| 研究生: |
李姿萱 Lee, Tzu-Hsuan |
|---|---|
| 論文名稱: |
基於居住生活行為的綠色品質與居住品質實證分析 Empirical Analysis of Green Quality and Housing Quality Based on Residential Behaviors |
| 指導教授: |
陳彥仲
Chen, Yen-Jong |
| 學位類別: |
碩士 Master |
| 系所名稱: |
規劃與設計學院 - 都市計劃學系 Department of Urban Planning |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 中文 |
| 論文頁數: | 111 |
| 中文關鍵詞: | 綠色品質 、居住品質 、差異比較分析 、迴歸分析 |
| 外文關鍵詞: | Green Quality, Housing Quality, ANOVA, Regression analysis |
| 相關次數: | 點閱:73 下載:15 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
在現代社會,隨著都市化和全球化的迅速發展下,使得對永續發展和提升居住品質的關注成為全球性的重要趨勢。因此,深入探討基於居住行為方面的綠色品質與居住品質情況,成為迫切且重要的課題。
近年來,居住環境及住宅市場長期以來都是臺灣政府和社會關注的焦點。政府積極參與住宅相關政策和計畫,致力於提供安全、健康、和宜居的住宅環境。無論是從官方指標、政策或政策工具的角度來看,無不反映出民眾和政府對居住品質的高度關注。而在聯合國永續發展目標(SDGs)的框架下,SDG 11 永續城鄉中明確強調。進一步凸顯綠色居住成為全球可持續發展議程中的一個關鍵焦點。在這脈絡下,研究如何評估並改善居住環境的可持續性和品質變得至關重要。然而,綠色居住並非僅停留在環境友善層面,同時還涵蓋了更廣泛的居住生活品質指標。
本研究的目標是建立兩套全面且適用於臺灣的綠色品質和居住品質指標系統。透過敏感度分析,選擇最合適的模型架構。最終決定使用等加權重法,賦予各指標項目之間相同的權重。此外,為彌補過去研究的局限性,本研究以全臺為範圍進行深入研究,但外島地區情況不同不予納入。通過深入分析永續發展目標、國民幸福指數、綠色成長指標、綠色國民所得帳以及過往居住相關文獻等多個方面的資源,以建立可持續使用的指標體系,將有助於評估及瞭解各區域的綠色品質和居住品質狀況。並通過差異比較分析探討不同區域之間的差異,提出有針對性的建議。另外,透過一般線性迴歸,探討綠色品質對居住品質影響程度,以及找出影響兩者的關鍵因素,做出彈性分析,以找出綠色品質指標項目對居住品質較敏感的地區。
研究發現,居住品質在不同尺度下,區域差異皆達顯著,而綠色品質則是在都市化地區分類中未達顯著。綠色品質指標如空氣汙染指標、污水處理率及自來水普及率對居住品質有顯著的正面影響,尤其是空氣汙染指標。而每人平均享有的公園綠地面積則對居住品質有顯著負面影響。這些結果突顯了改善這些環境指標對於提升居住品質的重要性。
本研究不僅是建置了評估綠色品質與居住品質的有效框架系統,並在學術上提供了對居住生活和綠色品質關係的新見解。也探討不同地區之差異,為政府和相關組織提供了實際針對性的政策建議,藉由改善居住環境與提高綠色品質,使臺灣能夠朝著更可持續、有彈性、健康和包容的社會邁進。
Amid rapid urbanization and globalization, sustainable development and Housing Quality are now global focuses. Therefore, This study establishes two sets of indicator systems for Green Quality and Housing Quality that are applicable to Taiwan and can be used repeatedly over time. The indicator systems were developed by selecting the most appropriate model framework through Sensitivity analysis, and ultimately, an Equal Weight Method was used to assign equal weights to each indicator. Additionally, the study conducted ANOVA to explore the differences between various regions, and used Regression analysis to assess the impact of Green Quality on Housing Quality, identifying the key factors influencing both. Elasticity analysis was also performed to identify the regions where Green Quality indicators are more sensitive to Housing Quality, leading to targeted recommendations.
The results of the study indicate significant differences in Housing Quality across different regions, while Green Quality did not reach significance in urbanized areas. Air pollution indicators, sewage treatment rates, and tap water coverage rates have a significant positive impact on residential quality, whereas the per capita park and green space area has a significant negative impact. These findings provide valuable policy recommendations for improving Taiwan's residential environment and contribute to promoting sustainable social development.
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