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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]

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

Financial development and energy consumption are expected to exert a significant effect on CO 2 emission changes. However, there remains a research gap in optimizing pathways to carbon peaking under financial and energy drivers. This paper constructs a novel model covering finance and energy consumption using the generalized Divisia Index (GDI) method. The GDI allows to quantify the drivers behind the CO 2 emission change in China during 2005–2020. Furthermore, it combines a Monte Carlo simulation with scenario analysis to forecast the path of CO 2 emissions during 2021–2030. The results demonstrate that energy structure and carbon factor remain the principal drivers for CO 2 emission growth in China. Thus, it is still pivotal to optimize energy saving and carbon reduction policies. Financial energy saving has enormous potential for reducing CO 2 emissions over time, even though the immediate influence on CO 2 emission is not significant. China has a substantial probability of reaching the peaking target in 2026 under the technology breakthrough scenario, whereas in the other two scenarios the target cannot be achieved. • The main factor in mitigating CO 2 emissions is the carbon intensity of financial development. • Energy consumption and carbon factor are essential factors to the increase in CO 2 emissions. • Energy intensity of financial development presents a strong potential for emission reductions. • Peaking targets will be achieved by 2030 only under the technology breakthrough scenario. [ABSTRACT FROM AUTHOR]

• Analyze the synergy of CO 2 emission factors of energy-intensive industries via EMD. • Synergy of CO 2 emission factors: long-term trend, short-term volatility. • Elastic coefficient model is used to reveal the heterogeneity of the 10 factors. • Six industries are divided into "stable and balanced" and "sensitive and uneven". Carbon emission is a global problem that countries around the world are paying attention to and urgently need to be resolved. Under the requirements of China's high-quality economic development and the carbon peaking and carbon neutrality goals, energy-intensive industries, as a key link in the industrial chain and the main source of China's CO 2 emissions, have attracted much attention. This paper made a statistical analysis of the scale development of six energy-intensive industries, and explored the fluctuation characteristics of their carbon emissions. The empirical mode decomposition (EMD) model was further introduced to analyze the and synergistic effect among the driving factors of carbon emission, revealing the synergistic mechanism of long-term trend and short-term volatility of factors. Then, the elastic sensitivity analysis model of carbon emissions in energy-intensive industries was constructed to study the heterogeneity of driving effects of factor indicators. From the perspective of factors driving, this article reveals the heterogeneity of the driving effects of ten key factor indicators including economic level (GDP), urbanization level (UP), industrial structure (IS), technological innovation (TI), and environmental regulation (ER); from the perspective of industry development, this paper summarized the "stable and balanced" and "sensitive and uneven" trends presented by the heterogeneity of the six energy-intensive industries. Combining the characteristics of industry emission fluctuations, driving factors' synergies and heterogeneity, from the perspective of short-term and long-term development, four targeted emission reduction control strategies were proposed to provide reference for the low-carbon development of energy-intensive industries. [ABSTRACT FROM AUTHOR]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

The increasing contribution of refrigeration systems to global warming has led to widespread demand for low global warming potential (GWP) refrigerants as alternatives. This paper proposed a comprehensive environmental assessment method for evaluating the carbon footprint of units charged with low-GWP refrigeration. Experimental and numerical studies were used to investigate the carbon footprint impact of using alternative refrigerants at different ambient temperatures in China. Initially, the model was validated through experimental case studies on the use stage of cold storage units. Subsequently, a comparison was made between the cooling capacity (CC) and energy efficiency ratio (EER) of cold storage units charged with different refrigerants. Additionally, the impact of alternative refrigerants in the use stage was analyzed by numerical calculations, and the adaptability of the cold storage unit to the ambient temperature and the national energy structure was also studied. The results indicate that among the four stages of the cold storage unit, the use stage is the hotspot of carbon footprint due to the large amount of energy consumption and the existence of refrigerant leakage and other behaviors. Utilizing low-GWP alternative refrigerants could significantly reduce the carbon footprint of the cold storage units' use stage by 10.56%–16.39%, thereby lowering the whole stage carbon footprint. Furthermore, the ambient temperature and national energy structure could also effectively reduce the carbon footprint of cold storage units' whole stage by 1%–27.17%. Furthermore, after adopting low GWP refrigerants, the life-cycle cost (LCC) of the units have also decreased by 6.1%–12%. Taking into account both environmental impact and economic considerations, it is recommended to directly replace R404A with R448A in the units in regions where the average ambient temperature is above 15 °C. • A carbon footprint evaluation model for the units was established. • Influencing factors of carbon footprint are studied numerically and experimentally. • By GHG assessments, the optimal refrigerant and external conditions were determined. • Alternative refrigerants reduce carbon emissions for cold storage units by 8.8%–24.92%. [ABSTRACT FROM AUTHOR]

