Régionalisation en analyse du cycle de vie : Analyse conséquentielle des filières alternatives pour le transport en france

RÉSUMÉ: Prendre une décision dans une perspective de développement durable nécessite d’évaluer ses bienfaits économiques, sociaux et environnementaux, et donc de sa durabilité. L’Analyse du Cycle de Vie (ACV) environnementale est un des outils d’évaluation de la durabilité, dans la mesure où il permet d’évaluer les impacts environnementaux potentiels des produits, services ou décisions. Ses principales forces sont : (i) la quantification des impacts environnementaux tout au long de la chaine de valeur (vision cycle de vie : depuis l’extraction des matières premières, leur transformation, leur utilisation et leur fin de vie), et (ii) la traduction des émissions et prélèvements vers et depuis l’environnement (inventaire) en impacts sur les changements climatiques, sur la santé humaine et sur la qualité des écosystèmes grâce à l’utilisation de facteurs de caractérisation (FC). Elle offre la possibilité d’identifier des pistes de réduction des impacts environnementaux avec une vision systémique. En particulier, l’ACV conséquentielle, complémentaire à l’approche attributionnelle classiquement utilisée, permet de quantifier les conséquences environnementales potentielles d’une décision, telle que la mise en place d’une politique publique ou le développement de nouvelles technologies. Son utilisation s’avère pertinente pour les études d’impact effectuées avant l’adoption d’une politique publique. Toutefois, comme pour tout outil d’évaluation, l’ACV comporte des limites. Une de ses principales limites porte sur l’incertitude de ses résultats, qui peut être importante et n’est encore que trop rarement évaluée. En tant qu’outil d’aide à la prise de décision, une attention particulière doit être portée à la réduction des incertitudes en ACV, afin de réduire l’incertitude totale de la prise de décision. La régionalisation, qui consiste à améliorer la représentativité géographique des résultats d’ACV, est un des moyens de réduire l’incertitude liée à la variabilité spatiale en ACV. À cet effet, diverses approches à différents niveaux en ACV existent et peuvent être intégrées par le praticien ACV, telles que la régionalisation de l’inventaire et la spatialisation de l’inventaire. Cependant, les approches existantes demeurent aujourd’hui mal identifiées et leurs conditions d’utilisation pertinentes restent à clarifier. De plus, l’intégration de la régionalisation peut induire une charge supplémentaire de travail pour le praticien ACV, notamment au niveau de la collecte des données et de la modélisation. Afin d’optimiser l’effort du praticien dans la réduction des incertitudes spatiales, il est donc nécessaire de mettre en place une méthodologie de priorisation dans la collecte des données pour la régionalisation. Cette priorisation viserait à identifier les données les plus contributrices à l’incertitude, c’est-à-dire les plus sensibles, et donc où le potentiel de réduction est le plus élevé. Or, les rares méthodologies existantes pour prioriser l’effort de collecte des données en ACV s’avèrent peu adaptées à la structure de l’ACV et la validité de leur priorisation reste discutable. Par ailleurs, il n’existe pas de méthodologie pour prioriser l’effort de régionalisation en ACV. Afin de traiter ces différentes limites, l’objectif général de ce projet est de développer un cadre méthodologique permettant de prioriser l’effort de réduction de l’incertitude en ACV à travers l’opérationnalisation de la régionalisation, dans le but d’améliorer la prise de décision. Il se décline en quatre objectifs spécifiques : 1) formuler un cadre structurant et opérationnel pour l’utilisation des approches existantes pour la régionalisation en ACV, 2) développer une méthodologie de priorisation de collecte des données en ACV pour la réduction des incertitudes spatiales afin de prioriser l’effort de régionalisation en ACV, 3) appliquer les cadres méthodologiques développés à des cas d’études en ACV attributionnelle, 4) appliquer la méthodologie développée à un cas d’étude en ACV conséquentielle pour évaluer les conséquences environnementales d’une politique publique pour le transport en France. Les réponses à ces objectifs spécifiques ont donné lieu aux contributions suivantes. La première contribution est une revue critique des approches existantes avec des recommandations pour intégrer la dimension spatiale en ACV. Les recommandations ont ensuite été appliquées pour accompagner une organisation du secteur agroalimentaire dans la spatialisation à court terme de sa propre base de données ACV. Ensuite, une méthodologie de priorisation de l’effort de régionalisation a été proposée afin d’identifier les principaux contributeurs à l’incertitude spatiale, en prenant en compte l’incertitude de l’inventaire et des FC. Pour ce faire, des analyses de sensibilité globale ont été menées en utilisant les indices de Sobol qui prennent en compte les interactions entre variables dans le modèle ACV. Cette méthodologie itérative destinée aux praticiens ACV et aux développeurs de base de données ACV permet de prioriser par étapes :1) les scénarios de l’étude ACV pour lesquels l’incertitude spatiale doit être réduite, 2) les