Policy-aware cross-city learning: A framework for sustainable urban governance

Incorporating knowledge of policy transfer into urban governance frameworks fosters cross-city learning and facilitates a transition from prediction-based to policy-guided decision-making. This approach combines data with policy insights, expanding the scope of cross-city learning and fostering collaborative governance. Unlike traditional qualitative policy transfer studies, the envisioned policy-aware framework computationally translates policy contexts into quantitative representations for direct integration into machine learning, enabling automated strategy adaptation rather than relying solely on human interpretation.

The need for policy awareness in cross-city learning

Cross-city learning holds significant potential for advancing smart cities and fostering sustainable urban development, promising to make global urbanization processes more effective and sustainable. However, its practical applications remain limited, requiring further exploration of effective implementation strategies.

Cross-city learning is fundamentally data driven in rationale and methodology. Conceptually, this data-driven approach is necessitated by the complexity of urban systems, inter-city heterogeneity, and the need for evidence-based governance at scale. Methodologically, it employs computational techniques, statistical analysis, and machine learning to extract transferable knowledge and quantify the effectiveness of strategies. These mechanisms enable cross-city learning to translate urban data into transferable knowledge, providing empirical foundations for the adaptation and implementation of urban policies.

Transfer learning has demonstrated remarkable success in addressing data sparsity and domain inconsistency across diverse fields, including computer vision, natural language processing, medical diagnostics, and autonomous driving. Cross-city learning seeks to transfer knowledge from source cities—prediction models, decision strategies, and data distributions—to target cities for prediction, detection, and deployment tasks. Despite its promise, existing research predominantly focuses on data features and algorithmic performance, neglecting deeper contextual factors like policy regulations, social culture, and public acceptance. Consequently, relying solely on prediction accuracy or error metrics leads to real-world challenges: execution difficulties, social rejection, and inadequate adaptability.

To address these challenges, we advocate for a policy-aware framework for cross-city learning. This framework accounts for variations in data distribution between cities and incorporates key contextual factors, such as policy environments, legal frameworks, social feedback, and cultural differences, into the learning models. By integrating these elements, the framework aims to generate actionable and contextually relevant strategic recommendations tailored to the specific needs of target cities.