1
|
Li K, Gao L, Guo Z, Dong Y, Moallemi EA, Kou G, Chen M, Lin W, Liu Q, Obersteiner M, Pedercini M, Bryan BA. Safeguarding China's long-term sustainability against systemic disruptors. Nat Commun 2024; 15:5338. [PMID: 38914536 PMCID: PMC11196269 DOI: 10.1038/s41467-024-49725-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
China's long-term sustainability faces socioeconomic and environmental uncertainties. We identify five key systemic risk drivers, called disruptors, which could push China into a polycrisis: pandemic disease, ageing and shrinking population, deglobalization, climate change, and biodiversity loss. Using an integrated simulation model, we quantify the effects of these disruptors on the country's long-term sustainability framed by 17 Sustainable Development Goals (SDGs). Here we show that ageing and shrinking population, and climate change would be the two most influential disruptors on China's long-term sustainability. The compound effects of all disruptors could result in up to 2.1 and 7.0 points decline in the China's SDG score by 2030 and 2050, compared to the baseline with no disruptors and no additional sustainability policies. However, an integrated policy portfolio involving investment in education, healthcare, energy transition, water-use efficiency, ecological conservation and restoration could promote resilience against the compound effects and significantly improve China's long-term sustainability.
Collapse
Affiliation(s)
- Ke Li
- Business School, Sichuan University, Chengdu, 610065, China
| | - Lei Gao
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Waite Campus, Adelaide, South Australia, 5064, Australia
| | - Zhaoxia Guo
- Business School, Sichuan University, Chengdu, 610065, China
| | - Yucheng Dong
- Business School, Sichuan University, Chengdu, 610065, China.
| | - Enayat A Moallemi
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Black Mountain, ACT, Australia
| | - Gang Kou
- Xiangjiang Laboratory, Changsha, 410205, China
- School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 610074, China
| | - Meiqian Chen
- Business School, Sichuan University, Chengdu, 610065, China
| | - Wenhao Lin
- Business School, Sichuan University, Chengdu, 610065, China
| | - Qi Liu
- Business School, Sichuan University, Chengdu, 610065, China
| | - Michael Obersteiner
- International Institute for Applied Systems Analysis, Laxenburg, 2361, Austria
- The Environmental Change Institute, University of Oxford, Oxford, UK
| | | | - Brett A Bryan
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Australia
| |
Collapse
|
2
|
Gao L. Accelerate progress towards sustainable development goals: Insights from China. Sci Bull (Beijing) 2024; 69:574-577. [PMID: 38184387 DOI: 10.1016/j.scib.2023.12.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Affiliation(s)
- Lei Gao
- Commonwealth Scientific and Industrial Research Organisation, Waite Campus, Urrbrae SA 5064, Australia.
| |
Collapse
|
3
|
Shi H, Luo G, Sutanudjaja EH, Hellwich O, Chen X, Ding J, Wu S, He X, Chen C, Ochege FU, Wang Y, Ling Q, Kurban A, De Maeyer P, Van de Voorde T. Recent impacts of water management on dryland's salinization and degradation neutralization. Sci Bull (Beijing) 2023; 68:3240-3251. [PMID: 37980171 DOI: 10.1016/j.scib.2023.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 11/20/2023]
Abstract
Reducing soil salinization of croplands with optimized irrigation and water management is essential to achieve land degradation neutralization (LDN). The effectiveness and sustainability of various irrigation and water management measures to reduce basin-scale salinization remain uncertain. Here we used remote sensing to estimate the soil salinity of arid croplands from 1984 to 2021. We then use Bayesian network analysis to compare the spatial-temporal response of salinity to water management, including various irrigation and drainage methods, in ten large arid river basins: Nile, Tigris-Euphrates, Indus, Tarim, Amu, Ili, Syr, Junggar, Colorado, and San Joaquin. In basins at more advanced phases of development, managers implemented drip and groundwater irrigation and thus effectively controlled salinity by lowering groundwater levels. For the remaining basins using conventional flood irrigation, economic development and policies are crucial for establishing a virtuous circle of "improving irrigation systems, reducing salinity, and increasing agricultural incomes" which is necessary to achieve LDN.
Collapse
Affiliation(s)
- Haiyang Shi
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Department of Geography, Ghent University, Ghent 9000, Belgium; School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
| | - Geping Luo
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China; Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium.
| | - Edwin H Sutanudjaja
- Department of Physical Geography, Utrecht University, Utrecht 3584, Netherlands
| | - Olaf Hellwich
- Department of Computer Vision & Remote Sensing, Technical University of Berlin, Berlin 10587, Germany
| | - Xi Chen
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China; Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium.
