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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.)

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.)

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.)

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.)

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.)

The main objective of this paper is to look at how environmental degradation in the form of climate change and air pollution affect international tourism for five countries such as Brazil, Russia, India, China, and South Africa (BRICS) over the years 1990-2019. Other independent variables include information and communication technology (ICT) and democracy. We also look at the role of environmental regulation to see the validity of porter hypothesis in the tourism sector. To achieve this objective, we apply a novel method of moments quantile regression approach as well as a robust causality technique. The result shows that at lower and medium quantile, CO 2 emission has positive impact on tourism while at higher quantile, CO 2 emission has negative but insignificant effect on tourism in BRICS countries. The result for PM2.5 is uniform across all the quantiles, showing the negative effect on tourism. ICT and human capital positively affect the tourism while democracy has negative impact on the tourism sector of the BRICS nations. The result also validated the Porter hypothesis for tourism sector. We conclude that tourism industry stakeholders and the environmental policymakers must work together to integrate tourism policies with BRICS countries' environmental conservation policies as part of the transition to sustainable tourism industry., (© 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.)

This paper presents the progress made by India and China in the field of waste management and its disposal since l996 to 2020. To access it, bibliometric analysis has been carried out using SCOPUS linked SCImago electronic database. Different bibliometric indicators such as documents, citable documents, external and self-citations, and external and self-citations per document along with their annual corresponding growth (ACG) have been calculated in order to explore the progresses made in both the countries. China has contributed 16.3% and India 4.3% to the total documents produced worldwide during 1996-2020 in waste management with the average ACG of 26.42% and 15.37%, respectively, during the same time span. Also, the average ACG for self-citations of published research documents is more than that of external citations for both the countries. In addition to that, the average self-citation magnitude reported for China (28,475) is more than that of India (5223), whereas this trend reverses in the case of average external citation per document with values 16.9 and 28.94, respectively. Moreover, the waste management practices being followed in both countries have also been compared and also presented so that it could be beneficial for the country which is relatively not good in the same., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Poverty eradication and environmental degradation are the two crucial challenges, and they are highly interlinked in the modern era. However, countries are still more emphasized in attaining poverty alleviation and alleviating environmental pollution which require enormous attention. Our study is a novel attempt to scrutinize the effect of poverty on carbon dioxide (CO 2 ) emissions for India and China over the sample period 1987-2019. Findings attained from the ARDL model suggest that a rise in poverty contributes to growing CO 2 emissions in India only in short run. Further, findings show that poverty is the principal source of pollution in the long term in India and China. Empirical results proposed focal policy implications in the light of sustainable development goals for India and China., (© 2021. 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.)

Fertilizer, though one of the most essential inputs for increasing agricultural production, is a leading cause of nitrous oxide emissions from agriculture, contributing significantly to global warming. Therefore, understanding factors affecting farmers' use of fertilizers is crucial to develop strategies to improve its efficient use and to minimize its negative impacts. Using data from 2528 households across the Indo-Gangetic Plains in India, Nepal, and Bangladesh, this study examines the factors affecting farmers' use of organic and inorganic fertilizers for the two most important cereal crops - rice and wheat. Together, these crops provide the bulk of calories consumed in the region. As nitrogen (N) fertilizer is the major source of global warming and other environmental effects, we also examine the factors contributing to its overuse. We applied multiple regression models to understand the factors influencing the use of inorganic fertilizer, Heckman models to understand the likelihood and intensity of organic fertilizer (manure) use, and a probit model to examine the over-use of N fertilizer. Our results indicate that various socio-economic and geographical factors influence the use of organic and inorganic fertilizers in rice and wheat. Across the study sites, N fertilizer over-use is the highest in Haryana (India) and the lowest in Nepal. Across all locations, farmers reported a decline in manure application, concomitant with a lack of awareness of the principles of appropriate fertilizer management that can limit environmental externalities. Educational programs highlighting measures to improving nutrient-use-efficiency and reducing the negative externalities of N fertilizer over-use are proposed to address these problems., (© 2021. The Author(s).)

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.

