Features and drivers for energy-related carbon emissions in mega city: The case of Guangzhou, China based on an extended LMDI model

PLoS One. 2019 Feb 11;14(2):e0210430. doi: 10.1371/journal.pone.0210430. eCollection 2019.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants / chemistry*
  • Carbon Dioxide / chemistry*
  • Carbon Footprint*
  • China
  • Economic Development*
  • Humans
  • Models, Theoretical*

Substances

  • Air Pollutants
  • Carbon Dioxide

Grants and funding

This work was supported by the National Natural Science Foundation of China (Nos. 41501144, 41671130), the High-level Leading Talent Introduction Program of GDAS (2016GDASRC-0101), and the Scientific Platform and Innovation Capability Construction Program of GDAS (2016GDASPT-0210) and the GDAS' Project of Science and Technology Development (2017GDASCX-0101, 2018GDASCX-0101, 2019GDASYL-0302001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.