Esra Ballı

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 paper investigates the impact of oil, coal, renewable energy consumption, CO₂ emissions in the ASEAN-5 countries for the period from 1971 to 2020 utilizing the panel ARDL and MMQR models. The panel autoregressive distributed lag (ARDL) model estimates average short- and long-run effects, whereas the method of moments quantile regression (MMQR), a panel quantile technique, estimates how these effects vary across the distribution of emissions. The MMQR results shed light on the impacts of variables on CO₂ emissions estimated at different emission levels. The results show that oil, coal consumption, and GDP are significant factors influencing CO₂ emissions. Conversely, renewable energy sources contribute significantly to reductions in CO₂ emissions. Also, the results reveal that trade activities have a significant impact on emissions in countries exhibiting high emission levels. In particular, the effects of GDP and renewable energy become stronger at higher quantiles, whereas those of oil and coal remain relatively stable. Furthermore, the panel causality tests reveal bidirectional causality between CO₂ emissions and oil consumption and economic growth. The results also support unidirectional causality from coal consumption to emissions, and causality from emissions to renewable energy. The results indicate the need for comprehensive efforts across various policy areas to reduce emissions and address climate change. Reducing coal and oil dependence should be a priority for all ASEAN-5 countries, while faster renewable deployment matters most for high emitters.

Acknowledgment
Esra Balli would like to acknowledge the financial support provided by the Council of Higher Education (YÖK) through the Academic Development Program (AKAP).