Land Use and Land-use Changes in Life Cycle Assessment: Green Modelling or Black Boxing?

Ecological Economics 144 (2018) 73–81

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Land Use and Land-use Changes in Life Cycle Assessment: Green Modelling or Black Boxing? Michele De Rosa Aarhus University, Department of Agroecology, Denmark

a r t i c l e

i n f o

Article history: Received 1 November 2016 Received in revised form 5 June 2017 Accepted 15 July 2017 Available online xxxx Keywords: Land-use change Life cycle assessment Land deals Positivism Dialectic

a b s t r a c t The assessment of Land Uses and Land-use Changes (LULUC) impacts has become increasingly complex. Sophisticated modelling tools such as Life Cycle Assessment (LCA) are employed to capture both direct and indirect damages. However, quantitative assessments are often incomplete, dominated by environmental aspects. Land uses are a multidisciplinary matter and environmental and sustainable development policies intertwine. Yet, LCAs mostly focus on environmental impacts excluding socioeconomic implications of land occupation. This paper investigates the limitations of current LULUC modelling practices in LCA. Common LCA assumptions harbor value choices reflect a post-positivist epistemology that are often non-transparent to e.g. policymakers. They particularly influence the definition of the functional unit, the reference system and system boundaries, among other LCA methodological choices. Consequently, results informing land policies may be biased towards determined development strategies or hide indirect effects and socioeconomic damages caused by large-scale land acquisitions, such as violation of tenure rights, speculation and displacement. Quantitative assessments of LULUC impacts are certainly useful but should holistically encompass both direct and indirect impacts concerning the environmental and the social science dimension of LULUC. An epistemological shift towards a dialectic approach would facilitate the integration of multiple tools and methods and a critical interpretation of results. © 2017 Published by Elsevier B.V.

1. Introduction Mark Twain, the famous American writer, has been attributed with saying, “Buy land, they do not make it anymore.” With the backdrop of current debates on land-use changes, deforestation and large scale land acquisitions, the quote may appear today to encourage land speculation. Land acquisition, not based on necessity but foresight into its future scarcity, may indeed cause increasing land prices, preventing access to those who do not have alternative sources of livelihood. In contemporary policymaking, it would be labelled as unsustainable development, though Twain would have not been familiar with the concept. The World Commission on Environment and Development (WCED), better known as Brundtland Commission, has popularized sustainability in the modern definition only in 1987. The commission notoriously defined sustainable development as a “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” UNWCED Our Common Future, 1987. In 2005, the World Summit on Social Development formalized the three goals of sustainability, the triple bottom line: the three interdependent pillars on which sustainability lies are economic development, social development and environmental protection (United Nations, 2005).

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http://dx.doi.org/10.1016/j.ecolecon.2017.07.017 0921-8009/© 2017 Published by Elsevier B.V.

The sustainable development framework has been extensively applied to the food industry and, more broadly, to the agriculture and forestry sector (Pacini et al., 2003; Ridoutt and Pfister, 2010; Čuček et al., 2012). The objective has been to measure the performance of a product or process. However, quantifying the environmental, economic and social performances of several activities occurring along the product chain of an industrial sector is not a trivial task. The complexity of the modern world lies as a backdrop, where a globalized economic system interlinks different socio-political and economic contexts. Several integrated assessment tools for evaluating agricultural systems and land use have been developed since the 1990s to address this complexity (Bezlepkina et al., 2011). Challenges have concerned (1) reaching consensus on methods and procedures to measure the intergenerational effects (temporal dimension), (2) the regional distribution of final impacts (spatial dimension) and (3) identifying the cause-effect relationships between product demand and damages generated (impact pathways). Assessments should cover the entire supply-chain and end-of-life and include indirect effects that occur in different geographical locations because of global impact pathways. Not surprisingly, Life Cycle Assessment (LCA) gained momentum shortly after the formalization of the sustainable development concept. LCA was born in 1991 (Jensen and Postlethwaite, 2008) as a “cradle-tograve” environmental assessment framework. After 25 years of constant development, today it is an established science-based comparative

