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• Bottom-up approach for predicting near-optimal building retrofit solutions. • Artificial neural networks ensure scalability for large-scale retrofit analysis. • Retrofit model for residential buildings of various climatic regions. • Trade-off between accuracy, computational cost, and ease of application. • Near-optimal retrofit solutions for residential buildings in Geneva municipality. Coordinated large scale building retrofit is one of the most effective approaches not only to reduce the environmental footprint of existing buildings but also to assure that climate goals can be met. Existing bottom-up retrofit approaches become infeasible or lack in terms of accuracy and/or computational cost especially when the scale of application increases in size. Hence, the aim of this paper is to propose and develop a bottom-up approach for large-scale building retrofit with the use of a scalable building-level surrogate retrofit model. To ensure scalability and generalization to large areas, we use artificial neural networks. The developed approach consists of a set of artificial neural networks trained on building archetypes, aiming to predict near-optimal building retrofit solutions for all residential buildings in Switzerland. This is achieved with a small set of inputs, and within a short run time. In order to validate the developed approach, we compare it with a conventional mixed building simulation and optimization approach. The surrogate model computes near-optimal building retrofit solutions, with an average coefficient of determination of 0.91 and an average f1 score of 0.89. To illustrate the advantages of this model, we perform a large-scale retrofit analysis of a case study in Geneva municipality. Such a surrogate model is capable of providing insights into the overall retrofit potential of large areas and thus shall support reaching energy emission reduction targets of existing residential buildings. [ABSTRACT FROM AUTHOR]

• Levelized costs, economic impact multiplier and share of domestic impact are assessed for geothermal district heating. • Choosing the right share of geothermal heat in the district heating is crucial for levelized costs. • The choice of auxiliary heat strongly influences district heating's economic impacts. • Standalone geothermal systems have higher domestic economic impacts. • Integrating waste incineration heat often maximizes both competitiveness and economic impacts. Geothermal district heating could play an important role towards climate change mitigation, but its slow uptake implies that it is cost competitive only under specific sets of conditions, including drilling success and design of the district heating. Even in cases when geothermal district heating may be less cost competitive than other alternatives, expenditure that occurs during its development brings indirect economic impact that could also benefit domestic economy. Using the case study of the State of Geneva in Switzerland, this paper analyzes cost competitiveness and economic impacts of 5′892 decision paths (combinations of decisions) to develop geothermal district heating, including the choice of auxiliary heat source, and uses comprehensive sensitivity analysis to identify the most influential decisions. The right share of geothermal coverage in the heat mix to achieve an optimum combination of investment costs and geothermal heat production, and the integration of waste incineration as auxiliary heat source are crucial for cost competitiveness. High economic impact multipliers are created by geothermal systems incorporating individual pellet boilers as auxiliary heat source. High share of domestic impact consistently occurs in the case of standalone geothermal installations, especially when they are used with second or third generation of district heating. Combining geothermal district heating from medium and deep wells (1′500 m and 2′500 m) with heat from waste incineration creates synergies where above-average cost competitiveness and economic impacts are achieved. [ABSTRACT FROM AUTHOR]