Academy of Interdisciplinary Studies Division of Environment and Sustainability 233 Exploring Private-Led Adaptation Financing Opportunities in Hong Kong Supervisor: Laurence Laurencio DELINA / ENVR Student: GOH Jinghann / CPEG Course: UROP 1000, Summer This study explores how private finance can be mobilised to support climate adaptation in Hong Kong, evaluating the city’s potential to become a leading hub for private-led adaptation finance. Drawing on primary data from interviews with 57 experts across various fields, the research examines stakeholders’ perceptions of the initiative’s feasibility. The study uses MAXQDA for qualitative analysis and systematically codes interview transcripts to identify and assess the frequency of key themes, highlighting both its potential and its challenges. This qualitative analysis identified several predominant themes—Climate Risk Management, Disaster Preparedness, Governance and Urban Adaptation—alongside less frequently discussed topics such as Language Barriers, Other Extreme Weather Events, the Greater Bay Area (GBA), and Small Ticket Sizes. In light of these findings, future research will delve deeper into the interactions among these themes to offer a more in-depth thematic analysis. This will help address critical implications related to sea level rise, the Hong Kong-ASEAN Free Trade Agreement, and innovative financing mechanisms, such as disaster risk insurance pooling, catastrophe bonds, and insurance bonds, as well as the impact of the Belt and Road Initiative. This UROP aims to provide valuable insights to assist policymakers and stakeholders navigate the complexities of climate adaptation finance. The project also investigates Hong Kong’s potential to become a centre for private-led adaptation finance by leveraging its strengths in financial and professional services and enhancing its role as an emerging green finance centre. The objective is to understand the mechanisms that could support adaptation finance and the barriers that may hinder progress, through qualitative thematic analysis of interview data, identification of key findings, and discussion of emerging themes. A Machine Learning Approach to Study the Relationship Between Urban Morphology and Urban Heat Island Supervisor: FUNG Jimmy Chi Hung / ENVR Student: XIAO Xiuquan / MATH-STAT Course: UROP 3200, Spring This study investigates the extraction of building heights in dense urban environments using Sentinel-1 Synthetic Aperture Radar (SAR) data. Building on the methodology proposed by Frantz et al. (2021), a machine learning framework was adapted to process dual-polarized SAR features (VV/VH bands) and derive spectral-temporal and spatial-spectral-temporal metrics, such as statistical aggregates and morphological operations. The Random Forest model exhibited promising potential for mapping vertical urban morphology. However, challenges arose in high-density areas due to radar signal saturation caused by overlapping shadows and complex building geometries. Future research aims to integrate multi-sensor data and advanced algorithms to improve height estimation accuracy. This work contributes to the development of a scalable framework for analyzing urban vertical structures, supporting urban planning and environmental studies.
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