Uncertainty Quantification in Life Cycle Assessments: Exploring Distribution Choice and Greater Data Granularity to Characterize Product Use

The life cycle environmental profile of energy‐consuming products is dominated by the products’ use stage. Variation in real‐world product use can therefore yield large differences in the results of life cycle assessment (LCA). Adequate characterization of input parameters is paramount for uncertainty quantification and has been a challenge to wider adoption of the LCA method. After emphasis in recent years on methodological development, data development has become the primary focus again. Pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐stage parameters. Illustrating a data‐driven approach, we examine energy use in domestic cooling systems, focusing on climate change as the impact category. Specific objectives were to examine: (1) how characterization of the use stage by different probability distributions and (2) how characterizing data aggregated at successively higher granularity affects LCA modeling results and the uncertainty in output. Appliance‐level electricity data were sourced from domestic residences for 3 years. Use‐stage variables were propagated in a stochastic model and analyses simulated by Monte Carlo procedure. Although distribution choice did not necessarily significantly impact the estimated output, there were differences in the estimated uncertainty. Characterization of use‐stage power consumption in the model at successively higher data granularity reduced the output uncertainty with diminishing returns. Results therefore justify the collection of high granularity data sets representing the life cycle use stage of high‐energy products. The availability of such data through proliferation of pervasive sensing presents increasing opportunities to better characterize data and increase confidence in results of LCA.     

Uncertainty Quantification in Life Cycle Assessments: Interindividual Variability and Sensitivity Analysis in LCA of Air‐Conditioning Systems

The life cycle environmental profile of energy‐consuming products, such as air conditioning, is dominated by the products’ use phase. Different user behavior patterns can therefore yield large differences in the results of a cradle‐to‐grave assessment. Although this variation and uncertainty is increasingly recognized, it remains often poorly characterized in life cycle assessment (LCA) studies. Today, pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐phase parameters, in turn facilitating quantification and reduction of this uncertainty in LCA. This study examined the case of energy use in building cooling systems, focusing on global warming potential (GWP) as the impact category. In Singapore, building cooling systems or air conditioning consumes up to 37% of national electricity demand. Lack of consideration of variation in use‐phase interaction leads to the oversized designs, wasted energy, and therefore reducible GWP. Using a high‐resolution data set derived from sensor observations, energy use and behavior patterns of single‐office occupants were characterized by probabilistic distributions. The interindividual variability and use‐phase variables were propagated in a stochastic model for the life cycle of air‐conditioning systems and simulated by way of Monte Carlo analysis. Analysis of the generated uncertainties identified plausible reductions in global warming impact through modifying user interaction. Designers concerned about the environmental profile of their products or systems need better representation of the underlying variability in use‐phase data to evaluate the impact. This study suggests that data can be reliably provided and incorporated into the life cycle by proliferation of pervasive sensing, which can only continue to benefit future LCA.     

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

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

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

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

Life cycle assessment of emerging technologies: A review

In recent literature, prospective application of life cycle assessment (LCA) at low technology readiness levels (TRL) has gained immense interest for its potential to enable development of emerging technologies with improved environmental performances. However, limited data, uncertain functionality, scale up issues and uncertainties make it very challenging for the standard LCA guidelines to evaluate emerging technologies and requires methodological advances in the current LCA framework. In this paper, we review published literature to identify major methodological challenges and key research efforts to resolve these issues with a focus on recent developments in five major areas: cross‐study comparability, data availability and quality, scale‐up issues, uncertainty and uncertainty communication, and assessment time. We also provide a number of recommendations for future research to support the evaluation of emerging technologies at low technology readiness levels: (a) the development of a consistent framework and reporting methods for LCA of emerging technologies; (b) the integration of other tools with LCA, such as multicriteria decision analysis, risk analysis, technoeconomic analysis; and (c) the development of a data repository for emerging materials, processes, and technologies.     

