Your search found 2 records
1 Horvath, S.- M.; Muhr, M. M.; Kirchner, M.; Toth, W.; Germann, V.; Hundscheid, L.; Vacik, H.; Scherz, M.; Kreiner, H.; Fehr, F.; Borgwardt, F.; Guhnemann, A.; Becsi, B.; Schneeberger, A.; Gratzer, G. 2022. Handling a complex agenda: a review and assessment of methods to analyse SDG entity interactions. Environmental Science and Policy, 131:160-176. (Online first) [doi: https://doi.org/10.1016/j.envsci.2022.01.021]
Sustainable Development Goals ; Assessment ; Interactions ; Indicators ; Synergism ; Policy coherence ; Statistical methods ; Collaboration ; Models ; Network analysis
(Location: IWMI HQ Call no: e-copy only Record No: H050937)
https://www.sciencedirect.com/science/article/pii/S1462901122000351/pdfft?md5=092037f5be9c77a2b4475bab596c75c6&pid=1-s2.0-S1462901122000351-main.pdf
https://vlibrary.iwmi.org/pdf/H050937.pdf
https://vlibrary.iwmi.org/pdf/H050937.pdf
(2.78 MB) (2.78 MB)
The interlinked character of the 2030 Agenda poses both a challenge and an opportunity in terms of coherent policy making. Accordingly, different methods have been used in approaching the interactions between SDG entities (goals, targets, indicators, policies, external entities) in several recent publications.
In this paper, we provide a review and assessment of methods used for analysing SDG entity interactions. Specifically, we assess the suitability of these methods for addressing policy coherence at different levels and from different perspectives.
A total of 30 methods are categorised into argumentative, literature, linguistic, simulation, statistical, and other quantitative methods and are examined with expert elicitation applying a range of criteria on the basis of the following factors: the ability to give detailed information about effects between SDG entities, practicability, sensitivity to interdisciplinarity, and collaboration and systems thinking.
No single method, category, or research tradition (i.e. quantitative or qualitative) can be regarded as the most suitable for analysing SDG entity interactions. Quantitative methods (i.e. statistical, simulation, and other quantitative) are most frequently applied in the scientific context, although assessment results suggest that argumentative methods are particularly useful for obtaining information about effects while enabling interdisciplinarity and collaboration. In contrast, literature, linguistic, and quantitative methods can not be used to process different kinds of information. However, regarding the effort required, quantitative methods (except simulation methods) seem to require fewer resources. Although argumentative methods are evaluated best overall in our assessment, different implementation contexts and the importance assigned to the criteria may justify the application of most other methods as well.
In this paper, we provide a review and assessment of methods used for analysing SDG entity interactions. Specifically, we assess the suitability of these methods for addressing policy coherence at different levels and from different perspectives.
A total of 30 methods are categorised into argumentative, literature, linguistic, simulation, statistical, and other quantitative methods and are examined with expert elicitation applying a range of criteria on the basis of the following factors: the ability to give detailed information about effects between SDG entities, practicability, sensitivity to interdisciplinarity, and collaboration and systems thinking.
No single method, category, or research tradition (i.e. quantitative or qualitative) can be regarded as the most suitable for analysing SDG entity interactions. Quantitative methods (i.e. statistical, simulation, and other quantitative) are most frequently applied in the scientific context, although assessment results suggest that argumentative methods are particularly useful for obtaining information about effects while enabling interdisciplinarity and collaboration. In contrast, literature, linguistic, and quantitative methods can not be used to process different kinds of information. However, regarding the effort required, quantitative methods (except simulation methods) seem to require fewer resources. Although argumentative methods are evaluated best overall in our assessment, different implementation contexts and the importance assigned to the criteria may justify the application of most other methods as well.
2 Jie, Y.; Xiaoshu, C.; Jun, Y.; Zhewen, K.; Jianxia, C.; Yimin, W. 2024. Geographical big data and data mining: a new opportunity for “water-energy-food” nexus analysis. Journal of Geographical Sciences, 34(2):203-228. [doi: https://doi.org/10.1007/s11442-024-2202-6]
Food security ; Water resources ; Water quality ; Energy ; Climate change ; Nexus approaches ; Big data ; Data mining ; Interactions ; Sustainability ; Sustainable development ; Urbanization ; Land use ; Socioeconomic aspects ; Policies ; Artificial intelligence ; Machine learning ; Models ; Decision-support systems
(Location: IWMI HQ Call no: e-copy only Record No: H052609)
(2.54 MB)
Since the Bonn 2011 conference, the “water-energy-food” (WEF) nexus has aroused global concern to promote sustainable development. The WEF nexus is a complex, dynamic, and open system containing interrelated and interdependent elements. However, the nexus studies have mainly focused on natural elements based on massive earth observation data. Human elements (e.g., society, economy, politics, culture) are described insufficiently, because traditional earth observation technologies cannot effectively perceive socioeconomic characteristics, especially human feelings, emotions, and experiences. Thus, it is difficult to simulate the complex WEF nexus. With the development of earth observation sensor technologies and human activity perception methods, geographical big data covering both human activities and natural elements offers a new opportunity for in-depth WEF nexus analysis. This study proposes a five-step framework by leveraging geographical big data mining to dig for the hidden value in the data of various natural and human elements. This framework can enable a thorough and comprehensive analysis of the WEF nexus. Some application examples of the framework, major challenges, and possible solutions are discussed. Geographical big data mining is a promising approach to enhance the analysis of the WEF nexus, strengthen the coordinated management of resources and sectors, and facilitate the progress toward sustainable development.
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