A GIS-based statistical approach to prioritize the retrofit of housing stocks at the urban scale

14467 11 76596 2 211316 document 76596 A GIS-based statistical approach to prioritize the retrofit of housing stocks at the urban scale.pdf application/pdf 6466ef7cc9c2dc57a7c90c2b76f97aeb MD5 600350 2017-03-09 09:40:03 https://pure.iiasa.ac.at/id/eprint/14467/1/A%20GIS-based%20statistical%20approach%20to%20prioritize%20the%20retrofit%20of%20housing%20stocks%20at%20the%20urban%20scale.pdf 14467 1 1 application/pdf text en public cc_by

A GIS-based statistical approach to prioritize the retrofit of housing stocks at the urban scale.pdf

published 76598 1 211328 document 76598 indexcodes.txt text/x-c 5194ed99e84eb8cc99f0216739fa9bf4 MD5 5230 2017-03-09 09:50:01 https://pure.iiasa.ac.at/id/eprint/14467/2/indexcodes.txt 14467 2 2 text/x-c other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/76596 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/76596 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/76596 archive 353 disk0/00/01/44/67 2017-03-09 09:43:36 2021-08-27 17:28:43 2017-03-09 09:43:36 article show Mastrucci A. 2129 0000-0002-5611-7780 Baume O. Stazi F. Leopold U. A GIS-based statistical approach to prioritize the retrofit of housing stocks at the urban scale pub prog_ene Building stock; Energy savings; Costs; CO2 emissions; Sustainable urban planning Cities are responsible for about 70% of the overall primary energy consumption in Europe and play a major role in addressing carbon mitigation. In this respect, the housing s ector has been identified as a key sector for its high energy savings potential achievable by implementing retrofit measures. However, a detailed characterization of the housing energy consumption profile and spatial distribution is needed to properly asse ss the energy saving potential at the urban scale and further support sustainable urban planning and energy policies. This study focused on a statistical approach based on Geographical Information Systems (GIS) developed to identify the energy consumption profile of urban housing stocks, the energy savings potential achievable by implementing retrofit measures and their respective spatial distribution across one entire city. The final energy consumption of individual dwellings was predicted by running a mul tiple linear regression model based on measured energy consumption available at aggregated level (post - code area level) and GIS data about characteristics of buildings and household. Energy savings potential and cost - effectiveness of standard retrofit meas ures were subsequently calculated and results were finally displayed as maps for decision support in sustainable urban planning. The methodology was applied to the exemplary housing stock of Rotterdam city, consisting of almost 300,000 units. Relevant res ults were provided to prioritize retrofit measures implementation according to energy savings potential and cost - effectiveness. Different types of maps were produced to show energy consumption and energy saving potential patterns across the city. The metho dology is generically applicable to other contexts and provides an effective tool for decision support in carbon mitigation policies of housing stocks at the urban scale. 2016-12 published 2872 public Journal of Energy Challenges and Mechanics 3 4 186-190 TRUE 2056-9386 FALSE no info:eu-repo/semantics/article