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The European Council completed the legislative procedure to establish the Carbon Border Adjustment Mechanism (CBAM) on April 25, 2023, which will be launched in 2027. The iron and steel sector is the main target of the forthcoming CBAM due to the industry's energy-intensive consumption with high carbon dioxide (CO 2 ) emissions. However, minimal existing research has been conducted in this regard. This study employs GTAP-e 11.0 and TOPSIS models to estimate the effects of CBAM implementation on the major nations around the world from 2027 to 2030, examining countries' GDP, social welfare, iron and steel production, trade balance, and CO 2 emissions to the global environment. This study concludes: (1) The GDP and social welfare of important iron and steel trade partners throughout the world will be significantly impacted by the application of CBAM. Most nations, including those in the EU, will experience negative GDP effects, with China undergoing the most pronounced social welfare declines followed by India. In contrast, the EU27 will benefit the most in terms of social welfare, followed by the US, Japan, and Russia. (2) Iron and steel production will decrease in all countries outside the EU, but it will have a positive impact on the trade balance of most countries. (3) The CO 2 emissions of all countries except for the EU and Japan will decrease, with a positive impact on preventing carbon leakage in the international iron and steel trade. (4) Comprehensive analysis demonstrates that the EU will benefit the most, and China will suffer the most from CBAM application. Based on the above conclusions, this study proposes corresponding policy recommendations., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper investigates the time-varying effects of fossil fuel consumption on CO2 emissions in India utilizing the time-varying cointegration test, allowing for multivariate long-run time-varying cointegration parameter developed by Bierens and Martins (2010) and the time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005). The long-run time-varying coefficients reveal that GDP has a positive and increasing impact on CO2 emissions over time. Moreover, results confirm the polluting effects of all fossil fuels. Besides, the TVP-VAR model findings also demonstrate that changes in income and fossil fuel consumption have a positive and significant impact on environmental degradation. Coal is found to be the most polluting fuel, followed by oil consumption. Furthermore, the time-varying responses show that increased natural gas consumption has the least influence when compared to other fossil fuels on CO2 emissions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The focus of sustainable development goals (SDGs) is to promote the use of renewable energy so that countries can achieve better environmental quality. However, the progression is plodding, and still, 80% of energy comes mainly from conventional sources in developing countries. The implementation of procedures depends on the political attitudes, political stability, and quality of institutions. India has a diverse political structure ranging from central government to state government to local governments. In the late '80 s, India witnessed a stiff rise in regional and national political parties, which leads more political competition. This paper tries to explain the possible relationship between political competition and CO 2 emission in India. With the application of the time series non-linear ARDL (NARDL) model, this study tries to find the asymmetric relationship between political competition and CO 2 emission. In our empirical model, we also include other important elements of environmental quality like innovation and fossil fuel consumption. Empirical results show that political competition is asymmetrically related to CO 2 emissions in the long run. Fossil fuel consumption and innovation also have a significant relationship with emissions. Based on the results, a few policy recommendations have been discussed., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In recent years, environmental issues have become controversial, and policymakers are discovering new predictors of carbon emissions. Some economists/researchers have advocated for fiscal decentralization to improve the quality of the environment by offering more financial authority to provincial/local and sub-national governments. Therefore, this work aims to inspect the effect of fiscal decentralization on economic growth and environmental quality in India by taking data from 1996 to 2021. This work applies both ARDL and NARDL econometric models for empirical examination. The findings of this study suggest that expenditure decentralization has asymmetric long-term and short-term consequences on economic growth, and carbon emission in India. The result of the asymmetric ARDL model also indicates that positive and negative shock in expenditure decentralization contrarily affects economic growth and carbon emission. Moreover, the positive and negative shock in revenue decentralization helps in reducing carbon emissions both in the long run and short run in India. These outcomes are useful for policy analysis from the Indian economic policy perspective. The study also laid out potential outcomes that may benefit India's local governments and central government in resolving the issues of economic growth and environmental degradation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A key strategy for social development and sustainable urban expansion is building sustainable and affordable transport systems. In this study, we aim to test the validity of the environmental Kuznets curve (EKC) hypothesis and reveal the impact of infrastructure investment in transportation systems in China, Turkey, India, and Japan over the period 1995-2020 on environmental degradation. According to dynamic ordinary least squares (DOLS) method analysis, per capita GDP and per capita GDP 3 have a significant positive impact on per capita CO 2 emission, while per capita GDP 2 has a significant adverse impact on per capita CO 2 emission. These results confirm the validity of the N-shaped EKC assumption, while inconsistent with the results of the FMOLS technique, showing that per capita GDP is significantly positive, while per capita GDP 2 and per capita GDP 3 have a significant negative impact on per capita carbon emissions. Moreover, as clarified by the fully modified ordinary least squares (FMOLS) and DOLS methods, road infrastructure investment (RO), aviation infrastructure investment, trade openness, and foreign direct investment (FDI) have significant positive effects, while railway infrastructure investment (RA) has a significant negative effect on per capita carbon emission. Country-level estimates of per capita carbon emission-based DOLS strategies in the model show that only China and Japan have N-shaped EKC hypothesis. Road infrastructure investment, aviation infrastructure investment, and trade openness have significant positive effects, while railway infrastructure investment has a significant negative effect on per capita CO 2 emission in selected central and east Asian countries. The latest electrified rail systems are well thought out and less polluting, so the contribution of rail infrastructure investment to the transport mix can support sustainable and safe transport systems at city center and intercity levels and reduce environmental pollution in central and east Asian countries. Moreover, the implementation of the basic environmental settings of trade agreements should be strengthened to condense the growing impact of free trade on environmental pollution., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Green growth refers to the economic growth strategy, which is less detrimental to natural assets, predominantly the environment. Therefore, the main motive of investment and innovation activities should be to attain economic growth while sustainably utilizing natural assets. In this regard, we aim to explore whether ICT diffusion and financial development matter for attaining green economic growth in BRICS economies. The long-run autoregressive distributed lag (ARDL) results show that internet development exerts a positive influence on green growth in four BRICS economies excluding India. However, the long-run estimates of mobile cellular subscriptions are positive only in Russia and China. On the other side, the increase in bank credit and insurance premium also contributes to the long-run green economic growth in almost all BRICS economies. Policymakers should focus on the increased use of ICT in the economy that would replace the physical resources in the economy with information resources. Besides, financial services should be provided to individuals and small and medium enterprises involved in green consumption and production activities., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The 2030 United Nations Sustainable Development Goal (SDG) 13 agenda hinges on attaining a sustainable environment with the need to "take urgent action to combat climate change and its impacts". Hence, this study empirically revisits the debate on the effect of nonrenewable energy and globalization on carbon emissions within the framework of the Kuznets hypothesis using an unbalanced panel data from seven South Asian countries (Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) covering 1980-2019. The variables of interest are carbon emissions measured in metric tons per capita, energy use measured as kg of oil equivalent per capita, and globalization index. To address five main objectives, we deploy four techniques: panel-corrected standard errors (PCSE), feasible generalized least squares (FGLS), quantile regression (QR), and fully modified ordinary least squares (FMOLS). For the most part, the findings reveal that the (1) inverted U-shaped energy-Kuznets curve holds; (2) U-shaped globalization-Kuznets curve is evident; (3) inverted U-shaped turning points for nonrenewable energy are 496.03 and 640.84, while for globalization are 38.83 and 39.04, respectively; (4) globalization-emission relationship indicates a U-shaped relationship at the median and 75th quantile; and (5) inverted U-shaped energy-Kuznets holds in Pakistan but a U-shaped nexus prevails in Nepal and Sri Lanka; inverted U-shaped globalization-Kuznets holds in Bangladesh and Sri Lanka, but U-shaped nexus is evident in Bhutan, Maldives, and Nepal. Deductively, our results show that South Asia countries (at early stage of development) are faced with the hazardous substance that deteriorates human health. Moreover, the non-linear square term of the nonrenewable energy-emissions relationship is negative, which validates the inverted U-shaped EKC theory. Overall, the effect of energy and globalization on carbon emissions is opposite while the consistency at the 75th quantile result indicates that countries with intense globalization are prone to environmental degradation., (© 2022. The Author(s).)

