湘渝川滇黔桂地市城镇化减贫效应的异质性及演变特征分析

doi:10.13249/j.cnki.sgs.2021.03.017

地理科学 ›› 2021, Vol. 41 ›› Issue (3): 522-533.doi: 10.13249/j.cnki.sgs.2021.03.017

湘渝川滇黔桂地市城镇化减贫效应的异质性及演变特征分析

丁建军¹(), 金宁波¹, 王璋¹, 刘超²

1. 吉首大学商学院，湖南吉首 416000
2. 河南理工大学测绘与国土信息工程学院，河南焦作 454000

收稿日期:2019-08-29 出版日期:2021-03-10 发布日期:2021-05-11
作者简介:丁建军（1980−），男，湖南衡东人，教授，博导，主要从事区域贫困与发展研究。E-mail： latt514@163.com
基金资助:
国家自然科学基金项目（41761022）、湖南省杰出青年科学基金项目（2020JJ2025）、湖南省社科基金重点项目（18ZDB031）、武陵山片区扶贫与发展2011协同创新中心开放基金项目（19JDZB069）、生态旅游湖南省重点实验室平台项目（STLV1815）资助

Heterogeneity and Evolution Characteristics of Poverty Reduction Effect of Prefecture-level Cities’ Urbanization in Hunan, Chongqing, Sichuan, Yunnan, Guizhou and Guangxi

Ding Jianjun¹(), Jin Ningbo¹, Wang Zhang¹, Liu Chao²

1. Business School, Jishou University, Jishou 416000, Hunan, China
2. School of Surveying and Mapping and Territorial Information Engineering, Henan University of Technology, Jiaozuo 454000, Henan, China

Received:2019-08-29 Online:2021-03-10 Published:2021-05-11
Supported by:
National Natural Science Foundation of China (41761022), Science Fund for Distinguished Young Scholars of Hunan Province (2020JJ2025), Key Program of Social Science Foundation in Hunan Province (18ZDB031), Open Foundation Program of the 2011 Collaborative Innovation Center of Wuling Mountain Area’s Poverty Alleviation and Regional Development (19JDZB069), Platform Program of Key Laboratory of Ecotourism in Hunan Province (STLV1815)

摘要/Abstract

摘要：

从地市空间尺度出发，应用空间聚类分析方法和地理加权回归模型，研究了2006、2011和2016年湘渝川滇黔桂111个地市城镇化减贫效应的空间异质性及其时空演变趋势。结果表明：① 湘渝川滇黔桂地市城镇化减贫效应存在明显的空间异质性，其中，人口城镇化率提升对农民收入的影响总体逐步增强，且影响系数最高值区域总体稳定在湖南境内的中东部地市；② 产业城镇化总体具有益贫性，但在部分地区表现为抑制农民增收，其总体正向影响变大的同时差异性也随之扩大。在空间上，产业城镇化的减贫效应最低值区域从湖南、广西境内集中分布及其它省域分散分布向湖南东南部和广西东北部收缩，人口城镇化减贫效应低值区域和产业城镇化的高值区域基本重合；③ 土地城镇化对农民收入影响有正有负，但回归系数的均值一直为负，总体上抑制农民增收，其减贫效应空间上呈现差异扩大趋势，其最高值由四川北部及东北部、重庆东北部、广西东南部等地区逐步向广西境内集中，总体上呈现“南高北低”分布格局；④ 城镇化质量对农民收入均有显著的正向影响，且总体减贫效应逐步提升，但这一效应呈现出倒“U”型特征。在空间上，湖南地市城镇化质量减贫效应强于其他省份地市。据此，提出加快推进人口城镇化进程、合理优化二三产业结构、制定科学的征地补偿制度和因地制宜地促进城镇化高质量发展的建议。

关键词: 城镇化, 减贫效应, 空间异质性, 连片特困区

Abstract:

