Linking data and model outputs with local demand for resources and local emissions An application to the Life Cycle Indicators framework
Lorenzo Benini, Serenella Sala, Ine Vandecasteele 2013
Report EUR 26453 EN
European Commission Joint Research Centre Institute for Environment and Sustainability Contact information Lorenzo Benini Address: Joint Research Centre, Via Enrico Fermi 2749, TP 270, 21027 Ispra (VA), Italy E-mail:
[email protected] Tel.: +39 0332 783 687 Fax: +39 0332 786 645 http://www.jrc.ec.europa.eu/ http://www.jrc.ec.europa.eu/ Cover page picture: Children’s poster exhibition at the World Resources Forum, Davos (CH), 7-8 October 2013, photo by Serenella Sala This publication is a Reference Report by the Joint Research Centre of the European Commission. Legal Notice Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed.
A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server http://europa.eu/. JRC87703 EUR 26453 EN ISBN 978-92-79-35155-6 ISSN 1831-9424 doi: 10.2788/61087 Luxembourg: Publications Office of the European Union, 2013 © European Union, 2013 Reproduction is authorised provided the source is acknowledged. Printed in Luxembourg
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Executive summary Reducing the environmental impacts of EU production and consumption is one of the main goals of the Roadmap to a resource efficient Europe (EC, 2011a). Additionally, the need of integrating life cycle thinking in environmental and resource policies is increasingly mentioned as strategic asset for comprehensively capture environmental dimension of sustainability. Such ambitious objectives require the development of a common, internally consistent framework for integrated sustainability assessment of European Union policies. This framework should accommodate not only a spectrum of sustainability indicators, but also should demonstrate the capacity to assess each indicator against defined sustainability thresholds and targets. This document defines the framework for the integration of the Life Cycle Indicators (EC, 2012a, 2012b, 2012c) with dynamic models, both spatially resolved and not, to the purpose of contributing to the development of an Integrated Sustainability Assessment Platform (ISAP). Three main elements can be potentially linked together in the framework of the LC Indicators: a macroeconomic model, the Land Use Modelling Platform (LUMP) (EC, 2013c) and the life cycle impact assessment methods (EC, 2012d). A description of the main elements of this framework is provided as well as discussed. As example of the potential link and mutual benefit between LCA and LUMP, the last section of the document is dedicated to the refinement of an impact assessment model (Pfister et al., 2009) for the assessment of water scarcity, on the basis of the outputs of the LUMP.
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Table of Contents 1
2
Introduction .................................................................................................................................... 8 1.1
LC indicators as ex-post methodology for macro-scale analysis............................................. 9
1.2
LC Indicators: from ex-post to ex-ante methodology for macro-scale consequential analysis 9
Key elements of the LC Indicators ex-ante modelling framework................................................ 10 2.1
2.1.1
Bottom-up, consequential LCA ..................................................................................... 10
2.1.2
The Environmentally Extended Input Output Tables (EEIOT) ....................................... 12
2.2
Dealing with the spatial dimension....................................................................................... 14
2.2.1
The Land Use Modelling Platform................................................................................. 14
2.2.2
An operational framework for integrating LUMP and LCIA indicators ......................... 14
2.3
3
Life cycle-based integrated economic-environment modelling ........................................... 10
Life Cycle Impact Assessment and related indicators ........................................................... 16
2.3.1
Environmental impact assessment in LCA .................................................................... 16
2.3.2
Spatially resolved impact assessment methods within LCA ......................................... 17
Mutual synergies between LCA and LUMP for impact assessment: a case study on water. ....... 19 3.1
Methodology......................................................................................................................... 19
3.2
Results and discussion .......................................................................................................... 22
3.3
Conclusions ........................................................................................................................... 28
3.3.1
Further developments .................................................................................................. 28
4
Acknowledgments......................................................................................................................... 28
5
References .................................................................................................................................... 28
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Glossary Definiendum
Definition
Area of protection (AOP)
A cluster of category endpoints of recognisable value to society, viz. human health, natural resources, natural environment and sometimes man-made environment (Guinée et al., 2002)
Cause-effect chain
or environmental mechanism. System of physical, chemical and biological processes for a given impact category, linking the life cycle inventory analysis result to the common unit of the category indicator (ISO 14040) by means of a characterisation model.
Characterisation
A step of the Impact assessment, in which the environmental interventions assigned qualitatively to a particular impact category (in classification) are quantified in terms of a common unit for that category, allowing aggregation into one figure of the indicator result (Guinée et al., 2002)
Characterisation factor
Factor derived from a characterisation model which is applied to convert an assigned life cycle inventory analysis result to the common unit of the impact category indicator (ISO 14040)
Characterisation methodology, methods, models and factors
Throughout this document an “LCIA methodology” refers to a collection of individual characterisation “methods” or characterisation “models”, which together address the different impact categories, which are covered by the methodology. “Method” is thus the individual characterisation model while “methodology” is the collection of methods. The characterisation factor is, thus, the factor derived from characterisation model which is applied to convert an assigned life cycle inventory result to the common unit of the category indicator.
Classification
A step of Impact assessment, in which environmental interventions are assigned to predefined impact categories on a purely qualitative basis (Guinee et al 2002)
Elementary flow
Material or energy entering the system being studied has drawn from the environment without previous human transformation (e.g. timber, water, iron ore, coal) , or material or energy leaving the system being studied that is released into the environment without subsequent human transformation (e.g. CO 2 or noise emissions, wastes discarded in nature) (ISO 14040)
Endpoint method/model
The category endpoint is an attribute or aspect of natural environment, human health, or resources, identifying an environmental issue giving cause for concern (ISO 14040). Hence, endpoint method (or damage approach)/model is a characterisation method/model that provides indicators at the level of Areas of Protection (natural environment's ecosystems, human health, resource availability) or at a level close to the Areas of Protection level.
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Definiendum
Definition
Environmental impact
A consequence of an environmental intervention in the environment system (Guinée et al 2002)
Environmental intervention
A human intervention in the environment, either physical, chemical or biological; in particular resource extraction, emissions (incl. noise and heat) and land use; the term is thus broader than “elementary flow” (Guinée et al 2002)
Environmental profile
The result of the characterisation step showing the indicator results for all the predefined impact categories, supplemented by any other relevant information (Guinée et al 2002)
Impact category
Class representing environmental issue of concern (ISO 14040). E.g. Climate change, Acidification, Ecotoxicity etc.
