Article

Illustrating Anticipatory Life Cycle Assessment for Emerging Photovoltaic Technologies

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Abstract

Current research policy and strategy documents recommend applying life cycle assessment (LCA) early in research and development (R&D) to guide emerging technologies toward decreased environmental burden. However, existing LCA practices are ill-suited to support these recommendations. Barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. Overcoming these challenges requires methodological advances that help identify environmental opportunities prior to large R&D investments. Such an anticipatory approach to LCA requires synthesis of social, environmental, and technical knowledge beyond the capabilities of current practices. This paper introduces a novel framework for anticipatory LCA that incorporates technology forecasting, risk research, social engagement, and comparative impact assessment, then applies this framework to photovoltaic (PV) technologies. These examples illustrate the potential for anticipatory LCA to prioritize research questions and help guide environmentally responsible innovation of emerging technologies.

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... Research, development, deployment and widespread diffusion of new environmentally sound technologies is a major route towards achieving sustainability (United Nations, 2017). To support the research and development of claimed environmentally sustainable technologies, international research frameworks such as the European Horizon 2020 program (European Commission, 2017) demand the application of quantitative methods, such as life cycle assessment (LCA) (Wender et al., 2014b). ...
... The available data and knowledge is specific for the case and context at hand (Gavankar et al., 2014;Hospido et al., 2010) and is highly subjective to a variety of factors that cannot be controlled (Miller and Keoleian, 2015). Existing LCI databases are based on historic data and the new technology under study is not available therein (Kunnari et al., 2009a;Wender et al., 2014b). The data that are available most likely originate in lab experiments or pilot projects and are therefore not representative of operational scales (Arvidsson et al., 2014;Gifford et al., 2016;Hesser et al., 2017a;Schulze et al., 2018). ...
... In ex-ante LCA studies it is important to realize that potential environmental impacts of new technologies are not automatically covered by the existing impact categories commonly used in expost LCA studies described in the LCA handbook (Guin ee et al., 2002) or in the ReCiPe life cycle impact assessment method (Huijbregts et al., 2017). Recent ex-ante LCA studies of new technologies and new materials stress the great limitation of the lack of characterization factors at the life cycle impact assessment (LCIA) phase of LCA studies (Deng et al., 2016;McKone et al., 2011;Tufvesson et al., 2012;Wender et al., 2014b). The absence of suitable impact categories and specific characterization factors may mask the true environmental performance of a new technology compared to the incumbent technology, as many biosphere flows with a potential environmental impact are left unclassified due to a lack of adequate models and data. ...
Article
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LCA is a well-known assessment tool that identifies and provides insights on the environmental impacts of products and services over their lifecycle. The guidance provided by the existing manuals typically applies to modelling and assessing environmental impacts ex-post, meaning that information is available from empirical experience after products have been commercially in use for extended periods of time. This information is not available if LCA is applied in an ex-ante manner before a technology is commercially deployed at scale. We identify the major challenges of applying LCA in an ex-ante manner and propose a route forward in dealing with these challenges that combines intuitions from other disciplinary fields. The first challenge is how to model consistent future foreground systems for the incumbent and new technology systems. Learning curves and scenario approaches are the way forward. The second challenge is how to model future background systems. Here a solution is to transform existing LCI databases towards future contexts, informed by the Integrated Assessment Models (IAMs) that provide scenarios in line with the Shared Socioeconomic Pathways (SSPs). Finally, uncertainty in ex-ante LCA is of a different nature as in ex-post LCAs. The main difference with conventional LCA studies is the highly uncertain information for the future. To acknowledge this. considerate attention should be attributed to the discussion on these uncertainties, both in the design of the assessment and the data used. Responsive evaluation can play a supportive role here. This will increase the transparency and efficacy of the results because the relevant stakeholders and experts are involved. In this way technology designers and other stakeholders derive insights on the influence of design choices or contextual factors (that are important, but hard to influence) on the potential environmental impacts of their foreseen technology.
... However, typically, LCA approaches rely on detailed and specific data from throughout the life-cycle of processes and products that are already in the market (Cherubini et al., 2009;Spath et al., 1999;Vink et al., 2003;Williams et al., 2006). In recent years an increasing volume of research has attempted to conduct LCA on products at low TRLs, such as nanomaterial production, graphene, biofuels and carbon capture and utilisation (Arvidsson et al., 2014;Cuellar-Franca et al., 2016;Gavankar et al., 2015;Hischier and Walser, 2012;Rajagopalan et al., 2017;Wender et al., 2014b). While it appears that the existing underlying framework for conducting LCA is appropriate for emerging technologies, there is no wellestablished approach to the use of LCA under such circumstances (ISO, 2006a,b;Klöpffer et al., 2007). ...
... • Using Monte Carlo simulations and probabilistic comparisons can enable the high parameter uncertainty experienced at low-TRLs to be propagated and explored (Wender et al., 2014b). However, this uncertainty is not always communicated in the results, with many studies still attempting to present simple, aggregated results, which can be misleading for policy-makers (Stirling, 2008). ...
... Thorstensen and Forsberg (2016) articulate SLCA as a tool for anticipation at the level of specific products, allowing the systematic study of social sustainability issues, and operationalisation of RRI. According to Wender et al. (2014b), life-cycle tools enable an approach which: ''systematically and iteratively explores uncertainties across the life cycle of an emerging technology to prioritise research with the greatest potential for environmental improvement and contributions to responsible innovation''. Helping technological actors to view and understand the variety of avenues and possibilities available and their wide-ranging implications helps to open-up governance approaches, questioning current technological expectations and commitments and promotes governance that emphasises informed experimentation and ''directed incrementalism'', preserving developmental flexibility for longer (Grunwald, 2010). ...
Article
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Emerging technologies are increasingly promoted on the promise of tackling the grand challenge of sustainability. A range of assessment and governance approaches seek to evaluate these claims, but these tend to be applied disparately and lack widespread operationalisation. They also face specific challenges, such as high levels of uncertainty, when it comes to emerging technologies. Building and reflecting on both theory and practice, this article develops a framework for Constructive Sustainability Assessment (CSA) that enables the application of sustainability assessments to emerging technologies as part of a broader deliberative approach. In order to achieve this, we discuss and critique current approaches to analytical sustainability assessment and review deliberative social science governance frameworks. We then develop the conceptual basis of CSA - blending life-cycle thinking with principles of responsible research and innovation. This results in four design principles - transdisciplinarity, opening-up, exploring uncertainty and anticipation - that can be followed when applying sustainability assessments to emerging technologies. Finally, we discuss the practical implementation of the framework through a three-step process to (a) formulate the sustainability assessment in collaboration with stakeholders, (b) evaluate potential sustainability implications using methods such as anticipatory life-cycle assessment and (c) interpret and explore the results as part of a deliberative process. Through this, CSA facilitates a much-needed transdisciplinary response to enable the governance of emerging technologies towards sustainability. The framework will be of interest to scientists, engineers, and policy-makers working with emerging technologies that have sustainability as an explicit or implicit motivator.
... Performing an LCA on emerging technologies such as HS-DAC is challenging since empirical data other than laboratory implementations of the technology are lacking and therefore uncertainty is introduced where assumptions and predictions of real-world implementations need to be used (Frischknecht et al. 2009;Hetherington et al. 2014;Hospido et al. 2010;Miller and Keoleian 2015;Wender et al. 2014b). However, since energy demand is often responsible for the largest share of the environmental impacts in LCA studies (Huijbregts et al. 2010;Sugiyama et al. 2008;Patel et al. 2012), energy demand is considered an important focal point in LCAs of new technologies (Patel et al. 2012;von der Assen et al. 2016) and a key factor for the costs and environmental impacts of CO 2 capture (von der Assen et al. 2016). ...
... Research, development, deployment and widespread diffusion of new environmentally sound technologies is a major route towards achieving sustainability (United Nations 2017). To support the research and development of claimed environmentally sustainable technologies, international research frameworks such as the European Horizon 2020 program (European Commission 2017) demand the application of quantitative methods, such as life cycle assessment (LCA) (Wender et al. 2014b). ...
... The available data and knowledge is specific for the case and context at hand (Gavankar et al. 2014;Hospido et al. 2010) and is highly subjective to a variety of factors that cannot be controlled (Miller and Keoleian 2015). Existing LCI databases are based on historic data and the new technology under study is not available therein (Kunnari et al. 2009;Wender et al. 2014b). The data that are available most likely originate in lab experiments or pilot projects and are therefore not representative of operational scales (Arvidsson et al. 2014;Gifford et al. 2016;Hesser et al. 2017a;Schulze et al. 2018). ...
Thesis
Full-text available
The development of new environmentally sound technologies is seen as a key route towards achieving sustainability. Also technology is regarded as the most important factor in the scientific field of industrial ecology for reducing environmental impacts of anthropogenic action. Developing greener, cleaner and more efficient technologies (using fewer resources or using them more efficiently) has therefore increasingly become the focus of many research projects that include the need to assess the potential future environmental impacts of technologies in the early development stages. The aim of this thesis was to develop a forward-looking assessment method based on LCA that can be used to integrate early insights in potential environmental impacts in R&D, with a specific focus on new energy technologies.
... They defined an LCA as prospective "when the (emerging) technology studied is in an early stage of development (e.g., small-scale production), but the technology is modeled at a future, more-developed stage (e.g., large-scale production)." Prospective LCAs are also called anticipatory [18] or ex-ante LCAs [19]. For a more in-depth discussion on the definitions of different modes of LCA including future states of product systems, see Cucurachi et al. [19] and Buyle et al. [20]. ...
... After scale-up, the impacts are mostly lower when compared to the impacts of the lab-scale system [18,[41][42][43][44]. Nevertheless, Arvidsson and Molander [23] found that scale-up behavior is case-specific and lower environmental impacts at a developed technology stage cannot be taken for granted. ...
... Several authors mentioned specific issues while defining the functionality of their observed system. Arvidsson et al. [22]; Arvidsson and Molander [23]; Arvidsson et al. [53] and Wender et al. [18] are aware of the functionality issue and noticed the issues concerning the definition of the functional unit. Still, the authors did not vary the functional unit in their studies and, therefore, did not investigate the Sustainability 2020, 12, 1192 8 of 23 effects of different functional units. ...
Article
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Emerging technologies are expected to contribute to environmental sustainable development. However, throughout the development of novel technologies, it is unknown whether emerging technologies can lead to reduced environmental impacts compared to a potentially displaced mature technology. Additionally, process steps suspected to be environmental hotspots can be improved by process engineers early in the development of the emerging technology. In order to determine the environmental impacts of emerging technologies at an early stage of development, prospective life cycle assessment (LCA) should be performed. However, consistency in prospective LCA methodology is lacking. Therefore, this article develops a framework for a prospective LCA in order to overcome the methodological inconsistencies regarding prospective LCAs. The methodological framework was developed using literature on prospective LCAs of emerging technologies, and therefore, a literature review on prospective LCAs was conducted. We found 44 case studies, four review papers, and 17 papers on methodological guidance. Three main challenges for conducting prospective LCAs are identified: Comparability, data, and uncertainty challenges. The issues in defining the aim, functionality, and system boundaries of the prospective LCAs, as well as problems with specifying LCIA methodologies, comprise the comparability challenge. Data availability, quality, and scaling are issues within the data challenge. Finally, uncertainty exists as an overarching challenge when applying a prospective LCA. These three challenges are especially crucial for the prospective assessment of emerging technologies. However, this review also shows that within the methodological papers and case studies, several approaches exist to tackle these challenges. These approaches were systematically summarized within a framework to give guidance on how to overcome the issues when conducting prospective LCAs of emerging technologies. Accordingly, this framework is useful for LCA practitioners who are analyzing early-stage technologies. Nevertheless, further research is needed to develop appropriate scale-up schemes and to include uncertainty analyses for a more in-depth interpretation of results.
... use it to track progress throughout the funding cycle (EC, 2018(EC, , 2019. Despite the growing use of LCA at these early stages, there is a lack of a systematic guidance for LCA analysts to address the particular challenges of emerging technologies (e.g., Wender et al., 2014b). Specifically, there remains confusion about how LCA can (or should) be used at different stages of technology development and market adoption. ...
... The literature on LCA of emerging technologies includes many case studies, variously using terms such as prospective (Mendoza Beltran et al., 2018;Cooper & Gutowski, 2018;Raugei & Winfield, 2019;Sathre et al., 2014;Wender & Seager, 2011), early stage (Cramer, 2000;Hetherington et al., 2014;Hung, Ellingsen, & Majeau-Bettez, 2018), ex ante (Hesser, 2015;Villares, Işıldar, Van der Giesen, & Guinée, 2017;Zhou et al., 2012), anticipatory (Gifford, Chester, Hristovski, & Westerhoff, 2016;Kendall & Yuan, 2013;Mattick, Landis, Allenby, & Genovese, 2015;Ravikumar, Seager, Cucurachi, Prado, & Mutel, 2018;Tsang, Philippot, Aymonier, & Sonnemann, 2016;Wender et al., 2014b;Wender, Foley, Guston, Seager, & Wiek, 2012), explorative (Steubing, Mutel, Suter, & Hellweg, 2016), and scenario-based (Arvidsson et al., 2017) LCA. The diversity of terms mirrors the wide range of available methods and disparate language employed across the LCA community. ...
