Localizing urban SDGs indicators for an integrated assessment of urban sustainability: a case study of Hainan province

ABSTRACT

Due to the Sustainable Development Goals (SDGs) being designed at both national and globally applicable level, it is challenging to adequately reflect the local context and characteristics in different urban regions without fully utilizing big earth data. To effectively address this issue, this study localized 73 urban indicators for 13 SDGs and both assessed and forecasted urban sustainability across 18 cities in Hainan (2010-2030) using big earth data. Our analysis specifically focused on indicator score, goal score, SDG index, SDG spatial spillover effect, and trade-offs and synergies. The results indicated an overall upward trend in sustainable development in Hainan province, predicting achievement of the SDGs by 2030. The SDG index and spatial spillover showed a pattern of ‘high in the north and south, low in the middle’. While synergies outweighed trade-offs, trade-offs increased at a faster pace. More specifically, the average SDG index increased from 29.85 in 2010–60.09 in 2018, with a projected score of 89.76 by 2030. During 2010–2018, the synergy-to-trade-off ratio declined from 3.91–1.84, driven by a trade-off growth rate 2.03 times higher than synergy. Our work provides a valuable localized case method, and data support for monitoring sustainable development at the global urban level.

KEYWORDS:

• Localized urban SDGs
• spatial spillover effect
• trade-offs and synergies
• SDG index
• 2030 SDGs projections

Acknowledgments

We appreciate the critical and constructive comments and suggestions from the reviewers that helped improve the quality of this manuscript.

Author contributions

Conceptualization Z.S.; methodology, S.L. and Z.S.; formal analysis, S.L.; writing – original draft preparation, S.L.; writing – review and editing, Z.S., Z.L., H.J., H.L. and X.O.; supervision, H.G.; funding acquisition, H.G. and Z.S. All authors have read and agreed to the published version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data presented in this study are available on request from the corresponding author.

Additional information

Funding

This research was funded by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (Grant No. CBAS2022IRP04), the Key R&D Program Projects in Hainan province (grant number ZDYF2020192), National Key R&D Program of China (Project No. 2022YFC3800700), and National Natural Science Foundation of China (grant number 42171291).