Impact category indicator
Quantifiable representation of an impact category (ISO 14040). E.g. Kg CO2equivalents for climate change
Life cycle impact assessment (LCIA)
"Phase of life cycle assessment involving the compilation and quantification of inputs and outputs for a given product system throughout its life cycle." (ISO 14040) The third phase of an LCA, concerned with understanding and evaluating the magnitude and significance of the potential environmental impacts of the product system(s) under study
Midpoint method
Sensitivity analysis
The midpoint method is a characterization method that provides indicators for comparison of environmental interventions at a level of cause-effect chain between emissions/resource consumption and the endpoint level. A systematic procedure for estimating the effects of choices made regarding methods and data on the outcome of the study (ISO 14044)
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Acronyms AoP CF ELCD EPLCA GWP ILCD LCI LCIA LUMP SETAC UNEP
Area of Protection Characterisation Factors European Reference Life Cycle Data system European Platform on Life Cycle Assessment Global Warming Potential International Life Cycle Data system Life Cycle Inventory Life Cycle Impact Assessment Land Use Modelling Platform Society of Environmental Toxicology and Chemistry United Nations Environment Program
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1 Introduction Reducing the environmental impacts of EU production and consumption is one of the main goals of the Roadmap to a resource efficient Europe (EC, 2011a). More specifically, the Roadmap states that “….by 2050, the EU economy shall have developed in such a way as to accommodate resource constraints and planetary boundaries…” and that “…also impacts related to the EU economy which do not take place in the EU boundaries will be assessed….”. Such ambitious objectives require the development of a common, internally consistent framework for integrated sustainability assessment of European Union policies; this framework should accommodate not only a spectrum of sustainability indicators, but also the capacity to assess each indicator against defined sustainability thresholds and targets. In response to policy needs of the Roadmap to a resource efficient Europe, the JRC-IES has developed a framework of life-cycle based indicators, with the aim of quantifying the overall environmental impact potential of production and consumption in the EU-27, while taking into account traded commodities (EC, 2012a, 2012b and 2012c). The methodology combines territorial statistics on emissions and resource extraction with and trade statistics and product life-cycle inventories, in order to assess the impacts associated with the ‘apparent consumption’1 of the EU economy. The overall framework is depicted in figure 1. Life Cycle Assessment (LCA) 2 is adopted as methodology in different steps: (i) in the evaluation of the impacts associated with import and export as this is based on product LCA for representative products, properly scaled up (detailed explanation in EC, 2012a); (ii) in the evaluation of domestic (EU territory) impacts, associated to domestic resource and emission, are assessed through life cycle impact assessment (LCIA) models.
Figure 1. Environmental impact associated with EU27 apparent consumption (EC, 2012b)
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The impacts associated with the imported products are summed to the impacts taking place within the EU27 boundaries, minus the impacts associated to the exported products. APPARENT CONSUMPTION = IMPORTS + TERRITORIAL - EXPORTS 2 Life Cycle Assessment is a methodology for integrated impact assessment in which the impact associated to the life cycle of product are assessed from raw material extraction to manufacturing, transport , retailing, up to end of life. LCA is based on four steps: (i) goals and scope definition- in which system boundaries and unit of the analisys are set-; (ii) life cycle inventory- the collection of all elementary flows of input and output of the system in terms of resource used and emission; (iii) life cycle impact assessment- the assessment of the impact assocaited to the flows in the inventory, covering a wide variety of environmental impact categories ( e.g. climate change, ecotoxicity, euthrophication etc); and (iv) interpretation.
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1.1 LC indicators as ex-post methodology for macro-scale analysis The life cycle inventories (LCI) developed at country and EU-27 scales are composed by the emissions and resource extractions taking physically place within the EU27 boundaries (territorial) and those emissions and resource consumption associated to imported and exported products (trade). The nomenclature of the elementary flows is consistent to the nomenclature of the (EC, 2010a) and the environmental impacts are assessed by following the impact assessment methods recommended within the ILCD (EC, 2012d). The adoption of such framework has several advantages and limitations (see EC, 2012a) and one of its most relevant pros is that such macro-scale estimation makes use of the same impact assessment methods generally applied at micro-scale assessments (products). Such characteristics allows for comparability across scales (i.e. from products to countries) in assessing environmental impacts. For this reason, the dataset has been recently used in the context of the calculation of normalization factors (EC, 2013a) for the Product Environmental Footprint (EC, 2013b). Normalisation factors reflect the magnitude of environmental impact by impact category and are the results of the multiplication of the emissions occurring by the associated characterization factors. According to the ILCD handbook - general guide for Life Cycle Assessment (EC, 2010a), the decision-context of the LC indicators belongs to ‘Situation C’, meaning that the LC indicators mainly aim at “accounting” for environmental impacts without implying an explicit decision context that accounts for potential consequences of choices on other systems. Despite of its relevance in ex-post monitoring, the methodology in its present form cannot be used for assessing consequences of changes in production or consumption patterns within the EU as it does not build on a modelling framework that would allow for consequential analysis and, more general, for future studies.
1.2 LC Indicators: from ex-post to ex-ante methodology for macro-scale consequential analysis Economy, population dynamics and, more specifically, production and consumption patterns have been driving, in modern societies, the consumption of resources such as energy, water, land and raw materials along with emission of pollutants to air, soil and water. Modelling the dynamics and interconnections between the driving forces and their effects on the system is considered a valuable step towards the inclusion of future effects into current policy making, as demonstrated e.g. by the attention posed to the future climate, in the framework of global change. According to the European Environment Agency (EEA) “environmental scenarios, outlooks and other types of forward studies help us to address discontinuity and uncertainties of future developments and to design robust policies that can withstand the test of time” (EEA, 2010). In order to move from an ex-post to an ex-ante methodology, the LC Indicators should be coupled with a macroeconomic model as well as with a bio-physical model spatially resolved. On that basis, a set of environmental indicators could be calculated consistently to the impact assessment methods used in life cycle assessment, in order to keep the consistency between macro and micro scale assessments. Such integration would allow for consequential analysis and future oriented studies, nevertheless, this integrated platform would allow for sound scenario building and impact assessment of policies on production and consumption, while explicitly (spatially) identifying distributional issues. Three main elements can be potentially linked together in the framework of the LC Indicators: a macroeconomic model, the Land Use Modelling Platform (LUMP) (EC, 2013c) the life cycle impact assessment methods (EC, 2012d). This document has the purpose of defining the framework for the integration of the LC Indicators with dynamic models, both spatially resolved and not, to the purpose of contributing to the development of an Integrated Sustainability Assessment Platform (ISAP). In the following sections a description of the main elements of this framework is provided as well as discussed. As example of the potential link and mutual benefit between LCA and 9
LUMP, the last section of the document is dedicated to the refinement of an impact assessment model (Pfister et al., 2009) for the assessment of water scarcity, on the basis of the outputs of the LUMP.
