A comparative analysis of Chinese regional climate regulation policy: ETS as an example

Carbon Abatement and Leakage in China's Regional Carbon Emission Trading

Emission trading schemes (ETS) are increasingly becoming a popular policy instrument to balance carbon abatement and economic growth. As a globally unified carbon pricing system has not yet been established, whether regionally operated ETSs cause carbon leakage remains a major concern. Taking China's regional pilot ETSs as a quasi-natural experiment, the study uses the spatial difference-in-differences method to examine how regional ETSs affect carbon emissions in and outside cities of policy implementation. Our analysis finds that China's regional ETS policy contributes to a 6.1% reduction in urban CO2 emissions and a 6.6% decline in emissions intensity in regulated cities, causing carbon leakages that increase CO2 emissions in neighboring cities by 1.7% on average. Our finding further suggests that regional ETSs mitigate local CO2 emissions through outsourcing production, improving energy efficiency and decarbonizing energy structure, whereas the outsourcing of industrial production drives up CO2 emissions in adjacent cities. Moreover, the performances of regional ETSs vary largely by socioeconomic context and mechanism design. China's regional ETSs reduce CO2 emissions more effectively in central and industrial cities but with more severe carbon leakage, while rigorous compliance mechanisms and active market trading help deepen carbon abatement and alleviate carbon leakage.

Intervention factors associated with environmental stressors resulting from cross-provincial transfers by coal resource-based enterprises

The environmental stressors associated with the cross-provincial transfer of coal resource-based enterprises (CREs) have become a critical concern for the green, sustainable, and high-quality development of resource-rich areas in central and western regions. This study referred to socioeconomic statistics and carried out an interview survey, literature review, and systematic analysis to clarify the mechanism underlying environmental stressors arising from the cross-provincial transfer of CREs. The intervention factors associated with such environmental stressors were identified, and the study conducted an empirical analysis of relevant data related to the coal-resources industry in three central and western provinces in China for the period 1997-2016. Research findings: (1) The intensity ranking of the influencing factors associated with environmental stressors caused by cross-provincial transfers of CREs has certain rules. The 'level of the enterprise's investment in environmental protection' is the weakest, the 'enterprise's development mode level' is slightly stronger, the 'enterprise scale' is stronger, and 'environmental regulation' is the strongest. (2) Stricter endogenous and exogenous policy regulations for environmental governance in rich coal resource-based regions are associated with weaker negative externalities in respect of resource development and the intensity of stressors. (3) Larger CREs are associated with a better green mining capacity, environmental repair cost advantages, social constraints, self-discipline, and thus, a weaker stress effect. (4) CREs that adopt more superior modes of development that focus on the utilization of the 'three wastes' are associated with a weaker stress effect. (5) The higher the level of investment by CREs in environmental protection technology, facilities, and equipment, the weaker the stress effect. The conclusions of the study can provide a theoretical basis to assist the Chinese government to develop relevant regulations to control inter-provincial transfers by CREs and to thereby diminish environmental stressor effects.

China's agricultural GHG emission efficiency: regional disparity and spatial dynamic evolution

Improving China's agricultural greenhouse gases (GHG) emission efficiency has become an important way to cope with climate change in an ecologically-and ethically responsible manner. In this paper, we use a global slacks-based inefficiency model to evaluate the agricultural greenhouse gases (GHG) emission efficiency levels in China during 2000-2015. The regional disparity of China's GHG emission efficiency is examined by using a Dagum Gini coefficient. A spatial Markov chain technique is also employed to investigate the spatial dynamic evolution of agricultural GHG emission efficiency. The results show that: (1) China's agricultural GHG emission efficiency increased steadily during the study period; a certain gap in efficiency among provinces and regions also exists. (2) Between-group disparity is the main source of the overall regional disparities in China's agricultural GHG emission efficiency. The disparities between regions are on the rise, while the disparities within regions are relatively stable. (3) China's agricultural GHG emission efficiency demonstrates significant spatial dependence. This study provides policy implications for the sustainable development of China's agricultural sector.

Does China's carbon emission trading policy improve regional energy efficiency?-an analysis based on quasi-experimental and policy spillover effects

Carbon emission trading policy is of great importance for addressing climate change and reducing carbon emissions. Reducing carbon emissions could further affect energy efficiency (EE). Based on the data from 30 provinces in China from 2006 to 2017, this paper first calculated EE by using the super slack-based model (Super-SBM) and then analysed the theoretical mechanism of the impact of carbon emission trading policy on EE. We also used a difference-in-difference (DID) model and mediation effect model for empirical analysis. Finally, we established the spatial difference-in-difference (SDID) model to test the policy spillover effects of carbon emission trading policy. The results showed that the high EE areas have gradually shifted to the central and eastern regions during 2006-2017 in China. The EE value in the pilot area of the carbon emission trading policy was obviously higher than that in the non-pilot area. Carbon emission trading policy had a significant positive effect on improving EE overall. In particular, green technology innovation and energy structure both had positive mediation effects on carbon emission trading policy affecting EE. However, the industrial structuring adjustment had no significant mediation effect in its influencing mechanism. Additionally, the spatial spillover effects test showed that the carbon emission trading policy had a positive effect on the EE of the pilot areas but a negative effect on that of the non-pilot areas.

Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China

The contradiction between the development of urban agglomerations and ecological protection has long been a challenging issue. China has experienced an astonishing expansion of its urban scale in the past 40 years, and nearly 783 million of the nation's people now live in cities. Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta have been prioritized to become world-class clusters by 2020. The health effects of air pollution in these three urban agglomerations are becoming increasingly formidable. Given these conditions, using the daily mean PM_2.5 concentration in 40 cities from January 2014 to December 2016, this research explored the spatial-temporal characteristics of PM_2.5 concentrations in these three urban agglomerations. The annual mean PM_2.5 concentrations in Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta are 35.39 µg/m³, 53.72 µg/m³ and 78.54 µg/m³, respectively. Compared with the other two urban agglomerations, abundant rainfall causes the Pearl River Delta to have the lowest PM_2.5 level. Furthermore, a general regression neural network (GRNN) method is developed to predict the PM_2.5 concentration in these clusters on the second day, with inputs including the average, maximum and minimum temperature; average, maximum and minimum atmosphere; total rainfall; average humidity; average and maximum wind speed; and the PM_2.5 concentration measured 1 day ahead. The results indicate that the GRNN method can precisely predict the concentration level in these clusters, and it is especially useful for the Pearl River Delta, as the underlying influence mechanism is more specified in this cluster than in the others. Importantly, this 1-day-ahead forecasting of PM_2.5 concentrations can raise awareness among the public to improve their precautionary behaviours and help urban planners to provide corresponding support.