the portfolio records. It is recommended to group the portfolio into clusters of similar products. The quantification could be based on: • Physical terms: unit of goods or number of services or in terms of weight or volume. • Non-physical terms: economic revenue or number of employees. To be used only in certain situations: e.g., big and very diverse portfolios or when each product in the portfolio is unique. Ideally, apart from the type of products and the amounts produced for each of them, the reporting flow should also include information about the quality and the durability of the products, particularly when actions are taken that change any of those characteristics. Table 20.4 The three features to define the reporting organisation Feature Guidelines Comments Subject of study The subject selected should represent a clear unit of operation, and shall be transparently justified and reported The assessment of the full organisation is recommended


If properly justified, the assessment may focus on segments or selected parts of an organisation (e.g

Hauschild et al., LCA: Theory and Practice


business divisions, brands, regions or facilities) Assessing a segment of the organisation could be preferred if it is: ∙A pilot for a broad application in the future ∙An autonomous part pioneering the application Consolidation method It is a systematic approach that specifies which parts of the organisation should be considered in the study and which should not It is particularly important for big or complex organisations (i.e., including wholly owned operations, incorporated and non-incorporated joint ventures, subsidiaries, etc.) The consolidation approach should be chosen depending on the complexity of the organisation and what is to be prioritised: risk or effective tracking and implementation of management policies Three methods exist: ∙Financial/operational control approaches: the organisation includes units over which it has financial/operational control ∙Equity share approach: the organisation includes units according to its share of equity interest Reference period Time


the organisation includes units over which it has financial/operational control ∙Equity share approach: the organisation includes units according to its share of equity interest Reference period Time period for which the organisation is reporting It is recommended to assess one operation cycle or fiscal year For example 2016 Organisational LCA


20.5.2 System Boundary: Direct and Indirect Activities The other key element in Fig. 20.2 is the system boundary. The system boundary is the set of criteria specifying which activities are part of the studied system and which resource use and emissions associated with them are included in the study. It is in accordance to the reporting organisation previously selected. Figure 20.3 shows the list of proposed activities at the reporting organisation and through the value chain and examples of emissions and resources to include. The system boundary includes and differentiates resource use and emissions and also linked activities that are: • Direct: from sources that are owned or controlled by the reporting organisation, like, e.g. combustion or process emissions, natural resources consumption and leachates at facilities of the organisation. • Indirect: occur at sources owned or controlled by another organisation or the consumer


• Indirect: occur at sources owned or controlled by another organisation or the consumer. They take place throughout the value chain (upstream or downstream) and are consequence of the activities of the reporting organisation. Examples include purchased electricity, employee commuting and use of sold products. Unlike in corporate carbon footprinting, where only greenhouse gas emissions from the generation of electricity are mandatory apart from direct emissions, in organisational LCA direct resource use and emissions and all the relevant upstream resource use and emissions shall be included and it is also recommended to assess downstream burdens. Downstream activities should be included always, particularly if products use energy or generate emissions during their use phase. Fig. 20.3 Direct and indirect activities to be considered in an organisational LCA J. Martínez-Blanco and M. Finkbeiner


20.5.3 Inventory: Collecting Data from Whole Organisation As in product LCA, life cycle inventory is the phase that addresses data collection and modelling of the system and it should include the whole set of inputs and outputs from activities involved in the provision of the reporting flow and within the system boundary. Two approaches can be used to collect the data of the inventory in organisational LCA: • Bottom-up approach: it entails adding the different LCAs of the products in the portfolio of the reporting organisation, weighted by the amount of products that are produced during the reference period, together with the supporting activities. The organisation may define clusters or families of products and assess only representative or proxy products. See an example of clustering in Milà i Canals et al. (2011)


The organisation may define clusters or families of products and assess only representative or proxy products. See an example of clustering in Milà i Canals et al. (2011). • Top-down approach: it considers the reporting organisation as a whole, and adds upstream (cradle-to-gate) models for all inputs of the organisation and downstream (gate-to-grave) models for all outputs. Additionally, a hybrid approach or intermediate approach that is using both bottom-up and top-down data may be feasible. As presented in Fig. 20.4, the data collected in the inventory should be differentiated between direct and indirect activities. The organisational LCA should include supporting activities. Those are activities of the organisation that are not directly involved in the production process (like heating, cleaning, canteen services, commuting of employees, research and marketing activities, etc.) but are part of the organisation’s activities


Different plans for the collection of the data may be designed for direct and indirect activities (see Fig. 20.4). In general, better quality and more specific data is expected for activities inside the reporting organisation, and for indirect activities identified as significant (on environmental terms, mass, economic or others). Specific data are also welcomed to model indirect activities, but higher use of assumptions, extrapolations and generic data is expected for them. 20.6 Final Remarks Organisational LCA application may reveal environmental hotspots where the organisation should focus energies and intervention, throughout the value chain and among all the products and operations involved in the provision of the portfolio. Understanding risk and impact reduction opportunities gives a solid ground to strategic decisions at different levels, for instance, when making decisions on technologies, investments and new product lines. It may also serve as the Organisational LCA


framework for tracking environmental performance over time and for informing corporate sustainability reporting. Most of the principles, requirements and guidelines for product LCA apply also for organisational LCA with some minor terminology amendments. Major discrepancies between the product and organisational LCA are during the definition of the unit of analysis and the associated system boundary and for the completion of the inventory. Organisational LCA studies are not thought for comparative assertions intended to be disclosed to the public. Therefore, the need of consistency at the scope phase is for environmental performance tracking. This still relatively new member of the LCA family has the potential to promote and spread life cycle approaches especially for those actors which do not apply them yet. Many organisations have implemented EMS over several years—in 2014 more than 300,000 organisations in about 170 countries had certified EMS according to ISO 14001 (ISO 2015b)


Many organisations have implemented EMS over several years—in 2014 more than 300,000 organisations in about 170 countries had certified EMS according to ISO 14001 (ISO 2015b). Organisational LCA can complement and refresh the mature EMS of these organisations and pinpoint significant and more cost-effective improvement options upstream and downstream the gates of the organisation’s sites. Organisations of all sizes and sectors have a key responsibility in the efforts to reduce environmental impacts. Large corporations play a promising role, but the contribution of medium and small organisations is also important if addressed as a collective. In developing countries, almost 200,000 EMS were certified in 2013 (ISO 2015b), while there is still a need for checking and promoting the application of LCA


In developing countries, almost 200,000 EMS were certified in 2013 (ISO 2015b), while there is still a need for checking and promoting the application of LCA. LCA of organisations may overcome some of the barriers for the SPECIFIC DATA Directly measured or collected data representaƟve of processes or acƟviƟes at a specific facility or set of faciliƟes Significant indirect acƟviƟes (highly recommended) Direct acƟviƟes (mandatory) GENERIC DATA Data sourced from a third-party like: life cycle inventory database, industry-average, scienƟfic papers and government staƟsƟcs source Other indirect acƟviƟes Other indirect acƟviƟes (recommended) Fig. 20.4 Specific and generic data prioritisation J. Martínez-Blanco and M. Finkbeiner


implementation of LCA in SMEs and developing countries, since LCA of organisations provides an overall idea of the environmental performance without having to perform independent LCAs for many products. Furthermore, the threat of selecting against non-best available technologies is reduced because comparative assertions are discouraged in organisational LCA. The existing initiatives for the LCA of organisations and consequently also this chapter focus so far on environmental impacts only. However, the organisational LCA approach can also be promising for assessing further sustainability dimensions, like social aspects. Social performance is determined by how an organisation conducts towards its stakeholders, while current Social LCA (S-LCA) rather focuses on the product level and has to face the challenges in relating the social impacts to the product when in fact they are mainly caused by the behaviour of organisations (see Chap. 16). Martínez-Blanco et al


16). Martínez-Blanco et al. (2015b) demonstrate and discuss how a social organisational LCA approach can overcome some of the methodological and practical challenges of the more product related S-LCA. Clift, R., Wright, L.: Relationships between environmental impacts and added value along the supply chain. Technol. Forecast Soc. Change 65, 281–295 (2000) Downie, J., Stubbs, W.: Evaluation of Australian companies’ scope 3 greenhouse gas emissions assessments. J. Clean. Prod. 56, 156–163 (2013). doi:10.1016/j.jclepro.2011.09.010 European Commission: Commission recommendation of 9 April 2013 on the use of common methods to measure and communicate the life cycle environmental performance of products and organisations (2013/179/ EU). Off. J. Eur. Union (2013). http://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=CELEX%3A32013H0179. Accessed June 2016 Finkbeiner, M.: Product environmental footprint—breakthrough or breakdown for policy implementation of life cycle assessment? Int. J


