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AbstractThe relationship between economic growth and environmental pollution continues to attract significant research interest for researchers, practitioners, and policymakers all over the globe. Theoretically, the environmental benefit of economic growth should be greater than its negative externality with higher level of development. However, from the African perspective, countries with higher economic performances often face several environmental challenges, which raises the doubt whether economic growth helps or constrains environmental quality improvement. Under the environmental Kuznets curve (EKC) hypothesis, this study re-examined the effect of economic growth on CO2 emissions conditional on the dynamics of urbanization, renewable energy, and good governance across 47 African countries using panel data from 1996 to 2019. We employ panel cointegration tests to establish whether there is a long-run equilibrium relationship among our variables. We also apply pooled mean group ARDL (PMG-ARDL) techniques and the Dumitrescu-Hurlin causality test to determine the long- and short-run effects of economic growth, urbanization, renewable energy consumption, and good governance on CO2 emissions. The results from the PMG estimator validate the EKC hypothesis since a 1% surge in GDP per capita increases emissions by 0.61% in the long run, while a 1% increase in its square decreases emissions by 0.03%. In the short-run, economic growth does not exercise any significant effect on emissions. Furthermore, results indicate a significantly negative and positive long-run effect of renewable energy and governance, respectively. Finally, our causality test shows bidirectional relationship between CO2 emissions and all the explanatory variables. Henceforth, we provided policy implications based on the study's results.

AbstractThe article explores the causal effect of local climate conditions on household consumption in South Africa. The climatic conditions are represented by monthly average temperature and precipitation. The study utilises the nationally representative 2017 National Income Dynamics Study (NIDS), wave 5 data and 2017 Climate Research Unit (CRU) climate data. The parsimonious quantile regression shows that climatic conditions (precipitation, temperature, wet days, and cloud cover) impact household per capita consumption. The quadratic quantile regression model analysis shows that household per capita consumption is convex in precipitation. Below the turning point, increased precipitation is associated with decreased household per capita consumption. Above the turning point, increased precipitation is related to increased household per capita consumption. Regions that receive very low precipitation or experience extreme temperatures (very cold or very hot) require tailor-made interventions to alleviate consumption. When we control for household characteristics, the impact of climatic conditions on household per capita consumption is weak. Providing inclusive development policies and programmes can mitigate the impact of climatic conditions on household per capita consumption.