The objective of this paper is to analyze structural characteristics and the evolution of "international trade-carbon emissions" network in the equipment manufacturing industry. To do so, we construct four kinds of networks, namely export trade network (E network), export implied carbon emission network (EC network), foreign value-added network (FVA network), and foreign value-added implied carbon emission network (FVAC network) for the said industry. The data obtained from world-input-output table and social network analysis method was used for empirical analysis. The results indicate that (1) the four types of networks show a trend of denseness, which also has the characteristics of trade clustering effect and small-world network. As the equipment manufacturing industry in various countries is increasingly embedded in the global value chain, the value chain is extended globally, which leads to the complexity of the international carbon transfer network. (2) The four types of networks' pattern are basically stable. Germany plays an important intermediary role. Whereas China is deeply involved in the division of global value chain and has achieved the shift from the edge to the core in all four types of networks, but there is still a big gap compared with Germany. (3) The E network and EC network have the obvious core-edge feature, and the two kinds of networks can be divided into developed countries block represented by Germany, Japan, the USA; developing countries block represented by China and India; and European Union block represented by Spain and France. The developing world receives carbon emission transfer from the developed world. Finally, (4) the division of FVA network and FVAC network segment shows that the global value chain under the current international division of labor is characterized by multi-regionalization. Therefore, actively embedding in the regional value chain has become an important way for China's equipment manufacturing industry to upgrade to low carbon.

This study explores the relationship between remittances received, education expenditure, energy use, income, poverty, and economic growth for a panel of the nine selected remittance-receiving countries (Bangladesh, China, Egypt, India, Indonesia, Mexico, Nigeria, Pakistan, and Philippines). World Development Indicator database is used for retrieving data from the period of 1990 to 2014. Panel cointegration technique is used to test the long-run relationship among studied variables. Furthermore, the autoregressive distributive lag (ARDL) model is applied to confirm the presence of a long-run and short-run relationship. The findings of the ARDL model indicate that remittances received positively influence economic growth, and there is a significant relationship between remittances received and economic growth during the long-run. Education expenditure, energy use, and income also positively and significantly impact economic growth during the long-run. In contrast, final household consumption used in this study as a proxy of poverty showed a significant negative effect on economic growth during the long-run, which indicates that increasing poverty will reduce economic growth; on the other hand, reducing poverty will boost economic growth in the selected countries during the long-run.

This paper aims to define the effects of military expenses and renewable energy consumption on carbon dioxide emissions for the ten countries with the highest military expenses, namely, Saudi Arabia, Israel, Russia, the USA, South Korea, India, France, Australia, China, and the UK from 1993 to 2017. The research applied the common correlated effects mean group estimator (CCEMG), dynamic CCEMG, and cross-sectional augmented autoregressive distributed lag (CS-ARDL) approaches. These dynamic techniques elucidate slope heterogeneity and cross-sectional dependency and solve the problem of unit root bias. It is found that the environmental Kuznets curve (EKC) hypothesis does not apply for this region. The findings demonstrate that military expenses increase carbon dioxide emissions; thus, the treadmill theory of destruction is valid for the panel of these countries, and it is also found that the consumption of sustainable energy decreases CO2 emissions. This suggests that a reduction in pollution can be achieved by increasing sustainable energies in the use of military vehicles to decrease emissions. Further important policy implications for the 10 countries with the highest military expenses are provided at the end of the paper.

Energy stocks have become an essential segment of the investment portfolios of both households and institutional investors. This study investigates the dynamic aspect of evolving weak-form efficiency in six energy stock markets: those of the United States (US), Canada, China, Australia, India, and Saudi Arabia. The generalized autoregressive conditionally heteroskedastic in the mean GARCH-M(1,1) method is applied, alongside the state-space time-varying approaches with the Kalman filter estimation, to detect the evolving efficiency for periods ending in November 2019. The empirical results reveal that the studied markets undergo various extents of time-varying efficiency, containing periods of efficiency enhancement as well as periods of deviation from efficiency. Meanwhile, the 2007-2009 global financial crisis and the 2015 changes in the energy sector-in addition to other contemporaneous crises-have a profound influence on the timeline of market efficiency evolution. Overall, all of the markets gradually became more efficient, apart from India's energy market as a result of the current energy crisis in India. Amid the energy markets explored in this study, the US energy market was found to be the most efficient.

