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基于主成分分析法的秦巴山脉区域林业产业发展研究 被引量:5

Study on the Development of Forestry Industry in Qinba Mountain Area Based on Principal Component Analysis
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摘要 林业产业作为国民经济的重要基础性产业,在生态建设中的地位日益突出。本文系统梳理了秦巴山脉区域的林业产业发展现状,详细开展了区域林业产业的优势、劣势、机遇和威胁(SWOT)分析,以专家问卷调查作为数据来源,运用主成分分析方法,以统计分析的方式寻找出对秦巴山脉林业产业发展有较大影响的要素,以之作为区域林业产业发展战略制定的科学依据。研究表明,生态环境问题、集约化程度低、森林旅游业发展机遇、区位优势、资源优势等方面是秦巴山脉区域林业产业发展的主要影响因素;区域林业产业发展重在于生态建设,应利用区位和资源优势,做大做强特色产业,优化产业结构;同时大力发展以森林旅游为主的第三产业,保障区域林业产业的高质量发展。 As a fundamental industry of national economy,the forestry industry plays an increasingly important role in ecological construction.This paper systematically introduces the current situation of the forestry industry in the Qinba Mountain Area,and analyzes the strengths,weaknesses,opportunities,and threats(SWOT)of the forestry industry.The paper uses expert questionnaires and principal component analysis to identify the factors that have great impacts on the forestry industry in the Qinba Mountain Area,so as to provide a scientific basis for the formulation of the forestry industry development strategy.The research reveals that the threat of ecological environment problems,the disadvantage of low intensive level,the opportunity of ecotourism development,and resource advantages have major impacts on the development of forestry industry in the Qinba Mountain Area.Therefore,the Qinba Mountain Area should focus on ecological construction,make use of its location and resource advantages to develop characteristic industries,optimize the industrial structure,and accelerate the development of forest tourism,thus to promote the high-quality development of the local forestry industry.
作者 郑东晖 后华 翟明普 渠美 Zheng Donghui;Hou Hua;Zhai Mingpu;Qu Mei(College of Economics&Management,Northwest A&F University,Yangling 712100,Shaanxi,China;College of Forestry,Beijing Forestry University,Beijing 100083,China)
出处 《中国工程科学》 CSCD 北大核心 2020年第1期120-126,共7页 Strategic Study of CAE
基金 中国工程院咨询项目“秦巴山脉绿色循环发展战略研究(二期)”(2017-ZD-02)。
关键词 秦巴山脉区域 林业产业 SWOT分析 主成分分析法 生态环境 Qinba Mountain Area forestry industry SWOT method principal component analysis ecological environment
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