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5 Core, Semi-periphery, Periphery SDG performances in SSA (Authors' visualisation using SocNetV 2.2) Notes Left: Overall SDG performance-Right: SDG 9 performance
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Although most scholars acknowledged that development is a transnational process, existing discussions usually focus on negative externalities such as pollution, epidemics, violent conflicts and economic crises. This chapter considers a form of positive externality, knowledge spillovers and argues that countries can innovate in policymaking, both de...
Citations
... Second, learning among public and private actors for SDG implementation should be fostered at all levels and scales (Andonova et al., 2022;Türkeli et al., 2020) with large potential for cities and local governments to function as transmission belts between the 2030 Agenda and actions for sustainable development on the ground (Hickmann, 2021). Third, governments must substantially reallocate funding for sustainable development and establish stronger incentive structures to guide public and private funding to promote sustainability transformations in different sectors (Biermann, Hickmann, Sénit, Beisheim, et al., 2022). ...
The millennium development goals (MDGs) were an important precursor to the sustainable development goals (SDGs). Hence, identifying the conditions that made the MDGs successful enhances our understanding of global goal‐setting and informs the global endeavour to achieve the SDGs. Drawing on a comprehensive review of 316 articles published between 2009 and 2018, we identify six factors that have enabled or hindered MDG implementation. Our analysis stresses the importance of path dependencies and shows that the MDGs catalysed changes only for those countries with sufficient resource availability, administrative capacity and economic development, as well as adequate support from external donors. National ownership and NGO pressure bolstered efforts to implement the MDGs. These findings suggest that globally agreed goals do not easily trickle down from the global to the national level. Thus, this article adopts a forward‐looking perspective and draws key lessons for the current implementation of the SDGs in developing countries.
... Most analytical efforts such as descriptive ( Diaz-Sarachaga et al., 2018 ), correlation ( Warchold et al., 2021 ), clustering ( Türkeli et al., 2020 ), regression ( Dotun-Olujinmi et al., 2021 ;Tesema et al., 2021 ), structural equation modelling ( Denny et al., 2018 ), gap ( Bjegovic-Mikanovic, 2019 ), content ( Chirambo, 2018 ), and network analyses ( Jiménez-Aceituno et al., 2020 ) have not been able to explain the significant variations existing across countries in terms of development as they are less spatially orientated ( Li et al., 2020 ;Nabiyeva and Wheeler, 2020 ;Otekunrin et al., 2019 ;Cling et al., 2018 ;Ogu et al., 2016 ). Also, the conventional analysis constraints the ability of governments, donor partners and impact investors in factoring space in designing Africa's developmental interventions, understanding synergies and trade-offs, and monitoring the SDGs ( Golding et al., 2017 ). ...
Challenges faced by African countries in achieving the goals of sustainable development are similar and transboundary. Analysis of sustainable development goals (SDGs) has largely been non-spatial, reducing the ability to find spatial relationships between countries and SDGs to help cooperation and proffer country-specific interventions. This study adopted techniques of exploratory and inferential spatial statistics to assess the successes of African countries from 2016 to 2020 in achieving the goals of sustainable development. Also, the study sought to understand how the spatial synergies and trade-offs between SDG's vary per country and time. Analysis revealed that spatial hotspots of countries with high SDGs scores were mostly confined to northern African countries with significant cold spots within central and eastern Africa and few patches in western and southern Africa for 2016. In 2020, the number of countries forming hotspots reduced, with Central African countries as significant cold spots. Five main spatial relationship such as positive linear, negative linear, concave, convex and undefined complex were found among countries and SDGs. However, these spatial relationships were fluid as they changed over time and with different levels of influence from 2016 to 2020. The study concludes that generic solutions and policies by development agencies and governments will not be enough in achieving SDGs because of the spatial heterogeneity of the continent. Hence policies should be country-specific based on results of spatial statistics.
... Given our focus on regional innovation, this study provides additional evidence on the work-innovation linkage which hitherto has been mostly restricted to the firm level. As the RIS approach and the recent open innovation literature suggest, innovation often occurs in non-firm settings and involves actors outside the industry [21][22][23]. Our study addresses this gap and generalizes these insights at a regional level. ...
This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.
... Pradhan et al. conducted a systematic study of SDG interactions by correlation analysis [18]. Türkeli SDGs and k-means clustering for all countries in Sub-Saharan Africa [19] using the SDSN and Bertelsmann Stiftung SDG Index data. Dörgő et al. before the following section dedicated to discussions. ...
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.
