life cycle assessment research paper

Advances in application of machine learning to life cycle assessment: a literature review

• LIFE CYCLE MANAGEMENT
• Open access
• Published: 28 March 2022
• Volume 27 , pages 433–456, ( 2022 )

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• Ali Ghoroghi   ORCID: orcid.org/0000-0001-5594-7275 1 ,
• Yacine Rezgui 1 ,
• Ioan Petri 1 &
• Thomas Beach 1  

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Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making.

In this study, previous research on ML for LCA were considered, and a literature review was undertaken.

The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases.

Conclusions

The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

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1 Introduction

Life Cycle Assessment (LCA) is a series of procedures set for the collection and assessment of the inputs and outputs of materials or energy, as well as the subsequent impacts on the environment incurred due to the running of a system or product throughout that entity’s life cycle (ISO 14040.2 Draft). The LCA provides a framework for the definition of the scope, and the goal of the assessment, analysis of the inventory (LCI, life cycle inventory), assessment of the impact (LCIA, life cycle impact assessment), and finally, the interpretation from these procedures (Guinee 2002 ). The purpose, entities (systems, products) and the degree of sophistication are defined in the LCA framework’s goal and scope definition step. The life cycle inventory (LCI) is the step in which the system boundaries are defined. The key outcome from the LCI is the inventory which collates inputs and outputs to the environment. The life cycle impact assessment (LCIA) is how its relevance expresses the inventory to the impact categories. This step quantifies the impact through weighting and normalization. The interpretation is the final step in which the results from the LCIA are evaluated and used to make recommendations (Guinee 2002 ). LCA is a vital instrument to help reduce the overall environmental burden and provide insights into upstream and downstream trade-offs associated with environmental pressures, health & wellbeing, and the consumption of natural resources. As such, LCA can inform policy-making by providing valuable information on environmental performance, and thus contributing to performance targets within the Environmental Technology Action Plan (ETAP) and for Energy-using Products within the EuP Directive, in green public procurement (GPP), and in Environmental Product Declarations (EPDs).

In addition, the recent special report on the impacts of global warming of \(1.5^{\circ }\) C was yet another call to implement measures to mitigate GHG emissions and to devise new adaptation scenarios (IPCC 2021 ; Sala et al. 2021 ). In this context, LCA helps quantify the environmental pressures, the trade-offs, and areas for achieving improvements considering the entire life cycle of built assets from design to recycling. However, current approaches to LCA do not consistently factor in (both in the foreground and background inventory systems) life cycle variations in: (a) building usage, (b) energy supply (including from renewable sources), and (c) building and environmental regulations; as well as other changes over the building/district lifetime (Anand and Amor 2017 ; Bueno et al. 2016 ; Skaar and Jørgensen 2013 ). These include (a) change in the energy mix of a building/district or upgrading/retrofitting the energy system(s) in place; and (b) time-increase of energy demand during the lifetime of a building due to a wide range of reasons, including changes in occupancy patterns.

As such, LCA is an important instrument to help reduce the overall environmental burden of buildings and provide insights into the upstream and downstream trade-offs that are associated with environmental pressures, health and wellbeing, and the consumption of natural resources. As such, LCA can inform policymaking by providing valuable information on the environmental performance of built assets. However, the current LCA methods and tools face several limitations and challenges, including: (a) site-specific considerations (Bueno et al. 2016 ), several local impacts need to be considered in building assessments, such as the microclimate; (b) model complexity (Anand and Amor 2017 ), buildings involve a wide range of material/products, interacting as part of a complex assembly or system; (c) scenario uncertainty (Anand and Amor 2017 ; Bueno et al. 2016 ), the long use phase of buildings, including the potential for future renovation, poses uncertainty problems in LCA that are not currently addressed; (d) health and wellbeing (Bueno et al. 2016 ; Skaar and Jørgensen 2013 ), traditional LCA methodologies do not address indoor and outdoor environmental impacts on health and well-being; (e) recycled material data (Anand and Amor 2017 ; Negishi et al. 2018 ), lack of data on using waste and recycled materials as new building materials; and (f) lack of consideration for social and economic aspects (Anand and Amor 2017 ; Negishi et al. 2018 ).

The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence the need to adopt a cartesian, i.e., “Divide and Conquer”, or systems engineering approach, whereby the strategy to reduce and mitigate the environmental impact of a complex artefact, in our case a built asset, should be divided into an ensemble of discrete and manageable scenarios, such as optimizing the energy mix of an energy system. By addressing these discrete scenarios in isolation using ML, a broader reduction of environmental impacts via LCA is feasible.

ML methods are from a subtype of Artificial Intelligence (AI) methods that learn from data to improve their accuracy without the need to be programmed again. ML is creating such a model that can find patterns by studying a set of training data and developing an algorithm without human involvement (Mitchell 1997 ). ML algorithms are typically categorized into four groups: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning as shown in Fig. 1 .

Various types of ML techniques

Areas of data science, including ML, are presently used to fill gaps in data for LCA. Furthermore, they have been used to develop accessory tools for LCA that can model and predict a product’s environmental impact based on information from the design phase. ML has the capability of being integrated as a real-time algorithm, assessing production or changes in processes and responding with potential alternatives for better or less environmentally impactful production. ML approaches have been applied to different disciplines of LCA. These include the prediction of missing data, forecasting impact parameters both directly and indirectly, and optimization algorithms in LCA. ML methods have also been used to overcome incompleteness or uncertainty in data to deliver actionable recommendations for the LCA (Algren et al. 2021 ). One potential advantage of ML in LCA is that it can reduce the cost of data collection. In other words, with ML, the most informative attributes can be identified and focused on collecting them while ignoring other attributes that may not contribute significantly to the model’s accuracy. Sometimes missing data in LCA is encountered, and ML can help predict those missing values, improving the data available for LCA. Based on the type of ML, various statistics and visualizations can be used to evaluate the predicted data, including accuracy, confusion matrix, receiver operating characteristic curve, cluster distortion, and means squared error.

ML thrives in applications where there is a requirement to solve mathematical models accurately and efficiently. Consequently, it can be adapted to provide ideas or methods for an optimization process. It can be implemented as part of a real-time decision-making process where potential improvements in the performance of a system throughout its life cycle are identified. Optimization methods can then be applied to the process. This makes it particularly useful in the design process instead of the entire LCA. This study shows that the ML can be coupled with standard optimization methods to increase their capability of quickly exploring promising regions. Figure 2 provides the standard ML and LCA deployment processes which should be considered in the investigation of ML methods in LCA.

ML and LCA deployment processes

The paper reviews the application of ML to LCA with a focus on Buildings, Districts and Cities, while also including several useful related applications described under a “Miscellaneous” heading. A plethora of studies has explored the application of LCA in buildings with most studies focusing on energy use and GHG emissions (Asif 2019 ; Elkhayat et al. 2020 ; Lyu and Chow 2020 ). However, the literature thus far lacks a comprehensive review of the different applications of ML in LCA, the trends in current practices as well as some of the gaps in research. This review aims to address this by proposing and answering research questions which will establish the current practices and future works which are required in this field.

1.1 Goal and scope

This paper aims to investigate the role of ML methods in LCA across three levels;

Districts and cities

Miscellaneous

As such, our review focuses on built assets considered (a) in isolation or (b) within a District or wider city level. Built assets can be of any type, including residential, public, or industrial. At the districts and cities level, the role of ML in human structures like roads, pavements, bridges, parks, railways is investigated, informed by the literature. ML in other related studies like chemical, agriculture, and products is considered and reported at the “Miscellaneous” level. As to the investigated LCA requirements, this review adopts an exploratory approach in that it reports use cases involving the application of ML in Buildings and wider Districts. As such, a bottom-up approach, driven by applications of ML in LCA, has helped identify the most common requirements addressed in the literature. It is worth noting that environmental certification schemes, such as BREEAM, are not considered in this paper. When ML is used at the districts level, the building as an attribute (categorical attribute) can be considered in the model, which will help capture the differences between different building types within the districts. This attribute will have to be tested to see the level of information (accuracy, for example) that it brings to the model.

In this study, fundamental limitations and challenges faced by current ML methods in LCA, applications, motivations, constraints and their role in predictions and optimizations are considered (Fig. 3 ).

The applications, motivations, constraints and ML methods in LCA that are considered in this review

The significant contributions of this paper are collating a literature survey to determine use of ML techniques for LCA by answering the following research questions:

How has ML been used in LCA?

What is the role and efficacy of ML methods in optimization in LCA?

Can ML methods integrate and contextualize existing inventory databases to provide a sound basis to streamline the LCA?

What are the gaps in research in order to guide future research for ML in LCA?

LCA is explored, and the current state of the art reported in the literature is identified to answer these questions. ML techniques tailored to LCA and specific AI techniques that can advance LCA’s establishment and delivery of the smart technology are investigated. Gaps in research will then be identified in order to guide future research for ML in LCA.

The contents of this paper are organized as follows: Sect. 2 lays out the methodology for identifying and including studies for the review. Section 3 discusses research and provides an overview of ML methods in LCA. Section 4 talks about ML and optimization in LCA. The results and discussion are described in Sect. 5 . Finally, the findings are evaluated and concluded in Sect. 6 .

2 Methodology

A literature review in applying ML in LCA was performed, and 81 relevant studies were analysed according to the research questions. The review presented here aims to identify, evaluate and interpret all available research relevant to LCA using ML models. This section outlines the process for selecting included papers. This methodology was based on five phases.

Planning phase

In this phase, scope, literature research questions and databases were determined. Google scholar was chosen for the search database as well as Scopus and Web of Science. Citavi (SWISS ACADEMIC SOFTWARE GMBH, 2021) was used for managing the collected references because of its broad functionality. The publication years of studies were determined to be between the years 2000 and 2021.

Search phase

In this step, the search process was developed to select appropriate studies. After defining the research questions in the planning phase, the main terms were defined. Similar terms or interchangeable terms were identified and connected using Boolean OR and AND operators. Table 1 shows the search terms used.

Filtering phase

At first, the contents of the papers were assessed through screening of titles and abstracts and the following of inclusion criteria were applied.

Language: English

Document types: Only full-text, conference or journal papers or books

Time interval: The publication years of selected primary studies are between the years 2000 and 2021 to narrow to more relevant results based on current practices in the field of LCA.

After removing the duplicates, the papers selected through their abstract screening were reviewed in full, and those that did not consider ML techniques in LCA or provide primary research findings in this topic were excluded. In the next step, the relevance of a paper based on its introduction and the conclusion/discussion was determined. In total, this yielded 81 primary research papers. These references were imported into our reference manager Citavi.

Evaluation phase

In this phase, the articles were assessed for their quality and impact. Three main points were considered for this phase:

Is the methodology clear?

Are results provided in full?

Is the paper relevant to the research questions of this review?

Finally, a decision is made regarding the inclusion of the paper in a full review for this paper. Some papers may have been included for context or interest despite a lack of methodology.

Extraction phase

The collected references were managed using Citavi. For each selected paper, relevant information was collected and a justification for each inclusion was noted. Each paper was then analysed and the following information was extracted and recorded: the model used, the optimal model found by the authors, the type of application that ML was targeting in the paper, and finally, the scale at which LCA was applied in this paper.

3 ML methods in LCA

In this section, related works about ML methods and motivation are presented. For ML methods, each studied zones are made bold.

Luque et al. presented a conceptual framework for the integration of AI and LCA. Throughout their study, the relevance of using sensing when addressing an objective of intelligent sustainability in engineering projects has emerged (Luque et al. 2020 ). Adedeji et al. present a roadmap to using AI techniques in LCI. The data chain for efficient resident data availability for LCA studies was considered to focus on AI integration. Also, a framework for using AI in LCI was developed (Adedeji et al. 2020 ).

At the buildings level, through the combined use of ML in LCA, it may be possible to significantly reduce environmental impacts (Barros and Ruschel 2021 ). D’Amico et al. employed ML methods in civil and structural engineering in order to reduce building impacts (D’Amico et al. 2019b ). Barros and Ruschel performed a systematic literature review of the scientific research conducted for architecture, engineering and construction industries in the context of LCA and ML (Barros and Ruschel 2021 ). They show that the most investigated environmental indicators were energy consumption and Global Warming Potential (GWP). Significantly, they found that ML was predominantly used for prediction. In the case of a regionalized bottom-up model created using ML techniques, environmental profiles for individual households were assessed by (Frömelt et al. 2020 ). At the districts and cities level, Manfren et al. presented a review of modelling tools for identifying optimal solutions for district-wide energy systems. They introduced a framework for the key concepts of a local energy management system in an urban area. This framework has a multicriteria perspective and uses ML to find optimal solutions for providing energy services through distributed generation (Manfren et al. 2011 ). Furthermore, DeRousseau et al. examined the various problem formulations which are commonly seen in the field of concrete mixture design optimization that can necessitate models based on the linear combination, statistics, ML, and physics (DeRousseau et al. 2018 ). Also, in LCA at the miscellaneous level of production, ML algorithms can have an impact in reducing GHG emissions in LCA for geographically differentiated and contextualized design measures; however, they are still underutilized for such applications (Milojevic-Dupont and Creutzig 2021 ). Kurdi et al. reviewed methods for simulation in tribology to model tribo-contact scenarios and investigated LCA with simulation combined with ML (Kurdi et al. 2020 ). Wu and Wang reviewed ML methods applied to toxicity prediction and discussed the ML algorithm’s input parameter to enhance prediction accuracy (Wu and Wang 2018 ). Gust et al. demonstrated that in toxicological and regulatory assessment for novel materials where fewer characterization data are available, probabilistic adverse quantitative outcome pathway can leverage using supervised ML models (Gust et al. 2015 ). In later sections, we discuss the most commonly used ML techniques in LCA.

