Carbon dioxide intensity and income level in the Chinese megacities' residential building sector: Decomposition and decoupling analyses

Towards a sustainable construction: A newly proposed Tapio-global meta-frontier DEA framework for decoupling China's construction economy from its carbon emissions

Uncovering the decoupling degree and its influencing factors is an important work to look for the carbon abatement and sustainable development in China's construction industry (CCI). To arrive at a solution, we proposed a research framework by taking into account the Tapio decoupling model and biased directional distance function based on global meta-frontier DEA, so as to reveal out the motive force and resistance that can help decouple construction industry economy from its carbon emissions. Discussion results indicate that during the sample period: (1) China's construction economy and carbon emissions exhibited weak decoupling. The advances of energy/production technology were two dominant factors in helping construction economy decoupled from carbon emissions, while construction economic activity exerted the largest effects in impeding decoupling process. (2) The decoupling degree and driving effects differed significantly in three regions. Regional energy/production technology disparity narrowed in central and western region, and further contributed to their decoupling. Energy-biased and output-biased scale change exerted impeding effects on the decoupling in eastern and central region, while exerted diametrically opposite effects on western region's decoupling. Besides, the change of energy/production resource allocation efficiency in eastern and western region acted motivating effects in their decoupling, while formed restriction in central region's decoupling.

City-level building operation and end-use carbon emissions dataset from China for 2015-2020

The building sector, which accounts for over 20% of China's total energy-related carbon emissions, has great potential to reduce emissions and is critical to achieving China's emissions peak and carbon neutrality targets. However, the lack of data on operational carbon emissions and end-use carbon emissions in the building sector at the city level has become a major barrier to the development of building energy conservation policies and carbon peaking action plans. This study uses a combination of "top-down" and "bottom-up" methods to account for the operational carbon emissions of buildings in 321 cities in China from 2015 to 2020. The energy consumption in buildings is further broken down into six end uses: central heating, distributed heating, cooking and water heating (C&W), lighting, cooling, appliances and others (A&O). The dataset can serve as a reference to support city-level policies on peak building emissions and is of great value for the improvement of the carbon emissions statistical accounting system.

Prediction of carbon emissions peak and carbon neutrality based on life cycle CO2 emissions in megacity building sector: Dynamic scenario simulations of Beijing

In order to design an optimal carbon peak and carbon neutralization pathway for the high-density building sector, a dynamic prediction model is established using system-dynamics coupled building life cycle carbon emission model (SD-BLCA) with consideration of future evolutionary trajectory and time constraints. The model is applied in Beijing using the SD-BLCA combined with scenario analysis and Monte Carlo methods to explore optimal trajectory for its building sector under 30-year timeframe. The results indicate that by increasing the proportion of renewable energy generation by 7% and retrofitting 60 million m2 of existing buildings, these two mature measures can offset the growth of carbon emissions and achieve the peak target by 2025. However, achieving carbon neutrality necessitates a shift from isolated technologies to a comprehensive net-zero emissions strategy. The study proposes a time roadmap that integrates a zero-carbon energy supply system and the carbon reduction measures of the whole life cycle. This strategy primarily relies on renewable sources to provide heat, power, and hydrogen, resulting in estimated reductions of 29.8 Mt, 28.1 Mt, and 0.7 Mt, respectively. Zero energy buildings, green buildings, and renovated buildings can reduce carbon emissions through their own energy-saving measures by 8.4, 18.2, and 11.8 kg/m2, respectively.

Complexity influence of societal development comprehensive indicators on building carbon emission: empirical evidence from China

Carbon mitigation in the building sector is crucial for China to fulfill its commitments towards achieving a carbon peak and carbon neutrality. However, the impact of societal development and ecological indicators on building carbon emissions remains unclear. This study employs the panel smooth transition regression model to investigate the complex implications of societal development comprehensive indicators, characterized by harmonious development, decoupling, and technological advances, on buildings' total carbon emissions, based on the evidence from China's 30 provinces for the period between 2007 and 2020. Additionally, the robustness of the model confirms that the conclusion is still valid. The empirical results indicate a strongly non-linear relationship between societal development comprehensive indicators and building carbon emissions. Both the harmonious development and technological advances exhibit two transition functions, and decoupling features a single transition function. Harmonious development is more sensitive to the impact of building carbon emissions, while technological advances have tremendous emission reduction potential. From the time dimension, fluctuation trends and ranges are different. From the spatial dimension, the inhibiting and promoting effects on each province have regional heterogeneity. Our results entail suggestions for reduced building total carbon emissions and practical strategies for regional climate resilience and efficiency in mitigating climate change.

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China

BACKGROUND

Conducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China.

RESULTS

Based on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons.

CONCLUSIONS

China's provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.

Learning dispatching rules via novel genetic programming with feature selection in energy-aware dynamic job-shop scheduling

The incorporation of energy conservation measures into production efficiency is widely recognized as a crucial aspect of contemporary industry. This study aims to develop interpretable and high-quality dispatching rules for energy-aware dynamic job shop scheduling (EDJSS). In comparison to the traditional modeling methods, this paper proposes a novel genetic programming with online feature selection mechanism to learn dispatching rules automatically. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives considered energy consumption. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 12.67%, 15.38%, and 11.59% in the meakspan with energy consumption (EMS), mean weighted tardiness with energy consumption (EMWT), and mean flow time with energy consumption (EMFT) scenarios, respectively.

