Impact of Climate Policy Uncertainty on Agriculture Development: Multidimensional Analysis From Land Use, Food Structure, and Carbon Emissions

Global climate change poses unprecedented challenges for agricultural sustainability, yet significant knowledge gaps persist regarding the multidimensional impacts of climate policy uncertainty (CPU) on agricultural systems. Previous research has primarily focused on isolated aspects such as land-use changes, production decisions, or emission patterns, neglecting the integrated effects across interconnected agricultural dimensions. This fragmentation underscores the need for comprehensive analytical frameworks that capture complex interactions between policy uncertainty and agricultural development trajectories. This study investigates these relationships through two-way fixed effects and spatial error model (SEM) applied to panel data from Chinese provinces spanning 2011–2022. Empirical findings reveal that CPU significantly impedes agricultural development across three critical dimensions: reducing land utilization efficiency, disrupting optimal food structures, and affecting carbon emission profiles. Regional heterogeneity analyses demonstrate that western regions exhibit heightened vulnerability to policy uncertainty effects on land use and food structures, while central regions show pronounced sensitivity regarding agricultural carbon emissions. Furthermore, spatial econometric modeling identifies significant negative spillover effects, whereby policy uncertainty in one region diminishes land utilization efficiency and food structure optimization in neighboring areas, while simultaneously influencing interregional emission patterns through resource allocation mechanisms. These findings contribute to the theoretical understanding of policy–agriculture interactions and provide empirical foundations for developing differentiated climate policy approaches that balance food security imperatives with environmental sustainability objectives.