LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity analysis

Global sensitivity analysis reduces data collection efforts in LCA: A comparison between two additive manufacturing technologies

Accounting for the environmental impacts in the design of technologies is becoming a necessity for manufacturers. Life cycle assessment (LCA) is a well-established method to quantify the environmental impacts of products and services through a holistic perspective and is increasingly used to support the eco-design of products and technologies. However, LCA generally faces an inherent issue with data availability. Given the constraints on both time and cost for collecting inventory data to feed the LCA model, a trade-off between data cost robustness is required with an efficient data collection strategy. The objective of this study is to develop a framework to prioritize data collection efforts in LCA using uncertainty analysis. This starts with a screening life cycle inventory analysis systematically informing all input parameters with uncertainty ranges. Monte Carlo analysis is then used to propagate the uncertainty through the model. Stochastic results are then compared with an acceptable confidence level set by the decision maker. This is followed by a global sensitivity analysis using Sobol' indices to rank different input parameters based on their contribution to the variability of the results. This paves the way for an iterative process prioritizing further data collection focusing on the most sensitive parameters. A case study comparing cold spray and wire arc additive manufacturing illustrates how to operationalize the framework. Learnings from the case study highlight the importance of defining the uncertainty ranges and the convergence criterion, where more work is needed in that domain.

Parametric Life Cycle Assessment of Nuclear Power for Simplified Models

Electrifying the global economy is accepted as a main decarbonization lever to reach the Paris Agreement targets. The IEA's 2050 Net Zero transition pathways all involve some degree of nuclear power, highlighting its potential as a low-carbon electricity source. Greenhouse gas emissions of nuclear power reported in the life cycle assessment literature vary widely, from a few grams of CO2 equivalents to more than 100 g/kWh, globally. The reasons for such a variation are often misunderstood when reported and used by policymakers. To fill this gap, one can make LCA models explicit, exploring the role of the most significant parameters, and develop simplified models for the scientific community, policymakers, and the public. We developed a parametric cradle-to-grave life cycle model with 20 potentially significant variables: ore grade, extraction technique, enrichment technique, and power plant construction requirements, among others. Average GHG emissions of global nuclear power in 2020 are found to be 6.1 g CO2 equiv/kWh, whereas pessimistic and optimistic scenarios provide extreme values of 5.4-122 g CO2 equiv/kWh. We also provide simplified models, one per environmental impact indicator, which can be used to estimate environmental impacts of electricity generated by a pressurized water reactor without running the full-scale model.

Variance-based sensitivity analysis of tuberculosis transmission models

Mathematical models are widely used to provide evidence to inform policies for tuberculosis (TB) control. These models contain many sources of input uncertainty including the choice of model structure, parameter values and input data. Quantifying the role of these different sources of input uncertainty on the model outputs is important for understanding model dynamics and improving evidence for policy making. In this paper, we applied the Sobol sensitivity analysis method to a TB transmission model used to simulate the effects of a hypothetical population-wide screening strategy. We demonstrated how the method can be used to quantify the importance of both model parameters and model structure and how the analysis can be conducted on groups of inputs. Uncertainty in the model outputs was dominated by uncertainty in the intervention parameters. The important inputs were context dependent, depending on the setting, time horizon and outcome measure considered. In particular, the choice of model structure had an increasing effect on output uncertainty in high TB incidence settings. Grouping inputs identified the same influential inputs. Wider use of the Sobol method could inform ongoing development of infectious disease models and improve the use of modelling evidence in decision making.

