Abstract
Due to the characteristics of poverty alleviation and cleanness, the photovoltaic poverty alleviation project (PPAP) plays an important role in consolidating the link between poverty alleviation and “rural revitalization” in China, which has attracted the attention of all sectors of society. In practice, EPC (engineering, procurement, and construction) mode is widely used in PPAP, which brings considerable benefits, but also has many risks. To ensure the successful development of the project, it is necessary to carry out risk assessment when selecting the scheme. However, this complex multi-attribute decision-making problem can be improved. First, the risk of PPAP is lack of targeted research; second, the uncertainty in the evaluation process has not been fully solved; third, the method of sorting is unreasonable. To solve these problems, this study establishes the risk assessment framework of PPAP. Firstly, a comprehensive index system suitable for PPAP risk assessment is established, including 5 first-level indexes and 22 second-level indexes. Then, the ANP method based on fuzzy environment is used to determine the weight. Finally, the cloud model and PROMETHEE are combined to select a higher priority solution. Case study and sensitivity analysis prove the robustness and feasibility of the framework. This study puts forward the corresponding risk response measures, to provide reference for the future practice of risk prevention and control.
1. Introduction
1.1. Background
Photovoltaic power generation is capable of long-term and sustainable power transmission and is led by government departments to achieve investment. At present, China’s PV power generation is mainly concentrated in uninhabited areas in the west or poor areas in the northwest, and the low energy and labor costs in the west attract PV manufacturing companies to invest there. By the end of 2016, 40% of the country’s PV installations were in the Western provinces, making full use of the abundant local solar resources and deserted land, and can effectively improve the desertification of the land [1]. Due to its many advantages, photovoltaic power generation also plays an important role in poverty alleviations [2]. Escaping poverty is an important goal that most developing countries are striving to achieve in their socioeconomic development, and it is also a focus of global development governance. Although many Chinese have shaken off poverty and entered a happy, well-off life, there are still countless Chinese people living below the lowest level. In response to the national poverty alleviation, environmental protection, sustainable development, and other requirements, China combined photovoltaic power generation with the precision poverty alleviation target and formally launched the first PPAP in 2014. Among the 832 poor counties in China, 451 counties have more than 1,100 hours of effective sunshine per year and are suitable for promoting PV poverty alleviation projects [3]. In addition, the cost reduction in the photovoltaic industry also brings more opportunities for photovoltaic poverty alleviation. The new installed capacity and growth rate of photovoltaic power generation in China from 2007 to 2018 are shown in Figure 1.
PPAP refers to the laying of solar panels on the roofs of houses and agricultural sheds in poor areas. Through these solar panels, each household can become a miniature solar power station. In addition to using electricity for themselves, the people can also sell the surplus electricity to the power grid and get some income. While making full use of solar energy resources, photovoltaic poverty alleviation realizes the combination of poverty alleviation, new energy utilization, energy conservation, and emission reduction. In 2015, photovoltaic poverty alleviation became one of the “ten precise poverty alleviation projects” identified and implemented by the China State Council Poverty Alleviation Office, which is one of the most effective ways to use photovoltaic power generation to help poor people get out of poverty. Since the emergence of photovoltaic poverty alleviation projects, it has provided a large number of employment opportunities and financial gains for people in poor areas, with obvious success [4].
As the main project in targeted poverty alleviation, PPAP has attracted increasing attention from the public and government. In terms of investment benefits, PPAP is a precise way to alleviate poverty, which has the characteristics of less investment, stable return, and sustainable development. The lifespan of PV poverty alleviation projects is more than 25 years, with subsidies reaching 20 years [5]. Therefore, PV poverty alleviation has significant economic and social significance. In addition, PV poverty alleviation projects have various forms, such as village-level photovoltaic power stations to alleviate poverty, photovoltaic greenhouse to alleviate poverty, and photovoltaic ground power station to alleviate poverty. In particular, the village-level photovoltaic poverty alleviation project has the advantages of not occupying arable land and forest land, flexible capacity selection, and easy management.