Do tourism clusters contribute to low-carbon destinations? The spillover effect of tourism agglomerations on urban residential CO2 emissions

A burgeoning literature has investigated the relationship between tourism and regional CO₂ emissions. However, the results are equivocal. Some scholars find that tourism induces more emissions, while others find that tourism benefits emission mitigation. Previous research suffers three deficiencies: (1) the CO₂ emissions induced by tourism beyond the tourism sectors are neglected; (2) the role of tourism agglomerations in sustainable destination development is underrated; (3) the tripartite mechanism of tourism agglomerations, household income, and urban direct residential CO₂ emissions (DRCEs) remains to be determined. Based on the theories of complex adaptive system and an empirical study of 30 provinces in China, we examine the relationship between the tripartite roles via multiple spatial econometric models. The results disclose that tourism agglomerations have dual effects on urban DRCEs and an inverted U-shaped effect on household income. The growth of tourism agglomerations has generally promoted urban DRCEs in many provinces of mainland China during the last fourteen years because its indirect effect (positive) outweighs the direct effect (negative). However, tourism agglomerations are feasible for low-carbon destinations once the emission-income decoupling happens. This study contributes to low-carbon destination management by presenting a more holistic image of the social-economic-environmental impacts of tourism agglomerations.

Decoupling analysis and peak projection of manufacturing CO2 emissions from the perspective of investment

Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon emissions into seven influencing factors (i.e., population, urbanization, economic development, investment share, energy intensity, energy structure and emission intensity), in order to explore the factors driving manufacturing carbon emissions during 2003-2018. Then, the paper analyzes the decoupling relationship between manufacturing investment and carbon emissions in 30 provinces. Finally, three scenarios are developed to project future manufacturing emissions at the provincial level up to 2035, and whether manufacturing emissions in 30 provinces can realize peak is discussed. The paper results in three main findings. First, we find that energy intensity played the most important role in decreasing the manufacturing emissions during the whole study period, while the economic development and investment share were the main effect promoting manufacturing carbon emissions. Second, China experienced a process from weak decoupling to strong decoupling between manufacturing invest and emissions. Third, China's manufacturing carbon emissions can only achieve the carbon peaking target in 2030 under the High scenario, and 7 provinces cannot reach the peak before 2035 under the three scenarios.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-03047-w.

Analysis of carbon emission drivers of secondary industries in Energy "Golden Triangle" area based on LMDI and two-dimensional decoupling model

As an essential energy and chemical base in China, carbon reduction in the Energy "Golden Triangle" (EGT) area is significant. This paper used the logarithmic mean Divisia index (LMDI) method to analyze the drivers of carbon emissions from secondary industry energy consumption (CESEC) in EGT from 2005 to 2019 and then used the GM (1,1) method to simulate carbon emissions in 2030. Meanwhile, the decoupling relationship between carbon emissions and economic development was also analyzed using the two-dimensional decoupling model to test the effectiveness of carbon reduction by the region's government. This paper showed the following: (1) CESEC in the EGT area increased from 1.89×10⁸t to 2.617×10⁸ t; (2) the economic output effect is the main factor influencing carbon emissions in the EGT area, followed by population effect and energy structure effect, while energy intensity effect mitigates carbon emissions; and (3) CESEC will peak at 12.362×10⁸t in 2030, leaving an arduous task on carbon reduction. The two-dimensional decoupling condition between carbon emissions and economic growth in the EGT area is low level-weak decoupling (WD-LE) for 2005-2019. The decoupling condition in Yulin and Ningdong is concentrated in low level-expansion connection (EC-LE) and low level-weak decoupling (WD-LE). Furthermore, Erdos reached high level-expansion negative decoupling (END-HE) condition during 2015-2019. Based on the above findings, a low-carbon development strategy for EGT should consider improving emission reduction technologies for high-carbon energy sources like coal, adjusting the energy consumption structure and seeking government policy support for carbon reduction.

Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China

China's household energy consumption has obvious regional differences, and rising income levels and urbanization have changed the ability of households to make energy consumption choices. In this paper, we analyze the energy consumption characteristics of urban village residents based on microlevel household survey data from urban villages in Guangzhou, China. Then, the results of modeling the material flows of per capita carbon emissions show the most dominant type of energy consumption. OLS is applied to analyze the influencing factors of carbon emissions. We find that the per capita household carbon emissions in urban villages are 722.7 kg/household.year, and the average household carbon emissions are 2820.57 kg/household.year. We also find that household characteristics, household size, household appliance numbers, and carbon emissions have a significant positive correlation, while income has no significant effect on carbon emissions. What is more, the size and age of the house have a positive impact on carbon emissions. Otherwise, the new finding is the demonstration that income is not significantly correlated with household carbon emissions, which is consistent with the characteristics of urban villages described earlier. On the basis of this study, we propose more specific recommendations regarding household energy carbon emissions in urban villages.

Analyzing the Spatio-Temporal Dynamics of Urban Land Use Expansion and Its Influencing Factors in Zhejiang Province, China

The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study's results revealed substantial urban growth of about 6899.59 km², i.e., 6.6%, whereas agricultural land decreased by 4320.68 km², i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua-Yiwu, and Wenzhou-Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study's findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area's urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.

Carbon Mitigation in the Operation of Chinese Residential Buildings: An Empirical Analysis at the Provincial Scale

The rapidly growing carbon emissions of residential building operations have become an obstacle to China’s commitment to achieving its carbon-neutral goals by 2060, but they also demonstrate great carbon mitigation potential. To help buildings reach carbon neutrality targets, this study decomposes the drivers of carbon emissions and evaluates the changes in carbon mitigation of residential buildings across 30 Chinese provinces from 2000 to 2018. The results indicate that (1) the operational carbon intensity increased in most provinces and the average annual growth rate across the 30 provinces was 4.2%; (2) from 2001 to 2018, North China and Northeast China had the highest average annual carbon mitigation intensity, at 602.7 and 376.9 kg of carbon dioxide per household, respectively. However, Northwest China had the highest carbon mitigation efficiency, with a carbon mitigation rate of 23.5%; and (3) in most cases, the total carbon mitigations of the operational residential buildings assessed at the provincial scale higher than those assessed nationwide, with a difference of 14.4 million tons of carbon dioxide on average. In addition, this study reviewed the energy efficiency codes for residential buildings and summarized effective energy efficiency measures. Overall, this study fills a gap in our understanding of carbon mitigation tools and provides a reference for the evaluation of historical carbon mitigation effects in the operation of residential buildings.

