Multi-dimensional poverty measurement for photovoltaic poverty alleviation areas: Evidence from pilot counties in China

Journal of Cleaner Production 241 (2019) 118382

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Multi-dimensional poverty measurement for photovoltaic poverty alleviation areas: Evidence from pilot counties in China Huiming Zhang a, b, Zhidong Xu b, Kai Wu c, * , Dequn Zhou d, Guo Wei e a

NUIST-UoR International Research Institute, Nanjing University of Information Science & Technology, Nanjing, 210044, China School of Management Science and Engineering, Nanjing University of Information Science&Technology, Nanjing, 210044, China c School of Finance, Central University of Finance and Economics, Beijing, 100081, China d School of Economics and Management & Research Centre for Soft Energy Sciences, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, China e Department of Mathematics and Computer Science, University of North Carolina at Pembroke, NC, 28372, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 March 2019 Received in revised form 7 August 2019 Accepted 9 September 2019 Available online xxx

Poverty alleviation based on the use of photovoltaic panels has been designated one of “the ten largescale poverty alleviation programs in China” since 2015. Given that most of the poverty objects to be identified are low-income families instead of regions, 30 pilot counties in China eligible for photovoltaic poverty alleviation projects have been measured by using a developed index system incorporating six dimensions. The results indicate: 1) Pilot counties vary widely in their poverty, with the highest poverty index more than three times that of the lowest. 2) The total annual poverty index declined from 2.01295 in 2014 to 1.89661 in 2016. 3) Among the six indices, finance and social production are the largest contributors to poverty, followed by income and social security. 4) The poverty indices of Qinghai, Ningxia and Gansu in Western China are smaller than that of Anhui and Shanxi, Central provinces. From the above results, this study recommends: strengthen the dynamic management of poverty-stricken areas, promote the development of “photovoltaicþ” industry and evade a one-size-fits all poverty alleviation policy in the central and western regions. © 2019 Elsevier Ltd. All rights reserved.

Handling editor: Yutao Wang Keywords: PV-based poverty relief Multi-dimensional poverty measures Deprived function Regional disparity

1. Introduction Photovoltaic (PV)-based poverty relief concerns “farmers, rural areas and agriculture” and aims to aid in poverty elimination by using the PV-based power generation. With such advantages including stable power generation income, realistic new-energy promotion and innovative measures of energy saving and emissions reduction, the PV-based poverty relief has drawn the attention of both Chinese central and local governments. Indeed, it has been designated as “one of the ten poverty-relief projects in China” since 2015. The other nine programs include providing microcredit, poverty alleviation using e-commerce, planting paper mulberry, etc. In terms of policies, in 2014, the National Energy Administration and the State Council Poverty Relief Development Leading Group

* Corresponding author. School of Finance, Central University of Finance and Economics, Beijing, China E-mail address: [email protected] (K. Wu). https://doi.org/10.1016/j.jclepro.2019.118382 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

Office jointly issued The Work Scheme on Carrying out PV-based Poverty Relief Projects, deciding to develop poverty-relief projects connecting to the PV-based power generation industry within 6 years. In 2015, the Department of New Energy and Renewable Energy of the National Energy Administration set out the Program for Compilation of PV-based Poverty Relief Implementation Scheme (Trial). In 2016, the National Development and Reform Commission, the State Council Poverty Relief Development Leading Group Office, the National Energy Administration and both the China Development Bank and the Agricultural Development Bank of China jointly issued Proposals on Implementing the Poverty Relief of PV-based Power Generation, stipulating that “Before 2020, specifically in about 35 thousand poverty-stricken villages (for which files and cards have been established) located in 471 counties in 16 provinces in which pilot projects have been carried out to better sunlight conditions, boost overall-village incomes. Each of the 2 million poverty-stricken families without capacity to work and, for which files and cards have been established (including the handicapped), shall earn an additional income of more than 3000 RMB every year”. Local governments, including the governments of Shandong,

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Gansu and Jiangsu provinces, have also successively issued The Implementation Scheme of PV-based Poverty Relief of Shandong Province, “The Thirteenth-Five-Year” Development Plan of Gansu Province, etc., planning the layout of PV-based power-generation systems, projects of non-local poverty relief relocation, and other feasible measures. From a microscopic perspective, some listed companies also participate in PV targeted poverty relief in, for example, investments in project construction and project financing. Akcome Solar Science & Technology Co., Ltd (AKCOME) and Xunwu County, Ganzhou City, Jiangxi Province signed a long-term agreement of strategic cooperation, developing a construction project of 100MW PV-based power station based on agriculture-PV mutual supplementation in Xunwu County, with a total investment of up to 980 million RMB. This project effectively implements PV-based poverty relief, meets the basic living requirements of rural lowincome families, brings them a stable income for 20e30 years and helps get rid of poverty through continuous income growth. Jetion Solar also provided 48 sets of new-type roof distributive solar panels in the first phase of their PV-based poverty relief project in Yingjisha, Xinjiang Uygur Autonomous Region, an investment of more than 80 million RMB. Statistics show that, by 2015, the scale of construction of PV-based poverty relief pilot projects had reached 1836 MW nationwide, with an annual average income of 2.26 billion RMB and an investment return rate of nearly 13.72%. Nearly 430,000 low-income families, for whom files and cards have been established, had increased their income, including some 88,000 incapacitated low-income families. The problem that 956 lowincome villages did not have a collective income had been solved. Jinzhai County in Anhui Province is one of the typical cases of successful PV-based poverty relief in China. According to the State Grid Jinzhai County Power Supply Company, by February 2016, Jinzhai County had assisted 320,000 kW for PV-based power generation, 8741 low-income families for installing 3 kW (KW) distributive PV-based power stations. The average annual income from grid-combined power generation by farmer families had reached more than 3000 RMB, 218 administrative villages had set up collective PV-based power stations with a total capacity of 39.3 MW and a total power generation of 18,235,100 kwh, thus bringing the annual average income to more than 60,000 RMB for the whole village. Due to the PV-based poverty relief, a total income of 18 million RMB for low-income families had been created. The scales for the PV poverty alleviation projects mainly include three types. The smallest scale projects are rooftop installations, generating several kilowatts power. The centralized power station, with the largest scale, can generate more than 1 MW power. Several financial modes were employed to support the PV poverty alleviation projects. 100% investment by government was used in Hefei, Anhui Province to promote the construction of 100 3 kW household distributive power stations. Both Yunnan and Jiangsu use poverty alleviation funds þ bank loans to farm families. In Guizhou, Anhui and Hebei, all the initial fund investment is provided by both government and enterprises. However, PV-based poverty relief still faces numerous difficulties in such aspects as financing, maintenance of equipment and power-grid renovation (Shi et al., 2016). The most important problem is the accurate identification of the identification of poverty-stricken areas, and low-income families, which is a precondition for the creation of the PV-based poverty-relief strategy, and the allocation of funds. At present, both domestic and overseas models for the accurate-identification of poverty are chiefly made on the basis of the A-F poverty measurement where, given that most of the objects to be identified are low-income families, regions are ignored. Moreover, in the literature, measures of poverty-stricken areas focus on identifying low-income

families from the dimensions including income, education, health and living conditions but fail to consider the heterogeneity of some industries. For example, the implementation of PV-based povertyrelief projects requires some sunshine and/or areas of land, therefore, the indexes of poverty measures may be imperfect if these heterogeneous features are not met. In practice, poverty-stricken counties will generally be selected before PV-based poverty relief projects are implemented in China. Following this, the projects are implemented village by village. Regional standards are mostly based on local per-capita gross domestic product (GDP) or the collective income of a village and town without consideration of such indexes as finance, social production and education. As such, they cannot provide a scientific analysis for decision-making from the government. Therefore, this paper will build a six-dimensional index system covering natural conditions, finance, social production, income, social security and health to improve the Alkire-Foster multidimensional poverty measure model, focusing on measuring the feasibility of a county to be lift out of poverty by the PV project. 2. Literature review An important measure taken by some developing countries to increase the income of their poverty-stricken population is the use of energy, a measure that has caught the attention of some scholars (Urge-Vorsatz, Herrero, 2012; Chakravarty and Tavoni, 2013). The studies related to this topic can be generally be grouped into the selection of poverty alleviation measures in developing countries (Jennifer et al., 2012; Sarakikya et al., 2015; Lo, 2016; Diouf, 2016; Liu and Li, 2017; Xue, 2017) and the performance of and the problems associated with poverty relief (Piazza et al., 2001; Lin, 2017; Jiao, 2015). Although research into the problem of both poverty identification and measures directly related to this article started in the 1980s and was not directly aimed at PV-based poverty relief, it is of use to us. From such dimensions as income and leisure, Hagenaars (1987), for the first time, set up multi-dimensional indexes of poverty. After that, some scholars comprehensively measured poverty from both economic perspectives and from such noneconomic perspectives as education, health, nutrition, resource endowment, environment, location and fragility (Tsui, 2002; Bourguignon, Chakravarty, 2003). Alkire and Foster (2011), researchers of the Oxford Poverty & Human Development Initiative (OPHI), identified and summed poverty from multi-dimensional perspectives and provided a general model of multi-dimensional poverty (which was later referred to as the A-F Model). This model can not only measure multi-dimensional poverty indexes (MPI), but can also categorize them according to regions, dimensions, etc. Followed by that, Alkire et al. (2017) proposed a set of indicators to measure temporary poverty and conducted an empirical analysis on the data of Chile from 1996 to 2006. By using the multi-dimensional poverty indexes of Alkire and Foster (2011) as a reference, Gao and Ma (2016) and Chen and Zhang (2016) measured poverty in China; the former classified poverty as to education, health and living standards while the latter added an income dimension to the above-mentioned three. Santos (2014) measured the poverty of more than 100 developing countries and discussed the data challenge and methodology problem. Padda and Hameed (2018) identified multidimensional poverty levels in rural Pakistan by examining agricultural and non-agricultural assets, housing, education, energy, sanitation and access to clean drinking water. Different from the above studies, a few scholars such as Vivi (2012) only defined poverty with a single indicator. On the measures of poverty, some scholars have considered: (1) the identification of poverty under equal dimensions or weights.

H. Zhang et al. / Journal of Cleaner Production 241 (2019) 118382

Chen (2006) raised the following doubts: given an individual universally applied poverty threshold value, how does this threshold value change among different families along with population characteristics. Accordingly, he assessed the equivalent dimension of Chinese urban families by using the data of the Chinese urban household survey (UHS). This finding provided a quantitative reference for calculating the poverty threshold when comparing households with different population structure characteristics. In the opinion of Pasha (2017), the ranking of countries is sensitive to the selection of different weights and indexes. Therefore, he applied an alternative weighting scheme for multi-dimensional poverty indexes and analyzed its impact on the scores and relative ranking of 28 countries. As his result indicated, giving equal weights to the three dimensions is statistically unreasonable. (2) The fuzzy measures of poverty. To avoid the dichotomy of strict poverty and non-poverty, Belhadj (2011) and Belhadj and Limam (2012) used fuzzy sets to measure poverty. Based on this, they carried out empirical analyses by connecting fuzzy sets to the individual happiness data of Tunisian families in 1990. This method was a breakthrough for traditional poverty measures. (3) Poverty measures based on indexes or income distribution. Zheng (2001) carried out statistical reasoning to test the decomposable poverty indexes of relative poverty thresholds. The results indicated that the poverty indexes of relative poverty threshold are asymptotically distributed and that the covariance structure can be continuously estimated. According to the findings of Chattopadhyay and Mallick (2007), when income adheres to a logarithmic normal distribution, the rise of mean income usually means a reduction in poverty. In contrast, once poverty indexes are discovered to adhere to a Pareto distribution, the increase of income will aggravate poverty. Obviously, the above two papers focused on the statistics of poverty measures but do not extend to policy connotations. In contrast, Idris et al. (2015) highlighted the practical applicability of poverty measures. They used the FGT index to measure the degree of poverty of farmers in the comprehensive agricultural development zone of the State of Sarawak in Malaysia. They suggested that Malaysia should broaden the non-agricultural income sources of farmers and thus help farmers to throw off poverty. (4) Poverty measures based on first-order dominance method. Arnd (2018) employed both multidimensional poverty index and first-order dominance to measure the poverty of 26 African countries, and concluded that the two measurements were useful complements. In conclusion, a lot of scholars have already explored differing measures of poverty identification. However, few studies have focused on PV-based poverty identification. This type of poverty alleviation uses both land and solar which are different from the other forms of poverty alleviation. Another demerit of current studies is that most of the poverty objects of identification are lowincome families instead of regions. To make these amends, we have selected pilot counties for PV-based poverty relief, designated in 2014 by the National Energy Administration and State Council Poverty Relief Development Leading Group Office as research objects, and conducted poverty alleviation using multi-dimensional poverty indexes.

3. Model and deprived critical value 3.1. Multi-dimensional poverty measurement model (1) Suppose that the total number of samples is n, and each sample has poverty measure values of m dimensions, then the matrix of sample observation will be:

2

x11 6 x21 6 6 « 6 4 « xn1

/ / « « /

x12 x22 « « xn2

/ / « « /

3

3 x1m x2m 7 7 « 7 7 « 5 xnm

(1)

where xij denotes the value of sample i on dimension j, i ¼ 1; 2; 3; / ; n , j ¼ 1; 2; 3; /; m. The row vector Xi ¼ ðx1j /x1n Þ denotes the value of sample i on the m dimensions, and the column vector Xj ¼ ðx1j /xmj Þ denotes the value of n samples on dimension j. (2) The deprived critical value refers to the poverty standard of each index and can also be regarded as the poverty threshold of each index, determining the poverty judgment of the sample on this dimension and the depth of poverty. Therefore, we determine k deprived critical values on each dimension j and define the deprived matrix 3 2 g11 g12 / / g1j 6 g21 g22 / / g2j 7 7 6 « « « « 7 G¼6 where the vector 7 6 « 4 « « « « « 5 gk1 gk2 / / gkj gj ¼ ðg1j ; g2j ; ///; gkj ÞT denotes that k deprived critical values on dimension j. The poverty deprivation result yij ð0  yij  kÞ of sample i on dimension j can be determined through the following function:

8 0 > > >1 > < yij ¼ 2 > > > >« : k

8 xij  gkj 0 > > > > g ðk1Þj xij < gkj <1 gðk2Þj  xij < gðk1Þj or yij ¼ 2 > > « > « > : k xij < g1j

xij < g1j g1i  xij < g2j g2i  xij < g3j « xij  gkj (2)

According to the original values and deprived critical values, we determine the matrix of poverty deprivation of n samples on all dimensions:

2

  Y ¼ yij

nm

y11 6 y21 6 ¼6 6 « 4 « yn1

y12 y22 « « yn2

/ / « « /

/ / « « /

3 y1m y2m 7 7 « 7 7 « 5 ynm

(3)

(3) With the method of principal component analysis, we determine the index weight of each dimension wj , subject to Pm j¼1 wj ¼ 1. The weight vector of weight composition of each index dimension is then Wj ¼ ðw1 ;w2 ;w3 ;////;wj Þ, 1  j  m; through Y  ðWj ÞT , we can obtain the matrix of multi-dimensional poverty indexes of each county Q ¼ ðq1 ; q2 ; q3 ; ///; qn ÞT , where qi ð1  i  nÞ denotes the measure of multi-dimensional poverty indexes of sample i . (4) Define the index weight matrix of all sample observation 3 2 w1 w2 / / wj 6 w1 w2 / / wj 7 7 6 « « « « 7 values as W ¼ 6 7 , where wj denotes 6 « 4 « « « « « 5 w1 w2 « « wj the weight of the sample on dimension j. For each dimension, all the samples have the same the index weight. Therefore, all the row data in W are same as the first row.

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H. Zhang et al. / Journal of Cleaner Production 241 (2019) 118382

Then, we conduct product operation to the matrix of poverty deprivation results Y ¼ ðyij Þnm and the index weight matrix W, and obtain the matrix of the weight deprivation results of fractional dimension

2

0

6 y11 6 0 6y 6 21 0 6 Y ¼6 « 6 6 « 6 4 0 yn1

y12

0

/

/

y22

0

/

/

« «

« «

« «

yn2

/

/

0

3 0 y1j 7 7 0 y2j 7 7 7 « 7 7 « 7 7 5 0 ynj

(4)

0

Where yij denotes the weight deprivation result of sample i on dimension j, which can be used to examine the contribution of each different dimension poverty index to the total poverty. The contribution of dimension j to multi-dimensional poverty indexes Pn 0 y i¼1 ij is rj ¼ Pn P m 0 . i¼1

y

j¼1 ij

(5) Define the population weight of each region as pi , i ¼ 1; 2;/ P //;n. Through ni¼1 qi  pi , we can obtain the measurement result of the total multi-dimensional poverty index of the region. We can further conduct a time sequence investigation on the total multi-dimensional poverty index and evaluate the overall effect of the implementation of PV-based poverty relief in the region.

3.2. Data and samples The current studies of poverty measures focus on the unit of farmer families instead of counties. County is the basic unit of local administration in China; census data and survey data usually carry out detailed statistics at the county level but not at further lower level. Considering the differences in poverty, if we take an administrative unit at the level above county, it would ignore these differences and make our policy invalid. On the other hand, if we select an administrative unit at the level below county, we would make some data difficult to track down due to the decentralization of the research object. It is therefore more suitable to choose the administrative unit at the level of county to be the research object in terms of both policy formulation and policy implementation. In 2014, both the National Energy Administration and the State Council Poverty Relief Development Leading Group Office proposed that the pilot projects of PV-based poverty relief should be conducted in 34 national poverty-stricken counties in 6 provinces (autonomous regions) including Hebei, Shanxi, Anhui, Gansu, Ningxia and Qinghai. After having eliminated four poverty-stricken counties for the lack of relevant data, this article selects 30 pilot PV-based poverty relief counties as research objects. All the data used in this article come from the Hebei Statistical Yearbook, Shanxi Statistical Yearbook, Anhui Statistical Yearbook, Gansu Statistical Yearbook, Ningxia Statistical Yearbook and Qinghai Statistical Yearbook as well as the China County Territory Statistical Yearbook. 3.3. Identifying poverty dimensions, indexes and deprived critical value Dimension selection is the first crucial issue for constructing multi-dimensional poverty indexes. At the county level, selected poverty dimensions and indexes should not only reflect the social and economic development levels of various poverty-stricken

counties but also reflect the living standards of residents of the various poverty-stricken counties. By referring to the studies of He et al. (2016), this article selects six dimensions: natural conditions, finance, social production, income, social security and education, and 13 indexes. These indexes are specified as: regional per-capita tax and the expenditure and income ratio of public finance can directly reflect the financial level of a region. The financial development is critically important for the renewable energy growth (Ji and Zhang, 2019) and is the manifestation of the poverty-relief capability of the region. Generally speaking, high per-capita tax and low ratio of public expenditure to income in a region indicate a good financial standing and a low degree of poverty. Different from the case of Sri Lanka where the poor families would not be able to benefit from their rooftop PV due to a lack of investment, China has attached great importance to the financial arrangement. This could justify the value of Chinese case studies to the developing world (Shi et al., 2018). Per-capita GDP is a widely used index for measuring the economic development level of regions. The percentage of value added from the primary industry to the total GDP of a region and the percentage of value added from the secondary industry are important indexes for measuring the industrial structure. A high percentage of value added of the former implies that the region focuses on agriculture and has an extremely low level of local economic development. In contrast, a high percentage of the latter shows an upgraded industrial structure, which has positive effects on green investment (Liao and Shi, 2018). It follows that the application of these three indexes can comprehensively embody the social and economic development level of the region. Grain is one of the most basic subsistence primary products for human beings. As grain is closely related to poverty, solving the problem that relying on grain creates is a precondition for supporting rural development and thus the poverty problem. In view of the above, this article incorporates per-capita grain output into the system of poverty indexes. Income has always been the most valued poverty-relief standard in China and most directly reflects the poverty-relief effect of the government. Because of this, we take per-capita disposable income of rural residents as poverty indexes. Savings are the surplus part of the people's income and can laterally reflect the standard of living of residents in various regions. Savings are thus taken as one of the poverty measure indexes. In poverty-stricken areas, the phenomena of disease-caused poverty and returning-to-poverty are prevalent. Moreover, special groups in poverty-stricken areas (e.g. elderly people, orphans, disabled people and mental patients lacking self-care abilities of daily living) cannot obtain necessary relief due to family poverty or low levels of regional social and economic development. Therefore, we select the number of beds in medical and health institutions owned per 10,000 people and the number of beds in social-welfare adoption institutions owned per 10,000 people as the poverty indexes of social security dimension (see Table 1). By referring to the goal of basic primary education in the Millennium Development Goals of the United Nations, we measure the regional development levels of primary and secondary education by using the ratio of primary and secondary school students to the total population of the county. Current studies ignore local natural factors but the amount of sunshine a region gets will have a direct impact upon the stability and durability of PV-based power generation. Especially, a number of photovoltaic agricultural projects are promoted with arable land although current policy requires that this type of land not be used for the PV-based alleviation. The more land the PV-based projects occupy, the larger the scale of photovoltaic poverty alleviation is likely to be. The longer the sun shines, the more power is likely to be generated. These two indicators may have positive impacts on the income of poverty alleviation projects. Therefore, ‘natural condition’ is taken as one of the

H. Zhang et al. / Journal of Cleaner Production 241 (2019) 118382

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Table 1 Studies of measuring poverty. Authors

