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

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

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

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

The rapid urbanization in China over the last decade has resulted in increased carbon emissions from the building materials industry. This paper seeks to reach a trade-off between economic benefits and carbon emissions in the building materials industry by integrating a carbon emissions quota allocation (CEQA) based equilibrium strategy under a carbon emissions trading (CET) mechanism that fully considers the stakeholder relationships between the regional authority and the building materials suppliers. An interactive solution approach with fuzzy random variables (FRVs) is designed to determine the trade-off between these decision makers. The methodology is then applied to a real world case to demonstrate its practicality and efficiency. Comprehensive discussions under different policy control scenarios and comparative analysis of two carbon reduction policies are conducted to assist policy makers. It was found that the proposed methodology encouraged building materials suppliers to adjust their production and carbon emissions quota purchase plans, further assisting regional authority to control the industry's emissions performance by providing a “win-win” solution for the regional authority and suppliers when making logical economic-environmental trade-off decisions. [ABSTRACT FROM AUTHOR]

Cities have been one of the most important areas of CO 2 emissions. It is increasingly important to research the effect of urbanization on CO 2 emissions, especially in large emerging and developing economies, due to the indispensable need for understanding the effect of urbanization on CO 2 emissions, evaluating carbon reduction tasks and providing the scientific basis for low-carbon urbanization. Utilizing a balanced panel dataset in the Yangtze River Delta (YRD), China, during the period of 2000–2010, this paper employed data envelopment analysis (DEA) window analysis and a spatial lag panel Tobit model to investigate the effect of urbanization on CO 2 emissions efficiency (the ratio of the target CO 2 emissions to the actual CO 2 emissions). The results show that the average CO 2 emissions efficiency was 0.959 in 2010, and CO 2 emissions efficiency ranged from 0.816 to 1 and exhibited spatial clustering in the region. The larger potential of CO 2 emissions reduction appeared in Zhenjiang and Yangzhou, indicating that more CO 2 emissions reduction tasks should be allocated to these two cities. Urbanization has negative effects on improving CO 2 emissions efficiency, and there is a U-curve relation between CO 2 emissions efficiency and urbanization, indicating that CO 2 emissions efficiency decreases at the early stage of urbanization, then increases when urbanization reach a high level. There is spatial spillover effect among the prefecture-level cities, suggesting that different prefecture-level governments should coordinate with each other to improve CO 2 emissions efficiency in the whole area. Gross domestic product (GDP) per capita also plays a markedly positive role in improving CO 2 emissions efficiency. This research highlights the effect of urbanization on CO 2 emissions efficiency and the importance of improving CO 2 emissions efficiency in developing countries. [ABSTRACT FROM AUTHOR]

The surge in energy consumption and CO 2 (carbon dioxide) emissions is exacerbating global warming. The Chinese government has promised to reduce CO 2 emission by 60%–65% per unit of GDP over the 2005 level by around 2030. Analyzing the factors influencing CO 2 emissions is one of the key aspects involved in effectively reducing greenhouse gas emissions. In this paper, we add climatic factors based on socio-economic factors, and adopt the extended STIRPAT model to investigate the mechanism driving emissions by the co-action of natural and socio-economic factors. The results show that the 1995–2014 emissions in China's Zhejiang province had an upward trend with growth rates varying between intermediate periods. The order of the factors' elasticity coefficients is urbanization rate (0.181) > GDP per capita (0.180) > population (0.177) > energy intensity (0.170) > trade openness (0.170) > cooling degree days (0.127) > mean temperature anomaly (0.116). Economic development and urbanization were the main factors promoting CO 2 emissions in 1996–1999, while economic development and foreign trade were the main factors in 2000–2014. Policy makers should pay more attention to keeping a healthy pace of economic development, introducing more low energy consumption and low pollution enterprises and encouraging technological innovation. Improving the quality of urbanization, guiding a sustainable lifestyle, adjusting the structure of foreign trade and curbing energy intensity are also expected to reduce emissions. [ABSTRACT FROM AUTHOR]

