Research on the Capability to Prevent Returning to Poverty and Its Enhancement Path for the Ecologically Fragile Areas: A Case Study of Enshi Prefecture

Abstract

According to the strategic plan for rural revitalization and the consolidation of poverty alleviation achievements, this research has developed an evaluation indicator system encompassing three dimensions: environment, social support, and economic resilience, viewed through a sustainable development lens. This system is designed to gauge the capacity to forestall a relapse into poverty in ecologically fragile regions and can also serve as a foundation for the government to establish a comprehensive early-warning and monitoring system. An integrated approach, combining the TOPSIS and entropy methods, was employed to assess the capability to prevent a recurrence of poverty based on data from Enshi Tujia and Miao Autonomous Prefecture spanning 2016 to 2022. Subsequently, the obstacle degree model was utilized to pinpoint critical barriers to enhancing its capability to mitigate the risk of reverting to poverty. The findings clearly indicated that, compared to other regions, Enshi City and Lichuan City maintained the most robust comprehensive capabilities to avert poverty recurrence between 2016 and 2022. Furthermore, the evaluation of capabilities across various dimensions revealed that, with the exception of Enshi City, other counties and cities demonstrated lower capacities in the environmental, social support, and economic resilience dimensions. Moreover, in 2020, the capabilities of all counties and cities deteriorated, and the capabilities under the dimensions of social support and economic resilience had not returned to their former levels by 2022, suggesting that the social and economic systems are susceptible to emergency public crises. A spatiotemporal analysis of the factors impeding the enhancement of capabilities in the counties and cities of Enshi Prefecture showed that the inhibiting factors varied by region, with the most prevalent obstacles stemming from economic resilience. In terms of environmental dimensions, the total regional water supply played a pivotal role in Enshi Prefecture. There was a pronounced regional disparity in the development of capabilities to prevent the recurrence of poverty, and the evolution of systems, such as the environment, social support, and economic resilience, was markedly uncoordinated. Finally, strategic recommendations and measures were formulated to bolster the capabilities to avert returning to poverty in ecologically fragile areas across these three dimensions.

1. Introduction

Poverty remains a pressing issue globally, posing a significant challenge to the international community. As the world’s largest developing country, China eradicated absolute poverty and achieved comprehensive moderate prosperity in 2020, guided by targeted poverty alleviation strategies [1,2,3]. This milestone significantly contributed to global poverty reduction efforts and offers valuable insights for other developing nations [4,5]. However, the eradication of absolute poverty in China does not imply the complete resolution of poverty issues, as households emerging from poverty are vulnerable to relapsing [6,7,8,9]. Sustainable and stable development is crucial in poverty-relief regions to prevent such recurrence, particularly once governmental support ceases. In recent years, various global emergencies and disasters have impacted economic development and heightened the risk of poverty relapse, especially in ecologically vulnerable areas [6]. China exhibits significant spatial disparities in poverty occurrence [4]. Specifically, the level of poverty and the risk of relapse into poverty are higher in western China compared to central and eastern regions, with an increasing trend from east to west [10,11,12]. Similarly, the effectiveness of poverty alleviation follows the same pattern. One possible reason for this phenomenon is that many rural areas in western China are remote, with poor transportation and infrastructure, as well as harsh natural environments, leading to a higher risk of relapse into poverty among residents [13]. Current research on the relapse into poverty is scant, with existing studies primarily addressing the causes and qualitative countermeasures. There is a notable absence of quantitative analysis and a comprehensive indicator system to evaluate the resilience against poverty recurrence in these regions.

Economic factors play a pivotal role in the livelihood capabilities of rural households, particularly in ecologically vulnerable regions, where they face a wide range of external risks. In the initial study of poverty, it was simply defined as an economic issue because family or individual income directly determines whether they can meet basic living standards [4,14]. From a sustainable development perspective, the stability and growth of the economic system cannot be isolated from its broader context [15,16]. Hence, in addition to monetary poverty, households are at greater risk of falling into poverty in terms of education, health, employment, and living conditions [17,18]. With further research on poverty issues, many scholars realized that income- or money-related indicators are not the sole criterion for determining poverty, prompting a shift from the monetary dimension to multidimensional poverty measurement [11,19,20,21,22]. Although economic factors remain important indicators of household poverty in multidimensional poverty measurement, more scholars have begun to focus on the impact of education, living conditions, and health status on household poverty [13,23,24]. Qi et al. [4] pointed out that household income and expenditure are still the main contributors to the occurrence of poverty, but transportation, housing conditions, education, communication, and healthcare are also significant factors that cannot be ignored. Zhai et al. [3] found that capability poverty is the main form of poverty at present, and the strengthening of participation ability, social resource utilization ability, and access to information ability can contribute to the reduction in poverty recurrence. Dou et al. [14] demonstrated that the proportion of highway entrances and exits, water-saving irrigation area, the number of health staff in institutions, the number of medical beds, the water supply, the water conservancy project, and the number of teachers have been identified as significant factors contributing to poverty. From the perspective of poverty alleviation, China not only provides the most basic material support but also explores poverty alleviation paths from the aspects of industrial development, enhancing education, developing the tourism industry, and innovating agricultural development [5]. Wang and Qi [12] explained that while the influence of economic factors on the effectiveness of poverty reduction is waning, the impact of social security, infrastructure, healthcare, and cultural education on poverty alleviation is steadily increasing. This suggests a shifting focus toward non-economic interventions for more effective poverty alleviation efforts. Moreover, ecological fragility has increased the risk of returning to ecological poverty [6] and ecological environment and natural conditions have become key factors in poverty alleviation [12]. Thus, it is essential to comprehensively analyze the factors influencing the capability to prevent a relapse into poverty, incorporating the three dimensions of environment, social support, and economic resilience, as illustrated in Figure 1.

The economic factors influencing household poverty levels mainly include net income per capita [25,26], out-of-pocket healthcare payments, and health insurance [10]. From the perspective of regional development, economic factors affecting the overall poverty status of an area also include the gross regional product (or per capita gross regional product), the proportion of agricultural output value to the total output value of the region, the proportion of non-agricultural industries, per capita annual savings, and per capita disposable income and consumption expenditure [4,12,20,27].

As for social factors, social support helps prevent poverty-stricken households from falling back into poverty to some extent [1]. Education and health issues are important factors causing multidimensional poverty, with significantly higher rates of multidimensional poverty observed in families with lower levels of education [28]. China’s experience in poverty alleviation governance has also proven that eliminating poverty relies on intellectual support, with education being the best intellectual investment [29,30]. The chronic poverty reduction effect of medical insurance makes a significant contribution to the overall chronic multidimensional poverty reduction among rural residents in China [26]. Households lacking labor and livelihood skills and capabilities are at higher risk of falling back into poverty [25]. For specific groups, such as the elderly and left-behind children, social support plays a more crucial role. Public pension programs reduce both monetary and non-monetary poverty in rural areas [31], and further development of social security and pensions is needed to fully compensate for the medical expenses of the elderly [32]. Additionally, social support can mitigate the impact of poverty on the psychological health of left-behind children [33]. Research on China’s poverty relapse issue has found that the poverty level of rural households is higher than that of urban households [28], and the poverty return index in rural and western regions is higher than that in urban areas and other regions of China [26]. The urbanization rate, number of rural employees, number of welfare institutions, and road network density are all major obstacles to vulnerability to poverty relapse in rural areas [27].

For ecologically fragile areas, environmental issues cannot be ignored. These regions often have harsh natural environments and are typically located in remote areas with slow development, aging infrastructure, and a lack of public services [34]. When the carrying capacity of the natural environment is weak, and human activities exceed this capacity, combined with conflicts between ecological governance policies and economic development, the risk of returning to poverty increases [6]. Mohanty et al. [28] verified that people living in mountainous regions are vulnerable and more likely to experience multiple deprivations. Even within the same area, differences in terrain and infrastructure can lead to varying risks of returning to poverty. Factors such as annual precipitation, altitude, surface softness, and the vegetation index can all affect this risk [27]. To address poverty issues in ecologically fragile areas, China has implemented poverty alleviation relocation projects to improve living conditions. However, families resettled after relocation rely more on external support compared to ordinary families [35]. Poverty alleviation through relocation reduces income poverty for rural households by increasing wage income and property income. Additionally, relocation policies can improve living conditions and reduce the incidence of multidimensional poverty [35,36,37]. However, the success of poverty alleviation relocation also depends on local socioeconomic conditions, and different effects will be achieved in different regions [38]. By leveraging the natural advantages of ecologically fragile areas, characteristic industries can be developed to reduce conflicts between economic development and environmental protection, thereby reducing the risk of poverty. Solar photovoltaic projects have been proven to effectively alleviate poverty in terms of economic, social, and human capital in these areas [39,40]. Consequently, a framework has been developed to assess the capability of preventing households in these areas from reverting to poverty. This framework aims to provide comprehensive guidance to enhance this capability and strengthen the theoretical basis for developing risk warning and surveillance mechanisms against poverty recurrence.

2. Literature Review

The existing research on the factors leading to relapsing into poverty from many aspects mainly reveals the impact of the natural environment, society, and economy on poverty-stricken areas and poor groups. In the late 1980s, some scholars generally pointed out that natural disasters, rising prices, diseases, and other factors were the main reasons for relapsing into poverty [41,42]. With the in-depth study of poverty alleviation in China, it is considered that the fundamental reason for poverty returning is economic vulnerability. The key measure to preventing people from falling back into poverty is to improve regional economic resilience [43,44]. Therefore, long-term and effective measures should be taken to promote regional economic development so as to continuously increase the property income of rural residents and enhance their ability to resist the risk of relapsing into poverty [45]. Moreover, there are still poverty-stricken marginal groups in poverty-relief areas with a poor ability to deal with economic risks. Their economic vulnerability is prominent, so related subjects need to focus on the factors affecting regional economic development [46].

Beyond the direct economic determinants that precipitate the onset of new poverty or widespread return to poverty, the role of social support mechanisms in conjunction with economic factors in causing poverty recurrence warrants considerable attention. Some academia supposed that lower income is synonymous with diminished labor market participation, healthcare access, and educational attainment safeguards. Confronted with diverse socioeconomic adversities, households bereft of adequate self-sufficiency measures are notably susceptible to reverting to impoverished conditions [47]. Furthermore, an array of studies underscores the significance of social assistance factors, including educational opportunities, participation in social–cultural endeavors, access to healthcare, and labor resource availability, directly influencing individuals’ propensity to return to poverty [8,9,10,11,48,49,50,51]. In line with the extant research, the phenomenon of poverty reoccurrence attributable to inadequate social support can be categorized into three distinct types, as follows.

The first category is related to policy elements, such as the weak public infrastructure, weak social security, and the transformation of development mode in the poverty-relief areas, leading to the relapse into poverty again because they cannot obtain corresponding social resources. The second class is described as the lack of ability to deal with relapse into poverty in sudden accidental disability, the shortage of interrelated knowledge and skills, the lack of market risk response ability, and other factors. In addition, as to the third one, the phenomenon of returning to poverty is caused by other restrictive factors, such as education and spirituality. Furthermore, the problems of poverty caused by illness, disability, and school are more prominent [26,52,53,54,55]. Currently, China’s social security system still has the characteristics of “wide coverage and low level”. The strategy of preventing relapsing into poverty not only needs to consider economic factors but also monitor the investment in social support, such as education, medical treatment, public infrastructure, and social service level, so as to improve the vulnerability of the households [10].

Besides the elements mentioned above, natural environmental factors also play an essential role in consolidating the success of poverty alleviation [20,27,44,56,57]. A recent study demonstrated that returning to poverty is closely related to the natural ecological environment around low-income households [58]. The vulnerability of the ecosystem in impoverished areas is more likely to cause sudden natural disasters, so ecological vulnerability always aggravates the risk of relapsing into poverty for ecologically fragile regions [59]. It is evident that many deeply poverty-stricken areas have the nature of harsh ecological environments. Those areas lack natural resources, especially water resources for living life, agricultural irrigation, and industrial production. The households face more frequent threats from the external environment, with insufficient resistance to natural disasters, which makes them need more stable and sustainable resistance when resisting external risks to avoid falling into the vicious circle of relapsing into poverty. Due to the long-term natural evolution and historical reasons, most of the poverty-stricken areas in China are old revolutionary base areas, ethnic minorities regions, and border areas located in the mountains. The natural environment is relatively harsh, leading to poverty-stricken areas creating additional environmental risks, so the residents are at a high risk of returning to poverty again. Therefore, considering environmental factors is also indispensable in investigating the mechanism of returning to poverty. Apart from the risk of returning to poverty caused by the natural environment, the benign development of the natural environment in poor areas is also a critical development path to achieve the goal of poverty eradication. Scholars have pointed out that ecotourism based on local natural resources is a critical way to improve the property income of residents in these areas [60], which is in line with the essential connotation of ecological civilization construction and green rural development. Ecotourism plays a vital role in improving the capability to resist the risk of relapse into poverty in the areas [61]. Indeed, the natural environment of ecologically fragile regions can impose restrictions on economic expansion, yet prudent management of natural resources holds the potential to drive sustainable development in these areas.

An in-depth review of existing research highlights the complexity of factors leading to a household’s return to poverty, encompassing economic, social, cultural, and environmental aspects [10,53,54,58,62]. Households that have previously overcome poverty might face it again due to various challenges, including economic setbacks, social issues, natural disasters, and other unexpected events [63,64,65,66]. This situation points to the need for a detailed investigation into the inherent reasons that can help communities in ecologically sensitive areas avoid falling back into poverty, requiring an approach considering multiple dimensions [67]. Thus, it is critical to examine the interconnections between environmental, economic, and social factors to better understand the fundamental reasons behind the recurrence of poverty.

In addition to exploring factors contributing to poverty reoccurrence, it is crucial to monitor the risk of returning to poverty as a strategy to bolster preventive measures. However, existing literature often prioritizes economic factors as the leading indicators for monitoring this risk. These studies suggest that household income and the economic robustness of a region are direct determinants for assessing the likelihood of falling back into poverty [44,45]. Some studies realize the quantitative monitoring of returning to poverty risk in the poverty-relief areas based on social support factors [62,68]. In contrast, some studies have combined multiple dimensions, including social and economic elements, to assess this risk, but excluded the environment [4,10]. Compared with the survey on the risk of returning to poverty, there are relatively few academic studies considering environmental factors, which makes the existing research unable to measure the potential risk comprehensively. Moreover, there needs to be more comprehensive quantitative research on the capability to prevent poverty reoccurrence, the research objects of which should be primarily concentrated in poor households inhabiting ecologically fragile regions. According to the existing research, this study proposes a comprehensive model with the three-dimensional factors of environmental, social support, and economic resilience elements. This study also constructs the monitoring and evaluation system of the capability to prevent returning to poverty and quantitatively evaluates the risk of returning to poverty in Enshi Prefecture, covered with vast virgin forests. At the same time, the evaluation indicator system can also be applied as a reference for further study on monitoring the risk in other ecologically fragile areas.

3. Materials and Methods

3.1. Overview of the Study Area

Enshi Tujia and Miao Autonomous Prefecture (referred to as Enshi Prefecture), situated in Hubei Province, China, stands out as the province’s sole ethnic minority autonomous region. Enshi Prefecture is located at the junction of three provinces, bordering Chongqing Municipality to the west, Yichang City in Hubei Province to the east, and Xiangxi Tujia and Miao Autonomous Prefecture in Hunan Province to the south, as shown in Figure 2. It is notably positioned within the impoverished Wuling Mountain area covered by vast primeval forests, with ethnic minorities constituting over half of its population. Enshi Prefecture is a typical ecologically fragile area, with natural protected areas accounting for approximately 12.1% of its total land area and a forest coverage rate of 70.14% [69]. The prefecture, encompassing Enshi City, Lichuan City, and Xuanen, Laifeng, Hefeng, Badong, Jianshi, and Xianfeng counties, is home to approximately 4.021 million people, covering an area of 24,000 square kilometers. This represents 13% of the province’s total land area and nearly 7% of its population. Despite its size, the prefecture’s GDP was only 2.67% of the provincial total in 2022, underscoring its economic challenges. Besides, characterized by multidimensional poverty, Enshi Prefecture grapples with obstacles, such as unfavorable geographic conditions, underdeveloped infrastructure, frail industrial bases, and insufficient public services. Notably, the region’s heavy reliance on tourism—a sector that contributed 31.27% to its GDP in 2022—poses a substantial risk of poverty return, as indicated by several studies [60,70]. The high dependency on tourism revenue makes the local economy vulnerable to fluctuations in the tourism market, thus elevating the risk of economic instability and potential poverty relapse. This susceptibility is particularly pronounced in ecologically fragile areas, where the combined challenges of economic dependency on tourism and the threat of natural disasters amplify the risk factors, thereby increasing the likelihood of poverty resurgence. Several key points are evident from the above description. Firstly, Enshi Prefecture’s mountainous terrain and limited plains are unfavorable for agricultural development and hinder infrastructure construction, such as roads and railways. This severely impedes communication with the outside world, contributing to its economic underdevelopment compared to other counties and cities. Since the implementation of China’s green development strategy, Enshi Prefecture faces greater challenges in balancing economic development and environmental protection. Secondly, located at the junction of three provinces, Enshi Prefecture connects the central and western regions of China. Its socioeconomic development is influenced by policies from various provinces, and its remote location further hinders communication with other cities. Thirdly, Enshi Prefecture is home to multiple ethnic groups, including the Tujia, Miao, Dong, Han, Hui, Yi, and Zhuang, each with its own characteristics. This complex social structure poses challenges for governance and administration. Meanwhile, while Enshi Prefecture has made strides in poverty alleviation, the persistent risk of falling back into poverty necessitates continued vigilance and targeted interventions. The Hubei Province Rural Revitalization Bureau has recognized the importance of this region by selecting it for further evaluation to consolidate poverty alleviation achievements. In summary, the inherent geographical location and natural environment of Enshi Prefecture severely limit its economic development. The complex social structure further complicates grassroots governance. Additionally, the development of the tourism industry underscores the conflict between environmental protection and economic growth. These issues increase the risk of returning to poverty in various counties and cities within Enshi Prefecture. Therefore, selecting the eight counties and cities in Enshi Prefecture as the focal point for the investigation of improving the capacity to prevent poverty return holds significant practical relevance. The insights gained from this study could also be applicable to similar poverty-stricken regions, offering a broader perspective on effectively addressing poverty relapse risks.

3.2. Methods

• Entropy weight TOPSIS method

The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is a well-regarded approach in multi-attribute decision-making. It differentiates itself by utilizing both the best possible outcome (positive ideal solution) and the worst possible outcome (negative ideal solution) as benchmarks to evaluate various options. This dual-reference standard allows for a more balanced and comprehensive assessment compared to methods considering only one ideal solution. The essence of TOPSIS is to rank the alternatives based on their proximity to the positive ideal solution and their distance from the negative ideal solution. The positive and negative ideal solutions are virtual solutions, embodying an optimal and a least desirable scenario in the given criteria.

It is assumed that there is a total of $m$ evaluation objects (alternatives), and each evaluation object has $n$ indicators (criteria). $X={(x_{ij})}_{m\times n}$ is the decision matrix, composed of data under each indicator, where each element, $x_{ij}$, represents the i-th $(i=1,2,\cdots ,n)$ indicator value for the j-th $(j=1,2,\cdots ,m)$ evaluation object. The steps of the entropy weight TOPSIS method are as follows.

Step 1: Standardization of the decision matrix. There are differences in the scales of each indicator. In order to eliminate the influence of various scales, the value of each indicator needs to be standardized. Begin with the normalized decision matrix $Y$, where each element, $y_{ij}$, is calculated from the original decision matrix, ensuring that the data across different indicators are comparable. The specific operations are in line with Equations (1) and (2):

For positive indicators:

$$y_{ij}=(x_{ij}-\min x_j)/(\max x_j-\min x_j)$$

(1)

For negative indicators:

$$y_{ij}=(\max x_j-x_{ij})/(\max x_j-\min x_j)$$

(2)

Step 2: Calculation of the entropy weight. Calculate the entropy for each indicator to determine its weight, reflecting the amount of information or variation each indicator contributes to the decision-making process. The size of information entropy is related to the variation of the indicator value in the evaluation system. The greater variation of the related indicator implies a smaller information entropy, which suggests that the indicator provides much more information. Thus, the weight of this indicator is more significant than the indicator with less variation. On the contrary, the minor variation means that the indicator’s weight is low. Therefore, the weight of each indicator can be learned according to the corresponding information entropy. The process of the entropy weight is as follows:

Step 2.1: The information entropy, $E(y_j)$, is calculated using Equation (3):

$$E(y_j)=-{\displaystyle \sum _{i=1}^m{y}_{ij}\ln y_{ij}}$$

(3)

Step 2.2: Let $E_j$ be the entropy value of the $j\mathrm{th}$ indicator, which is yielded based on Equation (4):

$$E_j=E(y_j)/\ln m,j=1,2,\cdot \cdot \cdot ,n$$

(4)

Step 2.3: Let $d_j$ represent the degree of diversification of the $j\mathrm{th}$ indicator and calculate it using Equation (5):

$$d_j=1-E_j,j=1,2,\cdot \cdot \cdot ,n$$

(5)

Step 2.4: Computation of weights. Compute the entropy weight of each indicator according to Equation (6):

$$w_j=\frac{d_j}{{\displaystyle \sum _{j=1}^n{d}_j}},\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\forall \hspace{0.33em}\hspace{0.33em}j=1,2,\cdot \cdot \cdot ,n$$

(6)

Weights computed in this manner are used to adjust the significance of each indicator in the subsequent TOPSIS analysis. The entropy weight effectively balances the influence of each criterion in the decision-making model, assigning higher weights to indicators that offer greater differentiation among alternatives.

Step 3: Construct the normalization of the original matrix.

Based on the entropy weight produced by step 2, the normalized value can be weighted. The weighted normalization of the original matrix is computed by Equation (7):

$$V={(v_{ij})}_{m\times n}=\left[\begin{array}{cccc}{w}_1{y}_{11}& w_2{y}_{12}& \cdots & w_{\mathrm{n}}{y}_{1\mathrm{n}}\\ w_1{y}_{21}& w_2{y}_{22}& \cdots & w_{\mathrm{n}}{y}_{2\mathrm{n}}\\ ⋮& ⋮& & ⋮\\ w_1{y}_{\mathrm{m}1}& w_1{y}_{\mathrm{m}1}& \cdots & w_{\mathrm{n}}{y}_{\mathrm{mn}}\end{array}\right]$$

(7)

The weighted normalized matrix represents the decision matrix adjusted by the importance of each criterion, reflecting both the performance of the options in each criterion and the significance of each criterion in the decision-making framework.

Step 4: Determine the positive ideal solution, $V^{+}$, and negative ideal solution, $V^{-}$, for all the evaluation objects.

The positive ideal solution is the hypothetical alternative that has the best possible values for all evaluated criteria. It is determined by selecting the maximum values from the weighted normalized matrix for each criterion. Conversely, the negative ideal solution represents the hypothetical alternative with the worst possible values for all criteria. It is identified by selecting the minimum values from the weighted normalized matrix for each criterion. Based on the weighted normalized matrix, $V$, constructed in step 3, the positive ideal solution, $V^{+}$, and negative ideal solution, $V^{-}$, are defined as Equations (8) and (9), respectively:

$$V^{+}={(v_j^{+})}_{j\in J}=\left\{(\max v_{ij}|j\in J)|i=1,2,3,\cdots ,m\right\}$$

(8)

$$V^{-}={(v_j^{-})}_{j\in J}=\left\{(\min v_{ij}|j\in J)|i=1,2,3,\cdots ,m\right\}$$

(9)

Besides being calculated in this manner, these two hypothetical solutions can also be the option to tailor these hypothetical solutions according to the specific demands of real-world application contexts.

Step 5: Calculate the distances to the ideal solutions.

The distance of each evaluation object to the positive ($d^{+}$) and negative ($d^{-}$) ideal solutions is yielded based on Equation (10):

$$\left\{\begin{array}{c}{d}_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^n{(v_{ij}-v_j^{+})}^2}}\\ d_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^n{(v_{ij}-v_j^{-})}^2}}\end{array}\right.$$

(10)

Step 6: Calculate the relative closeness between the evaluation object and the ideal solution, $C_i$:

$$C_i=\frac{d_i^{-}}{d_i^{-}+d_i^{+}}$$

(11)

The relative closeness, $C_i$, can reflect the quality of the evaluation object. The value of the relative closeness of each evaluation object is obtained by the TOPSIS method, which can be used to judge and measure the ranking of the evaluation object. The closer the evaluation object is to the optimal solution in the evaluation, the higher the rank of the corresponding evaluation object is.

The relative closeness index, $C_i$, indicates the proximity of each alternative to the ideal solution, with a higher value representing a better alternative. This value is used to rank the alternatives, where the alternative closest to the positive ideal solution is considered the best alternative.

2.

Identification of Obstacle Factors

The analysis of obstacle factors is centered on identifying and analyzing the key barriers that impede the enhancement of measures to prevent returning to poverty. This approach aims to facilitate the development of targeted policies to strengthen poverty prevention capabilities.

Therefore, the obstacle degree model is introduced and computed as the specific calculation Formula (12):

$$O_j=\frac{C_j{F}_j}{{\displaystyle \sum _{j=1}^n{C}_j{F}_j}}\times 100\%$$

(12)

where $O_j$ is the obstacle degree, which indicates the influence of each indicator on the capability to prevent returning to poverty. $F_j$ is the factor contribution degree, which indicates the contribution of a single indicator to the overall goal. In this study, the factor contribution degree can generally be expressed by the weight, $w_j$, of each indicator. $C_j$ is the indicator skewness, which indicates the gap between the actual value of each indicator and the optimal target value, and it can be expressed by the difference between 1 and the standardized value of each indicator.

Based on the analysis of the obstacle degree of a single indicator to the overall target, the obstacle degree of each criterion to the overall target can be further analyzed as Equation (13):

$$A_i={\displaystyle \sum _{j=1}^p{O}_j}$$

(13)

where $p$ is the indicator under the dimension layer and $A_i$ is the total target obstacle.

4. Evaluation Indicator System

The indicators should be meticulously selected to ensure a robust and objective assessment of the capability to prevent returning to poverty in ecologically fragile areas lifted out of poverty. These indicators are designed to be comprehensive, allowing for a multidimensional measure of the capability to prevent returning to poverty. To ensure the effectiveness and reliability of the evaluation system, the selected indicators should adhere to specific principles. Firstly, the indicators should have the nature of availability and reliability. The chosen indicators were based on readily available data, ensuring that the findings are grounded in reliable and verifiable information. This approach helps mitigate the influence of subjective biases, thereby enhancing the scientific rigor and precision of the evaluation. Secondly, the indicators should align with comparability. The indicators were derived from objective data, allowing for an analytical comparison across different regions. This comparability is essential for identifying relative strengths and vulnerabilities, enabling a balanced assessment of various areas. Thirdly, indicators in this study were combined with relevant literature, such as the study on relapsing into poverty, vulnerability, and urban, social, and economic resilience [44,52,65,71]. In light of these principles, this research developed an indicator framework designed to assess the capability of ecologically fragile areas to sustain poverty alleviation efforts and prevent relapse into poverty.

The criteria of the evaluation indicator system cover three dimensions: environmental factors, social support factors, and economic resilience factors, as illustrated in Figure 3. These dimensions comprehensively describe the factors affecting the capability to prevent returning to poverty in ecologically fragile areas. According to the principles of the indicator system and data requirements, this study will obtain relevant data from the publicly available dataset provided by the Enshi Prefecture government. For fairness and to avoid any privacy ethical concerns, all data were sourced from the official statistics published by the Enshi Prefecture Statistics Bureau.

The existing research underscores that environmental factors play a crucial role in the risk of households falling back into poverty. Households in economically disadvantaged areas, particularly those in ecologically fragile regions, heavily rely on their natural surroundings for their livelihood and subsistence. Consequently, their sustainable progress out of poverty is highly susceptible to environmental risks, increasing the likelihood of reverting to poverty. In this paper, we considered various environmental factors, such as the levels of regional wastewater discharge, air quality, the greening ratio in urban areas, the state of the safe treatment for household waste, availability of water resources, energy intensity (energy consumption per unit of GDP), and the amount of park area per capita. These indicators collectively shed light on the condition of natural resources and the quality of the living environment in regions out of poverty and with a fragile natural environment. This enhanced understanding is vital for tailoring effective and sustainable poverty alleviation strategies that are resilient to environmental challenges.

Social support encompasses various aspects, including material economy, life care, medical support, and psychological and emotional comfort. It is categorized into formal and informal types, where formal support refers to governmental assistance and other official aid, while informal support involves assistance provided by family, friends, and volunteers [72]. This research is designed to assess the capability to prevent returning to poverty in areas targeted by poverty alleviation, specifically focusing on the role of formal social support.

The indicators selected to gauge social support’s impact include the availability of medical beds, population metrics, total investment in fixed assets, the level of urbanization, and financial expenditures allocated to education, healthcare, cultural services, and investments in the industry of science and technology. These metrics serve as tangible representations of formal support, offering insights into the level of governmental and institutional backing. For example, expenditure on education highlights the emphasis placed on educational development, an essential aspect of social infrastructure. The number of available medical beds correlates with the region’s healthcare capacity, which is necessary to maintain the population’s health and well-being. Fixed-asset investment encompasses the total value of fixed assets built and acquired by all sectors of society over a specific period. It serves as a comprehensive indicator that reveals the magnitude, structure, and pace of investment in fixed assets. Increasing fixed assets in a region enhances local economic development and elevates the foundational living conditions for its residents, so investment in fixed assets provides an understanding of the region’s infrastructure development. Moreover, the investment in the science and technology sector is a direct indicator of the extent of local industrial restructuring and reflects the local government’s commitment to upgrading industries. Additionally, enhancing urbanization levels not only helps to bridge the social resource gap between rural and urban residents, but also facilitates grassroots management and improves the livelihood capacity of rural residents. These indicators collectively offer a comprehensive view of the formal social support structures essential for sustaining poverty alleviation efforts and preventing vulnerable populations from falling back into poverty. By examining these aspects, the study highlights the importance of robust social support systems in maintaining long-term poverty reduction outcomes.

The assessment of economic factors in evaluating the capacity to prevent reverting to poverty leverages indicators associated with economic resilience. These indicators include the regional gross domestic product (GDP) and the level of financial self-sufficiency, which reflect the region’s capability for economic adaptation and adjustment. The measurement also incorporates indicators related to the dependence on foreign trade, the degree of industrial advancement, and the contribution of high-tech industries to the regional gross domestic product, all of which represent the region’s innovative capacity and transformative potential in addressing poverty. Further, the region’s economic stability and recovery prowess are evaluated using indicators such as the total retail sales of consumer goods, disposable income of rural residents per capita, the balance of resident savings, and the Herfindahl–Hirschman Index (HHI) related to industrial structure. The total retail sales of social consumer goods act as a proxy for the economic vibrancy of a region, reflecting the size and activity of its market. The HHI is particularly insightful, as it quantifies the diversification in the industrial sector: a lower HHI signifies greater industrial variety and, consequently, a robust anti-risk capacity [73]. Moreover, the remaining two indicators directly demonstrate the residents’ economic capacity to resist the risk of reverting to poverty, higher values for which suggest that residents possess greater economic strength to mitigate external risks and prevent relapsing into poverty autonomously.

Additionally, the region’s reliance on foreign trade was scrutinized to understand its economic vulnerability or resilience to external shocks. These selected economic indicators collectively provide a comprehensive view of the region’s economic health and its preventive strength against the recurrence of poverty, especially in areas that have benefited from poverty alleviation interventions. Focusing on indicators such as industrial diversity and foreign trade dependency is crucial, as they offer cost-based insights into the economic dynamics that could influence the sustainability of poverty reduction efforts.

5. Case Study

5.1. Analysis of the Weight of Each Indicator

The data under this evaluation system in Enshi Prefecture from 2016 to 2022 were retrieved from the statistical yearbook and bulletin of the national economic and social development of the relevant counties (cities). This comprehensive data collection enabled a detailed analysis of the region’s capacity to prevent the recurrence of poverty, underpinned by a variety of indicators and dimensions. In the analysis, the specific indicators collectively determined Enshi Prefecture’s resilience against returning to poverty. Then, the weights were crucial, as they quantify the relative importance of each factor in the overall evaluation and were calculated in accordance with the method mentioned above. Figure 4 illustrates the distribution of these weights across the various dimensions, providing a visual representation of their significance in the analysis framework. Moreover, Figure 5 offers a detailed view, showing the percentages that correspond to the weights of each individual indicator within their respective criteria. This percentage bar chart is instrumental in understanding how each indicator contributes to the dimension it belongs to and, subsequently, to the overall capacity of the area to sustain poverty alleviation gains.

As seen in Figure 4 and Figure 5, it can be found that the factor of economic resilience still accounts for the largest proportion of the weight in the evaluation indicator system. Key indicators with large weight under this dimension included regional GDP ($x_{16}$), residents’ savings balance ($x_{20}$), proportion of high-tech industry added value to GDP ($x_{24}$), total retail sales of consumer goods ($x_{17}$), and disposable income of rural residents per capita ($x_{19}$). These heavily weighted indicators underscore the critical importance of the economic development level, the advancement of high-tech industries, the activity of the regional economy, and the financial conditions of residents in mitigating the risk of reverting to poverty in Enshi Prefecture. Per capita income is the primary factor influencing household poverty status [4,10], and low-income families are more prone to poverty [13,74]. Economic development serves as a vital external indicator of a region’s self-sufficiency and developmental capabilities, providing a clear reflection of a region’s progress and its capacity to effectively manage both internal and external challenges. For instance, the total retail sales of consumer goods indicate the size of a regional market and the residents’ capacity to adapt to risks. In the event of sudden external shocks to the market, higher retail sales suggest a larger market size and a greater capacity to withstand these shocks, thus reducing the likelihood of slipping back into poverty. It is imperative to monitor these influential factors continuously, as they significantly impact the capability to prevent returning to poverty.

Then, social support factors also account for a large proportion. Under this dimension, total investment in fixed assets ($x_{10}$), the number of available medical beds ($x_8$), education expenditure, and total population ($x_9$) account for a relatively large proportion. These indicators can reflect the market size, medical carrying capacity, emphasis on education, and infrastructure construction in Enshi Prefecture. Fixed-asset investment is related to regional infrastructure investment and can reflect the degree of social support that residents can access. Medical beds show the region’s capability to cope with the risk of illness and can be effective in reducing the risk of returning to poverty as a result of illness. Social support factors related to medical resources, such as the number of medical personnel, clinics, medical equipment, and hospital beds, also affect poverty alleviation outcomes [4,14,19]. The significance assigned to the year-end total population highlights the critical importance of human resources in the development of regions, especially those that are ecologically vulnerable. Such areas are challenged not only by slow population growth but also by a higher risk of experiencing increased emigration. As a scarce resource in poor areas, knowledge is a restrictive factor in its development [70,75]. Education plays a basic, leading, and sustainable role in poverty alleviation: it can block the intergenerational transmission of poverty, and it is conducive to consolidate the effectiveness of poverty alleviation [29,30,66,76]. The role of education expenditure in preventing relapsing into poverty in poverty-relief areas is self-evident. These social support factors are also the key problems. Those problems need to be solved by relevant departments in consolidating the achievements of poverty alleviation.

In Enshi Prefecture, despite the relatively low overall significance of environmental factors, water resources are a critical part of the area’s natural assets, which is in alignment with the existing research demonstrating that water resources play a crucial role in the region’s economic and social frameworks [77,78]. The proportion of water supply in the environmental dimension carries the most weight within the indicator system, underlining the importance of water in combating poverty in ecologically fragile areas, such as Enshi. Per capita water supply [14] and water area [6] are significant factors influencing multidimensional poverty, with a more direct impact on ecological poverty. A reliable water supply supports both socioeconomic progress and the harmonious development of resources and the environment in poverty-stricken areas. Although environmental factors typically hold less weight, their impact on poverty alleviation efforts is profound. Monitoring efforts to prevent relapse into poverty should particularly address the poverty risks associated with environmental challenges. It is vital for relevant authorities to invest in environmental conservation, ensuring sustained vitality for the region’s future. A robust ecological environment can leverage natural resource advantages into ecological and economic gains, fostering prosperity.

5.2. Analysis of the Capability to Prevent Returning to Poverty

Upon establishing the weights for each indicator, the TOPSIS method was employed to evaluate the capability of various regions within Enshi Prefecture to prevent reverting to poverty from 2016 to 2022, with detailed results presented in

Table 1. Comprehensive evaluation results of the capability to prevent returning to poverty.

County (City)	2016	2017	2018	2019	2020	2021	2022
Enshi	0.5049	0.5547	0.6098	0.7663	0.7381	0.8058	0.8449
Lichuan	0.3512	0.3810	0.4061	0.4755	0.4655	0.5079	0.5504
Jianshi	0.2333	0.2378	0.2409	0.2594	0.2547	0.2894	0.3312
Badong	0.1800	0.2160	0.2352	0.2581	0.2580	0.2996	0.3362
Xuanen	0.1762	0.1762	0.1874	0.2127	0.2372	0.2692	0.3107
Xianfeng	0.1824	0.1738	0.2009	0.2154	0.2112	0.2431	0.2713
Laifeng	0.1598	0.1762	0.1821	0.2185	0.2199	0.2449	0.2749
Hefeng	0.1487	0.1516	0.1863	0.1931	0.1967	0.2265	0.2802

and Figure 6. Initially, it was evident that Enshi City, the administrative capital, consistently ranked highest, outshining other areas, primarily due to its advanced infrastructure, higher level of urbanization, and more robust economic development and structure. Lichuan City, classified as a county-level city, secured the second rank, markedly surpassing other counties yet still lagging significantly behind Enshi City in the capability to prevent the relapse into poverty. Notably, Jianshi County was ranked third from 2016 to 2019, while Badong County maintained third place from 2019 to 2022. However, the gap between these and lower-ranked counties, such as Xuanen, Xianfeng, Laifeng, and Hefeng, was slight, with scores below 0.4, reflecting their limited efficiency in averting poverty recurrence, which highlights considerable disparities and imbalances in the capability to prevent returning to poverty within the prefecture. Furthermore, a decline in their capabilities was observed in 2020, particularly in Enshi and Lichuan Cities, with a notable shift in rankings between Jianshi and Badong Counties. This decline was mainly due to the adverse effects of COVID-19 on the development of ecologically sensitive areas, necessitating further in-depth research to uncover the factors driving these trends.

Thus, to gain a comprehensive understanding of regional disparities in the capability to prevent returning to poverty and to identify the causes of their decline, the analysis was expanded to measure these capabilities within Enshi Prefecture’s cities from 2016 to 2022 across three dimensions: environment, social support, and economic resilience, with detailed findings shown in

Table 2. The capability to prevent returning to poverty under each dimension.

County (City)	2016	2017	2018	2019	2020	2021	2022
	Environment
Enshi	0.6588	0.6818	0.7226	0.7725	0.7408	0.7144	0.7590
Lichuan	0.3993	0.4677	0.4234	0.4398	0.4715	0.4954	0.4401
Jianshi	0.3017	0.1864	0.1647	0.1415	0.2128	0.2459	0.2283
Badong	0.1765	0.2066	0.1925	0.1684	0.2000	0.2549	0.2284
Xuanen	0.4235	0.3289	0.2947	0.2719	0.2987	0.3194	0.2841
Xianfeng	0.3395	0.2846	0.1870	0.1476	0.1798	0.2560	0.2416
Laifeng	0.2062	0.2160	0.1703	0.1828	0.2146	0.2727	0.2574
Hefeng	0.3405	0.2695	0.2913	0.2373	0.2678	0.2859	0.2646
	Social Support
Enshi	0.8102	0.8017	0.8441	0.8728	0.8030	0.8152	0.8107
Lichuan	0.6163	0.6747	0.6414	0.6291	0.5865	0.5942	0.5921
Jianshi	0.3016	0.3815	0.3529	0.3177	0.2658	0.2927	0.3048
Badong	0.2817	0.3362	0.3123	0.2832	0.2997	0.3295	0.3189
Xuanen	0.2147	0.1472	0.1529	0.1900	0.2431	0.3271	0.2596
Xianfeng	0.3288	0.2070	0.2476	0.2513	0.2174	0.2365	0.2173
Laifeng	0.2149	0.1780	0.1839	0.1578	0.2290	0.2054	0.1584
Hefeng	0.1268	0.1124	0.1149	0.1119	0.1148	0.1256	0.1225
	Economic Resilience
Enshi	0.7600	0.7749	0.9411	0.9650	0.9207	0.9318	0.9354
Lichuan	0.4423	0.4292	0.4772	0.4818	0.4862	0.4784	0.4970
Jianshi	0.3717	0.3738	0.2553	0.2273	0.2384	0.2573	0.2508
Badong	0.2221	0.2712	0.2638	0.2354	0.2816	0.3106	0.2592
Xuanen	0.0908	0.1400	0.1196	0.1396	0.1498	0.2073	0.1771
Xianfeng	0.1552	0.1573	0.2086	0.1204	0.1138	0.1454	0.1480
Laifeng	0.2030	0.2443	0.2308	0.2292	0.2270	0.2037	0.2337
Hefeng	0.3320	0.1582	0.1493	0.1353	0.1498	0.2038	0.1740

. Moreover, to facilitate a direct comparison of how these capabilities have evolved across different dimensions within the prefecture, the rates of change in the capability to prevent returning to poverty for each dimension are graphically illustrated in Figure 7, Figure 8 and Figure 9.

Table 2 illustrates the general trends in the capability to prevent returning to poverty across three dimensions for all cities within Enshi Prefecture. First, the capability of Enshi City and Lichuan City continued to rank first and second in the comprehensive measurements across these dimensions. Moreover, Enshi City significantly led other counties and cities in all dimensions, particularly in economic resilience and social support. Additionally, it can be observed that all cities experienced a decline in their capability to prevent returning to poverty across the three dimensions in 2020. The specific details are as follows.

For the environmental dimension, meaningful conclusions can be drawn from Table 2 and Figure 7. Firstly, although Enshi Prefecture consistently led in the capability to prevent returning to poverty under the environmental dimension, there was a decline of 4% in 2020. Other cities experienced varying degrees of growth, with Jianshi increasing by nearly 50% and ranking first, followed by Xianfeng County with a 21% increase, and Badong County with an 18% increase. The main reason for this result is due to the pandemic’s impact, where Enshi City’s total water supply decreased that year, and its energy intensity increased. Whereas in Jianshi, the total regional water supply significantly increased, and energy intensity decreased, Xianfeng County and Badong County saw significant reductions in regional wastewater discharge in 2020. Secondly, the capability of Xuanen and Xianfeng Counties in the environmental dimension decreased year-by-year from 2016 to 2019. A potential underlying reason could be the pursuit of economic development, leading to an increase in regional wastewater discharge while not significantly reducing energy intensity. Thirdly, the capability of Laifeng County in the environmental dimension improved after 2020 but still lagged behind other cities. Analysis of existing data revealed that the main reason is a significant disadvantage in regional water supply. Finally, the evaluation results of Jianshi County, Laifeng County, Badong County, Xuanen County, and Xianfeng County showed a significant gap compared to Enshi City, but the gap among these counties was narrower. Regional water supply is the main reason for the gap between these five counties and Enshi City. This underscores the vital importance of sufficient water resources for sustainable development and economic stability in impoverished regions, especially in ecologically fragile areas. Therefore, counties ranked lower in environmental factors should be wary of the risk of reverting to poverty due to water shortage and pollution and need to further reduce energy intensity in regional economic development.

Regarding the dimension of social support, Table 2 and Figure 8 show that Enshi City and Lichuan City continue to lead other cities in the capability to prevent returning to poverty, but they also experienced various degrees of decline in 2020. Notably, Figure 8 highlights that Jianshi County experienced the largest reduction in this capacity, by 16.3%, followed by Xianfeng County with a 13.5% drop, primarily due to significant cuts in fixed-asset investments without comparable improvements in other aspects. On the other hand, Laifeng and Xuanen Counties saw increases in this capacity in 2020, particularly Laifeng, with a 45% rise, primarily due to enhanced expenditures in education, cultural, sports, and healthcare sectors, despite slight cuts in fixed-asset investments. Additionally, Hefeng County consistently showed subpar performance in this dimension, mainly due to its lower population totals, urbanization rates, and investments in fixed assets, education, and health services compared to other cities. Furthermore, data from Table 2 and Figure 8 clearly indicate that as of 2022, the prevention of poverty reoccurrence capabilities of most cities in this dimension had still not returned to their 2019 levels.

Economic development has a significant impact on the quality of life for residents in any region and serves as the foundation for infrastructure construction and public service expenditures, especially in environmentally fragile areas. Firstly, it can be observed from Table 2 that the capability to prevent reverting to poverty in Enshi City under the economic dimension was superior to other cities, including Lichuan City. From Figure 9, it is evident that the score for Enshi City’s capabilities in this dimension decreased by 5% in 2020. This decline is attributed to reductions in Enshi City’s regional GDP, total retail sales of social consumer goods, fiscal self-sufficiency, and the proportion of high-tech industry value added to GDP, coupled with stagnant growth of rural residents’ disposable incomes. In contrast, the capabilities related to the economic dimension for other cities did not experience drastic declines in 2020 but rather relatively improved slightly. The main reason is that these cities have minor economic scales, and the declines in various indicators in 2020 were not significant. Secondly, combining Table 2 and Figure 9, it can also be observed that the capability of Enshi City under the economic dimension has been increasing every year since 2020 but has not yet returned to the level of 2019, and the growth rate each year is also lower than the average before 2019. Furthermore, it is worth noting that compared to the other two dimensions, the economic dimension revealed a more pronounced imbalance in this capability across regions. Apart from Enshi City, the capabilities of other cities have been consistently low, indicating poor economic resilience. Through analyzing the raw data of the indicators of the lower-ranked counties, it can be observed that the main reason is the large gap between these counties and Enshi City in terms of regional GDP, resident savings balances, disposable income, and financial self-sufficiency level. Furthermore, the reasons resulting in the weaknesses in the economic dimension varied among these counties, among which Hefeng County, although ranked third overall, was the weakest in terms of four particular factors, including regional GDP, resident savings balance, and disposal income. In contrast, Xuanen County, which had the worst evaluation results, was also weak in terms of financial self-sufficiency and industrial advancement, in addition to the abovementioned indicators. Therefore, for the economic development of various regions and the existing weaknesses, targeted measures need to be developed to promote the sustainable economic development of the regions out of poverty.

5.3. Obstacle Analysis

After assessing the levels of the capability to prevent returning to poverty in each dimension for every city, an obstacle model was employed to pinpoint critical factors that inhibit the enhancement of these capabilities in each city and to identify obstacles to the collaborative progress of environmental, social, and economic systems from a sustainable development perspective, thereby better understanding the critical factors for enhancing the capability in ecologically vulnerable areas. Using data from 2016 to 2022 from Enshi Prefecture, the degrees of barriers across environmental factors, social support, and economic resilience were initially calculated annually for each city (as shown in

Table 3. Obstacle degrees of various dimensions in Enshi Prefecture from 2016 to 2022.

		Enshi	Lichuan	Jianshi	Badong	Xuanen	Xianfeng	Laifeng	Hefeng
2016	Environmental Factors	37.19%	31.35%	25.27%	27.01%	15.21%	20.35%	26.74%	19.16%
	Social Support	22.62%	17.98%	34.40%	29.13%	33.40%	27.76%	31.90%	36.59%
	Economic Resilience	40.19%	50.66%	40.33%	43.86%	51.40%	51.89%	41.35%	44.24%
2017	Environmental Factors	37.05%	24.97%	27.00%	23.39%	16.98%	19.39%	23.39%	18.25%
	Social Support	25.91%	17.21%	33.26%	33.33%	37.51%	35.26%	36.79%	38.09%
	Economic Resilience	37.04%	57.82%	39.75%	43.28%	45.51%	45.35%	39.83%	43.66%
2018	Environmental Factors	49.33%	28.91%	25.03%	23.39%	17.25%	22.01%	21.49%	16.19%
	Social Support	30.71%	22.09%	33.28%	35.40%	38.45%	35.76%	37.32%	44.05%
	Economic Resilience	19.96%	48.99%	41.69%	41.21%	44.31%	42.23%	41.19%	39.77%
2019	Environmental Factors	51.53%	22.13%	21.84%	21.44%	16.07%	19.65%	20.17%	14.71%
	Social Support	28.58%	30.09%	37.61%	36.30%	37.06%	33.61%	40.52%	43.08%
	Economic Resilience	19.89%	47.78%	40.56%	42.27%	46.86%	46.74%	39.31%	42.21%
2020	Environmental Factors	48.71%	21.06%	19.84%	20.17%	16.36%	18.81%	19.75%	13.14%
	Social Support	39.41%	35.10%	38.97%	38.20%	35.82%	36.54%	38.35%	45.18%
	Economic Resilience	11.88%	43.84%	41.19%	41.63%	47.82%	44.64%	41.90%	41.68%
2021	Environmental Factors	63.97%	23.45%	24.74%	16.53%	17.97%	16.46%	19.24%	14.13%
	Social Support	21.98%	28.79%	35.84%	37.60%	35.80%	39.14%	39.50%	45.63%
	Economic Resilience	14.04%	47.76%	39.41%	45.87%	46.23%	44.40%	41.27%	40.23%
2022	Environmental Factors	51.37%	21.40%	23.35%	17.11%	15.99%	17.39%	18.18%	13.54%
	Social Support	32.08%	31.44%	34.68%	36.61%	36.05%	38.21%	41.19%	44.80%
	Economic Resilience	16.55%	47.16%	41.97%	46.29%	47.95%	44.40%	40.63%	41.66%

), which helped provide a comprehensive view of how various dimensions affect the coordination and development of environmental, social, and economic systems.

According to Table 3, the main obstacles to preventing returning to poverty in all cities of Enshi Prefecture were primarily manifested in the economic system. Economic factors have been confirmed as the primary determinants of poverty levels and the risk of returning to poverty [4,59]. The high proportion of barriers in the economic resilience dimension reflects the fragility of the economic system and development in ecologically vulnerable areas and hinders the harmonious development of the regional environmental, social, and economic systems. Moreover, it is noteworthy that after 2018, the degree of barriers in the environmental dimension in Enshi City began to increase, likely due to its economic development relying on traditional industries, leading to increased regional energy intensity and, consequently, worsening local air quality. In the later stages of poverty alleviation, ecological poverty alleviation becomes a key focus [12]. A good ecological environment can enhance the effectiveness of poverty alleviation in ecologically fragile areas [79], while natural disasters or ecological destruction can increase local poverty levels or the risk of returning to poverty [6,25]. Last but not least, after 2020, the barriers in the social support dimension for all cities increased, indicating that the investment in social public services during the progress of poverty alleviation in ecologically vulnerable areas lacks sustainability and is also susceptible to emergency public crises. Increased social security support and public health expenditure are significant factors in promoting poverty alleviation [20]. Additionally, the increase in barriers in the social support dimension also reflects the vulnerability of the economic system from another perspective. Public crises, such as COVID-19, could impact these systems more directly and intensely [52]. Therefore, in the short term, to enhance the capabilities to prevent a recurrence of poverty in these areas, it is essential to remain vigilant against emergency crises and continue providing necessary social support. In the long term, there is a need to transform economic development strategies, strengthen economic resilience, and enhance oversight and support for families at risk of poverty, thus expanding the role of social support in these vulnerable areas. Additionally, it is crucial to enhance the capacity of impoverished families, enabling them to acquire more job skills and adapt quickly to environmental changes. Job skills training and improved production capacity for impoverished families can rapidly improve individual and household living conditions [3].

Additionally, the obstacle degrees of individual indicators were calculated annually for each city, with the top five barriers for each city per year presented in

Table 4. The top five obstacles to the capability to prevent returning to poverty.

County (City)	Year	Obstacle Degree Rank
		1		2		3		4		5
Enshi	2016	$x_{23}$	14.32%	$x_{24}$	13.89%	$x_1$	12.70%	$x_7$	11.77%	$x_{15}$	11.72%
	2017	$x_{24}$	16.33%	$x_7$	13.87%	$x_{15}$	10.61%	$x_{23}$	9.35%	$x_{17}$	8.28%
	2018	$x_7$	22.12%	$x_{15}$	16.26%	$x_{21}$	14.84%	$x_4$	9.44%	$x_{14}$	9.03%
	2019	$x_1$	20.07%	$x_7$	19.12%	$x_{15}$	18.50%	$x_3$	12.33%	$x_{14}$	7.36%
	2020	$x_{15}$	28.45%	$x_7$	14.21%	$x_1$	14.08%	$x_4$	9.08%	$x_{24}$	8.45%
	2021	$x_1$	13.17%	$x_2$	11.12%	$x_6$	10.79%	$x_4$	10.24%	$x_{18}$	9.95%
	2022	$x_{15}$	21.01%	$x_1$	14.66%	$x_2$	10.18%	$x_4$	9.58%	$x_{16}$	8.85%
Lichuan	2016	$x_5$	11.99%	$x_{19}$	11.97%	$x_{23}$	8.30%	$x_{14}$	6.64%	$x_{16}$	6.40%
	2017	$x_5$	12.71%	$x_{24}$	12.57%	$x_{19}$	9.96%	$x_{14}$	7.75%	$x_{21}$	7.05%
	2018	$x_5$	13.60%	$x_{19}$	10.87%	$x_{14}$	9.25%	$x_{18}$	7.99%	$x_{16}$	7.77%
	2019	$x_5$	12.52%	$x_{19}$	8.80%	$x_{15}$	8.77%	$x_{16}$	8.42%	$x_{19}$	8.41%
	2020	$x_{15}$	11.47%	$x_{19}$	10.76%	$x_5$	9.92%	$x_{17}$	7.76%	$x_{19}$	7.35%
	2021	$x_{19}$	12.32%	$x_{17}$	9.56%	$x_5$	8.72%	$x_{16}$	8.38%	$x_{12}$	8.22%
	2022	$x_{19}$	10.34%	$x_{17}$	9.54%	$x_5$	9.07%	$x_{16}$	8.71%	$x_{12}$	8.62%
Jianshi	2016	$x_5$	13.49%	$x_{20}$	8.18%	$x_{19}$	7.83%	$x_{17}$	7.32%	$x_{12}$	6.46%
	2017	$x_5$	13.56%	$x_{20}$	8.11%	$x_{17}$	7.14%	$x_{14}$	7.01%	$x_{14}$	6.54%
	2018	$x_5$	12.78%	$x_{20}$	7.47%	$x_{17}$	7.32%	$x_{14}$	7.22%	$x_3$	6.77%
	2019	$x_5$	12.83%	$x_{16}$	8.07%	$x_{17}$	7.34%	$x_{14}$	6.64%	$x_{17}$	6.46%
	2020	$x_5$	10.12%	$x_{10}$	8.96%	$x_{16}$	7.38%	$x_{17}$	7.37%	$x_{19}$	6.93%
	2021	$x_{10}$	8.80%	$x_5$	8.74%	$x_{17}$	8.02%	$x_{19}$	7.92%	$x_{18}$	7.83%
	2022	$x_{10}$	9.31%	$x_5$	8.88%	$x_{16}$	8.59%	$x_{17}$	8.49%	$x_{18}$	7.32%
Badong	2016	$x_5$	10.51%	$x_{19}$	7.43%	$x_{24}$	6.48%	$x_{20}$	6.47%	$x_{16}$	6.24%
	2017	$x_5$	12.00%	$x_{24}$	9.25%	$x_{14}$	7.32%	$x_{20}$	6.79%	$x_{16}$	6.36%
	2018	$x_5$	12.66%	$x_{14}$	8.30%	$x_{19}$	7.21%	$x_{17}$	7.14%	$x_{16}$	7.02%
	2019	$x_5$	11.92%	$x_{16}$	7.34%	$x_{14}$	7.25%	$x_{11}$	7.05%	$x_{22}$	6.83%
	2020	$x_5$	10.84%	$x_{15}$	7.75%	$x_{19}$	7.35%	$x_{17}$	7.11%	$x_{14}$	6.93%
	2021	$x_5$	10.62%	$x_{19}$	9.59%	$x_{17}$	8.94%	$x_{16}$	8.39%	$x_{20}$	7.64%
	2022	$x_5$	9.79%	$x_{17}$	8.61%	$x_{16}$	8.48%	$x_{19}$	8.37%	$x_{17}$	7.30%
Xuanen	2016	$x_5$	13.16%	$x_{19}$	9.29%	$x_{20}$	8.72%	$x_{17}$	7.87%	$x_{14}$	7.41%
	2017	$x_5$	12.16%	$x_{20}$	8.12%	$x_{14}$	7.40%	$x_{17}$	7.20%	$x_{11}$	6.86%
	2018	$x_5$	11.63%	$x_{14}$	7.99%	$x_{20}$	7.90%	$x_{17}$	7.64%	$x_{11}$	7.28%
	2019	$x_5$	11.17%	$x_{16}$	8.05%	$x_{10}$	7.49%	$x_{17}$	7.29%	$x_{10}$	7.15%
	2020	$x_5$	10.53%	$x_{10}$	9.42%	$x_{16}$	8.03%	$x_{17}$	7.98%	$x_3$	7.73%
	2021	$x_5$	9.33%	$x_{10}$	9.23%	$x_{17}$	9.15%	$x_{16}$	8.63%	$x_{16}$	8.51%
	2022	$x_{10}$	9.71%	$x_{17}$	9.70%	$x_5$	9.44%	$x_{16}$	9.42%	$x_{19}$	8.01%
Xianfeng	2016	$x_5$	12.66%	$x_{19}$	8.44%	$x_{20}$	7.81%	$x_{17}$	7.79%	$x_{16}$	6.96%
	2017	$x_5$	11.36%	$x_{24}$	8.67%	$x_{20}$	7.00%	$x_{17}$	6.78%	$x_{19}$	6.54%
	2018	$x_5$	12.14%	$x_{17}$	8.07%	$x_{20}$	7.62%	$x_{19}$	6.98%	$x_{19}$	6.88%
	2019	$x_5$	11.79%	$x_{16}$	8.10%	$x_{17}$	7.67%	$x_{10}$	7.17%	$x_{16}$	6.60%
	2020	$x_5$	10.34%	$x_{10}$	8.79%	$x_{17}$	7.72%	$x_{16}$	7.54%	$x_{20}$	7.28%
	2021	$x_{10}$	9.53%	$x_{17}$	9.51%	$x_5$	8.66%	$x_{16}$	8.65%	$x_7$	7.39%
	2022	$x_{10}$	9.85%	$x_{17}$	9.63%	$x_{16}$	9.09%	$x_5$	8.44%	$x_{14}$	6.96%
Laifeng	2016	$x_5$	11.55%	$x_{19}$	8.66%	$x_{20}$	7.52%	$x_{17}$	7.38%	$x_{16}$	6.06%
	2017	$x_5$	11.63%	$x_{20}$	7.49%	$x_{17}$	7.28%	$x_{19}$	7.27%	$x_{14}$	7.18%
	2018	$x_5$	11.26%	$x_{17}$	7.78%	$x_{19}$	7.57%	$x_{20}$	7.36%	$x_{19}$	6.77%
	2019	$x_5$	11.02%	$x_{16}$	8.12%	$x_{10}$	7.75%	$x_{17}$	7.56%	$x_{19}$	7.30%
	2020	$x_5$	10.26%	$x_{10}$	9.81%	$x_{19}$	8.35%	$x_{17}$	8.35%	$x_{16}$	8.14%
	2021	$x_{10}$	9.89%	$x_{17}$	9.32%	$x_{19}$	8.98%	$x_5$	8.97%	$x_{20}$	8.68%
	2022	$x_{10}$	9.79%	$x_{17}$	9.25%	$x_{16}$	8.88%	$x_5$	8.33%	$x_7$	7.99%
Hefeng	2016	$x_5$	12.23%	$x_{20}$	8.30%	$x_{17}$	7.88%	$x_{14}$	6.87%	$x_{15}$	6.66%
	2017	$x_5$	11.83%	$x_{24}$	8.50%	$x_{20}$	7.95%	$x_{17}$	7.37%	$x_{19}$	7.16%
	2018	$x_5$	12.94%	$x_{17}$	8.82%	$x_{20}$	8.74%	$x_{14}$	8.59%	$x_{14}$	7.83%
	2019	$x_5$	11.78%	$x_{16}$	8.51%	$x_{17}$	7.96%	$x_{14}$	7.31%	$x_{20}$	7.22%
	2020	$x_5$	10.99%	$x_{10}$	9.33%	$x_{17}$	8.56%	$x_{16}$	8.46%	$x_{20}$	7.73%
	2021	$x_5$	9.92%	$x_{10}$	9.85%	$x_{17}$	9.79%	$x_{16}$	9.24%	$x_{19}$	8.10%
	2022	$x_{10}$	10.07%	$x_{17}$	9.99%	$x_5$	9.72%	$x_{16}$	9.70%	$x_{20}$	8.06%

. The frequency of these indicators was also illustrated in Figure 10. Meanwhile, to further understand the main issues faced by different cities, the frequencies of the primary barriers encountered by each city are shown in Figure 11. Similarly, the barriers faced by the region each year are displayed in Figure 12.

Table 4 shows the main obstacles faced by various cities in different years in enhancing their capabilities to prevent relapsing into poverty, while Figure 10 clearly presents a comprehensive view of the challenges encountered across Enshi Prefecture. Through Table 4 and Figure 10, it can be seen that the most frequent factor among the top five barriers in Enshi Prefecture during the study period was the total regional water supply ($x_5$), which indicates a significant issue of water resource supply scarcity across a wide area, and water supply has become the most significant challenge in enhancing the capability in Enshi Prefecture. In fact, water scarcity is a significant challenge that constrains social and economic sustainable development [80]. Existing research indicates a strong correlation between rural poverty and water resource accessibility [81] and demonstrates that adequate irrigation water can effectively reduce poverty, but water depletion can exacerbate it [82]. Further, the subsequent barriers are economic indicators, such as total retail sales ($x_{17}$), regional GDP ($x_{16}$), rural residents’ disposable income ($x_{19}$), and residents’ end-of-year deposit balances ($x_{20}$), which reflect an overall lack of development drive in Enshi Prefecture’s economic growth process, and residents’ poor risk resistance capacity in economic dimensions. From a regional development perspective, a higher GDP level remains the primary factor in poverty reduction, as areas with low economic activity often have higher risks of multidimensional poverty [20,74]. At the individual and household levels, income levels, disposable income, and per capita savings are the main economic factors that enhance the ability to prevent a return to poverty [10,20,83]. Moreover, the following barriers are the urbanization rate ($x_{14}$) and total fixed-asset investment ($x_{10}$) under the social support dimension, indicating that rural development generally lags behind urban development due to the urbanization process after poverty alleviation, and there may also be a shrinkage in investment in infrastructure that affects residents’ living standards. This underscores the increasing importance of factors related to social security and infrastructure, such as sanitation facilities, housing conditions, road construction, and living conditions, in poverty alleviation outcomes [12,24]. Additionally, other recurring concerns include the share of scientific and technological fund expenditures in GDP ($x_{15}$), the proportion of high-tech industry-added value in GDP ($x_{24}$), and energy consumption per unit of GDP ($x_7$), which indicates insufficient investment in high-tech industries in Enshi Prefecture, a weak proportion of high-tech industries in the economic system, leading to an irrational economic structure, and a lack of capability to innovate. The development of high-tech industries can promote local economic development, foster innovation, and reduce local energy consumption [84]. Additionally, research and development expenditure is a significant factor in poverty reduction [20].

Additionally, a horizontal analysis using data from Table 3 and Figure 11 shed light on the distinct challenges faced by different cities. Notably, Enshi City, as the prefecture’s capital, encounters unique obstacles. Six of these challenges pertain to environmental issues, with energy intensity being the most prominent, followed by concerns about air quality and wastewater discharge. This result indicates that Enshi City may have neglected environmental considerations during its development, leading to exacerbated issues in this sector. Moreover, in Enshi City, the primary barriers within the social dimension are associated with the proportion of expenditures on science and technology to GDP. In contrast, in the economic dimension, the challenges include the industrial structure, dependency on foreign trade, and the share of the high-tech industry’s added value to GDP. These factors indicate an irrational economic structure characterized by inadequate investment in and output from high-tech industries. Consequently, the economic system of Enshi City is vulnerable to emergency crises, significantly impacting its capacity to enhance resilience against returning to poverty.

Further analysis from a longitudinal perspective, using data from Table 3 and Figure 12, explored how Enshi Prefecture’s main challenges have evolved over time. In the environmental dimension, the consistent and most prominent obstacle across the years has been the supply of water resources, indicating its longstanding role as a critical developmental challenge and a consistent impediment to enhancing the capabilities to avoid the poverty reoccurrence. In the social support dimension, before 2019, the most notable factors were the urbanization rate and the proportion of expenditures on science and technology to GDP, pointing to challenges in urban development following poverty alleviation and a lack of investment in high-tech sectors. After 2019, the frequency of issues related to total fixed-asset investment began to increase, implying it became a major barrier in the social support dimension, illustrating the impact of the COVID-19 pandemic on public welfare investment in ecologically vulnerable areas and highlighting the vulnerability of the regional social support system. These results also confirm that COVID-19 indeed increased the poverty levels of households in impoverished areas [52,85]. Lastly, in the economic dimension, before 2019, the main barriers were total retail sales of consumer goods, rural residents’ per capita disposable income, and residents’ savings balances. After 2019, the frequency of regional GDP as an obstacle began to increase, becoming a major factor impeding the enhancement of their capabilities to prevent returning to poverty, while the frequency of issues related to residents’ savings balances, although decreased from before, still remained a significant barrier. These trends reveal Enshi Prefecture’s weak economic foundation, limited development drive, and a vulnerable economic system prone to external disruptions.

6. Conclusions, Discussion, and Limitations of the Study

6.1. Conclusions

From a sustainable development perspective, an index system encompassing environmental, social, and economic factors was established to assess the capabilities to prevent returning to poverty in ecologically vulnerable areas. Specifically, Enshi Prefecture, a typical ecological conservation area, was evaluated using a multi-criteria assessment model to assess the capabilities to prevent falling back into poverty across all its cities and to analyze their evolutionary patterns. Additionally, the obstacle model was used to analyze the main factors hindering the enhancement of their capabilities. The following conclusions were drawn:

(1)

Enshi City and Lichuan City exhibited significantly stronger capabilities to prevent returning to poverty than other regions. In contrast, areas such as Jianshi, Badong, Xuanen, Xianfeng, Laifeng, and Hefeng Counties showed lower capabilities, with minimal variance among them. In 2020, most regions saw a decline in their capabilities, illustrating the ongoing vulnerability of ecological, social, and economic systems in post-poverty alleviation phases, susceptible to disruptions from unforeseen events.

(2)

Across the dimensions of environmental, social support, and economic resilience, Enshi City’s capabilities far exceeded those of other counties, particularly in economic resilience. However, Enshi City saw a significant decline in the capabilities to prevent returning to poverty across all three dimensions in 2020, and other counties also experienced declines in different dimensions in 2020, with the largest decrease in the social support dimension. More notably, the corresponding capabilities of all cities across all dimensions have not recovered to previous levels since 2020, indicating that sudden public crises can cause long-term impacts on the ecological, social, and economic systems of ecologically fragile areas.

(3)

The analysis results of the obstacle model showed that the factors hindering the enhancement of the capabilities to prevent returning to poverty in Enshi Prefecture came from environmental, social, and economic dimensions. It was also found that factors related to economic resilience remained the most significant barrier to enhancing the capabilities for all counties, consistent with the highest weighting of economic resilience in the evaluation system. The total regional water supply was the most frequently occurring factor among the top five barriers, corresponding to it being the most heavily weighted indicator in the model. Enshi Prefecture needs to strengthen the management of water resources in future development, actively adjust the economic structure, increase economic resilience, and optimize water resource utilization efficiency.

(4)

Horizontal and vertical analyses of the barrier factors faced by different counties revealed significant spatial imbalances in environmental management, social public welfare, and economic development among different cities. There was also an imbalance in the development of environmental, social, and economic systems across regions, which cannot develop in coordination. Moreover, the social and economic systems of ecologically vulnerable areas were quite fragile and easily affected by sudden events, and this result is consistent with the comprehensive assessment results of the capabilities to prevent returning to poverty.

6.2. Discussion

Ecologically fragile areas inherently face economic development constraints. These regions often heavily rely on traditional agriculture or plantation industries due to their geographical location, resulting in a monotonous economic development pattern. Consequently, residents’ income sources are limited, reducing the community’s resistance to the risk of poverty recurrence. Despite poverty alleviation measures implemented by local governments, the quality of poverty alleviation and the resistance of the community in Enshi Prefecture require ongoing attention to prevent widespread relapse into poverty. Therefore, this study constructed a measurement system from the dimensions of economic resilience, social support, and environmental factors to evaluate the capability to prevent poverty recurrence, using Enshi Prefecture as a case study to investigate its resilience to poverty relapse and the obstacles it faces.

Firstly, the analysis of the weights of relevant factors revealed the significance of economic factors in constructing the capability to prevent poverty relapse, indicating that economic development remains the primary driving force for sustainable poverty alleviation. Continuous efforts are necessary to enhance regional economic development to enhance the community’s resilience to the risk of poverty recurrence. Additionally, social support factors significantly influenced the resistance of ecologically fragile areas to poverty. Public healthcare expenditure, education expenditure, cultural expenditure, fixed-asset investment, and urbanization all affect poverty alleviation efforts, especially in ecologically fragile areas. Furthermore, post-poverty alleviation efforts in these areas should focus on water resource supply, as inadequate water supply directly impacts the living environment of local communities, reducing their resistance to environmental risks.

Secondly, the research results indicated that Enshi City and Lichuan City exhibited a significantly higher comprehensive capability to prevent poverty relapse compared to other counties and cities, highlighting severe regional disparities in the resistance to poverty risks within Enshi Prefecture. This disparity mainly arises because Enshi City, as the administrative and economic center of Enshi Prefecture, benefits from more policy assistance, leading to better infrastructure development compared to other counties and cities. However, post-poverty alleviation, Enshi City has faced increased obstacles to resilience against poverty relapse in the environmental dimension, primarily due to inadequate investment in high-tech industries, excessive energy consumption driven by reliance on traditional industries, and challenges in water resource supply. This underscores the importance of prioritizing natural resource conservation, especially water resources, in enhancing the capability to avoid poverty relapse. Water resource management and industrial restructuring should be guided by local realities to optimize water resource utilization. Besides Enshi City, other counties and cities face significant obstacles to enhancing their capability to mitigate the recurrence of poverty, primarily in economic and social dimensions. Economic obstacles highlight the importance of local economic development in enhancing community resistance. Moreover, social factors, such as fixed-asset investment, education expenditure, and healthcare expenditure, hinder community resistance to poverty risks, emphasizing the significant impact of local fiscal expenditures on grassroots infrastructure construction and public security, even after poverty alleviation. Therefore, enhancing regional capability to prevent poverty relapse requires comprehensive consideration of economic, social, and environmental factors, diversification of economic development models, improved efficiency in natural resource utilization, and increased public fiscal investment in social support to comprehensively enhance the resistance of the community.

Finally, another significant finding of this study was that the capacity to mitigate poverty recurrence across all regions decreased after 2020, and even by 2022, some areas had yet to recover to previous levels. This result demonstrated that public emergencies have significant negative impacts on the community resistance of ecologically fragile areas, particularly on economic resilience and social support systems. Public emergencies disrupt economic circulation, leading to an immediate reduction in local household income resources and a decline in living conditions. When there is a collective decline in living conditions, the community’s resistance to external risks begins to diminish. Furthermore, a decrease in economic levels leads to a reduction in local fiscal revenue, further diminishing the local government’s investment in various social supports, directly weakening the community’s resistance to social risks. If this situation persists for a long time, it will lead to poverty relapse or even large-scale regional poverty relapse. In summary, addressing the issue of enhancing the capability to avoid the relapse into poverty in ecologically fragile areas requires a focus on the quality of economic development from the poverty alleviation stage, while balancing the relationship between economic development and environmental protection and continuously enhancing social support. After poverty alleviation, it is essential to continuously monitor the development of community resistance from economic, social, and environmental dimensions to continually strengthen it.

6.3. Limitations and Future Research Direction

This study provided an in-depth analysis of the changing trends of poverty recurrence risk factors in various counties and cities in Enshi Prefecture but had some data limitations. Firstly, although considering indicators reflecting regional industrial structures, the data did not fully account for the geographical characteristics and local industries of each county and city, which may limit the explanation of industrial structure adjustment and optimization. Secondly, the research relied primarily on macro-level data on regional development, which can measure the risk of poverty recurrence and aid local governments in mitigating regional-scale poverty recurrence. However, it lacks micro-level data on households at risk of falling back into poverty, preventing accurate identification of the poverty recurrence risk of individuals and households. Thirdly, the data used were mainly official statistics on social development and economic construction, lacking direct investigations into the local enterprises and residents. This absence may result in an inaccurate reflection of local economic development and household living conditions. Therefore, future research will explore the capability to prevent relapse into poverty in the following directions: Firstly, future research will conduct detailed investigations into the industrial development of various counties and cities in Enshi Prefecture. By integrating local characteristics, the aim is to establish an updated and refined resilience measurement system against poverty recurrence. This system will comprehensively assess the industrial risks of each county and city, identify key factors influencing industrial upgrading, promote the development of characteristic industries, and seek industrial upgrading to enhance the resistance of the community. Secondly, future research should involve on-site investigations to directly obtain data from local households and grassroots management departments. This approach will facilitate the calculation of poverty recurrence risks across economic, environmental, and social support dimensions. Furthermore, this study will be expanded to research other ecologically fragile areas to validate the effectiveness of the proposed measurement system of the capability to prevent a relapse into poverty. This expansion also aims to provide more suitable strategies for enhancing resilience against poverty recurrence in regions similar to Enshi Prefecture.

7. Challenges, Recommendations, and Policy Implications

In this section, detailed policy suggestions and measures are proposed from three aspects: specific challenges faced by each county and city in Enshi Prefecture and corresponding countermeasures, the role of research results in poverty alleviation, and the impact of research conclusions on enhancing the capability to prevent poverty from returning in ecologically vulnerable areas.

7.1. Challenges and Recommendations for Enshi Prefecture

Based on the analysis of the capacity to avoid poverty recurrence and its obstacles in each county and city of Enshi Prefecture, specific recommendations are proposed to address the challenges faced in enhancing the capacity. (1) Enshi City: The main challenge is the conflict between economic development and environmental protection, with a low contribution of high-tech industries to GDP. To address this issue, the economic development model needs to be transformed by upgrading the industrial chain, increasing the output value of high-tech enterprises, establishing deep-processing industry chains for agricultural products, and enhancing the added value of local specialty agricultural products. Additionally, new tourism models should be developed to cultivate supporting industries and increase the resilience of the tourism industry. It is essential to adjust the industrial structure and improve the efficiency of water resource utilization to resolve the conflict between economic development and environmental protection. (2) Lichuan City: Challenges mainly stem from the social support dimension, such as the urbanization rate and the proportion of expenditure on science and technology to GDP, as well as economic factors, such as regional GDP and total retail sales of social consumer goods. Lichuan City needs to introduce high-tech enterprises, upgrade and transform the traditional tea industry to increase the added value of industries, promote economic development, and elevate residents’ income levels. (3) Jianshi County: The primary obstacle to enhancing resilience against poverty recurrence is from the economic dimension. Increasing investment in infrastructure construction is necessary, along with promoting urbanization and improving residents’ living conditions. By leveraging traditional agricultural products, such as oranges, pomelos, and pears, Jianshi County can develop deep-processing industries, diversify fruit varieties, increase farmers’ income, and enhance local financial revenue. (4) Badong County: The main challenge arises from the economic dimension, with sufficient regional economic development but poor economic conditions for local families. Badong County needs to upgrade and transform traditional fruit, tea, and medicinal herb industries, promote refined and standardized management, increase farmers’ income, and develop new industries to enhance economic resilience. (5) Xuan’en, Xianfeng, Laifeng, and Hefeng Counties: These areas face challenges due to low levels of local economic development. Unified planning should be conducted to develop characteristic industries in each county and city, promote joint development of regional industrial chains, enhance regional industrial resilience, and introduce new business models, such as e-commerce, to increase sales of local agricultural products and effectively improve residents’ economic income.

Additionally, all counties and cities in Enshi Prefecture face some common challenges. It is obvious that all counties and cities face a high risk of inadequate water supply. Measures should be taken to ensure water supply, including constructing new water conservancy projects, promoting water conservation, and controlling excessive water consumption by adjusting industrial structures. Moreover, inadequate urbanization levels are common across all counties and cities, except Enshi City. Despite geographical limitations, increased government investment in fixed assets is needed to promote local urbanization, improve residents’ living conditions, and enhance their resilience against poverty recurrence. Lastly, Enshi Prefecture should plan for renewable energy industries in advance to avoid environmental damage during economic development. Utilizing its unique forest resources can increase the energy supply for residents and enterprises and reduce environmental problems caused by energy consumption.

7.2. Implications for Policy in Poverty Alleviation

Based on the research findings and implementation experience presented in this paper, the following recommendations are proposed for poverty alleviation efforts in impoverished areas.

Firstly, there is a need to adjust the industrial structure and introduce high-tech industries to improve economic development. Long-term industrial planning should be formulated rather than focusing solely on short-term economic interests. Moreover, it is essential to balance resource allocation across regions to avoid uneven development due to unequal resource distribution. Secondly, cultivating environmental protection awareness during the poverty alleviation process is crucial. Although environmental problems in impoverished areas may not be immediately apparent, increased resource consumption with economic development can directly affect the environment, negatively impacting residents’ lives. More resources should be invested in protecting the natural environment, especially water resources. Monitoring water consumption, planning water conservancy and sewage treatment projects in advance, and strengthening water conservation awareness are essential. Lastly, increasing social support for impoverished areas, including financial support for education, medical care, culture, and other public affairs, is necessary. Insufficient educational resources increase the risk of poverty recurrence. Priority should be given to educational investment, not only in compulsory education but also in vocational skills training to improve employability and provide more economic opportunities for impoverished families. Additionally, medical issues are a challenge for impoverished families. Major medical expenses can burden families and affect their economic stability. Therefore, poverty alleviation efforts need to increase medical investment to ensure that impoverished families do not fall back into poverty due to medical expenses. In conclusion, constructing a sustainable path out of poverty requires a multi-faceted approach, including economic development, social support, and environmental protection. Continuous monitoring of the effectiveness of resilience against poverty recurrence is essential, even after poverty alleviation.

7.3. Suggestions for Ecologically Vulnerable Areas

Based on the research conclusions, the following recommendations are made to enhance the capability to prevent falling back into poverty in ecologically vulnerable areas:

(1)

Adhere to the principles of green and sustainable development, ensure an adequate supply of regional water resources, and improve water resource utilization efficiency.

The issue of regional water supply is the most critical natural environmental factor affecting the capability to prevent returning to poverty, especially for the ecologically fragile region. Insufficient water supply will seriously affect the sustainable development of the local economic system. Therefore, it is essential to implement measures to ensure water security in ecologically fragile and impoverished areas. On the one hand, grassroots governments should continuously improve the construction of water conservancy infrastructure, build water conservancy projects, and achieve scientific allocation of water resources through optimizing the layout of water facilities, such as constructing dams, reservoirs, and inter-regional water transfer projects. On the other hand, it is necessary to optimize industrial structures and production technologies to enhance water resource utilization efficiency. In regions prone to water scarcity and potential waste, promoting water conservation, implementing tiered water pricing, and encouraging the reuse of water are crucial steps. Even in less developed areas, development must adhere to sustainable principles, as compromising the environment for economic gains can lead to severe, unforeseen consequences. Governments must enforce eco-poverty alleviation policies and hold local leaders accountable for environmental protection. All stakeholders should avoid compromising these regions’ ecological integrity for short-term gains. Local authorities and citizens should collaborate to monitor and promptly address pollution issues in business operations.

(2)

Strengthen social support measures, improve the monitoring and early-warning mechanisms for poverty relapse, and reduce the threats that emergency public crises and natural disasters pose to the economic and social systems of ecologically vulnerable areas.

In regions with inadequate infrastructure, it is crucial for government-led social assistance initiatives to be varied. For infrastructure projects, especially water conservancy projects, local governments should develop sustainable policies, increase fixed-asset investments, and ensure the stable operation, maintenance, and continuous enhancement of infrastructure functions. Additionally, governments should fully recognize the critical role of social support factors, such as education, healthcare, and investment in innovative technologies, in preventing poverty reoccurrence. It is necessary to increase funding so as to improve public health and education guarantee systems and address difficulties in accessing healthcare and education. Simultaneously, it is also advisable to ensure a sufficient supply of various consumer goods and to expand the market size, providing fundamental safeguards for populations in impoverished areas prone to falling back into poverty due to natural disasters and calamities. Local governments should strengthen the joint revisit mechanism of grassroots cadres to households lifted out of poverty and enhance the collection and analysis of data on residents’ participation in medical care and education. Lastly, leveraging advanced technologies, such as big data, cloud computing, and intelligent monitoring, is crucial for thoroughly tracking changes across multiple indicators in vulnerable areas to proactively mitigate the risk of returning to poverty.

(3)

Considering the disparities in regional development, leveraging emerging technologies and digital economy strategies can harness ecological resource advantages to foster industries with unique local characteristics.

In ecologically vulnerable areas, it is essential to tailor development strategies to the specific challenges faced by each county or city. Developing industries that reflect local traits can optimize the industrial structure, diversify industry types, and improve the overall economic resilience of the region. At the same time, establishing a government-led, industry-driven, and resident participation coordinated development model is crucial, as it helps establish industries with local characteristics and regional specificity. Additionally, grassroots governments should understand the advantages of advanced technologies in industrial chain upgrading and economic transformation. Hence, local governments should capitalize on the transformative power of technologies, such as short videos, live streaming, and cloud tourism, to develop new digital economic models, enhancing connectivity in remote areas and supporting industrial chain optimization. Advanced technologies, such as big data, can also be employed to accurately predict industry demands, boost market competitiveness, and increase the overall benefits of local industries. Developing and enhancing regional industrial chains plays a crucial role in increasing rural incomes, reducing unemployment rates, and strengthening resilience against the risk of returning to poverty.