Agricultural Science and Technology Innovation, Spatial Spillover and Agricultural Green Development—Taking 30 Provinces in China as the Research Object

In the face of increasingly severe resource and environmental constraints, accelerating the transformation of agricultural green development through agricultural science and technology innovation is an effective measure to reduce agricultural pollution and improve agricultural production efficiency. From the perspective of multidimensional proximity, this paper expounds the mechanism of agricultural science and technology innovation on agricultural green development through spatial spillover from two perspectives: factor spillover path and product spillover path. Based on panel data of 30 provinces in China from 2006 to 2019, using the gray correlation analysis method, the level of agricultural green development in China was measured, and its spatial–temporal evolution trend was analyzed. The spatial economic matrix was selected as the spatial weight matrix, and the spatial econometric model was used to analyze the spatial spillover effect of agricultural science and technology innovation on agricultural green development. The results showed the following: (1) Agricultural green development had distinct spatial characteristics. The development level of green agriculture in eastern and northwestern China showed a trend of fluctuation decline, while that in southwest China showed a trend of fluctuation increase. The overall spatial distribution of green agriculture was high in the east and low in the west. (2) The spatial distribution of agricultural science, technological innovation and the agricultural green development level showed a significant positive global spatial autocorrelation, and the local spatial pattern characteristics of a number of provinces showed high-value agglomeration (HH), low-value agglomeration (LL), low-value collapse (LH) and high-value bulge (HL) as the auxiliary local spatial distribution. (3) Under the economic matrix, the improvement of the agricultural science and technology innovation level not only had a significant promoting effect on agricultural green development within each province but also promoted agricultural green development in neighboring provinces through positive spillover effects. This study provides insights that can help make up for the lack of regional agricultural science and technology investment, formulate scientific regional agricultural science and technology innovation policies and promote agricultural green development.

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

Research on the Spatial Spillover Effect of Provincial Final Consumption Level in China Based on the Complex Network

A spatial spillover correlation network is an excellent representation for expressing the relationship of consumption levels among regions, which provides a way to study the evolution mechanism of the spatial influence of the consumption level. Using data on the consumption levels of 29 provinces (or municipalities or autonomous regions) during the global stage (1978–2020) and two separated stages (1978–2001 and 2002–2020) after China’s reform and opening up, this paper analyzes the topological characteristics and driving factors of provincial residents’ consumption level spatial spillover network by applying the Granger causality test of Vector Autoregression (VAR) model and a complex network analysis method. The results show that the number of spatial spillover relationships of provincial residents’ consumption level in the second stage increases significantly in comparison with that in the first stage and the scope of mutual influence among provinces increases rapidly in the second stage; that eastern coastal regions play a net spillover role in the network and some central and western provinces play an increasingly important broker role; and that the members of the network compose four communities with different gradients, with Beijing, Shanghai, and Jiangsu in the leading positions. The network shows neighborhood spillover and club convergence, and these characteristics are more evident in the second stage; moreover, spatial adjacency, residents’ disposable income, urbanization level, consumer credit, and consumption environment similarity have significant driving effects on the spillover correlation of the consumption level.

Carbon Emission Trading Scheme, Carbon Emissions Reduction and Spatial Spillover Effects: Quasi-Experimental Evidence From China

The carbon emission trading scheme (ETS) is an essential policy tool for accomplishing Chinese carbon targets. Based on the Chinese provincial panel data from 2003 to 2019, an empirical study is conducted to measure the effects of carbon emission reduction and spatial spillover effect by adopting the difference-in-differences (DID) model and spatial difference-in-differences (SDID) model. The research findings show that: 1) The ETS effectively reduced the total carbon emissions as well as emissions from coal consumption; 2) such effects come mainly from the reduction of coal consumption and the optimization of energy structure, rather than from technological innovation and optimization of industrial structure in the pilot regions; and 3) the ETS pilot regions have a positive spatial spillover effect on non-pilot regions, indicating the acceleration effect for carbon emission reduction. Geographic proximity makes the spillover effect decrease due to carbon leakage.

Spatial Spillover Transmission Effects of Financial Development on Economic-Zone Financial Efficiency in China

This article attempts to fill important knowledge gaps to explore the spatial spillover effects of financial markets on regional financial efficiency in eight economic zones using three-stage superefficiency data envelopment analysis (DEA) and Durbin’s spatial econometric model. The average financial efficiencies in the North coast, East coast, and South coast economic zones reach the superefficiency DEA relatively efficient level, while the average financial efficiencies in the Northeast, Middle Yellow River, Middle Yangtze River, and large West-south and West-north economic zones reach the superefficiency DEA relatively inefficient level. Except for the North coast economic zone, seven equity markets have significant impacts on regional financial efficiency, and local equity markets in the Northeast, South coast, Middle Yellow River, and Middle Yangtze River economic zones generate significant spatial spillover effects on neighboring regions’ financial efficiency. Local credit markets only in the Northeast and South coast economic zones have significant spatial spillover influences on neighboring regions’ financial efficiency. Debt markets in the North coast, East coast, South coast, Middle Yangtze River, and large West-south economic zones have significant influences on regional financial efficiency, and local debt markets in the East coast and Middle Yangtze River economic zones generate significant spatial spillover effects on neighboring regions’ financial efficiency.