Cargando…

Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives

Peaking industrial carbon dioxide (CO(2)) emissions is critical for China to achieve its CO(2) peaking target by 2030 since industrial sector is a major contributor to CO(2) emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO(2) emissions, which also have...

Descripción completa

Detalles Bibliográficos
Autores principales: Duan, Haiyan, Dong, Xize, Xie, Pinlei, Chen, Siyan, Qin, Baoyang, Dong, Zijia, Yang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9266074/
https://www.ncbi.nlm.nih.gov/pubmed/35805488
http://dx.doi.org/10.3390/ijerph19137829
_version_ 1784743373537542144
author Duan, Haiyan
Dong, Xize
Xie, Pinlei
Chen, Siyan
Qin, Baoyang
Dong, Zijia
Yang, Wei
author_facet Duan, Haiyan
Dong, Xize
Xie, Pinlei
Chen, Siyan
Qin, Baoyang
Dong, Zijia
Yang, Wei
author_sort Duan, Haiyan
collection PubMed
description Peaking industrial carbon dioxide (CO(2)) emissions is critical for China to achieve its CO(2) peaking target by 2030 since industrial sector is a major contributor to CO(2) emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO(2) emissions, which also have huge CO(2) emissions reduction potential. It is significant to accurately forecast CO(2) emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO(2) emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO(2) emission trends during 2018–2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO(2) emission in four scenarios. The results indicate that the peak value of CO(2) emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO(2) emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO(2) emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO(2) emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.
format Online
Article
Text
id pubmed-9266074
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-92660742022-07-09 Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives Duan, Haiyan Dong, Xize Xie, Pinlei Chen, Siyan Qin, Baoyang Dong, Zijia Yang, Wei Int J Environ Res Public Health Article Peaking industrial carbon dioxide (CO(2)) emissions is critical for China to achieve its CO(2) peaking target by 2030 since industrial sector is a major contributor to CO(2) emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO(2) emissions, which also have huge CO(2) emissions reduction potential. It is significant to accurately forecast CO(2) emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO(2) emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO(2) emission trends during 2018–2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO(2) emission in four scenarios. The results indicate that the peak value of CO(2) emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO(2) emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO(2) emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO(2) emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries. MDPI 2022-06-26 /pmc/articles/PMC9266074/ /pubmed/35805488 http://dx.doi.org/10.3390/ijerph19137829 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Duan, Haiyan
Dong, Xize
Xie, Pinlei
Chen, Siyan
Qin, Baoyang
Dong, Zijia
Yang, Wei
Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives
title Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives
title_full Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives
title_fullStr Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives
title_full_unstemmed Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives
title_short Peaking Industrial CO(2) Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives
title_sort peaking industrial co(2) emission in a typical heavy industrial region: from multi-industry and multi-energy type perspectives
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9266074/
https://www.ncbi.nlm.nih.gov/pubmed/35805488
http://dx.doi.org/10.3390/ijerph19137829
work_keys_str_mv AT duanhaiyan peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives
AT dongxize peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives
AT xiepinlei peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives
AT chensiyan peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives
AT qinbaoyang peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives
AT dongzijia peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives
AT yangwei peakingindustrialco2emissioninatypicalheavyindustrialregionfrommultiindustryandmultienergytypeperspectives