Sustainable livelihoods and rural sustainability in China: Ecologically secure, economically efficient or socially equitable?

Resources, Conservation and Recycling 120 (2017) 1–13

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Sustainable livelihoods and rural sustainability in China: Ecologically secure, economically efficient or socially equitable? Heyuan You a , Xiaoling Zhang b,∗ a b

School of public administration, Zhejiang University of Finance and Economics, Hangzhou, China Department of Public Policy, City University of Hong Kong, Hong Kong, China; City University of Hong Kong, Shenzhen Research Institute, Shenzhen, PRC

a r t i c l e

i n f o

Article history: Received 2 August 2016 Received in revised form 19 December 2016 Accepted 25 December 2016 Keywords: Sustainable livelihood security Ecological security Economic efficiency Social equity Fuzzy comprehensive method Rural sustainability

a b s t r a c t Sustainable production and consumption in the rural regions remains a barely tried yet important issue for contributing to rural sustainability these days. In particular, the sustainable livelihood of rural farmers has not been fully investigated for those in rural areas with high agricultural pollution emissions and a poor ecological quality of agricultural production in China. Also affected are farmers with a low living standard and output, or suffer from social inequity. The sustainable livelihood security (SLS) index therefore provides a useful means of identifying the existence of the conditions necessary for sustainable livelihood or sustainable development. Using the fuzzy comprehensive method, this paper aims to assess the level of sustainable livelihood security of China’s provincial farmers and its three components of ecological security, economic efficiency and social equity. A SLS index is established and the entropy weight method used to determine the weight of the indices and analyze spatial distribution. The results indicate that the sustainable livelihood security index and its components vary between provincial regions, with the western provinces being most adversely affected, sustainable livelihood, economic efficiency and social equity being the least secure (or relatively insecure) in the western provinces while economic efficiency is most secure (or relatively secure) in the eastern and middle provinces, and social equity most secure in the eastern provinces. Concluding remarks suggest policies designed to improve the sustainable livelihood security of farmers according to local regional circumstances. © 2017 Elsevier B.V. All rights reserved.

1. Introduction China’s 600+ million rural farming community occupies a relatively lowly position compared to urban dwellers, with low incomes from agricultural production because of land fragmentation (Nguyen et al., 1996). In addition, many farmers suffer from exposure to ecological risks such as drought, soil erosion, environmental pollution and land degradation, especially in the north and northeast provinces (Chen et al., 2014; Ongley et al., 2010; Zhang et al., 2014; Xu et al., 2014). Farmers in different districts adopt different livelihood strategies such as pluriactivity and rural livelihood diversity for survival (Kinsella et al., 2000; Ellis, 1999; Cofie et al., 2010). Since the Brundtland Commission on Environment and Development, sustainable livelihood − being able to make a living in an economically, ecologically and socially sustainable manner − is

∗ Corresponding author. E-mail addresses: [email protected] (H. You), [email protected] (X. Zhang). http://dx.doi.org/10.1016/j.resconrec.2016.12.010 0921-3449/© 2017 Elsevier B.V. All rights reserved.

now regarded as providing the broad goal for such breadline communities (Bull, 2015). However, achieving a sustainable livelihood is not a deterministic issue (Scoones, 2009) and strategies aimed at improving livelihoods are always limited by unsustainable rural resources, high population growth rate, a vulnerable agricultural environment and significant social inequity, such as in the disparate distribution of wealth and allocation of land rights (Qu et al., 2011; Shaw and Kristjanson, 2014; Ouyang et al., 2014; Dai and Dien, 2013; Wu, 2004). Consequently, although immense changes have occurred in rural areas since China’s Reform and Opening Up in 1978 (Rozelle, 1996; Gao et al., 2014; De Brauw et al., 2002), the sustainable livelihoods of farmers have yet to be fully realized. The UK Department for International Development has developed a sustainable livelihood framework to analyze the factors that affect sustainable livelihoods (Scoones, 1998). Similarly, the Cooperative for Assistance and Relief Everywhere (CARE) USA’s program uses a household livelihood security framework to understand the relationship between households and society (McCaston, 2005). Household livelihood security covers six security areas: food, health, economics, education, shelter and community participation and emphasizes the multi-dimensional dynamics of the factors

