Abstract
Carbon emissions research based on regional perspective is necessary and helpful for China to achieve its reduction targets. This research aims at analyzing the energy-related carbon emissions and finding out the most important driving forces for the carbon emissions increments in Guangdong province. LMDI (Logarithmic Mean Divisia Index) method based on the extended Kaya identity has been used to explore the main driving factors for energy-related carbon emissions in Guangdong province annually between 1990 and 2014. Research results show that the impacts and influences of various factors on carbon emissions are different in the different development stages. Economic growth effect and population size effect are the two most important driving factors for the increased carbon emissions. Energy intensity effect played the dominant role in curbing carbon emissions. Energy structure effect and technical progress effect had different but relatively minor effects on carbon emissions during the five different development stages.
Highlights
Controlling greenhouse gas emissions and curbing global warming have become priorities in developing strategies for a sustainable future [1–7]
I In Equation (1), the subscript i is the various fuels in our study, the subscript t represents the time in years, Ct on the left in Equation (1) represents total energy-related carbon emissions in year t, Eti on the right in Equation (1) represents the total energy consumption including the various fuels in year t; LCVi, CFti and Oi represent the lower calorific value, the carbon emissions factors, and the oxidation rate of fuel type i, respectively
In Equation (4), ∆C on the left represents the difference of carbon emissions between a base year 0 and a target year t. ∆C can be further decomposed to five influencing factors as follows: ∆Cp−e f f ect represents the population size effect, ∆Cg−e f f ect represents the economic growth effect, ∆Ce−e f f ect represents the energy intensity effect, ∆Cs−e f f ect represents the energy structure effect, and ∆Cf −e f f ect represents the technical progress effect, respectively
Summary
Controlling greenhouse gas emissions and curbing global warming have become priorities in developing strategies for a sustainable future [1–7]. The carbon emissions problem is receiving the extensive and sustained interest of government policy makers, industrial manufacturers, and researchers [11–15] This is due to the pressure of public opinion from international climate negotiations, and due to the need to reduce and conserve domestic energy consumption in relation to resource and environmental constraints. In-depth studies on the factors driving energy-related carbon emissions need to be performed for China, especially at the regional and provincial levels [20–23] This is required because there are profound differences in resource endowment, economic structure, development models, residential lifestyles, consumption levels, and available technology across the different provinces within China [24–27]. Wang et al [26] used the LMDI method to decompose the changes of carbon emissions in Shandong province from 1995 to 2011, and demonstrated that rapid economic growth and coal based energy consumption structure played strong driving effects on carbon emissions. It was aimed to provide theoretical references for making more targeted policies on “energy saving and emission reduction” in Guangdong
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