Is the photovoltaic poverty alleviation project the best way for the poor to escape poverty? ——A DEA and GRA analysis of different projects in rural China

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Is the photovoltaic poverty alleviation project the best way for the poor to escape poverty? ——A DEA and GRA analysis of different projects in rural China Zihan Wang, Jiaxin Li, Jing Liu, Chuanmin Shuai * School of Economics and Management, Research Center of Resource and Environmental Economics, China University of Geosciences (Wuhan), Wuhan, 430074, PR China

A R T I C L E I N F O

A B S T R A C T

Keywords: Solar photovoltaic poverty alleviation project (PPAP) Targeted poverty alleviation (TPA) Efficiency assessment Data envelopment analysis (DEA) Grey relation analysis (GRA)

The solar photovoltaic poverty alleviation project (PPAP) is an important innovation in China’s targeted poverty alleviation (TPA) mission. Through investment in the renewable energy industry and an emphasis on poverty alleviation in rural areas, China’s TPA has achieved great success. Although China has invested large amounts of money in PPAP, its actual contribution to rural poverty alleviation has not been verified. This paper analyzes the contribution of PPAP’s efficiency in TPA via data envelopment analysis (DEA) and grey relation analysis (GRA). The results show that: 1) the overall efficiency of TPA is high; 2) the overall efficiency of TPA has large geographical differences; 3) the inputs of TPA have a great impact on the efficiency of poverty alleviation; and 4) China’s investment in PPAP is indeed effective, but its impact on poverty alleviation is overestimated. Therefore, we propose four policy recommendations: 1) the scale and proportion of financial investment in TPA should be optimized; 2) local governments should allocate poverty alleviation funds according to local situations; 3) the Chinese central government should strengthen macro control and reduce support for PPAP; and 4) local gov­ ernments should balance the allocation of funds for PPAP and other poverty alleviation projects.

1. Introduction Since the 1980s, China has given strong policy support to rural poverty alleviation, and the targeting has been fine-tuned from poor counties to poor villages and to poor households. In 2013, Xi Jinping, President of China, proposed the concept of targeted poverty alleviation. He called for refining the poverty alleviation work to specifically impact individuals and poverty alleviation policies. Since then, China began to promote targeted poverty alleviation (TPA) on a country-wide scale. After five years of hard work, the Chinese government has made great progress in targeted poverty alleviation. From 2012 to 2018, the number of rural poor in China had decreased from 98.99 million to 16.6 million, and the poverty incidence dropped from 10.2% to 1.7% (National Bu­ reau of Statistics, 2019). To support the national strategy of rural poverty alleviation, the Chinese government is expected to invest 126.1 billion RMB in TPA in 2019. At the 18th National Congress of the Communist Party of China, the Chinese government proposed an ambitious goal of lifting 100% of the rural poor by the end of 2020. To achieve this goal, China allocates fiscal

funds to many specific policies, such as the whole village approach (WVA), relocating the poor (RP) and the solar photovoltaic poverty alleviation project (PPAP). As an important part of China’s TPA, PPAP has received extensive attention from society. In the meantime, China’s photovoltaic industry has also developed very rapidly (see Fig. 1). In 2014, China began implementing the so-called PPAP strategic program, which provided a means of sustainable and pollution-free poverty reduction for the poor in rural China. This program not only provides a new pollution-free source of income for poor households, it also promotes clean energy adoption for the improvement of the ecological environment. Through implementing the PPAP, the Chinese government has made an attempt to further promote its achievements in poverty reduction and clean energy adoption in rural areas. China has formulated policies to mobilize resources and to strengthen the entire society to promote PPAP implementation. The central government en­ courages local governments and photovoltaic manufacturers to invest in solar PV power stations for poverty reduction. The government sub­ sidizes the construction of solar PV power stations via financial grants and subsidized low-interest loans. The Chinese government also

* School of Economics and Management, China University of Geosciences (Wuhan), PR China E-mail address: [email protected] (C. Shuai). https://doi.org/10.1016/j.enpol.2019.111105 Received 19 June 2019; Received in revised form 14 October 2019; Accepted 9 November 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Zihan Wang, Energy Policy, https://doi.org/10.1016/j.enpol.2019.111105

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encourages large state-owned enterprises and government agencies to provide donations for the construction of solar PV power stations. The electricity generation revenue of solar PV power stations is subsidized by the central government and local governments through Feed-in Tariffs (FIT). Poor households attain improved living standards due to the solar PV power stations by selling the electricity generated by the roof-top solar PV panels on their own house to the State Grid utilities company or by participating in village jobs paid with the profits generated from the village-level or multi-village level solar PV power stations for poverty reduction. The earliest PPAP involved installation of solar PV panels on the roofs of poor houses or greenhouses to generate electricity, and the farmers could then use the electricity generated from the solar panels and sell excess power to the State Grid. Currently, China’s PPAP has three mature models: 1) establishing distributed solar PV power gen­ eration systems on the roof-tops of poor houses; 2) constructing a 100–300 kw village-level solar PV power station at or near the village, and all of the poor households in the village will share the PPAP income; and 3) building large-scale (centralized) solar PV power stations in appropriate places, such as on the waste hills close to a village, and all of the poor households from several villages will share the PPAP income. In 2016, the first round of PPAP in China involved 14 provinces, including village-level solar PV power stations (2181.7 MW) and centralized solar PV power stations (2980 MW) (National Energy Administration, 2016). At the end of 2017, China’s National Energy Administration (NEA) announced that the first round of PPAP in the “13th Five-Year Plan” will increase new village-level solar PV power stations to achieve a combined capacity of 4938.3 MW (National Energy Administration, 2018). At the end of 2018, the scale of China’s solar PV power station for poverty alleviation has reached 15,440 MW. According to the unit cost of 8000 RMB/kW, China has invested more than 120 billion RMB in photovoltaic poverty alleviation projects (National Energy Administration, 2019). So far, China’s PPAP has made great achievements. Despite the general success of China’s PPAP, there are still many problems in helping farmers shake off poverty via solar photovoltaic power generation. The rapid boom and excessive scale of the expansion of solar PV stations is a controversial issue, and the public doubts about whether PPAP can truly achieve targeted poverty alleviation as it was originally designed. The main problems include: 1) the construction and maintenance costs of solar PV power stations are very high, and in­ vestment sources are very complex and profit distribution is chaotic; 2) the distribution of income is confusing, some solar PV power stations