Recyclability Definition of Recycled Nanofiltration Membranes through a Life Cycle Perspective and Carbon Footprint Indicator

The direct end-of-life recycling of reverse osmosis membranes (RO) into recycled nanofiltration (r-NF) membranes has been pointed out as a circular technology. For the first time, an environmental analysis of the whole life cycle of r-NF membranes was performed, focused on their usage. The carbon footprint (CF) of NF water treatment processes (Functional Unit: 1 m³ of treated water) with different pressure vessel (PV) designs and energy sources using r-NF and commercial NF-270-400 was quantified. Moreover, to compensate for the lower permeability of the r-NF, two design strategies were assessed: A) an increment in inlet pressure, and B) an increase in the number of modules. The inventory included energy modelling for each design and membrane. The interaction of both strategies with the permeability and service life of r-NF, together with different energy sources, was assessed using a novel hybrid analytical-numerical method. The relevance of energy use at the usage stage was highlighted. Therefore, r-NF permeability is the foremost relevant parameter for the definition of CF. The low impact of the r-NF replacement favoured strategy B. The use of an environmental indicator (CF) made it possible to identify the frontiers of the recyclability and applicability of r-NF membranes.

Evaluating the Technical and Environmental Capabilities of Geothermal Systems through Life Cycle Assessment

In these days of heightened environmental consciousness, many countries are shifting their focus towards renewable energy sources for both large-scale uses (such as power plants that generate electricity) and smaller-scale applications (e.g., building heating and cooling). In this light, it is not surprising that there is a growing interest in technologies that are reliant on non-conventional sources of power, such as geothermal energy. This study is making an effort to provide a comprehensive understanding of the possible advantages and multiple uses of geothermal energy systems, in the context of their technical and environmental evaluation through Life Cycle Assessment. A brief description of the analyzing methods and the tools used to study a particular system or application is presented. The geothermal technologies and the applications of specific systems are discussed in detail, providing their environmental advantages and their technical barriers as well. District and domestic heating systems cover a significant fraction of the geothermal energy potential. The majority of the discussed studies cover the electricity production as the most important application of geothermal energy. The overall conclusion of the current work is that geothermal energy is an extremely viable alternative that, combined with other renewable energy systems, may mitigate the negative effects of the existing energy mix worldwide.

A systematic review of life cycle assessment of solid waste management: Methodological trends and prospects

Solid waste disposal has led to increasing concerns over resource, health, and environmental problems. These issues have been investigated using the Life Cycle Assessment (LCA) technique which helps identify the roots of varying hazards and allows decision-makers to improve the environmental performance of waste management practices. However, there is a lack of review studies that conducted meta-analysis related to developments in critical methodological steps of LCA on solid waste management. To fill this gap, this review paper examines 15 elements comprising the preference of journals, 13 LCA method-related characteristics, and economic assessment. Insights on the limitations and current practices of LCA applications, along with trends for future research, are provided. 240 studies on the LCA of SWM from 2009 until 2020 were systematically reviewed and classified into two major year-groups (i.e., 2009-2014 and 2015-2020) to investigate the trend changes. Among the studied elements, it is found that energy-related applications are on the increase in LCA studies on solid waste management. Anaerobic digestion facilities nearly double in appearance in the second year-group (2015-2020). There is also a more frequent occurrence of certain characterization methods like ReCiPe and CML. Functional units become more diverse, but are overall mostly defined on a mass basis. A frequently identified issue of many LCAs on solid waste management is the ambiguity of data sources such as out-of-date literature or inconsistent geographical references. By addressing issues of methodological standardization, this review study provides a basis to further increase the reliability of results of future LCA studies on solid waste management.

A holistic environmental and economic design optimization of low carbon buildings considering climate change and confounding factors

