Assessment of Life Cycle Impacts on Ecosystem Services: Promise

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Critical Review

Assessment of life cycle impacts on ecosystem services: promise, problems and prospects Benoit Othoniel, Benedetto RUGANI, Reinout Heijungs, Enrico Benetto, and Cees A.A.M. Withagen Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03706 • Publication Date (Web): 30 Dec 2015 Downloaded from http://pubs.acs.org on January 1, 2016

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Environmental Science & Technology

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Assessment of life cycle impacts on ecosystem services:

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promise, problems and prospects

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Benoit Othoniel,*,†,‡ Benedetto Rugani,† Reinout Heijungs,‡ Enrico Benetto,† and Cees

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A.A.M Withagen‡

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†

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& Innovation (ERIN) – 41 Rue du Brill, 4422 Belvaux, Luxembourg

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‡

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Boelelaan, 1081 HV Amsterdam, the Netherlands

Luxembourg Institute of Science and Technology (LIST) / Department of Environmental Research

Vrije University Amsterdam / Faculty of Economics and Business Administration – 1105 De

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ACS Paragon Plus Environment


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Abstract

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The analysis of ecosystem services (ES) is becoming a key-factor to implement policies on

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sustainable technologies. Accordingly, life cycle impact assessment (LCIA) methods are more and

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more oriented towards the development of harmonized characterization models to address impacts on

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ES. However, such efforts are relatively recent and have not reached full consensus yet. We

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investigate here on the transdisciplinary pillars related to the modelling of LCIA on ES by conducting

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a critical review and comparison of the state-of-the-art in both LCIA and ES domains. We observe

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that: current LCIA practices to assess impacts on ‘ES provision’ suffer from incompleteness in

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modelling the cause-effect chains; the multi-functionality of ecosystems is omitted; and the ‘flow’

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nature of ES is not considered. Furthermore, ES modelling in LCIA is limited by its static calculation

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framework, and the valuation of ES also experiences some limitations. The conceptualization of land

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use (changes) as the main impact driver on ES, and the corresponding approaches to retrieve

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characterization factors, eventually embody several methodological shortcomings, such as the lack of

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time-dependency and interrelationships between elements in the cause-effect chains. We conclude

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that future LCIA modelling of ES could benefit from the harmonization with existing integrated

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multi-scale dynamic integrated approaches.

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Graphical abstract

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1.

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Ecosystem services (ES) refer to the benefits provided to society by ecosystems that support human

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well-being.1 ES have thus an utilitarian and anthropocentric perspective, and aim to conceptualize the

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reliance of society’s development and welfare on the natural capital.2, 3 More specifically, ES are

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mainly meant to value ecosystems’ functions and their supporting biodiversity according to the

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benefits retrieved from using these functions.4, 5 From a sustainability viewpoint, maintaining ES

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supply or provision to society is a priori necessary, given that compensating the loss of some essential

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ES (e.g. soil fertilization or carbon sequestration) would imply tremendous costs that societies may

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not be able to cover.6 The analysis of ES is hence a key ingredient in the sustainability assessment of

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policy options and technological solutions. This includes cost-benefit analysis,7 green GDP,8 life cycle

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assessment (LCA),9 and more.

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In this article, we review the state of the art of life-cycle impact assessment on ES provision

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(primarily the characterization step). LCA is conceived here in the usual meaning of an ISO-

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standardized procedure for calculating the environmental impacts associated with a product over its

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entire life cycle, from raw materials extraction to final disposal, and including use and maintenance.10

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The existing literature on LCA in relation to ES can be roughly divided into two groups:

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Introduction

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General purpose guidelines and handbooks, such as ISO 14040 and 14044;11 ILCD

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guideline;12 ReCiPe 2008;13 IMPACT 2002+;14 CML;15 eco-indicator99.16 These do not

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generally incorporate the ES concept. The ILCD guideline does mention ecosystem functions

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and recommends to consider impacts on both the structure and functioning of the ecosystems

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under the Area of Protection (AoP) “Natural Environment”, but without recommending a

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specific methodology.

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Focused reviews and proposals. A first comprehensive review on the topic was published by

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Zhang et al. in 2010.17 Starting from the statement that no life cycle-oriented method was able

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to account for all ES at once, they reviewed pros and cons of different approaches on their 4


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capability to assess ES, including LCA, with the aim to integrate them into a unique

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accounting system. This resulted in the development of an “Ecologically-based LCA”18,

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which essentially combines economic input-output datasets with energy and cumulative

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exergy consumption values.19 However, their approach departs from traditional LCA, and, as

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a result, tends to be an independent method rather than an extension of LCA. The assessment

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framework recently proposed by Bakshi et al.,20 as a sort of follow-up, somehow anchors this

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separation from standardized LCA. Curran et al.21 also performed a critical review on the ES-

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LCA thematic, but narrowed to the modelling of impacts on biodiversity. They mainly argued

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that the functional and structural aspects of biodiversity are not enough considered compared

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to the compositional one, and, in compliance with Zhang et al.,17 acknowledged the need to

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evaluate life cycle impacts on the provision of services by ecosystems.

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Given this general interest in the ES concept, a consensus-building UNEP-SETAC Life Cycle

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Initiative22, as well as a few other research projects, recently focused on the development of spatially-

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explicit characterization models to assess land use-driven impacts on ES.23 This intensive research

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activity is fully acknowledged here for having introduced the study of ecosystems functionality into

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life cycle impact assessment (LCIA) characterization models. However, the developed framework still

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raises many issues and is not yet implemented in common LCA practices.

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The challenge to address is that ES have several characteristics that potentially conflict with the

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traditional development of characterization models:

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ES emanate from the spatially and temporally dynamic interactions between two different

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complex systems: human society and ecosystems, which together form so-called socio-

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ecological systems.24 In ecosystems, ES production is ruled by interlinked ecological

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processes, influenced by multiple environmental variables and happening at different scales,

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from micro to macro. In human society, ES use is influenced by multiple and diverse factors

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(technological, cultural or economic for instance). As a result, it is difficult to reduce ES

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provision to a straightforward mechanism. The definition and modelling of cause-effect

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chains (as required by the traditional logic of LCIA) from Life Cycle Inventory (LCI) 5



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stressors towards changes in the benefits they provide then becomes highly complex and often

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requires assumptions that tend to over-simplify the valuation of ES, thus losing the essence of

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ES assessment.

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ES vary over space and time, and their quantification and valuation are highly scale-

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dependent.25 Assessing impacts on their provision hence requires detailed knowledge about

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the location and time of occurrence of the impacting interventions, which is an information

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very often lacking in LCI.26 Again several assumptions can be made, but this raises the

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problem of modelling ES at a scale that is both relevant for their valuation and the spatial and

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temporal information associated to LCI flows.

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The ES concept, as mentioned in the beginning, is anthropocentric. As a result, their study

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implies modelling the societal mechanisms (e.g. economic or socio-cultural) that drive their

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actual use.3 This type of modelling is usually not necessary for most of LCIA impact

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categories. Therefore new questions and problems are raised for LCA researchers, mostly

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linked to the complex modelling of such human phenomena into a mechanistic cause-effect

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chain fashion.

