Distortion amplification effects caused by imperfect climate policies: Evidence from China's emissions trading scheme

In pursuit of decarbonization, a suite of stringent climate policies has been implemented by China, with the carbon emissions trading scheme (ETS) emerging as a pivotal market-based mechanism for carbon reduction. However, the ETS remains in a phase of development with an underdeveloped price discovery function, potentially exacerbating resource misallocations in the interconnected energy market. This study theoretically proves the impact of imperfect ETS on energy misallocation and employs the panel dataset encompassing 30 Chinese provinces spanning the years 2000-2022, utilizing both the difference-in-differences (DID) method and the double machine learning model (DML) for empirical analysis. Findings reveal that the imperfect ETS exacerbates energy misallocation, and raising carbon prices could further amplify this issue. Notably, our regression analyses suggest that the DID methodology may underestimate the extent of this impact compared with the DML approach. Results are both statistically significant and robust, passing rigorous validity tests. Finally, grounded in theory and empirical insights, we conclude with tailored policy proposals aimed at optimizing the evolution of the carbon market.