The energy-growth nexus has important policy implications for economic development. The results from many past studies that investigated the causality direction of this nexus can lead to misleading policy guidance. Using data on China from 1953 to 2013, this study shows that an application of causality test on the time series of energy consumption and national output has masked a lot of information. The Toda-Yamamoto test with bootstrapped critical values and the newly proposed non-linear causality test reveal no causal relationship. However, a further application of these tests using series in different time-frequency domain obtained from wavelet decomposition indicates that while energy consumption Granger causes economic growth in the short run, the reverse is true in the medium term. A bidirectional causal relationship is found for the long run. This approach has proven to be superior in unveiling information on the energy-growth nexus that are useful for policy planning over different time horizons.
The presented work analyzes the energy prices, climate shock, and health deprivation nexus in the BRICS economies for the period 1995-2020. Panel ARDL-PMG technique is used to reveal the underexplored linkages. The long-run estimates of energy prices are observed to be negatively significant to the health expenditure and life expectancy model, whereas, positively significant to the climate change model. These findings suggest that energy prices significantly reduce health expenditures and life expectancy and, thus, increase the death rate in the BRICS economies. The long-run country-wise estimate of energy prices is found negatively significant in case of Brazil, India, China, and South Africa. Alongside, the group-wise significance of CO2 emissions is discovered to be negatively, positively, and insignificant in the cases of life expectancy, death rate, and health expenditure models, respectively. Besides, country-wise long-run estimate of CO2 emissions witnesses negative significance for Russia, India, China, and South Africa.
This study analyzes the relationship between globalization, energy consumption, and economic growth among selected South Asian countries to promote the green economy and environment. This study also finds causal association between energy growth and nexus of CO2 emissions and employed the premises of the EKC framework. The study used annual time series analysis, starting from 1985 to 2019. The data set has been collected from the World Development Indicator (WDI). The result of a fully modified ordinary least square (FMOLS) method describes a significantly worse quality environment in the South Asian region. The individual country as Bangladesh shows a positively significant impact on the CO2 emissions and destroys the level of environment regarding non-renewable energy and globalization index. However, negative and positive growth levels (GDP) and square of GDP confirm the EKC hypothesis in this region. This study has identified the causality between GDP growth and carbon emission and found bidirectional causality between economic growth and energy use.
The Southeast Asian countries have experienced significant degrees of economic growth over the years but have not managed to safeguard their environmental attributes in tandem. As a result, the aggravation of the environmental indicators across this region casts a shadow of doubt on the sustainability of the economic growth achievements of the Southeast Asian countries. Against this milieu, this study specifically explores the influence of renewable electricity generation capacity, technological innovation, financial development, and economic growth on the ecological footprints in five Southeast Asian countries namely Indonesia, Malaysia, the Philippines, Thailand, and Vietnam during the period 1985-2016. One of the major novelties of this study is in terms of its approach to assess the renewable energy use-ecological footprint nexus using the renewable electricity generation capacity as an indicator of renewable energy use in the selected Southeast Asian nations. The econometric analysis involves methods that are robust to handling cross-sectional dependency and slope heterogeneity issues in the data. Accordingly, the recently developed Cross-sectional Augmented Autoregressive Distributed Lag estimator is used to predict the short- and long-run impacts on ecological footprints. The major findings suggest that higher renewable electricity generation capacity and technological innovation reduce ecological footprints, while higher financial development and economic growth increase the ecological footprints. Therefore, these findings imply that in forthcoming years, the selected Southeast Asian countries will need to tackle the environmental adversities by enhancing their renewable electricity generation capacities, increasing investment in technological development, greening the financial sector, and adopting environmentally-friendly growth policies. Hence, the implementation of relevant policies, in this regard, can be expected to ensure complementarity between economic growth and environmental welfare across Southeast Asia.
The well-established emissions-growth debate relies on the symmetric nexus between CO2 emissions and economic growth, thereby ignoring a fundamental component of macro economy in the form of asymmetric relation. This paper considers how CO2 emissions respond asymmetrically to changes in economic growth. While utilizing both linear and nonlinear time series approaches for an environmentally exposed country, Pakistan over the period 1971-2018, we find convincing evidence that CO2 emissions rise more rapidly during negative shocks to economic growth than increase during economic expansions. Thus, contrary to what has previously been reported, the effect is strong as holds both at short run and long run. This is partly due to the increase in informal sector as GDP declines. Our estimated results show that accounting for the shadow economy results a higher magnitude of CO2 emissions due to decrease in economic growth, thus question the traditional symmetric decoupling of economic growth and CO2 emissions. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and dynamic OLS (DOLS). Thus, the findings of this study call for a re-thinking on climate policy design that rarely pays attention to the aforementioned outcomes due to fall in economic growth.
