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This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

The escalating issue of global climate change necessitates urgent measures to reduce carbon emissions globally. Within this context, the construction industry emerges as a critical sector to address given its high energy consumption, substantial CO2 emissions, and low utilization rate. Therefore, it is pivotal to foster energy conservation and reduce emissions in this sector. To this end, this paper delineates two primary objectives: (1) identifying optimal research methodologies and index parameters for evaluating carbon emission efficiency in the construction industry, and (2) assessing the variance in carbon emission efficiency at disparate stages and regions. Leveraging the Malmquist index, we scrutinize the carbon emission data from 30 Chinese provinces spanning from 2010 to 2019. Our findings indicate a geographical dichotomy in China's construction industry's carbon emission efficiency—lower in the west and higher in the east. Additionally, this study delves into the distinguishing features of emission efficiency alterations across regions, the main influencing factors, and avenues for enhancement. Subsequently, it proposes policy recommendations tailored to the unique attributes of various regions and the overarching framework. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [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]

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people's everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China's carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper. [ABSTRACT FROM AUTHOR]

The low-carbon transformation of the power industry is of great significance to realize the carbon peak in advance. However, almost a third of China's CO2 emissions came from the power sector in 2019. This paper aimed to identify the key drivers of CO2 emissions in China's power industry with the consideration of spatial autocorrelation. The spatial Durbin model and relative importance analysis were combined based on Chinese provincial data from 2003 to 2019. This combination demonstrated that GDP, the power supply structure and energy intensity are the key drivers of CO2 emissions in China's power industry. The self-supply ratio of electricity and the spatial spillover effect have a slight effect on increasing CO2 emissions. The energy demand structure and CO2 emission intensity of thermal power have a positive effect, although it is the lowest. Second, the positive impact of GDP on CO2 emissions is decreasing, but that of the power supply structure and energy intensity is increasing. Third, the energy demand of the industrial and residential sectors has a greater impact on CO2 emissions than that of construction and transportation. For achieving the CO2 emission peak in advance, governments should give priority to developing renewable power and regional electricity trade rather than upgrading thermal power generation. They should also focus on promoting energy-saving technology, especially tapping the energy-saving potential of the industry and resident sectors. [ABSTRACT FROM AUTHOR]

China is confronted with the dual constraints of economic transformation and carbon emission reduction. As the digital economy is a key force in promoting economic transformation and optimizing industrial structure, it is crucial to analyze the digital economy's impact on carbon emission reduction from the perspective of energy consumption and industrial value chain implications. We selected data from 251 prefecture-level cities and above in China from 2011 to 2019 as research samples, measured the development level of the digital economy using the entropy value method, and constructed relevant regression models based on two-way fixed effects, intermediary analysis, and moderation analysis. The research reveals that: (1) The digital economy has a significant contribution to carbon emission efficiency, and there are significant regional heterogeneity and city size differences; (2) The digital economy can improve carbon emission efficiency by reducing energy consumption. (3) From a value chain perspective, industrial structure rationalization weakens the carbon emission efficiency improvement effect of the digital economy to a certain extent, whereas industrial structure upgrading obviously enhances the carbon efficiency improvement effect of the digital economy. The above findings enrich the research in the field of digital economy and environmental governance, contribute to a more comprehensive understanding of the mechanisms by which the digital economy affects the carbon emission efficiency, as well as provide policy implications for enhancing the use of the digital economy in the regional energy consumption and industrial value chain. [ABSTRACT FROM AUTHOR]

In response to extreme climate change, China has set a goal of reaching emission peak by 2030 and achieving carbon neutrality by 2050. Energy conservation and emission reduction of building heating in northern China are key to achieving this goal. Based on building area prediction and energy consumption intensity, this paper establishes a model for calculating heating energy consumption and carbon emissions in northern residential buildings at the macro level, which provides a basis for formulating policies related to heating and emission reduction in northern buildings. Based on the research method of scenario projection, combining the carbon emission subsets and future heating energy mix projections in northern China, the heating energy consumption in northern China decreases to 175, 149 and 135 Mtce in 2050 under the baseline, medium control and strict control scenarios, respectively. The heating energy consumption in the northern region should be controlled at least under the medium control scheme. Under this scenario, building heating carbon emissions in the north could be reduced to 450 MtCO2 by 2050, or 280 MtCO2 if more stringent abatement technologies are adopted. In order to achieve this goal, a combination of energy-saving technologies must be used. The use of biomass and solar technologies should be emphasized in rural heating, while envelope renovation makes the greatest contribution to heating energy savings, with envelope renovation contributing up to 92 Mtce to urban heating. [ABSTRACT FROM AUTHOR]

