Digital financial inclusion, industrial structure and urban-Rural income disparity: Evidence from Zhejiang Province, China.

Rising income inequality challenges economic and social stability in developing countries. For China, the fastest-growing global digital economy, it could be an effective tool to promote inclusive development, narrowing urban-rural income disparity. It investigates the role of digital financial inclusion (DFI) in narrowing the urban-rural income gap. The study uses panel data from 52 counties in Zhejiang Province, China, from 2014 to 2020. The results show that the development of DFI significantly reduces rural-urban and rural income inequality. The development of DFI helps optimize industrial structure and upgrade the internal structure of agriculture, facilitating income growth for people in rural areas. Such effects are greater in poorer counties. Our findings provide insights into why rapid DFI and the narrowing of the rural-urban income disparity exist in China. Moreover, our results provide clear policy implications on how to reduce the disparity. The most compelling suggestion is that promoting the optimization of industrial structure through DFI is crucial for narrowing the urban-rural income gap.

Study on the Spatiotemporal Evolution and Influencing Factors of Agricultural Carbon Emissions in the Counties of Zhejiang Province.

The accurate measurement of agricultural carbon emissions and the analysis of the key influential factors and spatial effects are the premise of the rational formulation of agricultural emission reduction policies and the promotion of the regional coordinated governance of reductions in agricultural carbon emissions. In this paper, a spatial autocorrelation model and spatial Dubin model are used to explore the spatiotemporal characteristics, influential factors and spatial effects of agricultural carbon emissions (ACEs). The results show that (1) From 2014 to 2019, the overall carbon emissions of Zhejiang Province showed a downward trend, while the agricultural carbon emission density showed an upward trend. ACEs are mainly caused by rice planting and land management, accounting for 59.08% and 26.17% of the total agricultural carbon emissions, respectively. (2) The ACEs in Zhejiang Province have an obvious spatial autocorrelation. The spatial clustering characteristics of the ACEs are enhanced, and the "H-H" cluster is mainly concentrated in the northeast of Zhejiang, while the "L-L" cluster is concentrated in the southwest. (3) The results of the Dubin model analysis across the whole sample area show that the ACEs exhibit a significant spatial spillover effect. The disposable income per capita in the rural areas of the county significantly promotes the increase in the ACEs in the neighboring counties, and the adjustment of the industrial structure of the county has a positive effect on the agricultural carbon emission reductions in neighboring counties. (4) The grouping results show that there is heterogeneity between 26 counties in the mountainous areas and non-mountainous areas. In the 26 mountainous counties, the urbanization rate, rural population, mechanization level and industrial structure have significant negative spatial spillover effects on the carbon emissions. In the non-mountainous counties, the agricultural economic development level and disposable income per capita of the rural residents have significant spatial spillover effects on the agricultural carbon emissions. These research results can provide a theoretical basis for the promotion of the development of low-carbon agriculture in Zhejiang according to the region and category.

Agricultural environmental total factor productivity in China under technological heterogeneity: characteristics and determinants.

With limited resources, growing environment constraints and downward pressure on the economy, increasing agricultural environmental total factor productivity (AETFP) and its contribution to agricultural growth is significant for transforming agricultural development to make it more resource efficient and environment-friendly. This paper considered technological heterogeneity in different regions of China and measured AETFP in 30 provinces from 1997 to 2015 using the Metafrontier Malmquist-Luenberger (MML) productivity index. Multi-dimensional analysis was made on temporal and spatial characteristics, evolution patterns, and influencing factors of AETFP in China. The results showed that: (1) AETFP increased in the Ninth, Tenth, Eleventh, and Twelfth Five-Year Plan periods, with average annual growth rates of 0.76%, 0.88%, 1.17%, and 0.87%, respectively. (2) The average annual growth rate of AETFP in the eastern, central, and western regions decreased successively. The eastern region generally had played a leading role. The central region had a catch-up effect on environmental production technologies from the eastern region, while the western region lacked the catch-up effect. (3) The dynamic evolution of AETFP had prominent features. For the whole nation, the kernel density curve of AETFP continuously moved to the right. The main peak value continuously decreased and the width of the main peak continuously increased. The internal differences of AETFP in the eastern and western regions exhibited an increasing trend, while the internal differences of AETFP in the central region showed little change. (4) There was an inverted U-shaped relationship between agricultural economic growth and AETFP. Both the disaster rate and planting structure had a negative impact on AETFP with varying degrees of significance. Income gaps between urban and rural areas can partially offset the role of urbanization in promoting the growth of AETFP. The greater the income differences between urban and rural areas, the weaker the role of urbanization in promoting the growth of AETFP. These findings can help the government determine policies to change the agricultural development mode and formulate effective measures to improve AETFP.