Interpretation of comparative LCAs: external normalization and a method of mutual differences

Purpose

Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead.

Methods

This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach.

Results and discussion

External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty.

Conclusions

The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.

     

INVESTIGATING EVOLUTIONARY TRADE‐OFFS IN WILD POPULATIONS OF ATLANTIC SALMON (SALMO SALAR): INCORPORATING DETECTION PROBABILITIES AND INDIVIDUAL HETEROGENEITY

Evolutionary trade‐offs among demographic parameters are important determinants of life‐history evolution. Investigating such trade‐offs under natural conditions has been limited by inappropriate analytical methods that fail to address the bias in demographic estimates that can result when issues of detection (uncertain detection of individual) are ignored. We propose a new statistical approach to quantify evolutionary trade‐offs in wild populations. Our method is based on a state‐space modeling framework that focuses on both the demographic process of interest as well as the observation process. As a case study, we used individual mark–recapture data for stream‐dwelling Atlantic salmon juveniles in the Scorff River (Southern Brittany, France). In freshwater, juveniles face two life‐history choices: migration to the ocean and sexual maturation (for males). Trade‐offs may appear with these life‐history choices and survival, because all are energy dependent. We found a cost of reproduction on survival for fish staying in freshwater and a survival advantage associated with the “decision” to migrate. Our modeling framework opens up promising prospects for the study of evolutionary trade‐offs when some life‐history traits are not, or only partially, observable.     

Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA)

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.     

Strong zonation of benthic communities across a tidal freshwater height gradient

相似文献   

Life Cycle Assessment of Advanced Industrial Wastewater Treatment Within an Urban Environment

A dissolved air flotation (DAF) system upgrade was proposed for an urban paper mill to recycle effluent. To understand the influence of operating variables on the environmental impacts of greenhouse gas (GHG) emissions and water consumption, a dynamic supply chain model was linked with life cycle assessment (LCA) to produce an environmental inventory. Water is a critical natural resource, and understanding the environmental impacts of recycling water is paramount in continued development of sustainable supply chains involving water. The methodology used in this study bridged the gap between detailed process models and static LCA modeling so that operating variables beyond discrete scenario analysis could be investigated without creating unnecessarily complex models. The model performed well in evaluating environmental impacts. It was found that there was no single optimum operating regime for all environmental impacts. For a mill discharging 80 cubic meters of effluent per hour (m³/hour), GHGs could be minimized with a DAF capacity of 17.5 m³/hour, while water consumption could be minimized with a DAF capacity of 25 m³/hour, which allowed insight into where environmental trade‐offs would occur. The study shows that more complexity can be achieved in supply chain modeling without requiring a full technical model. It also illustrates the need to consider multiple environmental impacts and highlights the trade‐off of GHG emissions with water consumption in water recycling. The supply chain model used in this water treatment case study was able to identify the environmental trade‐offs from the operating variables selected.     

Time Horizon and Dominance in Dynamic Life Cycle Assessment

Temporal aspects have traditionally not been recognized adequately in life cycle assessment (LCA). The dynamic LCA model recently proposed offers a significant step forward in the dynamic assessment of global warming impacts. The results obtained with dynamic LCA are highly sensitive to the choice of a time horizon. Therefore, decision making between alternative systems can be critical because conclusions are dependent on the specific time horizon. In this article, we develop a decision‐making methodology based on the concept of time dominance. We introduce instantaneous and cumulative time dominance criteria to the dynamic LCA context and argue why the dominance of an alternative should also imply preference. Our approach allows for the rejection of certain alternatives without the determination of a specific time horizon. The number of decision‐relevant alternatives can thereby be reduced and the decision problem facilitated. We demonstrate our methodology by means of a case study of end‐of‐life alternatives for a wooden chair derived from the original authors of dynamic LCA and discuss the implications and limitations of the approach. The methodology based on time dominance criteria is supplementary to the dynamic LCA model, but does not substitute it. The overall value of this article stretches beyond LCA onto more general assessments of global warming, for example, in policy where the choice of a time horizon is equally significant.     