lack effective supervision, and some rural poor have not benefited from the PPAP; and 3) governments and companies pay too much attention to the construction of solar PV power stations rather than post manage­ ment and maintenance, causing poor performance. Therefore, it is of urgent necessity to evaluate the efficiency of the existing PPAP in rural China. By comparing and analyzing the efficiencies of different types of poverty alleviation projects for China’s TPA, we can determine the specific causes of the problems and single out the anti-poverty project with the highest efficiency to avoid blindly increasing the investment in PPAP. To this end, we have conducted this study. The rest of the study is organized as follows: Section 2 is literature review; in Section 3, we introduce the methods and data sources used in this article; Section 4 analyzes TPA’s efficiency in our sampled 1251 poor households in 8 provinces of China, and the GRA of 6 financial inputs; Section 5 discusses the problems with TPA including PPAP; and finally, we draw conclusions and propose policy recommendations in Section 7. The abbreviations in this paper are provided in Table 1. 2. Literature review Improving energy efficiency and rural development play extremely Table 1 Abbreviations. Abbreviations

Terminology in Full

PPAP TPA DEA GRA NEA RTS DMU SE-DEA MF RP WVA SS IPA OE PTE SE GRC

Photovoltaic Poverty Alleviation Project Targeted Poverty Alleviation Data Envelopment Analysis Grey Relation Analysis National Energy Administration Returns to Scale Decision Making Unit Super-Efficient DEA Microfinance Relocating the Poor Whole-Village Approach Social Security Industry Poverty Alleviation Overall Efficiency Pure Technical Efficiency Scale Efficiency Grey Relational Coefficient

Fig. 1. China’s solar PV installed capacity and solar power generation. 2

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important roles in the sustainable development of countries. For coun­ tries with economic poverty and a small population, fossil energy still plays an irreplaceable role in reducing energy poverty and improving people’s quality of life (Nadimi and Tokimatsu, 2019). However, in developing countries, China’s emphasis on renewable energy and rapid improvement of energy efficiency still has important research signifi­ cance. Zhang et al. (2011) used DEA to explore energy efficiency in 23 developing countries. They found that among the five countries with increasing energy efficiency, China experienced the fastest growth, mainly due to effective energy policies and financial support from government. Although China’s geographical differences lead to different energy efficiencies and energy saving efficiencies in different provinces, its efficiency in energy saving and emissions reduction remains stable (Wu et al., 2015). At the same time, the development of nonfossil energy and the reduction of coal consumption have had a very positive impact on China’s energy efficiency. In addition, by promoting the process of energy price marketization (Ouyang et al., 2018) and encouraging the use of renewable energy in the power industry (Qi et al., 2014), China will greatly improve its energy efficiency. Rural development and poverty alleviation play extremely important roles in national sustainable development (Suganthi, 2018). Currently, these steps can improve the efficiency of poverty alleviation from many aspects. Developing agriculture remains the most important factor for poverty reduction (Wagan et al., 2018). Increasing vegetable planting efficiency helps farmers increase their income. The government should pay attention to the market access system, enhance the status of women and strengthen skills training (Shrestha et al., 2016). Although countries around the world have not neglected to improve public services in poor areas, the health service system in poor areas of India is currently inefficient. This deficiency is due to a shortage of health infrastructure and human resources (De et al., 2012). Unlike India, public health care in poor areas of China is more wasteful than in nonpoor areas. This feature shows that policy makers must not only consider infrastructure, they must also improve the technical efficiency of medical staff (Wang et al., 2016). In view of the impact of energy efficiency and rural development on national sustainable development, the use and promotion of renewable energy has gradually become a new approach for lifting poverty-stricken areas out of poverty. By analyzing sustainable development perfor­ mance in Italy, Bruni et al. (2011) found that concentrating on main­ taining low CO2 emission rates and promoting renewable energy had positive effects on poverty alleviation. Now, the combination of solar PV power stations and poverty alleviation has become a new research di­ rection. The solar PV industry can integrate with agricultural activities to provide green and sustainable electricity for agriculture (Xue, 2017). Poor families in rural areas have started to promote solar energy. In Bangladesh, PV technology in the Solar Home System (SHS) has been widely used to achieve rural electrification. This model can be replicated in other parts of Asia (Sharif and Mithila, 2013). Biswas et al. (2004) proposed a new model for poverty alleviation that involves the estab­ lishment of rural enterprises that sell solar power. Due to the financial difficulties faced by developing countries in expanding rural electrifi­ cation, Diouf (2016) proposed a new approach for financing SHS in poor areas. As the photovoltaic industry participates in rural poverty allevi­ ation, China listed PPAP as one of the top ten TPA projects in 2014. China’s photovoltaic industry has developed rapidly, and the Chinese government has started to formulate policies to promote the combina­ tion of photovoltaic industry and rural poverty alleviation. China’s PV poverty alleviation policy is based on a distributed energy policy (Zhang et al., 2019). The policy is mainly focused on project construction, electricity price and income distribution (Zhang et al., 2018). There are many problems in China’s PPAP, including subsidy shortages, insuffi­ cient infrastructure, inferior equipment and inflexible mechanisms for profit distribution (Li et al., 2018b). DEA is a widely used method for analyzing energy efficiency and poverty alleviation efficiency. Based on nonparametric estimation