Meeting energy needs and ensuring ecological sustainability are important tasks for policymakers. In particular, changing the energy mix used in daily electricity generation can have a major effect on ecological conditions, and this is a previously neglected research topic. Therefore, this paper aims to investigate the influence of daily electricity generation from nuclear power and renewables on the achievement of carbon neutrality targets in the four largest nuclear power countries (i.e., the U.S., China, France, and Russia). The study applies the wavelet-transform coherence (WTC), quantile-on-quantile regression (QQR), and Granger causality in quantiles (GCQ) approaches for the period from January 2, 2019 to December 29, 2022. The outcomes present that electricity generation from nuclear energy reduces carbon (CO 2) emissions only in Russia, while hydropower generation does not contribute to CO 2 emission reduction in any country. Solar electricity generation decreases CO 2 emissions in different quantiles in the U.S. and Russia, while electricity generation from wind power reduces CO 2 emissions in the U.S. and France. The empirical outcomes also reveal that expanding solar, wind, and nuclear power in the energy mix can enhance the potential of the U.S., France, and Russia to achieve carbon neutrality and secure a sustainable future. However, China is using nuclear and renewables inefficiently regarding carbon neutrality. Therefore, the empirical evidence for China is highly controversial compared to former studies. [Display omitted] • The study examines renewable and nuclear electricity effect on carbon neutrality. • The study focuses on top four major nuclear power countries. • The study applies nonlinear approaches by using high-frequency (daily) data. • Renewable and nuclear electricity have time, frequency, & quantile-varying effect. • The best electricity source change according to each country. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

Human activities are closely related to energy consumption and carbon emissions. Nevertheless, in the context of energy transition and carbon emission reduction policies, the impact of population migration on energy consumption and carbon emissions is still unclear. Based on the panel data of 30 provincial regions in China from 2000 to 2019, this paper uses the spatial Durbin model to perform a spatial econometric investigation on this issue. In particular, this study innovatively embeds two spatial weight matrices, an economic gravity weight matrix and a population migration weight matrix, into the spatial Durbin model to capture the spatial spillover effects of population migration. The results show that population migration has obvious adverse impacts on the provinces with net outward population migration. Specifically, on the one hand, population migration increases energy consumption and energy poverty, but energy poverty is more severe in provinces with net outward population migration. On the other hand, population migration increases the carbon emissions and carbon reduction barriers of the provinces with net outward population migration and has no significant impact on the carbon emissions and carbon reduction barriers of the provinces with net inward population migration. Besides, the results of heterogeneity analysis show that population migration causes gaps in the energy structure among regions and even triggers the carbon transfer to provinces with net outward population migration. On the whole, the research results of this paper all prove that population migration increases the carbon reduction barriers in the provinces with net outward population migration. These empirical insights enrich the relevant theoretical achievements of the impact of population migration on energy consumption and carbon emissions and have a high reference value for formulating more reasonable regional energy transition and carbon emission reduction policies. • The spatial spillover effects of population migration on energy consumption and carbon emissions are explored. • The spatial Durbin model is used in this paper. • The spatial Durbin model is embedded with the economic gravity weight matrix and the population migration weight matrix. • Population migration has obvious adverse impacts on the provinces with net outward population migration [ABSTRACT FROM AUTHOR]

Highlights • We estimate the energy rebound effect caused by technological progress in Shanghai. • We use the state space model with time-varying parameter based on the IPAT identity. • Shanghai has a high partial rebound effect on average at the overall-economy level. • Mitigating the energy rebound effect can significantly reduce carbon emissions. • Energy efficiency improvement should not be the only way to conserve energy. Abstract The energy rebound effect is regarded as an obstacle of achieving the expected target of energy-saving policies, especially under a rapid urbanization background in developing counties, such as China. This has become a substantial drag of sustainable development in some cities. Shanghai is the economic center of China, and it is also a typical energy import-dependent mega-city. Investigating the evolution of Shanghai's energy-saving performance and the energy rebound effect is significant for the implementation of energy-saving policies in other similar cities of China and other developing countries. Using the state space model with time-varying parameters and based on the IPAT identity and the Solow residual approach, this paper is the first study to present a specific estimation on Shanghai's energy rebound effect caused by technological progress. The results show that, during the period of 1991–2016, the average energy rebound effect of overall economy and secondary industry in Shanghai was 93.96% and 73.10%, respectively, indicating a high partial rebound effect. Most of expected energy saving caused by improved energy efficiency is offset by extra energy consumption caused by technological progress. Regarding tertiary industry, the average rebound effect was 146.61%, indicating a backfire effect. However, the average energy rebound amount of tertiary industry is less than that of secondary industry. In particular, there is an increasingly negative impact of the rebound effect of tertiary industry on energy conservation in recent years, with the sector's rapid expansion and corresponding increase in energy demand. Furthermore, we estimate the carbon rebound amount (i.e., carbon emissions caused by the energy rebound effect) and find that, on average, the energy rebound effect caused 13.1% and 0.41% increases in carbon emissions in Shanghai and China, respectively. Therefore, mitigating the energy rebound effect can significantly reduce carbon emissions. Due to the substantial impact of the rebound effect, technological progress and energy efficiency improvement should not be the only way to achieve energy-saving target, especially in energy import-dependent mega-cities like Shanghai. Some supporting policies should be implemented to ensure that the expected outcome of energy-saving effort can be realized as far as possible. [ABSTRACT FROM AUTHOR]

