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To ameliorate the efficiency of urban green innovation is the key to realizing green economic transition. This paper constructs a Super-NSBM model with green patents as the intermediate output, uses this model to assess and decompose the green innovation efficiency of 284 Chinese cities, and finally analyzes the spatiotemporal characteristics and influential factors. The research result showed the gap of urban green innovation total efficiency among various regions in China is narrowing, while the spatial differentiation of decomposition efficiency is deepening. This means that a spatial collaborative innovation division pattern of "Eastern Region R&D + Southwest and Northeast Region Transformation" has gradually formed. In the meantime, this paper also found that the spillover effects of the urban green innovation total efficiency and phased efficiency all can form a significant demonstration effect on the surrounding areas. Finally, financial agglomeration, industrial structure, knowledge sharing, economic activity, higher education, opening, and environmental regulations may affect urban green innovation total efficiency and phased efficiency, and this effect has regional heterogeneity. [ABSTRACT FROM AUTHOR]

China's rapid industrialization has brought about a series of severe resource and environment problems. The systematic analysis of industrial resource and environment pressures is of great theoretical and practical significance for promoting the green transformation of China's industrial. Taking 285 prefecture level and above cities in China as the research objects, this paper constructs the evaluation index system of industrial resource and environmental pressures, uses ArcGIS spatial analysis method, variation coefficient and Taylor index to analyze the spatial differentiation characteristics of industrial resource and environmental pressures in China from 2006 to 2019 at national and regional scales. Based on spatial Dubin model, the main influencing factors of industrial resource and environmental pressures in China are analyzed. The results show that: ① The industrial resource pressure in China is increasing and shows the spatial change characteristics of gradually decreasing from coastal to inland areas. The degree of spatial agglomeration is increasing, and the hot spots of industrial resource pressure are concentrated in the Yangtze River Delta and Pearl River Delta region, the scope of hot spots is expanding. ② The industrial environmental pressure in China shows a fluctuating downward trend, and the spatial differentiation characteristics are "high in the east and north, low in the west and south". Areas with high industrial environmental pressure are mostly located at the junction of provincial administrative regions, the degree of spatial agglomeration is declining, and hot spots are transferred to the Bohai Rim and Yangtze River Delta regions.③ The factors with significant positive correlation with China's industrial resource pressure are total industrial assets, industrial added value and three industrial wastes average treatment rate, and the factors with significant negative correlation with China's industrial resource pressure are the number of industrial enterprises, labor productivity and the proportion of the output value of foreign-invested industrial enterprises in the total industrial output value. ④ The factors with significant positive correlation with China's industrial environmental pressure are the total industrial assets, and the output value of foreign-invested industrial enterprises in the total industrial output value, the factors with significant negative correlation with China's industrial environmental pressure are the three industrial wastes average treatment rate, and labor productivity. [ABSTRACT FROM AUTHOR]

Agricultural green development is an essential direction for global sustainable agriculture. The academic literature, however, needs to place greater emphasis on studying the factors influencing agricultural green development performance and how such performance can be improved. A theoretical framework for agricultural green development performance was constructed in this paper using the Super-SBM model, which considers undesirable outputs, to measure the agricultural green development performance of 330 cities at or above the prefecture level in China (excluding Tibet Autonomous Region, Hong Kong, Macao and Taiwan of China) from 2007 to 2018. Furthermore, the influencing mechanism of agricultural green development performance was then analyzed using a spatial econometric model. The results show that: 1) from 2007 to 2018, China's agricultural green development performance experienced three stages of evolution: 'rise, decline and rise'. 2) The regions with high performance agricultural green development are mainly distributed in eastern China, northeastern China, and southern Qinghai Province. 3) The agricultural economic level, industrialization process, and labor quality play significant roles in promoting local agricultural green development performance, while such performance is obviously inhibited by the openness level and the government's environmental regulations. Local agricultural green development performance is significant inhibited by the agricultural economic level and accelerated industrialization process in neighboring cities, while significantly promoted by the agricultural industrial structure in neighboring cities. Some suggestions for improving agricultural green development performance are proposed based on these research results, which can provide scientific references for promoting sustainable agriculture. [ABSTRACT FROM AUTHOR]

Since China's reform and opening up in 1978, the acceleration of industrialization and urbanization in China had led to dramatic changes in the pattern of urban-rural land use. In this paper, we focused on the rural industrialized areas in central China (Xinxiang County and Changyuan City of Henan Province). We used the average nearest neighbor index, spatial statistical analysis, and a structural equation model to analyze the spatiotemporal evolution and influencing factors of urban-rural construction land based on multi-source spatial data and survey data. The results showed that: 1) from 1975 to 2019, the spatial distribution of urban-rural construction land in rural industrialized areas had evolved from homogeneous distribution to local agglomeration. In terms of comparative analysis of cases, the spatial distribution of urban-rural construction land in Changyuan City had shown a trend from diffusion to agglomeration, and Xinxiang County had overall shown a spatial change from homogenization to agglomeration and then to regional integration development. 2) The hot spots with increased urban-rural construction land significantly expanded, and they had a high degree of spatial overlap with industrial development. Among them, Xinxiang County was concentrated in central and marginal areas, and Changyuan was mainly concentrated in central urban areas. 3) From the evolution of spatial proximity of urban-rural construction land, rural industrialized areas generally decline, showing the characteristics of internal differentiation in the rate of change. 4) Industrial development, social economy, the policy environment, and urban development played a positive role in promoting the expansion of urban-rural construction land in rural industrialized areas. To promote the optimal use of regional land and the integrated development of urban-rural areas, we should combine the advantages of regional endowment, formulate development strategies according to local conditions, and adjust the way that land is used in a timely manner. [ABSTRACT FROM AUTHOR]

