Agricultural Energy Rebound Effect and Its Key Drivers in China: New Evidence from Machine Learning Model

Abstract

With the transformation of agricultural production methods, it was expected that the enhancement of energy utilization efficiency would lead to a decrease in energy consumption. However, contrary to expectations, energy consumption has increased. This study delves into the mechanisms behind China’s agricultural energy rebound effect. By employing a systematic generalized method of moments (GMM) model, the agricultural energy rebound effect in China from 2003 to 2022 is quantified, and various models are utilized to verify contributing factors. The findings indicated that Random Forest and Gradient Boosted Regression Tree models outperformed others in forecasting the factors influencing China's agricultural energy rebound effect. Among all the characteristic variables, the most influential factors were residents' income (RLI) levels, advancements in agricultural technology, the structure of the agricultural industry, and the degree of urbanization. The prediction patterns of the above four influences on the rebound effect of China's agricultural energy were further inferred through the accumulated local effects (ALE) Plot, respectively, and the results show distinctly different nonlinear characteristics.