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The divergence across ES is very large in terms of spatial extent, temporality or type of

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benefits provided. Their aggregation into a unique indicator is thus questionable.27 However,

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LCA typically provides results as aggregated indicator scores. Therefore the definition of an

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impact category for ES has to be clearly framed from a conceptual point of view, and

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communicable indicators that both translate the ecological nature of ES and their beneficial

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value have to be designed carefully.

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Through the present review analysis we attempt to identify the lacks in current LCIA practice

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concerning ES, highlighting the methodological or knowledge gaps that exist between the research in

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LCIA and the one conducted within the ES domain. We also wish to emphasize the limits of the

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cause-effect chains implemented in current characterization models and the potential biases they yield

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for LCIA outputs, their interpretation and future modelling developments. 6


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The review is structured according to the following steps: i) defining the impact category to be

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assessed; ii) identifying the LCI flows relevant to the impact category; iii) modeling the

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environmental mechanism underlying the studied impact category; iv) designing characterization

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models that take as input LCI flows to output changes in impact category indicators; v) refining the

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characterization model with regionalization and time-dependency aspects.

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2.

The position of ecosystem services in LCA 2.1.

ES in relation to the overall framework of LCA

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Standardized under ISO norms,11 LCA is divided into four methodological phases, two of which, the

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LCI and LCIA, are computational in essence and represent the core of the methodology. Accordingly,

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all the life cycle inputs and outputs related to a ‘product’ functional unit, inventoried as LCI flows, are

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characterized during LCIA into potential impacts on the environment and human health, referred to as

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impact categories (e.g., resource depletion, acidification, eutrophication, global warming, etc.). This

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characterization of LCI results into broad indicators, which may cover thousands of very specific

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substances emitted and resources extracted, scattered around the globe, makes the strength of LCA,

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since it allows moving from powerful and complex calculations to the communication of simple,

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straightforward and understandable information (see further in the Supplementary Information-SI1).

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Inclusion of ES in LCIA thus means creating a new impact category with related indicator(s),

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calculable via a set of characterization factors (CF). These CFs must connect to the LCI flows, either

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existing ones (e.g. emissions of CH4, land use) or newly defined (for instance “change of groundwater

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level”).

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2.2.

Definition of the impact indicator/category to be assessed

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A recent development in LCIA concerns the characterization of impacts on ES.23, 28 Given that ES

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conceptualize how ecological processes support society’s welfare, characterizing human interventions

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that impact ES in the framework of LCA is equivalent to assessing the hidden costs or benefits to

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society of the LCI flows degrading or supporting functional ecosystems. A huge amount of studies,

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supported by a large community of scientists and policy makers, provide a wide variety of definitions,

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notions and theories to build up or improve the accuracy, effectiveness and transparency of ES

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modelling and assessment. However, a first look at current LCIA practice reveals that only a few of

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the advocated methodologies refer to existing concepts from ES-dedicated research, such as 8


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conceptual models29 or the notion of ES flow30 (cf. SI2-SI3). “ES flow” refers to the conceptualization

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of ES as benefits being transferred, over space and time (“flowing”), from providing areas

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(ecosystems) to benefiting ones (society) and actually enjoyed or used.31 For instance, climate

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regulation is provided from a forest to the whole world, it is an omni-directional global flow; water

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supply occurs along a river stream, it is a directional local flow.30 Their modeling is essential for the

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spatialization and valuation of ES benefits.32

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This implicates, as already observed,33, 34 that the characterization of LCI flows into impacts on ES in

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LCIA typically stops at the functional level and does not propose a complete assessment of ES-

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impacting flows through the valuation of the benefits these ES provide to society, although they were

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designed in this sense.1, 24 The currently proposed LCIA models quantify the impact of land use

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stressors on the physical production of ecosystem functions (e.g.35-39) or on land/soil quality (e.g.40, 41),

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but do not assess the contribution of these quantities and quality to human welfare. As a result, the

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costs and benefits (in terms of ES) to society of land use (changes) are not accounted for in those

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models. For example, Saad et al.37 assessed the water purification potential of soils through their

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physic-chemical filtration and mechanical capacities expressed in cmol/kgsoil and cm/day. While

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these two functions influence the supply of ES to society, they do not represent the actual ES they

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support, which is the provision of clean water.27, 29 Another example comes from the characterization

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of land use in terms of impacts on the service of erosion regulation.37, 42 On the one hand, Núñez et

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al.42 assessed the impact of soil erosion, induced by land occupation, on the depletion of soils’

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resources and net primary productivity. As such, it can already be argued they did not assess land use

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with respect to impacts on soils’ erosion regulation, which would be the capacity of a soil to prevent

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its erosion, but rather the impact of soil erosion on other soils ES. On the other hand, Saad et al.37 did

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assess this capacity through soils’ erosion resistance (expressed in tons of soil eroded per hectare per

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year), but did not consider the benefits it provides, i.e. the maintenance of crop production or the

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protection against landslides of a down-slope inhabited area.

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In order to complete the cause-effect chains from LCI flows towards ES provision, it thus seems

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necessary to evaluate the effective use and value of the impacted functions,5, 27, 29 considering the flow 9



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nature of ES and their contribution to the economic, social and ecological development of society.34,

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43-45

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degradation through the induced loss of crop provision service, ES supported by soils’ functionality.

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This proposition was concretized by Cao et al.34 who presented a methodology to develop monetary

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indicators for the functional CF developed in the LULCIA project.23 In their valuation approach, they

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considered the beneficial aspect of ES by assessing the costs to society of losing ecosystem functions,

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thus proving the feasibility of integrating such considerations into LCIA models. Another opportunity

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was presented by Loiseau et al.46 who proposed a framework for the LCIA of territorial land planning

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scenarios on land use functions.47 Although land use functions are slightly different from ES, since

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they consider the benefits emerging from human activities and not only ecological processes, their

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approach also shows that ES benefits, as well as the multifunctionality of ecosystems, can be

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encompassed in characterization models.

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To overcome this challenge, Garrigues et al.41 for instance suggested to evaluate soils’ quality

2.3.

The special case of biodiversity

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Impacts on biodiversity due to different drivers (e.g. land use, water scarcity) are already considered

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in LCA through indicators of “affected/disappeared fraction of species”.12 Questions specific to the

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characterization of impacts on biodiversity, such as the choice of the best LCIA indicator(s), are

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considered out of scope here, and the reader is invited to consult dedicated reviews21, 48, 49 for further

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insights. Instead, we are interested in investigating how the implementation of an ES-based impact

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indicator/category can be coordinated with biodiversity issues.50

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Biodiversity and ES are linked through multiple relationships, although a large debate still exists on

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how to classify and harmonize biodiversity into the ES framework.27, 51, 52 While the intrinsic value of

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biodiversity (the diversity of species in itself) can be considered as a cultural ES, it mostly plays a

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supporting role for their provision.3, 51, 53-56 Taking a reversed point of view, the assessment of ES

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allows to evaluate biodiversity’s use value and should ideally promote its conservation.3, 24, 34, 49

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Furthermore, no method so far allows to evaluate with sufficient certainty ES provision from

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biodiversity indicators. Therefore the characterizations of impacts on biodiversity and ES, as currently 10


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proposed by LCA researchers, seem complementary, which encourages to keep them as distinct

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impact indicators/categories: specific biodiversity indicators would eventually relate to its intrinsic

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value while ES indicators would relate to its use value. Future developments in LCIA should then

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allow their joint interpretation and possibly aggregation in one unique characterization model.

