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Abstract: The pulp and paper industry is one of the most energy-intensive industries worldwide. In 2007, it accounted for 5% of total global industrial energy consumption and 2% of direct industrial carbon dioxide (CO2) emissions. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and CO2 mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8terajoules (TJ), and the total CO2 mitigation potential is equal to 93,453tonnes CO2 annually, representing 14.4% and 14.7%, respectively, of the mill’s total energy consumption and CO2 emissions during the audit period. [Copyright &y& Elsevier]

Reducing carbon emission intensity is crucial for achieving sustainable development. Carbon emission intensity is expressively affected by the issuance of green bonds. Thus, it is imperative to assess the influence of green bond issuance on carbon emissions and examine their correlation. Such research holds great potential to expedite the overhaul and modernization of businesses and to construct a circular economy system. This paper uses the spatial Durbin model to draw empirical conclusions by using data from 26 provinces in China between 2016 and 2021. Firstly, under different spatial matrices, it has been analyzed that an increase of 1% in the issuance of green bonds leads to a reduction of 0.306% or 0.331% in carbon emission intensity. It shows that green bonds have the potential to substantially reduce carbon intensity. Additionally, the intensity of emissions in the current period is driven by the intensity of emissions in the previous period. Secondly, the analysis of mediated transmission suggests that green bonds can ultimately reduce carbon emission intensity by changing the energy consumption structure or improving the efficiency of green technology innovation. Thirdly, the analysis of heterogeneity shows that the inhibitory effect of green bond issuance on carbon emissions is stronger in less economically developed regions than in economically developed regions. There is a significant inhibitory effect of green bond issuance in neighboring provinces on local carbon emission intensity. This effect is present only in provinces in less economically developed regions and not in economically developed regions. [ABSTRACT FROM AUTHOR]

This paper presents an empirical investigation into the effects of energy transition and digitization on carbon dioxide emissions, serving as a proxy for environmental sustainability, across 28 Chinese provinces from 2000 to 2021. Utilizing both static and dynamic regression analyses, the study reveals a significant driving effect of the energy transition on environmental sustainability, primarily through a reduction in carbon dioxide emissions. Notably, this influence is more pronounced in the eastern region of China, followed by the central and western regions, indicating regional heterogeneity in the impact of the energy transition. Furthermore, digitization is found to have a substantial moderating effect, enhancing energy conservation and emission reductions. As digitization progresses, its capability to diminish the impact of energy transitions on carbon dioxide emissions becomes more apparent, particularly in the eastern region, while this effect is less marked in central and western China. The study also delves into the nonlinear relationship between energy transitions and carbon dioxide emissions, discovering that increased levels of digitization can exacerbate the negative effects of energy transitions on emissions. These findings offer valuable insights into the dynamics of energy transition and digitalization, highlighting their crucial roles in shaping environmental sustainability in China. [ABSTRACT FROM AUTHOR]

The carbon quota allocation rules of China's pilot carbon emissions trading (CET) regions are various, which mainly include benchmarking, historical carbon intensity reduction and auctioning. When the allocation rules change, it is unresolved how to achieve the optimal product prices and effectively allocate the carbon emissions reduction profits of CET-covered enterprises in the cooperative supply chain. Thus, this paper uses the Stackelberg game, Nash equilibrium and the Shapley value based on cost modification to investigate these issues. The results indicate that: (1) The increasing carbon prices can always improve the retail prices only under the auctioning rule. Meanwhile, the growing low-carbon awareness of consumer cannot be always conducive to improving the wholesale and retail prices, and the similar product prices of non-CET-covered enterprises have greater impact on the wholesale prices than that on the retail prices. (2) Only under the free carbon quota allocation rules, can the optimal wholesale and retail prices under the Stackelberg game be always higher than those under the Nash equilibrium. Meanwhile, the auctioning rule can better reduce carbon emissions than the free allocation rules. (3) Improving carbon emissions reduction contribution and emission reduction costs can be conducive to increasing the carbon emission reduction profits of the supplier and retailer, while the impact of carbon emission reduction contribution on improving the carbon emission reduction profits is not always greater than that of the carbon emission reduction costs. [ABSTRACT FROM AUTHOR]

Climate anomalies are affecting the world. How to reduce carbon emissions has become an important issue for governments and academics. Although previous researchers have discussed the factors of carbon emission reduction from environmental regulation, economic development, and industrial structure, limited studies have explored the carbon emission reduction effect of a city's spatial structure. Based on 108 Chinese cities from the Yangtze River Economic Belt between 2003 and 2017, this paper examines the impact of the city cluster policy on city carbon emissions using the difference-in-differences (DID) method. We find that: (1) The city cluster policy has significantly reduced the cities' carbon emissions by 7.4%. Furthermore, after a series of robust and endogenous tests, such as parallel trend and PSM-DID, the core conclusion still remains. (2) We further identify possible economic channels through this effect, and find that city cluster policy would increase city productivity, city technological innovation, and industrial structure optimization. The conclusions of this paper have important practical significance for China to achieve carbon neutrality and facilitate future deep decarbonization. [ABSTRACT FROM AUTHOR]

The development of information technology such as the continuous improvement of mobile Internet infrastructure and the performance of computers has made it easy to process and share information. The huge market demand for location-based information services provides huge impetus to the generation and development of mobile terminal positioning technology. Generally speaking, the main causes of climate change can be summarized into two categories: natural climate fluctuations and the impact of human activities which is a major measure taken by China to actively respond to climate change. This is a successful approach to actively explore the rapid development of China's industrialization and urbanization, which not only develops the economy and improves people's livelihood but also responds to climate change and reduces carbon intensity. Firstly, this paper mainly is aimed at the connotation of regional low-carbon economic development mode, studying the basic mode of regional low-carbon economic development, and analyzing the characteristics and applicable conditions of each mode. Secondly, based on the machine learning algorithm of data mining, the main mode selection of regional low-carbon economic development is discussed. Thirdly and finally, when choosing the regional low-carbon economic development mode, comprehensive consideration should be given to the economic development basis, energy structure, resource characteristics, industrial status, development mode, geographic location, and other factors. This paper studies the basic conditions and applicable conditions of regional economic development models. The conclusion shows that from the perspective of regional economic evolution, low-carbon economy can be regarded as the decarbonization process of economic development. It is an economic form combining its own characteristics and an inevitable requirement for the transformation of regional economy from other economic models to low-carbon economic models. And other factors of Selection of regional economic development foundation, energy structure, resource characteristics, industrial status, development mode, geographical location, were also discussed. [ABSTRACT FROM AUTHOR]

Compared to cast-in-situ construction, prefabricated construction reduces the generation of construction waste; however, construction waste cannot be entirely eliminated through prefabrication and can be generated at each stage of its life cycle. This paper generates a system dynamics model, comprising two causal-loop diagrams and one stock-flow diagram, to simulate the effectiveness of policies on decarbonizing prefabricated concrete building waste. The model can predict the annual and total construction waste, along with the associated carbon emissions, under different policy scenarios in China by 2030. This paper focuses on prefabricated concrete buildings in China, with specific attention to the wastage of concrete, bricks, mortar, and metal. In single-factor scenario simulations, increasing landfilling fee is the most effective policy for waste decarbonization, potentially reducing carbon emissions by 31%. Increasing prefabrication rate and enhancing regulation degree at the design phase of prefabrication can directly reduce prefabricated building waste generation, subsequently resulting in an 18% and 7% carbon emissions reduction. Multi-factor scenario simulations demonstrate that the simultaneous application of all four policies is most effective, achieving a 44% reduction in carbon emissions. This research establishes a theoretical framework for prefabricated concrete building waste management and provides the Chinese government with practical insights on optimizing policies for waste decarbonization in the dynamic landscape of prefabrication. • This paper develops the prefabricated building waste generation and disposal systems. • The model predicts the construction waste and associated carbon emissions by 2030. • Raising landfill fee is an effective policy for decarbonizing construction waste by 31.06%. • Prefabrication rate reaching 100% can reduce 18.04% of carbon emissions from waste. • Applying all policies is most effective, achieving a 44.34% carbon emissions reduction. [ABSTRACT FROM AUTHOR]