... In addition to confusion in terminology, the procedures and tools employed to assess emerging technologies have yet to be well-defined or Gavankar, Anderson, & Keller, 2015;Gavankar, Suh, & Keller, 2015;Khanna, Bakshi, & Lee, 2008;Piccinno, Hischier, Seeger, & Som, 2018;Simon, Bachtin, Kiliç, Amor, & Weil, 2016;Wender & Seager, 2011), and photovoltaics (e.g., Jungbluth, Bauer, Dones, & Frischknecht, 2005;Ravikumar et al., 2018;Wender et al., 2014b), there is a need for additional cross-case analysis to provide more generalized guidance (Miller & Keoleian, 2015;Wender et al., 2014a). The discussion is further complicated by the diversity with respect to objectives of the analysis and methods to employ, for example, related to the use of attributional versus consequential LCA (ALCA vs. CLCA; Earles & Halog, 2011;Plevin, Delucchi, & Creutzig, 2014;Suh & Yang, 2014;Zamagni, Guinée, Heijungs, Masoni, & Raggi, 2012). ...
Article
Full-text available
Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle‐based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes.
... Wender et al. [26,76] PV panels ...
... They link the separate chemical steps to similar processes on an industrial scale, making it possible to estimate the environmental profile on an industrial scale (see Section 3.3.3). The ideal system baseline approach is also used to set a range of values in sensitivity analysis, i.e., to define yields, efficiency, etc., [67,76]. ...
... Villares et al. [75] introduce this idea from a conceptual point of view without applying it; they link it to an exploratory approach. In a less extreme way, this concept is taken into account by Blok et al. [43] to define functionalities of emerging technologies, and by Wender et al. [76] to combine stakeholder opinions with direct research efforts. ...
Article
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Every decision-oriented life cycle assessment (LCAs) entails, at least to some extent, a future-oriented feature. However, apart from the ex-ante LCAs, the majority of LCA studies are retrospective in nature and do not explicitly account for possible future effects. In this review a generic theoretical framework is proposed as a guideline for ex-ante LCA. This framework includes the entire technology life cycle, from the early design phase up to continuous improvements of mature technologies, including their market penetration. The compatibility with commonly applied system models yields an additional aspect of the framework. Practical methods and procedures are categorised, based on how they incorporate future-oriented features in LCA. The results indicate that most of the ex-ante LCAs focus on emerging technologies that have already gone through some research cycles within narrowly defined system boundaries. There is a lack of attention given to technologies that are at a very early development stage, when all options are still open and can be explored at a low cost. It is also acknowledged that technological learning impacts the financial and environmental performance of mature production systems. Once technologies are entering the market, shifts in market composition can lead to substantial changes in environmental performance.
... The need to evaluate and improve the environmental sustainability of emerging technologies early in the research and development (R&D) process, has been recognized widely and is steadily gaining interest among researchers (Moni et al., 2020). The application of LCA early in R&D stages has been acknowledged as a possible way to guide emerging technologies towards decreased environmental burden (Wender et al., 2014). Application of LCA to identify environmental hotspots has a great potential to guide and drive emerging technologies at lab scale. ...
... While exploring the possibility of applying conventional LCA to evaluate SL production with emerging technologies at lab scale, it was found that there was insufficient inventory data for novel materials, enzymes, and energy used in the production processes. Other general inhibiting factors include inherent uncertainty related to LCA models; rapid technology change; confidentiality related data collection and seclusion of environmental aspects from technical research (Chopra et al., 2019;Wender et al., 2014). Therefore, in this work, instead of using a retrospective (traditional) LCA, we propose to use the dynamic LCA approach. ...
Article
Microbial biosurfactants are surface-active molecules that are naturally produced by a range of microorganisms. They have certain advantages over chemical surfactants, such as lower toxicity, higher biodegradability, anti-tumor, and anti-microbial properties. Sophorolipids (SLs) in particular are one of the most promising biosurfactants, as they hold the largest share of the biosurfactant market. Currently, researchers are developing novel approaches for SL production that utilize renewable feedstocks and advanced separation technologies. However, challenges still exist regarding consumption of materials, enzymes, and electricity, that are primarily fossil based. Researchers lack a clear understanding of the associated environmental impacts. It is imperative to quantify and optimize the environmental impacts associated with this emerging technology very early in its design phase to guide a sustainable scale-up. It is necessary to take a collaborative perspective, wherein life cycle assessment (LCA) experts work with experimentalists, to quantify environmental impacts and provide recommendations for improvements in the novel waste-derived SL production pathways. Studies that have analyzed the environmental sustainability of microbial biosurfactant production are very scarce in literature. Hence, in this work, we explore the possibility of applying LCA to evaluate the environmental sustainability of SL production. A dynamic LCA (dLCA) framework that quantifies the environmental impacts of a process in an iterative manner, is proposed and applied to evaluate SL production. The first traversal of the dLCA was associated with the selection of an optimal feedstock, and results identified food waste as a promising feedstock. The second traversal compared fermentation coupled with alternative separation techniques, and highlighted that the fed-batch fermentation of food waste integrated with the in-situ separation technique resulted in less environmental impacts. These results will guide experimentalists to further optimize those processes, and improve the environmental sustainability of SL production. Resultant datasets can be iteratively used in subsequent traversals to account for technological changes and mitigate the corresponding impacts before scaling up.
... 14, 15 Gilbertson and colleagues emphasize the need to coordinate research between experimental environmental scientists and modelers to develop new datasets to addresses the problem of lack of data that is up to date with the evolution of the technology. 15 Researchers have invested considerable efforts to investigate the upstream life cycle impacts corresponding to the design decisions on NEP production, [16][17][18][19][20] as well as the downstream transportation and transformation of ENM released from NEPs. [21][22][23][24] While independently, these areas have made significant advances in our understanding of the environmental implications of NEPs, there are challenges faced by both life cycle modelers and experimentalists concerned with ENM release from NEPs. ...
... This is consistent with the anticipatory LCA approach that assesses scenarios to determine the future environmental burdens of such emerging technologies. 16,36 The dynamic approach differs from the retrospective in that levels of uncertainty in data quantity, quality, and impact assessment, and variations in stakeholder behavior and valuation are explicitly incorporated into the analysis. The framework is compatible with latest integrative assessment tools (such as LICARA nanoSCAN) that combine LCA and RA with structured decision analysis techniques, 37 as well as populate Ashby-like plots for ENM selection and design of sustainable NEPs. ...
Article
Full-text available
Life Cycle Assessment (LCA) is a powerful tool for assessing the environmental impacts of established processes and products. However, its use in decision-making for sustainable development of emerging technologies is challenging. High levels of uncertainty and lack of data over the complete value chain associated with nascent nano-enabled products (NEPs) makes it difficult to perform LCA studies early in the design process. This study addresses the uncertainty problem faced by LCA, and a demonstration is performed with a case study of quantum dot (QD)-enabled display. The study at hand proposes a dynamic life cycle assessment (dLCA) framework, which emphasizes iterative evaluation and collaborative efforts to tackle the data scarcity problem faced by retrospective (traditional) LCA. Experimental study of two commercially available QD-enabled displays (hand-held tablet with CdSe QD-enabled display and TV set with InP QD-enabled display) is performed for data collection of QD amount and release. After complete digestion, the experimental result shows that the concentration of CdSe (3.92 ± 0.32 µg/cm2) in the QD enhancement film (QDEF) of Tablet is comparable with the concentration of InP (3.56 ± 0.24 µg/cm2) in the QDEF of TV. After accounting for the experimental results, the second traversal of dLCA is performed, and it shows that cumulative energy demand (CED) per unit area for InP QD-enabled displays is 5.28 x 10-3 MJ/cm2 (first traversal was 2.59 x 101 MJ/cm2) and CdSe QD-enabled displays is 3.92 x 10-4 MJ/cm2 (first traversal was 4.32 x 10-2 MJ/cm2). This study highlights the role of collaborative research between life cycle modelers and experimentalists to improve the credibility of LCA results for emerging NEPs. Even though this study is based on the case of QD-enabled displays, the proposed dLCA framework and interdisciplinary collaboration method can also be applied to other emerging technologies.
... It is common practice to evaluate a future technology at a research scale before mass production. While it is advantageous to evaluate an emerging technology in the research and development phase of a product's development, LCA of emerging technologies is an ongoing challenge [13]. Emerging technologies are developed in inefficient smallscale laboratories [14]; however, process efficiency usually increases as the production scale increases [15]. ...
... To estimate the energy consumption of the fabrication machines at a different scale from that of the lab-scale inventory dataset, an indirect upscaling method [13] was used in this case study. Using this indirect method, the input energy required to produce a given output is expressed as follows: ...
Article
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In this paper, a possibility to reduce the environmental burdens by employing thermoelectric generators (TEGs) was analyzed with a cradle-to-grave LCA approach. An upscaling technique was newly introduced to assess the environmental impacts of TEGs over its life cycle. In addition to CO2 emissions, other environmental impacts as well as social impacts were assessed using the Life Cycle Impact Assessment Method based on Endpoint Modeling (LIME2). The analysis was conducted under two scenarios, a baseline scenario with a 7.2% conversion efficiency and a technology innovation scenario with that of 17.7% at different production scales. The results showed that while GHG emissions were positive over the life cycle under the baseline scenario, it became negative (−1.56 × 102 kg-CO2 eq/kg) under the technology innovation scenario due to GHG credits in the use phase. An increase in the conversion efficiency of the TEG and a decrease in the amount of stainless steel used in TEG construction are both necessary in order to reduce the environmental impacts associated with TEG manufacture and use. In addition, to accurately assess the benefit of TEG deployment, the lifetime driving distance needs to be analyzed together with the conversion efficiency.
... 1 Rather than treating environmental issues piece-wise, life cycle assessment (LCA) emerged as an analytical antidote to the environmental ills of manufacturing industries such as metals, chemicals, plastic, automobiles, paper, and fossil fuels. 2 While LCA also been imagined as a method for steering new technologies toward environmentally preferable outcomes, 3 the fact is that the dominant practices are inherently retrospective. 4 As a consequence, emerging technologies such as nanoenabled products present a significant challenge to LCA analysts seeking to mitigate prospective environmental risks. 5 Key among these challenges is developing informative models for decision support 6 despite extraordinary data scarcity and uncertainty. ...
... 20,22 When integrated with the novel anticipatory-LCA framework for emerging technologies, the decision-driven approach identifies the most promising option from competing alternatives in a specificenvironmental context and can prioritize further R&D efforts to inform the selection of the environmentally preferable emerging technology alternative for commercialization. 4 In addition, the anticipatory, decision-driven approach (hereafter anticipatory approach) avoids external normalization where characterized inventory midpoints are typically divided by reference values applicable to a chosen geographic region. 25 For example, if the climate impact of manufacturing a product is 1000 kg CO 2 -eq and the reference value for climate change for the world is 5.76 × 10 13 , 24 the externally normalized climate change score is 2.39 × 10 −11 . ...
Article
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It is now common practice in environmental life cycle assessment (LCA) to conduct sensitivity analyses to identify critical parameters and prioritize further research. Typical approaches include variation of input parameters one at a time to determine the corresponding variation in characterized midpoints or normalized and weighted endpoints. Generally, those input parameters that cause the greatest variations in output criteria are accepted as the most important subjects of further investigation. However, in comparative LCA of emerging technologies, the typical approach to sensitivity analysis may misdirect research and development (R&D) towards addressing uncertainties that are inconsequential or counterproductive. This paper presents a novel method of sensitivity analysis for a decision-driven, anticipatory LCA of three emerging photovoltaic (PV) technologies: amorphous-Si (a-Si), CdTe and ribbon-Si. Although traditional approaches identify metal depletion as critical, a hypothetical reduction of uncertainty in metal depletion fails to improve confidence in the environmental comparison. By contrast, the novel approach directs attention towards marine eutrophication, where uncertainty reduction significantly improves decision confidence in the choice between a-Si and CdTe. The implication is that the novel method will result in better recommendations on the choice of the environmentally preferable emerging technology alternative for commercialization.
... From a long-term perspective, future changes in various factors, such as product price and product lifetime, will influence the diffusion of low-carbon products. Until now, research has been conducted on the forecasting of product diffusion [1] [2] and life cycle assessment (LCA) for estimating the environmental impact of the entire life cycles of low-carbon products [3]. However, it has not been fully studied how low-carbon products will reduce regional CO2 emissions over a longer period of time (e.g., 30 years). ...
