Search

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening—energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process. • Green innovation resilience can promote energy efficiency. • Early digital economy development can lead to resource grabbing and technology constraints effects. • Pursuing the policy orientation of "digital greening—energy efficient". [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

Atmospheric pollution emissions have become a matter of public concern in recent years. However, most of the existing researches on NOx pollution are from the natural science and technology perspective, few studies have been conducted from an economic point, and regional differences have not been given adequate attention. This paper adopts provincial panel data from 2006 to 2013 and the LMDI model to analyze the key driving factors and regional dilemmas of NOx emissions. The results show that significant regional disparities still exit on NO x emissions and its reduction effect 27 provinces didn't accomplish their corresponding reduction targets. Economic development factor is the dominating driving factor of NO x emissions during the study period, while energy efficiency and technology improvement factors offset total NO x emissions in the majority of provinces. In addition, the industrial structure factor plays a more significant role in reducing the NO x emissions after 2011. Therefore, the government should consider all these factors as well as regional heterogeneity in developing appropriate pollution mitigating policies. It's necessary to change NOx emissions control attitude from original key areas control to divided-zone control, not only attaches great importance to the reduction of the original key areas, but also emphasizes the new potential hotspots with high NO x emissions. [ABSTRACT FROM AUTHOR]

Research on the ability of environmental regulations to achieve an innovation-offset effect by increasing clean energy use is required. This study aims to verify this by analysing the impact of market-incentive environmental regulation on enterprises' clean energy consumption. Using China's sulfur dioxide (SO 2) emission trading system (ETS) for 2007 as a quasi-natural experiment, the difference-in-differences model and data obtained from the Chinese Industrial Enterprises Database and Chinese Industrial Enterprises Pollution Database were used to determine whether an ETS affects enterprises' clean energy consumption. The results show that an ETS encourages enterprises to utilise clean energy and has a significantly positive impact on enterprises' clean energy consumption. Moreover, this study finds that an ETS promotes clean energy consumption by improving the production of enterprises. This study verifies the rationality of China's SO 2 ETS design. • This paper is the first to examine the impact of emission trading system (ETS) on enterprises' clean energy consumption. • We adopt the difference-in-differences model in the empirical section. • The ETS increases enterprise's clean energy consumption. • The improvement of enterprises' production structure is an important mechanism. [ABSTRACT FROM AUTHOR]

Protecting our environment while maintaining economic growth, requires a delicate balance among interlinked sustainable development policies. In this paper, we examine China's economic industries, including a high-resolution of the country's electricity sector during 2020–2030, using a multi-objective optimization model based on Input-Output analysis. This model, investigates the synergy and trade-offs of sustainable development goals in maximizing employment and GDP while minimizing energy and water consumption, CO 2 emissions, and five major pollutants to advance a sustainable industrial structure adjustment pathway for China. Our results reveal that there exists both synergies and trade-offs among multiple objectives, e.g., synergy among goals of minimizing air pollutant emissions and trade-offs between minimizing energy consumption and maximizing employment. Through the planned industrial restructuring period (2020–2030), the GDP, employment, carbon emission, and energy consumption will increase respectively by, 96.1%, 7.2%, 16.8%, 16.8%, and 6.3%, while pollutant emissions would decrease. Moreover, our research indicates that energy and water conservation should be prioritized in industrial structure adjustment strategies and policies. Our model demonstrates how the synergies and trade-offs among multiple policy targets can empower policy-makers, especially in developing nations, to make more informed and optimized industrial structure adjustment policies for sustainable development. • A multi-objective optimization model based on the Input-Output model is proposed. • Synergies and trade-offs among policy targets are examined. • The structural adjustment of economic sectors with fine-resolution power sectors is investigated. • The compromise solution and the most recommended policy priority are explored. [ABSTRACT FROM AUTHOR]

