We investigate the role of military expenditure on emission in USA during the period 1960-2015. To achieve the objectives of this study, two measures of military expenditure are utilised, while several timeseries models are constructed with the gross domestic product (GDP) per capita, population, energy consumption per capita, non-renewable energy consumption per capita, renewable energy consumption per capita, urbanisation, trade openness and financial development serving as additional determinants of air pollution. We also use ecological indicator as an alternative measure of pollution. Moreover, different timeseries methods are utilised including a likelihood-based approach with two structural breaks. The output of this research concluded that all the variables are cointegrated. It is found that military expenditure has mixed impact on CO2 emissions. Real GDP per capita, energy consumption per capita, non-renewable energy consumption per capita, population and urbanisation increase CO2 emissions per capita in the long-run, while renewable energy consumption, financial development and trade openness reduce it. There is also evidence for the mixed role of military expenditure, when ecological footprint is utilised as the environmental degradation index. From the output of this research, few policy recommendations are offered for the examined country.
The main objective of this study is to examine the environmental Kuznets curve (EKC) hypothesis by utilizing the ecological footprint as an environment indicator and GDP from tourism as the economic indicator. To achieve this goal, an environmental degradation model is established during the period of 1988-2008 for 144 countries. The results from the time series generalized method of moments (GMM) and the system panel GMM revealed that the number of countries that have a negative relationship between the ecological footprint and its determinants (GDP growth from tourism, energy consumption, trade openness, and urbanization) is more existent in the upper middle- and high-income countries. Moreover, the EKC hypothesis is more present in the upper middle- and high-income countries than the other income countries. From the outcome of this research, a number of policy recommendations were provided for the investigated countries.
The purpose of this study is to explore the effect of financial development on CO2 emission in 129 countries classified by the income level. A panel CO2 emission model using urbanisation, GDP growth, trade openness, petroleum consumption and financial development variables that are major determinants of CO2 emission was constructed for the 1980-2011 period. The results revealed that the variables are cointegrated based on the Pedroni cointegration test. The dynamic ordinary least squares (OLS) and the Granger causality test results also show that financial development can improve environmental quality in the short run and long run due to its negative effect on CO2 emission. The rest of the determinants, especially petroleum consumption, are determined to be the major source of environmental damage in most of the income group countries. Based on the results obtained, the investigated countries should provide banking loans to projects and investments that can promote energy savings, energy efficiency and renewable energy to help these countries reduce environmental damage in both the short and long run.
This study investigates the influence of Internet retailing on carbon dioxide (CO2) emission in 77 countries categorized into developed and developing countries during the period of 2000-2013. To realize the aims of the study, a model that represents pollution is established utilizing the panel two-stage least square (TSLS) and the generalized method of moments (GMM). The results for both regressions similarly indicated that GDP growth, electricity consumption, urbanization, and trade openness are the main factors that increase CO2 emission in the investigated countries. Although the results show that Internet retailing reduces CO2 emission in general, a disaggregation occurs between developed and developing countries whereby Internet retailing has a significant negative effect on CO2 emission in the developed countries while it has no significant impact on CO2 emission in the developing countries. From the outcome of this study, a number of policy implications are provided for the investigated countries.
This study aims at exploring the impact of corruption control on energy efficiency in 60 countries categorized by income: lower middle (LMI), upper middle (UMI), and high (HI). Panel methodology was utilized taking the period of 2000-2017. As cross-sectional dependence is confirmed among the tested equations, the Pesaran (J Appl Econ 22(2):265-312, 2007) unit root test and the augmented mean group estimator proposed by Eberhardt and Teal (2010) were utilized to overcome this matter. The results in general indicate that the lower the corruption is, the more the energy efficiency for all income group economies. Moreover, renewable energy reduces energy efficiency in lower-middle income and high-income economies while its effect is positive in middle-income economies. In addition, the environmental Kuznets curve (EKC) found to be present in all income group economies. Lastly, causality relationships among energy efficiency, corruption, and GDP were present mostly in upper-middle income and high-income economies. From the results, it was recommended that the countries from all income groups should increase their corruption control for the purpose of enhancing energy efficiency.
The objective of this research is to examine the effects of stock market on air pollution in Malaysia during the period 1980-2017. To realize this aim, a nonlinear autoregressive distributed lag (ARDL) model is constructed. The short results in general revealed that the increase in stock markets will increase CO2 emissions and its significance increases in the long run. Moreover, the decline in stock market will reduce Malaysia's CO2 emissions but only in the long run. From the outcomes obtained, a number of policy recommendations were provided for the investigated country.
The aim of this research is to explore the association between financial development, research and development (R&D) expenditures, globalization, institutional quality, and energy consumption in India by using the quarterly data of 1995-2018. Quantile Autoregressive Distributed Lag (QARDL) approach is employed to examine the relationship. An application of the QARDL approach suggests that the R&D, financial development, globalization, and institutional quality significantly influence energy utilization in India. R&D and institutional quality have a negative effect on energy utilization which shows that due to the increase in the quality of institutions and R&D in the country, energy utilization is likely to decrease. However, globalization and financial performance have a positive influence on energy which depicts that due to the increase in financial performance and globalization in India the energy consumption is likely to increase. According to the outcomes of this research, India should make a policy to ease the penalties of energy utilization by monitoring resource transfer by means of globalization and by implementing energy conversation procedures through the advancement of the financial sector.
