An empirical investigation of nuclear energy and environmental pollution nexus in India: fresh evidence using NARDL approach.

The present study is aimed to explore the relationship between coal consumption, industrial production, and CO2 emissions in China and India for the period of 1971-2011. The structural break unit root test and cointegrating approach have been applied. The direction of causal relationship between the variables is investigated by applying the VECM Granger causality test. Our results validate the presence of cointegration among the series in both countries. Our results also validate the existence of inverted U-shaped curve between industrial production and CO2 emissions for India, but for China, it is a U-shaped relationship. Coal consumption adds in CO2 emissions. The causality analysis reveals that industrial production and coal consumption Granger cause CO2 emissions in India. In the case of China, the feedback effect exists between coal consumption and CO2 emissions. Due to the importance of coal in China and India, any reduction in coal consumption will negatively affect their industrial value added as well as economic growth.

PurposeThe present study is a novel attempt to measure the impact of population growth, natural resource depletion, non-renewable energy consumption, growth of national income, remittances inflow and industrial output on carbon dioxide emissions in India during the period of 1980–2018.Design/methodology/approachAutoregressive distributive lag (ARDL) is used to achieve the objective. The application of FMOLS (fully modified ordinary least squares), DOLS (dynamic ordinary least squares) and CCR (canonical cointegrating regression) techniques illustrate statistical robustness.FindingsThe long-run ARDL results confirm that increase in population, national income and energy consumption have a positive and significant impact on pollution levels in India. In contradiction to this, long run results further reveal that the increase in natural resource depletion, industrial output and remittances inflow have insignificant and negative impact on pollution levels in India. Further, the empirical findings did not find any evidence for the applicability of the environmental Kuznets curve (EKC) in India during the study period.Research limitations/implicationsThe study is confined to only a few important determinants of CO2 emissions in India. However, there is a large chunk of studies that have incorporated other determinants of CO2 emissions. Specifying a few determinants of CO2 emissions in India is itself a lacuna in the present study. Moreover, taking the time period from 1980 to 2018 is also one of the limitations of the study.Practical implicationsPlenty of research has been devoted to the causal relationship between the environment and its various determinants. However, not much attention has been paid to investigating the association between population growth, natural resource depletion, energy consumption, GDP per capita, remittances inflow, industry and carbon dioxide emissions in India. Since, CO2 emissions are one of the widely accepted and applied emissions in EKC applications, which the present study intends to test. Moreover, the study employs advanced econometric techniques including ARDL framework, FMOLS, DOLS and CRR methodologies to achieve robust results. Such an investigation will potentially allow policymakers to frame efficient environmental and fiscal policies to achieve the desired results.Originality/valueThe continuous increase of CO2 emissions in India has compelled policy makers to prioritize this issue as soon as possible and formulate national environmental policy for reducing the share of carbon dioxides emissions in climate change. The study could constitute the focus of future research.

To become the fastest-growing large economy in the world, India has set a target growth rate of 9%, reaching an economy of $5 trillion by 2024-25. It is an immense challenge to meet the growth target and keep the CO2 emissions under control. The present paper aims to discover the determinants for explaining CO2 emissions in India by conducting a complete decomposition analysis, where the residuals are fully distributed to the determinants for the country from 1990-2018. The analysis reveals that the biggest contributor to the rise in CO2 emissions in India is the expansion of the economy (scale effect). The intensity of CO2 and the change in the composition of the economy, which nearly move in tandem, also contribute to the rise in CO2 emissions, although more slowly. A declining energy intensity of the Indian economy is responsible for a considerable reduction in CO2 emissions. As a typical result for an upcoming economy, this paper did not find evidence for an environmental Kuznets curve. This implies that continued economic growth will lead to increased CO2 emissions.

As the world’s third largest carbon dioxide (CO2) emitter, India has long been believed to mostly compromise with international environmental obligation. Using annual data for the period 1970–2013, the study investigates the impact of population density, energy consumption, economic growth and trade openness on CO2 emissions in India. It applies the autoregressive distributed lag bounds testing approach to cointegration for establishing the existence of a long-run relationship and uses vector error correction model to determine the direction of causality between the variables. The results indicate that there is a meaningful long-run relationship between CO2 emissions and socioeconomic factors. We find that population density, energy consumption and economic growth have statistically significant positive effect on CO2 emissions both in the short-run and long-run. Among these three drivers, population density proves the main influencing factor of CO2 emissions changes. Therefore, a cautious population stabilization policy in the country would assist in reducing CO2 emissions and sustaining long-run economic growth. The findings further support the continued policy actions to develop the alternative energy sources such as renewable, and to use green and clean technologies to curb CO2 emissions without reducing energy consumption.