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Potential solutions could be found in the numerous methods developed to analyse spatial correlations

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between ES and biodiversity.57-59 However, their incorporation in LCA would imply the

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communication of spatialized/mapped LCIA results, a practice not yet implemented in LCA

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standards.

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2.4.

LCI flows potentially harmful to ES supply

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Land use has almost been the sole driver of impacts on ES characterized so far in LCA (Figure 1).23

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However, other potential drivers of ES loss are already referenced in LCI for other impact categories,

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namely the emission of pollutants and wastes, and the extraction of resources. Still, their impacts on

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ES provision are not characterized in LCIA,12, 17, 28, 41, 60 in spite of their effect on biodiversity being

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assessed, thus recognizing their impact on ecosystems’ composition and functionality (cf. the

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characterization of climate change, ecotoxicity, acidification and eutrophication impacts on species

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richness,13 of river water use impacts on aquatic biodiversity,61, 62 etc.).

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Hayashi et al.63 did attempt to characterize the impact of ozone depletion on crop and timber

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productions, but their method was judged too uncertain to be incorporated in ReCIPE 2008.13

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Furthermore, the importance of understanding the impact of resources use or emissions release on the

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provision of ES is well documented in the literature. For example, Cooley et al.64 studied the impact

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of ocean acidification on the provision of fishery or recreational activities, Van Dingenen et al.65

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assessed the impact of UV-B radiations (linked to ozone depletion) on crops yields, and Dodds et al.66

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the impact of freshwater eutrophication on recreational uses or drinking water. Cucurachi and

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Heijungs67 also pointed out the effect that noise may have on animals’ ecology, thus disturbing their

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functionality among ecosystems and the provision of ES that depend on it. Due to these evidences,

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future LCA research should therefore focus on the implementation of such cause-effect chains in ES 11



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characterization models. However, it will be necessary first to evaluate the knowledge available from

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the specific fields in order to make their modelling transparent and substantiated enough.68

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It should also be noted that LCIA methodologies are already available for some of these missing

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cause-effect chains and could be adapted to fit the ES assessment framework. The characterization of

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water use impacts,69, 70 in particular, is close to the ES assessment concept from an

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operational/modelling perspective (e.g. assessment of water quality and availability issues), but the

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scope of the existing methods and their jargon are not designed to address problematics associated

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with ES analysis. Some of these “adaptable” methods are depicted in SI4.

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In addition, it has been observed that the range of ES considered in LCIA is still incomplete.18, 23, 33, 71

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Table 1 shows the current spectrum of ES modelled in LCIA, built using the most recent Common

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International Classification of Ecosystem Services (CICES)72 that divides ES in three categories:

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provisioning, regulation & maintenance, and cultural services. It is clear that the evaluation of ES

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included in the latter two groups is essentially missing. Three reasons were identified to explain this

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lack:

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1. The interests (usually economic) in studying and valuing ES are unequal between them,

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which limits the access to data and field measurements for some ES.17 Provisioning services

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that are usually marketed (e.g. crops, timber, water, fish) and thus monitored, in e.g. national

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accounting schemes,2, 5 are the most studied ones. In contrast, the evaluation of regulation &

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maintenance and cultural services, which have a less tangible value, is limited by the

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availability of data.

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2. There is a scarcity of knowledge about the ecological processes underlying the provision of

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some ES, in particular with regard to regulation & maintenance services.71 This is further

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amplified by the general lack of cross-fertilization of LCA with other disciplines.73

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3. The values underpinning ES are opposed to the will to translate them all into a single unit

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within a common impact category.17 Indeed, the LCA framework imposes an aggregation of

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impact indicators for their interpretation. However, ES are very variable in nature and value, 12


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and thus can hardly be aggregated under a unique metric. As a result, some have been

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excluded from LCIA methodologies.9, 17

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In the end, the lack of completeness in the range of ES assessed is troublesome because it can lead to

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biased LCA results, where the loss of an important ES (for example through a trade-off) is not

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reported. This is particularly true for some economic sectors such as agriculture or tourism that are

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much dependent on the state of, respectively, regulation & maintenance and cultural services.33

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2.5.

Characterization model and characterization factors

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2.5.1.

Models for quantification, mapping, and valuation of ecosystem services

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The literature on ES shows a common spatial analysis framework to assess ES,24, 29, 31, 74 usually

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decomposed in two main steps: the quantification and mapping of ES supply from ecosystem

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functions, and the evaluation of ES demand/use by society for the valuation of ES flows. However,

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LCIA methodologies do not typically assess the benefits provided by ES and hence do not value ES

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flows. Therefore, most ES models used in LCIA only encompass the former step.

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2.5.2.

Functional quantification and mapping

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For the spatially-explicit quantification of ecosystems’ functional production, considered as the

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potential supply of ES,3, 74 current LCIA research proposes different approaches. When looking at the

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three broad types of methods that can be identified in ES literature,29, 74-77 which are depicted in Table

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2, two of them are actually used in LCIA: proxy-based methods and process-modelling-based

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methods. The third one, which consists in using primary data from field sampling or observations, has

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not been used in LCIA.

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The proxy-based methods encompass those that make use of proxies (i.e. surrogates or “secondary

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indicators”) to assess ES supply. An example, which is also the main approach to proxy-based

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assessment, is the quantification of ES through land cover proxies (e.g. benefit transfer78 or expert

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based potential provision values31).29, 75 The main advantages of proxy-based methods are their low 13



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data requirement and their applicability to large scales. However, they provide a very coarse

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evaluation of ES supply and may carry large uncertainties mainly due to the poor consideration of ES

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spatial heterogeneity.76, 79 The LCIA models developed by Brandão et al.35 or Müller-Wenk et al.36 fit

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into this category: they assess biomass production and carbon sequestration through, respectively, soil

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organic carbon and terrestrial carbon stocks proxies assigned to land covers in different biomes.

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However, their low spatial resolution and lack of description of the ecosystems’ functional

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mechanisms may prevent their application to foreground systems and the implementation of

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supposedly more complex cause-effect chains from e.g. LCI emissions flows.

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The process modelling-based methods, rare in LCIA, regroup those attempts to explicitly model the

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ecological mechanisms underpinning ES production, using primary data80 or not.81 In a way, these

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methods can be considered as an improvement of the proxy-based ones, explicitly formulating causal

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relationships between ecosystems’ characteristics and using either land covers or gridded patterns as

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spatial units for mapping. Allowing a more precise assessment of ES supply,76 such models however

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exist for fewer services so far,75 are still subject to large uncertainties due to models’ assumptions,76

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and their high data requirements may hamper further application to regionalized background systems

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in LCA, potentially scattered around the globe.26

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Nevertheless, as argued by Reap et al.,82 such ecosystem process-based modelling approach should be

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further explored in LCIA since it can potentially capture the spatial and temporal variations in ES

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supply. Examples are the methods relying on the extrapolation of ecosystems functional production

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from biological data such as functional traits or habitats,80, 83, 84 or approaches aiming to define

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ecological production functions,51, 85 such as InVEST.86 Altogether they have the advantage to

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consider the link between biodiversity components and ES, which may be useful to define cause-

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effect chains from LCI, but are constrained by data shortages and computational issues. The LANCA

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tool,87 used by Saad et al.37 to calculate biophysical CFs for land use impacts on soils’ erosion

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resistance, physicochemical and mechanical filtration, and water purification, is to our knowledge the

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only LCIA-oriented model able to consider the ecological mechanisms supporting ES production.