(Charnes et al., 1978), DEA can calculate scores for project efficiency from multiple inputs and outputs (Moon and Min, 2017). It can also identify the influencing factors of the inputs and outputs (Wu et al., 2018). By comprehensively analyzing the application of DEA in energy ef­ ficiency, Mardani et al. (2017) concluded that DEA showed great promise to be an effective evaluative tool for future analysis of energy efficiency issues. This finding fits with the findings in a study by Song et al. (2012). These findings indicate that research on energy efficiency based on DEA methods is valuable. Hu and Kao (2007) confirmed this conclusion by calculating the energy-saving target ratios (ESTRs) of 17 APEC economies in a total-factor framework. Through DEA analysis of the energy efficiencies in different economies, Chien and Hu (2007) found that compared with non-OECD economies, OECD economies have higher technical efficiency in renewable energy but that the proportion of renewable energy in the energy supply of non-OECD economies is higher than that of OECD economies. These observations show that although renewable energy is gradually gaining more and more atten­ tion from countries, but different economies have different directions of energy development. DEA can assess not only the overall energy efficiency of a country or region but also the efficiency of a single energy source. DEA can also identify important factors that affect energy efficiency and provide di­ rections for improving energy efficiency (Mardani et al., 2017). DEA can also be used in research on renewable energy, especially solar energy efficiency. The use of renewable energy must consider the actual situ­ ation of each country. For example, the geographical conditions and climatic conditions in Korea are more suitable for wind power genera­ tion (Kim et al., 2015). However, Li et al. (2018a) used the DEA method to evaluate the efficiency of seven different distributed energy systems (DES) and found that the photovoltaic system is the best because of its comprehensive advantages in terms of cost, energy efficiency and environmental benefits. Through the DEA method, scholars can further analyze and optimize the location, efficiency and technical feasibility of solar PV power sta­ tions. Azadeh et al. (2008) presented an integrated hierarchical approach for placing solar PV power stations by DEA, which has enabled the government to choose the best locations for building solar PV power stations at the lowest price. Taking indicators related to solar PV power station as the input variables of DEA, scholars have found that energy policies, technical parameters and the management level of stations will have different degrees of impact on the efficiency of solar PV power stations. For example, Sueyoshi and Goto (2014) proposed that adjust­ ing the feed-in tariff (FIT) is a better choice for the US government. Ghosh et al. (2017) proposed a solution that uses unique aerosols to improve the techno-commercial viability of PV projects. In addition, poor management seriously reduces the electricity generation efficiency of power stations (Sueyoshi and Wang, 2017). Improving the perfor­ mance of solar photovoltaic panels (Mostafaeipour et al., 2016; Yi et al., 2019) and enhancing maintenance can improve the operating efficiency of photovoltaic systems (Liu et al., 2017). Evaluating and ranking the overall efficiency of solar PV power stations via the DEA method and establishing an efficiency evaluation model for solar PV power stations are of great significance to improve the investment environment and to promote sustainable development in countries. The DEA method is also widely used in the field of rural development and poverty alleviation efficiency. Josifidis et al. (2010) used DEA to demonstrate how to reform the European welfare system to improve poverty reduction. de Figueiredo and Marca Barrientos (2012) intro­ duced a decision analysis method based on the DEA model to assess the operational efficiency of poor schools in Bolivia. In addition, DEA gen­ erates easy-to-comprehend composite rankings of provincial perfor­ mances, identifies appropriate benchmarks for each inefficient province, and estimates sources and amounts of improvement needed to make the provinces efficient (Habibov and Fan, 2010). DEA can also serve as an assessment tool to provide decision support for poverty alleviation 3

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using data envelopment analysis (DEA) and grey relation analysis (GRA). A schematic illustration of the research framework is shown in Fig. 2.

projects. For example, although microfinance in poverty-stricken areas is an effective way to improve the efficiency of poverty alleviation, microfinance institutions must strike a balance between their financial sustainability and the development needs of the poor. Piot-Lepetit and Nzongang (2014) proposed that multiple DEA models can provide a tool for microfinance institutions to improve financial and social efficiency. Wijesiri et al. (2019) found that India’s microfinance institutions have already completed their dual mission. Grey system theory provides a simple and effective solution for studying the influencing factors of the operation efficiency of a given project. The goal of grey system theory is to understand the operational behavior and evolutionary rules of the system by mining known infor­ mation (Deng, 2019). Grey relational analysis (GRA) is a quantitative method for estimating the situational changes between data sequences based on grey system theory (Tian et al., 2018). GRA is an extremely effective method for research with multi-inputs and discrete data (Kao and Hocheng, 2003; Manikandan et al., 2017). GRA can reveal nonlinear relationships between multiple variables based on the similarity of geometric proximity and can arrange their relationships in descending order (Wong et al., 2006; Yang et al., 2013). As a multiattribute analysis method (Wang et al., 2018), GRA can be combined with multiple models to assess the influencing factors of projects (Haeri and Rezaei, 2019; Hashemi et al., 2015; Luthra et al., 2017; Rajesh and Ravi, 2015). Calculation of the efficiency of projects via DEA and using GRA to study factors that affect operational efficiency has been proven feasible by many scholars. Yu et al. (2019) use DEA and GRA to calculate the effi­ ciency index and to analyze the correlations between China’s carbon emissions, economy, energy and population. Li et al. (2019) proposed a new three-stage DEA model combined with GRA to explore the influ­ encing factors of China’s semiconductor industry. This paper explores the efficiency of PPAP based on the perspective of poverty alleviation efficiency of poor people and the efficiency of China’s TPA. The results could provide a theoretical basis for the existing PPAP evaluation and government policy formulation.