2 Key elements of the LC Indicators ex-ante modelling framework 2.1 Life cycle-based integrated economic-environment modelling As reported within the ILCD Handbook (EC, 2010a) one of the fields of application of LCA is the context of ‘Meso/macro-level decision support’, beyond the most common micro-level applications (i.e. products). Such macro-scale decision context is defined as ‘Situation B’ within the ILCD Handbook (EC, 2010a) and refers to the application of life cycle assessment for supporting decision making in the context of decisions which can have large-scale structural effects, both mid-term and long-term, mediated via market and non-market mechanisms (e.g. due to bio-physical constraints). This is, for instance, the case of new policy implementation which impact on pervasive technologies (i.e. raw material strategies), as well as the development of new technologies in key economic sectors. As reported by De Camillis and Pennington in the document ‘Sustainability assessment of future-oriented scenarios: a review of data modelling approaches in Life Cycle Assessment’ (EC, 2013d), such context is consistent with the definition provided by UNEP/SETAC Life Cycle Initiative (2011), of “Consequential LCA” (C-LCA), aimed “to provide information on the environmental burdens that occur, directly or indirectly, as a consequence of a decision (usually represented by changes in demand for a product)”. The consequential approach is also called “changeoriented approach”. As reported by (EC, 2013d), two main families of life cycle thinkingbased approaches are generally used for assessing future-oriented scenarios: process-based LCA and environmentally-extended input output analysis (EEIOA), which can be considered, respectively, bottom-up and top-down approaches. The following two sections provide an overall description of the methodologies, highlighting how the methods can be used in the framework of the LC Indicators modelling, along with the main limitations. 2.1.1 Bottom-up, consequential LCA An in-depth review of the state of the art on approaches to consequential ‘bottom-up’ LCA is provided by Maravuglia et al. (2013), with particular reference to the case of bio-energy production. According to the authors, three typology of modelling approaches for C-LCA can be found: partial equilibrium models, computable general equilibrium models as well as simplified approaches. Each of the methods has its own drawbacks and limitations on the assessment of the indirect effects to be included in C-LCA, as discussed by the authors. Among the relevant examples provided by Maravuglia et al. (2013), the approach developed by Dandres et al. (2011) and subsequently refined in Dandres et al. (2012) in the case of the European Union bioenergy policy is taken as representative as possible methodology to be applied for linking the LC Indicators with macroeconomic modelling. In their first attempt Dandres et al. (2011) have used the economic general equilibrium model GTAP (Hertel, 1997) to estimate global economic perturbation that would be caused by two different European energy policies (bioenergy policy and business as usual policy), by linking it to an life cycle inventory dataset and assessing the impacts associated to the policy through LCIA method Impact2002+ (Jolliet et al., 2003). In particular, as reported by the authors, the workflow consisted of the following steps: 1. Simulating changes in European electricity system and evolution of world economy are used through the GTAP macroeconomic model GTAP (Hertel, 1997); 2. GTAP database is mapped to public databases in order to compute the mass of commodities produced by each economic sector and energy generated in 2005;
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3. GTAP database is mapped to Ecoinvent v.2 LCI database3 as to establish the emissions and extractions from ecosystems caused by each economic sector; 4. Impact2002 + method is applied to compute potential indirect environmental impacts; 5. Direct environmental impacts are assesses directly from European electricity generation according to LCA method with Ecoinvent database and Impact2002 + method. In their subsequent paper (Dandres et al., 2012), the same authors have adopted a more refined approach to the same case study, called macro life cycle assessment (M-LCA). In both workflows the indirect impacts are calculated via running the macroeconomic model GTAP, estimating variations in masses of produced commodities by sector on the basis of the respective changes in gross value added (GVA) of the economic sectors, linking the sectors to the Ecoinvent via representative commodities, and finally using life cycle impact assessment methods to assessing direct and indirect environmental impacts. Such procedure, summarized in figure 2, suffers from methodological drawbacks and strong assumptions. Beyond the general limitations of computable general equilibrium models, additional limitations arise from the matching between the GTAP and the Ecoinvent database. In order to avoid double counting, the authors had to segment the LCI database by allocating processes to specific sectors.