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assessment tool consisting of a “compilation and evaluation of inputs, outputs and the potential environmental impacts of a product system through its life cycle” (ISO, 2006). It is considered by the European Commission as “the best framework for assessing the potential environmental impacts of products currently available” (European Commission European Platform on Life Cycle Assessment (LCA), n.d.). LCA is extensively used to assess the environmental performance of agricultural and forestry products (Roy et al., 2009; Dalgaard et al., 2008; Schmidt, 2010). LCA involving Land Use and Land-use Changes (LULUC) have informed to a great number of national agricultural policies (Schmidt et al., 2009; Cherubini and Strømman, 2011) and private investments (Tuomisto et al., 2012; Agusdinata et al., 2011; Yan et al., 2011), as well as European and American biofuel policies (Kim and Dale, 2005; Broch et al., 2013; Vázquez-Rowe et al., 2014). The European Commission sponsors a European Platform on Life Cycle Assessment; it promotes “Life Cycle Thinking” applied also to Life Cycle Costing (LCC) and Social Life Cycle Assessment (SLCA)(UNEP, 2009; Sala et al., 2013a), together forming the backbone of a Life Cycle Sustainability Assessment (LCSA). Despite the promising developments and improvements in LCA models of LULUC, currently an operational LCSA framework does not exist (Sala et al., 2013a; Sala et al., 2013b). LULUC analyses still concern mainly ecological aspects, poorly addressing the multifunctionality of land and agriculture (Binder et al., 2010). Environmental assessments of land use frequently focus on one or only a few impacts, such as biodiversity loss or carbon footprint (De Rosa et al., 2016a), a simplified environmental LCA accounting for only one impact category: global warming (De Rosa et al., 2016b). The focus on greenhouse gasses (GHG) emission reduction from agriculture and forestry resulted in ad hoc land use policies and carbon credit schemes as the Reduced Emission from forest Degradation and Deforestation scheme (REDD +). One of the most disputed issues remains how to account for both direct and indirect GHG emissions, triggered by land use and occupation. Direct Land-Use Changes (dLUC) are “changes in human use or management of land within the boundaries of the product system being assessed,” while indirect Land-Use Changes (iLUC) are “changes in the use or management of land which is a consequence of direct land use change, but which occurs outside the product system being assessed,” as defined by the International Standard Organization (ISO) in 2012(ISO/ TS-14067, 2013). Indirect effects can take place both temporally and spatially outside the product system, called “system boundaries” in LCA. Accounting for them requires an understanding of the cause-effect relationships between product demands and actual impacts generated. The attempt to model the impact pathways of globalized production processes has led to highly complex modelling practices (Warner et al., 2014) and the creation of specialist knowledge. Increasingly sophisticated computer-based quantitative assessments have often resulted in loss of studies' transparency, reproducibility and reusability (Pauliuk et al., 2015; De Rosa et al., 2016b). The ‘blackboxing’ critique of technology in science studies — “the way scientific and technical work is made invisible by its own success” (Latour, 1999) — has been also made against quantitative modelling in environmental science (Haas, 2011): quantifying sustainability performances implies a particular way of assessing evidences and expertise,(Brickman et al., 1985; Jasanoff, 2005; Duncan, 2008) reflected in decision-making. The specialization of LCA has translated into a wide range of methodological assumptions and uncertainties. The increasing complexity of quantitative LULUC models risks confining the sustainable development discourse in land use studies to a technocratic matter, uniquely addressed by a spiral of incremental methodological improvement. The hidden epistemological choices of models make it difficult to interpret and discuss their results in light of their value-based assumptions (Iofrida et al., 2016). This paper scrutinizes the underlying ontology that has brought about this development. It aims to re-politicize the debate on land use modelling and the damages caused by Land-Use Changes (LUC). The manuscript analyses current practices and

limitations in LCA models of LULUC impacts, arguing that the quantification of resource flows and product footprint is socially situated. Decisions with large scale and long-term consequences based on quantitative assessment tools are also prone to biases and qualitative assumptions. The following presentation of common LULUC methodological disputes does not intend to be (and could not be) exhaustive. There are numerous articles in thematic scientific journals, some cited here, discussing the topic more exhaustively. The illustrative examples provided are instead necessary to discuss with a broader perspective on the ongoing debates. The objectives are, therefore: (1) to examine the relationships among LULUC methodological choices and their relationship to the underlying theoretical framework and (2) to critically analyze how the current development is shaping the scientific debate and its influences on agro-forestry policies, investments and land use practices around the world. 2. The Ontological and Epistemological Foundations of Current LULUC Modelling Practices As with other disciplines, the rise of quantitative LULUC modelling has been possible thanks to the progresses in information and communication technologies during the second half of the 20th century. These technological developments occurred simultaneously with the rise of Ecological Modernization (Mol and Sonnenfeld, 2000a; Neale, 1997; Christoff, 1996) theories, influenced by the post-positivist approach (e.g. Karl Popper). Like logical positivist, the ontology of post-positivism sees reality as something that exists and can be known, although this knowledge is mediated by human conjectures. Models and theories, that simplify reality in order to understand and control it, reflect these conjectures. While sharing the ontological understanding of positivism, post-positivism differs in its epistemology. Empirical, ‘measurable’ experiences are still the way reality is known. Nevertheless, because models mediate such measurable experiences, they may not fully mirror reality. Thus, theories and models are held true until they are falsified. Popper called falsification the formulation of beliefs amenable to be proven false. If falsified, models are substituted with new ones informed by new theories. In a broader critique of positivism, Thomas Kuhn later argued that when a whole set of beliefs—an entire paradigm—is falsified, a paradigm shift occurs. New values are assumed to form a new explanatory framework and a period of regular scientific work—normal science—follows (Kuhn, 1970). The rise of the sustainable development framework in the 1990s has often been interpreted as a paradigm change (Sala et al., 2013a) to a more holistic understanding of what shall be sustained. However, in sustainable development discourses the broadening of perspective has not represented a true paradigm shift. Rather, the epistemological understanding of reality as knowable through models (based on observations and human conjectures) has simply expanded tout-court to include non-environmental aspects, such as socioeconomic performances. The development of LCA methodologies and their application to LULUC is a well-fitting example. LCA analyses are inherently quantitative assessments, aiming to measure and compare the performance and efficiency of production. The concepts of performance and efficiency have also gained considerable attention, simultaneous to the ascendance of Ecological Modernization (Mol and Sonnenfeld, 2000b) and Industrial Ecology as a new scientific field in the last 30 years. The Ecological Modernization and Industrial Ecology general ethos is that new technological improvements would compensate increasing environmental problems and natural resource scarcity. However, the rapid rise of this optimist approach might be due to its accordance to intellectual, political and economic factors, beyond the realm of environmental studies, rather than the result of its robustness as a social theory (Buttel, 2000). Consequently, we could tend to measure only aspects amenable to introducing technological improvements, disregarding other potential damages neutral or

