📈 economics

Designing Emissions Trading Systems in Emerging Economies: A Firm-Level Simulation Framework and Application to Thailand

This paper develops and applies a firm-level simulation framework to Thailand's industrial estates to demonstrate that a moderately stringent emissions trading system with partial coverage, gradual auctioning, and targeted revenue recycling offers a balanced transition profile for emerging economies, serving as a practical ex ante calibration tool for regulators.

Original authors: Wanyok Atisattapong, Pasin Marupanthorn

Published 2026-07-16

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Wanyok Atisattapong, Pasin Marupanthorn

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the Earth's atmosphere as a giant, shared bathtub. For a long time, we've been filling it up with dirty water—carbon emissions from factories and power plants—without worrying about the overflow. Scientists and policymakers have realized that to stop the tub from flooding, we need to slow down the tap. One popular tool for doing this is called "carbon pricing." Think of it like a toll booth for pollution: if you want to pour dirty water into the bathtub, you have to pay a fee.

There are two main ways to run this toll booth. One is a carbon tax, where the government sets a fixed price for every drop of pollution, but doesn't guarantee how much pollution stops. The other is an Emissions Trading System (ETS), which is more like a strict limit on the total amount of water allowed in the tub. The government issues a set number of "allowance tickets," and companies must buy a ticket for every drop of pollution they emit. If a company cleans up its act and pollutes less, it can sell its extra tickets to a neighbor who is still dirty. This creates a market where the price of pollution is discovered by the companies themselves, theoretically encouraging the cheapest and fastest cleanups. But here's the tricky part: designing this system for a country that is still growing its economy is like trying to build a complex race car while it's already driving down the highway. You need to make sure the engine doesn't stall (businesses stay competitive), the driver doesn't crash (the environment actually improves), and the mechanic doesn't get overwhelmed (the government can actually check the work).

This paper, titled "Designing Emissions Trading Systems in Emerging Economies: A Firm-Level Simulation Framework and Application to Thailand," tackles exactly that challenge. The authors, Wanyok Atisattapong and Pasin Marupanthorn, created a sophisticated computer simulation—a digital "flight simulator" for climate policy—to test how different versions of an Emissions Trading System would work in Thailand. Instead of waiting to build a real system and hoping it works, they built a virtual one to see what happens when you tweak the knobs: How strict should the pollution limit be? How many free tickets should companies get? Should the government sell tickets to raise money, or give them away?

The researchers focused on Thailand's industrial estates, which are like massive neighborhoods packed with factories. They used a "firm-level" approach, meaning they didn't just look at the country as a whole blob; they simulated individual factories, each with its own unique costs, profits, and ability to switch to cleaner technology. They ran thousands of scenarios to see which design would capture the most pollution while keeping the cost manageable for businesses and the government.

The results suggest that there is no single "perfect" design, but there are clear winners and losers depending on the goals. The simulation found that a "soft" start, where the government gives away almost all the tickets and sets a very loose limit, might be easy for businesses but won't actually reduce pollution much. On the other hand, a "strict" start, with very few tickets and high prices, might clean up the air quickly but could hurt businesses so badly that they might fail or move their factories to countries with no rules at all.

The sweet spot, according to the simulation, is a "balanced" approach that gradually tightens the rules. The authors suggest a design where the government starts by covering only the biggest polluters (factories emitting more than 20,000 tons of CO2 per year), which captures about 97% of the emissions from that sector while keeping the administrative burden low. They recommend a "moderate" cap reduction of 10% initially, with 70% of the tickets given away for free to help companies adjust, and 30% sold at auction to raise money. This money shouldn't just go into the government's pocket; the simulation suggests recycling it back into the system to help companies pay for cleaner technology.