Climate action got attention from the United Nation's Sustainable Development Goals (SDGs). Although a large number of studies are investigating the said phenomenon, however, the literature on the top five responsible countries is unable to evaluate the role of environment-related technologies (ERTs) and institutional quality (IQ). The top five consumption-based carbon dioxide (CBCO 2 ) emitting economies, i.e., China, India, Japan, Russia, and the USA, are considerable stakeholders in this challenge. To fill this void, with the latest data available from 1992 to 2017, short- and long-run relationships are estimated with dynamic common correlated effects estimator and augmented mean group in the framework of EKC hypothesis. Reported results indicate the negative effect of ERTs and IQ towards CBCO 2 , which means that adoption of ERTs and better IQ is supportive in controlling environmental degradation. Findings are also robust to the policy implications for the UN's SDGs., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

It is need of the hour to investigate the impacts of climate parameters on agricultural production in a developing region of South Asia. Therefore, this work attempts to explore the climatic indicators on agricultural production for selected South Asian countries over the annual data of 1961-2016. This study estimates the impacts of rainfall, temperature, rural population, land under cereal production, and CO 2 emissions (ECO 2 ) on agricultural production. For empirical analysis, we applied second-generation unit root tests. After examining the order of integration of time series, we check for the co-integration among the variables. Before the co-integration test, we check for cross-section dependence among the variables. CD and LM tests confirm the existence of cross-section dependence. Afterward, we apply the Westerlund co-integration test to confirm the strong association among the variables. Further, we applied two methods for long-run coefficients of independent variables. Dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) tests were applied to cross-check the findings. Our findings show that rural population and rainfall are negatively associated with agricultural production. Moreover, temperature, land area under cereal production, and ECO 2 are positively associated with agricultural production. Our findings shed light on some important policy implications for South Asian countries., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Environmental sustainability concerns are growing worldwide. Many recent studies have focused on key indicators of CO2 emissions, but less consideration has been given to human capital. This study examines the impact of human capital on CO2 emissions in Brazil, Russia, India, China, and South Africa (BRICS) economies from 1991 to 2019 using a nonlinear panel autoregressive distributed lag approach. Findings show that positive change in education has reduced CO2 emissions, while a negative change in education has increased CO2 emissions in the long run in a group of BRICS. Regarding economy-wise analysis, a positive change in education reduces CO2 emissions in Russia, China, and South Africa in the long run, but a negative change in education has an increasing impact on CO2 emissions in Brazil and China. The results of robustness are also maintained in group and economy-wise empirical analysis. Policymakers should develop the education sector infrastructure in order to support the decrease of CO2 emissions., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

China, United States, India, Russia, and Japan are regarded as the top five carbon dioxide-emitting nations in the world. These countries altogether account for more than half of the global annual discharges of carbon dioxide. Consequently, impeding the carbon emission-led environmental adversities in these countries is of critical emphasis for establishing environmental sustainability worldwide. In this regard, this study checks how economic progress, energy use intensification, and renewable energy use affect the annual growth rates of per capita carbon dioxide emission in these highly-polluted economies considering the study period from 1990 to 2021. Besides, for analytical purposes, advanced panel data estimation techniques have been utilized for detecting and neutralizing the impacts of cross-sectional dependency and slope heterogeneity-related problems in the data. Overall, the findings endorse that economic progress deteriorates environmental quality both in the short and long run. However, since the long-run unfavorable environmental impacts of economic growth are relatively lower compared with the short-run impacts, the environmental Kuznets curve hypothesis can be deemed valid. Besides, more intensive use of energy resources is witnessed to impose negative long-run environmental consequences while the adoption of renewable energy instead of fossil fuels is found to improve environmental well-being, both in the short and long run. Furthermore, the results affirm that economic progress and energy use intensification jointly degrade environmental conditions. By contrast, economic progress alongside greater adoption of renewable energy is observed to inflict an environmental quality-improving effect. Considering these findings, a couple of carbon dioxide mitigating policies are suggested to the concerned highly polluted developed and developing nations., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With rapid economic growth, BRICS is facing enormous burdens of carbon emission and severe issues of income inequality. However, behind this economic success, the BRICS economies also face few thoughtful challenges to improve environmental quality by catching up the sustainable development goals. Consequently, the existing empirical research is concerned with the dynamic links between income inequality and CO 2 emissions by using the novel nonlinear ARDL approach, but small attention has been paid to the BRICS in literature. Therefore, we observed that a negative and positive change in income inequality has positive effect on CO 2 emissions in Russia and South Africa in the long run, although a positive change in income inequality has positive effects on CO 2 emissions in Brazil, Russia, and China, while a negative change in income inequality has negative effect on CO 2 emissions in India, Brazil, and Russia in the short run. Hence, the findings value specific attention from policymakers in BRICS economies., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 CO 2 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 CO 2 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., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The relationship between financial development and environmental sustainability has received significant attention in academic discourse. This study explores the crucial role of financial development in addressing the challenge of increasing CO 2 emissions. Using quantile-on-quantile and nonparametric causality-in-quantile methodologies, this research investigates the impacts and causal links between financial market and institution development and CO 2 emissions levels in five major polluting countries from 1990 to 2019. The findings provide strong evidence regarding the impact of financial institutions and financial market development on CO 2 emissions, demonstrating the presence of shocks and nonparametric causality from financial institutions and financial market development to CO 2 emissions quantiles. However, the effects of financial institution shocks and nonparametric causality are diverse and asymmetric across the sample. Positive and negative shocks are observed in India, while only negative shocks are observed in China, the USA, Russia, and Japan. Moreover, the findings reveal that the influence of financial market development on CO 2 emissions varies across countries, with both positive and negative shocks transmitted to CO 2 emissions in the USA, Russia, and Japan, indicating higher volatility in these countries compared to China and India, where only negative shocks are observed. Therefore, our recommendation emphasizes the prioritization of environmentally conscious financial products and the enhancement of the financial system's capacity to mitigate positive shocks contributing to increased CO 2 emissions. Implementing this strategy requires collective efforts to embrace sustainable financial practices that consider the environmental impact of financial activities., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study investigates the effect of financial development and economic growth on ecological footprint by including non-renewable energy consumption and trade openness as additional determinants. For this purpose, annual data of 10 countries with the highest ecological footprint (China, the USA, India, Japan, Brazil, Indonesia, Mexico, Korea, Turkey, and the UK) for the period 1992-2017 is used. The Westerlund and Edgerton (2007) Panel LM bootstrap test results reveal that there is cointegration between the variables. Additionally, the results obtained from the Common Correlated Effects (CCE) coefficient estimator show that financial development, economic growth, and non-renewable energy consumption negatively affect environmental quality by increasing ecological footprint. On other hand, the effect of trade openness on ecological footprint is found to be statistically insignificant. In addition, according to the panel causality test results, a unidirectional causality from financial development to ecological footprint is found while bidirectional causality between economic growth and ecological footprint exists. Therefore, it would be beneficial for policymakers in such countries to direct financial resources to green energy production and consumption and to encourage projects and practices., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Since developing countries experience economic and environmental sustainability challenges, it is desirable digging into the linkages between economic and environmental parameters. The purpose of this work is to evaluate the existence of the environmental Kuznets curve (EKC) theory (i.e., the inverse U-shape connection between real GDP per capita and per capita carbon dioxide emissions) in the sample of 11 developing countries. By using balanced annual panel data in the period between 1992 and 2014 and two alternative estimation techniques, we explored the potential inverted U-shaped linkage between carbon dioxide emissions and real GDP per capita in the sample of interest. For analysis purposes, Pedroni and Westerlund co-integration techniques are employed. Then, fully modified ordinary least squares, pooled mean group methods are applied for long-run parameter estimations. And, the Dumitrescu-Hurlin causality approach is employed for causal directions. Firstly, this work's findings provide the supportive evidence to the inverse U-shaped linkage in the long-run, indicating that an increase in real GDP per capita and electricity consumption tends to mitigate long-run carbon dioxide emissions in the developing countries, for the whole sample. Secondly, the country-specific findings suggested the presence of EKC theory for Brazil, China, India, Malaysia, the Russian Federation, Thailand, and Turkey. It implicated that these countries are on the path of attaining environmental sustainability in the long-run. However, Mexico, Philippines, Indonesia, and South Africa failed to lend credence to the EKC theory. It manifested that these countries need to design strategies directed to reduce carbon dioxide emissions from economic activity and electricity generation through efficiency improvement or promotion of renewables. Finally, bidirectional causal links are observed among all the variables of interest. The findings suggest that country-specific targeted action plans should be implemented to ensure the environmental sustainability in the developing world.