3.1 Neural networks

Artificial Neural Networks (ANNs), also known as Neural Networks (NNs) or simulated neural networks (SNNs), are a subset of ML and are at the heart of Deep-Learning algorithms. Their name and structure are inspired by the human brain, mimicking how biological neurons signal to one another (Livingstone 2008 ). ANNs are favourable as they overcome some limitations commonly seen with traditional software, such as collecting environmental and energy data, physical problem and software language, long computational time, and the need to calibrate a model. Consequently, ANN models provide a superior and more reliable decision support tool for engineers and architects, reducing uncertainties in the LCA field. Furthermore, the implementation of ANN in software can accommodate the development of an appropriate decision support tool. Thus, ML algorithms and techniques may be capable of increasing accuracy in LCA and reducing the simulation time (Sharif and Hammad 2019 ; Barros and Ruschel 2021 ; D’Amico et al. 2019a ). However, the validity of the NN solution is directly and powerfully proportional to the reliability of the database, which tends to be the most difficult to implement. Ziyadi et al. implemented quantitative uncertainty analysis methods to characterize and quantify uncertainties in a Life Cycle Inventory Analysis (LCIA) model. An ANN model was trained and tested to propagate input variability through a system using interval analysis. Monte Carlo sampling was then used to propagate input uncertainty directly and was compared to an indirect nonlinear optimization method that tries to maximize output range (Ziyadi and Al-Qadi 2019 ; Barros and Ruschel 2021 ). At the buildings level, ANN mainly was used for optimizing building performance and for impact prediction of energy consumption and GWP. It was suggested that advances in LCA and ML could help calculate and analyze building environmental indicators and develop and improve LCA methods. Shi and Xu presented a systematic LCA method to analyze the environmental performance of construction materials. Furthermore, BPNN and the hybrid algorithm GA-BP were introduced to evaluate building materials. Compared with BPNN, the hybrid GA-BP algorithm was shown to be of better value for selecting construction materials environmentally and has greater precision (Shi and Xu 2009 ). D’Amico et al. used ANN to simultaneously solve the energy and environmental balance along the building life cycle. The authors developed a decision support tool that quickly and reliably determines buildings’ performance with minimum effort. The reliable data and ML combination significantly contribute to the increase in speed and accuracy of LCA (Barros and Ruschel 2021 ; D’Amico et al. 2019a ). The results showed that ANN helps predict energy demand and building LCA (D’Amico et al. 2019a ). Considering that the importance of the design phase to carbon emissions during a building’s life cycle, Xikai et al. presented a regression model of carbon emissions using designing factors. Also, to determine the designing factors for a predictive model; Multilayer Perceptron (MLP) was used to develop regression models (Xikai et al. 2019 ). Sharif and Hammad proposed an ANN model to obtain complex data generated from the simulation-based multiobjective optimization model. This model tried to predict energy consumption to improve buildings’ energy performance-critical element of building energy conservation. The outcome of this study showed that the proposed ANN models could efficiently predict the LCA for the whole building renovation scenarios considering the building envelope, HVAC, and lighting systems (Sharif and Hammad 2019 ). Also, Sharif proposed a simulation-based multiobjective optimization model for optimizing the selection of renovation scenarios for existing buildings by minimizing total energy consumption (TEC) considering LCA. He developed a surrogate ANN for selecting near-optimal building energy renovation methods; and developed deep ML Models to generate renovation scenarios considering TEC (Arani 2020 ). In the building sector’s construction, the material with their embodied energy of all the materials that fall under the main category like wood, cement, plastic and the material that release less energy is provided as input data to the NN (Mukherjeea et al. 2019 ). Płoszaj-Mazurek et al. showed the relationships between the parameters of buildings and the possibility of introducing Carbon Footprint estimation and implementing building optimization at the initial design stage. They used Convolutional Neural Networks (CNN) to analyze an image of the urban layout and consider its influence on the building’s Total Carbon Footprint (Ploszaj-Mazurek et al. 2020 ). Azari et al. investigated the ideal building envelope design using a multiobjective optimization algorithm. This was based on the office building’s energy use and life cycle environmental impacts. The input variables for design were insulation material, window type, window frame material, wall thermal resistance and south and north window-to-wall ratios. The optimal iteration of these variables was found to design the building with the smallest possible operational energy and environmental impact. The eQuest 3.65 simulation tool was used to calculate active energy. LCA and Athena IE was used to find an estimated LCA. In addition, an ANN and genetic algorithm (GA) approach were implemented to generate further combinations and find the ideal design iteration. The environmental impact categories included global warming, acidification, eutrophication, formation of air pollution, and ozone depletion (Azari et al. 2016 ; Barros and Ruschel 2021 ). Xia et al. introduced a green building assessment index, developed using the life cycle theory and a back-propagation neural network (BPNN), through a Chinese and international building classification system. The assessment index was intended for scientific assessment as the basis for choosing the best plan for green building systems (Xia and Liu 2013 ; Barros and Ruschel 2021 ). Oduyemi et al. produced an ANN model for estimating operation and maintenance costs of buildings (Oduyemi et al. 2015 ). Life cycle cost analysis (LCCA) compares different design elements, specifications, and materials based on the installation, operation, maintenance and residual costs to evaluate the total life cost of construction. Alqahtani et al. used ANNs to develop a framework for LCCA of construction projects. This was used to estimate the entire cost of construction and uses cost significant items to find the main cost contributions affecting the accuracy of estimation (Alqahtani and Whyte 2013 ). Wang and Shen created a stochastic Markov model to increase the accuracy of life cycle energy consumption forecasting. This was done by involving longitudinal uncertainties in building conditions, degree days, and valuable life. The Markov building deterioration model was developed using historical data of similar situations and was used to predict the building’s useful life and expected condition at any given time. Deterioration of building and temperature changes were used to simulate yearly variation in energy consumption. Energy consumption was estimated with the available data set to calculate annual energy consumption using NN. The proposed stochastic model results in a more restricted distribution, being similar to measured data. It may be implied that the longitudinal uncertainty in the thermal condition of the building and the temperature can account for some uncertainty in the variation of the energy performance (Wang and Shen 2013 ; Barros and Ruschel 2021 ). Duprez et al. developed a technique using ML for predicting GWP of building design alternatives with a high coefficient of determination. The original model was compared to three metamodels, Multiple Linear Regression (MLR), Support Vector Regression (SVR) and ANN, to compare their ability to estimate GWP accurately. The authors concluded that ANN offered better results than MLR and SVR (Duprez et al. 2019 ).

At the city level, Perrotta et al. used the application of Boruta Algorithm (BA) and NN to evaluate and calculate a fleet of trucks’ fuel consumption to estimate the emissions for pavement roads. The authors showed that NN is appropriate for analyzing data from fleet and road asset management databases. The resulting NN model was used to estimate the impact of rolling resistance parameters (pavement roughness and macrotexture) on fuel consumption (Perrotta et al. 2018 ). Furthermore, Perrotta et al. used truck telematics, road geometry and condition data to investigate the fuel consumption prediction of fleets of trucks. Three ML techniques, Support Vector Machine (SVM), Random Forest (RF) and ANN, were developed and compared in performance (Perrotta 2017 ).

In the miscellaneous level Wisthoff et al. studied the relationship between product design decisions and eventual LCA. Their study developed a search tree of sustainable design knowledge in the early design phase, and to assist in quantifying the impact of these design decisions; the study used an MLP method to relate the LCA of 37 case study products to product attributes to help the designer to redesign the product to reduce the impact (Wisthoff et al. 2016 ). Smetana et al. focused on analyzing evolutionary similarities and differences between two complex modular systems, NN and blockchain technologies, on evaluating their potential for application to material flow analysis (MFA) and LCA. The authors concluded that the combination of NN and blockchain could form a more efficient system for MFA and LCA (Smetana et al. 2018 ). Chiang et al. introduced a design for environment methodology to evaluate derivative consumer electronic product development using a BPNN model and a technique for order preference by similarity to ideal solution (TOPSIS) method (Chiang et al. 2011 ). Zhu et al. presented a research framework for greening the continuous sitagliptin manufacturing process with LCA and NN’s aid. Deep learning NN models were developed to predict LCA according to the chemicals in a database with known LCA values and corresponding molecular descriptors (Luque et al. 2020 ). Li et al. developed an ANN approach to estimate unknown eco-indicators for missing environment impact information for several vital materials used in electronic products and integrate recycling scenarios in LCA (Li et al. 2008 ). The result showed that the ANN-based approach was accurate enough in forecasting the missing materials. Kaab et al. employed two ANNs and an adaptive neuro-fuzzy inference system (ANFIS) model for predicting LCA and output energy of sugar cane production (Kaab et al. 2019 ). Romeiko et al. presented a model for estimating LCA spatially at the county scale, with corn production developed by applying ANN (Romeiko et al. 2020a ). For the cost estimation of a product’s life cycle in the product design process, Leszczyński and Jasiński used ANNs and compared them with a parametric estimation (Leszczynski and Jasinski 2020 ). Marvuglia et al. developed an automatic selection strategy using combinations of a General Regression Neural Network (GRNN) and a set of linear models, based on partial least squares (PLS) regression for USEtox factor. The authors found that linear models have lower predictive power (prediction of toxicity factors) compared to GRNN nonlinear model (Marvuglia et al. 2015 ; Barros and Ruschel 2021 ). Song et al. developed ANN models to estimate the LCA of chemicals in the market. Using molecular structure information, they trained multilayer ANNs for life cycle impacts of chemicals using six impact categories. The application domain (AD) of the model was estimated for each impact category within which the model exhibits higher reliability (Song et al. 2017 ). Also, Song continued an attempt to harness the power of ML techniques to address the data deficiencies in LCA and an ANN, and Random Forest predictive models were developed to estimate approximate life cycle impacts of chemicals (Song 2019 ). Li et al. used nine molecular fingerprints to describe pesticides, binary and ternary classification models constructed to predict aquatic toxicity of pesticides via six machine learning methods: ANN, Naïve Bayes (NB), K-Nearest Neighbours (KNN), Classification Tree (CT), RF and SVM (Li et al. 2017 ). Amini Toosi et al. explored the possibility of an ANN-based LCA model for the conceptual design phase by classifying products according to their environmental and product characteristics. The product classification ultimately identified was used to create classification schemes with the C4.5 decision tree algorithm. An ANN-based approach with product attributes as inputs and environmental impact drivers as outputs were developed to predict the approximate LCA of grouping members. The predicted results seemed to be satisfactory (Seo et al. 2005 ). Cornago et al. introduced a model which resembles the deep neural network (DNN) to forecast the hourly day-ahead electricity consumption in an LCA aware scheduling system. This information allows to schedule the production to minimize the LCA impacts relative to the electricity consumption. (Cornago et al. 2020 ). Understanding and developing the LCA of activated carbon produced from diverse biomass feedstocks is critical and time-consuming for biomass screening and process optimization for sustainability. Liao et al. addressed this problem by developing a high accuracy ANN model and kinetic-based process simulation to estimate primary energy consumption and GHG emissions across various woody biomass (Liao et al. 2020 ). Nabavi-Pelesaraei et al. used historical data to predict future agricultural energy, and they showed that agricultural energy output and its LCA could be readily predicted by ANN (Nabavi-Pelesaraei et al. 2018 ). Sousa et al. proposed an ANN model using product attributes, which are characteristics of product concepts, and environmental inventory data from pre-existing LCAs. The product design team then use the new high-level attributes to obtain LCA for a new quickly product concept (Sousa et al. 2000 ). Also, Sousa and Wallace developed an ANN-based learning surrogate in approximate LCA of product design concepts (Sousa and Wallace 2006 ). Kleinekorte et al. proposed a predictive LCA framework of chemicals using ANN networks. The results show that the proposed. ANN was able to predict whether a technology change has the potential to reduce climate change impacts (Kleinekorte et al. 2019b ). Park and Seo proposed a BPNN model for an approximate LCA for the conceptual design phase by classifying products according to their environmental and product characteristics. For approximate LCA, the product attributes and environmental impact drivers (EID) were identified to predict the environmental impacts of products. The results showed BPNN is more accurate than multiple regression analysis in the prediction of the results of LCA (Park and Seo 2003 ). Milczarski et al. applied ANN to validate the production process’s quality and parameters in the food processing industry (Milczarski et al. 2020 ).

3.2 Support vector machines

Support vector machines (SVMs) have been scarcely involved in LCA. SVM is an ML algorithm based on a theory proposed by Vapnik called the statistical learning theory. It has proven to have unique advantages when working with smaller samples, nonlinear and high dimensional pattern recognition and can also be used in conjunction with other ML problems such as function fitting. SVM aims to solve the optimization problem and to find the optimal classification hyperplane in the high-dimensional feature space in order to work with complicated data classification (Cortes and Vapnik 1995 ).

At the buildings level, Shan et al. explored ML-based electroencephalogram (EEG) methods in the human-computer interaction domain for a potentially more accurate and objective human-building interaction. The machine learning-based EEG methods can be the primary feedback mechanism of wellbeing and performance to the building life cycle platform. Linear discriminant analysis (LDA) and SVM machine learning classifiers were demonstrated. Together with EEG indices, these two ML-based EEG methods can be the primary feedback mechanism of wellbeing and performance to the building (Shan et al. 2017 ). Liu et al. proposed a methodology that couples multiobjective optimization and SVM and decision tree classifiers to extract design heuristics (Comfort temperatures, etc.). The methodology has been demonstrated on sustainable residential system design via Techno-Ecological Synergy in LCA (TES-LCA) methodology (Liu and Bakshi 2018 ).

At the districts and cities level, Perrotta et al. presented the application of SVM to fuel consumption modelling of articulated trucks for a large dataset. Again, SVM demonstrated a good level of accuracy (Perrotta 2017 ).

