Shoaib Ahmed Wagan, Qurat Ul Ain Memon, Yueji Zhu, Fang Wang
ABSTRACT Climatic variability and rising atmospheric CO 2 emissions have emerged as major drivers of land degradation through altering land productivity in developing countries. This study examines the long and short‐run impacts of climatic variability and CO 2 emissions on land degradation, using banana land productivity as a proxy indicator in Pakistan from 1991 to 2023. Banana ( Musa spp.) is a nutritionally and economically important crop that is particularly vulnerable to fluctuations in climatic variability and CO 2 emissions, rendering it a suitable indicator of climate‐induced land degradation. Time series data spanning three decades were compiled from the Food and Agriculture Organization (FAO), the World Bank Development Indicators (WDI), and the World Bank Climate Change Knowledge Portal (WBCCKP). The ARDL‐ECM model was applied to examine the dynamic relationship among banana land productivity, climatic drivers (precipitation and temperature), CO 2 emissions, harvested area, and agricultural labor. The results show that CO 2 emissions exert significant negative long‐run effects on banana land productivity, providing robust empirical evidence of climate‐induced land degradation. Precipitation exhibits negative effects on banana land productivity, demonstrating that climatic factors provide divergent land degradation outcomes depending on the land management system. Interestingly, temperature exerts a positive and significant long‐run effect on banana land productivity, suggesting that within the observed range, rising temperature improved banana growth by accelerating photosynthesis and crop development. Harvested area presents a positive and significant impact on banana land productivity, underscoring the importance of effective land management. These findings highlight the importance of adaptive strategies and sustainable land management practices to mitigate climate‐induced land degradation and sustain long‐term land productivity under climate variability.