Year

Countries or Economies

Alkire, Foster Gao and Ma Chen and Zhang Chen Pasha Belhadj Belhadj, Limam Zheng Chattopadhyay, Mallick Idris et al. Arnd et al. Vivi

2011 2013 2016 2006 2017 2011 2012 2001 2007 2015 2018 2012

China China China 28 countries Tunisia Tunisia China India Malaysia 26 countries in Africa Indonesia

Poverty measurements A-F poverty measurement model

the identification of poverty under equal dimensions or weights The fuzzy measures of poverty Poverty measures based on indexes or income distribution

The first-order dominance method Single indicator

Table 2 Multidimensional poverty index, weight and deprived critical value. Dimension

Indicator

Weight

Deprivation critical value

Natural condition(0.1776)

The number of annual effective sunshine hours (hour) Per capita sown area (square meter) The ratio of public expenditure to income (RMB) Regional per-capita tax (RMB) Per-capita GDP (RMB) The proportion of value added of the primary industry accounts for the regional GDP The proportion of value added of the second industry accounts for the regional GDP Per-capita grain output (kg) Per capita disposable income of rural residents (RMB) The rural and urban residents' deposit balance (RMB) The number of beds in medical and health institutions owned per 10,000 people The number of beds in social-welfare adoption institutions owned per 10,000 people The ratio of primary and secondary school students to the total population of the county

0.0822 0.0954 0.0993 0.0959 0.1165 0.0423 0.0504 0.0743 0.1015 0.0864 0.1158 0.0164 0.1082

(1500, 1700, 2000) (560, 840, 1200) (0.3, 0.7, 1) (1592.54, 3075.52, 4558.49) (18000, 24000, 30000) (10%, 15%, 20%) (35%, 43%, 47%) (400, 430, 453) (6977, 8000, 10489) (9642, 15374, 21106) (35, 42, 50) (25, 33, 40) (0.1, 0.13, 0.16)

Finance(0.1952) Social production(0.1990)

Income(0.1879) Social security(0.1322) Education(0.1082)

poverty dimensions and includes two indexes: the per-capita area able to be cultivated and the number of annual effective sunshine hours (See Table 2). Deprived critical value is the poverty standard of each index; this determines the poverty identification and poverty depth of one dimension. At present, a number of studies apply a single deprived critical value, only one deprived critical value being set for each index. However, a single deprived critical value neglects the inherent differences among the various poverty objects which is unfavorable for portraying the depth of poverty. Therefore, by referring to the opinions of He et al. (2016), we set three deprived critical values for each dimension index. For the deprived critical value of annual effective sunshine hours, it is set by reference to the national average sunshine hours. For the two deprived critical values of other indexes, they are set according to existing national standards for poverty-stricken counties and the national average index in 2014. A middle deprived critical value is set between the two above-mentioned deprived critical values in order to portray the difference in depth of poverty among different counties. The setting of weights is the second crucial issue for constructing multi-dimensional poverty indexes. Weights reflect the contributions of different dimension indexes to multi-dimensional poverty indexes. Considering that the various indexes selected in this article are somewhat correlated, this correlation is already manifested as there are overlapping points in the impacts that a single dimension index has on the overall poverty of county. Principal component analysis can help extract the common information among various variables which can, to a certain degree, eliminate the unreasonable weight distribution caused by index correlation. This method has been applied for investigating energy issues recently, say, measuring energy security and energy market integration (Sheng et al., 2013; Zhang et al., 2015; Li et al., 2016). This makes the calculations of multi-dimensional poverty indexes

more accurate, and therefore this approach is also incorporated into this study. 4. Empirical results and analysis 4.1. The comparison between the 2014 and 2016 multi-dimensional poverty indexes (1) The result of multi-dimensional poverty measurement With the populations of various counties as weights, we obtained the total multi-dimensional poverty index of the 30 pilot PVbased poverty relief counties for 2014 and 2016 through a weighted calculation (see Table 3). Results reveal that: ⅰ) In general, from 2014 to 2016, the multi-dimensional poverty index of pilot counties showed a downward trend, from 2.01295 in 2014 to 1.89661 in 2016. This poverty index of 26 out of 30 counties declined in 2016, which shows that the implementation of PV-based poverty alleviation policy has achieved some results. ⅱ)The multidimensional poverty index gap between counties is large. The lowest poverty index appears for Yanchi in Ningxia Hui Autonomous Region, Delingha in Qinghai Province, and Pingquan in Hebei Province. The poverty index of Yanchi in 2014, 2015 and 2016 was 0.99590, 0.97881 and 0.94363. Delingha's poverty index for 2014 was 0.95349, and it fell sharply to 0.62079 in 2016. The poverty index of Pingquan in the three years are respectively 1.22385, 1.41363, and 1.22398, which is significantly lower than the average level of all counties in each year. Gande in Qinghai Province and Dongxiang in Gansu Province have the highest poverty index. The average poverty index of Gande is 2.57750 from 2014 to 2016, and that of Dongxiang is 2.47844. The difference between the county with the lowest poverty index and the highest is 3e4 times, which highlights the gap between the counties. The county poverty index

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Table 3 Multi-dimensional poverty measurement results. Province Hebei

County

Pingshan Pingquan Chicheng Quyang Lincheng Julu Shanxi Tianzhen Hunyuan Ji Daning Fenxi Gansu Tongwei Qingshui Minle Li Dongxiang Lintan Ningxia Yanchi Tongxin Haiyuan Qinghai Yushu Datong Ledu Gande Delingha Anhui Jinzhai Yuexi Si Lixin Funan The annual average poverty index

Year(2014)

Year(2015)

Year(2016)