Based on the spatial scale of prefecture level cities, spatial clustering analysis method and geographically weighted regression model are applied to studying the spatial heterogeneity of poverty reduction effect of urbanization and the trends of spatiotemporal evolution with the data from 111 prefecture level cities of Hunan, Chongqing, Sichuan, Yunnan, Guizhou and Guangxi in 2006, 2011 and 2016. The results show that: 1) There is obviously spatial heterogeneity in poverty reduction effect of urbanization in Hunan, Chongqing, Sichuan, Yunnan, Guizhou and Guangxi Province. Among them, the increase of population urbanization rate has gradually increased the overall impact to farmers’ income, and the high impact coefficients are mainly concentrated in the central and western Hunan. 2) Industrial urbanization is generally beneficial to increase farmers’ income, but in some areas it shows the opposite effect. Besides, as the positive effects of industrial urbanization on the changing of farmers’ incomes gradually expanding, their differences in different regions also expand over time. In terms of space, the areas with lower poverty reduction effect of industrial urbanization which are gradually shrinking from Hunan, Guangxi and other prefecture level cities to northeastern Guangxi, eastern and southern Hunan, and the areas with low population urbanization poverty reduction effect and high industrial urbanization poverty reduction effect are overlapped. 3) The impact of land urbanization on farmers’ income varies greatly in different regions. However, the mean value of the regression coefficient is always negatively shows that it plays a role of constraining the growth of farmers’ income, and there is a trend of enlarging gaps between different areas, the high-value areas are concentrate in Guangxi, northern and northeastern Sichuan, northeastern Chongqing, and southeastern Guangxi, with an overall distribution pattern of “high in the south and low in the north”. 4) The quality of urbanization has a significant positive impact on farmers’ income, with a trend of rising as a whole, this impact also has a characteristic of inverted “U-shaped”. In terms of space, Hunan Province has a stronger effect than other provinces. Lastly, this article puts forward suggestions such as accelerating the process of population urbanization, rationally optimizing the structure of the secondary and tertiary industries, formulating a scientific land acquisition compensation system, and promoting high-quality urbanization development adapt to local conditions.

Key words: urbanization, poverty reduction effects, spatial heterogeneity, contiguous poverty-stricken areas

中图分类号:

790.19

丁建军, 金宁波, 王璋, 刘超. 湘渝川滇黔桂地市城镇化减贫效应的异质性及演变特征分析[J]. 地理科学, 2021, 41(3): 522-533.

Ding Jianjun, Jin Ningbo, Wang Zhang, Liu Chao. Heterogeneity and Evolution Characteristics of Poverty Reduction Effect of Prefecture-level Cities’ Urbanization in Hunan, Chongqing, Sichuan, Yunnan, Guizhou and Guangxi[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(3): 522-533.

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维度	指标代码	指标	指标释义
城镇化水平	RKCZ	人口城镇化率	城镇常住人口占地区常住总人口比例/%
	CYCZ	产业城镇化率	二三产业增加值占地区生产总值比重/%
	TDCZ	土地城镇化率	城镇建成区面积占地区总面积比重/%
	CZZL	城镇化质量	“人口−土地−产业”城镇化耦合协调度^*
交通设施状况	GLMD	公路密度	公路里程比上地区国土面积/（km/km²）
城市偏向性	CXZB	城乡居民人均收入之比	城镇居民人均收入/农村居民人均收入
农业发展状况	ZWJG	农作物播种面积中非粮食占比	1−粮食播种面积占农作物播种面积比重/%
	RJMJ	人均农作物播种面积	农作物播种面积比上农村常住人口数/hm²
	JXH	机械化程度	农林牧渔业单位产值农用机械总动力/(kW/万元)
农村市场化程度	RJXF	农村人均社会消费品零售额	农村消费品市场零售额比农村常住人口数/万元
家庭负担程度	PJSY	平均赡养比	乡村户籍人数/乡村从业人数