Abstract: This paper, by adopting the carbon emission calculating method for all kinds of energy proposed by IPCC in 2006, calculates the amount of carbon emissions of Shandong province. It turns out that carbon emissions in Shandong increased 2.63 times, carbon emission intensity increased 2.45 times and the carbon emission intensity and the energy intensity by GDP is obvious higher than the average level of China from 1997 to 2008.Taking into account the characteristics of carbon emissions in Shandong Province, the paper tries to propose the corresponding strategies to develop low-carbon economy. [Copyright &y& Elsevier]

The construction industry is one of the most energy-intensive industries in China, which contributes to high carbon emissions and hinders efforts to reduce the emissions. The high energy consumption and emission have seriously constrained the growth of low carbon economy in China's construction industry. And reducing the carbon emission from construction industry (CECI) has become the key to sustainable development and the achievement of "double carbon target". The paper firstly decomposes the main influencing factors and calculates their effects by LMDI model. Then, the Tapio decoupling index is used to explore the decoupling relationship between carbon emissions and economic output. Finally, the spatial-temporal evolution of carbon emissions is analyzed through spatial autocorrelation theory. The results show that the carbon emissions from construction industry are highly increased by the construction employment rate (EM), and may be reduced by the energy intensity (EI) and carbon emission efficiency (CI). And the regions with better decoupling status are the eastern and central regions, which have relatively developed construction industry. Moreover, the spatial-temporal characteristic of carbon emissions is "high in the east and low in the west, high in the south and low in the north," showing obviously regional differences. [Display omitted] • Factors about production technology are taken into account, reflecting the characteristics of the construction industry. • Developed regions have better decoupling potential and more responsibility for CO 2. • The spatial distribution is "high in the east and low in the west, high in the south and low in the north". • The provinces in a region have similar decoupling status and carbon emission distribution. [ABSTRACT FROM AUTHOR]

Energy Internet (EI) is typically characterized by digitalization and clean energy that seeks to revolutionize the energy system and reduce carbon emissions. Even though several scholars conclude that EI accelerates the clean energy transition of energy system, since the controversial relationship between digitalization and energy consumption in academia, current studies on the carbon emission reduction advantages of the EI are still in dispute. This paper employs the effect of EI's construction on the carbon emission peak goal of China's energy system based on the system dynamics model and different scenarios. The simulation results find that: (1) the construction of the EI could enable China's energy system to meet the carbon reduction commitment in 2028, with a carbon emission peak of 11.61 billion tons; (2) As digitalization and clean energy become more extensively employed, rising energy demand is satisfied by the manner of consuming low-carbon electricity energy. This is thought to be the primary factor that could promote China to achieve the carbon peak goal earlier than expected; (3) The advancement of digitalization has further promoted the decoupling of China's economic growth from carbon emissions. It is projected that China's energy system will still achieve the carbon peak goal by 2030 when the expected annual GDP growth rate is increased to 6.32%, 5.75%, and 4.6% for the years 2020–2025, 2026–2030, and 2031–2035, respectively. These findings might provide a new perspective for quantitative research on the environmental outcomes of the EI and the relationship between environmental improvement and economic growth. [ABSTRACT FROM AUTHOR]

