.16	100.00
S1-1: GHG emissions > 15,000 tCO2e	418	9.24	168.49	96.74
S1-2: GHG emissions > 20,000 tCO2e	354	7.83	167.38	96.11
S1-3: GHG emissions > 25,000 tCO2e	315	6.97	166.51	95.61
Scenario 2 (S2)	4336	100.00	79.72	100.00
S2-1: GHG emissions > 15,000 tCO2e	318	7.33	65.85	82.60
S2-2: GHG emissions > 20,000 tCO2e	256	5.90	64.78	81.26
S2-3: GHG emissions > 25,000 tCO2e	219	5.05	63.95	80.22
Scenario 3 (S3)	4336	100.00	116.10	100.00
S3-1: GHG emissions > 15,000 tCO2e	802	18.50	92.09	79.32
S3-2: GHG emissions > 20,000 tCO2e	625	14.41	89.03	76.69
S3-3: GHG emissions > 25,000 tCO2e	499	11.51	86.20	74.24

illustrates these effects, while

Table 5. Number of factories within each industrial sector covered under the various scenarios.

Industrial Sectors	Scenario 1 (S1)			Scenario 2 (S2)			Scenario 3 (S3)
	S1-1	S1-2	S1-3	S2-1	S2-2	S2-3	S3-1	S3-2	S3-3
Food and beverage	73	52	35	73	52	35	176	134	109
Stone, sand and clay	2	1	1	2	1	1	2	2	2
Textiles	25	22	16	25	22	16	56	46	35
Wood	0	0	0	0	0	0	11	11	11
Paper	22	20	20	22	20	20	36	33	29
Non-metallic	86	70	66	86	70	66	150	113	92
Basic metal	37	30	26	37	30	26	76	61	52
Fabricated metal	5	2	2	5	2	2	112	78	50
Water supply	0	0	0	0	0	0	6	4	3
Chemical	61	52	48	61	52	48	110	96	82
Gas	5	5	4	5	5	4	9	9	8
Other (unclassified)	2	2	1	2	2	1	58	38	26
Electricity	100	98	96	0	0	0	0	0	0
Total	418	354	315	318	256	219	802	625	499

provides a detailed breakdown of the specific sectors and the number of facilities involved. This information is crucial for informed policy and planning, target setting, impact analysis, and for estimating the potential GHG emission mitigations from implementing this mandatory measure.

5. Results

The design of a robust and effective ETS hinges on three critical elements: scope, cap setting, and allowance allocation. The scope determines which sectors and GHG emissions are included, directly influencing the system’s ability to achieve national climate targets. Meanwhile, the cap setting establishes the total quantity of allowable emissions, which in turn drives the scarcity and price of allowances, creating a financial incentive for mitigations. Finally, the allocation of allowances—whether through free distribution or auctioning—is a key policy instrument for managing competitiveness and administrative costs.

5.1. Scope

Based on the readiness and impact assessment of key industrial sectors, which was informed by qualitative insights from a public hearing, the study proposes a phased approach for the ETS. To ensure significant coverage of emissions from the outset, scenario 1 (S1) was selected for initial consideration, as it includes scope 1 GHG emissions from industrial facilities and the electricity generation sector. Consistent with the practices of the EU ETS and other global systems, a threshold of 25,000 tCO₂e was established to focus the ETS on a manageable number of high-emitting facilities. This threshold resulted in the identification of 222 facilities out of a total of 315, spanning across seven key sub-industrial sectors. These facilities collectively account for 159.42 MtCO₂e, representing approximately 42.64% of the country’s total GHG emissions in the base year. Additionally, a total of 75 facilities from four other sub-industrial sectors were identified as being suitable for inclusion in the subsequent phase of the ETS, bringing the total number of facilities to 297 for the full system. The remaining 18 facilities were deemed unsuitable for inclusion in the ETS due to their small number within their respective sub-industrial sectors, which would not foster sufficient competition or provide a basis for comparing production efficiency. The specific breakdown of the scope by sub-industrial sector is detailed in

Table 6. The proposed scope of Thailand’s ETS in this study.

