Systematic review of the life cycle optimization literature, and recommendations for performance of life cycle optimization studies
Bibliographic record
Abstract
Life cycle optimization (LCO) refers to the integration of objectives calculated using a life-cycle based framework into mathematical optimization problems. Application of LCO may allow for substantial sustainability improvements in many industrial sectors, and provide valuable decision support towards achieving the UN Sustainable Development Goals. This study performed a PRISMA systematic review of LCO literature published between 2012 and 2023 with the goal of developing general guidelines for performance of LCO studies. Three hundred and one sources were reviewed to determine the industrial sector of the modeled system, the life cycle assessment framework used, how objective functions were defined, if uncertainty was included, and the optimization framework used. Results indicate a shift towards evolutionary-based optimization methods relative to previous reviews of the literature. Economic and environmental objective functions were most commonly assessed, while some studies have begun incorporating social objectives into their optimization. Based on the collected data, additional discussion was included related to choice of optimization framework, and definition of objective functions. The collected data and these additional discussions were used to develop a decision tree to aid practitioners in making methodological choices when performing LCO studies. This decision tree will help practitioners manage the trade-offs between the accuracy and efficiency of optimization methods based on the goals of their particular LCO study, and support increased uptake of LCO methodologies across industrial sectors. Increased uptake may provide significant value to researchers and policy makers by enabling investigation of potential sustainability improvement measures where all metrics are simultaneously optimized. • A PRISMA systematic review of the LCO literature was performed. • LCO studies are performed across a wide array of industrial sectors. • A decision tree was generated to aid LCO practitioners with methodological choices. • The decision tree aims to help increase uptake of LCO methodologies in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.166 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.034 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".