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However, LANCA is limited to the assessment of a few soil-related services and highly depends on

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the availability of high resolution data on soils’ physicochemical characteristics.88

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2.5.3.

Valuation of ecosystem services

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The valuation of ES refers to the assessment of ecosystems’ contribution to society’s welfare, and can

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be economic, social or ecological in nature.44, 45, 79, 89, 90 Numerous techniques have been developed to

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value ES, the application of which varies according to the scope of the study or the service(s)

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analyzed.7, 89, 91-93 A most typical one is the monetization of ES. An extensive review of monetary

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valuation methods that may be of interest for LCA analysts, while common in the field of ES

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valuation,91 is provided by Pizzol et al.94 A first pioneering application of some of these methods was

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conducted by Cao et al.,34 who valued the functional CFs23 through the estimation of production loss

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and adaptation costs for society. However, their approach neither modelled ES flows nor spatialized

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the benefits they provide, as promoted in ES literature.7, 32 Apart from this, no other attempt so far

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exists from dedicated LCA research, despite the robustness of valuation methods and their effective

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use in LCA.94-96

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A first reason that can explain this gap is the choice of indicators used to quantify ES supply in LCIA.

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Mostly based on ecosystem functional proxies35, 37 or emergy,18, 97 the biophysical quantification of

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ecosystem functions should be in compliance with the aim to value them.27 However, the indicators

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chosen in LCIA do not usually encompass the actual benefits provided to society by ES, and thus

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cannot be valued. For example, the soil organic carbon proxy used by Brandão et al.35 to assess the

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biotic production potential of ecosystems, which is clearly linked to society’s welfare through the

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supply of food, can hardly be extrapolated into crop yields, the actual valuable benefit of crop

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provision. The same applies to emergy-based quantifications, although several assumptions could be

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made to roughly estimate the socio-economic value of ES.98-102 Nevertheless, emergy represents a

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special case: it allows to incorporate the notions of ecological processes and biogeochemical cycles

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into the life cycle modelling of any human-driven system (through the inventory of direct and indirect

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solar energy embodied in products and services). Accordingly, emergy is considered an ecological15



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oriented accounting methodology, which more than LCA can approach the concept of ES

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through a “donor” type value.103-105 In this regard, the opportunity to convey the emergy

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concept into LCA and vice versa has been discussed in order to mutually enhance their

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complementary features and synergies, such as for the evaluation of ES.18, 42, 106, 107 Presently,

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however, there is no consensus on how to integrate the two methods.108

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A second limit to the valuation of ES in LCA is the absence of a hierarchy among ES. As supported

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by Boyd and Banzhaf,5 who pursued a comparison with the accounting of conventional market goods,

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some ES are intermediate products to others.109 For instance, pollination is an input to the production

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of some crops. Therefore, according to the definition of ES (i.e. sources of well-being), it is necessary

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to differentiate between the ES that directly or indirectly (through other ES) contribute to society’s

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welfare, in order to avoid double-counting. Hence the authors defined final ecosystem goods and

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services as the “components of nature, directly enjoyed, consumed, or used to yield human well-

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being”(p.6195). While this requires the establishment of a new framework that links intermediary and

328

final ES, such considerations may be the key for the definition of LCIA cause-effect chains that allow

329

the valuation of the whole ecological mechanisms providing ES benefits. However, it also implies the

330

assessment of ecosystems multi-functionality, to date absent from characterization models.

331

Furthermore, the attribution of ES towards a single indicator, as currently proposed, bounds their

332

valuation to a unique aggregating unit, which may not be the most efficient way to value all ES.17, 50,

333

110

334

Finally, the implementation of markets data and economic models in LCA still represents a difficult

335

task for the establishment of methods that can be effective and representative for non-market goods,

336

such as most ES.34, 111

16


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337

3.

338

LCIA research mostly focused until now on characterizing land us-driven impacts on ES. Most efforts

339

have been conducted by the UNEP-SETAC life cycle initiative’s working group on ES,23 who

340

developed CFs to assess land use inventory flows impacts on several ES (functions)35-37 and

341

biodiversity indices 112, 113 in order to define a common methodology. Their characterization models

342

are grounded in the general land use oriented approach depicted by Milà ì Canals et al.28 and first

343

introduced by Lindeijer.60 This is based on the concept of land use-driven impacts being divided into

344

transformation and occupation impacts (each one with dedicated LCI flows and corresponding

345

CFs).23, 28 On the one hand, land transformation impacts represent the abrupt change in land quality

346

(or potential of an ecosystem to supply services) due to a change towards a new land use and its

347

associated land cover,23 i.e. the impact of a land cover change. On the other hand, land occupation

348

impacts represent the overall ES loss or gain due to maintaining a certain land use, thus delaying

349

ecosystems’ regeneration over a given period of time,23 i.e. the impact of occupying an area of land

350

compared to a reference situation.

351

To be characterized, land use thus has to be decomposed into a land transformation followed by a land

352

occupation. Hence, land use flows are referenced in LCI as transformation flows (transformation “to”

353

and transformation “from”), expressed in surface unit, and occupation flows, expressed in surface unit

354

 time unit.114 Occupation flows can be inventoried without transformation flows, e.g. in case a

355

production system requires to maintain a certain area of land under its current use. These flows are

356

then characterized with specific CFs, the calculation of which is presented in detail by Koellner et

357

al.,23. Overall, while this builds on a robust scientific basis and is well aligned to the LCA procedure,

358

the general methodology could be improved, as discussed below.

359

Calculation of characterization factors

3.1.

Conceptualization of land use changes

360

The first potential area of improvement concerns the way land transformation interventions (land

361

cover changes) are referenced in LCI, and more specifically their division into transformation “from” 17



362

and transformation “to” inventory flows. Although the rationale supporting this approach60, 115 is quite

363

nifty and well-designed, in practice it limits the modelling and characterization of land use impacts on

364

ES.

365

Simply put, it can be argued that the land transformation flows obtained from LCI do not represent

366

land cover changes but instead two abstract and distinct processes of “ecosystem subtraction”

367

(“from”) and “ecosystem addition” (“to”). SI5 illustrates the rationale behind this assertion using LCI

368

unit process data. As a result, these interventions have to be considered as independent when their

369

impact is modelled, as if they were occurring alone, and characterization models that can encompass

370

this conceptualization of land use change have to be designed. That is potentially why ES are

371

currently mainly modelled through proxies, since process-based models are usually spatialized and

372

tend to model flows of materials or species through landscapes, which is no longer possible (or at

373

least becomes inaccurate) when an area of land is removed without being replaced (or the opposite).

374

Overall, the concept thus seems quite unintuitive, which may not encourage the design of new adapted

375

ES models.