The reduction of carbon emissions in the power industry will play a vital role in global decarbonization. The power industry has three main strategies to achieve this reduction in emissions: to implement lean marketing strategies that effectively target users of power and encourage them to adopt decarbonizing technologies and services; to optimize the efficiency of these users of power; and to improve the efficiency of renewable energy sources. This paper establishes a comprehensive evaluation system of indexed data from power industry customers for the development of lean marketing strategies. This system evaluates indexes derived from customer data on renewable energy sources, carbon emissions, energy efficiency, and customer credit. It adopts the DEMATEL–CRITIC combination weight assignment and VIKOR method for system evaluation and conducts simulation experiments on customer data in a region of Northeastern China to give an example of how this method could be applied in practice to lean marketing. The results show that the evaluation system proposed in this paper can govern the lean marketing decision-making of power sales enterprises. [ABSTRACT FROM AUTHOR]

The income gap and global warming have always been topics of common concern to scholars worldwide. Internationally, there is no consensus yet about the impact of the income gap on carbon emissions, and there are few studies about that in China. To explore the effect of the income gap on carbon emissions at the provincial level in China, this paper first theoretically and qualitatively analyzes the non-linear impact of the income gap on carbon emissions. Then, the Gini coefficient of the resident income of different regions in China from 2010 to 2019 is calculated. Finally, a threshold regression model is used to quantitatively test the existence of a threshold effect between the income gap and carbon emission intensity in China. The threshold value is the per capita disposable income of residents. The results show that the income gap is positively related to carbon emission intensity in poor regions. In high-income areas, the widening income gap inhibits the increase in carbon emission intensity. Based on this, this paper proposes policy recommendations to narrow the income gap and reduce the intensity of carbon emissions. [ABSTRACT FROM AUTHOR]

Given the spatial heterogeneity of the social-economic situations across different regions in China, the decomposition of emission reduction targets should be designed according to the actual characteristics of the industrial economy. There is concern about the loss of industrial competitiveness and leakage of CO2 emissions if just seven pilot carbon markets operate independently, so the national carbon market of the power sector was established in 2021. In this study, a China two-region CGE model including Guangdong (GD) and the rest of China (ROC) is built on an analysis of the long-term effects of CO2 prices in industrial sectors at the target 2030 peak. Based on this model, we constructed one business-as-usual scenario and six comparison carbon tax scenarios to quantify the CO2 cost impact for a wide range of manufacturing sectors and identify specific economic activities that face relatively high CO2 costs between the two regions. Based on the China two-region CGE model, the risks of leakage and competitiveness distortions in these potentially exposed sectors are qualitatively assessed. The results show that chemical, nonferrous metal, and machinery are GD's competitive sectors, and agriculture, food, textile, paper, cement, construction, and service belong to ROC's competitive industry. Both GD and ROC need to further unify carbon pricing policies at the same time to effectively coordinate the carbon intensity reduction target and industrial development, which is 2.6% and 3.2% of the severe GDP loss compared with BaU when implementing carbon tax policy. The results can support the setting of the carbon tax and industrial competitiveness promotion policy and with a strong reference to support the provincial emission reduction path. [ABSTRACT FROM AUTHOR]

Based on the total carbon emission data of 30 provinces and cities in China from 2000 to 2020, this paper used non-parametric kernel density estimation and traditional and spatial Markov probability transfer matrix methods to explore the temporal and spatial dynamic evolution characteristics of carbon dioxide emissions in China and then used a super-SBM model to calculate the carbon emission reduction potential of each province. The results showed that: (1) from 2000 to 2020, the total carbon emissions in China showed an upward trend of fluctuation, from 1.35 Gt to 4.90 Gt year by year, with an annual growth rate of 13.10%. (2) The core density curve showed a double peak form of "main peak + right peak," indicating that a polarization phenomenon occurred in the region. (3) The overall trend of carbon dioxide emissions shifting to superheavy carbon emissions was significant, and the probability of transition was as high as 74.69%, indicating that it was challenging to achieve leapfrog transition in the short term. (4) Based on the principle of fairness and efficiency of provincial carbon emission reduction, mainland China's 30 provincial administrative regions can be divided into four types. Finally, the carbon emission reduction path is designed for each province. [ABSTRACT FROM AUTHOR]

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms' optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings. Fig. 1. Introduction of the research questions Fig. 2. Framework of this paper. [Display omitted] • Carbon emission trading and green certificate trading policies were introduced into a dynamic CGE model. • The comprehensive performance of different scenarios were measured using total factor productivity index. • The policy effects of different scenarios were explored from 2020 to 2060. • The even more effective reform measures were adopt, within a single policy. • A sensible mix were proposed, within different policies. [ABSTRACT FROM AUTHOR]

Effectively allocating emission control targets is critical for China to achieve its emission reduction goals. This study researched the efficiency of total carbon dioxide (CO2) emission control target allocations during the 13th Five-Year Plan period (2015–2020). The efficiency of carbon intensity reduction targets, allocated by the National Development and Reform Commission in 30 regions, was assessed using a Directional Distance Function (DDF) model. Then, a Zero-Sum Gains (ZSG)-DDF model was constructed to determine how to optimally allocate total CO2 emission, under the premise of maximizing economic benefits and minimizing CO2 emissions. The results showed that in the case of fixed total CO2 emissions, to improve the resource allocation efficiency, the quota should be increased in the regions with high efficiencies, and the quota should be reduced in the regions with low efficiencies. The results in this paper can help guide the future allocations of total CO2 emission in China. [ABSTRACT FROM AUTHOR]

Innovative emission reduction technologies combined with optimized processes can effectively reduce the environmental pressure in the iron and steel industry (ISI). Identifying and comparatively analyzing the carbon emissions (CE) from steel productions is conducive to achieving China's ambitious carbon reduction targets. In this paper, we investigated two innovative routes: the shaft furnace-induction furnace-basic oxygen furnace route (SF-IF-BOF) and shaft furnace-electric smelting furnace-basic oxygen furnace route (SF-ESF-BOF), alongside two traditional routes: the blast furnace-BOF route (BF-BOF) and SF-electric arc furnace route (SF-ESF). The analysis was based on a functional unit of 1000 kg of crude steel (CS). By establishing the mass and energy balance models and decision criteria, 12 typical smelting scenarios for these routes were analyzed and compared in terms of materials consumption and CE. The results showed that increasing the proportion of scrap was beneficial for reducing CE. Under the condition of a 50% scrap ratio, the CE of the SF-IF-BOF, SF-ESF-BOF, BF-BOF, and SF-EAF routes were 768, 823, 946, and 870 kg, respectively. The SF-IF-BOF route displayed the most significant advantage of CE reduction. In addition, by using 100% zero-carbon electricity substitution, the SF-EAF route exhibited the greatest CE reduction effect of 41% (from 870 to 510 kg/t). While deploying 100% biomass fuels to replace coal, the CE of the BF-BOF route reduced by 80%. Finally, after introducing biomass fuels and zero-carbon electricity, the CE of the SF-IF-BOF route was minimized to a significantly low 13 kg/t (from 768), nearing zero CE. This research is expected to provide a novel solution for further CE reduction in the future of ISI, contributing to environmental sustainability and carbon reduction goals. • Suggestions for solutions to reduce CO2 emissions in the steel industry are provided. • Emissions of the SF-IF-BOF and SF-ESF-BOF compared to the BF-BOF and DRI-EAF routes. • DRI is considered to be generated using COG zero reforming process. • Using zero-C electricity and biomass can reduce CO2 emissions for the steel industry. [ABSTRACT FROM AUTHOR]

