Synergistic effect of energy and industrial structures on carbon emissions in China

Does business environment optimization improve carbon emission efficiency? Evidence from provincial panel data in China

Previous research has yielded mixed conclusions regarding whether business environment (BE) optimization can enhance carbon emission efficiency (CEE). This study delves into the impact of the BE on CEE using panel data from 30 provinces in China, employing fixed effect, quantile, and mediated effect models. It innovates in three key areas: research perspective, mechanism of action, and heterogeneity analysis. The research found that the BE optimization enhances CEE. Meanwhile, the influence of the BE on CEE exhibits marginal decreasing characteristics. The mechanism analysis reveals that the BE enhances CEE through the industrial structure optimization effect and the progress of green technology, while it diminishes efficiency through the energy rebound effect. Heterogeneity analysis indicates that BE optimization has a stronger impact on improving CEE in provinces with robust government governance, younger governors, and highly educated officials. The policy implication suggests that local governments should continually optimize the BE, enhance government governance capacity, and prioritize the appointment of young and highly educated officials.

How does new energy demonstration city pilot policy affect carbon dioxide emissions? Evidence from a quasi-natural experiment in China

Energy transition policies are of great significance in adjusting the structure of energy supply and demand and coping with climate change. The new energy demonstration city pilot (NEDCP) policy, as an important pilot project in China's energy transition process, lacks a scientific assessment of the carbon reduction effect of the NEDCP policy and an in-depth explanation of the mechanism of the NEDCP. Based on panel data of 209 Chinese cities at the prefectural and higher levels from 2007 to 2019, this study takes the NEDCP policy as a quasi-natural experiment, using a difference-in-differences model combined with firm-level data to identify the impact of the NEDCP policy on urban carbon dioxide (CO2) emissions. This study analyzes the impact of heterogeneity of urban characteristics on the policy effect from multiple perspectives, and further investigates its mechanism. The conclusions are shown in the following aspects. (1) The implementation of the NEDCP policy decreases urban CO2 emissions significantly. Meanwhile, a series of robustness tests, including the instrumental variables method, propensity score matching difference-in-differences method, placebo test, exclusion of policy interference test, and machine learning method, support this conclusion. (2) The NEDCP policy achieves carbon reduction effects mainly through scale and structure effects. (3) The results of the heterogeneity test show that the NEDCP policy is more effective in cities with higher administrative levels, energy-demanding cities, cities in the southeast of Hu-line, and cities with a higher degree of nationalization. Therefore, the Chinese government should summarize the implementation experience of the NEDCP policy and expand its scope of application. The evaluation of the NEDCP policy in China has important reference value for the energy transition of other developing countries.

Can carbon emission trading improve carbon emission performance? Evidence from a quasi-natural experiment in China

Carbon emission trading policies play a key role in reducing carbon emissions through market-based mechanisms. In the context of China's carbon neutrality goals and carbon peaking targets, it is important to predict and evaluate the effectiveness of such policies. The combined impact of carbon trading policies on carbon emission reduction and economic output has not been well investigated in previous studies. In this study, the impact of carbon emission trading policies on regional carbon emission performance was assessed through mechanism analysis and empirical tests. The mechanism analysis showed that carbon emission intensity reduction relied on three mediating effects: technological innovation incentives, industrial structure optimization, and energy substitution. For the empirical test, the multi-time difference-in-differences (DID) method was adopted to study the impact using panel data from 30 provinces in China between 2005 and 2019. Moreover, the specific impact mechanism was further tested using mediating effects. The results showed that China's carbon trading policy has significantly affected the carbon emission performance of the pilot regions, and factors such as GDP per capita, urbanization level, and capital-labor ratio have notably contributed to the reduction of carbon emission intensity. The proportions of the three mediating effects in the total effect were estimated to be 60.98%, 23.17%, and 10.14%, respectively. This study provides an empirical approach to the study of the impact of carbon trading policy on carbon emission reduction and economic output and can serve as a reference for addressing climate change and alleviating conflicts between the environment and economic growth in similar regions.