Figure 2. Workflow for linking macroeconomic model GTAP to the Ecoinvent LCA database (Dandres et al., 2012)
In fact, as reported by Maravuglia et al. (2013), such operation is not straightforward because there is not exact correspondence between the GTAP and the Ecoinvent databases and many processes are not specific of unique sectors. Other issues highlighted by the authors are: the lack of accuracy in modelling the land demand and use within the GTAP and the Ecoinvent as well as the uncertainties associated with the bottom-up mapping of sectors and the lack of regionalized LCI. In addition, the mapping between the variation in GVA and the variation in production within the economic sectors could result in a very strong assumption, due to the volatility of prices, in the long run. This aspect should be better investigated by checking the consistency of such assumptions through means of viability and suitability analysis as proposed by Giampietro et al. (2009). Despite such limitations, this seems to be a viable strategy to perform a linkage between the LCI and economic sectors modelling. Some of the limitations in modelling the direct and indirect effect in land use could be addressed by further integrating the models with the LUMP, at least for assessing land use changes at European scale. 3 http://www.ecoinvent.org/database/ecoinvent-version-2/
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2.1.1.1 The potential role of the ILCD Data Network
As documented within the Roadmap for the European Platform on Life Cycle Assessment (EC, 2013e), the Data Network on life-cycle could represent in the near future a relevant source of life cycle inventory datasets to be used for estimating the demand of resources and the related emissions associated to economic sectors. By assessing the life-cycle of the representative goods produced within economic sectors and combining them with statistics on production and trade (PRODCOM4), it could be possible to estimate the inventory associated with a specific sector. On that basis it would be possible to use such datasets for bottom-up consequential assessments. However, this approach suffers of the same issues discussed for the consequential LCA which entails the use of LCA datasets mapped into representative products which map to sectors. In particular, there is a concrete risk of double-counting of environmental impacts if the dataset coming from the LC inventories are not separated into single processes. And, anyhow, this mapping operation is not straightforward, as many processes are found in several different sectors (e.g. production of thermic energy). Another limitation can be represented by the limited consistency between statistics nomenclature and the nomenclature of the ILCD datasets. To this purpose a preliminary analysis on the consistency of the nomenclature for plastic products has been carried out. 2.1.1.1.1
Preliminary analysis on plastic products -Sector NACE 20 ‘Other chemicals’
In order to check the degree of consistency between the dataset name of the ILCD data sets and the statistical definition of the product reported within the PRODCOM database, a case study was carried out. The plastic products group within sector 20 - ‘NACE 20 – Other chemicals’ were selected and compared to the LCI datasets on plastics products available within the ILCD Data Network provided by PlasticsEurope5. As a result, 25 to 54 out of 369 plastic products had an associated LCI dataset and they cover from 19.4% up to 33.6%, of the overall plastics sold (by mass) of the EU27 market for 2011. This means that that the coverage of the LCI datasets for plastic products available within the ILCD DN is relatively low, also due to nomenclature inconsistency. Hence, further LCI datasets on products missing from the ILCD DN should be gathered in order to fill the gap and to allow a finer representation of that economic sector. 2.1.2 The Environmentally Extended Input Output Tables (EEIOT) Another possible way to connect the LC Indicators framework to the macroeconomic dimension is through EEIOT. In the document on “Sustainability assessment of futureoriented scenarios: a review of data modelling approaches in Life Cycle Assessment” (EC, 2013d), Andreoni and Suh have provided an overview on EEIOT, introducing the methodological and operational features of this approach. As reported in the document, “The input-output modelling approach consists of a system of linear equations, each one of which describes the distribution of an industry’s product through the economy.” (Miller and Blair, 2009). Such system of equations can be extended by assuming flows of resources and emissions associated to the economic activity of the sector. According to De Camillis and Pennington (EC, 2013d), “EEIO tables and models are based on a comprehensive accounting framework covering all economic activities. EEIO tables bring together economic and environmental data in a consistent, related sectorial framework. EEIO models based on them allow for analysing such data via a great variety of cross-sections of the economic system, such as the product perspective, or a sector perspective.”
4 PRODCOM provides statistics on the production of manufactured goods. The term comes from the French "PRODuction
COMmunautaire" (Community Production) for mining, quarrying and manufacturing: sections B and C of the Statistical Classification of Economy Activity in the European Union (NACE 2) http://epp.eurostat.ec.europa.eu/portal/page/portal/prodcom/introduction 5 PlasticsEurope - http://www.plasticseurope.org/
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According to Reimann et al. (EC, 2010b), “hybrid approaches (to LCA) combine the scope of the economy-wide EIO-LCA model with the detail of process analysis. While process models improve and extend the possibilities for analysis, EIO-LCA simplifies the modelling effort and avoids errors arising from the necessary truncation or boundary definition for the network of process models.” However, as explained in EC (2010b) and (EC, 2013d), the extension of the economic input-output tables to environmental ones makes the assumption of proportionality of economic flows with environmental effects. This is because of the fact that the relations among economic sectors are described in terms of economic flows (i.e. gross value added) exchanged among sectors, and the environmental impacts related to consumption and production are modeled through linear coefficients . Such assumption limits indeed the resolution of the analysis and introduces a strong limitation in the estimation of the actual environmental impacts associated with sectors, as the environmental ‘burden’ is allocated to economic exchanges and, as pointed out by Giampietro et al. (2009), flow/flow ratios (e.g. GWht/euro) are not good proxies for estimation as they are volatile by definition, not being referred to any external referents (e.g. the size of the system under analysis, expressed in hours of human activity).
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2.2 Dealing with the spatial dimension 2.2.1
The Land Use Modelling Platform
The Land Use Modelling Platform (LUMP) (EC, 2013c) “was developed in order to provide projected land use maps at a detailed geographical scale (100m2, regional or country level), translating policy scenarios into land-use changes such as afforestation and deforestation; pressure on natural areas; abandonment of productive agricultural areas; and urbanization. Furthermore, indirect impacts can be assessed through indicators, such as changes in wateruse and landscape morphology. This modelling platform integrates data from sector-specific models in order to resolve what can often be conflicting land use claims. The solution given by the land use model is based on a number of criteria, namely land claimed per sector given by specialized models such as agriculture or energy models; land suitability to host given land use classes; accessibility to transport hubs; policy-driven restrictions and subsidies; as well as planned transport infrastructures.” Such modelling framework has been recently used for developing a Reference scenario, consistent to what defined in the Energy Trends to 2030 publication by DG ENER and DG CLIMA (EC, 2009) and the Impact Assessment, annex to the Energy Roadmap 2050 (EC, 2011b), as well as the Roadmap itself (EC, 2011c), assuming full implementation of the Climate and Energy package. In that context, a large number of thematic and land-use based indicators (e.g. Water Demand – Use, Biomass demand & supply, Green Infrastructures, Ecosystem Services, Land-use/cover allocation, Exposure to Climate Change) were assessed on the basis of the goal of the Climate and Energy package.
