Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020
Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/6/1668 |
| _version_ | 1827747803319762944 |
|---|---|
| author | Yaya Feng Chunlin Huang Xiaoyu Song Juan Gu |
| author_facet | Yaya Feng Chunlin Huang Xiaoyu Song Juan Gu |
| author_sort | Yaya Feng |
| collection | DOAJ |
| description | Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda). |
| first_indexed | 2024-03-11T05:57:24Z |
| format | Article |
| id | doaj.art-8fd9c3aa11354537b827c7bc66412e6c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:57:24Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8fd9c3aa11354537b827c7bc66412e6c2023-11-17T13:40:19ZengMDPI AGRemote Sensing2072-42922023-03-01156166810.3390/rs15061668Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020Yaya Feng0Chunlin Huang1Xiaoyu Song2Juan Gu3International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaScientific Information Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Western China’s Environmental Systems, Ministry of Education, Lanzhou University, Lanzhou 730000, ChinaRapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).https://www.mdpi.com/2072-4292/15/6/1668integrated assessmentSDG 11Yellow River Basingeospatial big data |
| spellingShingle | Yaya Feng Chunlin Huang Xiaoyu Song Juan Gu Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020 Remote Sensing integrated assessment SDG 11 Yellow River Basin geospatial big data |
| title | Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020 |
| title_full | Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020 |
| title_fullStr | Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020 |
| title_full_unstemmed | Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020 |
| title_short | Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020 |
| title_sort | assessing progress and interactions toward sdg 11 indicators based on geospatial big data at prefecture level cities in the yellow river basin between 2015 and 2020 |
| topic | integrated assessment SDG 11 Yellow River Basin geospatial big data |
| url | https://www.mdpi.com/2072-4292/15/6/1668 |
| work_keys_str_mv | AT yayafeng assessingprogressandinteractionstowardsdg11indicatorsbasedongeospatialbigdataatprefecturelevelcitiesintheyellowriverbasinbetween2015and2020 AT chunlinhuang assessingprogressandinteractionstowardsdg11indicatorsbasedongeospatialbigdataatprefecturelevelcitiesintheyellowriverbasinbetween2015and2020 AT xiaoyusong assessingprogressandinteractionstowardsdg11indicatorsbasedongeospatialbigdataatprefecturelevelcitiesintheyellowriverbasinbetween2015and2020 AT juangu assessingprogressandinteractionstowardsdg11indicatorsbasedongeospatialbigdataatprefecturelevelcitiesintheyellowriverbasinbetween2015and2020 |