Geographical Impact and Ecological Restoration Modes of the Spatial Differentiation of Rural Social-Ecosystem Vulnerability: Evidence from Qingpu District in Shanghai

doi:10.5814/j.issn.1674-764x.2021.06.013

Abstract

Abstract:

Vulnerability research is the core issue and one of the research hotspots of sustainable development science. Vulnerability and its evaluation framework provide a new perspective for rural social-ecosystem studies. This paper introduced the ‘input-output’ efficiency theory and constructed the ‘SEE-PSR’ framework for the analysis of social-ecosystem vulnerability in the rural area in Qingpu District of Shanghai City. The DEA models, spatial autocorrelation model, multivariate logistic regression model, geographical detector and hierarchical cluster model were used to analyze the spatial differences of social-ecosystem vulnerability, and its geographical impact mechanisms and ecological restorations, in 184 administrative villages in this area. The results can be divided into three main points. (1) The results of the ‘input-output’ efficiency model of the EW-DEA based on entropy weight aggregation crossover was more reliable and accurate for the evaluation of rural social-ecosystem vulnerability. The vulnerability of the social-ecosystems in the administrative villages showed a trend of gradual decline from east to west, with an average value of vulnerability of 0.583, and the vulnerability of social systems had become an important factor in constraining the decrease of the vulnerability of the social-ecosystems in the region. (2) The distances from the center of Shanghai City, from Dianshan Lake, from the center of Qingpu District and from the water area were the four dominant geographical factors affecting the vulnerability of the social-ecosystem in this region. The geographical impacts exhibited the spatial differentiations of systemic structure, the substitution of typological attributes and the transformation level. (3) The geographical factors coupling the impact types of the social-ecosystem vulnerability were divided spatially into 10 types. The geographic multi-factor coupling impact types were dominant, which presented multi-cyclic spatial patterns and were dominated by the central multi-factor which was surrounded by the single factor types on both sides. According to the different types, some feasible ways of ecological restoration were proposed, which drew on the experiences of integrated territory consolidation to remediate the vulnerability of rural social-ecological systems. The results of this study can provide scientific reference for rural spatial reconstruction, regional ecological restoration and sustainable development for the regions characterized by conflict in the ‘strict protection of the ecological environment and vigorous development of the economy’.

Key words: social-ecological system, vulnerability evaluation, efficiency, geographical factors, geographical impact modes, ecological restoration, Qingpu District

REN Guoping, LIU Liming, LI Hongqing, SUN Qian, YIN Gang, WAN Beiqi. Geographical Impact and Ecological Restoration Modes of the Spatial Differentiation of Rural Social-Ecosystem Vulnerability: Evidence from Qingpu District in Shanghai[J]. Journal of Resources and Ecology, 2021, 12(6): 849-868.

