NDLI: Causality in social life cycle impact assessment (SLCIA)

Content Provider	Springer Nature Link
Author	Wu, Susie R. Chen, Jiquan Apul, Defne Fan, Peilei Yan, Yanfa Fan, Yi Zhou, Peiling
Copyright Year	2015
Abstract	The social life cycle impact assessment (SLCIA) incorporates either a type I or type II characterization model. We improved both models by introducing explicit causality by using statistic modeling through development of (1) a quantitative approach to simultaneously identify impact pathways of type II models with multiple impact categories, targeting SLCIA method developers and (2) a new hybrid model to establish causality between inventory indicators and subcategories, targeting social life cycle assessment practitioners.Causality establishments for type II impact pathways and the new hybrid model are the core requirements for this study. We used structural equation modeling (SEM) to identify the impact pathways for type II characterization models, therefore resolving the issues of unobservability and unvalidatibility in type II models. Using country-level data from the World Bank, the method was applied to an example impact pathway at macro-scale. We applied Bayesian networks in our hybrid model to address the issues of relevance and representativeness in type I models, assuming the unobservable social performances of an organization are the causes for observable inventory indicators. The method was applied to a hypothetical example for the stakeholder of the worker at company scale. Temporal precedence (i.e., lag effects) was incorporated into both models.The results from the confirmatory SEM supported our hypotheses that comprised the impact pathway from economic development to health outcomes, which were fully mediated by health expenditures and health access. A 1-year lag between each impact category resulted in the best model fit. Limitations on the data as well as subjective choice of indicators to represent impact categories are subject to criticism. The results from the hybrid model showed that, depending on the likelihood of the inventory indicators, the posterior probability of subcategories either deviated from their prior probability or behaved similarly. The construction of proper conditional probability tables and the choice of probability distribution for the likelihood are major challenges for the hybrid model.This study was the first attempt in using statistic causal models to quantitatively identify unobservable impact pathways of the type II model and to develop a hybrid model for SLCIA. A SEM that incorporates temporal precedence enables identification of impact pathways with multiple unobservable impact categories. The hybrid model using Bayesian networks represents the subcategories in posterior probabilities instead of absolute scores, helping companies to better develop instructions for future management practices.
Starting Page	1312
Ending Page	1323
Page Count	12
File Format	PDF
ISSN	09483349
Journal	The International Journal of Life Cycle Assessment
Volume Number	20
Issue Number	9
e-ISSN	16147502
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-06-23
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian networks Causality Characterization models Impact pathway Social life cycle impact assessment Structural equation modeling Environment Environmental Economics Environmental Engineering/Biotechnology Environmental Chemistry
Content Type	Text
Resource Type	Article
Subject	Environmental Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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