# Machine learning algorithms for supporting life cycle assessment studies: An analytical review

Bishwash Neupane, Farouk Belkadi, Marco Formentini, Emmanuel Rozière, Benoit Hilloulin, Shoeib Faraji Abdolmaleki, Michael Mensah

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Bishwash Neupane, Farouk Belkadi, Marco Formentini, Emmanuel Rozière, Benoit Hilloulin, et al. Machine learning algorithms for supporting life cycle assessment studies: An analytical review. Sustainable Production and Consumption, 2025, 56, pp.37-53. 10.1016/j.spc.2025.03.015. hal-04997142

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## Review Article

# Machine learning algorithms for supporting life cycle assessment studies: An analytical review

Bishwash Neupane<sup>a, *</sup>, Farouk Belkadi<sup>a</sup>, Marco Formentini<sup>c</sup>, Emmanuel Rozière<sup>b</sup>, Benoît Hilloulin<sup>b</sup>, Shoeib Faraji Abdolmaleki<sup>a</sup>, Michael Mensah<sup>a</sup>

### ARTICLE INFO

Editor: Dr. Gonzalo Guillen Gosalbez

**Keywords:**

Life cycle assessment

Machine learning

Prediction

Analytical study

Multi criteria decision making

### ABSTRACT

Nowadays, industries face increasing pressure to enhance their environmental sustainability scores, particularly in reducing carbon footprints. Life Cycle Assessment (LCA) tools are commonly used to evaluate environmental impacts across organizational levels, enabling predictions for potential improvements. But complexity and diversity of factors influencing these assessments make prediction models difficult to build and validate. Machine learning (ML) techniques present viable solutions to these challenges.

This study presents a systematic literature review (SLR) of seventy-eight peer reviewed articles, evaluating the performance of different ML models in Life Cycle Assessments applications. An analytical ranking of these models is provided based on their effectiveness for LCA predictions using the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Results indicate that Support Vector Machine (SVM) achieve a score of 0.6412, followed by Extreme Gradient Boosting (XGB) at 0.5811 and Artificial Neural Networks (ANN) at 0.5650, and, positioning them as the most suitable models for LCA studies for prediction application. Random Forest (RF), Decision Trees (DT), and Linear Regression (LR) follow with scores of 0.5353, 0.4776, and 0.4633, respectively, while Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process Regression (GPR) rank lowest with scores of 0.4336 and 0.2791. Detailed interpretations and implications of these findings are discussed.

## 1. Introduction

Life Cycle Assessment (LCA) is the fundamental methodology to evaluate the environmental impacts of product(s), and/or services which assists in well-informed decisions making towards a sustainable world (Goglio et al., 2020; Joshi et al., 2022). Product Carbon Footprint (PCF) is the measurement of total quantity of greenhouse gases (GHGs) released by product during its life cycle, both directly and indirectly, and is expressed in CO₂ equivalent (Chen et al., 2020). The basic principle of these studies is to compile all the mass and energy flows that the product exchanges with the environment across its life cycle and convert this information into environmental impact assessments (Anand and Amor, 2017). These techniques are standardized procedures created to assess the ecological indicators in addition to a system's energy consumption and greenhouse gases (GHGs) emissions during every stage of its life cycle (Wong et al., 2021). The application of LCA has grown

significantly in decision making for green transition and sustainable product development. Although the methods are becoming increasingly important, there are several challenges the LCA experts and stakeholders face during practical application, which compromise the accuracy and dependability of the outcomes (Anand and Amor, 2017; Costa et al., 2019). LCA studies are broad and data intensive requiring extensive data collection and rigorous analysis from both upstream and downstream processes to produce the reliable end results (Romeiko et al., 2024). Large number of input parameters and their uncertainties, unavailability, temporal and spatial differences makes LCA studies even more complicated, time consuming and resource intensive (Cabot et al., 2022; Ghorghi et al., 2022). Sheer volume of data coming from the industries with the increasing digitalization fuels further complications to do comprehensive LCA studies (Rahman et al., 2022). Advancements in industrial digitalization, data generation, storage and analytics are driving the interest towards the application of artificial intelligence (AI),

<footnote><sup>a</sup> LS2N (Laboratory of Digital Sciences of Nantes), UMR-CNRS 6004, Ecole Centrale de Nantes, 1 Rue de la Noë, 44300 Nantes, France</footnote>

<footnote><sup>b</sup> GeM (Research Institute of Civil Engineering and Mechanics), UMR-CNRS 6183, Ecole Centrale de Nantes, 1 Rue de la Noë, 44300 Nantes, France</footnote>

<footnote><sup>c</sup> DISI (Department of Information Engineering and Computer Science), University of Trento, Via Sommarive, 9, 38123 Povo, TN, Italy</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: Bishwash.Neupane@univ-nantes.fr (B. Neupane).</footnote>
machine learning (ML) techniques and big data analytics in LCA studies to address the existing challenges (Rahman et al., 2022; Romeiko et al., 2024; Xu et al., 2015).

Several studies explored the application, development, and integration of ML-supporting LCA tools to resolve existing challenges (Akhshik et al., 2022; Javadi et al., 2021; Kleinekorte et al., 2023; Marvuglia et al., 2015; Nabavi-Pelesaraei et al., 2018; Zhao et al., 2021). ML algorithms have been applied in different disciplines and stages of LCA studies; filling the data gaps during life cycle inventory (LCI) analyses (Zhao et al., 2021), predicting/forecasting impact parameters, and missing characterizing factors (Hou et al., 2020; Kaab et al., 2019), predicting lifespan of buildings (Ji et al., 2021), developing accessory tools for LCA (Kleinekorte et al., 2023) and help in life cycle interpretation (Satinet and Fouss, 2022). These studies focused either on a single sector like building (Barros and Ruschel, 2021; Dong and Zhang, 2023), or additive manufacturing (Naser et al., 2023a, 2023b) or on a bigger scale, cities/communities (Ghoroghi et al., 2022). Few studies focused on the specific stage of LCA; (Köck et al., 2023) presented a critical review of developments in the field of Life Cycle Inventory Analysis (LCIA) using ML and AI. (Zargar et al., 2022) reviewed the inventory modeling method for missing data. (Romeiko et al., 2024) performed a more comprehensive and detailed review of the application of ML on LCAs focusing on different stages, datasets for ML models, and evaluation metrics, and made recommendations for future research direction, focusing on how to overcome the challenges of applying ML on LCA studies. However current research lacks guidance on selection of ML algorithms or techniques to the LCA experts and stakeholders for enhancing predictive LCA tools and their role in downstream decision making. Very few studies analytically compare different ML algorithms for the application in LCA studies but focus only on one product or sector. There is a lack of research examining the application of ML algorithms across a broader range of various industrial domains and assessing their scalability and adaptability in diverse scenarios to develop predictive LCA tools for LCA experts, practitioner and stakeholders. To address this research gap, in this paper, a systematic literature review is done to understand and collect data on state of art application on ML and AI along with LCA studies following the similar approach in the study done by (Romeiko et al., 2024). After the detailed data collection and analysis with SLR, a multiple criteria decision making (MCDM) method is used to rank the best ML algorithms among different alternatives depending on the several evaluation criteria. Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for MCDM. Relying on the ranking, a prediction module will be developed in future which will address the prediction and downstream decision-making issues in LCA studies.

The paper is structured as following: Section 1 introduces the context, problem statement and research gap; Section 2 describes the method used in this study which is further divided into two subsections: Section 2.1 outlines the method used for the systematic literature review, while Section 2.2 details MCDM method, elaborating on steps and equations for AHP and TOPSIS. Section 3 presents the result and discussion, which is further divided into Section 3.1 for the systematic review and Section 3.2 for the MCDM results and discussion. Section 4 provides the conclusion of the study along with suggestion for future studies.

## 2. Methods

The study is done in the two phases. The first phase is a systematic literature review (SLR), performed following the guidelines proposed by Xiao and Watson (Xiao and Watson, 2019) to explore the state of art machine learning application in LCA studies. In the second phase, using the data collected and synthesized from SLR, a Multiple Criteria Decision Making Model based on Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is constructed to evaluate the performance of machine learning

algorithms in different LCA studies. The overall research methodology is shown in Fig. 1.

### 2.1. Systematic literature review

#### 2.1.1. Planning

In this phase, the strategy of the exhaustive literature survey is defined and the research questions are formulated:

RQ1: What is the state of art development in the application of ML in LCA studies?

RQ2. How do ML algorithms perform in LCA studies, surrogate modeling and optimization?

RQ3. What synthesis could be drawn from analytical method assessing performance of ML algorithms for the development of ML based predictive LCA tool?

The search databases were determined as "Scopus" and "Science Direct". Zotero is used for managing the collected references given that it has broad functionality and ease of use in Microsoft Word using the plugin. The following inclusion criteria/exclusion were used:

1. **Language:** English

2. **Document Type:** Full-text, Conference or Journal Papers or Book Chapters

3. **Time Interval:** January 2013 to February 2025.

#### 2.1.2. Conducting

In the **Search Phase**, a search protocol was developed to select suitable studies. Following the identification of the research questions in the planning phase, relevant keywords were identified: life cycle assessment, machine learning, surrogate modeling, and optimization.

As presented in Table 1 two different keyword combinations were used to search articles on the databases; Science Direct and Scopus. The initial search yielded 152 and 318 papers respectively for first keyword combination and 70 and 92 papers for second keywords combination. Articles appearing on Science Direct were also present on Scopus. The total of 410 articles retrieved from Scopus for two combination of keyword (318 for first combination and 92 for second combination) were selected for screening phase.