Chapter
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Various low-carbon products, such as photovoltaic (PV) panels, have been diffused to reduce CO2 emissions across the world. When we consider future uncertainties and product lifetimes, the question arises of how such products will reduce regional CO2 emissions over a longer period of time (e.g., 30 years). With the aim to answer this question, we describe scenarios to analyze the relationship among various social changes in the future (e.g., energy policy), the amount of low-carbon products diffused in a region, and the resulting CO2 emissions throughout the life cycle of the products. In this paper, we develop an integrated model for estimating the diffusion of low-carbon products and the CO2 emissions due to product diffusion using life cycle simulation. In a case study, we described several PV diffusion scenarios toward 2045 for the Tokyo area, in which we evaluated PV installation capacity and the CO2 emissions caused by PV diffusion. The results showed that the ownership rate of PV in 2045 would account for 36.8–53.6% of households. In addition, it was revealed that the extension of product lifetime provides the opportunity to reuse secondhand PV, causing less CO2 emissions than other scenarios.
... The use of LCA in an early stage of technological development has been studied since the 1990s (e.g., Azapagic, 1999) and has since continued to develop under diverse names. These include anticipatory (Collier, Connelly, Polmateer, & Lambert, 2017;Wender et al., 2014), early stage (Hetherington, Borrion, Griffiths, & McManus, 2014), emerging (Barberio, Scalbi, Buttol, Masoni, & Righi, 2014;Tsang, Bates, Madison, & Linkov, 2014), ex ante (Fazeni, Lindorfer, & Prammer, 2014;Villares, Işıldar, Beltran, & Guinee, 2016;Villares, Işıldar, van der Giesen, & Guinée, 2017), prospective (Arvidsson et al., 2017;Miller & Keoleian, 2015;Simon, Bachtin, Kiliç, Amor, & Weil, 2016), and screening LCA (Hung, Ellingsen, & Majeau-Bettez, 2020). As aptly summarized by Bergerson et al. (2020), this "diversity of terms mirrors the wide range of available methods and disparate language employed across the LCA community." ...
Article
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Estimating the environmental impact of emerging technologies at different stages of development is uncertain but necessary to guide investment, research, and development. Here, we propose a systematic procedure to assess the future impacts of emerging technologies. In the technology development stage (technology readiness level < 9), the recommended experience mechanisms to take into account are (a) process changes, (b) size scaling effects, and (c) process synergies. These developments can be based on previous experience with similar technologies or quantified through regression or engineering dimension calculations. In the industrial development phase, (d) industrial learning, based on experience curves or roadmaps, and (e) external developments should be included. External developments, such as changes in the electricity mix can be included with information from integrated assessment models. We show the applicability of our approach with the greenhouse gas (GHG) footprint evaluation for the production of copper indium gallium (di)selenide (CIGS) photovoltaic laminate. We found that the GHG footprint per kilowatt peak of produced CIGS laminate is expected to decrease by 83% going from pilot to mature industrial scale production with the largest decrease being due to expected process changes. The feasibility of applying our approach in practice would greatly benefit from the development of a database containing information on size scaling and experience rates for a wide variety of materials, products, and technologies.
... Therefore, predefined value functions as in outranking are useful in the interpretation of comparative LCA type studies. Specifically, outranking based Stochastic Multi Attribute Analysis (SMAA) has been used in a number of comparative LCA studies as a way to aggregate results in a range of applications such as transportation fuels (Rogers and Seager, 2009), carbon nanotubes (Canis et al., 2010), detergents , biofuel feedstocks (Rajagopalan et al., 2016), and photovoltaics (Ravikumar et al., 2018;Wender et al., 2014). Note that SMAA can also be used to refer to stochastic methods which may have different underlying aggregation algorithms such as SMAA-TOPSIS (Zhu et al., 2018), which is fully compensatory. ...
Article
The selection of an alternative based on the results of a comparative environmental assessment such as life cycle assessment (LCA), environmental input-output analysis (EIOA) or integrated assessment modelling (IAM) is challenging because most of the times there is no single best option. Most comparative cases contain trade-offs between environmental criteria, uncertainty in the performances and multiple diverse values from decision makers. To circumvent these challenges, a method from decision analysis, namely stochastic multi attribute analysis (SMAA), has been proposed instead. SMAA performs aggregation that is partially compensatory (hence, closer to a strong sustainability perspective), incorporates performance uncertainty in the assessment, is free from external normalization references and allows for uncertainties in decision maker preferences. This paper presents a thorough introduction of SMAA for environmental decision-support, provides the mathematical fundamentals and offers an Excel platform for easy implementation and access.
... Wender et al. (2014) discussed anticipatory LCA which explored both reasonable and extreme-case situations of potential environmental impacts associated with emerging technologies. This LCA approach includes parameter uncertainty in the technology model and allow feedback to technology developers (Wender and Seager 2011;Wender et al. 2014b). Besides primary experimental results (lab-scale or pilot scale) and simulation data, scientific articles, patents, expert opinion can be utilized as data sources for both predictive scenarios and scenario ranges (Arvidsson et al. 2014. ...
Article
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In recent literature, prospective application of life cycle assessment (LCA) at low technology readiness levels (TRL) has gained immense interest for its potential to enable development of emerging technologies with improved environmental performances. However, limited data, uncertain functionality, scale up issues and uncertainties make it very challenging for the standard LCA guidelines to evaluate emerging technologies and requires methodological advances in the current LCA framework. In this paper, we review published literature to identify major methodological challenges and key research efforts to resolve these issues with a focus on recent developments in five major areas: cross‐study comparability, data availability and quality, scale‐up issues, uncertainty and uncertainty communication, and assessment time. We also provide a number of recommendations for future research to support the evaluation of emerging technologies at low technology readiness levels: (a) the development of a consistent framework and reporting methods for LCA of emerging technologies; (b) the integration of other tools with LCA, such as multicriteria decision analysis, risk analysis, technoeconomic analysis; and (c) the development of a data repository for emerging materials, processes, and technologies.
... The collaborative approach described in the previous section allowed the crucial exchange of data and knowledge to enable and guide the assessment of sustainability implications across social, environmental, and economic criteria (see methods). In doing so, we followed an approach similar to anticipatory LCA, whereby uncertainty becomes a fundamental feature of the analysis and is propagated and explored throughout (Design principle 3) 30 . We considered four feedstock scenarios for sugar production, from three geographical locations: ...
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Tackling the pressing sustainability needs of society will require the development and application of new technologies. Biotechnology, emboldened by recent advances in synthetic biology, offers to generate sustainable biologically-based routes to chemicals and materials as alternatives to fossil-derived incumbents. Yet, the sustainability potential of biotechnology is not without trade-offs. Here, we probe this capacity for sustainability for the case of bio-based nylon using both deliberative and analytical approaches within a framework of Constructive Sustainability Assessment. We highlight the potential for life cycle CO2 and N2O savings with bio-based processes, but report mixed results in other environmental and social impact categories. Importantly, we demonstrate how this knowledge can be generated collaboratively and constructively within companies at an early stage to anticipate consequences and to inform the modification of designs and applications. Application of the approach demonstrated here provides an avenue for technological actors to better understand and become responsive to the sustainability implications of their products, systems and actions.
... Recent studies on emerging technologies highlight the need to conduct exhaustive sensitivity analyses, not only to ensure reliability but also to prioritize further research (Ravikumar, Seager, Cucurachi, Prado, & Mutel, 2018;Wender et al., 2014). Uncertainties and variability affecting the LCA results in this work can be grouped into three categories based on the sources: ...
Article
Renewable energy systems are essential in coming years to ensure an efficient energy supply while maintaining environmental protection. Despite having low environmental impacts during operation, other phases of the life cycle need to be accounted for. This study presents a geo‐located life cycle assessment of an emerging technology, namely, floating offshore wind farms. It is developed and applied to a pilot project in the Mediterranean Sea. The materials inventory is based on real data from suppliers and coupled to a parameterized model which exploits a geographic information system wind database to estimate electricity production. This multi‐criteria assessment identified the extraction and transformation of materials as the main contributor to environmental impacts such as climate change (70% of the total 22.3 g CO2 eq/kWh), water use (73% of 6.7 L/kWh), and air quality (76% of 25.2 mg PM2.5/kWh), mainly because of the floater's manufacture. The results corroborate the low environmental impact of this emerging technology compared to other energy sources. The electricity production estimates, based on geo‐located wind data, were found to be a critical component of the model that affects environmental performance. Sensitivity analyses highlighted the importance of the project's lifetime, which was the main parameter responsible for variations in the analyzed categories. Background uncertainties should be analyzed but may be reduced by focusing data collection on significant contributors. Geo‐located modeling proved to be an effective technique to account for geographical variability of renewable energy technologies and contribute to decision‐making processes leading to their development.
... Recent developments in the literature have started to move away from the trend of analyzing systems ex-post that so much has marked the first decades of LCA practice. Of particular interest are new methodological proposals and studies that use scenarios in LCA to assess the projected future of emerging technologies [3], and to assess the largescale implementation of technologies (see e.g. [4]). ...
Article
Life cycle assessment (LCA) is a method that has been applied on numerous different types of product systems. Most of these LCA studies concern full-market existing systems. In our common search for a more sustainable society, new technology systems are proposed of which the environmental sustainability still needs to be proven. These emerging technologies often only function at lab- or pilot-scale, and process data are also only available at these scales, and not at observed full-market scales. Performing LCAs of emerging technology systems poses challenges because relevant observations are lacking with regards to the projected final system, projected unit process data, projected characterization factors of new chemicals, etc. These challenges are increasingly recognized and addressed by the LCA community. In this contribution we discuss these challenges, with a special focus on ongoing research and recent developments.
... 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 LCA is inherently retrospective as it relies on life cycle inventory data from mature industrial 1 processes. 143 There is significant scarcity and uncertainty in globally available and comprehensive 2 inventory data in the early stages of research and development, 144 making it difficult to apply 3 conventional LCA tools for quantifying the environmental impacts of emerging ENMs for use in 4 agriculture. 145 Furthermore, incorporation of uncertainty analysis is not universally included in LCA; the 5 use of deterministic values [146][147][148] in the results mask the underlying data uncertainty and undermines the 6 confidence in the findings to inform decisions on the choice of an environmentally preferred emerging 7 ...
Article
Engineered nanomaterials (ENMs) used as fertilizers, pesticides and growth regulators will involve direct application of large quantities of ENMs to the environment and products intended for human consumption. Assessing their life cycle environmental impacts to mitigate unintended consequences poses several challenges. In this perspective, we identify obstacles to the application of life cycle assessment (LCA) for evaluating environmental tradeoffs of nano-enabled agrochemical applications. These include: (1) defining functional units that represent the function provided by nano-enabled agrochemicals and that are proportional to the scale of the study (nano-scale vs. field scale), (2) limitations in availability of comprehensive data necessary to inform life cycle material flow (resource use and emissions) for inventory development specific to nano-enabled agrochemical applications, (3) human and environmental exposure and effects data relevant to the agricultural context for impact assessment models, (4) spatial and temporal dependent components that can affect the results of an LCA of nano-enabled agrochemicals, and (5) high data uncertainties and the possibility of their reduction through collaborative efforts between life cycle practitioners and experimental researchers using anticipatory decision-based models. While several of these challenges are experienced in LCA of emerging technologies generally, they are highlighted herein due to a unique or heightened relevance to the use of ENMs in agriculture applications. Addressing challenges in these areas are intended to inform research prioritization to ensure safe and sustainable design, development, and implementation of nano-enabled agrochemicals.
... Previous work centered at the Technical University in Denmark (Bhander et al. 2003) and Arizona State University (Wender and Seager 2011;Wender et al. 2014aWender et al. , 2014bWender et al. , 2018 has seen concepts developed around anticipatory/prospective LCAs of individual products or technologies that include parameter uncertainty in the technology model, allowing feedback to the technology developers. Uncertainties are high in these assessments. ...
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Prospective environmental assessment of emerging technology is necessary in order to inform designers of beneficial changes early in a technology's development, and policy makers looking to fund projects and nudge manufacturers toward the most sustainable application of a technology. Existing analyses often have shortcomings such as failing to consider the environmental impacts in all stages of a product's life cycle; implicitly assuming that the emerging technology will be cost‐effective wherever it is technically viable; and assuming optimistic application scenarios that discontinue long‐established trends in human behavior. In this article, we propose a new approach, complementary to the prospective and anticipatory life cycle assessment literature, addressing the above concerns and attempting to make sense of the large uncertainties inherent in such analyses by using distributions to model all the inputs. The paper focuses on emerging manufacturing technologies, such as incremental sheet forming (ISF), but the issues examined are also applicable to new end‐use products, such as autonomous vehicles. This paper makes use of approaches (such as Bass modeling and product cannibalization considerations) familiar to those in the business community who anticipate market diffusion of a new technology and the effect on existing technology sales. The proposed methodology is demonstrated by estimating the potential environmental impacts in the U.S. car industry by 2030 of an emerging double‐sided ISF process. Energy and cost models of ISF and drawing are used to estimate potential mean savings of around 100 TJprimary and 60 million U.S. dollars per year by 2030.
... Loon et al. and Aravind et al. conducted analysis on the concept of magnetic levitation on VAWT [35]. Wen et al. investigated performance optimization for small Horizontal Axis Wind Turbine (CS-SHAWT) [17,29]. Wang et al. studied the impacts of irregular wind on a newly developed cross-axis wind turbine performance [36]. ...
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The utilization of conventional sources of energy releases harmful pollutants to the environment causing global warming and acid rain. For that reason, it becomes necessary to use a non-depletable, sustainable and eco-friendly renewable energy as a mean of producing electricity. Malaysia is tropical country rich in resources beneficial in electricity generation as it is in equatorial region therefore it has an abundance of solar irradiance of average annually. In addition, Malaysia’s demand in electricity is increasing to 124,677 GWh by 2020. Therefore, the electricity generation from renewable sources in Malaysia is anticipated to grow in the future alongside the government endorsement due to its clean, eco-friendly and free source of energy which can highly reduce the dependency on oil and gas that emits harmful pollutants to the environment. This paper gives a comprehensive review on the renewable projects and researches in Malaysia, challenges that affect popularity of renewable energy in Malaysia and available and successful renewable energy system in Malaysia.
... And who gets to decide what the thresholds of significance are? Such questions-embedded as assumptions in determining the system boundaries of any life cycle analysis (Wender et al. 2014)-are contested. Questions of significant harm are therefore always 'trans-scientific' (Weinberg 1972); ones that, despite appeals to modernist approaches, will resist reduction, require situated interpretation, and demand debate. ...
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The European Union’s Green Deal and associated policies, aspiring to long-term environmental sustainability, now require economic activities to ‘do no significant harm’ to EU environmental objectives. The way the European Commission is enacting the do no significant harm principle relies on quantitative tools that try to identify harm and adjudicate its significance. A reliance on established technical approaches to assessing such questions ignores the high levels of imprecision, ambiguity, and uncertainty—levels often in flux—characterizing the social contexts in which harms emerge. Indeed, harm, and its significance, are relational, not absolute. A better approach would thus be to acknowledge the relational nature of harm and develop broad capabilities to engage and ‘stay with’ the harm. We use the case of European research and innovation activities to expose the relational nature of harm, and explore an alternative and potentially more productive approach that departs from attempts to unilaterally or uniformly claim to know or adjudicate what is or is not significantly harmful. In closing, we outline three ways research and innovation policy-makers might experiment with reconfiguring scientific and technological systems and practices to better address the significant harms borne by people, other-than-human beings, and ecosystems.
... This is regulated by the International Standard Organization (ISO, 14040 [22] and 14044 [23] ); it enables the qualification and quantification of environmental impacts and identifies improvement options throughout the life cycle of a product, process or activity. In this article, LCA is applied in an "anticipatory" [24][25][26][27][28][29] fashion at the early stages of the development of the CF synthesis of Rufinamide. ...
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In the pharma and fine chemical industries, the development of continuous flow technologies is a process intensification step of primary importance towards the manufacturing of high‐quality products, while reducing the environmental impact and cost of production. The sustainability and profitability of a process can be measured through life cycle Assessment and cost evaluation. However, when applied to emerging technologies, these need to be performed at different stages of the process development in order to limit the uncertainties arising from the scale‐up, and hence providing high‐fidelity projections of environmental impacts and costs at larger scales. The output of the assessment can in fact vary significantly depending on the maturity of the technology and this translates into having different results at commercial scale compared to early estimations. Therefore, in this article, we perform an assessment at two different scales of production, lab and mini‐pilot scale, with the aim of quantifying the uncertainties of the assessment related to the scale‐up, identifying the hotspots of the system, and hence providing guidelines for the further steps of process development. The subject of the assessment is the continuous flow synthesis of Rufinamide. It is the first time that this synthesis is evaluated at pilot‐scale. The results show that low yields in the cycloaddition drastically affect the waste management and the production of precursors, and hence increases environmental impacts and cost of production. This calls for the need of prioritizing the optimization of this synthesis step in order to deploy a green and economically competitive production technology.
... High initial costs in conjunction with high levels of uncertainty surrounding widespread implementation will remain primary obstacles to adoption, yet established uncertainty and techno economic approaches in conjunction with life cycle assessment can provide guidance for early decision-making in the product design to minimize cost and environmental impact. 211,212 ...
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The application of nanotechnology in agriculture and food systems is a new and rapidly evolving area of research with the potential to positively impact an industry that is experiencing increased demand under increasingly stressed resources. Given the intimate relationship between agriculture, the environment, and human health, a proactive approach to design is critical – one that is informed by considering the trade-offs between potential benefits realized by nano-enabling and potential adverse impacts imposed by their use. This tutorial review includes an overview of current and proposed nano-enabled applications that are unique to agriculture and food systems to identify, (i) the function provided and proposed benefits realized through nano-enabling, (ii) the efficiency of (nano)material use, and (iii) the proposed mechanism through which the ‘nano’ component of the design operates. It is through this review that three primary suggestions emerge, offering guidance for ongoing studies to inform design for enhanced agriculture sustainability: the need for (i) comprehensive data reporting, including material flows (input, emissions, and retention in the environment or product) of the ENM or active ingredient used, (ii) experimental design that includes non-nano controls, and (iii) identification and discussion of mechanisms underlying how the ‘nano’ aspect of the design enables the observed outcome. In addition to overarching guidance for continued research to inform design for enhanced agriculture sustainability, suggestions unique to each reviewed product class are also provided.
... Life Cycle Assessment (LCA) of laboratory activities is generally performed in the context of funded research projects, as a starting point for identifying hotspots of innovative processes and emerging technologies at low Technology Readiness Level (TRL) (Bergerson et al., 2020;Buyle et al., 2019;Cucurachi et al., 2018;Hetherington et al., 2014;Moni et al., 2020). Results of previous studies highlighted that impacts generated by a process at lab scale may have higher burdens associated to energy use during the examined processes, compared to the impact generated by a similar process conducted at pilot or industrial scale (Corona et al., 2018;Elginoz et al., 2020;Piccinno et al., 2018;Wender et al., 2014). This is not surprising, because industrial processes are usually more optimized compared to laboratory processes, especially in terms of resource efficiencies, and they can also take advantage from economies of scale, which at the laboratory scale are not applicable. ...
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The goal of the study was to identify the environmental hotspots of an experimental research work at lab scale consisting in the physical vapor deposition magnetron sputtering of aluminum titanium nitride based thin coatings onto commercial laminated steel. The findings can provide useful insights for supporting the design of future experimental research campaigns, or instrumentation setups , with lower environmental impacts. Results highlighted that the main driver of impacts in the analyzed laboratory activities was the electricity used for instruments operations, in particular for the vacuum keeping. Thus, several optimization strategies were evaluated to reduce the overall electricity consumption, and to improve the environmental profile of experimental activities.
... Recent literature has highlighted prospective LCA as an approach to model the future full-scale performance of technology at an early stage of development (Arvidsson et al., 2018;Bartolozzi et al., 2019). Other similar terms used are anticipatory (Wender et al., 2014) or ex-ante LCA's (Buyle et al., 2019;Cucurachi et al., 2018) predictive LCA (Karka et al., 2019), "process simulation based LCA" (Rathnayake et al., 2018), eco-design and product design evaluation (Suhariyanto et al., 2018). Prospective LCA's have the common need to utilise a range of data to model the future scenario of the foreground system such as scientific articles, patents, expert interviews, lab results and process simulations (Arvidsson et al., 2018). ...
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Technological developments are opening new avenues to facilitate the circular economy through resource recovery from industrial wastewater. This paper presents the use of Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) in the development of technology solutions for the treatment of brine wastewater and recovery of by-products. Four industrial case studies are assessed that apply different innovative technology configurations, to treat the brines and recover the water, salts and mineral compounds. The assessment focusses on identifying hotspots and potential design improvements for the four case studies. In addition, the development of a unified approach for prospective LCA and LCC is illustrated to promote robustness and consistency in the analysis of the four systems. The analysis reveals that the impact and cost of treatment is highly dependent on the wastewater composition. Critically, whether the recovery of compounds and deionised water can counteract the impact and cost of the treatment systems. The early analysis suggests that this is possible for two of the cases studies. Estimates of the GHG emissions for the initial system analysis, range from 10 to 17 kg CO2e/m³, whilst costs range from €10/m³ to €25/m³. However, both are expected to decrease at full scale and are sensitive to costs of energy, chemicals and revenue from recovered by-products. The LCA’s highlight chemical and energy consumption as critical hotspots. Design considerations therefore focus on the reduction of chemicals, reuse or switching to lower impact chemicals, and maximising by-product recovery, and using renewable energy.
... Another approach conducted Monte Carlo simulations using published partitioning coefficients; however, given that ENMs form thermodynamically unstable suspensions, this approach has not been applied widely. 30,99 The USEtox multimedia fate and exposure model suggests calculating the XF using eqn (11), which results in a dimensionless element to be incorporated into CFs. 17 ...
Article
Global production and consumption of silver nanoparticles (nAgs) are forecasted to increase due to their applications in modern technologies. This situation raises concerns related to their environmental and human health consequences, as nAgs potentially will be released to the environment during and/or at the end of the product life cycles. Environmental impacts due to the raw materials and manufacturing of nAgs are examined throughout the literature using cradle-to-gate life cycle assessments. However, calculating nano-specific emissions resulting from nAg release is occasionally overlooked, or modeled as ionic silver, due to the lack of widely accepted characterization factors (CFs) to define the associated impact. The current study seeks to calculate CFs for nAgs by combining the principles of colloidal science with the USEtox model to be integrated to cradle-to-grave life cycle assessments. In order to control the variables while modeling the fate and behavior of nAgs, data from published mesocosm conditions are used. Effect and fate factors for CFs are calculated considering certain physicochemical properties of nAgs in the mesocosm and the composition of aquatic media. Additionally, two different scenarios are computed where the hetero-aggregation is modeled as either a removal or a transformation process, which significantly changes the final results. Considering different scenarios, a CF range is proposed as 2.19 × 103-2.34 × 105 PAF m3 day kg-1 (PAF: potentially affected fraction) for polyvinylpyrrolidone (PVP)-coated nAgs. Moreover, as a result of sensitivity analysis, it is found that the characteristics of the suspended particulate matter largely affect the fate of nAgs under both scenarios. Results suggest that using ionic silver to model nAg release will potentially overestimate the environmental impacts. This journal is
... A relevant application of this concept would be to quantify the effect that increases in farm size would have on the emissions profile of the operation (kg CO 2 eq emitted per unit of kelp harvested), and thus the true additionality of kelp CDR (Faber et al., 2022). The lack of historical production data for kelp farming in emerging regions (i.e., outside of the Pacific Rim), as well as the low technology readiness level of kelp farming specifically for CDR, pose a challenge to accurate cost and climate potential forecasting (Wender et al., 2014). As the kelp aquaculture industry expands in North America, Europe, and South America, the growing body of economic and lifecycle benchmarking data should be utilized to resolve these uncertainties (Engle et al., 2020;Thomas et al., 2021). ...
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To keep global surface warming below 1.5°C by 2100, the portfolio of cost-effective CDR technologies must expand. To evaluate the potential of macroalgae CDR, we developed a kelp aquaculture bio-techno-economic model in which large quantities of kelp would be farmed at an offshore site, transported to a deep water “sink site”, and then deposited below the sequestration horizon (1,000 m). We estimated the costs and associated emissions of nursery production, permitting, farm construction, ocean cultivation, biomass transport, and Monitoring, Reporting, and Verification (MRV) for a 1,000 acre (405 ha) “baseline” project located in the Gulf of Maine, USA. The baseline kelp CDR model applies current systems of kelp cultivation to deep water (100 m) exposed sites using best available modeling methods. We calculated the levelized unit costs of CO2eq sequestration (LCOC; $ tCO2eq-1). Under baseline assumptions, LCOC was $17,048 tCO2eq-1. Despite annually sequestering 628 tCO2eq within kelp biomass at the sink site, the project was only able to net 244 C credits (tCO2eq) each year, a true sequestration “additionality” rate (AR) of 39% (i.e., the ratio of net C credits produced to gross C sequestered within kelp biomass). As a result of optimizing 18 key parameters for which we identified a range within the literature, LCOC fell to $1,257 tCO2eq-1 and AR increased to 91%, demonstrating that substantial cost reductions could be achieved through process improvement and decarbonization of production supply chains. Kelp CDR may be limited by high production costs and energy intensive operations, as well as MRV uncertainty. To resolve these challenges, R&D must (1) de-risk farm designs that maximize lease space, (2) automate the seeding and harvest processes, (3) leverage selective breeding to increase yields, (4) assess the cost-benefit of gametophyte nursery culture as both a platform for selective breeding and driver of operating cost reductions, (5) decarbonize equipment supply chains, energy usage, and ocean cultivation by sourcing electricity from renewables and employing low GHG impact materials with long lifespans, and (6) develop low-cost and accurate MRV techniques for ocean-based CDR.
