# The Relationship between Economic Growth and Disaster Losses-Based on Linear and Nonlinear ARDL Model in China

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## Abstract

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## 1. Introduction

## 2. Literature Review

## 3. Disaster Losses Measurement Based on Entropy Weight TOPSIS Model

_{ij}is the analyzed values of each sampled parameter, i = 0, 1, 2, …, m, and j = 0, 1, 2, …, n.

_{j}is the entropy of each factor and $k=1/\mathrm{ln}m$.

_{i}is the closeness coefficient.

## 4. Analysis of the Relationship between Carbon Emissions and Disaster Losses

#### 4.1. Variables and ARDL Model

_{i}is the optimal lag period that is determined by AIC or SC criterion.

#### 4.2. Results Analysis

## 5. Conclusions and Suggestions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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Variables | Direct Economic Losses | Affected Area | Affected Population | Dead Population | Emergency Displaced Population |
---|---|---|---|---|---|

Mean | 3220.61 | 39,365.42 | 33,610.22 | 6448.08 | 1005.56 |

Median | 2586.35 | 40,540.00 | 36,287.45 | 2400.00 | 810.00 |

Maximum | 11,752.40 | 54,690.00 | 49,745.90 | 88,928.00 | 2682.20 |

Minimum | 1602.30 | 18,480.00 | 3977.90 | 589.00 | 211.10 |

Std. Dev. | 2154.69 | 11,853.52 | 12,691.39 | 17,668.99 | 597.61 |

Variables | Instructions |
---|---|

gdp | The real output, deflated by the 1990 level |

fai | Investment in fixed assets, deflated by the 1990 level |

een | Fossil energy consumption |

nen | New energy consumption |

disa | Disaster losses |

Variables | ln(gdp) | ln(fai) | ln(een) | ln(nen) | ln(disa) |
---|---|---|---|---|---|

Mean | 10.859 | 10.861 | 12.369 | 9.999 | 8.579 |

Median | 10.892 | 10.900 | 12.529 | 10.010 | 8.485 |

Maximum | 11.815 | 12.322 | 12.889 | 11.125 | 10.926 |

Minimum | 9.825 | 9.279 | 11.721 | 8.987 | 7.672 |

Variables | Test Type | 5% Level | T-Statistic | Prob. |
---|---|---|---|---|

ln(gdp)I | level | −3.632896 | −1.540196 | 0.7834 |

1st difference | −3.658446 | −4.331060 | 0.0139 * | |

ln(een)I | level | −3.004861 | −1.533965 | 0.4982 |

1st difference | −3.012363 | −3.991766 | 0.0064 * | |

ln(fai)I | level | −3.644963 | −3.25911 | 0.9837 |

1st difference | −3.658446 | −4.542753 | 0.0092 * | |

ln(disa) | level | −3.622033 | −4.619307 | 0.0065 * |

ln(disa) * ln(disa) | level | −3.622033 | −4.672785 | 0.0058 * |

Significance | 10% | 5% | 1% | |||

I(0) | I(1) | I(0) | I(1) | I(0) | I(1) | |

Critical Value Bounds | 2.20 | 3.09 | 2.56 | 3.49 | 3.29 | 4.37 |

F-Statistic | F = 7.302214 |

Variables | Coefficient | Std. Error | T-Statistic | Prob. |
---|---|---|---|---|

d(ln(gdp)I(−1)) | 0.74999 | 0.099791 | 7.515585 | 0.0003 |

d(ln(disa)) | −0.002986 | 0.001879 | −1.58895 | 0.1632 |

d(ln(disa)(−1)) | −0.009231 | 0.001558 | −5.926444 | 0.0010 |

d(ln(fai)I) | −0.077158 | 0.017828 | −4.328016 | 0.0049 |

d(ln(fai)I(−1)) | −0.038881 | 0.015498 | −2.508755 | 0.0460 |

d(ln(nen)I) | 0.06142 | 0.010925 | 5.621976 | 0.0014 |

d(ln(nen)I(−1)) | −0.044117 | 0.009014 | −4.894484 | 0.0027 |

d(ln(een)I) | 0.314848 | 0.03789 | 8.309447 | 0.0002 |

d(ln(een)I(−1)) | −0.563146 | 0.054644 | −10.30568 | 0.0000 |

CointEq(−1) | −0.826767 | 0.090241 | −9.161735 | 0.0001 |

R^{2} | 0.957047 |

Variables | Coefficient | Std. Error | T-Statistic | Prob. |
---|---|---|---|---|

