Impact of surface temperature change on food production: Evidence from PLFC and MMQR models

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Özet

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. This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/11510Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.21511/ee.16(3).2025.08Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorCatık, Abdurrahman Nazif
dc.contributor.authorBucak, Çağla
dc.contributor.authorAkdeniz, Coşkun
dc.contributor.authorBallı, Esra
dc.contributor.authorKuziboev, Bekhzod
dc.contributor.authorMakhmudov, Samariddin
dc.contributor.authorAshurova, Nasiba
dc.date.accessioned2026-10-18T20:26:33.102919091Z
dc.date.available2026-10-18T20:26:33.102919091Z
dc.date.issued2025
dc.description.abstractThe 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. This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.identifier.doi10.21511/ee.16(3).2025.08
dc.identifier.endpage126
dc.identifier.issn1998-6041
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105027556220
dc.identifier.startpage112
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/11510
dc.identifier.volume16
dc.language.isoen
dc.publisherLLC CPC Business Perspectives
dc.relation.ispartofEnvironmental Economics
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectfood production index
dc.subjectnonlinear panel data
dc.subjectrenewable energy consumption
dc.subjecttemperature anomalies
dc.titleImpact of surface temperature change on food production: Evidence from PLFC and MMQR models
dc.typeArticle
dspace.entity.typePublication
rdl.citation.scopus2
rdl.oa.statusgold
rdl.source.indexSCOPUS