Substituting renewable energy for coal power under the carbon neutrality goal is an inevitable path for power system transformation, while the high share of renewable energy leads to a rapid rise in system flexibility demand. Currently, coal-fired power is still one of the most economical and reliable large-scale flexible resources in China. Therefore, the issue of how to mitigate the conflict between cleanliness and flexibility for coal-fired power is urgent to be solved. Accordingly, this paper established an optimization framework for the retirement and technology adoption of coal-fired power units with high spatial-temporal resolution, and the regional carbon emission budget is estimated from a top-down perspective. Secondly, a dynamic carbon quota allocation method considering technological innovation and adoption is proposed. The carbon trading cost of the units is evaluated from a bottom-up perspective, to realize the interaction between the obligatory policy constraints of the top-level target and the flexible incentives of the market mechanism. Finally, the empirical analysis was conducted in the Beijing-Tianjin-Hebei region of China. The optimal solution achieved by proposed method results in a substantial 27% reduction in total costs while keeping the increase in carbon emissions below 3%. By analyzing the dynamic evolution path of coal and the scale of diffusion of multi-type technologies, the differences in the roles coal power assuming at various stages of decarbonization are discussed. The robustness of the model and the validity of the proposed method were verified by comparing the dynamic/static carbon quota allocation. Overall, this study provides an effective optimization model for investigating the low-carbon technology adoption timing for coal power in Beijing-Tianjin-Hebei region, and offers valuable insights for other coal-dominated emerging economies seeking for power system transformation roadmap in the forthcoming net-zero carbon age. • A decision-making optimization model of CCPs low-carbon technology is proposed. • A dynamic CET system with upper-lower layer interaction is introduced. • The proposed dynamic CET system results in 27% reduction in total costs. • The high-resolution data of coal power in Beijing-Tianjin-Hebei region are used. • The strategy to solve the conflict between cleanliness and flexibility is given. [ABSTRACT FROM AUTHOR]

Mitigating greenhouse gas emissions is a significant global challenge, and precise prediction of carbon emissions holds the utmost importance for China to attain its dual carbon goal. To overcome the limitations of existing studies using a single forecasting method, based on quadratic exponential smoothing, multiple linear regression, and Gaussian process regression models, this paper constructs a carbon emission combination forecasting model with the generalized induced ordered weighted average (GIOWA) operator and analyzes carbon emission reduction performance. Empirical testing utilizing China's carbon emission data from 1980 to 2020 reveals the following findings: (1) The GIOWA combination forecasting model significantly enhances the accuracy of carbon emission forecasts, with an average accuracy exceeding 99.5% over the sample period, surpassing various single forecasting methods. (2) The carbon emission reduction target can be achieved under three different scenarios of low, average, and high GDP growth rates. Specifically, under the low growth rate scenario, China can achieve a 60% reduction in carbon intensity by 2025 and a 65% reduction by 2030. This study offers valuable decision support for the development of effective carbon reduction policies. [ABSTRACT FROM AUTHOR]

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO 2 emissions. Through an examination of the effects of the National E-Commerce Demonstration City (NEDC) policy from 2006 to 2017, this paper reveals that e-commerce growth facilitated by the NEDC policy resulted in a 7.89% reduction in total CO 2 emissions and a per capita reduction of 1.1146 tons in the pilot cities. Mechanism analysis demonstrates that the upgrading of industrial structure, development of digital finance, and the growth of innovation and entrepreneurship serve as primary pathways for this impact. The robustness of the findings is supported by parallel trend tests, placebo tests, and additional sensitivity analyses. Furthermore, the research reveals that the NEDC policy exhibits a more significant reduction in CO 2 emissions in cities with higher levels of economic development and non-resource-based cities. Welfare analyses show that the NEDC policy has significant socio-economic effects. These findings provide new evidence on the environmental effects of the digital economy and offer insights into achieving carbon neutrality. • The National E-commerce Demonstration Cities (NEDC) policy reduced CO 2 emissions. • Optimizing industrial structure, enhancing digital finance, and promoting innovation and entrepreneurship are mechanisms. • The effect is more pronounced in cities with high economic levels and high resource dependence. • The NEDC policy substantially enhanced social welfare by reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Introduction: Although forecasting electric vehicles' growth in China was frequently reported in the literature, predicting electric vehicles market penetration as well as corresponding energy saving and carbon dioxide mitigation potential in a more suitable method is not well understood. Methods: This study chose the double species model to predict electric vehicles' growth trajectory under mutually competitive conditions between electric vehicles and internal combustion engine vehicles. For comparison, it set two scenarios: with 200 and 300 vehicles per thousand persons at 2050. To give details on energy saving and carbon dioxide mitigation potential induced by electric vehicles' market penetration, it further divided electric vehicles into five subgroups and internal combustion engine vehicles into seven subgroups, therein forming respective measurement formulas. Results: This paper solved the double species model and thus got its analytical formula. Then it employed the analytical formula to conduct an empirical study on electric vehicles market penetration in China from year 2010 to 2050. Under scenario 300, electric vehicles growth trajectory will emerge a quick growth stage during 2021–2035, thereafter keeping near invariant till 2050. Meanwhile, current internal combustion engine vehicles' quick growth will continue up to 2027, then holding constant during 2028–2040, afterwards following a 10-year slowdown period. Scenario 200 has similar features, but a 2-year delay for electric vehicles and a 5-year lead time for internal combustion engine vehicles were found. On average, scenario 300 will save 114.4 Mt oil and 111.5 Mt carbon dioxide emissions, and scenario 200 will save 77.1 Mt oil and 73.4 Mt carbon dioxide emissions each year. Beyond 2032, annual 50.0% of road transport consumed oil and 18.6% of carbon dioxide emissions from this sector will be saved under scenario 300. Discussion: Compared with scenario 200, scenario 300 was more suitable to predict electric vehicle market penetration in China. In the short-term electric vehicle penetration only brings about trivial effects, while in the long-term it will contribute a lot to both energy security and carbon dioxide mitigation. The contribution of this article provided a more suitable methodology for predicting electric vehicle market penetration, simulated two coupled trajectories of electric vehicles and internal combustion engine vehicles, and discussed relative energy-saving and climate effects from 2010 to 2050. [ABSTRACT FROM AUTHOR]

Introduction: Although forecasting electric vehicles' growth in China was frequently reported in the literature, predicting electric vehicles market penetration as well as corresponding energy saving and carbon dioxide mitigation potential in a more suitable method is not well understood. Methods: This study chose the double species model to predict electric vehicles' growth trajectory under mutually competitive conditions between electric vehicles and internal combustion engine vehicles. For comparison, it set two scenarios: with 200 and 300 vehicles per thousand persons at 2050. To give details on energy saving and carbon dioxide mitigation potential induced by electric vehicles' market penetration, it further divided electric vehicles into five subgroups and internal combustion engine vehicles into seven subgroups, therein forming respective measurement formulas. Results: This paper solved the double species model and thus got its analytical formula. Then it employed the analytical formula to conduct an empirical study on electric vehicles market penetration in China from year 2010 to 2050. Under scenario 300, electric vehicles growth trajectory will emerge a quick growth stage during 2021-2035, thereafter keeping near invariant till 2050. Meanwhile, current internal combustion engine vehicles' quick growth will continue up to 2027, then holding constant during 2028-2040, afterwards following a 10-year slowdown period. Scenario 200 has similar features, but a 2-year delay for electric vehicles and a 5-year lead time for internal combustion engine vehicles were found. On average, scenario 300 will save 114.4 Mt oil and 111.5 Mt carbon dioxide emissions, and scenario 200 will save 77.1 Mt oil and 73.4 Mt carbon dioxide emissions each year. Beyond 2032, annual 50.0% of road transport consumed oil and 18.6% of carbon dioxide emissions from this sector will be saved under scenario 300. Discussion: Compared with scenario 200, scenario 300 was more suitable to predict electric vehicle market penetration in China. In the short-term electric vehicle penetration only brings about trivial effects, while in the long-term it will contribute a lot to both energy security and carbon dioxide mitigation. The contribution of this article provided a more suitable methodology for predicting electric vehicle market penetration, simulated two coupled trajectories of electric vehicles and internal combustion engine vehicles, and discussed relative energy-saving and climate effects from 2010 to 2050. [ABSTRACT FROM AUTHOR]