Accessed June 2016 Finkbeiner, M.: Product environmental footprint—breakthrough or breakdown for policy implementation of life cycle assessment? Int. J. Life Cycle Assess. 19, 618 (2013). doi:10. 1007/s11367-013-0678-x Finkbeiner, M., König, P.: Carbon footprint and life cycle assessment of organizations. J. Environ. Account. Manag. 1, 55–63 (2013). doi:10.5890/JEAM.2012.01.005 Finkbeiner, M., Wiedemann, M., Saur, K.: A comprehensive approach towards product and organisation related environmental management tools. Int. J. Life Cycle Assess. 3, 169–178 (1998). doi:10.1007/BF02978825 Galatola, M., Pant, R.: Reply to the editorial “product environmental footprint—breakthrough or breakdown for policy implementation of life cycle assessment?” written by Prof. Finkbeiner (Int J Life Cycle Assess 19(2):266–271). Int. J. Life Cycle Assess. 19, 1356–1360 (2014)


Finkbeiner (Int J Life Cycle Assess 19(2):266–271). Int. J. Life Cycle Assess. 19, 1356–1360 (2014). doi:10.1007/s11367-014-0740-3 Huang, Y.A., Lenzen, M., Weber, C.L., et al.: The role of input–output analysis for the screening of corporate carbon footprints. Econ. Syst. Res. 21, 217–242 (2009). doi:10.1080/ 09535310903541348 ISO: Environmental Management—Life Cycle Assessment—Requirements and Guidelines for Organizational Life Cycle Assessment (ISO/TS 14072). ISO, the International Organization for Standardization, Geneva (2014) ISO: Environmental Management Systems—Requirements with Guidance for Use (ISO 14001: 2015). ISO, the International Organization for Standardization, Geneva (2015a) Organisational LCA


ISO: ISO Survey 2014. ISO, the International Organization for Standardization, Geneva (2015b). http://www.iso.org/iso/iso_survey_executive-summary.pdf?v2014. Accessed June 2016 Makower, J., Mattison, R., Salo, J., Kelley, D.: State of Green Business 2014. GreenBiz Group and Trucost (2014). http://info.greenbiz.com/rs/greenbizgroup/images/state-green-business-2014. pdf. Accessed June 2016 Martínez-Blanco, J., Inaba, A., Finkbeiner, M.: Scoping organizational LCA—challenges and solutions. Int. J. Life Cycle Assess. 20, 829–841 (2015a). doi:10.1007/s11367-015-0883-x Martínez-Blanco, J., Lehmann, A., Chang, Y.-J., Finkbeiner, M.: Social organizational LCA (SO-LCA)—an organizational approach to strengthen the implementation of S-LCA. Int. J. Life Cycle Assess. 20, 1586–1599 (2015b). doi:10.1007/s11367-015-0960-1 Martínez-Blanco, J., Inaba, A., Finkbeiner, M.: Life cycle assessment of organizations. In: Finkbeiner, M. (ed.) The Encyclopedia of Life Cycle Assessment


doi:10.1007/s11367-015-0960-1 Martínez-Blanco, J., Inaba, A., Finkbeiner, M.: Life cycle assessment of organizations. In: Finkbeiner, M. (ed.) The Encyclopedia of Life Cycle Assessment. Springer, Dodrecht (2016). (Vol. 7, Special Forms of LCA) Milà i Canals, L., Sim, S., García-Suárez, T., et al.: Estimating the greenhouse gas footprint of Knorr. Int. J. Life Cycle Assess. 16, 50–58 (2011). doi:10.1007/s11367-010-0239-5 Natura: Natura report 2013 (2014). http://natu.infoinvest.com.br/ptb/4888/Relatrioanualemingls. pdf. Accessed June 2016 Taylor, A.P., Postlethwaite, D.: Overall business impact assessment (OBIA). In: Proceedings of the 4th SETAC LCA Case Study Symposium, Brussels, Belgium (1996) UNEP: Greening the Economy Through Life Cycle Thinking: Ten Years of the UNEP/SETAC Life Cycle Initiative. Life Cycle Initiative, United Nations Environment Programme and Society for Environmental Toxicology and Chemistry, Paris (2012) UNEP: Guidance on Organizational Life Cycle Assessment


Life Cycle Initiative, United Nations Environment Programme and Society for Environmental Toxicology and Chemistry, Paris (2012) UNEP: Guidance on Organizational Life Cycle Assessment. Life-Cycle Initiative, United Nations Environment Programme and Society for Environmental Toxicology and Chemistry, Paris (2015). http://www.lifecycleinitiative.org/wp-content/uploads/2015/04/o-lca_24.4.15-web.pdf. Accessed June 2016 UNEP/SETAC: Life cycle initiative website (2016). www.lifecycleinitiative.org. Accessed June UN Environment: Road testing Organizational Life Cycle Assessment around the world: Applications, experiences and lessons learned. Life-Cycle Initiative, United Nations Environment Programme andSociety forEnvironmental Toxicology and Chemistry, Paris (2017) Unilever: Unilever sustainable living plan 2014: scaling for impact (2015). https://www.unilever. co.uk/Images/uslp-2014-uk-ire.compressed–3-_tcm1252-469820_1_en.pdf


https://www.unilever. co.uk/Images/uslp-2014-uk-ire.compressed–3-_tcm1252-469820_1_en.pdf. Accessed June WRI and WBCSD: Corporate Value Chain (Scope 3) Accounting and Reporting Standard— Supplement to the GHG Protocol Corporate Accounting and Reporting Standard. World Resources Institute and World Business Council for Sustainable Development (2011). http:// www.ghgprotocol.org/files/ghgp/Corporate%20Value%20Chain%20(Scope%203)% 20Accounting%20and%20Reporting%20Standard.pdf. Accessed June 2016 Author Biographies Julia Martínez-Blanco Involved in the use and development of life cycle thinking methodology since mid-2000s. Contributed to several UNEP/SETAC working groups. Main LCA interests and experience are the application to the organization level and to the agricultural and waste sectors. Matthias Finkbeiner Developing and applying LCA, LCM, LCC, SLCA and footprints since the 1990s


Matthias Finkbeiner Developing and applying LCA, LCM, LCC, SLCA and footprints since the 1990s. Chair of the ISO-Committee for LCA, the LCM conference series and the UNEP/SETAC groups on carbon footprint and organizational LCA. Interested in the real world application of life cycle based sustainability assessment approaches in companies and policy. J. Martínez-Blanco and M. Finkbeiner


Chapter 21 Future-Oriented LCA Stig Irving Olsen, Mads Borup and Per Dannemand Andersen Abstract LCA is often applied for decision-making that concerns actions reaching near or far into the future. However, traditional life cycle assessment methodology must be adjusted for the prospective and change-oriented purposes, but no standardised way of doing this has emerged yet. In this chapter some challenges are described and some learnings are derived. Many of the future-oriented LCAs published so far perform relatively short-term prediction of simple comparisons. But for more long-term time horizons foresight methods can be of help. Scenarios established by qualified experts about future technological and economic developments are indispensable in future technology assessments. The uncertainties in future-oriented LCAs are to a large extent qualitative and it is important to emphasise that LCA of future technologies will provide a set of answers and not ‘the’ answer


The uncertainties in future-oriented LCAs are to a large extent qualitative and it is important to emphasise that LCA of future technologies will provide a set of answers and not ‘the’ answer. Learning Objectives After studying this chapter, the reader should be able to • Explain the challenges of prospective LCA studies. • Explain the differences between foresight and LCA. • Provide an overview of some tools for performing prospective assessments. S.I. Olsen (&) Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark e-mail: siol@dtu.dk M. Borup  P.D. Andersen Division for Technology and Innovation Management, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_21


21.1 Introduction: The Need for Future-Oriented LCA LCAs are often regarded as suitable for decision support also in decision-making that concerns the future, e.g. in technology and product development or strategic decision-making. Surveys on the use of LCA in Nordic countries and in European industry and business show that LCA is frequently used for strategic purposes (e.g. Roos et al. 2016; Frankl and Rubik 2000). Thus, LCA practitioners and researchers apply LCA as a tool in strategy processes and longer term planning. However, the basic economic, environmental and societal/social conditions change with time, and current conditions may therefore not be valid. The environmental interventions of future systems are a product of very complex interactions and dependencies of these basic conditions and thus uncertain. Additionally, scaling issues, i.e. that operational-scale technologies differ from laboratory or pilot scale, adds uncertainties (e.g. Frischknecht et al


Additionally, scaling issues, i.e. that operational-scale technologies differ from laboratory or pilot scale, adds uncertainties (e.g. Frischknecht et al. 2009; Caduff et al. 2014). Distinctions between retrospective and prospective LCAs, and between state-oriented LCA with an accounting perspective and change-oriented LCA (attributional or consequential) are made (Rebitzer and Ekvall 2004) and traditional life cycle assessment methodology must be adjusted or changed for the prospective and change-oriented purposes. However, no standardised way of doing this has emerged. As explained in Part II of this book, the core of process-based LCA has traditionally been a detailed analysis of the processes of the entire life cycle of the product. It is assumed that the product is known and fully specified and, in principle, all materials, resource consumptions, discharges and environmental impacts, including all subprocesses, should be assessed


