Evaluation and monetisation of ecosystem services with real-time weather data and machine learning

Urban Green Infrastructures (GI) and Nature-based Solutions (NBS) play an essential role in the adaptation of urban spaces to climate change. However, implementation has been very low so far because there are uncertainties regarding their effects and benefits. In this context, we developed a method to evaluate and monetise current ecosystem services (ESS) provided by such GI. Our approach links current weather data with the greenpass technology via machine learning. This cloud-based method was tested and applied with a case study focusing on a perimeter building in Vienna, Austria. The GI applied were trees, green roofs, façade greenings, lawn, shrubs and perennials. The ESS include impacts on urban climate, energy balance of adjacent buildings, water balance and sewer system relief, CO 2 sequestration and O 2 production, as well as designation of the impact on property value. For the Viennese case study, regular benefits from ESS provided by the applied GI add up to € 340,000.- per year, while a one-time increase in property value results in € 1,073,600.- for the whole perimeter. Knowing the value of GI supports city administrations and large companies to predict and record the benefits and include them in the accounting. Cost-benefit considerations become easier and financial reservations or barriers for implementation of GI can be overcome. • Monetary assessment of GI • Use of machine learning algorithms to calculate ESS in real-time • Fact-based and economic argumentation basis for the implementation of GI