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negatively affected by them. The risk is to provide formally correct though self-evident results. ‘Measuring sustainability’ is certainly scientifically relevant but not less controversial than contextualizing sustainability. The quantitative models of which LCA is a perfect example, their methodological and data collection biases are undoubtedly less political although they have contributed to a shift away from the institutional and socioeconomic analyses of sustainability. 3. Land-use Changes in Environmental LCA: Practices and Limitations The dominant LULUC methodological frameworks have mostly focused on directly measurable environmental impacts and specifically on climate change related impacts (De Rosa et al., 2015; Frischknecht et al., 2016). Measuring a physical quantity, such as greenhouse gasses (GHG) fluxes directly generated by a production process, may appear straightforward. Yet, even in this simplified assessment, several methodological choices affect the data selection (Life Cycle Inventory – LCI) (De Rosa et al., 2016b) process and the impact assessments (Shine et al., 2005) (Life Cycle Impact Assessment – LCIA). In terms of influence on policy and decision making, the definition of the functional unit and the reference system, or baseline scenario (Holtsmark, 2013) are particularly crucial modelling choices in LCA analyses. The functional unit (FU) provides a quantified reference unit to describe the performances of the product system in terms of e.g. quantity, duration and qualitative characteristics (ISO, 2006). It ensures that results of different LCA studies are correctly compared referring to an equal function, instead of similar products. Theoretically, the FU should be defined “in terms of the obligatory product properties required by the market segment” and “should as far as possible relate to the functions of the product rather than to the physical product” (Weidema et al., 2004). Nevertheless, in LCA applications, the FU commonly refers directly to a specific physical product, rather than to a function. This practice results in setting aside a priori the possibility of identifying a wider range of options to deliver the performance described by the functional unit (the reference flow in LCA). Consistent with a post-positivist epistemology, this practice limits the quantification of damages to direct impacts related to the product. Alternatively, an interpretivist approach, i.e. a dialectic epistemology, would define the functional unit in terms of need fulfillment, including the ‘social science’ dimension, examining the context and its contradictions, focusing on the power relationships among stakeholders and identifying winners and losers. Hence, it would provide the adequate theoretical ground to allow the inclusion of indirect consequences triggered by the production activity; a practice that necessarily requires investigating beyond the directly measurable ‘facts’ which are seen as themselves a consequence of direct (operational) and indirect (supply-chain) (Hoekstra and Wiedmann, 2014) processes forming the production system assessed. In other words, those facts can be assumed either as independent of the production system supplying the FU, thus included in the reference system, or as themselves shaped by the production system supplying the FU, thus excluded from the reference system. In environmental LCA of LULUC the reference system represents a situation in which the environmental impact is supposed to be null. Defining a reference system is necessary in order to inventory only the interventions related to the assessed human land management activities. “Interventions that would occur also if the site was unused shall not be inventoried” (ILCD Handbook, p. 238) (JRC-IES, 2010) in order to account only for the net impact (Milà i Canals et al., 2007). Land use policies and carbon credit schemes aimed at reducing GHG emissions from land use are fundamentally based on calculating the difference between GHG emissions, which occur when implementing a project, and the reference scenario. An example of such a payment mechanism is the project, Reducing Emissions from Deforestation and land Degradation (REDD+). Predicting a high level of future deforestation (i.e. a reference scenario with high negative environmental effects) coupled with high

