Congqi Wang

Exploring the spatial correlation network and formation mechanism of urban water resource utilisation efficiency in the Yellow River Basin is crucial for modernising national ecological security capacities and promoting high-quality economic development. It contributes to environmental protection, enhances model precision, and strengthens theoretical foundations. Based on panel data from 94 cities in the Yellow River Basin from 2010 to 2021, this study employs a modified gravity model, stochastic frontier analysis, kernel density estimation, social network analysis, and the Quadratic Assignment Procedure (QAP) regression to examine the characteristics and determinants of the spatial correlation network. The results reveal a consistent upward trend in water resource utilisation efficiency, with a spatial hierarchy of downstream > midstream > upstream. The network demonstrates significant spatial correlation effects, although its density slightly decreased from 0.1166 in 2010 to 0.1161 in 2021, indicating notable regional spillover effects. Geographical proximity, industrial structure, and informatisation significantly promote the formation of the spatial correlation network, while per capita GDP, openness to the outside world, and technological innovation have negative impacts. The findings provide important insights and policy implications for improving water resource governance in the region.

Economic complexity and environmental policies have significant impacts on environmental sustainability. Furthermore, high-quality institutions may be a necessary tool for ensuring environmental sustainability. This study investigates the impact of economic complexity and institutional quality on environmental degradation. It analyzes annual data from 116 countries from 2008-2021 using fixedeffects models and the method of moment quantile regression. The results indicate that increased economic complexity leads to higher carbon emissions. Conversely, high-quality institutions reduce carbon emissions. Moreover, institutional quality can effectively mitigate the environmental pressures arising from increasing economic complexity. Additionally, foreign direct investment is found to lower carbon emissions. It was recommended that the quality of institutions should be upgraded, that differentiated environmental policies be developed, that the sustainability of urban planning be emphasized, and that foreign direct investment and technology transfer be promoted to foster sustainable development.

Reducing carbon emissions is widely recognized as one of our nation's top priorities. At the national level, green financing has grown and received a lot of support. Using panel data from 30 towns and provinces in China between 2011 and 2021, this study mainly examines the effect of green money on carbon emissions. It delves into the processes by which it does so. Here are the study's findings: Results showing that green money substantially lowers carbon emissions hold up in multiple robustness tests. Secondly, by looking at the mechanics of intermediary effects, we can see that green financing might lower carbon emissions through, for example, technological innovation and better industrial structure. In conclusion, the heterogeneity analysis reveals that green financing's impact on carbon emissions varies by regional development level, with a more substantial effect observed in the western and eastern regions. Considering these results, the authors of this study suggest implementing policies tailored to individual regions and developing a diverse green finance system to promote the efficient reduction of carbon emissions.

The digital economy drives China’s high-quality economic growth and reduces carbon emissions. Urban carbon emissions reduction is crucial to China’s “dual carbon” development goal and climate change adaptation. Spatial econometric modeling, standard deviation ellipse analysis, kernel density estimation, the Moran index, and Moran’s index are used to study the effects of the digital economy on provincial carbon emissions in 30 Chinese provinces from 2012 to 2021. The study found that: (1) The digital economy has shown a rising trend over time, with the spatial distribution of “eastcenter- west” decreasing in space. Carbon emissions, on the other hand, show a decreasing trend, with a spatial distribution characterized by a high north and a low south. (2) As measured by the standard deviation ellipse model, the digital economy and carbon emissions have decreasing ellipse areas, with the former’s centers of gravity moving northward and the latter’s southward, indicating that their spatial agglomeration characteristics are growing. (3) The spatial Durbin model examined how the digital economy affects carbon emissions. The digital economy is reducing carbon emissions in China significantly. A national spatial spillover effect was also detected, with surrounding provinces’ carbon emissions being negatively affected.

China’s “dual carbon” agenda shows its proactive approach to global carbon reduction. This study uses a spatial panel model to examine financial development and carbon emissions in China. It also examines how financial development thresholds affect carbon emissions, using research and development intensity and technical market development level as key variables. The results show that: (1) Finance in China is concentrated in the Circum-Bohai Sea and eastern coastal regions, with lower levels in the central and western regions. China’s high-carbon zones are mostly in the northwest and northeast. However, low-emission areas are mainly in the south. (2) Financial development increases local carbon emissions and decreases neighboring carbon emissions. However, it still hinders carbon emissions in the region. (3) Financial development promotes more carbon emissions in the northwest than in other regions, possibly due to additional variables. (4) Financial development initially increases regional carbon emissions when R&D intensity is the threshold variable, but this effect fades. When technological market development is the threshold variable, financial development’s impact on carbon emissions has gone from insignificant to major to insignificant promotion.

With the continuous advancement of global scientific and technological capabilities, the issue of global warming caused by greenhouse gas emissions has received widespread attention from countries worldwide. Promoting carbon reduction and curbing the trend of global warming have become urgent and significant challenges for China and the world. Therefore, it is of great practical significance to explore the impact and mechanism of the digital economy on carbon reduction. This paper empirically analyzes the impact and means of the digital economy on carbon emissions using panel regression models and mediation effect models. The research indicates that the digital economy significantly impacts carbon emissions, and the following main conclusions are drawn: (1) The influence of the digital economy on carbon intensity exhibits an inverted U-shaped curve, starting with promotion and then inhibition. (2) The digital economy can affect carbon emissions through industrial structural upgrading and technological innovation. (3) Regions with a relatively high level of digital economy development also tend to have higher energy utilization efficiency, leading to a more pronounced impact of the digital economy on carbon emissions levels.

Under the new development model, the digital economy has become a new engine to promote the green development of the economy and achieve the goal of “double carbon.” Based on panel data from 30 Chinese provinces and cities from 2011 to 2021, the impact of the digital economy on carbon emissions was empirically studied by constructing a panel model and a mediation model. The results show that firstly, the effect of the digital economy on carbon emissions is a non-linear inverted “U” shaped relationship, and this conclusion still holds after a series of robustness tests; secondly, the results of the benchmark regression show that economic agglomeration is an essential mechanism through which the digital economy affects carbon emissions and that the digital economy can indirectly suppress carbon emissions through economic agglomeration. Finally, the results of the heterogeneity analysis show that the impact of the digital economy on carbon emissions varies according to the level of regional development, and its effect on carbon emissions is mainly in the eastern region, while its impact on the central and western regions is weaker, indicating that the impact effect is primarily in developed regions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the development strategy of the digital economy according to local conditions to promote a more significant carbon emission reduction effect of the digital economy.