catégories d’impacts les plus sensibles, et donc prioritaires dans la collecte de donnée d’inventaire, 3) l’ordre de priorité entre la régionalisation de l’inventaire et la spatialisation de l’inventaire, 4) les variables d’entrée du modèle ACV qui doivent être régionalisées ou spatialisées en priorité. Cette méthodologie a ensuite été utilisée dans la réalisation d’une méta-analyse des besoins en matière de régionalisation de deux secteurs économiques. Ainsi, le praticien ACV n’aurait pas besoin de réévaluer lui-même les besoins à chaque nouvelle étude mais pourrait réutiliser les recommandations précalculées pour le secteur associé. Les résultats des méta-analyses sectorielles suggèrent l’importance : (i) de la contribution de la variabilité spatiale des FC à l’incertitude des résultats justifiant l’utilisation de FC régionalisés, (ii) de l’utilisation des analyses de sensibilité globale plutôt que les analyses de contribution aux impacts pour prioriser la collecte des données en ACV. Finalement, la méthodologie de priorisation de l’effort de régionalisation en ACV a été appliquée à un cas d’étude en ACV conséquentielle issue de modèle économique d’équilibre partiel. Ce cas d’étude évalue les conséquences environnementales à l’horizon 2050 des scenarios alternatifs dans le secteur des transports résultants de la mise en place de la loi de transition énergétique en France. Cette application a mis en lumière une contribution importante de l’incertitude issue du modèle économique d’équilibre partiel à l’incertitude des résultats en ACV conséquentielle. Les principales limites de ce projet sont liées à l’opérationnalisation de la méthodologie de priorisation de l’effort de régionalisation en ACV : la prise en compte limitée de la corrélation spatiale entre les variables en ACV, le temps de calcul important pour mener des analyses de sensibilité globale sur un grand nombre de variables d’entrée, la non-implémentation des analyses de sensibilité globale et des méthodes d’impact régionalisées dans la plupart des logiciels ACV, le manque de disponibilité des données et des outils pour régionaliser ou spatialiser l’inventaire. Ces limites pourront cependant être traitées dans l’avenir grâce aux efforts conjoints des différentes parties prenantes en ACV. Notons toutefois que la démocratisation de la prise en compte de l’incertitude et de la régionalisation en ACV passe avant tout par une implémentation des méthodes associées dans les logiciels ACV. Plus largement, ce projet de recherche a contribué à éclairer un peu plus la communauté ACV sur plusieurs plans en : (i) améliorant la prise en compte de la régionalisation en ACV, (ii) explorant l’opérationnalisation des méthodes d’impacts régionalisées, notamment de la méthodologie d’impacts régionalisée IMPACT World+, (iii) explorant les liens entre incertitude et régionalisation en ACV pour aider à prioriser l’effort de régionalisation, (iv) améliorant la prise en compte et la réduction de l’incertitude en ACV, notamment en ACV-C issue de modèle économique. L’incertitude n’est plus perçue ici comme un défaut, mais comme un levier à actionner pour cibler la réduction d’incertitude grâce à l’utilisation d’analyses de sensibilité.----------ABSTRACT: Make a decision in a sustainable development perspective requires to assess its economic, social and environmental benefits, and thus its sustainability. The environmental life cycle assessment (LCA) is one of the tools to assess sustainability as it aims to estimate the potential environmental impacts of goods, services, and decisions. Its main strengths are: (i) to quantify environmental impacts all along the value chain (life cycle perspective: from extraction of raw materials, their transformation, use and end of life), and (ii) to convert the emissions and withdrawals from the environment (inventory) into impacts on climate change, human health and ecosystem quality thanks to characterization factors (CFs). LCA makes it possible to identify avenues for the reduction of environmental impacts with a systemic perspective. Complementary to the attributional approach traditionally used in LCA, consequential LCA allows quantifying the environmental consequences of a decision, like the implementation of a public policy or the development of new technologies. Its use could be very relevant to enhance impact assessments performed before implementing a public policy. However, as with every assessment tool, one of the main limitations of LCA is the uncertainty of its results which may be high and is still too rarely assessed. As a tool for decision-making support, special attention should be paid to uncertainty reduction in LCA, in order to reduce the overall uncertainty in decision-making. Regionalization is one way to reduce uncertainty due to spatial variability in LCA. It refers to the enhancement of the geographical representativeness of LCA results. It can be integrated into LCA using many approaches at the different stages of LCA, especially thanks to inventory regionalization or inventory spatialization for LCA practitioners. However, existing approaches are now misidentified, and their relevant use conditions should be clarified. In addition, integrating regionalization may induce additional workload for the LCA