Abstract

The study of the evolutionary patterns and trends of agricultural carbon emission intensity and agricultural economic development levels plays an important role in promoting the green and low-carbon sustainable development of agriculture. This paper adopts the carbon emission factor method to measure the agricultural carbon emissions in Jiangxi Province from 2001 to 2020, uses the LMDI decomposition method to explore the drivers of carbon emissions, and further analyzes the coupling relationship between agricultural carbon emissions and the agricultural economy using the Tapio decoupling model, based on which a GM (1,1) model is used to forecast the agricultural carbon emissions in Jiangxi Province from 2001 to 2015. According to the research results, agricultural carbon emissions in Jiangxi Province show a trend of “rising and then falling”, with the intensity decreasing; the level of economic development is the main factor that increases carbon emissions, while the efficiency of agricultural production, the size of the labor force, and the structure of agricultural production have positive effects in terms of reducing carbon emissions. How to reduce carbon emissions while promoting agricultural economic development is an issue that remains to be addressed in the future. Further analysis found that the decoupling states of Jiangxi Province from 2001 to 2009 switched between strong decoupling and weak decoupling, with weak decoupling dominating the years 2010–2015 and strong decoupling dominating from 2016 onwards. With the continuous promotion of carbon emission reduction, agricultural carbon emissions in Jiangxi Province will continue to show a decreasing trend over the next five years. Three policy recommendations are put forth in order to advance the effort to reduce agricultural carbon emissions in Jiangxi Province: cultivating high-quality and low-carbon rice varieties, switching to green agricultural production, and coordinating the connection between economic growth and agricultural carbon emissions.

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