Summary
This study constructs a comprehensive 17-indicator evaluation framework to assess food security resilience across China's 31 provinces, accounting for climate change as a key stressor. Using entropy-TOPSIS, spatial econometrics, and predictive modelling, the authors document a steady increase in national resilience from 0.224 in 2010 to 0.350 in 2024, with significant regional disparities driven primarily by inter-provincial differences (70.13% of variation). The work confirms that flood shocks and temperature variability directly undermine resilience and forecasts continued modest improvement to 0.42 by 2029, though with persistent geographic inequality.
Regional applicability
Whilst this study is specific to China's provincial context and policy frameworks, the methodological approach—combining multi-dimensional indicators, spatial analysis, and climate-shock econometrics—offers a transferable template for assessing food security resilience in the United Kingdom under climate scenarios. UK application would require localisation of indicators (e.g., substituting grain self-sufficiency with relevant commodity metrics and adapting broadband/soil measures to British agroecological conditions), but the framework's structure is internationally relevant to food security policy.
Key measures
Composite food security resilience score (0–1 scale); Dagum Gini coefficient for inequality; Moran's I for spatial autocorrelation; obstacle degree model ranking; grain self-sufficiency rate; rural broadband access; soil erosion control rate; temperature variability and flood shock impacts; GM(1,1) grey prediction forecasts to 2029
Outcomes reported
The study measured food security resilience across 31 Chinese provinces using a composite index of 17 indicators across four dimensions (supply availability, consumption access, food utilisation, system stability), tracking changes from 2010 to 2024 and forecasting to 2029. It quantified regional disparities, spatial autocorrelation patterns, and the impact of climate shocks (flood and temperature variability) on resilience trajectories.
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