practitioner, especially for data collection and modeling. Therefore, a methodology to prioritize data collection efforts for regionalization in LCA should be proposed to reduce the spatial uncertainty of the LCA results. This prioritization should aim to optimize the practitioner’s efforts by focusing on data that mostly contributes to uncertainty, i.e. the most sensitive, thus that has the highest potential for uncertainty reduction. The few existing methodologies to prioritize data collection efforts in LCA are ill-adapted to the LCA structure and the validity of their prioritization may be challenged. Besides, no methodology to prioritize regionalization efforts in LCA exists. To address those limitations, the main purpose of this research project is to develop a methodological framework to prioritize the efforts for uncertainty reduction in LCA through the operationalization of regionalization, and ultimately enhance the decision-making. Four objectives are thus devised: (1) develop a framework to structure and operationalize the use of existing approaches for regionalization in LCA, (2) develop a methodology to prioritize data collection in LCA for the reduction of spatial uncertainty to prioritize regionalization efforts in LCA, (3) apply the developed frameworks to prioritize the regionalization efforts to case studies in attributional LCA, (4) apply the methodology to prioritize the regionalization efforts to a case study in consequential LCA to assess the environmental consequences of a public policy in the transportation sector in France. The responses to those objectives have generated the following contributions. The first contribution is a critical review of existing approaches and recommendations to integrate the spatial dimension in LCA. Then it was applied to guide an organization for the agri-food sector to spatialize its internal LCA database on the short-term. Secondly, a methodology to prioritize the regionalization efforts in LCA was proposed to identify the main contributors to the spatial uncertainty, accounting for uncertainty from inventory and CFs. To do so, global sensitivity analyses are performed using Sobol indices that account for interactions between variables in the LCA model. This iterative methodology is designed for LCA practitioners and LCA database developers and allows to prioritize step by step: (1) the scenarios in the LCA study where the uncertainty should be reduced, (2) the most sensitive impact categories on which prioritizing the inventory data collection, (3) between inventory regionalization or spatialization, (4) the most sensitive input variables to be regionalized or spatialized in priority. Next, this methodology was used to perform meta-analyses on the regionalization needs of two economic sectors. Therefore, the LCA practitioner would no longer need to evaluate by himself the needs for a new study but would reuse precomputed recommendations for the associated sector. The results for the meta-analyses suggest the importance of (i) the contribution of the spatial variability of CFs to the results uncertainty which justifies using regionalized CFs, (ii) using global sensitivity analysis instead of impact contribution analysis to prioritize data collection in LCA. Finally, the methodology to prioritize the regionalization efforts was applied to a case study in consequential LCA from partial equilibrium economic modeling. This case study aims to assess the environmental consequences by 2050 of alternative transportation scenarios from the implementation of the energy transition law in France. This application highlights the important contribution of the uncertainty from the partial equilibrium economic modeling to the results uncertainty in consequential LCA. The main limitations of this project are associated with the operationalization of the methodology to prioritize the regionalization efforts in LCA: limited consideration of spatial correlations between LCA variables, important computational time when performing global sensitivity analysis with a high number of input variables, the non-implementation of global sensitivity analysis and regionalized impact methods in most of LCA software, the lack of available data and tools to regionalize and spatialize the inventory. Those limitations could be addressed in the future thanks to mutual efforts for the different stakeholders in the LCA community. Note, however, that democratizing the consideration of uncertainty and regionalization in LCA requires primarily an implementation of the associated methods in LCA software. In a broader way, this research project contributes to inform a bit more the LCA community by: (i) enhancing the consideration of regionalization in LCA, (ii) exploring the operationalization of regionalized impact methods, especially for the regionalized impact methodology IMPACT World+, (iii) exploring the links between uncertainty and regionalization to help prioritizing regionalization efforts, (iv) enhancing the consideration and the reduction of uncertainty in LCA, especially in consequential LCA from partial equilibrium economic modeling. Uncertainty is no longer seen here as a failure but is used as a tool to target the uncertainty reduction by using sensitivity analysis

Milk protein production by a more environmentally sustainable process : bipolar membrane electrodialysis coupled with ultrafiltration

The increased demand for food production to nourish the rapidly growing human population raises serious sustainability issues for the food sector. Indeed, conventional food production lines involve processes having a significant environmental burden. Hence, the present study aims to demonstrate an environmentally sustainable process of food production. The milk protein was chosen as a model food ingredient due to its exceptional role in the human diet. The proposed innovative process of milk protein production includes bipolar membrane electrodialysis coupled with ultrafiltration (EDBM-UF). The crucial problem during the EDBM-UF of milk, such as different types of membrane fouling, was successfully solved. Moreover, the life cycle assessment of the novel EDBM-UF protein production process was carried out and compared to a conventional acid/base process. Additionally, a sensitivity test of electricity supply at different geographical locations of the world was performed since electricity is the main energy source for the EDBM-UF process and it could be derived from different sources (renewable and non-renewable). The assessment results demonstrate that the proposed electromembrane process has significant environmental benefits compared to the conventional process using chemicals independently from the electricity supply mix from all considered geographical locations. Thus, EDBM-UF could become a prospective industrial technology taking into account environmental concerns and promoting the development of healthy human society

Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling

Purpose Consequential life cycle assessment (C-LCA) aims to assess the environmental consequences of a decision. It differs from traditional LCA because its inventory includes all the processes affected by the decision which are identified by accounting for causal links (physical, economic, etc.). However, C-LCA results could be quite uncertain which makes the interpretation phase harder. Therefore, strategies to assess and reduce uncertainty in C-LCA are needed. Part of uncertainty in C-LCA is due to spatial variability that can be reduced using regionalization. However, regionalization can be complex and time-consuming if straightforwardly applied to an entire LCA model. Methods The main purpose of this article is to prioritize regionalization efforts to enhance interpretation in C-LCA by assessing the spatial uncertainty of a case study building on a partial equilibrium economic model. Three specific objectives are derived: (1) perform a C-LCA case study of alternative transportation scenarios to investigate the benefits of implementing a public policy for energy transition in France by 2050 with an uncertainty analysis to explore the strength of our conclusions, (2) perform global sensitivity analyses to identify and quantify the main sources of spatial uncertainty between foreground inventory model from partial equilibrium economic modeling, background inventory model and characterization factors, (3) propose a strategy to reduce the spatial uncertainty for our C-LCA case study by prioritizing regionalization. Results and discussion Results show that the implementation of alternative transport scenarios in compliance with public policy for the energy transition in France is beneficial for some impact categories (ICs) (global warming, marine acidification, marine eutrophication, terrestrial acidification, thermally polluted water, photochemical oxidant formation, and particulate matter formation), with a confidence level of 95%. For other ICs, uncertainty reduction is required to determine conclusions with a similar level of confidence. Input variables with spatial variability from the partial equilibrium economic model are significant contributors to the C-LCA spatial uncertainty and should be prioritized for spatial uncertainty reduction. In addition, characterization factors are significant contributors to the spatial uncertainty results for all regionalized ICs (except land occupation IC). Conclusions Ways to reduce the spatial uncertainty from economic modeling should be explored. Uncertainty reduction to enhance the interpretation phase and the decision-making should be prioritized depending on the goal and scope of the LCA study. In addition, using regionalized CFs in C-LCA seems to be relevant, and C-LCA calculation tools should be adapted accordingly

IMPACT World+: a globally regionalized life cycle impact assessment method

