Life cycle assessment of emerging technologies: A review

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.     

Life cycle assessment of emerging technologies at the lab scale: The case of nanowire‐based solar cells

Nanomaterials are expected to play an important role in the development of sustainable products. The use of nanomaterials in solar cells has the potential to increase their conversion efficiency. In this study, we performed a life cycle assessment (LCA) for an emerging nanowire‐based solar technology. Two lab‐scale manufacturing routes for the production of nanowire‐based solar cells have been compared—the direct growth of GaInP nanowires on silicon substrate and the growth of InP nanowires on native substrate, peel off, and transfer to silicon substrate. The analysis revealed critical raw materials and processes of the current lab‐scale manufacturing routes such as the use of trifluoromethane (CHF₃), gold, and an InP wafer and a stamp, which are used and discarded. The environmental performance of the two production routes under different scenarios has been assessed. The scenarios include the use of an alternative process to reduce the gold requirements—electroplating instead of metallization, recovery of gold, and reuse of the InP wafer and the stamp. A number of suggestions, based on the LCA results—including minimization of the use of gold and further exploration for upscaling of the electroplating process, the increase in the lifetimes of the wafer and the stamp, and the use of fluorine‐free etching materials—have been communicated to the researchers in order to improve the environmental performance of the technology. Finally, the usefulness and limitations of lab‐scale LCA as a tool to guide the sustainable development of emerging technologies are discussed.     

When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment

Prospective life cycle assessment (LCA) needs to deal with the large epistemological uncertainty about the future to support more robust future environmental impact assessments of technologies. This study proposes a novel approach that systematically changes the background processes in a prospective LCA based on scenarios of an integrated assessment model (IAM), the IMAGE model. Consistent worldwide scenarios from IMAGE are evaluated in the life cycle inventory using ecoinvent v3.3. To test the approach, only the electricity sector was changed in a prospective LCA of an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using six baseline and mitigation climate scenarios until 2050. This case study shows that changes in the electricity background can be very important for the environmental impacts of EV. Also, the approach demonstrates that the relative environmental performance of EV and ICEV over time is more complex and multifaceted than previously assumed. Uncertainty due to future developments manifests in different impacts depending on the product (EV or ICEV), the impact category, and the scenario and year considered. More robust prospective LCAs can be achieved, particularly for emerging technologies, by expanding this approach to other economic sectors beyond electricity background changes and mobility applications as well as by including uncertainty and changes in foreground parameters. A more systematic and structured composition of future inventory databases driven by IAM scenarios helps to acknowledge epistemological uncertainty and to increase the temporal consistency of foreground and background systems in LCAs of emerging technologies.     

The Role of Scale and Technology Maturity in Life Cycle Assessment of Emerging Technologies: A Case Study on Carbon Nanotubes

Life cycle assessment (LCA) has been applied for assessing emerging technologies, where large‐scale production data are generally lacking. This study introduces a standardized scheme for technology and manufacturing readiness levels to contextualize a technology's development stage. We applied the scheme to a carbon nanotube (CNT) LCA and found that, regardless of synthesis technique, CNT manufacturing will become less energy intensive with increased levels of readiness. We examined the influence of production volume on LCA results using primary data from a commercial CNT manufacturer with approximately 100 grams per day production volume and engineering design of a scaled‐up process with 1 tonne per day production capacity. The results show that scaling up could reduce 84% to 94% of its cradle‐to‐gate impacts, mainly as a result of the recycling of feedstock that becomes economically viable only beyond certain minimum production volume. This study shows that LCAs on emerging technologies based on immature data should be interpreted in conjunction with their technology and manufacturing readiness levels and reinforces the need of standardizing and communicating information on these readiness levels and scale of production in life cycle inventory practices.     

Linking Data Choices and Context Specificity in Life Cycle Assessment of Waste Treatment Technologies: A Landfill Case Study

To generate meaningful results, life cycle assessments (LCAs) require accurate technology data that are consistent with the goal and scope of the analysis. While literature data are available for many products and processes, finding representative data for highly site‐specific technologies, such as waste treatment processes, remains a challenge. This study investigated representative life cycle inventory (LCI) modeling of waste treatment technologies in consideration of variations in technological level and climate. The objectives were to demonstrate the importance of representative LCI modeling as a function of the specificity of the study, and to illustrate the necessity of iteratively refining the goal and scope of the study as data are developed. A landfill case study was performed where 52 discrete landfill data sets were built and grouped to represent different technology options and geographical sites, potential impacts were calculated, and minimum/maximum (min‐max) intervals were generated for each group. The results showed decreasing min‐max intervals with increasing specificity of the scope of study, which indicates that compatibility between the scope of study and LCI model is critical. Hereby, this study quantitatively demonstrates the influence of representative modeling on LCA results. The results indicate that technology variations and site‐specific conditions (e.g., the influence of precipitation and cover permeability on landfill gas generation and collection) should be carefully addressed by a systematic analysis of the key process parameters. Therefore, a thorough understanding of the targeted waste treatment technologies is necessary to ensure that appropriate data choices are made within the boundaries of the defined scope of the study.     

Evaluating Human and Ecological Impacts of a Product Life Cycle: The Complementary Roles of Life-Cycle Assessment and Risk Assessment

Life-cycle assessment (LCA) is a tool for evaluating various health and environmental impacts throughout a product's life. When used as a screening tool, LCA can potentially identify the processes and materials most likely to pose a threat to human health and the environment, and to determine where a risk assessment is warranted. The European Union has issued a ban on lead-based solder from use in electronic equipment beginning in July 2006. In response, the Lead-Free Solder Partnership, involving the U.S. Environmental Protection Agency, several electronics manufacturers, and the University of Tennessee afforded a vehicle for conducting a thorough LCA of leaded and lead-free solders used in the electronics industry. Sixteen impact categories were evaluated in the LCA, including human toxicity.

A primary conclusion of the assessment for human and aquatic toxicity, across the entire life cycle of tin-lead solder, was the potential for impacts derived from the landfilling of lead. These results, based on broad assumptions about exposure, suggest that a more detailed risk assessment of the landfilling process would assist in better understanding the potential for health and environmental risks. We believe LCA data can be used to identify the need for focused risk assessments, allowing the two tools to effectively complement one another. Use of both methods could assist in understanding the effectiveness of the European ban on lead solder and its potential to improve public health.     

Exploring the Use of Ecological Footprint in Life Cycle Impact Assessment

Ecological footprint (EF) is a metric that estimates human consumption of biological resources and products, along with generation of waste greenhouse gas (GHG) emissions in terms of appropriated productive land. There is an opportunity to better characterize land occupation and effects on the carbon cycle in life cycle assessment (LCA) models using EF concepts. Both LCA and EF may benefit from the merging of approaches commonly used separately by practitioners of these two methods. However, few studies have compared or integrated EF with LCA. The focus of this research was to explore methods for improving the characterization of land occupation within LCA by considering the EF method, either as a complementary tool or impact assessment method. Biofuels provide an interesting subject for application of EF in the LCA context because two of the most important issues surrounding biofuels are land occupation (changes, availability, and so on) and GHG balances, two of the impacts that EF is able to capture. We apply EF to existing fuel LCA land occupation and emissions data and project EF for future scenarios for U.S. transportation fuels. We find that LCA studies can benefit from lessons learned in EF about appropriately modeling productive land occupation and facilitating clear communication of meaningful results, but find limitations to the EF in the LCA context that demand refinement and recommend that EF always be used along with other indicators and metrics in product‐level assessments.     

基于在线供应链调查的产品生命周期评价

产品生命周期评价(LCA)中的供应链数据收集非常困难而且效率低下,因而在产品LCA研究中常常近似处理、甚至忽略供应链实际生产过程,严重影响了LCA的数据质量与可信度。开发专门的供应链数据收集工具是解决这一问题的有效途径。介绍了可进行在线供应链调查、建模与计算分析的LCA系统e Footprint,基于该系统提出了在线的LCA工作方法,通过长虹美菱冰箱的应用案例验证了系统与方法的可行性,为提高产品LCA的效率和质量提供了新的解决方案。     

A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment

This article presents a framework to evaluate emerging systems in life cycle assessment (LCA). Current LCA methods are effective for established systems; however, lack of data often inhibits robust analysis of future products or processes that may benefit the most from life cycle information. In many cases the life cycle inventory (LCI) of a system can change depending on its development pathway. Modeling emerging systems allows insights into probable trends and a greater understanding of the effect of future scenarios on LCA results. The proposed framework uses Bayesian probabilities to model technology adoption. The method presents a unique approach to modeling system evolution and can be used independently or within the context of an agent‐based model (ABM). LCA can be made more robust and dynamic by using this framework to couple scenario modeling with life cycle data, analyzing the effect of decision‐making patterns over time. Potential uses include examining the changing urban metabolism of growing cities, understanding the development of renewable energy technologies, identifying transformations in material flows over space and time, and forecasting industrial networks for developing products. A switchgrass‐to‐energy case demonstrates the approach.     

