World News Assessing progress towards sustainable development over space and time

World News

Abstract

To deal with worldwide difficulties^{1, 2, 3, 4}, 193 countries have actually committed to the 17 United Nations Sustainable Development Goals (SDGs)⁵ Quantifying progress towards accomplishing the SDGs is necessary to track worldwide efforts towards sustainable development and guide policy development and application. Nevertheless, organized approaches for assessing spatio-temporal development towards attaining the SDGs are doing not have. Here we establish and test systematic techniques to measure development towards the 17 SDGs at national and subnational levels in China. Our analyses show that China’s SDG Index rating (an aggregate score representing the overall performance towards accomplishing all 17 SDGs) increased at the nationwide level from 2000 to2015 Every province also increased its SDG Index rating over this duration. There were large spatio-temporal variations across areas. For instance, eastern China had a greater SDG Index score than western China in the 2000 s, and southern China had a higher SDG Index score than northern China in2015 At the national level, the scores of 13 of the 17 SDGs improved gradually, but the ratings of 4 SDGs decreased. This study suggests the need to track the spatio-temporal dynamics of development towards SDGs at the worldwide level and in other countries.

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Acknowledgements

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

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Affiliations

Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
- Zhenci Xu
- , Sophia N. Chau
- , Yingjie Li
- , Thomas Dietz
- , Shuxin Li
- , Anna Herzberger
- , Ying Tang
- & Jianguo Liu
College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China
- Xiuzhi Chen
- , Jinyan Wang
- & Yunkai Li
School of Life Sciences, State Key Lab of Grassland Agro-ecosystems, Lanzhou University, Lanzhou, China
- Jian Zhang
Environmental Science and Policy Program, Department of Sociology and Animal Studies Program, Michigan State University, East Lansing, MI, U.S.A.
- Thomas Dietz
Department of Location, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA
- Julie A. Winkler
- & Ying Tang
School of Economics, Renmin University of China, Beijing, China
- Fan Fan
Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
- Baorong Huang
Institute of Land and Urban-Rural Advancement, Zhejiang University of Financing and Economics, Hangzhou, China
- Shaohua Wu
Department of Humanities and Info, Zhejiang College of Construction, Hangzhou, China
- Dequ Hong

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Contributions

Z.X. and J.L. developed the research study. 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 remarks on the manuscript. Z.X., J.Z., Yingjie Li and F.F. evaluated the information. Yunkai Li and X.C. helped to evaluate information connected to SDGs 2 and 6. Z.X., S.N.C., J.Z., Yingjie Li and J.L. composed the manuscript. All authors evaluated the manuscript.

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Correspondence to.
Yunkai Li or Jianguo Liu

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The authors state no competing interests.

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

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Prolonged data figures and tables

Extended Data Fig. 2 Unpredictability analysis for SDG scores ( n = 281,287) at the nationwide level in 2015 for different varieties of selected signs.

1–17 suggests 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 rectangular shape covers the first quartile Q1 to the third quartile Q3, which is the interquartile variety (IQR)^40,41(IQR = Q3 to Q1), while the line segment inside the rectangle shows the average. When the optimum observed SDG scores are higher than Q3 1.5 × IQR^40,41, the upper whisker (red) is Q3 1.5 × IQR^40,41 Otherwise, the upper hair is the optimum observed SDG score. When the minimum observed SDG ratings are less than Q1 − 1.5 × IQR^40,41, the lower hair (green) is Q1 − 1.5 × IQR. Otherwise, the lower hair is the minimum observed SDG score^40,41

Extended Data Fig. 3 Level of sensitivity of SDG scores to modifications in each indicator.

The sensitivity index S_x of SDG scores is shown when each SDG sign’s original data value decreased by 10%, (1 )–(16), or increased by 10%, (17)–(32), for China and for 3 example provinces (Beijing, Henan and Gansu) at 3 levels (high, middle and low) of the average SDG scores in 2000, 2005, 2010 and2015 The sample size n for each figure is 119 indications. The x axes show the SDG signs set up from 1 to119 The y axis is the level of sensitivity index S_x of SDG ratings due to the 10%reduction or increase in the initial worth of each indication.

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

The box plot illustrates the distribution of SDG Index scores ( n = 156) for 156 countries in one year. The central rectangle covers the very first quartile Q1 to the third quartile Q3, which is the IQR^40,41, while the line sector inside the rectangle shows the median. When the maximum observed SDG Index ratings are greater than Q3 1.5 × IQR, the upper hair is equivalent to Q3 1.5 × IQR^40,41 Otherwise, the upper whisker amounts to the maximum observed SDG Index rating. When the minimum observed SDG Index score is less than Q1 − 1.5 × IQR, the lower whisker amounts to Q1 − 1.5 × IQR^40,41 Otherwise, the lower hair is the minimum observed SDG Index rating^40,41 The green line section within package is the median worth of SDG Index ratings for the 156 nations.

Extended Data Fig. 5 Coefficient of variation for SDG ratings.

a, Coefficient of variation (CV) for SDG ratings of provinces in 2000, 2005, 2010 and2015 b, Typical value of the coefficient of variation for SDG scores at the provincial level in 2000, 2005, 2010 and 2015.

Supplementary info

Supplementary Info

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

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Xu, Z., Chau, S.N., Chen, X. et al. Assessing progress towards sustainable advancement over area and time.
Nature577, 74–78(2020) doi: 10.1038/ s41586-019-1846 -3

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Gotten
05 February 2018
Accepted
06 November 2019
Released
01 January 2020
Problem Date
02 January 2020
DOI
https://doi.org/101038/ s41586-019-1846 -3

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