At the level of miscellaneous , Hou et al. compared the performance of SVM beside other ML models with the performance of the Ecological Structure-Activity Relationships (ECOSAR) model. This is proven to be the best model among several existing aquatic ecotoxicity QSAR tools and linear regression models for estimating HC50 values of chemicals based on their physical-chemical properties and their classification of the mode of action (Hou et al. 2020 ). Pradeep Kumar et al. developed an SVM model to delineate vanadium-derived strengthening effects in HSLA steels in the field of production. In addition, they created a ML model to predict the yield strength of V-HSLA steels. Materials savings are translated to embodied energy and carbon savings using LCA databases in a life cycle inventory process, subtracting the costs incurred in the production of vanadium feedstock (Pradeep Kumar et al. 2021 ). Li et al. used SVM to predict the aquatic toxicity of pesticides and develop a tool for an early evaluation of aquatic pesticide toxicity in environmental risk assessment. They found that SVM exhibited high accuracy (Li et al. 2017 ). Romeiko et al. compared the SVM and Gradient Boosting Regressor (GBR) model for estimating spatially explicit life cycle global warming and eutrophication, with corn production. The results indicated that the GBR model built with monthly weather, features yielded higher predictive accuracy for life cycle, global warming impact, and life cycle EU (Romeiko et al. 2019 ). Milczarski et al. applied SVM, ANN, RF, KNN and C4.5 to validate the production process’s quality and its parameters in the food processing industry. The results showed that using the RF algorithm had the best results of processes classification (Milczarski et al. 2020 ).

3.3 Random forest

Random forest is a type of supervised learning algorithm. It is a collection of decision trees, each trained with the “bagging” method. The principle of the bagging method is that combining learning models can improve the outcome (Breiman 2001 ). This ML algorithm has been relatively well-used in the LCA due to its high predictive accuracy and its built-in variable importance measures (Hou et al. 2020 ; Hou 2019 ).

At the buildings level, Xikai et al. applied RF beside three regression techniques to develop regression models of carbon emissions to predict designing factor during the building’s life cycle (Xikai et al. 2019 ). Frömelt used RF, KNN and LASSO-Regression to attribute missing water supply, electricity, and heating information. The predicted data were then converted to quantities using price data. Household budget survey finds the existence of similar socio-economic household archetypes in consumption. These archetypes diverging from general macro-trends suggest that the proposed approach may be beneficial in improving understanding of consumption and informing policymakers’ future decisions for impactful environmental measures targeting specific consumer groups (Frömelt et al. 2018 ; Frömelt 2018 ). DeRousseau et al. applied RF as the best method between various ML methods like regression models and for predicting concrete compressive strength for field concrete mixtures given the model performance metrics in the field of concrete mixture design optimization (DeRousseau 2020 ).

At the districts and cities level, Perrotta et al. developed an RF model beside other ML algorithms to investigate the fuel consumption prediction of large fleets of trucks based on truck telematics and road geometry and condition data. The study also shows that although all three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN (Perrotta 2017 ).

At the miscellaneous level, Cheng et al. assessed the impacts of different combinations of feedstocks and pyrolysis conditions on climate change, energy, and economic performance. First, they built an RF model to predict the yields and characteristics of biochar for selected feedstocks at varied pyrolysis conditions. Then, they applied LCA and financial analysis to RF model outputs to determine GWP, energy return on investment (EROI), and minimum product selling price (MPSP) of biochar (Cheng et al. 2020a ). Also, Cheng et al. evaluated the energy, climate change, and economic performance of slow pyrolysis of multiple feedstocks under various processing conditions via the integration of RF, LCA, and financial analysis. The results showed this integration is helpful for efficiently evaluating many possible pyrolysis systems producing biochar to sequester atmospheric CO 2 (Cheng et al. 2020b ). Also, Cheng et al. evaluates the feasibility of hydrothermal treatment (HTT) with carbon capture and storage (CCS) as energy-producing negative emissions technology (NET) and compares such system with traditional bioenergy with carbon capture and sequestration (BECCS) system. RF was developed to predict product yields and characteristics from HTT of various feedstocks. The model results were then integrated into an LCA model to compute two metrics EROI and GWP. Results showed that RF models had better prediction accuracy than regression tree and multiple linear regression models for HTT of feedstocks and predicted the mass yields of various products and the energy and carbon contents of biocrude and hydrochar (Cheng et al. 2020a ). Rojek and Dostatni used RF beside some ML methods as modelling tools supporting selecting materials in ecodesign (Rojek and Dostatni 2020 ). Gu developed an LCA model to reduce the life cycle environmental impacts of metal-organic frameworks; he combined a conventional LCA with RF and yielded some preliminary heuristics for sustainable design of metal-organic frameworks with some life cycle impact (Gu 2018 ). Beyond LCA, Hou developed an RF model in chemical risk management to predict the ecotoxicity of new chemicals or as a screening process to identify chemicals with high predicted ecotoxicity potential to further test in priority (Hou 2019 ). Milczarski et al. applied RF to validate the production process’s quality and its parameters in the food processing industry. The results showed that using the RF algorithm had the best results of processes classification (Milczarski et al. 2020 ).

3.4 Hybrid and ensemble ML techniques

The use of ML methods, including singles, ensembles, and hybrids, have been dramatically increasing. Hybrid methods combine at least two ML and soft computing methods to achieve superior outcomes. Ensemble methods use a series of ML classification trees as opposed to one. By doing so, the accuracy of the model is significantly increased. Ensemble methods are categorized as supervised learning algorithms. Ensemble methods increase the training. The ensemble method allows for different training algorithms, making training more flexible. Kishk et al. proposed an integrated life cycle costing (LCC) that utilizes statistics, fuzzy set theory, and ANNs to deal with incomplete information, human judgment, and uncertainty. The authors claim that these models should also provide estimates from different levels of data, and information availability (Kishk and Al-Hajj 1999 ).

At the buildings level, Feng et al. developed a quantitative method using fuzzy C-means clustering and an extreme learning machine (FCM-ELM) for assessing buildings’ environmental performance in early decision stages, considering uncertainty associated with complex design decisions. The results show that the model is at least as reliable and accurate as the Monte Carlo methodology (Feng et al. 2019 ). Also, Feng developed an LCA method that integrated discrete-event simulation and process-based LCA using the Bayesian regularization back-propagation neural networks (BRBNN), RT, ensemble learning (EL) and ELM algorithms to extract knowledge about the relationships between construction planning and project performance (Feng 2020 ). Azari et al. proposed a hybrid ANN and GA approach as the optimization technique to explore optimum building envelope design concerning energy use and LCA in a low-rise office building. The categories within the LCA were global warming, acidification, eutrophication, smog formation, and ozone depletion (Azari et al. 2016 ). In the context of building material properties, Shi et al. considered a systematic method derived from LCA theory to analyze the green performance of construction materials. The authors proposed a BPNN and GA-BP hybrid algorithm to evaluate green building materials. They showed that with BPNN, the GA-BP hybrid algorithm is favourable for selecting green building materials and achieves higher accuracy (Shi and Xu 2009 ). Wang et al. introduced a Markov chain based stochastic approach and an ANN model to project periodic energy consumption distribution for each joint energy state of building condition and temperature. Comparing the traditional deterministic model and the developed model shows that the proposed model improved the result (Wang and Shen 2013 ). Duprez et al. combined Sobol Sensitivity Analysis (SA) and an ANN to building LCA. The Sobol method displayed satisfactory results with the computation of quantitative indices. SA was used in the ANN training, and the subsequent model predicted the GWP of new design alternatives. It was able to do this in a time-efficient manner and with a coefficient of determination higher than 0.9 (Duprez et al. 2019 ).

For miscellaneous uses, Kleinekorte et al. proposed a fully automated framework, including selecting suitable subsets of descriptors, called feature selection and optimization of the network architecture. They used a GA to determine the optimal network architecture and an ANN to predict the environmental impact for a given chemical. The results show that the environmental impact is expected correctly, and the framework can serve as an initial screening tool for identifying environmentally beneficial process alternatives (Kleinekorte et al. 2019a ). Lysenko et al. proposed a method that the gradient-boosted classifier tree ensemble model (GBM) is chosen for the small number of positive (toxic) drugs in a training dataset with missing values. The ML leverages the identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity (Lysenko et al. 2018 ). Li et al. introduced a modular Scorecard-based LCA architecture with a Bayesian Network (BN). The energy consumption is assessed by an overall modular Scorecard-based LCA architecture embedded with a BN energy prediction model. Seo and Kim proposed a hybrid GA and NN model for an approximate LCA. The GA was employed as an optimization method of relevant feature selection, determining the number of hidden layers and processing elements. For approximate LCA, the product attributes and environmental impact drivers (EID) were identified to predict the environmental impacts of products (Seo and Kim 2007 ). The results show that the hybrid model improves the prediction accuracy of the BN model, and the BN is suitable for small data sets (Li et al. 2017 ). Zhou et al. proposed integration of ANN with GA to optimize the multiobjective function of material selection in product design considering LCA (Zhou et al. 2009 ).

3.5 Other types of ML

Slapni et al. presented a framework that used Weka 3.6.10, a JAVA program package for machine learning algorithms to predict the missing characterization factors (CFs) in environmental interventions to reduce deviation from the European Union normalization factors (EU NFs) and a nominated reg regional NFs to calculate LCA (Slapnik et al. 2014 ).

At the buildings level, Duprez et al. proposed a method for predicting GWP of building design alternatives with a high coefficient of determination. The authors used MLR, SVR and ANN. MLR and SVR performed poorly when predicting new values as they could not cope with complexity as for MLR or were prolonged as for SVR models. (Duprez et al. 2019 ). Xikai et al. presented a study on the regression model of carbon emissions in residential buildings using designing factors. Four regression techniques, Principal Component Analysis (PCR), RF, MLP and SVR, were used to develop regression models, and the results show that SVR had the optimal predictive power (Xikai et al. 2019 ). Shan used LDA and SVM machine learning classifiers to established EEG based methods to improve human-building interaction in the indoor environment and use them in a building LCA platform (Shan et al. 2017 ). Płoszaj-Mazurek et al. introduced a study of regenerative design guidelines for parametric modelling of building designs with calculated total Carbon Footprint. They used the GBR model to predict optimal building features and the CNN to predict the total carbon footprint of a building design based on fundamental building features and the urban layout. The results of multicriteria analyses showed relationships between the parameters of buildings and the possibility of introducing carbon footprint estimation and implementing building optimization at the initial design stage (Ploszaj-Mazurek et al. 2020 ). Østergaard used an MLR model to estimate more accurate lifespans, which can help to reduce the uncertainty of sustainability assessments of buildings in LCA. The regression model proved to estimate the lifespan with lower errors than the general approach relying on a single fixed value for all building locations, uses and building materials (Østergaard et al. 2018 ). Feng developed an LCA method that integrated discrete-event simulation and process-based LCA. The BRBNN, RT, EL and ELM algorithms were used to extract knowledge about the relationships between construction planning and project performance (Feng 2020 ).

At the districts and cities level, Alam used multiple linear regression, polynomial regression, decision tree regression and support vector regression models using calculated CO 2 emission as a response variable for the LCA model for different phases of the pavement life cycle. The models determined the significant contributors and quantified the CO 2 emission in pavement material production, initial construction, maintenance and use phase; they found that SVM and ANN performed better than other methods (Alam 2020 ). Renard et al. developed a reinforcement learning (RL) decision support tool that minimizes the global warming impacts of a pavement system over its life cycle. Renard et al. presented an approach to LCA modelling that implements a reinforcement learning algorithm called Q-learning, which helps decision-makers account for several sources of uncertainty in pavement infrastructure (Renard et al. 2021a ).

In the context of miscellaneous applications, Romeiko et al. used the boosted regression tree (BRT) model to identify the leading contributors among soil, weather, and farming practice parameters affecting the life cycle impacts in Soybean Production. The authors used a combination of Environmental Policy Integrated Climate and process-based LCA models to quantify life cycle GWP, EU and acidification (AD) impacts. BRT has been used in discovering the driving factors for spatial and temporal trends in transportation, public health, and other disciplines (Romeiko et al. 2020b ). Also, Romeiko et al. compared the predictive accuracies of SVR, linear regression (LR), ANN, gradient boosted regression tree (GBRT), and extreme gradient boosting (XGBoost) for estimating spatially explicit LCA at the county scale, with corn production in a case study. The results indicated that the GBRT model yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts (Romeiko et al. 2020a ). Bui and Perera proposed a decision support framework comprising the life cycle cost analysis and advanced data analytics based on Gaussian Mixture Models (GMM) with the expectation-maximization (EM) algorithm for data clustering. GMM is a case of an unsupervised learning algorithm in which GMM is a probabilistic-model technique for distributing data into different clusters by Gaussian distributions. This framework prepared an intelligent decision support tool for ship owners to achieve optimized vessel performance and comply with stringent environmental regulations (Bui and Perera 2020 ). Hamrol et al. presented an integrated eco-design of products and technological processes, ensuring the appropriate selection of materials and connections from the point of view of recyclability. The method was implemented in an expert system using the classification method decision tree induction as the classification method. The expert system offers a practical solution that makes it possible to change material or connection without consulting the product designer. Moreover, it is consistent with concurrent engineering design (Dostatni et al. 2018 ). Hou et al. used KNN, SVM, ANN, RF, Adaptive boosting (AdaBoost) and Gradient boosting machine (GBM) for estimating HC50 values of chemicals based on their physical-chemical properties and their classification of the mode of action. Among the machine learning models, RF had the best predictive performance (Hou et al. 2020 ). Cheng et al. used the MLR, regression tree (RT), and RF to predict product yields and characteristics from HTT of various feedstocks. The model results were then integrated into an LCA model to compute EROI and net GWP. Results showed random forest models had better prediction accuracy than regression tree and multiple linear regression to model HTT of feedstocks (Cheng et al. 2020a ). Rojek and Dostatni compared the effectiveness of RBF networks, Kohonen networks, and RF as modelling tools supporting selecting materials in ecodesign and showed that ML methods effectively supported selecting materials in ecodesign. The study has proven ML methods to be highly useful and effective in selecting materials in designed products (Rojek and Dostatni 2020 ). Gu used a built-in decision tree model (ID3) package coupled with conventional LCA to speed up understanding metal-organic frameworks based via connecting the LCA results with ML technique (Gu 2018 ). Lee et al. developed a rapid predictive model to quantify life cycle GW and eutrophication (EU) impacts of corn production using the BRT model to estimate future life cycle environmental impacts of corn production (Lee et al. 2020 ). Nabavi-Pelesaraei et al. conduct energy output and environmental impact prediction of paddy production on ANN and adaptive neuro-fuzzy inference system (ANFIS). According to the results, multi-level ANFIS is chosen as a better model than ANN models due to higher computation speed, and higher accuracy (Nabavi-Pelesaraei et al. 2018 ). Ma and Kim (Ma and Kim 2015 ) presented an algorithm, predictive usage mining for life-cycle assessment (PUMLCA). This displayed a higher forecasting accuracy when data had complexity. Through modelling usage patterns, trend, seasonality and level, predictive LCA was performed for agricultural machinery in real-time. This showed an accurate estimate of environmental impact (Barros and Ruschel 2021 ). SAAB presents a proposed LCA calculator for implementing an efficient LCA computation; they used Spark MLlib, a library built on Apache Spark, MPI and OpenMP for LCA algorithms. The results showed that the combination of MPI/OpenMP provided much better performance for computing algorithms than Spark MLIB in LCA (Saab 2019 ). Abdella et al. presented a framework integrating the economic input-output LCA with logic regression and k-means clustering to deal with multiple decision-making units in food consumption categories and sustainability indicators (Abdella et al. 2020 ). Olafasakin et al. developed a Kriging-based reduced order model (ROM) to predict pyrolysis yields of feedstock samples based on the output of a detailed chemical kinetic pyrolysis mechanism for assessing the costs and emissions of a pyrolysis biorefinery (Olafasakin et al. 2021 ).

