Stakeholder Analysis for Designing an Urban Air Quality Data Governance Ecosystem in Smart Cities

Abstract

Cities, the world over, are fuelling economic growth. at the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. to address these threats, smart cities and clean air programs are on a rise. in smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. the 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. the research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential 'fit for the purpose' use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. the analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain

Department(s)

Biological Sciences

Comments

National Science Foundation, Grant None

Keywords and Phrases

Artificial intelligence; Big data; Pollution management; Smart city; Stakeholder analysis; Urban computing

International Standard Serial Number (ISSN)

2212-0955

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Elsevier, All rights reserved.

Publication Date

01 Mar 2023

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