Impact Assessment of the Implementation Effect of the Post-relocation Support Policies of Rural Reservoir Resettlers’ Livelihoods in Energy Transition

Energy transition is a major structural change in the whole social system, and the energy system must be changed globally to replace fossil fuels. Hydropower is one of the largest renewable energy in the world. However the involuntary resettlees due to the construction of hydropower projects are suffering from being far away from their native land, losing the land cultivated for generations and the houses they live in, and losing the social relationship network based on geography and blood ties. Based on the system evaluation theory of reservoir resettlement and referring to relevant research findings, this paper constructs a comprehensive evaluation index framework for assessing the implementation effect of the PostRelocation Support (PReS) policy. The research region is located in Zhijin County, Bijie City, Guizhou Province, China. Accordingly, a combined method of a structural equation model and a fuzzy comprehensive evaluationary model is used in this paper to analyze the implementation of the PReS policy. The results show that the total score of implementing effects of the PReS policy is 4.4, with dramatic significance. The subindex scores of the resettlers’ family income, living conditions, and production conditions; the local economy; and social stability are 4.3, 4.6, 4.4, 4.6, and 4.3, respectively, with dramatic significance. This paper has analyzed and summarized the successful practices of implementing the PReS policy for reservoir resettlers in three dimensions: poverty alleviation, beautiful home construction and accelerating rural revitalization. Research shows that China's rural reservoir resettlers' PReS policy has been more effective in restoring the livelihoods of reservoir resettlers.

Keywords:

Subject: Environmental and Earth Sciences - Water Science and Technology

1. Introduction

Energy transformation is a major structural change in the energy system. Historically, these changes are driven by the demand for different fuels and the availability of fuels. Utilization of hydropower resources is an important method of renewable and clean energy transformation and utilization while being able to effectively reduce carbon emissions (Perathoner et al.,2014). To use hydropower resources to reduce carbon emissions, it is often necessary to manually build hydraulic structures, such as dams and diversion pipe culverts, which can concentrate water drops and regulate flow(Jiang, Wang, Zhang, Shi, & Yan, 2021). On this basis, the following advantages of hydropower generation are fully realized: high-power generation efficiency, low cost, fast unit startup and easy regulation(Yan, Wang, Wang, & Shi, 2018). China has finished most of its hydropower projects, and solar, wind and even hydrogen will be China’s energy in the next generation, which matters more for China’s just transition. As an important part of the new power system, pumped storage plays an important role in the development of clean and low-carbon energy transition, and is an important initiative to build a new power system, promote sustainable development, and achieve the goal of peak carbon neutrality. Constructing hydropower projects produces involuntary resettlers and even sacrifices the interests of some involuntary resettlers by forcing resettlers to leave their hometowns; some resettlers lose land and houses, some lose social networks, and some fail to adapt to the production and life of resettlement sites (Yan et al.,2018). Involuntary resettlers are a worldwide problem, and compensation alone cannot fulfill the goals of livelihood restoration, income improvement, life improvement and shared development benefits for resettlers (Cernea,1995,1999, 2008). Reservoir resettlers are involuntary resettlers, and supporting their livelihood is bound to be a long-term, multistage and arduous process (Cernea,2007;Downing et al.,2021).

