# Risk Analysis of Reservoir Resettlers with Different Livelihood Strategies

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Risk Analysis Framework

#### 3.1. Risk Assessment

#### 3.1.1. Risk Perception

- (1)
- Economic risk

- (2)
- Social risk

- (3)
- Psychological risk

#### 3.1.2. Risk Identification

- Data standardization

_{ij}is the standardized value of the jth indicator of the ith object; X

_{ij}is the original value of the jth indicator of the ith object; X

_{min}and X

_{max}are the minimum and maximum values of the jth indicator, respectively.

- 2.
- Entropy weighting method to determine objective weights

_{ij}is the indicator characteristic weight; m is the total number of objects; n is the total number of indicators; E

_{j}is the entropy value of the jth indicator; W

_{j}

_{1}is the objective weight.

- 3.
- AHP determines subjective weights

_{max}is the maximum characteristic root; RI is the random consistency index (the standard value is shown in Table 2 below); moreover, CR is the random consistency ratio, and when CR < 0.1, this indicates that the judgment matrix has good consistency.

- 4.
- Determine the portfolio weights

_{j}is the combination weight, W

_{j}

_{1}is the objective weight, and W

_{j}

_{2}is the subjective weight.

- 5.
- Calculate the composite risk index

_{i}value is obtained by the following equation:

- 6.
- Risk Level Classification

_{1}, …, m

_{K}are given according to the data set distribution.

_{i}that satisfies

_{i}is the mean value of all points in S

_{i}.

#### 3.2. Risk Management

#### 3.3. Risk Communication

#### 3.4. Robustness Analysis

- Select the initial sample capacity m and the benchmark sample capacity m
_{1}. - Starting from the initial sample capacity m and gradually decreasing to m
_{1}, (m − m_{1}+ 1) results are obtained according to the calculation Equations (1) to (12), where the risk level is assigned from 1 to 5 from very low to very high, respectively. - Calculate the Kendall’s concordance coefficient according to the following equations.

_{i}has a different meaning from the previous section and is the sum of the risk level assignments when the sample size is i.

- 4.
- When the sample size changes, the more obvious the change in w
_{j}is, the less robust the model is, and the opposite is true.

## 4. Case Study

#### 4.1. Data Sources

_{i}and Y

_{i}according to different indicators). To facilitate participant understanding, the questionnaire employed a 10-point scale. The correspondence between the content of the questionnaire and the indicators of risk sources of reservoir resettlers, along with the risk probability and risk impact calculation formulae for each indicator, are shown in Table 4 below.

#### 4.2. Analysis of Results

#### 4.2.1. Comprehensive Risk Index Measurement

_{1}, the risk index, risk probability, and risk impact of pure-agricultural-employment resettlers are both the highest and significantly higher than the other two categories, while the risk index and risk probability of agriculture-and-employment and nonagricultural-employment resettlers are essentially comparable; moreover, the risk impact of nonagricultural-employment resettlers is significantly higher than that of agriculture-and-employment resettlers, indicating that the likelihood of risk and sensitivity to risk in terms of the income of pure-agricultural-employment resettlers are at the highest level, while agriculture-and-employment resettlers are better off overall than nonagricultural-employment resettlers. In terms of income stability R

_{2}, the pattern is essentially similar to R

_{1}. In terms of the expenditure level R

_{3}, the risk index of pure-agricultural-employment resettlers decreases significantly, while the overall risk level of non-agricultural-employment resettlers is significantly higher than that of the other two categories. In terms of expenditure as a share of income R

_{4}, the risk level of nonagricultural-employment resettlers is still at the highest level at this time, although the risk level of pure-agricultural-employment resettlers has also increased to a greater extent. In terms of neighborhood relations R

_{5}and family relations R

_{6}, nonagricultural-employment resettlers are both at the vulnerable level, while pure-agricultural-employment resettlers are better off than agriculture-and-employment resettlers. In terms of satisfaction with current life R

_{7}and optimism about future life R

_{8}, each of the three resettler livelihood strategy categories has advantages and disadvantages in terms of risk index, risk probability, and risk impact; the overall pattern shows that nonagricultural-employment resettlers are the best, agriculture-and-employment resettlers are the second best, and pure-agricultural-employment resettlers are the worst.