Figure 3. Land Use Modelling Platform, main components (Lavalle, 2013)
2.2.2
An operational framework for integrating LUMP and LCIA indicators
The LUMP modelling framework is dynamic by nature and thus is perfectly suited for scenario building and policy impact assessment. Currently, under the reference scenario implemented within the LUMP (EC, 2013c), a set of models estimate the trends in driving forces up to the year 2030 for the EU28, including projected sectorial economic performance within the EU economy. These models’ outputs are then combined together in order to estimate land use claim and these claim are reflected in the changes in land uses. Such modeled change in land use determines a demand in local natural resources such as 14
water and biomass, affecting then the quality of the environment. The same holds for what concerns emission of pollutants, with both local and global effects. Indeed, the outputs of the LUMP could be used for assessing the environmental impacts occurring within the EU boundaries by applying life cycle impact assessment methods recommended within the ILCD, to provide an overall assessment of the environmental impacts taking place within the EU. More wisely, as proposed by Loiseau et al. (2013), spatially explicit information can be used for assessing the environmental impacts associated with both production and consumption patterns occurring in territories. In table 1, the main characteristics of the domestic production and domestic consumption accounting frameworks are defined. Accounting framework
Purpose
Perspective allocation
Production
Consumption
Assessing environmental impacts of production patterns at territorial scale (i.e. industry, agriculture, transport, Energy & Mining, services, households)
Assessing environmental impacts at territorial scale of consumption (i.e. households)
and Producers are responsible for the Consumers are responsible for the direct emissions and resources consumed within the production process. Allocation is done on a mass basis
direct and indirect environmental impacts caused by the consumption of the product, along all the life cycle phases
Bottom-up modelling of economic sectors through LCI datasets;
Modelling of basket-of-products through LCI datasets;
LUMP outputs (Land Uses and Functions)
LUMP outputs (Land Uses and Functions)
Unit of analysis
NUTS2, NUTS1, EU, by sector or group of sectors (u.m.: hectars, total hours worked)
NUTS2, NUTS1, EU, by consumer groups (u.m.: hectars, total nonworking hours, body weight)
Spatial downscaling
downscaling of local resource demand (water, biomass) according to land use proxies (e.g. industrial land use)
downscaling distribution
Modelling framework
of
population
Table 1. Accounting frameworks for territorial environmental assessment in LCA
However, as reported by Loiseau et al. (2013), this approach suffers from several methodological bottlenecks among with the definition of the functional unit (land use functions), the attribution of responsibility of environmental impacts (boundary) as well as the issue of double-counting seem to be the most relevant ones. Moreover, it has to be considered that the quality in the estimation of the life cycle inventory associated to product, thus sectors, resides in their technological and geographical representativeness of the processes involved in production. Time-representativeness is an issue that per-se is already included within the technological representativeness, as technologies evolve over time. Using average representative values for macro-regions can lead to high inaccuracies threatening the robustness of the outputs. For this reason a probabilistic framework in the estimation assessment is envisaged. Nevertheless, the integration between LCIA and LUMP methods is seen as a viable option for developing a coherent framework for spatially-based territorial inventory aimed at representing emissions and resources consumption patterns occurring within the EU28 and allowing for spatial differentiation of emission factors and impacts, as well as for the use of LCIA spatially resolved models for estimating environmental impacts through spatially resolved LCIA.
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2.3 Life Cycle Impact Assessment and related indicators 2.3.1
Environmental impact assessment in LCA
Overall, the reason why LCA is recognized to be of help in assessing environmental impacts is because of its inclusive methodological framework which aims at assessing the potential impacts to the human health, natural environment and natural resource depletion (called areas of protection in LCA context), while addressing both the global and local dimension of impacts. Nowadays, at EU level, the ILCD Handbook on Life Cycle Impact Assessment has recommended a set of models for assessing at midpoint and at endpoint the impacts associated to 14 impact categories. The models recommended for midpoint are reported in table 2. Impact category
Recommended default LCIA method
Indicator
Class
Climate change
Baseline model of 100 years of the IPCC
Radiative forcing as Global Warming Potential (GWP100)
I
Ozone depletion
Steady-state ODPs 1999 as in WMO assessment
Ozone Depletion Potential (ODP)
I
cancer
USEtox model (Rosenbaum et al, 2008)
Comparative Toxic humans (CTUh)
Unit
for
II/III
non-
USEtox model (Rosenbaum et al, 2008)
Comparative Toxic humans (CTUh)
Unit
for
II/III
Respiratory inorganics
RiskPoll model (Rabl and Spadaro, 2004) and Greco et al 2007
Intake fraction for fine particles (kg PM2.5-eq/kg)
I
Ionising radiation, human health
Human health effect model as developed by Dreicer et al. 1995 (Frischknecht et al, 2000)
Human exposure relative to U235
II
Ionising ecosystems
No methods recommended
Human effects
toxicity,
Human toxicity, cancer effects
Photochemical formation
radiation, ozone
efficiency
Interim
LOTOS-EUROS (Van Zelm et al, 2008) as applied in ReCiPe
Tropospheric concentration increase
ozone
Acidification
Accumulated Exceedance (Seppälä et al. 2006, Posch et al, 2008)
Accumulated Exceedance (AE)
II
Eutrophication, terrestrial
Accumulated Exceedance (Seppälä et al. 2006, Posch et al, 2008)
Accumulated Exceedance (AE)
II
Eutrophication, aquatic
EUTREND model (Struijs et al, 2009b) as implemented in ReCiPe
Fraction of nutrients reaching freshwater end compartment (P) or marine end compartment (N)
II
Ecotoxicity (freshwater)
USEtox model, (Rosenbaum et al, 2008)
Comparative Toxic ecosystems (CTUe)
II/III
Ecotoxicity (terrestrial and marine)
No methods recommended
Land use
Model based on Soil Organic Matter (SOM) (Milà i Canals et al, 2007b)
Soil Organic Matter
Resource depletion, water
Model for water consumption as in Swiss Ecoscarcity (Frischknecht et al, 2008)
Water use related scarcity of water
Resource depletion, mineral, fossil and 6 renewable
CML 2002 (Guinée et al., 2002)
Scarcity
Unit
for
II
III to
local
III
II
Table 2. ILCD Recommended methods and their classification at midpoint
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Depletion of renewable resources is included in the analysis but none of the analysed methods is mature for recommendation 16
Beyond its traditional application at products scale the life cycle impact assessment methods have been used also for evaluating the consequences of changes at different geographical and economic scales, both for assessing the impacts associated with production, as in the case of the ‘Life cycle indicators – resources’, and consumption, as in the case of the’ LC indicators – basket of products’ (EC, 2012c). Nevertheless, life cycle assessment has been identified by Loiseau et al. (2012), as a promising methodology to perform environmental assessment at a meso-level (i.e., territories) also in the context of ex-ante environmental assessments such as the strategic environmental assessment (EU, 2001) although several bottlenecks of such application can limit the use of such framework in meso-scale analysis as suggested by Loiseau et al. (2013). 2.3.2
Spatially resolved impact assessment methods within LCA