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causing poverty (Ghanim, 2000). When combined with rapid and participatory rural appraisal, the household livelihood security approach can be used to measure the livelihood security of farmers at the family and community level (Lindenberg, 2002). Saleth and Swaminathan (1993) have proposed a sustainable livelihood security (SLS) index as a means of identifying the necessary conditions for sustainable livelihoods or sustainable development in a given region (Moser, 1996). SLS has, for example, been applied to evaluate the livelihood security of farmers in highland and lowland communities of the Kali-Khola agricultural watershed in western Nepal (Bhandari and Grant, 2007). It can reveal the impact of livelihood strategies on sustainable rural livelihoods, such as the dramatic improvement of sustainable rural livelihoods in the rapidly developing and transforming areas in China after the implementation of new agricultural practices (Tang et al., 2013). The SLS index has also been used to solve both macroand micro-level problems, and easily generalizes to different contexts, such as farmers in a village, villages in a district and states in a country (Singh and Hiremath, 2010; Hatai and Sen, 2008; Sajjad et al., 2014; Uma, 1993). This makes the index eminently suited to assessing the SLS of farmers in China. An object with many properties needs many aspects to be considered when evaluating how good it is (Liu, 2008). In addition, decisions made in complicated systems need the comprehensive consideration of many relevant factors (Qin, 2012; Vahabzadeh et al., 2015). To do this involves a comprehensive holistic evaluation (Koplovitz et al., 2011). In uncertain situations, fuzzy set theory, first introduced by Zadeh (1965) to solve problems involving vague or imprecise data, has been widely used in combination with comprehensive evaluations. The fuzzy comprehensive method − a reliable decision-making methodology based on fuzzy set theory − has been applied in the quantitative description of socioeconomic status and ecological characteristics (Feng and Xu, 1999; Meng et al., 2009). For example, it has been successfully used in describing complex nature interactions that occur during oil spill management (Liu and Wirtz, 2007) and evaluating the water quality of Lake Honghu in China (Li et al., 2009). The procedures involved in the fuzzy comprehensive method are: (1) selecting parameters and the classification threshold values; (2) formulating the membership functions; (3) calculating the weights matrix; and (4) computing the membership degrees and obtaining the assessment result. With the SLS index, multi-index evaluation is needed to assess the relevant indices that indicate the condition of impact factors. It is also difficult to determine the classification threshold values of criteria to grade the SLS of farmers strictly due to the imprecise relationship between impact factors and the SLS (Su et al., 2010). This makes the fuzzy comprehensive method an obvious candidate for dealing with the fuzziness in the index when evaluating SLS and is therefore adopted in this study. Although sustainable livelihoods of farmers have been receiving increasing attention in China, little progress has been made in practice. An understanding is urgently needed of the impact of ecological security, economic efficiency and social equity. This paper integrates the SLS index and fuzzy comprehensive method to determine the extent to which the necessary conditions for sustainable livelihoods or sustainable development exist for farmers in China. The findings can help formulate specific policies for improving the SLS of farmers. The remainder of the paper is organized as follows. The next section presents entropy weighting and the fuzzy comprehensive method and discusses the SLS index. A case study is then described in which the degree of SLS membership of provincial farmers and the spatial distribution of SLS are determined. The results indicate that the SLS index and its components varies between provincial regions, with the western provinces being most adversely affected, sustainable livelihood, economic efficiency and social equity being most insecure in the

western provinces, while economic efficiency is most secure in the eastern and middle provinces and social equity most secure in the eastern provinces. Finally, specific policies designed to improve the SLS of farmers are proposed according to the local circumstances of different provincial regions of China.

2. The concept of SLS and its indicators Sustainable livelihood has been defined as the means of living against further poverty which require necessary capabilities, assets and activities to maintain an economically, ecologically, and socially sustainable manner (Chambers and Conway, 1992). The SLS has emerged in response to the desire to check the extent to which peasant households have adequate and sustainable access to a sustainable livelihood (Saleth and Swaminathan, 1993). Swaminathan (1991) also proposes that the concept of SLS is focused on the three pillars of ecologically secure, economically efficient and socially equitable; underscoring ecology, economics and social dimensions. Many of the studies of the measurement of SLS are driven by a desire to understand why the gap between the rich and the poor is widening (Swaminathan, 2000). These include the search for a simple and flexible analytical framework of SLS to access long-term livelihood security (Singh and Hiremath, 2010). SLS measurement is sensitive to inter-relationships among the indicators. However, no widely accepted method is available to quantify such interrelationships, given the absence of standard for describing the good or bad degree of SLS (Kumar et al., 2014; Sajjad et al., 2014). There are also many common concerns about SLS and the obvious differences in its attributes identified in the literature. Singh and Hiremath (2010), for example, consider ecological security, economic efficiency and social equity to be the vital factors that determine SLS and Chambers and Conway (1992) emphasize that sustainable livelihood depends on whether a livelihood is sustainable environmentally and socially. Some studies argue that difficult access to land and an insecure land tenure system have a key impact on SLS, as they strongly influence the everyday choices and prospects of poor rural people (Clover and Eriksen, 2009). Other scholars report that the rapid population growth is strongly associated with the insecurity of SLS in one region since it can lead to resource scarcity, a direct driver of economic decline and poverty (Gecho et al., 2014). Therefore at present the multi-index comprehensive evaluation is widely accepted to measure SLS. For many studies, a great effort has been made to identify the appropriate indicators for assessing SLS. In the context of mining, these indicators capture changes in economic, socio-cultural, health, political and environmental conditions (Horsley et al., 2015). An improved Livelihood Sustainability Index (LSI) has been established to assess the vulnerability of livelihoods in environmentally fragile areas of southern China (Wang et al., 2016). The genuine saving indicator (GSI) is developed for the purpose of preventing decline in capital stock, comprising produced, human and natural capital (Hamilton et al., 1997). In addition, the sustainable net benefit index (SNBI) is an integrated index of economic development in order to describe a clearer picture of welfare (Pulselli et al., 2006). Many of the indicators have also been developed with respect to SLS security analysis. The SLS is not as easy to explain since it should fully integrate ecological, economic and social dimensions (Swaminathan, 1991). Due to differences in natural, social and economic conditions, the indicators should be selected according to local circumstances (Uma, 1993; de Sherbinin et al., 2008). There is a common understanding for assessing SL Moser (1996), for instance, concludes that economic efficiency, ecological security, solidarity and technical feasibility are essential for SLS and eradication of poverty; Sajjad et al. (2014) propose an SLS index that