Abstract China is under great pressure to reduce CO 2 emissions (CEs). Meanwhile, China's economy is more reliant on domestic consumption (particularly household consumption) than on exports. So, to achieve large-scale CEs' reduction for China, there is urgent need to study the CO 2 emissions from household consumption (CEs HC). Based on the environmentally extended Multiregional Input-Output model, this paper not only calculates both the direct CO 2 emissions from household consumption (DCEs-CH) and the indirect CO 2 emissions from household consumption （ICEs CH）at the provincial level in China, but also splits ICEs CH into domestic and foreign CEs. The findings of this paper are as follows: (1) between 2002 and 2012, CEs HC in China increased significantly, by 2.27 times, from 1306.17 Mt in 2002 to 2971.01 Mt in 2012. The CO 2 emissions from urban household consumption (CEs UHC) in China accounted for 75% of the total CEs HC in 2012. (2) The per capita CEs HC in China increased from 1.02 t/person in 2002 to 2.19 t/person in 2012. There was a prominent disparity in the total CEs and per capital CEs HC among the provinces in China. The per capita CEs HC were larger in provinces with a higher level of urbanization and higher per capita income. (3) The large-scale interprovincial transfer of ICEs caused by household consumption mainly occurred either within the Yangtze River Delta, the Pearl River Delta, and the Jing-Jin-Ji region or among these three regions. In addition, to service the household consumption, the energy-abundant provinces transferred a considerable amount of CEs to eastern coastal provinces. To achieve CEs' reduction, consumption-based strategies, such as lifestyle changes, should be required in parallel with strategies to reduce emission intensities on the producer side in China. Urban households should take more responsibility for the reduction of CEs in China. The interprovincial "Carbon leakage" should be taken into account when making policies for CEs' reduction in China. [ABSTRACT FROM AUTHOR]

Abstract With the development of China's economy, the use of fossil energy has become more and more, resulting in increasing carbon emissions. CO 2 emissions have caused global warming, threatening humans and creatures on Earth. In order to effectively suppress the growth of carbon emissions, it is necessary to analyze the influencing factors of carbon emissions and apply them to predict carbon emissions. This paper presents sixteen potential influencing factors and uses grey relational analysis to identify the factors that have a strong correlation with carbon emissions. The principal component analysis (PCA) is used to extract the four principal components, which reduce the redundancy of the input data. The long short-term memory (LSTM) method is established to predict carbon emissions in China. We use back propagation neural network (BPNN) and Gaussian process regression (GPR) to compare LSTM method. The simulation results show that the prediction accuracy of carbon emissions based on LSTM is better than that of BPNN and GPR, indicating the effectiveness of PCA and LSTM in prediction of carbon emissions. Finally, this paper provides the theoretical basis for China to reduce carbon emissions by studying prediction of carbon emissions. Highlights • Grey correlation analysis is applied to identify factors associated with carbon emissions. • Principal components analysis is used to simplify input variables and obtain the main factors affecting carbon emissions. • The long short-term memory model is used for the first time to predict carbon emissions. • The direction of reducing carbon emissions in China is given by analyzing the prediction results. [ABSTRACT FROM AUTHOR]

Abstract Cities do play significant roles in tackling global environmental changes. Decoupling economic growth from environmental pressures is critical for global sustainable development, however, few studies pay attention to the decoupling analysis at city level. This paper conducts a comparative study of the decoupling performance of economic output from CO 2 emissions between Beijing and Shanghai, the top two cities in China. In this paper, the decoupling trends and the decoupling effects are explored from a sectoral perspective, by combining the Tapio decoupling elasticity and the logarithmic mean Divisia index (LMDI) model. The results show that Beijing and Shanghai both experienced weak decoupling in construction, expansive negative decoupling in transport, expansive coupling in trade, and weak decoupling in others over the period 2000–2015. Moreover, agriculture appeared strong decoupling in Beijing, and recessive decoupling in Shanghai; industry showed strong decoupling in Beijing, and weak decoupling in Shanghai. The decoupling effects showed different magnitudes and patterns in industrial sectors between Beijing and Shanghai. Overall, per-capita GDP and population growth inhibited the decoupling, while energy intensity, industrial share, energy mix accelerated the decoupling process. Highlights • The decoupling of sectoral economic output from carbon emissions in Beijing and Shanghai are investigated. • Beijing's industry enjoyed better decoupling performance than Shanghai's industry. • Transport should be put more emphasis in further decoupling efforts. • Energy intensity is crucial effect accelerating the decoupling process. [ABSTRACT FROM AUTHOR]

Resource-based cities generally face the dual pressures of ecological damage and high carbon emissions. Taking the National Sustainable Development Plan for Resource-Based Cities (NSDP) issued in China as a quasi-natural experiment, this paper for the first time, uses the difference-in-difference method to test the coordinated effect of green expansion and carbon reduction brought by NSDP at the county level using rich remote sensing data. The results show that NSDP has significantly promoted the ecological quality improvement and CO 2 emission mitigation in resource-based cities, and the above coordinated benefits mainly occur in energy, mature, declining, northeastern, and municipal resource-based cities. Through the mechanism analysis, we find that NSDP mainly works by promoting the transformation of industry sector to agriculture, animal husbandry, accommodation and catering, finance, and environment management sectors, which are reflected in output, employment, product yield, land type and land transaction area. Further discussion on the tertiary industry suggests that developing tourism, easing financing constraint and strengthening environment regulation, are the key channels to borne fruit in green expansion and carbon reduction. • Analysis of placed-based policy on resource at the county-level. • Coordinated effects of green expansion and carbon reduction are tested. • Key areas of policy effects are identified. • Industrial structure transformation and its related mechanisms are discussed. [ABSTRACT FROM AUTHOR]

The significant carbon emissions generated by China's sludge treatment industry cannot be ignored. However, with only a few recent well-executed studies on carbon emission reduction in the sludge treatment field, the literature awaits a more comprehensive assessment and prediction whether the sludge treatment industry can achieve the dual-carbon target. This article aims to fill this gap, holistically examining the carbon emission reduction potential in the industry. Using the moving average method and IPCC method, the projected carbon emission value of sludge treatment in China from 2021 to 2030 indicates a total net carbon emission of 0.3 million tons by 2030. This suggests that the sludge treatment industry is on track to meet the carbon peak target, leaving surplus carbon quotas or credits available to assist other industries for carbon emissions reductions. The study also finds that only incineration and composting demonstrate the potential to be able to partially offset carbon emissions through resource utilization in certain stages or processes. Therefore, the current sludge treatment methods do not yield comprehensive emission reduction effects and cannot provide sufficient contribution to China's dual carbon goals. To address this issue, the paper explores a new scheme that focuses on improving the sludge treatment process. The scheme comparative analysis reveals that, even with all sludge treatment methods optimized, the total net carbon emissions of the industry could only be reduced to 0.058 million tons. This reduction falls short of significantly mitigating carbon emissions to achieve the carbon neutrality goal. Therefore, industries unable to achieve substantial net carbon reduction may need to explore alternative measures, such as carbon sinks, to ultimately attain carbon neutrality. [ABSTRACT FROM AUTHOR]