As a public welfare project invested by the government, PPAP focuses on social benefits rather than profitability. Therefore, the risks of PV poverty alleviation projects have received attention, while profitability has received less attention. PPAP combines poverty alleviation with green development, which is a sustainable means of poverty alleviation. At present, PV poverty alleviation projects are greatly promoted because of their green and pro-poor attributes, and there are many deficiencies in PV poverty alleviation that need to be improved, but little research has been conducted on the risks of PPAP. Firstly, there are deficiencies in the policy, overall planning, funding source, power plant quality and user income guarantee, and interest guarantee of all parties in PV poverty alleviation projects. Secondly, little research has focused on the interaction between risk factors affecting PV poverty alleviation projects. Unlike ordinary risk factor studies, there may be interaction effects between risk factors affecting PPAPs, which need to be further studied. Thirdly, the uncertainty of risk occurrence has not been adequately addressed. Uncertainty includes fuzziness and volatility. In the process of risk assessment, information loss, experts’ ambiguity in scoring, and uncertainty of risk occur. Therefore, this study evaluates the risk factors for PPAPs and provides guidance for the construction of PPAPs. In addition, this study discusses the risk of photovoltaic poverty alleviation projects under the EPC model.
1.2. Innovations and Contributions
This study proposed a comprehensive risk evaluation framework for PPAPs. The innovations of this study are as follows:(1)This study identifies 22 risk factors throughout the life cycle to the promotion of PPAPs, which are divided into 5 aspects of policy, economy, technology, environment, and pattern.(2)ANP methods based on the cloud models and IT2FNs are used to address the ambiguity and uncertainty of the decision environment.(3)This study establishes a comprehensive PPAP risk evaluation framework and proposes corresponding comprehensive risk response measures.
This study contributes to the study of PPAP from the perspective of risk evaluation, summarizing the risks and defects in the construction process of PV poverty alleviation projects and making a comprehensive evaluation of the risks of PPAP based on the current social situation and the characteristics of EPC model. The criteria system affecting the risk factors for PV poverty alleviation projects is established, and the ANP method is used to calculate the index weights, which takes into account the interrelationship between the criteria and reduces the objectivity. In addition, this study uses fuzzy number and cloud model to better solve the problem of incomplete information collection and fuzzy information in decision-making, which can serve as a reference for the research of related PPAPs.
The remainder of this study is as follows. In Section 2, the research background related to PV poverty alleviation risk, fuzzy number, and cloud model at home and abroad are summarized and analyzed. In Section 3, a comprehensive PV poverty alleviation project risk factor index system is constructed based on literature and information and news, combined with practice and theory. In Section 4, based on the indicator system and the characteristics of PV poverty alleviation projects, a comprehensive risk assessment framework for PV poverty alleviation projects is proposed. In Section 5, the practicality of the evaluation framework is validated by case studies. Sensitivity analysis is used to verify the stability of the method. In Section 6, the study analyzes and summarizes the study and makes recommendations on key risk factors.
2. Literature Review
PPAP in China is started in 2014, and the research on PPAP mainly focuses on the efficiency analysis and policy aspects of the project, as well as the research on the promotion model and poverty alleviation mechanism. In terms of policy research, Li et al. [6] introduced the current situation and support policies of PV projects and concluded that obstacles such as lagging subsidies, insufficient infrastructure, poor quality of PV equipment, and unreasonable profit distribution mechanism may reduce the returns of PV after summarizing and comparing representative business models of PV projects. Corresponding suggestions are also made. Zihan Wang et al. [7] analyzed the efficiency of PPAP through data envelopment analysis and gray relational analysis and concluded that China’s investment in PPAP is indeed effective, but its impact on poverty alleviation is overestimated. Therefore, suggestions such as optimizing the scale and proportion of financial investment in the early stage, strengthening macroeconomic control by the central government, and reducing support for PPAP were made. Zhang et al. [8] analyzed PPAP in China from both policy and project perspectives and found that demand-oriented policies are deficient compared with supply-based and environment-based policies. The policies of projects mainly focus on project construction, electricity sale aspects, and income distribution. Finally, recommendations such as reformulating the identification policy of low-income households and improving the development of performance evaluation policies are made. Shan and Yang [9] established a three-party evolutionary game model to simulate and analyze the behavioral strategies of PV enterprises, poor households, and the government and related influencing factors. It is concluded that the active support of PV enterprises, the participation of poor households, and less government supervision are conducive to the development of PV poverty alleviation, and the appropriate increase in subsidies and the improvement of income distribution mechanism can ensure the sustainable development of PPAP.
