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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]

The majority of the total greenhouse gas (GHG) emissions in China are energy-related. Thus, full understanding of energy-related GHG emissions is crucial for local governments to establish a baseline for tracking emission trends and developing mitigation strategies. In this paper, we present a survey-based GHG emission accounting method for sectoral energy consumption and apply it in Xiaolan, a typical town of Zhongshan. The method combines scope and sectoral analyses on the basis of local statistical approach, and pays more attention to data collection process. Scenarios of core findings in the study are list as below: (1) The energy-related GHG emissions of Xiaolan in 2010 was 2,072,444 tons of carbon dioxide equivalents (CO 2 e). Of this, 31.83% was Scope 1 emissions (direct emissions) and 68.17% was Scope 2 emissions (indirect emissions); (2) Emissions from “manufacturing”, “residents”, and “power, gas & water production and supply” made up 90.27%, among which, “manufacturing” represents the biggest emitting sector as 69.09%; (3) In 2010, the per capita GHG emissions was lower than that in most of the other Chinese cities, but higher than several Asian cities including Amman and Tokyo. Some strategic approaches to reduce GHG emissions were proposed: (1) save energy and improve energy efficiency; (2) optimize energy structure and develop low-carbon energy; (3) update manufacturing structure; (4) improve GHG emission management for the resident sector. Finally, we identified a number of key research issues to advance the town level GHG emission method for future research needs. This paper provides a useful method to understand and profile GHG emissions for towns. [ABSTRACT FROM AUTHOR]

Carbon footprint (CF) and inequality reductions were two of the most important sustainable development goals. However, there was a lack of understanding of the latest equality of carbon footprint across regions. Based on the multi-regional input-output table and the China Family Panel Studies survey data of 17 provinces, this paper examined the evolution of carbon footprint and inequality in China. The results showed a stark disparity between regions and a declined inequality in the last few years. Specifically, the CF per capita in northern provinces (Liaoning, Shanxi, Heilongjiang, Shandong and Shaanxi) exceeded the average level in 2017. From 2015 to 2017, the carbon inequality showed a downward trend, with a national CF-Gini coefficient decrease from 0.31 to 0.28. Meanwhile, the gap between CF per capita in urban areas and rural areas was declining, with a growth rate at 18% and 32% from 2015 to 2017, respectively. In addition, CF per capita from various consumption categories indicated that household equipment and daily necessities, residential, transportation and communication were the three most important sources, with a downward trend from 2015 to 2017. This study would provide policymakers with deep insights into policies to achieve just transition. [ABSTRACT FROM AUTHOR]