Towards the target of carbon neutrality, the need for deep decarbonization in iron and steel industry is increasingly recognized. Existing studies rarely mention the effects of collaboration with its source industries for carbon emission reduction in ISI. Furthermore, the fundamental laws of CO 2 emission reduction capacity with cost change of source substitutions for various steel production routes under market economics are unclear. To avoid the blind substitution of clean sources for meeting the urgent requirements for decarbonization in China's steel industry, which may result in increased carbon emissions and uneconomical carbon reduction from a lifecycle perspective. This paper integrates the production and utilization processes of source resources into the steel production process, establishes coupling models of carbon and value flows for four main types of steel production routes, and aims to quantitatively analyze the specific CO 2 emission reduction potential and costs. Three economic market development scenarios from 2021 to 2060 are set, and the marginal carbon prices of various source substitutions are determined. Subsequently, decarbonization pathways with the goal of minimizing production costs are developed. Finally. The future resource demand and the carbon emission reduction contributions of source industries collaborated are predicted. The results show that (1) about 81.7–89.9 % of CO 2 emissions could be reduced in the scenarios; (2) the demands of electric power and hydrogen energy will reach 54.48–511.90 BkWh and 7.47–10.31 Gt in 2060, respectively; and (3) collaboration with source industries could make 18.3–18.3% of CO 2 emission reduction contributions. All in all, this study could provide scientific support for the low-carbon transformation of China's steel industry. • Carbon and value flows of BF-BOF, Scrap-EAF, DRI-EAF, MOE-EAF routes are analyzed. • CO 2 emission reduction potential with marginal carbon price is determined. • Decarbonization paths during 2021–2060 for China's steel industry are developed. • Quantify the contribution of collaborative carbon reduction with source industries. [ABSTRACT FROM AUTHOR]

Under the situation of global low-carbon development, the contradiction among energy consumption, economic growth, and CO2 emissions is increasingly prominent. Considering the possible two-way feedback among the three, based on the panel data of 30 regions in China from 2000 to 2017, this paper establishes a spatial Durbin model including economic growth, energy consumption equation, and CO2 emissions and studies the dynamic relationship and spatial spillover among economic growth, energy consumption, and CO2 emissions effects. The results show that the economic growth can significantly improve carbon dioxide emissions, and China's economic growth level has become a positive driving force for carbon dioxide emissions. However, economic growth will not be significantly affected by the reduction of carbon dioxide emissions. There is a two-way relationship between energy consumption (ENC) and carbon dioxide emissions (CO2). Energy consumption and carbon emissions are interrelated, which has a negative spatial spillover effect on the carbon dioxide emissions of the surrounding provinces and cities. [ABSTRACT FROM AUTHOR]

The five major power generation corporations dominate the power industry in China, and play vital roles in China's carbon trading scheme. Under this circumstance, this paper studies the allocation of carbon emission quotas to China's five major power generation corporations based on the fairness and efficiency principles, which proves the primary prerequisite for setting up the national carbon market. Specifically, a bi-level programming model is developed to optimally allocate carbon emission quotas to the corporations, and then a zero sum gains data envelopment analysis (ZSG-DEA) model is adopted to evaluate the efficiency of the allocation so as to adjust towards the optimal solution. The results indicate that power generation has significantly positive influence on the allocation of carbon emission quotas. Moreover, the difference between the sum of optimal carbon emissions and the sum of carbon quotas allocated is 1.431 billion tons, which implies that the emission reduction potential of the five major power generation corporations is tremendous. Finally, the bi-level model in this paper can well capture the context of China's power industry and provide effective results for the allocation of carbon quotas. [ABSTRACT FROM AUTHOR]

To achieve the Chinese government's CO 2 emissions reduction target and build a carbon trading market in the Pearl River Delta (PRD) region, an initial allocation of CO 2 emissions quotas among cities in this region is fundamental. Different from the previous CO 2 emissions quotas allocation methods, this paper uses the maximum deviation method (MDM) to allocate CO 2 emissions quotas in the PRD region by taking the imbalanced development of different cities into consideration. Three principles including equality (represented by a population indicator), efficiency (represented by a GDP indicator) and feasibility (represented by a historical CO 2 emissions indicator) are considered in the method. The results reveal that the allocations derived by the MDM are more balanced than those derived using either single indicator approach or the information entropy method, and more closer to the development targets of Guangdong province. The more balanced allocations will be conducive to stimulating the development of under-developed cities and narrowing the gap between developed and under-developed cities, thus help to achieve the sustainable development in the PRD region. This paper helps to enrich the emissions quotas allocation methods and provides a new equitable method for policy makers to allocate emissions quotas under imbalanced development circumstances. [ABSTRACT FROM AUTHOR]

The metallurgy industry plays a very important role in China's economic growth. Due to its energy concentration, the energy consumption of the industry accounts for a large proportion of China's total energy consumption, and thus contributes to carbon emissions. Whether the metallurgy industry can successfully reduce emissions is related to the future of China's carbon emissions targets. In this paper, the logarithmic mean Divisia index model is used to analyze the change in China's carbon dioxide emissions from 1991 to 2014. The empirical results show that there are three main factors affecting the carbon dioxide emissions of China's metallurgy industry. The three factors are labor productivity, energy intensity and industry size. In addition, we further analyzed the deep motivation of carbon dioxide emissions in the industry, and calculated the future emission reduction potentials. Lastly, according to the empirical results, the paper proposes policy recommendations for the future emission reduction of China's metallurgy industry. [ABSTRACT FROM AUTHOR]

Ecological sustainability of the Chinese economy is vital for China as well as for the world. By integrating environmental input-output analysis, emergy theory, and a complex network model into one framework, this paper explores Chinese sectoral ecological sustainability with the most recent 2012 input-output table. The results show that there are significant differences in emergy intensities among sectors. The non-metal mining sector is the most emergy-intensive sector at 6.06E+17 sej/10000 Yuan, followed by the metal mining sector. Additionally, output connections have a positive impact on the emergy yield ratio, input connections exert a negative impact on the emergy yield ratio, and PageRank centrality has a positive effect on the environmental loading ratio. This paper updates the Chinese sectoral emergy assessment, identifies key sectors in the Chinese ecological network, and analyzes the impact of network indicators on sectoral emergy performance. These analyses will be useful in updating the guide for adjusting industrial structure in China. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

The commercial sector is one of the key sectors in China’s economic transformation and carbon emissions control. This paper applies VAR and STIRPAT models to investigate the major influencing factors of CO 2 emissions in China’s commercial sector over the period 1980–2014. According to the empirical results, per capita GDP, urbanization and energy structure have positive effects on CO 2 emissions while energy intensity has negative effect on CO 2 emissions in the long term. Besides, per capita GDP ranks as the most important driving factors of CO 2 in China’s commercial sector, followed by urbanization, energy structure and energy intensity in that order. This paper argues that reducing energy intensity and improving energy structure are the means to realize CO 2 emission reductions in the commercial sector in the short and long term, respectively. Besides, this paper forecasts that CO 2 emissions in China’s commercial sector will be 1942 Mt by 2030, which is huge and about half of the CO 2 emissions of the European Union in 2014. The findings have important implications for policy-makers to enact CO 2 emission reduction policies. [ABSTRACT FROM AUTHOR]