Figures/Tables 13

References 33

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Target	Criteria	Indicator	Indicator description and data source
Economic vulnerability	Pressure (Input)	Gross income of region (yuan)	The overall regional economic difference, using economic statistics data
		Growth rate of secondary and tertiary industries (%)	The non-agriculture development difference, using economic statistics data
		Total value of farm output (yuan)	The agriculture development difference, using rural statistics data
	State (Output)	Per capita income (yuan person^-1)	Individual resistance to vulnerability, using the household survey data
		Household income (yuan)	Family resilience to vulnerability, using the household survey data
		Net household income (yuan)	Family resilience to vulnerability, using the household survey data
	Response (Output)	Per capita disposable income (yuan person^-1)	Family environment improvement capacity, using economic statistics data
	Response (Output)	Road density (%)	Rural external communication capacity, using the land use vector data
Social vulnerability	Pressure (Input)	Urbanization rate (%)	Regional non-agricultural population pressure, using economic statistics data
		Total population (person)	Regional population carrying pressure, using economic statistics data
		Density of population (person km^-2)	The per unit area population carrying pressure, using economic statistics data
	State (Output)	Net population outflow (person)	State of social identity change, using the rural survey data
		Ratio of settled and farmland (%)	State ofhousehold livelihood capital, using the land use vector data
		Number of agricultural workers (person)	Rural social employment, using the rural survey data
		Deserted farmland area (ha)	State of agricultural behavior, using rural statistics data
		Grain yield (t)	State of regional food security, using economic statistics data
	Response (Output)	Fixed investments (yuan)	Resilience in social services, using economic statistics data
		Household education expenditure (yuan)	Family resilience to vulnerability, using the household survey data
		Medical insurance coverage (%)	Resilience in public services, using economic statistics data
Ecological vulnerability	Pressure (Input)	Fertilizer usage (t)	Environmental pollution pressure of agriculture, using the rural survey data
		Plastic film application (t)	Environmental pollution pressure of agriculture, using the rural survey data
		Pollution emissions (t)	Industrial pollution pressure, using the environmental survey data
	State (Output)	Vegetation coverage (%)	State of green land, using the land use vector data
		Land degradation index	State of regional land quality, using the land degradation classification data
		Land patch density (piece ha^-1)	State of land fragmentation, using the land use vector data
		Agricultural landscape fractal dimension	State of agricultural shape, using the land use vector data
	Response (Output)	Land consolidation area (ha)	Regional ecological improvement, using economic statistics data
		Environmental protection input (yuan)	Regional environmental protection efforts, using economic statistics data
		Grain production per unit area (kg ha^-1)	Regionalfood productivity, using economic statistics data

Target	Criteria	Indicator	Indicator description and data source
Economic vulnerability	Pressure (Input)	Gross income of region (yuan)	The overall regional economic difference, using economic statistics data
		Growth rate of secondary and tertiary industries (%)	The non-agriculture development difference, using economic statistics data
		Total value of farm output (yuan)	The agriculture development difference, using rural statistics data
	State (Output)	Per capita income (yuan person^-1)	Individual resistance to vulnerability, using the household survey data
		Household income (yuan)	Family resilience to vulnerability, using the household survey data
		Net household income (yuan)	Family resilience to vulnerability, using the household survey data
	Response (Output)	Per capita disposable income (yuan person^-1)	Family environment improvement capacity, using economic statistics data
	Response (Output)	Road density (%)	Rural external communication capacity, using the land use vector data
Social vulnerability	Pressure (Input)	Urbanization rate (%)	Regional non-agricultural population pressure, using economic statistics data
		Total population (person)	Regional population carrying pressure, using economic statistics data
		Density of population (person km^-2)	The per unit area population carrying pressure, using economic statistics data
	State (Output)	Net population outflow (person)	State of social identity change, using the rural survey data
		Ratio of settled and farmland (%)	State ofhousehold livelihood capital, using the land use vector data
		Number of agricultural workers (person)	Rural social employment, using the rural survey data
		Deserted farmland area (ha)	State of agricultural behavior, using rural statistics data
		Grain yield (t)	State of regional food security, using economic statistics data
	Response (Output)	Fixed investments (yuan)	Resilience in social services, using economic statistics data
		Household education expenditure (yuan)	Family resilience to vulnerability, using the household survey data
		Medical insurance coverage (%)	Resilience in public services, using economic statistics data
Ecological vulnerability	Pressure (Input)	Fertilizer usage (t)	Environmental pollution pressure of agriculture, using the rural survey data
		Plastic film application (t)	Environmental pollution pressure of agriculture, using the rural survey data
		Pollution emissions (t)	Industrial pollution pressure, using the environmental survey data
	State (Output)	Vegetation coverage (%)	State of green land, using the land use vector data
		Land degradation index	State of regional land quality, using the land degradation classification data
		Land patch density (piece ha^-1)	State of land fragmentation, using the land use vector data
		Agricultural landscape fractal dimension	State of agricultural shape, using the land use vector data
	Response (Output)	Land consolidation area (ha)	Regional ecological improvement, using economic statistics data
		Environmental protection input (yuan)	Regional environmental protection efforts, using economic statistics data
		Grain production per unit area (kg ha^-1)	Regionalfood productivity, using economic statistics data

