Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling

Life cycle assessment of plastic waste in Suzhou, China: Management strategies toward sustainable express delivery

The explosive growth of China's express delivery industry has greatly increased plastic waste, with low-value plastics not effectively utilized, such as PE packaging bags, which are often not recycled and end up in landfills or incinerators, causing significant resource waste and severe plastic pollution. A gate -to- grave life cycle assessment was adopted to assess the impacts of express delivery plastic waste (EDPW) management models (S1, landfill; S2, incineration; S3, mechanical pelletization), with Suzhou, China as a case. Results showed that mechanical pelletization, was the most environmentally advantageous, exhibiting a comprehensive environmental impact potential of -215.54 Pt, significantly lower than that of landfill (S1, 78.45 Pt) and incineration (S2, -121.77 Pt). The analysis identified that the end-of-life disposal and sorting stages were the principal contributors to environmental impacts in all three models, with transportation and transfer stages of residual waste having minimal effects. In terms of all environmental impact categories, human carcinogenic toxicity (HTc) emerged as the most significant contributor in all three scenarios. Specifically, S1 exhibited the most detrimental effect on human health, while S2 and S3 showed positive environmental impacts. Based on these findings, it is recommended that the application and innovation in mechanical recycling technologies be enhanced, the promotion of the eco-friendly transformation of packaging materials be pursued, and a sustainable express delivery packaging recycling management system be established. These strategies are essential for achieving more eco-friendly management of EDPW, reducing its environmental pollution, and moving towards more sustainable express delivery management practices.

Uncertainty analysis of greenhouse gas emissions of monorail transit during the construction

This paper examines the uncertainty of greenhouse gas (GHG) emissions during monorail construction. Firstly, a deterministic analysis is conducted. Subsequently, the obtained data are evaluated using the data quality indicator (DQI), and a Markov chain Monte Carlo (MCMC) simulation method is employed to assume different parameter distributions. The results of the deterministic calculation indicate that the calculated emissions per unit area of the station amount to 1.97 ton CO2e/m2, while the calculated emissions per unit section length reach 7.55 ton CO2e/m2. To simulate parameter distribution, we utilize a Beta distribution with good shape applicability. Furthermore, we establish scenarios involving system boundary reduction, low-emission factors, and reduced material and energy inputs in order to analyze scenario uncertainties. Regarding model uncertainty, this paper assumes that the material and energy quantity data conform to the normal, log-normal, uniform, and triangular distributions, respectively, subsequently analyzing the uncertainty distributions. This paper analyzes the GHG emission uncertainty evaluation of 16 monorail stations and sections during the construction period, which is divided into parameter, scenario, and model uncertainty. We provide a concrete framework for studying uncertainties related to GHG emissions at stations and sections during the monorail construction period. The scenario analysis results will help to make decisions about the choice of parameters, system boundaries, and other settings. It provides new guidance for emission reduction policies, such as reducing the use of steel-related products or using alternative environmentally friendly materials, considering emission reduction factors more comprehensively and setting emission reduction factors according to uniform distribution principle as far as possible.

Life cycle assessment of iron-biomass supported catalyst for Fischer Tropsch synthesis

The iron-based biomass-supported catalyst has been used for Fischer-Tropsch synthesis (FTS). However, there is no study regarding the life cycle assessment (LCA) of biomass-supported iron catalysts published in the literature. This study discusses a biomass-supported iron catalyst's LCA for the conversion of syngas into a liquid fuel product. The waste biomass is one of the source of activated carbon (AC), and it has been used as a support for the catalyst. The FTS reactions are carried out in the fixed-bed reactor at low or high temperatures. The use of promoters in the preparation of catalysts usually enhances C5+ production. In this study, the collection of precise data from on-site laboratory conditions is of utmost importance to ensure the credibility and validity of the study's outcomes. The environmental impact assessment modeling was carried out using the OpenLCA 1.10.3 software. The LCA results reveals that the synthesis process of iron-based biomass supported catalyst yields a total impact score in terms of global warming potential (GWP) of 1.235E + 01 kg CO2 equivalent. Within this process, the AC stage contributes 52% to the overall GWP, while the preparation stage for the catalyst precursor contributes 48%. The comprehensive evaluation of the iron-based biomass supported catalyst's impact score in terms of human toxicity reveals a total score of 1.98E-02 kg 1,4-dichlorobenzene (1,4-DB) equivalent.

A critical review of the life cycle assessment studies on road pavements and road infrastructures

In recent years, there has been a sharp rise in the number of life cycle assessment (LCA) studies related to road pavements and road infrastructures. The main aim of this study is to perform a critical analysis of various studies undertaken so far to examine goals, scopes, impact categories, life cycle phases, methods and approaches, and limitations. A total of 67 LCA studies reported in literature were analysed and categorized into four categories viz. flexible pavement; rigid pavement; flexible and rigid pavement; and road infrastructure. The analysis revealed that 80% of the studies were carried out in developed countries while just 20% of studies were from developing countries. Most of the road pavement LCA studies (∼76%) considered material and construction phase and assessed the impacts in terms of only two impact categories viz. global warming potential and energy demand. Only 10-15% of studies considered a wide range of impact categories and used commercial software such as GaBi and SimaPro for impact assessment. 19 studies were on flexible pavements, 4 on rigid pavements, 30 on both flexible and rigid pavements and 14 on infrastructure. Bridges, tunnels, drainage, lighting, and road marking were the major components of road infrastructure studied while other road infrastructures such as culverts, toll plazas, and vehicle underpasses were not included. Majority of the studies depended on secondary or background data for the development of life cycle inventory. Out of 67 studies, only 18 studies performed the sensitivity analysis while only 6 studies carried out uncertainty analysis. There is a need for inclusion of all supporting infrastructures along with road pavement, and also for paying greater attention to sensitivity and uncertainty analysis in studies pertaining to the transportation sector. During construction phase, no LCA study considered the important impacts due deforestation, defragmentation, restriction of free wildlife movement etc. Hence, future LCA road studies must evaluate the negative consequences of these as well as integrate social and economic impacts via Multi-Criteria Decision Making to make LCA a robust decision-making tool for sustainability.

