Abstract Summary
Within the context of the climate change adaptation and mitigation plans, the Netherlands is intensifying efforts to reduce greenhouse gas emissions. This pursuit requires substantial investments in energy infrastructure, which poses challenges in terms of cost and spatial feasibility. Spatial planning plays a pivotal role in shaping the built environment and, consequently, influencing the energy system. Currently, a critical gap exists in understanding the relationships between energy infrastructure and built environment characteristics, hindering effective spatial policy formulation. This research addresses this gap through a comprehensive spatial analysis of South Holland, aiming to uncover the intricate interplay between energy and built environment features to answer the following research question: “How can different sets of characteristics be used in spatial clustering to identify the relationship between the energy system and the built environment for use in spatial planning?”. First, we identify key characteristics, including energy supply and demand, land use, building typology, and social factors through a literature review and interviews with experts from energy and spatial planning fields. These characteristics are grouped with different priorities: (1) Energy (2) energy and built environment (3) energy and social (4) all characteristics. Then, using a Machine Learning algorithm, k-prototype cluster analysis, each group of characteristics is clustered to explore the relationships between the energy infrastructure and built environment characteristics in South Holland. The results reveal that while the final spatial energy clusters exhibit some overlap between the group of characteristics, they also successfully highlight the differences between cities in South Holland. For example, a notable correlational relationship exists between the rental housing and the electricity demand of households which revealed itself in the social cluster analysis. Another common trend between the clusters is that housing density and total heat demand are linked to each other. On the other hand, significant differences are observed between urban and rural or peri-urban areas within the groups of characteristics from the energy and build environment clusters and social clusters. Our results emphasize that the cluster analysis is suitable for providing exploratory insights into the energy system and built environment relationship. The clusters generated can serve as a foundation for informing decision-making in spatial planning. The findings underscore the potential of this approach in analyzing the complex dependencies between energy and landscape for developing evidence-informed spatial policies.