Glued Laminated Bamboo (GluBam) has been innovated under the support of modern industrial technology, and this renewable material can drive the development of building structural system in a fashion of low carbon footprint. From the perspective of carbon emission, energy consumption and cost-based economy, this paper presents a comprehensive life cycle assessment (LCA) of steel-GluBam hybrid truss and virtually designed steel truss for a comparison. A steel-GluBam hybrid truss constructed at the Nanjing Tech University is used to carry on this study. The system boundary (i.e., production phase, transportation phase, construction phase, operation phase and disposal phase) of life cycle are fist determined. The mechanical properties of the truss structure are considered as the functional equivalence. The inventory of construction materials and the elements in each phase in the life cycle is compiled from the SimaPro and the available literature/specification. Based on the inventory analysis, the Monte Carlo method is employed to conduct the uncertainty analysis of carbon emission of the truss. The assessment results show that the life cycle carbon emission, energy consumption and cost of the steel-GluBam hybrid truss are equal to 4572.5 kgCO 2eq , 71.15 GJ and 15626.6 CNY, respectively. The production phase is the largest contributor to the carbon emission, energy consumption and cost, and the following one is the operation phase. Whereas the transportation phase, the construction phase and the disposal phase together contribute to only the small parts of emissions and cost. GluBam has the function of carbon sequestration leading to the reduction of carbon emission and energy consumption in the production phase, and other phases have no advantage on the emissions and cost of GluBam. The 95% confidence interval of life cycle carbon emission based on the Monte Carlo simulation is estimated as [4290,6370] kgCO 2eq for the steel-GluBam hybrid truss. With respect to the whole life cycle, using GluBam to replace the steel pipe in the upper chord pole and the diagonal web pole of the truss has a little larger cost but its environmental impact and energy consumption reduce a lot. The railway transportation of GluBam reduces 36.5 kgCO 2eq carbon emission, 0.68 GJ energy consumption and 83.7 CNY cost in the life cycle with reference to the road transportation. The steel-GluBam hybrid truss optimized based on the functional unit occurs in the case of reducing the cross-section of steel pipe from 42 mm × 4 mm to 32 mm × 2.5 mm. [ABSTRACT FROM AUTHOR]

[Display omitted] • Calculate carbon emissions of energy consumption by using annual emission factors of indirect energy. • Put forward quantitative indicators of mine intelligence. • Impact of intelligence on carbon emissions of energy consumption in mining industry. • Prediction of carbon emissions in mining industry based on intelligent scenario analysis. The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China's mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R&D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, −0.1271%, −0.1207%, 0.7467%, −0.0595%, −0.09813%, 0.1143% and −0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China's mining industry is proposed. [ABSTRACT FROM AUTHOR]

To perform better future trend prediction for an economy-energy-environment (3E) system and address the shortcomings of traditional multivariate grey models, this paper introduces a spatial correlation term into the multivariate discrete grey model, thus creating the SLDGM(1,n) model, and improves the final calculation of the model according to the priority of new information. The validity of the SLDGM(1,n) model is assessed using data from the 3E system in North China, and the SLDGM(1,n) model is applied to predict the future trends of the 3E system in North China. The following conclusions are obtained. First, the introduction of the spatial correlation term and the improvement of the final calculation method are reasonable; the prediction accuracy of the multivariate grey model is improved, and multiple systems are modeled simultaneously. Second, the SLDGM(1,n) model calculates the spatial spillover effect, and according to the simulation results for North China from 2010 to 2019, Hebei's energy consumption and carbon emissions are subject to the largest influence from other provinces, while its economic development level is subject to the smallest influence, and the carbon emissions of Shanxi and Inner Mongolia are subject to a negative spatial influence effect. Third, the prediction results indicate that under the effect of spatial correlation, the energy consumption of all five provinces in North China will continue to rise; the carbon emissions of Beijing will gradually decline while the carbon emissions of the other four provinces will all gradually rise, and the per capita GDP of the five provinces is expected to increase by more than 50% by 2025. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta region is one of the key areas of energy transition for China's carbon neutrality. The pathways to carbon neutrality are diverse in the regional level due to the different conditions on the energy supply and demand side. How to construct comprehensive assessment index system and evaluate regional pathways to carbon neutrality is an urgent problem to be solved. To fill this gap, this paper evaluates the carbon neutrality pathways of supply-side and demand-side coordination in the Yangtze River Delta region using the Low Emissions Analysis Platform with five scenarios (64 sub-scenarios): Business as usual, energy intensity decrease, energy structure reform, power supply technology innovation and policies mix. The simulation results indicate that: (1) 22 of the 64 sub-scenarios can achieve carbon neutrality by 2060. (2) In energy intensity decrease scenario, an annual 6% reduction in energy intensity could achieve carbon neutrality before 2060. (3) The policies mix scenario can lead to 21 sub-scenarios with pathways to achieve carbon neutrality when the average annual energy intensity decreases by 4% or more. Additionally, the end-use sectors electrification level should reach 64.19% at least by 2060. (4) There can be 14 sub-scenarios to achieve zero carbon emissions in the power plant sector, but annual energy intensity should be reduced by at least 4%. Applying Analytic Hierarchy Process analysis, the article evaluated the 22 sub-scenarios with the index of regional pathways to carbon neutrality. The result shows that the mixed policies of energy intensity reduction, energy structure reformation and power technology innovation can promote the coordination of supply-side and demand-side. This study can provide reference for policymakers to formulate effective policies to realize carbon neutrality. • The coordination of energy supply and energy demand is assessed. • A model with five scenarios (64 sub-scenarios) is developed for carbon neutrality. • Energy intensity drops by 4% or more can lead to multiple carbon neutral pathways. • A comprehensive assessment index of regional carbon neutral pathways is developed. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