Effect of Absorption Time for the Preparation of Activated Carbon from Wasted Tree Leaves of Quercus alba and Investigating Life Cycle Assessment

In this article, the effect of absorption time on the surface chemistry and pore structure of activated carbon (AC) from waste leaves of Quercus alba with the H3PO4 chemical activation method. XRD, SEM, EDX, BET, TGA, and FT-IR analyses of prepared AC were used to figure out the properties of the activated carbon. The results demonstrated that the 48 h absorption time of H3PO4 contributed to the highest surface area, 943.2 m2/g, among all the prepared activated carbon samples. As the absorption time of the phosphoric acid activating agent was increased, the surface area initially increased and then started to decrease. The further surface chemical characterization of activated carbon was determined by FT-IR spectroscopic method. Life cycle assessment methodology was employed in order to investigate the environmental impacts associated with the laboratory steps for activated carbon (AC) production. The LCA approach was implemented using OpenLCA 1.10.3 software, while ReCiPe Midpoint (H) was used for environmental impact assessment. The results of the LCA study showed that the impact categories related to toxicity were particularly affected by the utilization of electrical energy (≈90%). The power utilized during laboratory procedures was the main cause of environmental impacts, contributing an average of nearly 70% across all impact categories, with the maximum contribution to the impact category of freshwater ecotoxicity potential (≈97%) and the minimum contribution to land use potential (≈10%).

Global Sensitivity Analysis of Background Life Cycle Inventories

In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity and includes extensive validation of GSA results. We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of noninfluential and ranking of influential uncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.

A Parametric Approach to Optimizing Building Construction Systems and Carbon Footprint: A Case Study Inspired by Circularity Principles

There is a global call for a paradigm shift in the construction industry towards carbon neutrality, but a scant effort has been madein practice, especially concerning circularity. This paper helps bridge the gap by introducing a parametric approach to optimize sustainable construction design. The methodology was tested on a newly constructed office building, inspired by circularity principles, in Westerlo, Belgium. The methodology consists of parametric construction-typological analysis, automated through One Click LCA software (Life Cycle Assessment) and Microsoft Excel with 21 alternate designs and 630 iterations. The parametric variations involved three key performance indicators: construction system, materials’ environmental impact, and materials; reuse of content. The environmental effects of both construction systems (i.e., structural system, foundation type, materials, and envelope details) and reused building materials content (i.e.,) were evaluated by the parametric analysis for four construction systems scenarios. Environmental impact analysis for timber, steel, concrete, and hybrid construction systems was conducted, following ISO 14040 and CEN/TC 350 standards. The focus of the whole life cycle assessment was mainly on carbon neutrality. Results indicate that using local biosourced materials, including timber, can remarkably reduce buildings’ environmental impact. The sensitivity analysis results provide hard evidence that the construction material’s weight, materials reuse potential, and construction dismantling ability are the most influential factors in carbon-neutral buildings. This paper should improve professionals’ understanding of the impact of different structural systems choices and inform building designers about the circularity potential, and carbon footprint of construction technologies.

Environmental and economic cost-benefit comparison of sponge city construction in different urban functional regions

This study conducts a life cycle environmental and economic quantification comparison of urban runoff source control facilities (URSCFs) through construction and operation stages in two urban functional regions (i.e., residential area and campus). From the environmental perspective, URSCFs construction in residential area has both higher environmental impacts and benefits than that in campus. The operation stage of URSCFs can observe significant benefit for both residential area and campus. We then develop a set of monetized method to make a comprehensive benefit evaluation (i.e., environmental, economic, and social benefit) of URSCFs. Overall, the two areas have payback time less than thirteen years for their investment which is acceptable when compared with the assumed total service period (30 years). Specifically, the payback time of campus is 5.62 years and residential area is 12.44 years. This implies that the campus has great potential to achieve high cost-benefit ratio and thus the Sponge City construction in campus can implement URSCFs with less engineering and material consumption due to its more spacious site than residential area with high building density. For both residential area and campus, permeable pavement has the highest environmental impact and economic cost because of the concrete consumption. Thus, we recommend that it should be cautious of the construction of concrete permeable pavement and find environmentally and economically alternatives in future URSCFs projects.

Resource recovery in life cycle assessment of sludge treatment: Contribution, sensitivity, and uncertainty

This study focused on the resource recovery of sludge treatment by quantifying the environmental contributions, identifying the influential factors, and comparing different scenarios. Life cycle assessment (LCA) of sewage sludge treatment was carried out to estimate the environmental impacts of six scenarios: (1) co-digestion of sludge and food waste; (2) co-gasification of sludge and woody waste; (3) co-incineration of sludge and used oil; (4) landfilling; (5) incineration; and (6) anaerobic digestion combined with incineration. Results demonstrate that the resource recovery had a substantial contribution to the environmental performance of the sludge treatment, while the degree of contribution was largely affected by various treatment scenarios and diverse impact categories. To gain deep insight into the parameters related to resource recovery, sensitivity analysis was performed to investigate the influence of the parameters on the LCA results, including the organic content, conversion efficiency of organic matter to methane, and other energy conversion efficiencies. After integrating the inventory variation of those parameters into the decision process via the Monte Carlo simulation, results indicate that no obviously superior scenario could be identified. Conversely, when parameter uncertainty was not considered, co-gasification of sludge and woody waste exhibited the most preferable environmental performance. Overall, this study demonstrates that considering the parameter uncertainty of resource recovery will contribute to a more transparent evaluation process, but will inevitably increase the complexity of the decision-making process based on LCA results because it is difficult to determine a sludge treatment scenario that decisively outperforms the others.