Purpose As one of the world’s fastest-growing economies, India’s energy demand is predominantly met by fossil fuels, which significantly contribute to greenhouse gas emissions. The purpose of this study is to investigate the asymmetric relationship between fossil fuel consumption (disaggregated into coal, oil and natural gas) and carbon emissions in India. Design/methodology/approach This study uses the nonlinear autoregressive distributed lag (NARDL) methodology, as proposed by Shin et al. (2014), to capture the asymmetric impacts of positive and negative shocks in fossil fuel consumption on carbon emissions. This advanced econometric approach allows for the exploration of non-linearities in the energy-emissions nexus. Findings The results reveal a significant asymmetric impact of fossil fuel consumption on carbon emissions. Negative shocks (reductions in fossil fuel use) have a more pronounced effect on emissions mitigation than positive shocks (increases in usage). Among the energy sources, oil consumption exhibits the highest emission intensity during negative shocks, underscoring its critical role in emissions dynamics. Practical implications The findings suggest the urgent need for policymakers and industry leaders to prioritise reductions in fossil fuel consumption, particularly oil, to achieve meaningful emissions mitigation. Investments in cleaner energy alternatives and the adoption of mitigation-oriented energy portfolios are essential to align with India’s net-zero emissions goal by 2070. Originality/value This study contributes to the existing literature by examining the non-linear and asymmetric relationship between fossil fuel consumption and carbon emissions in India, a perspective that has been largely overlooked in prior studies. The results provide actionable insights for policymakers to design effective strategies for sustainable energy transitions.

The study is conducted to quantify driving forces of carbon dioxide (CO₂) emissions using decomposition analysis. The paper investigates the determinants of CO₂ emissions using the Logarithmic Mean Divisia Index (LMDI) for the period 2011-2021. The objective of the paper is to find determinants of CO₂ emissions by applying LMDI, where residuals are fully distributed to the determinants. The drivers of CO₂ were divided into the population, economic activity, energy intensity, and energy structure effects. Using each fuel type's unique emission factor, CO₂ emissions were computed. Identifying the drivers responsible for CO₂ emissions in India would aid in determining the areas that require attention. The study finds that population and economic activity effects have always been positive contributors to CO₂ emissions in India. However, the contribution of the population has been positive while economic growth has made up for most of the total. The energy intensity effect and structural effect have showcased a negative trend from 2011 to 2021. Negative energy and structural effects suggest India is improving in terms of energy use.

Prospects of carbon capture and storage (CCS) in India’s power sector – An integrated assessment

This study analyses the short and long run impact of trade, energy consumption and CO2 emissions on economic growth in Brazil, India and China. The time series considered is from the period of 1978-2014. By employing ARDL bound testing approach, the long and short run effects are estimated. With Error Correction model, the findings provide that there is a short run relationship between energy and export, leading to CO2 emissions in China, there is no significant relationship between the variables and CO2 emissions in India and a short run relationship between CO2 emission and energy consumption in Brazil. The study implies that policy makers in China need to give special attention for energy sector and non-convertible energy in export sector for reduction in CO2 emissions. In India, it is essential to consider other factors that are responsible for CO2 emissions in the country. And in Brazil, the results suggest an inefficiency of the use of energy and that export oriented industries in Brazil have been diverted to convertible or renewable energy sources. DOI : 10.7176/JETP/9-7-03 Publication date :October 31 st 2019

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO2 emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide.

PurposeThe purpose of this study is to investigate the nonlinear effects of renewable energy (RE) consumption and economic growth on per capita CO2 emissions during the time span from 1980 to 2020.Design/methodology/approachThe study uses the logistic smooth transition autoregression (STAR) model to decipher the nonlinear relationship between RE consumption, economic growth and CO2 emissions in the Indian economy.FindingsThe estimated results confirm a nonlinear relationship between India’s economic growth, RE consumption and CO2 emissions. The authors found that economic growth positively impacts CO2 emissions until it reaches a specific threshold of 1.81 (per capita growth). Beyond this point, further economic growth leads to a reduction in CO2 emissions. Similarly, RE consumption positively affects CO2 emissions until economic growth reaches the same threshold level, after which an increase in RE consumption negatively impacts CO2 emissions.Research limitations/implicationsThe study suggests that India should optimize the balance between economic growth and RE consumption to mitigate CO2 emissions. Policymakers should prioritize the adoption of RE during the early stages of economic growth. As economic growth reaches the specific threshold of 1.81 per capita, the economy should shift to more sustainable and energy-efficient practices to limit the effect of further CO2 emissions on further economic growth.Originality/valueTo the best of the authors’ knowledge, this study represents the first-ever endeavor to reexamine the nonlinear relationship between RE consumption, economic growth and CO2 emissions in India, using the STAR model.