KNN classification is one of the most fundamental and straightforward classification models in traditional supervised learning. Consequently, it is often one of the first choices for a classification study when it is tiny or no prior knowledge about the data distribution (Peterson 2009 ; Frömelt et al. 2018 ; Hou et al. 2020 ). Hou et al. proposed three data-driven frameworks to estimate the missing data in LCA. The results show that KNN models have better prediction performance than ECOSAR and linear regression models for estimating some parameters for chemicals in USEtox (Hou et al. 2020 ). Serajiantehrani used KNN and MLR, decision tree regression, and gradient boosting regression methods for the complete construction and environmental costs of trenchless cementitious spray-applied pipe linings, cured-in-place pipe with polyester resin, and sliplining with high-density polyethylene pipe methods by evaluation and analysis of the construction and environmental costs based on the actual data. The results show that Multi-linear Regression had the optimal predictive (Serajiantehrani 2020 ). Milczarski et al. applied KNN and C4.5 to validate the production process’s quality and parameters in the food processing industry (Milczarski et al. 2020 ). Romeiko et al. compared the SVM and GBR model for estimating spatially explicit life cycle global warming and eutrophication, with corn production. The results indicated that the GBR model built with monthly weather, features yielded higher predictive accuracy for life cycle, global warming impact, and life cycle EU (Romeiko et al. 2019 ).

In this section, applying ML in LCA is explored, and the current state of the art reported in the literature is identified to answer the above questions. ML techniques tailored to LCA and specific AI techniques that can advance LCA’s establishment and delivery of the smart technology are investigated. Table 2 shows details of the literature survey on ML methods in LCA. The papers are divided into three types of prediction impact (PI), decision making (DM) and literature review (LR). The applied method in each paper is identified, and the scale at which the model was applied is shown. The majority of studies identified in this review were for impact prediction, but many had multiple objectives and often incorporated decision making. Many niche applications were also found in this review and the discussed studies show the adaptability of ML techniques for LCA.

4 ML and optimization in LCA

In its most basic form, LCA does not always include a systematic way of optimizing alternatives for environmental impacts mitigation. Combining LCA with ML methods may be a useful way of generating optimized process alternatives as part of an LCA (Wallace et al. 2014 ). ML models can perform faster and with lower storage requirements when estimating model outputs than other traditional process-based models. They are also more flexible when being integrated into other processes and simulation platforms. These allow ML models to attempt more runs of a simulation and achieve better outcomes for a range of computationally demanding tasks. These include optimization, prediction, and validation. Also, ML models can be fine-tuned by altering trainable parameters through an optimization procedure. LCA can be used to assess technological solutions from an environmental perspective. In conjunction, ML can be used as an optimizer alone or combined with other optimization algorithms to find the best solution according to constraints in LCA.

Luque et al. developed a conceptual framework for the integration of AI and LCA. The study focused on the sensorization of industrial plants and the treatment of data through ML algorithms in the field of sustainability optimization (Luque et al. 2020 ). Ziyadi et al. developed an ML surrogate model to perform direct Monte Carlo sampling as well as indirect nonlinear optimization to provide grounds for objective model uncertainty analysis for LCA applications (Ziyadi and Al-Qadi 2019 ).

At the buildings level, Sharif and Hammad developed an ANN model to analyze renovation scenarios to minimize total energy consumption in LCC and LCA. They developed a set of data to represent renewal scenarios from results obtained by Simulation-Based Multi-Objective Optimization (SBMO). ANNs were developed as surrogate models of actual computationally complex buildings. The computational time saved with the use of the proposed substitute models was found to be significant (Cornago et al. 2020 ). Also, Sharif proposed a simulation-based multi-objective optimization model for optimizing the selection of renovation scenarios for existing buildings by minimizing total energy consumption (TEC) considering LCA. Furthermore, he developed a surrogate ANN for selecting near-optimal building energy renovation methods; and developed deep ML Models (MLMs) to generate renovation scenarios considering TEC and LCC (Arani 2020 ). Azari et al. used a multi-objective optimization algorithm to explore ideal building envelope design by analyzing energy use and LCA of office buildings. Their approach combined an ANN and GA to find the optimal design (Azari et al. 2016 ). Feng developed an LCA method that integrated discrete event simulation and process-based LCA. The optimization method achieved real-time environmental optimization by introducing ML methods into simulation-based optimization. Płoszaj-Mazurek et al. applied the CNN method to optimize the carbon footprint of buildings in regenerative architectural design. The BRBNN, RT, EL and ELM algorithms were used to extract knowledge about the relationships between construction planning and project performance (Feng 2020 ). The results show ML methods could be a research tool for exploring vast design spaces in the field of sustainable architectural design (Płoszaj-Mazurek et al. 2020 ; Płoszaj-Mazurek 2020 ). Renard et al. implemented a Q-learning to optimize a pavement construction and maintenance plan to minimize the expected global warming impact of a pavement facility (Renard et al. 2021a ). Liu et al. proposed a methodology that couples multiobjective optimization and ML to extract design heuristics (comfort temperatures and other related parameters). The methodology has been demonstrated on sustainable residential system design via TES-LCA methodology (Liu and Bakshi 2018 ).

At the districts and cities level, Chen et al. used multi-agent deep reinforcement learning to optimize dissolved oxygen and chemical dosage in water treatment plants. The outcome was designed from an LCA perspective to achieve sustainable optimization. They showed that the optimization based on LCA had results that achieved lower environmental impacts compared to the baseline scenario (Chen et al. 2021 ). Abokersh et al. developed a multiobjective optimization framework using an ANN model comprising the Bayesian optimization approach; assisted sensitivity analysis. ANN method was used to inherent sustainability principles in the design of solar assisted district heating in different urban sized communities in an optimization framework (Abokersh et al. 2020 ). DeRousseau et al. examined the various problem formulations commonly seen in concrete mixture design optimization that can necessitate models based on the linear combination, statistics, ML, and physics. They used ML methods for predicting the compressive field strength of concrete (DeRousseau et al. 2018 ; DeRousseau 2020 ).

For miscellaneous uses, Zhou et al. proposed integration of ANN with GA to optimize the multiobjective of material selection in product design considering LCA (Zhou et al. 2009 ). In the context of decision-making support for LCA, Marvuglia et al. presented an evaluation of two different grouping techniques for categorizing materials based on their environmental performance. The agglomerative clustering technique and self-organizing map helped distinguish variables that could be used to establish classes of materials using their environmental performance (Marvuglia et al. 2015 ). The authors implemented GRNN and a set of linear models based on PLS regression, hoping to develop an automatic selection strategy of the critical variables according to the modelled output (USEtox factor) (Marvuglia et al. 2015 ; Barros and Ruschel 2021 ). Cornago et al. proposed an LCA aware scheduling framework, in which a production schedule is optimized with a lower environmental impact using predicted the hourly day-ahead electricity consumption by a DNN model (Cornago et al. 2020 ). Romeiko et al. presented a model for estimating LCA spatially at the county scale in corn production. This was developed by applying ML methods that could be used for corn supply chain optimization, corn-based biorefinery siting, and feedstock landscape optimization (Romeiko et al. 2020a ).

Figure 4 represents the general relation of ML methods in the field of Optimization in LCA in the reviewed researches.

ML and optimization methodology in LCA

5 Results and discussion

This paper collaborated a literature survey to determine the use of ML techniques for LCA by answering the research questions. Gaps in research for ML in LCA were identified to guide future research. In the following sections, the highlights of reviewed papers and the limitation of using ML methods in LCA will be discussed.

5.1 Limitations of ML methods in LCA

Based on reviewed papers, the limitations of ML methods in LCA are;

LCA and training powerful analytical models with ML are expensive and depend on large amounts of hand-crafted, structured training data. Computational cost and training time in ML methods are other important parameters related to the accuracy of outputs. The researchers should try to reduce the computational cost by reducing the dimensions of data sets and keeping the accuracy and validation of the results in good time.

Some ML models, known as black-box models such as DNN, RF and SVMs, are exceedingly complex and make it very difficult to predict how they will perform in a specific context. Similarly, their users may not be able to review and understand the recommendations given by these models for intelligent systems.

Early design stages often are limited in detailed information, which is typically required for thorough assessments and thus need quick decisions on varying, numerous and loosely-defined concepts. These make the early use of detailed LCA impractical. For predictive modelling and experimental studies to be compatible, standardization of the conditions, experiments and reporting are needed in order to achieve consistency and to be reproducible.

Data-based approach is a method to fill in data gaps in LCA studies. It depends on the available data and how we choose to use it statistically, so we can recognize a good pattern from the data and make a prediction (Song 2019 ). Therefore, a large amount of data is required for an LCA while one of the key limitations on the application of ML algorithms side is a lack of high-quality and real-world-collected data sets.

5.2 Highlights of reviewed papers

The significant contributions of this paper are collaborating literature survey to determine use of ML techniques for LCA by answering the following research questions:

Applying ML in LCA is explored, and the current state of the art reported in the literature is identified to answer the above questions. ML techniques tailored to LCA and specific AI techniques that can advance LCA’s establishment and delivery of the smart technology are investigated. In this section, the results of the research are shown by figures and tables.

How has ML been used in LCA? Table 2 shows details of the literature survey on ML methods in LCA. The papers are divided into three types of prediction impact (PI), decision making (DM) and literature review (LR). The applied method in each paper is identified, and the scale at which the model was applied is shown. The papers included in this paper answer and support the above research question. In the included literature, many different applications at different scales were demonstrated to be beneficial in accurate and efficient LCA.

The associated heatmap, Fig. 5 , shows that ANN is the most commonly applied method at all three levels of categorisation in this paper, particularly at the buildings level and then at the miscellaneous level which includes a variety of niche applications. Hybrid techniques were the next most used ML method at the building and districts and cities level.

Heatmap of hit-points for each ML method

Figure 6 is a radar graph showing that the most common application of ML methods have been for predictions. Individually, NN was most commonly used, followed by hybrid methods. In response to the first research question, Figs.  5 and 6 display a list of included studies that supported the use of ML-based prediction methods to predict LCA accurately.

Radar graph showing the applications of ML methods in prediction and decision making

What is the role and efficacy of ML methods in Optimization in LCA? The results of this study show that the ML methods are capable of matching detailed LCA results and predicting missing data or trends of variables while staying within the accuracy of typical LCA. Furthermore, ML extends outside of LCA in processes such as data cleaning, predicting system output or performance, ecosystem informatics, and optimization. ML algorithms could also be applied in screening or cleaning data for LCI, estimating flow data for unit processes, improving the quality and quantity of data used to determine CFs, and can be used to generate optimized scenarios. They are especially suitable for supporting real-time decisions of construction environmental optimization. This study shows that the ML can be coupled with standard optimization methods to increase their capability of quickly exploring promising regions.

Can ML methods integrate and contextualize existing inventory databases to provide a sound basis to streamline the LCA? Many included studies in this review utilised pre-established databases in order to perform LCA. ML methods are capable of integrating these existing databases, although with all LCA the quality of the data and the nature of the database may have an impact on the quality of LCA. However, ML methods identified in this paper can be used to fill in gaps if pre-existing databases are partially complete.

Figure  7 shows predictors and outcomes of ML methods that have been used in LCA applications. Characteristics are shown as the most commonly used inputs. Impact categories were the most frequently assessed outcome of these applications.

Sankey diagram to show the relationship between the inputs and outcomes of ML methods in LCA

What are the gaps in research in order to guide future research for ML in LCA? Table 3 shows details of the literature survey on ML methods in LCA for different levels of the built environment. In this paper, the levels are categorized as buildings vs district & cities . For each level of the built environment, different categories of LCA are identified. The applications in which ML methods have been utilized have been marked with an asterisk. This is a roadmap for researchers in LCA who want to apply ML techniques to identify gaps in research. This paper has identified a significant gap in research in the ‘End of Life’ phase and ‘Benefits beyond the system, for buildings. These include demolition, disposal and transport, as well as recycling. At the districts and cities level, the most significant opportunities for ML in LCA research lie in the ‘Networks’ and ‘Open Spaces’.