Rapid evolution in the population age structure of the Middle East countries has major economic, social, and environmental outcomes. Therefore, to fill the gap in the previous literatures, in this study, the effect of age structure on environmental degradation was investigated in the Middle East region. To achieve this goal, a panel data of 10 Middle East countries were examined over the period of 1990 to 2014. Moreover, the carbon dioxide emission per capita was used as an environmental pollution index in this study. According to the stationary property of the variables, small sample size data, and the assumptions of the model, the panel autoregressive distributed lag method of mean group, pooled mean group, and dynamic fixed effect estimators were investigated in this study. The empirical results implied that the pooled mean group model emerged as the most efficient among the three estimators. Also, results revealed that the age structure have a significant relationship with environmental pollution. Children and working age population have a positive elasticity, whereas elderly people have negative elasticity. Furthermore, the results showed that the working age population has the greatest explanatory power on the carbon emissions. Also, the relationship between per capita energy consumption and gross domestic product per capita with air pollution was positive. Overall, the empirical results showed that any attempt to decrease carbon dioxide emissions in the Middle East region should consider the population age structure.
Sustainable development inculcates the process of preserving the environment for future generations while maintaining existing human needs. This study attempts to empirically investigate the relationship between CO2 emissions, GDP, energy consumption, and imports and exports using yearly data between 1980 and 2014 for the panel of eight developing countries (i.e., Bangladesh, Egypt, Iran, Indonesia, Malaysia, Nigeria, Pakistan, and Turkey). All the tests for cointegration establish the long-run association among the variables and confirm the environmental Kuznets curve (EKC) hypothesis for the panel of eight countries. GDP and energy consumption remained significant factors for emission intensity both in the long and short run. However, exports found to be positive factor for emissions in the long run only and imports spur emissions in the short run. The country-specific results validate EKC hypothesis for Bangladesh, Iran, Nigeria, Pakistan, and Turkey. The findings are policy oriented and suggest that the countries' economic growth along with energy consumption and exports are highly emission intensive which require necessary adjustments at sustainable development policy front.
The reduction in oil prices might make crude oil a cheaper alternative to renewable energy (RE). Given this, the present paper examines the effect of fluctuation of oil prices on the use of RE in the United States (US) during the period 1970 to 2018. We constructed two nonlinear autoregressive distributed lag (NARDL) models to examine the effect of the positive and negative oil price shocks on the use of RE in the US. The RE consumption is taken as the dependent variable and the gross domestic product (GDP), Brent crude prices, population density, trade openness, and price index as independent variables. The result revealed that the rise in crude oil price, GDP, and population density will increase RE use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease RE use in the short run and its effect will eventually diminish in the long run. On the policy front, it is suggested that US should raise its energy security by reducing its dependency on imported crude oil and increase the role of RE through the imposition of taxes on oil and increase the base of production and consumption through a series of measures.
Tourism is one of the important factors that can affect the environmental and economic situation of any economy. This study investigates the relationship between tourist arrivals and CO2 emission in the top 20 tourist destinations using data from quarterly observations from 1995 to 2018. A unique technique via quantile-on-quantile regression and Granger causality in quantiles was used. In particular, how the quantiles of tourist arrivals impact quantiles of CO2 emission was analyzed. The empirical results suggest a combination of both positive and negative effects of tourist arrivals and CO2 emission in most tourist destinations. Predominantly, at both high and low tails, in the USA, Spain, Hong Kong, and Austria, tourist arrival has a positive effect on CO2 emission, whereas in the case of Canada, France, Germany, Mexico, and Malaysia, the association was negative. On the other hand, China, Greece, Russia, Japan, Italy, South Korea, Thailand, and Turkey have both positive and negative effects of tourism on CO2 emissions at low and high tails. Tourism can be an important factor while formulating policy for environmental and climate aspects.
This study employs dynamic panel data for 34 Sub Saharan Africa (SSA) countries for the period 1984-2016 to estimate the effects of renewable energy on environmental quality measured by three indicators, namely, per capita CO2 emissions, energy intensity (EI) and Aggregate National Savings (ANS). The study leveraged a battery of second-generation econometric tests and estimation and causality methods to obtain the coefficients between the regressed and the regressors. Results reveal that use of renewable energy reduces CO2 emissions and energy intensity while it enhances ANS. Economic growth still seems to be expensive for the region as it stimulates CO2 emissions. However, it has a positive effect on ANS. As expected, fossil fuels exacerbate CO2 emissions and energy intensity. FDI is found to be detrimental for the environment of SSA region with its positive significant coefficient on CO2 emissions. Financial development is reported to reduce CO2 emissions. Some causal links between variables are also noted.
Coronavirus disease 2019 (COVID-19) pandemic is continuing to have severe effects on tourism-related industries, as safety precautions have become essential to follow. Based on this, this study aims to explore the role of perceptions of the tourist of safety in tourism destination choice with the mediating effect of tourist trust (TT) in the context of the Chinese tourism sector. In addition, this study considers improvements to safety measures for sustainable tourism and the benefits of the technology transformation in the travel industry because of COVID-19. For this study, a quantitative approach was used, and data were collected through convenient sampling. The questionnaire was measured on a 5-point Likert scale, and a cross-sectional approach was adopted for data analysis. The findings of this study show that the effect of the perceived safety of the social environment, perceived safety of facility and equipment elements, perceived safety of human elements, perceived safety of management elements, and perceived safety of natural environments is significant and positive on the tourist destination choice (TDC). In addition, TT is a significant mediator between these elements and TDC. Furthermore, this study concluded that COVID-19 had increased travel anxiety, with particularly negative effects on the Chinese tourism sector, but that the adoption of perceived safety measures could be beneficial in regaining TT for traveling, eventually giving tourists confidence in choosing their traveling destination.