Complexity and the Sustainable Development Goals: A Computational Intelligence Approach to Support Policy Mix Designs-Journal of Sustainability Research-CSCIED科技核心评价数据库-手机版

Complexity and the Sustainable Development Goals: A Computational Intelligence Approach to Support Policy Mix Designs

Complexity and the Sustainable Development Goals: A Computational Intelligence Approach to Support Policy Mix Designs

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DOI	10.20900/jsr20200006
刊名	Journal of Sustainability Research JSR
年，卷(期)	2020, 2(1)
作者	Serdar Türkeli*,
作者单位	United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht University, Boschstraat No. 24 6211AX Maastricht, the Netherlands ;
Abstract	In this article, a 3-step neuro-fuzzy expert decision support system is constructed in order to investigate the multifaceted performance interdependencies among 17 SDG performance scores across 162 UN Member States. The direct influence matrix among 17 SDGs, which would be filled by policy experts in interpretive structural modeling, is instead populated by computational intelligence. Results indicate that, the most influential performance drivers are SDG12 (Sustainable Production and Consumption), SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities) at global level. Yet these findings highlight the importance of establishing and enhancing local infrastructures and communities, innovative and sustainable supply and demand content to increase overall SDGs performance globally. Performance linkages SDG 5 (Gender Equality) and SDG 13 (Climate Action) are global common denominators across localities for positive evolution of overall SDGs performance. Local policy mixes between performance driver and linkage SDGs are recommended by taking eight dependent SDG performances (SDG 10, 16, 15, 8, 6, 17, 7, 2) into account as action contexts. Four autonomous (less influential) SDG performances (SDG 1, 3, 4, 14) remain to be integrated. Conclusions call for a global unity in diversity, local policy mixes by all cities and communities around the globe.
KeyWord	complexity; Sustainable Development Goals; mixed method research; artificial neural networks; multilayer perceptron; fuzzy set calibration; impact matrix cross-reference multiplication; neuro-fuzzy expert decision support system; computational intelligenc
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引用本文

Serdar Türkeli*. Complexity and the Sustainable Development Goals: A Computational Intelligence Approach to Support Policy Mix Designs, Journal of Sustainability Research. 2020; 2; (1). https://doi.org/10.20900/jsr20200006.

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