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Pollution reduction and low-carbon development are two specific but intertwined goals of addressing climate change and sustainable development of the United Nations in 2030, and the effective use of digital financial inclusion is imperative for driving technical innovation, upgrading industrial structure, and addressing climate change. Therefore, this paper investigates the impact of digital financial inclusion on the coupling coordination between pollution reduction and low-carbon development using a panel dataset of 30 China's provinces from 2011 to 2020. The results show that the coupling coordination between pollution reduction and low-carbon development in China improves significantly during the sample period. The synergistic effect of different economic zones and provinces varies greatly in space and time. In addition, digital financial inclusion has a promoting effect on the coupling coordination of pollution and carbon emission reduction. A mechanism analysis also reveals that technological innovation, industrial structure and energy consumption are the core transmission channels through which digital financial inclusion influences the synergistic effect of pollution and carbon emission reduction. This suggests that the need of coordinated governance by policymakers is warranted, and digital financial inclusion is an important way to this coordinated governance. [ABSTRACT FROM AUTHOR]

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO 2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO 2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO 2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO 2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions. • Influencing factors are screened according to the low-carbon transition paths. • The distributed power installed capacity and load interaction coefficient are considered. • Multiple regression equations are developed between CO 2 emission targets and influencing factors. • A long-term electricity demand forecast model driven by CO 2 constraints is constructed. • The multi-scenario electricity demand in western China is forecasted in response to carbon peaking and carbon neutral targets. [ABSTRACT FROM AUTHOR]

[Display omitted] Waste-to-Energy (WtE) technology is the most effective solution for managing non-recyclable wastes through mass burning and energy recovery. Owing to the significant volumes of plastics in China's industrial solid wastes (ISW), a large amount of greenhouse gases (GHG) is generated during the incineration process. Therefore, monitoring GHG emissions from WtE facilities is essential. Owing to the lack of suitable accounting models and characterized fossil carbon fraction (FCF) data, current studies use default values provided by the Intergovernmental Panel on Climate Change's (IPCC), which increases calculation inaccuracies. Therefore, this study established an improved method to accurately account for carbon emissions during solid waste incineration by firstly using radiocarbon dating by accelerator mass spectrometry (AMS) technique to determine the FCF of the solid waste components in China. Monte Carlo analysis was employed to perform the sensitivity analysis, and the results indicated that there was a significant deviation between the measured value and IPCC's default values of FCF, 3.2, 32.48, 93.39, 93.76, 90.49, and 93.8 % for paper, cotton, synthetic textiles, artificial rubber, artificial leather, and plastics, respectively. By replacing coal with ISW in a 2 × 110 t/h circulating fluidized bed boilers, 9.251 × 104 t CO 2 -eq emissions were reduced, and the carbon emission factor reached 0.56 t CO 2 -eq/t waste. This study complements the research gap fossil carbon data of wastes in the IPCC guidelines and provides a more accurate and effective way to calculate carbon emissions during ISW incineration treatment. [ABSTRACT FROM AUTHOR]

Global environmental problems are worsening. Among various regulatory measures, market-oriented approaches are most favored by governments worldwide. Using provincial panel data from 2004 to 2020, a multi-period DID model was hereby constructed to empirically study the pollution reduction effects of the pilot policy for carbon emissions trading in China. Findings of this paper could be listed as follows: (1) The pilot policy has achieved notable reductions in waste gas and solid waste, although its effect on wastewater pollutants has been less pronounced; (2) Pollution reduction in the pilot regions can be achieved by optimizing the energy consumption structure; (3) Significant variations in pollution reduction effects are observed between the Northern and Southern pilot regions, with notably stronger policy effects in the north across various pollutants compared to the south. Overall, this paper offers pertinent policy recommendations to aid the Chinese government in enhancing its carbon market. [ABSTRACT FROM AUTHOR]