Chinese cities are pursuing an energy transition to decouple energy-related carbon emissions (ERCEs) from economic growth. Despite numerous studies focusing on the factors influencing carbon emissions, few have quantitatively analyzed their respective contribution rates, thus leaving a gap in effectively guiding policies. This study took 16 cities in the Yangtze River Delta (YRD) as the study area. The decoupling between ERCEs and economic growth was analyzed during 2000–2020, and the contribution rates of different factors were explored. The results showed that the total ERCEs increased from 413.40 million to 1265.86 million tons during 2000–2020, increasing by over three times. Coal and oil were the dominant energy sources in most cities, but natural gas consumption increased from 0.15% to 5.96%. Moreover, 14 cities showed a decoupling status, indicating a certain win–win situation between economic growth and ERCE reduction. Economic growth greatly increased ERCEs, with its contribution rate ranging from 114.65% to 493.27% during 2000–2020. On the contrary, energy structure and energy intensity both contributed to reducing ERCEs in most cities, and their maximum contribution rates reached −32.29% and −449.13%, respectively, which were the main forces for the win–win situation. Finally, carbon reduction proposals are put forward, which provide theoretical support for achieving the "Double Carbon" goal in the YRD. [ABSTRACT FROM AUTHOR]

Despite a gradual decline in rural population due to urbanization, as of 2022, approximately 35% of China's total population still resides in villages. Over a span of 40 years, carbon emissions from villages have significantly surged, with a sevenfold increase from energy consumption and a 46% rise from agriculture. Consequentially, the development of low-carbon villages is imperative. A comprehensive understanding of the primary sources of carbon emissions in villages is crucial for implementing practical and effective strategies towards low-carbon development. However, limited research has been conducted on quantifying carbon emissions and sinks for Chinese villages. This study aims to address this gap by proposing a methodology for assessing carbon emissions in villages, including the emissions of CO2, CH4 and N2O. Inspired by the IPCC standard methodology for greenhouse gas emissions at national levels and provincial greenhouse gas inventory guidelines customized for China's context incorporating localized characteristics, this approach has been applied to seven villages in Northern China based on field investigations. Employing a range of methods including field surveys, questionnaires, statistical records and big-data platforms, we collected the carbon emission activity levels of the seven villages using the most up-to-date carbon emission factors. Subsequently, the collected data and facts are quantitatively processed to generate results that are compared among the seven villages. These findings are also compared with those from other studies. The analysis indicates that the primary industries in these villages significantly influence the total carbon emissions. Moreover, the study reveals that energy consumption in buildings, agriculture, transportation and waste disposal are the most influential emission sources. These findings provide valuable insights into the carbon emission landscape of villages and can serve as a guide for implementing strategies and policies aimed at promoting low-carbon development in the rural areas of Northern China. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is vital for addressing climate change and reducing carbon emissions in emerging economies. Financial development (FD) is crucial for economic growth, and its environmental impact and the adjustment of the industrial structure (IND) is a crucial lever in China's economic transition period. This study explored the relationship between FD, IND, and China's green total factor productivity (GTFEE) from 2000 to 2020 using the super-efficiency SBM-undesirable model, which estimates China's GTFEE. The ARDL results suggest that FD and IND enhance GTFEE in the long term, with FD promoting GTFEE by facilitating industrial structure adjustments. The Dumitrescu–Hurlin panel causality tests supported this finding. The QRPD panel quantile regression and heterogeneity analysis revealed significant heterogeneity in the effects. With increasing GTFEE, FD exerts a restraining effect, gradually weakening and transitioning into a promoting effect, while the IND consistently plays a promoting role. [ABSTRACT FROM AUTHOR]