At present, China’s PV poverty alleviation mainly focuses on village-level power stations, which are more suitable for EPC mode construction due to the characteristics of village-level power stations. There exists a lot of research on EPC project risk management. Wu et al. [10] combined project life cycle theory and Delphi method to identify 18 risk factors. The weights of the indicators were determined using the DEMATEL method in an intuitive fuzzy environment. Finally, the overall risk was assessed based on preference theory and risk scenario analysis and concluded that the overall risk level of PPAP in China, especially in terms of technology, is relatively high. Pal et al. [11] analyzed how various aspects of the relationship with suppliers and subcontractors in the EPC model affect project outcomes. Logistic regression and neural networks were used to analyze the data and found that the services provided by suppliers, subcontractors, continuous improvement, reliability of supplier, subcontractor delivery, and effective problem-solving were the critical factors. Luo et al. [12] analyzed the project bidding, contract signing, design, procurement, construction, and grid acceptance of large PV power plant EPC projects, discussed the main risks and countermeasures of large PV power plant EPC projects, and proposed a series of scientific project management methods for PV power plants. Cao [13] takes the implementation process of Datang Qinghai 10 MW ground grid-connected photovoltaic project as the research object and analyzes the problems in the application of EPC contracting mode management of photovoltaic projects. Li et al. [14] evaluated the direct economic and social benefits of PV using a multivariate network model. The results indicate that investing in PPAP will promote the social reputation of third-party investors and lead to potential profits. In addition, targeted information policies are needed, including information exposure, and encouraging social discussion, to sustain the profit benefits from reputation enhancement.
There are some papers that used MCDM methods. Liu et al. [15] proposed an occupational health and safety risk assessment using TODIM-PROMETHEE. Wu et al. [16] conducted a study for improving quality function deployment with cloud MULTIMOORA. Liu et al. [17] used the cloud model and hierarchical TOPSIS method for improving risk evaluation in FMEA. Wu et al. [18] used a fuzzy MCDM method to evaluate renewable power in China. It is a fact that risk assessment of PV poverty alleviation projects is a multi-attribute decision problem and a complex process that involves multiple parties and multiple stakeholders. Therefore, the selection of MCDM method is of great importance. In particular, in the field of energy and power, several studies have been conducted using MCDM methods. Wu et al. [19] developed a fuzzy MCDM framework in the form of IT2FNs to adequately describe uncertainty. The interval type 2 fuzzy weighted average operator was used to aggregate the fuzzy values. Finally, a case study was conducted in southeast China and concluded that the GHG emission reduction standard has the highest weight. Xu et al. [20] explored the key barriers to HRM development in China based on an improved fuzzy DEMATEL method. The thresholds of the traditional fuzzy DEMATEL method were optimized using the K-means clustering algorithm to finally identify the key barriers affecting human resources. For the site selection of straw biomass power plant, Wu et al. [21] adopted a two-dimensional uncertain linguistic variable group decision-making model to solve the individual deviation and combined two-dimensional information language with the cloud model, solving the problem of ambiguity and subjectivity in site selection of straw biomass power plants under uncertain language environment. Lu et al. [22] proposed a multi-attribute decision framework based on a cloud model with a Monte Carlo model for the selection of remediation strategies for contaminated groundwater. The cloud model is used to deal with imprecise numerical quantities that can adequately and accurately describe the ambiguity and stochastic nature of the information. The efficiency and applicability of the method are illustrated by a case study.
Through the literature review, the findings can be summarized as follows:(1)Uncertainty has not been adequately described in the risk evaluation of PPAP, and most of the previous studies have been conducted in a deterministic setting.(2)The few literature reviews on risk evaluation of PPAP from the government’s perspective have some limitations.(3)The measures proposed by previous studies to deal with PPAP risks are not comprehensive enough, and therefore, a comprehensive framework of risk response measures for PPAPs is lacking.
Therefore, this study will investigate the PPAP from these aspects to fill the gaps of the previous studies.
3. Preliminary
Definition 1. If is a fuzzy set of interval type 2 on domain , then can be expressed by the membership function of interval type 2 as . The uncertainty and fuzziness of interval can be defined by a series of bounded areas; that is, the uncertainty coverage of interval type 2 fuzzy set can be expressed by the upper bound membership function and the lower bound membership function [23]. The IT2FN is shown in equation (1) and Figure 2.where , , , and .
Definition 2. Supposing two IT2FNs , , and constant , the addition, multiplication, division, and power algorithm between them are shown in equations.
Definition 3. Defuzzification of IT2FNs is as follows:
Definition 4. is a domain containing specific and explicit values, and is a qualitative concept in . There exists an exact numerical value . is a random implementation of qualitative concept . If, and , the determinacy of to subject to the following equation.where the distribution of on domain is called normal cloud.
Risk factors are highly uncertain and a qualitative concept. Using fuzzy theory to study language evaluation can describe the fuzziness of language information. It cannot reflect the randomness of natural language, while the cloud model can take both into account, which is more realistic and can increase the accuracy of decision-making. To realize the transformation between qualitative risk and quantitative risk score, the cloud model is used in this study. Besides, this study chooses language evaluation as a more suitable way of evaluation for human thinking and emotional expression.
