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This paper presents a model based on dynamic material flow analysis, general in its principles and applied to the dwelling stock of Norway for exemplification. The algorithm at the core of the model is presented in the form of a pseudo-code and is described in detail. The driving force in the model is a population’s need for housing and the necessary input are retrievable from national statistics on population, often dating back to around 1800, and its prognoses up to 2050 or beyond. Technical parameters such as the dwellings’ lifetime and the renovation cycles are expressed by probability functions. Outputs of the model are the flows of construction, demolition and renovation; analysis of the renovation activity is given particular attention. The model shows how the renovation rates are a result of the need for maintenance of an ageing stock, and provides quantitative estimates of the present and future natural renovation rates, i.e. without specific incentives. The paper shows how to validate the model against statistics and other data sources, and how to use the model’s future projections on construction, demolition and renovation activities in scenario based analyses of dwelling stocks’ energy demand and greenhouse gases emissions. [ABSTRACT FROM AUTHOR]

Models describing long-term development in energy consumption or greenhouse gas emissions from building stocks commonly model the underlying development in the building stock's size and composition using simple, linear trends. Uncertainty is rarely discussed. This study explores the importance of uncertainty in input parameters in a dynamic model for the Norwegian dwelling stock that is presented in a recent publication and provides valuable insights in expected future quantities of renovation activity and how different segments of the stock are subject to renovation. By use of a sensitivity analysis and a scenario analysis the present study focuses on how uncertainty in model input parameters affect the modeling results and the robustness of results and conclusions. The sensitivity analysis did not lead to unexpected changes in results, and showed the dynamic model being mostly sensitive to changes in population and dwellings’ lifetime. Further scenarios with extreme input values for population and dwellings’ lifetime were considered in order to investigate the consequences of low and high renovation options. Results prove that previous main conclusions still hold: renovation rates at levels necessary to achieve policy targets in energy and emission savings seem unrealistic to be achieved when modeling the ‘natural’ need for renovation. [ABSTRACT FROM AUTHOR]

Roadmaps and action plans for energy savings in building stocks commonly assume strong increases in the renovation rates to obtain future energy savings. This study presents a segmented dynamic dwelling stock model for understanding the nature of the long-term development in stocks, their turn-over and need for maintenance, including a case study for Norway. Segments are defined by dwelling type and construction period. Based on the underlying drivers in the system, population size and number of persons per dwelling, the dwelling stock demand and construction, demolition and renovation activity are estimated for each year towards 2050. Demolition and renovation activity is modeled using probability functions of different characteristics. The dynamic and segmented model gives useful insight to what segments of the dwelling stock that are expected to be exposed to renovation in the future. This is important for proper tailoring of building renovation and energy policies today and in future. In Norway, detached houses constructed between 1945 and 2011 will dominate the renovation activity in the coming decades. Although the renovation rate is expected to increase in the future, we have found that the rates commonly assumed in roadmaps and action plans in Europe do not seem to be realistic. [ABSTRACT FROM AUTHOR]