Based on dynamic monitoring data on China's population, by using complex networks, spatial analysis and mathematical measurement, this study reveals the spatial characteristics and influencing factors of the network of flows of highly educated talents in the Yangtze River Delta region from the national and local perspectives. In the two perspectives, the network has strong isomorphism and certain differences. The in-flow of highly educated talents from cities with high administrative levels and more developed economies to Shanghai constitutes the core of the entire network. From a national perspective, highly educated talents tend to converge to the Yangtze River Delta region. From a local perspective, it was found that these talents cluster towards a limited number of cities in the region. From both perspectives, the flow network has developed into a "core-periphery" progressive hierarchical structure, with Shanghai becoming the sole core city. There is little difference in the influencing factors of talent mobility from both macro and meso perspectives. Highly educated talents would frequently flow between cities with strong economic development levels, and cities with high education level, scientific and technological level, complete infrastructure, and good aesthetics. However, geographical distance still plays a hindering role in the flow of highly educated talents, and factors such as cultural identity, institutional, and social modality differences among regions also have a certain effect on the flow of these talents. [ABSTRACT FROM AUTHOR]

Urban agglomeration (UA) is an advanced spatial economic form formed and developed in the process of rapid industrialization and urbanization, and an important carrier of urbanization and economic development. The economy has developed rapidly in the recent decades of China, and the UAs have also developed rapidly. However, as a large population country, the population distribution and changes of UAs in China has unique characteristics. Using the fifth, sixth and seventh population census data, spatial auto-correlation and spatial econometric models, we analyzed the spatial-temporal evolution characteristics and influencing factors of population agglomeration in China's UAs. Results revealed that: 1) from 2000 to 2020, the population gradually converged into UAs, and the characteristics of population agglomeration in different development degree of UAs differ. The higher the development degree of UA, the higher the population agglomeration degree. Besides, UAs are the main area with the most significant population agglomeration degree, and the spatial autocorrelation show that the cities with similar degree tend to be concentrated in space. The urban population gathering in UAs has a certain positive spillover effect on population size of neighboring cities. 2) Economic development and social conditions factors are important factors affecting population agglomeration degree in UAs. The main factors of population gather into UAs are similar with the outside UAs, but the positive promotion of urbanization rate and proportion of tertiary industry in GDP on population agglomeration of UAs in China are enhancing from 2000 to 2020. Meanwhile, the other factors, such as high-quality public services, good urban living environment conditions, high-quality medical and educational resources, are also important factors to promote urban population gather into UAs. This study provides a basis for formulating the development planning of UAs in China, and enriches the relevant theoretical research of population evolution and influencing factors of UAs. [ABSTRACT FROM AUTHOR]

The synergy of the digital economy and green total factor productivity (TFP) is the foundation for achieving beneficial outcomes for both the economy and environment. This synergy is also the catalyst for high-quality development and sustainable economic growth in China. The study applied a modified Ellison-Glaeser (EG) index, super-efficiency slacks-based measure (SBM) with a Malmquist-Luenberger (ML) index, coupling coordination degree, and other models to explore the spatiotemporal heterogeneity of the coupling between the digital economy and green TFP from 2011 to 2020 and also analyzed the influencing factors of the coupling. The results show that the coupling between the digital economy and green TFP showed an overall upward trend from imbalance to synergy during the study period. The distribution of the synergistic coupling expanded from point-like to band-like, and there was a significant spreading pattern from the east to the center or west China. The number of cities in a transition state decreased significantly. Spatial jumps, a coupling linkage effect, and evolution in time were prominent. Additionally, the absolute difference among cities expanded. Although coupling in the west experienced the fastest growth rate, the coupling in the east and resource-based cities showed significant benefits. Coupling did not reach an ideal coordinated state, and a neutral interaction pattern remains to be formed. Industrial collaboration, industrial upgrading, government support, economic foundation, and spatial quality all positively impacted the coupling; technological innovation had a lagged effect; and environmental regulation has not reached its full potential. Further, the positive effects of government support and spatial quality performed better in the east and in non-resource-based cities. Due to the optimization of industrial structure, the coupling of the west and resource-based cities achieved better dividends; however, spatial quality needs further improvement. Therefore, the efficient coordination of China's digital economy and green TFP requires a scientific, reasonable, localized, and distinctive approach. [ABSTRACT FROM AUTHOR]