In a global context where sustainable growth is imperative, understanding carbon emissions in significant regions is essential. Henan Province, being a vital region in China for population, agriculture, industry, and energy consumption, plays a crucial role in this understanding. This study, rooted in the need to identify strategies that not only meet China's broader carbon neutrality objectives but also offer insights regarding global sustainability models, utilizes the STIRPAT model combined with scenario analysis. The aim was to forecast carbon emission trajectories from 2020 to 2060 across the key industries—electricity, steel, cement, transportation, coal, and chemical—that are responsible for over 80% of the total emissions in Henan. The findings suggest a varied carbon peak timeline: the steel and cement industries might achieve their peak before 2025, and the transportation, coal, and chemical sectors might achieve theirs around 2030, whereas that of the power industry could be delayed until 2033. Significantly, by 2060—a landmark year for Chinese carbon neutrality ambitions—only the electricity sector in Henan shows potential for zero emissions under an extreme scenario. This study's results underscore the importance of region-specific strategies for achieving global carbon neutrality and offer a blueprint for other populous, industrialized regions worldwide. [ABSTRACT FROM AUTHOR]

The development of new energy vehicles has become a common choice for countries worldwide to reduce greenhouse gas emissions and improve the global ecological environment, with China being no exception. However, challenges, such as finding charging stations, accessing residential areas, and highway charging, have hindered the green and high-quality growth of the new energy vehicle industry. This study, set against the backdrop of China's 2018 policy to gradually redirect local purchase subsidy funds for new energy vehicles towards supporting the construction and operation of charging infrastructure, utilizes panel data from 282 prefecture-level cities from 2016 to 2021. A difference-in-differences model is constructed to compare the impact of infrastructure development on carbon emissions before and after the policy's implementation. The study finds that policy has a negative effect on carbon emissions, especially in the second and third year after the policy's implementation. Even after controlling for variables such as residents' wealth levels, population size, environmental pollution, energy consumption, and government support, the results remain significant. Heterogeneity analysis reveals that the effect of the promotion of charging infrastructure on carbon emissions is greater in southern and central parts of China. [ABSTRACT FROM AUTHOR]

China's carbon emissions have a stable industrial concentration. In recent years, the carbon emissions of the six major high-carbon industries have accounted for approximately 80% of the national total and are thus priority areas for emission reduction. With the promotion of energy-saving and emission-reduction policies, the structure and scale of high-carbon industries in various regions have undergone changes, but their carbon reduction effects show significant regional differences. Based on China's provincial panel data from 2006 to 2020, this study discusses the structural characteristics of high-carbon industries with their proportion of energy-based industries and measures their scale characteristics with their output values. On this basis, a fixed-effects model is used to analyze the single and synergistic effects of the scale and structure of high-carbon industries on carbon emissions in each province. The results indicate that changes in the scale and structure of high-carbon industries significantly affect carbon emissions but show regional differences in both the single and synergistic effects. When considering these synergistic effects, the single effect of high-carbon industries on carbon emissions will be weakened. In regions with large-scale high-carbon industries, the increase in the proportion of energy-based industries significantly increases carbon emissions, but this effect gradually weakens as the overall scale expands. In areas with small-scale high-carbon industries, the increase in the proportion of energy-based industries has a relatively small effect on carbon emission growth that gradually increases with the overall scale. In addition, the implementation of the carbon emission trading policy has a significant moderating effect on the carbon emissions of high-carbon industries and strongly promotes its reduction. [ABSTRACT FROM AUTHOR]

The development of the tertiary industry is of great significance for promoting industrial structure, optimizing and upgrading it, and achieving regional energy conservation and emission reduction goals. This study adopts a quantitative method to analyze the spatio-temporal pattern of carbon emissions from China's tertiary industry from 2004 to 2019. In order to analyze emissions from aspects such as energy structure, energy intensity, energy carrying capacity, industrial structure, level of industrial development, income level, consumption capacity, energy consumption intensity, and population size, this study establishes a hybrid factor decomposition model called the "energy-industry-consumption" research framework. The study shows that carbon emissions from China's tertiary industry have been increasing year by year from 2004 to 2019, with a growth rate of 353.10%. Transportation is the largest contributor to the increase in carbon emissions from China's tertiary industry. The carbon emissions from the tertiary industry in each province show four types: high-speed growth, low-speed growth, fluctuating growth, and stable growth. During the study period, carbon emissions produce a spatial heterogeneity with the highest emissions in the south and lowest in the northwestern part of China. The spatial pattern of per capita carbon emissions is not significant. Guangdong has the highest carbon emissions, and Shanghai and Beijing have higher per capita carbon emissions. Industrial factors and consumption factors have a positive effect on carbon emissions in China's tertiary industry, while energy factors have a negative effect. The leading factor of carbon emissions in China's tertiary industry has gradually shifted from energy to industry. [ABSTRACT FROM AUTHOR]