**Screening Phase:** The papers were assessed by screening titles and abstracts in this phase. The abstracts were reviewed based on the inclusion/exclusion criteria, resulting in the exclusion of 273 articles deemed irrelevant to the study topic. This reduced the number of eligible papers to 137 full-text reviews. During the **Evaluation Phase**, the articles were assessed for their quality and impact considering a) methodological clarity, b) results comprehensiveness, and c) relevancy to the research questions defined previously in the planning phase. After this, a decision was made regarding the inclusion of the paper for a full review. Of those 137 articles, 64 articles did not meet the above-mentioned criteria and were thus excluded, leaving 73 articles for quantitative and qualitative assessment. Among them, 4 papers were qualitative (review papers) and were excluded leaving 69 papers for quantitative assessment. By tracking the citations of these 69 studies, 9 articles utilizing quantitative LCA and ML methods were included, bringing the total number of 78 primary quantitative studies for the systematic review. The references for the included papers were imported from the reference manager. (See Supplementary information SI1 for all the details information extracted from these 78 papers.)

#### 2.1.3. Extraction

From each selected paper, the following information was extracted: Application domain, ML model(s) used, optimal model, model performance, dataset and data sources, task (type of application) of ML model, optimization method, prediction application, and scale at which LCA was applied in the study. This was followed by the **analysis and**
<figure><img src="image_3.png" /><figcaption>Fig. 1. Methodology; systematic literature review and multiple criteria decision making model.</figcaption></figure>

<table><caption>**Table 1**
Keywords combination and numbers of papers retrieved.</caption><thead><tr><td>Keywords combination</td><td>Science Direct</td><td>Scopus</td></tr></thead><tbody><tr><td>“life cycle assessment” and “machine learning” (“life cycle assessment” and “machine learning” and (“surrogate modelling” or “optimization”))</td><td>152<br/>70</td><td>318<br/>92</td></tr></tbody></table>

synthesis phase of the collected data. The data collected and synthesized in this phase were used to develop the multiple criteria decision making model discussed in detail in Section 2.2.

## 2.2. Multiple criteria decision making model (MCDM)

This paper aims to analyze the performance of different ML algorithms used in LCA studies based on the information collected from SLR. Analyzing ML algorithms performance normally involves more than one criterion, such as accuracy, computation time, scalability and interpretability and others. One algorithm can perform better than other algorithms for one criterion and underperforms for another criterion. Well known free lunch theorem proposed by Wolpert and Macready mentions ‘no algorithm is superior on all problems and so is no single evaluation criteria is always superior for their evaluation’ (Wolpert and Macready, 1997). As a consequence, it cannot be simply recommended one particular ML algorithm just by single evaluation criteria. However it is possible to model the selection of suitable ML algorithms for an application on the hand as multiple criteria decision making problem (Ali et al., 2017; Khanmohammadi and Rezaeiahari, 2014; Peng et al., 2011). (Ali et al., 2017) in their study proposed accurate multi-criteria decision making methodology to empirically evaluate and rank ML classification algorithm allowing end users or experts to choose the top rank classifiers for their application to learn and build models for them. (Khanmohammadi and Rezaeiahari, 2014) proposed AHP based meta learning algorithm identifying Support Vector Machine (SVM) as the most suitable supervised classification algorithm for developing clinical

decision support system. Peng et al. (2011) in their study concluded MCDM methodologies as useful tools for evaluating multiclass algorithms.

In this study, AHP is used to calculate the weights of the evaluation criteria, and these weights are subsequently applied in the TOPSIS method to rank the performance of machine learning algorithms. The AHP method provides a clear and systematic way to assign relative importance to the selection criteria (Inés and Ben Ammar, 2015), while TOPSIS ranks the alternatives based on their performance relative to an ideal solution (Panda and Jagadev, 2018). This combined approach allows for a comprehensive evaluation of the machine learning algorithms across multiple criteria, leading to a robust and transparent decision-making process.

### 2.2.1. Selection of evaluation criteria

The frequency within which an ML algorithm is employed in LCA studies reflects its growing acceptance and use within the research community and across industries, which is influenced by factors such as ease of application and the availability of the supporting tools. Following the SLR, the most frequently used ML algorithms in the literatures were identified and selected as alternatives for MCDM modeling. After identifying the alternatives, 11 different criteria were selected to evaluate the ML algorithms; R² and RMSE for model accuracy, data size, data sources variation, model interoperability, model interpretability, tasks versatility, application domain, application LCA stages, computational complexity, and scalability. The algorithms were scored from 1 to 10, (10 being the highest and 1 being the lowest) for all criteria except computational complexity. Computational complexity here refers to the computational time for model to perform task. The lower the time (closer to 1), higher is the score for computational complexity. Model accuracy (R² and RMSE) and data size information were extracted directly from reviewed papers and scaled.

* Model accuracy (R² and RMSE scores): The accuracy of each model were assessed using the R² and RMSE metrics for different ML algorithms based on frequency of use across multiple studies. For each
model the highest reported performance value was identified then the best result for each model was selected based on highest R² score and lowest RMSE score.

*   **Data Size:** Another criterion used for model comparison is the sample data size. As the model training data size varied randomly and there was no pattern, for each model categories, the median data size is chosen directly from literatures.

*   **Data Sources Variations:** To assess the variation in data sources, the data sources from which ML models used data were categorized into different groups. For each model, how many different data sources were represented within these categories across the studies were reviewed. The model having data representation from all categories received the highest score, meanwhile, the model having data from few sources received lower score.

*   **Model Tasks Versatility:** For task versatility, the range of tasks performed by each ML model across different studies were reviewed. For each model, it was identified how many different tasks it had been applied to and assigned score accordingly. If the model was used for a variety of tasks across multiple studies, it received higher score. Models that had been applied to fewer tasks or more niche tasks were given lower scores.

*   **Application Domain and Application LCA stages:** Similarly, for application domain and application to LCA stages, it was identified how frequently each model was applied across different domains and stages of LCA studies. The models were scored based on how broadly they were applied in different domains (e.g., energy, manufacturing, transportation) and across different LCA stages (e.g., goal and scope definition, life cycle impact assessment). A higher score was given to models that were successfully applied across multiple domains and stages.

*   **Computational Complexity, Scalability, Interpretability and Interoperability:** These criteria were based on a combination of findings from the literature and expert opinions regarding the performance of models in practical scenarios. For example, models that were reported to be computationally efficient, scalable, or highly interpretable were given higher scores. Similarly, interoperability was evaluated based on how well the models were able to integrate with different tools or platforms in various studies. These scores reflect the practical challenges and advantages of using each model in real-world applications.

Some of the criteria involve qualitative assessments and there might be potential subjectivity while scoring the models. Every effort has been used to minimize the subjectivity through thorough literature review, consistent scoring protocol, cross checking and consensus with results from published scientific literatures. The selection of the criteria for evaluating ML algorithms are grounded in ensuring that the chosen models are not only scientifically robust but also practical for the relevant applications.

### 2.2.2. Analytical hierarchy process (AHP)

AHP is employed using python script to calculate the weights for various criteria influencing the model selection. Developed by Thomas Saaty (Saaty, 2003), AHP is a structured MCDM method that decomposes a complex problem into a hierarchy of interrelated elements, including criteria and alternatives as shown in Fig. 2. First level represents the goal, second level represents the criteria and third level represents the alternatives. In this study, the goal is to analytically assess the performance of the ML algorithms for LCA studies (level 1), based on 11 different selection criteria (level 2) for 8 different ML algorithm alternatives (level 3). AHP helps decision makers by converting both subjective and objective evaluations of alternatives into a set of pairwise comparison using nominal scale, allowing them to make the rational choices. The scale ranges from 1/9 for “the least valued than” to 9/9 for “absolutely more important than”, covering the entire spectrum of the comparison (Ali et al., 2017; Khanmohammadi and Rezaeiahari, 2014).

AHP involves four main steps:

1.  **Pairwise Comparison Matrix:** After identifying the goal, alternatives and criteria, the main step involves constructing a pairwise comparison matrix for the criteria and alternatives. For the given number of criteria (n), the number of pairwise comparison can be calculated as:

$$ \text{number of pairwise comparison} = n^* \frac{(n-1)}{2} \qquad (1) $$

and the pairwise matrix is defined as:

$$ A = \begin{bmatrix} a_{11} & \cdots & a_{1j} \\ \vdots & \ddots & \vdots \\ a_{i1} & \cdots & a_{ij} \end{bmatrix} \qquad (2) $$

where $a_{ij}$ represents the relative importance of element i compared to j, with values from 1 (equal importance) to 9 (extremely more important). Saaty's 9-point scale (Saaty, 2003) is used to quantify the relative importance of one criterion over another based on expert's judgement.

2.  **Calculating Eigenvector:** Once the comparison matrix is constructed, the principal Eigen vector w is determined by solving the following eigenvalue equation:

$$ A \cdot w = \lambda_{\max} \cdot w \qquad (3) $$

where $\lambda_{\max}$ is the maximum eigenvalue, and w is the eigenvector. This eigenvector represents the normalized weights for each criterion.

3.  **Consistency check:** A consistency ratio (CR) is calculated to ensure that the pairwise comparisons are consistent. The consistency index (CI) is computed as:

<figure><img src="image_4.png" /><figcaption>Fig. 2. Hierarchical decomposition of ML algorithms selection MCDM problem (RMSE: Root Mean Squared Error, ANFIS: Adaptive Neuro Fuzzy Inference System, ANN: Artificial Neural Network, DT: Decision Trees, GPR: Gaussian Process Regression, LR: Linear Regression, RF, Random Forest, SVM: Support Vector Machine, XGB: Extreme Gradient Boosting).</figcaption></figure>
$$CI = \frac{\lambda_{max} - n}{n - 1} \qquad (4)$$

where n is the number of criteria. The consistency ratio (CR) is calculated as:

$$CR = \frac{CI}{RI} \qquad (5)$$

where RI is the random consistency index. A CR value less than 0.1 is considered as acceptable (Saaty, 2003).

