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应用生态学报 ›› 2022, Vol. 33 ›› Issue (12): 3169-3176.doi: 10.13287/j.1001-9332.202212.039

• 产业生态学与社会生态系统管理专栏 • 上一篇    下一篇

数据驱动的社会-经济-自然复合生态系统研究:尺度、过程及其决策关联

薛冰1*, 李宏庆2, 黄蓓佳3, 王鹤鸣4, 赵雪雁5, 方恺6, 陈成7, 陈伟强8, 石磊9, 勾晓华10   

  1. 1中国科学院沈阳应用生态研究所, 沈阳 110016;
    2柏林工业大学循环经济与循环技术系, 柏林 10623;
    3上海理工大学环境与建筑学院, 上海 200093;
    4东北大学材料与冶金学院, 沈阳 110819;
    5西北师范大学地理与环境科学学院, 兰州 730070;
    6浙江大学公共管理学院, 杭州 310058;
    7莱布尼茨农业景观研究中心, 勃兰登堡明谢贝格 15374;
    8中国科学院城市环境研究所, 福建厦门 361021;
    9南昌大学资源与环境学院, 南昌 330031;
    10兰州大学资源环境学院, 兰州 730000
  • 收稿日期:2022-09-19 接受日期:2022-10-13 出版日期:2022-12-15 发布日期:2023-07-05
  • 通讯作者: * E-mail: xuebing@iae.ac.cn
  • 作者简介:薛冰, 男, 1982年生, 博士, 研究员, 博士生导师。主要从事人地系统分析与区域发展治理研究。E-mail: xuebing@iae.ac.cn
  • 基金资助:
    国家自然科学基金项目(41971166)、中国科学院区域发展青年学者项目(2021-003)、辽宁省兴辽英才计划项目(XLYC2007201)和中德农业科技合作项目(2018/2019)

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

摘要: 社会-经济-自然系统是人类赖以生存和发展的复合系统,而数据驱动下的系统研究为加强生态系统的认知提供了新的增值导向。在新的数据语境下,社会-经济-自然复合系统呈现出一些新的特征,研究对象逐渐从单一要素向多要素耦合的方向转变,使支撑量化研究的数据体系更多样化、数据来源更广泛化、数据表达更具可视化,并呈现出研究尺度逐渐扩大化、研究对象更精细化的特征。在对数据的识别、表达和可视化的过程中,既要加强对时间、空间、结构、数量和秩序的耦合,也要注重与决策制定和地方服务的结合。新时期复合生态系统的未来研究方向应该从关键科学问题及支撑技术、尺度作用和多要素耦合以及地方和全球治理的科技支撑等方面展开,在数据的不断革新下,加强对多源数据、长期监测和时间序列的认知仍是需要深入研究的课题。开展复合生态系统的数据驱动分析,不仅能为生态系统的服务及可持续发展提供技术支撑,增强数据的长效共享机制,同时可为实现决策制定和信息传播等方面提供更多的价值支持。

关键词: 数据驱动, 复合生态系统, 尺度, 决策支持

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