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• Carbon emissions from energy consumption of 282 prefecture-level cities in China were calculated. • A new method for calculating carbon emission factors for electricity is proposed. • Extending the Atkinson Index to the application of Carbon Fairness. • Measured the inequality and fairness indicators of carbon emissions from energy consumption at the prefecture-level city level in China. This paper focuses on the conceptualization and quantification of carbon emission inequality and fairness from energy consumption. In order to more accurately measure the carbon emissions generated by electricity consumption in each city, we firstly propose a new method for calculating the carbon emission factor for electricity, and calculates the carbon emissions from energy consumption in 282 prefecture-level cities in China from 2003 to 2019. Through the application of the Theil-Kaya decomposition mechanism and the improved Atkinson index, we conducted an in-depth study of the carbon emission inequality and fairness indicators from energy consumption. The results show that (1) Although China's overall inequality has declined, it remains significant in the western region, with its inequality indicator fluctuating from 0.548 in 2003 to 0.683 in 2019.(2) The decomposition of the drivers of inequality indicators shows that the contribution of GDP per capita to inequality is increasingly being replaced by energy intensity and the energy carbon emission factor, with energy intensity emerging as the main driver, contributing more than 40% to inequality in 2019, both overall and in the three regions.(3) The results for the fairness indicator, on the other hand, show a certain degree of divergence. Further Dynamic Time Warping fitting analysis shows that regardless of how the public's sensitivity to carbon emissions varies, the change trends of the fairness and inequality indicators for carbon emissions in the eastern region show a high degree of convergence, even in the two most extreme cases. The fit is poorest in the western region, but improves significantly if public's sensitivity to carbon emissions increases. (4)To sum up, some policies that are consistent with the context of the times, such as the East-West regional counterparts and the construction of a new type of power system, are worthy of priority consideration by the Government. [ABSTRACT FROM AUTHOR]

• The high-quality development index is introduced into STIRPAT model. • The average yield of ecological footprint is updated. • High-quality development can effectively reduce the increase of ecological footprint. • The improvement of population quality helps ease pressure on the environment. • Lower energy consumption intensity helps improve ecological quality. It is urgent to coordinate the development of economy and environment under the "dual carbon" goal. As China's new development stage and mode, high-quality development takes into account both the speed and quality of economic, providing the possibility of achieving a "win-win" situation for the economy and the environment. In order to test the practical impact of high-quality development on ecological, based on the STIRPAT model, this paper introduces high-quality development index instead of per capita GDP, so as to measure the current economic situation in China, and explore the relationship between high-quality development and ecological footprint, and demonstrates the feasibility from theory and practice. At the same time, it pays attention to the complex effects of population and technology on environmental quality. The results show that the contradiction between economy and ecology still exists in China. The ecological footprint index of 30 provinces in the past 21 years has increased as a whole, and the fworst ecological footprint has concentrated in the Yellow River basin. But since 2012, the growth of the footprint has slowed. The slow down time and low value region of ecological footprint were observed, and it was found that they overlapped with the proposed time and the high value area of high-quality development. The level of high-quality development in China has also continued to rise, with regional differences but gradually narrowing. Among them, green development and shared development contribute more to the improvement of high-quality development level. Compared with the traditional economic development mode, high-quality development can reduce the increase of ecological footprint, and the longer the time, the smaller the increase, more conducive to long-term sustainable development. In addition, population and technology factors are also profoundly affecting China's ecological pattern. The increase in population size and population density, as well as the increase in the proportion of coal-based energy consumption structure, will lead to increased environmental pressure. The improvement of population quality and the reduction of energy consumption intensity will help alleviate ecological pressure. The research is helpful to provide policy guidance and path combination reference for China's sustainable development, and also provide new ideas for the global coordination of the relationship between economy and environment. [ABSTRACT FROM AUTHOR]