	2006年	2011年	2016年
High-High（高高）	岳阳、长沙、益阳	岳阳、湘潭、株洲	岳阳、长沙、湘潭
	湘潭、株洲、郴州	资阳、潼南、合川	株洲、资阳、大足
	眉山、资阳、江津	北碚、渝北、巴南	铜梁、合川、北碚
	永川、巴南、璧山	南岸、江北、沙坪坝	渝北、江北、南岸
	北碚、渝北、江北	璧山、铜梁、大足	巴南、江津、永川
	沙坪坝、九龙坡	永川、江津、九龙坡	璧山、九龙坡、沙坪坝
	大渡口、南岸	大渡口	大渡口
Low-Low（低低）	巫溪、迪庆、丽江	迪庆、怒江、丽江、大理	奉节、迪庆、保山
	怒江、大理、保山	保山、临沧、凉山、曲靖	百色、河池、柳州
	临沧、百色、曲靖	六盘水、黔西南、安顺	怀化、铜仁、湘西
	安顺、六盘水、黔南	文山、百色、河池、柳州	黔东南、秀山、酉阳
	黔西南、黔东南、铜仁	黔南、黔东南、怀化	彭水、黔南、安顺
Low-High（低高）	湘西、秀山、酉阳、彭水	铜仁、湘西、秀山、酉阳	六盘水、曲靖、黔西南
High-Low（高低）	娄底	娄底	娄底
	贵阳	贵阳、桂林	贵阳、桂林、大理

变量	模型1			模型2
变量	2006年	2011年	2016年	2006年	2011年	2016年
RKCZ	14.646	32.874	49.481	—	—	—
CYCZ	11.428	32.630	34.822	—	—	—
TDCZ	−8.337	−4.150	−6.735	—	—	—
CZZL	—	—	—	1921.162***	4757.178**	5768.576**
GLMD	98.163	190.412	178.654	11.951	−52.390	3.002
CXZB	−418.116***	−1495.475***	−3146.819***	−412.290	−1430.679***	−3287.705***
ZWJG	0.468	4.194	−5.174	−1.695	−1.798	−9.918
RJMJ	−1178.759	−1171.128	1114.845	−927.107	418.604	2243.175
JXH	−316.164***	−78.133	−248.951	−247.053*	−46.210	−71.784
RJXF	2369.857***	408.709	583.946	2433.2478***	388.709	380.985
PJSY	−57.491	−782.486	−95.944	−65.676	−501.433	−142.256
AdjR²	0.907	0.943	0.922	0.930	0.940	0.912
AICc	1605.584	1688.386	1792.013	1570.037	1685.449	1800.945

		平均值	最大值	最小值	下四分位值	中位值	上四分位值
2006年GWR模型回归结果
模型1	RKCZ	14.646	50.645	−0.201	5.280	9.838	22.483
	CYCZ	11.428	39.045	−12.097	−1.857	11.912	22.640
	TDCZ	−8.337	21.232	−53.882	−25.817	−0.684	4.122
模型2	CZZL	1921.162	3957.637	1283.729	1576.668	1755.512	2096.129
2011年GWR模型回归结果
模型1	RKCZ	32.874	114.568	8.419	18.730	27.251	36.247
	CYCZ	32.630	69.152	−19.841	9.732	23.618	64.508
	TDCZ	−4.150	187.475	−104.475	−16.935	−13.545	9.027
模型2	CZZL	4757.178	11922.848	2565.120	3835.244	4344.302	4791.685
2016年GWR模型回归结果
模型1	RKCZ	49.481	184.962	17.027	28.562	33.256	50.848
	CYCZ	34.822	83.510	−92.375	9.457	57.652	66.271
	TDCZ	−6.735	56.083	−78.781	−15.009	−9.559	1.609
模型2	CZZL	5768.576	14911.361	3507.984	4372.606	4968.780	5691.480

湘渝川滇黔桂地市城镇化减贫效应的异质性及演变特征分析

Heterogeneity and Evolution Characteristics of Poverty Reduction Effect of Prefecture-level Cities’ Urbanization in Hunan, Chongqing, Sichuan, Yunnan, Guizhou and Guangxi

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