Environmental impact of bioplastic use: A review

Throughout their lifecycle, petroleum-based plastics are associated with many environmental problems, including greenhouse gas emissions, persistence in marine and terrestrial environments, pollution, etc. On the other hand, bioplastics form a rapidly growing class of polymeric materials that are commonly presented as alternatives to conventional petroleum-based plastics. However, bioplastics also have been linked to important environmental issues such as greenhouse gas emissions and unfavorable land use change, making it necessary to evaluate the true impact of bioplastic use on the environment. Still, while many reviews discuss bioplastics, few comprehensively and simultaneously address the positives and negatives of bioplastic use for the environment. The primary focus of the present review article is to address this gap in present research. To this end, this review addresses the following questions: (1) what are the different types of bioplastics that are currently in commercial use or under development in the industry; (2) are bioplastics truly good for the environment; and (3) how can we better resolve the controversial impact of bioplastics on the environment? Overall, studies discussed in this review article show that the harms associated with bioplastics are less severe as compared to conventional plastics. Moreover, as new types of bioplastics are developed, it becomes important that future studies conduct thorough life cycle and land use change analyses to confirm the eco-friendliness of these new materials. Such studies will help policymakers to determine whether the use of new-generation bioplastics is indeed beneficial to the environment.

Carbon footprint of anaerobic digestion combined with ultrasonic post-treatment of agro-industrial organic residues

Anaerobic digestion (AD) of organic waste, although widely practiced, may require suitable accompanying treatments to enhance the degradability of complex materials. Since these may require significant efforts in terms of energy and chemical demand, careful assessment of their overall environmental sustainability is mandatory to evaluate their full-scale feasibility. The study aims to represent the environmental profile of ultrasonication (US) applied as a post-treatment of anaerobic digestion of agro-industrial organic residues. There is an interest in the US treatment for the processing of complex organic materials prior to AD in order to enhance the hydrolysis of complex organic substrates and increase the biogas yield of the biological process. An attributional, process-based life cycle assessment (LCA) study was applied to quantify and compare the potential environmental impacts of an AD plant, the biogas utilization options as well as the different digestate processing alternatives grouped into a set of 16 scenarios. Based on the results, upgrading of biogas and bio-methane use as vehicle fuel instead of energy generation from CHP or fuel cell was recommended due to the lower impact on GWP. Similarly, composting was a suitable option to reduce environmental impacts compared to belt drying. From the uncertainty analysis, AD without US as post-treatment proves to be more sustainable in terms of GWP compared to when US is used, showing net savings in GHG emissions especially when upgrading of biogas is applied. The analysis provides useful indications to policy makers to define sustainable management alternatives for organic residues as well as identify the environmental advantages associated with biogas utilization and digestate treatment and disposal alternatives.

Addressing environmental sustainability of plasma vitrification technology for stabilization of municipal solid waste incineration fly ash

Fly ash from municipal solid waste incineration is considered as a hazardous waste, which would raise great threats on environmental safety due to the inherent toxic heavy metals and organic pollutants. In this study, we applied the life cycle assessment to evaluate the thermal plasma vitrification process for stabilization of fly ash from municipal solid waste incineration. We established four scenarios: (i) plasma vitrification, including centralized and off-site plasma treatment, (ii) fuel-based vitrification, (iii) water-washing treatment followed by a rotary kiln, and (iv) conventional solidification and landfill. We found that the environmental impacts, especially toxicity to ecosystem quality and human health, could be significantly reduced by deploying plasma vitrification technology. We also found that centralized plasma vitrification facilities possessing larger treatment capabilities with clean electricity could further reduce the environmental impacts. In contrast, the water-washing treatment exhibited the highest environmental impacts due to the emissions of vaporized heavy metals. Based on the LCA and sensitivity analysis, we confirmed that the thermal plasma vitrification should be considered as an environmentally-friendly solution to sustainable treatment of fly ash from municipal solid waste incineration. Lastly, we provided several perspectives and prospects of plasma vitrification for realizing the sustainable materials management.

Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options

Models that characterize life cycle greenhouse gases from electricity generation are limited in their capability to estimate emissions changes at scales that capture the grid-scale benefits of technologies and policies that enhance renewable systems integration. National assumptions about generation mixes are often applied at annual time steps, neglecting spatiotemporal resolutions that provide insights on impacts from time-variable emissions. Our grid-scale model incorporates details of transmission and generation planning that allows a geographically and temporally textured and more realistic assessment of the life cycle greenhouse gas emissions outcomes, using a case study of the Western Interconnection of North America. Results from a co-optimized model of generation, transmission, and operations-the Johns Hopkins Stochastic Multistage Integrated Network Expansion Model-provide a detailed characterization of twenty-one scenarios with different configurations of storage additions, new renewable capacity, and carbon prices. Life cycle results suggest that optimization models that focus on generation alone may underestimate emissions by 18-29% because only emissions from power generation are quantified (i.e., supply chain emissions are omitted) but also that carbon pricing is the predominant driver of reducing emissions in the scenarios we examine. Life cycle assessment of electricity generation should move beyond individual technologies toward capturing the influence of policies at the system level to better understand technology-policy dynamics for the grid.

Life Cycle Impact Assessment of Garbage-Classification Based Municipal Solid Waste Management Systems: A Comparative Case Study in China

Confronted with a series of problems caused by surging generation of municipal solid waste (MSW), the Chinese central and local governments have promulgated and implemented policies to deal with them, including promotions of the classification of MSW. However, to date, practical knowledge and understanding about benefits for garbage classification from its environmental performance perspective is still limited. The present study is purposed to comprehensively investigate the environmental effects of garbage classification on municipal solid waste management (MSWM) systems based on three proposed garbage classification scenarios in China, via a comparative life cycle impact assessment (LCIA). Taking advantage of Impact Assessment of Chemical Toxics (IMPACT) 2002+ method, this comparative LCIA study can quantitatively evaluate midpoint, endpoint, and single scored life cycle impacts for the studied MSWM systems. A Monte Carlo uncertainty analysis is carried out to test the effectiveness and reliabilities of the LCIA results. The LCIA and uncertainty analysis results show that MSWM systems based on various garbage classification scenarios have significant variations in the studied midpoint, endpoint, and single scored environmental impacts. Different garbage classification scenarios have their individual environmental-friendly superiority for specific impact categories. Overall, results of this study demonstrate that MSW treatment systems integrated with garbage classification are more environmentally friendly by comparison with non-classification; and that the more elaborate the level of MSW classification, the smaller its impacts on the environment.

