ArticlePloS one2019
Features and drivers for energy-related carbon emissions in mega city: The case of Guangzhou, China based on an extended LMDI model.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 32 citations in OpenAlex.
- Article
- Study on carbon emission driving factors and carbon peak forecasting in power sector of Shanxi province.PloS one · 2024Article
- Increased inequalities of per capita COScientific reports · 2021Article
- A regional-scale decomposition of energy-related carbon emission and its decoupling from economic growth in China.Environmental science and pollution research international · 2020Article
- Factor decomposition and decoupling analysis of air pollutant emissions in China's iron and steel industry.Environmental science and pollution research international · 2020Article
- Article
- What Are the Driving Forces of Urban COInternational journal of environmental research and public health · 2019Article
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Authors and funding
10 authors at 4 institutions in 1 country.
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Abstract
Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10th five-year plan period (2003-2005), the 11th five-year plan period (2005-2010), and the 12th five-year plan period (2010-2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.
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