This study addresses the question of why industrial restructuring towards light industries or services sometimes fails to achieve carbon emission mitigation goals. By employing a new perspective of dividing industry segments into emission-dominating and non-emission-dominating ones based on Logarithmic Mean Divisia Index (LMDI) decomposition method, this paper analyses city-level carbon dioxide emission reduction performance under three distinctive industrial restructuring directions. Results indicate that carbon dioxide emission dominating segments are relatively fixed across cities, regardless of the various city types in China. The key point to achieve emission reduction through industrial restructuring is to identify and control the emission-dominating segments, instead of economic-leading ones. Besides, emission reduction performance of industrial restructuring from emission-dominating industry segments to services is better than that to non-emission-dominating ones. More importantly, industrial restructuring not involving output scale controlling of emission-dominating segments, or that recklessly rushing towards services are likely to fail the emission mitigation goal. This paper presents a strong international reference that merits cities facing policy hesitation over industrial restructuring directions while in pursuit of emission mitigation. It suggests that cities first focus on identifying the carbon dioxide dominating segments, of which the output scale should be controlled. For cities whose emission-dominating segments are not economic-leading ones, it is necessary to carry out industrial restructuring towards services or non-emission-dominating segments; while for cities heavily dependent on emission-dominating segments, energy efficiency should also be improved. • Identifying CO 2 -dominating segments at city level with LMDI method. • Comparing CO 2 mitigation effects among different industrial restructuring directions. • Providing feasible restructuring paths for distinct cities to reduce CO 2 emissions. [ABSTRACT FROM AUTHOR]

Based on data of the manufacturing sector of China and Japan from 2003 to 2016, this paper attempts to measure the progresses in energy-biased technology and energy efficiency by constructing a threshold panel regression model with variables including foreign direct investment (FDI) and energy consumption structure to explain energy efficiency using energy-biased technology as the key explaining variable. The estimation indicates significant differences in the energy efficiency of China's and Japan's manufacturing industries. In general, Japan's total energy efficiency is higher than China's. The industry with more intensive technology has higher energy efficiency which rises much faster. The paper finds that the energy efficiency of China's manufacturing sector shows an upward trend in general, while Japan's fluctuates more, showing two peaks and two troughs. Our empirical results show that there is a threshold value of progress in energy-biased technology; below this, progress in energy-biased technology will have a positive effect on energy efficiency and beyond it, the effect will be negative. Since this effect is not one-way, we define it as a 'double-edged effect'. It is estimated that the level of energy-biased technology progress of most manufacturing industries in China is below the threshold value, indicating that the technology progress in China's manufacturing sector has not been excessively biased towards energy consumption, and the impact on energy efficiency is still positive. The China-Japan comparison shows that the threshold value for Japan's manufacturing sector is significantly lower than that for China's, indicating a marginal effect on the 'double-edged effect': The threshold value will decrease when energy efficiency reaches a certain level. Therefore, it is necessary to offset these negative externalities from technological progress with other factors such as by increasing FDI and improving energy consumption structure. • This paper examines the influence of energy-biased technological progress alone on energy efficiency. • It proves the influence of energy-biased technological progress on energy efficiency to be nonlinear. • It measures the energy-biased technological progress and energy efficiency of some industries of the manufacturing sector of China and Japan. • It proves the rebound effect to be conditional in the manufacturing sector of China and Japan. • It also proves the threshold effect in the influence of energy-biased technological progress on energy efficiency. [ABSTRACT FROM AUTHOR]

The mechanism between financial agglomeration and environmental pollution is an important concern for both academia and policymaking. The main objective of this paper is to study the nonlinear impacts of financial agglomeration on environmental pollution. A theoretical framework was first constructed based on the scale effect, structure effect, and technology innovation effect of financial agglomeration and a Copeland-Taylor endogenous growth model. Using the panel data of 281 Chinese prefectural-level cities from 2003 to 2019, a panel threshold regression model was introduced to estimate the nonlinear association between financial agglomeration and environmental pollution. Industrial smoke (dust) emissions and industrial wastewater discharge were adopted to quantify current environmental pollution in China. The results show that financial agglomeration had a significant effect on improving the environment characterized by gradient thresholds; also notable is that 68.64% of the cities crossed the threshold value, entering the decelerating phase of financial agglomeration inhibiting environmental pollution. Both upgrading industrial structure and enhancing marketization could reduce environmental pollution, whereas increasing human capital, environmental regulation, and energy consumption had a deteriorating effect. The three channels for financial agglomeration to reduce environmental pollution were revealed to be financial scale, financial structure, and financial technology innovation. Our findings provide strong evidence for policymaking in sustainable development. [Display omitted] • Financial agglomeration (FA) is a crucial way to improve environment pollution (EP). • We built the theoretical framework of FA and EP based on the Copeland-Taylor model. • We tested the nonlinear impacts of FA on EP using the panel threshold model. • We revealed the transmission mechanism between FA and EP. [ABSTRACT FROM AUTHOR]