Category	Indicator	Calculation method
Natural Geographical Attributes	Y₁ Slope (%)	Slope data (resolution 30 m). Using DEM data extraction and re-classification: 0°-1°, 1°-2° and 2°-3° assigned to 1, 2, 3
	Y₂ Elevation (m)	Elevation data (resolution 30 m). Using DEM data extraction
	Y₃ Agricultural acreage (ha)	Extraction of arable land areas from land vector maps
	Y₄ Water area (ha)	Extraction of water areas from land vector maps
	Y₅ Distance from nearest river (km)	Using the river grid data extraction statistics. Grid vector layer (30 m*30 m), overlay administrative village committee and river grid layer, and use grid calculator statistics
	Y₆ Distance from Dianshan Lake (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to Dianshan Lake Center
Economic Geographical Attributes	Y₇ Distance from nearest main road (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the nearest main road
	Y₈ Distance from township center (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the nearest township center
	Y₉ Distance from center of Qingpu District (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the Government of Qingpu District
	Y₁₀ Distance from center of Shanghai City (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the Government of Shanghai City
	Y₁₁ Space accessibility among village	The ‘grid calculator’ was used to calculate the minimum time cost of each grid after the superposition of the road grid layer and the ground grid layer The average value of all time costs associated with an administrative village were used as the index value of spatial accessibility of the village area

Category	Indicator	Calculation method
Natural Geographical Attributes	Y₁ Slope (%)	Slope data (resolution 30 m). Using DEM data extraction and re-classification: 0°-1°, 1°-2° and 2°-3° assigned to 1, 2, 3
	Y₂ Elevation (m)	Elevation data (resolution 30 m). Using DEM data extraction
	Y₃ Agricultural acreage (ha)	Extraction of arable land areas from land vector maps
	Y₄ Water area (ha)	Extraction of water areas from land vector maps
	Y₅ Distance from nearest river (km)	Using the river grid data extraction statistics. Grid vector layer (30 m*30 m), overlay administrative village committee and river grid layer, and use grid calculator statistics
	Y₆ Distance from Dianshan Lake (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to Dianshan Lake Center
Economic Geographical Attributes	Y₇ Distance from nearest main road (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the nearest main road
	Y₈ Distance from township center (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the nearest township center
	Y₉ Distance from center of Qingpu District (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the Government of Qingpu District
	Y₁₀ Distance from center of Shanghai City (km)	The calculation process was the same as above (Y₅). Statistics from Village Committee to the Government of Shanghai City
	Y₁₁ Space accessibility among village	The ‘grid calculator’ was used to calculate the minimum time cost of each grid after the superposition of the road grid layer and the ground grid layer The average value of all time costs associated with an administrative village were used as the index value of spatial accessibility of the village area

Region	CCR-DEA model		ACE-DEA model		EW-DEA model
Region	Average of RV_S-E	RV_S-E	Average of RV_S-E	RV_S-E	Average of RV_S-E	RV_S-E
Qingpu District	0.623		0.699		0.583
Eastern part	0.689		0.756		0.625
Central part	0.542		0.643		0.554
Western part	0.638		0.699		0.609
Guanglian Village in Xujing		0.598		0.665		0.662
Songshan Village in Huaxin		7.125		7.193		7.194
Chuiyao Village in Zhaoxiang		0.629		0.698		0.703
Zhongxin Village in Chonggu		1.000		0.924		0.648
Tangyu Village in Xiayang		0.554		0.655		0.592
Heqiao Village in Yingpu		0.665		0.777		0.706
Jinxing Village in Xianghuaqiao		1.000		0.964		0.614
Wangjing Village in Baihe		1.000		0.972		0.729
Wanlong Village in Zhujiajiao		0.551		0.632		0.580
Beidai Village in Liantang		1.000		0.985		0.732
Tianshanzhuag Village in Jinze		0.582		0.676		0.626