Exploring the impact of economic growth on environmental pollution in South American countries: how does renewable energy and globalization matter?

Most emerging economies and the South American Countries are no exception to the negative consequences of trade-off between economic growth and environmental sustainability decisions. This study draws strength from the United Nations Sustainable Development Goals (UN-SDGs-7, 11, 12, and 13). Therefore, this study examines the environmental nexus between economic growth, globalization, renewable, and non-renewable energy, in South America from 1995 to 2020. We deployed the pooled mean group (PMG), mean group (MG), and dynamic fixed effects (DFE). Cross-sectional dependence, panel unit root, and cointegration tests were performed. Finally, we used the Dumitrescu and Hurlin test of causality to determine the long-run association between variables. The finding indicates that while environmental pollution increases with increasing economic growth, it decreases with increasing renewable energy both in the short and long term. Whereas economic globalization positively affects environmental pollution in the long term, social globalization and the moderation effect between political globalization and renewable energy improves environmental quality in the long run. Finally, a bidirectional causality was found between economic growth and environmental pollution, with a unidirectional causality running from economic, political, and social globalization, renewable, and non-renewable energy to environmental pollution. Given these findings, we discussed potential policy measures.

Drought, Climate Change, and Dryland Wheat Yield Response: An Econometric Approach.

Agriculture has been identified as one of the most vulnerable sectors affected by climate change. In the present study, we investigate the impact of climatic change on dryland wheat yield in the northwest of Iran for the future time horizon of 2041-2070. The Just and Pope production function is applied to assess the impact of climate change on dryland wheat yield and yield risk for the period of 1991-2016. The Statistical Downscaling Model (SDSM) is used to generate climate parameters from General Circulation Model (GCM) outputs. The results show that minimum temperature is negatively related to average yield in the linear model while the relationship is positive in the non-linear model. An increase in precipitation increases the mean yield in either model. The maximum temperature has a positive effect on the mean yield in the linear model, while this impact is negative in the non-linear model. Drought has an adverse impact on yield levels in both models. The results also indicate that maximum temperature, precipitation, and drought are positively related to yield variability, but minimum temperature is negatively associated with yield variability. The findings also reveal that yield variability is expected to increase in response to future climate scenarios. Given these impacts of temperature on rain-fed wheat crop and its increasing vulnerability to climatic change, policy-makers should support research into and development of wheat varieties that are resistant to temperature variations.