Phase of ETS	Industrial Sectors	Total No. of Facilities (Places)	GHG Emissions Threshold > 25,000 tCO2e
			No. of Facilities (Places)	Annual Emissions (ktCO2e/y) *	Proportion of National GHG Emissions (%) **
First phase (2031–2035)	Electricity	172	96	94.44	25.26
	Paper	96	20	6.73	1.80
	Non-metallic
	- Cement	18	12	28.80	7.70
	- Glass	35	22	1.81	0.48
	Basic metal
	- Iron and steel	114	18	1.52	0.41
	- Aluminum	40	6	0.64	0.17
	Chemical	248	48	25.48	6.81
Total		609	222	159.42	42.64
Second phase (2036–2040)	Food and beverages	732	35	4.31	1.15
	Textiles	192	16	1.76	0.47
	Non-metallic
	- Ceramic	40	9	0.80	0.21
	- Rubber	163	15	1.27	0.34
Total		1127	75	8.14	2.18

* The average GHG emissions from 2018 to 2020 were calculated using the DEDE database. ** Thailand’s GHG emissions, excluding the LULUCF sector, had an average of 373.89 MtCO₂e from 2018 to 2020, as reported in the NC4 (2018) [51], BUR4 (2019) [52], and BTR1 (2020) [39] reports.

.

Because the proposed ETS initially covers only part of the economy, a comprehensive strategy is specified for non-ETS sectors—transport, waste, and agriculture—combining complementary instruments, coordination mechanisms, and clear governance. In Thailand’s context, the transport sector requires a dedicated MRV pathway distinct from stationary sources; near-term instruments should prioritize upstream measures (e.g., a carbon component in fuel excise or feebate schemes), efficiency and zero-emission vehicle standards, and crediting for verified demand-side measures, with future consideration of an upstream fuel-supplier module once MRV matures. The waste sector is presently constrained by data availability in municipal solid waste; therefore, the priority is to establish a standardized waste MRV registry, strengthen methane quantification (landfill gas capture and flaring standards), and align with the forthcoming industrial-waste regulation currently under consideration. The agriculture sector is not proposed for ETS coverage in the near term given measurement challenges, heterogeneous smallholder structures, and relatively high compliance costs per tonne abated; instead, targeted support should be directed to low-emission practices (e.g., alternate wetting and drying in rice, manure management) and high-integrity pilot crediting outside the compliance ETS. To coordinate these tracks, the government should mandate an inter-ministerial non-ETS steering committee, set quantitative non-ETS emission-mitigation targets synchronized with NDC trajectories, and establish reporting cycles and budget allocation rules (including use of ETS revenues) to protect vulnerable households and SMEs.

The 25,000 tCO₂e threshold is justified on administrative-economic grounds as a balance between regulatory bandwidth and a credible price signal. First, supervisory capacity and expected verifier resources indicate that a regulated population on the order of ≈300 installations is commensurate with current public-sector staffing and IT systems, reducing the risk of compliance backlogs while enabling effective enforcement. Second, sensitivity analysis of coverage versus entity count (the “elbow” of the curve) shows diminishing returns in additional coverage if the threshold is lowered further at this stage, while MRV costs per regulated tonne would rise sharply for smaller emitters. Third, focusing on large point sources ensures that the carbon price is material for investment and operational decisions (e.g., fuel switching, process upgrades), thereby maximizing near-term abatement and learning effects. The threshold should be reviewed after the pilot phase and the first compliance period—conditional on MRV automation and registry performance, the regulator may consider a staged reduction (e.g., to 20,000 tCO₂e) coupled with simplified MRV for small emitters and de minimis provisions, thereby expanding coverage without compromising administrative feasibility. This sequencing ensures that sectors outside the ETS are not neglected but addressed through fit-for-purpose instruments and governance, while the ETS itself delivers a strong and credible price signal where it is most effective at the current stage of institutional development.

