Work

On the Use of Machine Learning and Key Performance Indicators for Urban Planning and Design

Year: 2024

Type: article

Abstract: Global efforts to achieve climate neutrality increasingly rely on innovative urban planning and design strategies. This study focuses on the identification and application of key performance indicators (KPIs) to support policymakers and local authorities in driving sustainable urban transitions. Using a real-life case study of European cities and countries, this research leverages data analytics and machine learning to inform decision-making processes. Specifically, the k-means clustering algorithm was employed to group countries based on socioeconomic and environmental KPIs, while principal component analysis was used to rank the most influential indicators in shaping these clusters. The analysis highlighted GDP per capita, corruption perception, and climate-related expenditure as key drivers of clustering. Additionally, time series analysis of KPI trends demonstrated the impact of policy decisions over time. This study showcases how machine learning and data-driven approaches can provide valuable insights for urban planners, offering a robust framework for evaluating and improving climate-neutrality strategies at both city and country levels.

Source: Applied Sciences

DOI: 10.3390/app14209501

Authors: Majsa Ammouriova, Veronika Tsertsvadze, Angel A. Juan, Trinidad Fernandez, Leon Kapetas

Institutions: Universitat Oberta de Catalunya, German Jordanian University, Universitat Politècnica de València, Fraunhofer Institute for Industrial Engineering

Language: English

Topic: Climate Change and Sustainable Development

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728