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Meeting energy needs and ensuring ecological sustainability are important tasks for policymakers. In particular, changing the energy mix used in daily electricity generation can have a major effect on ecological conditions, and this is a previously neglected research topic. Therefore, this paper aims to investigate the influence of daily electricity generation from nuclear power and renewables on the achievement of carbon neutrality targets in the four largest nuclear power countries (i.e., the U.S., China, France, and Russia). The study applies the wavelet-transform coherence (WTC), quantile-on-quantile regression (QQR), and Granger causality in quantiles (GCQ) approaches for the period from January 2, 2019 to December 29, 2022. The outcomes present that electricity generation from nuclear energy reduces carbon (CO 2) emissions only in Russia, while hydropower generation does not contribute to CO 2 emission reduction in any country. Solar electricity generation decreases CO 2 emissions in different quantiles in the U.S. and Russia, while electricity generation from wind power reduces CO 2 emissions in the U.S. and France. The empirical outcomes also reveal that expanding solar, wind, and nuclear power in the energy mix can enhance the potential of the U.S., France, and Russia to achieve carbon neutrality and secure a sustainable future. However, China is using nuclear and renewables inefficiently regarding carbon neutrality. Therefore, the empirical evidence for China is highly controversial compared to former studies. [Display omitted] • The study examines renewable and nuclear electricity effect on carbon neutrality. • The study focuses on top four major nuclear power countries. • The study applies nonlinear approaches by using high-frequency (daily) data. • Renewable and nuclear electricity have time, frequency, & quantile-varying effect. • The best electricity source change according to each country. [ABSTRACT FROM AUTHOR]

The accurate and reasonable prediction of natural gas consumption is important for enhancing national energy security and alleviating the environmental problems caused by energy consumption. Starting with the traditional nonlinear grey Bernoulli model, in this paper, we expand the development coefficient a and grey action quantity b, propose a new self-adaptive time-varying grey Bernoulli prediction model, deduce the time response formula of the model, and explore the relationship between model parameters and model accuracy. Then, the nonlinear parameter and power parameter coefficient of the new model are optimized by a genetic algorithm, and the effectiveness of the model is analyzed by the natural gas consumption of China, the United States, and Russia. The results show that, compared with other traditional grey prediction models, the new model has a better simulation effect, higher prediction accuracy, and stronger applicability. Meanwhile, the model was used to forecast the natural gas consumption of China, the United States, and Russia in the period from 2022 to 2026. The prediction results show that China's natural gas consumption will continue to steadily increase in the future, which is consistent with the global goal of adhering to the orderly development of natural gas. The prediction results can provide a theoretical basis for relevant departments to formulate relevant natural gas policies. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the impact of both economic growth and financial development on environmental degradation using a panel cointegration technique for the period between 1980 and 2007, except for Russia (1992–2007). In long-run equilibrium, CO2 emissions appear to be energy consumption elastic and FDI inelastic, and the results seem to support the Environmental Kuznets Curve (EKC) hypothesis. The causality results indicate that there exists strong bidirectional causality between emissions and FDI and unidirectional strong causality running from output to FDI. The evidence seems to support the pollution haven and both the halo and scale effects. Therefore, in attracting FDI, developing countries should strictly examine the qualifications for foreign investment or to promote environmental protection through the coordinated know-how and technological transfer with foreign companies to avoid environmental damage. Additionally, there exists strong output-emissions and output-energy consumption bidirectional causality, while there is unidirectional strong causality running from energy consumption to emissions. Overall, the method of managing both energy demand and FDI and increasing both investment in the energy supply and energy efficiency to reduce CO2 emissions and without compromising the country’s competitiveness can be adopted by energy-dependent BRIC countries. [ABSTRACT FROM AUTHOR]

Eco-efficiency composite indicators are widely accepted as the ratio of environmental impact to created economic value. These indicators are realistic measures for assessing sustainability performance considering the economy and environment. The weights reflect the importance of indicators to the aggregated environmental impacts. Estimating the relative weight of indicators is highly subjective, and therefore the search for a single unique weighting method has been going on for years. The regression-based weights are one of the most recent trends in sustainability modeling. Since these methods are designed initially to investigate the impact of multiple variables on a response variable rather than to estimate weights, some drawbacks are associated with their potential to provide proper weights. This paper presents a novel weighting approach integrating linear mixed-effect models with Johnson's relative weights to address these drawbacks and provide meaningful relative weights for eco-efficiency composite indicators. The proposed approach's operational and computational procedures are illustrated using a real example, and the eco-efficiency of food consumption of 38 countries is estimated for the period between 1990 and 2012 using a consumption-based sustainability accounting method. The findings have shown that energy use and GHG indicators are the most critical contributor to global food consumption's environmental impacts. The country-based eco-efficiency performance in this work has shown that China, India, and Russia are located in the low eco-efficiency score class. The Spatio-temporal analysis downgraded the geographical location's significance on the trends of eco-efficiency behavior in time and space. On the other hand, it revealed the different types of emerging hot spots over the world. • Consumption-based environmental footprint analysis is conducted. • Dynamic ecoefficiency of food consumption is estimated for 38 countries. • A novel weighting approach integrating Johnson's relative weights is proposed. • China, India, and Russia are located in the low ecoefficiency-score cluster. [ABSTRACT FROM AUTHOR]

As the industrial structure adjusts and driving effects change in different countries, the regional pattern of industrial driving effects is reshaped, and the new pattern is bound to lead to changes in energy consumption. Therefore, this paper uses multiregional input-output analysis, complex network theory, and panel quantile regression model to explore the impact of the topological structure of the global industrial driving network on energy intensity. The empirical results show that China's ability to drive economic activity is becoming stronger, but its level of dependence is also increasing. The industrial dependence of Russia is also relatively high, but over time it has been gradually decreasing. Countries with high energy intensity should reduce their role in driving others and rely less on external resources. For countries with low energy intensity, they could conduct trade cooperation with more countries, but the driving effect strength with other countries cannot be too high. Understanding the relationship between industrial driving effects and energy intensity is of great significance for the government in the implementation of trade policies and industry policies, which have a large impact on energy intensity. • The relationship between industrial driving effect and energy intensity is studied. • Panel quantile regression is adopted in the analysis. • The industrial driving effects of various countries are changing over time. • The roles played by countries have different impacts on energy intensity. • Industrial driving effect should be considered for trade policies. [ABSTRACT FROM AUTHOR]