The Belt and Road Initiative (BRI) is closely linked to the ecological sustainability of the infrastructure ventures that intrinsically include the aspects of climate change and pollution. Though there exists literature on the environmental Kuznets curve (EKC) and pollution haven hypothesis (PHH), very few explore the scope in the light of Belt and Road host countries (B&RCs). Therefore, the study examines the income-induced EKC and Chinese outward foreign direct investment (FDI)-based PHH in the multivariate framework of people's connectivity and technology innovation in B&RCs from 2003 to 2018. The outcome of the study reveals that the observed relationship is quantile-dependent, which may disclose misleading results in previous studies using traditional methodologies that address the averages. Utilizing the novel "Method of Moments Quantile Regression (MMQR)" of Machado and Silva (J Econom 213:145-173, 2019), the findings confirm an inverted U-shape association between economic growth and CO2 emissions only at lower to medium emission countries, thus validating the EKC hypothesis. The Chinese outward FDI flows increase carbon emissions at medium to high emission countries, thereby confirming PHH. The findings also indicate that people's connectivity contributes to increasing emissions while innovation mitigates carbon emissions at lower to medium polluted countries. Moreover, the outcomes of Granger causality confirm one-way causality between economic growth and CO2 emissions, between FDI and CO2 emissions, between people's connectivity and CO2 emissions, and between innovation and CO2 emissions. The results offer valuable insight for legislators to counteract CO2 emissions in B&RCs through innovation-led energy conservation in infrastructure projects while adopting green and sustainable financing mechanisms to materialize mega construction projects under the BRI.
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.
This novel research is an argumentative subject which was needed to be addressed and to fill this gap, the author examined the effect of financial development, information and communication technology, and institutional quality on CO2 emission in Pakistan by using quantile autoregressive distributed lag (QARDL) model. The data were obtained for the period from 1995Q1 to 2018Q4. In the long run, GDP and institutional quality have a positive impact on CO2 emission when this emission is already high, which shows that if the GDP and institutional quality increases, the CO2 emission also increases. Moreover, financial development and ICT has a negative impact on CO2 emission irrespective of emission level that whether it is high or low in the country, which shows that if financial enhancement and ICT increases, carbon emission decreases. The study also supported the EKC hypothesis in Pakistan.
Economic integration in the form of Belt and Road Initiative project opens many opportunities and hazards, especially of the participating nations' environment. The current study attempted to empirically test the economic and energy usage (renewable and non-renewable) impact on some selected countries of belt and road projects. For this purpose, the panel data set of twenty-four emerging economies of belt and road projects was selected from 1995 to 2014. The autoregressive distributed lags technique of econometric applied to determine the effect of renewable and non-renewable energy, GDP and GDP2 for EKC, and gross fixed capital formation on carbon emission in the selected countries of Belt and Road Initiative project. The outcomes of this study confirm the existence of EKC in these underlined countries. Here, fossil fuel-based energy consumption is a source of environmental degradation, while renewable and clean energy usage can help sustain environmental conditions without affecting economic growth progress. Capital fixed formation in these economies can enhance economic growth and help to sustainable environmental conditions in the belt and road countries. Thus, based on these empirical outcomes, this study suggests economic and financial assistance in green renewable energy sources and clean technological innovation to enhance economic benefits of Belt and Road Initiative project without compromising the environmental conditions of the region.
This paper uses the quantile autoregressive distributed lag (QARDL) model to analyze the impact of economic growth, tourism, transportation, and globalization on carbon dioxide (CO2) emissions in the Malaysian economy. The QARDL model is employed utilizing quarterly data from 1995Q1 to 2018Q4. The results demonstrate that economic growth is significantly positive with CO2 emissions at lower to upper quantiles. Interestingly, tourism has a negative effect on CO2 emissions at higher quantiles. Moreover, globalization and transportation services are positive, with CO2 emissions at upper-middle to higher quantiles. Furthermore, we tested the environmental Kuznets curve, and the outcomes confirm the presence of the inverted U-shaped curve in the Malaysian economy. The results of this study suggest that ecotourism is beneficial for economic growth in underdeveloped areas; it increases employment opportunities and, thus, achieves a win-win situation for protection and development. The government should encourage the low-carbon development of ecotourism and achieve green development of both tourism and the economy.
