The impact of climate change on China's agricultural green total factor productivity

Using China's province-level panel data from 2001 to 2019 and the unexpected super efficiency MetaFrontier-Malmquist model (MinDS) to compute the super efficiency MinDS index for the green total factor productivity of China's agricultural sector, the paper analyses the temporal evolution in the agricultural sector's green total factor productivity across China's three economic regions by kernel density estimation. The effects of general climate factors and extreme weather events on the agricultural sector's green total factor productivity are empirically tested. The results show that precipitation, temperature, and humidity significantly impact agricultural green total factor productivity. The semi-parametric panel estimation method fits the nonlinear relationship between climate change and agricultural productivity. In addition, the impacts are found to vary across grouping levels. The mechanism test shows that climate change affects agricultural green total factor productivity through output, input, and structure effects. The test of the synthetic control method based on counterfactual thought shows that extreme weather has a significant negative impact on agricultural green total factor productivity in severely affected areas and is shown to display spatial and dynamic sustainability. The paper's results encourage policymakers in China to actively promote the coordinated and high-quality development of regional green agriculture under the condition of climate change.