A review of thermochemical upgrading of pyrolysis bio‐oil: Techno‐economic analysis,life cycle assessment,and technology readiness

Technologies for upgrading fast pyrolysis bio‐oil to drop‐in fuels and coproducts are under development and show promise for decarbonizing energy supply for transportation and chemicals markets. The successful commercialization of these fuels and the technologies deployed to produce them depend on production costs, scalability, and yield. To meet environmental regulations, pyrolysis‐based biofuels need to adhere to life cycle greenhouse gas intensity standards relative to their petroleum‐based counterparts. We review literature on fast pyrolysis bio‐oil upgrading and explore key metrics that influence their commercial viability through life cycle assessment (LCA) and techno‐economic analysis (TEA) methods together with technology readiness level (TRL) evaluation. We investigate the trade‐offs among economic, environmental, and technological metrics derived from these methods for individual technologies as a means of understanding their nearness to commercialization. Although the technologies reviewed have not attained commercial investment, some have been pilot tested. Predicting the projected performance at scale‐up through models can, with industrial experience, guide decision‐making to competitively meet energy policy goals. LCA and TEA methods that ensure consistent and reproducible models at a given TRL are needed to compare alternative technologies. This study highlights the importance of integrated analysis of multiple economic, environmental, and technological metrics for understanding performance prospects and barriers among early stage technologies.     

Assessment of lignocellulosic biorefineries in Germany using a hybrid LCA multi‐objective optimization model

In this study a tiered hybrid life cycle assessment (LCA) multi‐objective optimization model is developed and applied to determine the optimal choice of new biorefinery technologies in Germany. Thereby, several aspects can be explicitly addressed, including a regionally differentiated accountability of sustainable feedstock availability, identification of environmental impacts along global value chains, and identification of trade‐offs between different sustainability goals. The model is applied to assess the optimal choice between two lignocellulosic biorefinery concepts. Two optimization objectives are taken into account: maximizing the investor's profit and minimizing global impacts on climate change related to a specified demand for products. In terms of environmental impacts, the model also takes into account the comparison of new biorefineries with current available technologies producing the specified final demand. The results of the case study show that the biorefinery concept including the ethylene production is more beneficial in terms of reducing climate impacts, while on the other hand the biorefinery including the ethanol production is more cost‐effective. Depending on the decision‐maker's preference on weighting the two objectives, different capacities of biorefineries and optimal locations in Germany are identified. Furthermore, regions in Germany providing the necessary biomass feedstock can be identified on a county level. Finally, we argue that the extension of LCA by multi‐objective optimization is well suited guiding the way toward well‐informed decision‐making in the field of technological choices.     

Trade-offs in community ecology: linking spatial scales and species coexistence

Trade‐offs in species performances of different ecological functions is one of the most common explanations for coexistence in communities. Despite the potential for species coexistence occurring at local or regional spatial scales, trade‐offs are typically approached at a single scale. In recent years, ecologists have increasingly provided evidence for the importance of community processes at both local and regional spatial scales. This review summarizes the theoretical predictions for the traits associated with trade‐offs under different conditions and at different spatial scales. We provide a spatial framework for understanding trade‐offs, coexistence and the supportive empirical evidence. Predictions are presented that link the patterns of diversity observed to the patterns of trade‐offs that lead to coexistence at different spatial scales. Recent evidence for the evolution of trade‐offs under different conditions is provided which explores both laboratory microcosm studies and phylogenetic tests. Examining trade‐offs within a spatial framework can provide a strong approach to understanding community structure and dynamics, while explaining patterns of species diversity.     

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.     