International audiencePurpose This paper addresses the need for a globally regionalized method for life cycle impact assessment (LCIA), integrating multiple state-of-the-art developments as well as damages on water and carbon areas of concern within a consistent LCIA framework. This method, named IMPACT World+, is the update of the IMPACT 2002+, LUCAS, and EDIP methods. This paper first presents the IMPACT World+ novelties and results and then analyzes the spatial variability for each regionalized impact category. Methods With IMPACT World+, we propose a midpoint-damage framework with four distinct complementary viewpoints to present an LCIA profile: (1) midpoint impacts, (2) damage impacts, (3) damages on human health, ecosystem quality, and resources & ecosystem service areas of protection, and (4) damages on water and carbon areas of concerns. Most of the regional impact categories have been spatially resolved and all the long-term impact categories have been subdivided between shorter-term damages (over the 100 years after the emission) and long-term damages. The IMPACT World+ method integrates developments in the following categories, all structured according to fate (or competition/scarcity), exposure, exposure response, and severity: (a) Complementary to the global warming potential (GWP100), the IPCC Global Temperature Potentials (GTP100) are used as a proxy for climate change long-term impacts at midpoint. At damage level, shorter-term damages (over the first 100 years after emission) are also differentiated from long-term damages. (b) Marine acidification impact is based on the same fate model as climate change, combined with the H + concentration affecting 50% of the exposed species. (c) For mineral resources depletion Responsible editor: Serenella Sala Electronic supplementary material The online version of this article (https://doi. impact, the material competition scarcity index is applied as a midpoint indicator. (d) Terrestrial and freshwater acidification impact assessment combines, at a resolution of 2°× 2.5°(latitude × longitude), global atmospheric source-deposition relationships with soil and water ecosystems' sensitivity. (e) Freshwater eutrophication impact is spatially assessed at a resolution grid of 0.5°× 0.5°, based on a global hydrological dataset. (f) Ecotoxicity and human toxicity impact are based on the parameterized version of USEtox for continents. We consider indoor emissions and differentiate the impacts of metals and persistent organic pollutants for the first 100 years from longer-term impacts. (g) Impacts on human health related to particulate matter formation are modeled using the USEtox regional archetypes to calculate intake fractions and epidemiologically derived exposure response factors. (h) Water consumption impacts are modeled using the consensus-based scarcity indicator AWARE as a proxy midpoint, whereas damages account for competition and adaptation capacity. (i) Impacts on ecosystem quality from land transformation and occupation are empirically characterized at the biome level. Results and discussion We analyze the magnitude of global potential damages for each impact indicator, based on an estimation of the total annual anthropogenic emissions and extractions at the global scale (i.e., Bdoing the LCA of the world^). Similarly with ReCiPe and IMPACT 2002+, IMPACT World+ finds that (a) climate change and impacts of particulate matter formation have a dominant contribution to global human health impacts whereas ionizing radiation, ozone layer depletion, and photochemical oxidant formation have a low contribution and (b) climate change and land use have a dominant contribution to global ecosystem quality impact. (c) New impact indicators introduced in IMPACT World+ and not considered in ReCiPe or IMPACT 2002+, in particular water consumption impacts on human health and the long-term impacts of marine acidification on ecosystem quality, are significant contributors to the overall global potential damage. According to the areas of concern version of IMPACT World+ applied to the total annual world emissions and extractions, damages on the water area of concern, carbon area of concern, and the remaining damages (not considered in those two areas of concern) are of the same order of magnitude, highlighting the need to consider all the impact categories. The spatial variability of human health impacts related to exposure to toxic substances and particulate matter is well reflected by using outdoor rural, outdoor urban, and indoor environment archetypes. For Bhuman toxicity cancer^impact of substances emitted to continental air, the variability between continents is of two orders of magnitude, which is substantially lower than the 13 orders of magnitude total variability across substances. For impacts of water consumption on human health, the spatial variability across extraction locations is substantially higher than the variations between different water qualities. For regionalized impact categories affecting ecosystem quality (acidification, eutrophication, and land use), the characterization factors of half of the regions (25th to 75th percentiles) are within one to two orders of magnitude and the 95th percentile within three to four orders of magnitude, which is higher than the variability between substances, highlighting the relevance of regionalizing. Conclusions IMPACT World+ provides characterization factors within a consistent impact assessment framework for all region-alized impacts at four complementary resolutions: global default, continental, country, and native (i.e., original and non-aggre-gated) resolutions. IMPACT World+ enables the practitioner to parsimoniously account for spatial variability and to identify the elementary flows to be regionalized in priority to increase the discriminating power of LCA