5.2. Cap Setting

The cap for the ETS was established through a meticulous analysis of historical emissions data from the DEDE, with the aim of creating a realistic yet ambitious mitigation pathway. This process was critically informed by the draft of Thailand’s 2nd Nationally Determined Contribution (NDC 3.0), which is currently under development by the DCCE and is summarized in

Table 7. Thailand NDC 3.0 absolute emissions mitigation in 2035.

IPCC Sectors	Emissions in 2019 (MtCO2e)	Emissions Reduction/Removal in 2035 Compared to 2019 (MtCO2e)			Emissions in 2035 (MtCO2e)
		NDC 3.0	Unconditional	Conditional
Energy	185.2	68.1	48.1	20.0	117.1
Transport	76.8	22.6	16.6	6.0	54.2
IPPU	38.0	4.2	1.5	2.7	33.8
Agriculture	60.5	7.6	5.1	2.5	52.9
Waste	18.7	6.7	5.1	1.6	12.0
Total sources	379.2	109.2	76.4 (70.0%)	32.8 (30.0%)	270.0

. The primary focus for cap setting was the unconditional national target of 76.4 MtCO₂e. Since the ETS in this study’s scope applies exclusively to the energy and IPPU sectors, the cap was derived from their combined mitigation target of 49.6 MtCO₂e. To allocate this target effectively, sub-sector specific targets were then determined based on historical proportions of GHG emissions.

Based on scenarios developed by the authors, tracking of historical GHG mitigations, and the remaining GHG mitigation potential within the industrial sector, and in conjunction with public hearings, it was determined that the ETS should be responsible for approximately 50% of the total mitigation goal for the included sectors due to its practical feasibility. Other climate mechanisms will address the remainder. This methodology yielded a total GHG mitigation target of 18.83 MtCO₂e. For the purpose of establishing a clear and strategic target, this figure was subsequently scaled up to 20 MtCO₂e, not only to provide an easily communicable round number but also to introduce a ≈6% risk buffer that (i) internalizes plausible measurement and policy-overlap uncertainty, (ii) supports price credibility by reducing the likelihood of a near-zero scarcity regime in early years—thereby strengthening investment signals—and (iii) preserves policy optionality by setting a minimum floor that can be deepened at midterm review should conditional international support and system readiness materialize (see

Table 8. The GHG mitigation target for the Thailand ETS Phase 1 by 2035.

Industrial Sectors	Absolute GHG Mitigation Target (MtCO2e)
	Energy Sector	IPPU Sector	Total	Total (Scale-Up)
Electricity	13.200	-	13.200	13.200
Paper	0.877	-	0.877	1.058
Cement	2.021	0.353	2.374	2.865
Glass	0.173	0.005	0.178	0.215
Iron and steel	0.173	0.007	0.180	0.216
Aluminum	0.042	-	0.042	0.051
Chemical	1.742	0.244	1.986	2.395
Total	18.228	0.609	18.837	20.000

). These calculated mitigation targets form the basis for determining the total number of allowances to be issued under the ETS, which will then be allocated to each facility within the included industrial sectors.

The translation of Thailand’s national mitigation target into an ETS cap is grounded in three explicit assumptions. First, the coverage baseline is defined as verified emissions of the covered sectors in the base year (consistent with Table 6 and the NDC 3.0 accounting), excluding any mitigations already claimed by non-ETS instruments to avoid double counting. Second, the allocation of the ETS contribution to the national target is set on a budget basis over 2031–2035, such that the cumulative difference between a BAU projection and the ETS annual caps equals the sectoral mitigation attributed to the ETS. Third, interaction with other policies (energy efficiency standards, renewable obligations, and tax incentives) is treated via an overlap adjustment so that the ETS cap reflects the additional mitigation attributed to carbon pricing rather than double-counted policy effects.

