地理学报 ›› 2018, Vol. 73 ›› Issue (3): 487-502.doi: 10.11821/dlxb201803008

• 土地利用 • 上一篇    下一篇

中国土地流转的区域差异及其影响因素——基于2003-2013年农村固定观察点数据

王亚辉1,2,李秀彬1,2(),辛良杰1,谈明洪1,2,蒋敏1,2   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2017-05-23 出版日期:2018-03-20 发布日期:2018-03-23
  • 基金资助:
    国家自然科学基金项目(41571095, 41271119)

Regional differences of land circulation in China and its drivers:Based on 2003-2013 rural fixed observation points data

WANG Yahui1,2,LI Xiubin1,2(),XIN Liangjie1,TAN Minghong1,2,JIANG Min1,2   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-05-23 Online:2018-03-20 Published:2018-03-23
  • Supported by:
    [Foundation: National Natural Science Foundation of China, No.41571095, No.41271119]

摘要:

土地流转是实现农业适度规模经营的必经之路,理解土地流转的空间差异及影响因素具有重要意义。基于2003-2013年农业部农村固定观察点系统的169511个住户样本,本文揭示了中国土地流转的区域差异,并采用Heckman两阶段模型识别土地流转区域差异的影响因素,旨在为促进土地流转提供科学参考。结果发现:① 2003-2013年间,土地流转率从17.09%上升到24.1%,年均增幅近0.7个百分点;南方土地流转较为活跃,比如福建等地流转率超过30%,而北方土地流转率较低;② 转入土地支付租金平均为283.74元/亩,55.05%的转入户并未支付租金;相反,转出土地获得租金为243.23元/亩,52.63%的农户转出土地未获得租金;目前中国土地的“零租金”流转率超过50%;③ 土地质量、地理区位、交易费用、家庭及村庄特征等对土地流转的区域差异具有显著影响;土地质量和地理区位在平原地区的边际效应较大,但在丘陵和山区,交易成本已成为影响土地流转的重要因素。土地“零租金”流转的实质是土地资源错配的一种表现,丘陵和山区边际化及交易费用偏高降低了土地资源的潜在价值。政府应努力降低土地流转中面临的各类交易费用,建立健全土地流转的补偿机制,同时关注丘陵和山区的土地资产贬值和撂荒现象。

关键词: 土地流转, 区域差异, 零租金, 影响因素, Heckman模型, 中国

Abstract:

Land circulation is an important measure that can be utilized to enable agricultural management at a moderate scale. It is imperative to explore spatiotemporal changes in land circulation and the factors that drive these variations in order to increase the vitality of land rental market in China. Based on a sample of 169 511 farm households from the rural fixed observation point system between 2003 and 2013, this paper revealed the regional differences in land circulation and used Heckman two-stage models to identify the drivers of regional differences in land circulation. The results of this study show that: (1) the rate of land circulation in China rose from 17.09% to 24.1% over the course of the study period, an average rate of 0.7%. (2) The rate of land circulation in the south of China has been higher than that in the north, the average land rental payment was 283.74 yuan per mu, and 55.05% of farm households did not pay a fee in the process of land circulation. In contrast, the average rent that leasers received was 243.23 yuan per mu nationally even though 52.36% of households did not receive any payments from their tenants. At present, the rate of rent-free land circulation was more than 50% in China's land rental market. In addition, the average rent in developed provinces, such as Jiangsu, Shandong, Guangdong and Zhejiang, was 40% higher than the national average. (3) The results show that land quality, geographic location, transaction costs, and household characteristics have all significantly affected land circulation in different regions of China. The marginal effects of land quality and geographic location were larger in the plain regions, while transaction cost was the key factor influencing land circulation in the hilly and mountainous regions. The essence of rent-free land circulation was a sign of mismatch of land resources, and the marginalization of mountainous regions and higher transaction costs reduced the potential value of land resources. Thus, as the opportunity cost of farming continues to rise across China, the depreciation of land assets will become irreversible and land abandonment will be anabatic in the hills and mountains in the future. The transaction costs in land rental market should be reduced by establishing the land circulation intermediaries at the township level. Also, more attention should be given to the critical issues of farmland abandonment and poverty reduction in the hills and mountains.

Key words: land circulation, regional difference, zero rent, influencing factors, heckman model, China