Energy and space : the implication of spatial planning on energy demand and energy efficiency potentials

Abstract

How we arrange our activities (e.g., dwelling, work, education, leisure, mobility, etc.) in space has implications on energy demand and on energy efficiency potentials. Low density, sprawling development and monofunctional land use are generally thought to increase energy demand. However, findings indicate that density does not always (significantly) decrease energy consumption and the role of traffic in energy demand related to land use and urban density remains a contested issue. Land use and spatial planning affect many aspects of household energy demand. Land use planning intervenes on the level of settlement structures and building types, all of which has implications on the demand for heating, cooling, and illumination. Land use patterns and settlement structures also impact energy demand by increasing both the demand for technical infrastructure and public-distributive services and the demand for (private) motorized mobility. The purpose of this paper is to discuss the various potential implications of urbanization and land use patterns on household primary energy demand and to identify aspects of household energy demand that can be clearly linked to spatial structures of landuse. Furthermore, the objective is to develop a theoretical model for the assessment and quantification of land use and spatial pattern related energy demand. This involves the development of an indicator for urban form and function which can be used to compare different land use patterns in the Austrian context, and the description of required input parameters and data sources regarding availability and data quality. The analysis is split into a separate analysis of the subsystems 'energy demand related to dwelling', 'energy demand related to the provision of technical and public-distributive infrastructure', and 'energy demand for mobility' and a methodology was developed for each of the three subsystems. Settlement structure is operationalized by proposing a matrix classification for settlements that combines functional aspects of land use and morphological aspects of spatial patterns. Suitable assessment methods were identified for the assessment of building, technical infrastructure, and transport energy demand. Difficulties relate to finding the optimal trade off between necessary accuracy, on the one hand, and the applicability and comprehensiveness of the approach, on the other hand. Methods must be sensitive to even subtle relative differences between the settlement classes and be suited for a large representative sample, thus go beyond the application to few case studies. Furthermore, energy demand is difficult to disentangle from socio-economic variables that influence energy consumption patterns and from lifestyles and personal attitudes which must be controlled in order to avoid erroneous conclusions. Regarding data input, parts of the required input data are available in the necessary quality and spatio-temporal resolution. Data restrictions are related to the fact that some data are only available in aggregated format, outdated or not publicly accessible due to requirements on data protection. Lack of data concerns mobility behaviour and specific fuel consumption of public means of transport. The outcome of these findings was formalised in a theoretical model that restricts its focus to the typical average annual household primary energy demand for building and vehicle use, without accounting for upstream processes, and the typical annual primary energy demand of communities related to the provision of technical infrastructure for the total energy embodied in the process from production to disposal. More groundwork has to be done before the method can be readily implemented. Generally, the different methodological approaches for the assessment of primary energy demand must be validated by comparing results from samples with measured values. Input parameters for the settlement classification must be reviewed and the classification scheme should be subjected to practical testing. In the next step, the proposed method should be subject to testing and be implemented in a number of case studies.17