# Assessing progress towards sustainable development over space and time

## Abstract

To address global challenges1,2,3,4, 193 countries have committed to the 17 United Nations Sustainable Development Goals (SDGs)5. Quantifying progress towards achieving the SDGs is essential to track global efforts towards sustainable development and guide policy development and implementation. However, systematic methods for assessing spatio-temporal progress towards achieving the SDGs are lacking. Here we develop and test systematic methods to quantify progress towards the 17 SDGs at national and subnational levels in China. Our analyses indicate that China’s SDG Index score (an aggregate score representing the overall performance towards achieving all 17 SDGs) increased at the national level from 2000 to 2015. Every province also increased its SDG Index score over this period. There were large spatio-temporal variations across regions. For example, eastern China had a higher SDG Index score than western China in the 2000s, and southern China had a higher SDG Index score than northern China in 2015. At the national level, the scores of 13 of the 17 SDGs improved over time, but the scores of four SDGs declined. This study suggests the need to track the spatio-temporal dynamics of progress towards SDGs at the global level and in other nations.

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## Acknowledgements

We acknowledge edits from S. Nichols and K. Kapsar, and data compilation from Y. Liu, Y. Wang, Z. Zhou and Y. Sun. We are grateful for financial support from the National Science Foundation, Michigan State University, Michigan AgBioResearch, the China Scholarship Council and the National Natural Science Foundation of China (grant numbers 51621061 and 51321001).

## Author information

Z.X. and J.L. designed the research. Yunkai Li and X.C. contributed and checked data. Z.X., S.N.C., J.L., T.D., Yunkai Li, Y.T., X.C., S.L., B.H., A.H., J.A.W. and D.H. provided comments on the manuscript. Z.X., J.Z., Yingjie Li and F.F. analysed the data. Yunkai Li and X.C. helped to analyse data related to SDGs 2 and 6. Z.X., S.N.C., J.Z., Yingjie Li and J.L. wrote the manuscript. All authors reviewed the manuscript.

Correspondence to Yunkai Li or Jianguo Liu.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Peer review information Nature thanks Brett Bryan, Xiangzheng Deng, Lei Gao and Jürgen Kropp for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Extended data figures and tables

### Extended Data Fig. 1 Change in China’ s individual SDG scores at the national level from 2000 to 2015.

For data sources, see Methods.

### Extended Data Fig. 2 Uncertainty analysis for SDG scores (n = 281,287) at the national level in 2015 for different numbers of selected indicators.

1–17 indicates uncertainty analysis for SDG 1–17. Sample sizes are 63, 1,023, 262,143, 1,023, 63, 63, 7, 15, 16,383, 15, 63, 127, 31, 7, 127, 7 and 127 for box plots of SDG 1–17. In each box plot, the central rectangle spans the first quartile Q1 to the third quartile Q3, which is the interquartile range (IQR)40,41 (IQR = Q3 to Q1), while the line segment inside the rectangle shows the median. When the maximum observed SDG scores are greater than Q3 + 1.5 × IQR40,41, the upper whisker (red) is Q3 + 1.5 × IQR40,41. Otherwise, the upper whisker is the maximum observed SDG score. When the minimum observed SDG scores are less than Q1 − 1.5 × IQR40,41, the lower whisker (green) is Q1 − 1.5 × IQR. Otherwise, the lower whisker is the minimum observed SDG score40,41.

### Extended Data Fig. 3 Sensitivity of SDG scores to changes in each indicator.

The sensitivity index Sx of SDG scores is shown when each SDG indicator’s original data value decreased by 10%, (1)–(16), or increased by 10%, (17)–(32), for China and for three example provinces (Beijing, Henan and Gansu) at three levels (high, middle and low) of the average SDG scores in 2000, 2005, 2010 and 2015. The sample size n for each figure is 119 indicators. The x axes display the SDG indicators arranged from 1 to 119. The y axis is the sensitivity index Sx of SDG scores due to the 10% decrease or increase in the original value of each indicator.

### Extended Data Fig. 4 China’s SDG Index score compared with another 156 countries based on overlapping indicators.

The box plot depicts the distribution of SDG Index scores (n = 156) for 156 countries in one year. The central rectangle spans the first quartile Q1 to the third quartile Q3, which is the IQR40,41, while the line segment inside the rectangle shows the median. When the maximum observed SDG Index scores are greater than Q3 + 1.5 × IQR, the upper whisker is equal to Q3 + 1.5 × IQR40,41. Otherwise, the upper whisker is equal to the maximum observed SDG Index score. When the minimum observed SDG Index score is less than Q1 − 1.5 × IQR, the lower whisker is equal to Q1 − 1.5 × IQR40,41. Otherwise, the lower whisker is the minimum observed SDG Index score40,41. The green line segment within the box is the median value of SDG Index scores for the 156 countries.

### Extended Data Fig. 5 Coefficient of variation for SDG scores.

a, Coefficient of variation (CV) for SDG scores of provinces in 2000, 2005, 2010 and 2015. b, Average value of the coefficient of variation for SDG scores at the provincial level in 2000, 2005, 2010 and 2015.

## Supplementary information

### Supplementary Information

This file contains Supplementary Tables 1-7, Supplementary Discussion, Supplementary Methods and Supplementary References

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Xu, Z., Chau, S.N., Chen, X. et al. Assessing progress towards sustainable development over space and time. Nature 577, 74–78 (2020). https://doi.org/10.1038/s41586-019-1846-3