The research questions posed in this paper were answered through this literature survey. In the included papers, authors claimed and displayed that ML can be applied to different aspects of the LCA and be a useful tool. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated into existing inventory databases to streamline the LCA across many use cases. However, ML-based techniques have been employed less for real-time monitoring and control of real-world LCA.

Future research should focus on using ML technologies in real-time applications to monitor, optimize, and control the built-environment systems. ML models may be more comprehensible than other black-box approaches due to their transparency. Furthermore, hybrid ML applications may expand on the benefits of ML models and overcome limitations to case-specific scenarios for optimizing LCA through their interpolation and extrapolation capabilities. Advanced stochastic metaheuristics should be used in refining ML model training parameters to maximize their accuracy and reliability.

6 Conclusion

LCA, when done successfully, provides a systems view of products systematically and quantitatively and can thus act as a decision support tool. It can then guide the design and give insights on areas for improvement and innovation. However, performing detailed LCA is expensive, time-consuming, and requires a large amount of data.

The ML methods in LCA have received considerable attention as countries are continuing and growing to address the importance and protection of the environment. The climate regulations have encouraged industries to apply LCA using various intelligent technologies. The rapid development of modern technologies, including sensors, information, wireless transmission, network communication, cloud computing, and smart devices have been led to an enormous amount of data accumulation. Therefore, LCA researches have adopted the opportunities made possible by the development of computational techniques and ML methods to improve predictive models. ML methods belong to the category of data-based predictive models and thus aims to use computational methods to allow an algorithm to find a meaningful pattern from an extensive data set.

The contributions of this paper are as follows:

This study presented a review of ML models utilized for LCA. It presented a thorough review and critical discussion of various ML technologies to solve function approximation, optimization, monitoring, and control problems in LCA research. Moreover, the advantages and disadvantages of using ML technologies in LCA are highlighted to direct future policymakers efforts in this domain.

The reviews show that if computational levels in LCA are divided into three categories, inventory, modelling and optimization, ML is most used at the inventory level for prediction and finding the missing data; and optimizing during the model simulation. The fundamental limitations and challenges faced by applying ML methods in LCA are model complexity and scenario uncertainty.

The review identifies that developing ML techniques, including predictive model control and optimization algorithms, can help the policymakers deliver actionable knowledge to inform various control strategies and corrective measures to reduce the gap between predicted and actual environmental impact. This review finds that ML methods can match the LCA results within the accuracy of typical LCA studies and correctly predict the trends.

This review has identified research gaps and given an overview of the progression in this field to aid researchers’ understanding of key concepts for applying ML in LCA. Future research should focus on using ML technologies in real-time applications to monitor, optimize, and control the built-environment systems. ML models may be more comprehensible than other black-box approaches due to their transparency. Furthermore, hybrid ML applications may expand on the benefits of ML models and overcome limitations to case-specific scenarios for optimizing LCA through their interpolation and extrapolation capabilities. Optimization uses can be particularly impactful in life cycle alternatives where the environmental impact of a process, product or system can be reduced. ML has the capability of being integrated as a real-time algorithm, assessing production or changes in processes and responding with potential alternatives. These can be less environmentally impactful and help decision-makers choose the optimum available options of the design, construction/production, facilities management, and demolition processes. Advanced stochastic metaheuristics should be used in refining ML model training parameters to maximize their accuracy and reliability. Nevertheless, ML may not be appropriate for every application and should be considered alongside the cost, length of time and delays which incur from some ML techniques. In the future, the integration of ML models within LCA may be commonplace following further research into applications such as utilizing access to dynamic data and providing detailed and accurate environmental impacts.

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Ghoroghi, A., Rezgui, Y., Petri, I. et al. Advances in application of machine learning to life cycle assessment: a literature review. Int J Life Cycle Assess 27 , 433–456 (2022). https://doi.org/10.1007/s11367-022-02030-3

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How to simplify life cycle assessment for industrial applications—a comprehensive review.

1. Introduction

• Which approaches exist to increase the practicability of the LCA calculation of complex products and product portfolios consisting of a large number of components?
• How can these simplification approaches be categorized and what is their respective benefit for the LCA calculation?
• Which tools are available that can facilitate the assessment?

2. Methodology

2.1. selection of search categories, 2.2. literature review, 2.3. analysis of the search results, 3.1. parametric lca, 3.2. modular lca, 3.3. automation.

3.3.1. LCA Software

3.3.2. lci databases, 3.4. aggregation/grouping, 3.5. screening, 3.6. others, 4. discussion, 5. conclusions, 6. future prospects, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Kiemel, S.; Rietdorf, C.; Schutzbach, M.; Miehe, R. How to Simplify Life Cycle Assessment for Industrial Applications—A Comprehensive Review. Sustainability 2022 , 14 , 15704. https://doi.org/10.3390/su142315704

Kiemel S, Rietdorf C, Schutzbach M, Miehe R. How to Simplify Life Cycle Assessment for Industrial Applications—A Comprehensive Review. Sustainability . 2022; 14(23):15704. https://doi.org/10.3390/su142315704

Kiemel, Steffen, Chantal Rietdorf, Maximilian Schutzbach, and Robert Miehe. 2022. "How to Simplify Life Cycle Assessment for Industrial Applications—A Comprehensive Review" Sustainability 14, no. 23: 15704. https://doi.org/10.3390/su142315704

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• Published: 20 December 2022

Life cycle assessment of MycoWorks’ Reishi ™ : the first low-carbon and biodegradable alternative leather

• Ellie Williams 1 ,
• Katarzyna Cenian 1 ,
• Laura Golsteijn 1 ,
• Bill Morris 2 &
• Matthew L. Scullin 2  

Environmental Sciences Europe volume  34 , Article number:  120 ( 2022 ) Cite this article

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A Correction to this article was published on 29 May 2023

This article has been updated

Over the past few years, several alternative leather technologies have emerged and promise advantages over incumbent leathers with respect to sustainability despite most containing enough plastic to prevent safe and effective biodegradation. Of the alternative leathers in production or advanced development, few fit the dual criteria of low-carbon and near-zero plastic. Reishi ™ is a leather alternative, grown using MycoWorks’ Fine Mycelium ™ technology, with less than 1% polymer content and satisfies the same performance, quality, and hand feel as animal leather. We present here the first Life Cycle Assessment (LCA) of Reishi ™ , detailing its “cradle-to-gate” carbon footprint and broader environmental profile. The pilot- and full-scale production of 1 m 2 of post-processed, finished, and packaged Reishi™ both before and after production improvement implementations is modeled, and the environmental footprint impact assessment method is performed.

It was found that Reishi’s™ carbon footprint is as low as 2.76 kg CO 2 -eq per m 2 , or 8% of the value of the bovine leather benchmark modeled. Furthermore, it was found that Reishi ™ has a lower impact compared to bovine leather modeled across a number of impact categories, including eutrophication, ecotoxicity, human health effects, and others. Reishi’s ™ impact “hotspots” were determined, with the largest opportunity for further reduction being improved energy efficiency in the growth of mycelium, in particular, the process’s sterilization of raw material inputs via autoclave tools. It is also shown that MycoWorks’ passive process for growing mycelium has a carbon footprint two orders of magnitude lower than incumbent mycelium growth processes that actively consume carbon dioxide gas, which MycoWorks’ process does not require.

Conclusions

Reishi™ is shown to be a promising sustainable material through its unique combination of natural quality, low-carbon footprint as determined by this LCA, and biodegradability due to its lack of plastic or crosslinked content. Its manufacturing process is low impact even when produced at a scale of tens of thousands of square meters per year—a miniscule fraction of the billions of square meters of bovine leather already sold per year. With further use of this leather alternative, additional efficiency gains are likely to be realized.

The fashion and broader consumer goods industries are facing increasing pressure to reduce the environmental footprint of their products: to respond to consumer demand, to comply with evolving environmental regulations, and to meet shareholder- or industry-imposed targets, such as the Science Based Targets initiative [ 36 ]. Representative of this is the explosion in demand for both non-animal and non-plastic materials in consumer items, such as fashion goods [ 14 ], that has recently led to a proliferation of “alternative leather” technologies made from mycelium, cactus, grape, and various other “bio-based” materials. These alternative leathers are expected to comprise a US$2.2 Billion per annum market as early as 2026 [ 23 ]. While many of these alternative leather technologies promise advantages over incumbent leathers with respect to their carbon footprint, many still contain enough plastic to prevent effective biodegradation and therefore may offer little or no overall advantage over either animal or plastic leathers.

Leather’s importance is due to its unique position as both an esthetic and performance material. It is fundamentally an emotional product whose hand feel ("haptics”) and durability have invoked the definition of luxury by humans for thousands of years [ 38 ]. Today, “leather” is a blanket term for leather-like materials that can be characterized as either natural or plastic based on the primary constituents in the material. These constituents contribute to the materials’ structural (strength & durability) and haptic (hand feel) properties, the two main measures of a leather’s quality and performance, and there exists a wide range of properties achievable in leathers due to the various raw materials (cowhide, lambskin, polyurethane, etc.) used or a combination thereof. As such, the term “leather” encompasses a wide range of materials made from hides of various types of animals and/or made from plastics, such as polyurethane (PU) and polyvinylchloride (PVC).

Animal leathers are typically associated with a high carbon footprint and various other ecotoxicity, water, human health, and land-use impacts. Bovine leather, for example, is a by-product or co-product of the beef industry, which is a major source of greenhouse gas emissions [ 42 ], poses toxicity risks to humans and the environment due to high intensity of chemical use in the hide tanning process [ 8 , 16 , 18 ], and is a major source of water pollution with particular concern of Nitrogen and Chromium in effluent water from treatment and tanning [ 13 , 17 ].

Conversely, plastic-based leathers, such as “PU leather” (aka “Vegan Leather”), have a lower carbon footprint from production than bovine leather but yield adverse environmental impacts during and at the end of its lifecycle. They are dependent on fossil resources, their polymers take centuries to break down naturally, and their incineration releases toxic chemicals [ 16 , 32 , 40 ]. The branding of plastic leathers such as PU leather as “vegan leathers” is but a “red herring” that distracts from issues ranging from microplastic pollution to landfill and ocean accumulation, issues with such severe negative impacts that the United Nations recently resolved to end plastic pollution [ 41 ].

The global fashion holding company Kering estimates that PU leather has ~ 33% less impact than animal leather on a per-square-meter basis [ 5 ]; of course, an application requiring a 33% thicker PU leather would therefore nullify such an advantage, illustrating how such incremental improvements may find difficulty translating into macro-scale impact. Although the fashion industry has painted non-biodegradable and/or petroleum-based fabrics and leathers as more sustainable than their naturally derived counterparts, this position is becoming increasingly discredited as more thorough research is performed and more wholistic views are taken [ 39 ].

Today, virtually all “vegan leathers” in products available in the market are in fact made from polyurethane (PU) and/or polyvinylchloride (PVC), plastics that comprise a significant percentage of global landfill [ 11 ] and fall under a category of cross-linked polymers that cannot be readily recycled and so contribute to the 91% of plastic that is not recycled globally [ 30 ]. Plastic textiles, including “vegan leathers,” make up 36% of all global plastic waste [ 33 ]. In MacLeod et al.’s “The global threat from plastic pollution” published in Science [ 21 ], they state that “Potential impacts from poorly reversible plastic pollution include changes to carbon and nutrient cycles; habitat changes within soils, sediments, and aquatic ecosystems; co-occurring biological impacts on endangered or keystone species; ecotoxicity; and related societal impacts.” Furthermore, it was recently found that microplastic pollution, a by-product of plastic products, has become so ubiquitous that they are now present in Antarctic snow, further indicating that, like carbon emissions, plastics have the ability to negatively impact ecosystems globally even when used and emitted locally [ 2 ].

The use of plastic in “vegan leathers” and their lack of biodegradability have become extremely important product attributes in the eyes of both consumers and climate scientists alike [ 33 ]. It is apparent that a wholistic solution—one that avoids the carbon emissions and cruelty issues of an animal hide while simultaneously avoiding the use of plastic—would offer opportunities in the consumer economy. As such, to have significant impact, an alternative leather must have a near order-of-magnitude or greater reduction in both carbon footprint and plastic content so as not to compromise on structural and haptic properties.

Of the alternative leathers currently known to be in production or advanced development, few fit the dual criteria of low-carbon and near-zero plastic; all of those that do are mycelium based. Conversely, however, not all mycelium materials coming to market are plastic free. Table 1 lists all the alternative leathers known to be in or near production, their description, and classification. It can be seen from the description of the materials in this table that many alternative leathers marketed as “plant-based” are, in fact, plastic based.

Note that many animal and alternative leathers, including all those listed under Natural Materials, use a very thin “aniline” or “semi-aniline” finish that contains an overall negligible amount of PU as these coatings tend to be less than 10 µm in thickness, i.e., less than ~ 1% of the material by thickness and mass. However, many leathers, including alternative leathers, such as Mylo ™ , contain a thick polyurethane layer that plays an essential role in the materials’ structural performance and becomes the primary haptic of the material (i.e., governs the hand feel). These thick layers can be in the range of 100–500 µm, or 10–50% of the material by thickness and mass, and are therefore most akin to “Bicast Leather” or “Bonded Leather:” inexpensive, down-market products which use animal hide that is either coated or mixed with Polyurethane, respectively [ 4 ].