With China's rapid urbanization and industrialization, carbon emission in Chinese cities deserve special attention, and promoting low-carbon city (LCC) is considered essential for China. However, different cities present different development stages and carbon emission scenarios. This study presents the factors affecting carbon emission by introducing a city development-stage framework. The method of logarithmic mean Divisia index (LMDI) is adopted to decompose emission factors into energy structure, energy intensity, industrial structure, economic output and population scale. Beijing is chosen as the case city in this study, and four development stages for the city are identified by using the Environmental Kuznets Curve (EKC) theory, namely, S 1 (−1991), S 2 (1991–2004), S 3 (2004–2022) and S 4 (2022-). As the data collected for analysis are only available for the period of 1995–2014, this study focuses on the factor analysis for the stage S 2 and S 3 . The results show that the main driving factor for carbon emission increase in the stage S 2 is economic output, followed by population scale, while the main factor contributes to carbon emission reduction in this stage is the industrial structure. In the stage S 3 , the economic output is the top contributor to increase carbon emission, followed by population scale and energy structure, whereas energy intensity is the main influencing factor in curbing the carbon emission. Although the paper refers to the specific case of Beijing, the understanding on the driving emission factors in different development stages not only provides policy-makers and practitioners with valuable references for adopting effective measures to reduce carbon emission, but also provides other cities internationally with important lessons for accelerating the development of LCC. The innovative economic development stage framework incorporates carbon emission characteristics provides a new approach for understanding driving emission factors at city or national level. [ABSTRACT FROM AUTHOR]

At present, China's carbon dioxide (CO 2 ) emissions ranked first in the world. Moreover, the CO 2 emissions in the manufacturing industry accounted for 55.0% of total CO 2 emissions. Thus, investigating the main driving forces of CO 2 emissions in this industry is crucial for reducing China's CO 2 emissions. The traditional estimation method can only get the “global” and “average” parameter estimation, but obscures the difference in the “local” parameter estimation across region. Geographically weighted regression embeds the latitude and longitude of the sample data into the regression parameters, and uses the local weighted least squares method to estimate the parameters point–by–point. To reveal the nonstationary spatial effects of driving forces, geographically weighted regression model is employed in this paper. The results show that economic growth has a positive impact on CO 2 emissions, and the impact continuously declines from the eastern region to the central and western regions. However, the impact of urbanization in the western region is higher than that in the eastern region and the central region. The impact of energy efficiency in the eastern and central regions is stronger than that in western region. The effect of industrialization also has a similar story. Therefore, in order to effectively achieve emission reduction, we need to take full account of spatial differences in different regions. [ABSTRACT FROM AUTHOR]

At present, China's carbon dioxide (CO 2 ) emissions ranked first in the world. Moreover, the manufacturing industry is the biggest contributor to CO 2 emissions. Most of the existing studies use the average estimation method to investigate the main drivers of CO 2 emissions in this industry. However, the data distribution of economic variables is often not normal distribution, and the tail of the data hidden important information. In order to provide a realistic basis for emission reduction in this industry, this paper applies the quantile regression approach to investigate the driving forces of CO 2 emissions under high, medium and low emission levels. The empirical results indicate that the effect of economic growth on CO 2 emissions in the upper 90th and 75th–90th quantile provinces are higher than those in the other quantile provinces. The influence of energy intensity in the 25th–50th quantile provinces is lower than those in the other percentile provinces. The impact of urbanization in the upper 90th quantile provinces is the strongest in all the quantile provinces. However, the influence intensity of energy structure in the lower 10th quantile provinces is the highest in all the quantile provinces. Therefore, policymakers should focus on the heterogeneous effects of driving forces on CO 2 emissions in different quantiles during the process of CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

