Factor Decomposition Analysis of Energy-Related CO2 Emissions in Tianjin, China

Zhe Wang,Ben Wu,Lin Zhao,Guozhu Mao

doi:10.3390/su7089973

Zhe Wang, Ben Wu + Show 2 more

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https://doi.org/10.3390/su7089973

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Abstract

Tianjin is the largest coastal city in northern China with rapid economic development and urbanization. Energy-related CO2 emissions from Tianjin’s production and household sectors during 1995–2012 were calculated according to the default carbon-emission coefficients provided by the Intergovernmental Panel on Climate Change. We decomposed the changes in CO2 emissions resulting from 12 causal factors based on the method of Logarithmic Mean Divisia Index. The examined factors were divided into four types of effects: energy intensity effect, structure effect, activity intensity effect, scale effect and the various influencing factors imposed differential impacts on CO2 emissions. The decomposition outcomes indicate that per capita GDP and population scale are the dominant positive driving factors behind the growth in CO2 emissions for all sectors, while the energy intensity of the production sector is the main contributor to dampen the CO2 emissions increment, and the contributions from industry structure and energy structure need further enhancement. The analysis results reveal the reasons for CO2 emission changes in Tianjin and provide a solid basis upon which policy makers may propose emission reduction measures and approaches for the implementation of sustainable development strategies.

Highlights

Climate change is one of the most urgent global environmental challenges of the present time
In consideration of the methodology, this paper extends the common Logarithmic Mean Divisia Index (LMDI) decomposition analysis in CO2 emission changes from both production and household sectors with nine types of energy, and examines the effects of 12 causal factors simultaneously, which are divided into energy intensity effect, structure effect, activity intensity effect, and scale effect
Researchers have developed many methods to quantify the effects of different factors that contribute to changes in energy consumption and carbon emissions, i.e., the structural decomposition analysis (SDA) and the index decomposition analysis (IDA), are widely used as analytical tools for supporting policymaking on national energy and environmental issues [14,15,16,17]

Summary

Introduction

Climate change is one of the most urgent global environmental challenges of the present time. China has become the utmost primary energy consumer as well as the utmost CO2 emitter all over the world. In 2012, China consumed 2735 million tons of oil equivalent (Mtoe) of primary energy and the amount of China’s CO2 emissions reached 9.21 billion tons [1,2]. The combustion of fossil fuels contributes to CO2 emissions, and to air pollutants such as SO2 and NOx [3]. Change Conference in 2009, the Chinese government made a commitment to the world that China would reduce its carbon emissions per unit GDP in 2020 by 40%–45% compared with 2005 levels [4].

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