Modelling Spatial Heterogeneity in Environmental Resources and Consumer Preferences

The influence of geographical context on human behaviours and preferences has been a longstanding research focus across several disciplines. In environmental and resource economics, consumer preferences often exhibit spatial patterns driven by various factors, including socio-demographic characteristics, geographic features of residence, exposure to amenities, and psychological factors at individual and societal levels. Over the past decades, an increasing amount of empirical research has explored the impact of spatial dimensions on welfare changes. However, spatial heterogeneity remains frequently overlooked in environmental valuation studies due to its inherent complexity. This PhD thesis addresses two primary objectives: A) to advance the understanding of how spatial factors influence preference heterogeneity towards environmental and market goods and B) to provide methodological advancements in accounting for spatial heterogeneity in stated preference valuation. The resulting work consists of two main parts: Part I lays a foundation through progressive spatial integration, employing primarily traditional methods to explore individual attitudes (Paper 1), societal preferences (Paper 2), and behavioural responses (Paper 3), establishing a baseline for more advanced spatial analyses. Part II integrates advanced spatial approaches, beginning with a case study that applies spatial techniques to traditional sampling approaches (Paper 4), followed by an innovative framework that combines spatial sampling with a cross-disciplinary approach, offering an alternative to current methodologies (Paper 5). Finally, the limitations of conventional spatial models when applied to spatial sampling are discussed (Paper 6). This thesis addresses the longstanding call in non-market valuation studies for advancements in spatial heterogeneity analysis while emphasising the importance of integrating sophisticated geographical context models from other disciplines. It contributes to the stated preference and spatial heterogeneity literature by offering empirical results and improved methodological frameworks for preference data analysis. The insights into the spatial distribution of preferences aim to inform and improve political decision-making processes, potentially leading to more efficiently targeted and preference-consistent policy programs.