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引用本文:邹秀清,谢美辉,肖泽干,武婷燕,尹玉林.基于熵权-TOPSIS法的乡村发展评价及障碍因子诊断[J].中国农业资源与区划,2021,42(10):197~206
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基于熵权-TOPSIS法的乡村发展评价及障碍因子诊断
邹秀清1,2,谢美辉2,肖泽干3,武婷燕4,尹玉林2
1.上海电力大学经济与管理学院,上海 201306;2.江西财经大学经济学院,南昌 330013;3.南京农业大学公共管理学院,江苏南京 210095;4.江西中医药大学马克思主义学院,南昌 330004
摘要:
目的 基于《国家乡村振兴战略规划(2018—2022年)》的主要考核指标,系统剖析全国31个省(市、自治区)乡村振兴水平空间差异及其障碍因子,对掌握乡村发展实绩、发现乡村发展短板,纠正乡村发展偏差具有重要意义。方法 文章采用熵权TOPSIS评估当前背景下乡村发展现状;运用“障碍度”模型,探讨影响乡村发展水平提升的障碍因素。结果 (1)北京、上海、天津、江苏、浙江、广州等地区的乡村综合发展水平与城市经济状况密切相关。粮食综合生产能力与乡村旅游发展水平高的农业省,产业兴旺水平较高,经济发达地区、生态农业省生态宜居。尽管全国乡风文明水平并不高,仅上海、北京为理想状态,但全国乡村治理水平较高,生活富裕水平与城市经济和乡村农业发展水平关联紧密。(2)主要障碍因子分为4类。产业类:农业技术贡献率、乡村旅游发展水平、农药化肥施用强度、粮食综合生产能力;生活类:农村自来水普及率、农村信息化程度、农村恩格尔系数;生态类:农村公共厕所卫生情况、村生活污水处理程度;治理类:农村家庭美德覆盖率、乡村治理参与程度。北京、上海、天津等城市经济发达的地区障碍因子主要为产业类与生活类。结论 基于上述结果,各地区各部门在乡村振兴实施过程中应定期监测、评估乡村振兴的实施绩效,及时发现乡村振兴的短板,确保乡村振兴战略实施的每一步高质有效。
关键词:  乡村振兴  发展评价  障碍因子  熵权TOPSIS  障碍度模型
DOI:10.7621/cjarrp.1005-9121.20211021
分类号:F323.0
基金项目:国家社会科学基金项目“多利益主体视角下土地财政风险形成机理及防控研究”(19BGL214)
EVALUATION OF RURAL DEVELOPMENT AND DIAGNOSIS OF OBSTACLE FACTORS BASED ON ENTROPY WEIGHT TOPSIS METHOD
Zou Xiuqing1,2, Xie Meihui2, Xiao Zegan3, Wu Tingyan4, Yin Yulin2
1.College of Economics and Management, Shanghai University of Electric Power, Shanghai 201306, China;2.School of Economics, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, China;3.College of Public Administration, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China;4.Academy of Marxism, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, Jiangxi, China
Abstract:
The purpose of this paper is to systematically analyze the spatial differences and obstacles of rural development level in 31 provinces (cities and autonomous regions) based on the main assessment indicators of the National Rural Revitalization strategic plan (2018—2022), which is of great significance for mastering the actual achievements of rural development, discovering the shortcomings of rural development and correcting the deviation of rural development. The entropy weight TOPSIS was used to evaluate the current situation of rural development in the current context, and the "obstacle degree" model was used to discuss the obstacle factors that affected the improvement of rural development level. The results were showed as follows. Firstly, the level of comprehensive rural development in Beijing, Shanghai, Tianjin, Jiangsu, Zhejiang, Guangzhou and other regions was closely related to the urban economic situation, while other regions did not show obvious laws. Agricultural province with high comprehensive grain production capacity and rural tourism development level had a high level of industrial prosperity, economically developed areas and eco-agriculture provinces were ecologically livable. Although the country's rural civilization level was not high, only Shanghai and Beijing were ideal, but the country's rural governance level was better, and the level of living affluence was closely related to the development level of urban economy and rural agriculture.Secondly, the main obstacle factors were divided into four categories. On industry: agricultural technology contribution rate, rural tourism development level, pesticide and chemical fertilizer application intensity, grain comprehensive production capacity. On life: rural water penetration rate, rural informatization degree, rural Engel coefficient. On ecology: rural public toilet sanitation, rural domestic sewage treatment degree. On governance: rural family virtue coverage and participation in rural governance. Industry and life were the main obstacles to the economic development of Beijing, Shanghai, Tianjin and other cities. The conclusion of this article is that during the implementation of rural rejuvenation, various departments in various regions should regularly monitor and evaluate the performance of rural rejuvenation, timely discover the shortcomings of rural rejuvenation, and ensure that each step of the implementation of the rural rejuvenation strategy is of high quality and effectiveness.
Key words:  rural revitalization  development evaluation  obstacle factors  entropy weight TOPSIS method  obstacle degree model
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