Does China’s Nationwide CO₂ Emissions Trading System Improve Upon Traditional Cap-and-Trade?

This study examines how cost-effective China’s nationwide tradable-performance-standard (TPS) carbon market is compared with a traditional cap-and-trade (C&T) system. Using plant-level data and a dynamic general-equilibrium model for 2020–2035, the authors estimate that the TPS cuts about 18 billion tons of CO₂ (an 8.6% reduction) at an average cost of roughly 87 RMB per ton. Early on, TPS costs are similar to C&T because existing taxes narrow the efficiency gap, but TPS becomes less efficient as the policy tightens. Even so, its environmental benefits exceed costs by more than fivefold, and adding allowance auctions could reduce costs by about 30%.

The Policy Scientist's Perspective

This article addresses a policy issue of wide significance: how large economies can reduce carbon emissions at scale while containing economic costs. Its importance extends well beyond China, as jurisdictions worldwide are weighing the trade-offs between different market-based instruments. The paper is timely, given the rapid expansion of emissions trading programs and heightened scrutiny of their performance. Its model draws on unusually detailed plant-level data, improving credibility. Findings are broadly generalizable to other economies considering intensity-based systems, and the dynamic equilibrium framework represents a meaningful contribution to recent literature.

Full Citation and Link to Article

Goulder, L. H., Long, X., Qu, C., & Zhang, D. (2024). China’s nationwide CO₂ emissions trading system: A general equilibrium assessment. American Economic Journal: Economic Policy (Forthcoming). https://doi.org/10.1257/pol.20240652

Central Research Question

The article investigates whether China’s nationwide tradable-performance-standard (TPS) emissions trading system can deliver cost-effective carbon reductions relative to a traditional cap-and-trade (C&T) system, once China’s institutional and fiscal environment is accurately represented. The authors ask how the TPS affects sectoral output, allowance prices, economic welfare, emissions trajectories, and the distribution of costs across sectors and provinces from 2020–2035. A central component of the inquiry is whether pre-existing taxes and subsidies alter the theoretically expected cost disadvantage of TPS programs. The study also evaluates how alternative benchmark designs and the introduction of partial allowance auctioning modify policy performance.

Previous Literature

The article builds directly on long-standing work on rate-based environmental regulations, including early analytical treatments by Goulder, Fischer, and others, which established that tradable intensity standards typically generate higher social costs than mass-based cap-and-trade programs because they implicitly subsidize output. Subsequent U.S.-focused empirical and simulation studies examined intensity-based clean electricity or fuel-economy standards, identifying similar trade-offs between output distortion and emissions abatement. More recent work on China’s system has primarily relied on partial-equilibrium sector models or limited dynamic simulations. Only one earlier general-equilibrium analysis (Yu et al., 2022) attempted a broader accounting of economy-wide interactions, but that work focused on Phase 1 of China’s system and did not incorporate plant-level heterogeneity, sector-specific benchmarks, or detailed fiscal interactions. The current paper extends this literature by integrating institutional features rarely modeled in earlier work: administered electricity pricing, heterogeneous production technologies, SOE/POE differences, and the structure of China’s output-based tax system. It also clarifies disagreements in the existing literature by showing that the TPS’s cost disadvantage is not structural or unavoidable; rather, it depends critically on benchmark stringency and on the relative tax burden faced by covered and non-covered sectors.

Data

The study relies on a combination of national input-output tables, plant-level emissions and energy-use data from China’s Ministry of Ecology and Environment, tax and subsidy schedules from GTAP and Chinese fiscal reports, and detailed information on production technologies within four key sectors: electricity, cement, aluminum, and iron and steel. The plant-level dataset is unusually rich, enabling the authors to model differences in emissions intensity, heat-rate improvement potential, capital composition, and technology choice within subsectors. CO₂ emissions data are derived from China’s energy balance and CEADs inventories. The authors also integrate data on renewable generation costs, integration costs for wind and solar, and estimates of fossil-plant heat-rate improvement from engineering reports. Economic variables—wages, capital stocks, consumption shares, import and export patterns—are calibrated to 2020 levels, with the dataset adjusted to match published statistics on GDP, emissions totals, and sectoral value-added. These data enable the construction of a consistent, intertemporally linked dataset for model simulation over the 2020–2035 horizon and ensure that sectoral and subsectoral behaviors reflect actual technological and fiscal conditions.

