アイルランドホテル業界の物件レベル炭素ベンチマーキング

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:物件レベル炭素強度のばらつきと構造的予測因子を定量化した実証ベースを提供する。

🏢実務担当者:宿泊施設の算定境界に廃棄物・水を含めるべき根拠と、食品廃棄物・暖房が優先削減対象であることを示す。

🏛政策担当者:セクター別ベンチマーク整備とScope3算定境界の標準化を検討する際の参照事例となる。

Hotels face intensifying pressure to measure and reduce their carbon footprint under the Corporate Sustainability Reporting Directive and adjacent frameworks, yet property-level benchmark data at national scale remains scarce and the boundary conventions applied by widely deployed sector frameworks may not adequately capture the sector’s actual footprint. Drawing on primary carbon audit data from 36 Irish hotels across 15 counties and star ratings from 2 to 5, this paper establishes the first comprehensive property-level carbon benchmark for the Irish hotel sector. Non-energy consumption data was available for 31 of these properties, and composition analyses are reported on that sub-sample. Emissions were calculated across Scope 1 combustion, Scope 2 electricity, and Scope 3 waste and water categories using SEAI (Sustainable Energy Authority of Ireland) and DEFRA (Department for Environment, Food and Rural Affairs) emission factors, standardised on per-guest-per-night and per-room-per-year intensity metrics. The analysis proceeded through descriptive statistics, multivariate ordinary least squares regression, and k-means cluster analysis. The findings show a 10.5-fold variation in per-guest emissions across the sample (mean 9.6 kg CO2e, median 8.0 kg CO2e), with the widely used energy-only accounting boundary underestimating hotel emissions by approximately 16% because it excludes the waste and water categories, which this study finds account for a mean of 16% of total property emissions. Within the waste category, food waste at 43.9% is nearly equal to landfill at 55.8%, identifying food waste diversion as a specific high-leverage intervention. Combined fossil heating sources contribute 60.8% of energy emissions, identifying heating decarbonisation as the primary energy lever. Room count emerges as the sole significant structural predictor of intensity (β = −0.53, p = 0.002) within a statistically significant multivariate model (F(4,31) = 3.57, p = 0.017, R2 = 0.315), with the apparent luxury emissions premium operating through size distribution rather than star rating itself. The findings support a differentiated, size-sensitive, waste-inclusive approach to hotel decarbonisation that current sector benchmarks do not fully accommodate.

• openalex https://doi.org/10.3390/su182110898first seen 2026-10-29 04:58:22 · last seen 2026-11-06 04:58:18
• semanticscholar https://doi.org/10.3390/su182110898first seen 2026-10-31 05:56:05 · last seen 2026-11-06 05:51:13