An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm

Abstract

Agricultural and rural carbon (ARC) emissions are a major source of greenhouse gas emissions in China and have profound implications for implementing the rural revitalization strategy. This study takes Shandong Province, a leading agricultural province in China, as a case study to explore the relationship between ARC emissions and their influencing factors. It employs the Logarithmic Mean Divisia Index (LMDI) model to decompose changes in ARC emissions from 2000 to 2021, analyzing the contributions of factors such as agricultural production efficiency and agricultural industrial structure. The study then expands the indicator system and applies feature selection methods to identify the main influencing factors. It establishes Bayes model averaging (BMA), STIRPAT-Ridge regression and Long Short-Term Memory (LSTM) models to evaluate their performance in modeling historical ARC emissions. Finally, the study makes prospective forecasts of ARC emissions in Shandong Province from 2022 to 2050 under low, medium and high speed development scenarios. The findings show that from 2000 to 2021, ARC emission intensity decreased by 71.86% in Shandong. Key factors like agricultural production efficiency and agricultural industrial structure exhibited emission reduction effects. Agricultural production efficiency, electricity consumption, agricultural economic level, and transportation travel positively impact ARC emissions, with agricultural production efficiency and electricity consumption as the dominant factors. Under the development high-speed scenario, ARC emissions are projected to peak around 2030. Reducing carbon emissions intensity, improving resource use efficiency and maintaining steady economic growth are crucial for controlling future ARC emissions and achieving sustainable development in Shandong Province.