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Purpose The built environment consists of a huge amount of infrastructure, such as roads and utilities. The objective of this paper is to assess the life cycle financial and environmental impact of road infrastructure in residential neighbourhoods and to analyse the relative contribution of road infrastructure in the total impact of neighbourhoods.Methods Various road sections are analysed based on an integrated life cycle approach, combining Life Cycle Costing and Life Cycle Assessment. To deal with complexity, a hierarchic assessment structure, using the principles of the "element method for cost control", is implemented. Four neighbourhood models with diverse built densities are compared to gain insight in the relative impact of road infrastructure in neighbourhoods. ResultsThe results reveal important financial and environmental impact differences between the road sections analysed. Main contributors to the life cycle financial and environmental impact are the surface layer and electrical and piped services. The contribution of road infrastructure to the total neighbourhood impact, ranging from 2% to 9% of the total cost, is relatively limited, compared to buildings, but not negligible in low built density neighbourhoods.Conclusions and recommendations Good spatial planning of the neighbourhood is recommended to reduce the amount of road infrastructure and the related financial and environmental impact. The priority should be to design denser neighbourhood layouts, before decreasing the financial and environmental impact of the road sections.

The local outdoor climate and building characteristics influence the energy use of a building to an important extent. To design energy efficient and climate robust buildings, it is important to get insights into the energy demand over the building's service life from the early design phase onwards. This paper presents an overview of the different variants of the heating and cooling degree day method. A convection-permitting climate model is furthermore used to obtain heating and cooling degree days (for a baseline temperature of 18 °C) for the RCP 8.5 (high-end) climate change scenario for Belgium on a high-resolution grid of 2.8km. Area of focus for this paper is Belgium. The results demonstrate a decrease of the HDD with 27% between 1976-2004 (3189 HDD) and 2070-2098 (2337 HDD). In contrast, the CDD were found to increase with a factor 2.4 from 167 CDD to 401 CDD in the same timeline. Smaller reductions in average HDD were moreover found in urban areas compared to rural areas. For the CDD, a higher absolute increase was found for urban areas and the Northeast of Belgium.

As buildings have a relatively long life span, it is important to consider climate change in energy performance modelling. Good quality weather data are needed to obtain accurate results. This chapter discusses widely used methods to predict future weather data (dynamical downscaling, stochastic weather generators and morphing) and provides an overview of available weather datasets (multi-year, typical years, extreme years and representative years) for building simulations. A Flemish office building is used for a comparative analysis of the estimated heating and cooling load making use of one-year weather files (typical and extreme future climate conditions) derived from a recently developed convection-permitting climate model for Belgium. Climate models and weather generators are identified as the most preferred for the estimation of the average energy consumption and thermal comfort in average and extreme situations. Climate models have the advantage to better represent extreme weather events and climate differences due to territorial settings, while weather generators can generate multiple climate realizations. A combination of a typical year with an extreme cold and extreme warm year was found to result in an overall good representation of the energy need for heating and cooling in average and extreme weather conditions. Further, the influence of the methodological choices to extract one-year weather files (typical or extreme years) from the 30-year climate data is highlighted as different results were obtained when different meteorological variables were considered for the creation of the one-year files.