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esteem for avoided deforestation on account of particular project, renders a high number of carbon credits. The higher the future estimated deforestation, the more carbon credits the project obtains. Rajão and Marcolino (Rajão and Marcolino, 2016) accurately describe a case of complex black-box computer simulations to quantify the baseline scenario for a REDD+ project. The paper is a documented example of the consequences deriving from the choice of the reference scenario and the direct influence its definition has on policy implementations. When complying with scientific practices, LULUC assessments ought to clearly report and document materials and methods. Nevertheless, the complexity of those methods may hide paradoxical environmental management practices. Whether the transparency of scientific work is sufficient for its correct interpretation by policy and decision makers might be beyond the legitimate concern of scientists. However, assuming no voluntary biases, methodological choices may inherently reflect value-based assumptions, with the result that the link between the ontology and the method may not be explicitly evident to the assessors themselves. The most eloquent example is perhaps the debate around direct and indirect land-use changes impacts. For a long time the quantification of LUC impacts was limited to dLUC effects. Only recently the LCA community began to debate methodologies to include iLUC effects in LCA modeling (De Rosa et al., 2015; Fargione et al., 2008; Searchinger et al., 2008). Also in this case, the direct-indirect LUC debate has concrete effects on policies. For instance, the European Union biofuel support policy to reduce GHG emission from the transport sector first financed all biofuel production, and then the policy was radically modified to reduce and even cap conventional biofuels (Bourguignon, 2015). This example allows two considerations: (1) the mere account of directly measurable dLUC effects reflects a positivist epistemology; reality is limited to what we can observe and our knowledge of it derives from our capacity of measuring what we observe (2). The account of iLUC requires interpreting the facts in order to trace their indirect consequences. Thus, it implies a dialectic epistemology because the indirect effects are quantitatively measured only by negation of what is apparently a fact. The attempt to trace indirect damages moves beyond a post-positivist epistemology and reflects a more critical approach: dialectically, the analysis moves beyond the individual phenomenon – ultimately all impacts may be individually seen as direct effects of land uses - and considers the interactions and changes in time between several phenomena/facts. The concept of indirect effects has to pass through the negation of what appears to be all-direct effect to unveil their dependencies, relationships and inherent contradictions. Nevertheless, to date, the iLUC debate has only concerned the environmental assessment of LULUC, more precisely only CO2 emission reduction. Currently, there is still no consensus on how to measure iLUC effects (De Rosa et al., 2016a; Ahlgren and Di Lucia, 2014) and existing iLUC models focus almost exclusively on CO2 emissions (Warner et al., 2014). More recently, there have been methodological developments in LCA to measure the biodiversity losses (Chaudhary et al., 2015; UNEP, 2016) caused by (direct) LULUC (Michelsen et al., 2012; De Baan et al., 2015; Gaudreault et al., 2016; Teixeira et al., 2016), e.g. in terms of species loss (Souza et al., 2015). This has mainly happened thanks to the evidences provided by the Millennium Ecosystem Assessment (MEA Millennium Ecosystem Assessment, 2005) showing that LULUC is one of the five main drivers of terrestrial biodiversity loss. Yet, emphasis on one measurable environmental impact, climate changes, is indirectly present in predicting future environmental conditions potentially affecting biodiversity losses (Titeux et al., 2016). LCA assessments of LULUC rarely include other environmental aspects, such as water use impacts (Milà I Canals et al., 2009; Boulay et al., 2013; Boulay et al., 2011), with scope also limited to direct impacts. The influence of a post-positivist epistemology seems therefore to dominate the current practices of current LULUC assessments. Critical interpretations of models have led in some cases to the inclusion in LCA models of indirect LUC effects but the practice is still rare and limited to a single environmental impact. As discussed in more detail in the

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following section, the socioeconomic dimensions of LULUC also depend on the reference scenario considered, although they are most commonly excluded tout-court from quantitative assessments. 4. Socio-Economic Damages Caused by LULUC: What do Quantitative Assessments Capture and Why? The measurability of social conditions and socio-economic impacts is more elusive than the quantification of physical phenomena. Current impact assessments in environmental LCA do indirectly include some social aspects, such as the impact on human health and/or well-being (a social issue) caused by pollution (an environmental issue). Additional challenges exist to quantify systematically social impacts: large-scale LCI data on social performances are rare; LCIA of social issues involves to a large extent value-based choices. For example, the Social Hotspot Database (Benoit-Norris et al., 2012) – probably the most comprehensive social LCA database available – weights the impacts with a score from one to five that is ultimately an arbitrary choice. Although a number of social LCA guidelines and methodological frameworks exist (UNEP, 2009; Weidema, 2006; PROSA, 2007; Reitinger et al., 2011; Feschet et al., 2013), SLCA is currently not widely performed and quantitative analyses of LULUC rarely account for social effects. Thus, it is interesting to investigate the nature of those guidelines and their approach. Theoretically, the stakeholder categories (and respective subcategories) assessed by a SLCA should be of the kind in Table 1 designed by the UNEP/SETAC guidelines for SLCA (Table 3 page 16 of the Guidelines) (UNEP, 2009) or the infringements list in Weidema (Weidema, 2006). These are ‘midpoint’ impact categories indicators, i.e. they assess the impact not as final damage caused at a certain point along the impact pathway – typically used in LCA. The use of “end-point” indicator in LCA – expressing the impact more closely to the actual final damage caused – has been encouraged (Jolliet et al., 2004) in order to provide Table 1 Stakeholder categories and subcategories according to the UNEP/SETAC guideline for Social LCA (UNEP, 2009). Stakeholder categories

Subcategories

Stakeholder “worker”