[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

The support of digital technology is urgently needed to promote low-carbon development and green economic and social transformation and to realize the goal of "carbon neutrality" early. This paper utilizes the "Golden Tax III" project for the digital transformation of China's tax collection and administration and empirically confirms that digital tax collection and administration significantly reduces carbon emissions based on the panel data of 30 provinces (municipalities directly under the central government and autonomous regions) in China from 2010 to 2020, i.e., digital technology plays a positive role in carbon emission reduction. The mechanism test shows that digital technology indirectly affects carbon emissions through industrial structure, technological innovation, and tax structure. Heterogeneity analysis shows that digital technology has a significant inhibitory effect on carbon emissions in the northern region, non-Yangtze River region, provincial administrative areas with heavy local government debt, and a large proportion of tax revenue from the secondary industry and tax revenue from state-owned enterprises. Under the requirement of high-quality and green development of the economy, this paper is of great theoretical and practical significance for using digital technology to ensure the governance functions of tax, help low-carbon development, and reach the goal of "carbon neutrality" at an early date. • This study assesses the impact of digital technology on carbon emissions from the government's perspective. • This study enriches the mechanism by which digital technology affects carbon emissions. • This study provides suggestions to promote the digitization of government affairs and carbon emission reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Exploring the industrial water-land-energy-carbon interaction help promote resource conservation and low-carbon development. • Constructing evaluation indicators and decomposition model to assess the carbon emission effect of industrial water and land use. • Industrial carbon emission intensity of water and land use in China increases, and inter-provincial difference expands. • Industrial matching index of water and land resources in China presenting great spatial agglomeration. • Matching index of water and land resources is the main inhibitory factor for all provinces in China. The industrial production requires the input of water, land and energy, and the water-land-energy interaction generates carbon emissions. Investigating the industrial water-land-energy-carbon nexus and exploring the influencing factors of the carbon emissions help promote the intensive use of water resources, land resources, and energy, as well as the low-carbon development of industry. This paper applied kernel density estimation and spatial auto-correlation analysis to explore the spatio-temporal variation of industrial carbon emission effect of water and land use and the industrial matching status of water and land resources in China, and then analyzes the influencing factors to industrial carbon emissions by introducing water and land resource factor into Kaya identity and Logarithmic Mean Divisia Index (LMDI) model. The main conclusions are: (1) From 2006 to 2020, China's industrial carbon emissions increased by 82.02%. The industrial carbon emission intensity of water use (CEIWU) and the industrial carbon emission intensity of land use (CEILU) increased by 158.02% and 55.53% respectively. The inter-provincial difference of CEIWU and CEILU expanded considerably, and the number of provinces with high CEILU had a large increase. (2) The industrial matching index of water and land resources (MIWL) in China showed a fluctuating downward trend, presenting great spatial clustering, and the provincial MIWL were mainly concentrated in the H-H and L-L regions. (3) The contributing and inhibitory effect of each influencing factor varied on regional and provincial level. The economic output of water use is a contributing factor to all regions and provinces, while the MIWL is an inhibitory factor to all regions and provinces. [ABSTRACT FROM AUTHOR]

This paper presents a micro energy grid to a community and estimates the effect of the urban micro energy grid on the reduction of energy demand and carbon emissions. Micro energy grid enables to share various energy sources generated at local plants and renewable sources for multiple buildings with different energy profiles. A community in China was selected for the implementation of the micro energy grid. A number of key performance indicators(KPIs) were developed to act as a framework for the overall community micro energy grid design. Site-wide strategies were selected to meet the KPIs and minimize the capital cost of the project. Financial analysis on the community micro energy grid model was performed and different scenarios were set up in order to find the optimalconditions for the government incentive, the investment discount rate, system capacity and development (demand) size of the project. It was found that a community in China adopting micro energy grid can achieve 26% carbon emissions reduction and 25% energy consumption savings compared to the conventional building energy systems and its payback period is 51 years with 10% discount rate and a government incentive of $0.052 per kWh. [ABSTRACT FROM AUTHOR]