Managing Choice Uncertainties in Life-Cycle Assessment as a Decision-Support Tool for Building Design: A Case Study on Building Framework

To establish a circular economy in society, it is crucial to incorporate life-cycle studies, such as life-cycle assessment (LCA), in the design process of products in order to mitigate the well-recognized problem of the design paradox. The aim of the study was to provide means in a structured way to highlight choice uncertainty present in LCA when used as decision support, as well as to mitigate subjective interpretations of the numerical results leading to arbitrary decisions. The study focused on choices available when defining the goal and scope of a life-cycle assessment. The suggested approach is intended to be used in the early design phases of complex products with high levels of uncertainty in the product life-cycle. To demonstrate and evaluate the approach, a life-cycle assessment was conducted of two design options for a specific building. In the case study two types of building frameworks were compared from an environmental perspective by calculating the global warming potential, eutrophication potential, acidification potential, stratospheric ozone depletion potential and photochemical oxidants creation potential. In the study, a procedure named the Decision Choices Procedure (DCP) was developed to improve LCA as an effective tool for decision support concerning design alternatives when less information is available. The advantages and drawbacks of the proposed approach are discussed to spur further improvements in the use of LCA as a decision-support tool.

PET bottles recycling in China: An LCA coupled with LCC case study of blanket production made of waste PET bottles

A large number of polyethylene terephthalate (PET) bottles are discarded daily after usage. Thus, plastic bottle recycling has elicited considerable attention in recent years. In this context, this study aims to quantify the environmental and economic impacts of blanket production from 100% recycled waste plastic bottles in China through a life cycle assessment coupled with life cycle costing method. In addition, the environmental impact of replacing coal with natural gas and solar energy was evaluated. Results show that impact categories of global warming and fossil depletion have significant influence on the overall environment. Carbon dioxide, water, iron, coal and chromium (VI) to water are the main contributors to the overall environmental burden. The internal and external costs are $6433/metric ton and $370/metric ton, respectively. Analysis results indicate that the optimization of organic chemicals, recycled polyester filament and steam production processes can reduce environmental and economic burdens substantially. Energy substitutions with natural gas and the use of solar photovoltaic in steam production and electricity generation are effective measures for decreasing environmental impacts. Finally, suggestions based on research results and the current status of waste plastic bottle recycling in China are proposed.

The environmental impact of rock support for road tunnels: The experience of Norway

Despite the high levels of investment in the construction of underground space, there has been relatively little research so far on the environmental impact of tunnelling and rock support. In an attempt to fill this gap, this paper explores and compares the environmental impact of rock support on road-tunnel design in Norway for different rock-mass classes and tunnel sizes. Norwegian rock-support practices in road-tunnelling over the last twenty years are used to estimate figures for the consumption of material, equipment and energy. The background data are drawn from various Ecoinvent databases and environmental product declarations for major materials. The results indicate: 1) that the global warming potential (GWP) varies from 1 ton to 3.6 t per meter of rock support for different tunnel sizes and rock masses; 2) that all the environmental impacts of the shotcreting (or concrete-spraying) process are significantly greater than of all other processes; 3) that when a tunnel becomes larger or the rock mass becomes poorer, the relative contribution of the bolting process will increase; 4) that all environmental impacts are more sensitive to rock-class than to cross-section parameters; and 5) that potential improvements include reducing rebound, better designs of shotcrete admixtures and binders, improving durability and mechanical properties, and implementing the GWP or other environmental indicators during the design and tendering stages.

Uncertainty Analysis of Embedded Energy and Greenhouse Gas Emissions Using BIM in Early Design Stages

With current efforts to increase energy efficiency and reduce greenhouse gas (GHG) emissions of buildings in the operational phase, the share of embedded energy (EE) and embedded GHG emissions is increasing. In early design stages, chances to influence these factors in a positive way are greatest, but very little and vague information about the future building is available. Therefore, this study introduces a building information modeling (BIM)-based method to analyze the contribution of the main functional parts of buildings to find embedded energy demand and GHG emission reduction potentials. At the same time, a sensitivity analysis shows the variance in results due to the uncertainties inherent in early design to avoid misleadingly precise results. The sensitivity analysis provides guidance to the design team as to where to strategically reduce uncertainties in order to increase precision of the overall results. A case study shows that the variability and sensitivity of the results differ between environmental indicators and construction types (wood or concrete). The case study contribution analysis reveals that the building’s structure is the main contributor of roughly half of total GHG emissions if the main structural material is reinforced concrete. Exchanging reinforced concrete for a wood structure reduces total GHG emissions by 25%, with GHG emissions of the structure contributing 33% and windows 30%. Variability can be reduced systematically by first reducing vagueness in geometrical and technical specifications and subsequently in the amount of interior walls. The study shows how a simplified and fast BIM-based calculation provides valuable guidance in early design stages.

How to Conduct Prospective Life Cycle Assessment for Emerging Technologies? A Systematic Review and Methodological Guidance

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.

The environmental impacts and the carbon intensity of geothermal energy: A case study on the Hellisheiði plant

Geothermal energy, alongside other low-carbon and renewable energies, is set to play a key role in decarbonising the power generation industry to meet the Paris Agreement goal. Thus far the majority of Life Cycle Assessment (LCA) studies focused on enhanced geothermal plants. However, conventional geothermal plants that harness hydrothermal reservoirs dominate the production of electricity from geothermal energy worldwide. This article focuses on Hellisheiði, a combined heat and power double flash geothermal plant located in Iceland, with an installed capacity of 303.3 MW of electricity and 133 MW of hot water. The study has a twofold goal: (i) identify hot spots in the life cycle and, where possible, suggest improvements, and (ii) understand the potential of geothermal energy to decarbonise the power generation industry. First, a detailed LCA study has been performed on Hellisheiði, with cradle-to-grave system boundaries and detailed site-specific data obtained from the literature. The analysis identifies consumption of diesel for drilling and use of steel for wells casing and construction of the power plant as the main hot spots. Second, carbon intensities of electricity production for various possible configurations of the Hellisheiði power plant (including single flash, and power-only production) have been compared with those of other geothermal plants and other energy sources. Different allocation procedures have been used to allocate impacts between electricity and hot water where necessary, and Monte Carlo simulations have been used to estimate uncertainties of Hellisheiði's carbon intensities. The comparison shows that the carbon intensity of Hellisheiði is in the range of 15-24 g CO₂-eq./kWh, which is similar to those of binary cycle geothermal plants, solar (photovoltaic) and hydropower, lower than other geothermal technologies and fossil-based technologies, and higher than nuclear and onshore wind.