Optimization of China's provincial carbon emission transfer structure under the dual constraints of economic development and emission reduction goals

The contradiction between China's economic development and the reduction of carbon emission is increasingly deepening along with the complex carbon emission transfer. Optimizing provincial-level carbon emission transfer in China is important for facilitating economic development and carbon emission reduction. Under these dual constraints, this study uses the slacks-based measure, marginal abatement cost, and geographically and temporally weighted regression models to measure the economic and carbon emission reduction effects and the carbon emission reduction baseline. Then, the optimization strategy and path of provincial carbon emission transfer network structure are proposed to provide policy support for achieving the dual goals of economic development and carbon emission reduction in China. This article draws the following important research conclusions. First, under the three economic development scenarios, provinces in the eastern coastal developed regions are capable of completing the expected carbon emission reduction, whereas the underdeveloped provinces in the central and northern regions are not. Second, from the perspective of the economic effect of carbon emission transfer, carbon emission transfer from most provinces promotes economic development, whereas carbon emission transfer from a few economically underdeveloped provinces hinders economic development. Third, from the perspective of the carbon emission reduction impact of carbon emission transfer, carbon emission transfer in the northeast region has a negative impact on carbon emission reduction, and carbon emission transfer in developed regions also has a negative impact on carbon emission reduction. Fourth, the optimization of the carbon emission transfer can be divided into four categories.

Global evaluation of carbon neutrality and peak carbon dioxide emissions: current challenges and future outlook

With the acceleration of urbanization and industrialization, carbon neutrality and peak carbon dioxide emissions have become common sustainability goals worldwide. However, there are few literature statistics and econometric analyses targeting carbon neutrality and peak carbon dioxide emissions, especially the publication trends, geographic distribution, citation literature, and research hotspots. To conduct an in-depth analysis of existing research fields and future perspectives in this research area, 1615 publications from the Web of Science Core Collection, between 2010 and 2020, were evaluated by using three analysis tools, under the framework of the bibliometrics method. These publications are distributed between the start-up (2010-2015) and the stable development (2016-2020) phases. Cluster analysis suggests three areas of ongoing research: energy-related carbon emissions, methane emissions, and energy biomass. Overall, future trends in this field include cumulative carbon emissions, the residential building sector, methane emission measurement, nitrogen fertilization, land degradation neutrality, and sciamachy satellite methane measurement. Finally, this paper further examines the most comprehensive coverage of nitrogen fertilization and the most recent research of the residential building sector. In view of the statistical clusters from 1615 publications, this paper provides new insights and perspectives for climate-environment-related researchers and policymakers. Specifically, countries could apply nitrogen fertilizer to crops according to the conditions of different regions. Additionally, experiences from developed countries could be learned from, including optimizing the energy supply structure of buildings and increasing the use of clean energy to reduce CO₂ emissions from buildings.

Dynamics and Decoupling Analysis of Carbon Emissions from Construction Industry in China

The construction industry is the backbone of most countries, but its carbon emissions are huge and growing rapidly, constraining the achievement of global carbon-peaking and carbon-neutrality goals. China’s carbon emissions are the highest in the world, and the construction industry is the largest contributor. Due to significant differences between provinces in pressure, potential, and motivation to reduce emissions, the “one-size-fits-all” emission reduction policy has failed to achieve the desired results. This paper empirically investigates the spatial and temporal evolution of carbon emissions in China’s construction industry and their decoupling relationship with economic growth relying on GIS tools and decoupling model in an attempt to provide a basis for the formulation of differentiated construction emission reduction policies and plans in China. The study shows that, firstly, the changes in carbon emissions and carbon intensity in the provincial construction industry are becoming increasingly complex, with a variety of types emerging, such as declining, “inverted U-shaped”, growing, “U-shaped”, and smooth fluctuating patterns. Secondly, the coefficient of variation is higher than 0.65 for a long time, indicating high spatial heterogeneity. However, spatial agglomeration and correlation are low, with only a few cluster-like agglomerations formed in the Pearl River Delta, Yangtze River Delta, Bohai Bay, Northeast China, and Loess and Yunnan–Guizhou Plateau regions. Thirdly, most provinces have not reached peak carbon emissions from the construction industry, with 25% having reached peak and being in the plateau stage, respectively. Fourthly, the decoupling relationship between carbon emissions from the construction industry and economic growth, as well as their changes, is increasingly diversified, and most provinces are in a strong and weak decoupling state. Moreover, a growing number of provinces that have achieved decoupling are moving backward to re-coupling, due to the impact of economic transformation and the outbreaks of COVID-19, with the degraded regions increasingly concentrated in the northeast and northwest. Fifthly, we classify China’s 30 provinces into Leader, Intermediate, and Laggard policy zones and further propose differentiated response strategies. In conclusion, studying the trends and patterns of carbon-emission changes in the construction industry in different regions, revealing their spatial differentiation and correlation, and developing a classification management strategy for low carbonized development of the construction industry help significantly improve the reliability, efficiency, and self-adaptability of policy design and implementation.

Carbon Peak and Carbon Neutrality in the Building Sector: A Bibliometric Review

Due to large energy consumption and carbon emissions (ECCE) in the building sector, there is huge potential for carbon emission reduction, and this will strongly influence peak carbon emissions and carbon neutrality in the future. To get a better sense of the current research situation and future trends and to provide a valuable reference and guidance for subsequent research, this study presents a summary of carbon peak and carbon neutrality (CPCN) in buildings using a bibliometric approach. Three areas are addressed in the review through the analysis of 364 articles published from 1990–2021: (1) Which countries, institutions, and individuals have conducted extensive and in-depth research on CPCN in buildings, and what is the status quo of their collaboration and contributions? (2) What subjects and topics have aroused wide interest and enthusiasm among scholars, and what are their time trajectories? (3) What journals and authors have grabbed the attention of many scholars, and what are the research directions related to them? Moreover, we propose future research directions. Filling these gaps will enrich the research body of CPCN and overcome current limitations by developing more methods and exploring other practical applications.