Interestingly, the simulation showed that a specific scenario called "Auction with recycling" (Scenario P4) scored the highest. In this version, the government auctions off half the tickets and uses the revenue to support energy efficiency upgrades. This creates a strong price signal that encourages cleaning up, but the recycling of money keeps the financial hit to businesses manageable. The study also highlights that simply looking at how much pollution a factory makes isn't enough to know if it will survive; you also have to look at how much money it makes and how easily it can pass the extra costs on to customers.

Crucially, the authors are very clear about what their study is not. They did not build a real ETS in Thailand, nor did they measure the actual effects of one because Thailand hasn't implemented a mandatory system for these factories yet. Everything in this paper is a "what-if" simulation based on proxy data and assumptions. The numbers they report, like a carbon price of 297 Thai Baht per ton in one scenario, are not predictions of the future market price but rather the calculated result of their specific computer model. They explicitly warn that these are conditional rankings, not guarantees. If the real-world costs of cleaning up are higher than they guessed, or if the government changes the rules, the results would change.

Ultimately, this paper acts as a policy calibration tool. It gives regulators a way to test their ideas before they launch them into the real world. It suggests that for emerging economies like Thailand, the path to a greener future isn't about choosing between "strict" or "soft," but about finding a "gradual" rhythm that balances environmental goals with economic reality. By using a firm-level simulation, the authors show that a well-designed system can protect the environment, keep industries competitive, and even generate funds for further innovation, provided the rules are transparent and the transition is managed carefully.

Technical Summary: Firm-Level Simulation Framework for Emissions Trading System Design in Emerging Economies with Application to Thailand

Problem Statement
Emerging economies face a complex design challenge in implementing Emissions Trading Systems (ETS). Regulators must balance four often competing objectives: environmental integrity (ensuring emissions reductions), economic efficiency (minimizing abatement costs), administrative feasibility (managing reporting burdens with limited capacity), and competitiveness management (preventing carbon leakage). Existing literature often relies on ex-post evaluations of mature systems or aggregate data, which fails to capture the distributional effects of policy design on heterogeneous firms. Specifically, there is a lack of ex-ante tools that can simulate how specific design choices—such as coverage thresholds, cap stringency, allocation rules, and offset limits—translate into firm-level compliance costs, financial exposure, and transition risks before a mandatory system is implemented. Thailand serves as a timely case study, preparing a formal carbon-pricing architecture while facing external trade pressures like the EU's Carbon Border Adjustment Mechanism (CBAM).

Methodology
The study develops a firm-level simulation framework designed for ex-ante regulatory impact assessment. The framework integrates the following core components:

Coverage and Thresholds: The model selects regulated entities based on baseline emissions ( $E_{i0}$ ) and a regulatory threshold ( $\tau$ ). It explicitly models the trade-off between capturing a high share of emissions and minimizing the administrative burden (Monitoring, Reporting, and Verification - MRV) of regulating small emitters.
Cap and Allocation: The system sets a declining emissions cap ( $C_t$ ) and distributes allowances via a mix of free allocation (benchmark-based or grandfathering) and auctioning. The free allocation rule in the Thailand application uses a "disclosed benchmark-style proxy" because verified facility-level output data is not yet publicly available; it maps revenue proxies to sector benchmark intensities.
Abatement and Offsets: Firms choose abatement rates ( $q_{it}$ ) to minimize costs, facing convex abatement costs. The model incorporates a limited domestic offset market (T-VER credits) as an exogenous supply constraint rather than a jointly cleared market.
Market Clearing: The allowance price ( $P_t$ ) is determined endogenously as a shadow price that equates aggregate direct abatement supply with the remaining direct-abatement target after offset use. The model solves for this price using a bisection method.
Financial and Risk Metrics: The framework calculates total compliance costs (abatement + allowance purchase + administrative + offset costs) and derives affordability ratios (cost-to-revenue and cost-to-EBITDA). It also constructs a leakage-risk indicator based on emissions intensity, trade exposure, and cost pass-through ability.
Scenario Analysis: The authors apply this framework to Thailand's industrial estates using data from 61 estates (specifically the S2 scenario: Scope 1 emissions excluding electricity generation). They simulate five policy scenarios ranging from a "Soft transition" (P1) to a "Mature ETS" (P5), varying cap reductions, free allocation shares, auctioning rates, and offset limits.