Sectoral allocation is set proportionally to each sector’s verified base-year emissions, yielding fixed sectoral shares for phase I. The total ETS cap for 2031–2035 follows an annual mitigation trajectory specified by a constant linear-reduction factor; sectoral annual caps in each year are obtained by applying the fixed shares to the total cap. To preserve price credibility and environmental integrity, the system allows unlimited banking, limited interest-adjusted borrowing, and employs a rules-based stability mechanism combining a price corridor with an allowance reserve. Predefined surplus and price triggers enable temporary, transparent parameter adjustments with ex-post neutrality. A midterm review (e.g., 2033) may update sectoral shares only if structural shifts or measurement improvements warrant recalibration.

At this stage, the ETS design specifies only an unconditional cap trajectory deliverable through domestic policy effort and resources; all parameterization presented here refers to this unconditional case. A conditional pathway—predicated on externally supported ambition (e.g., concessional finance, technology transfer, or Article 6 cooperation)—has not been developed and lies outside the scope of this study. Accordingly, additional stringency, intersectoral reallocations, and fiscal implications associated with a conditional scenario are not quantified in this study. Future research should specify eligibility criteria, governance triggers, and parameter values for a conditional cap once credible international support commitments are available.

Robustness was explored through design-sensitivity tests. (i) Weights in the readiness/impact framework were perturbed within plausible intervals to assess ranking stability across sectors. (ii) Thresholds were varied (±5000 tCO₂e around 25,000 tCO₂e) to examine the coverage–administrative trade-off. (iii) Electricity inclusion/exclusion (S1 versus S2/S3) was toggled to test system outcomes under Thailand’s ESB/PPAs context. (iv) For the cap, alternative stringency settings around the 50% ETS contribution were simulated to check whether sectoral shares and allocation recommendations remain qualitatively unchanged. Results indicate that headline conclusions are stable across these perturbations; remaining sensitivities are flagged for pilot testing.

5.3. Allowance Allocation

Allowance allocation, which is the process of assigning GHG emission permits to individual facilities, is a critical design element that influences both the distribution of costs and the incentive for emissions mitigation. The choice of an allocation method should align with a country’s policy goals and industrial structure. Typically, two primary allocation methods are used: free allocation and auctioning. While auctioning is a supplementary approach that creates a flexible and transparent carbon price mechanism (with revenues that can be reinvested), it is often considered more suitable for later phases when the system is more mature. For the initial phase of the Thailand ETS, consultation with key stakeholders during a public hearing meeting identified two appropriate methods under free allocation: fixed historical benchmarking and output-based benchmarking.

• Fixed historical benchmarked allocation prescribes product-specific carbon-intensity benchmarks (e.g., tCO₂e per tonne of clinker, hot metal, container glass, paper) calibrated to best practice or a top-performer reference for each sub-sector. Free allowances are then computed against a fixed historical output level rather than contemporaneous performance. This standardized “per-unit of historical output” rule delivers a uniform efficiency signal and some protection against carbon leakage; however, by severing the link between current emissions intensity and allocation, it can create scope for windfall gains when opportunity costs are passed through to prices. Administratively, the approach is data-intensive, requiring robust historical activity and intensity records to derive benchmarks that are representative of domestic production technologies. However, this method is better suited for later phases with more ambitious targets, as it requires comprehensive and accurate data for benchmarking factors that are not yet readily available in Thailand;
• Output-based benchmarked allocation (OBA) retains product benchmarks while indexing assistance to verified contemporaneous output in each compliance period and is more feasible for the current context due to its greater data readiness and widespread use in other countries. Conceptually, each facility’s observed carbon intensity is compared with the best-in-class benchmark within the sub-sector; installations with higher intensity (lower efficiency) face proportionally more stringent reduction obligations or receive lower net assistance relative to efficient peers, whereas top performers are rewarded. This method provides incentives for facilities to enhance operational efficiency and sends a positive signal to high-performing factories; it also encourages less efficient facilities to improve their production processes to align with industry leaders, thereby preserving domestic production (mitigating leakage) while differentiating obligations by performance. Because total free allocation varies with realized output, cap integrity must be safeguarded by explicit ex-post correction mechanisms (e.g., cross-sectoral correction factors) to keep aggregate allocation within the cap.

