Human-centric buildings for a changing climate: Introducing a new International Energy Agency research network
Bibliographic record
Abstract
As building energy and health targets increase, occupants’ influence on (and interactions with) their buildings is becoming more significant. Behaviors, such as daily routines, purchasing decisions, and responses to extreme events, directly impact energy use and health-related conditions within buildings. This dynamic is further shaped by global shifts such as teleworking, co-working, and home-sharing, which disrupt traditionally assumed occupancy patterns. Additionally, growing expectations for comfort and the integration of new technologies intensify the need to reassess how humans are considered in building design, maintenance, renovation, and operation—bringing a human-centric lens to traditionally building-focused approaches. This paper introduces a new research network that explores four key areas in the context of human-centric buildings in a changing climate: (1) individual human-building interactions, (2) community-scale interactions, (3) building (re)design, and (4) building operations. The Human-Centric Buildings for a Changing Climate (HCB) Network includes over 210 researchers from approximately 30 countries and multiple disciplines, including engineering, architecture, computer science, psychology, urban planning, sociology, public health, economics, and medicine. The objective of this paper is to establish the importance and relevance of these topics and to summarize the planned outcomes of a joint International Energy Agency (IEA) initiative between the Energy in Buildings and Communities (EBC) Programme Annex 95 and a Users Technology Collaboration Programme Task. This work aligns with and advances the goals of the UN Sustainable Development Goals (SDGs), particularly SDGs 3 (Health), 7 (Energy), 11 (Sustainable Cities), and 12 (Responsible Consumption), by centering human agency in climate-resilient building strategies.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".