In practice, there is always a limit to how much information is included and a tendency to focus more on some parts and processes than others. These limitations can become a problem if the LCA is narrowly considered a question of obtaining the results and figures that come from the process. If instead the LCA is considered a process of learning in which the people and organisations involved in the process acquire knowledge about the product and its environmental impacts, the limitations will often appear less severe. One of the important outcomes of an LCA is the knowledge obtained about which parts of the life cycle process are uncertain and of which there exists no precise information. To make proper sense of the results, a detailed understanding of the background of the figures is needed. Over the years, prospective elements and learning/decision elements have increasingly been introduced into LCA (e.g. Wender et al. 2014)


Over the years, prospective elements and learning/decision elements have increasingly been introduced into LCA (e.g. Wender et al. 2014). The prospective elements of LCA concern both the production system or functional unit that is the object for the LCA, as well as the general societal conditions surrounding this system. In strategy management literature, the latter is often labelled the system’s strategic environment; not to be confused with the system’s physical environment which most often is in focus in life cycle assessments. In practice, it is often difficult S.I. Olsen et al.


to distinguish exactly between the product system and its societal condition. One practical way to distinguish this is that the characteristics of product systems can be affected (selection of materials and their flows) by its manufacturer whereas the societal conditions (such as cost of energy and commodities, availability of materials, cycles in national economies) cannot be affected. Second, future changes are not only affected by possible decisions. Future changes in both the product system and in its surrounding societal conditions are affected by four types of factors: (1) socio-cultural, (2) technological, (3) economic and (4) political/legal factors


With this in mind we define prospective or future-oriented LCA as a systematic assessment of future events and developments in society, technology, economy and policy that in the long-term could considerably influence the product system (and/or functional unit) and its societal conditions and hereby the environmentally relevant flows. It is argued that integration of long-term scenarios in LCA is needed for use of LCA in typical strategic planning and public policy planning. A more explicit and systematic handling of uncertain aspects are important dimensions in this and have been addressed, e.g. by Miller and Keoleian (2015). A working group (WG) was established by the Society for Environmental Toxicology and Chemistry (SETAC) to address the issues of developing systematic scenarios as a basis for studies of future product systems (Weidema et al. 2004). Its report provides rather detailed recommendations for development of scenarios with a particular focus on the needs in LCA


2004). Its report provides rather detailed recommendations for development of scenarios with a particular focus on the needs in LCA. Scenarios include three basic elements “the definition of alternative future circumstances, the path from the present to the future, and the inclusion of uncertainty about the future”. In this chapter, the aim is to provide a broader understanding of the concepts and methods for future-oriented studies as well as linking up to literature on future-oriented LCA. Scenario analysis is therefore just one of the methods included here to address the uncertain future. 21.1.1 Foresight and Future-Oriented LCA The practical processes to strategically deal with the future development of science, technology, economy and of the society has been studied since the 1940s (Jantsch 1967; Bell 2009)


Many of the widely used foresight techniques were developed by American military planners during the Cold War period—aiming to ‘think the unthinkable’ and to prepare for it—and later-on adapted by large corporations to strengthen their intelligence capability. Over time, the conceptual and methodological development of prospective technology assessment (also known as and used interchangeably with the term ‘foresight’) has broadened, and produced a considerable and varied toolbox with ample applications in governmental policy-making and corporate strategizing. Today, it has become an important Future-Oriented LCA


practice to deal with uncertainties in technology development and strategically guide decision-making, planning and actions. Whereas LCA seeks a systematic and comprehensive analysis of environmental impacts over a product’s life cycle, technology foresight usually does not focus particularly on environmental aspects of future technologies. On the contrary, technology foresight is often criticised for having a too optimistic and positive view on the future technologies disregarding the environmental impacts and risks that also are connected to the technologies. Furthermore, it is well documented that experts in general are over-optimistic in their assessment of the future potential of the technology in which they have their expertise (Tichy 2004). The principles of life cycle assessment are thus very different from the principles of technology foresight. On many points the two approaches are quite opposite, as shown in Table 21.1


The principles of life cycle assessment are thus very different from the principles of technology foresight. On many points the two approaches are quite opposite, as shown in Table 21.1. But how can technological foresight then be relevant to LCAs that are usually made for present-time products and systems? The key elements of life cycle assessment are the identification and analysis of all the different processes in the entire life cycle of an industrial product or a technological system, including careful accounting of the materials and energy flows associated with the processes. Technology foresight studies normally address partial elements of the (future) technological systems in the sense of the main technical functionality, while a life cycle assessment gives a more comprehensive picture of the actual technological system in use and its different components


In its traditional form, LCA is a very detailed method, focusing on certainties and the most precise data available, while a foresight study is sketchier, process-oriented and—at least to some extent—trying to deal with the uncertain aspects of future developments. The next section explains some of the methods applied in foresight studies a bit more in detail. Table 21.1 Characteristics of the methodological approaches of LCA and technology foresight— in a traditional and archetypical form Issue Life cycle assessment Technology foresight Concept of time Present and near future Future, long-term perspective Procedural focus Analysis, assessment, interpretation Synthesis, reflection, interpretation, elucidation Data sources Data, information, records, etc. Information, opinions, questionnaires, statements, etc


Information, opinions, questionnaires, statements, etc. Analysis object Products or services with the same functionality Technical functionality of technologies and systems Delimitation of analysis Life cycle perspective, environmental impacts Many issues and themes Key results Objectified results, aiming to provide precise answers Sketches, scenarios, strategies and uncertainties Adapted from Rasmussen et al. (2005), see also text S.I. Olsen et al.


21.2 Prospective Methods In this section the aim is to provide a broader view of different methods that can be applicable in future-oriented LCAs. Prospective technology analyses (or technology foresight) apply a variety of methods and approaches that have been adapted from standard social-science research methodologies. One group is the quantitative methods such as S-curve analyses, analogies, experience curves, and different sorts of extrapolations of time series. LCA studies most often apply this type of methods when there is need to predict the future and they are mainly applicable for the short-term analyses (see also Table 21.2). Quantitative methods are often difficult to apply in prospective analyses where uncertainty is high, time horizons long and changes in technology or market situation can be large. We are then left with qualitative methods (or judgmental methods) such as literature reviews, expert panels, scenarios, futures workshops and Delphi surveys


We are then left with qualitative methods (or judgmental methods) such as literature reviews, expert panels, scenarios, futures workshops and Delphi surveys. An overview of the use of technology foresight methods in policy making can be found in Popper (2008). An overview of their use in corporate contexts can be found in Daheim and Uers (2008). Over the last 50 years, systematic methods of analysing expected futures have been developed. Foresight has appeared as a common name designating these methods. The purpose of technology foresight is not prediction of the future or exclusively to identify data about a technology in the long-term future. The purpose is rather to establish a fuller understanding of the possible technology futures and the forces shaping the future developments. The goal is to support current strategic discussion and decision-making as well as possible rather than predicting precisely. Foresight develops a well-informed context for current decisions


The goal is to support current strategic discussion and decision-making as well as possible rather than predicting precisely. Foresight develops a well-informed context for current decisions. 21.2.1 Diffusion Modelling, S-Curves and Analogies Diffusion modelling, S-curves and analogies are all quantitative methods for prospective technology analyses. S-curves or ‘Utterback-curves’ are based on the assumption—or empirical observation—that technologies usually go through a distinct technological maturity life cycle on the market (Utterback 1996). Related models are diffusion models or Fisher-Pry substitution models (Fisher and Pry 1971). The S-curve hypothesis states that the market growth is slow in the early phase (infant or ascent phase). In the next phase market growth rapidly increases (growth phase), and in a third phase (market maturing) growth flattens out or even becomes negative, see Fig. 21.1


In the next phase market growth rapidly increases (growth phase), and in a third phase (market maturing) growth flattens out or even becomes negative, see Fig. 21.1. Hereafter, new technologies fulfilling the same needs or several needs in a better or cheaper way overtake the market. It is often useful to check scenarios for the future diffusion of the technology with traditional S-curve methodologies. Traditional market forecasts based on extrapolation of historical data in the early phases of a technology’s market presence tend to underestimate the growth rates of the market, whereas the later forecasts Future-Oriented LCA


overestimate the growth rate. The reason for this is that the technology has reached a more mature phase resulting in a less steep market growth. Anticipating an analogue technological maturity life cycle can be helpful, but the overall problem is to estimate when the market peak occurs. 21.2.2 Experience Curve (Learning Curve) The concept of experience curves is based on learning curves first introduced by T.P. Wright reporting on a study of cost reductions in airplane production in America in the 1920s and 1930s (Wright 1936). The experience curve in contrast to learning curves applies not only to labour-intensive situations, but also to process-oriented ones. During the 1960s and 1970s experience curves were increasingly used in industrial forecasting and marketing strategy (Fusfeld 1970; Boston Consulting Group 1972). The experience curve describes how cost reductions appear in line with accumulated production


The experience curve describes how cost reductions appear in line with accumulated production. Accumulated production is used as a substitute for accumulated experience in the learning system. The mathematical expression of the experience curve can be written in the following way: Ct 1⁄4 C0  Qk t t 1⁄4 1; . . .; T; ð21:1Þ where observations of the unit cost at time t, Ct are calculated as average over the observed variables. C0 is the cost of the first unit produced. Qt is the accumulated production at time t, and, finally, k is the learning factor. In future-oriented LCA learning curves can be used for estimating future efficiencies of new technologies, and it has been shown that the learning factor (k) is fairly stable for each specific technology and is typically between 0.9 and 0.75 (Weidema et al. 2004)


2004). It has also been shown that emission coefficients are closely related to the cumulative investment and it is suggested that conservative learning factors (0.85–0.95) can be used as proxies for the physical efficiency improvements in flows (Weidema et al. 2004). As examples, the performance and efficiency of operational-scale technologies will differ from those of laboratory-scale or pilot-scale equipment in terms of performance and efficiency figures gained with process modelling. Gavankar et al. (2014) suggest that LCAs based on immature data should be interpreted in conjunction with their technology and manufacturing readiness level. This can in practice relate to the learning curves. 21.2.3 Delphi Studies Delphi is characterised by Linstone and Turoff (1975) “as a method for structuring a group communication process so that the process is effective in allowing a group of S.I. Olsen et al.


individuals, as a whole, to deal with a complex problem”. In practice, Delphi studies are often based on questionnaires sent to a selected panel of experts. The foresight period may range from few years to 20 or 30 years. A Delphi process includes typically two rounds where the results from each round are communicated to the participants in order to achieve a consensus or reveal divergent (bipolar) viewpoints among the participants. Technology foresight studies based on the Delphi method have been carried out on a national level in Japan since the 1970s. Delphi surveys are often based on the formulation of ‘statements’ about the future societal, technological, economic or political development. An example could be ‘More than 75% of all new wind turbines are without gear-boxes’. Statements can be formulated as a desk-study or using iterative processes at workshops or via questionnaires


An example could be ‘More than 75% of all new wind turbines are without gear-boxes’. Statements can be formulated as a desk-study or using iterative processes at workshops or via questionnaires. The statements are then exposed to a number of questions such as: ‘Period in which the statement will have first occurred’, ‘If realised what will be the impact on the environment?’, etc. Lists of such statements and questions are sent in two rounds to selected experts. When filling in the questionnaire the experts are typically also asked to state their level of expertise on each of the statements. See the example in Table 21.2. For statement 1 some kind of normal distribution can be observed on the period in which the statement will first occur. For statement 4 the results indicate disagreement among the respondents. Market diffusion Ascent phase Market growth Market maturing Working prototype Commercial application Fast market increase Market saturation Development Fig


Market diffusion Ascent phase Market growth Market maturing Working prototype Commercial application Fast market increase Market saturation Development Fig. 21.1 Example of an S-curve Future-Oriented LCA


21.2.4 Scenarios Scenarios can be defined as stories describing different but plausible futures. They are developed using techniques that systematise the perceptions of alternative futures (Schwartz 1998). Scenarios are basically tools for taking a long-term view in a world of great uncertainty. Up until the 1970s, most future studies aimed at predicting the future using various computer-based forecasting techniques. The rise of scenario analysis in strategic planning activities has largely been ascribed to the inability to provide credible forecasts and the perceived need for introducing tools for imagining, analysing, discussing, suggesting and preparing for sets of equally ‘plausible’ futures and running scenarios is essential when handling prospective assessments. 21.2.5 Technology Roadmaps Technology roadmapping is a forward-looking approach developed and widely used to support strategic long-term planning within organisations like industrial companies (e.g. Phaal et al. 2004)


Phaal et al. 2004). As the name indicates, road map studies analyse and discuss the road ahead for the development of a specific industrial product or a specific technology. Roadmaps seek to capture the surrounding conditions, threats and opportunities for a particular group of stakeholders in a technology area or in an area of technology application. Table 21.2 Example of Delphi statements and affiliated questions related to future environmental impacts of wind turbines Your level of expertise on the field of the statement Period in which the statement will have first occurred Impact on wind power's cost competitiveness Environmental effects due to manufacturing and decommissioning of wind technology Statement No


Statements about future wind power technology Own field of work Knowledgeable No knowledge Before 2005 2006 – 2010 2011 – 2015 2016 – 2020 After 2021 Never Highly beneficial Beneficial Neutral Harmful Highly harmful Highly beneficial Beneficial Neutral Harmful Highly harmful 10% of Europe's electricity from wind power 14 28 28 11 15 19 15 12 More than half of all new turbines in Europe are placed offshore 11 29 10 10 11 15 40% cost reduction of wind produced electricity relative to 2001 13 24 Global implementation of Kyoto targets 13 18 10 19 12 50% increase in EU and European national expenditure on wind power related research 13 21 12 Other renewable source of energy (other than hydro) becomes fully competitive with wind 11 11 10 15 Competitive concept for storage of wind energy (e.g. based on hydrogen) 26 15 11 14 Source Andersen and Bjerregaard (2001) S.I. Olsen et al.


Technology roadmaps can take on different forms. Usually, they include a graphical representation of the future developments as a central element. These often appear as multi-layered charts describing connections between different sub elements and different expected trends and developments. The connections between the different developments also indicate how and in what time period different actors are meant to contribute. Figure 21.2 shows an example of such graphical representations of roadmaps. Horizontally, it goes from the past to a future vision and vertically it goes from identification of, e.g. specific skills through development of a technology based on these skills, a product delivered by the technology and to the market for that product. Usually, a graphical representation of the future development and the interplay between different sub-elements is a central element in a roadmapping exercise


Usually, a graphical representation of the future development and the interplay between different sub-elements is a central element in a roadmapping exercise. Through describing and discussing the possible road ahead, including the problems and risks that can be expected, the roadmap perspective is built up. The technology roadmapping approach is increasingly applied in foresight studies, especially in those exercises that are focused upon particular industrial sectors like, e.g. the energy sector. Traditional technology roadmapping describes a specific, partial perspective, e.g. the perspective of an industrial company or interest organisation with a clearly defined goal. The approach is thus explicitly subjective and normative


the perspective of an industrial company or interest organisation with a clearly defined goal. The approach is thus explicitly subjective and normative. Within the limitations of the subjective perspective, the approach of technology roadmapping can lead to a comprehensive and multi-facetted understanding of a desirable development path for a technology and of the interplay between different kinds of activities (e.g. market, scientific, or industrial activities), different drivers of change, etc. Fig. 21.2 Example of the architecture of a technology roadmap project. After Phaal et al. (2004) Future-Oriented LCA


21.3 Key Concepts in Future-Oriented LCA 21.3.1 Dealing with the Future Future orientation is often categorised into short-term (1–5 years), medium-term (3–15 years), long-term (more than 20 years), and very long-term (more than 50 or 100 years). Several types of approaches to the future can be distinguished depending on the purpose of the study. • Predictive approaches, which answer the question “What will happen?” are meant to be defined for simple objects and short-term studies. Predictive studies are forecasts (the likely scenario) and what-if (conditioned to some specific events). Predictive approaches aim at describing the most likely futures and generally involve forecasting current trends into the future creating ‘surprise-free’ or ‘business as usual’ like images of the future


• Explorative approaches, which answer the question “What can happen?” aim at describing a number of plausible futures, which may be possible, desirable/feared and/or realisable, and start out from present trends leading to equally likely futures. They are external (related to exogenous conditions) and strategic (conditioned to some actions completed in a certain way). Cornerstone scenarios are also defined as explorative scenarios and are meant for complex objects (e.g. energy systems) and long-term time horizons. • Anticipative or normative approaches, which answer the question “How can a specific target be reached?” are created on the basis of desirable or feared visions of the future. Anticipative approaches involve working backwards from a future state to find possible pathways to that particular future. This methodology is often termed ‘back-casting’. In practice, scenarios in future-oriented LCAs are often based on a mix of prediction, exploration and anticipation


This methodology is often termed ‘back-casting’. In practice, scenarios in future-oriented LCAs are often based on a mix of prediction, exploration and anticipation. The SETAC WG on scenarios in LCA defined all methods for dealing with the future in future-oriented LCA and divided them into six groups of methods: extrapolation methods, exploratory methods, dynamic modelling, cornerstone scenarios, participatory methods and normative methods, and put them into an LCA application setting as illustrated in Fig. 21.3. When choosing and applying a future-oriented method (e.g. a scenario approach) it is important to keep in mind that the time horizon must be consistent with the goal of the study (Weidema et al. 2004). An important distinction is made between ‘what-if’ scenarios and ‘cornerstone ‘scenarios. ‘What-if’ scenarios are used to compare two or more well-known situations. They are the most widely used and frequently applied in the sensitivity analysis as discussed in Chaps


‘What-if’ scenarios are used to compare two or more well-known situations. They are the most widely used and frequently applied in the sensitivity analysis as discussed in Chaps. 11 and 12. ‘Cornerstone ‘scenarios are more uncertain and do not necessarily provide quantitative results. They point out a potential direction of future development and have a more long-term perspective. Future-oriented technology assessment in most cases deals with cornerstone S.I. Olsen et al.