Key Contributions

Transferable Design Framework: The paper reframes the ETS design problem for emerging economies with incomplete facility data and limited administrative capacity, offering a structured approach to evaluate design choices before implementation.
Integrated Simulation Structure: It uniquely combines threshold-based coverage, dynamic caps, heterogeneous abatement costs, administrative burden modeling, and revenue recycling into a single simulation engine.
Firm-Level Financial Exposure: Unlike models focusing solely on emissions volume, this framework explicitly links ETS design to firm-level financial vulnerability (cost-to-revenue/profit) and leakage risk, providing a tool for supervisors and investors to assess transition risk.
Pathway to Causal Evaluation: The study identifies the specific facility-firm data requirements (e.g., verified emissions, ownership crosswalks) necessary to upgrade this screening tool into a causal empirical study (using Regression Discontinuity or Difference-in-Differences designs) once a mandatory Thai ETS is operational.

Results
The application to Thailand's industrial estates yields the following findings:

Threshold Efficiency: Partial coverage is highly effective. A threshold of 20,000 tCO2e (S2-2) covers 55 factories, capturing 9.43 MtCO2e (approx. 87% of the S2 universe) while significantly reducing administrative burden compared to lower thresholds.
Scenario Performance:
- P4 (Auction with recycling) emerges as the highest-ranked scenario. It features a 15% cap cut, 50% free allocation, 50% auctioning, and a 10% offset limit. It generates a shadow price of 463 THB/tCO2e and 1.86 billion THB in auction revenue, which is recycled to support transition. Crucially, it keeps the 90th-percentile cost-to-revenue ratio at 1.27%, staying within the study's 1.5% affordability screen.
- P2 (Balanced ETS) is a strong second choice, offering a lower burden (297 THB/tCO2e) but less revenue for transition finance.
- P3 (Strict ETS) and P5 (Mature ETS) are deemed less feasible in the near term due to high shadow prices (630–895 THB/tCO2e) and cost-to-revenue ratios exceeding the affordability threshold (2.81% and 4.06%, respectively).
Leakage Risk: The Basic Materials and Industrials sectors show the highest leakage risk indices due to high emissions intensity and trade exposure, suggesting these sectors require targeted transitional protection (e.g., benchmark-based free allocation) rather than broad exemptions.
Robustness: Monte Carlo sensitivity analysis confirms that P4 remains the top-ranked scenario across various uncertainty draws regarding abatement costs and financial proxies, though the most stringent scenarios (P5) show a non-zero probability of technical infeasibility.

Significance and Claims
The paper positions itself as a policy-calibration tool rather than a source of empirical estimates of realized ETS effects. Its significance lies in:

Operationalizing Sustainable Development: It demonstrates how ETS design can be aligned with SDG 13 (Climate Action), SDG 9 (Industry Innovation), and SDG 12 (Responsible Consumption) by balancing decarbonization with industrial competitiveness and transition finance.
Informing Regulatory Strategy: The results suggest that for Thailand (and similar emerging economies), a credible ETS should begin with a moderate threshold (e.g., 20,000 tCO2e), use benchmark-style transitional free allocation, gradually increase auctioning, and earmark auction revenue for low-carbon investment.
Limitations and Future Work: The authors explicitly state that the reported rankings are conditional on simulation assumptions and proxy data. They do not claim to estimate causal treatment effects. The framework is intended to guide ex-ante design and disclosure, with the specific data requirements outlined for future ex-post causal evaluation once official mandatory ETS data becomes available.

Technical Summary: Firm-Level Simulation Framework for Emissions Trading System Design in Emerging Economies with Application to Thailand

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