1.48017 1.22385 1.66676 2.23369 1.36555 2.14716 2.15532 2.12541 2.48404 2.29548 2.00562 2.13463 2.13463 1.38083 2.35371 2.45140 2.40813 0.99590 1.77937 2.02560 2.53221 1.75065 1.72512 2.59465 0.95349 2.34294 2.34157 2.06373 1.97675 2.17271 2.01295

1.43770 1.41363 1.67297 2.01201 1.53529 1.90440 1.86178 2.27320 2.22174 2.37961 2.26978 2.13463 2.04889 1.38083 2.35371 2.54648 2.21336 0.97881 1.77937 1.91656 2.10220 1.66491 1.77405 2.59465 0.74553 2.26366 2.18265 1.79559 1.99069 2.10142 1.94922

1.30371 1.22398 1.67332 2.10996 1.56371 1.79788 1.92927 1.96578 2.20118 2.24106 2.13383 2.13202 2.04889 1.25616 2.24468 2.43745 2.14473 0.94363 1.71927 1.99046 1.89372 1.66491 1.40233 2.54319 0.62079 2.15463 2.18265 1.89561 1.89484 2.10142 1.89661

also varies greatly in the same provinces: the average poverty index for the three years in Pingquan, Hebei Province is 1.2872, while the poverty index of Quyang is as high as 2.1186. The same examples include Delingha (0.77327) and Gande (2.5775) in Qinghai Province. ⅲ) Judging from the dynamic changes of the county poverty index, the multi-dimensional poverty index decreased continuously from 2014 to 2016 in only 8 counties, respectively, Pingshan and Julu of Hebei Province, Ji of Shanxi Province, and Lintan of Gansu Province, Yanchi County of Ningxia Hui Autonomous Region, Delingha and Yushu of Qinghai Province, and Jinzhai of Anhui Province. In the poorer counties, the multi-dimensional poverty index in one of these three years has risen, or is consistent with the previous year. In very rare cases, the poverty index has risen continuously for three consecutive years. For example, the multidimensional poverty index of Pingquan in Hebei Province in 2015 increased by 15.42% compared with 2014, while the multidimensional poverty index in 2016 decreased by 13.47% compared with 2015; The multidimensional poverty index of Jixian in Anhui Province in 2015 was 13.08% lower than that in 2014, while the multidimensional poverty index in 2016 increased by 5.57%. The multidimensional poverty index of Datong in Qinghai Province in 2015 was 4.92% lower than that in 2014, and the multidimensional poverty index in 2016 was consistent with 2015. In a few counties where the poverty index has risen continuously, Lincheng and Chicheng are both located in Hebei Province. The above characteristics also reveal the fact that the multidimensional poverty gap between counties is large. According to the poverty index of regional or provincial pilot counties, there is a serious polarization in the western region. The multidimensional poverty index of Ningxia and Qinghai is relatively low, with annual averages of 1.5699 and 1.6955, while Gansu is 2.0936. The annual average of the PV-based poverty index in Anhui and Shanxi in the

central region is 2.0974 and 2.1636, respectively. Obviously, the poverty situation of photovoltaic pilot counties in the central region should be highly concerned by the government. (2) Dimension decomposition The Multi-dimensional poverty index describes the total poverty state of a sample. In the next, we further analyze the contribution of each individual dimension to the multidimensional poverty index. When a dimension index has a higher contribution, the poverty condition of the sample in this dimension is more serious than that in other dimensions, suggesting that it is necessary for the government to formulate a policy to improve the index value of this dimension. Table 4 shows the decomposition results of multi-dimensional poverty in 2014 and 2015. As revealed by Table 4, the dimensions with the highest contribution were the finance and social production dimension, respectively. Next came to the income and social security. The dimensions with the lowest contribution were natural condition and education. The regions where PV-based poverty relief is carried out have good natural conditions, including sunshine and land-use area, higher than the average national level and suitable for poverty relief projects of this type. This is an important reason for the lower contribution by the dimension of natural conditions to poverty. The education dimension has a smaller contribution to poverty because the proportion of primary and secondary school population in county population selected in this paper is the rigid requirement for evaluating the level of primary and secondary education. For these indexes, there is not a large gap between poverty-stricken counties and the national average. Although the subsidy policy for PV-based poverty alleviation projects in China can alleviate the poverty of these counties in the financial dimension to a certain extent, excessive dependence on subsidies and information asymmetry may also lead to poor poverty alleviation effect. This also contributes to the high poverty index in the financial dimension. Apart from the natural conditions, the finance and social production, we note that the poverty index of other three dimensions declined considerably. This proves that the government has begun to pay attention to solving the poverty problem in a multidimensional way. Compared with 2014, the poverty contribution rate of the income dimension in 2016 has dropped by 14.58%, showing the significant role of PV-based poverty alleviation in raising the income of poor people in poverty-stricken counties. The poverty index of the natural condition dimension in 2016 is higher than that of 2015 because annual average sunshine hours of poverty-stricken counties decreased compared with the previous years. The poverty contribution of finance is only second to that of natural resources, which increased by 10.25% in 2016. It is evident that the “blood-transfusion” poverty relief style, meaning relying on governmental subsidies to fund projects, should be transformed into a “hematopoietic-style” poverty relief and/or a developmentstyle poverty relief. The poverty contribution of the dimension of social production rose instead of fell, meaning that agricultural and industrial development in poverty-stricken counties is weaker. It is urgent to work out a policy to boost agricultural and industrial development in these counties. 4.2. The comparative analysis of typical counties Table 3 shows that in 2014e2016, Gande and Dongxiang had the highest poverty index, while the figures of Yanchi and Delingha were the lowest. We look at the changes of poverty index of in 2016 compared with 2014. The poverty index of Delingha, Julu, Yushu and Ledu fell by more than 15%. Among them, the poverty index of

