Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models
The COVID-19 pandemic has posed severe threats to global sustainable development. However, a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals (SDGs) is still lacking. This research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine, random forest, and extreme gradient boosting. The results show that the overall SDG performance declined by 7.7% in 2020 at the global scale, with 12 socioeconomic SDG performance decreasing by 3.0–22.3% and 4 environmental SDG performance increasing by 1.6–9.2%. By 2024, the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories, while extra time will be gained for 4 environment-related SDGs. Furthermore, the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies, and the latter will recover more quickly to be closer to their pre-COVID-19 trajectories by 2024. Post-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental impacts. The results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.
Fundamental Research Funds for the Central Universities[2022CDJSKJC21];National Natural Science Foundation of China;
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||School of Environmental Science and Engineering|
1.School of Management Science and Real Estate,Chongqing University,Chongqing,China
2.School for Environment and Sustainability,University of Michigan,Ann Arbor,United States
3.Michigan Institute for Computational Discovery & Engineering,University of Michigan,Ann Arbor,United States
4.College of Economics and Management,Southwest University,Chongqing,China
5.Center for Systems Integration and Sustainability,Department of Fisheries and Wildlife,Michigan State University,East Lansing,United States
6.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
7.School of Management and Economics,Beijing Institute of Technology,Beijing,China
8.Center for Energy & Environmental Policy Research,Beijing Institute of Technology,Beijing,China
9.Key Laboratory of Jiangxi Province for Persistent Pollutants Control and Resources Recycle,Nanchang Hangkong University,Nanchang,Jiangxi,China
10.Department of Civil and Environmental Engineering,University of Michigan,Ann Arbor,United States
Shuai，Chenyang,Zhao，Bu,Chen，Xi,et al. Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models[J]. Fundamental Research,2022.
Shuai，Chenyang.,Zhao，Bu.,Chen，Xi.,Liu，Jianguo.,Zheng，Chunmiao.,...&Xu，Ming.(2022).Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models.Fundamental Research.
Shuai，Chenyang,et al."Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models".Fundamental Research (2022).
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.