The natural catastrophic events largely damage the country's sustainability agenda through massive human fatalities and infrastructure destruction. Although it is partially supported the economic growth through the channel of "Schumpeter creative destruction" hypothesis, however, it may not be sustained in the long-run. This study examined the long-run and causal relationships between natural disasters (i.e., floods, storm, and epidemic) and per capita income by controlling FDI inflows and foreign aid in the context of Malaysia, during the period of 1965-2016. The study employed time series cointegration technique, i.e., autoregressive distributed lag (ARDL)-bounds testing approach for robust inferences. The results show that flood, storm, and epidemic disasters substantially decrease the country's per capita income, while FDI inflows and foreign aid largely supported the country's economic growth in the short-run. These results are disappeared in the long-run, where flood and storm disasters exhibit the positive association with the economic growth to support the Schumpeter creative destruction hypothesis. The foreign aid decreases the per capita income and does not maintain the "aid-effectiveness" hypotheses in a given country. The causality estimates confirmed the disaster-led growth hypothesis, as the causality estimates running from (i) storm to per capita income, (ii) epidemic to per capita income, and (iii) storm to foreign aid. The results emphasized for making disaster action plans to reduce human fatalities and infrastructure for sustainable development.
Every country intends to enhance national production by achieving sustainable development. The purpose of this study is to examine whether there exists any long-run association among environmental deterioration measured by territorial emissions in CO2, demographic factors (total population, population density, and urban population) and some other variables, namely, energy use, per capita income, energy intensity, and industrial value added for the 16 countries from the Middle East and North African (MENA) over 1990-2018. We implemented the generalized method of moments (GMM), fully modified ordinary least square (FMOLS), robust least square estimators, and panel Granger causality techniques for estimation. The empirical estimates reveal that there exists a long run cointegration among the series. Results also exhibit that energy use, per capita income, energy intensity, industrial value added, population density, total population, and urban population have positive effects on CO2 emissions. Furthermore, in each panel, there is bi-directional causality between population density and CO2 emissions, total population and CO2 emissions, and urban population and CO2 emissions. These findings suggest that the policymakers need not exclusively to focus on the transformation of rural labor from an agricultural-based model to urban regions with powerful, dominant industry and services sectors but also related to the changing of rural establishments into urban spaces is required. These changes in demographics involve changes in the demand for additional transportation services, food, shelter, clothing, and other necessities.
Matched MeSH terms: Economic Development/statistics & numerical data
Environmental degradation is at an alarming level in developing economies. The present paper examines the direct and indirect impacts of corruption on environmental deterioration using the panel data of 64 developing countries. Adopting the generalized method of moments (GMM) technique, the paper finds evidence that corruption exhibits a positive impact on pollution. Subsequently, there is also evidence indicating that the level of pollution tends to be higher in countries with a higher level of corruption, eliminating the effectiveness of income effect on environmental preservation. These results also suggest that environmental degradation is monotonically increasing with higher corruption and invalidate the presence of the EKC. Hence, a policy focuses that an anti-corruption particularly in the environmental and natural resources sector needs to be emphasized and enforced in order to reduce or possibly to totally eliminate the rent for corruption.
This article aims to investigate the relationship among renewable energy consumption, carbon dioxide (CO2) emissions, and GDP using panel data for 24 Asian countries between 1990 and 2012. Panel cross-sectional dependence tests and unit root test, which considers cross-sectional dependence across countries, are used to ensure that the empirical results are correct. Using the panel cointegration model, the vector error correction model, and the Granger causality test, this paper finds that a long-run equilibrium exists among renewable energy consumption, carbon emission, and GDP. CO2 emissions have a positive effect on renewable energy consumption in the Philippines, Pakistan, China, Iraq, Yemen, and Saudi Arabia. A 1% increase in GDP will increase renewable energy by 0.64%. Renewable energy is significantly determined by GDP in India, Sri Lanka, the Philippines, Thailand, Turkey, Malaysia, Jordan, United Arab Emirates, Saudi Arabia, and Mongolia. A unidirectional causality runs from GDP to CO2 emissions, and two bidirectional causal relationships were found between CO2 emissions and renewable energy consumption and between renewable energy consumption and GDP. The findings can assist governments in curbing pollution from air pollutants, execute energy conservation policy, and reduce unnecessary wastage of energy.
The Sub-Saharan Africa (SSA) is far lag behind the sustainable targets that set out in the United Nation's Sustainable Development Goals (SDGs), which is highly needed to embark the priorities by their member countries to devise sustainable policies for accessing clean technologies, energy demand, finance, and food production to mitigate high-mass carbon emissions and conserve environmental agenda in the national policy agenda. The study evaluated United Nation's SDGs for environmental conservation and emission reduction in the panel of 35 selected SSA countries, during a period of 1995-2016. The study further analyzed the variable's relationship in inter-temporal forecasting framework for the next 10 years' time period, i.e., 2017-2026. The parameter estimates for the two models, i.e., CO2 model and PM2.5 models are analyzed by Generalized Method of Moment (GMM) estimator that handle possible endogeneity issue from the given models. The results rejected the inverted U-shaped Environmental Kuznets Curve (EKC) for CO2 emissions, while it supported for PM2.5 emissions with a turning point of US$5540 GDP per capita in constant 2010 US$. The results supported the "pollution haven hypothesis" for CO2 emissions, while this hypothesis is not verified for PM2.5 emissions. The major detrimental factors are technologies, FDI inflows, and food deficit that largely increase carbon emissions in a panel of SSA countries. The IPAT hypothesis is not verified in both the emissions; however, population density will largely influenced CO2 emissions in the next 10 years' time period. The PM2.5 emissions will largely be influenced by high per capita income, followed by trade openness, and technologies, over a time horizon. Thus, the United Nation's sustainable development agenda is highly influenced by socio-economic and environmental factors that need sound action plans by their member countries to coordinate and collaborate with each other and work for Africa's green growth agenda.
Matched MeSH terms: Economic Development/statistics & numerical data