Methods

The authors construct a dynamic computable general-equilibrium (CGE) model with 31 sectors, including multiple subsectors in emissions-intensive industries. The model captures intertemporal investment behavior, capital mobility constraints, heterogeneous technologies, and government budget balance conditions. Production is represented through CES nests allowing substitution across materials, energy, labor, and capital. SOEs and POEs are treated as profit-maximizing firms but incorporate differences in subsidies, labor costs, and capital inputs. The modeling framework incorporates pre-existing taxes, including output taxes, factor taxes, and subsidies, allowing the authors to evaluate tax-interaction effects that alter the marginal cost of environmental policy. The TPS is operationalized by linking free allowance allocations to output levels via sector-specific benchmarks, while the C&T counterfactual fixes total allowance quantities to match the TPS’s aggregate emissions path. The model also simulates alternative benchmark designs (varying the number of benchmarks in the electricity sector) and partial allowance auctioning scenarios, with associated revenue-recycling policies. Each year from 2020–2035 is solved as a general-equilibrium system in which all goods, factors, and allowances clear simultaneously. Emissions reductions are decomposed into three channels—intensity improvements, sectoral composition shifts, and output reductions—to compare the mechanisms used under TPS and C&T.

Findings/Size Effects

The TPS reduces cumulative CO₂ emissions by approximately 18 billion tons over 2020–2035, an 8.6 percent reduction relative to baseline. Phase 1 (electricity only) achieves modest reductions, while Phases 2 and 3—covering cement, aluminum, iron and steel, and additional heavy industries—drive the majority of abatement. The average cost per ton is about 87 RMB (GDP-based), or 21 RMB when measured through equivalent variation. Environmental benefits exceed costs by a factor of roughly five under standard social-cost-of-carbon assumptions, and by much more when local air-quality co-benefits are included (central estimate: 45 trillion RMB in net benefits). The model identifies strong size effects: roughly half of total emissions reductions come from the electricity sector alone; intensity improvements account for the largest share of reductions under TPS, whereas output reductions play a smaller role due to the system’s implicit output subsidy. Compared with C&T, TPS costs are similar in the early years because pre-existing output taxes reduce the relative distortion. However, as benchmarks tighten and allowance prices climb, the output-subsidy distortion becomes more costly, and the TPS’s cost disadvantage grows. By the mid-2030s, the TPS becomes meaningfully more expensive per ton than an equivalent C&T system, aligning with theoretical expectations once tax-interaction benefits diminish. Allowance auctioning reduces compliance costs by 29–43 percent, depending on revenue recycling; financing labor and capital tax reductions yields the largest gains. The number of benchmarks also matters: moving from four electricity benchmarks to a single benchmark reduces economy-wide costs by one-third but increases inequality in provincial income effects by more than 60 percent.

Conclusion

The article offers a comprehensive assessment of China’s TPS and clarifies several misunderstandings in previous work. By embedding the TPS within the full institutional, technological, and fiscal structure of China’s economy, the authors show that the system can deliver substantial emissions reductions at relatively low cost and with extremely favorable benefit-cost ratios. The TPS performs more efficiently than standard theory would suggest in the early years because China’s tax system dampens the output-subsidy distortion, but as stringency rises, the expected cost gap between TPS and C&T re-emerges. The study demonstrates that policy design choices—benchmark structure, auctioning rates, and revenue recycling—have substantial effects on cost-effectiveness and distributional outcomes. The article also highlights generalizability: other large developing economies with similar tax structures and high emissions-intensive output shares may observe comparable performance patterns under intensity-based trading programs. Overall, the study represents a technically strong contribution that integrates detailed data, a sophisticated modeling framework, and a rigorous evaluation of policy alternatives at a national scale.