Empirical analysis reveals complex relationship between non-agricultural jobs and agricultural carbon emissions across various cities.
In the context of China’s Carbon Peaking and Carbon Neutrality Goals, understanding how non-agricultural employment affects agricultural carbon emission intensity is critical for achieving green and sustainable agricultural development. This study asks whether the reallocation of labor away from agriculture increases or reduces agricultural carbon emission intensity, through which transformation-related pathways this relationship may operate, and whether the relationship changes across development stages and spatially connected regions. Using panel data from 295 prefecture-level cities in China from 2011 to 2023, this study constructs a factor-substitution framework and applies fixed-effects models, potential pathway analysis, threshold models, and Spatial Durbin Models. The results show that non-agricultural employment has a significant inverted U-shaped association with agricultural carbon emission intensity: at relatively low levels it is associated with higher emission intensity, whereas beyond the estimated turning point it is associated with lower emission intensity. Non-agricultural employment is also systematically associated with agricultural structural adjustment, mechanization transformation, and AI-related patenting activity, which are interpreted as potential pathways rather than definitive causal mediation channels. Threshold results indicate that economic development and urbanization condition the marginal effect of non-agricultural employment, while spatial estimates show that the nonlinear relationship extends to neighboring regions through significant spillover effects. These findings suggest that agricultural carbon-reduction policies should distinguish between cities at different stages of labor reallocation, promote low-carbon forms of capital and technology substitution, and strengthen cross-regional coordination in agricultural producer services and green technology diffusion.
No takes yet. Share an insight, caveat, or question.
Yang et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: