Value Creation through Corporate Sustainability in the Port Sector: A Structured Literature Analysis
Bibliographic record
Abstract
Corporate Sustainability (CS) in the port sector has emerged as an important driver behind strategy definition for port authorities globally. It has been argued that CS practices have the potential of delivering value for port users and, as such, grant port operators and port managing entities competitive advantages. There is, however, limited evidence behind this claim. The difficulty with collecting such evidence is that we lack measures of port value creation, and CS metrics have rarely been developed and applied in ports. This paper provides a framework for collecting empirical evidence aimed at assessing in what way CS can benefit port competitiveness. The framework is built on a systematic literature analysis of the past years. The literature analysis exceeds previous comparable contributions by its analytical detail and provides valuable new insights on sustainability in the maritime domain. The research indicates that the accurate measurement of CS initiatives in the port sector is urgent and meaningful. When appropriately measured, the value that CS can deliver to port users becomes apparent. This is, however, often created indirectly via branding, risk mitigation, etc. The paper contributes to academic knowledge as it is the first to develop a rigorous CS measurement framework usable for ports in terms of value.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".