Abstract
This manuscript develops a logarithmic mean Divisia index I (LMDI) decomposition method based on energy and CO2 allocation Sankey diagrams to analyze the contributions of various influencing factors to the growth of energy-related CO2 emissions on a national level. Compared with previous methods, we can further consider the influences of energy supply efficiency. Two key parameters, the primary energy quantity converted factor (KPEQ) and the primary carbon dioxide emission factor (KC), were introduced to calculate the equilibrium data for the whole process of energy unitization and related CO2 emissions. The data were used to map energy and CO2 allocation Sankey diagrams. Based on these parameters, we built an LMDI method with a higher technical resolution and applied it to decompose the growth of energy-related CO2 emissions in China from 2004 to 2014. The results indicate that GDP growth per capita is the main factor driving the growth of CO2 emissions while the reduction of energy intensity, the improvement of energy supply efficiency, and the introduction of non-fossil fuels in heat and electricity generation slowed the growth of CO2 emissions.
Highlights
Facing the challenge of global climate change, most countries have come to a consensus that it is urgent to control anthropogenic GHG emissions, especially energy-related carbon dioxide (CO2) emissions [1]
We developed an logarithmic mean Divisia index I (LMDI) method including both the conventional influencing factors and the technical influencing factors to decompose the contributions of each influencing factor to the growth of CO2 emissions
We further introduce the acquirement of the primary carbon dioxide emission factor (KC) which is a key parameter for establishing the connection between energy consumption expressed in primary energy quantity (PEQ) form and CO2 emissions
Summary
Facing the challenge of global climate change, most countries have come to a consensus that it is urgent to control anthropogenic GHG (greenhouse gas) emissions, especially energy-related carbon dioxide (CO2) emissions [1]. It is essential for policy makers in various countries to understand the main factors influencing the growth of energy-related CO2 emissions and quantitatively evaluate their contributions [2]. The environment would benefit by further improving the LMDI method and considering more technical details about the structural and efficiency changes through the national energy system including stages of energy sources, energy conversion, and energy end-use
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