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SDG indicator set as multi-level and multi-sectorial decision support tool combining national reporting and local knowledge on the built environment
IOP Conference Series: Earth and Environmental Science Pub Date : 2020-11-21 , DOI: 10.1088/1755-1315/588/2/022067
Andr Mueller Dr. 1
Affiliation  

Providing a sustainable built environment through appropriate actions and investment decisions taken by various decision-makers calls for a renaissance of planning and a targeted supply and use of data as information. Data is seen as lowest level of abstraction constituting the basis for creating information and knowledge through interpretation (Dhr et al. 2012). Data as interpreted information and knowledge would eventually turn into decision support systems (Evers 2008) and thus unfold its internal power related to the translation from a complex reality into data as well as its external power referring to the communicative function of data (Sderstrm 1996). Before completing this turn, data needs to be validated. This is a process that requires both, generating and aggregating data on higher levels of abstraction and governance as well as integrating data, which originates from local sources (Kytt et al. 2013), while pursuing the objective of providing a multi-level and multi-sectorial decision support tool for purposes of planning and decision-making. The 17 SDGs and their 169 sub-goals as part of the Agenda 2030 as well as the 175 paragraphs of the New Urban Agenda provided as starting points for this provision the basis for collecting in Germany in cooperation with cities and counties official data deriving from multi-level sources (Bertelsmann Stiftung 2018). A set of respective indicators has been developed in cooperation with local authorities and national statistical institutions as well as by using publically available open data sources (e.g. Open.NRW, Hackday Niederrhein) and is currently being tested. Considering the sustainable built environment, it is particularly SDG 11 that bridges between overall planning orientations and detailed building actions. The author of the paper suggests combining national reporting mechanisms and local knowledge, reflecting data sources and indicators in urban and spatial typologies as well as referencing the elements against other cultural approaches and international standards.



中文翻译:

可持续发展目标指标集作为多层次、多部门的决策支持工具,结合了国家报告和当地关于建筑环境的知识

通过各种决策者采取的适当行动和投资决策来提供可持续的建筑环境,这需要重新规划和有针对性地提供和使用数据作为信息。数据被视为最低层次的抽象,构成通过解释创建信息和知识的基础(Dhr et al. 2012)。作为被解释的信息和知识的数据最终将变成决策支持系统(Evers 2008),从而展现其与从复杂现实到数据的转换相关的内部力量以及涉及数据交流功能的外部力量(Sderstrm 1996) . 在完成这一回合之前,需要验证数据。这是一个需要两者的过程,生成和聚合更高层次的抽象和治理数据,以及整合源自本地来源的数据(Kytt 等人,2013 年),同时追求提供多层次和多部门决策支持工具的目标规划和决策。作为 2030 年议程一部分的 17 个可持续发展目标及其 169 个子目标以及《新城市议程》的 175 段作为该条款的起点,为在德国与市县合作收集来自多方级来源(Bertelsmann Stiftung 2018)。与地方当局和国家统计机构合作,并通过使用公开可用的开放数据源(例如 Open.NRW,Hackday Niederrhein),目前正在测试中。考虑到可持续的建筑环境,特别是可持续发展目标 11 是总体规划方向和详细建筑行动之间的桥梁。该论文的作者建议将国家报告机制和地方知识结合起来,反映城市和空间类型的数据源和指标,并参考其他文化方法和国际标准的元素。

更新日期:2020-11-21
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