| Titre : | Addressing spatiotemporal variability in life cycle assessment: review focused on applications relevant to agriculture (2025) |
| Auteurs : | S. Bahmutsky ; N. Bamber ; V. Arulnathan ; N. Pelletier |
| Type de document : | Article |
| Dans : | Journal of Environmental Management (vol. 394, November 2025) |
| Article en page(s) : | p. 127451 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.1 - Généralités. Situation AgricoleThésaurus IAMM AGRICULTURE ; ANALYSE DU CYCLE DE VIE ; ECHELLE SPATIALE |
| Résumé : | Agricultural life cycle assessment (LCA) studies often rely on aggregated, national-scale inventory data, which risks misrepresenting actual inventories and environmental impacts at regional or local levels. Variability in soil characteristics and climate conditions exacerbates this issue, particularly in field- or farm-level assessments. LCA accuracy improves when regionalized inventory data and updated methodologies are used, though practical implementation is often limited by the lack of standardized frameworks, spatially/temporally relevant inventory data, and software limitations. This study used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method to identify literature addressing spatiotemporal variability in LCA, and identification of software and/or techniques used in the domain. It evaluated methods currently used to incorporate such variability, highlighting the strengths and weaknesses of each. The contribution of the review is presented as the first systematic synthesis of spatial and temporal methodological approaches for agricultural LCA coupled with a practical decision-support framework for practitioners. Geographic information systems enhance LCA accuracy by modeling spatial and temporal patterns. Among the available tools, Brightway2, Temporalis, and OpenLCA are the most capable of dynamic and regionalized LCA, with Ecoinvent offering the most regionalized background data. While spatial differentiation is valuable, highly granular modeling (e.g., individual plant or row level) is often unnecessary for accurate results. However, detailed inventories are beneficial for specific applications like precision agriculture. Land use and soil organic carbon were the most commonly cited topics related to spatial and temporal variability. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.1016/j.jenvman.2025.127451 |
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