Correcting remaining truncations in hybrid life cycle assessment database compilation

Hybrid life cycle assessment (HLCA) strives to combine process‐based life cycle assessment (PLCA) and environmentally extended input–output (EEIO) analysis to bridge gaps of both methodologies. The recent development of HLCA databases constitutes a major step forward in achieving complete system coverage. Nevertheless, current applications of HLCA still suffer from issues related to incompleteness of the inventory and data gaps: (1) hybridization without endogenizing the capital inputs of the EEIO database leads to underestimations, (2) the unreliability of price data hinders the application of streamlined HLCA for processes in some sectors, and (3) the sparse coverage of pollutants in multiregional EEIO databases limits the application of HLCA to a handful of impact categories. This paper aims at offering a methodology for tackling these issues in a streamlined manner and visualizing their effects on impact scores across an entire PLCA database and multiple impact categories. Data reconciliation algorithms are demonstrated on the PLCA database ecoinvent3.5 and the multiregional EEIO database EXIOBASE3. Instead of performing hybridization solely with annual product requirements, this hybridization approach incorporates endogenized capital requirements, demonstrates a novel hybridization methodology to bypass issues of price unavailability, estimates new pollutants to EXIOBASE3 environmental extensions, and thus yields improved inventories characterized in terms of 13 impact categories from the IMPACT World+ methodology. The effect of hybridization on the impact score of each process of ecoinvent3.5 varied from a few percentages to three‐fold increases, depending on the impact category and the process studied, displaying in which cases hybridization should be prioritized. This article met the requirements for a Gold—Gold JIE data openness badge described at http://jie.click/badges

