Yes, we CAN! Data-Driven Decision Making with the Climate Action Navigator

Wilke, Emily and Ulrich, Veit and von Elverfeldt, Kirsten and Martin, Maria and Block, Sebastian and Chandran, Ashwin and Gatland, Danielle and Hatfield, Charlie and Kong, Gefei and Parashar, Satvik and Rafiei, Fatemeh and Schott, Moritz and Zipf, Alexander (2026) Yes, we CAN! Data-Driven Decision Making with the Climate Action Navigator. EVERYBODY PLANS ... SOMETIMES. Cherish Heritage, Plan Now, Create a Better Future! Proceedings of REAL CORP 2026, 31st International Conference on Urban Development, Regional Planning and Information Society. pp. 1243-1248. ISSN 2521-3938

Text (Yes, we CAN! Data-Driven Decision Making with the Climate Action Navigator) CORP2026_151.pdf - Published Version Download (2MB)

Abstract

Cities urgently need to reduce greenhouse gas (GHG) emissions to comply with climate targets. To support evidence-based planning, we present the Climate Action Navigator (CAN) (HeiGIT 2026), a co-created, data-driven dashboard designed to analyse urban environments by means of a broad set of spatial assessment tools. While CAN is neither the first nor only dashboard that builds on open data and open source software for monitoring urban climate action, its commitment to transparency, adaptability, usefulness, and usability makes it uniquely suited for NGOs, (small) municipalities, and citizen initiatives. CAN’s assessment tools are co-created with municipal partners, researchers and local stakeholders, and integrate heterogeneous datasets to offer a holistic and accessible dashboard to assist in decision-making. This is supported by the high spatial resolution of CAN’s tools, which reach down to the neighbourhood and even the street level. With intuitive, interactive maps, the tools empower decision-makers to identify and compare intervention areas, thereby supporting targeted, actionable insights. The dashboard focuses on two core thematic areas: active mobility and GHG emissions. For mobility, CAN offers assessments related to walkability (hiWalk), bikeability (hiBike), and multimodal accessibility (transportability) globally. For emissions, it provides high-resolution estimates of emissions related to heating, traffic, and land cover change, as well as CO₂-budgets for selected cities. Because of data limitations, these tools are currently constrained to Germany. In addition, CAN utilizes deep learning models to detect rooftop solar panels from remote sensing imagery, again currently restricted to Germany. With its intuitive interface, CAN enables stakeholders to more effectively urge for climate action measures, prioritise interventions, and evaluate policy plans. This contribution presents CAN’s co-created assessment tools, with a focus on the tools related to GHG emissions, and illustrates how the dashboard is designed to assist data-driven policy-making and accelerate climate action at the urban scale.

Item Type:	Article
Uncontrolled Keywords:	Big Data, Active Mobility, Heating Emissions, Traffic Emissions, Dashboard
Subjects:	H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA76 Computer software Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Depositing User:	REAL CORP Administrator
Date Deposited:	05 Apr 2026 15:55
Last Modified:	05 Apr 2026 15:55
URI:	http://repository.corp.at/id/eprint/1310

Actions (login required)

View Item