Built environment transformation for the transport energy transition

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel framework linking built environment variables to energy-relevant travel choices using discrete choice models, with implications for integrated assessment modeling.

🏢実務担当者:Urban planners and sustainability officers can use the findings to prioritize density and self-sufficient neighborhoods over single-destination proximity, but should also consider sociodemographic disparities.

🏛政策担当者:Highlights that urban planning can achieve modest but meaningful transport emission reductions (10% by 2050) and that addressing energy poverty through social housing is critical for maintaining public support.

This thesis aimed to answer the following central research question: what type of built environment design can best support the energy transition in the passenger transport sector? It employed discrete choice modeling frameworks to jointly analyze the influence of built environment variables on various energy-relevant travel choices, thus illuminating new energy transition pathways. In doing so, it explicitly bridged the divides between the energy science, transport modeling, and energy poverty disciplines. The results showed that the density in the walking range of residents had most effect on energy-relevant travel behavior. Distances to city centers mattered too. The residential location relative to train stations and supermarkets was of little importance. This indicates that travel energy consumption depends on the urbanity of the environment as a whole rather than on the distance to any single destination. The most important mechanism was the reduction in travel distances in urban environments. Policy should thus focus on creating neighborhoods that are not just dense, but also self-sufficient. The increased use of public and active modes by urban residents was relevant as well. Yet, the car remained the dominant mode in inner city areas, indicating that investments in BEV charging infrastructure are still required. Residents of non-green urban areas with limited parking space also owned lighter cars, which use less energy. However, the effect was limited. The indirect impact of the built environment on energy-relevant travel behavior through the intermediary variable of car ownership was likewise surprisingly limited. The above built-environment effects were considerably smaller than the differences between sociodemographic groups: households with high incomes, full-time jobs, and university degrees consistently used far more energy than their less advantaged peers. They owned new and electric cars, but also traveled much larger distances, made less use of public and active modes, and owned heavier cars. This shows the importance of looking beyond technology in the energy transition. Sociodemographic differences likewise dominated patterns of transport and domestic energy poverty. Analysis of spatial patterns showed that similar types of households did face higher domestic and transport energy costs in rural areas. Correcting for lower rural housing prices and urban-rural differences in income did not change this pattern: inhabitants of remote rural areas still spent the highest fraction of their available (housing-costs-corrected) budget on energy. Urban planning and housing policy aimed at providing (city) social housing apartments may therefore help reduce the vulnerability of low-income households to energy price shocks. The climate relevance of the above-described results was assessed through further calculations. These showed that ambitious urban planning can reduce transport energy consumption by approximately 0.5%/year. This means that The Netherlands’ total travel CO2 emissions can be reduced by 10% before 2050 (order-of-magnitude). Furthermore, reducing transport and domestic energy poverty by building sufficient urban social housing may help preserve societal and political support for the energy transition. Finally, urban planning can directly address a major rebound effect, as the rising car energy efficiency through technological climate change mitigation efforts can reduce driving costs and thus contribute to urban sprawl.

• openalex https://doi.org/10.33540/3680first seen 2026-07-27 04:55:58