A Thailand-specific justification for OBA rests on governance, data, and policy coherence. First, benchmark definitions should be formalized through clear product boundaries and system limits for carbon-intensity measurement, published methods for deriving “best-in-class” (best performer or top decile with stated data vintages), and periodic recalibration (e.g., every 3–4 years) to sustain dynamic efficiency. Second, data readiness for OBA is comparatively high because it relies on routinely collected output and fuel/activity data under MRV; remaining gaps can be closed via a targeted data-improvement program (harmonized templates, unit normalization, verification protocols, and product-mix documentation) within one compliance cycle. Third, treatment of outliers should be codified ex ante—minimum sample thresholds, trimmed means or winsorization for extreme observations, and transparent handling of atypical product mixes (including fallback heat/fuel benchmarks where product benchmarks are not yet feasible). Fourth, a Thai carbon leakage list should classify sub-sectors by objective indicators (trade intensity × emissions intensity, exposure to CBAM-type measures) to differentiate assistance rates across cement/clinker, iron and steel routes, chemical, glass, paper, and aluminum, with scheduled reviews and clear entry/exit criteria.

Potential risks and corresponding safeguards should be specified. High assistance rates can make OBA resemble an implicit output subsidy, risking production distortion; Thailand should therefore pre-commit declining assistance schedules, apply output bands/caps beyond which additional output earns no extra free allocation, and maintain a binding system-wide cap. To avoid double counting, priority rules must clarify the relationship between free allocation and other instruments (e.g., offset eligibility, pass-through compensation in regulated power contracts), backed by registry tagging and mutually exclusive eligibility conditions. Intra-sector equity concerns arising from heterogeneous technologies and multi-product plants call for product-specific benchmarks where feasible, transitional fallback benchmarks only where necessary, and time-limited plant-level correction factors subject to sunset clauses and external technical review.

A credible transition to auctioning is essential for price signal strength and international interoperability. Consistent with the timeline in Figure 2, Thailand should: (i) launch a pilot ETS to test MRV templates, benchmark definitions, outlier screens, leakage-list criteria, and ex-post correction factors, publishing a transparent pilot evaluation; (ii) in Phase I, implement OBA with differentiated assistance by leakage tier, paired with a modest auction share to support price discovery, and activate a cross-sectoral correction factor to keep total free allocation within the cap; (iii) in Phase II, reduce assistance by roughly 10–15 percentage points per compliance period while raising the auction share toward a majority (≥50%), tightening benchmarks using updated intensity distributions and recycling auction revenues to address distributional impacts; and (iv) in Phase III, converge to a predominantly auction-based regime, retaining carefully targeted, time-limited OBA only for the highest-risk EITE segments, subject to periodic leakage tests and sunset provisions. This sequencing maintains environmental integrity and competitiveness during the learning period while establishing a clear pathway toward a mature, auction-anchored Thai ETS.

The results of the allowance allocation study for each industrial sub-sector are presented in

Table 9. The results of the allowance allocation study for each industrial sub-sector.

Industrial Sectors	GHG Emissions in 2035 (MtCO2e)	Average Annual Mitigation Rate (MtCO2e/y)	Facility-Level GHG Mitigation Target (%)
Electricity	80.93	0.844	5.53–30.89
Paper	5.38	0.084	0.72–31.02
Cement	25.92	0.180	4.00–22.64
Glass	1.54	0.017	2.56–39.75
Iron and steel	0.85	0.042	7.40–34.27
Aluminum	0.42	0.014	3.84–15.34
Chemical	22.34	0.196	0.25–110.23
Total	137.38	1.377	-

.

5.4. Standard Cost Model

The standard cost model (SCM) is a methodological framework for quantifying the administrative burden that legislation imposes on the public. Its purpose is to provide a standardized way to estimate compliance costs so that results can inform improvements in regulatory quality. The SCM accounts for both time-related and monetary components of compliance. In its basic form as detailed in the equation:

Administrative Burdens (AB) = Time Costs (T × Q) + Financial Costs (C × Q)

(2)

where T is the time required per compliance activity, Q is the number or frequency of such activities, and C is the financial cost per activity. Accordingly, the total burden equals the sum of time costs and financial costs, each scaled by how often the activity must be performed. The calculation steps are summarized in Figure 9.