To examine the asymmetric effects of militarization on economic growth and environmental degradation, this empirical research analyzes annual data of Pakistan and India over the period 1985-2018 using the NARDL econometric model. The empirical results show significant positive militarization effects on economic growth, while non-militarization also shows positive effects on the economic growth in Pakistan and India. Estimation showed that a 1% increase in militarization (non-militarization) led to 8.818% (3.849%) increase in GDP growth, whereas a 1% increase in militarization (non-militarization) decreased carbon emissions by - 1.034% (- 0.225%) in the long run in Pakistan, while militarization has also decreased the carbon emissions - 0.337% in India in the long run. The relationship between militarization and economic growth has an asymmetry in Pakistan and India in the short and long run, while asymmetry also exists between militarization and CO 2 in Pakistan and India in the short and long run. Our findings offer significant policy implications for promoting economic growth and environmental quality in Pakistan and India.

For examining the short-run and long-run asymmetric effect of oil prices on stock prices, recent literature uses standard nonlinear autoregressive distributed lag model. However, this model decomposes oil price series into partial sum of positive and negative changes only and fails to examine the effect of extreme changes in the oil price series on stock prices. This study, therefore, extends the existing literature by focusing on the emerging seven countries, i.e., Brazil, India, Russia, China, Mexico, Indonesia, and Turkey, and uses multiple threshold nonlinear ARDL model. This extended model helps to examine the asymmetric effect of extremely small to extremely large changes in the oil price series on stock prices. The estimates from standard nonlinear ARDL model indicate that, in the short run, oil prices significantly and asymmetrically affect stock prices in the context of Russia, Indonesia, and India only, whereas in the long run, insignificant effect is found for all sample countries. On the contrary, multiple threshold nonlinear ARDL model supports asymmetric effect in long run and short run for all sample countries where this effect is stronger in short run. Moreover, all diagnostic tests indicate that this extended model enjoys a better fit and is more stable than the traditional models. The findings, based on this model, provide deeper insights on the relationship between oil prices and stock prices and can be used for investors, policymakers, and other stakeholders.

The nexus of remittances and CO2 emission is very important and gathers a significant place in empirical research. This paper tries to find out the asymmetric relationship between carbon emissions, remittances, and financial development in India for the period 1980-2014. Based on the theoretical linkages, we develop a nonlinear ARDL model with the use of time series data in this study. The results of the NARDL bound test suggest that there is long-run cointegration among the variables. The findings show that positive shock in remittances causes an increase in CO2 emissions, where negative shock reduces it. The coefficient for financial development is positive but becomes statistically insignificant. Empirical results also support the existence of asymmetric long-run relationship among the variables. Based on the findings, the paper recommends the proper channelization of remittances and financial development towards environment-friendly energy sources and projects without compromising economic growth.