4. **Final Score Calculation:** After calculating the weights for each criterion, these weights are applied to performance scores of each alternative to derive the final rankings of the alternatives.

### 2.2.3. Technique for order preference by similarity to ideal solution (TOPSIS)

The TOPSIS is another MCDM technique that ranks alternatives based on their proximity to an ideal solution. The ideal solution is characterized by the best performance in all criteria, while the negative ideal solution corresponds to the worst performance (Panda and Jaga-dev, 2018). In this study using python, TOPSIS is applied to rank the performance of ML algorithms. TOPSIS includes following main steps:

1. **Constructing the decision matrix:** A decision matrix X is constructed where each element $x_{ij}$ represents the performance of alternative i with respect to the criterion j:

$$A = \begin{bmatrix} x_{11} & \cdots & x_{1j} \\ \vdots & \ddots & \vdots \\ x_{i1} & \cdots & x_{ij} \end{bmatrix} \qquad (6)$$

where i is the number of alternatives and j is the number of criteria.

2. **Normalization:** The decision matrix is normalized to eliminate the effect of different units across criteria. The normalized value $r_{ij}$ is computed as:

$$r_{ij} = \frac{x_{ij}}{\sqrt{\sum_{i=1}^{n} x_{ij}^2}} \qquad (7)$$

3. **Weighted normalized matrix:** The weighted normalized decision matrix is constructed by multiplying each normalized value $r_{ij}$ by the corresponding weight $w_j$ for the criterion j:

$$v_{ij} = w_j \cdot r_{ij} \qquad (8)$$

4. **Determining the ideal and negative ideal solutions:** The ideal solution $A^+$ and the negative ideal solution $A^-$ are defined as:

$$A^+ = (\max(v_{ij} \text{ for benefit criteria}), \min(v_{ij} \text{ for cost criteria})) \qquad (9)$$

$$A^- = (\min(v_{ij} \text{ for benefit criteria}), \max(v_{ij} \text{ for cost criteria})) \qquad (10)$$

5. **Euclidean distance calculation:** The Euclidean distance from each alternative to the ideal solution and the negative solution is computed:

$$D_i^+ = \sqrt{\sum_{j=1}^{m} (v_{ij} - A_j^+)^2} \qquad (11)$$

$$D_i^- = \sqrt{\sum_{j=1}^{m} (v_{ij} - A_j^-)^2} \qquad (12)$$

where $D_i^+$ and $D_i^-$ are the Euclidean distance from each alternative to the ideal and negative solutions.

6. **Relative closeness to the ideal solution:** The relative closeness of each alternative to the ideal solution is calculated as:

$$C_i^* = \frac{D_i^-}{D_i^+ + D_i^-} \qquad (13)$$

where $C_i^*$ represents the relative closeness of alternatives i. Model with higher relative closeness are deemed closer to the ideal solution indicating better overall performance.

The results obtained from SLR and MCDM are explained and discussed in the following section.

## 3. Results and discussion

### 3.1. Systematic literature review

#### 3.1.1. Current trend in ML and LCA publications and applications by sectors

Seventy-eight papers were selected that explored ML and different aspects of life cycle assessment studies. Fig. 3 shows the publication trend from 2013 to 2025 (February 2025) with most papers published between 2019 and 2024. ML/AI integration in LCA and PCF has surged in the last five years with fewer than three articles per year before 2018. These papers appeared across forty-two journals from diverse fields. Popular Journals of ML in LCA studies include the Journal of Cleaner Production, Sustainable Production and Consumption, ACS Sustainable Chemistry and Engineering, Science of Total Environment, Sustainability, and the International Journal of Life Cycle Assessment. Fig. 4 illustrates the sectors where ML has been applied to LCA. The building construction and construction materials sector lead with almost 27 % of studies. Food and agriculture sector accounted for 17 % of articles, while 10 % focused on the chemical industry. Energy sectors were analyzed in around 13 % of papers followed by Solid waste management accounting for 9 %. Manufacturing sectors, fuel, automotive and transportation meanwhile accounted for 6.41 %, 5.13 % and 3.85 %. Almost 5 % of the studies didn't specify the specific application domain.

#### 3.1.2. Key issues addressed by ML application in LCA studies

The application of ML in LCA primarily addresses the prediction of missing values to bridge the data gaps, enhancing accuracy and reliability. Table 2 presents the different categories of key issues and gaps addressed by using ML and AI in LCA studies. A total of thirty-two papers have applied regression models to mitigate the data gaps in LCA. Thirteen of these papers concentrated in predicting inventory gaps crucial for accurate LCAs, using techniques like linear regression (LR), support vector regression (SVR), and neural networks (NN). ML algorithms have been used in three papers to predict missing characterization factors (CFs). Twenty-two papers focused on using ML to predict lifecycle impacts demonstrating improved precision with methods such as artificial neural networks (ANN) and ensemble techniques. Implementation of ML also addresses spatial and temporal limitations in traditional LCA methodologies. Five studies integrated and analyzed high dimensional, geographically and temporally variable data, thereby improving accuracy of impact prediction across different locations and timeframes. ML enabled for more precise modeling, scenario analysis and optimization, allowing for location-specific assessments of environmental impacts.

One of the critical issue in LCA domain is coupling with multi-objective optimization techniques for process design and optimization (Azapagic, 1999). Fifteen different studies focused on the intersection between life cycle assessment, surrogate modeling and optimization as well, one of the critical issue in LCA domain. Out of these fifteen studies, four studies used ML algorithms to develop surrogate models either for process modeling to predict the inventory data or facilitating the
<figure><img src="image_5.png" /><figcaption>Fig. 3. Publication trend: ML application in LCA studies.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. ML application in LCA studies for different sectors and industries.</figcaption></figure>

extensive LCA studies during initial product design and development phases. Eleven studies focused on the process optimization and life cycle optimization using ML based optimization techniques like genetic algorithm (GA), particle swarm optimization (PSW) and others.

ML techniques applied in LCA and PCF studies also enhanced uncertainty and sensitivity analyses of the results, with four studies each applying ML for these. Application of ML also aids in input parameter reduction, feature ranking, classification, and clustering with fifteen studies exploring these tasks using methods like principal component analysis (PCA), and random forest (RF) feature importance to streamline models and improve their interpretability.

### 3.1.3. ML application in various LCA stages

The application of ML in different phases of LCA studies has shown significant potential to enhance accuracy and reliability. As shown in Table 3 the relevant studies are categorized into four major groups based on their focus within the LCA framework. In the Goal and Scope definition phase, the use of ML is still largely unexplored, with only one study (Ji et al., 2021) highlighting its application to predict building life expectancy. This study demonstrated that deep learning (DL) models outperform classical ML algorithms in predicting building life expectancy, reducing estimation errors and thereby improving LCA accuracy. In the Life Cycle Inventory (LCI) phase, ML techniques have been extensively applied to predict product yield and characteristics, fill missing LCI data, and estimate the inventory data. Twenty-one studies
<table><caption>**Table 2**
Key issues addressed by application of ML in LCA studies.</caption><thead><tr><th>Key issues (gaps) addressed</th><th>References</th></tr></thead><tbody><tr><td>Data gaps - Predicting unknown life cycle inventories</td><td>(Ascher et al., 2022; Cheng et al., 2020a, 2020b; Ghafarian Nia et al., 2025; Hajabdollahi Ouderji et al., 2023; Ji et al., 2021; Khadem et al., 2022; Long and Liu, 2023; Meng et al., 2019; Omidkar et al., 2024; Ren et al., 2023; Walker et al., 2020; Wan et al., 2024)</td></tr><tr><td>Data gaps - Characterization factors</td><td>(Hou et al., 2020; Marvuglia et al., 2015; Song et al., 2022)</td></tr><tr><td>Data gaps - Impact assessment</td><td>(Akhshik et al., 2022; Arshad et al., 2023; Aseel et al., 2021; Chen et al., 2020; Javadi et al., 2021; Karka et al., 2022; Khoshnevisan et al., 2014; Kleinekorte et al., 2023; Koyamparambath et al., 2022; Mohammadi Kashka et al., 2023; Naser et al., 2023a; Naser et al., 2023b; Navas-Anguita et al., 2019; Nguyen et al., 2021; Peng et al., 2024; Ploszaj-Mazurek et al., 2020; Sharafi et al., 2023; Shekoohiyan et al., 2023; Tajuddeen et al., 2023; Xing et al., 2023; Li et al., 2023; Zhang et al., 2024)</td></tr><tr><td>Spatial and temporal considerations</td><td>(Lee et al., 2020; Romeiko et al., 2020a, 2020b; Slapnik et al., 2015; Su et al., 2023)</td></tr><tr><td>Surrogate modeling and optimization</td><td>(Amini Toosi et al., 2022; Corral-Bobadilla et al., 2024; Dabbaghi et al., 2021; Hajabdollahi Ouderji et al., 2023; Huntington et al., 2023; Jian et al., 2021; Karamian et al., 2023; Kazemeini and Swei, 2023; Navas-Anguita et al., 2019; Nguyen et al., 2021; Polo-Mendoza et al., 2023; Sharif and Hammad, 2019; Thrampoulidis et al., 2021; Ullah et al., 2022; Zhu et al., 2020)</td></tr><tr><td>Uncertainty and sensitivity of LCA models</td><td>(Baehr et al., 2024; Long and Liu, 2023; Meng et al., 2019; Ziyadi and Al-Qadi, 2019)</td></tr><tr><td>Dimension reduction, input parameters</td><td>(Chen et al., 2023; Manan et al., 2024; Marvuglia et al., 2015; Ozbilen et al., 2013; Su et al., 2023; Xikai et al., 2019; Li et al., 2023)</td></tr><tr><td>Feature ranking, correlation analysis</td><td>(Abdella et al., 2020; Naser et al., 2023b; Su et al., 2023; Romeiko et al., 2020a)</td></tr><tr><td>Classification and clustering</td><td>(Abdella et al., 2020; Lang et al., 2024; Prioux et al., 2023; Satinet and Fouss, 2022)</td></tr></tbody></table>

utilized models such as Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Random Forest (RF) and others for predicting inventory data. Noteworthy studies include those by (Omidkar et al., 2024) and (Hajabdollahi Ouderji et al., 2023), which used ML for bio-oil and biogas yield predictions, respectively. Studies by (Sharafi et al., 2023) and (Zhang et al., 2024), (Zhao et al., 2021) employed models like ANN and decision trees (DT) to forecast missing inventory data, demonstrating the utilization of ML in addressing data gaps in LCA.