#### 4.2.2. Robustness Analysis

#### 4.2.3. Risk-Benefit Analysis

_{r}is the risk–benefit rate of individual resettler families, Income is the corresponding annual per capita disposable income of households, Cost is the annual per capita disposable income of rural residents in Guangdong Province, and $\overline{{\mathrm{R}}_{r}}$ is the average risk–benefit rate of resettler families with different livelihood strategies.

## 5. Discussion

- Pure-agricultural-employment resettlers face greater income risks

- 2.
- Agriculture and employment is a transitional livelihood strategy

- 3.
- Higher social network risks for nonagricultural-employment resettlers

- 4.
- Need to be alert to the possible excessive gap between rich and poor in nonagricultural employment

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Change in resettlers’ livelihoods over time under different livelihood strategies. L

_{0}is the livelihood change curve, assuming that resettlers do not relocate; ${L}_{0}^{\prime}$ is the change curve of unassisted livelihoods of resettlers after the occurrence of relocation; L

_{1}is the livelihood change curve with support and nonagricultural employment; L

_{2}is the livelihood change curve with support and the agriculture-and-employment strategy; L

_{3}is the livelihood change curve with support and pure-agricultural employment after relocation. Source: Author’s design.

**Figure 3.**Distribution of risk levels and composite risk index of resettlers with different livelihood strategies. (

**a**) Line graph of risk levels of resettlers with different livelihood strategies. (

**b**) Composite risk index distribution of resettlers with pure-agricultural employment. (

**c**) Composite risk index distribution of agriculture-and-employment resettlers. (

**d**) Composite risk index distribution of resettlers with nonagricultural employment.

**Figure 4.**Line graphs for the 300 resettlers’ risk probability, risk impact, and risk index. (

**a**) Resettlers’ risk probability line graph. (

**b**) Resettlers’ risk impact line graph. (

**c**) Resettlers’ risk index line graph.

Primary Indicators | Secondary Indicators | Indicator Descriptions |
---|---|---|

Economic risk | Income level R_{1} | Evaluation of resettlers’ satisfaction with household income level |

Income stability R_{2} | Average unemployment rate of the actual participating workforce (disaster rate) | |

Expenditure level R_{3} | Evaluation of resettlers’ satisfaction with their level of household expenditure | |

Expenditure as a share of revenue R_{4} | Resettlers’ evaluation of household expenditure as a share of income | |

Social risk | Neighborhood R_{5} | Resettlers’ self-evaluation of neighborhood relations |

Family relations R_{6} | Resettlers’ self-assessment of family relationships | |

Psychological risk | Existing life satisfaction level R_{7} | Evaluation of resettlers’ satisfaction with their current life |

Future life optimism level R_{8} | Evaluation of resettlers’ optimism about their future life |

Matrix Order | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 |

Risk Level | Very Low | Low | Moderate | High | Very High |
---|---|---|---|---|---|

R | 0 < R ≤ 0.04 | 0.04 < R ≤ 0.16 | 0.16 < R ≤ 0.36 | 0.36 < R ≤ 0.64 | 0.64 < R ≤ 1 |

Indicators | Questionnaire Content | Risk Probability P | Risk Impact C |
---|---|---|---|

Income level R_{1} | Are you satisfied with the overall income of your family? How much would a low income affect your family’s life? | P_{1} = 1 − X_{1}/10 | C_{1} = Y_{1}/10 |

Income stability R_{2} | Calculate the probability of risk from the unemployment rate and the crop damage rate; How much do you think your family’s life would be affected if you lost your job (suffered a disaster)? | P_{2} | C_{2} = Y_{2}/10 |

Expenditure level R_{3} | Are you satisfied with your family’s overall consumer spending? How much of an impact would higher spending have on your family’s life? | P_{3} = 1 − X_{3}/10 | C_{3} = Y_{3}/10 |

Expenditure as a share of revenue R_{4} | Do you think your household consumption expenditure is a significant proportion of your income? If it is high, how much does it affect your family life? | P_{4} = X_{4}/10 | C_{4} = Y_{4}/10 |

Neighborhood R_{5} | Do you feel that you have a good relationship with your neighbors? If not, how much does it affect your family life? | P_{5} = 1 − X_{5}/10 | C_{5} = Y_{5}/10 |

Family Relations R_{6} | Do you feel that your family members have a good relationship with each other? If not, how much does it affect your family life? | P_{6} = 1 − X_{6}/10 | C_{6} = Y_{6}/10 |