A major challenge for humans in the twenty-first century is devising a way to minimize environmental pollution while fostering economic growth that will not deplete the planet's resources. Despite increased awareness of climate change and efforts to combat it, the amount of pollution emissions on the Earth continues to drop significantly. This study employs cutting-edge econometric methods to examine the long- and short-term asymmetric and causal impacts of renewable and non-renewable energy consumption and financial development on CO2 emissions in India at both aggregate and disaggregated levels. Thus, this work fills a significant gap in research. A time series from 1965 to 2020 was used for this study. Wavelet coherence was employed to investigate causal effects among the variables, while the NARDL model addressed long-run and short-run asymmetry effects. Our findings indicate that (i) REC, NREC, FD, and CO2 emissions are all interconnected in the long run, (ii) NREC and FD significantly trigger CO2 emissions in India in the long run, and (iii) the results of a wavelet coherence-based causality test support the long-term estimates of this study.

Purpose: The purpose of this article is to investigate the impact of economic development on the environment. Using time series data from 1991 to 2019, GDP per capita and CO2 emissions are utilised as indicators of economic development and environmental degradation, respectively.
 Design/Methodology/Approach: PP and ADF unit root tests verify variable stationarity. Econometric study using Johansen cointegration test and vector error correction model (VECM) and residual stability model.
 Findings: Study revealed that long-run association among Carbon dioxide emissions, gross domestic product per capita, and industrial Value added. Carbon dioxide emissions increased with the rised in the Gross domestic product per capita. GDP per capita was outcomes to be unfavorably related to Carbon dioxide emissions in India.
 Research Limitations/Implications: This study was suggest that environmental degradation can be reduced at the cost of economic development. Energy-efficient technologies should be encouraged to enhance the domestic product with the help of the industrial sector by improving environmentally friendly technologies from advanced economies. This study suggests the need for environmental policies to reduce emissions during periods of economic growth.
 Originality/Value: This paper used new econometric methods to study the relationship between economic development and environmental degradation in India year 1991 to 2019.

PurposeThe present study tries to examine the relationship between financial inclusion and environmental quality as proxied by carbon emissions in India covering the period from 2008 to 2018.Design/methodology/approachA financial inclusion index has been composed using principal component analysis (PCA) based on three dimensions: access, penetration and usage. After testing for stationarity of the data, the authors adopted the autoregressive distributive lag model (ARDL) methodology.FindingsThe study found that financial inclusion and growth lead to increased carbon emissions in India and the government must resort to greener policies, whereas empirical results support that globalization reduced the pollutants emissions in both the long term and short period in India.Practical implicationsBased on the results, several policy prescriptions are rendered for policymakers: (1) need to move toward greener energy policies and (2) enhance the awareness of green financing instruments such as green bonds in India. Therefore, policymakers should be more proactive in accepting green and sustainable financial alternatives.Originality/valueThe present study contributes to the scant literature on the financial inclusion–emission nexus in India. This study considers three inclusion parameters that are not present in previous studies.

The article investigates the co-integrating relation between carbon dioxide (CO2) emissions and economic growth of India and China: the two emerging markets of Asia. The article also tries to test the functional form of EKC as applicable to India and China. The study takes annual data for the 55 year period from 1960–2014 and the variables included are GDP per capita (a proxy for economic growth) and CO2 emissions per capita (a proxy for the environmental degradation). The co-integrating relation has been tested using Johansen (1988) co-integration test which is supplemented with VAR-VECM Model at p-1 lags with error correction mechanism showing adjustment between short- and long-run equilibrium. The diagnostics include stationarity, parameter stability and structural breaks (using Chow breakeven test & CUSUM plots) and serial correlation (using VEC-LM test). The empirical results showed long-term co-integration between GDP and CO2 emissions for India but not for China with lagged ECM term for India with a value of 0.0466 showed that the 4 per cent of the dis-equilibrium be corrected in same year itself. The stationary test showed that both our variables were stationary either at I(1) or I(2) but none of these were at levels. The null of serial correlation showed no serial correlation. The Chow and Stability tests revealed that for CO2 emissions in India, there was a break in 2009. VEC Granger causality tests showed that there was uni-directional causality for India flowing from GDP to CO2 emissions. EKC functional form of Cubic representation was proved for China where the curve was found to be ‘N’ shaped but EKC could not be proved in case of India.

Nuclear energy consumption and CO2 emissions in India: Evidence from Fourier ARDL bounds test approach