Definition 5. denotes a quantitative field containing specific explicit values and a qualitative concept in . If there is an exact value , is a random occurrence value of , and the determinacy of is a random number with a stable tendency. The distribution of on is a cloud, and an is a cloud drop in the cloud.The cloud has the following properties:(1)The universe is not limited by dimension. It can be one-dimensional or multi-dimensional.(2)The random occurrence mentioned in the definition refers to the random occurrence under a certain probability. The definition refers to the degree of membership under a fuzzy set, which refers to the distribution under a certain probability. It contains two characteristics: fuzziness and randomness.(3)For any mapping from to interval , it is a one-to-many transformation, and the determinacy of to is a probability distribution, not a fixed number.Cloud models represent primitive-linguistic values in natural languages, and the overall features of qualitative concepts represented by the models can be represented by the digital features of clouds. The clouds are represented by expected value , entropy , and hyper-entropy, the digital feature of the cloud. The language scales and corresponding clouds used to assess the risk of each indicator for alternatives are shown in Table 1.
Expectations represent the expected values of cloud droplets in the domain and directly reflect a digital feature of qualitative conceptual situations. Entropy represents the measurable granularity of qualitative concepts. The larger the size, the more macroscopic the common concepts are. At the same time, it represents the uncertainty degree of qualitative concepts. It reflects the dispersion degree of cloud droplets representing this concept and also reflects the range of cloud droplets that can be included in the concepts in the domain. Entropy values reflect the randomness and fuzziness of concepts. Then, the distribution of on domain is called normal cloud.
4. Establishment of Risk Criteria System of PPAP
The establishment of the risk indicator system plays a significant role in risk management. PPAP in EPC mode possesses the characteristics of both EPC and photovoltaic. By reviewing the literature and consulting experts, this study establishes a comprehensive system of risk factor indicators affecting PPAP, as shown in Figure 3. The full details of the risk indicator system are shown inTable 2.
5. Methodology
In this section, an integrated methodology is proposed for assessing the risk of photovoltaic poverty alleviation projects. This model consists of two main stages, and its flowchart is depicted in Figure 4.
5.1. Calculation of Weights
5.1.1. Step 1. Calculation of Criteria Weights by ANP
In this study, the experts’ opinions are used to determine the criteria weight. Experts are invited to use linguistic terms to judge the importance of criteria. The corresponding relationships between linguistic terms and the IT2FN are shown in Table 3.
The establishment of a risk indicator system plays an important role in risk management, but each risk factor in the project affects each other, so it is necessary to consider the influence of the indicators on each other. The network analytic hierarchy process (AHP) uses a network structure to express the relationships of the elements in the system [44]. The elements in the network layer may influence and dominate each other, so the ANP method can describe the relationships between objective things more accurately, which makes it a more effective method for decision-making. The ANP method takes into account the possible correlations between elements [45], its nonlinear structure replaces the linear hierarchy, and it incorporates a feedback mechanism between elements. This approach addresses the interactions between elements in a realistic system and can better reflect the actual problem, which is a more complete and scientific approach to system decision-making. Therefore, this study selects the ANP method to calculate the weights of the criteria.
5.1.2. Step 2. Establishment of Risk Network Structure
With the discussion among the members of the expert group, the interaction relationship (feedback or dependence) between the indicators at all levels can be determined by comparing the indicators in pairs. First, the opinions of each expert are collected separately, and then, the differences are discussed collectively until a consensus is reached. Figure x shows the interaction of every pair of factors. The symbol “√” indicates that the secondary index in the row will affect the secondary index in the column.
According to the interrelated situation, ANP structure diagram can be obtained. In this study, super decision software is used to build the structure diagram.
5.1.3. Step 3. Construction of Judgment Matrix
A judgment matrix is a matrix for judging the relative importance of each factor on the influence of a factor. Based on the relevance questionnaire of the secondary indicators shown in the above table, the judgment matrix of the primary indicators and the judgment matrix of the secondary indicators were constructed, respectively.
For the judgment matrix of secondary indicators, an expert committee was established to evaluate to obtain the weights of secondary indicators, and some of the evaluation results are shown in Supplementary Materials (Appendix Table A).
For every pairwise comparison matrix, the consistency of logical judgment should be checked using the consistency index (CI) and consistency ratio (CR). For each paired comparison matrix, CI should be used to check the consistency of the matrix, which is shown as follows:
If , the pairwise comparison matrix is consistent.