ln(disa) | 0.018152 | 0.004442 | 4.086328 | 0.0065 |

ln(fai)I | −0.055221 | 0.051423 | −1.073862 | 0.3242 |

ln(een)I | 0.442601 | 0.092770 | 4.770975 | 0.0031 |

ln(nen)I | 0.039811 | 0.046665 | 0.853122 | 0.4263 |

C | −0.090547 | 0.035298 | −2.565249 | 0.0426 |

Diagnostic Test | Statistic | Prob. |
---|---|---|

LM test | 0.3478 | 0.8404 |

Ramsey RESET test | 3.8854 | 0.0037 |

Jarque–Bera test | 2.9929 | 0.2239 |

ARCH test | 0.0002 | 0.9913 |

Breusch–Godfrey | 0.8102 | 0.6533 |

D.W. test | 2.1662 | / |

Variable | Coefficient | Uncentered VIF | Centered VIF |
---|---|---|---|

ln(gdp)I(−1) | 0.0332 | 260.4987 | 9.9242 |

ln(gdp)I(−2) | 0.0515 | 416.0837 | 14.2891 |

ln(disa) | 0.0009 | 22.3331 | 5.2048 |

ln(disa)(−1) | 0.0007 | 17.5831 | 3.0690 |

ln(disa)(−2) | 0.0007 | 15.9031 | 2.1514 |

ln(fai)I | 0.0004 | 6.5566 | 2.4137 |

ln(fai)I(−1) | 0.0004 | 6.0610 | 2.2690 |

ln(fai)I(−2) | 0.0003 | 4.1302 | 1.6457 |

ln(nen)I | 0.0042 | 21.3153 | 9.2274 |

ln(nen)I(−1) | 0.0086 | 43.88395 | 19.8813 |

ln(nen)I(−2) | 0.0079 | 40.20849 | 17.9757 |

ln(een)I | 8.58 × 10^{−6} | 623.0242 | 3.5162 |

ln(een)I(−1) | 1.66 × 10^{−5} | 1223.712 | 6.2682 |

ln(een)I(−2) | 6.74 × 10^{−6} | 503.0009 | 2.3843 |

C | 0.0016 | 1595.67 | NA |

Variables | Coefficient | Std. Error | T-Statistic | Prob. |
---|---|---|---|---|

d(ln(gdp)I(−1)) | 0.2631 | 0.0319 | 8.2608 | 0.0037 |

d(ln(disa)) | 0.1033 | 0.0075 | 13.6921 | 0.0008 |

d(ln(disa)(−1)) | −0.0787 | 0.0067 | −11.6927 | 0.0013 |

d(ln(disa) * ln(disa)) | −0.0058 | 0.0004 | −14.5870 | 0.0007 |

d(ln(disa) * ln(disa)(−1)) | 0.0041 | 0.0004 | 11.2809 | 0.0015 |

d(ln(fai)I) | −0.1568 | 0.0053 | −29.3641 | 0.0001 |

d(ln(fai)I(−1)) | −0.0777 | 0.0040 | −19.2634 | 0.0003 |

d(ln(nen)I) | 0.0878 | 0.0033 | 26.8876 | 0.0001 |

d(ln(nen)I(−1)) | −0.0605 | 0.0025 | −24.6893 | 0.0001 |

d(ln(een)I) | 0.3306 | 0.0106 | 31.3328 | 0.0001 |

d(ln(een)I(−1)) | −0.4612 | 0.0152 | −30.3914 | 0.0001 |

CointEq(−1) | −0.4513 | 0.0131 | −34.4072 | 0.0001 |

R^{2} | 0.998234 |

Variables | Coefficient | Std. Error | T-Statistic | Prob. |
---|---|---|---|---|

lndisa | 0.4303 | 0.1076 | 3.9988 | 0.0280 |

lndisa * lndisa | −0.0228 | 0.0060 | −3.8157 | 0.0458 |

lnfaiI | −0.1332 | 0.0404 | −3.2968 | 0.0458 |

lneenI | 0.7019 | 0.1022 | 6.8677 | 0.0063 |

lnnenI | 0.2413 | 0.0642 | 3.7604 | 0.0329 |

C | −1.9629 | 0.4888 | −4.0160 | 0.0277 |

Diagnostic Test | T-Statistic | Prob. |
---|---|---|

LM test | 0.276768 | 0.7652 |

Ramsey RESET test | 1.431627 | 0.1860 |

Jarque–Bera test | 0.584567 | 0.7466 |

ARCH test | 0.263787 | 0.6138 |

D.W. test | 3.4852 |

Variable | Coefficient | Std. Error | T-Statistic | Prob. |
---|---|---|---|---|

dln(een) | 0.22117 | 0.057324 | 3.858234 | 0.0014 |

dln(gt) | 0.046992 | 0.038101 | 1.233361 | 0.2353 |

dln(nen) | 0.002857 | 0.031252 | 0.091414 | 0.9283 |

lndisa | 0.038866 | 0.057975 | 0.670394 | 0.5122 |

lndisa * lndisa | −0.001615 | 0.003123 | −0.517206 | 0.6121 |

C | −0.143814 | 0.265624 | −0.541419 | 0.5957 |

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**MDPI and ACS Style**

Cang, D.; Xu, Y.; Wang, G.
The Relationship between Economic Growth and Disaster Losses-Based on Linear and Nonlinear ARDL Model in China. *Sustainability* **2022**, *14*, 9760.
https://doi.org/10.3390/su14159760

**AMA Style**

Cang D, Xu Y, Wang G.
The Relationship between Economic Growth and Disaster Losses-Based on Linear and Nonlinear ARDL Model in China. *Sustainability*. 2022; 14(15):9760.
https://doi.org/10.3390/su14159760

**Chicago/Turabian Style**

Cang, Dingbang, Yiming Xu, and Guiqiang Wang.
2022. "The Relationship between Economic Growth and Disaster Losses-Based on Linear and Nonlinear ARDL Model in China" *Sustainability* 14, no. 15: 9760.
https://doi.org/10.3390/su14159760