Carbon Kuznets curve in China's building operations: Retrospective and prospective trajectories

China has pledged to achieve peak carbon emissions by 2030 and to be carbon neutral by the mid-century, and buildings will be the "last mile" sector in the transition to carbon neutrality. To help buildings hit the carbon peak goal, we investigate the different emission scales of carbon emission changes of residential and commercial building operations across 30 provinces in China through the carbon Kuznets curve (CKC) model. We observe that (1) more than three-quarters of the samples can be fitted by the CKC model. Most CKCs are the inverted U-shaped, residential and commercial buildings occupying 93% and 90% at the total emission scale, respectively. In addition, the remains can be illustrated as N-shaped curves. (2) Under the premise of CKCs existence, approximately half of the provincial residential and commercial buildings peak at different emission scales, except for emission per floor space (residential: 89%; commercial: 81%). Provinces with better economic development have a higher peaking probability. In the total emissions, the peaking probability in residential buildings is 33% and 50% for provinces with economic indicators <20,000 Chinese Yuan and 30,000-40,000 Chinese Yuan, respectively, and 22% and 67% for commercial buildings, respectively. (3) Taking carbon intensity as a case study, decoupling analysis examines the robustness of the CKC estimation. Overall, we close the gap of the CKC estimation in commercial and residential buildings, and the proposed methods can be treated as a tool for other economies to illustrate the retrospective and prospective trajectories of carbon emissions in building operations.

A multi-sectoral decomposition and decoupling analysis of carbon emissions in Guangdong province, China

Quantifying the decoupling states of carbon emissions from a multi-sectoral and dual-perspective can guide more detailed emission reduction strategies. Based on the single-regional input-output (SRIO), Tapio decoupling analysis (TDA), and structural decomposition analysis (SDA), this study investigated the dynamic variation feature and decoupling state of multi-sectoral carbon emissions, and revealed their driving factors of consumption-based emissions in Guangdong province from 2002 to 2017. The main discovery can be summarized as follows from results analysis. Firstly, electricity production sector and construction sector were the largest direct and embodied carbon emission sources, and capital formation was the most important factor with the contribution of approximately 100 % that led to embodied carbon emissions of construction. For most of the manufacturing and service sectors, the embodied carbon emissions caused by international export exceed 50 %. Secondly, the consumption structure, consumption per capita, and population effect promoted the embodied emissions during 2002-2012, while the emission intensity effect was the greatest offsetting factor for all sectors. Consumption structure effect was becoming a major driver to the increase of embodied carbon emissions for construction. Thirdly, agriculture, mining, energy transformation, and service sector showed the unsatisfactory decoupling relationship between direct carbon emissions and economic output. According to the decoupling states, the decoupling relationships in some secondary industries were overestimated under the situation of only considering direct carbon emissions. The obtained results and policy implications are expected to provide holistic reference for policymakers to promote the short-term carbon peak and long-term carbon neutrality of Guangdong province from the sectoral perspective.

Carbon Neutral Roadmap of Commercial Building Operations by Mid-Century: Lessons from China

Carbon neutrality has positive impacts on people, nature and the economy, and buildings represent the “last mile” sector in the transition to carbon neutrality. Carbon neutrality is characterized by the decarbonization of operations and maintenance, in addition to zero emissions in electricity and other industry sectors. Taking China’s commercial buildings as an example, this study is the first to perform an extensive data analysis for a step-wise carbon neutral roadmap of building operations via the analysis of a dynamic emission scenario. The results reveal that the carbon emissions abatement of commercial building operations from 2001 to 2018 was 1460.85 (±574.61) mega-tons of carbon dioxide (Mt CO2). The carbon emissions of commercial building operations will peak in the year 2039 (±5) at 1364.31 (±258.70) Mt, with emission factors and energy intensity being the main factors influencing the carbon peak. To move toward carbon neutral status, an additional 169.73 Mt CO2 needs to be cut by 2060, and the low emission path toward carbon neutrality will lead to the realization of the carbon peak of commercial buildings in 2024, with total emissions of 921.71 Mt. It is believed that cutting emissions from the operation of buildings in China will require a multi-sectoral synergistic strategy. It is suggested that government, residents, enterprises, and other stakeholders must better appreciate the challenges to achieve a substantial carbon reduction and the need for urgent action in the building sector in order to achieve carbon neutrality.

The Impact of Government Subsidies on the Low-Carbon Supply Chain Based on Carbon Emission Reduction Level

To improve low-carbon technology, the government has shifted its strategy from subsidizing low-carbon products (LCP) to low-carbon technology. To analyze the impact of government subsidies based on carbon emission reduction levels on different entities in the low-carbon supply chain (LCSC), game theory is used to model the provision of government subsidies to low-carbon enterprises and retailers. The main findings of the paper are that a government subsidy strategy based on carbon emission reduction levels can effectively drive low-carbon enterprises to further reduce the carbon emissions. The government's choice of subsidy has the same effect on the LCP retail price per unit, the sales volume, and the revenue of low-carbon products per unit. When the government subsidizes the retailer, the low-carbon product wholesale price per unit is the highest. That is, low-carbon enterprises use up part of the government subsidies by increasing the wholesale price of low-carbon products. The retail price of low-carbon products per unit is lower than the retail price of low-carbon products in the context of decentralized decision making, but the sales volume and revenue of low-carbon products are greater in the centralized decision-making. The cost-benefit-sharing contract could enable the decentralized decision model to achieve the same level of profit as the centralized decision model.

Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

Currently, little attention has been paid to reducing carbon dioxide (CO₂) emissions of Gansu, and the two-dimensional decoupling model has been rarely used to study the relationship between the economic development and CO₂ emissions, especially in western China (e.g., Gansu). Thus, here, we first used the Logarithmic Mean Divisia Index (LMDI) to decompose the driving factors of Gansu's CO₂ emissions between 2000-2017 and then analyzed the decoupling relationship by using the two-dimensional model. Results showed: (1) Gansu's CO₂ emissions increased from 7805.70 × 10⁴ t in 2000 to 19,896.05 × 10⁴ t in 2017. The secondary industry accounted for the largest proportion in Gansu's CO₂ emissions, followed by the tertiary industry and the primary industry. (2) The economic output showed the dominant driving effect on Gansu's CO₂ emissions growth with the cumulative contribution rate of 201.94%, followed by the effects of industrial structure, population size, and energy structure, and their cumulative contribution rates were 9.68%, 7.81%, and 3.05%, respectively. In contrast, the energy intensity effect presented the most obvious mitigating effect with the cumulative contribution rate of -122.49%. (3) The Environmental Kuznets Curve (EKC) between CO₂ emissions and economic growth was demonstrated the inverted U-shape in Gansu. The two-dimensional decoupling status was the low level-weak decoupling (WD-LE) during 2000-2017. Thus, dropping the proportion of the secondary industry, reducing the use of carbon-intensive fuel like coal, introducing advanced technologies, and increasing the investment of new energy might effectively restrain the growth of Gansu's CO₂ emissions.

Decoupling analysis of the industrial growth and environmental pollution in the Circum-Bohai-Sea region in China

Based on a comprehensive consideration of waste water (WW) and waste gas (WG), the Tapio decoupling model is constructed to explore the decoupling relationship between industrial growth and industrial pollution in the Circum-Bohai-Sea region (CBSR) of China from 2003 to 2016. By dividing 37 sample cities into three sub-regions, we conduct a comparative analysis to describe the spatial-temporal evolution of the decoupling states of industrial growth and environmental pollution. The results show the following: (1) Overall, the industrial WW discharge in 37 key cities has been decoupled from industrial growth, and the industrial development mode is relatively ideal. (2) The decoupling between industrial growth and industrial WW and WG emissions is more ideal in Beijing-Tianjin-Hebei (BTH) than in Midsouthern Liaoning (MSL). (3) There are two nodes for the decoupling between industrial growth and WW and WG in Shandong Peninsula (SDP), and the decoupling state between industrial growth and WG is better than the decoupling state between industrial growth and WW from 2003 to 2016. (4) From 2003 to 2016, the decoupling state between industrial growth and WW and WG in MSL is not ideal. The conclusions show that the decoupling relationship between industrial growth and environmental pollution in the CBSR is still quite variable and unstable; thus, differential treatment measures should be taken. To enhance the effectiveness of these measures, we will further study the main factors affecting the decoupling relationship, and conduct a comparative study in a larger scale.

What is the relationship among environmental pollution, environmental behavior, and public health in China? A study based on CGSS

China's increasing environmental pollution has caused concern for public health, so it is worth demonstrating the relationship between environmental behaviors and public health. To this end, this study examines the relationship between public health and both pollution and environmental behavior, based on micro-data and the ordered multinomial logistic regression model. It has been found that (1) external environmental pollution and subjectively perceived pollution have negation correlation with public health. Specially, air pollution and domestic waste pollution have significant associations and mainly with public mental health, (2) the environmental behavior of government and individual has an intermediary role, and (3) there is regional heterogeneity in the association between environmental pollution and public health. Residents in eastern China are more willing to mitigate the health damage caused by environmental pollution through individual environmental behavior, whereas those in central and western China emphasize government behavior. Finally, it is encouraged to actively implement environmental protection behaviors to improve the external environment and pay attention to the public's mental health.

How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression

To verify how does the development of technological innovation effectively mitigate carbon dioxide (CO₂) emissions in Organization for Economic Co-operation and Development (OECD) countries, this study first investigates the direct impacts and moderating effects of technological innovation, measured by the development of patents on CO₂ emissions by employing a balanced panel dataset for 35 OECD countries covering 1996-2015. Also, to examine the potential heterogeneity and asymmetry, the panel quantile regression approach is utilized. The empirical results indicate that technological innovation directly reduces CO₂ emissions; however, this impact is significantly heterogeneous and asymmetric across quantiles. Furthermore, through analyzing the influencing mechanism, the technological innovation affects the impacts of economic growth and renewable energy through its moderating effects. Moreover, the moderating effects of technological innovation is also heterogenous. Accordingly, the main contribution of this study is that the potential heterogeneity and asymmetry of both the direct impact and moderating effect of technological innovation on CO₂ emissions in OECD countries are systematically analyzed by employing the panel quantile regression approach.

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

Existing studies have shown that the COVID-19 pandemic caused a sharp drop in carbon emissions in 2020. A recent example of the impact of sudden extreme events on carbon emissions occurred in the 2008 global financial crisis, in which carbon emissions droped in 2009, but jumped in 2010. This study is aimed to discuss how to prevent the retaliatory growth of carbon emissions post COVID-19 through learning the lessons from analysis of short-term and long-term drivers of carbon emissions. This study explored the short-term (annual) effects (population scale. affluence level, carbon intensity, energy intensity) of changes in carbon emissions by decomposing carbon emissions in the world, different income groups and selected countries before and after the 2008 financial crisis using LMDI technique. In addition, this study explored the long-term effects (energy consumption per capita, energy structure, energy intensity, foreign direct investment, and trade openness) of changes in carbon emissions by decomposing carbon emission in the world and different income groups from 1990 to 2014 using VAR technique. The decomposition results of short-term drivers of carbon emission uncovered that the deterioration in energy efficiency (increase in energy intensity) was the main reason for the retaliatory rebound in carbon emissions post-2008 financial crisis, especially in high-income countries. The decomposition results of long-term drivers of carbon emission uncovered that trade openness contributed to reduce carbon emission in the world and the incomes groups in the long term, although trade openness led to increase in carbon emission in developing countries in the short term. To prevent retaliatory rebound of carbon emissions, what we should learn two lessons from the decomposition of carbon emission: improving energy efficiency, and expanding trade openness. Unfortunately, energy efficiency has been neglected in the economic recovery plans to respond to COVID-19 of various countries, especially developed countries, and worse, trade protectionism is on the rise, especially in developed countries. Therefore, we are pessimistic about preventing a retaliatory rebound in carbon emissions post-COVID-19 for now.