... There exist in fact several definitions and proposed methods in literature that aim at the same objective. Ex-ante, prospective, and anticipatory LCAs (Villares et al., 2017;Wender et al., 2014;Schrijvers et al., 2014;Ravikumar et al., 2018;Cucurachi et al., 2018;Roes & Patel, 2011) share the common objective of screening of emerging technologies since the early stages of their development by assessing the impacts that the selected technology would have once fully deployed at commercial scale. In the author's view, the most comprehensive description of LCA applied to new technology has been proposed by Cucurachi et al. (2018): "performing an environmental life cycle assessment of a new technology before it is commercially implemented in order to guide R&D decisions to make this new technology environmentally competitive with the incumbent technology mix." ...
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This study investigates on the environmental impact of an intensified technology for the manufacturing of Zeolite A, one of the largest zeolites employed worldwide by volume and value. The technology under consideration is an oscillatory continuous‐flow synthesis, developed industrially by Arkema, and currently at pilot‐scale. Life cycle assessment (LCA) is used in this work to measure the sustainability of this emerging technology in an anticipatory fashion, before its full deployment, with the aim of driving the process development toward the minimization of the environmental footprint. The assessment explores the full life‐cycle of the production system and comprises comparative analysis, scenario analysis, and a hotspot analysis. Finally, the continuous‐flow technology is benchmarked against the environmental impact of a conventional batch production of zeolite A, based on a full‐scale commercial plant. The results evidence that significant benefits would stem from shifting from batch to continuous‐flow production. The comparative analysis reveals that the extent of the latter advantages depends on the impact category under consideration and directs the next steps of CF system's process development toward pivotal aspects such as the recirculation system to further reduce the system's environmental impacts. Regardless of the chosen production technology, a large share of the total environmental impact hinges on the production of NaOH, a building block of the synthesis, and hence is hardly mitigatable. On the whole, the findings of this work emphasize the need of prioritizing LCA during the development phase of emerging technologies and underline its efficacy to prevent waste of resources and capitals.
... A relevant application of this concept would be to quantify the effect that increases in farm size would have on the emissions profile of the operation (kg CO 2 eq emitted per unit of kelp harvested), and thus the true additionality of kelp CDR (Faber et al., 2022). The lack of historical production data for kelp farming in emerging regions (i.e., outside of the Pacific Rim), as well as the low technology readiness level of kelp farming specifically for CDR, pose a challenge to accurate cost and climate potential forecasting (Wender et al., 2014). As the kelp aquaculture industry expands in North America, Europe, and South America, the growing body of economic and lifecycle benchmarking data should be utilized to resolve these uncertainties (Engle et al., 2020;Thomas et al., 2021). ...
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To keep global surface warming below 1.5 °C by 2100, the portfolio of cost-effective CDR technologies must expand. To evaluate the potential of macroalgae CDR, we developed a kelp aquaculture bio-techno-economic model in which large quantities of kelp would be farmed at an offshore site, transported to a deep water "sink site", and then deposited below the sequestration horizon (1,000 m). We estimated the costs and associated emissions of land-based nursery production, permitting, farm construction, ocean cultivation, biomass transport, and C Monitoring, Reporting, and Verification (MRV) for a 1,000 acre (405 ha) "baseline" project located in the Gulf of Maine, USA. The baseline kelp CDR model applies current systems of kelp cultivation in a realistic way to deep water (100 m) exposed sites using best available modeling methods. We calculated the levelized unit costs of CO2eq sequestration (LCOC; $ tCO2eq-1). Under baseline assumptions, LCOC was $17,048 tCO2eq-1. Despite annually sequestering 628 tCO2eq within kelp biomass at the sink site, the project was only able to net 244 C credits (tCO2eq) each year, a true sequestration "additionality" rate (AR) of 39% (i.e., the ratio of net C credits produced to gross C sequestered within kelp biomass). As a result of optimizing 18 key parameters for which we identified a range within the literature, LCOC fell to $1,257 tCO2eq-1 and AR increased to 91%, demonstrating that substantial cost reductions could be achieved through process improvement and decarbonization of production supply chains. Kelp CDR may be limited by high production costs and energy intensive operations, as well as CDR MRV uncertainty. To resolve these challenges, R&D must (1) de-risk farm designs that maximize lease space, (2) automate the seeding and harvest process, (3) leverage selective breeding to increase C yield, (4) assess the cost-benefit of gametophyte nursery culture as both a platform for selective breeding and driver of operating cost reductions, (5) decarbonize equipment supply chains, energy usage, and ocean cultivation by sourcing electricity from renewables and employing low GHG impact materials with long lifespans, and (6) develop low-cost and accurate ocean CDR MRV techniques.
... If the uncertainty is too high to confidently rank the environmental performance of the different scale-up scenarios, then the dominant source of uncertainty is targeted for another round of data collection, refined process modeling, improved matching to LCA databases, and so on (criterion 2). The systematic exploration of uncertainties in life-cycle impacts to prioritize research becomes a core guiding and design principle [29,30], rather than an afterthought for results communication. This iterative cycle is repeated until we can, with the least effort (criterion 3), provide useful guidance to the design of the technology deployment. ...
... • Anticipatory LCA (Wender et al. 2014), which has methodological features that can better evaluate low-technology readiness level technologies. • Advanced methods of uncertainty and sensitivity analysis to better account for data uncertainty and variability (Cucurachi, Borgonovo, and Heijungs 2016;Ravikumar et al. 2018;Wender et al. 2017) • Methods of multi-criteria decision analysis (Prado-Lopez et al. 2013) that can better evaluate and account for trade-offs across economic and environmental impacts (and others). ...
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A review discusses key insights, gaps, and opportunities for research and implementation of a circular economy for two of the leading technologies that enable the transition to a renewable energy economy, solar PV and lithium-ion batteries (LIB); procedures to critically analyze over 3000 publications on the circular economy of solar PV and LIBs, categorizing those that pass a series of objective screens in ways that can illuminate the current state of the art; existing impediments to a circular economy; and future technological and analytical research.
... To evaluate the environmental performance of emerging technologies at an early stage of development, ex-ante (or prospective or anticipatory) LCA can be used (Roes and Patel 2011;Walser et al. 2011;Arvidsson et al. 2014;Wender et al. 2014;Villares et al. 2016Villares et al. , 2017). An LCA is ex-ante when a technology studied exists at an early stage of research and development (R&D)-i.e., at a lab or pilot scale-but is modeled for a projected future industrial/ commercial-scale production (Arvidsson et al. 2017;Guinée et al. 2018). ...
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PurposeThe goal of this study is to perform an ex-ante life cycle assessment (LCA) of the emerging gallium-arsenide nanowire tandem solar cells on silicon (GaAs/Si) and to provide a benchmark for the commercialization of the technology. The environmental impacts and energy payback time (EPBT) of the GaAs/Si modules are compared with those of the incumbent single-Si modules. Parameters and efficiencies most relevant to be optimized in order to commercialize the technology are identified and discussed.Methods Two production routes for GaAs/Si solar cells are being up-scaled: the growth of GaAs nanowires on a native substrate, peel-off, and transfer to a silicon substrate (transfer route) and the direct growth of GaAs nanowires on a silicon substrate with assistance of a silicon-dioxide (SiO2) nanotube template (direct growth route). Two ex-ante LCAs for the different manufacturing routes and an LCA for the incumbent single-Si technology were conducted. Environmental impacts of the GaAs/Si technology were assessed and compared with the incumbent. Various scenarios regarding sensitive parameters and processes were modeled—such as modeling several industrial scale tools, the energy consumption of sensitive processes, the number of substrate reuses, the frequency of re-polishing the wafer, and benchmarking the scale of improvement of major impact drivers.Results and discussionThe analysis showed that, if expected process efficiencies are achieved, a 28% efficient GaAs/Si module performs 5 to 20% better (transfer route) and 20 to 30% better (direct growth route, except the ozone depletion impact) compared with an 18% efficient single-Si module, for all impact categories assessed—climate change, land use, acidification, ozone depletion, freshwater, marine, terrestrial ecotoxicity, eutrophication, human toxicity, and photochemical oxidation. Critical hotspots identified include the use of gold, trifluoromethane (CHF3), and a GaAs wafer. The EPBT of the GaAs/Si nanowire tandem module is in between 1.37 (expected process efficiencies achieved) and 1.9 years (worst case scenario), while the EPBT of the single-Si module is 1.84 years. Results can be considered as a benchmark for the successful commercialization of the technology.Conclusions If 28% efficient GaAs/Si nanowire tandem modules are developed, expected process efficiencies are achieved, and at least 100 reuses of the GaAs substrate (transfer route) are realized; then, the GaAs/Si modules perform better compared with an 18% efficient single-Si module for most impact categories assessed. Conclusions from the ex-ante LCA are conditional (if-then) and can be used as a benchmark, allowing to quantify the efficiencies that need to be achieved to commercialize the technology.
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LEGO® SERIOUS PLAY® (LSP) method for facilitating deliberation in multidisciplinary teams of students considering the social, ethical, and environmental implications of nanotechnology. As a wicked problem, nanotechnology warrants thorough examination and deliberation involving multiple stakeholders to ensure responsible innovation and governance. However, many conventional approaches to wicked problems fail to address the difficulty of cross-disciplinary communication in the absence of interactional expertise, and overlook proven creative problem solving methods. Despite nearly five decades of maturation in practices since the term ‘wicked problems’ first appeared in the literature in 1967, a need remains for exploring new approaches. LSP is a content neutral, hands-on facilitation method using boundary objects as a metaphorical vehicle for lowering the barriers to communication, thereby building empathetic perspective taking and increasing the “collision” of ideas to boost the collective creativity. The curriculum effectiveness and student experience was evaluated through pre- and post-surveys as well as summative focus group sessions. Findings show that the LSP method was useful in three respects: 1) it accelerated the socialization process essential for generating and sharing creative ideas by structuring interactions with material boundary objects, 2) it enabled students to externalize their ideas and perspectives in more explicit forms through the use of material metaphors, and 3), it facilitated the internalization of new knowledge.
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Recent studies demonstrated that advanced aerogel composites (Aspen Aerogels® Spaceloft® [SL]) have the potential to transform oil remediation via high oil uptake capacity and selectivity, excellent reusability, and high mechanical strength. Understanding the life cycle environmental impacts of advanced aerogels can enable a more holistic decision-making process when considering oil recovery technologies following a spill. Here, we perform a cradle-to-grave streamlined life cycle assessment (LCA) following International Organization for Standardization (ISO) 14040 2006 for SL weighed against the conventional oil sorbent material, polyurethane foam. The model included alternative use and disposal scenarios, such as single or multiple uses, and landfill, incinerator, and waste-to-energy (WTE) approaches for cleaning 1 cubic meter (m³) of light crude oil. Results showed that the ideal case for SL application was comprised of multiple use and WTE incineration (68% reduction in material use, approximately 7 × 10³ megajoules [MJ] of energy recovery from WTE), but SL offered energy and materials savings even when used once and disposed of via traditional means (i.e., landfill). In addition to evaluating these already-scaled processes, we performed an anticipatory LCA for the laboratory-scaled aerogel fabrication process that might inform the sustainable design of next-generation aerogels. In particular, the model compared rapid supercritical extraction (RSCE) with two conventional supercritical extraction methods—alcohol and carbon dioxide supercritical extraction (ASCE and CSCE, respectively)—for silica aerogel monoliths. Our results showed that RSCE yielded a cumulative energy savings of more than 76 × 10³ and 100 × 10³ MJ for 1 m³ of monolithic silica aerogel manufacturing compared to ASCE and CSCE, respectively.
Thesis
The well-being of the society depends on a number of metals, including base metals, precious metals and increasingly rare earth elements (REE). The usage of these metals increased in numerous applications, including electrical and electronic equipment (EEE), and their interrupted supply is at stake. There is an increasing interest in the secondary sources of these metals, particularly waste electrical and electronic equipment (WEEE) in order to compensate their potential supply deficit. This PhD thesis demonstrates the advantages and bottlenecks of biological and chemical approaches, as well as the advances and perspectives in the development of sustainable processes for metal recovery from WEEE. Furthermore, a novel process for the recovery of metals from WEEE is described, and a techno-economic assessment is given. Discarded printed circuit boards (PCB) from personal computers (PC), laptops, mobile phones and telecom servers were studied. Following an extensive literature review, a novel characterization and total metal assay method is introduced and applied to waste board materials. Discarded PCB contained metals in the range of (%, by weight): copper (Cu) 17.6 - 39.0, iron (Fe) 0.7 - 7.5, aluminum (Al) 1.0 - 5.5, nickel (Ni) 0.2 - 1.1, zinc (Zn) 0.3 - 1.2, as well as gold (Au) (in ppm) 21 - 320. In addition, multi-criteria analysis (MCA) using the analytical hierarchical process (AHP) methodology is applied for selection of the best-suited technology. A proof-of-concept for a two-step bioleaching extraction was given, in which 98.4% and 44.0% of the Cu and Au, respectively, were extracted. The two-step extraction concept was applied to the chemical leaching of metals from PCB. Cu leaching was carried in an acidic oxidative mixture of H2SO4 and H2O2, whereas Au leaching for carried out by S_2 O_3^(2-) in a NH_4^+ medium, catalyzed by CuSO4. Under the optimized parameters, 99.2% and 96.6% of Cu and Au, respectively, were extracted from the board material. Selective recovery of Cu from the bioleaching leachate using sulfidic precipitation and electrowinning was studied. Cu was selectively recovered on the cathode electrode at a 50 mA current density in 50 minutes, with a 97.8% efficiency and 65.0% purity. The techno-economic analysis and environmental sustainability assessment of the new technology at an early stage of development was investigated
Article
In this research, a framework for performing Anticipatory Life Cycle Analysis (a-LCA) has been developed to identify the sustainable end of life (EoL) management option for crystalline silicon photovoltaic (PV) panels. a-LCA can be used to stimulate proactive and sustainable decision making for emerging technologies through stakeholder participation. In this research, stakeholders related to EoL management of PV panels participated through a survey to identify and prioritize economic, environmental, and social indicators for PV EoL management. Several EoL strategies like bulk material recycling (centralized and decentralized), high value material recycling, and landfilling were chosen and assessed for the prioritised sustainability indicators. The EoL strategies were then ranked through a multi-criteria decision analysis tool for their level of sustainability. High value material recycling (close to 100% material recovery) was identified as the most sustainable option followed by bulk recycling of PV panels that recover only the major constituents, such as aluminium, glass, and e-waste. Landfilling remained the least preferred option, although it currently has an economic advantage over other recycling options, highlighting the need to shift the user preferences. The developed a-LCA framework is iterative and can be applied by decision makers for different EoL management strategies in the future.