Carbon Nanofiber Production

Holistic understanding of nanotechnology using systems analysis tools is essential for evaluating claims about the potential benefits of this emerging technology. This article presents one of the first assessments of the life cycle energy requirements and environmental impact of carbon nanofibers (CNFs) synthesis. Life cycle inventory data are compiled with data reported in the open literature. The results of the study indicate relatively higher life cycle energy requirements and higher environmental impact of CNFs as compared to traditional materials, like primary aluminum, steel, and polypropylene, on an equal mass basis. Life cycle energy requirements for CNFs from a range of feedstock materials are found to be 13 to 50 times that of primary aluminum on an equal mass basis. Similar trends are observed from the results of process life cycle assessment (LCA), as conveyed by different midpoint and endpoint damage indicators. Savings in life cycle energy consumption and, hence, reductions in environmental burden are envisaged if higher process yields of these fibers can be achieved in continuous operations. Since the comparison of CNFs is performed on an equal mass basis with traditional materials, these results cannot be generalized for CNF‐based nanoproducts. Quantity of use of these engineered nanomaterials and resulting benefits will decide their energy and environmental impact. Nevertheless, the life cycle inventory and the results of the study can be used for evaluating the environmental performance of specific CNF‐based nanoproducts.     

Applying Leontief's Price Model to Estimate Missing Elements in Hybrid Life Cycle Inventories

This article presents an approach to estimate missing elements in hybrid life cycle inventories. Its development is motivated by a desire to rationalize inventory compilation while maintaining the quality of the data. The approach builds on a hybrid framework, that is, a combination of process‐ and input–output‐based life cycle assessment (LCA) methodology. The application of Leontief's price model is central in the proposed procedure. Through the application of this approach, an inventory with no cutoff with respect to costs can be obtained. The formal framework is presented and discussed. A numerical example is provided in Supplementary Appendix S1 on the Web.     

A Methodical Approach for Systematic Life Cycle Assessment of Wood‐Based Furniture

Existing life cycle assessment (LCA) studies for furniture focus on single pieces of furniture and use a bottom‐up approach based on their bill of materials (BOM) to build up the data inventories. This approach does not ensure completeness regarding material and energy fluxes and representativeness regarding the product portfolio. Integrating material and energy fluxes collected at company level into product LCA (top‐down approach) over‐rides this drawback. This article presents a method for systematic LCA of industrially produced furniture that merges the top‐down approach and bottom‐up approach. The developed method assigns data collected at the company level to the different products while, at the same time, considering that wood‐based furniture is a complex product. Hence, several classifications to reduce the complexity to a manageable level have been developed. Simultaneously, a systematic calculation routine was established. The practical implementation of the developed method for systematic LCA is carried out in a case study within the German furniture industry. The system boundary was set in accord with the EN 15804 specification cradle‐to‐gate‐with‐options. The analysis therefore includes the manufacturing phase supplemented by an end‐of‐life scenario. The case study shows that the manufacturing of semifinished products (especially wood‐based panels and metal components) as well as the electric energy demand in furniture manufacturing account for a notable share of the environmental impacts. A sensitivity analysis indicates that up to roughly 10% of the greenhouse gas emissions are not recorded when conducting an LCA based on a BOM instead of applying the developed approach.     

Dynamic life cycle assessment: framework and application to an institutional building

Purpose

This paper uses a dynamic life cycle assessment (DLCA) approach and illustrates the potential importance of the method using a simplified case study of an institutional building. Previous life cycle assessment (LCA) studies have consistently found that energy consumption in the use phase of a building is dominant in most environmental impact categories. Due to the long life span of buildings and potential for changes in usage patterns over time, a shift toward DLCA has been suggested.

Methods

We define DLCA as an approach to LCA which explicitly incorporates dynamic process modeling in the context of temporal and spatial variations in the surrounding industrial and environmental systems. A simplified mathematical model is used to incorporate dynamic information from the case study building, temporally explicit sources of life cycle inventory data and temporally explicit life cycle impact assessment characterization factors, where available. The DLCA model was evaluated for the historical and projected future environmental impacts of an existing institutional building, with additional scenario development for sensitivity and uncertainty analysis of future impacts.

