Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Fine-tuning energy efficiency subsidies allocation for maximum savings in residential buildings
Energy consumption in buildings accounts for more than a third of European CO 2 emissions. The existing building stock shows the most potential for energy savings but at the expense of costly renovations. Thus, public intervention is decisive in driving transformation in this sector. However, policymakers mostly rely on heat estimates to develop energy-saving policies, limiting the possibility of aligning renovation support policy with environmental gain, slowing down the decarbonization effort. This study explores the benefits of using metered heat demand data with detailed building archetypes for impactful renovation subsidy allocation. We quantify the missed CO 2 emissions due to inaccuracies in heat demand estimates and develop an optimization model to quantify the impact of such inaccuracies on subsidy allocation. For the case study of Lyngby-Taarbæk municipality in Denmark, we find systematic bias in heat demand estimates that attribute higher heat demand to older houses than reality and inversely to newer family houses. Such bias results in the misallocation of 39% of total CO 2 emissions and distortion of 40% of the total subsidy. Ultimately, our results help policymakers identify buildings that should be prioritized for a maximum decarbonization impact.
Fine-tuning energy efficiency subsidies allocation for maximum savings in residential buildings
Energy consumption in buildings accounts for more than a third of European CO 2 emissions. The existing building stock shows the most potential for energy savings but at the expense of costly renovations. Thus, public intervention is decisive in driving transformation in this sector. However, policymakers mostly rely on heat estimates to develop energy-saving policies, limiting the possibility of aligning renovation support policy with environmental gain, slowing down the decarbonization effort. This study explores the benefits of using metered heat demand data with detailed building archetypes for impactful renovation subsidy allocation. We quantify the missed CO 2 emissions due to inaccuracies in heat demand estimates and develop an optimization model to quantify the impact of such inaccuracies on subsidy allocation. For the case study of Lyngby-Taarbæk municipality in Denmark, we find systematic bias in heat demand estimates that attribute higher heat demand to older houses than reality and inversely to newer family houses. Such bias results in the misallocation of 39% of total CO 2 emissions and distortion of 40% of the total subsidy. Ultimately, our results help policymakers identify buildings that should be prioritized for a maximum decarbonization impact.
Fine-tuning energy efficiency subsidies allocation for maximum savings in residential buildings
Siddique, Muhammad Bilal (Autor:in) / Bergaentzlé, Claire (Autor:in) / Gunkel, Philipp Andreas (Autor:in)
01.01.2022
Siddique , M B , Bergaentzlé , C & Gunkel , P A 2022 , ' Fine-tuning energy efficiency subsidies allocation for maximum savings in residential buildings ' , Energy , vol. 258 , 124810 . https://doi.org/10.1016/j.energy.2022.124810
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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