Literature Review on Decarbonization Through Sustainability-Oriented Contractor Selection in IPD Projects: Bibliometric Analysis
Bibliographic record
Abstract
This paper presents a bibliometric and literature review on decarbonization strategies in sustainability-oriented contractor selection within Integrated Project Delivery (IPD) frameworks. The study analyzes 972 journal articles published between 2002 and 2024 from Scopus, complemented by Google Scholar for thematic insights. Bibliometric techniques in R were applied to identify influential publications, research trends, and thematic clusters. The review highlights documented benefits of integrating decarbonization into contractor evaluation, including lifecycle carbon reduction, ESG alignment, and early-stage material optimization. Challenges remain in terms of limited lifecycle data, absence of standard benchmarks, and organizational resistance. Critical success factors identified include policy alignment, availability of assessment data, and collaborative stakeholder engagement. The findings demonstrate that incorporating carbon-related performance indicators into early procurement decisions can reshape prequalification standards and strengthen sustainable project delivery. Citation and co-word analysis reveal emerging research trends, including digital innovations such as artificial intelligence for contractor evaluation and emissions tracking. This study provides both a research foundation and a strategic guide for construction professionals, policymakers, and sustainability advocates aiming to align IPD with global decarbonization targets.
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.117 | 0.167 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".