In the tourism industry, hotels and homestay facilities account for considerable amounts of energy consumption and CO 2 emissions. Unlike other types of buildings, occupancy has significant fluctuations over time, while mostly being ignored or using a simple assumption based on energy use intensity or setting the occupancy levels. Due to the lack of occupancy rate information, great uncertainties exist in hotel operations, which leads to erroneous estimates of energy consumption in existing hotel buildings. This paper examines hotel operation practices in China with real occupancy rate data which is critical for reducing the uncertainty of estimating energy consumption patterns. Specifically, this study proposes a two-stage comprehensive and quantitative performance assessment approach for the homestay hotel building considering the system's energy and economic performance simultaneously. Regarding energy performance, there are two key facets to consider: renewable energy penetration and grid impact performance. Additionally, the economic performance (annual income and extra annual investment) of various energy portfolios is evaluated using break-even analysis. The impacts of large fluctuation in occupancy rate on electrical loads are considered and assessed in typical homestay hotels. The proposed assessment approach can comprehensively quantify the impacts of the photovoltaics (PV) areas and battery capacities on the system performance in terms of energy and economics. According to the results of the occupancy-based assessment, the ideal PV area and battery capacity are 400 m2 and 16 kWh, respectively, as opposed to the optimal energy portfolios (PV area 600 m2 and battery capacity 24 kWh) determined from conventional assessment results. [ABSTRACT FROM AUTHOR]

• POE was carried out to compare the design target and operational effect after use. • A Large Irregular Green Commercial Building was chosen for in-depth analysis. • We recommend the building energy saving and carbon emission reduction strategy. Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO 2 /m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM 2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions. [ABSTRACT FROM AUTHOR]

Nowadays, China's economy and energy structure are deeply adjusting to the direction of low-carbonization and non-carbonization with unprecedented efforts. This paper considered carbon emissions and energy endowments, adopted SBM-Undesirable model and spatial Durbin model to calculate the energy efficiency of 30 provinces and municipalities in China from 2015 to 2019 and analyze the direct effect, indirect effect and total effect on energy efficiency of 7 variables. Empirical results show that: Energy efficiency has spatial positive correlation and agglomeration characteristics; The main influencing factor of China's energy efficiency is energy consumption structure; Regions with strong energy endowment bear the environmental pollution cost of energy consumption areas in China; Environment Kuznets Curve (EKC) exists in China, but different provinces and municipalities are distributed in disparate stages; Research and development (R&D) investment only affects local energy efficiency, and has no spillover effect on surrounding areas in China; Foreign direct investment (FDI) has negative impacts on China's energy efficiency, and there is a phenomenon of pollution paradise. The valuable conclusions obtained in this study deepens the insights about the influencing factors of China's energy efficiency under the constraints of carbon emission and energy endowment. Energy efficiency has spatial positive correlation and agglomeration characteristics; The main influencing factor of China's energy efficiency is energy consumption structure; Regions with strong energy endowment bear the environmental pollution cost of energy consumption areas in China; Environment Kuznets Curve (EKC) exists in China, but different provinces and municipalities are distributed in disparate stages; Research and development (R&D) investment only affects local energy efficiency, and has no spillover effect on surrounding areas in China; Foreign direct investment (FDI) has negative impacts on China's energy efficiency, and there is a phenomenon of pollution paradise. [Display omitted] • Energy efficiency has spatial positive correlation and agglomeration characteristics. • Energy consumption structure mainly affects energy efficiency. • Environment Kuznets Curve exists in China. • "Pollution paradise hypothesis" exists in foreign direct investment. • Environmental pollution is transferred to surrounding provinces through resource trades. [ABSTRACT FROM AUTHOR]