Integrating High-Resolution Material Flow Data into the Environmental Assessment of Waste Management System Scenarios: The Case of Plastic Packaging in Austria

The environmental performance of the waste management system of plastic packaging in Austria was assessed using a combination of high-resolution material flows and input-dependent life cycle inventory data. These data were used to evaluate different configurations of the waste management system, reflecting the system structure as it was in 1994 in Austria and still is in some of the new EU member states, as well as a situation achieving the increased circular economy targets to be met by 2030. For the latter, two options, namely single-polymer recycling and mixed-polymer recycling, were investigated. The results showed that the status quo achieves net benefits for 15 out of 16 impact categories evaluated. Regarding the alternative scenarios, for most impact categories these benefits increased with increasing recycling rates, although for four impact categories the highest net benefit was achieved by the status quo. For many impact categories the marginal environmental benefit decreased at higher recycling rates, indicating that there is an environmentally optimal recycling rate below 100%. The results also highlight the importance of high-quality single-polymer plastics recycling from an environmental perspective because utilizing mixed polymer recycling to achieve circular economy targets would result in lower environmental benefits than the status quo.

Insulation or Smart Temperature Control for Domestic Heating: A Combined Analysis of the Costs, the Eco-Costs, the Customer Perceived Value, and the Rebound Effect of Energy Saving

Calculating the environmental benefits of energy saving systems in dwellings in a life cycle assessment (LCA) has two major issues, namely: how to deal with the customer behaviour and how to deal with rebound effects. Both issues are important for sustainable strategies. From a user-centred design perspective, two fundamentally different strategies are observed, namely: a ‘passive’ end-user, who invests in insulating the building and maintaining their preferred behaviour routines, versus an ‘active’ end-user; who must change his or her behaviour in order to save energy. A combined analysis of cost, (market) value, and eco-burden is used to compare and evaluate the two strategies; by applying the methods of eco-costs/value ratio (EVR) and eco-efficient value creation. Simulation software is applied to calculate the results for the active end-user approach (by means of home energy management systems [HEMS]). The energy savings for a passive user approach (applying thermal insulation) are calculated with straightforward heat loss calculations. The rebound effect of energy savings is taken into consideration. From the environmental point of view, the optimal insulation thickness is calculated, by comparing the energy savings with the environmental burden of the insulation materials. This analysis shows that HEMS are effective for poorly insulated houses, but not for well insulated houses. Governmental policies that focus only on insulation, however, lack the urgency of greenhouse gas reduction; the HEMS for existing houses is an indispensable tool for a fast transition to less domestic energy consumption.

How to bring absolute sustainability into decision-making: An industry case study using a Planetary Boundary-based methodology

The Planetary Boundaries concept has emerged as a framework for articulating environmental limits, gaining traction as a basis for considering sustainability in business settings, government policy and international guidelines. There is emerging interest in using the Planetary Boundaries concept as part of life cycle assessment (LCA) for gauging absolute environmental sustainability. We tested the applicability of a novel Planetary Boundaries-based life cycle impact assessment methodology on a hypothetical laundry washing case study at the EU level. We express the impacts corresponding to the control variables of the individual Planetary Boundaries together with a measure of their respective uncertainties. We tested four sharing principles for assigning a share of the safe operating space (SoSOS) to laundry washing and assessed if the impacts were within the assigned SoSOS. The choice of sharing principle had the greatest influence on the outcome. We therefore highlight the need for more research on the development and choice of sharing principles. Although further work is required to operationalize Planetary Boundaries in LCA, this study shows the potential to relate impacts of human activities to environmental boundaries using LCA, offering company and policy decision-makers information needed to promote environmental sustainability.

A Cradle to Handover Life Cycle Assessment of External Walls: Choice of Materials and Prognosis of Elements

This research focuses on a comparison of 20 external wall systems that are conventionally used in Spanish residential buildings, from a perspective based on the product and construction process stages of the life cycle assessment. The primary objective is to provide data that allow knowing the environmental behavior of walls built with materials and practices conventionally. This type of analysis will enable promoting the creation of regulations that encourage the use of combinations of materials that generate the most environmentally suitable result, and in turn, contribute to the strengthening of the embodied stages study of buildings and their elements. The results indicate that the greatest impact arises in the product stage (90.9%), followed by the transport stage (8.9%) and the construction process stage (<1%). Strategies (such as the use of large-format pieces and the controlled increase in thickness of the thermal insulation) can contribute to reducing the environmental impact; on the contrary, practices such as the use of small-format pieces and laminated plasterboard can increase the environmental burden. The prediction of the environmental behavior (simulation equation) allows these possible impacts to be studied in a fast and simplified way.

Comparative life cycle assessment of MSWI fly ash treatment and disposal

Municipal solid waste incineration (MSWI) fly ash constitutes a hazardous waste. The possibilities for managing this waste comprise disposal at underground deposits or at above-ground landfills after cement stabilisation, application of the FLUREC process, thermal treatment in a dedicated furnace or thermal co-treatment together with combustible hazardous waste. A comparative life cycle assessment (LCA) study was conducted in order to assess the environmental impact of these five MSWI fly ash disposal options with regard to two different time horizons (100years, indefinite). The uncertainties of the input parameters were propagated by Monte Carlo simulations (MCS). As could be shown by the discernibility analysis, the FLUREC process has the lowest impact in more than 90% of the MCS results. In case long-term emissions (beyond 100years) are neglected, the second lowest impact is caused by thermal co-treatment in more than 90% of the MCS results. Consideration of long-term emissions indicates the disposal at underground deposits as second best option. Furthermore, it is shown that stabilisation with cement has the second highest and thermal treatment in a dedicated furnace has the highest environmental impact, mostly due to high CO₂ emissions. Therefore these two treatment options should be avoided in the future. Besides the comparative evaluation of the different options, it could be shown that uncertainty analysis is useful to determine the relevance of long-term emissions for the ranking of different systems.

Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded?

Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs-discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST-and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics' branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.

Second-Order Analytical Uncertainty Analysis in Life Cycle Assessment

Life cycle assessment (LCA) results are inevitably subject to uncertainties. Since the complete elimination of uncertainties is impossible, LCA results should be complemented by an uncertainty analysis. However, the approaches currently used for uncertainty analysis have some shortcomings: statistical uncertainty analysis via Monte Carlo simulations are inherently uncertain due to their statistical nature and can become computationally inefficient for large systems; analytical approaches use a linear approximation to the uncertainty by a first-order Taylor series expansion and thus, they are only precise for small input uncertainties. In this article, we refine the analytical uncertainty analysis by a more precise, second-order Taylor series expansion. The presented approach considers uncertainties from process data, allocation, and characterization factors. We illustrate the refined approach for hydrogen production from methane-cracking. The production system contains a recycling loop leading to nonlinearities. By varying the strength of the loop, we analyze the precision of the first- and second-order analytical uncertainty approaches by comparing analytical variances to variances from statistical Monte Carlo simulations. For the case without loops, the second-order approach is practically exact. In all cases, the second-order Taylor series approach is more precise than the first-order approach, in particular for large uncertainties and for production systems with nonlinearities, for example, from loops. For analytical uncertainty analysis, we recommend using the second-order approach since it is more precise and still computationally cheap.

Limitations of toxicity characterization in life cycle assessment: Can adverse outcome pathways provide a new foundation?

Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. Integr Environ Assess Manag 2016;12:580-590. Published 2015. This article is a US Government work and is in the public domain in the USA.

A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty

We propose a methodology for conducting robust comparative life cycle assessments (LCA) by leveraging uncertainty. The method evaluates a broad range of the possible scenario space in a probabilistic fashion while simultaneously considering uncertainty in input data. The method is intended to ascertain which scenarios have a definitive environmentally preferable choice among the alternatives being compared and the significance of the differences given uncertainty in the parameters, which parameters have the most influence on this difference, and how we can identify the resolvable scenarios (where one alternative in the comparison has a clearly lower environmental impact). This is accomplished via an aggregated probabilistic scenario-aware analysis, followed by an assessment of which scenarios have resolvable alternatives. Decision-tree partitioning algorithms are used to isolate meaningful scenario groups. In instances where the alternatives cannot be resolved for scenarios of interest, influential parameters are identified using sensitivity analysis. If those parameters can be refined, the process can be iterated using the refined parameters. We also present definitions of uncertainty quantities that have not been applied in the field of LCA and approaches for characterizing uncertainty in those quantities. We then demonstrate the methodology through a case study of pavements.

Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework

By combining life cycle assessment (LCA) with multiobjective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one.

Comparison of Asian Aquaculture Products by Use of Statistically Supported Life Cycle Assessment

We investigated aquaculture production of Asian tiger shrimp, whiteleg shrimp, giant river prawn, tilapia, and pangasius catfish in Bangladesh, China, Thailand, and Vietnam by using life cycle assessments (LCAs), with the purpose of evaluating the comparative eco-efficiency of producing different aquatic food products. Our starting hypothesis was that different production systems are associated with significantly different environmental impacts, as the production of these aquatic species differs in intensity and management practices. In order to test this hypothesis, we estimated each system's global warming, eutrophication, and freshwater ecotoxicity impacts. The contribution to these impacts and the overall dispersions relative to results were propagated by Monte Carlo simulations and dependent sampling. Paired testing showed significant (p < 0.05) differences between the median impacts of most production systems in the intraspecies comparisons, even after a Bonferroni correction. For the full distributions instead of only the median, only for Asian tiger shrimp did more than 95% of the propagated Monte Carlo results favor certain farming systems. The major environmental hot-spots driving the differences in environmental performance among systems were fishmeal from mixed fisheries for global warming, pond runoff and sediment discards for eutrophication, and agricultural pesticides, metals, benzalkonium chloride, and other chlorine-releasing compounds for freshwater ecotoxicity. The Asian aquaculture industry should therefore strive toward farming systems relying upon pelleted species-specific feeds, where the fishmeal inclusion is limited and sourced sustainably. Also, excessive nutrients should be recycled in integrated organic agriculture together with efficient aeration solutions powered by renewable energy sources.

Integrating remediation and resource recovery: On the economic conditions of landfill mining

This article analyzes the economic potential of integrating material separation and resource recovery into a landfill remediation project, and discusses the result and the largest impact factors. The analysis is done using a direct costs/revenues approach and the stochastic uncertainties are handled using Monte Carlo simulation. Two remediation scenarios are applied to a hypothetical landfill. One scenario includes only remediation, while the second scenario adds resource recovery to the remediation project. Moreover, the second scenario is divided into two cases, case A and B. In case A, the landfill tax needs to be paid for re-deposited material and the landfill holder does not own a combined heat and power plant (CHP), which leads to disposal costs in the form of gate fees. In case B, the landfill tax is waived on the re-deposited material and the landfill holder owns its own CHP. Results show that the remediation project in the first scenario costs about €23/ton. Adding resource recovery as in case A worsens the result to -€36/ton, while for case B the result improves to -€14/ton. This shows the importance of landfill tax and the access to a CHP. Other important factors for the result are the material composition in the landfill, the efficiency of the separation technology used, and the price of the saleable material.

