Life cycle assessment (LCA)

Life Cycle Assessment (LCA) is a globally recognized standardized methodology used to comprehensively evaluate the environmental burdens associated with a product, process, or service. It takes a “cradle-to-grave” (or “cradle-to-cradle”) perspective, encompassing all stages of a system’s life, from raw material extraction and processing, through manufacturing, distribution, use, and end-of-life management (e.g., recycling, disposal). This holistic approach is essential for identifying potential trade-offs and preventing problem-shifting, where solving one environmental issue might inadvertently create another elsewhere in the life cycle. The core principle of LCA is to provide a complete picture of environmental interactions, allowing informed decision-making in design, policy, and business strategy.

The LCA framework, as formalized by the International Organization for Standardization (ISO) in Standards 14040 and 14044, is structured around four interconnected phases: goal and scope definition, life cycle inventory (LCI) analysis, life cycle impact assessment (LCIA) and interpretation. These phases are iterative, meaning that findings in later stages may lead to refinements in earlier ones. The initial phase, goal and scope definition, is paramount, establishing the purpose, target audience, system boundaries, and the functional unit (a quantified measure of the service provided) to ensure fair comparisons between alternatives.

The LCI phase involves detailed accounting of all material and energy inputs and outputs (emissions and waste) associated with each process within the defined system boundaries. This data collection can be very demanding, often relying on a combination of primary data (directly from specific processes) and secondary data (from databases, literature, or industry averages). The LCIA phase then uses known characterization factors to turn the LCI data into a set of environmental impact indicators, including global warming potential, acidification, eutrophication, and resource depletion. Different LCIA methodologies exist, offering various perspectives and levels of detail (e.g., midpoint vs. endpoint indicators).

The final interpretation phase is where the results of the LCI and LCIA are analyzed to identify significant environmental hotspots, assess the robustness of the findings through sensitivity and uncertainty analyses, and draw conclusions. This phase is crucial to translating the quantitative results into actionable recommendations to improve environmental performance. LCA can be broadly categorized into attributional LCA, which provides a static snapshot of environmental flows, and consequential LCA, which models the dynamic consequences of decisions, including market and economic effects [11,12].

Machine learning (ML)

Machine Learning (ML), a core branch of artificial intelligence (AI), allows computer systems to learn from data without explicit programming. Instead of relying on predefined rules, ML algorithms identify patterns, make predictions, and adapt their performance as they are exposed to new data. This ability to learn and adapt makes machine learning particularly valuable for tackling complex, data-rich problems in various fields, including environmental science and sustainability assessment [13].

ML encompasses a spectrum of techniques, broadly categorized as supervised, unsupervised, reinforcement, and deep learning. Labeled data sets (with known inputs and outputs) are used in supervised learning to train models for regression (predicting continuous values) or classification (predicting categorical labels). Unsupervised learning, on the other hand, looks at data that hasn’t been labeled to find hidden structures, like groups of data points that are similar or simplified representations of large datasets. Reinforcement learning takes a different approach: training agents to make optimal decisions through interactions with an environment and learning from rewards and penalties [14].

Deep learning, a powerful subset of ML, utilizes artificial neural networks with multiple layers (deep neural networks) to learn intricate patterns from vast amounts of data [15]. These deep networks have achieved breakthroughs in areas such as image recognition and natural language processing and are increasingly being applied to sustainability challenges. Different kinds of neural networks have different strengths: for example, convolutional neural networks (CNNs) are well suited for image data, recurrent neural networks (RNNs) for sequential data, and autoencoders for reducing dimensionality.

The integration of ML with other analytical methods is also growing, with approaches such as physics-informed machine learning (PIML). Furthermore, ensemble methods, which combine predictions from multiple models, and techniques like Gaussian Process Regression provide enhanced robustness and uncertainty quantification. These advancements are continually developing a powerful toolkit for applications in various fields [16].

Synergies between LCA and ML

Integrating ML into LCA can effectively address traditional methodological limitations by automating data acquisition and harmonization through Natural Language Processing (NLP), filling data gaps and improving data quality using regression models and generative techniques, dynamically analyzing temporal changes, and simplifying complex multi-criteria decision-making processes through clustering, optimization, and explainable AI (XAI). Table 1 summarizes the synergies between ML and LCA along with relevant references from the literature.

