• Energy Research
• 12. Responsible consumption close
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• Frontiers in Environmental Science close

The global community has set intensive targets in Sustainable Development Goals (SDGs) to better people’s lives after closing the Millennium Development Goals (MDGs). It corresponds to the 2030 aspirations of the United Nations to enhance and promote the sustainable development of human society. The current paper explores the impact of fiscal hedging and R&D in energy Using a green-energy system in SDGs. To do this, we used TOPSIS and QARDL methodologies on a 21-year dataset of South and Southeast Asian economies from 2000 to 2020. The study results show that fiscal hedging contributes favourably to the environmental degradation of the underlying economy. Research and development (R&D) in renewables has contributed negatively to ecological degradation and SDGs in the economies of South & Southeast Asia. This study suggests policy guidelines for advanced and developing economies based on fiscal stability and technical innovation through R&D to meet SDG.

There is no consensus on whether state-owned enterprises (SOEs) or privately-owned enterprises (POEs) pollute more. This study explores the impact of ownership on pollution emission intensity using micro-data from Chinese industrial enterprises. From the perspective of energy efficiency, the mechanism of ownership affecting pollution emissions is explored further. Research results show that the pollution emission intensity of SOEs is significantly higher than that of POEs. The underlying reason is the low energy efficiency of SOEs, and energy efficiency plays an important mediating role in the relationship between ownership and pollution emissions. Among industrial waste gas, nitrogen oxides (NOx) and dust emissions, energy efficiency plays the largest mediating effect between ownership and NOx emissions. Additionally, in both high-polluting and low-polluting industries, SOEs’ pollutant emission intensity is higher than that of POEs, however, the mediation effect of energy efficiency is greater in low-polluting industries. In cities with high growth pressure, SOEs’ pollutant emission intensity is more significant than that of POEs. On the contrary, there are no noticeable differences in pollutant emission intensity between SOEs and POEs in cities with low growth pressure. But the mediation effect of energy efficiency is more significant in cities with high growth pressure. Industrial enterprises are the ultimate sources of industrial pollution. Therefore, the formulation of effective environmental policies cannot be separated from the analysis of enterprises’ emission behaviors and the assessment of micro factors affecting emissions. The conclusions of this study provide a basis for developing countries to formulate environmental policies for industrial enterprises.

Geothermal energy is a renewable energy source, and geothermal heating is a livelihood project, so a resource tax can protect resources and regulate prices. Reasonable geothermal energy resource tax collection standards are conducive to high-quality industrial development. This paper takes 15 provinces and cities that clearly levy geothermal resource taxes in China as the research object, constructs an System Dynamics model of geothermal resource tax, and studies the impact of geothermal resource tax on the income of geothermal enterprises and CO2 emission reduction. Then, the forecast simulates the economic and environmental benefits brought about by the use of geothermal resources from 2021 to 2035. The research results show that using geothermal resources for heating can significantly reduce CO2 emissions, and the higher the proportion of geothermal use is, the greater the CO2 emission reduction before 2035. The fifteen provinces and cities that clearly levy geothermal resource taxes will find it difficult to achieve corporate profits at the current residential heating utilization price and geothermal energy resource tax rate; that is, to achieve a balance of income and expenditures for geothermal enterprises without financial subsidies, the price of geothermal heating will be significantly higher than the heating price of traditional central heating and the price of electric self-heating.

This study aims to find the nexuses among energy efficiency, renewable energy consumption, foreign direct investment, logistics industry, manufacturing industry and global trade during the COVID-19 pandemic and their impact on global supply chains in exporting nations of the world. The data for this study has been extracted from the World Development Indicators and Statista 2021 for 13 years ranging from 2007-to 2020 for nine top exporting countries. The fixed effect panel estimation technique was implied to examine and analyze the data. The results of our study revealed that highly risky diseases significantly impact supply chain operations globally. Global supply chains, logistics and manufacturing industries significantly influence global trade operations. Our results implicate that the overall international trade and logistics can be enhanced by improving the manufacturing and logistics industries by coping with the risk of pandemic diseases. Moreover, by utilizing cost-effective, renewable and efficient energy resources companies address sustainability issues of global trade and operations. By exerting further attention to the proficiency of the levies approval process, competence and quality of logistics services, and ease of assembling competitively priced shipments, the governments can significantly enhance the export from the logistics industry. Also, increasing manufacturing and agricultural value-added healthier consequences might be acquired in global supply chain operations from the manufacturing industry.