A Commodity Supply Mix for More Regionalized Life Cycle Assessments

Supply chain information is invaluable to further regionalize product life cycle assessments (LCAs), but detailed information linking production and consumption centers is not always available. We introduce the commodity supply mix (CSM) defined as the trade-volume-weighted average representing the combined geographic areas for the production of a commodity exported to a given market with the goal of (1) enhancing the relevance of inventory and impact regionalization and (2) allocating these impacts to specific markets. We apply the CSM to the Brazilian soybean supply chain mapped by Trase to obtain the mix of ecoregions and river basins linked to domestic consumption and exports to China, EU, France, and the rest of the world, before quantifying damage to biodiversity, and water scarcity footprints. The EU had the lowest potential biodiversity damage but the largest water scarcity footprint following respective sourcing patterns in 12 ecoregions and 18 river basins. These results differed from the average impact scores obtained from Brazilian soybean production information alone. The CSM can be derived at different scales (subnationally, internationally) using existing supply chain information and constitutes an additional step toward greater regionalization in LCAs, particularly for impacts with greater spatial variability such as biodiversity and water scarcity

SPOT: A strategic life-cycle-assessment-based methodology and tool for cosmetic product eco-design

ABSTRACT: The cosmetics industry is facing growing pressure to offer more sustainable products, which can be tackled by applying eco-design. This article aims to present the Sustainable Product Optimization Tool (SPOT) methodology developed by L’Oréal to eco-design its cosmetic products and the strategies adopted for its implementation while presenting the challenges encountered along the way. The SPOT methodology is based on the life cycle assessment (LCA) of a finished product and its subsystems (formula, packaging, manufacturing and distribution). Several environmental indicators are assessed, normalized and weighted based on the planetary boundaries concept, and then aggregated into a single footprint. A product sustainability index (a single rating, easy to interpret) is then obtained by merging the environmental product rating derived from the single environmental footprint with the social rating (not covered here). The use of the SPOT method is shown by two case studies. The implementation of SPOT, based on specific strategic and managerial measures (corporate and brand targets, Key Performance Indicators, and financial incentives) is discussed. These measures have enabled L’Oréal to have 97% of their products stated as eco-designed in 2022. SPOT shows how eco-design can be implemented on a large scale without compromising scientific robustness. Eco-design tools must strike the right balance between the complexity of the LCA and the ease of interpretation of the results, and have a robust implementation plan to ensure a successful eco-design strategy

