A review of methods for characterizing the environmental consequences of actions in life cycle assessment
Bibliographic Data
| ID | 19468014 |
|---|---|
| Authors | Joseph Palazzo (0000-0001-5688-8588, Bren School of Environmental Science and Management University of California Santa Barbara California USA, corresponding author), Roland Geyer (0000-0001-7430-1058, Bren School of Environmental Science and Management University of California Santa Barbara California USA), Sangwon Suh (0000-0003-1014-3075, Bren School of Environmental Science and Management University of California Santa Barbara California USA) |
| Year | 2020 |
| Volume | 24 |
| Issue | 4 |
| Pages | 815-829 |
| Publication date | 2020-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Industrial Ecology (JOURNAL) |
| Journal identifiers | ISSN: 1088-1980 • E-ISSN: 1530-9290 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1111/jiec.12983 |
| OpenAlex | W3005057300 |
| Language | EN |
| Citations received | 7 |
| References cited | 78 |
Understanding the environmental consequences of actions is becoming increasingly important in the field of industrial ecology in general, and in life cycle assessment (LCA) more specifically. However, a consensus on how to operationalize this idea has not been reached. A variety of methods have been proposed and applied to case studies that cover various aspects of consequential life cycle assessment (CLCA). Previous reviews of the topic have focused on the broad agenda of CLCA and how different modeling frameworks fit into its goals. However, explicit examination of the spectrum of methods and their application to the different facets of CLCA are lacking. Here, we provide a detailed review of methods that have been used to construct models of the environmental consequences of actions in CLCA. First, we cover the following structural modeling approaches: (a) economic equilibrium models, (b) system dynamics models, (c) technology choice models, and (d) agent‐based models. We provide a detailed review of particular applications of each model in the CLCA domain. The advantages and disadvantages of each are discussed, and their relationships with CLCA are clarified. From this, we are able to map these models onto the established aspects of CLCA. We learn that structural models alone are not sufficient to quantify the uncertainty distributions of underlying parameters in CLCA, which are essential components of a robust analysis of consequences. To address this, we provide a brief introduction to a counterfactual‐based causal inference approach to parameter identification and uncertainty analysis that is emerging in the CLCA literature. We recommend that one potential research path forward is the establishment of feedback loops between empirical estimates and structural models
Machine learning · Climate Change Policy and Economics · Computer Science · Energy, Environment, and Transportation Policies · Environmental Impact and Sustainability · Artificial Intelligence · Ecology
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| Unique citing works | 7 |
|---|---|
| Citations per year | 1,17 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |