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评估印度洪水脆弱性的社会经济和环境决定因素：一种面板数据方法。

Assessing the socioeconomic and environmental determinants of flood vulnerability in India: a panel data approach.

作者信息

Felix Kannan Thomas, Balasubramanian M, Govindarajan Padma Lakshmi, Kesav B

机构信息

Agriculture Development and Rural Transformation Centre, Institute for Social and Economic Change, Bengaluru, Karnataka, 560 072, India.

Centre for Ecological Economics and Natural Resources, Institute for Social and Economic Change, Bengaluru, Karnataka, 560 072, India.

出版信息

Sci Rep. 2025 Jul 30;15(1):27762. doi: 10.1038/s41598-025-09442-9.

DOI:10.1038/s41598-025-09442-9
PMID:40739148
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12311002/
Abstract

This study investigates the socioeconomic and environmental determinants of flood vulnerability in Indian states using a panel data approach. Drawing on state-level flood damage data from 1953 to 2020, sourced from the Ministry of Jal Shakti and affiliated agencies, this study employs a robust econometric framework to identify the key drivers of flood-related damage. The dataset included 16 variables, and rigorous preprocessing steps, such as data cleaning, interpolation, stationarity testing, and multicollinearity checks, to ensure model reliability. Endogeneity was tested using IV regression, and the absence of such issues allowed for the application of Fixed Effects (FE) and Random Effects (RE) models. The Hausman test favored the RE model, which was further validated through diagnostic and robustness tests. The findings reveal that capital expenditure, government spending on natural calamities, and mangrove coverage significantly reduce flood damage, highlighting the importance of both fiscal and ecological interventions. Conversely, population affected, damage to houses, and damage to public utilities were positively associated with flood damage, indicating the need for targeted infrastructure and community resilience strategies. This study offers actionable insights for policymakers, emphasizing the integration of environmental conservation with strategic public investment to enhance flood resilience in India.

摘要

本研究采用面板数据方法，调查印度各邦洪水脆弱性的社会经济和环境决定因素。本研究利用来自水利和相关机构部的1953年至2020年邦级洪水损失数据，采用稳健的计量经济学框架来确定洪水相关损失的关键驱动因素。数据集包含16个变量，并进行了严格的预处理步骤，如数据清理、插值、平稳性检验和多重共线性检查，以确保模型的可靠性。使用工具变量回归检验内生性，由于不存在此类问题，因此可以应用固定效应（FE）和随机效应（RE）模型。豪斯曼检验支持随机效应模型，该模型通过诊断和稳健性检验得到进一步验证。研究结果表明，资本支出、政府在自然灾害上的支出以及红树林覆盖面积显著减少了洪水损失，凸显了财政和生态干预的重要性。相反，受影响人口、房屋受损情况和公共设施受损情况与洪水损失呈正相关，这表明需要有针对性的基础设施和社区复原力战略。本研究为政策制定者提供了可操作的见解，强调将环境保护与战略性公共投资相结合，以增强印度的洪水复原力。

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本文引用的文献

1
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J Environ Manage. 2022 Mar 1;305:114317. doi: 10.1016/j.jenvman.2021.114317. Epub 2021 Dec 24.
2
Modeling, mapping and analysis of urban floods in India-a review on geospatial methodologies.印度城市洪水的建模、制图和分析-地理空间方法综述。
Environ Sci Pollut Res Int. 2021 Dec;28(48):67940-67956. doi: 10.1007/s11356-021-16747-5. Epub 2021 Oct 9.
3
Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future.
未来气候和土地利用变化下洪水灾害风险的时空变化测绘。
J Environ Manage. 2021 Nov 15;298:113551. doi: 10.1016/j.jenvman.2021.113551. Epub 2021 Aug 17.
4
The human gut microbiome and health inequities.人类肠道微生物组与健康不平等。
Proc Natl Acad Sci U S A. 2021 Jun 22;118(25). doi: 10.1073/pnas.2017947118.
5
Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future.气候变化影响评估、洪水管理和缓解策略在巴基斯坦的可持续未来。
Environ Sci Pollut Res Int. 2021 Jun;28(23):29720-29731. doi: 10.1007/s11356-021-12801-4. Epub 2021 Feb 10.
6
Contribution of historical precipitation change to US flood damages.历史降水变化对美国洪水灾害的影响。
Proc Natl Acad Sci U S A. 2021 Jan 26;118(4). doi: 10.1073/pnas.2017524118.
7
Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks.基于RBF-NARX神经网络耦合的城市内涝预警系统的建立
Water Sci Technol. 2020 Nov;82(9):1921-1931. doi: 10.2166/wst.2020.477.
8
Ecosystem Services of Mangrove Forests: Results of a Meta-Analysis of Economic Values.红树林生态系统服务：经济价值的荟萃分析结果。
Int J Environ Res Public Health. 2020 Aug 12;17(16):5830. doi: 10.3390/ijerph17165830.
9
Land use change impacts on floods at the catchment scale: Challenges and opportunities for future research.流域尺度上土地利用变化对洪水的影响：未来研究的挑战与机遇
Water Resour Res. 2017 Jul;53(7):5209-5219. doi: 10.1002/2017WR020723. Epub 2017 Jul 2.
10
Development and implementation of South Asia's first heat-health action plan in Ahmedabad (Gujarat, India).印度古吉拉特邦艾哈迈达巴德市制定并实施南亚首个热健康行动计划。
Int J Environ Res Public Health. 2014 Mar 25;11(4):3473-92. doi: 10.3390/ijerph110403473.