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Risk priorities and their co-occurrences in smart city project implementation: Evidence from India's Smart Cities Mission (SCM)

机译:风险优先事项及其在智能城市项目实施中的共同事件:来自印度智能城市的证据(SCM)

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With an increasing number of smart cities initiatives in developed as well as developing nations, smart cities are seen as a catalyst for improving the quality of life for city residents. However, current understanding of the risks that may hamper successful implementation of smart city projects remains limited due to inadequate data, especially in developing nations. The recent Smart Cities Mission launched in India provides a unique opportunity to examine the type of risks, their likelihood, and impacts on smart city project implementation by providing risk description data for area-based (small-scale) development and pan-city (large-scale) development projects in the submitted smart city proposals. We used topic modeling and semantic analysis for risk classification, followed by risk likelihood–impact analysis for priority evaluation, and the keyword co-occurrence network method for risk association analysis. The risk classification results identify eight risk categories for both the area-based and pan-city projects, including (a) Financial, (b) Partnership and Resources, (c) Social, (d) Technology, (e) Scheduling and Execution, (f) Institutional, (g) Environmental, and (h) Political. Further, results show risks identified for area-based and pan-city projects differ in terms of risk priority distribution and co-occurrence associations. As a result, different risk mitigation measures need to be adopted to manage smart city projects across scales. Finally, the paper discusses the similarities and differences in risks found in developed and developing nations, resulting in potential mitigation measures for smart city projects in developing nations.
机译:随着越来越多的智慧城市倡议和发展中国家,智能城市被视为改善城市居民生活质量的催化剂。但是,由于数据不足,特别是在发展中国家,目前对可能妨碍智能城市项目成功实施的风险仍然有限。最近在印度发起的智能城市任务提供了一个独特的机会,可以通过提供基于区域(小规模)开发和泛城的风险描述数据来检查风险的类型,可能性和对智能城市项目实施的影响。(大型-scale)提交的智能城市提案中的开发项目。我们使用了风险分类主题建模和语义分析,其次是风险似然 - 影响分析优先考虑,以及风险关联分析的关键字共同发生网络方法。风险分类结果确定了八个基于面积和泛城项目的风险类别,包括(a)财务,(b)伙伴关系和资源,(c)社会,(d)技术,(e)调度和执行, (f)机构,(g)环境,(h)政治。此外,结果表明为面积的基于面积和泛城项目确定的风险在风险优先分布和共同发生协会方面不同。因此,需要采取不同的风险缓解措施来管理跨尺度的智能城市项目。最后,本文讨论了发达国家中发现的风险的相似性和差异,从而导致发展中国家智能城市项目的潜在缓解措施。

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