A carbon reduction labelling scheme is an effective policy tool for motivating enterprises or organizations to reduce their life-cycle-based carbon emissions to benefit the environment. This paper uses system dynamics to model enterprises' compliance with a combined government policy instrument for a carbon reduction labelling scheme, that is, government subsidies and economic sanctions. A case study of paper enterprises in China is provided to demonstrate the application of the proposed model in which two optional technological plans to reduce the enterprises' carbon emissions are examined. The technological plans are categorized as a low-cost and high-cost plan. The simulation results show that both the technological plans are vulnerable to the combined policy making, and the high-cost plan shows superior environmental performance for carbon emissions reduction. However, the enterprises are more inclined to choose the low-cost plan because of superior economic performance. The study offers insights to help enterprises select optimal actions on carbon emissions reduction and to inform government of possible sustainable policy design to promote low-carbon development. The model limitations are discussed as a basis for further improvements in low-carbon development. [ABSTRACT FROM AUTHOR]

Solar energy is a clean energy resource, so the large-scale deployment of photovoltaic ( PV) power generation is of great significance for achieving carbon emission reductions in the electric power industry. This paper proposes models and methods for evaluation of the low-carbon benefits of grid-connected PV power generation projects. The carbon emission (or emission reduction) characteristics and the economic benefits of PV generation are analyzed using the following four metrics: generation capacity revenue, PV cost, loss efficiency improvement, and reserve capacity cost. The corresponding low-carbon benefit models and economic benefit models are then established. By combining the low-carbon and economic characteristics of photovoltaic power generation, a model is proposed for evaluating the low-carbon comprehensive benefits ( LCBs) of photovoltaic power generation and the concept of a carbon emission payback period ( CPP) is put forward. Examples with typical operating data from real-world applications are used to verify the validity of the models and methods proposed in this paper. The analysis results show that photovoltaic power generation has great potential in terms of low-carbon comprehensive benefits compared with conventional power generation. [ABSTRACT FROM AUTHOR]

As the largest emitter of greenhouse gases in the world, the peak values of Chinese CO2 emissions have attracted extensive attention at home and abroad. The carbon dioxide emissions of the Chinese transportation industry, accounting for 9.5% of total carbon dioxide emissions, is one of the high-emission industries, and its total carbon dioxide emissions continue to rise. Therefore, the accurate prediction of the peak values of carbon dioxide emissions from the Chinese transportation industry is helpful for China to formulate a reasonable policy of carbon dioxide emissions control. This paper, firstly, selects six major factors affecting the carbon dioxide emissions of the Chinese transportation industry. They are the Gross Domestic Product (GDP), population, urbanization rate, energy consumption structure, energy intensity, and industrial structure. Then, it builds a prediction model of carbon dioxide emissions based on Support Vector Regression (SVR). Finally, it analyses the sensitivity of each factor. The predicted results show that, under the baseline scenario, they will reach a peak of 1365.71 million tons in 2040; under the low-carbon scenario, the carbon dioxide emissions of Chinese transportation will peak at 1115.43 million tons in 2036; and in the high-carbon scenario, the peak value will occur in 2046 and the carbon dioxide emissions will be 1738.18 million tons. In order to promote the early peak of carbon dioxide emissions from the transportation industry, it is, firstly, necessary to change the mode of economic growth and appropriately reduce the speed of economic development. Secondly, the energy intensity of the transportation industry is reduced and the utilization rate of clean energy is improved. Thirdly, the industrial structure is optimized. Fourthly, the carbon dioxide emissions of the transportation industry caused by the increased urbanization rate are reasonably controlled. [ABSTRACT FROM AUTHOR]

E-commerce has emerged through digital platforms and Internet construction as an important way to achieve low-carbon development in China. To evaluate the mitigation effect of e-commerce, this paper conducts a quasi-natural experiment in terms of the pilot policy of national e-commerce demonstration cities (NEDC) to explore the changes in CO 2 emissions of 270 cities from 2004 to 2017 in China. We also check the asymmetry, mechanism effects and heterogeneity. The results show that the treated cities reduce CO 2 emissions by 4.1% due to the NEDC pilot policy compared to the untreated cities. The estimated results pass the parallel trend test, robustness tests, and the Goodman-Bacon decomposition. Moreover, the NEDC pilot policy increases the CO 2 emissions of treated cities by promoting economic growth (scale effect), and decreases the carbon emissions of treated cities by upgrading the industrial structure (structural effect) and increasing the technological innovation level (technical effect). Furthermore, the CO 2 mitigation effects of the NEDC pilot policy are significantly concentrated in the higher quantiles. Finally, the NEDC pilot policy has significantly negative effects on CO 2 emissions in "postage-free regions," southern China, and cities with large e-commerce sales. The results of this paper provide theoretical and practical references for China to achieve low-carbon development. • Evaluating the impact of pilot policies of national e-commerce demonstration cities (NEDC) on carbon emissions in China. • The NEDC pilot policy reduces carbon emissions of treated cities by 4.1%. • The NEDC pilot policy reduces carbon emissions through structural and technical effects. • The emission-reduction effects are significant at the higher quantiles. • The impact of the NEDC pilot policy on carbon emissions is obviously heterogeneous. [ABSTRACT FROM AUTHOR]

Based on semi-structured interviews with key stakeholders in Nanhai, this paper adopts an analytical framework to examine how a combination of infrastructure-pull, supply-push and demand-pull initiatives implemented by a small district government in southern China were able to facilitate socio-technical transitions to the public transport decarbonization through the establishment of hydrogen refuelling infrastructures, the adoption of fuel cell electric vehicles (FCEVs), and the development of production network on FCEVs. With neither massive investment from the central government nor captive linkage with global automakers, Nanhai out-performs other automotive powerhouses in China by accounting for 9.85 percent of FCEVs and 4.59 percent of all operating hydrogen refuelling stations in China, despite its land area only accounting for 0.01 percent of the country. The provision of hydrogen infrastructures and other pro-active local government initiatives (including financial incentives) facilitate the development of a competitive regionalized production network of about 90 (privately-owned) fuel cell electric buses makers and parts suppliers around the Xianhu Hydrogen Valley in Nanhai. This finding enhances our understanding on effects of (local) state policies on the socio-technical transitions to public transport decarbonization and the long-term development of hydrogen economies in other countries. [ABSTRACT FROM AUTHOR]

Sustained increase in Chinese household income has exerted positive impact on the consumption of goods and services. This paper aims to analyze the unequal impacts of household consumption across different income households on the whole CO 2 emissions, by utilizing a semi-closed input-output model and hypothetical extraction method. The semi-closed input-output model moves household consumption activities into the intermediate use along with dividing urban and rural households to several groups by their income levels, and the hypothetical extraction method (HEM) is used to estimate the reductions of CO 2 emissions relative to total emissions after extracting the consumption of each household group. The results show that: (1) the impact on CO 2 emissions generated by urban households’ consumption are 1.8 times as much as that of rural ones; (2) excluding consumption of higher-income households from the economy would cause greater CO 2 emission reductions than lower ones. Specifically, urban highest-income households have the most relative reductions of CO 2 emissions, which are −12.2% in 2007 and -13.8% in 2012, while for rural highest-income households are −5.1% in 2007 and -5.1% in 2012. And urban and rural highest-income households are respectively 3.8 and 1.8 times on the relative reductions of the lowest-income ones; (3) the relative reductions of household CO 2 emissions from food sectors is the most, while from residence sectors is the least. Based on the above conclusions, this paper puts forward consumption-based policy implications. [ABSTRACT FROM AUTHOR]

