高密度台北における都市形態の進化と炭素排出の空間再編：3D歴史GIS・XAI・MGWR分析、1980–2025年

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

🔬研究者:都市炭素排出の空間分析におけるXAIとMGWRの統合手法に関心のある研究者に有用。

🏢実務担当者:都市計画や不動産開発における炭素排出評価の空間的アプローチの参考になるが、直接的な企業開示への応用は限定的。

🏛政策担当者:高密度都市のコンパクト開発政策が炭素排出に与える影響を評価する際の空間分析手法として参考になる。

Compact development is often treated as a low-carbon strategy; yet, high-density cities may face new carbon pressures as vertical development, agglomeration, and cross-district mobility intensify. This study examines Taipei using 27,239 100 m × 100 m grid cells and 1980–2025 data integrating Historical GIS, remote sensing, official LOD1 building models, zone-level energy statistics, and anonymized origin–destination data. Zone-level energy totals were converted to CO2 and allocated to 100 m cells through a mass-preserving proxy surface; the resulting grid target is therefore a reconstructed allocation rather than an independently observed cell-level outcome. XGBoost, TreeSHAP, multiscale geographically weighted regression (MGWR), CASA, and network analysis served as complementary instruments. Random ten-fold cross-validation provided internal reconstruction diagnostics but was not spatially blocked and does not establish spatial transferability. The mean carbon-emission compound annual growth rates of existing built-up cells were 2.994% in the historic western districts and 2.890% in the emerging eastern districts (Welch’s test, p = 0.104). This non-significant result does not establish equivalence or identify the contributions of existing and newly built-up areas to aggregate growth; the extensive-margin hypothesis remains untested. TreeSHAP identified model-specific empirical breakpoints at 12.83 for a standardized, unitless nighttime-light index, 197.67 buildings/km2 for the building density, and 99.61 for the dimensionless FAR × Nightlight composite. These values indicate changes in the model-predicted feature contributions; they are not physical emission triggers, causal transition points, universal planning standards, or transferable regulatory cut-offs. MGWR achieved an adjusted R2 of 0.886 and revealed marked multiscale spatial non-stationarity; its coefficients are interpreted only as conditional spatial statistical associations. The observed Taipei Metro OD flows further indicated a cross-boundary carbon-burden asymmetry, but the network covers metro travel only and excludes buses, motorcycles, private cars, walking, cycling, and other modes. As an observational and proxy-based reconstruction, the study supports predictive and spatial associations rather than strict causal effects.

• openalex https://doi.org/10.3390/su18199708first seen 2026-09-24 04:47:28