H. Zhang et al. / Journal of Cleaner Production 241 (2019) 118382

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Table 4 Poverty dimension decomposition. Poverty dimension

Poverty contribution rate of each dimension in Poverty contribution rate of each dimension 2014 in 2015

Poverty contribution rate of each dimension in 2016

Falling rage

Natural condition Finance Social production Income Social security Education

5.59%

5.14%

6.41%

14.68%

27.05% 19.59%

28.71% 20.48%

29.83% 19.90%

10.25% 1.60%

19.74% 17.73% 10.30%

17.75% 17.87% 10.04%

16.86% 17.16% 9.84%

14.58% 3.21% 4.45%

2014

2015

2016

average poverty index

2.75 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25 0.00 Dongxiang

Yanchi

Gande

Delingha

Fig. 1. Multi-dimensional poverty index of four counties.

2014 2.75 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25 0.00 -0.25 -0.50

Lincheng

2015

2016

Julu

2016/2014 varia on range

Yushu

Ledu

Delingha

Fig. 2. Multi-dimensional poverty index and index variation of five counties.

the Delingha fell by 34.89%, while the poverty index of Lincheng, with a maximum increase of 14.51%, changed from 1.36555 in 2014 to 1.56371 in 2016 (see Fig. 1 and Fig. 2). Considering that the annual poverty index of these eight counties, or the index's rise and fall is at an extreme value, it is necessary to further analyze the poverty index of each dimension. By a comparison of Table 5, the two counties with the lowest differential poverty index, Yanchi and Delingha, both natural conditions and education accounted for greater contribution to poverty alleviation. For Gande and Dongxiang, their poverty index in the

three dimensions of finance, social production and income is higher, all exceeding 0.5, and this is the main constraint to poverty alleviation.Among the eight counties of Gande, Dongxiang, Yanchi, Delingha, Julu, Yushu, Ledu and Lincheng, although the poverty index of Yanchi is low, the poverty index of the finance has increased by 152.95%, and this should be an additional attention by government. When we look at the changes of dimensions, the poverty index of income in Delingha fell the most in 100% compared with other dimensions in 2016. The poverty index of several dimensions in Ledu fell significantly in 2016, but the

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H. Zhang et al. / Journal of Cleaner Production 241 (2019) 118382

Table 5 Poverty index of each dimension in typical counties. County

Yanchi, Ningxia

Delingha, Qinghai

Gande, Qinghai

Dongxiang, Gansu

Julu, Hebei

Yushu, Qinghai

Ledu, Qinghai

Lincheng, Hebei

Year

2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016 2014 2015 2016

Poverty dimension Natural condition

Finance

Social production

Income

Social security

Education

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2853 0.2853 0.2853 0.0951 0.1902 0.1902 0.0831 0.0000 0.0831 0.2853 0.2853 0.2853 0.0951 0.0951 0.0951 0.0831 0.0831 0.2494

0.1917 0.3862 0.4849 0.2904 0.1917 0.1917 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.5835 0.4877 0.5835 0.5835

0.0000 0.0000 0.0000 0.2821 0.2821 0.2731 0.5956 0.5956 0.5441 0.5956 0.5956 0.5956 0.5217 0.4478 0.3739 0.5956 0.4927 0.4412 0.2524 0.2949 0.1246 0.0307 0.1046 0.1851

0.3077 0.2051 0.1026 0.1026 0.0000 0.0000 0.5649 0.5649 0.5649 0.5649 0.5649 0.5649 0.3934 0.3077 0.3077 0.3934 0.3934 0.2051 0.2909 0.1883 0.1026 0.3077 0.3077 0.2051

0.2784 0.2784 0.2472 0.2784 0.1627 0.0469 0.3473 0.3473 0.3473 0.3942 0.3942 0.3942 0.3473 0.3473 0.2315 0.3473 0.3473 0.3785 0.3942 0.3942 0.2784 0.3473 0.3473 0.2315

0.2181 0.1090 0.1090 0.0000 0.1090 0.1090 0.2181 0.2181 0.2181 0.2181 0.2181 0.1090 0.2181 0.2181 0.2181 0.3271 0.0000 0.0000 0.1090 0.2181 0.2181 0.1090 0.1090 0.1090

poverty index of education in this county has increased significantly, which is worthy of vigilance. Another poor county worthy of government attention is Yushu, and its poverty index in the social security dimension has increased in 2016. Also in the same year, Julu had the largest decline at 33.34% in the poverty index of social security dimension. This was an important driving force for the addressing poverty. The reason for the highest increase in the poverty index of Lincheng in 2016 was that social production and natural conditions have deteriorated, and the poverty index of social production has increased by 502.94%. As for each poverty dimension, the poverty index of social production in Gande has a slightly decline although the comprehensive index is high, which means that the development of agriculture and industry has been improved to some extent. The biggest increase in the dimensional poverty index of Dongxiang is the natural conditions, indicating that the climate or land conditions in 2016 are inferior to 2014. Based on the above analysis, the central and local governments need to formulate PV-based poverty alleviation policies suitable for the county conditions. 5. Conclusions and policy suggestions Using the model of multi-dimensional poverty measures, this article constructs an index system for the Chinese PV-based poverty relief from six dimensions: natural conditions, finance, social production, income, social security and education. With 30 pilot counties of PV-based poverty relief as samples, multidimensional poverty conditions were investigated. The findings include: 1) the multidimensional poverty index of PV-based pilot counties showed a general decreasing trend from 2014 to 2016, while there were also some counties, such as Lincheng and Chicheng in Hebei province, where the poverty index increased in 2015 and 2016. 2) Pilot counties differ greatly in poverty. The county with the highest degree of poverty has a poverty index more than three to four times that of the county with the lowest degree of poverty. Even some counties in the same province, say, Delingha (0.77327) and Gande (2.5775) of Qinghai province, Pingquan (1.2872) and Quyang (2.1186) of Hebei province, have significant

