High-Technology Development in Regional Economic Growth最新文献

High-Technology Development in Regional Economic Growth Pub Date : 2019-06-04 DOI: 10.4324/9781315192017-4

Byung-Rok Choi

{"title":"Empirical Findings","authors":"Byung-Rok Choi","doi":"10.4324/9781315192017-4","DOIUrl":"https://doi.org/10.4324/9781315192017-4","url":null,"abstract":"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.","PeriodicalId":269550,"journal":{"name":"High-Technology Development in Regional Economic Growth","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115157050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}