scenarios and focus of the remainder of this chapter will be on approaches in this category, since these are less developed in the LCA literature. 21.3.2 Dealing with Time Horizons Time horizons in a future-oriented LCA is a central issue. Choice of time horizon depends on the focus area and the goal of the LCA. For example, foresight for areas that are difficult to change, such as roads and energy supply infrastructure, needs to have a different time horizon than foresight for areas that change quickly, such as information and communication technology. In practice, future-oriented LCA studies often look towards the future in the long-term or medium-term, but set up possibilities for action and recommendations in the short-term. 21.3.3 Dealing with Uncertainty In this context the term uncertainty does not refer to statistical uncertainty or uncertainty on measurements or data values of given parameters, but uncertainty in Fig


21.3 Relevance of different future-oriented approaches in relation to applications of LCA (Weidema 2003) Future-Oriented LCA


a more qualitative sense. It is uncertainty about what the characteristics of the technology are, how widespread the technology is going to be used, and other open questions and issues that are yet unknown. Therefore, uncertainty in future-oriented LCA studies will necessarily need to be addressed in a qualitative manner, at least in part of the analysis, and cannot be reduced to stochastic, systemic or methodological uncertainties only. Traditionally, many scientific areas aim at identifying and describing certainties and neglect or underestimate the uncertain aspects and risks connected to the field and to the new knowledge produced. In connection with technology development and techno-scientific activities, this has been called the tradition of objectification or purification (Latour 1993). For example, questions of how the technology will be produced, which use-context it implies, which support technologies, infrastructures and support systems it requires, etc


For example, questions of how the technology will be produced, which use-context it implies, which support technologies, infrastructures and support systems it requires, etc. can be hidden or ignored. Moreover, analyses within the history of science and technology and within the sociology of knowledge, show that a considerable amount of new uncertainties and risks are generated in connection with development of new technology and new knowledge (e.g. Beck 1992). As a response to this, new forms of analytical practices that focus more explicitly on the uncertainties have been developed (e.g. Harremoës 2003). When dealing with sustainability and environmental aspects of new knowledge and technologies, the way uncertainties and risks are addressed by the different knowledge communities becomes of completely central importance (Funtowicz et al. 1999; Hisschemöller et al. 2001; EEA 2001; Lemons 1995). An illustration of the traditional focus on certainties is a figure with a circle


1999; Hisschemöller et al. 2001; EEA 2001; Lemons 1995). An illustration of the traditional focus on certainties is a figure with a circle. Inside the circle is the known. Outside the circle is the unknown, called ‘no-know’ in Fig. 21.4. In between is a large, fuzzy area of partially known but uncertain issues. The fully known area is in fact very small and therefore, dealing with technology development and transition processes to new technological systems is identical to working with the uncertain issues. To be capable of analysis and assessment of the uncertainties is of central importance for the development of new technology areas since they include ‘positive’ opportunities as well as ‘negative’ effects and risks of the new developments. Two main types of uncertainty are usually pointed out in the uncertainty literature: Epistemic uncertainty (lack of knowledge) and variability uncertainty (ontological uncertainty—due to inherent variability and indeterminacy). See Chap


See Chap. 11 for further details on both. In connection with decision support through analysis and modelling, a distinction between three levels of uncertainty in between fully determined and total ignorance and indeterminacy have been pointed out (Walker et al. 2003): 1. Statistical uncertainty; 2. Scenario uncertainty; 3. Recognised ignorance. Scenario uncertainty refers to assessment of possible, plausible futures and the making of—to some degree unverifiable—assumptions in connection with this. S.I. Olsen et al.


The uncertainties of future-oriented LCA activities belong to a large extent to level 2 and 3, rather than level 1. From a technology foresight perspective, the differences between technology foresight and LCA can in practice be used productively in the design of strategic, future-oriented studies with sensitivity to environmental aspects (Rasmussen et al. 2005). For example, a combination of technology foresight and LCA were employed in a project in the wind energy area, making it possible to keep a strategic environmental perspective throughout the project (Andersen et al. 2007). From the technology foresight methods, trends mapping, a Delphi questionnaire, scenarios and a number of different expert panels were employed. In the first phase, a full present-time LCA was carried out. A later step in the process was a simplified LCA ‘scanning’ of selected aspects of the future wind power technology. Figure 21.5 shows the different phases of the project


A later step in the process was a simplified LCA ‘scanning’ of selected aspects of the future wind power technology. Figure 21.5 shows the different phases of the project. On the other hand, the need to forecast future product systems in prospective LCA can also draw a lot upon the principles of foresight, e.g. in the attempt to build scenarios. Some suggestions for the inclusion of forecasting methods are provided by Weidema et al. (2004) in a systematic form as presented in Table 21.3 and in Fig. 21.3. There has been a number of studies applying prospective LCA in practice with some of them aiming more at the methodological aspects, in particular for assessing emerging technologies (Wender et al. 2014; Frischknecht et al. 2009). When performing LCA for emerging technology cases, practitioners have responded with a number of strategies to be prospective, including: • Developing structured scenarios within LCA models (Pesonen et al. 2000; Hospido et al


2000; Hospido et al. 2010) • Statistical time-resolved data (Zimmermann et al. 2015) • Thermodynamic process modelling (Grubb and Bakshi 2011) Fig. 21.4 Understanding of ‘known’ and unknown (‘no-know’) (Harremöes 2003) Future-Oriented LCA


• Consideration of experience curves from analogous industries to identify potential future improvements in efficiency (Wender and Seager 2014) • Dimensional analysis to explore scaling effects (Caduff et al. 2014; Gavankar et al. 2014) • Exploring market-driven impacts through consequential LCA (Weidema 2003), and • Uncertainty bounding analyses to provide upper and lower limits to environmental impact (Eckelmann et al. 2012). These advances allow the development of life cycle inventories descriptive of future technological developments and accounting for parameter and scenario uncertainty in exploring how the life cycle inventory may change with future developments and alternative process configurations (Wender et al. 2014). However, many of the advances do not address the complex long-term forecasting (Table 21.3)


2014). However, many of the advances do not address the complex long-term forecasting (Table 21.3). LCA at a state-of-the-art wind power system Mapping current trends of wind power technologies and concepts Expert panel brainstorm on future wind technology Delphi survey on future wind technology Scenarios of future wind power systems Expert panel brainstorm on environmental aspects of decommissioning current and future wind technology LCA scanning of future wind power systems Reporting Fig. 21.5 Example of methodological design of a technology foresight-LCA project in the wind energy area. Modified from Andersen et al


21.5 Example of methodological design of a technology foresight-LCA project in the wind energy area. Modified from Andersen et al. (2007) Table 21.3 Suggested forecasting methods for studies with different time frames and complexity Term Complexity Specific and predictable Less predictable and more complex Short (1–5 years)– medium (3–20 years) Extrapolation methods Dynamic modelling and participatory methods Long (>20 years) Dynamic modelling, exploratory and normative methods Cornerstone scenarios methods Reproduced from Weidema et al. (2004) S.I. Olsen et al.


21.4 Concluding Remarks LCA is to a large extent being applied to support decisions that extend into the future. There are a number of challenges related to such future-oriented LCAs to which foresight methods may provide important inputs even though both methods are very different. LCAs aim to provide quantitative results, but much of our knowledge of the future is only qualitative. Therefore, LCA of future technologies will provide a set of answers and not ‘the’ answer. In addition to the steps already taken in any LCA, which to a large extent relate to different extrapolation methods, future-oriented LCA need to establish scenarios and to relate to uncertainties that are not just stochastic but rather linked to scenario uncertainty and recognised ignorance. Scenarios should be established through the help of qualified experts about future technological and economic developments, which are indispensable in future technology assessments


Scenarios should be established through the help of qualified experts about future technological and economic developments, which are indispensable in future technology assessments. Different types of scenarios are relevant to different situations, e.g. what-if scenarios are relevant in comparison of well-known situations in the short-term and in specific cases, whereas cornerstone scenarios aim to point out a potential direction in the future development with a long-term perspective. A number of prospective or foresight methods, from extrapolation to technology roadmaps, were presented which all can play a role when performing future-oriented LCA. However, providing more specific guidance in performing future-oriented LCA is difficult due to different requirements and conditions for each specific case in terms of, e.g. time horizon and complexity


time horizon and complexity. Nonetheless, a set of questions about the future technology development in a given field is a helpful tool developed to systematically address the different kinds of driving forces shaping the future technology development in this field. The questions help getting, at first, an overview of the driving forces and barriers and, later, maintaining this overview in the further discussion of the driving forces. The set of questions is presented in Appendix. Appendix: Questions The questions are formulated as standard questions which can be specified further in the work in the different technology areas according to the needs. The questions ensure that different types of development mechanisms can be addressed in a systematic manner. It is not expected that answers can be found to all questions in all cases. There will probably be questions which cannot be answered or where only vague guesses can be suggested