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 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.)

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.)

Vegetation dynamics is an important aspect for determining climate change trends. The present study delineates to examine spatiotemporal changes of vegetation cover in Pindari valley (Kumaun Himalaya) from the 1972 to 2018 timeline. The study includes the calculation of vegetation spectral indices of normalized vegetation index (NDVI), extraction of different vegetation classes, and statistical analysis of the Mann-Kendall (MK) test on historical metrological data (especially precipitation and temperature) of the study site. For the statistical analysis of metrological data, the power data access viewer datasets have been used. The central feature classes of the study are grassland, scrubland, and forest cover. The results revealed that the region's forest cover significantly decreased by 24.74 sq. km from 1972 to 2018, increased in grassland cover by 17.84 sq. km, respectively, and a slight increase in scrubland class by 3.13 sq. km for the study period. The calculated NDVI shows significant changes over the study location; it has been noticed that the maximum values of the NDVI decreased by 0.24, and the minimum values show growth of about 0.047. The analysis indicates that climatic parameters such as precipitation and temperature are the main limiting factors affecting vegetation growth. The annual mean maximum temperature showed a decreasing trend. The estimated results show an increase in annual rainfall and annual minimum temperature, while the decreasing trend is observed in the case of maximum annual temperature. Objectives of the study are (1) spatiotemporal analysis of the vegetation cover, (2) identification of the main causes of change in the vegetation cover, and (3) statistical trend analysis of long-term metrological data. The outcome of the presented research work would be beneficial for the proper management and monitoring of the forest ecosystem., (© 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.)

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.)

Global warming and food security have led to increasing concern about agricultural crop production efficiency, especially wheat and rice farming. The purpose of the current study is to measure wheat and rice production efficiency scores with environmental quality in China, India, and Pakistan by using a data envelopment analysis (DEA) model. The DEA results show that China and India are more efficient in wheat and rice production but it is not efficient in the environment in the study period. The results also show that Pakistan has also relatively small wheat and rice efficiency compared with China and India and increased the efficiency with the passage of time. The practical outcomes also show that Pakistan has the most efficient and effective states from the periods 2008 to 2019 in terms of wheat and rice efficiency and also a small increase in carbon emission. Based on the findings, policymakers should pay attention to the role of green technology in reducing agricultural CO2 emissions., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study examines links between Morgan and Stanley capital Investment (MSCI), foreign direct investment (FDI), renewable energy, urbanization, and trade openness on environmental degradation in (Brazil, Russia, India, China, South Africa) BRICS countries. In this study, generalized method of moment (GMM) estimation is applied on a data set ranging from 1993 to 2018. Results illustrate that stock market index price (MSCI) has negative relationship on CO 2 emissions in India, China, Russia, and South Africa and has positive relationship in Brazil. One possible reason for this is strong environmental regulations and their enforcement by Brazilian government. The study also finds that trade openness, FDI, and urbanization have a significant positive relationship on environmental degradation. The impact of stock market development on environmental degradation varies among BRICS countries. Our outcomes have significant policy implications. For example, the policy makers have to initiate effective strategies to promote the renewable energy sources to meet the increasing demand for energy by replacing the use of conventional energy such as coal, gas, and oil. This will help to reduce the CO 2 emissions from fossil fuel and ensure sustainable stock market development in the BRICS nations. BRICS countries who have taken the initiative and formulated policies for businesses to conserve the environment play a positive role compared to those who do not.