Confronting Uncertainty in Life Cycle Assessment Used for Decision Support

The aim of this article is to help confront uncertainty in life cycle assessments (LCAs) used for decision support. LCAs offer a quantitative approach to assess environmental effects of products, technologies, and services and are conducted by an LCA practitioner or analyst (AN) to support the decision maker (DM) in making the best possible choice for the environment. At present, some DMs do not trust the LCA to be a reliable decision‐support tool—often because DMs consider the uncertainty of an LCA to be too large. The standard evaluation of uncertainty in LCAs is an ex‐post approach that can be described as a variance simulation based on individual data points used in an LCA. This article develops and proposes a taxonomy for LCAs based on extensive research in the LCA, management, and economic literature. This taxonomy can be used ex ante to support planning and communication between an AN and DM regarding which type of LCA study to employ for the decision context at hand. This taxonomy enables the derivation of an LCA classification matrix to clearly identify and communicate the type of a given LCA. By relating the LCA classification matrix to statistical principles, we can also rank the different types of LCA on an expected inherent uncertainty scale that can be used to confront and address potential uncertainty. However, this article does not attempt to offer a quantitative approach for assessing uncertainty in LCAs used for decision support.     

Use of LCA as a development tool within early research: challenges and issues across different sectors

Purpose

The aim of this paper is to highlight the challenges that face the use of life cycle assessment (LCA) for the development of emerging technologies. LCA has great potential for driving the development of products and processes with improved environmental credentials when used at the early research stage, not only to compare novel processing with existing commercial alternatives but to help identify environmental hotspots. Its use in this way does however provide methodological and practical difficulties, often exacerbated by the speed of analysis required to enable development decisions to be made. Awareness and understanding of the difficulties in such cases is vital for all involved with the development cycle.

Methods

This paper employs three case studies across the diverse sectors of nanotechnology, lignocellulosic ethanol (biofuel), and novel food processes demonstrating both the synergy of issues across different sectors and highlighting the challenges when applying LCA for early research. Whilst several researchers have previously highlighted some of the issues with use of LCA techniques at an early stage, most have focused on a specific product, process development, or sector. The use of the three case studies here is specifically designed to highlight conclusively that such issues are prevalent to use of LCA in early research irrespective of the technology being assessed.

Results and discussion

The four focus areas for the paper are system boundaries, scaling issues, data availability, and uncertainty. Whilst some of the issues identified will be familiar to all LCA practitioners as problems shared with standard LCAs, their importance and difficulty is compounded by factors distinct to novel processes as emerging technology is often associated with unknown future applications, unknown industrial scales, and wider data gaps that contribute to the level of LCA uncertainty. These issues, in addition with others that are distinct to novel applications, such as the challenges of comparing laboratory scale data with well-established commercial processing, are exacerbated by the requirement for rapid analysis to enable development decisions to be made.

Conclusions

Based on the challenges and issues highlighted via illustration through the three case studies, it is clear that whilst transparency of information is paramount for standard LCAs, the sensitivities, complexities, and uncertainties surrounding LCAs for early research are critical. Full reporting and understanding of these must be established prior to utilising such data as part of the development cycle.     

Critical Components of Uncertainty Communication in Life Cycle Assessments of Emerging Technologies

Because of their recognition as a comprehensive tool of environmental assessments and their increasing use by governments and industries, life cycle assessments (LCAs) are positioned to be prominent sources of mass media information on new products and technologies. The LCA studies underlying media reports are often viewed by nonexperts after the initial reporting. However, uncertainty is rife in early assessments of emerging technologies, and LCA's ability to inform environmental opinions and decisions is limited without the accompanying communication on uncertainty. Though approaches to the technical aspects of uncertainty analysis in LCA are available in the literature, those on communicating that uncertainty, in ways that are cognitively accessible to the nonexperts, are still lacking despite their highlighted importance across various disciplines. With the focus on communication, this article uses the existing literature to derive five criteria for making uncertainty communication accessible to a nonexpert audience. Then, LCAs on engineered nanomaterial (ENM) and ENM‐enabled products, as a case study of emerging technologies where uncertainties abound, are reviewed for whether they meet these five criteria. The study concludes with recommendations for communicating uncertainty in LCAs in order to enhance their role as decision‐ and public opinion–informing assessments.     

LiSET: A Framework for Early‐Stage Life Cycle Screening of Emerging Technologies

While life cycle assessment (LCA) is a tool often used to evaluate the environmental impacts of products and technologies, the amount of data required to perform such studies make the evaluation of emerging technologies using the conventional LCA approach challenging. The development paradox is such that the inputs from a comprehensive environmental assessment has the greatest effect early in the development phase, and yet the data required to perform such an assessment are generally lacking until it is too late. Previous attempts to formalize strategies for performing streamlined or screening LCAs were made in the late 1990s and early 2000s, mostly to rapidly compare the environmental performance of product design candidates. These strategies lack the transparency and consistency required for the environmental screening of large numbers of early‐development candidates, for which data are even sparser. We propose the Lifecycle Screening of Emerging Technologies method (LiSET). LiSET is an adaptable screening‐to‐LCA method that uses the available data to systematically and transparently evaluate the environmental performance of technologies at low readiness levels. Iterations follow technological development and allow a progression to a full LCA if desired. In early iterations, LiSET presents results in a matrix structure combined with a “traffic light” color grading system. This format inherently communicates the high uncertainty of analysis at this stage and presents numerous environmental aspects assessed. LiSET takes advantage of a decomposition analysis and data not traditionally used in LCAs to gain insight to the life cycle impacts and ensure that the most environmentally sustainable technologies are adopted.     