Ammonia is a very important chemical industry product. It has a wide range of applications in refrigeration, energy, and fertilizers, and 80% of ammonia is used as a nitrogen fertilizer. China mainly produces ammonia and urea from coal gasification, which leads to difficult CO 2 capture and high energy consumption. This study proposed a new process for the production of ammonia and urea by coal direct chemical looping hydrogen production process (CDCLTU). The coal direct chemical looping hydrogen production replaces the air separation unit and water gas conversion unit of the traditional coal-to-urea process (CTU), which effectively separates and captures CO 2 and significantly reduces the energy consumption. A model of the CDCLTU was established using Aspen Plus. Parameter optimization, technoeconomic, and environmental analysis were conducted through simulations. The results showed that CDCLTU could completely replace the air separation system that the CTU relies on. Compared with the traditional process, the utilization rate of C increased from 53.46% to 67.95% in the new process. The energy consumption of CO 2 separation and capture was reduced by 97.04%. The CDCLH had a 79.7% energy efficiency in the hydrogen production process and 99% capture rate of CO 2. The total capital investment was dropped by 41%, unit urea production cost dropped by 12.1%, and the payback period dropped from 7.5 to 3.5 years. This paper provides a new direction for clean and efficient utilization of coal resources. [Display omitted] • Proposed a novel coal-to-urea process by chemical looping technology. • Conducted main parameters optimization and performance analysis for the new process. • The new process reduced 97.04% energy consumption of CO 2 separation and capture. • Production cost of the new process is 12.1% lower than that of the conventional process. • The new process achieved 67.95% C utilization rate and 1.39 t/t lower CO 2 emission. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

Properly addressing the environmental issue requires suitable treatment for energy use and capital investment. This paper links the Bounded-adjusted Measure (BAM) model to the managerial disposability model. The additive structure allows measuring inefficiency with regards to individual input/output variables and identifying whether a certain unit should follow the natural or managerial disposability to reduce the overall inefficiency. Using this BAM model, the environmentally-adjusted technical inefficiency of 30 province-level regions in China is measured during the period of 2006–2015. From the perspective of natural disposability, carbon dioxide emissions resulting from primary energy use, along with sulfur dioxide emissions, constitute the main causes of the atmospheric environmental inefficiency. From the perspective of the managerial disposability, sulfur dioxide emissions and nitrogen oxides emissions appear as the main sources of the inefficiency. From the joint perspective (in terms of disposability), carbon dioxide emissions resulting from primary energy consumption, sulfur dioxide emissions and nitrogen oxides emissions constitute the major contribution to inefficiency in China. In addition, inefficiency related to energy consumption and atmospheric emissions is higher in North China. Different province-level regions should choose appropriate energy-saving paths according to environmental inefficiency associated with different disposability assumptions. • Managerial disposability for BAM model is proposed. • Atmospheric emissions contribute most to the inefficiency both assuming natural and managerial disposability. • We compare overall inefficiency for 30 provincial-level regions in China under ND-BAM and MD-BAM models. • Government can opt for input expansion or technology upgrading according to the estimated ranking. [ABSTRACT FROM AUTHOR]

Since the combustion of fossil fuel became the largest contributing factor of greenhouse gas emissions in China, the growth of energy consumption caused by urbanization has led to significant increase in carbon emissions. Applying both the carbon-emissions-coefficient and the sector-energy-consumption method, we predicted the energy consumption and carbon emissions, explored the characteristics of energy consumption and carbon emissions in different urbanization stages, and analyzed the energy crisis in Shandong which is a typical province in China. The results indicate that in 2015 and 2020, the total primary energy consumption will be 1.57 times and 1.85 times higher than that of in 2009, and the carbon emissions are estimated to be 1.48 times and 1.67 times respectively. The urbanization has distinct positive effects on energy consumption and carbon emissions, and the influence is more obvious in the intermediate-stage than in the early-stage. From a policy perspective, this paper could provide valuable information for planners and decision makers to formulate feasible and practical polices with a view to optimize energy structure and mitigate carbon emissions. [ABSTRACT FROM AUTHOR]

The increasing energy and material consumption associated with global economic growth has resulted in the need for more severe efforts at mitigating global climate change. The iron and steel industry consumes 8% of energy and emits 7% of total CO 2 globally. China's iron and steel industry contributes to 15% of that country's total CO 2 emissions. Therefore, there is an urgent need to explore the possibility of net zero emissions in the iron and steel industry in China to meet China's goal of carbon neutrality before 2060. In the study presented in this paper, the MESSAGEix–China iron and steel model was developed by integrating the process-based technology of the sector into the IIASA's MESSAGEix framework to explore zero CO 2 emission pathways and their associated impacts on resources, energy, and water in China's iron and steel industry up to 2100. We found that there are multiple pathways to achieving zero CO 2 emissions in the Chinese iron and steel industry by the end of the 21st century. More specifically, in all the pathways developed in this study, CO 2 emissions decreased significantly between 2030 and 2060 due to the rapid application of 100% scrap-based Electric Arc Furnaces (EAFs) and hydrogen-based Direct Reduced Iron (DRI)-EAFs steel-making technologies. However, by 2060, there will still be 70–360 Mt of CO 2 emissions from China's iron and steel industry; consequently, carbon sink or negative emission technologies are required to offset this and achieve the country's carbon neutrality goal. Furthermore, technologies for achieving zero emissions differ widely in terms of their impacts on the consumption of materials and energy. Compared to the electric (ELE) scenarios, 25–40% of extra iron ore is consumed in the current and new national policy (NPS) scenarios and the DRI scenarios, but 25–220% of scrap is required. At the same time, 20–150% more energy will be saved in the ELE scenarios than in the NPS and DRI scenarios. Finally, we recommend that policy makers design a cross-cutting strategy to achieve zero CO 2 emissions and enhance efforts for material recycling and the provision of clean energy and water. • There are multiple pathways to achieving net zero emissions in China's iron and steel industry. • Steel outputs are essential for reducing CO 2 emissions and conserving materials, energy, and water. • Hydrogen-based DRI and 100% scrap-EAFs technologies are key to achieving zero emissions. • CO 2 mitigation measures have limited impacts on water consumption. [ABSTRACT FROM AUTHOR]