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Table 1. Synergies between ML and LCA stages.

https://doi.org/10.1371/journal.pclm.0000732.t001

ML in goal and scope definition

The initial goal and scope definition phase of LCA involves defining the functional unit, the system boundaries, and the data requirements. Natural Language Processing (NLP), a prominent subfield of ML, offers significant potential to automate the extraction of information from unstructured data such as scientific publications, technical reports, and product specifications. For example, regression-based modeling combined with optimization can streamline LCA processes by automatically identifying critical indicators, potentially aiding early-stage definitions by efficiently identifying key variables [9,37]. Furthermore, recent studies underscore the utility of automation and NLP for managing unstructured data, improving the initial stages of LCA [32]. However, NLP-based approaches face challenges regarding the transparency of data sources and context interpretation, as large language models often leverage web-scraped information without clear provenance. Despite this potential, explicit applications of ML in the definition of goals and scope remain limited, presenting opportunities for future research to develop robust NLP models.

ML in life cycle inventory (LCI) analysis

The LCI stage, known for its high data intensity, has seen extensive use of ML to address data scarcity, improve data quality, and enable dynamic evaluations.

ML in life cycle impact assessment (LCIA)

ML in interpretation and decision support

Data availability and quality

A fundamental challenge in ML applications within LCA is obtaining extensive, high-quality, and representative datasets for model training. Data scarcity, heterogeneity, and uncertainty significantly impact model accuracy and reliability. LCA datasets frequently suffer from data gaps and inconsistencies due to variations in data collection methods, regional differences, and incomplete documentation of processes [17,18,36]. Furthermore, uncertainty associated with measurement errors and subjective expert judgments can introduce significant bias into training datasets, affecting the accuracy and credibility of ML predictions [19,27,41]. Additionally, LCA data come in a variety of formats (e.g., EcoSpold, ILCD, JSON-LD), depending on the database or software used, which adds another layer of complexity to data harmonization [10].

Model generalizability and transferability

Developing ML models that generalize across different products, processes, or geographical regions remains a significant obstacle. Models trained on specific datasets, such as the Canadian fuel LCI database [19], often struggle to maintain predictive accuracy when applied outside of their initial scope. Transfer learning, which involves leveraging knowledge gained from one context to enhance model performance in another, presents a promising approach to improve generalizability. However, its effectiveness in LCA contexts remains underexplored and requires further research to evaluate its viability and potential limitations [8,38]. Additionally, validating ML models in LCA is challenging—supervised learning approaches require ground-truth values for training and testing, but many environmental impact measures lack a single accepted true value. In practice, model accuracy must often be inferred from proxy metrics or expert judgment, introducing uncertainty and limiting confidence in claims of improved predictive performance.

Model interpretability and transparency

The interpretability and transparency of ML models are critical to ensuring their adoption and trustworthiness in decision-making processes [25,42–44]. Complex models like neural networks and ensemble methods frequently function as “black boxes,” obscuring the underlying decision logic from users and stakeholders [35]. Consequently, there is a growing emphasis on the development and implementation of Explainable AI (XAI) techniques—such as surrogate models, feature importance analyses, and visualization tools—to elucidate ML model results. Enhancing transparency through XAI is essential to gain the trust of stakeholders and to enable informed decisions [35].

Integration with existing LCA frameworks and software

Integrating ML methodologies into existing LCA frameworks and software systems presents significant technical challenges. Current LCA software tools typically lack the ability to directly incorporate sophisticated ML algorithms, requiring additional software interfaces or dedicated platforms. This integration gap hinders the widespread adoption and practical implementation of ML-enhanced LCAs, highlighting the need for more robust and flexible integration solutions, standardized data formats, and improved compatibility between ML tools and traditional LCA software [21,30].

Computational cost

Training and deploying complex ML models require substantial computational resources, including high-performance computing infrastructure and extended processing times. The computational burden of sophisticated algorithms (such as deep learning and Bayesian methods) limits their applicability to routine or real-time evaluations, particularly in resource-constrained settings [20]. Moreover, the energy consumption and associated carbon emissions of training large ML models can partially offset their sustainability benefits if not managed with efficient hardware or renewable energy sources. Future research must focus on developing efficient algorithms and computational strategies that reduce resource demands while preserving model accuracy and predictive capabilities.

Lack of standardized methodologies

Currently, there are no widely accepted standardized methodologies or guidelines for integrating ML techniques within LCA processes. The absence of standards complicates comparisons between studies and reduces the reliability and reproducibility of ML-based LCA results. Establishing clear and universally applicable methodological standards and guidelines would significantly facilitate broader adoption, improve the comparability of results, and enhance methodological transparency and rigor in the LCA community [9,17,29,45].

Domain expertise

The effective application of ML within LCA requires close interdisciplinary collaboration between experts in the environmental assessment domain and ML specialists. The development and deployment of ML models demand both a deep understanding of environmental processes and proficiency in advanced computational methodologies. Consequently, fostering interdisciplinary collaborations is essential to successfully bridge knowledge gaps, improve model relevance, and ensure accurate interpretation and implementation of ML results within the practical context of LCA [24,31,46].