Determinants of environmental quality in India: evidence using the bootstrapped ARDL model with structural breaks.

This current study examines the impact of renewable energy consumption, agriculture, and globalization on carbon emissions in India over the period from 1980 to 2018. For long-run estimates, we apply Gregory-Hansen's co-integration test, bootstrap ARDL approaches, fully modified ordinary least squares, and dynamic OLS. The empirical results of long-run estimates indicate that a 1% increase in renewable energy consumption, agriculture, and economic globalization will increase carbon emissions by 0.764%, 1.675%, and 0.517%, respectively. Moreover, this study confirms the detrimental effect of these variables on carbon dioxide emissions. Economic globalization coefficients indicate that the scale effect is valid in India. The 2002 economic crisis slowed down the country's growth rate, which reduced the ecological pollution. Several policy recommendations are derived from the empirical findings.

Impact of coal rents, transportation, electricity consumption, and economic globalization on ecological footprint in the USA.

Over the last three decades, the world has been facing the phenomenon of the ecological deficit as the ecological footprint is continuously rising due to the persistent decline of the per-capita bio-capacity. Moreover, there is a substantial increase in globalization and electricity consumption for the same period, and transportation is contributing to economic prosperity at the cost of environmental sustainability. Understanding the determinants of ecological footprint is thus critical for suggesting appropriate policies for environmental sustainability. As a result, this study analyzes the impacts of economic globalization, transportation, coal rents, and electricity consumption in ecological footprint in the context of the USA over the period 1995 to 2018. The data have been extracted from "Global Footprint Network," "Swiss Economic Institute," and "World Development Indicators." The current study has also applied the flexible Fourier form nonlinear unit root test to examine the stationarity among variables. For the empirical estimation, a novel technique, the "quantile auto-regressive distributive lag model," is applied in the study to deal with the nonlinear associations of the variables and to evaluate the long-term stability of variables across quantiles. The study's findings indicate that coal rents, transportation, and globalization significantly and positively contribute to the deterioration of ecological footprints at different quantile ranges in the short and long run. Electricity consumption is found to have a positive and significant impact at lower quantile ranges in the long run but not have a significant impact in the short run. The study suggested that lowering the dependence of the transport sector on fossil fuels, more use of hydroelectricity, and stringent strategies to curb coal consumption would be helpful to reduce the positive influence of these variables on ecological footprints in the USA.

Dirty versus clean fuel for cooking in Pakistan: regional mapping and correlates.

Based on the Pakistan Demographic and Health Survey (PDHS) 2017-18 data, the study intends to present the relative status of a particular fuel (type) in terms of its level of use in different regions of Pakistan. At the same time, the study investigates the determinants of the household type of fuel used for cooking in Pakistan. Socioeconomic characteristics of the households and household heads are taken as determinants of the main type of fuel (clean or dirty) used for cooking by the households. Binary logistic regression is applied as an estimation technique. Over the country, wood stands first as the most widely used type of fuel followed by natural gas followed by LPG. Animal dung and charcoal are respectively ranked as the fourth and fifth most commonly used types of fuel for cooking by households in Pakistan. In comparison amongst the four provinces of Pakistan, the use of wood as well as charcoal as the fuel type for cooking (in relative terms) is the most common in Balochistan province. Likewise, Punjab province is the most frequent user of liquefied petroleum gas (LPG), crop residues, and animal dung, while Sindh province is the most frequent user of natural gas. Households possessing agricultural land and livestock and with large household sizes had more probability to use dirty sources of fuel for cooking. Households with better wealth status and residing in urban areas were more likely to use clean sources of fuel for cooking. Household head's higher educational status and male-gender had more likelihood to use clean sources of fuel for cooking.

Role of tourism development in environmental degradation: A step towards emission reduction.

Globally, the interaction and vulnerability of tourism and climate change have recently been in focus. This study examines how carbon dioxide emissions respond to changes in the tourism development. Panel data from 2000 to 2017 for 70 countries are analyzed using spatial econometric method to investigate the spatial spillover effect of tourism development on environmental pollution. The direct, indirect, and overall impact of tourism on environmental pollution are estimated after the selection of the most appropriate GNS method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1 % level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and carbon emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness and economic growth significantly influence on environmental pollution through spatial spill over. In addition, education expenditure and infrastructure play a significant moderating role in the relationship among tourism development and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and environmental pollution and indicate that while a country's emissions initially rise with the tourism industry's growth, they begin declining after a limit.

Unlocking the role of energy poverty and its impacts on financial growth of household: is there any economic concern.

The major purpose of this study is to assess racial disparity and energy poverty index by measuring energy poverty index by using data envelopment analysis and regression equation from South Asia (2001-2018). An energy poverty index is quantifying the size and scope of energy poverty, and DEA is used to investigate the relevance of socioeconomic position to multidimensional energy poverty. In multidimensional energy poverty, location, house ownership position, number of dependents, and the age of the main caregiver have an important positive impact. Our research has shown that Bhutan is the most susceptible nation with an energy poverty index of (0.02), Maldives (0.03), and Bangladesh (0.11), while India (0.86) and Pakistan (0.49) are the least likely to be energy poor as regards energy poverty. Of the total energy production, 78% is based on traditional fuels, followed by 12% based on petroleum products. The Gini index indicates a positive association with the energy poverty index at a 5% significance level. This signifies that these socioeconomic indicators positively contribute to the energy poverty index level. This study developed more synchronized policies to eradicate energy poverty and can provide a way forward for policymakers to develop strategies to implement them suitably in the regional power sector.

