The impact of geopolitical risk, governance, technological innovations, energy use, and foreign direct investment on CO 2 emissions in the BRICS region.
Ijaz UddinMuhammad UsmanNajia SaqibMuhammad Sohail Amjad MakhdumPublished in: Environmental science and pollution research international (2023)
Geopolitical risk (GPR) and other social indicators have raised many somber environmental-related issues among government environmentalists, and policy analysts. To further elucidate whether or not these indicators influence the environmental quality, this study investigates the impact of GPR, corruption, and governance on environmental degradation proxies by carbon emissions (CO 2 ) in BRICS (Brazil, Russia, India, China, and South Africa) countries, namely Brazil, Russia, India, China, and South Africa, using data over the period 1990 to 2018. The cross-sectional autoregressive distributed lag (CS-ARDL), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods are used for empirical analysis. First and second-generation panel unit root tests report a mixed order of integration. The empirical findings show that government effectiveness, regulatory quality, the rule of law, foreign direct investment (FDI), and innovation have a negative effect on CO 2 emissions. In contrast, geopolitical risk, corruption, political stability, and energy consumption have a positive effect on CO 2 emissions. Based on the empirical outcomes, the present research invites the concentration of central authorities and policymakers of these economies toward redesigning more sophisticated strategies regarding these potential variables to protect the environment.
Keyphrases
- south africa
- life cycle
- healthcare
- cross sectional
- human health
- randomized controlled trial
- municipal solid waste
- mental health
- systematic review
- type diabetes
- skeletal muscle
- fatty acid
- quality improvement
- hiv positive
- risk assessment
- machine learning
- climate change
- electronic health record
- metabolic syndrome
- hiv infected
- artificial intelligence
- big data
- data analysis