As previously mentioned, within the ILCD Handbook (EC, 2011d), a set of impact assessment methods has been recommended to be adopted within LCIA. Traditionally, LCA has been developed as a site-independent methodology. Only in recent years, some development has been performed toward spatially resolved methods. However, very often the impact assessment methods - for both mid-point and end-point indicators -developed in LCA do not take into consideration the relevance of spatial differentiation on a finer scale than country. This limitation is reflected also within the ILCD recommended methods, as only three impact categories have spatially resolved factors, namely: water resource depletion, terrestrial eutrophication and acidification. The resolution of the characterization factors is the country scale. As a matter of fact, in order to properly assess environmental impacts it would be beneficial to account for spatial differences also for ecotoxicity, human toxicity, aquatic eutrophication, ionizing radiation and photochemical ozone formation, as implemented in by Sleeswijk and Heijungs (2010), Gandhi et al. (2011), Van Zelm et al. (2008), Preiss and Klotz (2008) and Gallego et al. (2010). Also the land use impact category should be made spatially resolved as well. Of course, regionalized CFs related to impacts are only half the picture – life cycle inventory data should also be provided on a regional or local level if the full potential of LCA is to be regionalized. The debate is ongoing as to whether the exclusion of spatial information in applications such as LCA may give misleading results, which can influence decisions on the environmental risk and performance of products (e.g. Pennington et al., 2005, Armitage et al., 2007, Sala et al., 2011). Contrary to the global drivers of environmental impacts, such as climate change and ozone depletion, the need to have spatially differentiated models for regional/local impacts (Huijbregts et al., 2003, Geisler et al., 2004) has been recognised following evidence that differences in fate and exposure mechanisms and differences in effects due to sensitivity and background levels can vary significantly depending on the geographical context (Udo de Haes, 2002). Indeed, it is argued that results could present high variability among different models and could sometimes be highly sensitive to spatial differentiation. This is due to a complex interaction between emissions’ location patterns, chemical properties, and landscape parameters. A complete assessment of the spatial differentiation of impact categories- where a “fate component” has to be taken into account- requires at least two levels of analysis (Sala et al., 2011): assessment of spatial distribution (the range of potential environmental geographical distribution of a the substance) in order to understand at which scale a this is typically distributed (local, regional, global, etc.) and assessment of spatial variability (the variability of the distribution and fate of the substance for various scenarios, countries, continents). To date, several spatially distributed fate and transport models e.g. of chemicals (i.e. models that allow for spatially explicit assessment of contaminants from a given spatial distribution of emission) have been developed at various resolutions (e.g. Woodfine et al., 2001, 17
MacLeod et al., 2001, Sweetman et al., 2002, Toose et al., 2004, Pennington et al. 2005, Luo et al., 2007, Wegener Sleeswijk and Heijungs, 2010). These models allow the distribution and fate of chemicals to be assessed in the environment after their emission, based on their chemical (viz. physical chemical) properties and the landscape-characteristics of the emission compartment. It has to be noted that parameterization of spatially resolved multimedia models is usually based on scenarios of an evaluative environment, or on geographical resolution related to administrative boundaries (e.g. countries/continents) or landscape-based areas (e.g. watersheds, eco-regions). Indeed, one of the main challenges of spatially resolved characterization factors is related to identify, if possible, a common resolution for reporting the results. Actually, this is far from reality. Indeed, resolution of impacts affecting ecosystem and human health are very different and, additionally, some impacts are considered global (e.g. those related to climate change) and other has a high relevant at local scale (e.g. human toxicity related to particulate matter inhalation). For each impact category, three scales could be identified:
Optimal: the scale at which the model gives the best results in terms of reducing uncertainty
Relevant: the scale at which the uncertainty are partially reduced but the most relevant impact are addressed
Feasible: the scale at which is feasible collecting data at the inventory level, reducing the differentiation in data demanding
In fact, the parameterization of spatially resolved models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. Besides, some key questions for the development of spatially resolved characterization factors are: which is the optimal/feasible resolution (local, regional, country, continent, global)?; which model is comprehensive enough to explore completely spatial variability?; How to properly address uncertainties related to spatial (temporal) differentiation? Unfortunately, asking a practitioner to address spatial differentiation has important drawbacks in terms of workload (e.g. input data to be provided) and computational capacities. For this reason, multimedia models adopted so far are mainly simplistic box models in which the concept of spatial differentiation is based on scale and resolution (cell, country, and basin). This approach may reduce the uncertainty of the assessment depending on the extent to which the “box” is really representative of the removal/transport processes. However it has been proven that this is not necessarily the case, and that proper emission archetypes (i.e. the geographical unit showing similar behavior in the removal rates of a chemical, suitable for building realistic scenarios of chemical distribution in the environment) should be used to simplify the model (Gandhi et al. 2011). The choice of the most appropriate scale and scenario is a prominent step from a management perspective, as a trade-off should be identified between an extremely simplified approach and computationally complex multimedia models. Therefore, three main approaches for integrating spatial differentiation may be envisaged:
box models, evaluative environment properly refined to capture variability of sensitive parameters
fully spatially resolved models, computationally intensive but able to reduce uncertainty 18
archetypes, as trade-off between the two aforementioned possibilities.
The identification of suitable archetypes requires a thorough understanding of how the removal process is represented in the model and particularly of the main factors influencing the process. Global sensitivity analysis can be used to identify the factors playing the main role in determining the value of the model output (Ciuffo et al 2012). A recent application of sensitivity analysis for building climate based archetypes for chemicals has been proposed by Ciuffo e Sala, 2013 highlighting that: global sensitivity analysis can support identification of emission archetypes; climate-based archetypes can mimic spatial variability in multimedia models better than those based on administrative boundaries (e.g. countries/ regions); simplified archetype-based models can increase reliability of results.
3 Mutual synergies between LCA and LUMP for impact assessment: a case study on water. This section has the purpose of presenting how the LUMP outputs can be used to refine the existing impact assessment methods used in the context of life-cycle impact assessment of water scarcity. A new set of characterization factors (CFs) of water stress has been calculated to this purpose on a spatially resolved basis (catchments) at country scale in Europe. The methodology proposed by Pfister et al. (2009) has been adopted by using data on water consumption and hydrologic availability coming from the LUMP. The results have been compared to the values provided by Pfister et al. (2009) at country scale as well as to the values calculated by Frischknecht et al. (2008), suggested within the ILCD (EC, 2011d; EC, 2012e).
