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Abstract Cities do play significant roles in tackling global environmental changes. Decoupling economic growth from environmental pressures is critical for global sustainable development, however, few studies pay attention to the decoupling analysis at city level. This paper conducts a comparative study of the decoupling performance of economic output from CO 2 emissions between Beijing and Shanghai, the top two cities in China. In this paper, the decoupling trends and the decoupling effects are explored from a sectoral perspective, by combining the Tapio decoupling elasticity and the logarithmic mean Divisia index (LMDI) model. The results show that Beijing and Shanghai both experienced weak decoupling in construction, expansive negative decoupling in transport, expansive coupling in trade, and weak decoupling in others over the period 2000–2015. Moreover, agriculture appeared strong decoupling in Beijing, and recessive decoupling in Shanghai; industry showed strong decoupling in Beijing, and weak decoupling in Shanghai. The decoupling effects showed different magnitudes and patterns in industrial sectors between Beijing and Shanghai. Overall, per-capita GDP and population growth inhibited the decoupling, while energy intensity, industrial share, energy mix accelerated the decoupling process. Highlights • The decoupling of sectoral economic output from carbon emissions in Beijing and Shanghai are investigated. • Beijing's industry enjoyed better decoupling performance than Shanghai's industry. • Transport should be put more emphasis in further decoupling efforts. • Energy intensity is crucial effect accelerating the decoupling process. [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]

As CO 2 emissions differ in households with different characteristics, mitigation policies still address the contributions of these characteristics and their effects on specific households. Using a representative resident survey in urban China, this paper compares how household characteristics differ in their associations with household embedded carbon emissions (HECEs) at different levels by quantile regression; then, this paper analyzes the contributions of these characteristics by Shapley decomposition. The findings include the following: (i) household income is the most important contributor to the difference of HECEs, and its positive effect increases as HECEs rise; (ii) the use of wealth has different effects on HECEs because household housing ownership and deposits contribute little to HECEs, but the positive effects of household car ownership contribute 25.8%; (iii) young people and children will emit more HECEs than adults; the employed emit more than persons who are unemployed or retired; (iv) education increases HECEs overall but mainly at the low quantiles; the negative effect of the marriage increases as HECEs rise; (v) income, car ownership, marital status, size and constitution of households with high HECEs should be addressed to reduce the inequality of HECEs. Indeed, education on the low-carbon concept is another way forward. [ABSTRACT FROM AUTHOR]

Cities contribute to most of the CO 2 emissions. And the economic system at city level is much complex due to various linkaged sectors. This paper aims to analyze the economy-wide contribution of sectors and households to CO 2 emissions in Beijing (China) by utilizing a semi-closed input-output model integrated with a modified hypothetical extraction method. Results show that, compared with 2005, in 2012 (1) interprovincial export caused the largest amount of CO 2 emissions [135.5 Mt] with the main contributions arising from manufacturing (42.1 Mt); transportation, storage, and post (TSP in short, 29.1 Mt); and households (23.6 Mt); (2) across the intermediate input-output system, real estate activities accounted for the largest amount of embodied CO 2 intensity (0.07 kg per yuan) and more sectors outsourced CO 2 ; (3) tracing the integrated sector network, CO 2 linkages pointed to manufacturing and TSP dominating the internal linkages, manufacturing prominent in mixed linkages, secondary industry leading the net forward linkages, and tertiary industry dominant in terms of net backward linkages, helping control CO 2 according to its origin; (4) CO 2 emissions induced by household strikingly affected total CO 2 emissions in Beijing, mainly coming from income-oriented affects, with a large rural-urban disparity and a similar sectoral distribution pattern. Finally, we propose suggestions on carbon reduction in terms of technological interlinkages, final demand and household participation. [ABSTRACT FROM AUTHOR]