376

Furthermore, since land uses are somehow modelled as independent elements of a landscape, no

377

landscape-effect can be apprehended this way, despite the acknowledged influence of landscape

378

composition on the provision of ES.49, 116, 117 A direct consequence of this can be observed in the

379

modelling of land transformation flows. Given the way CFs are calculated for these flows, the

380

transformations “from” and “to” a given land cover will be characterized by opposite CFs. Therefore,

381

when aggregating characterization results, transforming 1 ha of land “from forest” and 1 ha of land

382

“to forest” in the same region will have a null impact, despite they might occur through different land

383

use changes linked to different unit processes. Once characterized, two product systems that require

384

either e.g. (1) the transformation of 10 ha of land from forest to agriculture and 10 ha from pasture to

385

forest, or (2) the transformation of 10 ha of land from pasture to agriculture, will thus have the same

386

impact on ES (if these land use changes happen in the same location). Such non-realistic results testify

387

the need to encompass landscape-effects in characterization models, and perhaps to rethink the

388

inventory framework for land use flows. 18


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389


3.2.

Selection of a reference situation

390

The choice of a reference situation to calculate land use impacts23, 118 also represents an issue.49 As

391

implied in the definition of transformation and occupation impacts, the calculation of their respective

392

CFs requires a reference state to which the assessed land use (after transformation or during

393

occupation) can be compared. To this end, three options have been proposed by Koellner et al.:23 the

394

potential natural vegetation (PNV) state, the (quasi-) natural land cover in the region, and the current

395

mix of land uses. However, only the PNV has been used so far in LCA research,49 whereas this choice

396

may not be the most relevant one, depending on the scope of the study.23, 118 Indeed, as observed by

397

several authors,23, 49, 118-120 using the PNV as a baseline implies the comparison of the obtained results

398

with a hypothetical ideal situation that may never actually happen. In this sense, the results obtained

399

do not really represent the actual impact of land use, but rather how far the use of an area of land (or

400

its transformation) puts society away from a theoretical “healthy” state of the environment.118

401

Implying the consideration of “forces of nature”23 and resulting into negative impacts for almost any

402

land use assessed,49 the use of PNV thus fosters the myth of the “Garden of Eden” in which humans

403

live in perfect harmony with a wild and free nature. However, the way towards sustainable

404

development does not reside in attaining a static idealistic state of nature, but rather in defining

405

trajectories that can improve our co-existence and the long-term sustainability of our interactions with

406

our environment.121 As a consequence, the alternative approach of referring to the most recent

407

available mix of land use should be further studied (Figure 2b illustrates the use of this reference

408

situation).122 Such assessment would allow highlighting improvement strategies, with positive results

409

in terms of ES supply, instead of always negative ones.49 This would also avoid the consideration of

410

ecosystems’ regeneration, which is a source of sensible uncertainties.35, 37, 112 Finally, decision makers

411

may be more interested in obtaining information based on a realistic situation than on a theoretical

412

one.23, 49, 123

413

3.3.

Supply of ecosystem services from land occupation 19



414

When calculating land occupation impacts, the provision of ES (land quality) is assumed constant

415

over time,49 an assumption that, although practical, seems too simplistic. Whether the occupation

416

takes place after a land transformation or not, ecological processes always occur in the area of land

417

occupied and the surrounding ecosystems, all inter-connected.33, 124 The ecosystems and their

418

provision of services thus keep evolving over time, following natural mechanisms and under the

419

influence of different pressures both linked and non-linked to the product system under study.125, 126 A

420

linked impact would be, for example, the loss of a supporting function initially provided by an

421

ecosystem that has been transformed during the life cycle of the assessed functional unit. A non-

422

linked one would be climate change effects on the functionality of the ecosystems, which would

423

happen independently from the analyzed product system. However if one assumes a constant quality

424

during land occupation, then none of these impacts occur, which means that any land occupation can

425

be durably sustained (or implanted in case of a pre-transformation), whatever are its location, the

426

surrounding environment or the history of land uses. As a consequence, CFs values may encompass

427

the provision of ES from land uses that in fact would not be functional, such as from agricultural uses

428

implemented on exhausted soils, or from ecosystems meant to disappear with temperature rise.

429

In addition, neglecting interactions between ecosystems implies that a land use change does not affect

430

ES supply from its surrounding environment. However, the provision of some services is almost

431

entirely supported by such inter-ecosystem exchanges (usually maintained by regulating and

432

supporting services125). Considering the crop provision and pollination services as an example,

433

although the ecosystem functional loss (the impacted pollinators) due to occupying a former nesting

434

habitat (after transformation) happens at the location of the land use, the affected provision of services

435

does not. This latter is observed in the surrounding agricultural land uses producing pollination-

436

dependent crops.127

437

Therefore, it appears that the dynamic and complex characters of ecosystems functional mechanisms,

438

as well as the effects of landscape composition and external pressures, should be encompassed in

439

characterization models (the varying trends in ES provision during land occupation this would

440

potentially yield are illustrated in Figure 2b).33, 49, 82, 116, 124 This brings back to the earlier conclusion 20


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Page 21 of 57


441

that the use of process-based ES models at the core of characterization models should be further

442

studied.82, 122

443

Nevertheless, such characterization is extremely complicated, mainly because it should generate time-

444

dependent CFs (the value of which would vary with the time horizon considered) and would require

445

the calculation of temporally-explicit LCI flows (at least for the occurrence of transformations and the

446

duration of occupations) and the design of prospective characterization models, all of which do not

447

represent easy tasks.122, 128, 129 Moreover, it should encompass notions such as threshold effects and

448

ecosystems’ resilience that are at the heart of their integrity and functionality. However, these are still

449

complex challenges for LCIA methods’ development and ES assessment in general.73, 130-132

450

3.4.

Multi-functionality of ecosystems

451

Because interactions among ecosystems and their services are not modelled in current LCIA practice,

452

it appears that none of the existing characterization models can actually assess land use impacts on

453

multiple ES at the same time (or ES bundles133), i.e. considering the multi-functionality of

454

ecosystems. However, the assessment of synergies and trade-offs among ES is of primary

455

importance.4, 133-136 In this sense, after assessing the life cycle impacts of beef production systems,

456

both Pelletier et al.137 and Beauchemin et al.138 concluded that grass-fed systems are somehow more

457

beneficial than crop-fed ones since they sustain pastures that provide a wider range of ES (e.g.

458

biodiversity, aesthetic landscape, water purification) than croplands. Similarly, Loiseau et al.46

459

proposed a framework to assess the life cycle impacts of land planning scenarios on the multi-

460

functionality of land uses, but adopting the concept of land use functions instead of ES.

461

To some extents, synergies and trade-offs represent the balance society has to perform between its

462

direct needs for provisioning and cultural services and the need to preserve the quality of ecosystems

463

and biodiversity to sustainably maintain its well-being (i.e. its indirect needs for supporting and

464

regulating services).29, 139, 140 Therefore, ES synergies and trade-offs are the most understood way to

465

evaluate the costs and benefits induced by impacting ecosystems, especially in the case of land use

466

changes.4, 85, 134 The goal is to avoid undesired trade-offs that could ultimately decrease the supply of 21



467

essential ES,4, 85, 134, 141 such as the loss of soil fertility or erosion control (regulating services) due to

468

the intensification of crop production (provisioning service).139, 140, 142 In addition, assessing trade-offs

469

and synergies makes it possible to highlight the hierarchy of ES143 with some services (the

470

‘intermediate’ ones)109 underpinning other services in turn (the ‘final’ ones).5

471

As a result, LCIA research should further investigate the use of multi-functional ecosystem models in

472

order to implement cause-effect chains towards ES bundles (Figure 1).50 Figure 2b shows the likely

473

results of such improvement. However, following Rodriguez et al.139 and Foley et al.,140 it is worth

474

noticing that trade-offs have a spatial dimension (are the effects taking place in close or long

475

distance?), temporal characteristics (are the effects occurring instantaneously or in the future?) and

476

reversibility features (can the ES provision return to its previous state or not?). These characteristics

477

are difficult to integrate in LCIA models since they imply a location-, time- and context-dependent

478

modelling at a resolution usually too high to fit current LCI frameworks.