Flower power: Floral and resource manipulations reveal how and why reproductive trade‐offs occur for lowbush blueberry (Vaccinium angustifolium)

Plant reproductive trade‐offs are thought to be caused by resource limitations or other constraints, but more empirical support for these hypotheses would be welcome. Additionally, quantitative characterization of these trade‐offs, as well as consideration of whether they are linear, could yield additional insights. We expanded our flower removal research on lowbush blueberry (Vaccinium angustifolium) to explore the nature of and causes of its reproductive trade‐offs. We used fertilization, defoliation, positionally biased flower removal, and multiple flower removal levels to discern why reproductive trade‐offs occur in this taxon and to plot these trade‐offs along two continuous axes. We found evidence through defoliation that vegetative mass per stem may trade off with reproductive effort in lowbush blueberry because the two traits compete for limited carbon. Also, several traits including ripe fruit production per reproductive node and fruit titratable acidity may be “sink‐limited”—they decline with increasing reproductive effort because average reproductive structure quality declines. We found no evidence that reproductive trade‐offs were caused by nitrogen limitation. Use of reproductive nodes remaining per stem as a measure of reproductive effort indicated steeper trade‐offs than use of the proportion of nodes remaining. For five of six traits, we found evidence that the trade‐off could be concave down or up instead of strictly linear. Synthesis. To date, studies have aimed primarily at identifying plant reproductive trade‐offs. However, understanding how and why these trade‐offs occur represent the exciting and necessary next steps for this line of inquiry.     

Environmental context alters ecological trade‐offs controlling ant coexistence in a spatially heterogeneous region

1. Ecological trade‐offs in ant (Hymenoptera: Formicidae) assemblages and their implications for coexistence boast a rich history in entomology. Yet investigations of trade‐offs have largely been limited to homogeneous environments. We examined how environmental context modifies trade‐off expression in an ant assemblage spanning a heterogeneous region in central Florida, U.S.A. 2. We examined how trade‐off expression is altered among two contrasting habitat types: open shrub and forest. We tested for the presence of the dominance‐discovery trade‐off and two dominance‐thermal tolerance trade‐offs by estimating behavioral dominance, discovery ability, and thermal tolerance (foraging thermal limit, lethal temperature, and maximal abundance temperature) for a wide range of interacting ant species. 3. We found significantly linear dominance hierarchies in both shrub and forest habitats, showing dominant species out‐compete subordinates for food resources. In thermally stressful shrub habitats, subordinates exhibit higher thermal tolerances, take greater thermal risks, and reach maximum forager abundances at higher temperatures than do dominant species. This suggests temperature mediated trade‐offs control coexistence in shrub habitat. In thermally moderate forest habitat, we found limited evidence for trade‐offs between competitive dominance and resource discovery or between dominance and thermal traits, implying other processes control coexistence. These results demonstrate that trade‐offs controlling ant coexistence may be contingent on environmental context.     

Using Attributional Life Cycle Assessment to Estimate Climate‐Change Mitigation Benefits Misleads Policy Makers

Life cycle assessment (LCA) is generally described as a tool for environmental decision making. Results from attributional LCA (ALCA), the most commonly used LCA method, often are presented in a way that suggests that policy decisions based on these results will yield the quantitative benefits estimated by ALCA. For example, ALCAs of biofuels are routinely used to suggest that the implementation of one alternative (say, a biofuel) will cause an X% change in greenhouse gas emissions, compared with a baseline (typically gasoline). However, because of several simplifications inherent in ALCA, the method, in fact, is not predictive of real‐world impacts on climate change, and hence the usual quantitative interpretation of ALCA results is not valid. A conceptually superior approach, consequential LCA (CLCA), avoids many of the limitations of ALCA, but because it is meant to model actual changes in the real world, CLCA results are scenario dependent and uncertain. These limitations mean that even the best practical CLCAs cannot produce definitive quantitative estimates of actual environmental outcomes. Both forms of LCA, however, can yield valuable insights about potential environmental effects, and CLCA can support robust decision making. By openly recognizing the limitations and understanding the appropriate uses of LCA as discussed here, practitioners and researchers can help policy makers implement policies that are less likely to have perverse effects and more likely to lead to effective environmental policies, including climate mitigation strategies.     

