Increasing household energy consumption, mainly due to consumption upgrading, will create tough challenges for China if that country is to achieve peak carbon emissions in 2030 and carbon neutrality in 2060. However, this critical issue has not been explored comprehensively in the literature. Using China Family Panel Studies data and the distribution dynamics approach, this article is the first study to examine the relationship between quality of life (QOL) (proxied by consumption upgrading) and relative household energy consumption (RHEC). The results show that convergence clubs exist in all QOL groups for the RHEC, but they are more evident in the groups with lower middle and low QOL. This is encouraging because they suggest that an improvement in QOL does not necessarily lead to a higher level of energy consumption. The dataset was then divided into rural‐urban and regional subgroups to further explore the impacts of these different characteristics on energy consumption. Significant disparities are found among the same QOL groups between urban and rural households and among different regions. The results derived from this study lead to pragmatic policy suggestions in areas including energy saving, emissions reduction, and particularly alleviation of inequality.

Economic shocks from COVID-19, coupled with ongoing US-China tensions, have raised debates around supply chain (or global value chain) organisation, with China at the centre of the storm. However, quantitative studies that consider the global and economy-wide impacts of rerouting supply chains are limited. This study examines the economic and emissions impacts of reorganising supply chains, using Australia-China trade as an example. It augments the Hypothetical Extraction Method by replacing traditional Input-Output analysis with a Computable General Equilibrium analysis. The estimation results demonstrate that in both exports and imports, a trade embargo between Australia and China – despite being compensated for by alternative supply chains—will cause gross domestic production losses and emissions increases for both countries and the world overall. Moreover, even though all other economies gain from the markets left by China, many of them incur overall gross domestic production losses and emission increases. The finding that the Association of Southeast Asian Nations and India may also suffer from an Australia-China trade embargo, despite a gain in trade volume, suggests that no country should add fuel to the fire. The results suggest that countries need to defend a rules-based trading regime and jointly address supply chain challenges.

This paper examines income convergence by using a continuous distribution dynamics approach which takes both population and economic size into consideration. The study is based on a panel dataset of 31 Chinese provinces from 1952 to 2013. The impacts of geographical location, capital accumulation, trade openness and industrial structure are also evaluated by conditional analysis. This paper provides a new angle for examining the spatial distribution dynamics of China’s provincial income. The results show that the ergodic distribution of the pre-reform period is multimodal, while the ergodic distribution of the post-reform period is bimodal. However, the ergodic distribution of the more recent period 2000–2013 is found to be unimodal. All three ergodic distributions are right-skewed. The weighted analysis shows that by neglecting population and economic size, the economic performance in the pre-reform period may be overestimated, while the poverty reduction effect in the post-reform period may be underestimated. The results suggest that relocating people and economic activities to high-income provinces is an important approach to reduce relative poverty. Conditional analysis results indicate that geographical location, capital accumulation, trade openness and industrial structure have a significant influence on the distribution dynamics of per capita GDP. However, the observed bimodality cannot be explained by one of the four factors.

The aim of this paper is to examine the transitional dynamics of carbon dioxide emission in China by using a prefecture-level database. Convergence analysis is conducted and mobility probability plots (MPPs) are employed to examine the distribution dynamics of 286 cities from 2002 to 2011. The empirical investigation is conducted in three steps. In the first step, the research is carried out at the national level, and it provides an overall view of the evolution of carbon dioxide emission in China. In step two, the database is divided into smaller spatial groupings so as to investigate the relationship between carbon dioxide emission and geographical location. In the third step, the evolution of carbon dioxide emission in the key cities and in other non-key cities is examined, along with its implications for environmental protection. This study offers valuable information on convergence of carbon dioxide emission in China.

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.