Freedom of association and collective bargaining Child labour Fair salary Working hours Forced labour Equal opportunities/discrimination Health and safety Social benefits/social security Health & safety Feedback mechanism Consumer privacy Transparency End of life responsibility Access to material resources Access to immaterial resources Delocalization and migration Cultural heritage Safe & healthy living conditions Respect of indigenous rights Community engagement Local employment Secure living conditions Public commitments to sustainability issues Contribution to economic development Prevention & mitigation of armed conflicts Technology development Corruption Fair competition Promoting social responsibility Supplier relationships Respect of intellectual property rights

Stakeholder “consumer”

Stakeholder “local community”

Stakeholder “society”

Value chain actors (excluding consumers)

clearer indication to decision makers (Pizzol et al., 2016). The use of “end-point” indicator encompasses the aggregation of “mid-point” impacts, moving further down along the impact pathways and requires value-based choices in weighting diverse damage categories. “Endpoint” results can further be aggregate in a single-score LCA indicator, such as Disability Adjusted Life Years (DALY)(Murray and Lopez, 1996) or Quality Adjusted Life Years (QALY) (Weidema, 2006), to compare the impacts of alternative products. In analogy with environmental LCA, a reference system shall also be defined to model socio-economic damages related to LULUC. Once again, the reference system defines what would have occurred without the studied activity. In this case however, its definition is more nuanced: the political and economic milieu under which the impact pathways unfold can either be included or excluded from the baseline scenario depending on whether they are considered as pre-existing or conditions to be created for the activity to occur, therefore not neutral. Because of the dominant positivist epistemology in LCA, quantitative LULUC impact assessments tend to trace directly measurable effects. The quantification in single-score indicator such as DALY or in economic losses or gains (monetarization) (Hellweg et al., 2003; Weidema, 2015) focuses mostly on the directly measurable impacts of Table 1, concerning for example employment conditions. Indirect determinants, more difficult to trace and measure, are de facto excluded. Land uses are a good case to show the dependency between production activities and political and institutional factors. Land belongs to the natural environment (ecosphere) and it is a production asset shaped by human activities (technosphere). Human-mediated conditions, such as land tenure, land governance, institutional and economic factors, determine the sphere to which land belongs. Ultimately, power relationships between stakeholders influence these conditions. Socio-economic damages caused by LULUC are not only directly affecting workers' health and rights. Again, indirect impacts play a major role on the broader communities whose livelihood depends on the land. For this reason, large-scale land acquisitions by companies or governments in lower income countries, “land grabbing” (Borras et al., 2013), has been a contentious issue. Land deals carry several risks, not only of environmental damages, but also “of dispossession and loss of livelihoods, corruption, deterioration in local food security” and longterm social polarization (Arezki et al., 2011). Over the past decade, a significantly large amount of land deals have transferred long term land rights over large areas to companies and foreign states for agricultural investments (Cotula, 2012; Anseeuw et al., 2013). However, land deals are not necessarily fulfilling foreigner interests (Wolford et al., 2013). There is extensive scientific literature dealing with the conditions under which land uses take place and land deals are determined. Reflecting the multifunctionality of land, this literature belongs not only to environmental science but also to diverse branches of science: agrarian studies, ecological economics, development studies, political ecology to mention some. The following sub-sections draw from these fields to discuss what the LCA of bio-based products has not been able to capture yet. 4.1. The Role of National Authorities Holding weak democratic governance responsible for the negative consequences of land deals (Arezki et al., 2011; Doss et al., 2014) implies to assume that weak governance is pre-exist and independent from the planned land use. Lack of transparency, accountability and land tenure insecurity are a fertile ground for corruption and exploitative behaviors (Anseeuw et al., 2013). Indeed, one may assume poor governance, or its enduring nature, as an induced condition necessary to guarantee a certain land use instead of a fact preceding land deals. It is a matter of fact that local states play an active role in governing land deals and granting land rights: they prepare the legal regime and pave the road to investment flows, often at the expenses of environmental regualtions (Cotula, 2015). They act as land-brokers (Levien, 2013), calculating,

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legitimizing (Oliveira, 2013) or forcing (Levien, 2013; Grajales, 2013) land deals. Colonial and post-colonial land policies have influenced land tenure, leaving in many cases local people with little or no land entitlement (Chomba et al., 2016). The direct socio-economic consequences of land deals may not be the most severe if weak governances are considered as an indirect effect of an investment that allows the production activities. The interest of domestic elites over international agribusiness investments facilitate large-scale land deals (Fairbairn, 2013; Burnod et al., 2013) with enormous impacts on local economic system, causing directly or indirectly land dispossession (Cotula and Vermeulen, 2009). Land deals put at risk not only land property right but also land access, i.e. the ability to derive benefits from land (Ribot and Peluso, 2003). Land use changes in favor of highly productive managements can determine displacement of labour, local small-scale farming and loss of livelihood (Li, 2011). In the absence of wealth redistribution mechanism, the economic consequences on communities can be severe. Ignoring the link between large-scale land deals and indirect socio-economic impacts means to systematical exclude these dynamics from the assessment. New tools and approaches may be required to quantify a wider range of indirect socio-economic consequences of land deals. Nevertheless, it seems worth to investigate how much they would alter the results of land-use LCA models.

