ReviewCritical review and practical recommendations to integrate the spatial dimension into life cycle assessment
Introduction
Life cycle assessment (LCA) methodology was first developed without considering any spatial aspects because spatial differentiation was historically related to site-specific risk assessment while LCA was designed for global pollution prevention (Potting and Hauschild, 2006). But misleading conclusions that may be drawn from a site-generic LCA and the importance of taking spatial differentiation into account in life cycle impact assessment (LCIA) was demonstrated (Ross and Evans, 2002). First, activities along the product life cycle and related elementary flows (EF) that constitute the life cycle inventory (LCI) may be geographically scattered owing to the globalization of supply chains. Also, an EF (emission or extraction) in a given area may have a different impact depending on its location. The environmental consequences of the EF may be local, regional, continental or global depending on the type and characteristics of the EF and receiving environment (Potting and Hauschild, 2006).
Addressing the geographic aspects in LCA appears to be a promising avenue to increase the representativeness and reliability of the results (Mutel and Hellweg, 2009). Ultimately, it could improve the discrimination power for comparative LCA (Udo de Haes et al., 1999). Regionalization provides a representative description of processes and phenomena that are spatially variable. Variability involves current variations in the real world and is distinguished from uncertainty, which refers to a lack of knowledge on reality (Huijbregts, 1998). Providing more representative descriptions of spatially variable processes and phenomena should reduce the uncertainty associated with the shortage of information on their spatial location. In addition, more and more regionalized LCI databases (Colomb et al., 2015, Durlinger et al., 2014, Lansche et al., 2013, Vionnet et al., 2012, Weidema et al., 2012) and regionalized LCIA methods are being developed (Bulle et al., 2017, Verones et al., 2016), offering opportunities for LCA practitioners to improve the quality of their studies. However, enhancing geographic representativeness may require an increased workload for the LCA practitioner, specifically in terms of data collection and modeling (Baitz et al., 2012). One of the challenges of integrating regionalization is therefore to find a level of geographic representativeness that is adapted to the study objectives (Patouillard et al., 2016).
It is possible to consider geographic aspects at every phase in LCA methodology (Aissani, 2008; International Organization for Standardization ISO, 2006a, ISO, 2006b):
- •
The goal and scope (G&S) when defining the object of the study and its spatial requirements
- •
When regionalizing the inventory, the LCI ensures the better geographic representativeness of the studied systems (inventory regionalization). In addition, attributing a spatial location to the EFs (inventory spatialization) makes it possible to use regionalized characterization factors (CF).
- •
The LCIA when assessing the spatial variability of impact scores as a function of the characteristics of the receiving environment (impact regionalization)
- •
The interpretation when identifying the potential transfer of impacts from one geographic location to another
To our knowledge, there is no comprehensive literature review, i.e. peer reviewed article with an exhaustive overview of existing literature, on how to integrate geographic aspects at every stage of LCA or provide guidance on how to regionalize inventory data or handle different resolution scales between the inventory and impact assessment. Furthermore, there is no framework or consistent terminology in relation to spatial aspects in LCA. In this context, the SCORELCA association, which includes leading stakeholders in life cycle thinking (EDF, ENGIE, Renault, Total, Veolia) and the French environmental protection agency (ADEME), and the authors initiated a research study to investigate the interest and relevance of considering and implementing geographic aspects in LCA. This study intends to assist the LCA community to consistently integrate the spatial dimension and create a common language. It further aims to guide LCA practitioners gathering relevant spatial information to increase the robustness of the results through a streamlined process.
Therefore, the main aim of this study is to build a framework to structure and provide recommendations on the use of the different existing approaches to integrating the spatial dimension in LCA. The three objectives of this study are to (a) synthesize and classify current recommendations and approaches to integrate the spatial dimension in LCA, (b) analyze each identified approach based on their level of relevance, development and operationalization and (c) formulate recommendations on how to integrate the spatial dimension in LCA. To achieve those goals, this article is structured in three sections: (a) literature review of existing approaches to integrating the spatial dimension in LCA, (b) critical analysis of the selected approaches, and (c) practical recommendations for the implementation of the approaches by LCA practitioners. The article builds on the report of the SCORELCA study by Patouillard et al. (2015). This work benefitted from the active participation of SCORELCA member experts on the steering committee.
Section snippets
Terminology related to the spatial dimension in LCA
In the literature, numerous terms related to the spatial dimension in LCA are inconsistently used and often not clearly defined. Therefore, we propose the following terminology and definitions based on the literature review.
- •
Economic flow: An exchange with the technosphere, i.e. an intermediate exchange of goods or services
- •
Elementary flow (EF): An exchange with the ecosphere (to/from the environment)
- •
Process: A unit process that describes an activity (ecoinvent). It lists the exchanges with the
Literature review
In total, 75 references were consulted as part of the literature review, leading to 33 recommendations on geographic representativeness requirements in LCA and 37 approaches addressing the spatial dimension in LCA. The 15 references used to identify the recommendations have been written between 2006 and 2014, and are standards or directives or reports that claim to provide recommendations in LCA. The 60 references used to identify the approaches have been written between 1996 and 2017 but 50%
Conclusion
Building on an extensive critical review from the literature, this study identified the state of the art on how to integrate the spatial dimension in LCA, highlighted practical and conceptual obstacles and created a common language. Recommendations were formulated for LCA practitioners, LCI database developers, LCIA method developers, LCA software developers and LCA researchers to enhance the integration of the spatial dimension in LCA on the short and the long term.
