Recent advances and opportunities in planning green petroleum supply chains: a model-oriented review
Bibliographic record
Abstract
Today, petroleum companies cannot be competitive and efficient without considering potential solutions provided by sustainable supply chain management (SSCM) optimization models. SSCM can solve different challenges faced by this sector. Academics and practitioners consider the opportunities offered by decision-making tools for planning sustainable petroleum supply chains. Moreover, the ever-increasing number of publications applying decision-making models to the petroleum industry also attests to this fact. Therefore, the primary objective of this study is to understand the evolution of sustainable supply chain planning in the petroleum industry and highlight the specificities of the body of knowledge in this area. A comprehensive analysis is performed using 23 papers published from 2010 to 2019. This paper proposes a classification framework to analyze different factors in developing mathematical models, including the triple bottom line pillars of sustainability (economic, environmental, and social). The main observation is that planning models that focus on all three sustainability dimensions in the petroleum sector are scarce. Regarding the environmental dimension, the analysis demonstrates that consideration of greenhouse gas emissions, especially CO2 impacts, is dominating planning models. Furthermore, there is an absence of quantitative models that include social dimensions, and this gap must be addressed in the future. Finally, we propose future extensions to develop research in SSCM in the petroleum sector, keeping in mind recent developments from both technological and economic drivers in this specific sector.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".