Results and discussion

Results showed that overall life cycle impacts varied greatly in some categories when compared to static LCA results, generated from the temporal perspective of either the building's initial construction or its recent renovation. From the initial construction perspective, impacts in categories related to criteria air pollutants were reduced by more than 50 % when compared to a static LCA, even though nonrenewable energy use increased by 15 %. Pollution controls were a major reason for these reductions. In the future scenario analysis, the baseline DLCA scenario showed a decrease in all impact categories compared with the static LCA. The outer bounds of the sensitivity analysis varied from slightly higher to strongly lower than the static results, indicating the general robustness of the decline across the scenarios.

Conclusions

These findings support the use of dynamic modeling in life cycle assessment to increase the relevance of results. In some cases, decision making related to building design and operations may be affected by considering the interaction of temporally explicit information in multiple steps of the LCA. The DLCA results suggest that in some cases, changes during a building's lifetime can influence the LCA results to a greater degree than the material and construction phases. Adapting LCA to a more dynamic approach may increase the usefulness of the method in assessing the performance of buildings and other complex systems in the built environment.     

Emergy as a Life Cycle Impact Assessment Indicator

Founded in thermodynamics and systems ecology, emergy evaluation is a method to associate a product with its dependencies on all upstream environmental and resource flows using a common unit of energy. Emergy is thus proposed as an indicator of aggregate resource use for life cycle assessment (LCA). An LCA of gold mining, based on an original life cycle inventory of a large gold mine in Peru, is used to demonstrate how emergy can be incorporated as an impact indicator into a process‐based LCA model. The results demonstrate the usefulness of emergy in the LCA context. The adaptation of emergy evaluation, traditionally performed outside of the LCA framework, requires changes to the conventional accounting rules and the incorporation of uncertainty estimations of the emergy conversion factors, or unit emergy values. At the same time, traditional LCA boundaries are extended to incorporate the environmental processes that provide for raw resources, including ores. The total environmental contribution to the product, doré, is dominated by mining and metallurgical processes and not the geological processes forming the gold ore. The measure of environmental contribution to 1 gram (g) of doré is 6.8E + 12 solar‐equivalent Joules (sej) and can be considered accurate within a factor of 2. These results are useful in assessing a process in light of available resources, which is essential to measuring long‐term sustainability. Comparisons are made between emergy and other measures of resource use, and recommendations are made for future incorporation of emergy into LCA that will result in greater consistency with existing life cycle inventory (LCI) databases and other LCA indicators.     

Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity

Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle‐based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes.     

Let's Be Clear(er) about Substitution: A Reporting Framework to Account for Product Displacement in Life Cycle Assessment

The multifunctional character of resource recovery in waste management systems is commonly addressed through system expansion/substitution in life cycle assessment (LCA). Avoided burdens credited based on expected displacement of other product systems can dominate the overall results, making the underlying assumptions particularly important for the interpretation and recommendations. Substitution modeling, however, is often poorly motivated or inadequately described, which limits the utility and comparability of such LCA studies. The aim of this study is therefore to provide a structure for the systematic reporting of information and assumptions expected to contribute to the substitution potential in order to make substitution modeling and the results thereof more transparent and interpretable. We propose a reporting framework that can also support the systematic estimation of substitution potentials related to resource recovery. Key components of the framework include waste‐specific (physical) resource potential, recovery efficiency, and displacement rate. End‐use–specific displacement rates can be derived as the product of the relative functionality (substitutability) of the recovered resources compared to potentially displaced products and the expected change in consumption of competing products. Substitutability can be determined based on technical functionality and can include additional constraints. The case of anaerobic digestion of organic household waste illustrates its application. The proposed framework enables well‐motivated substitution potentials to be accounted for, regardless of the chosen approach, and improves the reproducibility of comparative LCA studies of resource recovery.     