In the Life Cycle Impact Assessment (LCIA) phase, ML techniques were applied in thirty-six studies, focusing on predicting environmental impacts and missing characterization factors for chemicals, agricultural products, and building materials. (Zhang et al., 2024) developed a deep learning model to predict the carbon footprints of chemicals, while (Kleinekorte et al., 2023) used GPR for process-specific LCA frameworks. Studies by (Lee et al., 2020) and (Tajuddeen et al., 2023) explored ML models for predicting agricultural impacts and the global warming potential (GWP) of building materials, respectively. In the interpretation and decision-making phases, eight studies highlighted the potential of ML to provide accurate predictions, identify key impact drivers, and facilitate scenario analyses. (Arshad et al., 2023), integrated LCA results with ANN for predictive assessments, and (Naser et al., 2023b), used ML for correlation analysis in additive manufacturing. Twelve studies have also demonstrated ML applications across multiple LCA phases, enhancing overall analysis comprehensiveness and accuracy. Out of those twelve, seven papers used ML algorithms during Life Cycle Inventory and Impact Assessment phases, two papers applied ML during Life Cycle Inventory and Interpretation phase. (Wan et al., 2024) used ANN for both inventory data prediction and interpretation, while (Kaab et al., 2019) and (Khanali et al., 2017) applied ML during inventory and impact assessment phases to predict energy consumption and environmental impacts, underscoring the integrative power of ML in LCA.

<table><caption>**Table 3**
ML application in various life cycle stages.</caption><thead><tr><th>LCA phases</th><th>Application subcategories</th><th>References</th></tr></thead><tbody><tr><td>Goal and scope definition</td><td>Life span prediction</td><td>(Ji et al., 2021)</td></tr><tr><td>Life cycle inventory</td><td>Foreground inventory data</td><td>(Ascher et al., 2022; Cheng et al., 2020a, 2020b; Corral-Bobadilla et al., 2024; Ghafarian Nia et al., 2025; Hajabdollahi Ouderji et al., 2023; Huntington et al., 2023; Liao et al., 2020; Long and Liu, 2023; Negri et al., 2022; Omidkar et al., 2024; Polo-Mendoza et al., 2023; Ren et al., 2023; Ullah et al., 2022; Walker et al., 2020)</td></tr><tr><td></td><td>Filling in missing inventory data</td><td>(Khadem et al., 2022; Meng et al., 2019; Nguyen et al., 2019; Sharafi et al., 2023; Sharif and Hammad, 2019)</td></tr><tr><td rowspan="3">Life cycle impact assessment</td><td>Chemical sector-impact prediction</td><td>(Kleinekorte et al., 2023; Zhang et al., 2024; Zhu et al., 2020)</td></tr><tr><td>Chemical sector - Missing characterizing factors prediction</td><td>(Hou et al., 2020; Marvuglia et al., 2015; Slapnik et al., 2015; Song et al., 2022)</td></tr><tr><td>Food and agriculture sector impact prediction</td><td>(Khoshnevisan et al., 2014; Lee et al., 2020; Mohammadi Kashka et al., 2023; Mousavi-Avval et al., 2017; Romeiko et al., 2020a, 2020b)</td></tr><tr><td></td><td>Building and building materials impact prediction</td><td>(D'Amico et al., 2019; Koyamparambath et al., 2022; Li et al., 2023; Ploszaj-Mazurek et al., 2020; Sharif and Hammad, 2019; Tajuddeen et al., 2023; Thrampoulidis et al., 2021; Xing et al., 2023)</td></tr><tr><td></td><td>Others</td><td>(Aseel et al., 2021; Asif et al., 2019; Chen et al., 2023; Elouariaghi et al., 2022; Javadi et al., 2021; Karka et al., 2022; Lamnatou et al., 2024; Lang et al., 2024; Navas-Anguita et al., 2019; Nguyen et al., 2021; Ozbilen et al., 2013; Peng et al., 2024; Shekoohiyan et al., 2023; Su et al., 2023)</td></tr><tr><td>Life cycle interpretation</td><td>Downstream decision-making, uncertainty analysis, identifying key drivers, scenario analysis</td><td>(Arshad et al., 2023; Kazemeini and Swei, 2023; Naser et al., 2023a; Prioux et al., 2023; Ziyadi and Al-Qadi, 2019)</td></tr><tr><td rowspan="3">Multiple phases</td><td>Inventory and interpretation</td><td>(Wan et al., 2024)</td></tr><tr><td>Inventory and impact assessment</td><td>(Kaab et al., 2019; Khanali et al., 2017; Long and Liu, 2023; Manan et al., 2024; Martínez-Ramón et al., 2024; Nabavi-Pelesaraei et al., 2018)</td></tr><tr><td>Parameter selection, emission prediction</td><td>(Thilakarathna et al., 2020; Xikai et al., 2019; Naser et al., 2023a)</td></tr><tr><td></td><td>Impact assessment and interpretation, uncertainty assessment</td><td>(Baehr et al., 2024)</td></tr></tbody></table>

### 3.1.4. ML models in LCA studies

The application of ML models in LCA and PCF spans a range of tasks including prediction, forecasting, correlation analysis, extrapolation, classification, clustering, and uncertainty analysis as shown in Fig. 5. Individual models such as ANN, RF, SVM, and Gradient Boosting Regression Trees (GBRT) are used, with ANN being the most frequently used.
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** ML models and their tasks in different literature studies (GBR: Gradient Boosting Regression, BRT: Boosted Regression Tree, KRR: Kernel Ridge Regression, ENR: Ensemble Nearest Neighbors, GB: Gradient Boosting, NN: Neural Network, GBM: Gradient Boosting Machine, GEP: Gene Expression Programming, GBRT: Gradient Boosted Regression Tree, XGB: Extreme Gradient Boosting, RR: Ridge Regression, BR: Bayesian Regression, DNN: Deep Neural Network, PIANN: Physics Integrated Artificial Neural Network, ABC: Ant Bees Colonies, LASSO: Least Absolute Shrinkage and Selection Operator, LRGB: Linear Regression Gradient Boosting, LGB: Light Gradient Boosting, RBFN: Radial Basis Function Network, DL: Deep Learning, LR: Linear Regression, ANN: Artificial Neural Network, RF: Random Forest, MLR: Multiple Linear Regression, SVM: Support Vector Machine, ANFIS: Adaptive Neuro-Fuzzy Inference Systems, PR: Pattern Recognition, DT: Decision Tree, kNN: k Nearest Neighbors, MLP: Multilayered Perceptron, CFNN: Counter propagation Feedforward Neural Network, PCA: Principle Component Analysis, LSR: Least Square Regression, RNN: Regression Neural Network, GPR: Gaussian Process Regression, BSL: Bayesian Semi supervised Learning, DBN: Deep Belief Network, GA: Genetic Algorithm, PSO: Particle Swarm Optimization).</figcaption></figure>

employed model. (Omidkar et al., 2024) used ANN for regression tasks, while (Zhang et al., 2024) applied ANN and RF for prediction, demonstrating the versatility and robustness of these models in handling various LCA-related tasks. Other studies, like those by (Hajabdollahi Ouderji et al., 2023), employed a combination of models including ANN, support vector machines (SVM), and GPR, with GPR emerging as the best model for prediction tasks. (Long and Liu, 2023) utilized RF alongside optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) for accurate predictions.

Ensemble methods and more complex frameworks are also prominent in LCA applications. For instance, (Naser et al., 2023b) compared multiple models including Linear Regression (LR), Decision Trees (DT),

RF, and extreme gradient boosting (XGB), with XGB and Multilayer Perceptron (MLP) performing best in correlation analysis tasks. Studies like those by (Kleinekorte et al., 2023) leveraged GPR and ANN for prediction tasks, while (Tajuddeen et al., 2023) and (Akhshik et al., 2022) compared various models such as Multiple Linear Regression (MLR) and boosted decision trees for prediction and regression tasks, respectively. Advanced tasks such as dimension reduction and clustering were addressed using models like Principal Component Analysis (PCA) and k-means clustering, as seen in the work by (Xikai et al., 2019) and (Prioux et al., 2023). These diverse applications highlight the significant role of ML in enhancing the accuracy, reliability, and comprehensive-ness of LCA studies.

Among seventy eight papers studied, thirty six studies utilized a
single ML models, highlighting the preference for dedicated and specialized application of individual models. Similarly thirty six other papers employed multiple models, often comparing their performance to identify the most effective approaches for specific tasks. Remaining six papers used optimization algorithms alongside ML models which indicates that the integration of optimization techniques is relatively uncommon, however increasing in this field. Fig. 5 presents the Sankey diagram prepared based on the ML models used for the different tasks in each paper. Artificial Neural Network (ANN) is one of the most commonly used models followed by Random Forest (RF) along with Decision Trees (DT), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB). Fig. 6 summarizes the inputs used by different ML models and predicted outputs. The most common outputs of the ML models are impact categories (carbon footprints, global warming potentials). The input used in ML models varied from feedstock characteristics, operational parameters, agricultural parameters, energy variables, design parameters and so on.

