|dc.description.abstract||Residential buildings are responsible for 24% of the global final energy consumption, and the housing sector is important for future mitigation of greenhouse gases. Scenario models are important tools for estimation of the energy-saving potential in dwelling stocks. A dynamic and segmented dwelling stock model is developed to simulate long-term development of dwelling stocks in terms of stock size and building typology composition. The model gives a detailed understanding of the dynamics in the system. Renovation activity is estimated as the need for renovation during the ageing process of the stock, in contrast to exogenously defined and often unrealistic renovation rates applied in other models.
The dynamic dwelling stock model is applied for energy analyses for the Norwegian dwelling stock. In a historical analysis covering the period 1960-2015, the energy model is used to explore the causes of historical changes in dwelling stock energy use. Energy savings by large-scale energy-efficiency improvement of the stock and a strong increase in the average heating system efficiency are found to be offset by changes in user heating habits.
The case study of Norway 2016-2050 shows that despite stock growth, the total theoretical estimated delivered energy is expected to decrease. A large share of the energy-efficiency potential of the stock is already realized through standard renovation. The potential for further reductions through more advanced and/or more frequent renovation, compared to current practice, is surprisingly limited. However, extensive use of heat pumps and photovoltaics will give large additional future energy savings. Finally, user behavior is highly important. A strong future rebound effect is expected as the dwelling stock becomes more energy efficient.
The presented model is highly policy-relevant as it suggests a new way to generate more realistic estimates of future renovation activity, which should be applied in political road maps and action plans. Furthermore, a model like this could be applied to estimate the likely and possible future levels of e.g. renovation activity or introduction of heat pumps and photovoltaics. This information is useful for policymakers when developing subsidy schemes.||nb_NO