Abstract
Based on provincial panel data for the past 15 years in China, the SBM-ML index method was used to measure agricultural productivity under the environmental-constraint perspective with agricultural surface source pollution as the non-desired output. A dynamic panel regression model was used to empirically analyze the factors influencing agricultural productivity to provide a reference for formulating policies to alleviate the conflict between economic development and environmental pollution. The results show that the green total factor productivity of Chinese agriculture exhibits a slow, incremental trend year by year. The growth of green total factor productivity in agriculture mainly comes from the increase in the rate of green technological progress. In terms of geographical disparity, the eastern, central, and western regions show a high-to-low gradient of agricultural green total factor productivity. Agricultural green total factor productivity showed a significant positive spatial correlation in some years. As for the influencing factors, foreign trade in agricultural products is conducive to enhancing green total factor productivity in agriculture, whereas foreign direct investment in agriculture and agricultural technology input inhibit the growth of green total factor productivity in agriculture. This research also found a significant U-shaped relationship between environmental management inputs and green total factor productivity in agriculture. Accordingly, suggestions are provided to optimize the international trade structure of agricultural products, selectively introduce high-quality green foreign investment projects, drive the efficiency of R&D investment through digital technology, and increase investment in special funds for agricultural pollution control.
Highlights
Introduction published maps and institutional affilThe ravages of the novel coronavirus pandemic pose a new challenge to vulnerable agriculture
This research attempts to make the following extensions and contributions: measurement of agricultural surface source pollution output by the unitsurvey assessment method to compensate for the lack of estimation of agricultural pollution emissions; measurement of agricultural productivity from the perspective of environmental constraints to obtain more accurate productivity measurement results; analysis of the driving factors affecting agricultural productivity to provide a theoretical basis for the government to implement targeted policies
In Equation (1), Ej is the emissions of agricultural pollutants total nitrogen (TN), total phosphorous (TP), and chemical oxygen demand (COD) in each province; EU is the index statistics of each source of pollution; ρij is the pollution production intensity factor of pollutant j of unit I; ηi is a coefficient characterizing the efficiency of the relevant resource use; PEij is the amount of pollutant j generated, Cij is the emission factor for pollutant j of cell i and is determined by the cell and spatial feature
Summary
China’s Agricultural Productivity from the Perspective of Environmental Constraints. Department of International Economics and Trade, School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China.
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