Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism

Reducing CO<sub>2</sub> emissions of industrial energy consumption plays a significant role in achieving the goal of CO<sub>2</sub> emissions peak and decreasing total CO<sub>2</sub> emissions in northeast China. This study proposed an extended STIRPAT model to pr...

Full description

Bibliographic Details
Main Authors: Zhiyuan Duan, Xian’en Wang, Xize Dong, Haiyan Duan, Junnian Song
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/3/791
id doaj-3e11989aabfa43d3b6cfe927a2871879
record_format Article
spelling doaj-3e11989aabfa43d3b6cfe927a28718792020-11-25T01:42:24ZengMDPI AGSustainability2071-10502020-01-0112379110.3390/su12030791su12030791Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and MechanismZhiyuan Duan0Xian’en Wang1Xize Dong2Haiyan Duan3Junnian Song4Key Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, ChinaKey Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, ChinaKey Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, ChinaKey Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, ChinaKey Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, ChinaReducing CO<sub>2</sub> emissions of industrial energy consumption plays a significant role in achieving the goal of CO<sub>2</sub> emissions peak and decreasing total CO<sub>2</sub> emissions in northeast China. This study proposed an extended STIRPAT model to predict CO<sub>2</sub> emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO<sub>2</sub> emissions and explore the reduction path and mechanism to achieve CO<sub>2</sub> emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO<sub>2</sub> emissions efficiency of energy consumption &gt; industrialized rate &gt; GDP &gt; urbanization rate &gt; industrial energy intensity &gt; the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.https://www.mdpi.com/2071-1050/12/3/791industrial energy consumptionco<sub>2</sub> emissionsreduction pathpeakstirpat model
collection DOAJ
language English
format Article
sources DOAJ
author Zhiyuan Duan
Xian’en Wang
Xize Dong
Haiyan Duan
Junnian Song
spellingShingle Zhiyuan Duan
Xian’en Wang
Xize Dong
Haiyan Duan
Junnian Song
Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism
Sustainability
industrial energy consumption
co<sub>2</sub> emissions
reduction path
peak
stirpat model
author_facet Zhiyuan Duan
Xian’en Wang
Xize Dong
Haiyan Duan
Junnian Song
author_sort Zhiyuan Duan
title Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism
title_short Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism
title_full Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism
title_fullStr Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism
title_full_unstemmed Peaking Industrial Energy-Related CO<sub>2</sub> Emissions in Typical Transformation Region: Paths and Mechanism
title_sort peaking industrial energy-related co<sub>2</sub> emissions in typical transformation region: paths and mechanism
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2020-01-01
description Reducing CO<sub>2</sub> emissions of industrial energy consumption plays a significant role in achieving the goal of CO<sub>2</sub> emissions peak and decreasing total CO<sub>2</sub> emissions in northeast China. This study proposed an extended STIRPAT model to predict CO<sub>2</sub> emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO<sub>2</sub> emissions and explore the reduction path and mechanism to achieve CO<sub>2</sub> emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO<sub>2</sub> emissions efficiency of energy consumption &gt; industrialized rate &gt; GDP &gt; urbanization rate &gt; industrial energy intensity &gt; the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.
topic industrial energy consumption
co<sub>2</sub> emissions
reduction path
peak
stirpat model
url https://www.mdpi.com/2071-1050/12/3/791
work_keys_str_mv AT zhiyuanduan peakingindustrialenergyrelatedcosub2subemissionsintypicaltransformationregionpathsandmechanism
AT xianenwang peakingindustrialenergyrelatedcosub2subemissionsintypicaltransformationregionpathsandmechanism
AT xizedong peakingindustrialenergyrelatedcosub2subemissionsintypicaltransformationregionpathsandmechanism
AT haiyanduan peakingindustrialenergyrelatedcosub2subemissionsintypicaltransformationregionpathsandmechanism
AT junniansong peakingindustrialenergyrelatedcosub2subemissionsintypicaltransformationregionpathsandmechanism
_version_ 1725036761640337408