The Impact of Digital Inclusive Finance on Agricultural Green Total Factor Productivity: Evidence From China

Abstract

This manuscript applies the GML model with unexpected output to measure agricultural green total factor productivity (GTFP) in 30 provinces in China from 2011 to 2019. We explore the effect and mechanism of digital inclusive finance (DIF) on agricultural green total factor productivity. Our empirical results show that during the sample period, China’s agricultural green total factor productivity has shown an increasing trend. Digital inclusive finance mainly promotes agricultural GTFP by improving green technology level. The coverage rate, the application rate and the digitalization rate of digital inclusive finance all generate positive effects on agricultural green total factor productivity, among which the coverage rate contributes the most. Besides, the positive effect of digital inclusive finance in the eastern coastal areas is more significant than in other areas. The analysis of the mechanism shows that digital inclusive finance can indirectly help improve agricultural green total factor productivity through motivating agricultural technology innovation and industrial structure optimization. The research results of this manuscript are extremely meaningful for better implement DIF-related policies, and promote the green development of agriculture.

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

“…The index could effectively avoid the possibility of no solution, meet the requirements of circularity, as well as allow technical regression. The global benchmark enveloped the whole current benchmark into a single set of global production possibilities, serving as a common reference set for each period [ 30 , 31 ]. The sets of production possibilities for current and global benchmarks were shown as follows: The current benchmark: The global benchmark: …”

Spatial correlations and driving mechanisms of low-carbon agricultural development in china

“…Yang et al (2019) explored the degree of spatial divergence of agricultural green TFP. The main influencing factors of low carbon development in agriculture are crop insurance (Carter et al, 2016;Fang et al, 2021), digital inclusive finance (Gao et al, 2022), agricultural financial subsidies (Li et al, 2021), industrial agglomeration (Wu J. et al, 2020), farmers' characteristics, economic development level, farmers' income level, financial support to agriculture (Adnan et al, 2018), agricultural structure, resource utilization, and environmental pollution control level , the foreign trade of agricultural products, and foreign direct investment in agriculture and agricultural technology input (Chen Y. et al, 2022), all of which showed that the agricultural green development in China showed a good trend, but the interprovincial differences widened, and the spatial distribution gradually became uneven, with significant spatial dependence. Hence, it is necessary to pay attention to the spatial interaction effect between regions and gradually reduce the regional disparity in agricultural development (Li et al, 2019).…”

“…The essence of the urbanization process is a multidimensional transmutation process accompanied by the flow of capital, labor, technology, and other factors from the countryside to the city, and the reconfiguration of factors between urban and rural areas, which has a great impact on the production scale and cultivation structure of agriculture (Tian et al, 2016;Xiong et al, 2020;Joséf, 2022); 4) rural labor education level differences (ED) is expressed by the average years of education of rural residents. The labor force is the decision maker of agricultural production methods and its level of education has a significant impact on the adoption and application of pioneering technologies (Wang et al, 2019;Wu et al, 2021;Khanh and Nguyen, 2022); 5) financial development level differences (FI) is expressed by the ratio of deposit and loan balance of financial institutions to GDP, a sound financial service system can provide financial support for agricultural transformation and upgrading and green technology progress (Huang et al, 2014;Cao et al, 2022;Gao et al, 2022); 6) agricultural irrigation water utilization rate differences (WA) is expressed by the ratio of effective irrigated area to cultivated area in each region, agricultural irrigation water use efficiency can affect agricultural carbon emissions and output efficiency by changing inter-regional agricultural production costs and intra-agricultural production structure (Xu et al, 2022); 7) farmland operation scale differences (SC) is expressed by the per capita crop sown area, it has been proven that the scale of agricultural production leads to differences in the cost of adoption of agricultural technology, and that larger scale of operation makes it easier to obtain economies of scale and adopt advanced technology (Helfand and Taylor, 2021;Mao et al, 2021); 8) financial support differences (IN) is expressed by the proportion of local financial expenditure on agriculture, many scholars have found that financial support for agriculture significantly affects agricultural carbon emissions (Guo et al, 2022); 9) marketization level differences (MA) is expressed by the marketization index measurements, according to (Fan et al, 2011). The level of marketization determines the flow and allocation of production factors and therefore has an impact on the spatial association network .…”

Evaluation and Determinants of the Digital Inclusive Financial Support Efficiency for Marine Carbon Sink Fisheries: Evidence from China

“…Combining digital technology and financial services to achieve financial innovation plays a crucial role in the transformation and upgrading of agribusiness [ 26 ]. Gao et al (2022) found that the development of digital inclusive finance was an important path to motivate agricultural technology innovation and industrial structure optimization in China, which helped drive Chinese agriculture to improve agricultural total factor productivity and achieve high-quality development [ 27 ]. Based on a panel dataset of Chinese provinces from 2011 to 2019, Guo et al (2022) constructed agricultural green development indicators from four dimensions of resource savings, environmental protection, ecological conservation, and quality industrialization and confirmed that digital inclusive finance have effectively promoted the green development of Chinese agriculture [ 28 ].…”