The role of government support in sustainable urban development has always been a research topic of scholars, but research focusing on the relationship between government innovation support and urban green sustainable development is still relatively rare. This article uses China’s innovative city pilot policy (ICPP) to represent the innovation support provided by the government and address the interaction mechanism and the spatial spillover effect of China’s innovative city pilot policy (ICPP), green technology innovation (GTI), and green sustainable development performance (GSDP) with the support of the mediating effect model and the spatial econometric model. Based on panel data of 24 cities in the Yangtze River Delta urban agglomeration from 2001 to 2020, this paper establishes an evaluation index system of green sustainable development performance (GSDP), measuring with the SBM directional distance function based on the undesired output. This paper adopts the spatial difference-in-difference model (SDID) to study the impact mechanism of the ICPP on the GSDP in the Yangtze River Delta. The results show that (i) there is a positive spatial spillover effect of GSDP in the urban agglomeration of the Yangtze River Delta urban agglomeration; (ii) ICPP has a significantly positive effect on GSDP, as verified by several robustness checks; (iii) green technology innovation plays a partial mediating effect in the relationship of the ICPP and GSDP.

This paper is based on the research hypothesis that the development of the digital economy can enable urban carbon emission reduction. We use the panel data of 275 prefecture-level cities in China from 2011 to 2019, the static panel-data interaction-effect model, and the panel-threshold model to verify the non-linear impact mechanism and heterogeneity of the digital economy in industrial structure upgrading affecting urban carbon emissions. The results demonstrate the following insights. First, due to the heterogeneity of industries, an increase in the proportion of the tertiary industry cannot reduce urban carbon emissions. Second, the digital economy has an inverted U-shaped adjustment effect on the process of industrial structure upgrading, affecting urban carbon emissions. Consequently, the integration and development of the tertiary industry and the digital economy can achieve urban carbon emission reductions. Finally, the digital economy has a double threshold effect on the process of industrial structure upgrading, affecting urban carbon emissions. The carbon-emission-reduction effect of industrial structure upgrading only appears after the scale of the digital economy crosses the first threshold. As the scale of the digital economy continues to increase, the carbon-emission-reduction effect of industrial structures is likely to continue increasing significantly. We recommend that local governments achieve urban carbon reduction by encouraging the development of high-end service industries and strengthening digital infrastructure.

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

Improving energy efficiency is an effective way to achieve carbon neutrality in the context of growing advocacy for a green economy in both academia and government. In this study, we analyzed the impact mechanism of the manufacturing agglomeration on total factor energy efficiency. Based on this work, we identified 30 provinces (autonomous regions and municipalities) in China using 2002 to 2017 panel data. We used the data envelopment analysis game cross-efficiency model to calculate the interprovincial current state of energy efficiency. We constructed a spatial Durbin model and used an adjacent space weight matrix, geographic distance weight matrix, and economic distance weight matrix to study the impact of manufacturing agglomeration on total factor energy efficiency. The results showed that under different spatial weights, the interprovincial total factor energy efficiency had a significant spatial dependence; under the three spatial weights, we identified a U-shaped relationship between manufacturing agglomeration and total factor energy efficiency. Industrial agglomeration had obvious spatial spillover effects on total factor energy efficiency, and the spillover effects under the weight of geographic distance were higher than other spatial weights. During the sample period, the direct, indirect, and total effects of the manufacturing industry had an impact on energy efficiency. It all had a negative number indicating that there was a crowding effect in manufacturing agglomeration, and it had an inhibitory effect on energy efficiency.

Whether financial inclusion and economic growth can sustainably release poverty alleviation effects in long term has been the focus of academia and government sector. This article uses provincial panel data from 2004 to 2019 to examine the dynamic nonlinear connectedness between the financial inclusion, economic growth, income inequality, and poverty alleviation; the main objective is to reveal the direction and intensity of the long‐term and short‐term impact of each factor on poverty alleviation. By building a panel vector autoregression model (PVAR), the comparison analyses of national, eastern, central, and western sample groups verify the existence of dynamic nonlinear connectedness among the four variables. The study found that there is complex bidirectional causality between these variables, financial inclusion has the long‐term impact on promoting poverty alleviation in China, the impact of economic growth is relatively weak, and income inequality has weakened the positive impact of financial inclusion on poverty alleviation. Through the analysis of impulse response function, variance decomposition, and time‐varying nonparametric estimates in different economic regions, we find that the impact of financial inclusion on poverty alleviation presents a U‐shaped characteristic, and the contribution of financial inclusion to poverty alleviation in western regions is significant, but poverty reduction in eastern and central region mainly depends on economic growth. For policymakers, financial inclusion can be an effective way to alleviate relative poverty, but the poverty governance should focus more on reducing income inequality in China.

This paper presents novel empirical evidence on the welfare impacts of forest protection programs, focusing on both income and material welfare at the household level. Specifically, we conduct a household survey of 1271 households in forestry communities that participate in the Natural Forest Protection Program (NFPP) in China. The survey data are collected from 56 state forest enterprises (SFEs) across three provinces in China. We employ the Alkire–Foster method to calculate and decompose the material deprivation index based on household income levels. Our analysis reveals that forestry communities exhibit a significant proportion of households in low-income welfare states, comprising nearly one-fourth (25.41%) of the sample. Furthermore, we observe that the percentage of households in a low-material welfare state is nearly one-fifth (21.70%), with 7.79% of households experiencing both low-income and low-material welfare. Importantly, we find that welfare disparities persist across population subgroups based on occupation and geography. In addition, we assess the impact of the NFPP on household welfare outcomes and identify an elite group of technicians residing in urban communities down the hill who experience positive welfare effects from the program. These findings provide critical insights beyond a single welfare dimension and contribute to the growing literature on evaluating forest protection policies. Furthermore, the results offer valuable lessons for designing and implementing forest protection programs in other developing countries.