Decoupling or delusion? Mapping carbon emission per capita based on the human development index in Southwest China

Reducing energy-related carbon emissions has become the essential measure to mitigate global climate change. Based on decoupling analyses and index decomposition, this study is the first to explore whether carbon emission per capita decouples from the human development index from 2000 to 2015 at the provincial level in Southwest China [Chongqing (CQ), Sichuan (SC), Guizhou (GZ), and Yunnan (YN)]. We demonstrate the following. (1) The economic output and energy intensity effects among the five drivers are the strongest to promote and suppress the growth of carbon emission per capita from 2001 to 2015. (2) At the provincial level, we observed four decoupling statures, and the decoupling impact was organized in decreasing order: CQ > GZ > SC > YN (2001-2005), GZ > YN > CQ > SC (2006-2010), YN > SC > GZ > CQ (2011-2015). (3) The overall decoupling effect of Southwest China has been generally reinforced from 2000 to 2015, and finally entered a strong decoupling status in 2013-2015; an environmental Kuznets curve explained that this finding is related to historical peaks in total carbon emissions. Overall, this study provides guidance for the government on carbon emissions mitigation strategies and a valuable decision-making reference for other regions attempting to accelerate low-carbon development.

Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy

Improving energy efficiency and building a low-carbon economy are the important ways to resolve the current contradiction between economic growth and the environment in China. In this paper, we use the super-efficiency Slack-Based Measure model (super-efficiency SBM model) to measure the energy efficiency of 30 provinces in China, and then conduct Empirical Orthogonal Function (EOF) to analyze its spatial-temporal evolution. Moreover, we use the Geographically and Temporally Weighted Regression (GTWR) to analyze the spatial-temporal heterogeneity of its driving factors. The results show that: (i) during the sample period, China's energy efficiency shows a rapidly upward trend, accompanied by the gradually strengthening spatial pattern of the "eastern>central>western"; (ii) the spatial pattern of the "southern>northern" exhibited by the annual growth rate of energy efficiency experienced a process of weakening first and then gradually strengthening; (iii) the influencing effects of market openness, relative energy price and industry structure on energy efficiency have no significant heterogeneity as a whole; (iv) the effects of environmental regulation intensity, the marketization level, the technical level, energy consumption structure and economic development level have significant spatial heterogeneity, and the effects of energy conservation and emission reduction policies has significant temporal heterogeneity.

Can urban sprawl be the cause of environmental deterioration? Based on the provincial panel data in China

Based on the provincial panel data in China for 2001-2017, this article explores the impact of incoordination between land and population urbanization on environmental quality from the perspective of urban sprawl. It can be found that: (1) Most provinces are suffering from the urban sprawl, land finance has made urban sprawl more serious. (2) The results of SDM show that the impact of urban sprawl on the CO₂ emissions can be regarded as a N-shaped curve. Both excessively rapid expansion of urban space and excessively rapid growth of urban population can intensify the CO₂ emissions. (3) The tests of regional heterogeneity show that in underdeveloped provinces, the effect of urban sprawl on CO₂ emissions could also be a N-shaped curve, but it is an inversed U-shaped for the developed provinces. Thus, it can diminish the emissions of CO₂ in developed provinces, through strengthening environmental regulations and restraining the excessively rapid growth of the urban population. However, the underdeveloped provinces should actively promote economic development and create more jobs to avoid the loss of labors. These conclusions are also applicable to the tests of regional heterogeneity based on total factor productivity.

Is India on the right pathway to reduce CO2 emissions? Decomposing an enlarged Kaya identity using the LMDI method for the period 1990-2016

Nowadays, India is the third-largest CO₂ emitter and energy consumer in the world, and, it is soon expected to surpass China as the most populated country. Therefore, it is of great interest to analyse how India is developing its energy transition to a lower-carbon economy. This work analyses the evolution of the main driving forces of CO₂ emissions in India during the period 1990-2016 through the use of an enlarged version of the Kaya identity, which establishes a link between CO₂ emissions, types of energy sources (16), size of the economic sectors (3) and value of the Gross Domestic Product. India's CO₂ emissions increased by 276% in the period under study, due to the rapid economic growth of India, which has been the dominating driving force contributing to the increase in CO₂ emissions by 241%, while the energy intensity has been the main one reducing them by approximately -47%. So far, the use of coal has supported the rapid economic growth and the contribution of renewable energy, although significant, is still short compared to the total amount of energy employed. Remarkably, the estimated value of the emission intensity for 2020 supposes a 26% reduction concerning the value in 2005. According to this result, India is on the right pathway to fulfil its Nationally Determined Contribution but not to reduce its net CO₂ emissions.

Structural and social-economic determinants of China's transport low-carbon development under the background of aging and industrial migration

This paper aims to explore structural and social-economic determinants of China's transport CO₂ emissions (TCEs) from 2004 to 2016, by using logarithmic mean Divisa index (LMDI). Compared with existing studies, two new factors, i.e., spatial pattern and age structure, that should have impacts on TCEs, are considered in this study. Results show that during 2004-2016: (1) transportation demand and urbanization were dominant in TCEs growth, while energy intensity and industrial structure were the key factors of CO₂ emissions reduction. (2) The effects exerted by age structure and CO₂ emission factor change were relatively mild. The former one has a stable promoting effect, while the latter one inhibits TCEs growth. Additionally, the influence of spatial pattern on the growth of CO₂ emissions from transportation was limited. (3) Energy structure and energy intensity played more important roles in reducing emissions of the transport sector in the eastern region. The effects of population size on the eastern region were much greater than those of the central and western regions, presenting the phenomenon of "eastern agglomeration". (4) In Tianjin, energy intensity and energy structure did not perform well for TCEs.

