Exploring the driving forces for emission reduction strategies in Henan by combining spectral clustering with two-layer LMDI decomposition

Wen, Lei; Li, Zhenkai

Exploring the driving forces for emission reduction strategies in Henan by combining spectral clustering with two-layer LMDI decomposition

2019

Wen, Lei | Li, Zhenkai

To comprehend city-level driving mechanisms of carbon emissions, this paper utilizes spectral cluster and two-layer LMDI (logarithmic mean divisia index) method to systematically assess the contribution values of correlative factors from each cluster to Henan’s carbon emissions increments, and accordingly comes up with more strategies about how to reduce carbon emissions for each cluster on the basis of driving forces of carbon emissions. The results of clustering and the decomposition are as follows: (1) the 18 prefecture-level cities in Henan were divided into five categories by spectral clustering, and there are similar development patterns within each category, so they can learn from each other to improve their own defects of development; (2) this paper utilizes the two-layer LMDI method to divide the factors affecting each cluster of carbon emissions into four types, which includes energy structure, energy intensity, per capita GDP, and population, and calculates the contribution value of each factor. It was concluded that the contribution value of per capita GDP dominantly drove up carbon emissions, while energy intensity played a significant role in offsetting them. Therefore, it is important for Henan’s low-carbon development to control the expansion of economic activity and improve energy efficiency in the future.

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