whether political resource endowment affect regional low-carbon economic development has attracted the attention of many relevant stakeholders. Given that, by using remote sensing data such as nighttime light data and introducing the spatial econometric models, this paper selected 519 counties covering 69 prefecture-level cities in China's 14 provinces to explore the impact of political resource endowment on carbon productivity at the county level. The results showed that regional carbon productivity along the Long March areas was more 12% higher than that along non-Long March areas. Moreover, a series of robustness tests also verify that political resource endowment can effectively promote regional carbon productivity. Additionally, regional industrial structure is the most significant influencing factor in reducing carbon productivity. Our results also showed economic benefits generated from per unit carbon emissions in these counties along the Long March areas were obviously higher than those of counties along the non-Long March areas, indicating that counties along non-Long March areas should pay more attention to improving the balanced development of economic growth and environmental protection. Finally, for counties along the Long March areas and their surrounding regions, their carbon productivity spatially presents the typical spatial autocorrelation trend. Therefore, it is necessary to combine the transformation of industrial structure with the optimization of industrial spatial layout, and formulate the targeted improvement plan on carbon productivity for counties along the Long March areas to achieve coordinated low-carbon development. • The impact of political resource endowment on regional carbon productivity is explored; • Carbon productivity of counties along the Long March areas is higher than that of other counties; • Industrial structure hindered the growth of carbon productivity along the non-Long March areas; • Carbon productivity of counties benefitted from the endowment of political resources. [ABSTRACT FROM AUTHOR]

Abstract: The regional condition and natural resources of the Yellow River Delta are superior, but the issues and constrains there are also quite pronounced. So the development of high-efficiency ecological economy is confronted with serious challenges. Take the Yellow River Delta High-efficiency Eco-economic Zone as the evaluation object and at the basis of the analyses of the development situation in the area, the energy consumption and carbon emissions were forecasted and estimated in the paper. The results indicate that the total energy use in 2015 and 2020 would be 1.62 and 2.22 times higher than that in 2009, respectively. The average annual growth rate of carbon emissions is 8.40% from 2009 to 2015 and that would be 6.50% from 2015 to 2020. The amount of carbon emissions of the area in 2015 and 2020 will take up 13.35% and 16.16% of Shandong Province, respectively. Finally, some suggestions were proposed to guarantee the sustainable energy use and the realization of carbon emission reduction. [Copyright &y& Elsevier]

Abstract: Urban building system assumes significant environmental and ecological implications in terms of a contribution of emissions of CO2 and other greenhouse gases. In order to understand the roles of urban building system in the global and regional climate changes, we set up an integrated model to analyze the carbon emissions of urban building system during its life cycle in this paper, which is called LCCE Model. The further analysis is made to examine the sources of CO2 emissions and the life cycle characteristics of typical architectural structures as masonry-concrete and steel-concrete. We first identified four major sources of CO2 emissions during the whole life cycle of urban building system, which are industrial process emissions, energy consumption emissions, fugitive emissions and land footprint emissions. Given an assumption of a building life cycle of 50years, we took urban residential buildings as an example and calculated CO2 emissions in the main five phases of an overall life cycle of a residential building system, including constructive materials preparation, building construction and reformation, building operation, building demolition as well as wastes treatment and recycling. A comparison was made to examine the differences of CO2 emissions among buildings with two typical architectural structures as masonry-concrete and steel-concrete. The results show that the latter produces less CO2 emission than the former per unit area. Specifically, the amount of CO2 emission is 329.61t for masonry-concrete buildings and 315.79t for steel-concrete buildings per 100m2. Most emissions come from energy consumption and land footprint, accounting for 78–83% and 13–20% of the total emissions respectively. According to our LCCE model, there is a great potential of reducing carbon emissions in urban building system. The key to reduce carbon emissions during the life cycle of urban buildings is directed to building wastes recycling, improvement of consumption patterns of energy and materials, preferential use of buildings with a moderate floor area ratio and effective utilization of natural energy and ecologically friendly building materials according to the characteristics of local urban development. [Copyright &y& Elsevier]