differences in the average poverty index. 3) By analyzing each  and social production account for dimension, we found that fiance the greatest contribution to poverty. This means that the bloodtransfusion style of poverty relief that relies on governmental fiscal funds and credit loans may be necessarily transformed into a type of “hematopoietic-style” poverty relief. Among the six dimensions, the natural conditions and education only make the least contribution. 4) The poverty index of western provinces is polarized, and Ningxia and Qinghai have performed better than Gansu. In the practice of poverty alleviation, Anhui and Shanxi, which locate in the central areas, should be given more attention by the government. 5) For those counties with extremely high or low poverty index, or exceptional increase or decrease in poverty index, the poverty relief policies should focus on anomalous changes of poverty dimensions. The policy suggestions are as follows: First, face up to the long-term and arduous nature of poverty relief and reinforce the dynamic management of poverty-stricken areas. After three years of targeted photovoltaic poverty alleviation from 2014 to 2016, poverty in Chinese poverty-stricken areas in most pilot poverty-stricken counties has been changed to some extent, however, PV-based poverty relief is still a long-term job. The Outline for Poverty Relief Development in Chinese Rural Areas (2011e2020) states that “By 2020, all objects of poverty relief in China should have gotten rid of their worries about food and clothing and should already enjoy guaranteed compulsory education, basic medical treatment and housing. The growth range of the per-capita pure income of farmers in poverty-stricken areas should be higher than the national average. The indexes in the main fields of basic public service should have approached the national average and the trend of the expansion of development gap should have been reversed”. The arduous nature of the policy objective highlights the difficulty of PV-based poverty relief. It is necessary to carry out dynamic monitoring over pilot PV-based poverty relief regions with an eye on the contributions of the indexes of the various dimensions to the poverty index. Also, we need to formulate appropriate policies to reduce the index values with bigger contributions, connect “the dynamics of poverty identification” and

H. Zhang et al. / Journal of Cleaner Production 241 (2019) 118382

“the long-term nature of poverty relief”, and promote the implementation effect of PV-based poverty relief policies. Second, stick to the policy of poverty relief combining “pervasiveness” and “accuracy”. As indicated in the results, there is a big gap in poverty indexes among the different counties. However, in terms of dimension structure, different regions also show similarities. For example, finance and social production contribute the greatest to poverty, while the national conditions and education make the least contribution to the poverty. In view of these reasons, government can focus on dimensions of finance and social production with the purpose of “hematopoietic-style” poverty alleviation, and of reducing the dependence of poverty alleviation on government financial support measures. The agricultural poverty PV relief also should be carried out according to local conditions and vigorously develop industries related to PV-based poverty relief. Local governments need to, in connection with regional realities and based on the overall arrangement of such public resources as education, health, industrial parks, low-income farmer families and barren mountains and slopes, encourage a combination of PV-based power generation methods and agricultural facilities. Make full use of existing resources in poverty-stricken areas, using PV installations in rural tourism, fish breeding, greenhouse vegetable and fruit facilities to promote incomes through the poverty-relief industry, complete the shift of industrial targets from poverty relief to poverty elimination, and enhance the overall economic development level of the various regions. Third, although the multidimensional poverty index in the Eastern China is generally low (1.6537), the poverty index of minority counties, such as Quyang and Julu is in a larger value. Therefore, the latter poverty-stricken counties need a special attention. The average poverty index of Anhui and Shanxi in Central China and Gansu, which located in western areas are higher than that of Qinghai and Ningxia. For example, the poverty index of Tianzhen, Hunyuan, Ji, Daning and Fenxi in Shanxi Province, and Tongwei, Qingshui, Li, Dongxiang and Lintan in Gansu province exceeds 1.8 in the past three years. In view of this, selective instead of one size fits all policies are available for the central and western regions. The multi-dimensional poverty measurement should be fully used to accurately identify the weakest poverty dimensions of pilot counties with a purpose of providing stronger support for the policies. Our work still remains limitations because of small sample size. In 2016, the number of pilot counties authorized by central government has been increased to 471. The studies for the expanding samples in the future may provide a more valuable reference. Acknowledgements Huiming Zhang is grateful for the financial support from the National Social Science Fundation of China (19BGL185). Kai Wu acknowledges the financial support from the Program for Innovation Research in Central University of Finance and Economics. Dequn Zhou acknowledges the financial support from the National Science Foundation of China (71834003). References Alkire, S., Foster, J., 2011. Counting and multidimensional poverty measurement. J. Public Econ. 95 (7), 476e487. Alkire, S., Apablaza, M., Chakravarty, S., Yalonetzky, G., 2017. Measuring chronic multidimensional poverty. J. Policy Model. 39, 983e1006. Arnd, C., Mahrt, K., Hussain, M.A., Tarp, F., 2018. A human rights-consistent approach to multidimensional welfare measurement applied to sub-Saharan

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