4.2. The Role of International Institutions Transnational institutions can affect national policies and economic strategies to favor some production activities at national level with large socio-economic consequences. Increasing land productivity in the global south is portrait as a necessary strategy to boost food supply (Deininger et al., 2011) and replicate the so-called green revolution occurred in the '50s and '60s in the western world (Perkins, 1997). To achieve this goal the World Bank has supported the displacement of smallholders in favor of industrial food production international: the World Development Report (World Bank, 2008) explicitly recommends uncompetitive smallholders to cease activities and take up waged labor (Li, 2012; Li, 2009). The discourses of food-security and social improvements are often present in market-based policies and investment interventions (Li, 2007; World Economic Forum, 2013). Supported by a market-based approach to food security, depesantisation (Araghi, 1995) is depicted as not only possible but even necessary (Li, 2011; Nally, 2015). Nevertheless, the World Food Program shows that increasing productivity does not guarantee food supply to the people that need it the most (World Food Programme, 2009). Moreover, the benefits and damages caused by large-scale production are not equally distributed, with local and most vulnerable stakeholders carrying most of the burden. Land acquisitions are not the neutral result of products demand as typically assumed in LCA. They are the indirect consequence of determined economic and political choice to produce those products. All direct and indirect consequences, including on broader societal and economic level, of this choice shall be holistically attributed to the products. Large-scale land acquisitions can also occur for environmental ends, e.g. forest conservation programs and carbon credit schemes as REDD+. Evictions, dispossessions and loss of livelihood should not be excluded in these cases either. Studies have shown that while having positive impacts in terms of carbon emissions and biodiversity, conservation programs can also exacerbate poverty locally by restricting access to land and forest resources (Chhatre et al., 2012; Beymer-Farris and Bassett, 2012; Poudyal et al., 2016), leading some authors to talk about “green grabs” (Fairhead et al., 2012). The current LULUC assessment framework in LCA is not able to capture these negative socio-economic consequences on local population. The following subsection discusses another risk of market-based policies in a context of fully liberalized

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trades that seems to slip away from quantitative assessments: the damages caused by land speculation. 4.3. The Role of Markets and Money One of the core assumptions of LCA is that damages are a caused by the production of goods and services along the whole product chain, or of changes in product demand (in consequential LCA modelling) leading in turn to a change in future production volumes. However, in liberalized markets influenced by financial capitals changes in production levels may also occur because of expectations of rising value of a commodity, before the actual demand changes. With this respect, LULUC represent again an interesting example. Market demand is not the only driver of investments. Speculations on future rising land or commodity prices may also increase the production of land-based commodities, and the occurrence of large-scale land acquisitions (AlonsoFradejas et al., 2015). Speculation on land-based product, especially palm oil, seems to have increased in the last decade: instability in real estate, bond and equity markets and low purchase or lease prices turned land in the global South an increasingly attractive object of speculation (Simms, 2015). In recent years, the so-called flex crops (Borras et al., 2013) have critically emerged as global commodities. The flexibility to supply alternatively different markets, depending on the highest market prices, characterizes flex-crops used for food, feed, fuel or industrial materials. Soya, palm oil, sugarcane, corn and cassava are among the most popular flex crops (Borras et al., 2013). Currently, LCAs of these types of crop tacitly assume that the damages are exclusively cause by product demand. Nevertheless, finance is also an “industry” whose “product” (the return on investments) may be intangible but not necessarily damage-free. Defining the new scenario determined by the emerging of these crops introduces additional challenges for researchers (Borras et al., 2016). Tracing the link between production activities, related damages and actual product demand in LCA becomes especially challenging in this context. Flex crops can be anticipated speculating on rising demand of biofuels and flex tree plantation can even “be used to speculate on carbon offset schemes such as REDD+” (Borras et al., 2013). While waiting for more profitable demands to rise, flex crops can supply different markets minimizing the risk for investors. This may determine a change in the marginal supplier (an important consequence, especially in consequential LCA modelling). Marginal suppliers are the “suppliers that will change their production capacity in response to an accumulated change in demand for the product” (Consequential-LCA Marginal suppliers, n.d.). Taking advantage of economy of scale, extensive plantations of flex crops, allow more competitive production prices than their substitutable products. FAOSTAT statistics show that land occupation by palm oil plantations in Indonesia increased by more than 400% in the last 15 years. Despite palm oil is currently only partially supplying biofuel, speculation on increasing biofuel demand (Koswanage, n.d.) have served as an argument to attract investors (Stevenson, 2012). In the meantime, palm oil has already become the most competitive vegetable oil for many uses (Schmidt and Weidema, 2008; Schmidt, 2015) displacing earlier production sources (change of marginal supplier). The damages currently quantified in LCA of bio-based product deliberately excluded the consequences of land displacement in terms of socio-economic impacts on local economies and people. Moreover, the cause-effect relationship established in LCA modelling between the environmental damages (most notably deforestation) and the production of palm oil is also nuanced. Around 90% of global supply of palm oil comes from Indonesia and Malaysia. Currently Indonesia shows the highest deforestation rate in the world and ranks third behind China and USA in CO2 emissions. Are changes in market demand for one or more products uniquely responsible for these impacts? Even though currently palm oil mainly supplies the market for vegetable oil, without the expectation generated by the market for biofuel, (regardless of whether they will ever be fulfilled), there would have not been massive