5.1.4. Step 4. Establishment of Initial Supermatrix
Forming the Supermatrix: the local priorities are entered in the appropriate columns of a supermatrix to obtain the global priorities in a system with interdependent influences. As a result, a supermatrix is created, which is in fact a partitioned matrix, where each segment represents a relationship between two clusters in a system.
With both the external and internal relationships between the elements of other element sets compared and the supermatrix without weight composed of the sorting vectors affected by each element in the network layer, can be obtained by equation.
The matrix is composed of matrixes, of which the column sum is 1. Nevertheless, the matrix has not been normalized. To make computation more convenient, it is necessary to normalize the supermatrix, which means the weighting the elements of to obtain a weighted supermatrix.
5.1.5. Step 5. Calculation of Weighted Supermatrix
To obtain a weighted supermatrix, each column of the unweighted supermatrix should be normalized.
The importance of with is compared to obtain a sort vector, which is denoted as . After this, a weighted matrix is established.
The weighted supermatrix obtained by multiplying is denoted as .
After that, for the sake of representing the relevance between these elements, the stability of the weighted supermatrix by equation (12) needs to be dealt with. Stability treatment means to calculate the limit relative rank vector of each supermatrix.
The prerequisite for the value of the corresponding row of the original matrix becoming stable weights of evaluation indicators is that the limit is convergent and unique. The result calculated by the above formula is the weights of each indicator.
5.2. Risk Ranking of Projects
After determining the weights of the secondary indicators, a questionnaire is designed to collect the linguistic scores of the evaluation experts on the secondary indicators of each alternative.
An investigation team was set up to collect relevant information of the four schemes and form an investigation report. The investigation and analysis report of the basic situation of each alternative is then sent to the expert evaluation group for reference and benefit evaluation form. The evaluation results of the expert group were collected through a questionnaire survey. Experts score the indicators of each project.
Decision-makers use natural language to express their scores and then translate these natural languages into a series of clouds with corresponding digital characteristics. The cloud model is represented by , where represent expected value, entropy, and hyper-entropy, respectively [46]. In this study, an improved language expression model was used, the scoring area , and five levels of natural language were used to evaluate the scoring of each alternative. Table 2 gives the language scales and corresponding clouds used to assess the risk of each indicator for alternatives. Step 1: calculation of the index scores. Cloud arithmetic averaging algorithm is used to aggregate the opinions of each decision-maker to get the score of alternative under index , which is represented by cloud , in which and are the operation symbols of cloud model. The algorithm is as follows: Step 2: construction of the priority function. PROMETHEE is based on pairwise relationships to define appropriate priority functions first [47]. There are six kinds of priority functions in the paper, shown in Supplementary Materials (Appendix Table B). The type of priority function used by each indicator is selected according to the characteristics of each factor. Based on the cloud model, alternative represents the priority of alternative under indicator as follows: Step 3: determination of the preference index. The preference index based on the cloud model is defined as follows: In the above equation, is the weight of index , is the preference degree of over , and is the preference degree of over . Step 4: calculation of the inflow . is the degree to which alternative is superior to other alternatives, and the calculation formula is as follows: Step 5: determination of the outflow . is the degree to which alternative is inferior to other alternatives, and the calculation formulas are as follows: Step 6: calculation of the net flow value like equation (23). The higher the inflow of a scheme, the lower the outflow, and the higher the ranking of the scheme. Step 7: comparison of the clouds of each alternative to sort. In this study, a forward normal cloud generator is used to theoretically compare alternative clouds. Three parameters and the number of cloud droplets are used to calculate the expected value of cloud A. The formulas are as follows:where is a cloud drop in the domain and is the determinacy of . Two clouds and are compared; if , then .