The current study explores the relationship between water resources and tourism in South Asia for the period of 1995-2017. The study employs the CIPS unit root test for stationarity of the variables and the CD test for cross-sectional dependence among cross-sectional units. As for the long-run parameters, a novel technique, known as dynamic common correlated effect (DCCE) model, is used which was recently developed by Chudik and Pesaran (J Econ 188:393-420, 2015b). The outcomes from the DCCE method suggest that water resources have a positive impact on tourism in South Asia. It is also proven that ignoring cross-sectional dependence among the cross-sectional units may bring about misleading outcomes. The findings of the study can be helpful for policymakers to understand the role of water resources in boosting tourism and contributing to the economic prosperity of South Asian countries.
In this modern era, the global warming issue has been on the front burner of almost all countries including Malaysia. This study utilizing time series data spanning from 1970 to 2018. To this end, a linear and nonlinear autoregressive distributed lag model was conducted to reveal the foreign direct investment-growth-environment nexus. The conclusion validates the existence of the pollution haven hypothesis in Malaysia. Specifically, the empirical results of the linear autoregressive distributed lag model indicate that foreign direct investment and real gross domestic product have a significant positive impact on CO2 emission while carbon damage cost and the interaction term of foreign direct investment and carbon damage cost have a negative impact in the long run and short run. To find the asymmetric behavior of the foreign direct investment our study employed a nonlinear autoregressive distributed lag model. The findings confirmed the asymmetry association of foreign direct investment with CO2 emission. Interestingly, our results of the interaction term in both models are significant with a negative sign that shows the mediating effect of carbon damage cost that converts the positive effect of foreign direct investment on CO2 emission to negative. Thus, it is vital to reinforce the use of significant regulation as the Malaysian economy opens up to attract more foreign direct investment.
This paper investigates the non-linear impacts of the agricultural, industrial, financial, and service sectors on environmental pollution in Malaysia during the 1980-2018 period. It employs the extended STIRPAT model and two indicators of environmental pollution (carbon dioxide emissions and ecological footprints). It uses the autoregressive distributed lag (ARDL) technique to estimate the parameters. Evidence from the study indicate that the agricultural, industrial, and service sectors have inverted U-shaped non-linear impacts on carbon dioxide emissions and ecological footprints, while the financial sector has a U-shaped non-linear relationship with carbon dioxide emissions and ecological footprint. These empirical outcomes are robust to diagnostic tests, structural breaks, and alternative estimation technique and proxies. The economic implication of this paper is that, at the early stage of sectoral growth, the pollution intensity of sectoral output increases, but after a certain turning point, a further increase in sectoral output will reduce environmental pollution. Precisely, environmental pollution will reduce if the agricultural, industrial, and service sectors exceed threshold levels of 11%, 44%, and 49% of GDP, respectively, while environmental pollution will be aggravated if financial sector exceeds a threshold level of 94%. Therefore, efforts to mitigate environmental pollution in Malaysia should integrate sectoral growth to attain sustainable development.
This paper examines the effect of climate change and financial development on agricultural production in ASEAN-4, namely Indonesia, Malaysia, the Philippines, and Thailand from 1990 to 2016. Further, we explore the role of renewable energy, institutional quality, and human capital on agricultural production. Since the shocks in one country affect another country, we use second-generation modeling techniques to find out the relationship among the variables. The Westerlund (2007) cointegration tests confirm long-run relationship among the variables. The results from cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model reveal that climate change negatively affects agricultural production; on the other hand, renewable energy, human capital, and institutional quality affect positively agricultural production. Moreover, renewable energy utilization, human capital, and intuitional quality moderates the effect of carbon emission on agricultural production. In addition, a U-shaped relationship exists between financial development and agricultural production, suggesting that financial development improves agricultural production only after reaching a certain threshold. Hence, this study suggests that ASEAN-4 countries must adopt flexible financial and agricultural policies so that farmers would be benefitted and agricultural production can be increased.
Several studies have highlighted the significant impact of climate change on agriculture. However, there have been little empirical enquiries into the impact of climate change on marine fish production, particularly in Bangladesh. Hence, this study aims to investigate the impact of climate change on marine fish production in Bangladesh using data from 1961 to 2019. Data were obtained from the Food and Agriculture Organization, Bangladesh Meteorological Department, the World Development Indicators, and the National Oceanic and Atmospheric Administration. The autoregressive distributed lag (ARDL) model was used to describe the dynamic link between CO2 emissions, average temperature, Sea Surface Temperature (SST), rainfall, sunshine, wind and marine fish production. The ARDL approach to cointegration revealed that SST (β = 0.258), rainfall (β =0.297), and sunshine (β =0.663) significantly influence marine fish production at 1% and 10% levels in the short run and at 1% level in the long run. The results also found that average temperature has a significant negative impact on fish production in both short and long runs. On the other hand, CO2 emissions have a negative impact on marine fish production in the short run. Specifically, for every 1% rise in CO2 emissions, marine fish production will decline by 0.11%. The findings of this study suggest that policymakers formulate better policy frameworks for climate change adaptation and sustainable management of marine fisheries at the national level. Research and development in Bangladesh's fisheries sector should also focus on marine fish species that can resist high sea surface temperatures, CO2 emissions, and average temperatures.