The low carbon building design has become critical given the urgent need to reduce global carbon emissions. Reducing operational energy use through multi-objective optimizations used to be a common approach, but its validity is impaired by surging embodied impacts. Therefore, a life cycle optimization becomes necessary to improve the overall carbon performance of buildings. However, current research lacks an application of multi-objective optimizations to explore the energy use, carbon emission and cost considering both embodied and operational impacts. Impacts of confounding design factors and climate change on achieving low carbon designs are also not sufficiently revealed by existing studies. To address these gaps, this study: (i) proposes a parametric design optimization method for low carbon buildings considering cost-effectiveness, (ii) explores the impacts of confounding factors on achieving low carbon designs and (iii) evaluates the impact of climate change on the life cycle performance of buildings with proper scenario assumptions. A case study is conducted to explore passive design parameters and integrated photovoltaic (PV) applications to reduce the energy use and carbon emissions in a cost-effective approach. The joint optimization of embodied and operational impacts can reduce the energy use, carbon emission and cost by 42%, 58% and 32%, respectively. Also, variation of confounding factors can lead to different optimized designs with carbon reduction difference up to 75%. The results also show that global warming will lead to higher energy use and carbon emissions in tropical regions within the near future, while stringent mitigation strategies aligned with RCP 2.6 can reverse the trend after two decades.

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 Content Review of Life Cycle Assessment of Nanomaterials: Current Practices, Challenges, and Future Prospects

This paper provides a comprehensive review of 71 previous studies on the life cycle assessment (LCA) of nanomaterials (NMs) from 2001 to 2020 (19 years). Although various studies have been carried out to assess the efficiency and potential of wastes for nanotechnology, little attention has been paid to conducting a comprehensive analysis related to the environmental performance and hotspot of NMs, based on LCA methodology. Therefore, this paper highlights and discusses LCA methodology’s basis (goal and scope definition, system boundary, life cycle inventory, life cycle impact assessment, and interpretation) to insights into current practices, limitations, progress, and challenges of LCA application NMs. We found that there is still a lack of comprehensive LCA study on the environmental impacts of NMs until end-of-life stages, thereby potentially supporting misleading conclusions, in most of the previous studies reviewed. For a comprehensive evaluation of LCA of NMs, we recommend that future studies should: (1) report more detailed and transparent LCI data within NMs LCA studies; (2) consider the environmental impacts and potential risks of NMs within their whole life cycle; (3) adopt a transparent and prudent characterization model; and (4) include toxicity, uncertainty, and sensitivity assessments to analyze the exposure pathways of NMs further. Future recommendations towards improvement and harmonization of methodological for future research directions were discussed and provided. This study’s findings redound to future research in the field of LCA NMs specifically, considering that the release of NMs into the environment is yet to be explored due to limited understanding of the mechanisms and pathways involved.

A Software Tool for a Stochastic Life Cycle Assessment and Costing of Buildings’ Energy Efficiency Measures

This article presents a novel software tool for the assessments of life-cycle environmental impacts and costs, which is aimed to support decision-making in the design phase of retrofit interventions in the building sector. By combining Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) calculations and functionalities, this tool allows evaluating the long-term trade-offs between economic and environmental performance of investment projects in energy efficiency for buildings, while accounting for uncertainties in input parameters and economic scenarios. A major novelty of the software tool is the stochastic nature of both the LCC and LCA dimensions. The LCA is implemented with Monte-Carlo methods, while the LCC accounts for the probabilistic interdependence of macroeconomic variables over time. The software also includes advanced specific tools for parametrization and sensitivity analysis. Exemplary applications are presented in order to illustrate the novelty and the functionalities of the software tool.

How Far Can Life Cycle Assessment Be Simplified? A Protocol to Generate Simple and Accurate Models for the Assessment of Energy Systems and Its Application to Heat Production from Enhanced Geothermal Systems

Life cycle assessments (LCAs) quantify environmental impacts of systems and support decision-making processes. LCAs are however time-consuming and difficult to conduct for nonexperts, thus calling for simplified approaches for multicriteria environmental assessments. In this paper, a five-step protocol is presented to generate simplified arithmetic equations from a reference parametrized LCA model of an energy system and its application illustrated for an enhanced geothermal system for heat generation with very low direct emissions in continental Europe. The simplified models estimate seven environmental impacts (climate change, freshwater ecotoxicity, human health, minerals and metals, and fossil resources depletion, and acidification) based on six technological parameters: number of injection and production wells, power of the production and injection pump, average well length, thermal power output, and eight background parameters defining the European electricity mix. A global sensitivity analysis identified these parameters as influencing the variance of the environmental impacts the most. Ensuring the representativeness of the reference LCA model and the validity of the simplified models requires thorough assessment. This protocol allows to develop relevant alternatives to detailed LCAs for quick and multicriteria environmental impact assessments of energy systems, showing that LCAs can be simplified to system-specific equations based on few, easily quantified, parameters.