3.1. Phase one: BCC-DEA efficiency analysis Data envelopment analysis (DEA) is a nonlinear parameter method for system analysis. This method uses multiple inputs and outputs as decision variables. DEA uses mathematical programming models to measure optimal inputs and outputs. Widely used DEA models include the BCC and CCR models. CCR emphasizes that the returns to scale (RTS) are constant. BCC extends the use of CCR to variable returns to scale. The BCC model combines the inputs and outputs of each decision making unit (DMU) into the production frontier formed by the production of possible sets. By measuring the distance between each DMU and the frontier, the rationality of the DMU’s inputs and outputs can be judged (Xiaojuan et al., 2013). Based on the actual situation of poverty allevi­ ation fund investment, this paper uses the BCC model for efficiency analysis. The BCC model is shown below: 8 " !# m m > > > min θ ε X S þ X Sþ > > i r > > > i¼1 i¼1 > > > > n X > > > > xij λj þ Si ¼ θxij ; i 2 ð1; 2; ⋯; mÞ > > > j¼1 > > > > n > j¼1 > > > > n > X > > > λj ¼ 1 > > > > j¼1 > > > > > θ; λj ; Si ; Sþ > r � 0 > > > : j ¼ 1; 2; ⋯; n we assume that there are n DMUs, each with m inputs and s outputs. xij and yrj represent the input and output of DMUj . λj is the weight of the P P input and output of n DMUs. nj¼1 xij λj and nj¼1 yrj λj are the weighted

3. Materials and methods This paper evaluates the overall efficiency of TPA and the extent to which different kinds of inputs have affected poverty alleviation by

inputs and outputs of the DMU. θ represents relative efficiency, Si and Sþ r represent slack variables, and ε is the non-Archimedean infinitesimal

Fig. 2. Schematic drawing of research framework. 4

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(ε¼0.000001).

3.4. Variables and data

3.2. Phase two: SE-DEA efficiency analysis

The data in this paper are from interview questionnaires obtained from field surveys of 1251 poor households in 8 poverty-stricken counties in 8 provinces throughout China, namely, Anhui (AH), Shanxi (SX), Henan (HA), Hubei (HB), Inner Mongolia (IM), Ningxia (NX), Qinghai (QH) and Gansu (GS). The poverty-stricken counties in these 8 provinces cover different levels of PPAP in China, which is beneficial for fully studying the impact of PPAP on TPA operation effi­ ciency. Fig. 3 and Table 2 show the sample area and the demographic statistics of the sample. Table 3 lists the variables used for DEA, including 6 inputs and 2 outputs. The six input variables used for DEA calculations are the six major capital investment projects of China’s TPA. The output variable Y1 uses the China Rural Poverty Scorecard compiled by Grameen Foundation to measure the rural poverty situation in China (Schreiner, 2012). The rural poverty index calculated via this scorecard can avoid measurement errors caused by time span and currency value fluctuations and can measure the changes in the farmers’ poverty more objectively and comprehensively (Shuai et al., 2016). This paper uses the electricity consumption of poor households as the output variable Y2, because electricity consumption is an important part of the multidimensional poverty evaluation system (Bennett and Mitra, 2013; Mudombi et al., 2018). Walker et al. (2016) and Townsend (1979) believe that most of the daily necessities needed to survive in current society require elec­ tricity. If there is a shortage of electricity, the resources of poor house­ holds become significantly lower than the resources of ordinary families or individuals, resulting in exclusion of the poor from normal society. Therefore, electricity consumption is a suitable proxy that comprehen­ sively reflects the improvement of poor families’ overall living stan­ dards. Among all of the dimensions of multidimensional poverty assessment, electricity consumption has a relatively high priority (Rao and Pachauri, 2017). Because of the lack of quantitative evaluation of electricity consumption in variable Y1, this paper uses electricity con­ sumption as an output variable to comprehensively evaluate the poverty status of rural poor households. Because the data in this paper came from interview questionnaires, we performed a reliability and validity analysis of 5-point Likert Scales items in all questionnaires. The results in Table 4 show that the Cron­ bach’s Alpha and KMO of the questionnaires are both greater than 0.8. Thus, the data have good stability and consistency.

We found that there are too many effective DMUs in DEA based on the BCC model. It was impossible to further evaluate and compare multiple effective DMUs by relying on this model. To solve this problem, Banker and Gifford (1988) and Banker et al. (1989) proposed to separate effective DMUs from the frontier during analysis. They built a super-efficient DEA model (SE-DEA) based on the CCR model. Andersen and Petersen, 1993 later perfected this mathematical model. The SE-DEA algorithm is as follows: 8 > > > > minθ > > > > N > X > > > xnk λn þ s ¼ θxnk ; k 2 ð1; 2; ⋯; kÞ > > > > n¼1 > > > n6¼j > > > < N subject ​ to ​ X (2) > ynv λn sþ ¼ ynv ; v 2 ð1; 2; ⋯; vÞ > > > > n¼1 > > > n6¼j > > > > > > > λn � 0; n ¼ 1; 2; ⋯N > > > > þ > > s > : � 0; s � 0 The idea of solving the problem by using the SE-DEA model is to replace the inputs and outputs of DMUj with the linear combination of inputs and outputs of all of the other DMUs in the DMUj evaluation. An effective DMU can change its inputs and outputs in a certain proportion, while its technical efficiency remains unchanged. The ratio of this change is its super efficiency score. Where xnk represents the k-th input of the n-th DMU, ynv represents the v-th output of the n-th DMU, and θ represents the relative efficiency. 3.3. Phase three: grey relation analysis By calculating the DMU’s super efficiency score, we can further analyze the impact of different types of inputs on the efficiency of the DMUs. Grey relation analysis (GRA) is part of grey system theory. This method is suitable for analyzing complex interrelationships between �n et al., 2006). The specific multiple factors and multiple variables (Mora steps of GRA are as follows:

4. Results and analysis 4.1. DEA efficiency analysis

(1) Determination of the reference and comparability sequences. The reference sequence Y is a sequence of data reflecting the behavior of the system, and the comparison sequence Xi is a sequence of data that affects the behavior of the system. In this paper, the reference sequence consists of the DMU’s super efficiency index and the comparison sequence includes 6 TPA inputs. The formula is as follows: �

Y ¼ yðkÞ; k 2 ð1; 2; ⋯; nÞ Xi ¼ xi ðkÞ; k 2 ð1; 2; ⋯; nÞ; i 2 ð1; 2; ⋯; mÞ

Table 5 shows the DEA scores calculated for the 40 sample villages based on the BCC-DEA approach. The overall efficiency (OE) of the 40 sample poor villages is at a high level. The average overall efficiency score is 0.802, the average pure technical efficiency (PTE) score is 0.931, and the average scale efficiency (SE) score is 0.868. If the villages can achieve the optimal input ratio, the average output could be increased by 6.9%. If the villages can achieve the optimal production scale, the average output could be increased by 13.2%. Undoubtedly, there is still room for improvement in the existing funding ratio to accommodate the increase in scale and the overall efficiency. As shown in Table 6, 26 poverty-stricken villages showed overall inefficiency. The average pure technical efficiency is 0.894 and the average scale efficiency is 0.797, which is 9% lower than the average pure technical efficiency. This result shows that the proportion of different funds and the scale inefficiency together lead to overall in­ efficiency, in which the scale inefficiency has a greater impact. From the perspective of returns to scale, 24 DMUs showed increasing returns to scale and 2 DMUs showed decreasing returns to scale in the 40 povertystricken villages. Therefore, adding large-scale investment and increasing financial support could significantly improve the poverty

(3)

(2) Standardization of the super efficiency index and input data of the DMUs. (3) Calculation and ranking of the grey relational coefficients, where ρ indicates the distinguishing coefficient. ρ 2 ð0;∞Þ: the smaller ρ is, the better the discrimination. In general, ρ ¼ 0:5 (Xu et al., 2018). The formula is as follows: minminjyðkÞ ξi ðkÞ ¼

i

k

jyðkÞ

xi ðkÞj þ ρmaxmaxjyðkÞ i

k

xi ðkÞj þ ρmaxmaxjyðkÞ i

k

xi ðkÞj

xi ðkÞj

(4)

5

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Fig. 3. Distribution of China’s PPAP and the sample area.

alleviation effects of poor villages. From the perspective of provincial differences, as shown in Table 7 and Fig. 4, the average pure technical efficiency for Hubei, Qinghai, and Inner Mongolia are less than 0.75. The average scale efficiency of Hubei is 0.693, which is much lower than the average scale efficiency of the total samples (0.868). The results show that Hubei must urgently in­ crease the scale of funds to improve its overall efficiency. The average pure technical efficiencies of Qinghai and Inner Mongolia are 0.819 and 0.847, which are lower than the average pure technical efficiency of the total samples (0.931). These results show that the two provinces should focus on optimizing the proportion of different funds and increasing financial support to improve their overall efficiency.

efficiencies of Hubei, Inner Mongolia and Qinghai are very low (0.643, 0.705 and 0.776, respectively). These results show that all three prov­ inces must optimize the capital structure and increase the scale of funds. Table 8 lists the grey relational coefficient (GRC) of the inputs and SE-DEA scores that are both greater than 0.6. The results show that all six inputs have a greater impact on the TPA efficiency. Specifically, the whole-village approach (WVA) has the greatest impact on poverty alleviation efficiency, and its grey relational coefficient is 0.703. It is followed by industry poverty alleviation (IPA) and social security (SS), whose grey relational coefficients are 0.693 and 0.678, respectively. PPAP has the least influence, and its grey relational coefficient is 0.649. By comparing the proportions of the 6 grey relational coefficient to the proportion of inputs, as shown in Fig. 5, differences between the six inputs and their grey relational coefficients can be observed. Among them, industry poverty alleviation (IPA) (9.11%), social security (SS) (3.97%) and relocating the poor (RP) (10.3%) are lower than the optimal proportion, while the whole-village approach (WVA) (34.46%), microfinance (MF) (19.94%) and PPAP (22.23%) are higher than the optimal proportion. The proportions of the whole-village approach (WVA), social security (SS), and industry poverty alleviation (IPA) have large gaps with their grey relational coefficients. The proportion of so­ cial security (SS) only accounts for 23.62% of its grey relational coeffi­ cient. The provinces should readjust the proportion of inputs as soon as possible to improve poverty alleviation efficiency. It can be seen from Fig. 5 that the proportion of PPAPs’ capital in­ vestment (22.23%) is greater than its grey relational coefficient ratio (16.1%), indicating that the current PPAPs’ capital investment is greater than its actual demand. As shown in Fig. 6, there are regional differences in PPAP funding. The input ratios of Hubei, Henan, Ningxia and Gansu exceed the standard. The input ratios of Hubei and Gansu are 46.31% and 61.61%, respectively, far above the optimal ratio. Qinghai, Inner Mongolia and Anhui could increase capital investment in PPAP. Shanxi’s PPAP capital investment ratio is 16.18%, which is close to the grey relational coefficient ratio.