Inter-basin water transfers are intended to have a positive impact on the development of a region, but are always accompanied by huge energy consumption. The feasibility and effectiveness of water transfer projects have not yet been clearly evaluated, especially in the light of their energy consumption and effects on social development. This paper develops a method that combines life cycle approach and system dynamic model to reveal the effectiveness of different water supply alternatives (transferred water, reclaimed wastewater and rainwater harvesting) when they are all constrained to consume equal energy. This approach is applied to the case of Jinan city in Shandong province, China, the main water-receiving city on the Eastern Route of the South-to-North Water Transfer Project (ER-SNWTP). The results show that in normal years, Jinan's reclaimed water and rainwater harvesting supply are effective replacements for transferred water under the constraints of equal energy consumption and capital costs; even in dry years these two alternatives still play an important role. The results of this study indicate the relative effectiveness of reclaimed water and rainwater harvesting in supplying urban water while conserving energy and capital. • Using a new evaluation approach for urban water supply alternatives analysis. • Local water and transferred water have similar supporting role to the receiving city in the east route of SNWTP; • Local water option has less environmental impact and lower financial cost. [ABSTRACT FROM AUTHOR]

Considering the enlarging inter-provincial disparities in China as regards carbon emissions and carbon intensity (carbon emissions per unit gross domestic product), this paper is the first study to investigate the inter-provincial carbon imbalance by constructing and employing the Kaya-Zenga index. We use China's panel data of provincial-level carbon emissions over 1995–2016 to quantitatively measure the levels of inter-provincial imbalance and polarization in carbon emissions and carbon intensity. Further, we decompose the Kaya-Zenga index into different contributing factors both regionally and structurally and perform a scenario analysis to identify the corresponding regionally differentiated countermeasures regarding carbon emission reduction. The results show that the imbalance in carbon emissions is mainly caused by imbalances in population scale and income level, while the imbalance in carbon intensity predominantly results from imbalances in energy efficiency and energy mix. In addition, for heavy manufacturing provinces, the respective emission-reduction strategy should aim at lowering energy intensity through local technology improvement and inter-regional technology transfer. For light manufacturing and high technology provinces, carbon emission reduction is harder to be achieved; however, a mix of policies of improving energy efficiency, optimizing energy mix, and industrial upgrading should be implemented. The results of the scenario analysis indicate that reducing imbalance in carbon intensity under different scenarios can lead to a substantial reduction in carbon emissions (up to 10%). • We investigate the inter-provincial carbon imbalance within China. • We construct the Kaya-Zenga index and decompose the index into four contributors. • We use a panel data set of 30 provincial-level regions in China over 1995–2016. • Carbon imbalance is mainly caused by imbalances in population and energy efficiency. • Reducing intra-group imbalance in carbon intensity can abate carbon emissions. [ABSTRACT FROM AUTHOR]

To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and profitability of a given industry. However, this negative effect is weaker for more complex industries. Only for industries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time. • We investigate the impact of Emission reduction target policy(ERT) on industrial performance from complexity perspective. • We apply the method of reflection to calculate the Economic Complexity of provinces and Industry Complexity of industries. • More complex industrial structure contributes to the coal consumption reduction. • ERT has negative effects on the probability of branching into new industries, lowers the productivity and profitability of a given industry. • ERT has weaker negative effects for more complex industries. [ABSTRACT FROM AUTHOR]