Dynamic Models of Fixed Capital Stocks and Their Application in Industrial Ecology

Industrial assets or fixed capital stocks are at the core of the transition to a low‐carbon economy. They represent substantial accumulations of capital, bulk materials, and critical metals. Their lifetime determines the potential for material recycling and how fast they can be replaced by new, more efficient facilities. Their efficiency determines the coupling between useful output and energy and material throughput. A sound understanding of the economic and physical properties of fixed capital stocks is essential to anticipating the long‐term environmental and economic consequences of the new energy future. We identify substantial overlap in the way stocks are modeled in national accounting, dynamic material flow analysis, dynamic input‐output (I/O) analysis, and life cycle assessment (LCA) and we merge these concepts into a common framework for modeling fixed capital stocks. We demonstrate the usefulness of the framework for simultaneous accounting of capital and material stocks and for consequential LCA. We apply the framework to design a demand‐driven dynamic I/O model with dynamic capital stocks, and we synthesize both the marginal and attributional matrix of technical coefficients (A‐matrix) from detailed process inventories of fixed assets of different age cohorts and technologies. The stock modeling framework allows researchers to identify and exploit synergies between different model families under the umbrella of socioeconomic metabolism.     

Import‐based Indicator for the Geopolitical Supply Risk of Raw Materials in Life Cycle Sustainability Assessments

There is a growing concern over the security and sustainable supply of raw material among businesses and governments of developed, material‐intensive countries. This has led to the development of a systematic analysis of risk incorporated with raw materials usage, often referred as criticality assessment. In principle, this concept is based on the material flow approach. The potential role of life cycle assessment (LCA) to integrate resource criticality through broadening its scope into the life cycle sustainability assessment (LCSA) framework has been discussed within the LCA communities for some time. In this article, we aim at answering the question of how to proceed toward integration of the geopolitical aspect of resource criticality into the LCSA framework. The article focuses on the assessment of the geopolitical supply risk of 14 resources imported to the seven major advanced economies and the five most relevant emerging countries. Unlike a few previous studies, we propose a new method of calculation for the geopolitical supply risk, which is differentiated by countries based on the import patterns instead of a global production distribution. Our results suggest that rare earth elements, tungsten, antimony, and beryllium generally pose high geopolitical supply risk. Results from the Monte Carlo simulation allow consideration of data uncertainties for result interpretation. Issues concerning the consideration of the full supply chain are exemplarily discussed for cobalt. Our research broadens the scope of LCA from only environmental performance to a resource supply‐risk assessment tool that includes accessibility owing to political instability and market concentration under the LCSA framework.     

Life cycle assessment of emerging environmental technologies in the early stage of development: A case study on nanostructured materials

The use of nanostructured materials has been recently proposed in the field of environmental nanoremediation. This approach consists in using nanomaterials not directly, but as building blocks for the design of nano‐porous micro‐dimensional systems, overcoming the eco‐ and health‐toxicology risks generally associated with the use of nano‐sized technologies. Herein we report the use of life cycle assessment (LCA) as an eco‐design tool for optimizing the production of cellulose nanosponges (CNS), nanostructured materials recently developed for water remediation purposes. LCA was applied from the acquisition of raw materials to the synthesis of CNS (from cradle‐to‐gate), considering three production systems, from the lab‐level to a modeled scale‐up system. The lab‐scale LCA identified the main environmental hotspots, namely the energy‐consuming steps and the final purification of the material (washing step). In a second lab‐scale production, an improvement action could be implemented, switching the washing solvent from methanol to water and decreasing the washing temperature. A second LCA showed a reduced contribution to the impacts from the materials, while the global impacts remained within the same order of magnitude. A simulated scale‐up of the process allowed to optimize the energy‐consuming steps and the water consumption, through internal recycling. A third LCA assessed the resulting benefits and a decrease in the global impacts by two orders of magnitude. Our study contributes to the discussion of LCA community, providing a focus on the importance of scaling‐up of emerging technologies, namely nanostructured porous materials, highlighting the benefits of a LCA based approach since the very beginning of product design (eco‐design).     

