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High-Technology Development in Regional Economic Growth最新文献

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Empirical Findings 实证研究结果
Pub Date : 2019-06-04 DOI: 10.4324/9781315192017-4
Byung-Rok Choi
In this chapter, the previously developed methodology of the MLSDI (see Chapter 4) is computed for a sample region, and the empirical findings are presented and discussed. Thereby, the knowledge (see Section 2.3.3; e.g. Weitz et al., 2018) and the sustainability gap (see Section 2.3.4; e.g. Hall et al., 2017) are tackled. This chapter is structured as follows. First, the sample (except the key figures x and the key indicators y) is introduced in Section 5.1. Hereafter, results of the sustainable development key figures are exhibited in Section 5.2: Section 5.2.1 presents results of the data collection and preparation process, and Section 5.2.2 fills the incomplete sample’s data gaps. Section 5.3 deals with the multilevel key indicators y. First, they are derived from the meso GRI and the macro SDG frameworks in Section 5.3.1, and second, their empirical findings are analysed. To this end, summary statistics of the unscaled growth indicators yg are investigated in Section 5.3.2, whereas an analysis of the unscaled ratio indicators yr is refrained from, given their non-comparability. The key indicators’ outlier detection and treatment are outlined in Section 5.3.3, and the empirical findings of the cleaned and rescaled key indicators ys are examined in Section 5.3.4. The main contribution to the knowledge gap’s missing understanding of the dynamic interactions of the individual sustainable development elements makes Section 5.4. A comparative analysis of weights ω and importance factors ψ by the three applied weighting methods – PCA, PTA, and MRMRB algorithm – is carried out in Section 5.4.3. Section 5.4.1 and Section 5.4.4 deal with the PC family’s statistics, and Section 5.4.2 outlines the MRMRB algorithm’s diagnostics. Section 5.5 analyses the four composite sustainable development measures’ summary statistics (see Section 5.5.1) and results for the selected branches (see Section 5.5.2). Last, sensitivities of the applied methods are tested in Section 5.6.
在本章中,对样本区域计算了先前开发的MLSDI方法(见第4章),并介绍和讨论了实证结果。因此,知识(见第2.3.3节;例如Weitz et al., 2018)和可持续性差距(见第2.3.4节;例如Hall et al., 2017)被解决。本章的结构如下。首先,在5.1节中介绍了样本(关键数字x和关键指标y除外)。接下来,可持续发展关键数字的结果将在5.2节中展示:5.2.1节给出了数据收集和准备过程的结果,5.2.2节填补了不完整样本的数据空白。第5.3节涉及多层次关键指标。首先,它们来自第5.3.1节中的中观GRI和宏观可持续发展目标框架,其次,对它们的实证结果进行分析。为此,第5.3.2节将对未标度的增长指标yg进行汇总统计,而未标度的比率指标yr由于不具有可比性,因此不进行分析。5.3.3章节概述了关键指标的异常值检测和处理,5.3.4章节检验了清洗后和重新标度后的关键指标ys的实证结果。知识差距的主要原因是缺乏对个体可持续发展要素动态相互作用的理解,这使得第5.4节。通过三种应用的加权方法- PCA, PTA和MRMRB算法-对权重ω和重要因子ψ进行比较分析,在第5.4.3节中进行。章节5.4.1和5.4.4处理PC族的统计,章节5.4.2概述了MRMRB算法的诊断。5.5节分析了四项综合可持续发展措施的汇总统计数据(见5.5.1节)和所选分支的结果(见5.5.2节)。最后,在第5.6节中测试了所应用方法的灵敏度。
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引用次数: 0
Summary and Policy Implications 摘要及政策影响
Pub Date : 2019-06-04 DOI: 10.4324/9781315192017-5
Byung-Rok Choi
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引用次数: 0
Theoretical Background and Policy Issues 理论背景与政策问题
Pub Date : 2019-06-04 DOI: 10.4324/9781315192017-2
Byung-Rok Choi
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引用次数: 0
Research Questions and Modeling Strategy 研究问题和建模策略
Pub Date : 2019-06-04 DOI: 10.4324/9781315192017-3
Byung-Rok Choi
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引用次数: 0
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High-Technology Development in Regional Economic Growth
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