Since the inception of the twenty-first century, there has been a profound upsurge in economic policy uncertainty (EPU) with several economic and environmental impacts. Although there exists a growing body of literature that probes the economic effects of EPU, the EPU-energy nexus yet remains understudied. To fill this gap, the current study probes the impact of disaggregated EPU (i.e., monetary, fiscal, and trade policy uncertainty) on energy consumption (EC) in the USA covering the period 1990M1-2020M12. In particular, we use sectoral EC (i.e., energy consumed by the residential sector, the industrial sector, the transport sector, the electric power sector, and the commercial sector) in consort with total EC. The findings from the bootstrap ARDL approach document that monetary policy uncertainty (MP) plunges EC, whereas trade (TP) and fiscal policy uncertainty (FP) escalate EC in the long run. On the contrary, there is a heterogeneous impact of FP and MP across sectors in the short run, while TP does not affect EC. Keeping in view the findings, we propose policy recommendations to achieve numerous Sustainable Development Goals.
The Sustainable Development Goal (SDG) 10 focuses on combating the climate change and its effects. The inclusion of this agenda in the Sustainable Development Goals by the United Nations has shown that worsened environmental degradation is currently a major threat facing humankind. The World Commission on Environment and Development 2015 has highlighted that income inequality is one of the major causes for environmental deterioration. Hence, reducing environmental degradation requires a look at the problem of unequal income distribution. Moreover, educational attainment plays a vital role in providing relevant knowledge and skills to people in handling environmental problems. Thus, the objective of the study is to investigate the relationship between income inequality, educational attainment, and CO2 emissions by employing a panel data analysis for a group of 64 countries from 1990 to 2016.The study uses mainly dynamic common correlated effects (DCCE) estimator to take into account the issue of cross-section dependence which has been ignored by most of the previous studies. By tackling the problem of cross-section dependence, unbiased and reliable results could be produced in estimations. Our results portray that an inverted U-shaped environmental Kuznets curve (EKC) is found to be valid. Additionally, income inequality has a negative impact on environmental degradation. Likewise, educational attainment and CO2 emissions are revealed to be negatively correlated. The findings of the study could provide a better understanding on the root causes of environmental degradation, and further suggest remedial actions to overcome the problem.
The aging population is a common problem faced by most countries in the world. This study uses 18 years (from 2002 to 2019) of panel data from 31 regions in China (excluding Hong Kong, Macao, and Taiwan Province), and establishes a panel threshold regression model to study the non-linear impact of the aging population on economic development. It is different from traditional research in that this paper divides 31 regions in China into three regions: Eastern, Central, and Western according to the classification standard of the National Bureau of Statistics of China and compares the different impacts of the aging population on economic development in the three regions. Although this study finds that the aging population promotes the economy of China's eastern, central, and western regions, different threshold variables have dramatically different influences. When the sum of export and import is the threshold variable, the impact of the aging population on the eastern and the central region of China is significantly larger than that of the western region of China. However, when the unemployment rate is the threshold variable, the impact of the aging population on the western region of China is dramatically higher than the other regions' impact. Thus, one of the contributions of this study is that if the local government wants to increase the positive impact of the aging population on the per capita GDP of China, the local governments of different regions should advocate more policies that align with their economic situation rather than always emulating policies from other regions.
Present climate change consists of global warming that is caused by the emission of greenhouse gases, generally carbon dioxide. The study examines the pollution haven, pollution halo, and environmental Kuznets curve for a number of Asian countries during the period of 1985 to 2020. Outcomes suggest that urbanization, gross domestic product per capita, energy consumption, and foreign direct investment inflow have positive effects, while gross domestic product square, foreign direct investment square, and tourism have negative effects on emissions of carbon dioxide. Furthermore, findings support the validity of the environmental Kuznets curve, pollution haven, and pollution halo hypothesis for the selected Asian countries. We also find robust results of rationality of the environmental Kuznets curve hypothesis for Pakistan, Bangladesh, India, China, Indonesia, Korea, Japan, Malaysia, Vietnam, and Singapore; of pollution haven hypothesis for Bangladesh, China, Indonesia, Japan, Pakistan, and Singapore; and of pollution halo hypothesis for Bangladesh, China, Indonesia, Japan, Pakistan, and Singapore.
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.