Analyzing the influence of external shocks on the pricing dynamics of the live pig industry chain is essential for effective macroeconomic control. Utilizing monthly data spanning from January 2010 to August 2023, this study employs the TVP-SV-VAR (Time-Varying Parameter—Stochastic Volatility—Vector Autoregression) model to analyze the effects of EPU (Economic Policy Uncertainty) and INU (Live Pig Industry News Uncertainty) on industry pricing. The findings are as follows: Firstly, the impacts of EPU and INU on industry prices exhibit time variability and distinct characteristics. Specifically, the impact magnitude of EPU ranges between [−0.025, 0.025], and that of INU between [−0.01, 0.01]. These differences in impact magnitude elicit varied responses from manufacturers and consumers to the indices. Secondly, uncertainty shocks at particular time points show high consistency, suggesting a patterned influence of external shocks on industry pricing that aligns with historical trends. Thirdly, robustness tests with alternative explanatory variables confirm the reliability of the findings. An uncertainty index, crafted from more comprehensive information sources, more accurately captures the effects of external shocks on industry pricing. Additionally, the volume of live pig slaughters illustrates the potential interaction between external shocks and pricing dynamics. In an era marked by increasingly frequent external shocks, this research offers valuable insights for policymakers to implement macro-control and foster high-quality industrial development.

Exploring the influence of external shocks on the pricing dynamics of the live pig industry chain is essential for effective macroeconomic control. Utilizing monthly data spanning from January 2010 to August 2023, this study employs a Time-Varying Parameter - Stochastic Volatility - Vector Autoregression (TVP-SV-VAR) model to analyze the effects of Economic Policy Uncertainty (EPU) and Live Pig Industry News Uncertainty (INU) on industry pricing. The findings are as follows: Firstly, the impacts of EPU and INU on industry prices exhibit time variability and distinct characteristics. Specifically, the impact strength of EPU ranges between [-0.025, 0.025], and that of INU between [-0.01, 0.01]. These differences in impact strength elicit varied responses from manufacturers and consumers to the indices. Secondly, uncertainty shocks at particular time points show high consistency, suggesting a patterned influence of external shocks on industry pricing that aligns with historical trends. Thirdly, robustness tests with alternative explanat ory variables confirm the reliability of the findings. An uncertainty index, crafted from more comprehensive information sources, more accurately captures the effects of external shocks on industry pricing. Additionally, the volume of live pig slaughters illustrates the potential interaction between external shocks and pricing dynamics. In an era marked by increasingly frequent external shocks, this research offers valuable insights for policymakers to implement macro-control and foster high-quality industrial development.

Climate change, more frequent extreme events, and tightening resource and environmental constraints are increasing uncertainty in grain production, making agricultural risk assessment essential for food security. Yet high realized production capacity does not necessarily imply low risk, and output-based assessments may obscure climatic, ecological, and socioeconomic vulnerabilities. Using a panel of 4140 city-year observations for 180 Chinese prefecture-level cities (2000–2022), we construct an Agricultural Comprehensive Risk Index (ACRI) spanning climate–meteorological exposure, ecological–environmental conditions, and socioeconomic vulnerability, and examine its joint spatial patterns and evolution with grain production capacity. Explainable machine learning (XGBoost + SHAP) identifies key predictors, nonlinear effects, interactions, and regional heterogeneity. We find pronounced spatial mismatches and asynchronous dynamics between risk and capacity. High-capacity, medium-to-high-risk areas concentrate in northeast, north, and parts of central/eastern China, while parts of southwest and western China show low capacity with medium-to-high risk. Ecological–environmental variables contribute 77.6% of global SHAP importance, with strongly nonlinear associations and non-additive interactions involving thermal, vegetation, and productivity factors. Over the sample period, predictions grow more dependent on thermal, ecological, and selected drought- and flood-related features, with clear differences between northern and southern China. These findings show that realized grain production capacity cannot substitute for agricultural risk assessment. Food-security governance should therefore evaluate capacity and risk jointly and target region-specific constraints.