4.2. Grey relation analysis

5. Discussion

Table 2 Demographic statistics. Respondents’ attributes

Quantity

Proportion

Region

154 151 154 165 152 150 158 167 947 304 9 35 82 286 335 504 215 641 325 70

12.31% 12.07% 12.31% 13.19% 12.15% 11.99% 12.63% 13.35% 75.70% 24.30% 0.72% 2.80% 6.55% 22.86% 26.78% 40.29% 17.19% 51.24% 25.98% 5.60%

Gender Age

Education

HB HA QH NX GS SX IM AH male female �19 20–29 30–39 40–49 50–59 �60 illiteracy primary junior Senior and higher

The average SE-DEA scores in Table 7 show that the average overall

By analyzing the overall TPA efficiency, we found that there are 6

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Table 3 Description of variables.

Table 4 Reliability and validity analysis.

Variable

Name

Description

NO.

Cronbach’s Alpha

KMO

X1

Photovoltaic Poverty Alleviation Project (PPAP)

This variable refers to the total amount of investment for PPAP during 2013–2017 TPA. PPAP mainly helps poor households out of poverty through the increased power generation income. This variable refers to the total amount of investment for MF during 2013–2017 TPA. MF is a kind of credit loan specially designed for poor households with less than 50,000 RMB per loan, less than 3 years, no mortgage, low interest rate and financial discount. This variable refers to the total amount of investment for RP during 2013–2017 TPA. RP refers to the relocation of the poor, to help them shake off poverty by improving living conditions in resettlement areas, adjusting economic structure and expanding income-increasing channels. This variable refers to the total amount of investment for WVA during 2013–2017 TPA. WVA is a poverty alleviation method that aims to improve infrastructure construction, develop social welfare, and improve people’s living conditions. This variable refers to the total amount of investment for social security during 2013–2017 TPA. SS includes medical insurance, pension insurance and minimum living allowance. This variable refers to the total amount of investment for IPA during 2013–2017 TPA. IPA is a poverty alleviation method that develops local special industries, adopts a shareholding system to connect poor households and enterprises and promote the development of povertystricken areas. This variable reflects the changes in the poverty situation of the rural population in the sample area in 2013–2017. The Rural Poverty Index measures the overall economic living standard of rural households from multiple dimensions such as family members, education, number of workers, income, housing materials, cooking fuel, drinking water sources, electrical appliances, transportation, farming equipment, household insurance and social security. This variable reflects the changes in electricity consumption of poor farmers in the sample area in 2013–2017. Changes in electricity consumption can reflect the improvement in the overall living standards of poor households.

46-51,53,54

0.809

0.828

58–60 61–64 67–72 73–76 77–82 83–85 86–88 89–91 92–95 96–98 99–101 102–104 105–108 109,110 111–116 117–122 123–125

0.875 0.882 0.804 0.919 0.931 0.887 0.816 0.838 0.823 0.815 0.845 0.806 0.852 0.956 0.913 0.937 0.945

0.920

X2

Microfinance (MF)

X3

Relocating the Poor (RP)

X4

Whole-Village Approach (WVA)

X5

Social Security (SS)

X6

Industry Poverty Alleviation (IPA)

Y1

Changing Rate of Rural Poverty Index

Y2

Changing Rate of Rural Electricity Consumption

Note: Since the questionnaire is divided into two parts, this paper conducted two KMO tests. Table 5 The results of overall efficiency, pure technical efficiency, scale efficiency and SE-DEA score.

Note: TPA’s funds are issued for every poor village. In order to maintain the consistency of the variable units, this paper takes the average of each village for the variables Y1 and Y2.

regional differences in the efficiencies of some provinces. The reasons may be because the natural environments of Qinghai and Inner Mongolia differ from the inland natural environment, which consists wide areas and sparse populations. As seen from the sample, an increase in infrastructure has led to excessive proportions of capital investment in the whole-village approach (WVA), i.e., 60.9% and 46.31% in Qinghai and Inner Mongolia, respectively. This trend leads directly to low pure technical efficiency in the two provinces. Hubei has a dense population and a large number of poor people. Its per capita poverty alleviation funds are low, thus the scale efficiency is low. In 2017, the per capita poverty alleviation funding for the poverty-stricken population in Hubei was only 46.36 thousand RMB, while the funding in Inner Mongolia and

DMUs

OE

PTE

SE

SE-DEA

DMU01 DMU02 DMU03 DMU04 DMU05 DMU06 DMU07 DMU08 DMU09 DMU10 DMU11 DMU12 DMU13 DMU14 DMU15 DMU16 DMU17 DMU18 DMU19 DMU20 DMU21 DMU22 DMU23 DMU24 DMU25 DMU26 DMU27 DMU28 DMU29 DMU30 DMU31 DMU32 DMU33 DMU34 DMU35 DMU36 DMU37 DMU38 DMU39 DMU40 Mean Scores

