Abstract

Finding environmentally significant clusters in global supply-chain networks of goods and services has been investigated by Kagawa et al. (Soc Netw 35(3):423–438, 2013a; Econ Syst Res 25(3):265–286, 2013b; Glob Environ Chang, 2015), using the popular clustering method of nonnegative matrix factorization, which actually yields sensitive cluster assignments. Due to this sensitivity issue, there is a danger of overfitting of the results. In order to confirm the robustness of the obtained clusters, which in fact have strong implications for international climate change mitigation, especially for the US-induced Chinese clusters, we design a simulation-based experiment. Empirical findings of the proposed approach are compared with those of Kagawa et al. (Glob Environ Chang, 2015). The environmental implications are reported as well.

Highlights

  • Graph partitioning methods or clustering methods in general have been widely used for understanding and visualizing fundamental features of social and economic network complexity, e.g., Newman and Girvan (2004), Kagawa et al (2013a, b), Liang et al (2015)

  • A striking environmental study has been provided by Kagawa et al (2015); the authors identified CO2 emission clusters within global supply-chain networks formed by the final demand impulse of a specific final product and argued how the identified emission clusters have contributed to increasing CO2 emission transfers and have grown over time [see Davis et al (2011) and Peters et al (2011) for the analysis of CO2 emission transfers]

  • 5 Conclusion In this study, we establish a sampling-based procedure in order to examine the robustness of clusterings that could be found using nonnegative matrix factorization or spectral clustering methods

Read more

Summary

Introduction

Graph partitioning methods or clustering methods in general have been widely used for understanding and visualizing fundamental features of social and economic network complexity, e.g., Newman and Girvan (2004), Kagawa et al (2013a, b), Liang et al (2015). A striking environmental study has been provided by Kagawa et al (2015); the authors identified CO2 emission clusters within global supply-chain networks formed by the final demand impulse of a specific final product and argued how the identified emission clusters have contributed to increasing CO2 emission transfers and have grown over time [see Davis et al (2011) and Peters et al (2011) for the analysis of CO2 emission transfers]. By setting the parameter K = 10, we can obtain a set of clusters, but if we instead set the parameter K = 11, the obtained clusters could include very different sectors from the ones of K = 10.

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call