China is one of the biggest energy consumers and carbon emitters in the world. Understanding the factors affecting carbon emissions is critical for policymakers to control the rising trend of carbon emissions. This paper investigates the relative importance of carbon emissions drivers in China. Literature review has been carried out to determine a set of predominant independent variables; the LASSO model is then introduced to rank the relative importance among the set of independent variables. The results find that 1) carbon emissions were mainly driven by economic growth and energy consumption followed by population size and industrialization; and 2) income growth slowed down carbon emissions during the studied period, but it is the least significant factor among the other factors. The ranking allows policy makers to focus on the most critical contributors to carbon emissions and gives policymakers more flexibility in determining policy interventions.

Utilizing an advanced machine learning algorithm, particularly the Artificial Neural Network (ANN) framework, this study reveals a significant nonlinear and even cyclical relationship between public concern about environmental issues and the ESG performance of Chinese A-share listed companies, covering the period from 2004 to 2020. The findings highlight the effectiveness of the Self-Organizing Map (SOM)-ANN framework in elucidating the empirical relationship between these variables. We contend that robust public monitoring can enhance companies’ ESG initiatives, and we recommend that policymakers implement a series of measures to safeguard and promote public involvement in decision-making processes. Furthermore, our analysis of the combined effects of public concern and various performance metrics on firms’ ESG outcomes indicates that the diversity among firms is crucial for determining the most appropriate level of public participation in their sustainable development efforts. Therefore, managers and policymakers should focus on firm-specific attributes instead of adopting a “one-size-fits-all” approach to maximize the benefits of public engagement.

This paper investigates environmental efficiency and its distribution dynamics in Chinese cities. The analyses are applied the meta-frontier data envelopment analysis and the continuous dynamic distribution approach together with panel data of 286 Chinese cities at the prefecture and above-prefecture levels. The results show that during the period of 2002–2011, the average environmental efficiency in Chinese cities is well below the production frontier. The coastal cities show better performance than the central and western cities; large and extra-large cities are more efficient than small and medium cities; and the environmental efficiency for the ‘key’ cities is slightly better than that for the ‘non-key’ cities. The spatial dynamic distribution analyses indicate that the environmental efficiency of Chinese cities tends to converge. To improve environmental efficiency in the future, the government should pay more attention to the central cities, medium and large size cities in western China, and the non-key cities. Abbreviations: CPI- Consumer price index; DEA- Date envelopment analysis; DMU- Decision making unit; GDP- Gross domestic product; GMI -Group- frontier managerial inefficiency; IPCC- Intergovernmental Panel on Climate Change; MTR- Meta- technology gap ratio; NTP- Net transition probability; PAA- Prefecture and above-prefecture levels; PIM Perpetual inventory method; SFA Stochastic frontier analysis; TGI Technology gap inefficiency

Empirical studies suggest that globalization (FDI and international trade) has been greatly affected by the COVID-19 and related anti-pandemic measures imposed by governments worldwide. This paper investigates the impact of globalization on intra-provincial income inequality in China and the data is based on the county level. The findings reveal that FDI is negatively associated with intra-provincial inequality, intra-provincial inequality increases as the primary industry sector (agriculture) declines. The result also finds that the increase in inequality stems not from the development in the tertiary or secondary industry sectors per se, but the unevenness in the distribution of these sectors.