In the relevant report of the World Commission on Dams (WCD), those who are negatively affected by the development and utilization of water resources (such as the construction of hydropower stations) are called displaced people, resettlers or affected people. The activities of these people are divided into displacement, resettlement, rehabilitation, repair and development, that is, DRRD (Downing,2002;Koranteng et al.,2018;Oliver-Smith,2010). DRRD is also the whole resettlement process, which includes various risks and may even lead to poverty (SI-IAIK,2009). The original life system of reservoir resettlers will be destroyed; economic activities and income will be interrupted(Raihan & Milon, 2021); and clothing, food, housing and transportation will not be guaranteed. Additionally, reservoir resettlers may lose necessary social public services, such as medical care and education; deviate from the mainstream of society; be marginalized; and even become refugees (Shaojun,2018;Shi et al.,2021). Michael Cernea, a famous sociologist at the World Bank, proposed the Impoverishment Risks and Reconstruction (IRR) model to describe the inherent risks of involuntary resettlement. According to the IRR model, involuntary resettlement includes eight key poverty risks: land loss, unemployment, loss of home, marginalization, unsafe food, disease, loss of access to public property and services, and community disintegration (Cernea,1997). Mahapatra (1999), an Indian scholar, fully verified the feasibility of the IRR model by studying and analyzing the data of Indian resettlers over the past 20 to 30 years. He evaluated the PostRelocation Support (PReS) policy for Indian resettlers with the help of the IRR model and pointed out that the focus of the PReS policy for Indian resettlers is to prevent poverty caused by land loss (Cernea,2000). There are many reasons for the above problems in resettlement practice; some of these problems are related to the resettlers themselves, some to the planning and management work, and some to resettlement policies and regulations (Vanclay,2017). To solve the abovementioned resettlement problems, the World Bank issued a guiding document on resettlement in 1990. The document, titled the World Bank working guidelines: involuntary resettlement (operational guidelines OD4.30), pointed out that priority should be given to the resettlement issue and that the best scheme should be selected in the early planning stage of the project (Fox et al.,1998). The inadequacy of the World Bank document is that it advises that the compensation claim for the resettlers be paid in full before the relocation and that compensation not involve assistance for the problems and difficulties faced by the resettlers after the relocation(Xu et al., 2021). The resettlement policy focuses mainly on project construction while ignoring concerns about the development of resettlers, resulting in insufficient resettlement funds and prominent environmental problems (McDonald-Wilmsen et al.,2010). Cernea, a research expert at the World Bank, pointed out that the purpose of resettlement policy formulation should be to help resettlers recover and improve their living conditions (Cernea et al.,2000). Therefore, to further properly address the livelihood restoration of involuntary resettlers, the World Bank subsequently issued the World Bank's work guide on involuntary resettlement in 1995. This document specifies the policy objectives, planning points, tasks and project selection requirements of involuntary resettlement in detail (Guoqing,2018). The main highlight is that the policy formulation fully considers the people-oriented principle (such as minimizing the number of resettlers and transforming temporary compensatory and relief measures for resettlers into comprehensive development-oriented solutions), and all projects involving resettlement are covered (Park,2010;Price,2009).

In fact, the impact of relocation on the social system of resettlers cannot be completely avoided (Zaman et al.,2021). As long as there is project construction, A World Bank study on reservoir resettlement focuses mainly on examining the early compensation and relocation of resettlers and advocates giving one-time compensation to resettlers before relocation (Shi & Yu et al.,2021). However, the study pays insufficient attention to PReS for reservoir resettlers and rarely involves application analysis, especially research on the economy, society, culture and other aspects closely related to reservoir resettlers (Peng et al.,2021). China has carried out large-scale water conservancy and hydropower projects for social and economic development. Many reservoir, embankment, river and canal construction projects have led to widespread resettlements. The population of reservoir resettlers alone has reached more than 20 million. China is the country with the most involuntary resettlers in the world, so China is very representative of the research on the just transition of migrant livelihoods (Robinson,2003;Jackson et al.,2000). To help the reservoir resettlers eliminate poverty and prosper and to help promote the economic and social development of the reservoir area and the resettlement area, ensure the healthy development of water conservancy and hydropower work in China in the new period, and build a socialist harmonious society, China issued the opinions of the State Council on improving PReS for large and medium-sized reservoir resettlers in 2006 and established the late stage support fund for large and medium-sized reservoir resettlers and the reservoir area fund for large and medium-sized reservoirs. For the resettlers of large and medium-sized reservoirs, the unified support is 20 years, and each person is 600 yuan per year. The use of the support funds is determined according to the principle of "one try and two can": "one try" means that the government should try to provide resettlers directly cash compensation as much as possible; "Two can" means a combination of cash compensation and project support. This study attempts to solve the problems of reservoir resettlers, especially the livelihood restoration of reservoir resettlers in poor areas, by constructing an impact assessment system of the PReS policy effects on rural reservoir resettlers; this system uses a structural equation model to determine the weight of impact assessment indicators and uses a mathematical Fuzzy comprehensive evaluation method to evaluate the PReS policy for Chinese resettlers(Downing, Shi, Zaman, & Garcia-Downing, 2021).