Life cycle assessment of ultra-low treatment for steel industry sintering flue gas emissions

The largest contributor to pollutant emissions is the sintering process in steel industry. Ultra-low emission policy for the Chinese steel industry states that emission concentrations of particulate matter, SO₂ and NO_x should not exceed 10, 35 and 50 mg/m³ respectively. The emission concentrations of the steel industry are the same as the ultra-low emission policy for the coal-fired power industry, but the pollutant control technologies of the two industries are different. Life cycle assessment method is applied to analyze the latest ultra-low treatment process for sintering flue gas emissions which includes electrostatic precipitation, ozone oxidation, wet desulfurization, wet denitration, condensation dehumidification and wet electrostatic precipitation. Following this novel ultra-low emission treatment, the concentrations of particulate matter, SO₂, NO_x, and PCDDs in the sintering flue gas decreased very significantly, attaining the new emission standard. With 1 ton of sinter as the functional unit and "cradle to gate" as the system boundary, the environmental impact of the process is 0.1811 and the total economic cost is 172.79 RMB, of which internal cost is 34.64 RMB and external cost is 138.15 RMB. The main environmental impacts result from applying the wet denitration and ozone oxidation processes. Sodium sulfite in the wet denitration process, and electricity and liquid oxygen in the ozone oxidation process are the key inputs that cause environmental impact. These findings are useful for a further optimization of the ultra-low emissions process from both the environmental and economic perspective, which is applicable in other regions of the world.

A regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth in China

China, known as the largest carbon emitter and the second largest economy worldwide, has continued to put effort into the understandings of the main drivers of carbon emission and their decoupling statuses from its economic growth. Considering the significant differences of natural and social environments in different regions of China, this paper presents a regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth by using the Logarithmic Mean Divisia Index (LMDI) and the Tapio decoupling method. The decoupling results indicate that carbon emissions in all regions show a stable decoupling trend from their economic development, which means that China is now on the right road for achieving a low-carbon economy. However, the decoupling status by the end of 2016 also indicates that most of the regions are still in the states of expansive coupling or weak decoupling, especially in Northwest (NW), which implies that the speed of decarbonization process is still not high enough. The decomposition results show that in all regions except NW, GDP per capita is the most influential factor leading to increasing carbon emissions, while energy intensity is the largest factor in reducing carbon emissions. In NW, both GDP per capita and energy intensity drive the increase in carbon emissions. The results in this paper could benefit China's regional policy-making and national strategies.

Unbalanced economic benefits and the electricity-related carbon emissions embodied in China's interprovincial trade

Quantifying the economic benefits and environmental costs brought about by trade can help reveal the environmental inequalities behind regional trade. There have been many studies on the accounting of greenhouse gas emissions and pollutants embodied in regional trade, but there are insufficient studies analyzing the imbalance between the economic benefits and environmental costs embodied in trade. Electricity-related carbon emissions are the main contributor to global warming, explaining more than 40% of carbon emissions both globally and in China. This study uses the network approach and multiregional input-output (MRIO) model to quantify the electricity-related carbon emissions and value added embodied in China's interprovincial trade from 2007 to 2012 and also applies the regional environmental inequality (REI) index to measure the imbalance of electricity-related carbon emissions and economic benefits embodied in such trade. The results show that 20-80% of the electricity-related carbon emissions and 15-70% of the value added of a province's final demand are outsourced to other provinces. The major directions of the net value added and electricity-related carbon emissions embodied in China's interprovincial trade were from north to south and from the center to the east. Unequal bilateral interprovincial trade mainly occurred between inland provinces and developed provinces, and western provinces (such as Guizhou, Gansu, and Ningxia) suffered economic and environmental losses from interprovincial trade. This study can promote understanding of the distribution impacts of domestic trade on environmental costs and economic benefits and provide a reference for China's cross-provincial carbon emission mitigation policies.

Driving forces of China's CO2 emissions from energy consumption based on Kaya-LMDI methods

Anthropogenic carbon emission gives rise to a situation where global warming is becoming serious. China is paying for reducing carbon emissions. The concept of carbon curse suggests that countries rich in fossil fuels tend to be closely linked to high carbon emissions, but this is not absolute, which reminds policymakers that the policies implemented are positivelycorrelateswith carbon emission reduction. This study is also aimed at this, hoping to provide some proposals about reducing CO₂ emissions to policy-makers by decomposing and analyzing the important factors. To achieve this target, this paper employs the extended the Kaya identity, combines the LMDI method to analyze the impact factors of carbon emissions in China from 1996 to 2016 and discusses the effects and causes of each factor according to the actual situation. It is found that the economic activity is the greatest driving force to promote carbon emissions, while on the contrary, energy intensity is the biggest suppressor. Optimizing industrial structure, improving the structure of energy and export-import trade and intensifying the development of clean energy can effectively restrain the growth of carbon emissions. In addition, the relative innovation point in this study is to analyze carbon emissions with the combination of electricity trading and discusses that increasing imported electricity is also a strategy to reduce carbon emissions.

Temporal and Spatial Variability of Carbon Emission Intensity of Urban Residential Buildings: Testing the Effect of Economics and Geographic Location in China

The role of urban residential buildings (URBs) in the carbon reduction goal of China is becoming increasingly important because of the rising energy consumption and carbon emission of such buildings in the region. Considering the increasing spatial interaction of the carbon emission of URBs (URBCE) in the region, this study investigates the influence of climate and economic factors on the URBCE in North and South China. First, the URBCE is calculated by using a decomposition energy balance table based on the carbon emission coefficient of electric and thermal power, thereby improving the estimation of the basic data of URBCE. Second, the influence of economic and climatic factors on the URBCE intensity in 30 provinces of China is explored by using a spatial econometric model. Results show that the URBCE intensity in China had a spatial autocorrelation from 2000 to 2016. Climatic and economic factors have great differences in the degree and direction of influencing the URBCE intensity in the country. Formulating emission reduction policies for climate or economic zones is more scientific and effective than developing national policies. Among these factors, urbanization rate, climate, and GDP per capita have a significant positive impact on the URBCE intensity in the region, whereas other factors have varying degrees of negative impact. In addition, climate, consumption level, and building area have significant spatial spillover effects on URBCE intensity, whereas other factors do not pass the significance test. Relevant conclusions should be given special attention by policymakers.