Using the SCM and the Phase I scope of 222 facilities, the assessment treats compliance as an annual obligation. The time requirement aggregates three activities—GHG report preparation, third-party verification, and formal sign-off/attestation—yielding 97 h per facility. The financial cost is priced at current Thai market averages for the same three activities (reporting, verification, attestation), amounting to THB 190,000 per facility. Aggregated to the Phase I population, the estimated administrative burden equals a time demand of 21,534 h and a financial cost of THB 42.18 million per reporting year. These figures provide a baseline for staffing, budgeting, and burden-reduction targeting in MRV and verification scheduling and can be refined during the 2029–2030 pilot.

6. Discussion

Designing an effective ETS for Thailand requires a comprehensive framework that addresses scope, cap setting, and allowance allocation. This study proposes such a framework based on a detailed analysis of key design elements.

The scope was defined through a readiness and impact assessment, identifying a core group of 222 facilities across seven sub-industrial sectors, with an emissions threshold of 25,000 tCO₂e. This group accounts for approximately 42.64% of the country’s total GHG emissions in the base year. The study also suggests a phased approach, with an additional 75 facilities suitable for later inclusion, while excluding the remaining 18 due to their small size and lack of competitive incentive within their sectors.

The cap for the ETS was set based on an analysis of historical emissions data and informed by Thailand’s draft NDC 3.0. The ETS would be responsible for approximately 50% of the mitigation target for the energy and IPPU sectors, resulting in a final scale-up cap of 20 MtCO₂e. This target forms the basis for the total number of allowances to be issued.

Regarding allowance allocation, the study recommends a free allocation approach for the initial phase, with a focus on output-based benchmarking. While benchmarking promotes efficiency and fairness, its data requirements are not yet fully met in Thailand. Therefore, output-based benchmarking is the most feasible starting point, as it provides strong incentives for improved efficiency and is widely used internationally.

Beyond the allocation design and compliance cost estimations, it is also important to recognize that the introduction of an ETS will have broader economic and distributional consequences. Potential effects include the influence of carbon prices on firms of different sizes, particularly SMEs, as well as implications for households and trade-exposed sectors subject to CBAM. Although a full CGE or input–output (IO) modeling analysis lies beyond the scope of this study, international evidence shows that even modest carbon prices can produce differentiated outcomes across sectors and social groups. Accordingly, complementary measures such as revenue recycling or targeted support will be essential to ensure that Thailand’s ETS delivers not only environmental benefits but also equitable and welfare-enhancing outcomes.

In conclusion, the proposed ETS framework is a practical model tailored to Thailand’s specific context. It provides a strategic and phased approach to implementing a market-based mechanism that can effectively support the nation’s ambitious climate goals.

7. Limitations and Research Agenda

This analysis acknowledges limitations typical of pre-implementation ETS work in emerging contexts—namely data-quality and coverage gaps (with reliance on 2018–2020 activity and default factors), expert-based judgments in multi-criteria scoring, and the absence of equilibrium/dispatch models to quantify general-equilibrium effects, price pass-through, and power-sector re-dispatch. It should also be acknowledged that the present study did not employ a formal Multi-Criteria Decision Analysis (MCDA) technique such as AHP or TOPSIS. Instead, a structured expert elicitation combined with sensitivity analysis was adopted to ensure transparency and feasibility. This represents a methodological trade-off, and future research could extend the framework by applying formal MCDA approaches to further validate and refine the weighting process. These constraints underscore the need for sequenced learning and institution-building ahead of the first compliance period (2031–2035). In addition, the present study did not explicitly model carbon price trajectories or assess distributional consequences across firms, SMEs, households, and CBAM-exposed sectors. Likewise, broader macroeconomic and welfare effects remain outside the scope, as full CGE or IO modeling was considered beyond the present analysis.

