RESUMEN
Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward.
RESUMEN
Understanding the development mechanism of drought events, characterization of future drought metrics, and its impact on crop yield is crucial to ensure food security globally, and more importantly, in South Asia. Therefore, the present study assessed the changes in future projected drought metrics and evaluated the future risk of yield reduction under drought intensity. We characterized the magnitude, intensity, and duration of future drought by means of the SPEI drought index using CMIP6 (Coupled Model Inter-comparison Phase-6) climate models. The impact of future drought on crop yield was quantified from the ISI-MP (Inter-Sectoral Impact Model Inter-comparison Project) crop model by a proposed non-linear ensemble of Random Forest (RF) and Gradient Boosting Machine (GBM). Results suggested that high drought magnitude with a longer drought duration is projected in some regions of South Asia while high drought intensity comes with a shorter duration. It was also found that Afghanistan, Pakistan, and India will experience a longer drought duration in the future. Our proposed ensemble machine learning (EML) approach had high predictive skill with a minimum value of RMSE (0.358-0.390), MAE (0.222-0.299), and a maximum value of R2 (0.705-0.918) compared to the stand-alone methods of RF and GBM for yield loss risk projection. The drought-driven impact on crop yield demonstrates a high risk of yield loss under extreme drought events, which will encounter 54.15%, 29.30%, and 50.66% loss in the future for rice, wheat, and maize crops, respectively. Furthermore, drought and yield loss risk dynamics suggested a one unit decrease in SPEI value would lead to a 14.2%, 7.5%, and 10.9% decrease in yield for rice, wheat, and maize crops, respectively. This study will provide a notable direction for policy agencies to build resistance to crop production against the drought impact in the regions that are critical to climate change.