Who affects carbon emissions? Drivers and decoupling effects of agricultural carbon emissions—evidence from Sichuan, China

Abstract

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Mitigating agricultural carbon emissions is pivotal for attaining the objectives of carbon peak and carbon neutrality. Utilizing a time-varying parametric Cobb-Douglas (C-D) production function, this study employs an enhanced Logarithmic Mean Divisia Index (LMDI) decomposition approach, the Tapio decoupling model, and Monte Carlo simulations to investigate the determinants and decoupling dynamics of agricultural carbon emissions within Sichuan Province from 2010 to 2020. The findings reveal that: (1) Factors such as carbon emission intensity, agricultural structure, labor inputs, and capital stock played a significant role in suppressing agricultural carbon emissions, collectively contributing to a reduction of 484.12 million tonnes. (2) The unstable decoupling of agricultural carbon emissions from economic development in Sichuan Province. Capital stock, alongside carbon emission intensity and agricultural structure, significantly contributed to this decoupling. To harmonize agricultural economic growth with carbon emission reduction, emphasis should be placed on manure management and resource utilization in livestock and poultry farming. Furthermore, leveraging technological advancements to enhance resource efficiency is crucial for reducing carbon emissions. Simultaneously, strategic management of fixed asset growth, focused on energy conservation, can catalyze the synergistic effects of economic development and technological spillovers.

Keywords

• agricultural carbon emissions
• logarithmic mean divisia index model
• Tapio decoupling model
• scenario analysis
• carbon peak and carbon neutrality