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Energies
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Energies
Article . 2022
Data sources: DOAJ
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Peaking Carbon Emissions in a Megacity through Economic Restructuring: A Case Study of Shenzhen, China

Authors: Yang Yang; Fan He; Junping Ji; Xin Liu;

Peaking Carbon Emissions in a Megacity through Economic Restructuring: A Case Study of Shenzhen, China

Abstract

With the growing cost of carbon emissions reduction, the application of industrial restructuring to suppress carbon emissions is becoming more attractive. By constructing an input-output optimization model, this study explored how industrial restructuring helps megacities synergistically achieve carbon peak and high-quality development. The results showed that through contributing 164.4% of the reduction in emissions from 2020 to 2025, industrial structure optimization significantly inhibited the growth of carbon emissions; From 2020 to 2025, the manufacturing structure continued to be high-end, which resulted in a reduction in industrial carbon emissions by 10.3%; through vigorous development of the low-carbon service industry, the carbon emission of the service industry would continue to slow down at an average annual rate of 2.4%. Industrial premiumization and the low-carbonization of the modern service sector are the key driving forces for Shenzhen to achieve low-carbon transformation. The results also showed that the power and retail sectors are the most important for emissions reduction. This study can provide a roadmap for megacities on how to explore potential emission reduction via optimizing their economic structure to help them achieve their carbon emissions peak.

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Keywords

Technology, I-O optimization model, emission reduction potential, T, industrial restructuring, I-O optimization model; industrial restructuring; emission reduction potential

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    9
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
gold