6. Case Study
The second batch of PPAP is being carried out enthusiastically and is planned to cover 15 provinces (autonomous regions) and 165 counties. To ensure the goal of poverty alleviation and the effective use of clean energy and to respond to the national call for poverty alleviation, four PPAPs will now be selected for investment in EPC mode. In this section, based on the risk assessment model proposed in this study, a case study will be conducted to measure the risk level of the four alternatives P1, P2, P3, and P4, at the same time, to verify the robustness of the framework. The information related to these four options is shown in Table4. Step 1: information and materials related to the alternatives are collected. The local area is visited to learn about the special human and natural environment of these areas. Fifteen experts specializing in photovoltaic power generation, poverty alleviation, and EPC were invited to set up three expert groups through the design of the questionnaire to collect experts’ comments. Step 2: the weight of the criteria is determined. Through the discussion among the members of the expert group, a pairwise comparison between indicators is made to determine the interaction relationship (feedback or dependence) between indicators at all levels. The influence between secondary indicators is reflected by IT2FNs, which can solve the fuzziness of expert description and is more in line with human expression habits. The scores of risk factors were determined by experts, and due to space limitations, only one expert’s evaluation results are shown in this study, which can be found in Supplementary Materials (Appendix Table A), and the experts are from the fields of PPVP, risk management, and EPC. After calculation, the weights of the first- and second-level risk factors are acquired, which are shown in Figure 5. Step 3: sorting. The evaluation value of each attribute is transformed into a cloud model to reflect the volatility and probability of risk factors. Seven levels of cloud are used to transform language term values. According to their experience, the expert group scored the risk factors according to the local characteristics of the project, and the scoring results are shown in the figure. Different risk factors have different attributes, so the risk evaluation score cannot be generalized. Therefore, according to the different characteristics of each risk indicator, different priority functions are selected, and their parameters are the same. The priority functions selected by the indicators and their parameters are shown in Supplementary Materials (Appendix Table B). Therefore, the distance between two alternatives under each index is obtained. According to the above explanation, different preference functions are used to calculate the priority between all alternatives, and the global priority between paired alternatives can be calculated. Finally, the inflow, outflow, and net flow of each scheme are acquired. The alternatives can be arranged in the order of according to their net flow. Obviously, among the four alternatives, P4 should be chosen as the best location.
7. Sensitivity Analysis
To verify the robustness of the model raised in this study, sensitivity analysis is conducted to inspect the changes in the ranking of schemes, in cases of variation with weights and k.
Firstly, weight fluctuations observe the corresponding risk levels of alternatives in case of fluctuations in the weight of each factor, including 10%, 20%, and 30% fewer alterations and 10%, 20%, and 30% more of them. The impacts on the fluctuations of criteria are reflected in Figure 5 intuitively. It can be obviously concluded that changes in C1 weight bring in different impacts on the net flow of alternatives. With the increase in the proportion of C1, the net flows of P4 and P1 tend to ascend, while those of P2 and P3, on the contrary, descend apparently, resulting in enlargement in the distance between alternatives. Hence, C1 is a sensitive factor among all the solutions. Therefore, it should be taken into consideration the impact of local policy guidance on the PVAP project at the stage of scheme selection.
As for C2, the results of them have little fluctuation, especially P2 and P3 that are relatively stable, as opposed to P1 and P4, more vulnerable to economic risks. Figure 6 indicates a notable phenomenon that the risk of P4 remains exceedingly high with the change in C3 weight, surpassing the others utterly. Thus, changes in the weight of technology factors will produce more risks to P4. Under the situation of C4 weight fluctuation, the net flow of P1 shows a significant decrease; on the contrary, P2 and P3 tend to increase. Different features of their natural environment led to this discrepancy, in geography, climate, culture, and so on. With regard to C5, an increase in weight gives distinct rise to the score of P4, opposite to P2. Obviously, the behaviors of all participants in the operation of construction projects will have different impacts on different schemes.
(a)
(b)
(c)
(d)
(e)
As is shown in these cases, P4 can surpass other schemes and keep in the first ranking place, while P3 holds the lowest risk score no matter which criteria weight changes. It testifies that the framework proposed in current studies is of pragmatic robustness and reliability.
Secondly, the sample size may exert an impact on the results. To verify the influence of the net flow cloud obtained by PROMETHEE on the sorting results, the operation times k of the forward normal cloud generator is analyzed. Let , respectively, and changes in program evaluation can be depicted in Figure 7. Four lines all have a smooth trend and gradually get close to the expectation of each net flow cloud. The numbers of cloud drops selected, and the operation of the forward normal cloud generator will not cause accidental evaluation results.
The above sensitivity analysis proves that the method proposed in this study has excellent stability in the process of weight and sorting.
8. Recommendations for Risks of PPAP
According to the above analysis, PPAP is confronted with challenges in China, and the next batch of it is supposed to learn practical lessons from the previous cases. Recommendations for significant risks are given as follows, which require the joint efforts of the government, power grid, and poor households.
