Coupling Coordination and Spatiotemporal Dynamic Evolution between Agricultural Carbon Emissions and Agricultural Modernization in China 2010–2020

Abstract

Modern agriculture contributes significantly to greenhouse gas emissions. How to reduce such emissions without sacrificing agricultural development is a common issue concerning most developing countries. In China, a rural revitalization strategy proposed in 2018 aims to achieve agricultural modernization by 2050, while reaching a carbon emission peak by 2030 and neutrality by 2060. However, China’s progress towards these goals is largely unknown. This study evaluates the coupling coordination and spatiotemporal dynamic evolution between agricultural carbon emissions and agricultural modernization in China from 2010 to 2020 through a joint employment of spatial autocorrelation and coupling coordination degree modeling. The results show that from 2010 to 2020, the agricultural modernization level increased from 0.155 to 0.272, and the agricultural carbon emission intensity decreased from 4.9 tons per CYN million to 2.43 tons. Agricultural carbon emissions and the agricultural modernization level manifest significant spatially agglomerative patterns with noticeable discrepancies across different regions. Moreover, the coupling coordination degree between agricultural carbon emissions and agricultural modernization has increased every year, but disparities among provinces continued to widen. Specifically, coupling coordination in northern China is significantly higher than that in the south, and its spatial distribution exhibits a positive correlation and increasing levels of clustering. These results point to the continued need for sustainable agricultural development efforts, such as strengthening rural infrastructure and diffusing green technologies in achieving China’s dual carbon emission and agricultural modernization goals. This study also examines the sustainable agricultural development issue from a new perspective, and the findings can provide policy references for sustainable agricultural development policies in China.

Carbon-Emission Density of Crop Production in China: Spatiotemporal Characteristics, Regional Disparities, and Convergence

“…If a spatial correlation exists, spatial econometric models, such as spatial lag model (SAR), spatial error model (SEM), and spatial Durbin model (SDM), are appropriate to use in the conditional β-convergence test. SDM is utilized as the benchmark since it is the general form of SAR and SEM, and the relevant equation of the convergence test is represented as Equation (11).…”

“…Based on life cycle assessment, many researchers applied some widely recognized emission coefficients to calculate the carbon emissions from crop production. This idea, which requires simple operation and eases regional comparison, is commonly used in national and provincial scale studies [10,11]. With different research aims, accounting based on the emission coefficients can be summarized into two perspectives: one is to focus on a certain emission source, such as rice fields [12], soil management [13], and agricultural waste [14][15][16], which is conducive to investigating the greenhouse gas emissions in specific links; the second is to measure the total emissions generated by multiple emission sources [17,18], grasping the carbon effect of the whole process of crop production.…”

Spatial Association Network and Driving Factors of Agricultural Eco-Efficiency in the Hanjiang River Basin, China

“…The challenges of greenhouse gas emissions and pollution have become critical [1,2]. Agriculture is a major contributor to global carbon emissions, with emissions from the agricultural sector accounting for 13.5% of the global total [3,4]. Non-point source pollution from agricultural activities is highly detrimental to the water environment, soil quality, and ecosystems, and also poses a range of environmental and human health risks [5].…”

“…When measuring agricultural eco-efficiency, research needs to take into account the negative environmental impacts of undesirable outputs. However, studies have considered both non-point source pollution and carbon emissions to a limited extent [4,5,8,10,13,14]. In general, the average level of agricultural efficiency decreases when a broader consideration of non-desired outputs is taken into account, but this is a more accurate reflection of efficiency.…”

Assessment of agricultural carbon emissions reduction potential and optimisation pathways based on a framework of equity and efficiency principles: Evidence from Fujian Province in China

“…However, most studies tended to ignore agricultural carbon emissions as a necessary input constraint to economic output, which is not consistent with actual carbon abatement policymaking (Ma et al 2019;Tang and Ma 2022). This is because agricultural economic growth has always depended on the necessity of carbon emissions (Xia et al 2022). Although the application of energyefficient and clean technologies in agricultural practice reduces carbon emissions, the link between agricultural economic development and carbon emissions cannot be broken (Elahi et al 2022).…”