479

3.5.

The employed vocabulary

480

The vocabulary employed for the characterization of land use impacts on ES may be source of

481

misunderstanding and incompatibility with the one formulated in ES literature. In the domains of ES

482

assessment29 and land change science,144 land cover and land use are distinct elements. As reported by

483

Koellner et al.,114 who follow the work of Di Gregorio,145 a land cover is defined by the physical

484

features (natural or human) present on the land surface.146 ‘Corn crops’, for example, is a type of land

485

cover. It is different from a land use, defined according to the actions and purposes of land

486

management and the benefits (material or non-material) it provides to people.146 ‘Agriculture’ is a

487

land use. A land use thus plays the role of intermediary process between a land cover and the benefits

488

society obtains from it.114 This distinction is important because a change in land cover or in land use

489

would not have similar effects on ES supply.147 Also, a given land cover can support several land

490

uses, while a land use can take place on different land covers.146 However, distinguishing both is not

491

an easy task, mainly because of classification, modelling and monitoring issues.144, 147

22


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492

For the characterization of land use interventions, Koellner et al.114 proposed a common classification

493

to inventory LCI flows of land occupation and transformation. Based on a review of available land

494

use-land cover classifications, they designed a 4-level grouping from generic global classes to more

495

detailed classes that encompass the intensity of land use. This classification seems appropriate for the

496

assessment of impacts on ecosystem functions and services since it has already been successfully

497

implemented in other LCA studies.35, 37, 112

498

Nevertheless, most studies mention land use and land use changes as impacting ES (e.g.35, 37).

499

However, land cover conversion is identified in the MEA1 as an impacting intervention driven by land

500

use and land use change. Accordingly, it is worth remarking that: i) if frameworks from ES research

501

are to be used to characterize impacts on ES, a clear distinction between land use and land cover is

502

necessary;29 ii) referring to our previous observation about the assumption of constant land quality

503

over time, it can be argued that the actual land use (activity) is not assessed in current LCIA. As land

504

uses represent the concrete actions undertaken to manage land, these have an impact on ecosystems

505

other than the change of land cover. For example, the emission of chemical compounds is linked to

506

the land use activity (e.g. pesticides for agriculture) and occurs during land occupation, but its impact

507

on ES provision is not characterized in current LCIA methods.

23



508

4.

Increase complexity in characterization models: regionalization

509

and temporality 4.1.

510

Regionalization of characterization models

511

Due to the consideration of land use impacts and the need to better model impacts on biodiversity and

512

ecosystems, LCA researchers have advocated the spatialization (or regionalization) of characterization

513

models.17, 23, 26, 148-152 This aims at retrieving regionalized CFs, the values of which depend on the

514

location of the characterized LCI flows. The rationale supporting this development is that,

515

historically, LCA considered similar the impact on biodiversity and ecosystems of a given emission or

516

land use wherever it took place.17, 28, 41 However, ecosystems sensitivity and quality, as well as other

517

parameters that influence the response of ecosystems to different stressors, vary considerably in

518

space.27, 28

519

Hauschild153 framed three levels of spatial differentiation for the characterization of impacts:

520



Site-generic: no spatial differentiation is performed.

521



Site-dependent: some spatial differentiation is applied (at a more or less high resolution scale)

522 523

to separate sources of impacts and receiving natural compartments. 

Site-specific: a very detailed spatial differentiation localises the sources of impact flows.

524

In the context of characterizing impacts on ES supply, research aims towards a site-dependent global

525

characterization of impacts on ES for background systems,23 while a site-specific approach for the

526

foreground system can still be conducted, although the resulting outputs would rather serve as a first

527

“screening” to identify potential impacts on ES. These could then be assessed through other

528

environmental impact assessment methods more adapted to a very local scale.34

529

From a methodological point of view, regionalizing impacts on ES is equivalent to defining spatial

530

patterns in the cause-effect chains from the initial environmental intervention flows towards the final

531

benefiting society. Once defined, it is then necessary to properly choose the scale at which ES are

532

modelled and input data retrieved.23, 41, 42, 151, 154 24


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Page 25 of 57

533


4.1.1.

Spatialization of cause-effect chains

534

The current framework underpinning the cause-effect chains implemented to characterize land use

535

impacts on ES appears overall incomplete in terms of spatialization properties. Taking as reference

536

the cause effect chain depicted in Figure 1, the regionalization of the two elements it includes can be

537

analyzed: the impact of land use flows on ecosystems functionality, and the supply of services from

538

ecosystems towards society.

539

At the functional level, all the methodologies based on Milà i Canals et al.’s characterization

540

framework115 assign the loss or gain of functionality to the same location than the land transformation

541

or occupation. Such assumption seems appropriate since a land use (change) is a point-based

542

intervention. However, since no connections between ecosystems or landscape-effects are modelled,

543

they only characterize land use impacts on the functionality of the transformed or occupied ecosystem

544

and not on the surrounding ecosystems, although their functionalities may be inter-dependent. This is

545

due to the non-modelling of ecological cascading effects, which occur over space and encompass the

546

flow nature of ES30 and the roles of intermediary ES109 and regulating and supporting ES.

547

The same observation applies to the modelling of ES provision towards society. So far only

548

characterized by Cao et al.34, this provision also has a spatial extent, i.e. a flow nature.30 However,

549

their characterization resulted in CFs attributing the gain or loss of ES to the same location than the

550

characterized intervention flow. Although a scale issue exists that may have limited their analysis,

551

Cao et al.34 still did not spatialize the use of ecosystem functions by society. Through the calculation

552

of “effect factors”, they did encompass the fact that ecosystem functions do not directly and totally

553

serve the locally present populations, but are rather distributed towards several entities (e.g. other

554

“societies”, ecosystems) through different ES flows. However, since these are not spatially explicit,

555

the authors could not identify the ES beneficiaries affected by impacts on ecosystems’ functionality.

556

Such information is however primordial to inform decision-makers, especially for the identification of

557

off-site effects.125 In the end, if the costs and benefits of impacts on ES are not localized, the

558

regionalization of characterization models arguably becomes purposeless. 25



559

4.1.2.

The assessment scale

560

The choice of an assessment scale represents an issue inherent to ES modelling25, 31, 155, 156 and that has

561

strong implications for the characterization of life-cycle impacts on ES. Defining this scale, which has

562

to be relevant with the studied processes, 23, 41, 42, 154 enables to choose the appropriate resolution of the

563

spatial units at the basis of characterization models, according to which input data are collected. As

564

observed by Mutel et al.,151 the spatial units at the basis of LCIA models cover a wide range in

565

resolution (e.g. at grid, country or watershed scale) and depend on the scope of the impact under

566

focus. While it is not possible to define a perfect scale, its choice has to be tested and substantiated for

567

any LCIA model, since it can be a source of large errors in the calculation of CFs.151 Furthermore, the

568

assessment scale should be linkable with the spatial information contained in LCI databases, which is

569

often poor in resolution (e.g. typically only country-scale).26

570

When characterizing impacts on ES, the main issue is that the bigger the scale of the model, the more

571

variable and uncertain is the CF, and the smaller the scale, the more data and accuracy in localizing

572

LCI flows are required. As a result, the choice of an assessment scale has to balance the accuracy and

573

representativeness of the model used to retrieve CFs, the availability of data to run this model, and the

574

computability with LCI results.

