The aim of this study is to examine the impact of air pollutants, including mono-nitrogen oxides (NOx), nitrous oxide (N2O), sulfur dioxide (SO2), carbon dioxide emissions (CO2), and greenhouse gas (GHG) emissions on ecological footprint, habitat area, food supply, and biodiversity in a panel of thirty-four developed and developing countries, over the period of 1995-2014. The results reveal that NOx and SO2 emissions both have a negative relationship with ecological footprints, while N2O emission and real GDP per capita have a direct relationship with ecological footprints. NOx has a positive relationship with forest area, per capita food supply and biological diversity while CO2 emission and GHG emission have a negative impact on food production. N2O has a positive impact on forest area and biodiversity, while SO2 emissions have a negative relationship with them. SO2 emission has a direct relationship with per capita food production, while GDP per capita significantly affected per capita food production and food supply variability across countries. The overall results reveal that SO2, CO2, and GHG emissions affected potential habitat area, while SO2 and GHG emissions affected the biodiversity index. Trade liberalization policies considerably affected the potential habitat area and biological diversity in a panel of countries.
This study aims to determine an interactive environmental model for economic growth that would be supported by the "sustainability principles" across the globe. The study examines the relationship between environmental pollutants (i.e., carbon dioxide emission, sulfur dioxide emission, mono-nitrogen oxide, and nitrous oxide emission); population growth; energy use; trade openness; per capita food production; and it's resulting impact on the real per capita GDP and sectoral growth (i.e., share of agriculture, industry, and services in GDP) in a panel of 34 high-income OECD, high-income non-OECD, and Europe and Central Asian countries, for the period of 1995-2014. The results of the panel fixed effect regression show that per capita GDP are influenced by sulfur dioxide emission, population growth, and per capita food production variability, while energy and trade openness significantly increases per capita income of the region. The results of the panel Seemingly Unrelated Regression (SUR) show that carbon dioxide emission significantly decreases the share of agriculture and industry in GDP, while it further supports the share of services sector to GDP. Both the sulfur dioxide and mono-nitrogen oxide emission decreases the share of services in GDP; nitrous oxide decreases the share of industry in GDP; while mono-nitrogen oxide supports the industrial activities. The following key growth-specific results has been obtained from the panel SUR estimation, i.e., (i) Both the food production per capita and trade openness significantly associated with the increasing share of agriculture, (ii) food production and energy use significantly increases the service sectors' productivity; (iii) food production decreases the industrial activities; (iv) trade openness decreases the share of services to GDP while it supports the industrial share to GDP; and finally, (v) energy demand decreases along with the increase agricultural share in the region. The results emphasize the need for an interactive environmental model that facilitates the process of sustainable development across the globe.
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.
The objective of the study is to examine the impact of natural disasters on external migration, price level, poverty incidence, health expenditures, energy and environmental resources, water demand, financial development, and economic growth in a panel of selected Asian countries for a period of 2005-2017. The results confirm that natural disasters in the form of storm and flood largely increase migration, price level, and poverty incidence, which negatively influenced country's economic resources, including enlarge healthcare expenditures, high energy demand, and low economic growth. The study further presented the following results: i) natural resource depletion increases external migration, ii) FDI inflows increase price level, iii) increase healthcare spending and energy demand decreases poverty headcount, iv) poverty incidence and mortality rate negatively influenced healthcare expenditures, v) industrialization increases energy demand, and vi) agriculture value added, fertilizer, and cereal yields required more water supply to produce greater yield. The study emphasized the need to magnify the intensity of natural disasters and create natural disaster mitigation unit to access the human and infrastructure cost and attempt quick recovery for global prosperity.
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.
It is well documented that carbon emissions can be reduced by replacing conventional energy resources with renewable energy resources; thereby, the role of green technology is essential as it protect natural environment. Given that, the United Nations' agenda of "green is clean" may be achievable by adoption of green technologies. The objective of the study is to examine the link between information and communication technology (ICT), economic growth, energy consumption, and carbon dioxide (CO2) emissions in the context of South Korean economy, by using a novel Morlet wavelet approach. The study applies continuous wavelet power spectrum, the wavelet coherency, and the partial and the multiple wavelet coherency to the year during 1973-2016. The outcomes reveal that the connections among the stated variables progress over frequency and time domain. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages. From the time-domain sight, the results display robust but not consistent associations among the considered variables. From an economic point sight, the wavelet method displays that ICT helps to reduce environmental degradation in a medium and long run in the South Korean economy. This emphasizes the significance of having organized strategies by the policymakers to cope up with 2 to 3 years of the occurrence of the huge environmental degradation in South Korea.
The study examines the role of quality education in access to justice, using a panel data of 21 diversified countries for the period of 1990-2015. The findings show that there is a positive relationship between the presence of scientific and technical journals (STJ) articles and crime rates. The R&D expenditures does not substantially reduce crime rate while per capita income, trademark applications, and technical cooperation grants significantly reduce crime rates across countries. The panel fixed effect (FE) model confirmed the inverted U-shaped relationship between per capita income (GDPpc) and crime rate in the presence of STJ, while this result is changed in the case of GMM estimator. The results of panel causality confirmed the unidirectional causality running from crime rate to STJ and R&D expenditures, while there is bidirectional causality between i) GDPpc and technical cooperation grants, and between ii) energy efficiency and refugee population by country. The variance decomposition analysis (VDA) shows that R&D expenditures have a greater share to influence crime rate, while technical cooperation grants will affect STJ for the next 10 years time. This finding bolsters the conversation on the relationship between education and a reduction in crime rates.