H. You, X. Zhang / Resources, Conservation and Recycling 120 (2017) 1–13

contains 11 indicators, with the density of population, proportion of forest area, cropping intensity and livestock density being selected as ecological security indicators, the yield of total agricultural output, proportion of net irrigated area to net sown area, per capita value of agricultural output and fertilizer consumption as economic efficiency indicators, and rural female literacy, villages having a paved road facility and electrified villages as social equity indicators; while Dalei and Gupt (2014) use an intertemporal sustainable livelihoods security index (ISLSI) that incorporates ecological security, economic efficiency and social equity to analyze livelihood sustainability in a mine-spoiled degraded ecosystem over a period of time. More recently, the SLS index highlights the supporting ecosystem for improving the quality of farmers’ life (de Sherbinin et al., 2008; Gecho et al., 2014); green economics have an important impact on the SLS (Cato, 2009); and Ferrol-Schulte et al. (2013) finds that the rampant pollution is being further severely stretched on SLS. Meanwhile, there are significant relationships between the pollution of poverty (which is confined to poor areas) realized in the form of lack of services, amenities and infrastructure and reduction of human welfare (Krieger et al., 2014). The SLS index in this study, representing the ecological, economic and social dimensions of SLS, is selected based on the necessary conditions for Chinese farmers’ sustainable livelihood. Therefore, ecological security mainly considers aspects such as agricultural pollution emissions and the ecological conditions of agricultural production (Chen et al., 2016; Huang et al., 2007); economic efficiency paying more attention to farmers’ living standards and land output (Bull 2015; Liang and van Dijk, 2011); while social equity mainly includes the difference in economic status between urban residents and rural farmers, and the farms’ ability to improve livelihood.

3

The matrix R, whose elements are normalized values adopted in the entropy weight method is

⎡

a11  a12  · · · a1j  · · · a1n 

⎢ a a  · · · a  · · · a  ⎢ 21 22 2n 2j ⎢ R=⎢ ⎢ · · · · · · · · · · · · · · · · ·· ⎣ · · · · · · · · · · · · · · · · ··

⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(3)

am1  am2  · · · amj  · · · amn  where the proportion of the i-th index of the j-th province (Pij ) is Pij =

aij  m 

aij

(4) 

j=1

and the entropy of the i-th index is defined as ei = −K

m 

Pij · ln Pij

(5)

j=1

with K = ln1n , and if Pij = 0, then Pij ·lnPij = 0 (Yan et al., 2014). The entropy weight of the j-th index is then given by wi =

1 − ei

n 

(6)

(1 − ei )

i=1

where 0 ≤ wi ≤ 1,

n 

wi = 1.

i=1

3. Methodology 3.1. Weight of the indices

3.2. Fuzzy comprehensive method

Subjective methods, such as Delphi and the analytic hierarchy process (AHP), are widely used for assigning weights to indices due to their simplicity (Aminbakhsh et al., 2013; Stefanidis and Stathis, 2013), but are often criticized for their imprecision in relation to investors’ judgments for example. An alternative that is used here is the entropy weight method (Chu et al., 2015). This is an objective method based on probability theory that measures the relative importance of the variables involved (Chu et al., 2014). As the original values of the indices are measured on different scales, the entropy weight method is used to transform them to eliminate the influence of different dimensions. This is done by transforming the original values of the indices into a 0–1 range by normalization. Classified as positive indices (I+ ) or negative indices (I− ) according to their different effects, the normalization equations are

Assume that SLS is assessed by q indices. The index set U can be expressed as

aij  =

Max aij − Min aij

1≤j≤n

j ∈ I+

(1)

1≤j≤n

Max aij − aij

aij  =

1≤j≤n

Max aij − Min aij

1≤j≤n

j ∈ I−

(2)

u1 , u2 , u3 , · · ·, uq



(7)

The SLS is differentiated into p levels using fuzzy language. Therefore, the appraisal set V for assessing SLS is expressed as V=



v1 , v2 , v3 , · · ·, vp



(8)

The fuzzy matrix reveals the mapping relationship between the index set and appraisal set, with elements calculated using membership functions f : U → V ; uq → f (uq ) = (zq1 , zq2 , · · ·, zqp )

(9)

⎡

z11 z12 · · · · · ·z1p

⎤

⎢ z z · · · · · ·z ⎥ ⎢ 21 22 2p ⎥ ⎢ ⎥ Z˜ = ⎢ ⎥ ⎢···············⎥ ⎣···············⎦

(10)

zq1 zq2 · · · · · ·zqp

1≤j≤n

where Max aij is the maximal original value of the i-th index, Min aij 1≤j≤n



The fuzzy matrix Z˜ can be expressed as

aij − Min aij 1≤j≤n

U=

1≤j≤n

is the minimal original value of the i-th index, aij is original value of the i-th index of the j-th province and aij  is the normalized value.

where zqp is the membership degree of the q-th index to the p-th appraisal level in the appraisal set. The membership functions have a direct impact on the assessment of SLS. The SLS has four classes: insecurity, relative inse-

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curity, relative security and security, with membership functions expressed as

˜ 1 (x+ ) = A

⎧ + 1x ≤ x1 ⎪ ⎪ ⎨ + x2 − x

x −x ⎪ ⎪ ⎩ 2+ 1

x1 < x+ ≤ x2

⎧ x+ ⎪ x+ ≤ x1 ⎪ ⎪ x1 ⎪ ⎪ ⎪ ⎨ 1x1 < x+ ≤ x2 ⎪ x3 ⎪ ⎪ x2 < x+ ≤ x3 ⎪ x3 − x2 ⎪ ⎪ ⎩ − x+

0x+

˜ 3 (x+ ) = A

(11)

x2 < x+ ≤ x3

(14)

˜ 1 (x+ ), A ˜ 2 (x+ ), A ˜ 3 (x+ ) where x+ are the values of positive indices, A ˜ 4 (x+ ) are the degrees of membership of x+ to the four levand A els (Fig. 1a–d), x1 , x2 , x3 are the segmentation points for the four appraisal levels of positive indices respectively. If x+ ≤ x1 , then x+ ∈ insecurity. If x1 x3 , then x+ ∈ security.

(12) ˜ 1 (x- ) = A

> x3

⎧ 1x ≥ x1 ⎪ ⎪ ⎨ x2 − x

x2 − x1 ⎪ ⎪ ⎩ -

x2 < x- ≤ x1

(15)

0x < x2

⎧ + 0x ≤ x1 ⎪ ⎪ ⎪ ⎪ x+ − x ⎪ 1 ⎪ ⎨ x1 < x+ ≤ x2 x1 − x2

⎪ 1x2 < x+ ≤ x3 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ x3 x+ > x x+

x − x2

x −x ⎪ ⎪ ⎩ 3+ 2

1x > x3

0x > x2

˜ 2 (x+ ) = A

˜ 4 (x+ ) = A

⎧ + 0x ≤ x2 ⎪ ⎪ ⎨ +

3

(13)

˜ 2 (x- ) = A

⎧x 1 ⎪ x ≥ x1 ⎪ ⎪ x⎪ ⎪ ⎪ ⎨ 1x2 ≤ x- < x1 x3 − x⎪ ⎪ x3 ≤ x- < x2 ⎪ ⎪ x3 − x2 ⎪ ⎪ ⎩ 0x- < x3

Fig. 1. Membership functions.

(16)

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˜ 3 (x- ) = A

⎧ 0x ≥ x1 ⎪ ⎪ ⎪ ⎪ x − x⎪ ⎪ ⎨ x 1 − x x2 ≤ x- < x1 1

2

(17)

⎪ 1x3 ≤ x- < x2 ⎪ ⎪ ⎪ ⎪ ⎪ x- ⎩ x < x3 x3

˜ 4 (x- ) = A

⎧ 0x ≥ x2 ⎪ ⎪ ⎨ x2 − x

x2 − x3 ⎪ ⎪ ⎩ -

x3 ≤ x- < x2

(18)

1x < x3

˜ 1 (x- ), A ˜ 2 (x- ), A ˜ 3 (x- ) and where x− are the values of negative indices, A − ˜ A4 (x ) are the degrees of membership of x to four levels (Fig. 1e–h), and x1 , x2 , x3 are the segmentation points for the four appraisal levels of negative indices respectively. If x− ≥ x1 , then x− ∈ insecurity. If x2 ≤ x−
(19)

where Sj is the membership degree of SLS of the j-th province, wi is the weight of the i-th index, Z˜ j is the fuzzy matrix of the j-th province and ◦ is the fuzzy operator. The M(·,⊕) operator is used as the fuzzy operator applied in assessing SLS. The M(·,⊕) operator is the weighted mean operator and SLS is therefore assumed to be the sum of the values of the indices multiplied by their weights. 3.3. SLS index and its segmentation points

5

were therefore chosen to indicate agricultural pollution emissions and the ecological conditions of agricultural production including cultivated land occupied for construction, disaster area of crops and the rate of forest coverage. 2. Economic efficiency is tightly correlated with the farms’ living standard and land output. Farms’ living standard always involves a variety of factors such as net income, consumption expenditure and grain possession. Indices were therefore selected from these aspects. 3. Social equity mainly includes the difference in economic status between urban residents and rural farmers, and the farmers’ ability to improve livelihood. The difference in economic status between urban residents and rural farmers is associated with the level of income disparity and Engel’s coefficient. Although the Gini coefficient, Theil index and Atkinson index are important measures of income disparity, these indices are not publically accessible in China. Therefore, income disparity is defined as the ratio between per capita incomes of farmers divided by per capita disposable income of urban residents. Education level also has an important impact on the skill of the farmers and rural relief helps to improve insecure livelihood. The indices were therefore selected to indicate these aspects. The indices used to measure these components and the SLS index are summarized in Table 1. According to Sajjad et al. (2014) and Singh and Hiremath (2010), SLS is categorized into four classes. The maximum and minimum segmentation points are determined by national policies and expert suggestions. Generally, the middle segmentation points are the arithmetic mean of maximum and minimum segmentation points, with a few middle segmentation points adjusted according to the experts’ suggestions. The segmentation points for indices that are selected to assess the degrees of membership are showed in Table 2.

The SLS index is composed of three interacting components of ecological security, economic efficiency and social equity (Scoones, 2009). The specific indices of the three components are selected according to the factors that affect the SLS of the farmers. The reasons for selecting the indices are briefly summarized as follows.

4. Case study

1. Ecological security is defined as the status of ecological threat and environmental pollution during yield and living. The indices

China’s administration involves 34 provincial level divisions that comprised 23 provinces (including Taiwan), 4 municipali-

4.1. Study area

Table 1 SLS index used in assessing sustainable livelihood security of farmers in China.

SLS Index

Components

Indices

Units

Definitions

Index feature

Ecological security

Agricultural pollution emissions (I1 ) Cultivated land occupied for construction (I2 ) Disaster area of crops a (I3 ) Forest coverage (I4 ) Per capita net income of farmers (I5 ) Per capita consumption expenditure of farmers (I6 ) Per capita grain possession (I7 ) Per land agricultural output (I8 )

104 t

Agricultural pollution emissions comprises COD, TN, TP and NH3 -N −

−

− The rate of forest coverage The data comes from the household survey in China The data comes from the household survey in China Per capita annual grain possession Agricultural gross value divided by agricultural land area Per capita net income of farmers divided by per capita disposable income of urban residents Engel’s coefficient of farmers in certain province Proportion of population whose education are elementary school, semiilliteracy and illiteracy Relief fund comprises social relief fund and natural relief fund

− + +

Economic efficiency

Social equity

hm2 103 hm2 % Yuan/person Yuan/person kg/person yuan/hm2

Income disparity (I9 )

−

Engel’s coefficient (I10 )

%

Education level (I11 )

%

Per capita rural relief fund (I12 )

Yuan/person

Note: +is the positive index, and − is the negative index. a: disaster area of crops is the area of crop whose output is reduced by over 10% since disaster.