To recover the global economy, China in 2013 called for a new global strategy, namely, "One Belt and One Road Initiative" (BRI), which aims at reinforcing regional economic cooperation, enhancing regional collaboration of economic policy, and realizing the goal of rapid economic development of member countries. Accelerating industrialization not only has been recognized as an effective way to stimulate economic development, but also lead to the serious issue of environmental pollution, which challenges the environmental sustainability. In this study, we focus on the industrializing region as a study area to investigate the driving factors of environmental pollution. Technically, we utilized satellite observation technique to obtain NO 2 columns data to denote environmental pollution and then applied dynamic spatial panel data models to evaluate what affects NO 2 pollution levels. The findings are the following. (1) NO 2 pollution exhibits significant and positive spatial autocorrelation, indicating spatial spillovers of NO 2 pollution. (2) Lebanon, Bangladesh, Kyrgyzstan, and India experienced the largest increase of NO 2 pollution while NO 2 pollution in Singapore, Hungary, Greece, and Ukraine was substantially reduced. (3) The results of the dynamic spatial panel data models show that both the time dynamics effects and the spatial spillover effects are found to be significant and positive. In other words, both effects should be considered. Population is the foremost contributor to increase NO 2 pollution while urbanization is an effective way to reduce pollution. An EKC relationship between NO 2 pollution and per capita income was verified. Besides, industrialization, foreign direct investment, and trade openness have positive impacts on NO 2 pollution.

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.

Traditional medicine (TM) also known as folk medicine consists of medical knowledge systems that were developed over generations in various countries before the era of modern medicine. Over the last few years, there has been a shift in universal trend from modern medicine to TM because it is believed that these medicines are non-toxic to humans, have little to no side-effects, are readily available and affordable. The purpose of this study was to investigate the essential and non-essential elemental concentrations of ayurvedic medicines (AM), Ghanaian traditional medicines (GTM) and Chinese traditional medicines (CTM). The results indicate that the mean elemental concentrations in TM varied greatly. The mean concentrations of copper and mercury in AM, GTM and CTM ranged from 4 to 45 mg/kg and 0.01 to 2 mg/kg, respectively. Multivariate statistical analyses applied to recognise inter-element associations indicated that at 0.05 significant level, there was a positive correlation between elements suggesting they may have originated from the same source. The study concludes by highlighting the need for monitoring of the elemental concentrations in commonly consumed traditional medicines regularly to detect contamination in these medicines since the occurrence of metals in these medicines above or below legally permissible limits can be harmful to consumers.

This paper provides an assessment of energy density and energy efficiency and creates an important indicator of environmental performance. This article applied two mathematical models and econometric techniques to obtain detailed and specific results. The DEA and the non-normative account aggregation mean a collective aggregation to form a mathematical aggregation tool to create an environmental index for the BRICS countries (Brazil, Russia, India, China, and South Africa) based on available data from 2011 to 2016. The advantage of the proposed approach is to manage the irregularities of the data and follow the desired properties of the index number. The current paper is relevant for the broad scope of construction, the environmental index, and the evolution of the rankings of countries based on multiple indicators. Our results indicate that Brazil and Russia have the highest values of the Environmental Performance Index, which range between 67.44 and 60.70, respectively. India has a minimum value of 30.57 of the environmental index. The analysis shows that Brazil, Russia, and South Africa have the best scores and that these countries have the best results, while China and India also have the best results. This study can help form a valuable political tool for the development and development of the country's politics.

Heavy metal contamination of river water has become a major issue affecting environmental and human health worldwide. This study makes an effort to observe the heavy metal incidence of River Brahmaputra, India, and its river restoration assessment for 2 years, 2017-2018 and 2018-2019. River Brahmaputra is the second longest and a major transboundary river of the South Asian region, which transcends the political boundaries of China, India and Bangladesh. The Pearson correlation analysis was used to analyse the interrelationships between physico-chemical parameters (which was also analysed during our study) and heavy metals. Heavy metal pollution index and multivariate analyses such as hierarchical cluster analysis and principal component analysis were used to establish our scientific observations. An assessment involving people's willingness to pay for river restoration was also done. Arsenic (As), lead (Pb), antimony (Sb), nickel (Ni), chromium (Cr), mercury (Hg) and cadmium (Cd) were monitored in three stations, namely, Dibrugarh Town (Tinkunia Ghat), Guwahati City (Bharalumukh) and Dhubri Town (Jahajghat), for the periods 2017-2018 and 2018-2019. The overall mean concentration was in the order Hg > Ni > Cr > Pb > Cd > As > Sb. Heavy metal pollution index was found in the order station 2 (344) > station 3 (320) > station 1 (317). Results clearly indicate the presence of heavy metal pollution in the Brahmaputra river, with very high levels in Guwahati City. Concentrations of Ni, Cr, Hg and Cd were found to be very high while As presence was marginally high. Concentration of Pb and Sb were nearly the prescribed World Health Organization and Bureau of Indian Standards limits. Multivariate analyses suggest that the heavy metal pollution is linked to similar sources in all the sampled sites. Further, the river restoration assessment revealed that citizens are willing to pay an average of INR 22.6 (USD 0.30) for restoring the water quality. Therefore, given the high levels of heavy metal contamination witnessed in the Brahmaputra river and citizens willingness to contribute towards restoring its water quality, the need for an appropriate river restoration strategy and water policy is strongly felt as an outcome of this study.