It is not expected that answers can be found to all questions in all cases. There will probably be questions which cannot be answered or where only vague guesses can be suggested. One’s first answers to the set of questions can be taken up again later in the analysis process, whereby some of the answers and the understanding of the development dynamics can be refined. The set of questions below is an example of questions that can be used for illuminating future developments in an area (the area in the example is energy Future-Oriented LCA


technology). The questions can either be used in a questionnaire survey, for dialogue with individual experts or for reflection internally among the LCA analysts. It is structured in three sections: • Basics—what technology are we talking about. • Drivers for technology change. • Changes resulting from the drivers. Both ‘positive’ and ‘negative’ drivers (barriers) are meant to be included. The types of driving forces addressed are: • Technical and technological issues. • Science and knowledge developments. • Energy systems—infrastructures. • Use of the technology—e.g. what role in the electricity systems? • Where, on which markets, is the technology used—how wide spread is the use? • Regional and geographical aspects. • Industrial production of the technology. • Innovation networks and innovation communities of the technology. • Public regulation and public support. • Societal and political concerns. • Environmental challenges and possible risks


• Innovation networks and innovation communities of the technology. • Public regulation and public support. • Societal and political concerns. • Environmental challenges and possible risks. Each question can be asked for (a) the near future; (b) the midterm future and (c) the long-term future. 1. Basics (in brief) 1:1 What technology is addressed? 1:2 What different basic technology concepts are available or seen as possible alternatives in the future? By technology concepts we mean for example, thin-film PV, silicon PV, etc. 1:3 What are the main elements (sub-technologies) of these technology concepts? e.g. tower, blades, foundation, net connection, etc. of off-shore wind farms, etc. 2. Drivers for technology change By ‘drivers’ is both meant ‘positive’ and ‘negative’ (limiting) factors influencing the development of the technology


of off-shore wind farms, etc. 2. Drivers for technology change By ‘drivers’ is both meant ‘positive’ and ‘negative’ (limiting) factors influencing the development of the technology. 2:1 For each main element: What are the relevant developments in techniques and technological knowledge connected to this component? 2:2 For each main element: What relevant influences from generic techno-scientific areas as material research, nanotechnology, biotechnology, biochemistry and information and communication technology can be identified as drivers for change? For example, functional surfaces, biochemical processes, corrosion knowledge, material techniques, sensor technology, microbiologic processes, etc. S.I. Olsen et al.


2:3 What are the relevant developments in integration of the technology in the energy systems and infrastructures? For example, integration technologies, institutional and organisational arrangements, development of fuel supply chains, regulatory procedures, etc. 2:4 What are the relevant developments in the use of the technology: what role will it have in electricity systems? For example, central/decentral production; general purpose or specific purpose, niche markets, etc. 2:5 What are the relevant developments in dissemination of the technology— how widespread will the use be; on what specific markets? 2:6 What regional/national/geographical aspects can be identified as drivers for technology change? For example, specific conditions in some regional electricity systems, climatic aspects, etc


2:7 What relevant developments in industrial production of the technology can be identified as drivers for technology change? 2:8 What relevant developments in the knowledge community and the network of innovators of the technology can be identified as drivers for technological change? For example, developments in the ‘industrial sector’ of the technology, industrial innovators/manufacturers, research programmes, other support institutions, etc. 2:9 What public regulation and public support can be identified as drivers for technology change? For example, market support, development programmes, etc. 2:10 What public, societal and political concerns can be identified as drivers for technology change? e.g. security of supply, employment, safety issues, emission restrictions, etc. 2:11 What developments in environmental challenges and risks can be identified and become drivers for technology change? 3


security of supply, employment, safety issues, emission restrictions, etc. 2:11 What developments in environmental challenges and risks can be identified and become drivers for technology change? 3. Resulting changes from the drivers This section concludes from section 2, sketching the picture of the technology in the short-term future, medium-term future and long-term future and pointing out relevant LCI issues. 3:1 Taken into account the questions in section 2—What main development path can be identified for the technology? 3:2 Taken into account the questions in section 2—What relevant alternative/extreme development paths can be identified? 3:3 Direct changes: Technology change. Picture of the future technology: What will, in brief, be the characteristics of the technology, its design, costs, use and life cycle? • Total design and selection of technology concept (also covering material use). • Design and the main sub-technologies/main parts. • Production processes


• Design and the main sub-technologies/main parts. • Production processes. Future-Oriented LCA


• Installation, e.g. system/support structure, foundation, site preparation, power conditioning equipment, land requirement and storage requirement. • Operating and maintenance. • Dismantling and waste handling. 3:4 Expected impacts on LCI issues How will these changes lead to changes in LCI issues (material/resource consumptions, environmental impacts, etc.) • Total design and selection of technology concept (also covering material use) • Design and the main sub-technologies/main parts • Production processes • Installation, e.g. system/support structure, foundation, site preparation, power conditioning equipment, land requirement and storage requirement. • Operating and maintenance • Dismantling and waste handling Andersen, P.D., Bjerregaard, E.: Prospective life-cycle assessment on wind power technology 2020. In: TA-Datenbank-Nachrichten, nr. 4, vol. 10 (2001) Andersen, P.D., Borup, M., Krogh, T.: Managing long-term environmental aspects of wind turbines: a prospective case study


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Integr. Environ. Assess. Manag. 11(3), 425–434 (2015). doi:10.1002/ieam.1646 Author Biographies Stig Irving Olsen LCA expert both as researcher and as consultant. Involved in the development of LCA methodologies since mid 1990s. Contributed to UNEP/SETAC working groups on LCIA methodology. Main LCA interest is human toxicity impacts, emerging technologies, and decision making. Mads Borup Analysed energy technology development and energy sector change towards sustainability since early 2000s. Works with analyses of innovation system dynamics, transition processes, sustainable consumption and production system, and green technology foresight. Interested in socio-technical, systemic perspectives on technology innovation. Per Dannemand Andersen Since mid-1990s research interests were technology foresight, strategy and strategy processes in industrial sectors, and innovation and research strategy in energy technologies


Besides technology foresight practice, interests included methodology development for foresight and the position of foresight exercises within policy development. S.I. Olsen et al.


Chapter 22 Life Cycle Management Niki Bey Abstract This chapter gives an overview of Life Cycle Management (LCM)—a discipline that deals with the managerial tasks related to practicing sustainable development in an organisation. Just as Life Cycle Assessment, LCM advocates the life cycle perspective, and it applies this perspective in decision-making processes. The chapter shows that LCA can play a key role in LCM since LCA provides quantitative performance measurements. It also explains, which stakeholders need to be considered, how LCA and LCM relate, how LCA can be used to develop Key Performance Indicators, and addresses how LCM can be integrated into an organisation


Learning Objectives After studying this chapter, the reader should be able to • Define Life Cycle Management (LCM) and describe its links to related approaches and terms • Give an overview of the central elements of LCM and how LCM relates to Life Cycle Assessment (LCA) • Identify central stakeholders and their areas of influence on decisions • Describe factors needed to make LCM work in organisations • Describe how to develop Key Performance Indicators for use in LCM practice • Practice LCM activities and decision-making contexts by means of a case study In general, this chapter takes the viewpoint of a Life Cycle Management practitioner. N. Bey (&) Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kongens Lyngby, Denmark e-mail: niki@dtu.dk © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_22


22.1 Introduction—What Is LCM? 22.1.1 Definition of Life Cycle Management—And Distinction Against Related Terms Private individuals and managers in organisations constantly make choices in the context of all kinds of activities; be it shopping in supermarkets, choosing a holiday destination, selecting means for business travel or refurbishing a family home or equipping an entire office building. When working towards sustainable development, any such choice will imply the question, which option may be the preferable one in a holistic perspective. The way advocated and described in this book is to answer this question through taking a view on the life cycles of the involved products and systems. Practicing the above in managerial decision-making is Life Cycle Management (LCM); i.e. the making of managerial decisions with a sustainability-oriented, holistic view on the life cycles of the products, service activities and systems that the managerial decisions are dealing with


the making of managerial decisions with a sustainability-oriented, holistic view on the life cycles of the products, service activities and systems that the managerial decisions are dealing with. Once adopted, Life Cycle Management means taking a constant view on many life cycles of products and systems, and practicing LCM thus influences decision-making processes in many contexts, in fact in more or less all activities an individual or an organisation pursues. Examples of decision-making in an organisation may be selecting engineering materials for products (e.g. for window frames), choosing suppliers providing materials and services, selecting logistics solutions (e.g. rail transport vs. air transport), designing or redesigning factories— or schools or residential houses—and even selecting among different options for public transportation systems to be established by a municipality—and all this potentially in different geographical regions


