Artikel

Role of linkage structures in supply chain for managing greenhouse gas emissions

This study describes a structural decomposition analysis (SDA) of Japanese greenhouse gas (GHG) emissions from 1990 to 2005, focusing on four linkage structures in the Leontief inverse representing supply chains in Japan. The developed RAS-invariant decomposition was applied to Japanese linked input-output tables for the three 5-year periods studied. It examined the effect of the Leontief inverse on emissions changes into the specific effects of forward linkage, backward linkage, the average of forward/backward linkage and kernel structure. Our SDA method solves the problem of parameter independence completely. The accuracy of those effects has been improved mathematically compared with conventional methods. For example, it was detected that backward linkage contributes to an increase in GHG emissions, while conventional methods erroneously determine a decrease. The results of the SDA confirmed that forward linkage and kernel structure contributed to a rise in GHG emissions, and that backward linkage consistently increased emissions in the three periods. Some sectors have robust linkage in the supply chain with consistently increasing emissions, which should be preferentially improved to mitigate their indirect GHG emissions in Japan.

Language
Englisch

Bibliographic citation
Journal: Journal of Economic Structures ; ISSN: 2193-2409 ; Volume: 7 ; Year: 2018 ; Issue: 7 ; Pages: 1-21 ; Heidelberg: Springer

Classification
Wirtschaft
Subject
Structural decomposition analysis
Input-output analysis
RAS
Information geometry

Event
Geistige Schöpfung
(who)
Morioka, Ryoko
Nansai, Keisuke
Tsuda, Koji
Event
Veröffentlichung
(who)
Springer
(where)
Heidelberg
(when)
2018

DOI
doi:10.1186/s40008-018-0105-3
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Morioka, Ryoko
  • Nansai, Keisuke
  • Tsuda, Koji
  • Springer

Time of origin

  • 2018

Other Objects (12)