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.)

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.)

The role of government size and digitization in the process of environmental quality is a matter of considerable debate in the field of environmental economics. BRICS economies have suffered from environmental pollution. This paper scrutinizes that how CO 2 emissions respond to government size and digitization in BRICS economies. Empirical estimates of the ARDL approach show that government size has a positive impact on CO 2 in Brazil, India, and China, while negative impact on CO 2 in Russia in the long run. The long-run estimates reveal a negative and significant effect of digitization on CO 2 in Brazil, India, and China. Education and e-learning activities have a favorable and crucial role played in environmental quality in Brazil, India, and China. Based on these findings, BRICS authorities should improve the efficiency of government expenditures and invest more in digitization to improve the quality of the environment., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Waste foundry sand (WFS) is the by-product of the foundry industry, which is produced about 0.6 tons per 1 ton of foundry industry production. While it cannot be recycled or reused, it will be disposed of in landfills. Today, with increasing attention to environmental issues, the reuse and recycle of materials because of limited resources have been considered. Due to the desirable properties of WFS, many studies have been done on the properties and performance of its use in various industries, especially in concrete technology. The purpose of this paper is to conduct a bibliometric analysis of foundry sand research during the years 1971-2020. Various aspects, such as document types, languages, major journals, key countries, authors, and keywords, have been examined. The collaborations among authors and countries were constructed, visualized, and evaluated through the application of the social network analysis method based on co-authorship relations. Also, keyword cluster analysis has been performed using co-occurrence relations to discover the most prominent issues related to WFS. The results showed that the number of publications (TP) has improved significantly in recent years, especially in 2018-2020. It has increased from 1 in 1971 to 38 in 2020. The engineering subdivision with 33% has had the highest number of papers. Also, India, China, and the USA have the highest number of publications, respectively. Statistical data from the author keyword study showed that in general, papers can be classified into three categories in terms of subject: first, characteristics of WFS and its importance; second, the use of WFS in concrete and the study of mechanical and physical properties as one of its applications; and the third, investigation on environmental effects and damage caused by disposal and landfill of WFS and efforts to find solutions for recycling and reuse. The keywords "Waste Foundry Sand," "Foundry Sand," and "Compressive Strength" with a growth rate of 2700%, 1900%, and 1100%, respectively, are important topics in the field of WFS research., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study examines the relationship between FDI and CO 2 emissions in a nonlinear framework using data from countries along the "One Belt, One Road" from 1979 to 2017 as the sample. First, the linear analysis method was used to examine the stability of FDI and CO 2 emissions in countries along the "One Belt, One Road." In addition, the BDS (Brock-Dechert-Scheinkman test) method and nonlinear Granger causality test are used to investigate the nonlinear relationship between variables. Finally, a threshold vector autoregressive model (TVAR) is used to analyze the dynamic change mechanism between China's FDI and CO 2 emissions, and a threshold vector error correction model (TVECM) is used to test how the variables respond to deviations from equilibrium. Then, the Markov switching model is used to test the robustness of the results. The research results show that China, India, South Africa, and other countries all have a nonlinear causal relationship between FDI and CO 2 emissions. At the same time, the comovement of FDI and CO 2 emissions in China has obvious structural break features, which are relevant for the underlying regime. Furthermore, the results also show that the adjustment process of the FDI toward equilibrium is highly persistent in the first regime, and CO 2 emissions will adjust to an equilibrium state at a faster speed in the second regime. Therefore, this paper puts forward different policy suggestions for different countries. For China, we should pay attention to the long-term benefits of FDI and introduce high-tech green FDI.