The current study is designed to analyze the relationship between, environmental innovations, globalization, financial development, and CO 2 emissions in the South Asian region over the period of 1996 to 2019. In this regard, the role of governance is also incorporated as a moderator along with Environmental Kuznets Curve (EKC) hypothesis. The sample size includes Bangladesh, India, Pakistan, Nepal, and Sri Lanka. The results of the robust least square show the validity of EKC in the sample countries. Environmental innovations show desirable results on CO 2 emissions, while globalization, financial development, and governance are increasing environmental degradation. The role of governance as a moderator is only effective and favorable with environmental innovation. However, in the case of globalization and financial development, governance appeared to be ineffective in lessening the rate of emissions; rather, it contributes to emissions. It clearly shows the missing link in formulating coherent policy to achieve sustainability targets. Therefore, it is desirable to improve the role of governance with respect to environmental policies not only to handle directly environmental issues but also indirectly while promoting the process of globalization and financial development., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study aims to investigate the impacts of traditional and green economic growth on poverty, income inequalities, and environmental degradation in the case of South Asian economies. In this regard, the study collects data for the period 2000 to 2018 from five South Asian economies (Pakistan, India, Sri Lanka, Nepal, and Bangladesh) from world development indicators (WDI). The study applies second-generation unit root test and co-integration technique, CIPS unit root and Westerlund co-integration tests, to establish the stationarity of the series and co-integration relationship among variables. Furthermore, this study utilized dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) models to investigate the long-run empirical estimates. The study finds that both traditional economic growth (GDP) and green economic growth (GGDP) have a negative impact on poverty and inequality; however, GGDP contributes more than traditional GDP to reducing poverty and inequality. Moreover, the study shows that economic growth has a positive long-run impact on environmental degradation while GGDP has a negative and significant long-run association with environmental degradation. The study recommends that policymakers should develop policies to develop green economic growth to save the environment and for the reduction in poverty and income inequalities in south Asian economies., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A key factor in social development and sustainable urban expansion is the establishment of sustainable and affordable transport systems. This study aims to investigate the impact of transport infrastructure investment on environmental degradation and economic growth and to test the validity of the EKC hypothesis in China, India, Russia, and Japan over the period 1995-2020. The results show that GDP has a significant positive effect, and GDP 2 and GDP 3 have significant adverse effects on environmental degradation, respectively. These results confirm the validity of the inverted U-shaped EKC hypothesis in selected emerging Asian economies. Rail infrastructure investment has significant adverse effects, while road infrastructure investment and aviation infrastructure investment have significant positive effects on environmental degradation. Likewise, the impact of investment in transport infrastructure system (roads, rail, and aviation) on economic growth is positive and statistically significant. Country-level estimates confirm the validity of the inverted U-shaped EKC hypothesis in China, India, and Russia, while the N-shaped EKC is only valid in Japan. Investments in rail infrastructure based on modern rail systems that run on electricity are believed to be less polluting in the transport mix, help create sustainable and safe transport systems, and reduce emissions at the urban and intercity levels in emerging Asian countries. In addition, the growing impact of free trade on environmental pollution should be strengthened to harmonize the strict enforcement of environmental conditions dominated by trade agreements., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The trade-off between environmental degradation and unemployment has been recently termed as Environmental Philips Curve (EPC). This research attempts to investigate the presence of EPC in the Indian context utilizing time series data for the period 1990-2019. Besides contributing to the meagre empirical literature on this issue, the present study makes a novel contribution by introducing a gender dimension to this investigation. In particular, this study investigates whether the nexus between environmental degradation and unemployment is different for men and women. We examine the presence of the EPC by estimating a model that considers growth in CO 2 emission as a function of economic growth, trade openness, and gender segregated unemployment rates. Our findings based on autoregressive distributed lag, fully modified ordinary least squares, and dynamic ordinary least squares estimators generate robust evidence for a negative impact of male unemployment rate on growth in CO 2 emission that validates the existence of the Environmental Philips Curve for the male unemployment rate. However, there is no trade-off between environmental quality and women's employment. In fact, the results point to a favourable effect of reduction in female unemployment on environmental quality. The results of the Block Exogeneity test indicate a unidirectional causality from male unemployment rate to environmental degradation. However, a bidirectional causal relationship exists between female unemployment and environmental degradation. The existence of a trade-off between environmental quality and male employment suggests that India is yet to find viable technologies that can curtail pollution without compromising its livelihood. An optimistic conclusion emanating from our findings is the existence of a virtuous cycle between female employment and environmental quality. An integrated approach to improve environmental quality and increase women's economic activity may facilitate a speedy realization of sustainable development goals for India as both the goals complement and reinforce each other., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Irrespective of the vast array of empirical evaluations pertaining to the environmental Kuznets curve (EKC) hypothesis, both for India and other countries, previous studies, amid divergent submissions, inadvertently failed to highlight the relevant threshold that ensures significant reductions in environmental decay. Additionally, the implications of environmental-control technology on environmental quality are also lacking mostly in the context of Indian economy. Thus, this study enlists environmental-control technology and other relevant factors over the period 1980-2018 and employs the novel multiple threshold nonlinear ARDL technique, a model rarely applied in previous studies for updated empirical narratives. Accordingly, the empirical evidence rectifies that the variables converged to long-run equilibrium. Furthermore, from the tercile partial deviations, it is established that at the middle threshold (GDP 2 ), pollution shrinks more significantly amid rising income, thereby validating the EKC hypothesis for India. Likewise, environmental-control technologies provided only a short-term insignificant carbon neutrality pathway, whereas they provided long-term insignificant emission increasing effects. This implies that the depth of such technology in India is inadequate to invoke cleaner environments at all times. Likewise, energy consumption and urbanization processes are significant environmental polluters, while trade openness provides insignificant long- and short-term carbon emission effects. Against this background, economic growth within the middle threshold promises a more sustainable environment amid rising national income at all times. Moreover, given its short-term outcomes, strengthening the depth of environmental-control technology is imperative to ensure a long-lasting clean environment in India.W2 ), pollution shrinks more significantly amid rising income, thereby validating the EKC hypothesis for India. Likewise, environmental-control technologies provided only a short-term insignificant carbon neutrality pathway, whereas they provided long-term insignificant emission increasing effects. This implies that the depth of such technology in India is inadequate to invoke cleaner environments at all times. Likewise, energy consumption and urbanization processes are significant environmental polluters, while trade openness provides insignificant long- and short-term carbon emission effects. Against this background, economic growth within the middle threshold promises a more sustainable environment amid rising national income at all times. Moreover, given its short-term outcomes, strengthening the depth of environmental-control technology is imperative to ensure a long-lasting clean environment in India., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This research examines the linkage between financial risk and carbon emissions using a quarterly dataset spanning from 1991 to 2019 for top carbon emitting countries. To achieve the study objective, this study apply quantile-on-quantile regression (QQR), the quantile regression (QR) approach for robustness check, and the nonparametric predictive test that identifies causality in mean and variance. Empirical findings from the QQR technique disclose the following: (i) financial risk decreases carbon emissions in the USA, Russia, Germany, and Canada; (ii) in China, India, Japan, Brazil, and Indonesia, financial risk enhances carbon emissions (iii) while we find mixed reactions in the case of South Korea. The outcomes of the conventional quantile regression also confirm the QQR outcomes, while that of nonparametric causality discloses evidence of causality in majority of quantiles from financial risk to carbon emissions. Based on these empirical outcomes, policymakers in the financial risk-induced-environmental degradation regions should consider implementing policies or reforms that would keep financial systems sound, in order to prevent shocks to the environment, and its attendant multiplier impact on the environmental sustainability targets implemented to protect both the immediate and the future generations., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Over the period 1980-2016, this study looks into the causal relations between carbon dioxide (CO 2 ) emissions, energy consumption (EC), foreign direct investment (FDI), and gross domestic product (GDP) in five South Asian countries (Bangladesh, India, Nepal, Pakistan, and Sri Lanka). To achieve the research objectives, panel unit root tests, panel co-integration, autoregressive distributed lag model, and Granger causality tests are used. In the long run, GDP has a positive impact on CO 2 emissions, while squared GDP has a negative impact, confirming the framework of the environmental Kuznets curve (EKC) in Pakistan and Sri Lanka. However, in the short run along with these two countries, Bangladesh also confirms the EKC hypothesis. Among these five countries, Bangladesh and Nepal support the pollution haven hypothesis, but India, Pakistan, and Sri Lanka support the FDI halo hypothesis. The EC has a large positive impact on CO 2 emissions across five countries. In the long run, the Granger causality test confirms one-way causation from EC to CO 2 emissions and bidirectional causality of FDI and CO 2 . These countries might encourage clean energy technology through FDI without jeopardizing GDP and environmental quality. The findings of the study provide a guideline for these countries to reduce CO 2 emissions, achieve a long-term green GDP, and combat global warming., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