8.1. Policy Risks
(1)Unstable Policy. Local governments should clarify the division of administrative responsibilities for poverty alleviation, improve relevant policies, and give corresponding policy guidance, such as financial subsidy mechanisms, to guide the development of PPAP. The government should strengthen the propaganda of the PV poverty alleviation policy so that the public knows what benefits they will get after the policy is implemented. At the same time, the PV poverty alleviation policy should be effectively combined with the local actual situation to create conditions for implementation. It is necessary to create a good atmosphere for the poverty alleviation policy and mobilize the enthusiasm of all parties. For example, leadership groups and special work teams can be set up in cities and counties to carry out policy research and special publicity to strengthen the organizational leadership of PV poverty alleviation.(2)Land Acquisition. On the one hand, in the process of site selection, poverty alleviation photovoltaic power station location should be promoted according to local conditions. The new mode of “photovoltaic +” can be actively explored. On the premise of not changing the nature of the land, the way of industrial integration and three-dimensional development can be adopted to solve the problem of power station land. This way not only effectively avoids the problem of PPAP land acquisition but also achieves the purpose of giving full play to the comprehensive benefits of land. On the other hand, the government should strengthen the construction of governing capacity, improve the administrative efficiency, and reduce the obstacles to PPAPs in the process of land acquisition.(3)Imperfect Laws. Relevant laws and regulations are supposed to be improved, and standardized contracts with long-term legal effects should be signed to clarify the responsibilities and obligations of each participant and protect their legitimate rights and interests. The local government can form a special supervision group to visit the local area and provide some necessary information, legal advice, dispute mediation, and other services for the villagers when signing the contract.(4)Imperfect Supervision. On the one hand, relevant departments can form a supervision group to effectively supervise the income and income distribution of the project, so as to ensure that PPAP can really bring economic support to poor households. On the other hand, suitable poor households should be chosen, especially the special poor households that need the most support. Relevant departments need to improve the process of poor households’ filing and card information, carry out qualification examinations for poor households applying for installation one by one, and understand the current situation of poor households through visiting households.
8.2. Economic Risks
(1)Declining Feed-in Tariff. The government should strengthen the coordination with the power grid and residents to ensure the income of villagers. With the reduction in subsidies for the photovoltaic industry, photovoltaic technology and quality need to be improved to reduce costs and obtain sufficient profits. Power grid enterprises shall timely settle part of the electricity charge of benchmark electricity price in full, accelerate the progress of electricity charge settlement and subsidy distribution of photovoltaic poverty alleviation projects, and timely apply for national special subsidies according to policies to ensure the benefit of photovoltaic poverty alleviation.(2)Operation and Maintenance Costs. After-sales service and operation and maintenance system of PPAP need to be improved. The main body of operation and maintenance responsibility should be defined, and the operation and maintenance system should be improved. During the construction of poverty alleviation photovoltaic power station, it is necessary to clarify the operation and maintenance responsibilities and default consequences of equipment manufacturers, construction enterprises, power grid enterprises, and owners, respectively. The intelligence and information level of power plant equipment is improved. The Internet of things and other technologies are used to build the remote centralized control management platform of photovoltaic power stations, so as to know the operation status of the equipment in time.
8.3. Technical Risks
(1)Technical Imperfection. Advanced technology and economic and reasonable photovoltaic technology are used. For example, the “leader” standard selects manufacturers through advanced technical indicators, and enterprises with high-quality, high-tech, and advanced capital strength will stand out. No matter the product quality or after-sales service, they are more guaranteed, which can ensure the long-term stable power generation benefits of photovoltaic poverty alleviation projects.(2)Grid-Connected Risk. The local government is responsible for coordinating with the power grid, guiding the residents to improve the power consumption capacity. The power grid company shall timely repair the power grid, optimize, simplify the local power grid using supporting policies or funds, and solve technical problems such as power operation and dispatching, short-circuit current, and unqualified power quality.(3)Lacking Uniform Standard. Access standards are established for poverty alleviation photovoltaic power plant equipment, to ensure the qualification of photovoltaic poverty alleviation power plant construction enterprises and ensure the quality safety and investment income of photovoltaic poverty alleviation power plants. Strict supplier qualification shall be strictly supervised, and unqualified supplier behaviors shall be severely punished by the government.(4)Equipment Management Risk. User training needs to be carried out to actively popularize operation and maintenance knowledge to users and improve the self-management and protection ability of poor households. Enterprises should also be encouraged to develop more maintainable, self-checking, and self-cleaning equipment to reduce the frequency of operation and maintenance.(5)Poor Grid Infrastructure. Power enterprises should strengthen the transformation and upgrading of the rural power grid, actively promote the construction of supporting grid connection projects for photovoltaic poverty alleviation projects, and ensure the construction, operation, and revenue of photovoltaic poverty alleviation projects.