| | - Jianli Ding
- College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China
| | - Shixin Wu
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Xiufeng He
- School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
| | - Chunbo Chen
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Friday U Ochege
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Department of Geography and Environmental Management, University of Port Harcourt, Port Harcourt 500004, Nigeria
| | - Yuangang Wang
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Qing Ling
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Alishir Kurban
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China; Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium
| | - Philippe De Maeyer
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Department of Geography, Ghent University, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium
| | - Tim Van de Voorde
- Department of Geography, Ghent University, Ghent 9000, Belgium; Sino-Belgian Joint Laboratory of Geo-Information, Ghent 9000, Belgium
| |
Collapse
|
4
|
Ge Y, Hu S, Song Y, Zheng H, Liu Y, Ye X, Ma T, Liu M, Zhou C. Sustainable poverty reduction models for the coordinated development of the social economy and environment in China. Sci Bull (Beijing) 2023; 68:2236-2246. [PMID: 37604723 DOI: 10.1016/j.scib.2023.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 06/23/2023] [Accepted: 06/25/2023] [Indexed: 08/23/2023]
Abstract
Sustainable development in impoverished areas is still a global challenge owing to trade-offs between development and conservation. There are large poverty-stricken areas (PSAs) in China, which overlap highly with ecologically sensitive areas. China has made great efforts to alleviate poverty over the years. The coordinated relationship between the social economy and the environment in PSAs, however, remains under-recognized. This study developed a county-level index system encompassing the socioeconomic and environmental sectors of China's PSAs. The integrated indexes of the two sectors were developed to reveal the spatial-temporal socioeconomic and environmental patterns and coupling coordination degree (CCD) levels were calculated to assess the coordinated relationships between them. The CCD indicated the increasingly coordinated development of socioeconomic and environmental conditions in China's PSAs from 2000 to 2020. Meanwhile, although the socioeconomic index achieved considerable growth with a growth rate of 58.4%, the environmental index was mildly improved with a growth rate of 19.6%, instead of a reduction. PSAs still have a large gap in socioeconomic development compared to non-poor areas; however, PSAs perform better in environmental index. Overall, the increased coordinated development between the social economy and the environment from 2000 to 2020 can be attributed to China's long-term, large-scale, and targeted interventions in poverty reduction and environmental conservation. Further, benefiting from the geodiversity of China, we identified four poverty reduction models which include advantageously, sustained, periodic, and limited effective models, on the basis of CCD change patterns. The four models can provide valuable experience for the rest of the world in tackling similar trade-offs of poverty reduction and environmental challenges.
Collapse
Affiliation(s)
- Yong Ge
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519015, China.
| | - Shan Hu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongze Song
- School of Design and the Built Environment, Curtin University, Perth WA 6102, Australia
| | - Hua Zheng
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yansui Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station TX 77940, USA
| | - Ting Ma
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengxiao Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenghu Zhou
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
5
|
Cao M, Tian Y, Wu K, Chen M, Chen Y, Hu X, Sun Z, Zuo L, Lin J, Luo L, Zhu R, Xu Z, Bandrova T, Konecny M, Yuan W, Guo H, Lin H, Lü G. Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios. Sci Bull (Beijing) 2023; 68:740-749. [PMID: 36934012 DOI: 10.1016/j.scib.2023.03.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 03/18/2023]
Abstract
Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.
Collapse
Affiliation(s)
- Min Cao
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Ya Tian
- School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
| | - Kai Wu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| | - Yu Chen
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Xue Hu
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; The Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
| | - Zhongchang Sun
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Lijun Zuo
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Jian Lin
- Sierra Nevada Research Institute, University of California, Merced CA 95348, USA
| | - Lei Luo
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Rui Zhu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632, Singapore
| | - Zhenci Xu
- Department of Geography, the University of Hong Kong, Hong Kong 999077, China
| | - Temenoujka Bandrova
- Laboratory on Cartography, University of Architecture, Civil Engineering and Geodesy, Sofia 1164, Bulgaria
| | - Milan Konecny
- Laboratory on Geoinformatics and Cartography, Institute of Geography, Masaryk University, Brno 601 77, Czech Republic
| | - Wenping Yuan
- School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, China
| | - Huadong Guo
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Hui Lin
- School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
| | - Guonian Lü
- Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
| |
Collapse
|
6
|
Affiliation(s)
- Xin Li
- National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.
| |
Collapse
|
7
|
Global assessment of nature's contributions to people. Sci Bull (Beijing) 2023; 68:424-435. [PMID: 36732118 DOI: 10.1016/j.scib.2023.01.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/05/2022] [Accepted: 11/07/2022] [Indexed: 01/22/2023]
Abstract
Synergistically maintain or enhance the numerous beneficial contributions of nature to the quality of human life is an important but challenging question for achieving Sustainable Development Goals. However, the spatiotemporal distributions of global nature's contributions to people (NCPs) and their interactions remain unclear. We built a rapid assessment indicator framework and produced the first spatially explicit assessment of all 18 NCPs at a global scale. The 18 global NCPs in 1992 and 2018 were globally assessed in 15,204 subbasins based on two spatial indicator dimensions, including nature's potential contribution and the actual contribution to people. The results show that most of the high NCP values are highly localized. From 1992 to 2018, 6 regulating NCPs, 3 material NCPs, and 2 nonmaterial NCPs declined; 29 regulating-material NCP combinations (54 in total) dominated 76% of the terrestrial area, and the area with few NCPs accounted for 22%; and synergistic relationships were more common than tradeoff relationships, while the relationships among regulating and material NCPs generally traded-off with each other. Transitional climate areas contained few NCPs and have strong tradeoff relationships. However, the high synergistic relationship among NCPs in low latitudes could be threatened by future climate change. These findings provide a general spatiotemporal understanding of global NCP distributions and can be used to interpret the biogeographic information in a functional way to support regional coordination and achieve landscape multifunctionality for the enhancement of human well-being.
Collapse
|