Visualising and weighting the multiple effects of building energy efficiency in conditional network structures

Buildings play a versatile role in the life of humans and its surroundings. Occupants spend in average more than 90% indoors, buildings consume around 40% of our resources and energy. With increasing awareness, innovation and technology and the pressure of climate change, the quality and energy efficiency of buildings increased. However, A study by BPIE (2017) together with data of the EU Building Stock Observatory concluded that 97% of the building stock needs to be upgraded and only less than 3% achieve an energy standard of A. Motivation for deep building renovation is low and the high investment costs are mostly considered as investment with long payback periods. The reason for these unfavorable balances is the focus on only one main benefit of improving the energy performance of buildings: energy, respective energy cost savings. In the presented work extends this framework by introducing a complex network of multiple, wider benefits of energy efficiency, beyond energy cost savings. For the first time, this conditional network highlights the causal chain of direct and indirect effects of renovation measures in social, environmental, and economic means. It defines parameters, expected utilities and output formats which will support the quantification of these benefits and therefore enriches the long-term building renovation strategies with new arguments and monetized values which can even lead to reduced payback periods. The presented network is currently transposed into a Bayesian network which will be integrated into a comprehensive decision support tool.     «

Buildings play a versatile role in the life of humans and its surroundings. Occupants spend in average more than 90% indoors, buildings consume around 40% of our resources and energy. With increasing awareness, innovation and technology and the pressure of climate change, the quality and energy efficiency of buildings increased. However, A study by BPIE (2017) together with data of the EU Building Stock Observatory concluded that 97% of the building stock needs to be upgraded and onl...     »