Citation

Tan RR, Aviso KB, Lao AR, Promentilla MAB. Clean Technol. Environ. Policy 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s10098-021-02096-x

PMID

33994908

Abstract

P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.

Language: en

Keywords

Industrial accidents; Causal chain; Circular economy; Fuzzy cognitive maps; Process network synthesis; Vicious cycle