We examine the linkages of economic freedom (ECF), energy use, and CO 2 emissions in selected South Asian countries of Pakistan, India, Bangladesh, and Sri Lanka. Annual data from 1995 to 2018 are analyzed by employing second-generation methodologies. Cross-sectional autoregressive distributed lag (CS-ARDL) is used because this method incorporates the cross-sectional dependence among the data. This work uses three models, where the dependent variables are gross domestic product (GDP), CO 2 emissions, and energy use. The findings reveal that ECF and energy use contributes to more economic development. ECF is improving air quality by lowering CO 2 emissions. The findings suggest that these countries need to increase the percentage of renewable energy in their energy generation mix. At the same time, there is a need to integrate ECF with environmental awareness programs. This will not only increase air quality but also increase economic growth. GDP is found to be dependent on energy use; however, increased energy use from non-renewable also contaminates the environment. Therefore, South Asian countries need to invest more in research and development projects to promote renewable energy., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO 2 ) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO 2 emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO 2 emissions; correspondingly, this study builds a model for CO 2 emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO 2 emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO 2 emissions. Finally, this study forecasted a CO 2 emission trend, with a slowdown predicted in China by 2022; however, CO 2 emission's reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO 2 emissions without harming economic development., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In recent literature, scholars discussed the role of export diversification in environmental quality. However, most studies analyzed the role of export diversification in influencing carbon dioxide emissions with mixed results. However, since carbon dioxide emissions specifically capture the environmental effects of energy utilization, a change in the level of carbon dioxide emissions cannot be regarded as a comprehensive measure of environmental deterioration. Also, many previous studies use the original form of the Theil index to measure export diversification, and during the interpretation of the results, they disregard the fact that the lower value of the Theil index indicates higher diversification and vice versa. In this context, to address these gaps in the literature, a study on the contribution of export diversification in ecological footprint is necessary to understand the ecological impacts of export diversification. Therefore, this study analyzes the contribution of export diversification in ecological footprint covering the period between 1965 and 2017 using the STIRPAT model in the context of India which is required to fulfill the demands for resources of over 1.3 billion people. The study relied on the environmental Kuznets curve hypothesis framework to understand the role of export diversification in ensuring environmental sustainability. Using the newly developed Augmented ARDL test, the study established that variables of interest are cointegrated. In the long-run estimation, export diversification reduces the ecological footprint of India and helps establish the inverted-U-shaped nexus between ecological footprint and economic growth. Thus, the environmental Kuznets curve hypothesis was evidenced to hold for India. This important finding divulges that India can control the level of environmental footprints, and therefore decrease environmental degradation by continuously increasing export product diversification. Also, India is on the right path to achieve a reduction in ecological footprint associated with more development when accounting for export diversification in the model. Moreover, energy intensity boosts environmental deterioration, while population density reduces it. Finally, the study discusses strategies to achieve environmental sustainability through increasing export diversification., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper compares four prediction methods, namely random forest regressor (RFR), SARIMAX, Holt-Winters (H-W), and the support vector regression (SVR), to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models and suggest an effective model for the prediction of total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crops in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE) highlights the differences in accuracy among the four models. The mean absolute percentage eror (MAPE) and the mean square error (MSE) for the four methods are RFR (MAPE: 5.67; MSE: 549,900.02), SARIMAX (MAPE: 1.67; MSE:70,422.35), H-W (MAPE:0.75; MSE:16,648.58), and SVR (MAPE: 0.91; MSE: 17,832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the support vector regression (SVR) are relatively low as compared to SARIMAX and RFR. Based on these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crops. Holt-Winters model predicted 14,364.97 for the year 2025, and SVR predicted 13,696.67 for the year 2025. The decision-maker can use these predictions to build a suitable policy for the future. This approach can be contrasted with other forecasting methods, such as the neural network, and train the model to achieve better forecast accuracy., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The objective of this study is to contribute to the existing debate of green economic growth by empirically investigating the role of cleaner energy production, green innovation, and green trade in green economic growth in the context of South Asian countries. For this purpose, the study collects the data of South Asian Economies for 2000-2018 from different sources such as world development indicators (WDI), International Energy Statistics (IES), and Organization for Economic Co-operation and Development (OECD) statistics. The study applied Pesaran's (2007) second-generation unit root test to test the stationarity of the data. Wasteland's (2007) test of cointegration was applied to examine the long-run association among modeled variables. The study confirmed the long-run association among modeled variables that turn to be stationary at the first differences. Moreover, the study applied fully modified least square (FMOLS) and dynamic least square (DOLS) to estimate the empirical results of the study. Results of the study show that the production of clean energy, green innovation, and green trade positively contributes to the green economic growth of South Asian Economies Graphical abstract., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Rivers engage in carbon cycle by transporting the dissolved products of weathering of rocks to the oceans, and this process is sensitive to the global climatic changes. The present study was carried out with an objective of estimating the spatial and temporal variation in carbon consumption due to rock weathering in Cauvery, which is a major non-perennial tropical river in the peninsular India. The samples of all the rock types of this river basin were collected and subjected to mineralogical analysis. The water samples from this river were collected three times a year from 2013 to 2017 at 28 locations and were analysed for pH, EC and major ions. The spatiotemporal variations in the chemistry of river water were used to understand the amount of carbon dioxide consumed by rock weathering. The contribution of weathering to dissolved load of the river was higher followed by the contribution of anthropogenic activities and rainfall. The contribution of silicate weathering is dominant during the high river flow, whereas during low flow time periods, the contribution of carbonate weathering is on par with silicate weathering. The carbon consumption due to weathering in the Cauvery river was higher when the flow was significant, and it was lower during summer months. It is also evident that the carbon consumption is high in the upper and middle regions of the basin due to the weathering of gneissic and granodiorite rocks. Thus, the carbon consumption and flux in this basin are dynamic, both spatially and temporally. The east flowing rivers draining through the peninsular India, which is mostly composed of massive rocks, also functions as carbon sink, thus benefitting the environment by reducing the excess CO 2 in the atmosphere.

The present study explores the effect of renewable energy consumption and public-private partnership investment in energy on consumption-based carbon dioxide emissions for India from 1990Q1 and 2015Q4 whilst controlling technology innovation and economic growth. The study employs the Maki cointegration, Bayer-Hanck cointegration, fully modified ordinary least squares, dynamic ordinary least squares, and frequency-domain causality tests to explore these dynamics. The outcomes of the present study reveal that (i) there is a long-run cointegration equation between consumption-based carbon dioxide emissions and its possible determinants; (ii) whilst renewable energy consumption is beneficial for lowering consumption-based carbon dioxide emissions, public-private partnership investment in energy makes a positive contribution to consumption-based carbon dioxide emissions in the long-run; and (iii) public-private partnership investment in energy and renewable energy consumption also significantly causes consumption-based carbon dioxide emissions at different frequency levels in India. The present study recommends that policymakers in India should apply a series of policies to discourage the use of non-renewable energy and raise the share of renewable energy in order to reduce consumption-based carbon dioxide emissions in the country. The present study also recommends that public-private partnership investment in renewable energy should increase to achieve cleaner production processes.

