Using Kaya and LMDI models to analyze carbon emissions from the energy consumption in China

Pingguo Yang, Xiao Liang, Patrick J. Drohan

Research output: Contribution to journalArticle

Abstract

China has become the largest carbon-emitting country in the world since 2007. To achieve national environmental goals by 2030, the carbon emissions per unit of gross domestic product (GDP) will need to fall to 60–65% of 2005 levels. Such a dramatic decrease presents a challenge for a nation in adjusting its energy source and usage, but via monitoring of reductions, greater understanding can be gained of how carbon emitters are responding to national goals. We analyzed the change in carbon emissions from China’s fossil energy consumption from population, per capita GDP, energy efficiency improvements and energy structure using a Kaya identity model and Logarithmic Mean Divisia Index (LMDI) factor decomposition method from 2006 to 2018. Results suggest that trends in carbon emissions from 2006 to 2018 can be broken down into four periods: a rapid increase period during 2006–2011, a slowdown increase period during 2011–2014, a consecutive decline period during 2014–2016 and a rebound during 2017–2018. Trends in carbon emissions were greatly affected by per capita GDP and energy efficiency. While per capita GDP increased carbon emissions, energy efficiency had a countering effect on carbon emissions. Our results suggests that China’s measures in the past decade to reduce carbon emissions (i.e. carrying out carbon emissions trading on a fixed basis, readjusting the economic structure, optimizing the energy structure, improving energy efficiency and increasing forest carbon sinks) have helped to reduce carbon emissions. However, China should continue to actively respond to climate change while striving to achieve of economic sustainable development and social progress.

Original languageEnglish (US)
Pages (from-to)26495-26501
Number of pages7
JournalEnvironmental Science and Pollution Research
Volume27
Issue number21
DOIs
StatePublished - Jul 1 2020

All Science Journal Classification (ASJC) codes

  • Environmental Chemistry
  • Pollution
  • Health, Toxicology and Mutagenesis

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