2. Research method

2.1. Construction of the Impact Assessment Index System

In May 2011, the National Development and Reform Commission, the Ministry of Finance and the Ministry of Water Resources jointly issued a notice on monitoring and evaluating the implementation of the PReS policy for large and medium-sized reservoir resettlements (this notice is hereafter referred to as Document No. 1033) after expert consultation and interministerial joint meetings widely solicited opinions throughout the country. Document No. 1033 clearly stipulates the impact assessment indicators of the implementation effects of the PReS policy; these indicators include 5 secondary indicators and 14 tertiary indicators. See Figure 1 for details.

This study has fully considered the requirements of impact assessment objectives in building a comprehensive impact assessment index system for the implementation effects of the PReS policy. Under the framework of Document No. 1033 on impact assessment indicators for the implementation effects of the PReS policy and the impact assessment theory of rural reservoir resettlement system, combined with the actual situation of the surveyed area and the research results of experts and scholars in this field, it also follows the general principles of index selection and the specific principles related to the research topic. The implementation effects of the PReS policy should be evaluated from multiple angles(Lian, Shi, & Xu, 2022). Figure 2 shows the comprehensive impact assessment index system of the implementation effects of the PReS policy constructed in this study, and 5 secondary indicators and 17 tertiary indicators have been established.

2.2. Research Areas

In this paper, Zhijin County is selected as the survey sample to study the impact assessment of the PReS policy. Zhijin County is located in west-central Guizhou Province, south of the Bijie Experimental Area, and in the triangle at the intersection of the Liuchong and Sancha Rivers, the tributaries of the Wujiang River. With a total area of 2865.28 square kilometers, the county administers 578 villages (communities) in 33 townships (subdistricts) and is inhabited by 1.256 million people from 26 ethnic groups, including Han, Miao, Yi, Bai and Buyi. Zhijin County is an important part of the "Qianzhong economic zone". Zhijin County has 6 large and medium-sized reservoirs, including 5 large reservoirs and 1 medium-sized reservoir. Zhijin County began to investigate and register the immigrant population near large and medium-sized reservoirs in the county in July 2006, with a total of 17154 people registered.

Zhijin County is selected as the survey sample because, on the one hand, Zhijin County has many PReS resettlers. In 2021, the county comprised 24 towns, and 483 villages and had 785 groups, 5077 households, and 25681 people. Zhijin County used to be deeply poverty stricken. Many resettlers are both the supporting population and the poor population. Their multiple identities are intertwined. In the postpoverty alleviation era, how to realize the just transition of resettlers' livelihood is a good breakthrough from the perspective of the implementation effects of the supporting policy for rural resettlers against the background of China's rural revitalization. It is typical and representative to select Zhijin County as the survey sample.

2.3. Data Source

Questionnaire Design

To use a structural equation model to analyze the implementation effects of the PReS policy, this study, based on the comprehensive impact assessment index system of the implementation effects of the PReS policy, selects the 5-point scale method and designs 17 Indicator Options, which mainly reflect the impact assessment of the survey objects on the implementation effects of the PReS policy. The scale includes these five options: 1 is “significantly reduced”, 2 is "slightly reduced", 3 is "no change", 4 is "slightly increased or improved" and 5 is "significantly increased or improved". The five options are defined in Table 1.

From December 21, 2021, to January 20, 2022, with the support and cooperation of the People's Government of Zhijin County and the Rural Revitalization Bureau, we carried out field surveys, questionnaire surveys, household interviews, township interviews and county-level data collection in sample villages. We strove to make the questionnaire for evaluating the PReS policy objective and fair. We distributed questionnaires to reservoir resettlers, ordinary farmers, village cadres, reservoir resettlement cadres at all levels, county and township cadres and other stakeholders. A total of 372 questionnaires were distributed, 28 invalid questionnaires were eliminated, and 344 valid questionnaires were finally obtained.

We adopt stratified sampling in this sampling survey. Samples are taken from high-income resettlement groups, middle-income resettlement groups and low-income resettlement groups. The income levels in this study are divided on the basis of the per capita income of resettlement households. To be specific, low-income resettlement households with per capita income ranging from 0 yuan to 5303 yuan; middle income resettlement households with per capita income of 5304 yuan to 15303 yuan; high income resettlement households with per capita income exceeding 15303 yuan. The income level ratio of sampled resettlement households and the overall income level ratio of resettlement are shown in Figure 4.