With the "carbon neutrality" target proposal, China is facing severe pressure on carbon reduction. Industrial economic activities form an essential emission source of carbon dioxide. Therefore, it is necessary to promote industrial sectors' coordinated emission reduction work. This paper uses non-competitive input-output model and structural path decomposition method to analyze the carbon emission changes from three levels of total, production layer and industrial chains, identifying the main paths and driving factors of carbon emission changes in China from 2017 to 2018. The results show that: (1) The first layer presents obvious low-carbon characteristics as a direct emission level, but the change in its carbon emissions accounted for 41.8%. (2) The increased emission of industrial chains is mainly caused by consumer spending and fixed capital formation, and emission reduction is mainly triggered by consumer spending. (3) Demand scale is an essential driving factor of carbon emission increase. The contribution rate of demand structure to emission increase and reduction industry chains is 55.1% and −16.5%, respectively, which is the biggest reason for the difference between them. Finally, targeted policy recommendations are put forward from the perspective of the emission transmission path and driving factors in the industrial chain. • The non-competitive IO model of China's CO 2 emissions is established. • SPD is applied to identify the emission increase and reduction industrial chains. • The driving factors of CO 2 emission changes in industrial chain are explored. • The comparative analysis of increase and reduction industrial chains is conducted. [ABSTRACT FROM AUTHOR]

The issue of climate change has become an increasing concern for the international community. At the same time, climate change and, particularly, global warming affects the amount of energy required in China and the structure of this demand, thereby influencing the amount of CO 2 (carbon dioxide) emissions produced. To investigate the possible influence of climate change on China's CO 2 emissions, this paper utilizes the degree-day method to calculate the heating degree days (HDD) and cooling degree days (CDD) in 29 provinces in China from 1995 to 2011. A series of appropriate econometric models are subsequently estimated to measure the impact of climate change on CO 2 emissions per capita. To enhance the explanatory power of the estimation results, several indicators, such as income per capita, industry structure, urbanization rate and population density, are incorporated in the estimation equations as control variables. The estimation results indicate that the feedback effect of climate change on China's carbon emissions is statistically significant but not large in magnitude. During the sample period between 1995 and 2011, approximately 1.687% of China's increased total CO 2 emissions could be attributed to climate change. Moreover, the impacts of climate change on different regions of China are notably different. For example, the influence of HDD on CO 2 emissions per capita are most significant in the middle and southern regions, while the impacts of CDD are most significant in the eastern and northern regions. However, there is no obvious evidence that climate change has affected CO 2 emissions in the western region. [ABSTRACT FROM AUTHOR]

As the world's largest carbon emitter, China faces serious air pollution problems. Given that both carbon dioxide and air pollutants mainly come from burning of fossil fuels, the coal-to-gas policy has received widespread attention as a way to steer the public towards cleaner energy. However, inflexibility in the early implementation of the coal-to-gas policy caused a shortage of natural gas, and thus had negatively influenced households' lives. Considering the importance of clarifying the economic and environmental impacts of the coal-to-gas policy, this paper employs a dynamic recursive computable general equilibrium (CGE) model and sets different substitution scenarios, to simulate the impacts of the coal-to-gas policy on gross domestic product (GDP), sectoral output, trade, carbon emission reduction, and air quality improvement. The simulation results show that the long-term damage to GDP will be weaker than the short-term one, and carbon emissions would be reduced by 280 and 300 million tons in 2030 under the high and full substitution scenarios, respectively. The emissions of air pollutants (SO 2 and NO x) would also decrease significantly. Moreover, our results indicate that it is feasible to increase the substitution ratio in a stepwise manner and eventually achieve full substitution, which can not only effectively avoid shortages of natural gas, but also promote the coordinated control of carbon dioxide and air pollution emissions. • Coal-to-gas policy is a more effective way to reduce CO 2 emissions than SO 2 and NO x emissions. • The full alternative scenario with a step-by-step substitution approach should be adopted. • Coal-to-gas policy will reduce GDP, and the long-term impact is weaker than the short-term impact. • Some subsidies or tax incentives could be given to exports of resource-intensive goods. [ABSTRACT FROM AUTHOR]

