Information Disclosure, Coal Withdrawal and Carbon Emissions Reductions: A Policy Test Based on China’s Environmental Information Disclosure

Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China's experience

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China's eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran's index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China.

Towards zero-carbon cities: how does environmental information disclosure impact the risk of carbon locking?

Utilizing panel data from 119 cities spanning from 2013 to 2019, the study rigorously investigates the influence of environmental information disclosure (EID) on mitigating the risk of urban carbon lock-in through the application of both two-way fixed effects and spatial econometric models. The findings robustly indicate that EID plays a significant role in facilitating carbon unlocking within cities, a conclusion that remains steadfast even after addressing potential endogeneity issues. Mechanistically, it is revealed that EID can effectively decrease the risk of carbon lock-in by enhancing urbanization levels and reducing energy intensity. Furthermore, the study unveils that the efficacy of EID in curbing urban carbon lock-in risk is also contingent upon factors such as carbon intensity, population size, and environmental quality, highlighting the multifaceted nature of EID's impact on urban carbon management. Specifically, the increase in carbon intensity, the expansion of population size, and the deterioration of environmental quality will reduce the carbon unlocking effect, which may not be conducive to the achievement of the goal of "carbon neutrality" in cities. The spatial effect test found that EID and carbon locking risk are both spatially significant positive autocorrelation, the deepening of local EID can significantly cut the risk of carbon locking in neighboring cities, and the urbanization pathway and energy saving pathway exist both locally and in neighboring places.

Research on influencing factors of carbon information disclosure quality in China's power industry

High-quality carbon information disclosure is required with the development of China's national carbon emission trading market. It is still controversial what factors influence carbon information disclosure quality in China's power industry. This paper involved four factors: government regulation, media reports, enterprise size, and directors' professional backgrounds based on strategic change theory. The carbon information disclosure quality was measured using the analytic hierarchy process (APH) and entropy weight method (EWM). Then, panel data regression was used to analyze the relationship between the factors and carbon information disclosure quality. The results showed that (1) the quantity of media reports has no significant correlation with carbon information disclosure quality. (2) The proportion of directors with legal backgrounds to all directors is significantly and negatively correlated with carbon information disclosure quality. (3) Government regulation, the media value orientation, enterprise size, and the proportion of directors with accounting backgrounds to all directors are significantly and positively related to the carbon information disclosure quality.

Market incentives, carbon quota allocation and carbon emission reduction: Evidence from China's carbon trading pilot policy

As a major carbon dioxide-emitting country, China set carbon trading market to reduce enterprise carbon emissions through the rational allocation of carbon quotas among different enterprises and regions. The market has also conducted a preliminary exploration for the country to achieve carbon dioxide emissions peak in 2030 and carbon neutrality in 2060 while actively addressing the challenges of global climate change. This study analysed the emission reduction effect of China's carbon trading pilot policy, especially the role of carbon quota and carbon trading price. The analysis used county-level panel data from 1997 to 2017, regarded the implementation of the carbon trading pilot policy as a quasi-natural experiment, and used the difference-in-differences method. The results showed that, first, the policy implementation not only reduced regional carbon emissions but also inhibited carbon dioxide emissions per capita, with long-term effects. Second, the carbon emission reduction effect brought by the carbon pilot policy showed significant heterogeneous results with the different degrees of regional carbon emissions and environmental supervision. The effect was greater in areas with higher carbon emission density and stronger legal supervision. Third, the difference in carbon quota allocations resulted in different emission reduction effects, among which the historical method had the strongest effect. The carbon quota price and number of enterprises participating carbon trading market were the key factors affecting carbon emission reduction.