−

+ + + −

− −

−

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H. You, X. Zhang / Resources, Conservation and Recycling 120 (2017) 1–13

Table 2 The segmentation points for indices. Indices

I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12

Table 3 Statistical description of SLS index in 31 provinces.

Segmentation points x1

x2

x3

90 40000 800 10 5500 6000 100 6000 3 50 30 220

55 30000 400 20 7000 7250 300 8000 2.5 40 25 180

40 10000 200 30 8300 8500 500 10000 2 35 20 140

ties, 5 autonomous regions and 2 special administrative regions in 2014. Hong Kong, Macao and Taiwan are not included due to the lack of original data. Due to their distinct differences in natural, economic and social features, the provinces are partitioned into western, middle and eastern regions according to the 1986 “7th Five-Year” Plan. China’s Western Development Program, began in 2000, aiming to narrow the gap between the east coast and the western provinces and is a little different − covering 6 provinces, 5 autonomous regions and 1 municipality. The provinces partitioned in the “7th Five-Year” Plan are therefore adjusted to suit China’s Western Development Program to give: 1. The western provinces, consisting of Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. 2. The middle provinces, consisting of Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan. 3. The eastern provinces, consisting of Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan.

Indices

Obs.

Min.

Max.

Mean

Std.dev.

I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12

31 31 31 31 31 31 31 31 31 31 31 31

1.09 2926.00 14.40 4.02 4506.70 3098.00 51.80 103.57 2.06 29.70 7.30 111.77

204.20 334657.00 2429.40 63.10 17803.70 18512.00 1502.70 50897.71 3.93 53.60 92.80 399.21

55.97 92694.29 805.23 30.03 8495.28 7570.00 435.70 15072.34 2.88 39.62 33.21 205.28

49.33 77874.29 647.03 17.63 3339.76 3341.27 315.61 12024.38 0.51 5.86 15.43 82.12

Date processing system (DPS) is used for the calculations. Based on the fuzzy comprehensive procedure, using the related indices and corresponding weights, the membership degrees of SLS, ecological security, economic efficiency and social equity of farmers in the 31 provinces are summarized in Table 4, while the levels of SLS obtained according to principle of maximum membership degree are shown in Table 5. As Table 5 indicates, the farmers’ SLS is secure in 7 provinces, accounting for 22.6% of all provinces; relatively secure in 13 (41.9%); relatively insecure in 8 (25.8%); and insecure in the 3 provinces of Gansu, Qinghai and Xinjiang. The security of the farmers in the majority of provinces is therefore relative, implying that their SLS could be raised to a more desirable condition. For the three components of SLS

5. Results and discussion

• there are 11, 7, 8 and 5 provinces in which farmers are at the secure, relatively secure, relatively insecure and insecure SLS level respectively in terms of ecological security; 8, 11, 10 and 2 provinces respectively for economic efficiency; and 1, 15, 9 and 6 respectively for social equity. • Farmers with a secure SLS generally have ideal ecological security, economic efficiency and social equity. • For relatively secure SLS, the proportions of secure, relatively secure and relatively insecure levels are 30.8%, 38.5% and 30.7% respectively for ecological security; 15.4%, 76.9% and 7.7% respectively for economic efficiency; and 7.7%, 53.8% and 38.5% respectively for social equity. The farmers’ with relatively secure SLS have shortcomings in one of ecological security, economic efficiency and social equity. • For relatively insecure SLS, the proportions of secure, relatively secure, relatively insecure and insecure levels are 25.0%, 25.0%, 50.0% and 0% respectively for ecological security; 0%, 0%, 87.5% and 12.5% respectively for economic efficiency; 0%, 12.5%, 37.5% and 50.0% respectively for social equity. • For insecure SLS, the proportions of secure, relatively secure, relatively insecure and insecure levels are 0%, 0%, 0% and 100.0% respectively for ecological security; 0%, 0%, 66.7% and 33.3% respectively for economic efficiency; 0%, 0%, 33.3% and 66.7% respectively for social equity. The simultaneous occurrence of risk or danger in ecological security, economic efficiency and social equity has led to the relative insecurity and insecurity of farmers’ SLS.

5.1. Results of SLS

5.2. Spatial distribution

The weight vector obtained by the entropy weight method is W = [0.033, 0.034, 0.045, 0.116, 0.139, 0.105, 0.132, 0.142, 0.066, 0.049, 0.022, 0.117] Therefore the weights of I1 , I2 , I3 , I4 , I5 , I6 , I7 , I8 , I9 , I10 , I11 , I12 are 0.033, 0.034, 0.045, 0.116, 0.139, 0.105, 0.132, 0.142, 0.066, 0.049, 0.022, 0.117, respectively.

The spatial distribution of SLS and its components is shown in Fig. 2. With the exception of Heilongjiang (middle provinces), the provinces with secure SLS located in the eastern regions. And the provinces with relatively secure SLS located in the eastern and middle regions. In contrast, western province farmers have insecure and relatively insecure SLS. Hence, regional characteristics have an

4.2. Data collection The data for the eight indices used in the assessment are from the China Rural Statistical Yearbook 2013. These include the disaster area of crops, forest coverage, per capita net income of farmers, per capita consumption expenditure of farmers, per land agricultural output, income disparity, Engel’s coefficient and education level. The data for COD, TN, TP and NH3 -N used to compute agricultural pollution emissions are from the 2013 China Environment Yearbook. The data for cultivated land occupied for construction was collected from the 2012 China Land and Resources Statistical Yearbook. The area of cultivated land occupied for construction is statistical data in 2008 since the detailed outcomes of the 2012 s National Land Survey have not yet been published. The data for per capita grain possession and the per capita rural relief fund is from the 2013 China Rural Statistical Yearbook. The descriptive statistics of the SLS index are provided in Table 3.