Phylogenetic analysis reveals positive correlations between adaptations to diverse hosts in a group of pathogen‐like herbivores

A jack of all trades can be master of none—this intuitive idea underlies most theoretical models of host‐use evolution in plant‐feeding insects, yet empirical support for trade‐offs in performance on distinct host plants is weak. Trade‐offs may influence the long‐term evolution of host use while being difficult to detect in extant populations, but host‐use evolution may also be driven by adaptations for generalism. Here we used host‐use data from insect collection records to parameterize a phylogenetic model of host‐use evolution in armored scale insects, a large family of plant‐feeding insects with a simple, pathogen‐like life history. We found that a model incorporating positive correlations between evolutionary changes in host performance best fit the observed patterns of diaspidid presence and absence on nearly all focal host taxa, suggesting that adaptations to particular hosts also enhance performance on other hosts. In contrast to the widely invoked trade‐off model, we advocate a “toolbox” model of host‐use evolution in which armored scale insects accumulate a set of independent genetic tools, each of which is under selection for a single function but may be useful on multiple hosts.     

Evolution of the leaf economics spectrum in herbs: Evidence from environmental divergences in leaf physiology across Helianthus (Asteraceae)

The leaf economics spectrum (LES) describes a major axis of plant functional trait variation worldwide, defining suites of leaf traits aligned with resource‐acquisitive to resource‐conservative ecological strategies. The LES has been interpreted to arise from leaf‐level trade‐offs among ecophysiological traits common to all plants. However, it has been suggested that the defining leaf‐level trade‐offs of the LES may not hold within specific functional groups (e.g., herbs) nor within many groups of closely related species, which challenges the usefulness of the LES paradigm across evolutionary scales. Here, we examine the evolution of the LES across 28 species of the diverse herbaceous genus Helianthus (the sunflowers), which occupies a wide range of habitats and climate variation across North America. Using a phylogenetic comparative approach, we find repeated evolution of more resource‐acquisitive LES strategies in cooler, drier, and more fertile environments. We also find macroevolutionary correlations among LES traits that recapitulate aspects of the global LES, but with one major difference: leaf mass per area is uncorrelated with leaf lifespan. This indicates that whole‐plant processes likely drive variation in leaf lifespan across Helianthus, rather than leaf‐level trade‐offs. These results suggest that LES patterns do not reflect universal physiological trade‐offs at small evolutionary scales.     

Construction Matters: Comparing Environmental Impacts of Building Modular and Conventional Homes in the United States

Modular construction practices are used in many countries as an alternative to conventional on‐site construction for residential homes. While modular home construction has certain advantages in terms of material and time efficiency, it requires a different infrastructure than conventional home construction, and the overall environmental trade‐offs between the two methods have been unclear. This study uses life cycle assessment to quantify the environmental impacts of constructing a typical residential home using the two methods, based on data from several modular construction companies and conventional homebuilders. The study includes impacts from material production and transport, off‐site and on‐site energy use, worker transport, and waste management. For all categories considered, the average impacts of building the home are less for modular construction than for conventional construction, although these averages obscure significant variation among the individual projects and companies.     

Predicting shifting sustainability trade‐offs in marine finfish aquaculture under climate change

Defining sustainability goals is a crucial but difficult task because it often involves the quantification of multiple interrelated and sometimes conflicting components. This complexity may be exacerbated by climate change, which will increase environmental vulnerability in aquaculture and potentially compromise the ability to meet the needs of a growing human population. Here, we developed an approach to inform sustainable aquaculture by quantifying spatio‐temporal shifts in critical trade‐offs between environmental costs and benefits using the time to reach the commercial size as a possible proxy of economic implications of aquaculture under climate change. Our results indicate that optimizing aquaculture practices by minimizing impact (this study considers as impact a benthic carbon deposition ≥ 1 g C m^?2 day^?1) will become increasingly difficult under climate change. Moreover, an increasing temperature will produce a poleward shift in sustainability trade‐offs. These findings suggest that future sustainable management strategies and plans will need to account for the effects of climate change across scales. Overall, our results highlight the importance of integrating environmental factors in order to sustainably manage critical natural resources under shifting climatic conditions.     

Environmental impacts of European trade: interpreting results of process-based LCA and environmentally extended input–output analysis towards hotspot identification

Purpose

Trade is increasingly considered a significant contributor to environmental impacts. The assessment of the impacts of trade is usually performed via environmentally extended input–output analysis (EEIOA). However, process-based life cycle assessment (LCA) applied to traded goods allows increasing the granularity of the analysis and may be essential to unveil specific impacts due to traded products.