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investments in large-scale plantations. This means that speculation (or “financial products”) may be responsible for impacts caused by shifting the marginal supplier of vegetable oil to palm oil. 5. Discussion and Conclusion LULUC related issues well reflect the multidisciplinary character of environmental science. The limited availability of land represents itself a tangible metaphor of the planetary boundaries and natural resource constraints. Natural and ecological limits challenge the current development paradigm, which requires increasing and continuous economic development. Since those limits cannot be overcome, the ecological modernization approach relies on increasing efficiency to reduce damages, achieved through continuous technological improvements. The optimization of production processes is therefore not simply an option but becomes an imperative in modern society. The optimization requires to quantitative assess the performances of those processes, in order to determine their efficiency and potential improvements. For this reason, LCA is considered as a valid tool to scientifically ground policies and support decision making. The sustainable development challenge is intrinsically multidisciplinary. Analogously, LCA is an eclectic interdisciplinary tool: its hybrid nature draws from different branches of science: LCI dipping into engineering (applied and natural science) and economy (social science); LCIA drawing knowledge from environmental mechanisms (life and natural science) and social science (waiting system of different impacts). However, both the social and natural science LCA components make use of methodologies reflecting in many cases a post-positivist epistemology. With this respect, LCA does not make epistemological distinction between the natural world and the social realm. It treats biophysical and socioeconomic issues with similar methodologies. However, the additional challenges in measuring social damages have slowed down the development of social LCA assessment. Different disciplines propose different interpretations of interacting phenomena. Studies on the nature of LULUC and impacts on livelihood of land acquisitions exist in different fields and results show a focus on other aspects than the one captured in LCA. LCA of bio-based products do not focus critically on the dynamicity of LULUC. The choice of the FU and the reference scenario of land-use models in LCA are particularly delicate. Current practices tend to focus on a narrow range of product-based alternatives rather than addressing the fulfillment of the needs identified by the FU. Long-term socioeconomic implications of land deals (and following land uses) affecting the livelihood of local populations may be excluded when assumed as part of a pre-existing reference scenario. The analyses and results are therefore not only affected by data availability, modelling assumptions and uncertainties, typically addressed in quantitative models, but reflect a particular epistemological choice. They reflect a determined vision of the world. Policies informed by these models, e.g. REDD +, reflect the same vision. Although this is a licit perspective, black boxing value choices undermine transparency, a fundamental requirement of scientific work. LCAs of bio-based products consider the socio-economic context as independently predetermined. Nevertheless, environmental, social and economic alterations are consequences of global political and economic processes influenced by power relationships. The same power relationships determine the perspective of observation (between developing-developed context, consumer-producer perspective, business manager-policy maker perspective etc.) and the focus of research (the impacts assessed). Consequently, economically developed countries designed environmental targets and policies based on carbon foot printing and carbon credits (e.g. the reduction of GHG emissions by 20% within 2020 or the REDD+ carbon credit scheme). The transition to the service economy is already ongoing in their economies and those targets do not compromise the economic performances, or even represent an opportunity to improve them. A developing context

instead prioritizes economic targets even at the expenses of environmental performances, because it guarantees favorable conditions to the industrial sector and a tempting opportunity to attract foreign capitals. The paradoxical result of assessing the environmental effects mostly in terms of GHG emissions is that nature conservation is in this way turned into a business. Since businesses are profitable only when successful, the quantification of impacts tends to concentrate on successful changes, depicting a win-win scenario. A change of perspective and methodology could tell a different story. Wealthy organizations buy carbon credits and the “right to pollute” and local elites the legitimacy to perpetuate their elitist position, at both the expenses of collective good and resources. What are the terms of conservation strategies, the background conditions of environmental targets, how they affect the livelihood of locals and of the broader community, who among them are the most vulnerable to changes, clearly cannot be concern of carbon foot printing or generally of environmental foot printing. The current epistemological approach in LULUC assessments tends to overlook the relationship between impacts and the context under which they occur, hiding the different level of vulnerability of groups prone to them and the reasons for their higher vulnerability. Financial and institutional power relationships can determine the lack of access to economic and institutional alternatives (Vermeulen and Cotula, 2010). This lack of alternatives limits the capacity of people to bargain or even force them to give free consent to investments. In other words, investment in production activities cannot be considered neutral to the institutional context in which they occur. The assumption of neutrality leads to ignore a wide range of socio-economic consequences and risks to disguise modest improvements of production performances as a sustainable solution. 6. Outlooks The dominance of unbalanced and non-transparent assessment practices plays a role in shaping the public opinion globally, the sustainable development discourse and the public acceptance of policies. A more holistic approach for the identification of potential impacts and definition of impact pathways appears urgent. The multidisciplinary character of land use issue should be reflected in: a broader range of items inventoried and damage categories included in assessments, equally reflecting the three dimensions of sustainable development; in the choice of methodological approaches, moving beyond the post-positivist framework. This should not necessarily limit the application of qualitative analyses. Quantitative analysis could also unveil power relationships, livelihood dependencies on natural assets, the links between land-use changes, environmental incomes and socioeconomic impacts. Tailored bottom-up data collection efforts could facilitate broadening the scope of quantitative analyses. For example, the Poverty and Environment Network (PEN) database, contains quantitative detailed socioeconomic data that allow a comprehensive global analysis of tropical forests and poverty (Wunder et al., 2014). The database contains survey data on more than 8000 households in 25 developing countries and has supported global analyses of the relationship between tropical forests and poverty. Analyses of the database have pointed out the importance of environmental income in reducing inequalities and identified the vulnerability of the poorest group to loss of this income (Angelsen et al., 2014). PEN-based studies also demonstrated that state-owned forest resources in tropical area around the world generate more income than private forest (Jagger et al., 2014). These conclusions are contrasting with many marked-based solutions and depesantisation strategies, whose socioeconomic impacts result as positive in several LULUC assessments. Unfortunately, the PEN database targeted already deforested areas at the beginning of the data collection. Therefore, it does not allow to compare socio-economic effects of deforestation (occurred during the data collection process) based on the reference scenario of adjacent non-deforested areas. Nevertheless, a database