Rapid urbanization has exerted substantial pressure on China’s energy system and contributed to climate change. To find the key drivers of urban residential energy consumption and CO 2 emissions, this paper uses an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model that employs city-level data to examine the influences of population scale, income level, population compactness and price on house-based residential energy consumption, energy-related CO 2 emissions and private vehicle ownership. The empirical results indicate that factors such as population scale, affluence, and population compactness can lead to increases in residential energy consumption and CO 2 emissions. In terms of transportation, income and population scale positively drive the growth of private vehicle ownership, while the fuel price negatively influences private vehicle ownership. Moreover, population scale is the most important factor in residential energy consumption and CO 2 emissions. Finally, policy recommendations are suggested for China’s urban development strategy and urban design and to encourage technology innovations that reduce residential energy consumption and CO 2 emissions. [ABSTRACT FROM AUTHOR]

Beijing’s residential CO2 emissions have become the main growth point of CO2 emissions. However, the impact factors of the direct energy-related CO2 emissions from residents in Beijing have not been previously evaluated. This paper accessed the key factors that affect the residents’ CO2 emissions in Beijing from 1995 to 2015, using a newly built decomposition model with generalized Fisher index (GFI) and M-P model. The results were compared between urban and rural areas. Urban residential CO2 emissions did not change during 1995–1999 but then grew rapidly after 1999, while rural residential CO2 emissions fluctuated during the studied period. Increased per capita income is the most important pulling factor for the growth of residential CO2 emissions, while energy consumption intensity is a decisive factor in inhibiting residential CO2 emissions. Population size plays a pulling role in the growth of residential CO2 emissions. Energy structure exerts a role in inhibiting residential CO2 emissions, and its inhibition effect is stronger for urban residents. Average consumption propensity inhibits urban residential CO2 emissions but has little effect on rural residential CO2 emissions. The population migration from rural to urban areas in the urbanization process increases residential CO2 emissions. To reduce residential CO2 emissions, several recommendations have been proposed. [ABSTRACT FROM AUTHOR]

Currently, China is the largest emitter of carbon dioxide in the world. Moreover, the manufacturing industry is the largest contributor to China's CO 2 emissions. Therefore, investigating the main driving forces of CO 2 emissions in the manufacturing industry has great practical significance. Using panel data from 2000 to 2014, this paper examines the driving forces of the industry's CO 2 emissions at the regional level. The results show that economic growth dictates the industry's CO 2 emissions, and its impact varies across regions. The influence of industrialization on CO 2 emission in the central region is stronger than in the eastern and western regions due to differences in industrial structure and the building industry. The impact of urbanization declines continuously from the western region to the central and eastern regions because of differences in human capital accumulation and private car ownership. Energy structure also produces a different effect in the three regions on account of differences in coal consumption. Furthermore, the role of energy efficiency in reducing CO 2 emissions in the three regions is markedly different owing to big differences in R&D funding and R&D personnel investments. Considering the significant regional differences, we propose policy recommendations to mitigate CO 2 emissions in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Urbanization results in a considerable economic disparity between urban and rural areas in developing countries, which has had a consequent significant impact on CO 2 emissions. To accommodate this directly, this paper presents a modified version of the Kaya Identity formula, U-Kaya, that includes a direct urbanization factor to determine the relationship between energy consumption intensity, population growth, urbanization rates, urban and rural GDP per capita and the volume of carbon emissions, for application in the dynamic forecasting of carbon emissions by Monte Carlo simulation. The method is demonstrated in forecasting China's likely carbon emissions in 2020 by three different modes of urbanization policy: the government-dominant mode, market-dominant mode and a hybrid government market-dominant mode. The results indicate that a higher urbanization rate, energy carbon emission coefficient and energy intensity will lead to increased carbon emissions. Finally, three policy implications are identified, comprising the narrowing of the economic gap between urban and rural areas, adjustments to the energy structure and technical innovation, which will provide a valuable reference for developing countries in their efforts to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

