Causal inference to scope environmental impact assessment of renewable energy projects and test competing mental models of decarbonization
| dc.contributor.author | Gazar, Amir M. | en |
| dc.contributor.author | Borsuk, Mark E. | en |
| dc.contributor.author | Calder, Ryan S. D. | en |
| dc.date.accessioned | 2025-01-31T14:52:19Z | en |
| dc.date.available | 2025-01-31T14:52:19Z | en |
| dc.date.issued | 2024-11-25 | en |
| dc.description.abstract | Environmental impact assessment (EIA), life cycle analysis (LCA), and cost benefit analysis (CBA) embed crucial but subjective judgments over the extent of system boundaries and the range of impacts to consider as causally connected to an intervention, decision, or technology of interest. EIA is increasingly the site of legal, political, and social challenges to renewable energy projects proposed by utilities, developers, and governments, which, cumulatively, are slowing decarbonization. Environmental advocates in the United States have claimed that new electrical interties with Canada increase development of Canadian hydroelectric resources, leading to environmental and health impacts associated with new reservoirs. Assertions of such second-order impacts of two recently proposed 9.5 TWh yr−1 transborder transmission projects played a role in their cancellation. We recast these debates as conflicting mental models of decarbonization, in which values, beliefs, and interests lead different parties to hypothesize causal connections between interrelated processes (in this case, generation, transmission, and associated impacts). We demonstrate via Bayesian network modeling that development of Canadian hydroelectric resources is stimulated by price signals and domestic demand rather than increased export capacity per se. However, hydropower exports are increasingly arranged via long-term power purchase agreements that may promote new generation in a way that is not easily modeled with publicly available data. We demonstrate the utility of causal inference for structured analysis of sociotechnical systems featuring phenomena that are not easily modeled mechanistically. In the setting of decarbonization, such analysis can fill a gap in available energy systems models that focus on long-term optimum portfolios and do not generally represent questions of incremental causality of interest to stakeholders at the local level. More broadly, these tools can increase the evidentiary support required for consequentialist (as opposed to attributional) LCA and CBA, for example, in calculating indirect emissions of renewable energy projects. | en |
| dc.description.version | Published version | en |
| dc.format.extent | 18 page(s) | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | ARTN 045005 (Article number) | en |
| dc.identifier.doi | https://doi.org/10.1088/2634-4505/ad8fce | en |
| dc.identifier.eissn | 2634-4505 | en |
| dc.identifier.issn | 2634-4505 | en |
| dc.identifier.issue | 4 | en |
| dc.identifier.orcid | Calder, Ryan [0000-0001-5618-9840] | en |
| dc.identifier.orcid | Mortazavig, Amir [0000-0002-8962-4279] | en |
| dc.identifier.uri | https://hdl.handle.net/10919/124463 | en |
| dc.identifier.volume | 4 | en |
| dc.language.iso | en | en |
| dc.publisher | IOP Publishing | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | renewable energy | en |
| dc.subject | causal inference | en |
| dc.subject | cost benefit analysis | en |
| dc.subject | life cycle assessment | en |
| dc.subject | sociotechnical systems | en |
| dc.subject | energy policy | en |
| dc.title | Causal inference to scope environmental impact assessment of renewable energy projects and test competing mental models of decarbonization | en |
| dc.title.serial | Environmental Research: Infrastructure and Sustainability | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
| dc.type.other | Article | en |
| dc.type.other | Journal | en |
| pubs.organisational-group | Virginia Tech | en |
| pubs.organisational-group | Virginia Tech/Veterinary Medicine | en |
| pubs.organisational-group | Virginia Tech/Veterinary Medicine/Biomedical Sciences and Pathobiology | en |
| pubs.organisational-group | Virginia Tech/Veterinary Medicine/Population Health Sciences | en |
| pubs.organisational-group | Virginia Tech/Faculty of Health Sciences | en |
| pubs.organisational-group | Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | Virginia Tech/Veterinary Medicine/CVM T&R Faculty | en |
| pubs.organisational-group | Virginia Tech/Graduate students | en |
| pubs.organisational-group | Virginia Tech/Graduate students/Doctoral students | en |