Carbon dioxide (CO 2 ) is the key atmospheric gas that controls the earth's greenhouse effect, and forests play a major role in abating the atmospheric CO 2 by storing carbon as biomass. Therefore, it is vital to understand the role of different forests in regulating the spatiotemporal dynamics of atmospheric CO 2 concentration. In this study, we have used eddy covariance (EC) tower-based atmospheric CO 2 concentration measurements and satellite-retrieved column average CO 2 concentration of 2018 to understand the diurnal and seasonal dynamics of atmospheric CO 2 concentration over the sub-tropical forest in the foothills of northwest Himalaya, Uttarakhand, India. EC study revealed that the CO 2 concentration over the forest canopy peaks during mid-night to early morning and drop to a minimum during the afternoon. On a monthly scale, peak atmospheric CO 2 concentration was observed during July in both the sites, which was a result of more release of CO 2 by the forest ecosystem through ecosystem respiration and microbial decomposition. Enhanced photosynthetic activities during the late monsoon and post-monsoon resulted in the decrease of atmospheric CO 2 concentration over the forest ecosystem. Among the meteorological variables, rainfall was found to have the highest control over the seasonal variability of the atmospheric CO 2 concentration. Orbiting Carbon Observatory-2 (OCO-2) satellite-retrieved column average CO 2 (XCO 2 ) was also examined to comprehend its reliability on an ecosystem scale. The OCO-2 retrieved XCO 2 value was higher than the EC carbon flux tower-measured atmospheric CO 2 concentration, which might be due to differences in the vertical resolution of the CO 2 column and scale difference. However, the monthly atmospheric XCO 2 retrieved from OCO-2 strongly adheres with the ground-measured monthly pattern. Our study highlights that forests with varying functional traits within the same climatic conditions show variability in the regulation of atmospheric CO 2 concentration.

Climate change resulting from the higher concentration of greenhouse gases in the atmosphere is a threat to the sustainability of life on earth. To ensure sustainable development, the study analyzed the impact of financial development, globalization, and energy use on the environmental quality of South Asian economies over the period 1990-2014. To account for cross-sectional dependence, Breusch-Pagan-Lagrange multiplier, Pesaran-scaled Lagrange multiplier, bias-corrected-scaled Lagrange multiplier, and Pesaran cross-sectional dependence tests are used. The second-generation tests are used to determine the stationarity level of the variables. Furthermore, the Westerlund panel cointegration test confirms cointegration among the variables. For long-run association, fully modified ordinary least squares, dynamic ordinary least squares, and pooled mean group estimators are used. The results suggest that financial development contributes to carbon emissions, whereas globalization has the potential to control emissions. The study also used Dumitrescu and Hurlin's (2012) panel causality test to explore the causal relationship among the variables. Unidirectional causality is observed from economic growth, globalization, and financial development to environmental degradation and from emissions to energy use, respectively. As financial development deteriorates environmental quality, therefore, the government should monitor the disbursement of loans for research and development, green financing, and efficient production that reduce resource consumption and improves environmental quality. Financial development should not compromise environmental quality and endanger sustainability. Furthermore, South Asian countries should promote globalization to support the inflow of green technologies to enhance environmental quality.

In this paper, the BASIC (Brazil, South Africa, India, and China) countries, which play an increasingly significant role in combating climate change, are selected as the research objects. First, we investigate the driving factors of the evolution of carbon emissions in the BASIC group from 2000 to 2016. Second, we use the Monte Carlo simulation to analyze the potential evolution trend of carbon emissions in the benchmark scenario, optimization scenario, and enhanced scenario in the BASIC group from 2017 to 2030 to judge whether the countries can achieve their own autonomous contribution goals. Third, we evaluate whether the BASIC INDC can provide practical support for making the global temperature control goals described in the Paris Agreement. The results show that first, the contribution rate of the energy consumption structure and population size to the carbon emissions of the BASIC are low, and the contributions of R&D efficiency, R&D intensity, and investment intensity to the carbon emission change in the BASIC group are more prominent. Second, under the benchmark scenario, Brazil, South Africa, India, and China from the difference between the complete INDC targets respectively is 265.50 Mt, 207.86 Mt, 1034.70 Mt, and 4660.82 Mt. Under the optimization scenario, China could meet the INDC goals. The gap between Brazil, South Africa, and India is 60.06 Mt, 9.50 Mt, and 413.74 Mt, respectively. Under the enhanced scenario, China and South Africa could meet the INDC target, while Brazil and India are 15.69 Mt and 228.28 Mt away from the INDC target, respectively. Third, although the INDC goals of Brazil and India can achieve the carbon reduction allocated under the 2 °C targets, the INDC targets submitted by the BASIC are not enough to reach the carbon reduction allocated under the global 1.5 °C destination; Brazil, South Africa, India, and China's gap is 64.14 Mt, 246.15 Mt, 236.84 Mt, 3399.64 Mt, respectively. Fourth, Brazil, South Africa, and China should increase its R&D on energy-saving and emission reduction technologies, and India should develop reasonable population policies and green economic development policies.

The growth stimulating effect of energy has been long established in economic literature. Consequently, researchers and policy makers are trying to identify socio-economic determinants of energy consumption in both developed and developing countries to conserve energy. In the present study, we attempt to examine the non-linear and asymmetric effect of human capital and income disparity on energy demand in India over the period 1970-2014. The findings propose long-run co-integrating relationship between human capital, income inequality, and energy demand in the presence of asymmetric framework. It is found that increased level of human capital results in energy conservation as it decreases the energy demand, and higher income gap also results in lower energy demand as poor section may have restricted access to energy sources. Finally, the asymmetric causality test results reveal that only positive shock in income inequality tends to cause energy demand. From policy stand point, it can be recommended that government policies should be designed and implemented towards improvements in human capital to achieve sustainable economic growth in India since human capital found to be a significant component in energy conservation.

Modern bioenergy is being recognized as an increasingly important low-carbon resource by policy-makers around the world to meet climate policy targets. In India also, there is a clear recognition of the significant role of bioenergy in electricity generation as well as in other applications. In this study, a preliminary attempt has been made to assess the techno-economic feasibility of biomass pellets-based power (BPBP) generation in India. Surplus availability of biomass feedstock from agriculture and forestry/wasteland sector is estimated at 242 million tonnes (Mt) for 2010-11 and is expected to rise to 281 Mt in 2030-31 due to increased crop production and associated waste/residue availability. In terms of related capacity, the potential of BPBP projects is estimated at 35 GW for 2030-31. The associated carbon dioxide mitigation potential resulting from the substitution of coal is estimated at 205 Mt in 2030-31 if the entire biomass surplus is to be diverted for power generation. The levelized cost of electricity is estimated at €0.12 per kWh for BPBP projects as compared to €0.10 per kWh for imported coal based power plants. For states with the lower tariff for biomass power, the break-even price of carbon for BPBP projects is estimated at €18 per tonne. Additionally, BPBP projects will generate employment of more than 5 million person-months in the construction of biomass power plants and over 200,000 full-time employments in the operation of BPBP plants and in the production of biomass pellets.

This research paper aims to provide a comparative trend analysis of CO 2 emissions from the two largest emitters, India and China. The analysis focuses on the main sources of CO 2 emissions-coal, oil, cement, and gas and their annual data and global share percentages from 1960 to 2019. The study uses non-parametric trend analysis methods, which do not rely on assumptions of normality, outliers, or data length. Pettitt's test, a well-established non-parametric method, was used to detect sudden shifts in the data. The Mann-Kendall (MK) test and Sen's slope estimator were then applied to identify the presence or absence of monotonic linear trends and assess the magnitude of the slopes. In addition, the innovative trend analysis (ITA) method was used, which is particularly effective in detecting and visualizing monotonic, non-monotonic, and sub-trends. The ITA method has the advantage of presenting these trends in a graphical format. According to the results of Pettitt's test, an abrupt change was detected in India in 1989 for all sources of CO 2 emissions. In China, however, an abrupt change was detected in 1989 for coal and gas-related sources, while other sources showed a change point in 1990. The results of the MK test and the ITA method showed that all sources show only monotonic increasing trends. Based on the results of Sen's slope estimator, the average rate of change of CO 2 emissions is significantly higher in China than in India in all categories after the detection of the abrupt change point. Policymakers should promote the adoption of renewable energy sources, such as solar, wind, hydro, and geothermal, and also implement strategies to control deforestation to counteract the abnormal increase in CO 2 emissions. Finally, this research lays a solid foundation for future studies on CO 2 emissions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Carbon dioxide (CO 2 ) emissions from the cement industry account for 26% of the total industrial emissions, and the need to develop low-carbon techniques within the cement industry is extremely urgent. Low-carbon projects and technologies for the cement industry in different regions and countries have been thoroughly reviewed in this manuscript, and the low-carbon development concept for each county has been analyzed. For developing countries such as China and India, energy saving and efficiency enhancement are currently the key points, while for developed countries and regions such as Europe, more efforts have been focused on carbon capture, utilization, and storage (CCUS). Global CCUS projects have been previously conducted, and the majority of CCUS projects are currently performed in Europe where major projects such as the CEMCAP, CLEANKER, and IEILAC projects represent the latest research progress in cement production technologies and low-carbon technologies for the global cement industry. The development of low-carbon cement technologies has changed from focusing on the end point to instead focusing on the source and process through the exploration of hydrogen and solar energies, and more disruptive and original technologies are expected to be developed, particularly in the cement industry in China., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

China, India, and the USA are the countries with the highest energy consumption and CO 2 emissions globally. As per the report of datacommons.org , CO 2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO 2 emission effect with the prediction of CO 2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO 2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO 2 emission prediction., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper aims to investigate the dynamic nexus between economic complexity index (ECI), technological development (TIN), human capital (HC) and environmental quality in India for transition towards a sustainable environment. This study is based on secondary data covering the period from 1985 to 2018. For empirical analysis, this study applied "Stochastic Impacts by Regression on Population, Affluence, and Technology" (STIRPAT) model framework under the estimation of autoregressive distributed lag (ARDL) model and vector error correction model (VECM) model. The empirical findings of model 1 show ECI, TIN, HC and urbanization (URB) as the helping hands to mitigate the problem of environmental degradation by shrinking the level of EF, whereas for model 2, ECI and TIN failed to influence the CO 2 emissions, but HC served as a stimulant for environmental quality enhancement by declining the level of CO 2 emissions. In contrast, GDP growth and URB strengthen the CO 2 emissions levels. Moreover, in VECM framework, estimated findings reveal that the covariables Granger-cause EF and CO 2 emissions, inferring that causality flows asynchronously from its covariables to EF and CO 2 . Impulse response function (IRF) revealed that the responses in EF and CO 2 emissions ascribed to changes in its covariables. The outcome of the study has some implications for environmental policy strategists to prepare sustainable environment policies and other responsible authorities for sustainable development goal (SDGs), academician and scholars. All the stakeholders involved in environmental economics and policymakers can evaluate this study to design proper policy framework with respect to the environment. There are few studies that explore the dynamic nexus between ECI, TIN and HC with environmental quality in the control environment of URB and GDP growth using the STIRPAT model for India., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The current study projects the increase in surface temperature and CO 2 emissions using the EGM (1,1, α, θ) grey model for the six most significant CO 2 contributing countries, namely China, the USA, India, Russia, Japan, and Germany. The study uses time series data for surface temperature (in degree celsius) from 2010 to 2020, and CO 2 emission (metric tons per capita) data from 2009 to 2019. The empirical results show a downward trend in CO 2 emissions from Japan, Germany, the USA, and Russia by 2028. However, in the same time period, CO 2 emissions are expected to increase for India and remain nearly constant for China. This study indicates an increase in surface temperature at a significant rate in all the 6 countries: by 6.70 °C for China, 7.52 °C for Germany, 2.95 °C for India, 2.66 °C for Japan, 3.61 °C for Russia, and 13.48 °C for the USA by the end of 2028. The study compares the EGM (1,1, α, θ) grey model with the general EGM (1,1) grey model and finds that the EGM (1,1, α, θ) model performs better in both in-sample and out-of-sample forecasting. The paper also puts forward policy suggestions to mitigate, manage, and reduce increases in surface temperature as well as CO 2 emissions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Growing food demand, environmental degradation, post-harvest losses and the dearth of resources encourage the decision makers from developing nations to integrate the economic and environmental aspects in food supply chain network design. This paper aims to develop a bi-objective decision support model for sustainable food grain supply chain considering an entire network of procurement centres, central, state and district level warehouses, and fair price shops. The model seeks to minimise the cost and carbon dioxide emission simultaneously. The model covers several problem peculiarities such as multi-echelon, multi-period, multi-modal transportation, multiple sourcing and distribution, emission caused due to various motives, heterogeneous capacitated vehicles and limited availability, and capacitated warehouses. Multiple realistic problem instances are solved using the two Pareto based multi-objective algorithms. Sensitivity analysis results imply that the decision makers should establish a sufficient number of warehouses in each producing and consuming states by maintaining the suitable balance between the two objectives. Various policymakers like Food Corporation of India, logistics providers and state government agencies will be benefited from this research study. [ABSTRACT FROM AUTHOR]

From the perspective of ecological environmental protection, higher education can not only train a large number of professionals dedicated to environmental protection for the construction of environmental quality, but more importantly, it can promote the improvement of the quality of ecological civilization in the whole society, so as to achieve the effect of indirectly improving environmental quality. BRICS economies, as the most important economies in the world, have an impact on the development of the world in many aspects. However, under the guidance of the concept of sustainable development, whether the environmental quality of BRICS economies meets the standards has also become the focus of scholars. Starting from the sustainable development path, this paper summarizes the model of environmental education in BRICS national higher education. At the same time, through the establishment of the BRICS environmental quality assessment system, the environmental quality of the BRICS countries after the implementation of environmental education was assessed based on the content of the sustainable development system. This paper performs matrix operations on the weight values of the indicators at all levels and the membership vector to obtain a 4 × 4 matrix, that is, the evaluation result Q of each indicator is [0.050, 0.200, 0.113, 0,011], which proves that the evaluation results in this paper are basically reasonable. It can be seen that in terms of forest coverage and environmental protection areas, environmental education in higher education has the deepest impact on Russia. In terms of carbon emissions, South Africa benefits the most from environmental education in higher education, so each country should take corresponding actions according to different environmental assessment results., Competing Interests: The author declares no conflicts of interest., (Copyright © 2022 Miao Guo.)

