Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.

The development of the digital economy holds great significance for alleviating haze pollution. To estimate the impact of the digital economy on haze pollution, this paper explores the spatiotemporal evolutionary characteristics of the digital economy and PM2.5 concentration in the middle and lower reaches of the Yellow River Basin from 2011 to 2019 and conducts regression analysis by combining a fixed effect (FE) model and the spatial Durbin model (SDM). Moreover, this study divides the mitigation effect of haze pollution into a direct effect and a spatial spillover effect, and it further analyzes the mechanism from the perspectives of technological innovation and the industrial structure. The empirical results show that the development level of the digital economy increases year by year and that the concentration of PM2.5 decreases year by year. The digital economy level and PM2.5 concentration in the downstream region are higher than those in the middle region, and the digital economy is negatively correlated with haze pollution. Similarly, the spatial spillover effect of the digital economy is conducive to curbing haze pollution. The robustness test also supports this conclusion. In addition, there is regional heterogeneity in the impact of the digital economy on haze pollution. The direct effect and spatial spillover effect of the digital economy on haze pollution in the downstream region are greater than those in the middle region. This study suggests that to realize air pollution prevention and control, it is necessary to strengthen the construction of digital infrastructure and create a good digital economy development environment based on local conditions. Encouraging the development of digital technological innovation and promoting industrial digital transformation hold great significance for alleviating haze pollution.

No abstract

With the acceleration of urbanization, domestic waste has become one of the most inevitable factors threatening the environment and human health. Waste classification is of great significance and value for improving urban environmental quality and promoting human well-being. Based on the theory of planned behavior, we added external and socio-economic factors to systematically examine how they affect residents’ waste classification behavior (WCB). We collected 661 valid data through a questionnaire survey conducted in Jinan, a pilot city for waste classification in China. Key driving factors were identified by combining binary logistic regression and the principal component analysis. The results showed that the elderly, women, and people with higher education are more likely to participate in waste classification. Attitude, collaborative governance, and institutional pressure positively affect WCB, while subjective norm and infrastructure have a negative effect. Knowledge mastery and degree of publicity are positively and significantly related to WCB, but other perceived behavioral control sub-variables negatively affect WCB. Based on the results and status of waste classification in Jinan, we propose the multi-agent linkage governance pattern from various dimensions to explore a powerful guiding incentive that can enhance WCB and provide a reference for waste management policymakers.