IMPACT World+: a globally regionalized life cycle impact assessment method

Purpose This paper addresses the need for a globally regionalized method for life cycle impact assessment (LCIA), integrating multiple state-of-the-art developments as well as damages on water and carbon areas of concern within a consistent LCIA framework. This method, named IMPACT World+, is the update of the IMPACT 2002+, LUCAS, and EDIP methods. This paper first presents the IMPACT World+ novelties and results and then analyzes the spatial variability for each regionalized impact category. Methods With IMPACT World+, we propose a midpoint-damage framework with four distinct complementary viewpoints to present an LCIA profile: (1) midpoint impacts, (2) damage impacts, (3) damages on human health, ecosystem quality, and resources & ecosystem service areas of protection, and (4) damages on water and carbon areas of concerns. Most of the regional impact categories have been spatially resolved and all the long-term impact categories have been subdivided between shorterterm damages (over the 100 years after the emission) and long-term damages. The IMPACT World+ method integrates developments in the following categories, all structured according to fate (or competition/scarcity), exposure, exposure response, and severity: (a) Complementary to the global warming potential (GWP100), the IPCC Global Temperature Potentials (GTP100) are used as a proxy for climate change long-term impacts at midpoint. At damage level, shorter-term damages (over the first 100 years after emission) are also differentiated from long-term damages. (b) Marine acidification impact is based on the same fate model as climate change, combined with the H+ concentration affecting 50% of the exposed species. (c) For mineral resources depletion impact, the material competition scarcity index is applied as a midpoint indicator. (d) Terrestrial and freshwater acidification impact assessment combines, at a resolution of 2° × 2.5° (latitude × longitude), global atmospheric source-deposition relationships with soil and water ecosystems’sensitivity. (e) Freshwater eutrophication impact is spatially assessed at a resolution grid of 0.5° × 0.5°, based on a global hydrological dataset. (f) Ecotoxicity and human toxicity impact are based on the parameterized version of USEtox for continents. We consider indoor emissions and differentiate the impacts of metals and persistent organic pollutants for the first 100 years from longer-term impacts. (g) Impacts on human health related to particulate matter formation are modeled using the USEtox regional archetypes to calculate intake fractions and epidemiologically derived exposure response factors. (h) Water consumption impacts are modeled using the consensus-based scarcity indicator AWARE as a proxy midpoint, whereas damages account for competition and adaptation capacity. (i) Impacts on ecosystem quality from land transformation and occupation are empirically characterized at the biome level. Results and discussion We analyze the magnitude of global potential damages for each impact indicator, based on an estimation of the total annual anthropogenic emissions and extractions at the global scale (i.e., Bdoing the LCA of the world^). Similarly with ReCiPe and IMPACT 2002+, IMPACT World+ finds that (a) climate change and impacts of particulate matter formation have a dominant contribution to global human health impacts whereas ionizing radiation, ozone layer depletion, and photochemical oxidant formation have a low contribution and (b) climate change and land use have a dominant contribution to global ecosystem quality impact. (c) New impact indicators introduced in IMPACT World+ and not considered in ReCiPe or IMPACT 2002+, in particular water consumption impacts on human health and the long-term impacts of marine acidification on ecosystem quality, are significant contributors to the overall global potential damage. According to the areas of concern version of IMPACT World+ applied to the total annual world emissions and extractions, damages on the water area of concern, carbon area of concern, and the remaining damages (not considered in those two areas of concern) are of the same order of magnitude, highlighting the need to consider all the impact categories. The spatial variability of human health impacts related to exposure to toxic substances and particulate matter is well reflected by using outdoor rural, outdoor urban, and indoor environment archetypes. For Bhuman toxicity cancer^ impact of substances emitted to continental air, the variability between continents is of two orders of magnitude, which is substantially lower than the 13 orders of magnitude total variability across substances. For impacts of water consumption on human health, the spatial variability across extraction locations is substantially higher than the variations between different water qualities. For regionalized impact categories affecting ecosystem quality (acidification, eutrophication, and land use), the characterization factors of half of the regions (25th to 75th percentiles) are within one to two orders of magnitude and the 95th percentile within three to four orders of magnitude, which is higher than the variability between substances, highlighting the relevance of regionalizing. Conclusions IMPACT World+ provides characterization factors within a consistent impact assessment framework for all regionalized impacts at four complementary resolutions: global default, continental, country, and native (i.e., original and non-aggregated) resolutions. IMPACT World+ enables the practitioner to parsimoniously account for spatial variability and to identify the elementary flows to be regionalized in priority to increase the discriminating power of LCA

Sensitivity of Technical Choices on the GHG Emissions and Expended Energy of Hydrotreated Renewable Jet Fuel from Microalgae

Taking into account the environmental impacts of biofuel production is essential to develop new and innovative low-emission processes. The assessment of life cycle GreenHouse Gas (GHG) emissions of biofuel is mandatory for the countries of the European Union. New biomass resources that hardly compete with food crops are been developed increasingly. Microalgae are an interesting alternative to terrestrial biomass thanks to their high photosynthetic efficiency and their ability to accumulate lipids. This article provides an analysis of potential environmental impacts of the production of algal biofuel for aviation using the Life Cycle Assessment (LCA). Evaluated impacts are GHG emissions and the primary energy consumption, from extraction of raw materials to final waste treatment. This study compared two management choices for oilcakes generated after oil extraction from microalgae. In the first system, these cakes are treated by energetic allocation and in the second by anaerobic digestion. In both cases, the steps of cultivation and harvesting have the highest impact on the results. Sensitivity analyzes are performed on technical choices of operating systems (choice of the type of nutrients, mode of harvesting, drying and oil extraction) as well as a Monte-Carlo analysis on key parameter values for GHG emissions (concentration of microalgae in ponds, productivity and oil content). The results highlight the impact of the use of chemical fertilizers and the importance of the concentration of algae on GHG emissions and energy consumption