### 3.1.5. ML models data sources and data sizes in LCA studies

The reviewed literature highlights a diverse range of data sources used in training ML models for LCA applications. Presented in Table 4, data sources are classified into four categories; scientific literature and lab experiments (eighteen studies), LCA and other simulation models (fifteen studies), established databases (nine studies), and empirical and industry-specific data (sixteen studies). Nineteen studies did not mention the data sources. Studies employed simulation models like GHGenius and Monte Carlo to generate detailed datasets; with (Khadem

et al., 2022) using 917 lifecycle GHG emission samples and (Li et al., 2023) generating 1.6 million data points from Monte Carlo simulations. Established databases such as Eco Invent, and OKOBAUDAT are also used as data sources (Zhang et al., 2024) using data from Eco Invent v3.8 and PubChem to generate 547 chemical datasets. Datasets from scientific literature and lab experiments were commonly used (Hajabdollahi Ouderji et al., 2023) compiling 167 biogas yield datasets from previous studies. Empirical and industry-specific data were significant as well (Naser et al., 2023a) including 200 entries from additive manufacturing data, and (Ji et al., 2021) utilizing over 1 million historical building data entries from Korea. These varied sources provide comprehensive and diverse datasets crucial for accurate ML model training and evaluation in LCA studies.

The sample sizes for training ML models varied significantly across these studies, ranging from a few hundred to several thousand even millions of data points. The smallest sample included only thirty-three experimental data on the study done by (Long and Liu, 2023) and the largest data sample was 1.6 million samples through Monte Carlo simulation by (Li et al., 2023). Around 68 % of the data size were less than 1000, 25 % of data size were between 1000 and 10,000 and just 7 % (only three out of seventy-eight) studies had data size more than 10,000. Established databases and LCA simulation models provided larger data size. Industry-specific sources, laboratory experimental data and scientific literature-based data often offered smaller yet highly relevant datasets depending on the study's scope and focus.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Categories of inputs and outputs used for different ML models in literature studies.</figcaption></figure>
<table><caption>**Table 4**

Data sources and sample size used in ML models in LCA studies.</caption><thead><tr><th>References</th><th>Data Sources and sample sizes</th></tr></thead><tbody><tr><td><strong>Scientific literature and lab experiments</strong></td><td></td></tr><tr><td>(Omidkar et al., 2024)</td><td>245 samples datasets based on previous literature</td></tr><tr><td>(Hajabdollahi Ouderji et al., 2023)</td><td>258 Biogas yield and seventy-one methane yield datasets from the previous literature</td></tr><tr><td>(Long and Liu, 2023)</td><td>65 bioethanol production experimental datasets from fifteen research publications</td></tr><tr><td>(Tajuddeen et al., 2023)</td><td>Scientific literature containing data from environmental product declarations</td></tr><tr><td>(Akhshik et al., 2022)</td><td>Limited datasets based on scientific literature</td></tr><tr><td>(Cheng et al., 2020a)</td><td>538 measurements from fifty-four literature sources</td></tr><tr><td>(Thilakarathna et al., 2020)</td><td>Literature and Experimental datasets from 26 six research papers</td></tr><tr><td>(Cheng et al., 2020b)</td><td>800 entries from seventy literature studies</td></tr><tr><td>(Meng et al., 2019)</td><td>114 observations from field test of Dual Fuel engines</td></tr><tr><td>(Asif et al., 2019)</td><td>756 data samples from the literature</td></tr><tr><td>(Prioux et al., 2023)</td><td>Secondary data from scientific articles</td></tr><tr><td>(Ghafarian Nia et al., 2025)</td><td>1765 data samples from scientific articles</td></tr><tr><td>(Ullah et al., 2022)</td><td>305 Scientific literature</td></tr><tr><td>(Ren et al., 2023)</td><td>324 data collection from scientific literatures</td></tr><tr><td>(Ascher et al., 2022)</td><td>312 data collected from scientific research articles</td></tr><tr><td>(Manan et al., 2024)</td><td>270 experimental Data</td></tr><tr><td>(Dabbaghi et al., 2021)</td><td>240 experimental data</td></tr><tr><td>(Xing et al., 2023)</td><td>570 experimental data</td></tr><tr><td>(Corral-Bobadilla et al., 2024)</td><td>33 production process experimental data</td></tr><tr><td><strong>Simulations models</strong></td><td></td></tr><tr><td>(Khadem et al., 2022)</td><td>917 sample Lifecycle GHG emission data GHGenius model</td></tr><tr><td>(Ploszaj-Mazurek et al., 2020)</td><td>5000 datasets of single/multiple building layouts simulation</td></tr><tr><td>(Sharif and Hammad, 2019)</td><td>463 renovation modeling scenarios and ten renovation methods</td></tr><tr><td>(D'Amico et al., 2019)</td><td>1506 building datasets based on LCA simulation models</td></tr><tr><td>(Xikai et al., 2019)</td><td>Process-based LCA model for 207 residential buildings</td></tr><tr><td>(Nguyen et al., 2019)</td><td>Day Cent biogeochemical simulation model</td></tr><tr><td>(Khanali et al., 2017)</td><td>200 samples from LCA data modeling</td></tr><tr><td>(Ozbilen et al., 2013)</td><td>150 datasets from LCA simulation models</td></tr><tr><td>(Li et al., 2023)</td><td>1,600,000 datasets obtained from Montecarlo simulations</td></tr><tr><td>(Abdella et al., 2020)</td><td>Input-Output tables of the U.S. economy for twenty-nine sectors</td></tr><tr><td>(Jian et al., 2021)</td><td>772 process simulation data</td></tr><tr><td>(Huntington et al., 2023)</td><td>10,000 process simulation data</td></tr><tr><td>(Negri et al., 2022)</td><td>320 process simulation data</td></tr><tr><td>(Polo-Mendoza et al., 2023)</td><td>4096 process simulation data</td></tr><tr><td>(Kazemeini and Swei, 2023)</td><td>10,000 process simulation data</td></tr><tr><td><strong>Established database</strong></td><td></td></tr><tr><td>(Zhang et al., 2024)</td><td>547 IDEA v2.3, Eco invent v3.8, PubChem, ChEMBL</td></tr><tr><td>(Tajuddeen et al., 2023)</td><td>120 datasets from open-source OKOBAUDAT and Eco-invent</td></tr><tr><td>(Koyamparambath et al., 2022)</td><td>980 Environmental Product Declarations (EPDs), and OKOBAUDAT</td></tr><tr><td>(Hou et al., 2020)</td><td>2307 observations from EPA Comptox Chemistry dashboard</td></tr><tr><td>(Slapnik et al., 2015)</td><td>176 chemical substances, PPDB, ReCiPe, FURS</td></tr><tr><td>(Marvuglia et al., 2015)</td><td>3051 number of observations from scientific literatures</td></tr><tr><td>(Zhu et al., 2020)</td><td>224 samples from Eco Invent v3.5 database</td></tr><tr><td>(Baehr et al., 2024)</td><td>5251 Environmental Product Declaration data</td></tr><tr><td>(Q. Nguyen et al., 2021)</td><td>7096 Public ESG reports</td></tr><tr><td><strong>Empirical and industry-specific data</strong></td><td></td></tr><tr><td>(Zhang et al., 2024)</td><td>547 Chemical Production Data from the chemical industry</td></tr><tr><td>(Naser et al., 2023b)</td><td>200 data entries compromising five design features and seven process features from additive manufacturing data</td></tr></tbody></table>

<table><caption>**Table 4 (continued)**</caption><thead><tr><th>References</th><th>Data Sources and sample sizes</th></tr></thead><tbody><tr><td>(Sharafi et al., 2023)</td><td>50 years of historical datasets from the agriculture industry</td></tr><tr><td>(Su et al., 2023)</td><td>8659 survey datasets from thirteen cities</td></tr><tr><td>(Mousavi-Avval et al., 2017)</td><td>150 individual canola farms</td></tr><tr><td>(Karka et al., 2022)</td><td>138 datasets from the flow model of the pilot plant, industry experts, and literature</td></tr><tr><td>(Satinet and Fous, 2022)</td><td>1136 clothing products data from industry experts</td></tr><tr><td>(Ji et al., 2021)</td><td>1,010,190 recorded historical data describing the building and built and demolished</td></tr><tr><td>(Romeiko et al., 2020a)</td><td>6000 records from the year 2000-2008 from agricultural sectors</td></tr><tr><td>(Kaab et al., 2019)</td><td>120 agricultural industries</td></tr><tr><td>(Nabavi-Pelesaraei et al., 2018)</td><td>240 paddy producer farm data</td></tr><tr><td>(Khoshnevisan et al., 2014)</td><td>130 farm data</td></tr><tr><td>(Mohammadi Kashka et al., 2023)</td><td>175 farm data</td></tr><tr><td>(Aseel et al., 2021)</td><td>Industry Specific reports</td></tr><tr><td>(Thrampoulidis et al., 2021)</td><td>11,525 residential building data</td></tr><tr><td>(Walker et al., 2020)</td><td>47 different commercial building data</td></tr></tbody></table>

### 3.1.6. ML model training and evaluation in LCA studies

#### 3.1.6.1. Data splitting, model training, and evaluation method.
Across the different studies, the datasets were sampled and split randomly between training, validation, and testing sets. The 70/30 and 80/20 splits are the most common, balancing between a substantial training set and a meaningful testing set to validate the model's performance. Some studies used more complex splits to incorporate the validation sets in addition to training and testing sets. Training datasets represented a wide range of the total data in the reviewed studies using holdout methods. (Omidkar et al., 2024) divided their data into a training set of 183 samples and a test of 62 samples. In contrast D'Amico et al. (2019) allocated 85 % of the data for training and 15 % for validation. Nguyen et al. (2021) varied the training ratio from 10 % to 70 %, incremented by steps of 20 %, with a constant 20 % test set. Mousavi-Avval et al. (2017) partitioned the 80 % training and 20 % for testing. In the studies using cross-validation, the datasets were divided into training, validation, and testing subsets. The most common split ratio was 70 % training, 15 % validation, and 15 % testing, as used by multiple studies including Khanali et al. (2017), Asif et al. (2019) and Naser et al. (2023b) used 80 % for training and 20 % for validation/testing. Some studies applied more unique strategies. Meng et al. (2019) in their study split their datasets 1000 times randomly using Monte Carlo cross-validation; Karka et al. (2022) split their datasets 500 times. Before modifying to 80 % training and 20 % testing, Akhshik et al. (2022) divided the data into 70 % training, 15 % testing, and 15 % validation. The testing dataset made about 10 % to 30 % of the total, whereas the combined training and validation datasets accounted for 70 % to 90 % of the total. The division of training, validation, and testing datasets was not mentioned in nine papers. Table 5 summarizes the model training and evaluation methods used for ML models in LCA studies.