2.4. Research Methodology

2.4.1. Structural Equation Model

There are two methods to determine weight: the subjective weighting method and the objective weighting method. The disadvantage of the former is that it is too subjective and depends somewhat on the selected experts. The disadvantage of the latter is that researchers have no discretion and can provide only public factors based on data information. In practical applications, two apparently unrelated factors are often classified into the same category, and researchers cannot correct this mistake. Therefore, this paper selects a method combining subjective and objective factors to determine the index weight by constructing a structural equation. This method can also reflect the structural validity of the questionnaire. We use amos23.0 software to output the model parameter estimation and model goodness of fit test.

2.4.2. Fuzzy Comprehensive Evaluation Method

Since all kinds of qualitative and quantitative impact assessment methods are used to evaluate the implementation effects of the PReS policy mostly from a certain side, the final impact assessment conclusion should be obtained by comprehensive evaluation. There are many kinds of comprehensive impact assessment methods, such as the expert scoring method, AHP, and Fuzzy comprehensive evaluation method. However, the basic idea is to comprehensively consider various qualitative and quantitative factors, combine qualitative impact assessment with quantitative impact assessment, and usually use mathematical processing to quantify qualitative indicators and convert multiple objectives into a single objective to determine the final impact assessment. In this study, the structural equation is constructed to determine the index impact assessment weight, and the implementation effects of the PReS policy is comprehensively evaluated by using mathematical fuzzy comprehensive evaluation.

The Fuzzy comprehensive evaluation method uses fuzzy membership degree theory to reasonably quantify qualitative indicators and highly integrates qualitative and quantitative methods. Because many impact assessment factors are involved in the comprehensive impact assessment of the implementation effects of the PReS policy, if only the main factors are considered, some important information will be lost, and the impact assessment results will be distorted. The Fuzzy comprehensive evaluation method can better address the problems of multiple factors and qualitative interval impact assessment criteria. To comprehensively consider all the impact assessment factors, this paper adopts a two-level fuzzy comprehensive impact assessment model. The mathematical fuzzy comprehensive impact assessment model is:

B=A⸰R=（b₁，b₂，...，b_n）

(1)

In the formula, B represents the impact assessment set, b_n represents the membership degree of the survey object to the nth impact assessment, A represents the weight vector, R represents the fuzzy relationship matrix, and ⸰represents the fuzzy operation method, which is called the fuzzy operator. The fuzzy operator is an artificial definition, and "⸰" can be defined differently to compare which fuzzy operator has the best and most reasonable control effects in the fuzzy reasoning process. There are four common fuzzy operators: "min-max", "product-sum", "min-sum" and "product-max" fuzzy operators. This paper evaluates mainly the implementation effects of the PReS policy. There are many impact assessment index systems, so it is more scientific to choose the product-sum fuzzy operator. This method considers all factors according to weight and is suitable for situations where each factor is effective. For the comprehensive impact assessment of multilevel factors, the multilevelFuzzy comprehensive evaluation method can be used to solve the problem; that is, from the bottom to the top, until the final impact assessment set B is obtained, the k-th impact assessment result is the membership degree of the k-1st factor. Finally, the fuzzy value is removed; that is, the comprehensive impact assessment score W of the impact assessment object is calculated using the fuzzy comprehensive impact assessment set B and the measurement scale H:

W=B×H

(2)

In Formula (2), H = ("significantly reduced", "slightly reduced", "no change", "slightly increased or improved", "significantly increased or improved") = (1, 2, 3, 4, 5).

The multilevel fuzzy comprehensive impact assessment model is suitable for evaluating the implementation effects of the PReS policy. The steps are as follows: First, five secondary indicators are evaluated. Then, the impact assessment results at this level are taken as the fuzzy relationship matrix of the implementation effects of the PReS policy. Finally, the fuzzy comprehensive impact assessment of the implementation effects of the PReS policy is performed, and the overall comprehensive impact assessment results are obtained. This article defines comments as follows: {[0-1)significantly reduced;[1-2)slightly reduced;[2-3)no change;[3-4)slightly increased or improved；[4-5]significantly increased or improved｝.