3.1 Methodology According to Pfister et al. (2009), the stress associated to the extraction of surface waters in river catchments can be expressed through an index of stress (Water Stress Index) and this value can be used as characterization factor (CF) in LCA to assess the impact associated with the consumption of 1 m3 of surface water resource, at mid-point level. The WSI is defined in Eq. 1 as a logistic function of the multiplication between the withdrawal-to-hydrologic availability index (WTA) (Eq. 3) and a variation factor (VF) representing monthly and yearly variability observed in precipitation (Eq. 4). WTA* is defined according to Eq. 2 and it expresses the ratio of the total withdrawals of surface water over the hydrologic availability of water in that specific catchment adjusted by the variation factor. VF values at catchment scale are estimated through a weighted average of the values observed within the catchment (Eq. 5). According to Pfister et al. (2009), watersheds with strongly regulated flows (SRF) have storage structures weaken the effect of variable precipitation significantly; hence the effect of VF is less relevant (see Eq. 2), but may cause increased evaporation and reduction of water resource availability.
Eq. 1
Eq. 2
Eq. 3
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Eq. 4 Eq. 5
Where: WSI: is the Water Stress Index of a given catchment WTAi : is WTA in watershed i and user groups j are industry, agriculture, and households VF: is the variation factor estimated as combination of the standard deviations of the precipitations (monthly S*month and yearly S*year) SRF are watersheds with strongly regulated flows as reported by Pfister et al. (2009) on the basis of Nilsson et al. (2005). VFws: is the variation factor calculated for a given watershed ‘ws’ P = rainfalls
In this assessment the same methodology as the one proposed by Pfister et al. (2009) is adopted for calculating the WSI values for 30 European countries (EU28 + 2), with few methodological exceptions. These exceptions are: the calculation of the VF factor, as there is no differentiation between SRFs and non-SRF, and the introduction of a rescaling factor for VF values. As the supporting information provided in Pfister et al. (2009) do not specify the time scale on which to calculate the VF factors (e.g. days, weeks, months or years), in this assessment a monthly scale has been adopted and, additionally, a linear rescaling factor has been introduced in order to allow for comparability among the estimations carried out by Pfister et al. (2009). The average WSI values (WSI_LUMP) are calculated at country scale on the basis of the water statistics in output from the LUMP platform. The WSI_LUMP figures have been calculated within the ArcGIS 10.1 ESRI® environment by combining the WEI and VFs maps. The VFs figures have been calculated in Matlab MathWorks® environment for each grid-cell of the precipitation dataset (Haylock et al., 2008). Pfister et al. (2009) have estimated WSI for 10’000 individual catchments by using the outputs of the WaterGAP2 global model (Alcamo et al., 2003), whereas in this assessment the WTA* values has been calculated by using the results of the LUMP. In particular, the Water Exploitation Index (WEI) was estimated by Vandecasteele et al. (2013), and subsequently refined in Vandecasteele et al. (2014), in the framework of the supporting document to the EU Blueprint to Safeguard Europe’s Waters (EC, 2012f), for the year 2006. The WEIabs values are computed according to Eq. 6, whereas WEIcons figures are computed according to Eq. 7, as described in (EC, 2012f). WEIabs is hence conceptually identical to the Withdrawals to hydrologic-to-availability index used in Pfister et al. (2009) and the WEI values can be used instead. Both the WEI and the WTA are based on hydrological models’ outputs which have been calibrated on the climate normal period (1961 – 1990). In this assessment the data on precipitation are taken from Haylock et al. (2008) and refer to the same time frame. Eq. 6
WEIabs = abstraction / (external inflow + internal flow)
Where: WEIabs: Water Exploitation Index based on water abstraction Internal flow: net generated water (rainfall – evapotranspiration + snowmelt) External inflow: inflow from upstream areas Abstraction: water abstraction in the region
Eq. 7
WEIcons = (abstraction – return flow) / (external inflow + internal flow)
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Where: WEIabs: Water Exploitation Index based on water abstraction Internal flow: net generated water (rainfall – evapotranspiration + snowmelt) External inflow: inflow from upstream areas Abstraction: water abstraction in the region Return flow: water abstraction minus water consumption in the region
The results of both of the WSI_LUMP values (withdrawal and consumption) estimated at country scale are then compared to characterization factors for water depletion reported by Pfister et al. (2009) and to the ones developed in the Eco-scarcity methodology (Frischknecht et al., 2008). The Eco-scarcity is the method suggested within the ILCD Handbook recommendations on impact assessment methods (EC, 2010a) developed by the JRC-IES and it is based on the withdrawals-to-availability ratio. In order to compare the characterization factors a MIN-MAX normalization (EC-OECD, 2008) has been applied, so to normalize in a range from 0 (min) to 1 (max) and allowing for comparability among different unit of measurement. Such comparison allows to evaluating differences and similarities between such characterization factors which are used in the context of life cycle impact assessment. These results will be used to improve the ILCD Handbook recommendations on impact assessment methods (EC, 2010a).
Figure 4a. Variation factors (VF) for precipitation, as estimated according to Eq. 4 on the basis of the dataset provided by Haylock et al. (2008) for the normal climate period (1961-1990). Estimations for Turkey are currently under revision.
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Figure 4b Variation factors (VF) for precipitation, as estimated according to Eq. 5 on the basis of the dataset provided by Haylock et al. (2008) for the normal climate period (1961-1990). Estimations for Turkey are currently under revision.