The natural materials in Table 1 (i.e., those derived from mycelium and without a thick polyurethane layer applied to the surface) are those made by MycoWorks, Mogu, and Ecovative. The carbon intensity of production for each of these has not yet been published. The production techniques differ, where Ecovative’s Forager™ material uses an active growth process while both Mogu and MycoWorks’ Reishi ™ use a passive growth process. The active growth process involves pumping large amounts of CO 2 into the growth chamber because it is well understood that a high CO 2 environment promotes the growth of mycelium and prevents the growth of mushrooms [ 43 ]. The carbon footprint for these “mushroom leathers” produced using an active growth process is likely very high due to the combustion of fuels at the CO 2 production source, and in turn the release of this CO 2 into the atmosphere during their mycelium growth process [ 3 ]. This is in contrast to MycoWorks’ and Mogu’s passive growth process that consumes no gases.

Based on publicly available information regarding the Ecovative process that is used to produce both their Forager ™ material and the mycelium for Bolt Threads’ Mylo ™ material [ 43 ], the consumption of CO 2 is so high as to potentially exceed that of animal leathers by an order of magnitude (a calculation for this is presented in the SI). As such, it appears that only two possible candidates currently exist for both a low-carbon and biodegradable material with a natural haptic: mycelium materials from both MycoWorks Inc. and Mogu. While the carbon footprint of Mogu has not yet been published, the carbon footprint for MycoWorks’ Reishi™ is detailed herein.

Reishi ™ , a relatively new biomaterial produced solely by and proprietary to MycoWorks Inc. (California, USA), is an alternative leather that has both a substantial reduction in carbon footprint while using no plastic as a structural base or haptic, in addition to having other environmental benefits, as we will demonstrate in this paper. Reishi ™ compares in quality, hand feel, and performance to animal leathers [ 26 ] and has been adopted by leading global luxury brands, demonstrating its capability to have impact. Reishi ™ is created with Fine Mycelium™, a biotechnology platform invented by MycoWorks to engineer mycelium (the thin fibrous network which makes up the fungal organism) into custom-grown, made-to-order materials. Fine Mycelium™ also refers to the material created via this proprietary process, which undergoes dyeing, lubricating, and finishing processes to be made into Reishi ™ , a leather-like product [ 28 ].

While the structural composition of Reishi ™ as non-crosslinked, pure biomass material means it is biodegradable in nature, whether it meets the low-carbon criteria was not yet published. A Life Cycle Assessment (LCA) has been performed on Reishi ™ , outlined below, to determine whether it meets this dual criteria essential for low-impact alternative leathers. Biodegradable fungi-based materials do not rely on livestock farming, and require minimal fossil resources and natural land, thereby suggesting a material with a favorable environmental performance is possible if no substantial CO 2 is required during the growth of such fungi [ 12 , 16 , 34 ]. As outlined herein, active-gas mycelium growth processes require additional fuels to meet high CO 2 input requirements, whereas MycoWorks’ passive mycelium growth process results in a drastic reduction in total emissions. Through this analysis, these ostensible advantages of Reishi ™ have been thoroughly challenged, and the detailed manufacturing process for the material has been scrutinized for its environmental impact.

The primary aim of this research is to assess the environmental footprint of Reishi ™ as a material and compare how it performs in different production-scale scenarios.

In this study, a Life Cycle Assessment (LCA) following the ISO14040/14044 standard is used to quantify the environmental footprint of Reishi ™ . Life Cycle Assessment is a standardized methodology which compiles the inputs and outputs of a product system and evaluates its potential environmental impact [ 15 ]. Life Cycle Assessment can be used to quantify the impact of a product on a number of impact categories, such as water scarcity, ecotoxicity, land use, eutrophication, and climate change. Climate change is considered one of the most pressing environmental issues of our time and is also a good predictor for several other environmental impacts [ 1 ]. Accordingly, quantifying the carbon footprint of a product (i.e. the impact on climate change) is seen as an entry point for assessing environmental performance [ 45 ]. Additional impact categories are also presented.

It should be noted that current LCAs do not account for potential risks and impacts from emissions of micro- and nano-plastics in the environment [ 22 ]. Therefore, further contemplation of other factors, such as biodegradability as presented in the introduction above, helps present a comprehensive environmental performance of Reishi ™ . The following sections detail the product system evaluated and the technical specifications of the study.

Product under study

Reishi™ is a custom-made biomaterial that compares in quality, performance, and hand feel to high-quality animal leathers [ 27 ]. Fine Mycelium™ is the underlying technology in Reishi ™ and engineers mycelium cells into three dimensional structures that are densely entwined to result in enhanced strength, durability, and haptics compared to naturally occurring mycelium or “mushroom leather” (see Figs.  1 ,   2 ).

Visual representation of the cross section of Fine Mycelium without (left) and with (right) an embedded fabric material

Simplified visual representation of the Reishi ™ production process

MycoWorks produces Reishi ™ tailored with an in situ embedded fabric material or with no fabric at all. There is a range of fabric materials which can be chosen to be embedded within Reishi ™ . The fabric material (also known as the “structural base addition”) is placed in the tray, and the mycelium grows to fill into the fabric and then above it, thereby embedding it within the mycelium (as shown in Fig.  1 ).

As an additional option, the mycelium can grow to an adequate strength and robustness with no added textile as the structural base addition. For the sake of this research, three material variations were modeled (where the material input is the only variable changing and all other processes are held constant):

mycelium with cotton (100% cotton material embedded in mycelium)

mycelium with recycled polyester (100% recycled non-woven polyester embedded in mycelium)

mycelium only (no fabric material embedded within the mycelium as a structural base, using solely mycelium).

Note that for each of these model variations, the only aspect which varies is the material for the embedded fabric material, with all other inputs and outputs remaining the same.

The Fine Mycelium ™ process uses a natural feedstock, including waste sawdust as its food source, requires minimal water, and is grown with little-to-no light exposure in a facility climate controlled to room temperature.

Functional unit and reference flow

The functional unit describes the unit of analysis which the inputs and outputs are collected in reference to in a qualitative and quantitative way. The functional unit of this study is 1 m 2 of Reishi ™ , i.e., post-processed*, finished, and packaged leather-like** material with natural haptics, satisfying similar performance, quality, and hand feel as animal leather.

*In the case of Reishi ™ , this means dyed and lubricated.

**Refer to the Background section, where leather is defined as “a blanket term for leather-like materials that can be characterized as either natural or plastic based on the primary constituents in the material.”

The reference flow is the amount of product required to fulfill the functional unit accounting for product or material losses throughout the value chain, denoting that more than 1 m 2 of Reishi ™ is required to fulfill the 1 m 2 being available for sale.

System boundaries

In LCA, the system boundaries indicate which stages and activities of a product’s lifecycle are included in the assessment. Reishi ™ is considered an intermediate product with a large range of potential final uses. Consequently, the product system analyzed is cradle-to-gate, meaning it includes the processes from raw materials production and pre-processing through Reishi ™ leaving a tannery facility (Fig.  3 ). The main processes involved are the production of raw materials for sheet production, growth of the Reishi ™ sheet, transport from the production factory to the tannery, post-processing and finishing, and packaging of the final product.

Schematic overview of Reishi ™ production

Data collection

We collected primary foreground data on the material requirements for production and post-processing, transport distances of procured materials, energy and water usage for the production and tannery sites, waste disposal for the production and tannery sites, the site-specific electricity mix and heat source for the production facility, transport distance between production and tannery site, and packaging materials. Variations of these data are used in different scenarios as detailed below.

Ecoinvent 3.6 (allocation, cut-off by classification) was used as the main background database [ 35 ], with use of processes from Agri-footprint 4.0 (economic allocation) [ 9 ] when necessary. These two databases are considered to be compatible for use in combination; both databases use economic allocation (which is consistent with the allocation procedure used in the foreground system), and while some Agri-footprint processes use other data in the background (e.g., ELCD for electricity and transport), this was not the case for the processes used.

These databases include data on emissions, raw material inputs, technology, energy inputs, water, and transport for materials and processes. In instances where an appropriate secondary dataset was not available, proxies were used. Refer to Additional file 1 for supplementary details on the datasets used and Additional file 2 for the LCI file of the foreground system.

Capital goods were excluded from this analysis. Aligned with the PEF methodology which states that “Capital goods (including infrastructure) and their EoL [end of life] should be excluded, unless there is evidence from previous studies that they are relevant,” evidence for relevance was sought and not found. While this product system is within an emerging industry, there is no obvious argument for including capital goods in the analysis. Further, the leather Product Environmental Footprint Category Rules (PEFCR) does not identify capital goods as mandatory company-specific data [ 6 ], implying that capital goods are not among the most relevant processes.

Additional processes are also justified to be excluded from the system modeled. Production of packaging materials for procured raw materials were excluded due to their assumed negligible contribution to the product carbon footprint. Tertiary packaging was also excluded, given the very large quantities (thousands) of Reishi ™ sheets that can be shipped on a single palette. Energy use for office space and quality control was excluded to isolate the inputs associated solely with the production of Reishi ™ .

Modeling choices

Life Cycle Assessment modeling was conducted using SimaPro 9.0. In instances where multi-functional processes occur (i.e., processes or facilities providing more than one function), all inputs and emissions associated with the process were partitioned between the multiple goods and/or services. The only multi-functional process in the foreground system is the production process which produces the Reishi ™ sheet and spent substrate “brick.” As neither subdivision nor a system expansion was possible, economic allocation was used to divide the environmental impacts between the two outputs as successively justified. Consequently, the allocation procedure in the foreground system (economic) is consistent with the allocation procedure in the background datasets (economic).

For every sheet of Reishi ™ produced, the sheet is accompanied by a by-product “brick” of loosely bound substrate (sawdust) and mycelium, which is a biodegradable substance that has been used in agriculture as a soil amendment, among others. When the substrate brick is sold externally it has an economic value and is therefore considered a co-product. If mass allocation were used, a disproportionately high fraction would be attributed to the brick given its large mass and relatively low value. Consequently, economic allocation was chosen to allocate the environmental impact of production up until the point of sale of the brick between the two co-products. By choosing economic allocation, more of the impact is therefore assigned to the Reishi ™ sheet versus its co-product brick. To split the facility-wide usage between the processes occurring before and after the substrate brick is sold the time required for each production process was used.

For end-of-life allocation, the cut-off approach was used in the background and foreground data. This means that in instances where recycled material is used throughout the value chain the recycled materials come burden free, accounting for only the resources and emissions associated with the recycling process, including transportation.

End-of-life allocation is particularly relevant for the footprint of the version of Reishi™ produced with recycled polyester. In practice, the recycled polyester could be sourced from systems which consider it either as a recyclable material, as having economic value, or alternatively as a waste product. As a result, each system would require different modeling. The recycled polyester in this product system modeled is considered a recyclable material and is modeled accordingly with the cut-off approach. This means that the fabric impact includes only the processes associated with recycling virgin material into secondary material and not the production impacts of the virgin material.

For facility-wide energy usage, the impacts were allocated to the sheet and brick based on the operational capacity of the facility (e.g., when the operational capacity is at x sheets per day, this daily usage is divided between x sheets and substrate bricks).

Scenario modeling

Reishi ™ is currently being produced at pilot scale in California and will soon be produced at full scale at a production facility currently under construction in August 2022. Given Reishi ™ is a material still in its relative infancy, there are still several opportunities for reducing the product's environmental footprint. The Life Cycle Impact Assessment (LCIA) results from the initial LCA revealed some improvement opportunities that MycoWorks subsequently implemented in the pilot-scale facility and have designed into the full-scale facility.

To make a fair assessment on the imminent environmental footprint of the material, scenarios for before and after improvement implementation are modeled for both the pilot-scale and upcoming full-scale operations. This section summarizes the details of these four scenarios modeled. The details of production are as per the “product under study” section above, with the following scenarios each being adaptations of this product system. Table 2 shows an overview of the main differences between the scenarios. For further detail on how the scenarios differ, please refer to the Additional files.

Scenario 1: Pilot-scale day one

The “pilot-scale day one" scenario represents the production of Reishi ™ using California facility operations at first production, i.e., upon opening of the first-ever Fine Mycelium ™ plant. Primary data on the bill of materials (BOM) and manufacturing details are based on the actual operations in November 2021. This included a nutritional grain feedstock, post-processing (including tanning, lubrication, and dyes) and finishing chemistry of the state-of-the-art at the time, the autoclaving tool in sheet production powered with electricity, the natural gas source for Heating, Ventilation, and Air-conditioning (“HVAC”), and electricity powered by 100% wind energy. Energy data were collected from a 12-month period and daily averages were calculated. When produced at the pilot-scale, Reishi ™ sheets are produced on-site in California and are then shipped to Spain for post-processing.

Scenario 2: Pilot-scale current

The “pilot-scale current” scenario represents pilot-scale production using current (2022) operations in California that includes improvements that were implemented after the plant was opened. The following improvements were made to the pilot-scale day one scenario: energy for the facility HVAC is now using a more efficient generation technology, there is lower sale price for Reishi ™ , the brick is sold as a co-product, there has been a replacement of tanning chemistry with less impactful lubrication fatliquors, and there is now a reuse of some of growth tray components. As traditional tanning was eliminated for the production of Reishi ™ , it was determined that 10% of the total tannery energy use, water consumption, and waste production are required for the remaining finishing process. In addition, sheet growth was improved to allow for a slightly larger harvested area and concomitantly higher mass using the same inputs.

Scenario 3: Full-scale day one

The “full-scale day one” scenario represents the day one production anticipated at the full-scale facility in South Carolina, expected to be operational in 2023. Some of the main differences to the current pilot-scale production are on-site post-processing (eliminating the need to ship to tanneries), a higher production capacity per square meter of facility, a lower sale price for Reishi ™ , a different electricity mix (2/3 solar and 1/3 hydropower compared to 100% wind power), factory-wide energy use for HVAC derived from biogas (instead of natural gas), and autoclaving for sheet production derived from biogas (instead of electricity).