Existing life satisfaction level R_{7} | Are you satisfied with your current living situation? If not, how much does it affect your family life? | P_{7} = 1 − X_{7}/10 | C_{7} = Y_{7}/10 |

Future life optimism level R_{8} | Do you feel optimistic about your future? If not, how much does this affect your family life? | P_{8} = 1 − X_{8}/10 | C_{8} = Y_{8}/10 |

Crop Damage Area (Million Mu) | Crop Cultivation Area (Million Mu) | Urban Registered Unemployment Rate (%) | |
---|---|---|---|

2010 | 916.21 | 6394.16 | 2.52% |

2011 | 694.9145 | - | 2.46% |

2012 | 625.844 | - | 2.48% |

2013 | 1726.65 | 7047.125 | 2.43% |

2014 | 1265.723 | 7117.43 | 2.44% |

2015 | 1268.9 | 7177.08 | 2.45% |

2016 | 1121.25 | 7246.252 | 2.47% |

2017 | 427.7 | 6341.26 | 2.47% |

2018 | 823.4 | 6419.04 | 2.41% |

2019 | 162.54 | 6536.07 | 2.25% |

2020 | 126.15 | 6677.71 | 2.53 |

Risk Probability | 12.64% | 2.45% |

Tier 1 Indicators | Secondary Indicators | Risk Probability Weighting | Risk Impact Weighting |
---|---|---|---|

Economic risk | Income level R_{1} | 0.238 | 0.272 |

Income stability R_{2} | 0.154 | 0.187 | |

Expenditure level R_{3} | 0.101 | 0.106 | |

Expenditure as a share of revenue R_{4} | 0.115 | 0.099 | |

Social risk | Neighborhood R_{5} | 0.087 | 0.052 |

Family relations R_{6} | 0.141 | 0.137 | |

Psychological risk | Existing life satisfaction level R_{7} | 0.075 | 0.068 |

Future life optimism level R_{8} | 0.089 | 0.079 |

Pure-Agricultural Employment | Agriculture and Employment | Nonagricultural Employment | |
---|---|---|---|

Very low | 5 | 13 | 17 |

Low | 17 | 37 | 42 |

Medium | 25 | 37 | 18 |

High | 41 | 11 | 18 |

Very high | 12 | 2 | 5 |

Risk Level | 1 | 2 | 3 | 4 | 5 | w_{j} | |
---|---|---|---|---|---|---|---|

Sample Size | |||||||

300 | 35 | 96 | 80 | 70 | 19 | 1.65 × 10^{−4} | |

297 | 35 | 94 | 79 | 71 | 18 | 1.05 × 10^{−5} | |

294 | 34 | 92 | 80 | 70 | 18 | 3.47 × 10^{−6} | |

291 | 33 | 92 | 77 | 69 | 20 | 1.78 × 10^{−6} | |

288 | 34 | 91 | 74 | 70 | 19 | 1.08 × 10^{−6} | |

285 | 32 | 91 | 76 | 68 | 18 | 7.08 × 10^{−7} | |

282 | 32 | 90 | 74 | 67 | 19 | 5.22 × 10^{−7} | |

279 | 32 | 88 | 72 | 67 | 20 | 4.06 × 10^{−7} | |

276 | 31 | 86 | 72 | 66 | 21 | 3.23 × 10^{−7} | |

273 | 32 | 84 | 71 | 67 | 19 | 2.62 × 10^{−7} | |

270 | 32 | 84 | 70 | 66 | 18 | 2.17 × 10^{−7} |

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**MDPI and ACS Style**

Wang, F.; Yao, K.; Liu, B.; Zhang, D. Risk Analysis of Reservoir Resettlers with Different Livelihood Strategies. *Water* **2022**, *14*, 3530.
https://doi.org/10.3390/w14213530

**AMA Style**

Wang F, Yao K, Liu B, Zhang D. Risk Analysis of Reservoir Resettlers with Different Livelihood Strategies. *Water*. 2022; 14(21):3530.
https://doi.org/10.3390/w14213530

**Chicago/Turabian Style**

Wang, Feilong, Kaiwen Yao, Bingwen Liu, and Dan Zhang. 2022. "Risk Analysis of Reservoir Resettlers with Different Livelihood Strategies" *Water* 14, no. 21: 3530.
https://doi.org/10.3390/w14213530