Table 4 The membership degrees of sustainable livelihood security of farmers in China. Province

Ecological security

Economic efficiency

Social equity

IV

III

II

I

IV

III

II

I

IV

III

II

I

IV

III

II

I

0.199 0.273 0.121 0.391 0.498 0.092 0.093 0.276 0.351 0.241 0.245 0.200 0.299 0.115 0.198 0.158 0.142 0.266 0.365 0.404 0.232 0.321 0.332 0.644 0.631 0.648 0.446 0.711 0.631 0.578 0.562

0.282 0.396 0.372 0.793 0.528 0.365 0.322 0.332 0.312 0.501 0.335 0.527 0.367 0.452 0.358 0.432 0.437 0.520 0.446 0.667 0.537 0.587 0.771 0.738 0.670 0.668 0.717 0.669 0.565 0.608 0.546

0.619 0.549 0.770 0.597 0.390 0.800 0.774 0.548 0.386 0.598 0.565 0.798 0.533 0.819 0.673 0.703 0.785 0.639 0.483 0.591 0.579 0.609 0.654 0.327 0.307 0.255 0.517 0.273 0.328 0.376 0.394

0.627 0.573 0.577 0.190 0.290 0.613 0.662 0.600 0.547 0.469 0.602 0.433 0.592 0.528 0.571 0.514 0.519 0.445 0.514 0.303 0.445 0.390 0.190 0.156 0.200 0.130 0.219 0.163 0.242 0.321 0.259

0.001 0.150 0.112 0.147 0.112 0.067 0.071 0.112 0.150 0.185 0.051 0.086 0.034 0.065 0.150 0.112 0.097 0.110 0.067 0.054 0.000 0.035 0.107 0.050 0.079 0.094 0.046 0.186 0.116 0.116 0.161

0.034 0.120 0.150 0.185 0.196 0.080 0.099 0.055 0.122 0.198 0.049 0.120 0.014 0.061 0.157 0.172 0.067 0.077 0.104 0.065 0.000 0.063 0.079 0.070 0.033 0.116 0.060 0.185 0.055 0.150 0.127

0.168 0.043 0.116 0.071 0.116 0.144 0.145 0.082 0.008 0.043 0.116 0.145 0.120 0.107 0.078 0.116 0.127 0.080 0.115 0.122 0.124 0.161 0.107 0.149 0.087 0.036 0.151 0.026 0.071 0.090 0.067

0.194 0.078 0.027 0.033 0.000 0.126 0.116 0.116 0.078 0.000 0.149 0.070 0.194 0.149 0.000 0.002 0.116 0.116 0.116 0.149 0.228 0.149 0.116 0.148 0.149 0.112 0.149 0.033 0.110 0.077 0.019

0.132 0.121 0.009 0.244 0.247 0.000 0.000 0.142 0.132 0.000 0.105 0.092 0.148 0.008 0.048 0.046 0.032 0.077 0.188 0.197 0.117 0.105 0.100 0.386 0.386 0.365 0.261 0.386 0.376 0.323 0.262

0.118 0.156 0.164 0.476 0.213 0.118 0.172 0.145 0.123 0.139 0.211 0.227 0.237 0.179 0.127 0.188 0.204 0.227 0.237 0.348 0.332 0.273 0.445 0.464 0.423 0.366 0.479 0.319 0.378 0.350 0.294

0.279 0.256 0.426 0.274 0.204 0.434 0.435 0.283 0.211 0.359 0.285 0.421 0.291 0.501 0.350 0.355 0.417 0.384 0.233 0.368 0.316 0.376 0.418 0.132 0.132 0.152 0.257 0.132 0.142 0.176 0.239

0.341 0.362 0.355 0.036 0.197 0.400 0.346 0.363 0.332 0.379 0.307 0.291 0.281 0.339 0.391 0.330 0.315 0.291 0.281 0.154 0.186 0.241 0.074 0.007 0.051 0.007 0.021 0.087 0.074 0.132 0.132

0.066 0.002 0.000 0.000 0.139 0.025 0.022 0.022 0.068 0.056 0.088 0.022 0.117 0.043 0.000 0.000 0.013 0.079 0.111 0.153 0.115 0.181 0.125 0.208 0.166 0.188 0.138 0.139 0.139 0.139 0.139

0.129 0.121 0.058 0.132 0.118 0.167 0.051 0.131 0.068 0.164 0.075 0.180 0.116 0.212 0.074 0.072 0.166 0.216 0.106 0.254 0.205 0.251 0.248 0.204 0.214 0.186 0.178 0.165 0.133 0.109 0.126

0.172 0.250 0.229 0.252 0.070 0.222 0.193 0.182 0.167 0.196 0.164 0.232 0.122 0.211 0.245 0.232 0.241 0.175 0.135 0.101 0.139 0.073 0.129 0.046 0.088 0.066 0.109 0.115 0.115 0.110 0.088

0.092 0.133 0.196 0.122 0.093 0.087 0.200 0.121 0.137 0.090 0.145 0.072 0.117 0.040 0.180 0.182 0.088 0.038 0.117 0.000 0.031 0.000 0.000 0.000 0.000 0.011 0.049 0.044 0.058 0.112 0.108

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Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

Sustainable livelihood security

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Table 5 The levels of sustainable livelihood security of farmers in China. Province