0.342 0.625 0.477 1.000 0.650 0.619 0.804 1.000 0.614 0.908 1.000 0.865 0.822 0.403 1.000 0.742 0.306 0.817 1.000 1.000 0.561 1.000 0.781 0.999 0.894 0.820 0.660 0.891 0.853 1.000 0.899 1.000 0.868 0.690 0.891 0.447 0.669 0.665 1.000 0.607 0.802

1.000 0.628 1.000 1.000 1.000 0.926 0.997 1.000 0.619 1.000 1.000 0.923 1.000 0.458 1.000 0.819 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.991 0.675 1.000 0.933 1.000 1.000 1.000 1.000 0.781 0.956 0.466 1.000 1.000 1.000 0.616 0.931

0.342 0.995 0.477 1.000 0.650 0.693 0.807 1.000 0.993 0.908 1.000 0.942 0.822 0.879 1.000 0.900 0.306 0.817 1.000 1.000 0.561 1.000 0.781 0.999 0.894 0.828 0.978 0.891 0.918 1.000 0.899 1.000 0.868 0.884 0.930 0.960 0.669 0.665 1.000 0.984 0.868

0.3421 0.6249 0.4769 1.122 0.6497 0.8038 1.0279 0.6145 0.9084 1.1169 0.8224 0.4031 1.1037 0.3057 0.8164 1.5436 1.1013 0.5609 1.3817 0.9989 0.8944 0.8205 0.6601 0.8915 1.2271 0.8987 1.0477 0.8678 0.6899 0.447 0.6688 0.6647 1.1717 0.6069 0.6725 1.1089 0.9668 1.4778 2.1764 1.4866 0.9043

Qinghai amounted to 152.9 thousand RMB and 71.4 thousand RMB, respectively (Hubei Government, 2018; Inner Mongolia Government, 2018; Wang, 2018). 7

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Table 6 Efficiency of the inefficiency DMUs and the changes of Returns to Scale.

Table 8 The grey relational coefficient of inputs and SE-DEA scores.

DMUs

OE

PTE

SE

RTS

Inputs

GRC

Rank

DMU01 DMU02 DMU03 DMU05 DMU06 DMU08 DMU09 DMU11 DMU12 DMU14 DMU15 DMU18 DMU20 DMU21 DMU22 DMU23 DMU24 DMU26 DMU28 DMU29 DMU30 DMU31 DMU32 DMU34 DMU35 DMU37 Mean Scores

0.342 0.625 0.477 0.650 0.804 0.614 0.908 0.822 0.403 0.306 0.817 0.561 0.999 0.894 0.820 0.660 0.891 0.899 0.868 0.690 0.447 0.669 0.665 0.607 0.673 0.967 0.695

1.000 0.628 1.000 1.000 0.997 0.619 1.000 1.000 0.458 1.000 1.000 1.000 1.000 1.000 0.991 0.675 1.000 1.000 1.000 0.781 0.466 1.000 1.000 0.616 1.000 1.000 0.894

0.342 0.995 0.477 0.650 0.807 0.993 0.908 0.822 0.879 0.306 0.817 0.561 0.999 0.894 0.828 0.978 0.891 0.899 0.868 0.884 0.960 0.669 0.665 0.984 0.673 0.967 0.797

irs irs irs irs irs irs irs irs drs irs irs irs irs irs irs drs irs irs irs irs irs irs irs irs irs irs –

WVA IPA SS RP MF PPAP

0.703 0.693 0.678 0.658 0.651 0.649

1 2 3 4 5 6

Source: calculated results using GRA model by the authors.

village approach (WVA) as an important measure in the “Poverty Alle­ viation Program for Rural China”. This policy is very targeted and effec­ tive, and it plays an essential role in the scale effect of funds and the cluster effect of projects (Ruiqiang et al., 2016). PPAP has the least impact, which may be due to the generally low operating efficiency of China’s solar PV power stations. These solar PV power stations waste many national financial subsidies and social resources (Wu et al., 2018). At the same time, the allocation mechanism of solar PV power station is not perfect, and some poor households must repay bank loans with in­ come, which leads to lower final benefits. In addition, the income of village-level solar PV power stations is distributed by village committees and poor households. This may lead to difficulties in ensuring reason­ able incomes for poor households and conflicts with nonpoor house­ holds (Jian-yu and Ting, 2018). The geographical differences found in the analysis of the poverty alleviation efficiency of PPAP may be because local governments have not systematically planned the scale of solar PV power station con­ struction. PPAP is only determined based on the location of solar energy resources, and local governments have different subsidy policies (Li et al., 2018b). The “Photovoltaic Poverty Alleviation Project Plan”, which is based on solar PV power station scale control, is based on the number of applications and poor villages in each province. This policy does not fully consider the needs of local power generation and the poverty alleviation efficiency of PPAP (National Energy Administration, 2017). It is worthwhile noting that the Chinese government intends to establish a renewable energy quota system from 2018. The government will set the proportion of renewable energy (excluding hydropower) in the total energy consumption (Li, 2019). How to achieve a balance between PPAP regulation and the determination of renewable energy quotas and considering renewable energy quotas in PPAP efficiency evaluation will be a valuable future research direction.

