Advancing life cycle assessment through data science: A critical review of algorithms, tools, and data challenges
Bibliographic record
Abstract
A well-executed life cycle assessment requires thorough data collection across all relevant processes, combined with advanced data analysis. Common data-related issues in life cycle assessment research include the absence of necessary data, low data quality, inconsistencies, uncertainty, and failure to account for variations over time and location. In this context, data science, the discipline of extracting meaningful insights from data, has the potential to address these challenges. While the integration of data science with life cycle assessment holds significant potential, best use cases depend on the goal of the study, as well as the data type and volume required, underscoring the necessity of reviewing the intersection of data science and life cycle assessment. This study used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method to identify literature addressing the use of data science elements to support life cycle assessment. It evaluated which data science techniques are appropriate for specific life cycle assessment stages or problem areas and the strengths and weaknesses of current data science applications in life cycle assessment. Key opportunities identified revolve around solutions for dealing with missing or poor-quality data, expensive/prohibitive data collection, and improving the accuracy of life cycle assessment results. The currently most feasible pathways appear to involve use of machine learning techniques, as these types of studies were the most conducted and generated tangible results. Extreme gradient boosting, random forest, and artificial neural networks were particularly prominent algorithm choices. Data collection and transferability using ontologies and semantic tools were also highlighted as important strategies for improving data flow in life cycle assessment, including the integration of a wide variety of databases and non-life cycle assessment data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".