As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology.

This paper illustrates the direct and indirect effects of democracy on CO 2 emissions in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 1992 to 2018. In view of the distribution heterogeneity of CO 2 emissions, the panel quantile regression model is especially used to explore the nexus among different variables. Furthermore, in order to predict the trends of CO 2 emissions in different countries, we also estimate the kernel density function of CO 2 emissions in the BRICS countries by the quantile-fitted values. The results indicate that the direct impact of democracy on carbon dioxide emissions is significantly negative and great at high-emission countries. Although the indirect effect of democracy is positive in China and negative in Brazil and South Africa, the total effect of democracy on CO 2 emissions remains negative in all BRICS countries. The estimation of kernel density function shows that the distribution of CO 2 emissions in each country is gradually concentrated. Moreover, there is an environmental Kuznets curve depicting the linkage of urbanization and carbon dioxide emissions in Brazil and South Africa. These findings further highlight that the impact of democracy on high-emission and low-emission countries should be taken into account in policymaking to achieve sustainable developments.

In this paper, we investigated the association between CO 2 emissions, remittances, income, energy use, and foreign direct investment (FDI) through panel cointegration analysis, common correlated effect mean group (CCEMG), fully modified least squares (FM-LS) estimates, and panel causality test for the Brazil, Russia, India, China, and South Africa (BRICS) economies over 1986-2016. The empirical outcomes of CCEMG and FM-LS clarified that remittance inflows into BRICS region are contributing to environmental degradation. Similarly, results in the case of Brazil, Russia, and China supported the remittances-led emission hypothesis. On the other hand, different results have been reached in India, thus the remittances; inflows have dictated that they are helping to combat CO 2 emission. Furthermore, FDI has a positive and significant sign for BRICS economies. This relationship narrated that the FDI inflows to this region increase the CO 2 emission, hence obeying the pollution haven hypothesis. Finally, energy use also supports the energy-led emanation phenomenon in BRICS panel and alongside the countries. The panel causality test confirms the bidirectional linkage between remittances and CO 2 emissions.

In this paper, we revisit the environmental Kuznets curve (EKC) hypothesis by using estimations that account for cross-sectional dependency (CSD) and asymmetry effect in 76 countries for the period 1971-2014. Our results lend moderate support to the EKC hypothesis. The country-specific results unfold that a total of 16 out of 76 countries support the EKC hypothesis using CCEMG estimator. Results from AMG reveal that the EKC hypothesis holds in 24 out of 76 countries. It is worth highlighting that 11 countries (Australia, China, Congo Dem. Rep., Costa Rica, Gabon, Hong Kong, India, Korea, Myanmar, Turkey, and Uruguay) exhibit an inverted U-shaped curve regardless of whether CCEMG or AMG is used. The asymmetry analysis using PMG is also able to support the EKC hypothesis. We conclude that the EKC hypothesis does not fit all countries. Policy implication and recommendation in designing appropriate energy and economic policies are provided.

Chemical sector contributes extensively to the economic development of countries, however, it is one of the main water-consuming industries. Considering that the corporate water accounting along complete SCs can help companies in improving their sustainable water managements along their value chains, this paper aims to study the water consumption in the entire supply chain of the chemical industry from a multi-regional input-output perspective. In this regard, six Asian countries including Indonesia, Taiwan, China, Japan, South Korea and India are selected to be studied based upon the availability of their data. In the following, the direct water consumed by themselves and the indirect water used by their suppliers are measured for each country using input-output analysis method. Moreover, to draw on the conclusions of selected Asian countries, the amount of consumed water in their supply chains is compared with each other using one-way analysis of variance method. The results from input-output analysis show that the indirect water consumption in the chemical SCs of Indonesia, China, India and South Korea are 20.66, 4.62, 1.37 and 1.08 times greater than their direct water use, while the indirect water consumption for Japan and Taiwan are 0.73 and 0.13 times less than their direct values. The final results from one-way analysis of variance indicate that direct and indirect amount of water consumptions in the chemical supply chains of the selected countries do not follow the same trend over 15 years. However, India and China are the top two countries in terms of both direct and indirect amounts of water consumption due to their higher population. This study presents valuable information for authorities and policymakers in terms of proper water consumption management in chemical industry and other industrial sectors. In this matter, technology-based or production-related actions are beneficiary for affecting direct water use, while consumption-based or end-use actions are more appropriate for affecting indirect water consumption.