Note: The irs means increase and the drs means decrease. Table 7 The average efficiency of 8 provinces. Provinces

OE

PTE

SE

SE-DEA

AH SX HA GS NX QH IM HB

0.993 0.891 0.865 0.853 0.781 0.742 0.677 0.619

1.000 0.956 0.923 0.933 1.000 0.819 0.847 0.926

0.993 0.930 0.942 0.918 0.781 0.900 0.825 0.693

1.443 0.946 0.894 0.853 0.952 0.776 0.705 0.643

6. Conclusions and policy implications This paper analyzes the contribution of PPAP’s efficiency in TPA via data envelopment analysis (DEA) and grey relation analysis (GRA), with the following conclusions. 1) The overall efficiency of targeted poverty alleviation is high. The proportion of different funds and the scale inefficiency together lead to overall inefficiency. The average pure technical efficiency is 0.894, and the average scale efficiency is 0.797. It can be seen that the inefficiency of scale has a greater impact on project inefficiency. The returns to scale of inefficient DMUs mainly show an increasing trend, indicating that increasing the investment scale and financial support can significantly improve the poverty alleviation effect in poor villages. 2) The overall efficiency of targeted poverty alleviation has large geographical differences. The average overall efficiency levels of Hubei, Qinghai and Inner Mongolia are lower than 0.75, in which Hubei’s scale efficiency is low and Qinghai and Inner Mongolia’s pure technical efficiency are low. In addition, the average SE-DEA scores for Hubei, Inner Mongolia, and Qinghai are 0.643, 0.705, and 0.776, respectively, which are much lower than those of other provinces. This trend is mainly because these three provinces have

Fig. 4. Average efficiency of 8 provinces.

In the ranking of the impacts of the TPA’s 6 funding inputs, the whole-village approach (WVA) has the highest degree of influence. This outcome could be because the Chinese government has listed the whole8

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Energy Policy xxx (xxxx) xxx

Fig. 5. The proportion of inputs and GRC

Fig. 6. Proportion of PPAP funds and GRC in 8 provinces.

less efficient DMUs and a higher proportion of inefficient samples with low overall efficiency. The low overall efficiency levels of Hubei, Inner Mongolia and Qinghai have universality. 3) The inputs of targeted poverty alleviation have a great impact on the efficiency of poverty alleviation. The grey relational co­ efficients of each input and the SE-DEA scores are whole-village approach (WVA) (0.703), industry poverty alleviation (IPA) (0.693), social security (SS) (0.678), relocating the poor (RP) (0.658), microfinance (MF) (0.651) and PPAP (0.649). 4) The PPAP’s impact on poverty alleviation is overestimated, and the proportion of investment is not appropriate. In the invest­ ment of TPA, the proportion of PPAP (22.23%) exceeded its optimal ratio (16.1%), which wastes the existing limited resources and re­ duces the efficiency of poverty alleviation. At the same time, there are geographical differences in the investment of PPAP. The pro­ portions of PPAP in Hubei (46.31%) and Gansu (61.61%) are too high. Henan (24.73%) and Ningxia (27.58%) must also reduce the proportion of PPAP. Qinghai (6.45%), Inner Mongolia (11.99%) and Anhui (8.73%) have lower PPAP ratios.

efficiency and average scale efficiency of TPA are much lower than 1, TPA should optimize its investment structure while expanding the investment scale. In addition, the average scale efficiency of TPA is greater than the average pure technical efficiency, which indicates that the scale effect of funds in TPA is still strong at this stage. By increasing financial support for TPA, the government could signifi­ cantly improve the poverty alleviation effect on poor households. 2) Local governments should allocate poverty alleviation funds according to local situations. The overall efficiency of TPA reflects significant regional differences. The government should control the proportion of poverty alleviation funds according to the specific conditions of each province and determine appropriate measures. Specifically, Hubei should focus on increasing investment in poverty alleviation. Local governments should speed up infrastructure con­ struction in poverty-stricken areas, introduce more new industries to promote the employment of the poor, and comprehensively improve the human, material and financial resources of TPA. Qinghai and Inner Mongolia should balance the allocation of poverty alleviation funds and reduce the loss of poverty reduction efficiency caused by uneven distribution of funds. 3) The central government should strengthen macro control, and reduce the support for PPAPs. The PPAP’s investment ratio in TPA is not appropriate. Overall, its impact on poverty alleviation effi­ ciency is overestimated. The Chinese government should reduce the proportion of PPAP in TPA’s financial allocation, reduce subsidies for

Based on the above conclusions, we propose the following policy recommendations. 1) Optimizing the scale and proportion of financial investment in targeted poverty alleviation. Since the average pure technical 9

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photovoltaic manufacturers, and slow down the investment in PPAP and the growth rate of solar PV power station installed capacity. The Chinese government should play a more guiding role in the con­ struction of local PPAP. The central government should improve the basis for the preparation of the programmatic document “Photovol­ taic Poverty Alleviation Project Plan” and adopt more macrocontrol methods, including comprehensive consideration of local PPAP operation efficiency and actual poverty alleviation effects instead of relying on local government reporting quotas and the size of the poverty-stricken population. 4) Local governments should balance the allocation of funds for PPAP and other poverty alleviation projects. From different geographical perspectives, the use of PPAP funds varies from prov­ ince to province, lacking rational planning. For local governments, the proportion of PPAP funds should be balanced in the overall financial poverty alleviation grants. For example, Hubei and Gansu should significantly reduce the proportion of PPAP funds and reduce the approval and construction of solar PV power stations, while Qinghai, Anhui and Inner Mongolia should increase the installed capacity of solar PV power stations and encourage solar PV power station contractors to increase investment.

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