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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. • Large discrepancies exist between estimated heat demand & observed heat data • This leads to CO 2 reductions being wrongly estimated & misinforms policy • Subsidies designed with heat demand estimates may distort 40% of subsidy budget. • And miss allocate up to 39% of CO 2 emissions from dwellings in Denmark • Fine-tuned subsidies can achieve 12% more CO 2 savings in the studied buildings [ABSTRACT FROM AUTHOR]