Based on a unique micro-level dataset of 30,689 mining enterprises from 2008 to 2011, this paper uses the non-radial directional distance function (NDDF) to calculate the unified efficiency index (UEI) and energy-environmental performance index (EEI) for China's mining enterprises. The double bootstrap method is then used to test how tax incentive policies affect the UEI and EEI of China's mining enterprises. The results show that: (1) the UEI and EEI of Chinese mining enterprises first decreased and then increased over the sample period; overall, Chinese mining enterprises had low energy and environmental efficiency, especially the coal mining enterprises, private mining enterprises and mining enterprises in the central and western regions. (2) Tax incentives positively affect the energy and environmental efficiency of mining enterprises, especially the efficiency of coal mining enterprises, non-state-owned mining enterprises and mining enterprises in the central and western regions. Our results remain robust after using the propensity score matching estimator (PSM). (3) There is a positive feedback between tax incentives and energy and environmental efficiency, more efficient mining enterprises receive more government incentives. Further analysis shows that although tax incentives do not reduce the total energy consumption of enterprises, reducing the energy consumption of enterprises can improve their UEI and EEI. In addition, R&D investment, profitability and resource taxes all contribute to improving the UEI and EEI of mining enterprises. • The NDDF is used to calculate the UEI and EEI of mining enterprises in China. • How tax incentive policies affect the energy and environmental efficiency is tested. • Chinese mining enterprises had low energy and environmental efficiency. • There is a positive feedback between tax incentives and enterprises' efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for measuring energy efficiency inequality based on the Shephard energy distance function and the "double" stochastic meta-frontier. To incorporate the regional inequality, we use three different grouping criteria, geography, city features, and strategic development goals. We also explore whether the different city grouping criteria can influence the energy efficiency. A panel data of 284 cities in China from 2003 to 2013 is used for empirical analysis. The results indicate that the regional heterogeneity has significant impact on the energy efficiency. We find that different grouping criteria affect energy efficiency, especially, the urban feature criteria. These results highlight the importance of choosing a proper grouping criteria in meta-frontier energy efficiency analysis. • The inequality of city-level energy efficiency needs evaluation. • We introduce the "double" stochastic meta-frontier method to perform analysis. • Three grouping criteria are proposed for visualizing the technology heterogeneity. • The regional heterogeneity different grouping criteria affect energy efficiency. • A proper grouping criterion is needed in meta-frontier analysis. [ABSTRACT FROM AUTHOR]

China is now facing the formidable tasks of saving energy and reducing emissions, so it is very important to analyze China's energy and environmental efficiency. However, previous studies have rarely paid attention to the cross-impacts that different forms of public environmental concern (PEC) have simultaneously on energy and environmental efficiency. To investigate how these two types of efficiency change along with PEC variations, this paper employs six extended programming models which are developed based upon the theory of Data Envelopment Analysis (DEA) and the directional distance function (DDF). An empirical analysis of data from 239 Chinese prefecture-level cities demonstrates the techniques. The empirical results show that PEC variations can significantly affect the environmental efficiency only if the city is experiencing unsustainable development. This work also shows that PEC about input resources may exaggerate the environmental efficiency, implying that the public may be more tolerant of resource over-consumption than of pollutant over-emission. • How public environmental concern (PEC) affects environmental efficiency is studied. • A series of PEC-based DDF-DEA models are proposed. • An empirical analysis of data from 239 Chinese prefecture-level cities demonstrates. • PEC variations affect environmental efficiency only if it is unsustainable. • PEC over input resource may exaggerate environmental efficiency. [ABSTRACT FROM AUTHOR]

Crucial to the green and sustainable development of China's industry are both choosing an appropriate path of directed technical change and optimizing and improving energy consumption pattern. In this paper we incorporate fossil energy, non-fossil energy and environmental factors into the traditional production function and calculate the factor output elasticity, the growth rate of environmental total factor productivity, the factor-biased degree of directed technical change and factor substitution elasticity in China's industrial sector based on stochastic frontier analysis of the transcendental logarithmic production function. The results show that the high output elasticity of capital leads to the highest factor-biased degree of directed technical change of capital although it is accompanied by an increase in energy consumption and environmental degradation. At present, the factor-biased degrees of directed technical change in China's industry, from high to low, are capital, the environment, fossil energy, labor and non-fossil energy; these can be seen as the root cause of, among other things, overcapacity, energy shortages and environmental pollution. Optimizing the path of directed technical change of labor, capital and non-fossil energy is an advisable way to solve the above problems. The patterns of energy consumption in industrial production in most provinces are unsustainable and each province should optimize its local energy consumption pattern according to those technical changes biased towards non-fossil energy and the positive factor substitution relationship between two kinds of energy. • We calculate the growth rate of environmental total factor productivity. • We measure the factor-biased degree of directed technical change. • The factor-biased degrees of capital and environment are relatively high. • The patterns of energy consumption in most provinces are unsustainable. [ABSTRACT FROM AUTHOR]