Evaluation of Life-Cycle Assessment Analysis: Application to Restoration Projects and New Construction in Alpine Climate, Japan

The present paper discusses the applications of life-cycle assessment (LCA) to construction works in Japan. LCA has been frequently used to assess the environmental impacts of new construction. Nonetheless, the applications of LCA to restoration have not been fully confirmed to date. It is said that historical buildings may contribute to sustainable development. Nonetheless, as for heritage buildings, since the protection of cultural value is usually prioritised, their environmental impacts may not be sufficiently explored. To this aim, this paper evaluated the environmental impacts of the restoration of heritage buildings. This paper consisted of two tasks. First, the restoration projects of heritage buildings in Japan were introduced. The restoration of two heritage houses was discussed, referring to heritage protection legislation in Japan. Second, LCA was performed on the restoration of heritage houses and the construction of contemporary houses. Environmental impacts were compared between the restoration and new construction with regard to greenhouse gas emissions and operational energy use. A focus was given to the amount of materials used. Restoration consumes a limited amount of materials compared to new construction, although the energy use of heritage buildings is considerable. The environmental impacts of restoration were quantified so that they were compared with those of new construction. The comparison indicated issues applying LCA to heritage buildings.

Incorporating upstream emissions into electric sector nitrogen oxide reduction targets

Electricity production is a major source of air pollutants in the U.S. Policies to reduce these emissions typically result in the power industry choosing to apply controls or switch to fuels with lower combustion emissions. However, the life-cycle emissions associated with various fuels can differ considerably, potentially impacting the effectiveness of fuel switching. Life-cycle emissions include emissions from extracting, processing, transporting, and distributing fuels, as well as manufacturing and constructing new generating capacity. The field of life-cycle analysis allows quantification of these emissions. While life-cycle emissions are often considered in greenhouse gas mitigation targets, they generally have not been included in air quality policymaking. We demonstrate such an approach, examining a hypothetical electric sector emission reduction target for nitrogen oxides (NO_x) using the Global Change Assessment Model with U.S. state-level resolution. When only power plant emissions are considered in setting a NOx emission reduction target, fuel switching leads to an increase in upstream emissions that offsets 5% of the targeted reductions in 2050. When fuel extraction, processing, and transport emissions are included under the reduction target, accounting for 20% of overall NO_x reduction goal, the resulting control strategy meets the required reductions and does so at 35% lower cost by 2050. However, manufacturing and construction emissions increase and offset up to 7% of NO_x reductions in electric sector, indicating that it may be beneficial to consider these sources as well. Assuming no legal obstacles exist, life-cycle-based approaches could be implemented by allowing industry to earn reduction credits for reducing upstream emissions. We discuss some of the limitations of such an approach, including the difficulty in identifying the location of upstream emissions, which may occur across regulatory authorities or even outside of the U.S.