A decision-support diagram
Acknowledgements
This work was supported by the SCORELCA Foundation. We would like to express our gratitude to the SCORELCA Foundation and to all SCORELCA members for their contributions to this research.
References (85)
- et al.
Macroanalysis of the economic and environmental impacts of a 2005–2025 European Union bioenergy policy using the GTAP model and life cycle assessment
Renew. Sustain. Energy Rev.
(2012) - et al.
Environmental assessment: (LCA) and spatial modelling (GIS) of energy crop implementation on local scale
Biomass Bioenergy
(2011) Identification of key issues for further investigation in improving the reliability of life-cycle assessments
J. Clean. Prod.
(1996)- et al.
Effects of land-use change on the carbon balance of 1st generation biofuels: an analysis for the European Union combining spatial modeling and LCA
Biomass Bioenergy
(2013) - et al.
Spatial analysis of toxic emissions in LCA: a sub-continental nested USEtox model with freshwater archetypes
Environ. Int.
(2014) - et al.
Environmental assessment of a territory: an overview of existing tools and methods
J. Environ. Manage
(2012) - et al.
Modelling approaches for consequential life-cycle assessment (C-LCA) of bioenergy: critical review and proposed framework for biogas production
Renew. Sustain. Energy Rev.
(2013) - et al.
Geospatial characterization of building material stocks for the life cycle assessment of end-of-life scenarios at the urban scale
Resour. Conserv. Recycl.
(2017) - et al.
Data quality management for life cycle inventories—an example of using data quality indicators
J. Clean. Prod.
(1996) Intégration des paramètres spatio-temporels et des risques d’accident à l’analyse du cycle de vie: application à la filière hydrogène énergie et à la filière essence
Ecole Nationale Supérieure des Mines de Saint-Etienne
(2008)
LCA's theory and practice: like ebony and ivory living in perfect harmony?
Int. J. Life Cycle Assess.
The tool for the reduction and assessment of chemical and other environmental impacts
J. Ind. Ecol.
Feasibility of applying site-dependent impact assessment of acidification in LCA (8 pp)
Int. J. Life Cycle Assess.
Vers une caractérisation spatiotemporelle pour l’analyse du cycle de vie
École nationale supérieure des mines de Paris
Survey of approaches to improve reliability in LCA
Int. J. Life Cycle Assess.
Analysis of water use impact assessment methods (part A): evaluation of modeling choices based on a quantitative comparison of scarcity and human health indicators
Int. J. Life Cycle Assess.
Regional characterization of freshwater use in LCA: modeling Direct impacts on human health
Environ. Sci. Technol.
Gestion de l’incertitude causée par l’incohérence d’échelle spatiale à l’interface de l’inventaire et de l’analyse des impacts en ACV
Impact World+: globally regionalized life cycle impact assessment
Int. J. Life Cycle Assess. Rev.
Quantifying land use impacts on biodiversity: combining species-area models and vulnerability indicators
Environ. Sci. Technol.
Geographical and technological differences in Life Cycle Inventories shown by the use of process models for waste incinerators Part II
J. Life Cycle
openLCA 1.4 Overview and First Steps
AGRIBALYSE, the French LCI database for agricultural products: high quality data for producers and environmental labelling
OCL Oilseeds Fats, Crop. Lipids
Land use impacts on biodiversity in LCA: a global approach
Int. J. Life Cycle Assess.
Agri-footprint; a life cycle inventory database covering food and feed production and processing
Proc. 9th Int. Conf. Life cycle assess. Agri-Food Sect
Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC
Off. J. Eur. Union L
Health effects of fine particulate matter in life cycle impact assessment: findings from the Basel Guidance Workshop
Int. J. Life Cycle Assess.
Coupling GIS and LCA for biodiversity assessments of land use Part 2: impact assessment
Int. J. Life Cycle Assess.
Coupling GIS and LCA for biodiversity assessments of land use Part 1: inventory modeling
Int. J. Life Cycle Assess.
ReCiPe 2008. A LCIA method which comprises harmonised category indicators at the midpoint and the endpoint level. Characterisation
A Life Cycle Impact
Sensitivity coefficients for matrix-based LCA
Int. J. Life Cycle Assess.
The Computational Structure of Life Cycle Assessment
Confronting uncertainty in life cycle assessment used for decision support
J. Ind. Ecol.
Global Trade Analysis: Modeling and Applications
Uncertainty in LCA LCA methodology application of uncertainty and variability in LCA Part I : a general framework for the analysis of uncertainty and variability in life cycle assessment
Int. J. Life Cycle Assess.
Geographically Differentiated Life-cycle Impact Assessment of Human Health
Analysing Land-use Effects on the Carbon Balance of Biofuels by Coupling a Spatial Model to LCA
ISO14040:2006 Environmental Management-life Cycle Assessment-principles and Framework
ISO14044:2006 Environmental Management - Life Cycle Assessment - Requirements and Guidelines
Cited by (55)
Classification of sources of uncertainty in building LCA
2024, Energy and BuildingsSpatialized carbon-energy-water footprint of emerging coal chemical industry in China
2024, Renewable and Sustainable Energy ReviewsCoupling big data and life cycle assessment: A review, recommendations, and prospects
2023, Ecological IndicatorsRegional nitrogen resilience as distance-to-target approach in LCA of crop production systems
2022, Environmental Impact Assessment Review