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

Individual adaptation of industry LCA practice: Results from two case studies in the Swedish forest products industry

Goal, Scope and Background The mere existence of life cycle assessment (LCA) methodology and general acceptance of the life cycle philosophy is not enough to make their use widespread in industry. To gain a better understanding of factors shaping LCA studies and life cycle related practice, field studies of the development of LCA practice in two companies were carried out. Methods In order to obtain a deeper understanding of LCA practice, the number of ‘variables’ was minimized and two similar companies were chosen for study: Stora Enso and SCA. Both companies are part of the Swedish forest products industry, are large multinational enterprises and have been working with LCA since the early 1990s. Both interviews and document studies were used to collect data regarding LCA work from its introduction until 2003. Results and Discussion We found fundamental differences in LCA practice between two similar companies in regard to LCA studies per se (the number of studies undertaken and methodological preferences) and also in regard to the organisation of and approach to LCA work. By testing various theoretical explanations of these divergent LCA practices, we identified the actions of individuals and their understanding of the situation as important factors shaping LCA practice. Conclusions Although sector-wide recommendations on LCA practice are common in the LCA community, this study indicates that companies use LCA differently despite similar structural conditions such as company size or sector affiliation. Recommendations and Perspectives Since the understanding and actions of individuals are important in shaping LCA practice, people working with LCA in industry probably have greater scope for action than they recognise and than sector recommendations may imply when it comes to organising and carrying out their work. Thus, those working with life cycle issues, even in different sectors, can learn much from each other about ways of organising and benefiting from LCA work.     

Comparative life cycle assessment of current and future electricity generation systems in the Czech Republic and Poland

Purpose

The purpose of the study was to perform a comparative life cycle assessment of current and future electricity generation systems in the Czech Republic and Poland. The paper also outlines the main sources of environmental impact for the different impact categories for the electricity generation technologies analyzed. The analyses covered the years 2000–2050, and were conducted within the framework of the international programme Interreg V-A Czech Republic-Poland, Microprojects Fund 2014–2020 in the Euroregion Silesia.

Methods

Environmental assessment was done using the life cycle assessment (LCA) and ReCiPe Midpoint and Endpoint methods, which allowed the presentation of different categories of environmental impact and damage. The LCA was based on ISO 14040 and ISO 14044, using SimaPro 8.2.3 software with the Ecoinvent 3.2 database. The analyses cover both the current electricity production structures in the Czech Republic and Poland, and the projected energy production.

Results and discussion

The LCA analyses performed for the energy systems under consideration in the Czech Republic and Poland enabled a comparative analysis of current and forecast energy systems in these countries, as well as identification of the main sources of environmental impact. Comparative analysis of the LCA results showed that current and future electricity generation systems in Poland caused higher environmental impact there, than in the Czech Republic.

Conclusions

The assessment of the life cycle of electricity sources showed that the main determinant of the negative impact on the environment of energy systems in both Poland and the Czech Republic was the consumption of solid fuels, and in particular, the consumption of lignite. It is important to highlight that this is the first attempt of a comparative LCA of electricity production in the Czech Republic and Poland. This is also the first approach that contains analyses of the life cycle assessment of both present and future energy systems. The economic assessment and eco-efficiency of current and future electricity generation systems in European Union countries will be addressed in future research.

     

Approaches for Addressing Life Cycle Assessment Data Gaps for Bio‐based Products

There is an increasing need for life cycle data for bio‐based products, which becomes particularly evident with the recent drive for greenhouse gas reporting and carbon footprinting studies. Meeting this need is challenging given that many bio‐products have not yet been studied by life cycle assessment (LCA), and those that have are specific and limited to certain geographic regions. In an attempt to bridge data gaps for bio‐based products, LCA practitioners can use either proxy data sets (e.g., use existing environmental data for apples to represent pears) or extrapolated data (e.g., derive new data for pears by modifying data for apples considering pear‐specific production characteristics). This article explores the challenges and consequences of using these two approaches. Several case studies are used to illustrate the trade‐offs between uncertainty and the ease of application, with carbon footprinting as an example. As shown, the use of proxy data sets is the quickest and easiest solution for bridging data gaps but also has the highest uncertainty. In contrast, data extrapolation methods may require extensive expert knowledge and are thus harder to use but give more robust results in bridging data gaps. They can also provide a sound basis for understanding variability in bio‐based product data. If resources (time, budget, and expertise) are limited, the use of averaged proxy data may be an acceptable compromise for initial or screening assessments. Overall, the article highlights the need for further research on the development and validation of different approaches to bridging data gaps for bio‐based products.