Abstract: From the time of reform and opening up, China''s rapid economic growth attracts worldwide attention, which also brings the accompanying problems that high energy consumption, high pollution and high emission are more prominent. Therefore, the low carbon economy development model has become an inevitable choice for China''s sustainable economic development. However, the stage of economic development is insurmountable and the continuing growth and the disadvantaging structure of energy consumption will become a serious obstacle for China''s low-carbon economy. Selecting the relevant data from 1980 to 2009, and using not only Hebei Province as its study area but also the whole country as its reference region, this paper analyzes the relationship between the changes in energy consumption structure and the carbon emissions in Hebei Province, and basing on which predicts the energy consumption and carbon emissions in the future in order to provide policy recommendations for the optimization of energy consumption structure, the upgrade of industrial structure, and the development of carbon economy [Copyright &y& Elsevier]

Abstract: Based on 1992-2005 input-output tables at comparable price and using structural decomposition method (SDA), the paper decomposes carbon emissions growth from energy consumption in China into 4 categories of factors: carbon emissions intensity, technology, domestic final demand and trade. Carbon emissions have a trend of accelerated growth and the decisive factor is domestic final demand, not trade. Intensity of carbon emissions plays a significant restrain effect, entirely benefit from energy efficiency, rather than energy substitution. The effect of technical change leads to the growth of carbon emissions, reflecting the technology structure developed towards high energyconsumption and emissions. [Copyright &y& Elsevier]

China is committed to peaking its carbon emissions by 2030 and become a carbon-neutral society by 2060. The building sector that accounts for over one-third of the total carbon emissions is expected to face a great challenge in helping China achieve this goal. Shenzhen, as a low-carbon pilot city, whether its low-carbon work of urban buildings reaches the target is crucial. An attempt has been made in this study to assess the intensity of carbon emissions and associated reduction efficiency of urban buildings (operation stage) in Shenzhen by using the life cycle assessment method. The results show that the total carbon emissions generated from the buildings' operation stage have increased from 22 million metric tons (Mt) CO 2 eq in 2005 to 42 (±13%) Mt. CO 2 eq in 2019. Carbon emissions mainly result from the buildings' electricity use (79%), followed by refrigerant release emissions (12%). The energy conservation and carbon emissions reduction intensity in Shenzhen is at the middle level in China, and there is considerable space for improvement. According to scenario-based analysis, the carbon emission of the buildings sector can probably reach its peak by 2025 with the implementation of suitable policies – 5 years earlier than national target by 2030. Overall, this study makes a systemic analysis of the characteristics of urban buildings energy consumption and carbon emissions reduction, which can provide supportings for justifying the effectiveness of low-carbon activities in Shenzhen and beyond. • This paper evaluated the emissions reduction of civil buildings in Shenzhen city. • LCA method is employed to quantify the efficiency of emissions reduction. • Public buildings consume more energy per unit area than residential buildings. • Electricity use is the focus of buildings emission, accounting for 79% of the total. • Current efforts cannot meet the ambitious carbon peak and neutrality target. [ABSTRACT FROM AUTHOR]

• A joint decomposition model based on meta-frontier was proposed. • The effect of scale efficiency was captured based on variable return to scale model. • The catch up effect of carbon emission and energy usage technology was captured. Production-theoretical decomposition analysis (PDA) is widely used to explore the factors related to changes in carbon emissions. However, traditional PDA mainly focuses on scenarios of decision making units (DMUs) with constant returns to scale, and neglects technology heterogeneity among DMUs. Therefore, this paper proposes a joint approach combined PDA and index decomposition analysis (IDA) based on the variable returns to scale (VRS) and the meta-frontier model, and further decomposed the change of carbon emissions into 9 driving factors. Based on above, we investigated the effects of the nine factors on the change of carbon emissions at national, regional and provincial levels in China during the period of 2007–2016. The results indicated that the increase of carbon emissions in China have slowed down since 2012, but the overall situation is not optimistic, especially in the western region. Economic activity played a dominant role in increasing carbon emissions for each region. Carbon emission efficiency and potential carbon factor were also the two important factors related to increase carbon emissions. Conversely, energy intensity, the catch-up effect of pure technical efficiency and scale efficiency on carbon emissions played a positive role in decreasing CO 2 emissions, especially for the eastern region. However, the driving factors related to energy usage, such as the catch-up effect of energy usage scale efficiency and the pure energy usage technical efficiency had trifling and unstable effects on the change of carbon emissions. Based on the above analysis, we propose suggestions to reduce carbon emissions. [ABSTRACT FROM AUTHOR]