Assessing GHG mitigation goals of INDCs (NDCs) considering socio-economic and environmental indicators of the parties

Seyed Alireza Modirzadeh (), Mohsen Nasseri (), Mohammad Sadegh Ahadi () and Farzam Pourasghar Sangachin ()
Additional contact information
Seyed Alireza Modirzadeh: University of Tehran
Mohsen Nasseri: University of Tehran
Mohammad Sadegh Ahadi: Mehr Renewable Energy Co.
Farzam Pourasghar Sangachin: Iran Chamber of Commerce, Industries and Agriculture

Mitigation and Adaptation Strategies for Global Change, 2021, vol. 26, issue 8, No 5, 33 pages

Abstract: Abstract Based on the announced (Intended) Nationally Determined Contributions (INDCs), parties to the United Nations Framework Convention on Climate Change (UNFCCC) have introduced greenhouse gas emission reduction goals. These goals are based on the country-specific long-term climate policy. However, these goals do not necessarily match the countries’ socio-economic statuses and environmental performances, which are the main pillars of sustainable development. This paper proposes unconditional mitigation goals for 190 countries considering their socio-economic and environmental status in such a way that the total emission reductions are equal to that of (I)NDCs. To do so, different data mining methods including k-means clustering, Decision Trees (DT), Multivariate Adaptive Regression Splines (MARS), and Generalized Regression Neural Networks (GRNNs) are employed. Different clusters of countries are described and an appropriate range as unconditional mitigation goal for each country is introduced. In addition, the sufficiency of the current targets for each country is analyzed based on the proposed ranges. The results indicate that among top emitters, (I)NDCs of India, Russia, and Japan do not match their socio-economic status. On the contrary, Indonesia, Germany, and Mexico have targeted stricter mitigation goals than the proposed ranges of this research.

Keywords: GHG mitigation goals; Nationally Determined Contribution (NDC); Sustainable development; Clustering; Decision trees; Multivariate Adaptive Spline Regressions; Generalized Regression Neural Networks (GRNNs) (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11027-021-09975-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:masfgc:v:26:y:2021:i:8:d:10.1007_s11027-021-09975-0

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11027

DOI: 10.1007/s11027-021-09975-0

Access Statistics for this article

Mitigation and Adaptation Strategies for Global Change is currently edited by Robert Dixon

More articles in Mitigation and Adaptation Strategies for Global Change from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().