In the holdout methods, datasets are split into training and testing sets. The ML model is trained on the training set and evaluated on the testing set. Four studies used this strategy and these studies had well-defined training and testing sets. Cross-validation divides the entire dataset into multiple training, validation, and testing sets. The training and validation sets are used to optimize the model and its parameters, while the testing set assesses the models' performance and generalization capability. Twenty papers utilized the K-fold cross-validation techniques as they provide a more comprehensive evaluation of the model's performance across different datasets. The 10-fold cross-validation technique is preferred more than other cross-validation techniques.
<table><caption>**Table 5**
Summary of model training strategies in different studies.</caption><thead><tr><th>Model training strategy</th><th>Types</th><th>References</th></tr></thead><tbody><tr><td>Holdout strategy</td><td></td><td>(Abdella et al., 2020; D'Amico et al., 2019; Mousavi-Avval et al., 2017; Nguyen et al., 2019)</td></tr><tr><td rowspan="3">k-Fold cross validation</td><td>One-fold cross-validation</td><td>(Asif et al., 2019; Khanali et al., 2017; Ozbilen et al., 2013; Zhu et al., 2020)</td></tr><tr><td>Three-fold cross-validation</td><td>(Koyamparambath et al., 2022; Romeiko et al., 2020a)</td></tr><tr><td>Five-fold cross-validation</td><td>(Ascher et al., 2022; Hajabdollahi Ouderji et al., 2023; Hou et al., 2020; Naser et al., 2023b; Satinet and Fouss, 2022; Song et al., 2022; Zhang et al., 2024)</td></tr><tr><td rowspan="3">Monte Carlo cross validation</td><td>Ten-fold cross-validation</td><td>(Cheng et al., 2020a; Cheng et al., 2020b; Ghafarian Nia et al., 2025; Khadem et al., 2022; Lee et al., 2020; Manan et al., 2024; Nguyen et al., 2021; Romeiko et al., 2020b; Slapnik et al., 2015; Thilakarathna et al., 2020; Walker et al., 2020; Xikai et al., 2019)</td></tr><tr><td></td><td>(Meng et al., 2019; Tajuddeen et al., 2023)</td></tr></tbody></table>

**3.1.6.2. Model training and evaluation metrics.** Different evaluation metrics are used to assess model performance in the reviewed studies. Since most ML models aim to map inputs to a continuous response variable, metrics that measure the correlation between predicted and observed response variables are commonly used. The most frequently used evaluation metrics are Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Mean Absolute Percentage Error (MAPE). R² is the most frequently used metric appearing in thirty-two studies, RMSE in twenty-two papers, and MAPE, and MSE in ten and nine studies, respectively. The derivatives from these three metrics were also used frequently for evaluating the prediction accuracy. These include Mean Square Error (MSE), mean absolute error (MAE), correlation coefficient, coefficient of variance, root relative square error, and normalized root mean square error (NRMSE). Table 6 classifies the selected studies based on the evaluation metrics. Many studies used more than one evaluation metric, and the values for these metrics vary across different studies. For instance (Romeiko et al., 2020a), achieved R² values ranging from 0.64 to 0.78 for predicting life cycle global warming, eutrophication, and acidification impacts. (Hou et al., 2020) reported an R² of 0.63 for predicting hazardous concentrations. (Liao et al., 2020) achieved an R² of 0.971 for predicting total activated carbon (AC) yield, which was used as inventory data to understand the environmental implication of activated carbon is diverse biomass feedstock.

**3.1.7. Advantages of ML applications in LCA studies**

ML based LCA enhance the predictive accuracy and efficiency of LCA. By uncovering complex patterns within large datasets, ML models achieve superior predictive accuracy, as demonstrated by Zhang et al. (2024), who reported RMSE values significantly lower than conventional models. Leveraging large datasets allows ML algorithms to provide precise estimates, reducing uncertainty in LCA calculations, as demonstrated by (D'Amico et al., 2019) who achieved a high R² of 0.99991 in predicting carbon footprints of buildings. ML algorithms streamline data processing and analysis, automating labor-intensive tasks and facilitating real-time monitoring and analysis. Naser et al. (2023a) showcased automation in LCA studies of additive manufacturing products. The same authors in another study proposed a framework for real-time data integration to ensure timely and accurate assessments (Naser et al., 2023b).

ML models also excel in handling complex, high-dimensional data, enabling multi-factor analysis and effective utilization of big data. Studies done by Song et al. (2022) and Abdella et al. (2020)

<table><caption>**Table 6**
Subgrouping of the studies based on evaluation metrics.</caption><thead><tr><th>Evaluation metrics</th><th>References</th></tr></thead><tbody><tr><td>Root Mean Square Error (RMSE)</td><td>(Akhshik et al., 2022; Ascher et al., 2022; Baehr et al., 2024; Cheng et al., 2020a, 2020b; Corral-Bobadilla et al., 2024; D'Amico et al., 2019; Ghafarian Nia et al., 2025; Hajabdollahi Ouderji et al., 2023; Hou et al., 2020; Ji et al., 2021; Kaab et al., 2019; Karka et al., 2022; Khadem et al., 2022; Khoshnevisan et al., 2014; Lang et al., 2024; Long and Liu, 2023; Manan et al., 2024; Mousavi-Avval et al., 2017; Nabavi-Pelesaraei et al., 2018; Naser et al., 2023b; Omidkar et al., 2024; Polo-Mendoza et al., 2023; Ren et al., 2023; Sharafi et al., 2023; Shekoohiyan et al., 2023; Slapnik et al., 2015; Tajuddeen et al., 2023; Thilakarathna et al., 2020; Ullah et al., 2022; Walker et al., 2020; Wan et al., 2024; Xikai et al., 2019; Xing et al., 2023; Zhang et al., 2024)</td></tr><tr><td>Coefficient of Determination (R²)</td><td>(Arshad et al., 2023; Ascher et al., 2022; Baehr et al., 2024; Cheng et al., 2020a; Cheng et al., 2020b; Corral-Bobadilla et al., 2024; D'Amico et al., 2019; Ghafarian Nia et al., 2025; Hajabdollahi Ouderji et al., 2023; Hou et al., 2020; Ji et al., 2021; Jian et al., 2021; Kaab et al., 2019; Karka et al., 2022; Khanali et al., 2017; Kleinekorte et al., 2023; Koyamparambath et al., 2022; Lee et al., 2020; Li et al., 2023; Liao et al., 2020; Long and Liu, 2023; Manan et al., 2024; Meng et al., 2019; Mohammadi Kashka et al., 2023; Mousavi-Avval et al., 2017; Nabavi-Pelesaraei et al., 2018; Naser et al., 2023b; Negri et al., 2022; Nguyen et al., 2019; Omidkar et al., 2024; Ozbilen et al., 2013; Polo-Mendoza et al., 2023; Ren et al., 2023; Romeiko et al., 2020a; Sharafi et al., 2023; Shekoohiyan et al., 2023; Song et al., 2022; Tajuddeen et al., 2023; Thilakarathna et al., 2020; Thrampoulidis et al., 2021; Ullah et al., 2022; Wan et al., 2024; Xikai et al., 2019; Xing et al., 2023; Zhao et al., 2021; Zhu et al., 2020)</td></tr><tr><td>Correlation Coefficient (R)</td><td>(Khoshnevisan et al., 2014; Romeiko et al., 2020a; Slapnik et al., 2015)</td></tr><tr><td>Mean Absolute Error (MAE)</td><td>(Akhshik et al., 2022; Mousavi-Avval et al., 2017; Omidkar et al., 2024; Su et al., 2023; Tajuddeen et al., 2023; Thilakarathna et al., 2020; Xikai et al., 2019)</td></tr><tr><td>Mean Squared Error (MSE)</td><td>(Arshad et al., 2023; Hajabdollahi Ouderji et al., 2023; Koyamparambath et al., 2022; Li et al., 2023; Negri et al., 2022; Polo-Mendoza et al., 2023; Romeiko et al., 2020a; Satinet and Fouss, 2022; Sharif and Hammad, 2019; Song et al., 2022; Su et al., 2023)</td></tr><tr><td>Nash-Sutcliffe Coefficient (NSE)</td><td>(Sharafi et al., 2023)</td></tr><tr><td>Mean Absolute Percentage Error (MAPE)</td><td>(D'Amico et al., 2019; Hajabdollahi Ouderji et al., 2023; Kaab et al., 2019; Khoshnevisan et al., 2014; Meng et al., 2019; Nabavi-Pelesaraei et al., 2018; Ploszaj-Mazurek et al., 2020; Zhang et al., 2024)</td></tr><tr><td>Median Percentage Error (MPE)</td><td>(Zhang et al., 2024)</td></tr><tr><td>Kendall's Tau</td><td>(Satinet and Fouss, 2022)</td></tr><tr><td>Ordinal Classification Index (OCI)</td><td>(Satinet and Fouss, 2022)</td></tr><tr><td>Wilcoxon Signed Rank Test</td><td>(Nguyen et al., 2021)</td></tr></tbody></table>

demonstrated the ability of ML models to analyze large number of factors influencing environmental impacts. ML models offer customization and adaptability, with the capacity for tailored LCA models and continuous improvement through adaptive learning. They also enhance decision support by facilitating scenario analysis and optimization (Karka et al., 2022; Long and Liu, 2023). Improved data integration and interoperability allow ML models to provide holistic environmental impact analyses (Hajabdollahi Ouderji et al., 2023; Zhang et al., 2024). ML models' scalability and ability to translate data into actionable insights ensure they are valuable tools for large-scale assessments and practical applications (Meng et al., 2019; Satinet and Fouss, 2022).
### 3.1.8. Challenges and limitations of ML application in LCA studies

The application of ML in LCA studies faces significant challenges. Conducting LCA and training ML models are costly and require extensive manually curated, well-structured training data (Venkatraj and Dixit, 2022). High computational costs and training times impact the accuracy of results, necessitating strategies like dimensionality reduction to maintain accuracy and timely validation (Omidkar et al., 2024). The complexity and black-box nature of ML models such as Deep Neural Networks (DNN), RF, ANN, and SVR/SVM make it difficult to interpret the model performance (Hou et al., 2020; Xikai et al., 2019). The lack of standardization in model training and evaluation criteria further complicates the selection and implementation of these models (Ghoroghi et al., 2022).

