Spatial Spillover Effects and Dynamic Evolution of Agricultural Carbon Sinks Under China’s Dual-Carbon Goals

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a methodological framework (spatial Durbin model, kernel density, Markov chains, ML) for analyzing spatial dynamics of carbon sinks that can be replicated in other regions.

🏢実務担当者:Highlights the importance of interregional coordination and technology transfer for agricultural carbon management, suggesting practical measures like cross-provincial sink trading.

🏛政策担当者:Demonstrates that region-specific policies leveraging spatial spillovers are effective for enhancing national carbon sinks, with scenario projections showing 18% improvement under high-policy intensity.

This study investigates the spatial spillover effects and dynamic evolution of agricultural carbon sinks in China, contextualized within the nation’s dual-carbon goals of achieving carbon peaking by 2030 and carbon neutrality by 2060. While prior research has extensively examined agricultural carbon emissions, the spatial and temporal dynamics of carbon sinks—the capacity of agricultural systems to absorb and store atmospheric carbon—remain underexplored. Utilizing panel data spanning 30 Chinese provinces from 2000 to 2022, this research constructs a comprehensive agricultural carbon sink index, integrating cropland, grassland, and forestland components. Spatial econometric models, including the Spatial Durbin Model, are employed to quantify direct and indirect effects, while kernel density estimation and Markov chain analysis trace the dynamic evolution of sink levels over time. The findings reveal several critical patterns. First, agricultural carbon sinks exhibit significant positive spatial autocorrelation, indicating that provinces with high sink capacities tend to cluster geographically, particularly in northeastern and southwestern regions. Second, spatial spillover effects are substantial: a one-unit increase in a province’s carbon sink level is associated with an average 0.35-unit increase in neighboring provinces’ sink levels, driven by shared agro-ecological conditions, technology diffusion, and policy coordination. Third, the dynamic evolution analysis demonstrates a gradual upward trend in national average sink levels, yet with persistent regional disparities. Provinces in the eastern and central regions show moderate convergence, while western areas lag, creating a “low-sink trap” that hinders nationwide progress. Key influencing factors, identified through a machine learning framework akin to random forest analysis, include agricultural mechanization intensity, land-use efficiency, and fertilizer application rates, which jointly explain over 60% of the spatial variation in sink levels. The study further constructs scenario-based projections to 2035, revealing that under a high-policy-intensity scenario combining improved soil conservation, afforestation programs, and reduced chemical inputs, agricultural carbon sinks could increase by 18% relative to baseline levels. However, without targeted interventions to address spatial spillover effects, western provinces risk falling further behind. These results underscore the necessity of regionspecific policies that leverage spatial interdependencies, such as cross-provincial carbon sink trading mechanisms and coordinated technology transfer programs. By elucidating the spatial dynamics and temporal trajectories of agricultural carbon sinks, this research provides actionable insights for optimizing China’s agricultural carbon management under the dual carbon framework, contributing to both climate mitigation and sustainable rural development.

• openalex https://osf.io/prgq6first seen 2026-06-29 05:33:54 · last seen 2026-06-29 05:33:59