This empirical study investigates the dynamic interconnection between fossil fuel consumption, alternative energy consumption, economic growth and carbon emissions in China over the 1981 to 2020 time period within a multivariate framework. The long-term relationships between the sequences are determined through the application of the Autoregressive Distributed Lag (ARDL) bounds test and augmented by the Johansen maximum likelihood procedure. The causal relationships between the variables are tested with the Granger causality technique based on the Vector Error Correction Model (VECM). Empirical results reveal the existence of a statistically significant negative relationship between alternative energy consumption and carbon emissions in the long-term equilibrium. Furthermore, the VECM results demonstrate that both carbon emissions and fossil fuel consumption have unidirectional effects on economic growth. Additionally, the study highlights a short-term unidirectional causal relationship from economic growth to alternative energy consumption. These findings suggest that a reduction in fossil fuel consumption in the short run may indirectly impede the development of alternative energy. The study proposes that China should expedite the development of alternative energy and control the expansion of fossil fuel consumption to attain its carbon reduction target without hindering economic growth. [ABSTRACT FROM AUTHOR]

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China's increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China's short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China's short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China's CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China's CO2 emissions. [ABSTRACT FROM AUTHOR]

In order to fulfill the commitment of China to achieve carbon peak by 2030 and carbon neutrality by 2060, all industries have been taking their respective carbon reduction actions. The transportation industry accounts for 11% of CO2 emission of the whole society, and its energy conservation and carbon reduction benefit is of great significance to the national carbon reduction process. New energy vehicles are undoubtedly one of the most important means of carbon emission reduction in the transportation sector. However, electric vehicles still have CO2 emissions, as the fossil fuel use comes from upstream power. To systematically and comprehensively evaluate the CO2 emissions of HEV, PHEV and BEV in the whole process, this study introduces the life-cycle method to research on the past and current situations, and predict future scenarios for ICEV and EV light-duty vehicles at the national and regional levels, by deeply analyzing the generation mix and generating efficiency from the WTT stage, and fuel economy from the TTW stage. The study shows that compared with ICEV, HEV and PHEV could reduce around 30% of CO2 emissions. Currently, BEV could reduce 37% of CO2 emission in the region where the proportion of coal-fired power is high, and 90% of CO2 emission in the region where the proportion of hydro power is high. This study discusses the impact of the proportion of renewable energy application on the carbon emissions from electric vehicles, analyzes the environmental benefits of promoting electric vehicles in different regions, and lays a foundation for the promotion strategy of electric vehicles for different regions in the future. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency is globally regarded as an effective way to reduce carbon emissions. In recent years, the energy efficiency of China has gradually improved; however, energy consumption and CO2 emissions are still increasing. To better understand the reasons for this, we evaluated the energy rebound effect (RE) of 30 provinces in China over the period 2001–2017 by employing stochastic frontier analysis (SFA) and the system generalized method of moments (system-GMM) approach, and explored the extent to which the RE affects CO2 emissions. Asymmetric and regional heterogeneity analyses were also conducted. The results indicate that the national average RE was 90.47% in the short run, and 78.17% in the long run, during the sample period. Most of the provinces experienced a partial RE, with a backfire effect occurring in some provinces such as Guangxi and Henan. The RE was associated with significant increases in CO2 emissions; specifically, a 1% increase in the short-run RE led to an increase in CO2 emissions of approximately 0.818%, and a 1% increase in the long-run RE resulted in an increase in CO2 emissions of approximately 0.695%. Moreover, significant regional differences existed in the impact of the RE on CO2 emissions; in regions with high emissions and a high RE, the CO2 reduction effect from the marginal decline in the RE was much more pronounced than that in other regions. [ABSTRACT FROM AUTHOR]

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors. [ABSTRACT FROM AUTHOR]