Sustainable livelihood security

Ecological security

Economic efficiency

Social equity

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

I I II III III II II I I II I II I II II II II II I II II II III III III III III IV IV III IV

I IV III III III II II I IV III I II I I III III II I I I I II I II I III II IV IV III IV

I I II III IV II II I I I I II II II I II II II I II III II III III III III III IV III III III

II II II II IV II I II II II II II II III II II II III II III III III III IV III IV III III IV IV IV

Note: IV is insecurity, III is relative insecurity, II is relative security, and I is security.

important impact on SLS, with high rates of economic development and favorable ecological conditions producing better SLS in the eastern and middle provinces compared with the less prosperous western provinces. Basically each level of ecological security is distributed over the three provincial regions. The low ecological security in the western provinces is associated with the poor environmental quality in this region. Our results are consistent with previous findings (Cai, 2008), that unfavorable conditions for agricultural production such as soil erosion, desertification and water shortage are major threats to SLS. Accordingly, there is little ecological security in western provinces such as Gansu, Qinghai and Xinjiang. Surprisingly, ecological security in some eastern and middle provinces, where ecological conditions are better than the western provinces, are also insecure or relatively insecure. The main reasons are intensive agricultural land use and rapid conversion of agricultural land to non-agricultural use. The over-utilization of some inputs such as pesticide and chemical fertilizers from more intensive use of the land has generated considerable agricultural pollution emissions, including COD, TN, TP and NH3 -N (You, 2016a). Rapid conversion of agricultural land to non-agricultural use has also caused the loss of high quality agricultural land, especially agricultural land situated in suburbs. Yu and Ng (2007) also find that land fragmentation, which appears in the process of agricultural land conversion, can lower ecological security. The eastern and middle provinces are associated with secure and relatively secure economic efficiency while, with the exception of Hainan and Shanxi, the western provinces have insecure and relatively secure economic efficiency. These results are consistent with the views of rural development in China. Economic efficiency has a significantly positive correlation with local rural economy (Benjamin and Brandt, 2002). Rural development has been continuing since the advent of rural reform in 1978. However, there are large discrepancies between provinces because the rural economy

departed from the originally intended growth-with-equity direction. In particular, the rural economy and farmers’ living standards have observably improved in the eastern provinces since implementation of the East Coast-first policy (Rozelle, 1996). Therefore, some factors reflecting the development of the rural economy have spatial differences. For example, the per capita net income and per capita consumption expenditure in the eastern and middle provinces are clearly higher than the western provinces, which has had a good effect on the farmers’ ability to obtain a sustainable livelihood in these regions. In addition, there is higher agricultural production efficiency in the eastern and middle provinces than the western provinces due to more intensive agricultural land use (Gao et al., 2014). The excellence yield of farming in the eastern and middle provinces also helps to improve SLS. Social equity in the eastern provinces is more secure than in the middle provinces. The number of relatively secure eastern provinces is twice the number of middle provinces. Social equity of the middle provinces on the other hand is more secure than the western provinces, where it is mostly at the insecure and relatively insecure level. Previous studies reveal that the provincial differences in farmers’ social equity are associated with public service provision and the local economy in rural areas (West and Wong, 1995; Démurger et al., 2002). Farmers in the eastern provinces have relatively fair access to resources and livelihood. For example, a higher education level is beneficial to the non-agricultural employment of farmers and the narrow income disparity reduces the rural-urban wealth gap (Yang, 2004). In addition, the public service provision of relief, such as through the rural relief fund, eases distress in times of catastrophe (Zhou et al., 2014). This implies the local farmers do not have sufficient resources to deal with risks from disasters and therefore the farmers who receive such assistance are mainly distributed in the western provinces where the SLS may be pessimistic.

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The results show that there are disparities of SLS in ecological, economic and social dimensions. The high economic efficiency in these provinces is always accompanied by high social equity, but it may be associated with insecure or relatively insecure ecological security. The disparities should relate to the stage of rural development in China. The process of rural development in China has focused more on the economic well-being of farmers (Fan et al., 2015). While more public investment is being directed at improv-

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ing the farmers’ social equity, the process of improving the quality of economic well-being and social equity is not coordinated with ecology; even weakening the ecology (Sun et al., 2012). Low ecological security, low economic efficiency and low social equity always occur simultaneously in some provinces. The provinces in western region, such as Tibet and Gansu or example, have suffered from such occurrences. The overall deterioration of SLS reveals that farmers in these provinces do not have adequate and sustainable access

Fig. 2. Spatial distribution of sustainable livelihood security and its components.

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H. You, X. Zhang / Resources, Conservation and Recycling 120 (2017) 1–13

Fig. 2. (Continued)

that includes ecological, economic and social dimensions to income and resources to meet sustainable livelihood. Previous studies have found similar cases in rural poverty. Ecological, economic and social problems have a positive correlation with the poor livelihoods of farmers who are living in poverty (Yu, 2013; You, 2016b).

6. Policy implications and conclusions The results of the SLS of farmers in China presented in this study are helpful for designing policies to improve the conditions for sustainable livelihoods or sustainable development. The specific