This paper conceptualises the factors responsible for water quality change in the aquifer on the basis of a study of flow dynamics, hydro-chemical, and bacteriological processes in a gypsum, lignite, oil, and gas-rich Barmer sedimentary basin in India. The change in colouration, odour, and presence of gases in flowing wells has been analysed. The study revealed that such a change in the quality of the water is mainly occurring in those wells that are located in the transition zone between unconfined and confined parts of the same aquifer. The field and lab analyses have further revealed the presence of pyrites, other iron-rich minerals, hydrogen sulphide (H2S), carbon dioxide (CO2), and methane (CH4) gases in the aquifer. The study suggests that changes in flow dynamics, hydro-chemical equilibrium, and microbiological processes like sulphate reduction, methanogenesis, and reverse methanogenesis are responsible for such changes in groundwater quality. The change in water quality in the unconfined parts of the aquifer is mainly due to in situ pyrite dissolution, bacteria-mediated sulphate reduction, or reverse methanogenesis taking place in the nearby confined parts of the aquifer. The presence of CH4 in non-flowing wells or while drilling new wells appears to be biogenic in nature and has migrated from deeper, confined parts of the same aquifer. The overexploitation of the aquifer has changed the flow dynamics and hydro-chemical equilibrium within the aquifer, and it has prompted the migration of gases from deeper, confined parts of the same aquifer. The water quality change is more prominent in the transitional zone between unconfined and confined parts of the aquifer. [ABSTRACT FROM AUTHOR]

With the growth of industries and population, the need for energy consumption has increased, which has inevitably increased greenhouse gas emissions. Further use of fossil fuel for energy consumption exacerbates the situation making it one of the major issues for climate change. China, India, the USA, and Russia are the world's leading countries in energy consumption and emissions and are responsible for climate change. These countries account for 54% of carbon dioxide (CO2) emissions in the global environment. This paper investigates the energy consumption of China, India, the USA, and Russia and its trend in greenhouse gas emissions. Using four available datasets from 1980 to 2018 for China, India, USA, and 1992 to 2018 for Russia, we employed three advanced machine learning algorithms (support vector machine, artificial neural network, and long-short term memory) and verified its predicted capability with actual greenhouse gas emissions. The obtained results were evaluated with three statistical metrics (route mean square, mean absolute percentage error, and mean bias error). The predicted results with three machine learning algorithms were very close to actual greenhouse gas emissions. Besides, we forecasted the trend of greenhouse gas emissions in these countries from 2019 to 2023. The forecasted results with the long-short term memory model confirm an increase in CO2, methane, and Nitrous oxide (N2O) emissions in the case of China and India; in contrast, the results indicate a slowdown of CO2, methane, and N2O emissions in the USA and Russia. [ABSTRACT FROM AUTHOR]

Carbon credit is an element, which is used to aid in regulation of the amount of carbon dioxide that is being released into the air. This is the part of a global plan which has been created in an effort to reduce the global warming and its effect. This global plan works by capping the amount of total emissions that can be released by one organization. If there is a shortfall in the amount of gases that are used, there is a monetary value assigned to this shortfall and it may be traded and the trading of these carbon credits is known as Carbon Credits Trading. This research paper is focused on the Carbon Credits Trading Practices within Indian organizations. The main objective of this research paper is to explore the different carbon credits trading practices which are adopted by the Indian organizations. [ABSTRACT FROM AUTHOR]

Top-down modeling estimates are among the most reliable information available on the CO 2 fluxes of the earth system. The inadequate coverage of CO 2 observing stations over the tropical regions adds a limitation to this estimate, especially when the satellite XCO 2 is strictly screened for cloud contamination, aerosol, dust, etc. In this study, we investigated the potential benefit of a global ground-based observing station network, 17 newly proposed stations over India, and global satellite XCO 2 in reducing the uncertainty of terrestrial biospheric fluxes of Tropical Asia-Eurasia in TransCom cyclo-stationary inversion. The data from selected 80 global ground-based CO 2 observation stations, together with two additional stations from India (i.e., Cape Rama and Sinhagad) and satellite XCO 2 , helps to reduce the temperate Eurasian terrestrial flux uncertainty by 23.8%, 26.4%, and 36.2%, respectively. This further improved to 54.7% by adding the newly proposed stations over India into the inversion. By separating the Indian sub-continent from temperate Eurasia (as inspired by the heterogeneity in the terrestrial ecosystems, prevailing meteorological conditions, and the orography of this vast region), the inversion evinces the capacity of existing CO 2 observations to reduce the Indian terrestrial flux uncertainty by 20.5%. The largest benefit (70% reduction of annual mean uncertainty) for estimating Indian terrestrial fluxes could be achieved by combining these global observations with data from the newly proposed stations over India. The existing two stations from India suggest Temperate Eurasia as a mild source of CO 2 (0.33 ± 0.57 Pg C yr -1 ), albeit with prominent anthropogenic influences visible in these two stations during the dry seasons. This implies that the proposed new stations should be cautiously placed to avoid such effects. The study also finds that the newly proposed stations over India also have an impact in constraining nearby oceanic CO 2 fluxes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

This paper scrutinizes the asymmetric impact of education and education expenditure on clean energy consumption and CO2 emissions in the BRICS economies using annual data for the period 1991-2019. The analysis employs a nonlinear autoregressive distributed lag (ARDL) framework. Findings unfold that a positive change in education contributes to increasing clean energy consumption in Brazil, Russia, India, and China. This finding implies that a negative change in education contributes to reducing clean energy consumption in Brazil, Russia, and India in the long run. Nonetheless, a positive change in education expenditure increased the clean energy consumption in Brazil, Russia, and India, while it has decreased in South Africa. On the dark side, a negative change in education expenditure degrades clean energy consumption in India, China, and South Africa in the long run. The asymmetric empirical results of CO2 emissions are mixed, economy-specific, and vary across group countries in the long run. We find that the education and education expenditure has long-run asymmetric effects in BRICS industries. Thus empirical findings give us robust policy implications for BRICS economies., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)