• GDIM is used to investigate the influencing factors of energy-related PM 2.5 emissions in China. • A novel decoupling indicator is constructed based on GDIM decomposition results. • The increase of PM 2.5 emissions is primarily due to output scale and energy use effects. • Emission intensity and emission coefficients are the main factors decreasing PM 2.5 emissions. • Technical effect plays an important role in promoting the decoupling of PM 2.5 emissions. Haze pollution has become a severe threat to China's sustainable development. It is crucial to provide a complete explanation for the mechanism behind the changes in PM 2.5 pollution and explore how to decouple economic growth from PM 2.5 pollution. Compared with the traditional decomposition analysis methods, the Generalized Divisia Index Method (GDIM) integrates multiple influencing factors and provides a fuller understanding of the influence mechanism of PM 2.5 pollution. In addition, the decomposition of the monitored PM 2.5 concentration in previous studies leads to ambiguous economic meanings of some relative impact indicators. In this paper, GDIM decomposition is applied to study the driving factors of energy-related PM 2.5 emissions changes in China. Then, based on the GDIM decomposition results, this paper constructs a novel decoupling indicator to study the decoupling relationship between PM 2.5 emissions and economic growth, and the contributions of the technical and non-technical factors to the decoupling indicator are quantified. The main results are summarized as follows. (1) The output scale effect and energy use effect are the main reasons for the increase of PM 2.5 emissions, while the emission intensity effect and emission coefficient effect make major contributions to decreasing PM 2.5 emissions. The effects of population, per capita emissions, per capita output, and energy intensity are relatively minor. (2) China and its three economic regions move from strong decoupling in 1998–2000 to weak decoupling during 2000–2010, and then into strong decoupling over the period of 2010–2014. (3) Technical effect plays an important role in promoting the decoupling between PM 2.5 emissions and economic growth, but its contribution shows a downward trend over time. In contrast, non-technical effect hinders the decoupling process, and its contribution is decreasing from 2000 to 2014. This paper is supportive for formulating emissions reduction policies and improving China's air quality. [ABSTRACT FROM AUTHOR]

Eco-efficiency is of great significance in facilitating a region’s sustainable development. However, there are some inconsistencies among the concepts and also relationships of eco-efficiency and its relevant efficiencies in recent studies. This paper integrates eco-efficiency and three related-efficiencies (economic efficiency, energy efficiency, and environmental efficiencies) into a total-factor analysis framework, regarding them as composite eco-efficiency indicators. Employing a new data envelopment analysis approach, named Meta-US-SBM, which considers meta-frontier, undesirable outputs, super efficiency, and slacks simultaneously, the measure system of these four efficiencies is established. An empirical study is conducted using Chinese provincial data from 2001 to 2014. Results indicate that, overall, the regional economic efficiency is better than other related efficiency indicators. Furthermore, different provinces are adopting different development modes, as denoted by significant heterogeneity among eco-efficiency indicators. [ABSTRACT FROM AUTHOR]

• We measure the green growth degree of China's industrial sectors by considering the constraint of total energy consumption. • We proposed a new common equilibrium efficient frontier, which considers each DMU's own adjustment strategy on fixed-sum energy input. • The proposed approaches are applied to analyze the green growth and energy and environmental efficiency of 36 of China's industrial sectors. Tight energy constraints and severe environmental pollution are two main problems affecting economic growth and sustainable development. As an important economic growth mode, green growth seeks to achieve both economic growth and environmental sustainability. Previous studies on the efficiency evaluation of green growth ignored fixed-sum energy consumption; this is a significant omission because this constraint is general in China: the government has set targets to reduce energy consumption by 20% and 16% per unit of GDP during its the eleventh (2006-2010) and twelfth (2011-2015) five-year plans. To fill this gap, we propose a new data envelopment analysis (DEA) model to analyze the green growth degree of China's industrial sectors. The proposed DEA model considers the constraint of total energy consumption in order to conform to the policy of energy and emission reduction. Previous theoretical studies on fixed-sum output construct the common equilibrium efficient frontier (CEEF) only by considering the minimal reduction strategy for the fixed-sum output of all decision making units (DMUs), while ignoring each DMU's adjustment strategy for fixed-sum input/output. In this paper, we construct a new CEEF by considering each industrial sector's adjustment of energy consumption. The new CEEF not only guides the industrial sectors to alter their energy consumption structure to achieve green growth but also allows a more accurate evaluation of the energy and environmental efficiency (EEE) of industrial sectors. Last, our proposed model is applied to analyze the green growth and EEE of 36 of China's industrial sectors. [ABSTRACT FROM AUTHOR]

• Studying on dynamic ecological footprint (EF) in Jing-Jin-Ji from 1996 to 2020. • Six impact factors for driving EF is decomposed. • The EF in Jing-Jin-Ji will increase of 10% from 2015 to 2020. The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region. [ABSTRACT FROM AUTHOR]