575

Koellner et al.114 proposed 5 levels of regionalization (with an increasing resolution) for the inventory

576

of land use flows and their impact characterization on ES. This framework uses the classifications

577

developed by Olson et al.157 and Spalding et al.158 to disaggregate terrestrial and marine biomes, and

578

obtain data at a sufficient level of detail. Following their recommendations, Brandão et al.35 retrieved

579

soil organic carbon data at the climatic regions level to model impacts on biotic production (using

580

IPCC data).159 However, these levels are not standardized160, 161 and other boundaries can also be used.

581

Saad et al.37 considered Holdridge life zones and regions162 to collect soil, landscape and climatic data

582

(in parallel to Olson terrestrial biomes and a non-spatial model), and Cao et al.34 retrieved economic

583

data at the national scale to monetize land use impacts on ES. Their common rationale behind

26


Page 26 of 57

Page 27 of 57


584

choosing these scales is that they were more in compliance with the modelled mechanisms (ecological

585

or economic), and the data they needed were mostly available at these scales.

586

While these efforts help moving towards a more detailed regionalization, it appears that the scales

587

currently considered to model ES in LCIA are very large compared to what is recommended in the ES

588

literature. Indeed, given the very local dimension of most ES, research tends to support the assessment

589

of ES supply at the landscape scale or lower.3 While such finer scale cannot be used to regionalize

590

CFs, since LCI flows are not (yet) localized at such resolution, the ecological mechanisms underlying

591

ES provision occur and differ in their magnitude mostly at this level. Therefore, when CFs are

592

retrieved at a higher scale, a high variability in their value has to be encompassed. However, since

593

current characterization models consider the same scale to model ES and retrieve CFs, no variability

594

is communicated in their values, thus supporting the assumption that a given land use provides the

595

same quantity of ES wherever it is e.g. in an entire biome. To overcome such limitation, LCIA

596

research shall then focus on modelling ES provision at finer scales inside characterization models and

597

develop solutions to downscale the results obtained at the scale of LCI flows spatial information. This

598

would ideally result in CFs that describe potential impacts on ES not as a unique value but rather as a

599

range.

600

Finally, along the chain of ES provision, three types of scales can be identified: i) the ecosystem scale,

601

from where ES are originally supplied; ii) the socio-economic scale, where ES benefits are enjoyed;

602

and iii) the scale of ES flows, representing the distance between the two previous scales (see further

603

details in SI6). This heterogeneity implies the consideration of multiple scales along a unique cause-

604

effect chain from one LCI flow towards the provision of one or multiple ES.42 Such option seems the

605

most promising if the costs and benefits induced by LCI flows, which represent interventions

606

occurring at ecosystem scale, are to be assigned to their beneficiaries, localized at the scale of socio-

607

economic systems. However, current LCIA models do not incorporate such multi-scale notion, but

608

consider the supply and use of ES at the same scale all along their provision.

609

4.2.

Temporality aspects 27



610

Similarly to the spatial differentiation challenges, research on LCA has grown towards the integration

611

of temporal dynamics in LCI and LCIA. This rationale, designed to output time-dependent CFs, was

612

initially proposed by Pehnt163 and further developed until today.128, 164-167

613

When focusing on ES, the consideration of temporal dynamics seems crucial27, 126 and thus should be

614

treated in LCIA.17, 33, 122, 128, 129 The supply of ES is varying over time, primarily because of temporally

615

dynamic natural processes at the basis of ecosystems functionality.125, 126 For example, the coastal

616

protection value of coastal plant communities depends on their biomass, which varies over seasons.126

617

Some external drivers varying over time can also influence (directly or indirectly) the functionality of

618

ecosystems, such as population growth, economic development or climate change.1 Finally, demand

619

for ES has a temporal dimension too. Back to the example of coastal protection, its value will increase

620

during storm season (high demand/need) and decrease during the rest of the year (low

621

demand/need).126 The timing between the supply of and demand for ES thus plays a role as well (e.g.

622

a coastal plants community will have a low coastal protection value if its biomass volume is minimal

623

during the storm season).126

624

So far, no approaches exist to characterize impacts on ES provision in such a dynamic way. Instead,

625

the supply of ES is assumed constant over time in current LCIA models (cf. previous section). Two

626

main facts can explain this lack: first, no dynamic ecosystem functional models are used in

627

characterization models;17, 82 second, given that very little has been done to evaluate the beneficial

628

side of ES, no characterization model considers the dynamic interactions between society and

629

ecosystems.17, 33, 168, 169 This lack hampers the characterization of indirect effects, such as the impact of

630

land use on the provision of ES to society, which in turn influences its production systems (to which

631

ES are somehow inputs), thus generating new impacts on ecosystems, etc.156 These so-called

632

“rebound effects”170 are however judged critical for ES assessment, in particular with regard to biofuel

633

supply-chains. In this connection, integrated models that couple dynamic economic and ecological

634

models could be used,43, 171, 172 as well as scenario-based modelling (illustrated in Figure 2b),122, 173

635

although such approaches can propagate large uncertainties and hinder transparency.172, 174

28


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Page 29 of 57


636

Nevertheless, the main limiting factors to such developments are found again at the LCI stage, where

637

temporal information is usually absent128, 129 given the steady-state nature of attributional inventory

638

models.175, 176 In spite of the recent efforts to inventory indirect land use changes with a consequential

639

approach,23, 168, 177 much remains to be done in order to integrate useful temporal information in LCI.

640

Finally, it has also been argued that dynamic interactions between drivers of ES loss are not

641

characterized in LCIA.17, 23, 33, 168 Although modelling such combined effects is primordial within the

642

ES research domain, it is a well-known limitation of LCA that interactions between impact flows

643

cannot be characterized following the computational structure of LCIA. As a consequence, either new

644

ways to conduct LCIA are developed or the characterization of impacts on ES will continue embed

645

such time-dependent limitation.

29



646

5.

647

Interdisciplinary efforts are still required to efficiently assess life-cycle impacts on ES and build consensus in

648

LCIA. Dedicated ES research shows there is large room for possible modelling improvements. The following

649

actions can potentially drive the development of future LCIA characterization models to fully encompass the

650

essence of ES:

651

Final remarks and outlook



652 653

Page 30 of 57

The coupling of multifunctional process-based ecological models and dynamic socio-economic models to consider the inter-connections among socio-ecological systems, which are at the source of ES.



The modelling of ES as spatial flows between ecosystems and society, and the design of multi-scale

654

cause-effect chains, to regionalize CFs and integrate the beneficial aspect of ES for humans, ecosystems

655

quality and technological progress.

656



657 658

The definition of substantiated and transparent indicators that can clearly communicate the costs and benefits to society induced by life-cycle interventions on ecosystems.



The characterization of impacts due to other LCI flows than those based on land use, and the clear

659

articulation of biodiversity and ES indicators, to provide a more complete picture of the potential life-

660

cycle impacts on the natural environment of the assessed functional units.