8.4. Environment Risks
(1)Inconvenient Traffic. During the planning period of PPAPs, it is necessary to design reasonable transportation lines to ensure the transportation of personnel and materials. The local government should also consciously improve the local traffic convenience.(2)Irradiation Resource. Solar endowment. The photovoltaic module manufacturing industry is encouraged to innovate and break through key technologies, improve power generation efficiency, reduce power generation deviation, improve power quality, provide technical support, and reduce technical risks and power generation costs. In addition, the establishment of solar radiation observation stations in the field area can accurately grasp the local light resources.(3)Public Acceptance. The government should coordinate and solve the matching problem of fundraising. In addition, the publicity of the photovoltaic poverty alleviation project should be strengthened to stimulate the enthusiasm of villagers and mobilize more poor people to work. At the same time, the development of related industries should be accelerated to form a complete industrial chain, so as to realize the transformation from input to output in poverty alleviation work.(4)Uncontrollable Risk. PPAP’s comprehensive operation insurance shall be provided to prevent losses caused by natural factors such as mudslides, collapse, and floods. Appropriate photovoltaic module materials are selected according to local climate conditions, so that the equipment can withstand local extreme weather.
8.5. Pattern Risks
(1)Lacking Qualifications. It is necessary to introduce professional third-party certification bodies, regulatory bodies, and qualification deposit systems to supervise and assess the construction quality in the whole process. For suppliers and construction enterprises whose products or projects fail to meet the standards, the quality deposit shall be deducted and included in the national enterprises’ industry credit information disclosure system.(2)Unqualified Acceptance. It is required that PPAP around the country must employ a strong third-party acceptance agency to timely conduct quality inspection and acceptance of photovoltaic modules, inverters, and other equipment, to ensure the project quality. Effective rectification measures shall be formulated for the existing projects, and the acceptance report shall be submitted to the relevant departments.(3)Lacking Communication. During the preparation planning period of PPAP, the information flow and material flow of the project shall be well planned. During the construction and operation and maintenance period of PPAP, all participants shall ensure the timeliness and accuracy of information transmission during communication and ensure the communication at all stages of the project.
9. Conclusion
Photovoltaic poverty alleviation plays a remarkable role in the field of poverty alleviation and new energy in China. However, the first batch of photovoltaic poverty alleviation projects is found to have risks in practice, which will bring losses and failure to achieve the expected goals. At present, few studies involve the risk assessment of PPAP, especially the PPAP under the EPC mode. There are the following problems in the relevant research of photovoltaic poverty alleviation projects under the EPC mode: first, most of the studies focus on the risk of photovoltaic, without considering the particularity of poverty alleviation in the actual implementation process; second, incomplete description of information ambiguities and expert opinions in the decision-making process will lead to third that there is room for improvement in the evaluation method. The call effect among risk factors has not been dealt with, and the ranking method needs to be improved. To solve these problems, this study proposes a new risk assessment model for PPAPs under EPC mode. First of all, in view of the problems in a large number of social practice cases, a comprehensive risk index system of PPAP under the EPC model has been established, based on the literature collection and the Delphi method. Then, in the process of weight determination and scheme evaluation, interval two fuzzy numbers and cloud model are used to represent the attribute value of decision information, respectively, to solve the fuzziness of information and the probability of risk events in the evaluation process. ANP-PROMETHEE is applied to measure the risk based on the selection of the corresponding preference function, which can solve the correlation between risk factors and reflect the characteristics of each attribute. In addition, the applicability of the PPAP risk evaluation framework is demonstrated by evaluating the risks of the four selected PPAP scenario sets, and the stability of the framework is ensured after conducting a sensitivity analysis. Finally, comprehensive risk countermeasures are proposed to provide a reference for future project implementation to avoid risks. In conclusion, the study of PPAP risks in this study is reasonable and practical and can provide a theoretical reference for future PPAP decisions.
Nomenclature
| MCDM: | Multi-criteria decision-making |
| EPC: | Engineering, procurement, and construction |
| ANP: | Network analytic hierarchy process |
| PPAP: | Photovoltaic poverty alleviation project |
| IT2FN: | Interval trapezoidal type 2 fuzzy number |
| PROMETHEE: | Preference ranking organization method for enrichment evaluation |
| PV: | Photovoltaic. |
Data Availability
The data in this study are assessed by experts in this research area (expert advice and questionnaire).
Conflicts of Interest
The authors declare that they have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
Authors’ Contributions
Yunna Wu conceptualized the study, wrote the original draft, and acquired funding. Han Chu provided resources, curated the data, wrote and reviewed the manuscript, and designed the methodology. Minjia Xu involved in data processing and visualized the data.
Acknowledgments
This researchwas supported by the National Social Science Fund of China (19AGL027) and the Fundamental Research Funds for the Central Universities (Nos. 2021MS022 and 2021PT013).
Supplementary Materials
Risk management and EPC after inputting the data from experts, and six priority functions of the PROMETHEE method are provided in Appendix. (Supplementary Materials)