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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

台北縣垃圾費隨袋徵收政策移植過程之研究 / The policy transfer process of per-bag trash collection fee in Taipei County

黃秋燕, Huang, Chiu Yen Unknown Date (has links)
台北縣隨著國際世界的趨勢,搭乘著環保意識的潮流,對垃圾費的徵收制度進行了變革,由隨水費附徵的便宜行事作業規範中,改以污染者付費的公平合理的計量徵收方式—隨袋徵收垃圾費,此一改革突破了地方制度法及廢棄物清理法的框架限制,對民眾增加了直接的經濟性誘因,改變了民眾垃圾丟棄的歷來習性,如此政策的改革,不但擦亮了民眾的眼睛,更實踐「幸福、美麗、大台北」政策支票的兌現。 台北市在民國87年推動垃圾費隨袋徵收的政策,在實施長達10年之久,隨袋徵收政策才跨越了淡水河,由台北縣以一個縣(市)政府的層級,疏通所轄29個鄉鎮市公所的自治團體,進而推動此一環保政策;故台北縣是如何在層層的困境中尋求有效的突破?而其「垃圾費隨袋徵收」經歷過哪些過程?有那些因素影響台北縣垃圾費隨袋徵收的政策內涵? 從台北縣隨袋徵收政策的推動過程中,經由研究個案之研析,綜理出以下四點發現:一、在移植的過程中,創造有效層級運作。二、以現有的措施優勢,結合適當的宣傳,減少移植過程的磨擦。三、地域的毗鄰,加速了政策移植。四、領導者的作為,是決定移植成功與否的關鍵。另以政策移植學術討論面向有二:一、政策移植的動態過程會因個案的不同而有所簡易化。二、本土境內政策移植克服政策移植的諸多不當的變化,境內政策移植較國際間移植單純,但仍應考量移植政府本身客觀條件及移植政策的長遠影響,以達致可長可久成功的政策移植。
2

地方政府的政策趨同與政策學習 ─ 1999專線個案研究 / Policy Convergence and Policy Learning of Local Government: A Case of 1999 Citizen Hotline

陳序廷, Chen, Hsu Ting Unknown Date (has links)
在全球化與資訊通訊科技的脈絡下,各級政府之間為解決政策問題或改善政策績效而相互進行政策學習已成為趨勢。本研究應用政策趨同以及政策學習理論,分析在國內已形成顯著趨同現象的1999專線政策,並選取新北市政府為個案進行深入探討。研究結果顯示,行政區劃層級為直轄市、位於北部或中部、人口數百萬以上的縣市在1999專線上比較有採納趨同的傾向,趨同縣市在土地面積、高山地區比率、道路里程密度,行政機關公務人員數,以及歲入上也較未趨同縣市為多。在細部的服務機制和委外範圍上,各縣市仍多少有差異,較不完備之縣市則呈現人口數以及歲入較少的特徵。1999專線的趨同力量中並無中央政府的強制力介入,促成趨同的可能原因有觀念普及、政治壓力、競爭壓力、問題壓力,以及創新的認知屬性等因素導致。在政策學習分析的部分,新北市政府學習過程中的關鍵行動者為文官體系所形成的移植網絡,並以臺北市為最主要的學習對象,地理區位的接近性以及由此衍生的過往關係為雙方互動交流的有利基礎。然而新北市與臺北市之間存有結構性差異,無法全盤移植臺北市政府的經驗。本研究也發現,臺北市政府在國內1999專線的政策趨同或政策學習上扮演了重要的關鍵角色,其作為一個中介者向外國政府師法、本土化後將此政策資訊傳播給國內地方政府,成為許多地方政府的學習對象。本研究作為一初探性研究,建議未來除在學術上應豐富有關趨同及學習模式之研究外,並應將政策學習理論結合實務的管理面,建置政策知識庫以及制度化的政策學習網絡,讓地方政府的施政經驗得以保存、累積與流通應用,以作為政策知識管理和學習的有效後盾,提昇政策績效。 / Under the influence of globalization and information and communication technologies, governments at all levels get more chances to learn policy across boundaries as a way to solve policy problem or to enhance policy performance. The thesis applies policy convergence and policy learning theory to analyze the 1999 citizen hotline in Taiwan, takes New Taipei City as a case, and intends to examine the process and contributing factors for policy learning. The results show municipality, located in northern or central Taiwan with population of several million or more, tend to convergence in 1999 citizen hotline. There are some differences between convergence city and non-convergence city in land area, the ratio of total land area of alpine areas, density of the road mileage, number of public servants, and revenue. The possible reasons leading to convergence include the diffusion of concept, political pressure, the pressure of competition, the pressure of problem-solving, and the cognitive attributes of innovation. In the policy learning process of New Taipei City, the key actors are policy transfer network consist of civil servants. Taipei City is the main learning objects of New Taipei City. The proximity of geographic location and past relations is the basis for the interaction between New Taipei City and Taipei City. However, there are structural differences between the two cities; therefore, New Taipei City didn’t photocopy all the details in 1999 citizen hotline of Taipei City. This research also finds that the important role of Taipei City in the policy convergence or policy learning of 1999 citizen hotline. Taipei City, as an intermediary earned from a foreign government, localized the policy, and disseminated policy information to the domestic local governments. Based on the findings, the author suggests the government should construct policy knowledge base, and institutionalize the transfer network to improve policy performance. As an exploratory study, the author also suggests the academics could establish more appropriate convergence models by quantitative statistical analysis, or include more cities to compare models of policy learning in the future.

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