LCM can, with full legitimacy and good reasoning, be practiced without a focus on environment and sustainability, e.g. for pure cost optimisations. However, it will always involve a view on entire life cycles. LCM in this chapter—and in common understanding—does build on the principles of sustainable development and Life Cycle Thinking (LCT). And LCM applies LCT in a generic and flexible way to business management, potentially employing any life cycle-based approaches and methods, such as: – Life Cycle Assessment (LCA), [sometimes also referred to as ‘Environmental LCA’ (E-LCA)] – Social LCA (S-LCA or SLCA) (Chap. 16) – Life Cycle Costing (LCC) (Chap. 15); – Ecodesign/Life Cycle Design (LCD)/Sustainable Product Design (SPD), Design for Recycling/Circularity and others (Chap. 23)


16) – Life Cycle Costing (LCC) (Chap. 15); – Ecodesign/Life Cycle Design (LCD)/Sustainable Product Design (SPD), Design for Recycling/Circularity and others (Chap. 23). This also covers combinations and/or simplifications of such approaches as well as classic business tools, such as stakeholder analysis, SWOT (strengths, weaknesses, opportunities, and threats) analysis (with sustainability focus), etc. In the remainder of this chapter, the term ‘tool’ also stands synonymously for ‘method’ and ‘instrument’. N. Bey


It is very important to acknowledge that it is always up to the individual organisation to decide which tools their particular Life Cycle Management approach should comprise—there is no ‘one-size-fits-all’ LCM approach. Rather, LCM draws upon the above outlined tools and approaches—something that can be called a toolbox (UNEP/SETAC 2007)—from which the organisation picks its individual set leading to their tailor-made LCM approach. This is because a large number of factors such as product type, business model, market presence, organisational maturity, level of ambition, value chain position, regulatory frameworks and others can play a decisive role for what a meaningful and feasible approach would be for a given organisation. A prominent, already existing suggestion towards combining environmental, economic and social assessment is Life Cycle Sustainability Assessment (LCSA)


A prominent, already existing suggestion towards combining environmental, economic and social assessment is Life Cycle Sustainability Assessment (LCSA). The two principally possible methodological options to generate LCSA results are either to present three sets of impact category indicators next to each other (from LCA, LCC, and SLCA) or to develop methods for integrated inventory analysis and integrated impact assessment to reach integrated impact scores (Kloepffer 2008; Guinée et al. 2011). However, although principally targeted towards LCM, none of the two options seems immediately feasible for decision-support in Life Cycle Management for the following reasons: In the combined option, the resulting relatively large, combined number of indicators e.g. 20...30, may rather confuse than guide the decision-maker


20...30, may rather confuse than guide the decision-maker. The integrated approach, however, suffers from several methodological challenges, one of them being how to deal with location-specificity, which is crucial in both LCA and SLCA but not (yet) addressed to the extent needed. Thus, further research is required, potentially also integrating insights from, e.g. Multi-Criteria Analysis, Multi-Criteria Decision-Making, etc. (e.g. Linkov and Seager 2011; Prado et al. 2012). Life Cycle Management is applicable in businesses and organisations of all kinds, sizes, markets and supply chain/value chain positions—be it manufacturers, retailers, service providers or other organisations—be they for-profit or not-for-profit, i.e. even a sports club can practise LCM. This chapter therefore uses the term ‘organisation’ as a placeholder for ‘business’, ‘company’ or any other product producing company and/or service-providing entity practicing LCM


This chapter therefore uses the term ‘organisation’ as a placeholder for ‘business’, ‘company’ or any other product producing company and/or service-providing entity practicing LCM. Since this is a Life Cycle Assessment textbook, one can appropriately say ‘LCM puts LCA into practice’ and also ‘LCA is a key tool in the LCM toolbox’. However, unlike Life Cycle Assessment, LCM operates without being a methodology consisting of a number of well-described distinct phases or steps (and LCM is not ISO standardised either). In addition, also contrary to LCA, LCM has no deterministic character, i.e. from a given starting point and given constraints it does not necessarily lead to the same conclusions, for instance the same recommendations


from a given starting point and given constraints it does not necessarily lead to the same conclusions, for instance the same recommendations. Rather, LCM is a management concept with an underlying life cycle-sustainability-oriented mind-set, and it comprises a number of different tools that can be applied in combination or separately, by different departments of an organisation or any stakeholder, and in a variety of decision-making contexts, such as choice of manufacturing processes in production development or supplier choice in the purchase department. LCM thus pulls Life Cycle Assessment and other Life Cycle Management


quantitative or qualitative decision-support tools into concrete decision-making contexts. This is explained in Example Box 22.1. Example Box 22.1 A concrete LCM decision-making context is, for instance, whether or not to choose a certain new material that product developers—due to some technical properties—may find advantageous for a product type (e.g. for window frames). That material may also have adverse environmental properties, have a higher price per unit than alternative traditional materials, and may be more difficult to source and process in manufacturing than alternatives. In order to find out more on specifically the environmental properties of the material, either LCAs or other quantitative or qualitative assessments or data sources can be reviewed (in case they are at hand for the practitioner) or be actively commissioned


This would provide a basis for the material decision—and other factors, such as manufacturability, will have to be included in this decision context too. If it is decided to conduct an LCA, still some options exist as to how this LCA should be performed, e.g. whether a life cycle screening could suffice versus a full formal LCA, and whether to perform the assessment by own staff (that might even have to be hired/trained first) or by consultants (which may be expensive). Both choices influence the required time for the LCA, the incurred costs, and potentially other factors, e.g. the quality of the obtained LCA result. All this establishes the basis for the environment-oriented part of the decision of what material to select, and thus ultimately, all this may have an influence on the life cycle sustainability profile of the product, which the material is part of


In Life Cycle Management practice, the mechanical/technical, economic and manufacturing-related properties (as well as potentially additional ones) would have to be assessed as well—and communicated appropriately to individuals inside the organisation with different professional backgrounds, since the final decision is usually not made by one person alone. The above example indicates the breadth of contexts that the LCM practitioner may have to take into account, incl. the variety of tools, like LCA, that can be employed in support of making life cycle-spanning decisions. Qualitative tools such as guidelines and checklists may be employed as well. When looking at the LCA framework with its four phases and the Direct applications described in ISO 14040 (see Fig. 22.1), Life Cycle Management covers all of these Direct applications, such as Product development and improvement


22.1), Life Cycle Management covers all of these Direct applications, such as Product development and improvement. LCM could thus be called the initiator/trigger or 0th phase for making LCAs, but also the result-user/-executor or 5th phase of the LCA, which is indicated by the double arrow in Fig. 22.1. Life Cycle Management advocates taking a life cycle-wide view on business activities, and through this it can make the practitioner aware of two key N. Bey


circumstances: First, no life cycle stage can be neglected in the decision-making, since activities in any life cycle stage of a product or system (and thus decisions on whether or not to conduct those activities) can influence the sustainability profile of that product or system. Second, managing with a life cycle perspective initiates and sustains an organisational learning process of identifying what these influences may be, whether they are desired or non-desired ones, and which indicators to focus on. LCM thus enables identifying options and prioritising actions to reach more sustainable ways of running businesses. In essence, Life Cycle Management deals with the managerial tasks related to practicing sustainable development in an organisation. It could thus also be referred “Direct applications” are LCM activities


It could thus also be referred “Direct applications” are LCM activities. The bi-directional arrows indicate mutual influence: 1) The application context strongly influences the way the LCA is to be designed and conducted (not only in terms of Goal & Scope definition) and; 2) Methodological choices during the LCA can influence (i.e. support or reduce) the usefulness of LCA results for robust decision-making in the application context. Direct applications: • product development and improvement • strategic planning • public policy making • marketing • other Scope definition Inventory analysis Impact assessment Interpretation Goal definition Fig. 22.1 The LCA framework of ISO 14040 augmented from an LCM perspective: key for successful support of LCM by LCA is that the LCA relates to the decision context of the direct application. Therefore, the direct application context could be called the initial 0th and/or 5th phase of an LCA (based on ISO 14040:2006) Life Cycle Management


to as Sustainability Management—and is in some organisations also referred to as such, or as Responsibility Management, CSR management (CSR = Corporate Social Responsibility) or similar. However, the term ‘life cycle’ in the title underlines Life Cycle Management’s origins in the life cycle thinking (compare Sonnemann and Margni 2015, Chap. 2). Sonnemann and Margni (2015) also provide the below, all-embracing definition: “Life Cycle Management (LCM) is a management concept applied in industrial and service sectors to improve products and services while enhancing the overall sustainability performance of business and its value chains. In this regard, Life Cycle Management is an opportunity to differentiate through sustainability performance on the market place, working with all departments of a company such as research and development, procurement, and marketing, and enhance the collaboration with stakeholders along a company’s value chain