District Heating Energy Consumption of the Building Sector in the Jing-Jin-Ji urban Agglomeration: Decomposition and Decoupling Analysis

China’s rapid urbanization has caused dramatically increasing energy consumption in the district heating systems of the building sector in the Jing-Jin-Ji urban agglomeration, and this change has led to enormous air pollution issues in this region. However, the drivers and the sustainable development process of the district heating system of the building sector have not been investigated to understand the management of energy conservation and emissions reduction in the Jing-Jin-Ji urban agglomeration. This study investigates the drivers of the district heating energy consumption of the building sector (DHEB) in the Jing-Jin-Ji urban agglomeration between 2004 and 2016 by developing a decomposition framework. The decoupling status between the DHEB and gross domestic product (GDP) is then analyzed based on the Tapio decoupling index. The results show that a weak decoupling effect is mainly found between the DHEB and GDP in the Jing-Jin-Ji urban agglomeration from 2004 to 2016. The increase in the DHEB in 2004–2016 is largely driven by the growth of the district heating area and population, while the heating energy intensity negatively contributes to the increase. Significant differences in the effects of the share of the energy mix and share of heat production technology were found between subregions in response to government policy, which impacted levels in Beijing, Tianjin, and Hebei in decreasing order.

Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors

Carbon emission peak has become a focus of political and academic concern in global community since the launch of Kyoto Protocol. China, as the largest carbon emitter, has committed to reaching the carbon peak by 2030 in Paris Agreement. This ambitious national goal requires the endeavors of individual sectors, particularly those carbon-intensive ones. Predicting the sectoral peaks under current endeavors and understanding driving forces for the carbon emission changes in the past years are substantial for guiding the allocation of the country's future efforts. In the past studies contextualized in China, the prediction of its carbon peaks seldom appeared at the sectoral level, which is considered as a research gap. Therefore, this study predicts the peaks at four carbon pillar sectors (i.e. industrial, building, transport and agricultural sectors) and identifies the driving forces for the carbon emission changes of them. This study hypothesized Carbon Kuznets curve (CKC) as the theoretical model for predicting the peaks and used Logarithmic mean Divisia index (LMDI) as the method to identify the driving forces. The results show that the carbon emission in the country will peak in 2036, six years later than the agreed year. The lateness of the national peak can be attributed to the significant lateness of three pillar sectors' peaks, occurring in 2031 for the industrial sector, 2035 for the building sector, 2043 for the transport sector, peak for the agricultural sector occurs four years earlier in 2026 though. Furthermore, the results show that carbon emission is significantly driven by the booming economic output and inhibited by decreasing energy intensity, but the slight fluctuation of energy structure plays a minor role in the four sectors. Policy adjustments are proposed for effectively and efficiently urging the on-time occurrence of the national peak.

The conformable fractional grey system model

The fractional order grey models have appealed considerable interest of research in recent years due to its high effectiveness and flexibility in time series forecasting. However, the existing fractional order accumulation and difference are computationally complex, which leads to difficulties for theoretical analysis and applications. In this paper, new definitions of fractional accumulation and difference are proposed based on the definition of conformable fractional derivative, which are called the conformable fractional accumulation and difference. Then a novel conformable fractional grey model is proposed based on the conformable fractional accumulation and difference, and Brute Force method is introduced to optimize its fractional order. The feasibility and simplicity of the proposed model and the Brute Force method are shown in the numerical example. The conformable fractional grey model outperforms the existing fractional grey model and the autoregressive model in 1 to 3-step predictions with 21 benchmark data sets, and also outperforms the existing fractional grey model in predicting the natural gas consumption of 11 countries. The results indicate that the proposed conformable fractional grey model is more efficient in longer term prediction and non-smooth time series forecasting than the existing models.

Modernizing our way out or digging ourselves in? Reconsidering the impacts of efficiency innovations and affluence on residential energy consumption, 2005-2015

This study scrutinizes the impacts of efficiency innovations as well as affluence on residential energy consumption, which is a major driver of greenhouse gas emissions. The study draws on the ecological-modernization perspective, which is optimistic about how technological innovations and affluence can help societies overcome environmental challenges associated with production and consumption, and the political-economy perspective, which raises doubts about whether these factors are beneficial to the environment, given their tendency to drive more consumption. Analysis of nationally representative longitudinal data reveals mixed relationships between efficiency innovations and residential energy consumption: while some measures of efficiency innovations, generally those not requiring human-technology interactions, are negatively related to residential energy consumption, others are either unrelated to it or drive more consumption. These findings suggest efficiency innovations offer only minimal opportunities for conserving energy, and may depend on the nature of the innovation. Raising doubts about the potential for rising affluence to promote environmental protection, this study reveals positive relationships between our measures of affluence and residential energy consumption.

Estimation and decomposition analysis of carbon emissions from the entire production cycle for Chinese household consumption

To guide households on implementing low-carbon consumption patterns, it is necessary to comprehensively measure carbon emissions of household consumption. This study expands the input-output relationship into the production-consumption relationship. It uses optimized data of the relationship between household consumption and production industry to calculate the entire production-side carbon emissions, including from capital formation, of Chinese household consumption, and uses LMDI model to analyze the factors affecting the growth of carbon emissions from Chinese household consumption. The results show that the carbon emissions of Chinese household consumption grew steadily from 2005 to 2015 almost 50% of carbon emissions were accounted for by high growth rates in residence consumption. Carbon emissions and growth rate of urban households' consumption are significantly higher than the same figures for rural households. The carbon emissions intensity of all types of household consumption except residence and education has shown a downward trend. Household consumption structure and income level are the two main factors that promote the growth of household carbon emissions. Urbanization level and population size are secondary factors while household consumption carbon intensity is an important factor for curbing the growth of household consumption emissions. The study also proposes policy recommendations on how to improve the consumption structure of households, reduce the carbon intensity of household consumption, and curb the growth of carbon emissions from urban households.