Tools for citywide blue-green performance targeting and labelling of urban regreening measures

Summary

Barton, D.N., Woodville, P., Cimburova, Z., A’Campo, W., Korkou, M., Immerzeel, B. & Nawrath,M. 2025. Tools for citywide blue-green performance targeting and labelling of urban regreening measures. NINA Report 2620. Norwegian Institute for Nature Research The report discusses tools that support spatial prioritisation of regreening measures to areas with the greatest ecosystem service deficits in a city. These innovative tools include the development of an urban morphological classification methodology to help differentiate na-ture-based solutions (NBS) performance requirements by neighbourhood; an app to compute regreening performance scores and estimate costs, and a proposal for a performance labelling instrument to incentivise property owners to retrofit NBS to the existing urban fabric. Taken to-gether these tools provide decision-support and incentives for nature-based solutions that make the city more resilient. Blue-green infrastructure in cities provide ecosystem services on-site to property owners, locally to neighbourhoods and to the wider urban population. Urban ecosystem services are often un-derprovided by private property owners and developers because they are largely common or public goods. Public goods require policy instruments that internalise the benefits and costs of urban regreening measures for private actors. For this reason many municipalities are imple-menting blue-green performance requirements to increase provision of ecosystem services from private land. Most performance systems are regulations for ‘new builds’ on private land. This represents a small percentage of land in a city. In this report we present a ‘blue-green’ property labelling system to address the majority of built land with regreening measures integrated with the existing building infrastructure. Historically, the energy and climate performance requirements have typically been initiated as uniform standards designed to reduce initial implementation and transaction costs. As these sys-tems mature there is potential for greater differentiation and cost-effective targeting across a city. We foresee similar developments in blue-green performance standards. A blue-green perfor-mance indicator (BGPI) does not by itself prioritise urban regreening to neighbourhoods with the least ecosystem services, nor do BGPIs initially consider property management typologies. How-ever, as they mature, there is a potential for optimisation through differential requirements that are sensitive to the property specific costs and benefits of regreening. Adjustments need to be made for the diversity of urban morphologies and property management forms in cities, and their respective urban regreening costs and potential. Our report presents tools to tackle these chal-lenges. The report provides links to Google Earth Engine tools for multi-criteria spatial prioritisation of NBS to ecosystem service deficit areas, with tool examples provided for green roofs and urban trees in Oslo, Norway. The tool prototypes reported here can be adapted to assess spatial tar-geting of other types of urban regreening measures. Once a neighbourhood has been identified, performance targets for the neighbourhood’s particular physical and institutional conditions need to be determined. A BGPI provides a municipal regulatory requirement for developers and property owners to meet a minimum performance. Expanding on Norway’s Blue-Green Factor (BGF) metrics, we propose a labelling system that incentivizes broader adoption and innovation of nature-based solutions beyond these requirements. Using Oslo as a case study, the report develops an urban morphology typology that facilitates spatial differentiation of performance requirements. The morphology considers (i) physical re-greening potential defined by urban density and form, and (ii) implementation costs due to dif-ferent property management types. We use expert guided machine learning methods to develop an urban morphology for Oslo. While developed for Oslo, this is a generalizable GIS methodol-ogy for mapping any urban area. We then discuss how the morphology can serve as a basis for blue-green performance labelling of new and existing properties. Full documentation of the urban morphology classification methodology is provided in the appendix. Building on targeting tools and the urban morphology typology, the report presents a concept design for a blue-green performance labelling system, using Oslo’s Blue-Green Factor (BGF) as a starting point. The concept comes from energy performance labelling and incentives promoted by the Norwegian energy innovation programme ENOVA. Following their energy la-belling approach we discuss the possibilities of a new labelling system to incentivize urban re-greening, which we call “GRØNNOVA”. The concept design for a “Grønnova” labelling of properties is provided in the appendix for further dissemination of this idea with policy-makers and stakeholder. To help implement the performance label, the SPARE project also developed a QGIS app for digitising and computing a property’s current BGF score using high resolution aerial photos. The user can select a BGF scoring norm to apply, including the market leading systems by Standard Norge and Oslo Kommune, or define and test their own scoring. The BGF QGIS app includes a cost computing module based on a review of the NBS cost literature. The report provides links to the BGF QGIS App download and full Github documentation. As regreening measures in municipalities are implemented, they should be accounted for and evaluated. Blue-green performance metrics represents an innovative approach for urban ecosystem accounting and a robust proxy for ecosystem service provision. Moving beyond simple land-cover extent mapping, urban morphological classification can help explain how the capacity of urban ecosystems to deliver ecosystem services varies within cities. Map layers on urban morphology classification, BGF scores and ecosystem service provision can be cross-referenced with socio-demographic indicators such as urban public health and environmental justice to support urban municipal planning.