In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models. When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed. We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties. We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.

A simplified simulation module has been developed to calculate the thermal performances of multizone buildings, using modal analysis. This technique reduces the computation time and therefore allows architects to perform accurate simulations using a low cost micro computer (type AT for instance). The thermal calculations are based upon a data structure in which the building is decomposed into elements (thermal zones, walls, windows, materials, …). Each building element has been modeled as a computer object. Components of higher complexity are linked to simple components via pointers. Thus, if a component is modified, the modification is automatically transmitted through the whole structure. This environment is particularly adapted to connect the thermal calculation module (COMFIE) to an expert interface. At the moment, no expert system or inference engine is implemented, but simple algorithms which work in the following way. The user inputs a project and several characteristics (heat losses, solar gains, …) are studied by the expert tool, which then proposes alternative designs. The user may test these modifications by repeating the simulation. This tandem simulation/expert interface takes into account the specificity of the project and its climate when applying the expert knowledge. A few expert rules are proposed, in order to enhance the energy savings and the thermal comfort. New expert rules may also be generated and/or validated. This design tool not only provides the user with fast and accurate simulations, but also offers him an opportunity to include the thermal aspects in his design approach with the help of expert rules, and this constitutes a real computer aided design.

Purpose: Investigating potential social and socio-economic impacts should play a key role for the development of sustainable mobility alternatives. Social life cycle assessment (S-LCA) is becoming increasingly important to ensure holistic sustainability assessments. The present work aims at identifying and evaluating social and socio-economic impact subcategories in S-LCA. A novel participatory approach implying all concerned stakeholders is proposed to select relevant impact subcategories and thus contribute to a thorough interpretation of S-LCA results. It is applied to assess electric and conventional vehicles. Methodology: This paper describes a comprehensive step-by-step S-LCA framework. The innovation of this work consists in defining a structured S-LCA framework integrating a systematic approach based on two stages: (1) a sectorial risk analysis for the identification of impact subcategories and (2) a participatory approach for their prioritization. The proposed participatory approach considers all concerned stakeholders to enable the selection of the most relevant impact subcategories. A set of social inventory indicators is attributed to subcategories that were perceived as the most relevant. These are used to perform the social evaluation and carry out a full analysis in the result interpretation allowing thus to integrate a multi-actor perspective to the materiality assessment. Results: The defined S-LCA framework is implemented to compare two mobility scenarios, corresponding to electric and conventional vehicle technologies. A new set of mobility-related impact subcategories is proposed for users’ stakeholder. Following the new designed participatory approach, subcategories for all stakeholders are prioritized according to different actors’ perceptions. For example, “safe and healthy living conditions,” “local employment,” and “delocalization and migration” were perceived for local communities as the most relevant subcategories by the different consulted stakeholders (industrial, academic, and public actors and users). These results also showed that social significance varies depending on the consulted actors and on the geographical area of the study. Using PSILCA database, we have investigated the subcategories that were perceived as the most relevant. Results for the evaluation and interpretation phases are presented for both transportation technologies. Conclusions: This approach aims at increasing local relevance of S-LCA results and their representativeness. Results for the considered mobility scenarios have demonstrated the need to extend the scope of the materiality assessment, generally used for determining subcategories’ social significance from a single stakeholder perspective, by involving other stakeholders into the prioritization stage. Moreover, the proposed comprehensive S-LCA framework integrating the participatory approach is general enough to be applied to other product systems and sectors.

By analyzing the latest developments in the dynamic life cycle assessment (DLCA) methodology, we identify an implementation challenge with the management of new temporal information to describe each system we might want to model. To address this problem, we propose a new method to differentiate elementary and process flows on a temporal level, and explain how it can generate temporally differentiated life cycle inventories (LCI), which are necessary inputs for dynamic impact assessment methods. First, an analysis of recent DLCA studies is used to identify the relevant temporal characteristics for an LCI. Then, we explain the implementation challenge of handling additional temporal information to describe processes in life cycle assessment (LCA) databases. Finally, a new format of temporal description is proposed to minimize the current implementation problem for DLCA studies. A new format of process-relative temporal distributions is proposed to obtain a temporal differentiation of LCA database information (elementary flows and product flows). A new LCI calculation method is also proposed since the new format for temporal description is not compatible with the traditional LCI calculation method. Description of the requirements and limits for this new method, named enhanced structural path analysis (ESPA), is also presented. To conclude the description of the ESPA method, we illustrate its use in a strategically chosen scenario. The use of the proposed ESPA method for this scenario reveals the need for the LCA community to reach an agreement on common temporal differentiation strategies for future DLCA studies. We propose the ESPA method to obtain temporally differentiated LCIs, which should then require less implementation effort for the system-modeling step (LCA database definition), even if such concepts cannot be applied to every process.