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This thesis explores the non-market values of ecosystem services in Penang's urban green spaces, addressing threats posed by urbanization. Utilizing non-market valuation techniques, this study aims to provide a comprehensive understanding of the contributions of urban green spaces to the local economy. The first topic assesses urban residents' preferences using the best-worst scaling method, highlighting air quality as crucial but revealing inconsistencies in preferences. The second topic applies the travel cost method to analyse visit preferences and behaviours, emphasizing the significance of incorporating time-related costs for understanding travel patterns. The third topic employs a discrete choice experiment to reveal trade-offs between green space characteristics and travel distance, emphasizing the high value placed on improved air quality. The fourth topic uses a Seemingly Unrelated Regression model to explore the impact of spatial data on willingness to pay, showing insights into how spatial characteristics influence preferences. The fifth topic employs a simulation framework to assess the economic benefits of attribute changes in Penang's urban green spaces, emphasizing increased consumer surplus with improvements. Overall, these findings contribute valuable insights into resident preferences, spatial influences, and economic implications, offering guidance for sustainable urban development to local policymakers.

We examine if liquidity risk is priced in ETF equity returns and ETF equity premiums. We also show if these relationships hold in extreme market situations, which includes pre-and post-Covid periods. We find that liquidity risk is an important determinant of ETF equity valuation, and equity premiums are sensitive to liquidity levels. The equity premium tends to increase at the higher levels of bid-ask spread in the pre and post-Covid periods indicating that the information is not fully available to the public or to investors. Our results are robust across different sub-groups categorized based on the characteristics (age and size) of the ETFs. Our results have implications for asset pricing and price discovery and show that investors hold ETFs with high equity premiums even when the liquidity risk is high. A positive liquidity premium exists in Artificial Intelligence ETF markets and has implications for price discovery.