Transmission mechanism between energy prices and carbon emissions using geographically weighted regression

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Wei Li
  • Guomin Li
  • Baihui Jin
  • Wen Wu
  • Pengfei Cui
  • Guohao Zhao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)434-442
Journal / PublicationEnergy Policy
Volume115
Online published28 Jan 2018
Publication statusPublished - Apr 2018

Abstract

This work quantifies the conduction mechanism of energy prices on carbon emissions and carbon intensity from the perspective of space and quantile. Taking China's Eight Economic Regions as an example, we explore how to optimize energy price policy to promote regional carbon reduction by combining the GWR model, quantile regression and scenario analysis. The study finds that (1)the energy prices can promote or suppress carbon emissions and carbon intensity through five variables, including economic development, industrial structure, energy efficiency, energy investment and energy consumption etc.(2) the current energy investment and consumption structure lead to a high level of carbon emissions, while such effect from economic development is relatively limited, and the influence direction and level of industrial structure and energy efficiency on carbon emissions are regionally different. (3) with the exception of the energy consumption structure, four other factors have varying restraining effects on carbon intensity, and (4) scenario analysis shows that optimizing industrial and energy consumption structure are crucial to reduce carbon emissions,and optimizing industrial structure helps to reduce carbon intensity. Finally, this paper proposes policy suggestions, aiming to realize regional carbon reductions by using price leverage.

Research Area(s)

  • Carbon emissions, Energy price, GWR Model, Transmission mechanism

Citation Format(s)

Transmission mechanism between energy prices and carbon emissions using geographically weighted regression. / Li, Wei; Sun, Wen; Li, Guomin et al.
In: Energy Policy, Vol. 115, 04.2018, p. 434-442.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review