High energy intensity and energy consumption structure are the main contradictions restricting China’s green economic growth. Green technology innovation is an important factor to alleviate the energy dilemma. Referring to the IPC Green Inventory launched by the World Intellectual Property Organization, the green patents of 30 provinces in China from 2004 to 2019 are screened. Based on the panel data of 30 provinces in China, this study empirically tests the impact of green technology innovation on energy intensity and energy structure by employing fixed effect model and quantile regression model. The empirical results show that green technology innovation can significantly reduce energy intensity and energy structure, and the long-term effect is obviously higher than the short-term effect. Compared with the green utility model patent representing general innovation, the green invention patent representing disruptive innovation expresses more momentous long-term and short-term effect. The sustainable effect of green technology innovation will gradually weaken with the reduction of energy intensity and energy structure. For provinces with low energy structure, the impact of green technology innovation is no longer significant. Instrumental variable method and robustness test prove that the conclusion of this study is robust. This study provides support for the government and enterprises to promote technological innovation and play a part in alleviating the energy dilemma.

As is glaringly apparent, the world is now bedeviled with environmental challenges and ecological complications. This may not be unconnected with the persistent ecological challenges emanating from fossil fuel consumption and changing lifestyles. This is why various attempts are ongoing to advance the course of renewable energy and economic growth as a response to environmental change. Therefore, this study seeks to investigate the dynamic effect of external debt, energy usage, and real income on the ecological footprint in Turkey, covering the period 1985–2017. To achieve the stated objective, the study used the newly developed Bootstrap Autoregressive Distributed Lag (BARDL) test and the Granger causality test to unravel the co-movement as well as the direction of causality among the variables. The results revealed that external debt influences environmental quality in both the short and the long run. However, energy consumption and real income have been found to have retarded environmental quality in both the short and the long run. The Granger causality test results revealed that the causal flow direction runs from external debt, economic growth, and energy usage to environmental quality. Therefore, the study argues that for the Turkish government to achieve a long-term plan of environmental sustainability, there is a need for the government to venture into debt consolidation programs such as implementing tax increases and cutting public spending in order to increase fiscal space that would finance long-term environmental protection policies.

Some of the globe’s most economically advanced nations make up the G7 (Canada, Japan, France, Germany, Italy, United States and United Kingdom). Nevertheless, in tandem with such strong economic growth, the environmental conditions in these nations have deteriorated, raising serious issues among stakeholders. Therefore, we examine the effect of eco-innovation and trade openness on CO2 emissions in G7 nations. We also take into account the role of renewable energy, economic growth and nonrenewable energy use using a dataset covering the period from 1990–2019. We employed recent econometric techniques such as slope heterogeneity (SH) and cross-sectional dependence (CSD), Westerlund cointegration, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), panel quantile regression and panel causality tests to assess these associations. The outcomes of the CSD and SH tests disclosed that using a first-generation unit root test will produce biase outcomes. Furthermore, the outcomes of the Westerlund cointegration disclosed support long-run association between CO2 and its drivers. In addition, the results of the long-run estimators (FMOLS and DOLS) unveiled that nonrenewable energy and trade openness contribute to the damage to the environment while economic expansion, renewable energy and eco-innovation enhance the quality of the environment. Furthermore, the outcomes of GDP, REC and ECO curb CO2 while NREC energy and TO surge CO2. Finally, the outcomes of the panel causality test unveiled that CO2 emissions can be predicted by all the exogenous variables.

The emission peak and carbon neutrality targets pose a great challenge to carbon emission reduction in the coal industry, and the coal industry will face an all-around deep adjustment. The forecast of coal price is crucial for reducing carbon emissions in the coal industry in an orderly manner under the premise of ensuring national energy security. The volatility and instability of coal prices are a result of multiple influencing factors, making it very difficult to make accurate predictions of coal price changes. We propose in this paper an innovative hybrid forecasting method (CEEMDAN-GWO-CatBoost) for forecasting coal price indexes by combining machine learning models, feature selections, data decomposition, and model interpretation. By combining high forecasting accuracy with good interpretability, this method fills a gap in the field of coal price forecasting. Initially, we examine the factors that influence coal prices from five angles: Supply, demand, macroeconomic factors, freight costs, and substitutes; and we employ Spearman correlation analysis to reduce the complexity of the attribute set and devise a coal price forecasting index system. Secondly, the CEEMDAN method is used to decompose the raw coal price index data into seven intrinsic modal functions and one residual term in order to weaken the volatility of the data caused by complex factors. Next, the CatBoost model hyperparameters are optimized using the Grey Wolf Optimizer algorithm, while the coal price data is fed into the combined forecasting model. Lastly, the SHAP interpretation method is introduced for studying the important indicators affecting coal prices. The experimental results show that the combined CEEMDAN-GWO-CatBoost forecasting model proposed in this paper has significantly better forecasting performance than other comparative models, and the SHAP method employed in this study identifies the macroeconomic environment, freight costs, and coal import volume as significant factors affecting coal prices. As part of the contribution of this paper, specific recommendations are made to the government regarding the formulation of a regulatory policy for the coal industry in the context of carbon neutrality based on the findings of this research.