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

On the financial balance of input–output constructs: revisiting an axiomatic evaluation

Financial balance is fundamental to input–output (IO) analysis, and consequently the respect of this balance is one of the dominant criteria in evaluating IO constructs. Kop Jansen, and ten Raa [(1990) The Choice of Model in the Construction of Input–Output Coefficients Matrices. International Economic Review 31, 213] proved that the byproduct-technology construct (BTC) and the industry-technology construct (ITC) do not generally conserve financial balance. In contrast, Majeau-Bettez et al. [(2016) When do Allocations and Constructs Respect Material, Energy, Financial, and Production Balances in LCA and EEIO? Journal of Industrial Ecology 20, 67–84] demonstrated that the BTC necessarily respects financial balance and that the ITC is always financially balanced when applied to data recorded in monetary units. The present article resolves this paradox

ECOPT 2 : An adaptable life cycle assessment model for the environmentally constrained optimization of prospective technology transitions

Life cycle assessment (LCA) is a method to evaluate the environmental impacts of technologies from cradle to grave. However, LCAs are commonly defined in terms of the consumption of a single unit of a product and thus ignore scaling issues in large-scale deployment of technologies. Such product-level LCAs often do not consider capital manufacturing capacity and supply chain bottlenecks that may hinder the rapid, widespread uptake of emerging technologies entering the market; emerging technologies often require the expansion of existing supply chains or the development of entirely new supply chains, such as the manufacturing of novel materials. As a result, such LCA studies are limited in their ability to realistically assess impacts at the macro-scale and thus to guide large-scale decisions. In this work, we present ECOPT2, a generalized adaptable model that combines these constraints to the LCA approach using a mathematical programming approach and dynamic stock modeling. ECOPT2 combines LCA factors with transition scenarios from energy systems models to determine the environmentally optimal deployment of new technologies while accounting for material circularity constraints and barriers to uptake. We also introduce the structure of the software tool and demonstrate its features using a stylized vehicle electrification scenario. © 2022 The Authors. Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of International Society for Industrial Ecology

Lifting industrial ecology modeling to a new level of quality and transparency: a call for more transparent publications and a collaborative open source software framework

Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.FWN – Publicaties zonder aanstelling Universiteit Leide

Substitution modelling in life cycle assessment of municipal solid waste management

Life cycle assessment (LCA) is gaining importance worldwide in guiding waste management policies. The capacity of co-products such as recycled materials and recovered energy to avoid primary production of equivalent products largely determines the environmental performance of waste treatment technologies. Estimating the reductions in resource use, emissions, and impacts enabled by this substitution of primary production is often the most influential and controversial factor in quantifying the overall environmental performance of a waste management strategy. This study aims to critically evaluate the modelling of substitution in LCAs of recovered material from municipal solid waste management systems (MSWMS) by answering two questions. First, to what extent is substitution modelling transparently documented in the literature? Second, are the substitution ratios justified to represent physically realistic replacement of one product by another? To address these questions, we performed a systematic analysis of 51 LCA studies on MSWMS published in the peer-reviewed literature. We found that 22% of the substitution ratios are only implicitly expressed. A significant proportion of substitution ratios is not justified (65%), while for the remaining 35%, justifications do not represent physically realistic substitutions. We call for more rigor and transparency, and we propose guidance for the documentation of substitution ratios, with the aim of reaching more credible and robust analyses. For the justification of a substitution ratio to be considered physically realistic, information should notably be provided concerning loss of quality, the function performed by substitutable materials, and the sector of use

Lifting the veil on the correction of double counting incidents in hybrid life cycle assessment

Life cycle assessment (LCA) and environmentally extended input–output analyses (EEIOA) are two techniques commonly used to assess environmental impacts of an activity/product. Their strengths and weaknesses are complementary, and they are thus regularly combined to obtain hybrid LCAs. A number of approaches in hybrid LCA exist, which leads to different results. One of the differences is the method used to ensure that mixed LCA and EEIOA data do not overlap, which is referred to as correction for double counting. This aspect of hybrid LCA is often ignored in reports of hybrid assessments and no comprehensive study has been carried out on it. This article strives to list, compare, and analyze the different existing methods for the correction of double counting. We first harmonize the definitions of the existing correction methods and express them in a common notation, before introducing a streamlined variant. We then compare their respective assumptions and limitations. We discuss the loss of specific information regarding the studied activity/product and the loss of coherent financial representation caused by some of the correction methods. This analysis clarifies which techniques are most applicable to different tasks, from hybridizing individual LCA processes to integrating complete databases. We finally conclude by giving recommendations for future hybrid analyses