Dynamic material flow modeling: an effort to calibrate and validate aluminum stocks and flows in Austria

A calibrated and validated dynamic material flow model of Austrian aluminum (Al) stocks and flows between 1964 and 2012 was developed. Calibration and extensive plausibility testing was performed to illustrate how the quality of dynamic material flow analysis can be improved on the basis of the consideration of independent bottom-up estimates. According to the model, total Austrian in-use Al stocks reached a level of 360 kg/capita in 2012, with buildings (45%) and transport applications (32%) being the major in-use stocks. Old scrap generation (including export of end-of-life vehicles) amounted to 12.5 kg/capita in 2012, still being on the increase, while Al final demand has remained rather constant at around 25 kg/capita in the past few years. The application of global sensitivity analysis showed that only small parts of the total variance of old scrap generation could be explained by the variation of single parameters, emphasizing the need for comprehensive sensitivity analysis tools accounting for interaction between parameters and time-delay effects in dynamic material flow models. Overall, it was possible to generate a detailed understanding of the evolution of Al stocks and flows in Austria, including plausibility evaluations of the results. Such models constitute a reliable basis for evaluating future recycling potentials, in particular with respect to application-specific qualities of current and future national Al scrap generation and utilization.

Product carbon footprints and their uncertainties in comparative decision contexts

In response to growing awareness of climate change, requests to establish product carbon footprints have been increasing. Product carbon footprints are life cycle assessments restricted to just one impact category, global warming. Product carbon footprint studies generate life cycle inventory results, listing the environmental emissions of greenhouse gases from a product's lifecycle, and characterize these by their global warming potentials, producing product carbon footprints that are commonly communicated as point values. In the present research we show that the uncertainties surrounding these point values necessitate more sophisticated ways of communicating product carbon footprints, using different sizes of catfish (Pangasius spp.) farms in Vietnam as a case study. As most product carbon footprint studies only have a comparative meaning, we used dependent sampling to produce relative results in order to increase the power for identifying environmentally superior products. We therefore argue that product carbon footprints, supported by quantitative uncertainty estimates, should be used to test hypotheses, rather than to provide point value estimates or plain confidence intervals of products' environmental performance.

Framework for analyzing transformative technologies in life cycle assessment

Emerging products and technologies pose unique challenges for the life cycle assessment (LCA) community, given the lack of data and inherent uncertainties regarding their development. An emerging technology that has the potential to be transformative and effect broad-scale change within society, as well as the underpinning assumptions associated with its life cycle, is particularly difficult to analyze. Despite the associated challenges, LCA methods must be developed for transformative technologies. The greatest improvement potential occurs at the early phases of technology development; therefore, prospective LCA results can be used to anticipate potential unintended consequences and develop design pathways that lead to preferential outcomes. This paper identifies and categorizes ten factors that influence the LCA results of transformative technologies in order to provide a formal structure for determining appropriate factors for inclusion within an LCA. Appropriate factors for an analysis should be selected according to the overall research questions of the study and are applicable to both attributional and consequential approaches to LCA.

Uncertainty in multi-media fate and transport models: a case study for TNT life cycle assessment

Life cycle assessment (LCA) is an evaluation method used by decision-makers to help assess the relative environmental impacts of various industrial processes. Despite that many LCA methods remain sensitive to uncertain input data, which can reduce the utility of their results, uncertainty arising from constituent LCA models remains poorly understood. Here, we begin to address this problem by evaluating the extent to which parameter-value uncertainty affects the SimpleBox 2.0 fate and transport model, which serves as a backbone for many LCA ecotoxicological impact categories. Two Monte Carlo type sampling methods were used to evaluate dispersion in steady-state concentration values for three chemicals involved in grenade production: toluene, 2,4-dinitrotoluene (2,4-DNT), and 2,4,6-trinitrotoluene (TNT). Parameters were first sampled stochastically one-at-a-time, then by randomly exploring a local patch of the parameter space. We confirmed that global temperatures contribute primarily to the overall variance of model results, which at most spanned approximately 8 decades in magnitude. These results are consistent with previous results obtained for the whole of the LCA method. LCA methods carry out calculations iteratively; a reduction in the error of a single component, such as the fate and transport model, may therefore improve its performance and utility as a decision-making aid.

Emerging approaches, challenges and opportunities in life cycle assessment

In the modern economy, international value chains--production, use, and disposal of goods--have global environmental impacts. Life Cycle Assessment (LCA) aims to track these impacts and assess them from a systems perspective, identifying strategies for improvement without burden shifting. We review recent developments in LCA, including existing and emerging applications aimed at supporting environmentally informed decisions in policy-making, product development and procurement, and consumer choices. LCA constitutes a viable screening tool that can pinpoint environmental hotspots in complex value chains, but we also caution that completeness in scope comes at the price of simplifications and uncertainties. Future advances of LCA in enhancing regional detail and accuracy as well as broadening the assessment to economic and social aspects will make it more relevant for producers and consumers alike.

Simultaneous removal of sulfide, nitrate and acetate under denitrifying sulfide removal condition: modeling and experimental validation

Simultaneous removal of sulfide (S(2-)), nitrate (NO3(-)) and acetate (Ac(-)) under denitrifying sulfide removal process (DSR) is a novel biological wastewater treatment process. This work developed a mathematical model to describe the kinetic behavior of sulfur-nitrogen-carbon and interactions between autotrophic denitrifiers and heterotrophic denitrifiers. The kinetic parameters of the model were estimated via data fitting considering the effects of initial S(2-) concentration, S(2-)/NO3(-)-N ratio and Ac(-)-C/NO3(-)-N ratio. Simulation supported that the heterotrophic denitratation step (NO3(-) reduction to NO2(-)) was inhibited by S(2-) compared with the denitritation step (NO2(-) reduction to N2). Also, the S(2-) oxidation by autotrophic denitrifiers was shown two times lower in rate with NO2(-) as electron acceptor than that with NO3(-) as electron acceptor. NO3(-) reduction by autotrophic denitrifiers occurs 3-10 times slower when S(0) participates as final electron donor compared to the S(2-)-driven pathway. Model simulation on continuous-flow DSR reactor suggested that the adjustment of hydraulic retention time is an efficient way to make the reactor tolerating high S(2-) loadings. The proposed model properly described the kinetic behaviors of DSR processes over wide parametric ranges and which can offer engineers with basis to optimize bioreactor operation to improve the treatment capacity.