Early design stages often lack the detailed information needed for comprehensive assessments, making delayed LCA impractical (Naser et al., 2023b). Standardizing conditions, experiments, and reporting is essential for consistency and reproducibility between predictive modeling and experimental studies. The success of data-driven LCA approaches is hindered by the scarcity of high-quality datasets, with many studies relying on small sample sizes. Addressing these issues requires continuous efforts to reduce computational complexity, improve model transparency, standardize assessment protocols, and enhance data collection processes, thereby maximizing the potential of ML in environmental impact assessments (Ghoroghi et al., 2022; Romeiko et al., 2024).

## 3.2. Multiple criteria decision making

### 3.2.1. Selection and evaluation criteria for MCDM modeling

As discussed in Section 2.2.1 machine learning algorithms with highest frequency of occurrence were identified to select them as alternatives. Fig. 7 illustrates the most frequent distribution of machine learning algorithms across the reviewed studies. ANN is the most employed model appearing in 42 studies, followed by RF, with 17 occurrences. SVM, DT and XGB appear 15, 12 and 11 times respectively, meanwhile LR, GPR and ANFIS exhibit comparatively lower frequencies ranging from 5 to 8 appearances.

After identifying frequently used ML based tools, 11 criteria were identified to assess these algorithms analytically: model accuracy ($R^2$)

and RMSE scores), data size, data sources variations, model interoperability, model tasks versatility, application domain, application in different stages of LCA studies, computational complexity, scalability, and interpretability. The models are scored based on the selection criteria explained in methods Section 2.2.1. Table 7 summarizes the score given to each model based on the selection criteria (see Support information S1 and S2).

Fig. 8 visualizes the comparative performance of the models in the radar plots for different criteria identified above and the relevant score based on Table 7. These plots provide a holistic view of the strengths and weaknesses of each model allowing for an intuitive comparison. ANN demonstrates strong performance across multiple criteria like accuracy, application domain, data sources, task versatility and so on showing overall better performance. RF excels in data size and scalability, highlighting its robustness in handling larger dataset and scaling well for real time application. SVM and XGB exhibit superior $R^2$ performance with their ability to model data accurately. DT is distinguished for its interpretability and scalability, but its performance in RMSE and computational complexity is less favorable. LR excels in task versatility but lags in scalability and data sources. GPR performs the best in RMSE, suggesting it achieves the lowest prediction errors, though it struggles with scalability. ANFIS presents a balanced profile across most criteria but shows limitations in task versatility and data size. It can be clearly seen that one algorithm outperforms other on one criterion, and underperforms on other criteria. As explained in Section 2.2 MCDM approach using AHP and TOPSIS is used to analytically assess the performance of these models and the results are explained in upcoming section.

### 3.2.2. Priority weights from analytical hierarchy process

AHP is employed in python to calculate the weights to various criteria influencing the model performance. Using Eq. (1) the number of pairwise comparisons for 11 criteria used in this study was calculated to be 55. Expert judgement based on Saaty's 9-point scale (Saaty, 2003) was used to quantify the relative importance to criteria over another and to develop the pairwise decision matrix. (See Supplementary information SI3 for details, python script and pairwise decision matrix.)

The consistency of these judgement was verified through Consistency Ratio (CR) calculated using Eq. (5). It has the value of 0.05940

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** ML models and frequency of use in LCA studies (ANN: Artificial Neural Network, SVM: Support Vector Machine, RF: Random Forest, DT: Decision Tree, XGB: Extreme Gradient Boosting, LR: Linear Regression, ANFIS: Adaptive Neuro-Fuzzy Inference Systems, GPR: Gaussian Process Regression).</figcaption></figure>
<table><caption>**Table 7**

Evaluation criteria and model score.</caption><thead><tr><th>Models criteria and score</th><th>ANFIS</th><th>ANN</th><th>DT</th><th>GPR</th><th>LR</th><th>RF</th><th>SVM</th><th>XGB</th></tr></thead><tbody><tr><td>R<sup>2</sup> score</td><td>0.999</td><td>0.998</td><td>0.990</td><td>0.890</td><td>0.934</td><td>0.950</td><td>0.970</td><td>0.980</td></tr><tr><td>RMSE score</td><td>0.015</td><td>0.050</td><td>0.070</td><td>0.075</td><td>0.080</td><td>0.060</td><td>0.040</td><td>0.029</td></tr><tr><td>Data size (median)</td><td>140</td><td>463</td><td>697</td><td>281</td><td>200</td><td>673.5</td><td>547</td><td>668</td></tr><tr><td>Data sources variation</td><td>1</td><td>8</td><td>5</td><td>4</td><td>6</td><td>6</td><td>8</td><td>4</td></tr><tr><td>Interoperability</td><td>6</td><td>7</td><td>8</td><td>6</td><td>9</td><td>7</td><td>7</td><td>6</td></tr><tr><td>Tasks versatility</td><td>3</td><td>9</td><td>4</td><td>2</td><td>4</td><td>6</td><td>8</td><td>5</td></tr><tr><td>Application domain</td><td>1</td><td>9</td><td>5</td><td>3</td><td>5</td><td>6</td><td>9.5</td><td>5</td></tr><tr><td>Application LCA stages</td><td>5</td><td>7.5</td><td>7.5</td><td>5</td><td>7.5</td><td>7.5</td><td>7.5</td><td>7.5</td></tr><tr><td>Computational complexity</td><td>9</td><td>7</td><td>3</td><td>8</td><td>2</td><td>4</td><td>5</td><td>6</td></tr><tr><td>Scalability</td><td>7</td><td>8</td><td>7</td><td>6</td><td>9</td><td>8</td><td>7</td><td>8</td></tr><tr><td>Interpretability</td><td>4.0</td><td>3.0</td><td>9.0</td><td>8.0</td><td>10.0</td><td>7.0</td><td>5.0</td><td>6.5</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Eight frequently used ML algorithms performance comparison for selected 11 evaluation criteria's (RMSE: Root Mean Square Error, INTP: interpretability, SC: scalability, CC: computational complexity, ALS; application LCA stages, AD: application domain, TV: task versatility, INTO: interoperability, DSO: data sources variability, DSI: data size).</figcaption></figure>

which is within the acceptable limits; below 0.10 (Saaty, 2003), ensuring the decision making process was logical and coherent. This also validates the reliability and uniformity of the pairwise comparisons and weights assigned. Experts judgement was not used to obtain the weights of alternatives, as the information was extracted from Systematic Literature Review as presented in Table 7. The AHP process yielded a set of weighted criteria that reflect the decision priorities with accuracy (RMSE and R²) being the most significant determinant followed by scalability and interpretability. Table 8 presents the priority weights assigned to each criteria obtained using AHP process. It can be seen that the higher priority weight is assigned for accuracy underscoring the importance of model accuracy and error minimization in predictive

performance of ML algorithms in LCA studies. Moderately prioritized criteria include scalability, interpretability and data sources variability which reflect need for algorithms to be robust, easily understood and can handle varying data inputs ensuring practical applicability and reliability. Criteria such as data size, application domains, application domains application LCA stages are assigned lower priorities indicating while they contribute to the overall evaluation framework, they are considered less crucial relative to fundamental performance metric. Criteria like data size, application domain, application LCA stages are specific to the case studies and differ for different studies which justifies the assignment of lower priority weights and giving more priorities to the other performance metrics which are more generalizable.

<table><caption>**Table 8**

Priority weights assigned to the each criteria based on AHP.</caption><thead><tr><th>Criteria</th><th>Priority weights</th></tr></thead><tbody><tr><td>R<sup>2</sup> score</td><td>1.35E-1</td></tr><tr><td>RMSE score</td><td>1.55E-1</td></tr><tr><td>Data size</td><td>6.13E-2</td></tr><tr><td>Data sources variability</td><td>9.01E-2</td></tr><tr><td>Interoperability</td><td>7.11E-2</td></tr><tr><td>Task versatility</td><td>8.41E-2</td></tr><tr><td>Application domains</td><td>6.99E-2</td></tr><tr><td>Application LCA stages</td><td>4.47E-2</td></tr><tr><td>Computational complexity</td><td>8.32E-2</td></tr><tr><td>Scalability</td><td>1.01E-1</td></tr><tr><td>Interpretability</td><td>1.04E-1</td></tr></tbody></table>

### 3.2.3. Performance evaluation of ML algorithms using TOPSIS

TOPSIS is applied to calculate the performance score of the ML algorithms and rank them using a python script. The equations explained in Section 2.2.3 were used in the python script for each steps. Information collected from SLR as presented in Table 7 is used to develop the decision matrix. When assigning scores to the criteria for each model, they were identified as beneficial indicated by 1 and non-beneficial criteria indicated by -1. Beneficial criteria are those criteria for which higher the score is better. However, non-beneficial criteria are those criteria for which score closer to 0 is better (RMSE and computational complexity/computational time) in this case. Lower the RMSE and computational complexity, the better the model. The decision matrix is normalized using Eq. (7) and weighted using Eq. (8) and the criteria
weights derived from AHP.