LCM is used beyond short-term business success and aims at long-term achievements minimizing environmental and socioeconomic burden while maximizing economic and social value.” Since it is an overarching, cross-functional discipline, Life Cycle Management shares aim and focus with a number of other approaches and concepts, coming from the sustainability field, the managerial field and other fields. In practical application in the organisation, all such concepts are usually combined rather than pursued separately and exclusive to each other. Thus, applied Life Cycle Management often adds the aspect of sustainability to other more established management disciplines. An example is Supply Chain Management (SCM): Originally, this activity is about securing a required input flow of supply of raw materials and pre-manufactured goods, etc. into a manufacturing company with focus on the supplies being provided in time, and at required quality and cost


into a manufacturing company with focus on the supplies being provided in time, and at required quality and cost. LCM adds the sustainability angle on this activity, for instance in the form of making sure that in addition to the other requirements also specified work conditions at suppliers comply with company standards and/or in the form that emissions from in-flow logistics do not exceed certain emission values. Management approaches and tools, which LCM relates to in such a way, include e.g. • Product (life cycle) Data Management (PDM); • Sustainable Supply Chain Management (S-SCM); • Corporate Social Responsibility (CSR) and Corporate Responsibility (CR); • Environmental management (potentially according to ISO 14001, EMAS, etc.); • Environmental Health and Safety (EHS); • Compliance management; • Corporate governance; • Risk management. N. Bey


In order to avoid possible confusion, it is pointed out below in what ways LCM in this chapter’s context differs from the above and other quite similar terms and approaches: • Product Life Cycle Management (PLM) is often used as covering product-related data management, as is Information Life Cycle Management and Product Data Management (PDM). LCM in the context of this chapter has a broader scope than such PLM/PDM, since it does not only focus on data management but on all life cycle activities in an organisation. • In some companies, Life Cycle Management is referred to as an activity starting after launch of a product, i.e. subsequently to product development. In this chapter however, LCM covers product portfolio management, product development, and after-launch/after-sales activities until end-of-life of individual products and entire product types on the market


• LCM in this chapter’s context is not an activity that relates exclusively to the marketing life cycle (from product launch over market maturity until it is taken off the market). LCM rather relates to the physical life cycle(s) (cradle-to-grave) of a given palette of products that the organisation has a certain influence upon. • Life Cycle Management is also often referred to as an activity of Asset Management, e.g. for large infrastructural installations with relatively long life times. Again, LCM in the context of this chapter has a broader scope than just this management of use/after-sales and end-of-life. • Life Cycle Management also differs from environmental management systems and schemes, such as the European EMAS, the British BS7750 and the international ISO 14001, since these are designed for production (site) management


ISO 14001—according to ISO, one of the most widely applied standards for environmental management—in its 2015 revision now requires a view on life cycles of the produced products as well—in addition to the production focus. This, in turn, exemplifies a tendency towards integrated approaches rather than to pursue several single-issue-focussed management systems in parallel. Instead of focusing on a certain part of the organisation or of the product life cycle, Life Cycle Management takes as a starting point an overarching perspective, and it thus influences—and is influenced by—many parts of the organisation (Fig. 22.2). It is important to note already here that there are internal and external factors influencing the accomplishment of the managerial tasks. This will be elaborated later in this chapter (Sect. 22.2). Life Cycle Management


22.1.2 Brief History of LCM—Why Did It Arise? How Does It Evolve? Life Cycle Management is a relatively young concept, which came up only about three decades ago. This timing can be explained as a logic consequence of two factors: First, the need of practitioners to operationalise the, then, new concept of Sustainable Development. Since the life cycle concept had been known and applied already since the mid-1970s (e.g. for military asset procurement; see also Chap. 15 on Life Cycle Costing), dealing with sustainability matters in a life cycle perspective was an obvious path to go. Second, a number of environmental incidents and catastrophes happened during the 1970s and 1980s


Second, a number of environmental incidents and catastrophes happened during the 1970s and 1980s. Immense disasters for instance with chemicals in the cities of Bhopal and Seveso, with oil tankers such as Amoco Cadiz and Exxon Valdez, with nuclear power plants such as Three Mile Island and Chernobyl as well as the controversial end-of-life treatment of the off-shore oil storage buoy Brent Spar all filled much in the public debate as well as in public and corporate consciousness in those years—and they were increasingly communicated through rising global news networks and environmental NGOs Management Distribution Sales & Marketing Procurement Production Product Development Life Cycle Management Life cycle-based environmental policy and product strategy Green distribution Green marketing Cleaner production Green procurement Design for Environment/ Ecodesign Fig


22.2 Life Cycle Management as the central influencer and connector of different departments in an organisation and (shown on the circumference) examples of the outcome of its influence in each of those departments (based on UNEP 2007) N. Bey


(Non-Governmental Organisations). Therefore, the question of dealing with these issues in a strategically feasible and desired way—i.e. sustainably—became evident for governments, businesses, and the upcoming environmental NGOs alike. A key initiator and driver for LCM discussion and development since 2002 is the UNEP-SETAC Life Cycle Initiative, which has issued a large number of publications, e.g. the LCM Guide (UNEP/SETAC 2007) and online material such as the LCM Navigator for SMEs (UNEP/SETAC 2008). The UNEP-SETAC Life Cycle Initiative has also been patron and sponsor for the bi-annual conference series on LCM that took place for the first time in 2001 in Copenhagen, Denmark. The LCM field is also promoted and elaborated by particular companies and, to a certain extent, also by business organisations and other NGOs, e.g. the World Business Council for Sustainable Development (WBCSD), through e.g. guidelines and models developed in their regime


the World Business Council for Sustainable Development (WBCSD), through e.g. guidelines and models developed in their regime. Life Cycle Management is today an established discipline, albeit pursued under many different names (as explained in Sect. 22.1). Current trends in LCM include: 1. Interest in mainstreaming LCM, i.e. getting it better integrated in standard procedure of organisations in many contexts (Sonnemann and Margni 2015); 2. Using LCM in organisational capability and maturity development support (e.g. Swarr et al. 2015; Pigosso et al. 2013); 3. Improving data flow integration and exploitation, e.g. in relation to socio-technical trends such as Internet-of-Things (i.e. the interconnectedness of various products, other than computers, through the Internet), and Big Data (i.e. the tracking, storing and making-available of large amounts of data; in fact relating to ‘PLM/PDM’); 4. Implementing LCM coherently and in the long term in ever-changing organisations


the tracking, storing and making-available of large amounts of data; in fact relating to ‘PLM/PDM’); 4. Implementing LCM coherently and in the long term in ever-changing organisations. The 17 Sustainable Development Goals (SDGs) agreed upon in the UN General Assembly in late 2015, are increasingly understood as managerial targets that also drive future developments [e.g. in the SDG Compass (GRI/UN Global Compact/WBCSD 2015)]; 5. Developing approaches to ensure companies’ long-term survival, e.g. if their business offerings rely on non-renewable resources. 22.1.3 LCM as Integral Part of a Management System To manage means, in general, to have control of, to take care of and to make decisions about. The goal of Life Cycle Management is to contribute to sustainable development, through operationalising it in organisations. LCM does this through (1) requiring the practitioner to identify those sustainability matters that are relevant for the particular organisation (e.g


LCM does this through (1) requiring the practitioner to identify those sustainability matters that are relevant for the particular organisation (e.g. via conduction of LCAs and other assessments), and (2) connecting these with possible managerial actions (i.e. for instance advising on who shall do what and when). The subsequent managerial actions of Life Cycle Management


monitoring/checking and potentially initiating corrective or alternative actions are part of LCM in the same way as they are part of managerial procedure in general. Similar to any other management concept, LCM can be integrated into a management system that the organisation may be using, and LCM can herein serve as a tool, e.g. for Sustainability Strategy Development. In its support of continuous improvement, LCM fits seamlessly together with widely applied management systems developed by ISO, such as ISO 9001 for Quality management and ISO 14001 for Environmental management, the latter having undergone a substantial revision in 2015, now emphasising its relevance not only in production contexts but also for product life cycle improvements, see ISO (2015). The particular managerial tasks of Life Cycle Management, mentioned in Sect. 22.1.1, can be distinguished into four types: 1


The particular managerial tasks of Life Cycle Management, mentioned in Sect. 22.1.1, can be distinguished into four types: 1. Setting (measurable) targets—for the entire organisation or for parts of it, as part of, for instance, a strategy development process, as well as creating the basis for execution and planning, so that targets can be reached; 2. Executing the plan; 3. Tracking execution and performance; and 4. Taking corrective actions or setting new targets—depending on the performance. In shorter terms, this sequence is described as Plan, Do, Check, Act or just PDCA (UNEP/SETAC 2007; ISO 2015), and in the sense of continuous improvement, the four phases will be repeated cyclically for an infinite number of times (see Fig. 22.3), each time at a slightly higher level of sophistication. Such a standard managerial activity cycle is practiced in many organisations and is a backbone concept in many ISO standards


22.3), each time at a slightly higher level of sophistication. Such a standard managerial activity cycle is practiced in many organisations and is a backbone concept in many ISO standards. Life Cycle Management follows those same four phases but distinguishes itself particularly in what the first and second phase deal with, namely target-setting for above sustainability matters and creating structures so that sustainability targets can be reached. Plan Do Check Act Continuous improvement cycle Fig. 22.3 A generic PDCA cycle of continuous improvement, consisting of the four phases plan, do, check, and act, which are run through continuously in management concepts such as LCM (based on ISO work, e.g. ISO 2015) N. Bey