Paper Info
Title
Regional Input-Output Multiple Choice Goal Programming Model And Method For Industry Structure Optimization On Energy Conservation And Ghg Emission Reduction In China
Abstract
To assess the potential of China's industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input-output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product (GDP), minimization of energy consumption and GHG emission. They are subjected to the input-output balance, economy development, energy supply, and industry diversity. And four scenarios with different decision preferences are taken into accounted in the solutions of the industrial structure optimization model. The results demonstrate that industrial restructuring has potential in energy saving and emission reducing. First, after optimization, energy consumption intensity and GHG emission intensity can drop by 13.88246% and 5.33767% over 2012, and GDP can grow up at annual growth rate 6.6% from 2013-2020. Second, promoting the development of the low energy-intensive and low GHG emission intensive sectors is an effective method for energy conservation and emission reduction. Three, compared to energy intensity reduction, GHG emission intensity reduction is less effective for four scenarios. Four, there are several difficulties to achieve the amounts and intensity control targets of energy conservation and GHG emission reduction simultaneously. It is suggested that China had better strive to promote progress of technologies of energy conservation and GHG emission reduction while adjusting the industrial structure. (C) 2019 The Authors. Published by Atlantis Press SARL.
Year
DOI
Venue
2019
10.2991/ijcis.d.191104.002
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Keywords
DocType
Volume
Industrial restructuring, Energy conservation, Greenhouse gas emission reduction, Big data analysis, Input-output model
Journal
12
Issue
ISSN
Citations 
2
1875-6891
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ping Ping Lin100.34
Deng-Feng Li296846.12
Bin Qian Jiang300.34
An Peng Wei400.34
Gao Feng Yu500.34