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developed with a similar approach specifically designed to broaden the scope of LCA models, could provide new tools to include the socio-economic effects discussed in this paper. The continuous development of the System of Environmental-Economic Accounts and Hybrid LCA, integrating Input-Output (IO) models in LCA analysis also offer instead top-down opportunities for improving LCA methodology. The recently released EXIOBASE, a multi-regional IO database containing environmental-related activities, provides a more complete picture than traditional process-based LCA. It may serve to better define the product system and as an advanced starting point to facilitate a more detailed assessment of production-related impacts (Wood et al., 2015). Social LCA can benefit from the integration of traditional LCA with IO databases, revealing the contribution of unexpected sectors, such as hotspots for social indicators otherwise excluded in traditional process-based LCA (Zamani et al., 2016). Within the LCA context, a potential for empowering different actors along the value chain and to mainstream bottom-up eco-innovation and alternative thinking is offered by Life Cycle Management (LCM). LCM was conceived as a management tool aiming at improving the overall sustainability performances of industries. It is intended to facilitate the collaboration with stakeholders along a company's value chain. The potential of the Life Cycle Management (LCM) concept resides in its difference from the LCA concept: LCA is a tool to identify hotspots and potential improvements and for comparative assessments between predetermined options. LCM sheds light on the variety of possibilities to manage a product or process during its lifecycle stages. Theoretically, the two are ontologically different: LCA provides scientific bases to quantify and compare lifecycle performances of organizations; LCM provides a framework to re-think the way the lifecycle of products and services is managed. It is therefore potentially by any individual or community and to any alternative pathway. It provides so also a new epistemology, which empowers all people involved in the value chain and enables their creativity. For this reason, under the LCM framework a number of bottom-up eco innovations and non-traditional approaches can be categorized, e.g. jugaad and frugal innovations, arising often in difficult economic context due to necessity, economic constraints and resource scarcity. The potential of LCM and its novelty lies therefore in the opportunity to mainstream sustainable practices, providing a framework within which they can be reproduced elsewhere. LCM gives the opportunity to modify the baseline scenario against which the assessment is carried on. Acknowledgments This work was funded by Aarhus University (15766) through the project ‘Environmental and socioeconomic potential of new concepts and business models for increased production and utilization of biomass from agricultural land in Denmark (ECO-ECO)’and the Graduate School Science and Technology. The authors would like to thank the anonymous reviewers for their contribution. References Agusdinata, D.B., Zhao, F., Ileleji, K., Delaurentis, D., 2011. Life cycle assessment of potential biojet fuel production in the United States. Environ. Sci. Technol. 45 (21), 9133–9143. Ahlgren, S., Di Lucia, L., 2014. Indirect land use changes of biofuel production - a review of modelling efforts and policy developments in the European Union. Biotechnol. Biofuels 7 (1). Alonso-Fradejas, A., Borras, S.M., Holmes, T., Holt-Giménez, E., Robbins, M.J., 2015. Food sovereignty: convergence and contradictions, conditions and challenges. Third World Q. 36 (3), 431–448. Angelsen, A., Jagger, P., Babigumira, R., Belcher, B., Hogarth, N.J., Bauch, S., Börner, J., Smith-Hall, C., Wunder, S., 2014. Environmental income and rural livelihoods: a global-comparative analysis. World Dev. 64 (Supplement 1), S12–S28. Anseeuw, W., Lay, J., Messerli, P., Giger, M., Taylor, M., 2013. Creating a public tool to assess and promote transparency in global land deals: the experience of the land matrix. J. Peasant Stud. 40 (3), 521–530. Araghi, F.A., 1995. Global Depeasantization, 1945–1990. Sociol. Q. 36 (2), 337–368.

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