To meet the goal of being carbon neutral by 2050, the Netherlands needs new and interconnected circular and sustainable infrastructure in its cities. But, a critical gap exists in understanding the relationships between energy infrastructure and built environment characteristics, hindering effective spatial policy formulation.
Building city infrastructure that meets circular and sustainability targets will require new, or new use of, space. Spatial planning, therefore, is a necessary part of decision-making when investing in the built environment of the future.
This project focused on the relationship between energy infrastructure and spatial policy. It developed a machine-learning algorithm to make sense of data comprising three components: energy use, such as peaks in demand, and efficiency of use by the consumer; the built environment, such as building density, age, and space above and below ground; and the social environment, for instance, demographics and income levels.
The analytical tool revealed relationships between all three factors, across urban, semi-urban and rural settings in South Holland. When shared with experts in energy infrastructure planning, the validity and value of the analysis was confirmed.
“The inclusion of the social environment data was found especially helpful, as it had rarely been considered before.”
The solution: The research produced an analytical tool to support evidence-based decisions on investments in the built environment. More research will enable the tool to be tested and developed further, for example, by including data on district heating, industrial usage, and renewable energy streams.