3.2 Results and discussion The preliminary results of the estimations of WSI and WSI_LUMP_abs and WSI_LUMP_cons at country scale are reported in figures 4, 5 and 6; whereas in figure 7 the WSI figures calculated by Pfister et al. (2009) are reported. In figure 8 is presented a scatter plot of WSI and WSI_LUMP_abs results and the normalized CFs values are reported in figure 9 for comparison. The results of the VF factor representing the variability of the precipitation patterns are overall consistent with what reported by Pfister et al. (2009) for most of Europe with exception of Turkey. These differences are probably due to both the different datasets used within the methods and the coefficient of correction that has been introduced so to allow for comparability between the outputs and the VF values estimated by Pfister et al. (2009). The introduction of such re-scaling coefficient was needed for comparability as the VF is not independent to the unit of measurement, neither to the time frame of the analysis. A consolidated version of the VF values will be re-estimated so to improve the robustness of the figures. Overall, as it can be observed from the maps (figures 6, 7 and 8), WSI and WSI_LUMP_abs differ substantially both in absolute terms and in relative ranking among countries (figure 9). Moreover, as it is possible to see from figure 8, there is no significant correlation among the two datasets. The results on WSI_LUMP_abs show that the highest stress on water resources at country scale is occurring in Hungary which is followed in turn by Spain, Italy, Belgium, Romania, Bulgaria, Greece and the Netherlands. On the contrary, according to Pfister et al. (2009) the highest values of water stress at country scale are observed for Turkey, Greece, Spain and Belgium, followed by Italy, Portugal, United Kingdom, Bulgaria and Finland. The observed differences are probably due to both the VF values and the withdrawals-toavailability ratios. In both cases the statistical sources, as well as the underlying models used are different. In addition, it is reasonable to assume that there are outliers in the datasets and models used for such assessment which deeply affect the results both for WSI and WSI_LUMP_abs values, as it is hard to believe that Finland and the Netherlands are significantly affected by water stress issues. 22
It can be noted that the WSI estimated by Pfister et al. (2009) are, on average, higher than those estimated in this analysis for WSI_LUMP_abs. Again, this difference can be due to several factors, however, because of the fact that the WTA* and VF values are not directly available in Pfister et al. (2009), it is not possible to clearly identify the sources of such differences. Further refinements will better investigate the data sources of WTA* and VFs figures so to assess which are the key contributors to the differences observed. From the comparison of the normalized characterization factors (figure 9) it is possible to see that the results are extremely different among the methods, despite all these metrics belong to the same group of withdrawal-to-availability impact assessment methods, with exception of the WSI_LUMP_cons, as reported by Kounina et al. (2013). The differences observed for the Eco-points figures are due to the equation which defines the methodology rather than to the different sources of statistics. In fact, the calculation of the eco-points is based on the squared value of the withdrawal-to-availability ratio and it does not take into account climatic variability. Such approach, which partly reflects a general precautionary principle, attributes an excessive weight to the WTA (or WEI) ratio and then overestimates its relevance on the overall results, in comparison to the WSI and the WSI_LUMP_abs.
23
Figure 5a and 5b. Water exploitation index (WEI) estimated at country scale on the basis of overall withdrawals (a) and water consumption (b)
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Figure 6a and 6b. Water Stress Index WSI_LUMP values estimated on the basis of overall withdrawals (a) and consumption (b), preliminary results
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Figure 7. Average WSI values estimated at country scale reported in Pfister et al. (2009) dataset
Figure 8: Water Stress Index calculated at country scale, comparison between WSI and WSI_LUMP (withdrawals)
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Figure 9: Comparison among CFs, preliminary results. The Eco-factors have been normalized to the 0-1 scale through min-max normalization
Figure 10: WSI values at catchment scale calculated by Pfister et al. (2009)
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3.3 Conclusions As described in the previous sections, the ‘LC Indicators – Resources’ dataset combines together the impacts associated with the economic activities taking place within the EU boundaries and the impacts associated to trade. Indeed, being global exercise, a global method for assessing the impacts should be used instead of a regional one; otherwise a large share of the EU environmental burden could not be quantified. Even though, focusing on the impacts occurring at territorial scale within the EU is extremely meaningful, as the vast majority of those impacts take place within the EU boundaries if the ‘apparent consumption’ perspective is adopted (EC, 2013a). On the basis of the observed results it can be said that assessing water stress at country scale is not meaningful, as average national values hide local issues and can provide misleading figures. In fact, as it can be noted by comparing figures 7 and 10, regional figures vary sensibly within countries. The use of average country values leads inevitably to a loss in information on water stress variability over space, masking very critical local conditions which should be adequately reflected. For this reason, the use of regionalized impact assessment method is strongly recommended. Hence, it is of outmost relevance and priority for LCA impact assessment methodologies to deal with this issue, on the same line of what was developed by Pfister et al. (2009). The approach presented here allows to linking the outputs of the LUMP platform and to feed them into an impact assessment method with spatial resolution, going beyond the common methods which are based on country-scale characterization factors. 3.3.1
Further developments
Several possible developments of this approach are foreseen. First of all in very few LCIA methods for water scarcity assessment the temporal aspect has been assessed properly (Pfister et al., 2009; Pfister and Bayer, 2013). Seasonal variations dominate both the water demand and the hydrologic availability values, especially in livestock production and agriculture, as well as in households and industries. So far, no combination of such factors has been put together so to account for such variability; hence a further development of this work will look at these pictures. Additionally, it has to be noted that the effect of climate change could affect dramatically the availability of water resources to be used, creating competition in resource use. The linkage between the impact assessment method and the platform used for scenario analysis at EU level (LUMP) represents a powerful characteristic that could lead to the development of future-oriented impact assessment methods.
4 Acknowledgments The authors wish to thank Alessandra Bianchi for the work done in extracting the monthly and yearly precipitation maps which has been fundamental to the development of the case study.
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European Commission EUR 26453 – Joint Research Centre – Institute for Environment and Sustainability Title: Linking data and model outputs with local demand for resources and local emissions Author(s): Lorenzo Benini, Serenella Sala, Ine Vandecasteele Luxembourg: Publications Office of the European Union 2013 – 36 pp. – 21.0 x 29.7 cm EUR – Scientific and Technical Research series – ISSN 1831-9424 ISBN 978-92-79-35155-6 doi: 10.2788/61087 Abstract Reducing the environmental impacts of EU production and consumption is one of the main goals of the Roadmap to a resource efficient Europe (EC, 2011a). . Additionally, the need of integrating life cycle thinking in environmental and resource policies is increasingly mentioned as strategic asset for comprehensively capture environmental dimension of sustainability. Such ambitious objectives require the development of a common, internally consistent framework for integrated sustainability assessment of European Union policies. This framework should accommodate not only a spectrum of sustainability indicators, but also should demonstrate the capacity to assess each indicator against defined sustainability thresholds and targets. This document defines the framework for the integration of the Life Cycle Indicators (EC, 2012a, 2012b, 2012c) with dynamic models, both spatially resolved and not, to the purpose of contributing to the development of an Integrated Sustainability Assessment Platform (ISAP). Three main elements can be potentially linked together in the framework of the LC Indicators: a macroeconomic model, the Land Use Modelling Platform (LUMP) (EC, 2013c) and the life cycle impact assessment methods (EC, 2012d). A description of the main elements of this framework is provided as well as discussed. As example of the potential link and mutual benefit between LCA and LUMP, the last section of the document is dedicated to the implementation of an impact assessment model (Pfister et al., 2009) for the assessment of water scarcity, on the basis of the outputs of the LUMP.
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LB-NA-26453-EN-N
As the Commission’s in-house science service, the Joint Research Centre’s mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a cross-cutting and multidisciplinary approach.
ISBN:
978-92-79-35155-6
