Integrating Climate Resilience into Construction Project Management: A Global Narrative Review of Frameworks, Policies and Implementation Challenges
Bibliographic record
Abstract
Climate change increases the risk of floods, heatwaves, droughts, and sea-level rise for infrastructure investments. These dangers disrupt supplies, delay schedules, and raise costs, demonstrating that standard Construction Project Management (CPM)—focused on cost, scope, and time—must now include climate resilience. However, previous studies have not synthesized how resilience frameworks and policies translate into project management practices, leaving a gap between theory and implementation. This narrative study summarizes global frameworks, policy instruments, and implementation issues in integrating climate resilience into CPM. A structured literature search was conducted across Scopus, Web of Science, ScienceDirect, and Google Scholar using relevant keywords. Thematic analysis identified conceptual frameworks, governance mechanisms, and practice barriers from 2000 to 2025. Four main findings emerged: (1) existing frameworks such as the Sendai Framework, IPCC AR6 pathways, ISO 14090/14091, World Bank CSIF, and FIDIC guidance support adaptive project management; (2) policy deficiencies persist due to weak enforcement and lack of resilience KPIs; (3) barriers include financial disincentives, limited climatic data, and uneven capacity; and (4) emerging best practices suggest lifecycle alignment, digital integration (BIM–GIS–twins), and outcome-based procurement. Integrating resilience throughout the project lifecycle can shift construction management from reactive hardening to proactive, climate-informed infrastructure delivery.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".