Climate change has threatened our economic, environmental, and social sustainability seriously. The world has taken active measures in dealing with climate change to mitigate carbon emissions. Predicting the carbon emissions peak has become a global focus, as well as a leading target for China’s low carbon development. China has promised its carbon emissions will have peaked by around 2030, with the intention of peaking earlier. Scholars generally have studied the influencing factors of carbon emissions. However, research on carbon emissions peaks is not extensive. Therefore, by setting a low scenario, a middle scenario, and a high scenario, this paper predicts China’s carbon emissions peak from 2015 to 2035 based on the data from 1998 to 2014 using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The results show that in the low, middle, and high scenarios China will reach its carbon emissions peak in 2024, 2027, and 2030, respectively. Thus, this paper puts forward the large-scale application of technology innovation to improve energy efficiency and optimize energy structure and supply and demand. China should use industrial policy and human capital investment to stimulate the rapid development of low carbon industries and modern agriculture and service industries to help China to reach its carbon emissions peak by around 2030 or earlier. [ABSTRACT FROM AUTHOR]

Abstract The transport sector produced numerous global greenhouse gas emissions in China. It is important to promote low carbon commuting in China. This paper aims to investigate the determinants of intention and behavior of low-carbon commuting through bicycle-sharing (LCB) in China. Theoretical model and hypotheses are put forward by integrating the theory of planning behavior and theories of value and residual effects. Through structural equation model (SEM), the empirical analysis revealed that residual effect has the largest positive impact on intention of LCB for both males and females. Subjective norms positively affected intention of LCB for males. Attitude towards bicycle-sharing, perceived behavioral control positively affected intention of LCB for females. Intention has higher positive effect on females' behavior of LCB than males'. It is better for government to increase financial subsidies for bicycle-sharing users and producers. [ABSTRACT FROM AUTHOR]

Abstract As a significant basic industry for the national economic development, coal chemical industry is one of the main contributors to energy consumption and CO 2 emission. In this paper, the CO 2 emission in the time series of 2020 and 2030 was calculated and the Logarithmic Mean Divisia Index (LMDI) method was employed to explore the driving forces of CO 2 emission change. According to the industry development, the CO 2 emission in 2020 and 2030 was tentatively predicted. The results show that China's coal chemical industry has experienced a significant increase in CO 2 emission, from 177.87 million ton CO 2 (MtCO 2) to 427.19 MtCO 2 in 2005–2015, with an average annual growth rate of 9.16%. Currently, the coal-to-ammonia industry is the largest contributor. According to the decomposition analysis, the economic growth and energy intensity are the two positive factors and the industry structure is a negative factor to carbon emission change. In 2020, the CO 2 emission will be 617.34 MtCO 2 under slow development scenario and 759.69 MtCO 2 under rapid development scenario. In 2030, the CO 2 emission will reach 1041.90 MtCO 2 and 1440.13 MtCO 2 under slow and fast development scenarios, respectively. The coal-to-olefins industry will take the place of coal-to-ammonia and turn to be the largest one in 2020 and 2030 if no CO 2 capture, utilization and storage (CCUS) technologies are adopted. It is probable that the contribution from coal-to-methanol will also play important role for CO 2 emission due to its rapid development. In addition, by 2030, if coal-to-natural gas industry develops as planned, its CO 2 emission will significantly increase, which has the potential to be equivalent to that of olefins industry. Highlights • The CO 2 emission evolution trend in China's coal chemical industry is analyzed. • The major driving factors of CO 2 emission change are identified and the effect sizes of various factors are evaluated. • The CO 2 emission in 2020 and 2030 is forecasted. • Policy implications to policymakers are provided. [ABSTRACT FROM AUTHOR]

Abstract China is the world's largest producer and consumer of non-ferrous metals, and the non-ferrous metals industry is energy- and emission-intensive. The CO 2 emissions of the industry have increased nearly four times from the level in 2000 to reach 310 million tons in 2014. To cope with the issues of CO 2 emission and other environmental problems under the background of low-carbon development, it is necessary to analyze the CO 2 emission performance of China's non-ferrous metals industry. Based on group heterogeneity, this paper uses data envelopment analysis to calculate the Shephard distance function and then uses the Shephard distance function and non-parametric meta-frontier methods to construct the meta-frontier Malmquist CO 2 emission performance index (MMCPI) of China's non-ferrous metals industry during the period 2006–2014. The index is further decomposed into three parts: technical efficiency change, technological progress change and technological catch-up effect. The results showed that the MMCPI of China's non-ferrous metals industry exhibits an upward trend during the review period. The improvement in CO 2 emission performance is most significant in the central region, followed by the eastern and western regions. Based on the decomposition of MMCPI, the effect of technological progress are significantly different for the different regions, indicating that more targeted policies are need for different regions. [ABSTRACT FROM AUTHOR]

Abstract Theses on China's low-carbon economy focus on the driving forces of carbon emissions, energy–carbon efficiency, and the carbon–economy nexus. Meanwhile, comprehensive frameworks that assess China's green economy and also analyze its driving forces are lacking. This paper constructs a green productivity index based on the Global Malmquist–Luenberger productivity index to evaluate the development of the low-carbon economy in China. A panel vector auto-regression model is adopted to further analyze the endogenous interactions and dynamic relationships among aspects of the green economy and its influencing factors. Empirical results show that China has seen a balanced development of green economic growth in 2000–2016, achieved in a series of clear stages. To maintain the sustainable growth of the low-carbon economy in the future, China must rely more on technological progress. This research also makes some important discoveries that have not been found in previous studies: the impact of China's industrial structure and energy consumption structure on the green economy are short term, having a duration of impact of approximately four years. Finally, we provide an assessment of provincial green economy development and describe the corresponding policy implications. Graphical abstract Image Highlights • A comprehensive analysis of China's green economy. • A slack-based Global Malmquist–Luenberger index is adopted to calculate the Green Productivity Index in China. • The stage characteristics of green economic development are identified. • A panel vector autoregressive model is used to analyze the interactions among aspects of the environmental economic system. • The findings that the impact duration of energy structure, industry structure, and urbanization on the green economy is about four years. [ABSTRACT FROM AUTHOR]

Abstract Despite the impact of urbanization on carbon emission has been extensively studied in recent years, the mechanisms by which urbanization, real estate investment affect carbon emissions are still unclear. Different from previous studies focused on direct effect, this paper investigated the impact of urbanization and real estate investment on China's carbon emissions with considering of the provincial contribution and spatial spillover effect. Using the multiplicative logarithmic mean Divisia index (M- LMDI) model, spatial panel data model and panel data of 30 provinces in China over the period 1997–2015, the results suggested that the contributions to carbon emissions and their driving mechanisms were significantly different across provinces. The top five provinces in China that contributed the most to the growths of carbon emissions in China were Shandong, Guangdong, Hebei, Jiangsu, and Henan, with cumulative effects of 10.28%, 10.28%, 5.44%, 5.19%, and 5.09% respectively. The direct impact of urbanization on carbon emissions was significantly negative, but its spatial spillover effect was significantly positive, which suggested that the effect of urbanization on carbon emissions shows significant "pollution transfer effect" after introducing the spillover effect. Due to the characteristics of non-movability and long investment period, the impact of real estate investment on carbon emissions was fixed in the local region, and there was no spillover effect to the adjacent regions. Because of the existence of mutual promotion mechanism between urbanization and real estate investment, the interaction between the two had significant positive direct and spillover effects on carbon emissions, which indicated that the interaction between the two created a reduction effect on both local and adjacent regions. The research can effectively identified the provinces that contribute a lot to China's carbon emissions, and made the decomposition and implementation of the national carbon emission reduction targets more targeted. At the same time, urbanization policies should be combined with real estate policies in the process of carbon emission reduction. [ABSTRACT FROM AUTHOR]