Complete Data Inventory of a Geothermal Power Plant for Robust Cradle-to-Grave Life Cycle Assessment Results

Technologies to produce electric energy from renewable geothermal source are gaining increasing attention, due to their ability to provide a stable output suitable for baseload production. Performing life cycle assessment (LCA) of geothermal systems has become essential to evaluate their environmental performance. However, so far, no documented nor reliable information has been made available for developing robust LCA studies. This work provides a comprehensive inventory of the Italian Bagnore geothermal power plants system. The inventory is based exclusively on primary data, accounting for every life cycle stage of the system. Data quality was assessed by means of a pedigree matrix. The calculated LCA results showed, with an overall low level of uncertainty (2–3%), that the commissioning and operational phases accounted for more than 95% of the environmental profile. Direct emissions to atmosphere were shown to be the major environmental impact, particularly those released during the operational phase (84%). The environmental performances comparison with the average Italian electricity mix showed that the balance is always in favor of geothermal energy production, except in the climate change impact category. The overall outcome confirms the importance, for flash technology employing fluid with a high concentration of gas content, of using good quality primary data to obtain robust results.

Life Cycle Assessment for the Design of Chemical Processes, Products, and Supply Chains

Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to chemical engineering design because the design scope is expanded to include process, product, and supply chain. Here, we first provide a brief tutorial highlighting key elements of LCA. Methods to fill data gaps in LCA are discussed, as capturing the full life cycle is data intensive. On this basis, we review recent methods for integrating LCA into the design of chemical processes, products, and supply chains. Whereas adding LCA as a posteriori tool for decision support can be regarded as established, the integration of LCA into the design process is an active field of research. We present recent advances and derive future challenges for LCA-based design.

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.

Novel Method of Sensitivity Analysis Improves the Prioritization of Research in Anticipatory Life Cycle Assessment of Emerging Technologies

It is now common practice in environmental life cycle assessment (LCA) to conduct sensitivity analyses to identify critical parameters and prioritize further research. Typical approaches include variation of input parameters one at a time to determine the corresponding variation in characterized midpoints or normalized and weighted end points. Generally, those input parameters that cause the greatest variations in output criteria are accepted as the most important subjects of further investigation. However, in comparative LCA of emerging technologies, the typical approach to sensitivity analysis may misdirect research and development (R&D) toward addressing uncertainties that are inconsequential or counterproductive. This paper presents a novel method of sensitivity analysis for a decision-driven, anticipatory LCA of three emerging photovoltaic (PV) technologies: amorphous-Si (a-Si), CdTe and ribbon-Si. Although traditional approaches identify metal depletion as critical, a hypothetical reduction of uncertainty in metal depletion fails to improve confidence in the environmental comparison. By contrast, the novel approach directs attention toward marine eutrophication, where uncertainty reduction significantly improves decision confidence in the choice between a-Si and CdTe. The implication is that the novel method will result in better recommendations on the choice of the environmentally preferable emerging technology alternative for commercialization.

Integrating uncertainties to the combined environmental and economic assessment of algal biorefineries: A Monte Carlo approach

The economic and environmental performance of microalgal processes has been widely analyzed in recent years. However, few studies propose an integrated process-based approach to evaluate economic and environmental indicators simultaneously. Biodiesel is usually the single product and the effect of environmental benefits of co-products obtained in the process is rarely discussed. In addition, there is wide variation of the results due to inherent variability of some parameters as well as different assumptions in the models and limited knowledge about the processes. In this study, two standardized models were combined to provide an integrated simulation tool allowing the simultaneous estimation of economic and environmental indicators from a unique set of input parameters. First, a harmonized scenario was assessed to validate the joint environmental and techno-economic model. The findings were consistent with previous assessments. In a second stage, a Monte Carlo simulation was applied to evaluate the influence of variable and uncertain parameters in the model output, as well as the correlations between the different outputs. The simulation showed a high probability of achieving favorable environmental performance for the evaluated categories and a minimum selling price ranging from $11gal^-1 to $106gal^-1. Greenhouse gas emissions and minimum selling price were found to have the strongest positive linear relationship, whereas eutrophication showed weak correlations with the other indicators (namely greenhouse gas emissions, cumulative energy demand and minimum selling price). Process parameters (especially biomass productivity and lipid content) were the main source of variation, whereas uncertainties linked to the characterization methods and economic parameters had limited effect on the results.