Samariddin Makhmudov

Type of the article: Research Article

Abstract
The analysis of the mechanisms through which temperature variations affect food production has become a paramount concern for sustainable development and policy formulation, as global food security faces unprecedented challenges from accelerating climate change. Temperature anomalies are threatening agricultural systems that sustain billions of people worldwide. Using panel data from 40 countries from 1980 to 2021, this study investigates the influence of annual surface temperature fluctuations on food production. The Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model is estimated to analyze this correlation in the mixed evidence on the integration levels of the variables. The results corroborate the cointegration among the variables. Temperature change has a significant negative effect on food production in both the short and long run. Food production is positively influenced by economic growth and renewable energy consumption. The study also considers potential nonlinearity by utilizing the Partially Linear Functional Coefficient (PLFC) and the Method of Moments Quantile Regression (MMQR) model. The PLFC estimates imply that economies with lower GDP levels are more adversely influenced by temperature change, emphasizing the crucial role of economic growth in mitigating climate change. Significant negative effects of temperature change are also corroborated by the MMQR estimates in all quantiles, with the largest effects obtained at the higher quantiles. The variation in the impact of renewable energy consumption over quantiles implies that energy policies should be modified according to the developmental phases of countries. The empirical findings have significant implications for formulating sustainable agricultural policies and climate adaptation strategies.

Type of the article: Research Article

Abstract
This study aims to empirically examine the dynamic effects of geopolitical risk, economic policy uncertainty, and climate policy uncertainty on CO₂ emissions in G7 economies, utilizing annual data from 1990 to 2022. To account for cross-sectional dependence and parameter heterogeneity, the analysis employs a cross-sectional autoregressive distributed lag (CS-ARDL) model. Diagnostic tests confirm significant cross-sectional dependence and slope heterogeneity among the variables. All variables are integrated of order one, I (1), confirmed by unit root tests. In contrast, the cointegration test provides a strong indication of a stable long-run relationship among geopolitical risk, policy uncertainty measures, and CO₂ emissions. The outcomes show that a 1% rise in the geopolitical risk index leads to a statistically significant long-run rise of 0.042% in per capita CO₂ emissions. In addition, a 1% increase in economic policy uncertainty and climate policy uncertainty is associated with long-run increases of 0.028% and 0.015%, respectively. These results remain robust across alternative estimators. Overall, the evidence suggests that heightened geopolitical risk and policy-related uncertainties significantly exacerbate environmental degradation in G7 economies, highlighting the necessity for strategies that improve stability, reduce uncertainty, and encourage renewable energy adoption as part of a long-term environmental strategy.