Using anticipatory life cycle assessment to enable future sustainable construction

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.     

Environmental assessment of future technologies: how to trim LCA to fit this goal?

Introduction

New and innovative technologies may claim substantial efficiency gains in the future. However, they are often assessed based on their current performance, measured in the laboratory or in pilot plants. The goal of discussion forum 38 was, on one hand, to shed light on the main drivers and principles that ensure a sensible and fair assessment of far future technologies. On the other hand, the most recent European developments in prospective technology assessment of emerging energy technologies and the related quantification of external costs were touched upon.

Discussion

The discussion forum started with three talks dedicated to external costs and multicriteria decisions presenting results of the New Energy Externality Developments for Sustainability project. After three presentations considering long-term LCI modeling aspects, lectures were held covering industry implementation and case studies. The following main conclusions were drawn at the end of discussion forum 38: (a) life cycle assessment (LCA) is considered a useful tool for environmental assessments of future energy technology, (b) consistency in LCA modeling of future situations is achieved by adapting data in the foreground (electricity-generating technology) and in the background (electricity supply mix, material manufacture, transport services, etc.), (c) external cost assessments and multicriteria decision analysis involve value judgments and thus do lead to a variety of different conclusions, (d) the present situation must be known properly to be able to model possible future situations, and (e) challenges are the data availability and definition of consistent scenarios of the future.     

农业生命周期评价研究进展

作为评价产品系统全链条环境影响的有效工具,生命周期评价（LCA）方法已广泛用于工业领域。农业领域也面临着高强度的资源和环境压力,LCA在农业领域的应用应运而生。旨在综述已有农业LCA研究的基础上,鉴别农业LCA应用存在的问题,并为农业LCA未来的发展提出建议。目前农业LCA存在系统边界和功能单位界定不明晰、缺少区域清单数据库、生命周期环境影响评价模型（LCIA）不能准确反映农业系统环境影响、结果解释存在误区等方面的问题。为了科学准确地衡量农业系统的环境影响,促进农业系统的可持续发展,文章认为农业LCA应该从以下几个方面加强研究,即科学界定评价的参照系、系统边界的扩大及功能单位的合理选取、区域异质性数据库构建与LCIA模型开发、基于组织农业LCA的开发以及对于利益相关者行为的研究。     

生命周期评价方法在城市生活垃圾管理中的应用研究述评

结合城市生活垃圾管理系统特征,系统归纳基于生命周期评价(Life cycle assessment,LCA)方法的城市生活垃圾管理模型的发展现状,并对LCA方法在城市生活垃圾管理中的实践以及在我国开展城市生活垃圾管理LCA研究的应用前景进行评述。分析表明,LCA是城市生活垃圾管理领域的重要工具之一,基于LCA方法的城市生活垃圾管理模型在全生命周期环境影响评价与识别、处置工艺选择与改进、可持续生活垃圾管理决策支持等方面具有十分重要的应用价值。中国在本地化生活垃圾管理系统LCA模型开发、清单数据库和评价指标体系构建以及与其他研究方法集成等方面面临挑战。     

Analyzing the Practice of Life Cycle Assessment

Life cycle assessment (LCA) is a quantitative tool used to evaluate the environmental impacts of products or processes. With respect to buildings, LCA can be used to evaluate the environmental impacts of an entire building's life cycle. Currently LCA in the building area is used in a limited capacity, primarily to select building products. In order to determine the causality for the lack of whole‐building LCAs, focus groups with members of the architecture, engineering, and construction (AEC) communities were held. This article investigates the current level of knowledge of LCA in the AEC community and then discusses the benefits and barriers to the practice of LCA. In summary, the goal of the research was to identify why LCA is not used to its fullest potential in a whole‐building LCA. In an open forum and moderated setting, focus group participants were asked individually to self‐identify their experience with LCA, a brief education session on LCA was held, and then benefits and barriers to LCA were discussed. The focus group sessions were transcribed and systematically coded by social researchers in order to analyze the results. Hybrid flow and radar charts were developed. From the focus group results, the most important benefit to LCA was “provides information about environmental impacts.” The results did not identify a prominent barrier; however, building‐related metrics were ascertained to be one of the more crucial barriers. The benefits and barriers classified by this analysis will be utilized to develop a subsequent online survey to further understand the LCA and AEC community.