Abstract Economic development has promoted urbanization in the Beijing-Tianjin-Hebei (BTH) region. With the improvement of living standards, residential consumption have become an important contributor to regional carbon emissions. Using an input-output model, we calculated direct and indirect carbon emissions from both urban and rural residential consumption in the BTH region from 2002 to 2012, and then explored the main factors contributing to indirect carbon emissions from residential consumption using a structural decomposition analysis. Results showed that direct carbon emissions from residential consumption increased from 47.13 million tons (MtC) in 2002 to 123.34 MtC in 2012 and indirect carbon emissions from residential consumption increased from 210.36 MtC in 2002 to 550.00 MtC in 2012. Carbon emission intensity and residential consumption level were the main factors affecting indirect carbon emissions during 2002–2012, with varying effects in different sub-regions. Carbon emission intensity played negative effects in Beijing (−37.19 MtC) and Tianjin (−55.39 MtC), positive effects in Hebei (92.89 MtC). Residential consumption level played positive effects in the three sub-regions (79.94 MtC in Beijing, 31.91 MtC in Tianjin and 155.52 MtC in Hebei). Increases in intermediate demand, residential consumption level and population size enhanced indirect carbon emissions. Based on the analysis, this paper contributed to carbon emission reduction strategies in the BTH region. Highlights • Direct carbon emissions from residential consumption increased to 123.34 MtC in 2012. • Indirect carbon emissions from residential consumption increased to 550.00 MtC in 2012. • Carbon emission intensity and residential consumption level were the main impact factors. • Intermediate demand, residential consumption level and population size played positive effects. [ABSTRACT FROM AUTHOR]

Abstract Carbon emission rights allocation affects the effective operation of carbon trading system, and then the achievement of energy saving and emissions reduction objectives. Reasonable allocation scheme should meet the principle of justice. Carbon emission rights allocation based on energy justice is a novel and worth discussing subject. This paper explores the optimization of carbon emission rights allocation based on energy justice. The justice-based model is built with the aid of Gini coefficient. The way to optimize allocation scheme with constraints of population, ecological productive land, fossil energy resources and GDP is discussed. Results show that fossil energy resources and ecological productive land play more important role in the allocation. The prime example is Inner Mongolia, its quota percentage change reaches up to 150.01%. As the GDP and population growth rate are fixed in a given interval, the quota allocation in eastern China shows some interesting results. When the GDP growth rate is 6.25%, the quotas of eastern China will not change with varying population growth rate. The allocation will enter a relative stable stage as the population growth rate is greater than or equal to 6.4‰. These results can provide China with experience to deal with the new problem appeared in carbon trading system like two-children policy and the new normal of economic growth. Highlights • Energy justice-based carbon emission allocation with multiple constraints is explored. • Provincial allocation results in China are related to location-related factors. • Two-children policy has effect on optimal allocation results in eastern China. • The critical values of population and economic growth rate in this model are found. [ABSTRACT FROM AUTHOR]

Abstract The construction industry is one of the largest energy consumers and CO 2 emitters in China. This paper primarily aimed to explore the driving forces of energy-related CO 2 emissions (ECE) from the construction industry. Adopting a comprehensive decomposition approach, this study decomposes the changes in ECE into eight factors. The main results provide the following findings. (1) Industrial activity was the largest factor pushing the growth of CO 2 emissions, driving up CO 2 emissions in all years and contributing to a 174.65 Mt CO 2 emissions increase in total. (2) In contrast, advances in industrial output technology represented the dominant factor inhibiting CO 2 emissions, cumulatively reducing CO 2 emissions by 99 Mt. (3) The effects of potential energy intensity changes and industrial output technical efficiency fluctuated in different years but exerted positive effects on CO 2 emissions over the entire period. (4) Additionally, changes in spatial structure, energy-saving technology, and energy consumption structure as well as energy usage efficiency all contributed to emissions reductions to varying degrees. The CO 2 emissions of the construction industry increased in all provinces, while emissions changes and their factor effects varied distinctly across provinces. Highlights • Direct and indirect CO 2 emissions in China's construction industry are calculated. • Impacts of driving factors are quantified by combining the PDA and IDA methods. • Industrial activity is mainly responsible for CO 2 emissions increase. • Industrial output technology change is the dominant curbing factor. • Factor effects of China's construction industry vary distinctly across provinces. [ABSTRACT FROM AUTHOR]

China has become the global largest CO 2 emitter, and CO 2 emissions from China’s power industry account for almost half of the total. Therefore, the power sector plays a key role in achieving low-carbon economy. It is imperative to formulate comprehensive policy implications for the power sector from both macro and micro perspectives to achieve the target of emission reduction. This paper selected seven socio-economic and technological factors involved in the whole power industry to investigate the mechanism driving CO 2 emissions based on the extended STIRPAT model. The results show that the order of factors’ absolute elasticity coefficients is GDP per capita (0.245) > power generation efficiency (−0.237) > power consuming efficiency (−0.189) > line loss rate (−0.184) > electricity practitioner population (0.143) > industrial structure (0.082) > generation structure (0.068). Further, it is necessary to discuss how a specific power enterprise responds under the low-carbon generation policy to maximize its own interest. The evolutionary game model between power enterprise and government is established and results indicate that it is critical to slash low-carbon production cost, enhance returns on low-carbon production and strengthen the regulation of power enterprises, and reduce the supervision cost of government. Accordingly, policy recommendations are proposed from both macro and micro perspectives for CO 2 emission reduction of power industry. [ABSTRACT FROM AUTHOR]

Reliable energy statistics serve as the foundation and starting point for understanding the climate policies of any country. As the most comprehensive sources of energy data for China, China Energy Statistics Yearbooks (ESY), published by the National Bureau of Statistics (NBS), underwent three major revisions since 2005. This paper analyzes how these three major revisions correspond to data on China's energy use from 1996 to 2012, and theirs impacts on CO 2 emissions, emission intensity and the share of non-fossil energy in total energy consumption, which are key indicators of China's climate policies. Some notable changes include CO 2 emissions for 2012, which increased by 594 Million tons – a figure equivalent to South Korea's annual emissions, which implies that China's emissions at peak level will be higher than previous estimation. Meanwhile, revised emission intensity data show a higher amount and lower decreasing rate, which means that China's achievements in decreasing emission intensity during 2005–2012 have been overestimated, while future difficulties for achieving the goal of 40–45% reduction will be greater. On the share of non-fossil fuel energy in China's total energy mix, the adjustments increased the indicator by 1.26%, reflecting the fact that China's effort in developing non-fossil energy had been underestimated. Discrepancies in reported scattered coal in different statistics versions are the main contributors to these changes. To further enhance the reliability of its energy statistics, and thereby its climate policies, China should remove incentives for misreporting through legal means, and make the technical system for data collection more comprehensive and accurate, with particular emphasis on scattered coal. [ABSTRACT FROM AUTHOR]

Highlights • The input–output model and ecological network analysis (ENA) are integrated. • The control and dependence conditions of economic sectors in China are analyzed. • The key regions and sectors for carbon mitigation of China are identified. Abstract In this paper, multi-regional input–output analysis (MRIO) and ecological network analysis (ENA) are combined to assess carbon flows within China and identify key regions and sectors in the context of spatial heterogeneity for effective carbon mitigation. An interregional carbon network model is established by articulating the directions and magnitudes of carbon flows based on MRIO. ENA is then used to unveil indirect carbon flows and mutual relationships among regions. The results show that the northwest is the largest controller for most regions in China. Most carbon emissions in the rest of China are induced by the east’s final demand and substantial consumption. In addition, at sectoral level, the control and dependence abilities vary by region in China. This study provides an integrated framework to investigate interregional carbon emission structure, identify efficient pathways for coordinated emission mitigation, and reduce global carbon inequality across regions. [ABSTRACT FROM AUTHOR]