Climate Extreme and Agriculture Development: Fresh Insight From Top Agri-Economics - lisha, liu; Maneengam, Apichit; Chupradit, Supat; Albasher, Gadah; Alamri, Ohoud; Alsultan, Nouf Ahmed; Dar, Afzal Ahmed (Frontiers in Environmental Science, 2022)
A range of studies have been observed, covering the title of climate change and its linkage with the agriculture sector. This would justify the claim that changing environment has its several outcomes for which the agriculture sector cannot be ignored. The purpose of this study is to investigate the impact of various climate change dynamics and modelling on the four indicators of agriculture sector. Overall, five panel economies were selected having highest level of agriculture output in the world economy. The time duration of the study was during 1990-2018 with yearly data as collected from world development indicator or WDI. The study analysis was conducted while applying four panel regression models like ordinary least square, fixed effect estimator, least square dummy variable, and finally the random effect. For better understanding, study findings are empirically explained. The results confirm that both positive and negative impact of various proxies of climate change on agriculture dimension of selected economies. More specifically, it is observed that higher climate change in the form of carbon emission from different sources are causing a downturn effect on the agriculture export while at the same time, they are causing an up-ward shift in the agriculture import of selected economies. Besides, study has reasonably disrobed various policy implications both in theoretical and practical perspective. However, some limitations are also under observation. Firstly, this study considers the limited number of explanatory variables for reflecting the changing climate trends among top five agriculture economies of the world. However, there are still range of other factors which can be observed in the future studies to examine their influence on the selected indicators of agriculture industry. Secondly, this study has applied traditional panel models where no implication is observed for the dynamic panel methods like Generalized methods of Moments or GMM. Thirdly, this study has not provided any evidence for the cross-country analysis. Fourthly, this study has limited time span along with missing examination of both short run and long as well. Future studies may address these limitations for better implication in both theoretical and practical perspective.