The oxidation of sulfide ores is a common phenomenon. To better understand the current development and status of oxidation studies of sulfide ores (OSSO), a bibliometric analysis of OSSO was conducted by mapping the spatiotemporal distribution of the knowledge domain and the research focus using VOSviewer and Citespace tools. The data were derived from the Web of Science (WOS) core collection database from 2000 to 2018. Study emphases covered publication outputs, countries/regions, organizations, top journals, research focus and keyword co-occurrence network, and theme development. The results include the following findings: (1) The line of the 3-year moving average of publications (3-year MAP), h-index (3-year MAH), and authors (3-year MAA) increased from 2001 to 2018. Conversely, the h-index continuously declined. (2) Asia had the most publications, with 1052, followed by Europe, with 923, and America, with 767. China, the USA, and Australia are the most active countries. (3) The top 10 organizations with the most publications are five Chinese organizations and one organization from each of the following countries: Russia, Australia, France, and India. (4) Hydrometallurgy, Minerals Engineering, Ore Geology Reviews, Geochimica Et Cosmochimica Acta, and Economic Geology are among the top 10 journals that researchers are most concerned about. (5) Cooperation among different organization or different countries is the most effective way to produce the most influential papers. (6) The OSSO is still focused on the process of oxidation by using different methods and techniques. In future work, it is necessary to progress new methods to understand the process of self-heating and prevent spontaneous combustion disaster of sulfide ore which result from OSSO.

The related literature reveal that the papers on environmental pollution do not sufficiently analyse the cement production which is an important determinant of air pollution and health problems by using econometric methods. To fill this gap, this paper aims to examine the relationship between cement production, air pollution, mortality rate, and economic growth by employing MS Bayesian Vector Autoregressive (MScBVAR) and Markov Switching Bayesian Granger causality (MScBGC) approaches from 1960 to 2017 for China, Brazil, India, Turkey and the USA. MSIA(2)-BVAR(1) model for China, MSIAH(2)-BVAR(3) models for India, MSIAH(3)-BVAR(2) for Brazil, and MSIAH(3)-BVAR(1) for Turkey, and MSIAH(2)-BVAR(2) for the USA were selected. The MScBGC results revealed that the cement production is granger cause of mortality rate, air pollution, and economic growth in all regimes for China, India, Brazil, Turkey, and the USA.

In order to explore the research trends and hotspots related to the treatment of heavy metals in wastewater by graphene and its composites, this study collected information on 511 publications from the Science Citation Index-Expanded (SCIE) and conducted a quantitative and visual analysis. The article on the adsorption of heavy metals in wastewater by graphene and its composites first appeared in 2006 and continued to grow since 2011. It broke through 100 articles for the first time in 2016 and the overall trend is on the rise. The 511 articles were published in 185 journals, of which RSC Advances is the most dynamic journal, and Journal of Materials Chemistry A is the most authoritative journal. Asian authors published about 87.14% of papers, and China, Iran, and India played a leading role compared with other countries. The University of Chinese Academy of Sciences is the largest research institution for the adsorption of heavy metals in wastewater by graphene and its composites. Hot keywords are "heavy metal removal," "water sample," "recent advance," "reusable adsorbent," "graphene-based material," and "composite nanosheet." Combined with keywords and cluster analysis, the chemical modification of graphene oxide has become a hot research direction for graphene materials to remove heavy metals from wastewater. Among them, MnFe 2 O 4 -graphene composite is a hot spot for graphene modification. In general, HNO 3 , HCl, and EDTA are desorption reagents for graphene and its composites.

