Perceiving progress and imbalance of environmental SDG indicators in China using big data

ABSTRACT

In the past decades, the environment has changed drastically in China with rapid economic development. Lack of data, especially data with spatiotemporal information, has however hindered evaluating progress of environmental indicators in the global Sustainable Development Goal (SDG) framework. Here, we evaluate and explore geospatial information of 20 environmental SDG indicators from six SDGs in China from 2010 to 2020 using big data. We show China has improved rapidly in most of the 20 indicators, except for indicator 13.2.2 (CO₂ emission). 63.5% of China’s land mass showed improved states for the SDG indicators. By 2020, four of the indicators were found to have achieved their 2030 targets. It is predicted from the existing data that 10 of the 20 indicators can achieve their targets smoothly by 2030, but two indicators will lag behind. We quantitatively show China’s progress towards the environmental SDG indicators, and put forward timelines and suggestions for policy makers.

KEYWORDS:

• Big earth data
• sustainable development goals
• spatial information
• environment

Acknowledgments

This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDA19090000 and XDA19090122)

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author upon request.

Author contributions

H.G. and J.L. designed and led the study. L.H., Y.C., Z.S., X.L., S.L., L.Z., and F.W worked on the data acquisition, processing and analysis. L.H and Y.C designed the figures. I.N., Z.X., Q.L., L.L., M.W., Y. C. contributed to interpretation of the results. All of the authors led the writing.

Competing interests

The authors declare no competing interests.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 1 USD≈6.2 RMB in 2017, 1 USD≈6.7 RMB in 2017, 1 USD≈6.6 RMB in 2018.

2 1 PPP USD≈4.2 RMB in 2017.

Additional information

Funding

This work was supported by Key scientific and technological research projects in the Xinjiang Production and Construction Corps: [Grant Number 2023AB074]; the Strategic Priority Research Program of the Chinese Academy of Sciences: [Grant Number XDA19090122].