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

China is experiencing a stage of rapid urban development. The energy consumption and related carbon dioxide emissions of households continue to increase. This paper calculates direct and indirect carbon dioxide emissions of households based on the input–output method in China from 1996 to 2012. The results reveal that there were more total carbon dioxide emissions from urban households than from rural households, far more indirect emissions from urban households than from rural households, slightly more direct emissions from urban households than from rural households, and differences in direct carbon dioxide emissions from various fuels and in indirect emissions from various sectors between urban and rural households. To examine the causal relationship between urbanization and the carbon dioxide emissions of households, cointegration and Granger causality tests are applied. A unidirectional causal relation was found running from urbanization to both direct and indirect household carbon dioxide emissions, and the direct and indirect carbon dioxide emissions of households would increase 2.9% and 1.1%, respectively, for every increase of one percent in urbanization. We discuss the reasons of why the development of urbanization will lead to more household direct and indirect carbon dioxide emissions, and suggest certain policy implications for urbanization and carbon dioxide emissions based on the results of this study. [ABSTRACT FROM AUTHOR]

With fast economic development, industrialization and urbanization, China faces increasing pressures on carbon emission reduction, and especially on air pollutants (SO 2 , NOx, PM) reduction, particularly the notorious haze issue caused by air pollution in recent years. Pursuing co-benefits is an effective approach to simultaneously respond to both carbon and air pollutant problems. In this paper, the AIM/CGE (Asia–Pacific Integrated Assessment Model/Computational General Equilibrium) model and GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies)-China model are combined together to project future CO 2 and air pollutants emissions in China, as well as reduction costs and co-benefit effects. Considering implementation of carbon mitigation policy and air pollutant mitigation technologies, four scenarios (S1, S2, S3 and S4) are analyzed. Results indicate that by implementing both carbon and air pollutant mitigation (S4), CO 2 emission per GDP can be reduced by 41% by 2020, compared with the 2005 level, and SO 2 , NOx and PM2.5 emissions would change by a factor 0.8, 1.26 and 1.0 of the 2005 level, respectively in 2030. The real co-benefits of emission reductions (S2 minus S4) for SO 2 , NOx and PM2.5 are 2.4 Mt, 2.1 Mt and 0.3 Mt in 2020, and the corresponding cost reduction co-benefits are 4, 0.11, and 0.8 billion €, respectively. Provincial disparity analysis reveals that regions with higher co-benefits are those with higher GDP such as Guangdong, Shandong and Jiangsu, energy production bases such as Inner Mongolia and Shanxi, low coal quality provinces such as Sichuan (for SO 2 ), and industrial base provinces such as Liaoning. Cost-effectiveness is finally discussed for policy implications, which suggests that investment in less developed western regions is more cost-effective and easier in reducing CO 2 or air pollutant emissions than in developed eastern regions. [ABSTRACT FROM AUTHOR]

Based on analysis of input–output energy and household expenditure data (IO-EA-expenditure), this paper calculates China’s indirect carbon emissions from residential consumption in 2002 and 2007, by region. Then a new Structural Decomposition Analysis (SDA) model is proposed to investigate the regional variations of impacts of urbanization, consumption ratio and consumption structure on residential indirect CO 2 emissions in China during 2002–2007. As the results suggest, expansion of urbanization and upgrade of consumption structure play important roles in the growth of residential indirect emissions. Transformation of consumption ratio, contributing the most to emissions in the eastern region, reduces indirect emissions in all regions to a certain extent. The persistent decline of carbon emission intensity still contributes significantly to the decrease in emissions and the rise of per capita consumption plays a dominant role in the growth of residential indirect emissions. Based on empirical results, this article provides policy suggestions which can help reduce China’s residential indirect CO 2 emissions. [ABSTRACT FROM AUTHOR]

Abstract: To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions. [Copyright &y& Elsevier]