Climate-based archetypes for the environmental fate assessment of chemicals

Emissions of chemicals have been on the rise for years, and their impacts are greatly influenced by spatial differentiation. Chemicals are usually emitted locally but their impact can be felt both locally and globally, due to their chemical properties and persistence. The variability of environmental parameters in the emission compartment may affect the chemicals' fate and the exposure at different orders of magnitude. The assessment of the environmental fate of chemicals and the inherent spatial differentiation requires the use of multimedia models at various levels of complexity (from a simple box model to complex computational and high-spatial-resolution models). The objective of these models is to support ecological and human health risk assessment, by reducing the uncertainty of chemical impact assessments. The parameterisation of spatially resolved multimedia models is usually based on scenarios of evaluative environments, or on geographical resolutions related to administrative boundaries (e.g. countries/continents) or landscape areas (e.g. watersheds, eco-regions). The choice of the most appropriate scale and scenario is important from a management perspective, as a balance should be reached between a simplified approach and computationally intensive multimedia models. In this paper, which aims to go beyond the more traditional approach based on scale/resolution (cell, country, and basin), we propose and assess climate-based archetypes for the impact assessment of chemicals released in air. We define the archetypes based on the main drivers of spatial variability, which we systematically identify by adopting global sensitivity analysis techniques. A case study that uses the high resolution multimedia model MAPPE (Multimedia Assessment of Pollutant Pathways in the Environment) is presented. Results of the analysis showed that suitable archetypes should be both climate- and chemical-specific, as different chemicals (or groups of them) have different traits that influence their spatial variability. This hypothesis was tested by comparing the variability of the output of MAPPE for four different climatic zones on four different continents for four different chemicals (which represent different combinations of physical and chemical properties). Results showed the high suitability of climate-based archetypes in assessing the impacts of chemicals released in air. However, further research work is still necessary to test these findings.

Quantifying the trade-off between parameter and model structure uncertainty in life cycle impact assessment

To enhance the use of quantitative uncertainty assessments in life cycle impact assessment practice, we suggest to quantify the trade-off between parameter uncertainty, i.e. any uncertainty associated with data and methods used to quantify the model parameters, and model structure uncertainty, i.e. the uncertainty about the relations and mechanisms being studied. In this paper we show the trade-off between the two types of uncertainty in a case of maize production with a focus on freshwater ecotoxicity due to pesticide application in The Netherlands. Parameter uncertainty in pesticide emissions, chemical-specific data, effect and damage data, and fractions of metabolite formation of degradation products was statistically quantified via probabilistic simulation, i.e. Monte Carlo simulation. Model structure uncertainties regarding the concentration-response model to be included, the selection of the damage model, and the inclusion of pesticide transformation products were assessed via discrete choice analysis. We conclude that to arrive at a minimum level of overall uncertainty the linear concentration-response model is preferable, while the transformation products may be excluded. Selecting the damage model has a relatively low influence on the overall uncertainty. Our study shows that quantifying the trade-off between different types of uncertainty can help to identify optimal model complexity from an uncertainty point of view.

Exploring the viability of probabilistic under-specification to streamline life cycle assessment

Life cycle assessment (LCA) is a technique used to assess the environmental impact of products, processes, or materials. Recently, its importance as a decision-making tool to help evaluate current inventories and innovation of environmentally responsible products has grown; however, the amount of information needed to completely assess even the simplest product's environmental impact may require significant time and resources. Myriad quantitative and qualitative effort-reducing strategies have been considered to accelerate the pace and reduce the cost of LCA. Although these streamlining methodologies reduce the time and effort of conducting LCA, they introduce variability and uncertainty into the results, creating a challenge for stakeholders who may need to make decisions based on the information. This Article explores the impact of streamlining on the credibility of LCA results given the uncertainty in the context of several case studies related to materials production in common consumer products. A technique for the structured analysis of the bill of materials is proposed, which leverages statistical analysis in the context of uncertainty.

Quantitative uncertainty analysis of Life Cycle Assessment for algal biofuel production

As a result of algae's promise as a renewable energy feedstock, numerous studies have used Life Cycle Assessment (LCA) to quantify the environmental performance of algal biofuels, yet there is no consensus of results among them. Our work, motivated by the lack of comprehensive uncertainty analysis in previous studies, uses a Monte Carlo approach to estimate ranges of expected values of LCA metrics by incorporating parameter variability with empirically specified distribution functions. Results show that large uncertainties exist at virtually all steps of the biofuel production process. Although our findings agree with a number of earlier studies on matters such as the need for wet lipid extraction, nutrients recovered from waste streams, and high energy coproducts, the ranges of reported LCA metrics show that uncertainty analysis is crucial for developing technologies, such as algal biofuels. In addition, the ranges of energy return on (energy) invested (EROI) values resulting from our analysis help explain the high variability in EROI values from earlier studies. Reporting results from LCA models as ranges, and not single values, will more reliably inform industry and policy makers on expected energetic and environmental performance of biofuels produced from microalgae.

Quantifying uncertainty in LCA-modelling of waste management systems

Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) as a new approach, a combined sensitivity analysis providing a visualisation of the shift in the ranking of different options due to variations of selected key parameters. This tiered approach optimises the resources available to LCA practitioners by only propagating the most influential uncertainties.

Uncertainty propagation in life cycle assessment of biodiesel versus diesel: global warming and non-renewable energy

Uncertainty information is essential for the proper use of life cycle assessment and environmental assessments in decision making. To investigate the uncertainties of biodiesel and determine the level of confidence in the assertion that biodiesel is more environmentally friendly than diesel, an explicit analytical approach based on the Taylor series expansion for lognormal distribution was applied in the present study. A biodiesel case study demonstrates the probability that biodiesel has a lower global warming and non-renewable energy score than diesel, that is 92.3% and 93.1%, respectively. The results indicate the level of confidence in the assertion that biodiesel is more environmentally friendly than diesel based on the global warming and non-renewable energy scores.