Stochastic production function-based assessment of the performance of climate risk management in agriculture: Methods and applications

Abstract

[Objective] As global warming progresses, it is of utmost importance to scientifically evaluate the effectiveness of climate risk management to enhance the capacity to adapt to climate change. The econometric analysis framework based on the stochastic production function has been widely utilized in the assessment of climate risk management effectiveness. Nevertheless, a significant number of studies misinterpret the stochastic production function. This study aims to address the shortcomings of existing literatures, review the application of the stochastic production function in agricultural risk assessment, and further provide rational suggestions for optimizing resource management in response to climate change. [Methods] In light of the logical inconsistencies in the existing literatures that apply the stochastic production function to assess climate risks, this study proposes a shift in the application approach of the stochastic production function to optimize the method for evaluating the effectiveness of climate change risk management. Using the panel data of 335 cities in China from 2001 to 2020, employing the maximum likelihood estimation method to empirically test the applicability of the stochastic production function in evaluating the effectiveness of agricultural measures in adapting to climate change. [Results] (1) Increasing the sown area of crops, improving the proportion of effectively irrigated area, boosting the amount of fertilizer input per unit area, and raising the amount of agricultural electricity consumption per unit area can increase crop yield. (2) Expanding the sown area of crops can not only increase crop yield but also significantly reduce the production risks of crops. Improving the proportion of effectively irrigated area, boosting the amount of fertilizer input per unit area, and increasing the electricity consumption for agriculture per unit area, will expand the production risks while increasing crop yield. (3) The impact of conventional inputs on crop production varied with climate conditions, indirectly reflecting that under different climate conditions, various conventional resource management approaches have varying abilities to mitigate climate risks. [Conclusion] (1) Climate change itself is an important source of risk in agricultural production. Using a traditional production function to estimate the impact of climate change on risk with climate change as an explanatory variable is wrong, and the resulting estimates may lack substantive economic implications. (2) It is necessary to apply the stochastic production function to identify the effects of climate change and resource input combinations, thereby evaluating the effectiveness of different resource inputs in mitigating climate change risks, and providing reasonable suggestions for optimizing resource management to address climate change.

Keywords

• climate change risks|resource management measures|stochastic production function|performance of adaptation|agricultural production