661

Altogether, the use of multi-scale dynamic integrated models at the heart of characterization models,9, 33, 43

662

oriented towards the holistic description of the dynamic interactions between the technosphere and the biosphere,

663

seems a promising option. In addition to the ones analyzed in this review, other problematics, related for instance

664

to the inventory of ES change drivers or the scoping of LCA, also have to be acknowledged and tackled to fully

665

implement ES assessment in LCA.

30


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666

Supporting information

667

Additional information on LCIA calculations (SI1), framing and defining ES provision (SI2-SI3), LCA research

668

close to the characterization of impacts on ES (SI4), the analysis of land transformation flows (SI5), and the scales

669

identified along the supply chain of ES (SI6). This material is available free of charge via the Internet at

670

http://pubs.acs.org.

671

Acknowledgements

672

This work has been financed by the National Research Fund Luxembourg within the framework of the FNR

673

CORE 2013 project “VALUES” (C13/SR/5903117; http://www.list.lu/en/project/values/). Authors are grateful to

674

three anonymous reviewers for their comments that greatly helped improve the robustness, clarity and conciseness

675

of this article.

676

Author information

677

Corresponding Author

678

*Address: 41 rue du Brill, 4422 Belvaux, Luxembourg; phone: +352-275-888-5038; e-mail:

679

[email protected].

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Page 52 of 57

Figure 1. Simplified description of the cause-effect chain currently implemented in life cycle impact assessment models towards the supply of ecosystem services (ES). Considering the beneficial nature of ES, which flow from functional ecosystems towards society,29 the current characterization of land use impacts on ES arguably stops at the functional level, apart from the approach investigated by Cao et al.34 The multifunctionality of ecosystems is currently not encompassed, as well as impacts due to emissions and resources uses. Moreover, the ecosystems functionality and society’s mechanisms are modelled in a static way. Finally, the whole cause-effect chain is poorly spatialized, since no effect on ecosystems is considered outside the location of the characterized land use flow.


Page 53 of 57

Figure


2. Illustration and quick analysis of the potential implications the use of a multi-functional dynamic integrated

ecosystem services (ES) model would have on the calculation of characterization factors (CF) to assess land use flows impacts on their provision. The choice of the current land use as the reference situation to calculate CF is also studied. a. The Current modelling procedure broadly summarizes the characterization models proposed by current LCIA research. b. The “Upgraded” modelling procedure incorporates some of the seemingly most promising opportunities raised in literature to improve characterization models. Similar and complementary illustrations can be found in Milà i Canals et al. (2007)28 and Bare (2011)122. The spatialization and temporalization (e.g. time horizon) of the models are not dealt with. Keep in mind they should further increase the complexity of the “Upgraded” procedure. LUC: Land Use Change, represents the occurrence of a land transformation flow; occupation: represents the period encompassed in a land occupation flow.



Page 54 of 57

Table 1. Alignment between current life cycle impact assessment (LCIA) methodologies considering land use impacts on ecosystem services (ES) and the Common International Classification of Ecosystem Services (CICES).72 Ecosystem service (CICES classification a) 1 – 2 levels st

nd

rd

State of the characterization th

c

3 level (4 level ) Biomass (cultivated crops)

Provisioning – Nutrition, materials or energy b

Biomass

Water

Regulation & Maintenance – Mediation of wastes, toxics and other nuisances

Regulation & Maintenance – Mediation of flows

Regulation & Maintenance – Maintenance of physical, chemical, biological conditions

Characterization factor/Indicator unit

Functional level (midpoint) Beneficial level (endpoint)

Soil organic carbon in kgC.year.m-2 -1

Productivity in $.ha .year

-1 2

Regionalization (extent – differentiation)

Ref

Global – between biomes

35

Global – between countries

34

Functional and beneficial level (midpoint – endpoint)

Soil resource depletion in MJsol.eq.m .year and NPP change in %

Global – grid cell level (5 arcmin)

41

Functional level (midpoint)

Groundwater recharge in mm.year-1

Global – between biomes and Holdridge life zones

37

Beneficial level (endpoint)

Urban water supply in $.ha-1.year-1


34

Mediation by biota

No methodology proposed

Mediation by ecosystems (filtration/sequestration/ storage/accumulation)


Physiochemical filtration in cmolc.kgsoil-1 and mechanical filtration in cm.day-1


37


Replacement costs in $.ha-1.year-1


34


Erosion resistance in t.ha-1.year-1


37


Avoided costs in $.ha-1.year-1


34


Terrestrial green water flows and surface blue water production in m3.ha-1.year-1

Global - per climatic criteria

38


Changes in evapo-transpiration in mm

None

39


Carbon flow in t.m-1.year-1

Global – between biomes

36


Social cost of carbon in $.ha-1.year-1


34

Mass flows (control of erosion rates)

Gaseous/air flows


Lifecycle maintenance, habitat and gene pool protection

No methodology proposed d

Pest and disease control


Soil formation and composition

No methodology proposed e

Water conditions


Regulation & Maintenance – Maintenance of physical, chemical, biological conditions

Atmospheric composition and climate regulation (global climate regulation)

Cultural b

Interactions with biota, ecosystems and land/seascapes



Page 55 of 57


Only methods that explicitly aim to assess impacts on ES are considered here. However, numerous other methods propose to characterize land use (or other) based impacts on some related concepts, such as soil quality or water stress (e.g. 40, 69). A non-extensive list is presented in the SI-S3. Similar outputs are also proposed in 23 or 178. a) The CICES classification is divided into four levels, from the broad ES categories to the detailed benefits they provide.72 b) In the case of provisioning and cultural ES, these were merged at the second level of classification (which distinguishes the uses of ES), since such distinction is not considered in LCIA models. c) The fourth level is mentioned only in case a methodology has been proposed. However, every 3rd level is divided into at least two 4th level classes in the CICES. Overall, 48 4th level ES are included in the CICES, 7 of which are considered in LCIA according to our observations. d) Methodologies that assess impacts on biodiversity (e.g. 112, 113) are not considered here because it is assumed that they do not assess land use impacts on the capacity of ecosystems to provide suitable habitats, but rather the impact of land use on habitats themselves and the species they shelter. e) All the methodologies that relate to soil quality or soil content (e.g. 40, 41) could be included here. However, they usually take the viewpoint of the biomass provision that soils support and thus do not support the assessment of land use impacts on the processes of soil formation and composition per se.



Page 56 of 57

Table 2. Categories and main features of the methods currently used to model ecosystem services (set of information that summaries the outputs of previous critical reviews from the ecosystem services research domain).29, 74, 75, 76, 77

Modelling method

Main characteristics

Data requirement – Level of detail – Uncertainty

Proxy-based

Based on the use of proxy indicators (e.g. land use type) to extrapolate the provision of ES

Low – Low – High

Process modelling-based

Explicit modelling (often dynamic) of the underlying processes from which ES are provided in order to calculate their provision

Medium – High – Medium

Primary data-based

Based on monitored data to provide a direct assessment of ES


High – High – Low

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Land use change

Resource use

Emission

Ecosystems Spatial ecosystem services flows

CURRENT

FUTURE? Life cycle impact assessment

Societies

Landscape effect

Rebound effect


Benefits from ecosystems

Assessment of Life Cycle Impacts on Ecosystem Services: Promise

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