Accordingly, the research and implementation agenda prioritizes a set of workstreams that (i) close empirical and MRV gaps, (ii) establish a statistically robust benchmarking architecture, (iii) use the 2029–2030 pilot to pressure-test design choices under controlled conditions, and (iv) embed an evaluation framework that links market outcomes to policy adjustment. Future research should also extend the empirical agenda by incorporating simplified simulations or CGE/IO modeling to capture economy-wide and welfare dimensions, including competitiveness, windfall risks, and household-level impacts. Scenario analysis of allocation pathways (e.g., transitioning from free allocation to partial auctioning) would further clarify trade-offs in efficiency and fairness. The agenda is intended to de-risk key parameters, support transparent governance, and provide evidence for the midterm review of the cap and allocation settings.

• MRV improvements: digitize facility-level reporting, harmonize templates, codify quality assurance (QA) and quality control (QC) and uncertainty ranges, and publish grid and product-boundary definitions for carbon-intensity (CI) metrics.
• Benchmarking program: establish product-level CI datasets, outlier rules, periodic benchmark updates, and a transparent leakage-risk list to calibrate assistance rates.
• Pilot ETS (2029–2030): use the pilot to run ex ante robustness tests in vivo—vary thresholds, test alternative sector weights, and compare outcomes with/without electricity under controlled conditions; document operational readiness for allocation and cap governance.
• Empirical evaluation of prices and costs: during the pilot and early compliance years (2031–2035), implement a monitoring plan to track allowance price formation, compliance costs, pass-through to output prices/tariffs, and competitiveness indicators; publish open microdata to enable third-party evaluation. This should be complemented with structured assessments of distributional effects on SMEs, households, and CBAM-exposed sectors, ensuring that equity and welfare considerations are integrated into future policy adjustments.

8. Conclusions

This study proposes a tailored framework for establishing an ETS in Thailand, based on a comprehensive analysis of its core design elements: scope, cap setting, and allowance allocation. The framework recommends a phased implementation, starting with 222 high-emitting facilities from seven key industrial sub-sectors. This approach ensures significant coverage, accounting for approximately 42.64% of the country’s total GHG emissions in the base year. The ETS cap is set at 20 MtCO₂e, aligning with national climate targets. For allowance allocation, the study recommends free allocation using the output-based benchmarking method for the initial phase due to its data readiness and effectiveness in incentivizing efficiency improvements. This practical and strategic framework provides a clear pathway for Thailand to achieve its ambitious climate goals. Based on these findings, we recommend the following for policymakers:

• Implement a phased approach: begin with the high-readiness sectors and gradually expand the scope as the system matures;
• Utilize output-based benchmarking: adopt this allocation method initially to drive efficiency improvements while accommodating current data limitations;
• Strengthen data infrastructure: prioritize the development of a robust MRV system. This is a critical prerequisite for generating reliable and accurate GHG emissions data, which underpins the credibility of the entire market and will be essential for enabling more advanced allocation methods, such as auctioning, in the future;
• Implement a market stability mechanism: recommended to mitigate price volatility. International experience has demonstrated this to be a necessary, not merely theoretical, tool for ensuring a stable and consistent carbon price signal [8]. The mechanism should be designed with an appropriate cap target—neither too lenient nor too ambitious—and incorporate a government-determined penalty price that effectively influences market trading prices;
• Consider future transition: plan for a gradual transition to a hybrid model that includes auctioning to create a transparent carbon price signal and generate revenue for a sustainable transition;
• Explore international offsetting mechanisms: recommend that policymakers explore the feasibility of allowing companies to utilize high-quality international carbon credits to offset a portion of their emissions, which would provide operational flexibility, particularly for hard-to-abate sectors, while also contributing to global mitigation efforts under Article 6 of the Paris agreement;
• Evaluate regional market linkage: recommend evaluating the potential for linking Thailand’s ETS with other regional systems. An initial assessment could focus on the feasibility of a future ASEAN ETS, starting with a pilot linkage to the Singapore ETS, which would enhance market liquidity, stabilize the carbon price, and foster regional climate cooperation.