For the last three decades, both China and India are considered as the largest emerging market economies in the world. Both of these economies play an essential role in the global economy in terms of economic output and CO 2 emissions. Hence, these countries are expected to play an important role in setting up environmental and sustainable development policies. Therefore, our paper aims to examine the role of natural gas and renewable energy consumptions on CO 2 emissions and economic growth during 1965-2016 within a multivariate framework. The autoregressive distributed lag bounds testing approach to cointegration and vector error correction model (VECM) is employed to explore the long-run and causal nexus among the natural gas consumption, renewable energy consumption, coal and petroleum consumption, CO 2 emissions, and economic growth, respectively. The empirical results show existence of long-run equilibrium association among the variables. The Granger causality results indicate that the short-run bidirectional causality between renewable energy consumption and economic growth in India, while no causality is found between these two variables in China. However, natural gas consumption causes economic growth in China whereas no causality is confirmed in India in the short-run. The findings further suggest that there is long-run bidirectional causality among the considered variables in both countries. Our paper addresses several important policy implications.

Straw burning has become a hot topic in recent years as it poses a great risk not only to the lung health of residents in exposed areas but also to large-scale haze events. In order to have a more comprehensive understanding of straw burning research, based on the bibliometric analysis of Science Citation Index Expanded from Web of Science, the research progress of straw burning pollution from 1972 to 2016 and the future research trends were carried out in this paper. The research focuses on the document type, language, publication year, times cited and its output characteristics, subject category, journal, national and institutional distribution, author, etc. The results show that the study of straw burning pollution has shown a significant increase over the past 45 years. A total of 813 publications were found, and English was the most commonly used language. Articles were the most frequently appeared document types, and the researches were strongly embracing with the top 3 popular subject categories of "environmental sciences and ecology," "agriculture," and "meteorology and atmospheric sciences." We identified that the major journals publishing straw burning pollution research were Atmospheric Environment, followed by Atmospheric Chemistry and Physics. China as a leader in paper quantity played an important role in the research field of straw burning pollution, while the USA and India were located in the second and third positions. The most productive institution was Chinese Academy of Sciences, followed by Peking University and University Arkansas. Based on our analysis and the consideration of current environmental problems, more studies should focus on the following three aspects in the future: driving mechanism of emission characteristics, construction of high-resolution emission inventories, and the influencing mechanism of straw burning pollutants on climate change and human health. Our analysis and prospects can be served as a useful reference for future studies.

In a neo-classical aggregate production and Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) modeling framework, the paper attempts to explore the relationship between disaggregated energy consumption, economic growth, and carbon dioxide emissions in case of five emerging market economies-Brazil, Russia, China, India, and South Africa (BRICS) over the period 1992 to 2016. The study applied the robust unit root, cointegration, and long-run elasticity estimation methods like Pooled Mean Group and differenced panel generalized method of moments for empirical exercise. Having detected the panel heterogeneity and cross-sectional dependence, the cointegration tests documented the evidence of a long-run association among the variables. In the long-run, capital, labor, and non-renewable energy consumption are found to affect the economic growth positively. On the contrary, the impact of renewable energy consumption on the economic growth is found be positive but statistically insignificant. Moreover, population, per-capita income, and non-renewable energy consumption are found to increase the emissions whereas renewable energy consumption decreases them. Therefore, along with a proper emissions controls, BRICS countries should design and implement effective support policies so as to ensure the economic growth along with environmental sustainability.

This paper empirically examines the effects of urbanization and income inequality on CO 2 emissions in the BRICS economies (i.e., Brazil, Russia, India, China, and South Africa) during the periods 1994-2013. The method we used is the panel quantile regression, which takes into account the unobserved individual heterogeneity and distributional heterogeneity. Our empirical results indicate that urbanization has a significant and negative impact on carbon emissions, except in the 80 th , 90 th , and 95 th quantiles. We also quantitatively investigate the direct and indirect effect of urbanization on carbon emissions, and the results show that we may underestimate urbanization's effect on carbon emissions if we ignore its indirect effect. In addition, in middle- and high-emission countries, income inequality has a significant and positive impact on carbon emissions. The results of our study indicate that in the BRICS economies, there is an inverted U-shaped environmental Kuznets curve (EKC) between the GDP per capita and carbon emissions. The conclusions of this study have important policy implications for policymakers. Policymakers should try to narrow the income gap between the rich and the poor to improve environmental quality; the BRICS economies can speed up urbanization to reduce carbon emissions, but they must improve energy efficiency and use clean energy to the greatest extent in the process.