Article
Large-scale deployment of photovoltaic (PV) modules has considerably increased in recent decades. Given an estimated lifetime of 30 years, the challenge of how to handle large volumes of end-of-life PV modules is starting to emerge. In this Perspective, we assess the global status of practice and knowledge for end-of-life management for crystalline silicon PV modules. We focus in particular on module recycling, a key aspect in the circular economy of photovoltaic panels. We recommend research and development to reduce recycling costs and environmental impacts compared to disposal while maximizing material recovery. We suggest that the recovery of high-value silicon is more advantageous than the recovery of intact silicon wafers. This approach requires the identification of contaminants and the design of purification processes for recovered silicon. The environmental and economic impacts of recycling practices should be explored with techno–economic analyses and life-cycle assessments to optimize solutions and minimize trade-offs. As photovoltaic technology advances rapidly, it is important for the recycling industry to plan adaptable recycling infrastructure. The increasing deployment of photovoltaic modules poses the challenge of waste management. Heath et al. review the status of end-of of-life management of silicon solar modules and recommend research and development priorities to facilitate material recovery and recycling of solar modules.
Thesis
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Over 292 million tons of municipal solid waste (MSW) are generated annually in the U.S. Appropriately managing this waste can reduce greenhouse gas (GHG) emissions, conserve critical resources, and generate renewable fuels and electricity. Landfills are a critical component of the U.S. MSW management (MSWM) infrastructure that currently accepts over half of generated MSW. Landfills are also the third leading source of anthropogenic methane (CH4) emissions in the U.S., behind natural gas extraction and livestock. Life-cycle assessment (LCA) is a decision-support framework used to quantify and understand the environmental impacts and resource consumption of MSWM scenarios and processes, including landfills. However, landfill LCAs are complicated by their varying sizes, waste composition, gas collection and control regulations, and the dynamic nature of gas and leachate production and management. Thus, accurate models to estimate the environmental emissions and impacts attributable to landfills are important for guiding emissions and climate mitigation policymaking. While LCAs provide valuable insights, large data requirements limit their effective incorporation into guiding future decisions and allowing quick iterations of analyses. Reducing the data requirements for inventory and impact assessments will facilitate the wider use of LCAs during early system planning. The objectives of this research include (1) developing a life-cycle model to represent how a municipal landfill works in consideration of size, waste composition, and regulations that govern landfill gas collection and control; (2) evaluating how the dynamic nature of long-term emissions from landfills affects the associated global warming impacts; and (3) developing a streamlined
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The built environment is the largest single emitter of CO2 and an important consumer of energy. Much research has gone into the improved efficiency of building operation and construction products. Life Cycle Assessment (LCA) is commonly used to assess existing buildings or building products. Classic LCA, however, is not suited for evaluating the environmental performance of developing technologies. A new approach, anticipatory LCA (a‐LCA), promises various advantages and can be used as a design constraint during the product development stage. It helps overcome four challenges: (i) data availability, (ii) stakeholder inclusion, (iii) risk assessment, and (iv) multi‐criteria problems. This article's contribution to the line of research is twofold: first, it adapts the a‐LCA approach for construction‐specific purposes in theoretical terms for the four challenges. Second, it applies the method to an innovative prefabricated modular envelope system, the CleanTechBlock (CTB), focusing on challenge (i). Thirty‐six CTB designs are tested and compared to conventional walls. Inclusion of technology foresight is achieved through structured scenario analysis. Moreover, challenge (iv) is tackled through the analysis of different environmental impact categories, transport‐related impacts, and thickness of the wall assemblies of the CTB. The case study results show that optimized material choice and product design is needed to reach the lowest environmental impact. Methodological findings highlight the importance of context‐specific solutions and the need for benchmarking new products.
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Purpose Future scenarios and life cycle assessment (LCA) are powerful tools that can provide early sustainability assessments of novel products, technologies and systems. The combination of the two methods involves practical and conceptual challenges, but formal guidance and consensus on a rigorous approach are currently missing. This study provides a comprehensive overview of how different topic areas use future scenarios and LCA in order to identify useful methods and approaches, and to provide overall recommendations. Methods This study carried out a systematic literature review that involved searching for peer-reviewed articles on Web of Science, Scopus and Science Direct, utilising a rigorous set of keywords for future scenarios and for LCA. We identified 514 suitable peer-reviewed articles that were systematically analysed according to pre-defined sets of characteristics for the combined modelling of future scenarios and LCA. Results and discussion The numbers of studies combining future scenarios and LCA increase every year and in all of the 15 topic areas identified. This combination is highly complex, due to different sequences in the modelling between future scenarios and LCA, the use of additional models and topic area-specific challenges. We identify and classify studies according to three archetypal modelling sequences: input, output and hybrid. More than 100 studies provide methods and approaches for combining future scenarios and LCA, but existing recommendations are specific to topic areas and for modelling sequences, and consensus is still missing. The efficacy of many studies is hampered by lack of quality. Only half of the articles complied with the LCA ISO standards, and only one quarter demonstrated consistent knowledge of future scenario theory. We observed inconsistent use of terminology and a considerable lack of clarity in the descriptions of methodological choices, assumptions and time frames. Conclusions and Recommendations The combined use of future scenarios and LCA requires formal guidance, in order to increase clarity and communicability. Guidance should provide unambiguous definitions, identify minimum quality requirements and produce mandatory descriptions of modelling choices. The goal and scope of future scenarios and LCA should be in accordance, and quality should be ensured both for the future scenarios and the LCA. In particular, future scenarios should always be developed contextually, to ensure effective assessment of the problem at hand. Guidance should also allow for maintaining current modelling complexity and topic area differences. We provide recommendations from the reference literature on terminology, future scenario development and the combined use of future scenarios and LCA that may already constitute preliminary guidance in the field. Information collected and recommendations provided will assist in a more balanced development of the combined use of future scenarios and LCA in view of the urgent challenges of sustainable development.
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Building-integrated photovoltaic (BIPV) is a promising solar energy technology that looks set to grow in popularity in the pursuit of a sustainable future. It has the potential to mitigate some of the main concerns over ground-mounted solar energy systems such as land use. However, there is an apparent gap in our understanding of its life cycle environmental impacts. Very few life cycle analysis (LCA) studies have evaluated BIPV comprehensively in comparison with standalone PV systems and other energy technologies. In this paper, we review the limited existing LCA studies on BIPV and identify the challenges and future research needs. The findings will help researchers, industries and policy makers better understand the environmental sustainability of BIPV to facilitate its development.
Article
Water lubricated bearings have been named in literature as a sustainable alternative to their oil-based counterparts. In order to clarify when water lubricated bearings are or are not a sustainable alternative, and inform design decisions, an analytical design tool is introduced based on anticipatory Life Cycle Assessment (LCA). This model is based on data from the bearing geometries, materials, and material combinations that have been the subject of research attention in the past 20 years in water lubricated bearing design. The model provides simple equations, fed with data from literature on materials, production and their tribological combination to provide initial insight on the sustainability of these types of bearings and future designs. A case study illustrates that quantifying environmental impacts can help determine when lubricant loss is more important than material choice, or vice-versa. The method aids bearing designers towards more sustainable designs.
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The use of carbon dioxide as a feedstock for a broad range of products can help mitigate the effects of climate change through long‐term removal of carbon or as part of a circular carbon economy. Research on capture and conversion technologies has intensified in recent years and the interest in deploying these technologies is growing fast. However, a sound understanding of the environmental and economic impact of these technologies is required to drive fast deployment and avoid unintended consequences. Life cycle assessments and techno‐economic assessments are useful tools to quantify environmental and economic metrics; however, these tools can be very flexible in how they are applied, with the potential to produce significantly different results depending on how the boundaries and assumptions are defined. Built on ISO standards for generic life cycle assessments, several guidance documents have emerged recently from the Global CO2 Initiative, the National Energy Technology Laboratory, and the National Renewable Energy Laboratory that further define assessment specifications for carbon capture and utilization. Overall agreement in the approaches is noted with differences largely based on the intended use cases. However, further guidance is needed for assessments of early stage technologies, reporting details, and guidance for policymakers and non‐technical decision makers. This article is protected by copyright. All rights reserved.
Article
The role of life cycle assessment (LCA) in informing the development of a sustainable and circular bioeconomy is discussed. We analyse the critical challenges remaining in using LCA and propose improvements needed to resolve future development challenges. Biobased systems are often complex combinations of technologies and practices that are geographically dispersed over long distances and with heterogeneous and uncertain sets of indicators and impacts. Recent studies have provided methodological suggestions on how LCA can be improved for evaluating the sustainability of biobased systems with a new focus on emerging systems, helping to identify environmental and social opportunities prior to large R&D investments. However, accessing economies of scale and improved conversion efficiencies while maintaining compatibility across broad ranges of sustainability indicators and public acceptability remain key challenges for the bioeconomy. LCA can inform, but not by itself resolve this complex dimension of sustainability. Future policy interventions that aim to promote the bioeconomy and support strategic value chains will benefit from the systematic use of LCA. However, the LCA community needs to develop the mechanisms and tools needed to generate agreement and coordinate the standards and incentives that will underpin a successful biobased transition. Systematic stakeholder engagement and the use of multidisciplinary analysis in combination with LCA are essential components of emergent LCA methods. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)’.
Article
The frontier of lifecycle assessment (LCA) case studies has moved from understanding conventional technologies to analyzing emerging technologies at the research and development (R&D) phase. One new challenge in LCA that aims to deal with this phenomenon is the development of methods to estimate the environmental impacts of emerging technologies in the R&D phase while considering manufacturing at scale. This study proposes a simple and feasible method that allows LCA practitioners to consider scaling effects of energy consumption during production in a case study of the super growth method of producing carbon nanotubes. In this case, the greenhouse gas (GHG) impact of producing 1 kg of carbon nanotubes decreases from 47.05 t-CO2eq to 21.33 t-CO2eq by scaling up the process. Although the total GHG impacts at the commercial scale estimated from the lab scale inventory are similar to those estimated from the pilot scale inventory, the share of GHG emissions of materials are different because of a change in substrate and a change from batch processing to continuous processing. Carbon nanotube production by the super growth method is competitive with those made by other processes from the viewpoint of GHG impact and product quality.
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Life-cycle assessments (LCAs) of municipal solid waste management (MSWM) systems are time- and data-intensive. Reducing the data requirements for inventory and impact assessments will facilitate the wider use of LCAs during early system planning and design. Therefore, the objective of this study is to develop a systematic framework for streamlining LCAs by identifying the most critical impacts, life-cycle inventory emissions, and inputs based on their contributions to the total impacts and their effect on the rankings of 18 alternative MSWM scenarios. The scenarios are composed of six treatment processes: landfills, waste-to-energy combustion, single-stream recycling, mixed waste recycling, anaerobic digestion, and composting. The full LCA uses 1752 flows of resources and emissions, 10 impact categories, 3 normalization references, and 7 weighting schemes, and these were reduced using the streamlined LCA approach proposed in this study. Human health cancer, ecotoxicity, eutrophication, and fossil fuel depletion contribute 75–83% to the total impacts across all scenarios. It was found that 3.3% of the inventory flows contribute ≥95% of the overall environmental impact. The highest-ranked strategies are consistent between the streamlined and full LCAs. The results provide guidance on which impacts, flows, and inputs to prioritize during early strategy design.
