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Chinese Journal of Applied Ecology ›› 2022, Vol. 33 ›› Issue (12): 3169-3176.doi: 10.13287/j.1001-9332.202212.039

• Special Features of Industrial Ecology and Social Ecosystem Management • Previous Articles     Next Articles

Data-driven study of complex socio-economic-natural ecosystems: Scales, processes and decision linkages

XUE Bing1*, LI Hong-qing2, HUANG Bei-jia3, WANG He-ming4, ZHAO Xue-yan5, FANG Kai6, CHEN Cheng7, CHEN Wei-qiang8, SHI Lei9, GOU Xiao-hua10   

  1. 1Institute of Applied Ecology, Chinese Academy of Sciences, Shen-yang 110016, China;
    2Chair of Circular Economy and Recycling Technology, Technical University of Berlin, Berlin 10623, Germany;
    3School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China;
    4School of Materials and Metallurgy, Northeastern University, Shenyang 110819, China;
    5College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China;
    6School of Public Affairs, Zhejiang University, Hangzhou 310058, China;
    7Leibniz Centre for Agricultural Landscape Research, Müncheberg 15374, Brandenburg, Germany;
    8Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Fujian, China;
    9School of Resources & Environment, Nanchang University, Nanchang 330031, China;
    10College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • Received:2022-09-19 Accepted:2022-10-13 Online:2022-12-15 Published:2023-07-05

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Abstract: The social-economic-natural system is a complex system for human survival and development, and the data-driven system research provides a new value-added orientation to enhance the cognition of the ecosystem. Under the new data context, the social-economic-natural complex system shows new features. The research object is gradually changing from a single element to a multi-factor coupling direction, which makes the data system more diversified, data sources more extensive, data expression more visualized. The research scale shows the characteristics of gradually expanding, and the research object would be more detailed. In the process of data identification, expression and visualization, it is therefore necessary to strengthen the coupling of time, space, structure, quantity and order, as well as to focus on the integration with decision making and local services. The future research of complex ecosystems in the new era should be carried out in terms of key scientific issues and supporting technologies, the role of scale and multi-factor coupling, as well as scientific and technological support for local and global governance. Under the continuous innovation of data, strengthening the cognition of multi-source data, long-term monitoring and time series still needs to be studied in depth. Carrying out data-driven analysis of complex ecosystems not only provides technical support for ecosystem services and sustainable development and enhances the long-term data sharing mechanism, but also provides more value support for realizing decision making and information dissemination.

Key words: data-driven, complex ecosystem, scale, decision support