• Energy sustainability includes security, efficiency, cleanness, low carbon emissions. • Chinese energy sustainability has obvious spatial heterogeneity. • Cleanness and efficiency perform better than security, low carbon emissions in China. • Energy supply, consumption structure and pollution emissions are key impact factors. • Targeted measures are proposed to promote Chinese energy sustainability. Energy sustainability is one of the largest challenges for sustainable development. Energy sustainability in China varies by regions because of the differences in energy resources, technology and economic levels. Previous studies have assessed national energy sustainability, few to none have looked internally on a regional or provincial scale. This paper defined energy sustainability from the four dimensions of security, efficiency, cleanness and low carbon emissions, established an index system and evaluated the energy sustainability levels of 30 provinces in China by the Improved Entropy Weight-Cloud Model (IEW-CM). The results show that: (1) General energy sustainability in Chinese provinces is deficient, and energy cleanness and efficiency perform better than security and low carbon emissions. (2) Chinese energy sustainability has obvious spatial heterogeneity and within the same province, the level is unbalanced in four dimensions. The characteristics of energy sustainability exhibit significant differences in different regions. (3) Energy security and low carbon emissions play a crucial role in achieving energy sustainability. Natural gas deliverability, energy consumption elasticity coefficient, CO 2 emission per unit of energy and share of fossil energy consumption are the primary factors affecting energy sustainability differences among provinces. [ABSTRACT FROM AUTHOR]

This paper proposed a model-based framework to optimize the energy-water-emissions nexus (EWEN) for regional electric power system (EPS) planning. The framework integrated the superiority of bi-level programming (BLP), two-stage stochastic programming (TSP), mix-integer programming (MIP), interval parameter programming (IPP) models and carbon emission trading (CET) mechanism, which could effectively deal with the planning problems in the EWEN optimization process. BLP helped decision makers make leader-follower choices when they have conflicting preferences. TSP with IPP could validly address the uncertain information expressed as interval values with unknown distribution. MIP was introduced as an efficacious tool to evaluate the capacity expansion potential in EPS. CET mechanism was applied to reinforce the CO 2 mitigation effect during the EWEN optimization process. Then the proposed framework was applied into a case study in Shanxi Province, a main thermal power base in China to optimize the EWEN. Results indicated that the power supply structure was inclined to show a low-carbon transition trend in the planning periods. Meanwhile, coupled with the strict water conservation policy, every 5% reduction in carbon emission density would lead to an average decreasing rate of 8% for SO 2 , 3% for NOx and 12% for PM. This study may help provide an effective way to deal with planning problems with conflicting and sequential goals under uncertainty and make better tradeoff strategies in EWEN optimization in EPS. • Propose a stochastic leader-follower framework for energy-water-emission nexus. • Control uncertainty by the combination of TSP and with IPP. • Interactions of CO 2 emissions and air pollution emissions are revealed. • Introduce CET mechanism to help improve the CO 2 mitigation effect. • The empirical results demonstrate the excellent performance of the framework. [ABSTRACT FROM AUTHOR]

The aluminium industry, with its traditionally high energy consumption, high emissions and high pollution, is facing increasing pressure to reduce carbon emissions in China. Implementing effective policies and measures to promote carbon emissions reduction is of great importance. This study deploys the life cycle assessment (LCA) method to explore carbon emissions and CO 2 sources from aluminium manufacturing in China as well as a scenario analysis to evaluate the reduction potential and driving forces of carbon emissions in the industry. The evaluated scenarios include a new capacity allocation scheme, technological progress and energy structure optimization. The carbon emissions from the aluminium chain are mostly contributed by indirect emissions (more than 68%) from electricity generation. Significant regional discrepancies are observed in the total emissions and intensity, and a reasonable industry layout tends to transfer to the south and southwest areas from the perspective of the environmental pillar. Production output remains the dominant factors associated with total carbon emissions from manufacturing firms, such as the aluminium industry. Technological progress and the energy structure contribute to total reductions and carbon efficiency, although the former has a relatively limited scope for aluminium firms. This paper also shows that marginal carbon efficiency improvements are not very effective in lowering the total CO 2 emissions under the context of high output growth. [ABSTRACT FROM AUTHOR]