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학술논문관광레저연구2018.07 발행KCI 피인용 9

조건부가치측정법을 이용한 해양생태관광지 환경개선 사업의 경제적 가치추정: 거문도 바다숲 조성사업을 중심으로

Estimating the Economic value of the Marine Eco-tour site Environmental improvement project Using Contingent Valuation Method: Focused on the marine afforestation project in Geummundo

유윤희(세종대학교); 이희찬(세종대학교); 오희균(세종대학교)

30권 7호, 179~194쪽

초록

This study is aimed to analyze the economic value of the marine afforestation project in Geummundo region by using CVM. A questionnaire was conducted through one-on-one individual interviews with 1,002 households that were extracted from a proportional stratified sampling method. The research results indicate that the yearly Willingness to pay(WTP) for the construction work for the marine afforestation project in Geummundo region from 35,762won per household. In the case of number of households nationwide, the yearly WTP for the marine afforestation project in Geummundo was estimated to be approximately 6,995 billion won per year. This information could be applied with fundamental information which is important for evaluates the economical efficiency of marine afforestation project.

Abstract

This study is aimed to analyze the economic value of the marine afforestation project in Geummundo region by using CVM. A questionnaire was conducted through one-on-one individual interviews with 1,002 households that were extracted from a proportional stratified sampling method. The research results indicate that the yearly Willingness to pay(WTP) for the construction work for the marine afforestation project in Geummundo region from 35,762won per household. In the case of number of households nationwide, the yearly WTP for the marine afforestation project in Geummundo was estimated to be approximately 6,995 billion won per year. This information could be applied with fundamental information which is important for evaluates the economical efficiency of marine afforestation project.

발행기관:
(사)한국관광레저학회
분류:
관광학

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조건부가치측정법을 이용한 해양생태관광지 환경개선 사업의 경제적 가치추정: 거문도 바다숲 조성사업을 중심으로 | 관광레저연구 2018 | AskLaw | 애스크로 AI