China's carbon trading pilot policy, economic stability, and high-quality economic development

The study addresses whether China’s carbon emissions trading pilot (CET) policies hinder or support economic performance, focusing on two outcomes: macroeconomic stability and high-quality economic development. Against the backdrop of China’s “double carbon” goals (peak by 2030, neutrality by 2060) and the rollout of ETS pilots since 2013, the paper notes mixed evidence in prior research regarding economic impacts. The authors posit two hypotheses: H1: CET pilots differentially affect economic stability over time—negatively in the short term (increased volatility) but stabilizing in the long run. H2: CET pilots catalyze high-quality economic development through green transition, innovation, and structural upgrading. The work is motivated by China’s early-stage national carbon market, fragmented regional pilots, and the policy need to balance growth and emissions reduction in a large developing economy.

The paper reviews theoretical and empirical foundations for carbon trading. Externality and property rights theories (Marshall, Pigou, Coase) justify market-based instruments such as ETS to internalize environmental costs. China’s pilots (Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong, Shenzhen) cover multiple industries and have advanced low-carbon development but face issues of liquidity, fragmentation, and volatile prices. Empirical studies show: (a) emissions reduction effectiveness of ETS pilots; (b) positive effects on green innovation and export sophistication; (c) mixed effects on economic growth—some positive (productivity gains, employment expansion, coordinated development) and some negative (output and GDP reductions in simulations); (d) structural effects via industrial upgrading, energy structure optimization, and productivity; (e) spatial spillovers and heterogeneity across regions and industries, with stronger positive impacts often in the East or in non-resource-based cities, and varied timing across pilot regions. The mixed literature motivates examining both economic stability and high-quality development simultaneously and exploring regional heterogeneity.

Design: The pilots are treated as a quasi-natural experiment. The authors estimate difference-in-differences (DID) models with province and year fixed effects using balanced annual panel data for 31 mainland provincial-level regions from 2005–2021 (Hong Kong, Macao, Taiwan excluded). Software: StataMP 17. Models: (1) EF_it = α + α1·did_it + Σγ·X_it + region FE + year FE + ε_it, where EF is macroeconomic fluctuation. (2) HQD_it = β + β1·did_it + Σδ·X_it + region FE + year FE + ε_it, where HQD is high-quality economic development. Treatment: did_it = treat_i × time_it, with treat_i=1 for Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong (Shenzhen merged into Guangdong), 0 otherwise. time_it=1 for 2013 and after (pilot period), 0 otherwise. Outcome variables: (a) Economic fluctuation (EF): 3-year rolling standard deviation of provincial GDP growth rate: EF_it = sqrt[(1/3) Σ_{t-1 to t+1} (GDP_growth_it − mean)^2], larger EF indicates lower stability. (b) High-quality economic development (HQD): constructed via entropy-weighted TOPSIS from five primary dimensions—innovation, coordination, green, openness, sharing—with 11 secondary indicators (e.g., R&D intensity, patents per capita, tertiary/secondary industry ratio, urban-rural income disparity, green coverage, SO2 intensity, trade dependency, FDI intensity, wages, medical beds per capita, education spending intensity). HQD index is rescaled (expanded equivalently) for regression comparability. Controls: (1) Fixed asset investment growth (Assets). (2) Infrastructure: per capita road area. (3) Human capital: share of university students in resident population (Talent). (4) Industrial development: log number of industrial enterprises above scale (Enterprise). (5) Employment: unemployment rate (Employ). Data sources: National Bureau of Statistics of China and national/provincial statistical yearbooks (urban, science and technology). Missing values are interpolated. The final dataset is a balanced panel (31 regions, 2005–2021). Validity checks: Parallel-trend tests using tvdiff. For EF: individual trend F=0.33 (p=0.7174), time trend F=0.26 (p=0.6078). For HQD: individual trend F=2.53 (p=0.0823), time trend F=0.62 (p=0.4321). Placebo tests: 500 random reassignments; DID coefficients cluster around zero and differ significantly from the true estimates.

Economic stability (EF): Baseline DID shows that the CET pilot increases economic volatility (i.e., reduces short-term stability). Without controls: DID coefficient = 1.0289 (p=0.005). With all controls: DID = 1.1922 (p=0.005). Among controls, infrastructure is positively associated with volatility (e.g., 0.1333, p<0.05), while human capital reduces volatility (−0.4429, p<0.10). Heterogeneity in EF: By macro-region, the East exhibits a positive and significant effect of CET on volatility (intensifies fluctuations). The Central region’s coefficient is positive but not significant, and the West shows a negative, non-significant coefficient, suggesting a slight mitigating effect that is not statistically discernible. High-quality development (HQD): Across OLS, random effects, and fixed effects specifications, the CET pilot significantly raises HQD. In the two-way fixed effects model with full controls, DID = 1.0250 (SE 0.5300), significant at 5%. In alternative models: OLS DID = 8.6118 (SE 0.8500, p<0.01), RE DID = 4.0192 (SE 0.6914, p<0.01), FE DID = 3.3111 (SE 0.6878, p<0.01) before adding time FE. Heterogeneity in HQD: The West shows a strong positive effect (DID = 3.1613, p<0.01). Effects in the East (−1.5457, n.s.) and Central (0.4845, n.s.) are not statistically significant. Parallel-trend and placebo tests support robustness for both EF and HQD analyses. Descriptive HQD pattern: From 2005–2021, HQD rose nationwide, with Beijing, Shanghai, and Jiangsu leading; central/western provinces generally lagged coastal regions.

Findings support H1’s short-term destabilization effect: CET pilots, amid fragmented markets, liquidity constraints, and volatile prices, raise firms’ compliance and abatement costs and transmit to macro fluctuations. Over time, as markets mature and firms upgrade technologies, stability is expected to improve (cobweb convergence logic). Findings also support H2: CET pilots enhance high-quality development by incentivizing green innovation, industrial restructuring, and energy structure optimization; integrating emissions into market mechanisms induces efficiency and productivity gains. Regional heterogeneity reflects differences in marketization, industrial structure, innovation capacity, and policy implementation intensity. The East’s stronger immediate volatility likely stems from larger, more active markets and faster structural adjustment, while the West benefits more on HQD due to lower initial development levels and lower transition resistance, allowing larger marginal gains from low-carbon transformation.

The study contributes by jointly evaluating carbon trading pilots’ impacts on macroeconomic stability and high-quality development using a DID framework and a cobweb-theory lens. Empirically, CET pilots significantly increase short-term economic volatility but significantly promote high-quality development overall, with region-specific heterogeneity (strongest HQD gains in the West; volatility amplification in the East). Policy implications include: (1) coordinating carbon reduction with stable growth and high-quality development through calibrated reduction intensity, energy structure optimization, and technology diffusion; (2) accelerating integration and maturation of the national carbon market—improving pricing signals, quota allocation, regulatory frameworks, and market infrastructure; (3) boosting market participation and liquidity—broadening coverage, increasing paid quotas, diversifying products, linking with carbon tax offsets, and fostering carbon finance innovation. These steps can smooth short-term instability while deepening long-run quality-oriented growth.

The analysis is framed by a cobweb model but does not empirically estimate supply and demand elasticities in the carbon market or verify whether the evolving equilibrium aligns with the “double carbon” targets—an avenue for future work. Comparative analyses between China and developed countries are also suggested to understand institutional and market differences in CET impacts. Data limitations required interpolation for some series, and early-stage market imperfections (fragmentation, liquidity) may condition observed short-term effects.