Scholarlink

<p>Urban thermal risk assessments remain constrained by reliance on Land Surface Temperature (LST) and Land Use Land Cover (LULC) correlations, often overseeing human biometeorological stress and socioeconomic vulnerability. We introduce a human-centered framework, shifting focus from satellite-derived surface temperature to human biometeorological experience to overcome the “sensor-human disconnect” in Northern Pakhtunkhwa to address the challenges. The framework integrates three components: Firstly, the Embodied Thermal Mosaic, modeled by deriving the Universal Thermal Climate Index (UTCI) from micro-scale drivers (Sky View Factor, albedo, elevation). Secondly, a Socio-Thermal Vulnerability Index (STVI) by combining UTCI with household-level sensitivity and adaptive capacity score. Thirdly, Socio-Thermal Inertia, quantified via Cox proportional hazards survival analysis to capture lagged adaptive responses. Results show that Sky View Factor explains 38% of UTCI variance; STVI and LST are weakly coupled (R<sup>2</sup> = 0.18), with 60% of high-suffering zones (STVI &gt; 0.7) located in moderate-LST but high-vulnerability areas; and exposure to &gt; 50&#xa0;days above 35&#xa0;°C accelerates adaptation, with median migration latency dropping from 8.7&#xa0;years (20 hot days) to 2.1&#xa0;years (100 hot days) during 1985 to 2024. This physics-base policy-ready framework offers a robust tool for proactive thermal risk management in vulnerable regions.</p> Graphical Abstract <p>This snapshot describe that urban thermal risk assessments often rely on Land Surface Temperature (LST) and land cover, neglecting human biometeorological stress and socioeconomic vulnerability. We propose a human-centered framework— “human heat, not hot pixels”—for northwestern Pakistan to bridge the sensor–human gap. It integrates three components: (1) an Embodied Thermal Mosaic using microscale drivers (Sky View Factor, albedo, elevation) to model the Universal Thermal Climate Index (UTCI); (2) a Socio-Thermal Vulnerability Index (STVI) combining UTCI with household-level sensitivity and adaptive capacity; and (3) Socio-Thermal Inertia, quantified via Cox survival analysis to capture delayed adaptation. Results show Sky View Factor explains 38% of UTCI variance; STVI and LST are weakly correlated (R<sup>2</sup> = 0.18), with 60% of high-suffering zones (STVI &gt; 0.7) in moderate-LST but highly vulnerable areas. Exposure to &gt; 50&#xa0;days &gt; 35&#xa0;°C accelerates adaptation—median migration latency drops from 8.7&#xa0;years (20 hot days) to 2.1&#xa0;years (100 hot days). This physics-based, policy-ready framework enables proactive thermal risk management. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Human-Centered Framework for Assessing Socio-Thermal Vulnerability in Climate-Vulnerable Region of Northern Pakhtunkhwa, Pakistan

  • 期刊论文

  • 发表日期：

    2026年1月8日

查看全文
  • Akhtar Rehman,
  • Fu Benchen,
  • Naveed Rehman,
  • Irfan Ur Rashid,
  • Nazir Ur Rehman,
  • Shafee Ahmad,
  • Muhammad Hamza,
  • Maliha Ghani

摘要

Urban thermal risk assessments remain constrained by reliance on Land Surface Temperature (LST) and Land Use Land Cover (LULC) correlations, often overseeing human biometeorological stress and socioeconomic vulnerability. We introduce a human-centered framework, shifting focus from satellite-derived surface temperature to human biometeorological experience to overcome the “sensor-human disconnect” in Northern Pakhtunkhwa to address the challenges. The framework integrates three components: Firstly, the Embodied Thermal Mosaic, modeled by deriving the Universal Thermal Climate Index (UTCI) from micro-scale drivers (Sky View Factor, albedo, elevation). Secondly, a Socio-Thermal Vulnerability Index (STVI) by combining UTCI with household-level sensitivity and adaptive capacity score. Thirdly, Socio-Thermal Inertia, quantified via Cox proportional hazards survival analysis to capture lagged adaptive responses. Results show that Sky View Factor explains 38% of UTCI variance; STVI and LST are weakly coupled (R2 = 0.18), with 60% of high-suffering zones (STVI > 0.7) located in moderate-LST but high-vulnerability areas; and exposure to > 50 days above 35 °C accelerates adaptation, with median migration latency dropping from 8.7 years (20 hot days) to 2.1 years (100 hot days) during 1985 to 2024. This physics-base policy-ready framework offers a robust tool for proactive thermal risk management in vulnerable regions.

Graphical Abstract

This snapshot describe that urban thermal risk assessments often rely on Land Surface Temperature (LST) and land cover, neglecting human biometeorological stress and socioeconomic vulnerability. We propose a human-centered framework— “human heat, not hot pixels”—for northwestern Pakistan to bridge the sensor–human gap. It integrates three components: (1) an Embodied Thermal Mosaic using microscale drivers (Sky View Factor, albedo, elevation) to model the Universal Thermal Climate Index (UTCI); (2) a Socio-Thermal Vulnerability Index (STVI) combining UTCI with household-level sensitivity and adaptive capacity; and (3) Socio-Thermal Inertia, quantified via Cox survival analysis to capture delayed adaptation. Results show Sky View Factor explains 38% of UTCI variance; STVI and LST are weakly correlated (R2 = 0.18), with 60% of high-suffering zones (STVI > 0.7) in moderate-LST but highly vulnerable areas. Exposure to > 50 days > 35 °C accelerates adaptation—median migration latency drops from 8.7 years (20 hot days) to 2.1 years (100 hot days). This physics-based, policy-ready framework enables proactive thermal risk management.