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Abstract Reducing the total CO2 emissions intensity (TCEI) in the Chinese construction industry is an important strategy to cut its CO2 emissions, which share a considerable proportion of sectoral CO2 emissions in China. However, there is lack of an in-depth analysis of the effect of production structure on the TCEI. This study comprehensively investigates the effect of production structure on the TCEI in the Chinese construction industry by using structural decomposition analysis and sensitivity analysis methods collectively. The results show that the TCEI in the Chinese construction industry declined by 0.3505 tons CO2/104 RMB between 2007 and 2012, and the effect of production structure contributed 110.66% to such decline. Over 95% of the effect was contributed by the electricity, steam and hot water production and supply industry and the refined petroleum, coke products, and nuclear fuel industry. The results also demonstrate that the three most critical technical coefficients were for the nonmetallic mineral products industry consumed by the construction industry, the metal smelting and pressing industry consumed by the construction industry, and the metal smelting and pressing industry consumed by itself in 2012. Besides the first two coefficients, the coefficient for the electricity industry consumed by itself was also critical in 2007. The production chains of the construction industry itself and the metal smelting and pressing industry included over half of the twenty most critical technical coefficients. This study can provide valuable reference for Chinese policy makers to take proper strategies to adjust the production structure in order to reduce the TCEI. Highlights • The effect of production structure on TCEI in construction industry was analyzed. • The TCEI in the Chinese construction industry declined by 19% between 2007 and 2012. • The three most critical technical coefficients were identified. • The production chains of construction industry and metal industry were most critical. [ABSTRACT FROM AUTHOR]

Highlights • An economic performance-carbon index panel framework for classifying different regions is established. • Carbon emission are decomposed into four impact factors. • The understanding of impact factors offers valuable references for adopting low carbon economy measures. Abstract As the largest carbon emitter in the world, China is under pressure in addressing carbon emission reduction. It is important for the country to understand the impact factors on emission so that proper mitigation measures can be taken. Previous studies have analyzed emission factors through classifying regions mainly by a single economic index or carbon index, whereas this study introduces a two-dimensional panel framework to classify regions. These two dimensions are per capita GDP and carbon intensity. Based on this panel framework, the 30 provinces in China are classified into four regions, namely, high economy and high carbon intensity (Region I), high economy and low carbon intensity (Region II), low economy and low carbon intensity (Region III), and low economy and high carbon intensity (Region IV). The STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and the panel cointegration approach are employed to analyze the impacts of various carbon emission factors in the four classified regions. The results show that the most significant factor for Region I is industrial structure, and that for Regions II , III and IV is energy intensity, population size and per capita GDP respectively. The research results can help decision makers adopt effective emission control measures. The two-dimensional panel framework introduced can be extended for application to examine the emission impact factors at regional or national levels in other countries. [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]

In assuming its commitments to the reduction of CO 2 emissions, China is contributing great efforts to mitigate CO 2 emissions across its all economic sectors including construction industry. The Chinese construction industry is one of the main contributors to CO 2 emissions in China. This paper presents an empirical study on the CO 2 emissions in the Chinese construction industry across the period of 1995–2011. The input-output analysis method is employed for conducting the study. The data used for analysis are mainly collected from the world input-output database. The analysis results demonstrate that the CO 2 emissions in the Chinese construction industry increased by 388.7% from 1995 to 2011, reached to 3035.9 Mt in 2011. However, the carbon intensity in the construction industry decreased by 46.9% from 1995 to 2011, recorded as 0.00198 Mt/million dollars in 2011. The indirect CO 2 emissions were the majority of the construction CO 2 emissions, accounting for over 95% during 1995–2011. The process-based CO 2 emissions contributed 8.7%–12.6% of the total construction CO 2 emissions during 1995–2011. The electricity, gas and water supply industry, the other non-metallic mineral industry, and the basic metals and fabricated metal industry were the main contributors to the construction CO 2 emissions during 1995–2009. This study presents a comprehensive picture on the CO 2 emissions in the Chinese construction industry, which provides valuable references both to further study and to formulate more effective policies. [ABSTRACT FROM AUTHOR]

Global warming caused by carbon emission has been recognized as a threat to public health and welfare. Carbon emission reduction is therefore a necessary task for each country to address the severe challenges arising from global warming. This research combines the STIRPAT model with the use of the panel and time-series data to analyze the impacts of population, affluence and technology on the carbon emission of 125 countries at different income levels over the period of 1990–2011. The results show that the key impact factor (KIF) at global level is affluence, followed by technology and population in the order of their impacts on carbon emission. For countries at high-income (HI) level, technology has the greatest impact on carbon emission, while affluence has the least. Affluence, prior to technology and population, is identified as the KIF of carbon emission for countries at upper-middle-income (UMI) and lower-middle-income (LMI) levels. When it comes to the low-income (LI) level, affluence serves as the factor greatest affecting on carbon emission, and technology has the least impact. In particular, two generic patterns are identified based on the empirical results: higher income leads to greater impact of the technology and lower impact of the affluence on carbon emission. The KIFs of different income level countries identified in this study provide policy-makers and practitioners with valuable references for adopting effective policies and strategies to stimulate the global carbon emission reduction. [ABSTRACT FROM AUTHOR]

As the world's largest carbon emitter, China is confronting a great challenge to mitigate its carbon emissions. The construction industry is one of the major contributors to the carbon emissions in China. This study investigated the driving factors of the changes in the carbon emissions in the Chinese construction industry by using the structural decomposition analysis method and the data from the World Input-Output Database. The results show that the total final demand effect contributed most (131%) to the increase in the construction carbon emissions, while the energy intensity effect offset the largest part (108%) of the increase during the whole period. The production structure effect and final demand ratio effect contributed 49% and 35% to the total increase in the carbon emissions between 1995 and 2009, respectively. The carbonization factor effect had a very small decrease impact (7%) on the carbon emissions between 1995 and 2009. The trends of the effects of driving factors during 1995–2009 and the contributions by industries to the effects of driving factors between 1999 and 2009 have been further explored. This study can help the Chinese policymakers formulate more effective policies for mitigating the construction carbon emissions. [ABSTRACT FROM AUTHOR]