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policies targeted at imperfect ecological security, economic efficiency and social equity can support the realization of SLS. To do this, it is important to adjust key aspects of policies to suit the local circumstances of different provincial regions. In order to reduce risk and improve ecological security, farmers in the eastern provinces need to be encouraged to reduce agricultural pollution. The majority of eastern provinces have a lower proportion of GDP dependent upon agriculture and therefore a GDP oriented policy has less effect on the demand for agricultural output, which has allowed the intensity of agricultural production to decrease in these provinces. In addition, the conversion of agricultural land to non-agricultural use needs to be strictly restricted in the eastern provinces due to rapid industrialization and urbanization. This weakens the shocks from agricultural pollution and farmland conversion, which can destroy the assets for sustaining livelihoods. A large proportion of subsistence farmers are in the middle provinces, while the agricultural industry in the middle provinces is vital to ensure an adequate food supply to the country. Consequently, local governments need to increase financial investment to improve the agricultural infrastructure in this region, while at the same time paying more attention to ameliorating the farmland ecosystem. Land consolidation has the potential to improve the effectiveness of agricultural land cultivation and compensate for the conversion of agricultural land to non-agricultural use. It improves the farmers’ natural capital for sustaining livelihoods. Steps could be taken to induce the migration of farmers in the western provinces away from ecologically vulnerable areas to the township to lessen ecological pressure. Advanced agricultural technologies need to be adopted, such as water-saving and emission-reduction technologies, that promote agricultural production efficiency in arid and semiarid areas of the western provinces (Wu et al., 2015). The farmers in the western provinces need to achieve a balance between ecological pressure and agricultural production for sustaining livelihoods. The eastern provinces are low in per capita grain possession because they have a high population density and relatively small amount of agricultural land. Therefore, a key aspect of policies for improving economic efficiency would be to decrease the amount of agricultural-to-urban land transfer. Agricultural land-based activities are an important part of farmers’ sustainable livelihoods. The farmers in the middle provinces can raise their earnings from farm income. Hence, an agricultural subsidy policy could be adopted for sustaining livelihoods. The farmers’ income could be supplemented by government subsidy, since any increase in agricultural production costs, such as an escalation in fertilizer prices, undermines its vulnerability. Agricultural output in the middle provinces needs to be raised by the adoption of new techniques. As agricultural land in the middle provinces becomes more productive, the livelihoods of farmers would benefit from more competitive farming. The farmers’ income should be primarily raised in the western provinces. Increasing the farmers’ income enables farmers to adopt different livelihood strategies to achieve their livelihood goals. Income growth ought to be based on increasing non-agricultural income-earning opportunities, since there is limited potential for agricultural productivity gains in the western provinces. An important task in promoting the social equity of farmers’ SLS in the eastern provinces is accelerating new-type rural population urbanization (You, 2016c). Farmers migrating to towns or cities need to be provided with local public services that ensure they share the benefits of urbanization (You, 2016d). Therefore, farmers can obtain a wide range of opportunities for sustaining livelihoods during urbanization. The policies in the middle provinces need to pay more attention to narrowing the gap between the farmers’ and city residents’ livelihoods. Local governments should adequately coordinate their efforts to increase non-agricultural employment opportunities for farmers in the middle provinces

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during the process of transferring industries from the eastern to western provinces. Investment in infrastructure and education also needs to be encouraged. The purpose of local governments or independent providers in rural areas, such as enterprises supplying infrastructure (water supply, roads and water conservation facilities etc.), is to improve the social equity of the farmers’ SLS and enhance their living conditions. The farmers pursue sustainable livelihoods via a range of activities that are by nature specific to the local resources and infrastructure. The overall policy and institutional framework for rural development in China therefore needs to support the development of the western provinces. In addition, income inequality between farmers and urban dwellers is an important component of social inequality in the western provinces. One approach to solving this problem is to better support farmers’ education, since relative income is strongly dependent on level of education. Based on the fuzzy comprehensive method procedure of evaluation, the results show that the sustainable livelihood of farmers in most provinces is relatively secure or insecure. The differences between provinces and the spatial distribution of SLS and its components were analyzed. The demonstrated methodological framework is not restricted to assess the farmers’ SLS in China. When the SLS index is adjusted to fit local circumstances, it is also suitable for assessing the SLS of specific demographic groups in different study areas. This paper, therefore, further advances the method of assessing SLS. Some issues need to be further addressed. Firstly, the weight of the indices has an important effect on the evaluation of SLS. There are many methods for weighting the indices, but it is difficult to judge their accuracy. Therefore, a combination of weighting methods (considering both subjective and objective weight information) could be adopted in further research. Secondly, membership functions have a direct impact on the assessment of SLS. Their selection is subjective and therefore more attention needs to be paid in the future research to the effect of different membership functions on the results of assessing SLS. Acknowledgments The research reported in this paper received financial support from the National Natural Science Foundation of China under Grant (71403235; 71673232 and 71303203), Zhi-Jiang Young Scholar Program of Social Science of Zhejiang Province under Grant No. 16ZJQN026YB and the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ14G030016; This research is co-funded by the Environment and Conservation Fund (Project No: 92110732) funded by HKSAR Depts, the Hong Kong Research Grant Council, Early Career Scheme (Project No: 9048039), the General Research Funding of Hong Kong Research grant council (Project No: 9042363) and the Lincoln Institute of Land Policy Fundation project, (USA & China) (project no: R-IND6604). References Aminbakhsh, S., Gunduz, M., Sonmez, R., 2013. Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. J. Saf. Res. 46, 99–105. Benjamin, D., Brandt, L., 2002. Property rights, labour markets, and efficiency in a transition economy: the case of rural China. Can. J. Econ. 35 (4), 689–716. Bhandari, B.S., Grant, M., 2007. Analysis of livelihood security: a case study in the Kali-Khola watershed of Nepal. J. Environ. Manage. 85, 17–26. Bull, K., 2015. Improving Rural Livelihoods and Environments in Developing Countries. Report. Institute for Land, Water and Society, Charles Sturt University, http://www.csu.edu.au/research/ilws/research/sra-rurallive (Accessed 13 March 2015). Cai, X., 2008. Water stress, water transfer and social equity in Northern China-Implications for policy reforms. J. Environ. Manage. 87 (1), 14–25. Cato, M.S., 2009. Green Economics: an Introduction to Theory, Policy and Practice. Earthscan, London, U.K.

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