Toward Sustainable Urban Development: Identifying Principal SDG Indicators for Chinese Cities

Abstract Urban transformation plays a decisive role in China's achievement of the United Nations 2030 Agenda for Sustainable Development. However, a comprehensive and authoritative sustainable development goal (SDG) indicator framework is currently lacking at the city level. To address this gap, we first developed an integrated framework comprising 357 indicators for 297 Chinese cities through a literature review. Nevertheless, the sheer number of reviewed indicators presents significant challenges in data collection. The study then used principal component analysis and multiple regression to identify a small set of SDG indicators (principal indicators) with the consideration of data collection difficulty. Finally, we tested their effectiveness up to 2030. The key findings of our study are as follows: 1) 187 principal indicators are identified to explain the 90% variance of all the 357 indicators with lowest data collection difficulty, providing comprehensive coverage of the SDGs and achieving efficient information aggregation; 2) these principal indicators demonstrated good variance effectiveness and data availability in the vast majority of cities (284 out of 297), highlighting priority areas for future data infrastructure development; 3) the continued applicability of these principal indicators up to 2030 is validated. This study offers insights to guide investments in data infrastructure supporting China's sustainable urban development.

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Publication Details

Journal
Advanced Sustainable Systems
Published
2025-02-13
DOI
https://doi.org/10.1002/adsu.202400894
Citations
5
Primary Topic
Sustainable Building Design and Assessment
Type
article
Field-Weighted Citation Impact
5.03

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article

Toward Sustainable Urban Development: Identifying Principal SDG Indicators for Chinese Cities

Chenyang Shuai, Jingran Sun, Bu Zhao, Xi Chen et al.
5 citations
Advanced Sustainable Systems
Sustainable Building Design and Assessment
5.03
article

Toward Sustainable Urban Development: Identifying Principal SDG Indicators for Chinese Cities

Chenyang Shuai, Jingran Sun, Bu Zhao, Xi Chen, Lu Chen
article en
5 citations

Abstract

Abstract Urban transformation plays a decisive role in China's achievement of the United Nations 2030 Agenda for Sustainable Development. However, a comprehensive and authoritative sustainable development goal (SDG) indicator framework is currently lacking at the city level. To address this gap, we first developed an integrated framework comprising 357 indicators for 297 Chinese cities through a literature review. Nevertheless, the sheer number of reviewed indicators presents significant challenges in data collection. The study then used principal component analysis and multiple regression to identify a small set of SDG indicators (principal indicators) with the consideration of data collection difficulty. Finally, we tested their effectiveness up to 2030. The key findings of our study are as follows: 1) 187 principal indicators are identified to explain the 90% variance of all the 357 indicators with lowest data collection difficulty, providing comprehensive coverage of the SDGs and achieving efficient information aggregation; 2) these principal indicators demonstrated good variance effectiveness and data availability in the vast majority of cities (284 out of 297), highlighting priority areas for future data infrastructure development; 3) the continued applicability of these principal indicators up to 2030 is validated. This study offers insights to guide investments in data infrastructure supporting China's sustainable urban development.

Advanced Sustainable SystemsVol. 9(4)
Texas Department of Transportation (US), Southwest University (CN), Chongqing University (CN), Cornell University (US), University of Michigan (US), The University of Texas at Austin (US)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 6%
Sustainable Building Design and Assessment
5.03
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