Liu, Zihao; (2025) Impact of ESG Information Integration in Default Risk Analysis, Portfolio Diversification, and Optimal Geographical Locations of Powerplant Solar Farms. Doctoral thesis (Ph.D), UCL (University College London).

Text Final_Thesis_Completed_Version_ZihaoLiu.pdf - Submitted Version Access restricted to UCL open access staff until 1 March 2026. Download (24MB)

Abstract

As Environmental, Social, and Governance (ESG) considerations become increasingly central to regulatory and investment decisions, integrating these factors into risk and investment management is crucial for building a more resilient, sustainable, and profitable financial system. This dissertation investigates the impact of ESG integration across three key areas: credit risk, portfolio investment risk, and geo-finance. Through this research, we analyse how ESG factors influence financial decision-making and propose practical methods to leverage these factors for enhanced financial performance. First, we examine the influence of ESG on credit risk by analysing the relationship between firm-level ESG performance and credit quality. Using both linear and non-linear regression models, we predict loan spreads and assess creditworthiness. Our findings reveal that ESG factors significantly affect credit risk in specific European industries, leading to improved accuracy in loan pricing and default risk detection. To capture non-linear relationships, we employ various machine learning (ML) algorithms, demonstrating that the integration of ESG factors enhances the performance of default prediction models. Next, we focus on investment risk by evaluating the effectiveness of subdividing ESG assets within portfolio construction. Compared to broader ESG categories, subdivided ESG assets show lower pairwise correlations, higher Sharpe Ratios, and greater flexibility, making them ideal for portfolio diversification. Our analysis indicates that subdivided ESG portfolios outperform integrated ESG portfolios in terms of risk-adjusted returns, particularly during periods of market instability such as the COVID-19 pandemic. Using the Shapley Value method, we further demonstrate that subdivided ESG assets not only enhance returns but also mitigate potential losses. Finally, we investigate the direct impact of ESG on risk-return dynamics by proposing a hybrid geo-finance method for identifying optimal locations for solar farm development in the UK. By introducing an asset pricing model, this approach integrates weather and financial variables within a Multi-Criteria Decision-Making framework that combines multiple Geographic Information System layers. The method enables us to pinpoint ideal geographical sites for solar farms that balance environmental sustainability with economic viability. Overall, this dissertation bridges the gap between ESG integration and risk management by introducing an innovative framework that links financial performance with sustainability considerations, offering strategic insights for both investors and policymakers.

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