Preserving the Past, Protecting the Future: A Framework for Sustainable Climate Adaptation of Heritage Structures
Bibliographic record
Abstract
Climate change poses an unprecedented challenge to cultural heritage worldwide, requiring urgent adaptation strategies that reconcile preservation with resilience. This paper proposes a structured framework for assessing climate adaptation interventions in heritage structures, addressing the dual imperative of safeguarding authenticity while ensuring long-term sustainability and safety. Drawing on expertise from the International Scientific Committee on the Analysis and Restoration of Architectural Heritage Structures (Iscarsah), the study examines the multifaceted impacts of climate change on heritage sites and evaluates a spectrum of intervention strategies, ranging from minimal interference to more transformative measures. The proposed framework integrates key criteria, including conservation principles, resilience to climate hazards, environmental sustainability, technical feasibility, and sociocultural implications, thus enabling a comprehensive assessment of potential actions. The applicability of this framework is illustrated through case studies on flood and fire management, which demonstrate its capacity to guide decision-making in diverse heritage contexts. By systematically weighing the trade-offs between preservation, adaptation, and ecological impact, the framework provides a practical tool to structure dialogue between experts and stakeholders. In doing so, it fosters more holistic, interdisciplinary solutions for protecting cultural heritage in an era of climate uncertainty.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| 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".