Article
Economic and environmental impact assessments are increasingly being adopted in the design and implementation of emerging systems. However, their emerging nature leads to several assessment challenges that need to be addressed to ensure the validity and usefulness of results in understanding their potential performance and supporting their development. There is the need to (i) account for spatial and temporal variability to allow a broader perspective at an early stage of development; (ii) handle uncertainties to systematically identify the critical factors and their interrelations that drive the results; (iii) integrate environmental and economic results to support sound decision-making based on two sustainability aspects. To address these assessment challenges, this study presents an alternative approach with the following corresponding features: (i) multiple scenario development to conduct an exploratory assessment of the systems under varying conditions and settings, (ii) global sensitivity analysis to identify the main critical factors and their interrelations, and (iii) trade-off and eco-efficiency analysis to integrate the economic and environmental results. The integrated approach is applied to a case study on plasma gasification for solid waste management. The results of the study highlight how the approach allows the identification of the dynamic relations between project settings and surrounding conditions. For example, the choice of gasifying agent largely depends on the background energy system, which dictates the impacts of the process energy requirement and the savings from the substituted energy of the syngas output. Based on these findings, the usefulness and validity of the proposed integrated approach are discussed in terms of how the key assessment challenges are addressed and how it can provide guidance for the development of emerging systems.
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For sustainable production and consumption, emerging green technologies need to be optimized towards a minimal environmental impact and a maximal economic impact. In an early stage of technology development, more flexibility is available to adapt the technology. Therefore, a prospective environmental and techno-economic assessment is required. The prospective assessment differs at the different stages of technology development, as also the data availability and accuracy evolves. This paper reviews the different prospective technological, economic and environmental assessment methods which have been used to assess the potential of new green chemical technologies. Based on the current best practices, an overarching framework is introduced to assess the technological, economic and environmental potential of an emerging green chemical technology at the different stages of technology development.
Conference Paper
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Future energy technologies must be based on renewable sources of energy and they must be sustainable. This workshop will provide insight into unintended impacts of renewable energy and how they can be avoided. In order to steer away from the pitfalls and unintended effects it is essential that necessary knowledge is present to the developers and decision makers engaged in renewable energy. This is where this workshop is valuable in its discussion of unintended health and environmental impacts of various renewable energy technologies. The workshop give the participants an introduction the the concept of unintended consequences, in connection with renewable energy. Furthermore, several approaches to improve the understanding of these consequences and methods for predicting them, will be discussed. This will include the concepts of rebound effects and consequential life cycle assessments (LCA). The workshop will encompass presentations and discussions of results from cross-disciplinary research on implementation of the alternative fuels hydrogen, electricity and biodiesel in the transport sector, as well as the assessment of environmental impacts from the production of solar cells. This will also cover impacts of the use of nanotechnology and nanomaterials in the various energy technologies. In-dept focus will also be on the formation of nanoparticles during combustion of bio-blended diesel, and the toxic effects of these new emission components.
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Life cycle assessment (LCA) is increasingly recognized as the proper framework for understanding the environmental impacts of nanotechnologies. In practice, applying LCA to nanotechnology is problematic. The performance, emissions, and inventory data collected at the laboratory scale may not be representative of the commercial scale. Despite the high uncertainty, LCA may guide nascent technologies towards being environmentally beneficial through early identification of leverage points. This research applies novel LCA methods based on laboratory-scale manufacturing data and battery performance modeling to quantify the energy tradeoffs associated with nano-enabled lithium ion batteries. At present, the large energy demands of nanomanufacturing processes make commercial scale application of nano-enabled batteries impracticable. This case study reveals both the challenge and value of applying prospective LCA to nanomaterials.
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Anticipatory governance is ‘a broad-based capacity extended through society that can act on a variety of inputs to manage emerging knowledge-based technologies while such management is still possible’. It motivates activities designed to build capacities in foresight, engagement, and integration – as well as through their production ensemble. These capacities encourage and support the reflection of scientists, engineers, policy makers, and other publics on their roles in new technologies. This article reviews the early history of the National Nanotechnology Initiative in the United States, and it further explicates anticipatory governance through exploring the genealogy of the term and addressing a set of critiques found in the literature. These critiques involve skepticism of three proximities of anticipatory governance: to its object, nanotechnology, which is a relatively indistinct one; to the public, which remains almost utterly naïve toward nanotechnology; and to technoscience itself, which allegedly renders anticipatory governance complicit in its hubris. The article concludes that the changing venues and the amplification within them of the still, small voices of folks previously excluded from offering constructive visions of futures afforded by anticipatory governance may not be complete solutions to our woes in governing technology, but they certainly can contribute to bending the long arc of technoscience more toward humane ends.
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I provide a vision and definition of Responsible Research and Innovation and propose a broad framework for its implementation under Research and Innovation schemes around the world. I make the case that RRI should be understood as a strategy of stakeholders to become mutual responsive to each other and anticipate research and innovation outcomes underpinning the "grand challenges" of our time for which they share > responsibility.> Research and Innovation processes need to become more responsive and adaptive to these grand challenges. This implies, among other, the introduction of broader foresight and impact assessments for new technologies beyond their anticipated market-benefits and risks. Social benefits of new technologies need to take into account widely shared public values. This implies a paradigm shift in innovation policy, moving away from an emphasis on key technologies towards issue and mission oriented policies. Background information can be found on: http://Renevonschomberg.wordpress.com
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Prospective Hazard Analysis techniques such as Healthcare Failure Modes and Effects Analysis (HFMEA) and Structured What If Technique (SWIFT) have the potential to increase safety by identifying risks before an adverse event occurs. Published accounts of their application in healthcare have identified benefits, but the reliability of some methods has been found to be low. The aim of this study was to examine the validity of SWIFT and HFMEA by comparing their outputs in the process of risk assessment, and comparing the results with risks identified by retrospective methods. The setting was a community-based anticoagulation clinic, in which risk assessment activities had been previously performed and were available. A SWIFT and an HFMEA workshop were conducted consecutively on the same day by experienced experts. Participants were a mixture of pharmacists, administrative staff and software developers. Both methods produced lists of risks scored according to the method's procedure. Participants' views about the value of the workshops were elicited with a questionnaire. SWIFT identified 61 risks and HFMEA identified 72 risks. For both methods less than half the hazards were identified by the other method. There was also little overlap between the results of the workshops and risks identified by prior root cause analysis, staff interviews or clinical governance board discussions. Participants' feedback indicated that the workshops were viewed as useful. Although there was limited overlap, both methods raised important hazards. Scoping the problem area had a considerable influence on the outputs. The opportunity for teams to discuss their work from a risk perspective is valuable, but these methods cannot be relied upon in isolation to provide a comprehensive description. Multiple methods for identifying hazards should be used and data from different sources should be integrated to give a comprehensive view of risk in a system.
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The governance of emerging science and innovation is a major challenge for contemporary democracies. In this paper we present a framework for understanding and supporting efforts aimed at ‘responsible innovation’. The framework was developed in part through work with one of the first major research projects in the controversial area of geoengineering, funded by the UK Research Councils. We describe this case study, and how this became a location to articulate and explore four integrated dimensions of responsible innovation: anticipation, reflexivity, inclusion and responsiveness. Although the framework for responsible innovation was designed for use by the UK Research Councils and the scientific communities they support, we argue that it has more general application and relevance.
Conference Paper
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There is a critical need to incorporate life cycle assessment (LCA) into research and development of renewable energy technologies, so that cradle-to-grave environmental concerns are identified early and communicated to technology developers. For example, environmental analyses of photovoltaic (PV) technologies may call attention to energetically burdensome processes with room for improvement, and can be used to compare the net energy balance of competing PV technologies. However, existing LCA frameworks are largely retrospective (i.e., requiring detailed data from existing industries and supply chains), and are thereby unable to provide timely information to decision makers. Large growth in the photovoltaic industry necessitates the development of anticipatory LCA methods, which can be used to explore potential environmental impacts of technologies and industries as they evolve. While the economic experience curves (i.e., $/watt) in PV project the costs to continuously come down as efficiency improves, it is not obvious that environmental experience curves (i.e., embodied energy/watt) are also monotonically declining. We review the boundaries and assumptions of all published PV-LCAs, and develop environmental experience curves for mono-Si, multi-Si, amorphous Si, and cadmium tellurium PV cells. The curves show decreasing manufacturing energy burden for silicon cells and relatively little improvement for thin film technologies. Using these environmental experience curves, we calculate the Energy Returned on Energy Invested (EROI) from cradle-to-use for each technology, plotting historic improvements for each technology and calling attention to the different rates of improvement. Results suggest alternate research and policy agenda, for example, silicon research should focus on decreasing manufacturing and supply chain investments while the net energy production of thin film PV may be improved largely through increases in efficiency.
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The Energy Independence and Security Act of 2007 set an annual US national production goal of 39.7 billion l of cellulosic ethanol by 2020. This paper explores the possibility of meeting that target by growing and processing Miscanthus × giganteus. We define and assess six production scenarios in which active cropland and/or Conservation Reserve Program land are used to grow to Miscanthus. The crop and biorefinery locations are chosen with consideration of economic, land-use, water management and greenhouse gas (GHG) emissions reduction objectives. Using lifecycle assessment, the net GHG footprint of each scenario is evaluated, providing insight into the climate costs and benefits associated with each scenario's objectives. Assuming that indirect land-use change is successfully minimized or mitigated, the results suggest two major drivers for overall GHG impact of cellulosic ethanol from Miscanthus: (a) net soil carbon sequestration or emissions during Miscanthus cultivation and (b) GHG offset credits for electricity exported by biorefineries to the grid. Without these factors, the GHG intensity of bioethanol from Miscanthus is calculated to be 11–13 g CO2-equivalent per MJ of fuel, which is 80–90% lower than gasoline. Including soil carbon sequestration and the power-offset credit results in net GHG sequestration up to 26 g CO2-equivalent per MJ of fuel.
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This article is based on the work of the SETAC-Europe LCA Working Group ‘Scenario Development in LCA’, which has started its work in April 1998. The goal of the Working Group is to focus on the use of scenarios in Life Cycle Assessment (LCA). This article presents the results of the first phase of the Working Group. The previous definitions of scenarios include three common basic elements: the definition of alternative future circumstances, the path from the present to the future, and the inclusion of uncertainty in the concept. We define a scenario in LCA as “a description of a possible future situation relevant for specific LCA applications, based on specific assumptions about the future, and (when relevant) also including the presentation of the development from the present to the future.’ On the basis of the scenario definition we distinguish between two basic approaches for scenario development in LCA studies: What-if scenarios and Cornerstone scenarios. What-if scenarios are used to gain operational information and to compare two or more alternatives in a well-known situation with a short time horizon where the researcher is familiar with the decision problem and can set defined hypothesis on the basis of existing data. The Cornerstone scenario approach offers strategic information for long term planning, new ways of seeing the world, and also guidelines in the field of study. Results of a study using the Cornerstone scenario approach often serve as a basis for further, more specific research where the scenarios can be defined according to What-if scenarios. The frames of the scenarios are defined in the first phase of LCA, the goal and scope definition. Scenario development does, however, influence all of the following phases of LCA. The frames of the scenarios form the basis for modelling product systems and environmental impacts associated with products and services, which are not exactly known due to lacking information on parts of the life cycle.
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The performance of photovoltaic devices could be improved by using rationally designed nanocomposites with high electron mobility to efficiently collect photo-generated electrons. Single-walled carbon nanotubes exhibit very high electron mobility, but the incorporation of such nanotubes into nanocomposites to create efficient photovoltaic devices is challenging. Here, we report the synthesis of single-walled carbon nanotube-TiO(2) nanocrystal core-shell nanocomposites using a genetically engineered M13 virus as a template. By using the nanocomposites as photoanodes in dye-sensitized solar cells, we demonstrate that even small fractions of nanotubes improve the power conversion efficiency by increasing the electron collection efficiency. We also show that both the electronic type and degree of bundling of the nanotubes in the nanotube/TiO(2) complex are critical factors in determining device performance. With our approach, we achieve a power conversion efficiency in the dye-sensitized solar cells of 10.6%.
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Biofuels are widely touted as viable, albeit not straightforward, alternatives to petroleum-derived fuels. To best determine their utilization, many practitioners turn to life-cycle assessment (LCA) to ascertain the “environmental footprint”. Although parameters such as resource and land use, along with infrastructure, can be incorporated into LCA algorithms, many have noted that the methodological approach still needs careful attention. In this Feature, McKone et al. outline seven grand challenges that need to be engaged and surmounted to provide the best way forward for biofuel use. Second Runner-up, Top 2011 Feature Paper in ES&T
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Together with a number of PV companies an extensive effort has been made to collect Life Cycle Inventory data that represents the current status of production technology for crystalline silicon modules. The new data covers all processes from silicon feedstock production to cell and module manufacturing. All commercial wafer technologies are covered, that is multi- and monocrystalline wafers as well as ribbon technology. The presented data should be representative for the technology status in 2004, although for monocrystalline Si crystallisation further improvement of the data quality is recommended. On the basis of the new data it is shown that PV systems on the basis of c-Si technology are in a good position to compete with other energy technologies. Energy Pay-Back Times of 1.5-2.5 yr are found for South-European locations, while life-cycle CO2 emission is in the 25-40 g/kWh range. Clear perspectives exist for further improvements with roughly 25%.