A step toward reducing air pollution in top Asian economies: The role of green energy, eco-innovation, and environmental taxes.

Environmental degradation is significantly studied both in the past and the current literature; however, steps towards reducing the environmental pollution in carbon emission and haze pollution like PM2.5 are not under rational attention. This study tries to cover this gap while considering the carbon emission and PM2.5 through observing the role of renewable energy, non-renewable energy, environmental taxes, and ecological innovation for the top Asian economies from 1990 to 2017. For analysis purposes, this research considers cross-sectional dependence analysis, unit root test with and without structural break (Pesaran, 2007), slope heterogeneity analysis, Westerlund and Edgerton (2008) panel cointegration analysis, Banerjee and Carrion-i-Silvestre (2017) cointegration analysis, long-short run CS-ARDL results, as well as AMG and CCEMG for robustness check. The empirical evidence in both the short- and long-run has confirmed the negative and significant effect of renewable energy sources, ecological innovation, and environmental taxes on carbon emissions and PM2.5. Whereas, non-renewable energy sources are causing environmental degradation in the targeted economies. Finally, various policy implications related to carbon emission and haze pollution like PM2.5 are also provided to control their harmful effect on the natural environment.

Co-movement of energy prices and stock market return: environmental wavelet nexus of COVID-19 pandemic from the USA, Europe, and China.

This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic's severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

Assessing the efficiency and total factor productivity growth of the banking industry: do environmental concerns matters?

This paper investigates the efficiency and total factor productivity (TFP) growth of the Pakistani banking industry and determines the impact of risk and competition on the efficiency and TFP growth. The data envelopment analysis (DEA)-based Malmquist productivity index is used to measure efficiency and TFP growth of the Pakistani banking industry. The generalized method of moments (GMM) model is applied to observe the impact of risk and competition on efficiency and TFP growth. The motivation behind the use of GMM model is its ability to overcome unobserved heterogeneity, autocorrelation, and endogeneity issues. The results of the study show that the credit and liquidity risks have positive while insolvency risk has negative effect on the efficiency and TFP growth. The competition leads to improve technological efficiency but declines the technical efficiency growth. Among other explanatory variables, operational cost management, banking sector development, GDP growth rate, and infrastructure development show significant relationships with various efficiencies and TFP growth. The banks also facilitate for the purchase of carbon-intensive products in order to reduce carbon emissions. Strong banking development successfully allocate their financial resources for the development of energy-efficient technology while banking sector development is found to be negatively related with environmental sustainability. The strong banking sector possesses a significant negative influence on carbon reduction and environmental degradation.

Trilemma association of energy consumption, carbon emission, and economic growth of BRICS and OECD regions: quantile regression estimation.

Recent research has shown a huge impact of non-renewable energy (NRE) production on environmental health. In this context, this work analyzes the effects of GDP growth and long- and short-term consumption of renewable and non-renewable energy (RE and NRE, respectively) on carbon emission in BRICS and OECD economies. The quantile autoregressive distributed lag (QARDL) model was employed on the panel data from 1980 to 2016. Findings suggest a negative GDP-carbon emission correlation and a positive NRE-carbon emission correlation in the considered economies. Furthermore, carbon emission decreases with increase in gross capital formation, whereas trade openness does not have any significant effect on carbon emission. It has been determined that the application of the error correction method (ECM) has less effect on energy consumption as compared to the past levels and changes in energy consumption. In the long-term, a positive correlation of carbon emission and energy consumption is observed, whereas limited short-term effects of energy consumption on carbon emission are observed. Therefore, an RE-based energy production approach is recommended in the selected region for the future projects.

Nexus between green finance and climate change mitigation in N-11 and BRICS countries: empirical estimation through difference in differences (DID) approach.

Green finance is inextricably linked to investment risk, particularly in emerging and developing economies (EMDE). This study uses the difference in differences (DID) method to evaluate the mean causal effects of a treatment on an outcome of the determinants of scaling up green financing and climate change mitigation in the N-11 countries from 2005 to 2019. After analyzing with a dummy for the treated countries, it was confirmed that the outcome covariates: rescon (renewable energy sources consumption), population, FDI, CO2, inflation, technical corporation grants, domestic credit to the private sector, and research and development are very significant in promoting green financing and climate change mitigation in the study countries. The probit regression results give a different outcome, as rescon, FID, CO2, Human Development Index (HDI), and investment in the energy sector by the private sector that will likely have an impact on the green financing and climate change mitigation of the study countries. Furthermore, after matching the analysis through the nearest neighbor matching, kernel matching, and radius matching, it produced mixed results for both the treated and the untreated countries. Either group experienced an improvement in green financing and climate change mitigation or a decrease. Overall, the DID showed no significant difference among the countries.