[Article] Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings

Most building comfort assessments still answer yesterday’s question: Is this building comfortable today?

But heat waves, climate drift, and extreme years demand a different one: 👉 Will this building remain habitable under future climate stress?

Our recent paper in Energy & Buildings introduces a multi-criteria decision support framework designed exactly for this challenge: “Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings.”

🧩 What makes this framework different?

Instead of relying on a single comfort metric or a fixed temperature threshold, the framework connects four dimensions that are usually treated separately:

1️⃣ Building context: From hospitals and senior housing to renovated and post-war dwellings.

2️⃣ Operation mode: Free-running, mixed-mode, or mechanically cooled buildings.

3️⃣ Climate sensitivity: Explicitly accounting for heat waves and future climate scenarios, not only typical years.

4️⃣ Time-integrated performance indicators: Including indoor overheating degree, ambient warmness degree, climate-change overheating resistivity, hours of exceedance, and effective temperature.

The result is a decision logic, not a score. A framework that helps answer what matters, when, and for whom.

🏙️ Why this matters beyond academia

1. Designers gain a structured way to test whether passive and active strategies remain effective under future heat waves.

2. Engineers can compare comfort outcomes across building types and climates using consistent indicators.
3. Policymakers finally get a tool aligned with climate adaptation goals, not just energy efficiency targets.

The case study shows that even high-performance buildings can experience sharp increases in overheating risk toward 2100, underlining why comfort evaluation must evolve with the climate.

📄 Read the paper: https://doi.org/10.1016/j.enbuild.2023.113804

👥 Authors & institutions to follow: Deepak Amaripadath (Arizona State University), Ronnen Levinson (Lawrence Berkeley National Laboratory), Rajan Rawal (CEPT University), Shady Attia (University of Liège)

📚 Learn more about our research: https://www.sbd.uliege.be/

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