In 2015, China put forward five development concepts, propounded the concept of green development and green innovation, and adhered to the road of sustainable development. China also promoted the vision of high-quality economic development in 2017. It is very important to study the impacts of green innovation on high-quality development. However, scant research has been conducted on the impact of green innovation and institutional constraints on high-quality economic development simultaneously over both space and time in China. In this study, we analyze the impacts of green innovation on high-quality economic development across China from 2014 to 2018. The panel data model and the panel threshold model are developed, and the impact of green innovation and institutional constraints on high-quality economic development is empirically analyzed. Our results indicate that the comprehensive index and the level of high-quality economic development in all regions of China are increasing consistently. At the national level, addressing green innovation and institutional constraints have shown a significant positive impact on high-quality economic development. At the regional level, the interaction of green innovation and institutional constraints (i.e., mitigation of institutional constraints by green revolution) has a more positive impact on the economic quality of eastern China than that of central China. The interaction between green innovation and institutional constraints shows no positive impact on the high-quality economic development of the central and western regions because the green innovation, institutional constraints, and economic development in the central and western regions are weaker than those in the eastern regions. Therefore, it is suggested that different regions of China, especially the central and western ones, should strengthen green innovation, improve the market system, and increase government support.

This study empirically analyzes factors influencing the coupling and coordinated development of rural urbanization and rural finance. For this purpose, the study employs the coupling degree model and develops a panel quantile model to estimate the coupling degree and coupling coordination degree of rural urbanization and rural finance. Accordingly, the study presents panel data comprising 31 provinces, municipalities, and autonomous regions in China from 2010 to 2019. The empirical results reveal that the coupling degree of rural urbanization and rural finance is relatively low in most study areas. This result suggests that rural urbanization and rural financial development in most provinces in China have not exhibited coordinated development. Further, the results reveal that urban population density negatively affects the coupling and coordination degree of rural urbanization and rural finance. Moreover, the effects of urban population density are more significant in areas with a low coupling coordination degree compared with those with a higher coupling coordination degree. An increase in the quantile gradually decreases the effect of the proportion of educational expenditure to GDP on the degree of rural urbanization and rural financial coupling coordination. However, the effect of financial development efficiency increases accordingly. The per capita GDP, per capita fiscal expenditure, and per capita built-up area significantly affect all the quantiles, indicating a positive correlation. This correlation can promote the coupling and coordinated development of rural urbanization and rural finance. In areas with a low coupling coordination degree, the financial development scale significantly positively affects the coupling coordination degree of rural urbanization and rural finance. In areas with a high coupling coordination degree, the financial development scale significantly negatively correlates with the coupling coordination degree. In addition, a positive correlation exists between the financial development structure and the coupling coordination degree of rural urbanization and rural finance.

With the development of economic globalization, some local environmental pollution has become a global environmental problem through international trade and transnational investment. This paper selects the annual data of 30 provinces in China from 2000 to 2017 and adopts exploratory spatial data analysis methods to explore the spatial agglomeration characteristics of haze pollution in China’s provinces. Furthermore, this paper constructs a spatial econometric model to test the impact of foreign direct investment (FDI) and industrial structure transformation on haze pollution. The research results show that the high-high concentration area of haze pollution in China has shifted from the central and western regions to the eastern region and from inland regions to coastal regions. When FDI increases by 1%, haze pollution in local and neighboring areas will be reduced by 0.066% and 0.3538%, respectively. However, the impact of FDI on haze pollution is heterogeneous in different stages of economic development. FDI can improve the rationalization level of industrial structure, and then inhibit the haze pollution. However, FDI inhibits the upgrading level of industrial structure to a certain extent, and then aggravates the haze pollution. The research in this paper provides an important decision-making basis for coordinating the relationship between FDI and environmental pollution and realizing green development.

Improving the eco-efficiency of cultivated land use (ECLU) is important for ensuring food security, promoting social and economic development, and reducing carbon emissions. However, dynamic inter-period comparisons of the ECLU and clarifications of its influencing factors are limited. We calculated the ECLU at the county level in the Beijing–Tianjin–Hebei region, China, based on the super-efficiency slacks-based measure and global Malmquist–Luenberger index and analyzed its influencing factors utilizing a geographically and temporally weighted regression model. From 2000 to 2020, the number of higher counties decreased and that of medium counties increased. Geographically, the ECLU values in the north are higher than those in other districts and counties; counties in Beijing and Tianjin maintained moderate ECLU values, whereas Zhangjiakou and Chengde maintained high ECLU values. The ECLU value in the study area showed a trend of rapid decline–slow rise–continuous rise, with the upward trend of the ECLU value in Beijing–Tianjin–Hebei region being significantly less pronounced than those in most counties of Hebei Province. Resource allocation and scale expansion where initially dominant; however, technological progress and investment eventually prevailed. The ECLU is mainly affected by the multiple cropping index, industrial structure, irrigation index, mechanized farming level, and per capita cultivated land. This study assesses the ECLU in the Beijing–Tianjin–Hebei region, providing a scientific basis for the formulation and implementation of relevant policies for its improvement. Furthermore, this study enriches the theory and methods of research on the ECLU and has practical value and theoretical significance. Overall, the results have important social value as they contribute to ensuring national food security, reducing carbon emissions, promoting regional coordinated and sustainable development.

The development of renewable clean energy such as hydropower can not only ensure energy security, but also help achieve the United Nation’s Sustainable Development Goals. This paper uses the annual data of 30 provinces in China from 2000 to 2017, and constructs a dynamic spatial Durbin model and a geographically weighted regression model to empirically test the dynamic impact of hydropower on haze pollution and economic growth at the national and provincial levels. The empirical results show that the promoting effect of hydropower on economic growth in Western China is less than that in Eastern China, which further aggravates the economic development gap between the eastern and western regions. In addition, the suppression effect of hydropower on the haze pollution in the western region is greater than that in the eastern region, where the haze pollution is serious. From the national level, hydropower can promote regional economic growth and inhibit haze pollution, and the spatial spillover effects of these two effects are greater than the local effects, and the long-term impact is greater than the short-term impact. The research conclusions of this paper will help China realize the sustainable development goals of energy saving and emission reduction.

Urban agglomerations serve as essential platforms for regional innovation, while agricultural technology innovation and diffusion play pivotal roles in enhancing agricultural eco-efficiency (AEE). Based on panel data from the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) (2001–2023), this study employs a super-efficiency slacks-based measure model incorporating undesirable outputs to evaluate agricultural eco-efficiency. A modified gravity model is utilized to construct agricultural technology innovation networks (ATINs) in urban agglomerations, and a spatial Durbin model is applied to examine the spillover effects of network structure on eco-efficiency. The results indicate that: (1) Higher-degree centrality within the innovation network significantly improves local agricultural eco-efficiency and produces positive spillover effects on neighboring cities; (2) both direct and spillover effects are significant in central cities, whereas sub-central cities exhibit only a significant direct effect, and peripheral cities display an insignificant direct effect but a significant spillover effect; and (3) enhanced urban informatization, agricultural financial development, and industrial scale substantially strengthen the spatial spillover effects of the innovation network, thereby further advancing agricultural eco-efficiency within the agglomeration. These findings offer theoretical and empirical support for optimizing agricultural technology pathways and enhancing eco-efficiency in urban agglomerations.