Each model's distance to both the ideal and negative-ideal solutions was computed using Eqs. (11), and (12). The relative closeness to the ideal solution was determined for each alternatives using Eq. (13). Models with higher scores are deemed closer to the ideal solution; indicating better overall performance. (See Supplementary information SI3 for the details and python script.) Table 9 summarizes the result of TOPSIS identifying SVM, XGB, ANN, RF and DT as better performing models compared to LR, ANFIS and GPR.

SVM emerged as the top-performing model with a TOPSIS score of 0.6412, reflecting an optimal balance among accuracy, interpretability, and generalization capabilities (Xikai et al., 2019). This model is known for its robustness in handling non-linear data and ability to work well with small and moderate datasets, which likely contribute to its top ranking. SVM/SVR's capacity to optimize complex data structures aligns well with the high-dimensionality typical in LCA studies, as evidenced by its favorable performance across multiple evaluation criteria. This result aligns with findings from (Zhang et al., 2024), who emphasize SVM's effectiveness in handling non-linear data through its kernel-based approach—an important asset in LCA applications that deal with complex environmental data. While computationally more intensive than simpler models like linear models, its efficiency in parameter tuning likely made it highly adaptable to LCA applications (Su et al., 2023; Tajuddeen et al., 2023). Extreme Gradient Boosting (XGB), scoring 0.5811, ranked second and demonstrated high accuracy, robustness, and computational efficiency due to its ensemble based approach. Recent studies by (Naser et al., 2023b) emphasize XGB's strengths in managing data inconsistencies—common in LCA contexts—by handling missing data and outliers without substantial loss in predictive accuracy. XGB's ability to handle missing data and outliers makes it particularly useful in LCA settings, where data quality can vary. While XGB outperforms many models in terms of efficiency and scalability, its complexity and demand for careful parameter tuning limit its generalizability, contributing to its ranking just below SVM.

The third-ranked model, Artificial Neural Networks (ANN), achieved a score of 0.5650, showcasing robust predictive accuracy and adaptability across diverse LCA scenarios (Ji et al., 2021; Sharafi et al., 2023; Song et al., 2022). Its capacity for pattern recognition across complex input-output relationships allows it to accommodate the extensive variables involved in LCA data further support ANN's utility in environmental modeling, highlighting its capability to capture intricate, multidimensional interactions within ecological datasets (Wan et al., 2024). However, the ANN's lower interpretability and relatively high computational demands slightly reduced its score (Thilakarathna et al., 2020). RF's fourth-place ranking (0.5353) reflects its strong performance in accuracy and interpretability. RF's ensemble nature reduces overfitting risks, making it effective for diverse LCA data sources. However, it tends to be slower than other models like XGB, and its interpretability is lower than simpler models like Decision Trees, slightly affecting its rank (Satinet and Fouss, 2022). RF's robust predictive capacity, especially in handling large datasets, remains a favorable characteristic for prediction tool development.

With a score of 0.4776, DT offers interpretability and ease of use, aligning well with the goal of simplifying complex environmental data

<table><caption>**Table 9**
ML algorithms performance evaluation.</caption><thead><tr><td>ML algorithms</td><td>TOPSIS score</td></tr></thead><tbody><tr><td>SVM (Support Vector Machine)</td><td>0.6412</td></tr><tr><td>XGB (Extreme Gradient Boosting)</td><td>0.5811</td></tr><tr><td>ANN (Artificial Neural Network)</td><td>0.5650</td></tr><tr><td>RF (Random Forest)</td><td>0.5353</td></tr><tr><td>DT (Decision Tree)</td><td>0.4776</td></tr><tr><td>LR (Linear Regression)</td><td>0.4633</td></tr><tr><td>ANFIS (Adaptive Neuro Fuzzy Inference System)</td><td>0.4336</td></tr><tr><td>GPR (Gaussian Process Regression)</td><td>0.2791</td></tr></tbody></table>

for decision-making (Su et al., 2023). However, the model's relatively lower accuracy and susceptibility to overfitting can reduce its predictive reliability, especially in complex LCA scenarios. DT's utility in tasks requiring high interpretability still makes it a valuable component in ensemble approaches or as a secondary model. The lower-ranked models—Logistic Regression (LR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gaussian Process Regression (GPR)—though possessing specific strengths, faced limitations that impacted their suitability for LCA applications. For instance, LR's simplicity, though interpretable, lacks the flexibility required for high-dimensional LCA datasets, while ANFIS, despite its precision, is computationally intensive, and GPR's sensitivity to data variability can undermine its scalability (Akhshik et al., 2022; Kaab et al., 2019; Kleinekorte et al., 2023).

The analytical evaluation of ML algorithms based on AHP and TOPSIS using data collected from SLR provides the snapshot of performance of these algorithms for researchers and practitioners. However, it is critical to recognize that the evaluation criteria used in this analytical study such as data size, data sources variability, application domain, application LCA stages are collected from past case studies. These criteria, while reflective of past successful applications may not fully encapsulate the theoretical potential or adaptability of the algorithms in future. Consequently, the rankings presented herein primarily indicate which models have been most effective in previous implementations rather than providing a definitive foretelling of their perspective performance. Furthermore, the evaluation framework evaluated machine learning models by scoring them from scale 1 to 10 across several criteria as explained in Section 2.2.1 under methodology section. Although this scoring process involves some qualitative judgement, the subjectivity was minimized through a thorough systematic literature review, consistent scoring protocols and expert opinions. Similarly, the AHP used to build the pairwise comparison matrix using Saaty's 9-point scale also depend on expert judgement to set the relative importance of each criterion. To ensure the consistency by cross checking these assessments using the Consistency Ratio value, whether it is below 0.1 or not, the use of expert opinion does introduce some subjectivity. These limitations should be kept in mind, and future work may benefit from incorporating more objective, data-driven measures alongside expert judgement.

## 4. Conclusion

This study systematically reviewed 78 peer-reviewed articles on ML application in LCA studies to identify the current trends, commonly used models, models input and outputs, data sources and data sizes, and methodological challenges. Following the SLR, this study analytically assessed the performance of frequently used ML models across different LCA studies based on 11 different selection criteria using AHP and TOPSIS. The findings demonstrate the increasing role of ML in enhancing LCA processes by improving data collection, spatial and temporal analysis, surrogate modeling, life cycle optimization and uncertainty handling. The key insights from this review are summarized as:

* ML applications in LCA are predominantly utilized in the life cycle inventory (LCI) and life cycle impact assessment (LCIA) stages, where they facilitate predictive modeling based on diverse datasets, including scientific literature, experimental results, and industry databases.

* Among the various ML techniques, Artificial Neural Networks (ANNs) emerged as the most widely applied, followed by Random Forest (RF) and Support Vector Machine (SVM). The dataset sizes used in different ML models vary significantly, ranging from 33 to 1.8 million entries.

* The analytical performance of the ML algorithms using MCDM identified Support Vector Machine, Extreme Gradient Boosting, Artificial Neural Networks, Random Forest and Decision Tree as top
performing models in LCA applications based on the past studies and application on scientific research.

* This performance ranking of different ML provides the snapshot view for researchers and practitioners to identify which ML models could be used in case of LCA studies. However, it is important to note that some of the evaluation criteria used in analytical process such as data size, data sources variability, application domain, application life cycle stages are drawn from specific case studies from different papers and may not fully reflect the ranked models theoretical potential or performance of the future.

* Additionally, scoring process for models based on different criteria and the construction of the pairwise matrix in the AHP involve a degree of expert judgement, introducing some subjectivity into the evaluation. These limitations should be considered when interpreting the rankings.

* Future research should focus on validating these evaluations by testing ML models on more diverse datasets from multiple sources. Integrating hybrid evaluation frameworks that combine expert judgement with objective, data-driven measures could enhance the robustness and generalizability of the ranking process.

* The advent of artificial intelligence techniques including deep learning models and large language models (LLMs), presents promising opportunities to complement ML approaches in LCA. The SLR shows that application of these models in LCA studies is very few to none. In the future studies, exploring and applying these models in LCA studies supplementing to the traditional ML algorithms could further enhance automation, predictive accuracy and decision making capabilities for LCA practitioners and researchers.

While ML provides benefits like improved accuracy and automation, challenges remain in computational costs, model interpretability, data quality, and lack of standardization on model performance evaluation. Given the increasing complexity and significance of integrating ML into LCA, researchers, practitioners and industry stakeholders require robust and reliable decision making tools. These tools should offer methodological support by providing structured approaches for data collection and analysis as well as technical capabilities to handle large scale datasets efficiently. This study contributes towards the development of a decision-aid framework that will enable LCA experts to make informed predictions about environmental impacts while implementing targeted improvement strategies at both product and process levels using machine learning and artificial intelligence.

## CRediT authorship contribution statement

**Bishwash Neupane:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Farouk Belkadi:** Writing – original draft, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Marco Formentini:** Supervision. **Emmanuel Roziere:** Writing – review & editing, Funding acquisition, Supervision. **Benoît Hilloulin:** Writing – review & editing, Funding acquisition, Supervision. **Shoeib Faraji Abdolmaleki:** Writing – review & editing. **Michael Mensah:** Writing – review & editing.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgement

The author would like to acknowledge the ANR IDAVE project consortium for providing the opportunity to carry out the research activities and the French National Research Agency for financially

supporting this research and project.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.spc.2025.03.015.

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