Methods

This study assesses the environmental impacts of the European trade, considering two modelling approaches: respectively EEIOA, using EXIOBASE 3 as supporting database, and process-based LCA. The interpretation of the results is pivotal to improve the robustness of the assessment and the identification of hotspots. The hotspot identification focuses on temporal trends and on the contribution of products and substances to the overall impacts. The inventories of elementary flows associated with EU trade, for the period 2000–2010, have been characterized considering 14 impact categories according to the Environmental Footprint (EF2017) Life Cycle Impact Assessment method.

Results and discussion

The two modelling approaches converge in highlighting that in the period 2000–2010: (i) EU was a net importer of environmental impacts; (ii) impacts of EU trade and EU trade balance (impacts of imports minus impacts of exports) were increasing over time, regarding most impact categories under study; and (iii) similar manufactured products were the main contributors to the impacts of exports from EU, regarding most impact categories. However, some results are discrepant: (i) larger impacts are obtained from IO analysis than from process-based LCA, regarding most impact categories, (ii) a different set of most contributing products is identified by the two approaches in the case of imports, and (iii) large differences in the contributions of substances are observed regarding resource use, toxicity, and ecotoxicity indicators.

Conclusions

The interpretation step is crucial to unveil the main hotspots, encompassing a comparison of the differences between the two methodologies, the assumptions, the data coverage and sources, the completeness of inventory as basis for impact assessment. The main driver for the observed divergences is identified to be the differences in the impact intensities of goods, both induced by inherent properties of the IO and life cycle inventory databases and by some of this study’s modelling choices. The combination of IO analysis and process-based LCA in a hybrid framework, as performed in other studies but generally not at the macro-scale of the full trade of a country or region, appears a potential important perspective to refine such an assessment in the future.

     

Hotspot Scenario Analysis

Increasingly, organizations are working to reduce the environmental footprint of their supply chains. The use of environmentally preferable purchasing criteria is one strategy organizations use to address this goal. However, evaluating the environmental performance of these criteria (e.g., recycled content, biodegradable, renewable, and so on) has remained elusive. Life cycle assessment (LCA) can measure the impact reduction potential of sourcing strategies. However, full process‐based LCAs are time‐consuming and costly across multiple criteria of thousands of products and inputs purchased in an organizational setting. A streamlined “hotspot” methodology is presented using a combination of environmentally extended economic input‐output (EEIO) approaches and extant literature to identify hotspots in which to constrain a parameterized process‐based LCA. A case study of breakfast cereal manufacturing is developed to (1) assess the efficiencies associated with the hotspotting approach and (2) demonstrate its applicability in generating comparable decision signals of environmentally preferable sourcing criteria for procurement and supply‐chain managers along the dimensions of global warming potential and water use.     

Proposed Local Ecological Impact Categories and Indicators for Life Cycle Assessment of Aquaculture

In this study we discuss impact categories and indicators to incorporate local ecological impacts into life cycle assessment (LCA) for aquaculture. We focus on the production stages of salmon farming—freshwater hatcheries used to produce smolts and marine grow‐out sites using open netpens. Specifically, we propose two impact categories: impacts of nutrient release and impacts on biodiversity. Proposed indicators for impacts of nutrient release are (1) the area altered by farm waste, (2) changes in nutrient concentration in the water column, (3) the percent of carrying capacity reached, (4) the percent of total anthropogenic nutrient release, and (5) release of wastes into freshwater. Proposed indicators for impacts on biodiversity are (1) the number of escaped salmon, (2) the number of reported disease outbreaks, (3) parasite abundance on farms, and (4) the percent reduction in wild salmon survival. For each proposed indicator, an example of how the indicator could be estimated is given and the strengths and weaknesses of that indicator are discussed. We propose that including local environmental impacts as well as global‐scale ones in LCA allows us to better identify potential trade‐offs, where actions that are beneficial at one scale are harmful at another, and synchronicities, where actions have desirable or undesirable effects at both spatial scales. We also discuss the potential applicability of meta‐analytic statistical techniques to LCA.