Consequential life cycle assessment of agricultural products: state of the art and research needs

Abstract Purpose Consequential Life Cycle Assessment (cLCA) was developed to assess large-scale consequences, supporting applications like policy-making. Unlike the well-established attributional approach, cLCA is characterized by lower maturity. Specifically, a lack of consensus on the identification of marginal data, system boundary setting and handling of results’ uncertainties impedes its practical use. We examine how agricultural cLCA studies handle these methodological challenges, focusing on food and biobased materials. Methods A systematic literature review was carried out, initially yielding 229 cLCA studies. The eligibility screening led to the final selection of 23 studies on biobased materials and 32 studies on food. These were primarily analysed regarding marginal data identification, system boundary setting, and uncertainty assessment, alongside supplementary methodological aspects, such as functional unit, use of market models, study scope, and reasoning for consequential modelling. Results and Discussion The review identified a broad variety of approaches for addressing methodological challenges of cLCA, without significant differences between biobased materials and food, while revealing documentation gaps and inconsistencies in reported methodological procedures. Marginal data identification relied mostly on ecoinvent consequential and formalized guidelines, while 11% of the studies did not specify the procedure. Around a third reported using market models or projections. System boundary setting was generally well documented, yet often lacked details, raising concerns regarding the completeness of captured changes. For uncertainty, 25% applied statistical methods and 62% conducted at least a sensitivity analysis, while 22% relied on qualitative discussion only. Further observations included the absence of specifications for temporal scope and justification for consequential modelling. Conclusions and Recommendations To improve robustness, cLCA studies would benefit from standardized minimum procedural requirements, for which suggestions from this review can be viewed as a starting point. Specifically, we recommend i) using the JRC (2010) classification as a checklist to qualitatively discuss potential secondary consequences, ii) conducting sensitivity analysis, and iii) explicitly defining the temporal scope. Increased application of quantitative uncertainty assessment would strengthen the reliability of cLCA studies. Future research could test these suggestions in case studies, examine marginal data identification with respect to the practical applicability of existing procedures, and expand the review to biofuels. Overall, methodological harmonization and transparent study documentation are key to increasing the reliability and broader acceptance of cLCA.