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In assessing hazard for human health posed by newly engineered nanomaterials (ENM), approaches such as Weight of Evidence (WOE) and expert judgment are required to develop conclusions about the hazard of ENM. This is because all factors affecting hazard are not currently well defined and are often subject to different interpretation. Here we report the application of a WOE procedure to assess the potential of ENM to cause harm for human health, by integrating and combining physicochemical properties of NM and toxicity data obtained within the EU-funded Particle Risk project. The procedure was applied to carbon black (CB), single-walled carbon nanotubes (SWNT), C60 fullerene and quantum dots (QD) ENM tested during the Particle Risk project. The results show that some of the investigated ENM present a relatively higher hazardousness level on the basis of the integration of their physicochemical properties and toxicological effects, and that their hazard may be ranked as follow: QD > C60 > SWNT > CB. This case study shows the utility of WOE approach to obtain a hazard ranking of ENM.
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Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined and new entries since July 1998 are briefly described.
Article
Purpose Comparative life-cycle assessments (LCAs) today lack robust methods of interpretation that help decision makers understand and identify tradeoffs in the selection process. Truncating the analysis at characterization is misleading and existing practices for normalization and weighting may unwittingly oversimplify important aspects of a comparison. This paper introduces a novel approach based on a multi-criteria decision analytic method known as stochastic multi-attribute analysis for life-cycle impact assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces. Methods To contrast different valuation methods, this study performs a comparative LCA of liquid and powder laundry detergents using three approaches to normalization and weighting: (1) characterization with internal normalization and equal weighting, (2) typical valuation consisting of external normalization and weights, and (3) SMAA-LCIA using outranking normalization and stochastic weighting. Characterized results are often represented by LCA software with respect to their relative impacts normalized to 100 %. Typical valuation approaches rely on normalization references, single value weights, and utilizes discrete numbers throughout the calculation process to generate single scores. Alternatively, SMAA-LCIA is capable of exploring high uncertainty in the input parameters, normalizes internally by pair-wise comparisons (outranking) and allows for the stochastic exploration of weights. SMAA-LCIA yields probabilistic, rather than discrete comparisons that reflect uncertainty in the relative performance of alternatives. Results and discussion All methods favored liquid over powder detergent. However, each method results in different conclusions regarding the environmental tradeoffs. Graphical outputs at characterization of comparative assessments portray results in a way that is insensitive to magnitude and thus can be easily misinterpreted. Typical valuation generates results that are oversimplified and unintentionally biased towards a few impact categories due to the use of normalization references. Alternatively, SMAA-LCIA avoids the bias introduced by external normalization references, includes uncertainty in the performance of alternatives and weights, and focuses the analysis on identifying the mutual differences most important to the eventual rank ordering. Conclusions SMAA-LCIA is particularly appropriate for comparative LCAs because it evaluates mutual differences and weights stochastically. This allows for tradeoff identification and the ability to sample multiple perspectives simultaneously. SMAA-LCIA is a robust tool that can improve understanding of comparative LCA by decision or policy makers.
Article
Purpose More energy efficient lighting options, such as compact fluorescent bulbs and light emitting diodes are predicted to significantly reduce the amount of energy used for lighting. Such forecasts are predicated on the assumption of light saturation and do not take into account the potential for economic rebound. The potential of the rebound effect to reduce or negate predicted energy savings is explored here. Methods This work uses an agent-based model with a cellular automata approach to study the impact of rebound on the consumption of residential light and associated energy use, using three lighting technologies, and a time span from 2012 to 2030. Agents, representative of households, select between three lighting options using a multiplicative utility function and a probabilistic choice mechanism. Agents then decide whether to consume more light and potentially more energy based on the lighting technology selected and personal preferences. The agents are heterogeneous in nature, consisting of seven typologies, with their characteristics informed through survey data. Results and discussion The results of the model indicate that although the consumption of light may increase, overall changes in the consumption of energy compared to 2012 levels will be minor. If the consumption of light is held steady, assuming saturation, then there is the potential for the adoption of energy-efficient lighting to result in significant energy savings. However, if the rebound effect occurs, there will be a decrease in the consumption of energy for lighting as consumers adopt more energy efficient options. Overtime as the consumption of light continues to increase, those savings will largely be eroded. Conclusions This study suggests that the adoption of energy-efficient lighting in itself will not reduce the overall consumption of energy for lighting on a long-term scale although it may be successful in doing so in the short-term. The rebound effect will greatly reduce the projected energy savings from more efficient lighting technologies, with potential for direct rebound to exceed 100 %. In order for the quantity of energy utilized in residential lighting to decrease, solutions beyond that of efficiency gains must be considered.
Article
Purpose Ecoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation. This approach allows calculating uncertainties for all flows in the ecoinvent database. In ecoinvent 2 the uncertainty factors were provided based on expert judgment, without (documented) empirical foundation. This paper presents (1) a procedure to obtain an empirical foundation for the uncertainty factors that are used in the pedigree approach and (2) a proposal for new uncertainty factors, received by applying the developed procedure. Both the factors and the procedure are a result of a first phase of an ecoinvent project to refine the pedigree matrix approach. A separate paper in the same edition, also the result of the aforementioned project, deals with extending the developed approach to other probability distributions than lognormal (Muller et al.). Methods Uncertainty is defined here simply as geometric standard deviation (GSD) of intermediate and elementary exchanges at the unit process level. This fits to the lognormal probability distribution that is assumed as default in ecoinvent 2, and helps to overcome scaling effects in the analysed data. In order to provide the required empirical basis, a broad portfolio of data sources is analysed; it is especially important to consider sources outside of the ecoinvent database to avoid circular reasoning. The ecoinvent pedigree matrix from version 2 is taken as a starting point, skipping the indicator “sample size” since it will not be used in ecoinvent 3. This leads to a pedigree matrix with five data quality indicators, each having five score values. The analysis is conducted as follows: for each matrix indicator and for each data source, indicator scores are set in relation to data sets, building groups of data sets that represent the different data quality indicator scores in the pedigree matrix. The uncertainty in each of the groups is calculated. The uncertainty obtained for the group with the ideal indicator score is set as a reference, and uncertainties for the other groups are set in relation to this reference uncertainty. The obtained ratio will be different from 1, it represents the unexplained uncertainty, additional uncertainty due to a lower data quality, and can be directly used as uncertainty factor candidates. Results and discussion The developed approach was able to derive empirically based uncertainty factor candidates for the pedigree matrix in ecoinvent. Uncertainty factors were obtained for all data quality indicators and for almost all indicator scores in the matrix. The factors are the result of the first analysis of several data sources, further analyses and discussions should be used to strengthen their empirical basis. As a consequence, the provided uncertainty factors can change in future. Finally, a few of the qualitative score descriptions in the pedigree matrix left room for interpretation, making their application not ambiguous. Conclusions and perspectives An empirical foundation for the uncertainty factors in the pedigree matrix overcomes one main argument against their use, which in turn strengthens the whole pedigree approach for quantitative uncertainty assessment in ecoinvent. This paper provides an approach to obtain an empirical basis for the uncertainty factors, and it provides also empirically based uncertainty factors, for indicator scores in the pedigree matrix. Basic uncertainty factors are not provided, it is recommended to use the factors from ecoinvent 2 for the time being. In the developed procedure, using GSD as the uncertainty measure is essential to overcome scaling effects; it should therefore also be used if the analysed data do not follow a lognormal distribution. As a consequence, uncertainty factors obtained as GSD ratios need to be translated to range estimators relevant for these other distributions. Formulas for this step are provided in a separate paper (Muller et al.). The work presented in this paper could be the starting point for a much broader study to provide a better basis for input uncertainty in LCA, not only in ecoinvent.
Article
One promising future bulk application of graphene is as composite additive. Therefore, we compare two production routes for in-solution graphene using a cradle-to-gate lifecycle assessment focusing on potential differences in energy use, blue water footprint, human toxicity, and ecotoxicity. The data used for the assessment is based on information in scientific papers and patents. Considering the prospective nature of this study, environmental impacts from background systems such as energy production were not included. The production routes are either based on ultrasonication or chemical reduction. The results show that the ultrasonication route has lower energy and water use, but higher human and ecotoxicity impacts, compared to the chemical reduction route. However, a sensitivity analysis showed that solvent recovery in the ultrasonication process gives lower impacts for all included impact categories. The sensitivity analysis also showed that solvent recovery is important to lower the blue water footprint of the chemical reduction route as well. The results demonstrate the possibility to conduct a life cycle assessment study based mainly on information from patents and scientific articles, enabling prospective life cycle assessment studies of products at early stages of technological development.
Article
Purpose The need for a systematic evaluation of the human and environmental impacts of engineered nanomaterials (ENMs) has been widely recognized, and a growing body of literature is available endorsing life cycle assessment (LCA) as a valid tool for the same. The purpose of this study is to evaluate how the nano-specific environmental assessments are being done within the existing framework of life cycle inventory and impact assessment and whether these frameworks are valid and/or whether they can be modified for nano-evaluations. Method In order to do that, we reviewed the state-of-the-art literature on environmental impacts of nanomaterials and life cycle assessment studies on ENMs and nanoproducts. We evaluated the major characteristics and mechanisms under which nanomaterials affect the environment and whether these characteristics and mechanisms can be adequately addressed with current life cycle inventories and impact assessment practices. We also discuss whether the current data and knowledge accumulated around fate, transport, and toxicity of nanomaterials can be used to perform an interim evaluation while more data are being generated. Observations and recommendations We found that while there is plenty of literature available promoting LCA as a viable tool for ENMs and nanoproducts, there are only a handful of studies where at least some parts of life cycle were evaluated for nanoproducts or nanomaterial. None of the LCA studies on ENMs or nanoproducts that we came across assessed nano-specific fate, transport, and toxicity effects as part of their evaluation citing the lack of data as the primary reason. However, our literature review indicates that nano-LCA studies need not omit the assessment of nanomaterials’ human health and environmental impact due to incomplete data. There is some evidence that scalability may exist in certain types of nanomaterial, and traditional characterization can be applied even below 100 nm up to the scalability breakdown limits. For the size range where the scalability cannot be established, it may be more appropriate to explore empirical relationships, though possibly crude, between nanomaterial properties and their impact on human health and environment. Empirical relationships thus derived can serve as valid input for assessment until specific data points for nanomaterial fate, transport, and toxicity become available. Finally, where there is no quantitative data available, qualitative inferences may be drawn based on the known information of the nanomaterial and its potential release pathways.
Book
Energy technologies in the future will need to be based on renewable sources of energy and will, ultimately, need to be sustainable. This book provides insight into unintended, negative impacts and how they can be avoided. In order to steer away from the pitfalls and unintended effects, it is essential that the necessary knowledge is available to the developers and decision makers engaged in renewable energy. The value of this book lies in its presentation of the unintended health and environmental impacts from renewable energies. The book presents results from cross-disciplinary research on the implementation of alternative fuels in the transport sector, namely hydrogen, electricity and biodiesel. This is followed by an assessment of environmental impacts from the production of solar cells. Critical reviews on the use of nanotechnology and nanomaterials in the energy technologies is then provided, with the formation of nanoparticles during combustion of bio-blended diesel and their toxic effects, is discussed in detail. http://www.springer.com/energy/renewable+and+green+energy/book/978-1-4471-5531-7
Article
Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined and new entries since January 2004 are reviewed. Copyright # 2004 John Wiley & Sons, Ltd.
Article
Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined and new entries since January 1999 are reviewed.
Article
Consolidated tables showing an extensive listing of the highest independently con-®rmed eciencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since June 1997 are brie¯y described. # 1998 John Wiley & Sons, Ltd.
Article
The presence of value judgments in life-cycle impact assessment (LCIA) has been a constant source of controversy. According to a common interpretation, the international standard on LCIA requires that the assessment methods used in published comparisons be “value free.” Epistemologists argue that even natural science rests on “constitutive” and “contextual” value judgments. The example of the equivalency potential for climate change, the global warming potential (GWP), demonstrates that any impact assessment method inevitably contains not only constitutive and contextual values, but also preference values. Hence, neither life-cycle assessment (LCA) as a whole nor any of its steps can be “value free.” As a result, we suggest a more comprehensive definition of objectivity in LCA that allows arguments about values and their relationship to facts. We distinguish three types of truth claims: factual claims, which are based on natural science; normative claims, which refer to preference values; and relational claims, which address the proper relation between factual knowledge and values. Every assessment method, even the GWP, requires each type of claim. Rational arguments can be made about each type of claim. Factual truth claims can be assessed using the scientific method. Normative claims can be based on ethical arguments. The values of individuals or groups can be elicited using various social science methods. Relational claims must follow the rules of logic. Relational claims are most important for the development of impact assessment methods. Because LCAs are conducted to satisfy the need of decision makers to consider environmental impacts, relational claims about impact assessment methods should refer to this goal. This article introduces conditions that affect environmental decision making and discusses how LCA—values and all—can be defended as a rational response to the challenge of moving uncertain scientific information into the policy arena.