To estimate the energy usage for full-scale production the expected utilization of total electricity and gas capacity was calculated based on the planned running time of 24 h per day on two labor shifts. The split between consumption for sheet production process and factory-wide energy (for HVAC and lighting) was estimated using engineering designs for the full-scale plant. The annual production capacity was estimated using expert judgement assuming 365 production days per year.

Scenario 4: Full-scale with planned improvements

The fourth and final “full-scale with planned improvements” scenario represents the expected full-scale operations with planned improvements implemented, all of which are claimed by MycoWorks to be technically demonstrated and in-process for scaled implementation. In this scenario, the following changes were made to the full-scale day one scenario: a lower Reishi ™ sheet price, reuse of some of the growth tray components, energy for autoclaving derived from electricity instead of natural gas, a recycled corn-based nutrient source for the feedstock, and the brick substrate reused once.

Impact assessment method

The Environmental Footprint (EF) impact assessment method documented in the European Commission’s Product Environmental Footprint (PEF) method was used, namely, version 3.0 [ 46 ]. The EF method was selected as it is recommended by the European Commission and can be considered a general European consensus. Additionally, a majority of the luxury fashion and broader consumer goods industry (for whom this paper is largely relevant for) is based in Europe, further supporting this choice.

The characterized results, the normalized and weighted results, and the “single score” (i.e., the summed normalized and weighted results) are assessed in the interpretation. The characterized results are the impacts for each impact category expressed at the midpoint level (e.g., the carbon footprint expressed in kg CO 2 eq. per m 2 ). Normalization and weighting enable the results for each impact category to be expressed in the same unit, μPt per m 2 . This is calculated by applying the normalization factor (representing, e.g., the average impact per person per year) to the characterized results, and then applying the weighting factor (representing the perceived relevance and importance of each category) to the normalized results. These normalized and weighted results are subsequently summed into the “single score,” which in this paper is referred to as the “total environmental footprint.”

Note that weighting applies a value judgement, and therefore some subjectivity to the results. Consequently, caution should be taken when interpreting the normalized and weighted results, and the total environmental footprint. In this research, calculating the weighted and normalized results was deemed as useful to provide a basis for future research into impact categories potentially most relevant for Reishi ™ .

Firstly, the carbon footprint as well as process hotspots for Reishi ™ at pilot-scale and full-scale was assessed. Then, the characterized results of a wider range of impact categories were evaluated to identify any trade-offs associated with scaling-up production. Finally, the normalized and weighted results were assessed to identify the impact categories contributing most to the total environmental footprint.

For each scenario the overall carbon footprint results are presented for Reishi™ produced with the three fabric options included in this study: mycelium with cotton, mycelium with recycled polyester, and mycelium only. This is followed by the main findings from the contribution analysis for Reishi™ produced with mycelium only, which shows the relative contribution of different stages of the production and post-processing to the total product carbon footprint. To aid the interpretation of results the processes in the product system were grouped together (see Table 3 ).

Note that it has already been established that Reishi ™ qualifies for the qualitative aspects of the functional unit (material with natural haptics and satisfying the same performance, quality, and hand feel as animal leather), as justified in the background section of this paper.

The carbon footprint of 1 m 2 of post-processed, finished, and packaged Reishi™ produced in the relatively primitive and extremely low production volume (i.e., thousands of square feet per year, or many orders of magnitude smaller than global leather production) “pilot-scale day one” operations is 17.65 kg CO 2 eq. using cotton as the fabric material choice, 14.9 kg CO 2 eq. using recycled polyester, and 14.5 kg CO 2 eq. using mycelium only.

The contribution analysis in Fig.  4 illustrates that, for Reishi™ using mycelium only, facility energy usage (primarily for HVAC) has the greatest contribution (57%), where heat from natural (See Table 4 ) gas is the main driver (49%) followed by electricity use (8%). This can be explained by the facility having a higher HVAC use than a typical warehouse due to the facility’s relatively narrow required range that the temperature cannot deviate from. This is critical to the process and must be maintained throughout the entirety of production.

Carbon footprint contribution analysis for 1m 2 of finished Reishi ™

Production of raw materials for sheet production is the next largest contributor (15%), of which the grain-based nutrient feedstock is the largest contributor (9%) followed by some of the growth tray components (5%). The post-processing step has an only somewhat significant impact, with the chemicals and tannery operations contributing 10% and 6%, respectively. Impact from energy use for sheet production is somewhat minimal (7%) and is driven by the electricity use. The remaining groups have a relatively small impact, refer to Table 4 for further details.

The carbon footprint of 1 m 2 of post-processed, finished, and packaged Reishi ™ produced in the still miniscule “pilot-scale current” operations is 9.34 kg CO 2 eq. using cotton as the fabric material choice, 6.59 kg CO 2 eq. using recycled polyester, and 6.20 kg CO 2 eq. using mycelium only. Taking mycelium only as the fabric choice, this shows a 57% reduction compared to the day one operations that resulted from rapid efficiencies that were implemented after the pilot-scale plant began production.

Figure  4 shows the relative contribution of different stages of the sheet production and post-processing to this total footprint. A considerable difference compared to the day one scenario is the lower impact from facility energy use due to sourcing natural gas from a more efficient source (see Additional file 1 for more information). In this scenario, there is no longer a tanning step as it has been replaced by a simpler dyeing and lubrication step, which results in the impact from tanning chemicals being nearly entirely eliminated; impact is then only due to the finish. Removing the tanning step also considerably reduces the impacts of the tannery operations. The impact of growth tray impacts is noticeably lower which displays the benefit of its reuse.

Further reductions shown in the numerical footprint of Reishi ™ for processes occurring prior to the point of sale of the substrate brick are due to the economic allocation between the Reishi ™ sheet and brick. In this scenario, the substrate brick is sold (i.e., has an economic value) for ~ 1.1% of the value of a sheet of Reishi ™ , and the economic allocation is used to divide the environmental impact of all processes up until the point of sale for the substrate brick (raw materials for sheet production, energy for sheet production, and a part of facility electricity use, facility energy use, and production waste). This can be seen, for instance, in the lower impact of electricity for sheet production, where the electricity amount and source remain the same; however, impact allocated to the sheet is reduced. For more detail on the relative contribution of different processes for this scenario, refer to Table 4 .

The expected carbon footprint of 1 m 2 of post-processed, finished, and packaged Reishi ™ produced in the relatively primitive “full-scale day one” operations is 16.92 kg CO 2 eq. using cotton as the fabric material choice, 14.18 kg CO 2 eq. using recycled polyester, and 13.88 kg CO 2 eq. using mycelium only.

While the absolute carbon footprint is similar to that in the “pilot-scale day one” scenario (a 4.3% decrease using mycelium only), there are some differences in the hotspots. Figure  4 shows the greatest contributor is the energy usage for sheet production (60%) which is split almost equally between electricity and heat from biogas. This is considerably higher than in the pilot scale for two reasons; the autoclave tool uses heat from biogas instead of electricity (where energy from biogas has a higher impact per megajoule than the wind electricity used in the pilot scale) and the per-sheet energy consumption for production is higher (as shown in Table 2 ).

The next highest contributor is the facility-wide energy usage (21%), where most comes from electricity (18%) and the remainder from heat from biogas (3%). Compared to the current pilot-scale scenario, the impact from electricity use is 172% higher; however, the impact from heat usage is 77% lower. The full-scale operations have a greater production capacity, meaning that facility-wide energy demands are shared between a larger number of products. Although the per-sheet consumption is lower than in the pilot scale, the greater impact from electricity is driven by slightly higher impact electricity mix (1/3 hydro and 2/3 solar compared to 100% wind). The drastic reduction in impact from facility-wide HVAC is due to the energy source being a biogas from the local utility’s methane-recapture operations compared to natural gas, in combination with lower per-sheet energy demands. Raw materials for sheet production also have a significant contribution (15%), where the majority of its impact is coming from the nutrient feedstock (9%) followed by the growth tray components (5%). Refer to Table 4 for more detail on the hotspots.

Further reduction in the overall numerical footprint of Reishi™ is the lower price at which the sheets are sold. With larger-scale operations enabling sheets to be sold at a lower price, the economic allocation of the sheet is lower. Consequently, further overall reductions are seen in the processes occurring prior to the point of sale of the substrate brick due to the reduced sheet sale price altering the economic allocation, thus attributing a smaller portion of impact to the Reishi™ as previously explained.

Similar to the current pilot-scale scenario, the post-processing chemicals and operations have a minimal impact (0.02% and 0.87% respectively). The post-processing chemicals used remain the same as in the current pilot scale; however, further reduction in post-processing impact is due to the renewable electricity mix at the US tannery site (i.e., the same factory) having a lower impact than the Spanish grid mix (which is where the partner tannery for the pilot scale is located). Additionally, because the tannery is effectively on-site, the shipping impacts are eliminated.

The expected carbon footprint utilizing planned improvements to the full-scale operations, i.e., the plant’s designed operational point, for 1 m 2 of finished and packaged Reishi ™ is 5.80 kg CO 2 eq. using cotton as the fabric material choice, 3.06 kg CO 2 eq. using recycled polyester, and 2.76 kg CO 2 eq. using mycelium only. This shows an expected 80% reduction compared to the full-scale day one footprint. This is expected to be the steady-state carbon footprint emissions for the plant starting within a few months of its commissioning but does not represent the ultimate minimum value achievable.

Figure  4 shows the relative contribution from different stages of production and post-processing to the footprint using mycelium only production. The greatest contributor in this scenario is the energy use for mycelium sheet production (41%); however, in absolute value this is significantly lower than in the full-scale day one scenario. While the per-sheet energy consumption for sheet production remains the same, the large reduction is realized by switching the autoclaving energy source from heat from biogas to electricity (which has a lower impact per energy unit consumed). Facility-wide energy use is the next largest contributor (32%), although the absolute impact is also far lower than in the full-scale day one scenario. The reduction in electricity is largely driven by switching to a more efficient renewable electricity source (100% wind compared to 1/3 hydro and 2/3 solar) per the local utility. Like in scenarios above, further numerical reductions (for instance, in the facility-wide energy usage) are seen from the reduced sale price of Reishi™.

Notably, the raw materials for mycelium sheet production have a much lower impact than in all previous scenarios. This is largely a consequence of changing the nutritional grain feedstock to a recycled corn-based nutrient feedstock and also due to the reuse of the brick substrate, meaning less raw materials are required per sheet grown. For more detail on the relative contribution of different processes for this scenario, refer to Table 4 .

Implications of included fabric material choice on the carbon footprint

MycoWorks produces Reishi ™ tailored to include, or not include, an embedded fabric material specified by the customer. The impacts of different fabrics vary greatly; for example, virgin cotton, virgin polyester, and recycled polyester have a carbon footprint of 11.29, 5.59, and 1.11 kgCO 2 eq./kg of textile, respectively [ 25 ], indicating that the fabric choice plays a major role in the resulting total carbon footprint of Reishi ™ .

The contribution analysis in the previous section was shown for Reishi ™ made from mycelium only.

Table 5 displays the carbon footprint of Reishi™ using different fabric material choices. Figure  5 illustrates how heavily the carbon footprint of Reishi ™ is influenced by the impact of the fabric material chosen, and how this compares to a benchmark bovine leather we modeled (see Additional file 1 for details).

Carbon footprint of 1m 2 of traditional bovine leather modeled compared to 1m 2 of finished Reishi™ (produced in full-scale with planned improvements scenario using different embedded fabrics)

Environmental footprint: all scenarios

As outlined in the introduction, environmental impacts beyond carbon footprint should also be considered to establish a wholistic view of the material’s sustainability profile (Laurent et al. 2012). Accordingly, this section provides preliminary insights into the potential impact of Reishi™ on other environmental impact categories to identify any environmental trade-offs of scaling-up production.

Figure  6 shows that when looking at the characterized results for all impact categories there is a clear general trend that implementation of improvements at each individual scale greatly reduces the environmental footprint of Reishi ™ with no obvious trade-offs present. As expected, the full-scale day one impact is larger than the current pilot-scale impact. This demonstrates the importance of improvement implementation when scaling-up production to realize the most optimal environmental footprint.

Characterized midpoint LCIA results, where the highest score is scaled to 100%

Figure  7 shows the normalized and weighted results for each impact category and their contribution to the total environmental footprint. This illustrates that for each of the scenarios the impact categories contributing to most of the environmental footprint are resource use (minerals and metals), climate change, cancer human health effects, and resource use (energy carriers).

Contribution of each impact category to the total environmental footprint for each scenario. Calculated using the normalized and weighted results for each impact category

These results have shown that Reishi ™ holds promise as a low-impact leather-like material with natural haptics. This study has calculated the carbon footprint of Reishi ™ when produced under pilot-scale operations (day one and current scenarios) and prospectively calculated the footprint for full-scale operations (day one and with planned improvements scenarios) for a plant that has already been fully designed and engineered based on the existing pilot plant. The contribution analyses identified which parts of the cradle-to-gate product value chain contribute most to the carbon footprint for each of these scenarios. Finally, the impact categories which appear to be most relevant for Reishi ™ were identified. In this section we will discuss (1) production opportunities, (2) limitations of this study, and (3) practical implications for the sector.

Production opportunities

The day one results revealed several process “hotspots” in the production of Reishi ™ (see Fig.  4 ). These hotspots provide focus for where to target future impact reduction efforts. The most effective reduction opportunities identified (some of which have already been implemented) include the following: sourcing lower-impact biogas for HVAC (such as sequestered methane sourced from landfill or biogas from anaerobic digestion by the local utility supplying the full-scale plant in South Carolina), reduction in—or elimination of—chemical use in post-processing, use of electricity rather than gas for autoclaving, and sourcing 100% wind electricity. Alongside general improvements in energy efficiency of the process, such as changing sterilization from autoclaving to another suitable process, the scaling-up of Reishi ™ production will significantly reduce the carbon footprint with economies of scale allowing factory resources, in particular, HVAC, to be split between larger volumes of Reishi ™ sheets. It is important to note that although reducing the sale price of the sheet will also largely reduce the numerical footprint of Reishi ™ , this does not decrease the true environmental footprint of operations.