Balance issues in input–output analysis: A comment on physical inhomogeneity, aggregation bias, and coproduction

Recently, Merciai and Heijungs (2014) demonstrated that monetary input–output (IO) analysis can lead to system descriptions that do not conserve mass when the assumption of homogeneous prices is violated. They warn that this violation of basic balance laws can lead to biased estimates of environmental impacts, and they therefore recommend performing IO analysis in a physically accounted framework. We take a broader scope on this issue and present price inhomogeneity as a special case of product mix inhomogeneity. We demonstrate that even a fully physically accounted IO analysis or lifecycle assessment will violate balance laws if it suffers from inhomogeneous aggregation. The core issue is not whether a system is described using monetary or physical units, but rather whether product groups are too aggregated to allow for the concurrent respect of energy, mass, financial and elemental balances. We further analyze the link between the violation of physical balances and the introduction of biases. We find that imbalances are neither a necessary nor a sufficient condition for the presence of systematic errors in environmental pressure estimates. We suggest two ways to leverage the additional explanatory power of multi-unit inventory tables to reduce instances of imbalances and aggregation biases

Regionalized climate footprints of battery electric vehicles in Europe

The climate mitigation benefits of battery electric vehicles (BEVs) relative to internal combustion engine vehicles (ICEVs) are highly dependent on the carbon intensity of the electricity consumed during their production and use-phase. A consistent and dynamic approach to grid-mix regionalization of BEV life-cycle assessments in Europe is therefore necessary to offer accurate guidance to consumers and policy makers. To this end, we present ReDyFEV, a simple open-source software tool that can be used to calculate attributional, regionalized lifecycle climate impacts of BEVs in Europe for user-defined time periods, including near real-time. We determine the national lifecycle carbon footprints across all EU states for four BEV size segments and compare them to those of fossil-fuelled vehicles of similar sizes. Simplified sensitivity analyses investigate the effect of lifetime assumptions, electricity demand in battery production, and of relocating battery production to Europe on the carbon footprints of BEVs

Correcting remaining truncations in hybrid life cycle assessment database compilation

Hybrid life cycle assessment (HLCA) strives to combine process‐based life cycle assessment (PLCA) and environmentally extended input–output (EEIO) analysis to bridge gaps of both methodologies. The recent development of HLCA databases constitutes a major step forward in achieving complete system coverage. Nevertheless, current applications of HLCA still suffer from issues related to incompleteness of the inventory and data gaps: (1) hybridization without endogenizing the capital inputs of the EEIO database leads to underestimations, (2) the unreliability of price data hinders the application of streamlined HLCA for processes in some sectors, and (3) the sparse coverage of pollutants in multiregional EEIO databases limits the application of HLCA to a handful of impact categories. This paper aims at offering a methodology for tackling these issues in a streamlined manner and visualizing their effects on impact scores across an entire PLCA database and multiple impact categories. Data reconciliation algorithms are demonstrated on the PLCA database ecoinvent3.5 and the multiregional EEIO database EXIOBASE3. Instead of performing hybridization solely with annual product requirements, this hybridization approach incorporates endogenized capital requirements, demonstrates a novel hybridization methodology to bypass issues of price unavailability, estimates new pollutants to EXIOBASE3 environmental extensions, and thus yields improved inventories characterized in terms of 13 impact categories from the IMPACT World+ methodology. The effect of hybridization on the impact score of each process of ecoinvent3.5 varied from a few percentages to three‐fold increases, depending on the impact category and the process studied, displaying in which cases hybridization should be prioritized. This article met the requirements for a Gold—Gold JIE data openness badge described at http://jie.click/badges

Choice of allocations and constructs for attributional or consequential life cycle assessment and input-output analysis

The divide between attributional and consequential research perspectives partly overlaps with the long-standing methodological discussions in the life cycle assessment (LCA) and input-output analysis (IO) research communities on the choice of techniques and models for dealing with situations of coproduction. The recent harmonization of LCA allocations and IO constructs revealed a more diverse set of coproduction models than had previously been understood. This increased flexibility and transparency in inventory modeling warrants a re-evaluation of the treatment of coproduction in analyses with attributional and consequential perspectives. In the present article, the main types of coproductions situations and of coproduction models are reviewed, along with key desirable characteristics of attributional and consequential studies. A concordance analysis leads to clear recommendations, which call for important refinements to current guidelines for both LCA/IO practitioners and database developers. We notably challenge the simple association between, on the one hand, attributional LCA and partition allocation, and on the one hand, consequential LCA and substitution modeling