Is change of natural capital essential for assessing relocation policies? A case from Baihe county in western China

Figures & data

Table 1. Characteristics of Respondents

Property Variables		Frequency	Per cent (%)
Gender	Male	368	61.7
	Female	228	38.3
Age	Less than 18 years old	11	1.9
	18–35 years old	132	22.1
	36–45 years old	332	55.7
	46–60 years old	108	18.1
	More than 60 years old	13	2.2
Education	Illiterate	42	7.0
	Elementary school	246	41.3
	Junior middle school	213	35.7
	Senior middle school	66	11.1
	Undergraduate and above	29	4.9
Annual Income	1,000 and below (RMB)	129	21.6
	1,001–3,000	249	41.8
	3,001–5,000	67	11.2
	5,001–8,000	92	15.4
	More than 8,000	59	9.9
Relocating Mode	Collective mode	227	38.1
	Decentralized mode Relocated from village to town	125 61	21.0 10.2
	Autonomous emigration	88	14.8
	Unchanged residents	95	15.9

Figure 1. Research Framework

Table 2. Index System for Livelihood Assessment

Assets	Descriptions	Selected Indicators
Natural capital	Land and produce, water and aquatic resources, trees and forest products, wildlife, wild foods and fibers, biodiversity, environmental services	Farmland area, land produce, water consumed, tree products, environment quality
Human capital	Health, nutrition, education, knowledge and skills, capacity to work, capacity to adapt	Health, nutrition, education, pre-training opportunity, capacity to work
Social capital	Networks and connections (patronage, neighborhoods, kinship), relations of trust, mutual understanding and support, formal and informal groups, shared values and behaviors, common rules and sanctions, collective representation, mechanisms for participation in decision-making, leadership	Kinship contacts, making friends, relations of trust, available supports, convenience to participate in community decision-making
Physical capital	INFRASTRUCTURE (transport, roads, vehicles, secure shelter and buildings, water supply and sanitation, energy, communications), tools and technology (tools and equipment for production, seed, fertilizer, pesticides, traditional technology)	Transport infrastructure, secure shelter and buildings, water supply and sanitation, tools and technology, communication conditions
Financial capital	Savings, credit and debt (formal, informal), remittances, pensions, wages	Savings, credit and debt, pensions, annual income, compensation money for housing

Descriptions of the five components of livelihood assets come from Serrat (2017).

Table 3. Index System on Acceptance of Relocating Policy

Items and Factors		Standardized factor loadings			Cronbach’s Ahpha (Total Scale 0.87)
Safety	⑴ Losses caused by natural disasters are reduced after relocation.			0.779	0.77
	⑵ Incidence of diseases decreases among the residents after relocation.			0.817
	⑶ Public security cases, for example theft, robbery, are reduced after relocation.			0.727
Preparation	⑷ Transportation infrastructure is improved after relocation.		0.638		0.76
	⑸ Sanitation facilities are improved, with safer drinking water after relocation.		0.767
	⑹ Communication conditions are improved after relocation.		0.754
	⑺ It will be more convenient to get bank loans after relocation.		0.721
Satisfaction	⑻ How satisfied are you with the level of compensation for house relocation?	0.759			0.80
	⑼ How satisfied are you with the employment support and pre-job training after relocation?	0.808
	⑽ How satisfied are you with the management quality of displacement and resettlement?	0.694
	⑾ Do you think the resettlement project is for the benefit of the masses?	0.715
KMO (Bartlett sphericity test)		0.67 (<.001)	0.77 (<.001)	0.78 (<.001)

Figure 2. Livelihood Assets of the Resettlers Calculated by SLF

Note: Each Branch of the Spider Graph Depicts One Kind of Capital with a Value Area of [0, 100].

Figure 3. Natural Capital of the Resettlers Described by Five Indicators

Note: Each Indicator Has a Value Area of [0, 100].

Figure 4. Acceptance of Relocating Policy

Note: In the Coordinate System, Y-axis Is the Level of Policy Acceptance with Value Area of [0, 100], and X-axis Is the Code Provided by the Investigation, Used to Identify the Respondents.

Table 4. Correlations between Variables

Variables	1	2	3	4	5	6	7	8	9	10	11	12	VIF
Gender	1.000												1.04
Age	−.034	1.000											1.03
Education	.027	−.164**	1.000										1.06
Annual Income	−.111**	.002	.114**	1.000									1.08
Relocating Mode	−.022	.010	−.020	−.021	1.000								1.17
Natural Assets	−.006	−.048	−.135**	−.061	.293**	1.000							1.76
Physical Assets	−.052	.009	.127**	.178**	−.290**	−.583**	1.000						2.67
Social Assets	−.012	.024	.119**	.157**	−.275**	−.558**	.731**	1.000					3.13
Human Assets	−.067	.009	.107**	.057	−.201**	−.490**	.653**	.588**	1.000				2.20
Finacial Assets	−.032	.016	.115**	.144**	−.237**	−.544**	.693**	.662**	.537**	1.000			1.87
Livelihood	−.068	.010	.116**	.161**	−.268**	−.499**	.884**	.867**	.772**	.841**	1.000		1.04
Acceptance	−.051	.009	.119**	.123**	−.321**	−.528**	.688**	.723**	.655**	.648**	.774**	1.000

*<0.05, **<0.01.

Table 5. Statistical Models and Regression Results

	Acceptance of Relocating Policies
Variables	Model 1	Model 2	Model 3	Model 4
Control Variables
Gender (male for reference)	−0.011	−0.020	−0.014	−0.022
Age	0.004	−0.008	0.013	−0.000
Education(illiteracy for reference)	0.033	0.015	0.032	0.014
Annual income(1,000 yuan and below for reference)	−0.006	0.008	−0.009	0.006
Relocating model (collective mode for reference)			−0.069*	−0.057*
Variables of Interest
Natural capital		−0.047		−0.045
Physical capital		0.340***		0.335***
Social capital		0.119**		0.110*
Human capital		0.172***		0.168***
Financial capital		0.261***		0.261***
Livelihoods	0.770***		0.749***
Adj. R^2	0.596	0.635	0.600	0.637
F	172.824***	113.392***	146.318***	103.143***

*<0.05, **<0.01, ***<0.001

Serrat O. 2017. Knowledge solutions: tools, methods, and approaches to drive organizational performance. Singapore: Springer; p.21–26. accessed 2019 May 20. https://link.springer.com/chapter/ https://doi.org/10.1007/978-981-10-0983-9_5.

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