The two scenarios with improvements show the significant opportunity that technical development has on the impact footprint. The “pilot-scale current” scenario demonstrates that a 57% reduction in carbon footprint has already been realized after implementation of technical changes. Similarly, the “full-scale with planned improvements” scenario shows the potential carbon footprint savings (80% reduction) expected once improvements are implemented at this site. This illustrates the relative infancy of mycelium as an industrialized process relative to the maturity of animal leathers; it is conceivable that further significant improvements for mycelium’s environmental footprint are possible by extrapolating the efficiencies that other industries have seen in both energy usage and economies of scale over their decades of scale-up relative to Fine Mycelium’s ™ mere months devoted to scale-up thus far.

Limitations

The pilot-scale models (“day one” and “current”) are robust; however, some improvements could be made to increase certainty in the results. While primary data were used for processes under direct or operational control of MycoWorks, secondary data sources were relied on for upstream processes. When accurate background datasets were not available, proxies were taken (e.g., for the fatliquor which replaced the tanning chemicals). Further, when country-specific datasets were not available, global datasets were used. To further refine the results, primary data from suppliers could be sourced, such as from the nutrient feedstock, the gas used for HVAC, and the fabrics. Overall, the conclusions drawn from this study are not expected to be heavily influenced by these limitations.

More caution should be applied when analyzing results from the full-scale scenarios. While the pilot-scale models are based upon an actively operational product system, the “full-scale day one” and “full-scale with planned improvements” scenarios are partially prospective. The data points with the highest uncertainties are the energy consumption for mycelium sheet production and facility-wide energy usage, both of which have been estimated by MycoWorks engineers since actual consumed data are not yet available. This uncertainty is relatively low since the energy-consuming systems have already been selected and will be operating at their specified conditions. Consequently, the results from these scenarios should not be taken as concrete but rather serve to provide an estimation for the future footprint of Reishi ™ . The full-scale day one scenario serves to estimate the short-term (i.e., 2023) footprint, while the scenario with planned improvements looks further into the future. Once the full-scale facility is operational primary data should be collected to update the results to enable more concrete conclusions to be drawn for the full-scale operation.

Material maintenance requirements and longevity are integral to assessing the full lifecycle impacts of a textile. Consequently, to gain a more wholistic picture of the total lifecycle impact of the product—and to effectively compare this material to other alternatives for particular use cases—the scope could be expanded to a cradle-to-grave study for particular use cases in future assessments. As such, an element of time would be introduced into the functional unit that currently only addresses the processed level and quantity.

Practical implications for the fashion and broader consumer goods industries

Studies have begun to hypothesize the environmental impact of fungi-based leather-like materials [ 16 , 19 ]; however, the research presented in this paper is the first to use LCA to quantify the environmental impact of Reishi ™ using primary production data. While the comparison to a specific natural material was out of scope of this research, to place the environmental footprint of Reishi ™ in the context of other natural materials (i.e., natural haptics) in the market, we modeled a benchmark bovine leather (see Additional file 1 for details).

Comparison to the benchmark bovine leather modeled suggests Reishi™ has a significantly lower carbon footprint than traditional bovine leather. The carbon footprint of the benchmark leather product modeled is 32.97 kg CO 2 eq. per m 2 , which is significantly higher than the carbon footprint of Reishi ™ , which (with mycelium only) is currently 6.20 kg CO 2 eq. per m 2 (an 81% reduction) produced at the pilot scale and is likely to be reduced below 2.76 kg CO 2 eq. per m 2 (a 92% reduction) with upscaling production and further improvements. These reductions compared to traditional leather are within a similar range to those claimed by plastic haptic leather alternative products, although it is impossible to make any direct reduction comparisons due to a myriad of methodological differences. Modern Meadow estimates a 79% reduction for Bioleather1, Natural Fiber Welding estimates a 93% reduction for Mirium (although the benchmark is a literature source rather than a modeled benchmark), Adriano di Marti estimates a 94% reduction for Desserto (although limited methodological details are available in their early LCA), and a 94% reduction is estimated for Vegea (accounting for the full product life cycle, rather than just “cradle-to-gate”); see Additional file 1 for a summary table of, and sources for, these figures.

Comparing the total environmental footprint of Reishi ™ to benchmark bovine leather (i.e., after normalization and weighting of the midpoint results for all impact categories) suggests the lower carbon footprint of Reishi ™ comes without environmental trade-offs. The total environmental footprint of the benchmark leather infers that the impact from “cancer human health effects” is the largest in comparison to the other impact categories. While the “resource use” impact categories appeared as most relevant for Reishi ™ , when looking at absolute impact, the resource use impact of Reishi ™ is lower than the benchmark leather used. This thereby indicates that there is no obvious burden shifting if Reishi ™ was to be chosen as an alternative to the benchmark leather. When comparing the total environmental footprint, the impact of Reishi ™ is considerably lower (an 80–93% reduction for the four scenarios using mycelium only), indicating a promising overall environmental performance. Note that if the cutting efficiency of the benchmark product were accounted for then the footprint of the benchmark would be higher. It is important to note that these findings are specific to this case study and that the conclusions drawn should not be extrapolated to all bovine leather. Further, as explained in the methods section, interpretation of normalized and weighted results should be treated with caution due to the subjectivity in the weighting factors used.

These results are supported by existing literature [ 12 , 16 ] which indicate that Reishi ™ is a leather-like material with a lower carbon footprint than traditional bovine leather. However, to make conclusive statements on overall comparative conclusions and environmental trade-offs between two materials, the scope of this study would need to be expanded to include a more in-depth analysis of the alternative material.

While most of this study focused mainly on identifying the process hotspots of the impact on climate change, further research could investigate the main drivers of impact to other relevant impact categories: in particular, resource use (minerals and metals) and cancer human health effects and resource use (energy carriers), as these are most relevant environmental impact categories for Reishi ™ .

This research places Reishi ™ in the context of natural leather-like materials. Thus, contextualizing Reishi ™ within plastic and plastic-like materials (i.e., materials such as PU and rubber that both have plastic haptics) is out of scope of this study. We believe that current LCIA methods (specifically the factors for plastic impacts) are not yet mature enough to make a proper comparison between a natural and a plastic product. Until impacts, such as microplastic pollution, are accounted for, we believe a quantitative assessment of plastic alternatives would not holistically reflect the true impact of the material. Once LCIA methods can more effectively support this comparison, future research could expand the scope to contextualize the environmental footprint of Reishi™ to plastic leather-like materials.

Mycelium, in particular MycoWorks’ Fine Mycelium ™ , is a technology that has been in development for several years and has been undergoing industrialization and scale-up for months only. There have only been several thousand square meters ever produced, enough to provide an accurate analysis of its impact, but not enough to allow for drastic efficiencies to be unlocked. Despite this, its impact is already low relative to its scale and projected to be far lower with the modest increases in production volumes. This study has shown this material’s beneficial sustainability profile when produced at merely several hundred thousand square meters per year, including a carbon footprint of 2.76 kg CO 2 eq/m 2 (a 94% reduction compared to bovine leather), much lower impact on eutrophication, ecotoxicity, and human health (see Additional file 1 ). Note that billions of square meters of animal leathers are currently produced per year in an industry that has existed—and in turn participated in enormous efficiency gains—for many decades.

As argued above, a high-quality alternative leather suited for wide segments of the market will ideally have an order-of-magnitude reduction in carbon footprint while also not wielding a plastic hand feel and using only minimal plastic content in its thin surface coating. While the near-zero (less than 1%) plastic content of Reishi ™ was already known, this paper shows the extent to which the low-carbon footprint of Reishi ™ makes it an ideal alternative. As a result, Reishi ™ is one of the few leather-like materials validated to have a low-carbon footprint and also a natural, biodegradable composition.

The carbon footprint of 1 m 2 of post-processed, finished, and packaged Reishi ™ in the “pilot-scale day one” scenario was 17.65 kg CO 2 eq. using cotton as the fabric material choice, 14.9 kg CO 2 eq. using recycled polyester, and 14.5 kg CO 2 eq. using mycelium only. Following the implementation of technical changes and process optimization, the footprint was reduced to its current state at pilot scale of 9.34 kg CO 2 eq. using cotton as the fabric material choice, 6.59 kg CO 2 eq. using recycled polyester, and 6.20 kg CO 2 eq. using mycelium only.

At the full-scale facility slated to be operational in 2023, the day one carbon footprint is expected to be approximately 16.92 kg CO 2 eq. using cotton as the fabric material choice, 14.18 kg CO 2 eq. using recycled polyester, and 13.88 kg CO 2 eq. using mycelium only. With the material and process still undergoing optimization, with only demonstrated and planned improvements the carbon footprint is expected to be reduced considerably to 5.80 kg CO 2 eq. using cotton as the fabric material choice, 3.06 kg CO 2 eq. using recycled polyester, and 2.76 kg CO 2 eq. using mycelium only. The characteristics of this scenario with planned improvements are technically feasible, meaning that the realization of these footprints is dependent on implementation. This research should be refreshed periodically to maintain a true picture of the up-to-date product carbon footprint of Reishi ™ .

The embedded textile of choice has a large influence on the footprint of Reishi ™ produced at any scale and stresses that these results should not be extrapolated to Reishi ™ made with materials not covered in this study.

Regardless of the scenario, this study has shown that most of the climate change impact from the growth of mycelium comes from the energy use at the mycelium production facility (see Fig.  4 ). These results also suggest that MycoWorks’ passive growth process (i.e., free of any input gases) is key to its low-carbon footprint. These insights are already being used by MycoWorks as input to further improve production processes and have been used to make engineering decisions for the full-scale plant in South Carolina.

Given the urgency around taking action to reduce the impact on climate change from industries that use large amounts of carbon-intensive or polluting materials, like animal and plastic leathers, and the evolving climate policies to adhere to, it is increasingly essential for users of these materials to have access to high-quality, sustainable options. This research provides the first basis on which brands can assess the carbon footprint of Reishi ™ as a material choice. The large reductions in carbon footprints seen in the scenarios with implemented improvements provide an environmental incentive to continue R&D to further reduce the impact footprint, as passively grown mycelium, such as MycoWorks’ Fine Mycelium ™ , shows the most promise of any leather alternative when measured on haptics (natural), carbon footprint (extremely low), and plastic use (near zero).

Availability of data and materials

For some further detail on the data and results, refer to the SI. The datasets generated and analyzed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request.

Change history

29 may 2023.

A Correction to this paper has been published: https://doi.org/10.1186/s12302-023-00741-4

Abbreviations

Environmental footprint

Ethylene-vinyl acetate

Life cycle assessment

Life cycle impact assessment

Supporting information

Heating, ventilation, air conditioning

Product environmental footprint

Product environmental footprint category rules

Polyurethane

Polyvinyl chloride

Research and development

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Acknowledgements

The authors would like to acknowledge Cory Fulkerson for collecting and reporting facility and process energy data and Mike Lindheim for analysis and data collection of prospective scale-up scenarios. They thank Curtidos Badia for their cooperation in providing data on tannery post-processing.

This study was funded by MycoWorks Inc. The role of MycoWorks in this study was to provide primary data for the analysis, build realistic partially prospective scenarios for analysis, provide sector—and market-specific knowledge, co-author sections of the manuscript, and to review the draft manuscript.

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EW updated and adapted the Reishi ™ LCA model with the latest data and scenarios, analyzed the data, produced the figures, and was the primary author of the manuscript. KC built the initial LCA models for Reishi ™ and the benchmark bovine leather and reviewed the final Reishi ™ LCA model. LG substantially revised the manuscript. BM led the coordination for MycoWorks, Inc., including data collection and identifying scenario analyses, and revised the manuscript. MS co-authored the abstract, introduction and conclusions, substantially revised the final manuscript, and re-touched the graphs. All the authors read and approved the final manuscript.

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Correspondence to Bill Morris .

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Supplementary Information

Additional file 1:.

Table S1. Estimated CO2-eq. emissions from mycelium grown via the Ecovative process for Ecovative’s Forager ™ “mushroom leather” and as an input for Bolt Threads’ Mylo ™ material. Table S2. Overview of main proxies taken for raw materials. Table S3. Characterized LCIA results for 1m 2 of Reishi TM produced in the four scenarios, and for 1m 2 of the benchmark product (using mycelium only). Table S4. Comparison of all available published data for leather alternatives *calculated value as explained in Table 1 and above. Figure S1. Characterized midpoint LCIA results of 1m 2 of Reishi in the four scenarios compared to 1m 2 of benchmark bovine leather, where the highest score is scaled to 100%. Figure S2. Environmental footprint (normalized and weighted results using the EF method 3.0 (Zampori & Pant, 2019)) for 1 m 2 of Reishi ™ in the four scenarios compared to 1 m 2 of the benchmark bovine leather.

Additional file 2.

A complete LCI file of the foreground system for each of the four scenarios modelled.

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Williams, E., Cenian, K., Golsteijn, L. et al. Life cycle assessment of MycoWorks’ Reishi ™ : the first low-carbon and biodegradable alternative leather. Environ Sci Eur 34 , 120 (2022). https://doi.org/10.1186/s12302-022-00689-x

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Received : 16 August 2022

Accepted : 05 November 2022

Published : 20 December 2022

DOI : https://doi.org/10.1186/s12302-022-00689-x

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• Sustainable materials
• Sustainability
• Carbon footprint
• Sustainable fashion
• Mushroom leather
• Future of fashion
• Biomaterials
• Vegan leather
• Animal free