Ling Bai

Exploring the impact of economic modernization on carbon balance is an essential endeavor to achieve carbon neutrality and combat climate change. However, the spatial impact of economic modernization on carbon balance remains ambiguous. Therefore, this study aims to explore the spatial spillover effects of agricultural modernization, industrialization, and urbanization on carbon balance during the economic modernization process in China, taking 30 provinces and cities in China as examples from 2010 to 2021. This study utilizes the spatial Durbin model to derive the following results: In the past decade, the carbon balance ratio has shown a fluctuating and decreasing dynamic evolution trend. There is an increase in regions with serious carbon deficits. Further investigation into the spatial spillover effect of carbon balance unveils that for every 1% increase in the carbon balance ratio of a province, neighboring provinces experience a decrease of 0.833%. Additionally, the spatial spillover effects of the three modernizations in China on the carbon balance ratio behave differently. Agricultural modernization and urbanization demonstrate negative spatial spillover effects on the carbon balance in neighboring regions, while industrialization exerts a significant positive spatial spillover effect on the carbon balance of neighboring regions. Regarding control variables, the level of innovation solely contributes to local carbon balance realization without generating a trickle-down effect, whereas infrastructure development operates inversely. At the same time, there are differences in the spatial effects of agricultural modernization and industrialization on the carbon balance between the eastern region and the central and western regions. The study underscores the importance of economic modernization and development processes focusing on fostering synergistic growth between economic and environmental benefits within both local and neighboring areas.

It is unidentified whether information communication technology (ICT) agglomeration can contribute to carbon reduction and to what extent it plays a role in energy conservation and emission reduction, and further exploration is urgently needed. Based on the panel data of 108 cities in the Yangtze River Economic Belt from 2008 to 2019, the spatial panel Durbin model and intermediary effect model are employed to explore the effect of ICT agglomeration on carbon emissions and its pathways. It can be indicated from the results as below. (1) The local ICT agglomeration can reduce carbon emissions, but an increase in the level of ICT agglomeration in surrounding cities will increase local carbon emissions. (2) ICT agglomeration can reduce carbon emissions through reducing energy intensity and capital mismatch. (3) The effect of ICT agglomeration on carbon emissions is heterogeneous. ICT agglomeration can suppress carbon emissions in the middle and lower reaches of the Yangtze River, while it will increase carbon emissions in the upper reaches. ICT agglomeration increases carbon emissions in old industrial cities, reduces carbon emissions in non-old industrial cities, and has a more significant emission reduction effect in non-resource-based cities. We suggest promoting the formation of a coordinated linkage mechanism for ICT industry development and carbon emission reduction policies among regions, and implement differentiated ICT development strategies according to different industrial structure types.

Air pollutants, primarily PM_2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM_2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM_2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM_2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM_2.5 concentrations within the YREB. PM_2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM_2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM_2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM_2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management.

Ecological welfare performance contributes directly to human well-being and regional sustainable development. Improving the regional ecological welfare performance in the process of pursuing green and sustainable development demands theoretical innovation and empirical exploration. Based on the super-efficiency SBM model, this study evaluated the ecological welfare performance of 108 cities during the period of 2009 to 2019. The Dagum Gini coefficient decomposition and spatial convergence model were employed to analyze the differences in ecological welfare performance across and within the study area and explore the underlining causes of such spatial differentiation in the Yangtze River Economic Belt and the upper, middle and lower reaches. It can be seen from the results that: (1) the overall difference in the ecological welfare performance of the Yangtze River Economic Belt is associated with a fluctuating downward trend during the study period. Regional and inter-regional differences were revealed and hypervariable density was identified as the main source of the differences. (2) The ecological welfare performance of the Yangtze River Economic Belt has absolute and conditional β convergence, and the ecological welfare performance of each city-region and surrounding urban areas has a positive impact on each other. (3) The difference in the spatial-temporal differentiation trend is manifested by the difference in the convergence rate. The cities in the middle reaches of the Yangtze River have the fastest convergence rate, followed by the cities in the upper reaches, and the cities in the lower reaches are the slowest. This geographic difference is mainly driven by the combined effects of industrial structure, urban characteristics, environmental regulation, foreign direct investment, and transportation accessibility. Finally, it is proposed that future policies should focus on the imbalanced regional development in the study area, and each region needs to explore ways to improve local ecological welfare performance according to local conditions, and ultimately promote the overall green, coordinated and high-quality development in the Yangtze River Economic Belt.

The past four decades have witnessed rapid growth in the rate of energy consumption in China. A great deal of energy consumption has led to two major issues. One is energy shortages and the other is environmental pollution caused by fossil fuel combustion. Since energy saving plays a substantial role in addressing both issues, it is of vital importance to study the intrinsic characteristics of energy consumption and its relationship with economic growth. The topic of the nexus between energy consumption and economic growth has been hotly debated for years. However, conflicting conclusions have been drawn. In this paper, we provide a novel insight into the characteristics of the growth rate of energy consumption in China from a multi-timescale perspective by means of adaptive time-frequency data analysis; namely, the ensemble empirical mode decomposition method, which is suitable for the analysis of non-linear time series. Decomposition led to four intrinsic mode function (IMF) components and a trend component with different periods. Then, we repeated the same procedure for the growth rate of China’s GDP and obtained four similar IMF components and a trend component. In the second stage, we performed the Granger causality test. The results demonstrated that, in the short run, there was a bidirectional causality relationship between economic growth and energy consumption, and in the long run a unidirectional relationship running from economic growth to energy consumption.