3. Results

3.1. Basic Characteristics of the Sample Data

In the questionnaire survey group of the implementation effects of the PReS policy, the survey proportion of county and township cadres, reservoir resettlement cadres and village cadres is 61.33%, more than half, while the proportion of resettlers surveys is 13.95%, which is relatively small because during the implementation of the PReS policy, county and township cadres, reservoir resettlement cadres and village cadres have done much work, have a good understanding of the PReS policy, and know the problems and effects encountered in implementing the PReS policy, so the proportion of selection is relatively high. In terms of the proportion of educational level, 65.7% are university level or above; this result also corresponds to the high proportion of county and township cadres, reservoir resettlement cadres and village cadres. Their educational level is generally higher than that of resettlers. For the same reason, from the perspective of current residence, the proportion of villagers' committee residents, township residents and county residents is 74.41%. There are many ethnic minorities in Zhijin County. In this questionnaire survey, ethnic minorities accounted for 45.35% of the respondents. See Table 2 for details.

3.1.1. Independent Sample Test

Independent samples refer to two groups of samples that are relatively independent of each other. Using an independent sample test, we can determine whether all items can identify the reaction degree of different respondents. In this paper, the samples are first sorted according to the total score of the scale, and the top 30% (high group) and the bottom 30% (low group) are obtained. Then, the differences in the high and low groups’ answers to each question are tested. According to the output results of SPSS 26.0 software, when the confidence level is 0.05, all indicators pass the independent sample test.

3.1.2. Reliability Test of the Questionnaire

The reliability test of the questionnaire refers to the reliability of the questionnaire test results, which reflects mainly the consistency and stability of the questionnaire test results, that is, whether the questionnaire results can reflect the authenticity of the tested samples. Generally, the reliability test essentially tests whether the results of the questionnaire are reliable and whether the tested sample has answered truthfully. Cronbach's alpha is the most commonly used reliability test method in social science research. In some basic studies, the reliability is acceptable only when it reaches at least 0.80. In some exploratory studies, the reliability is acceptable as long as it reaches 0.70, and the range is between 0.70 and 0.99, indicating high reliability. A reliability lower than 0.5 is low and must be rejected. Using SPSS 26.0 statistical software, we obtain 0.9 as the internal consistency reliability coefficient value of the questionnaire on the "implementation effects of the PReS" policy; this result shows that the internal consistency of the questionnaire is very high, and we calculated the reliability coefficients of the five secondary indicators as 0.727, 0.858, 0.600, 0.742, and 0.684. Therefore, the reliability of all dimensions is acceptable.

3.2. Weights of Impact Assessment Indicators

See Figure 5 for the structural equation model for a comprehensive impact assessment of the implementation effects of the PReS policy. See Table 3 for the model-fitting degree test. The overall simulation results of the model are satisfactory. Although the fitting results of the X²/DF and the GFI slightly fail to meet the requirements due to the complexity of social problems, overemphasizing the fitting degree standard is unsuitable for social science research. Steiger proposed that a CFI less than 0.05 indicates a good fit, and a CFI less than 0.10 indicates a reasonable fit. A GFI value greater than 0.90 indicates a good fit, and a GFI greater than 0.80 indicates that the fit is reasonable. Therefore, the structural equation model for comprehensive impact assessment of the implementation effects of the PReS policy has passed the test. In this paper, the estimated value of the factor load and its significance level are expressed in table form. The significance level passed the test under the condition of 0.01. The load vector can be normalized, and the impact assessment index weight can be obtained after normalization 1. See Table 4 for specific results.

3.3. Mathematical Fuzzy Evaluation Results of Impact Assessment

According to the distribution probability of 344 effective samples' answers to each indicator, the membership degree of the indicator at the corresponding level is determined. For example, for the indicator "total income of resettlers", if 8 of the 344 valid samples answered "significantly decreased", the membership degree of the indicator for the subsystem comments "significantly decreased" is 0.023 (i.e., 8/344 = 0.023). By analogy, the fuzzy relationship matrix of the improvement of five secondary indicators can be constructed. Then, according to the index weight obtained above, the impact assessment result “Wi” of the improvement of each secondary index is calculated: 0 ≤ W1 ＜ 1 is marked as "significantly decreased"; 1 ≤ W2 ＜ 2 is "slightly decreased"; 2 ≤ W3 ＜ 3 is "no change"; 3 ≤ W4 ＜ 4 is "slightly increased or improved"; 4 ≤ W5 ≤ 5 is "significantly increased or improved" (Wi indicates the impact assessment result),the definition of indicator evaluation values is shown in Table 5.