Effect of climate change on fruit by co-integration and machine learning

Purpose The purpose of this paper is to assess the impacts on production five fruit crops from 1961 2018 energy use, CO 2 emissions, farming areas and labor force in China. Design/methodology/approach This analysis applied autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method Johansen co-integration test predict long-term relation between variables. Four machine learning methods are used for prediction accuracy climate effect production. Findings findings have shown that crop growth, harvested land a relation. outcome use emission rural population has negative influence crops. consumption, area, total yield agriculture positive six long-run relationships reveal 1% increase will decrease by −0.59 −1.97. 0.17%, 1.52%, 1.80% 4.33%, respectively. Furthermore, uni-directional correlated with growth consumption. Also, results indicate bi-directional impact varies emissions agricultural Originality/value study also fills literature gap implementing ARDL fruits China, examine change explore important issue.