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<p>This study examines the spatial association between water-related risks and international migration patterns using country-level data for 2019. Water risk indicators are constructed from the Aqueduct Water Risk Atlas 3.0 using a TOPSIS-based composite indicator approach, distinguishing Overall Water Risk (OWR), Physical Risks Quantity (PRQn), Physical Risks Quality (PRQl), and Regulatory and Reputational Risks (RRR). International emigration data are obtained from the World Bank-KNOMAD migration database. The analysis first evaluates spatial clustering through Global Moran’s I and then compares baseline ordinary least squares (OLS) models with spatial autoregressive (SAR), spatial error model (SEM), and spatial Durbin model (SDM) specifications. The dependent variable is transformed as log(Emigration + 1) to reduce skewness, while the TOPSIS-derived indicators are standardised before estimation. The results show significant spatial clustering in both water risk and migration indicators. Baseline OLS models exhibit residual spatial autocorrelation, whereas spatial econometric models substantially reduce this dependence. PRQn and RRR show positive associations with international emigration, while PRQl is less robust across specifications. The findings should be interpreted as cross-sectional spatial associations rather than causal effects. The study contributes to the environmental migration literature by showing how composite water risk indicators and spatial econometric models can be combined to identify geographically uneven patterns of vulnerability and human mobility.</p>

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Spatial associations between water risk and international migration using TOPSIS and spatial econometric models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Alessandro Indelicato,
  • Firuz Suleymanov,
  • Juan Carlos Martín

摘要

This study examines the spatial association between water-related risks and international migration patterns using country-level data for 2019. Water risk indicators are constructed from the Aqueduct Water Risk Atlas 3.0 using a TOPSIS-based composite indicator approach, distinguishing Overall Water Risk (OWR), Physical Risks Quantity (PRQn), Physical Risks Quality (PRQl), and Regulatory and Reputational Risks (RRR). International emigration data are obtained from the World Bank-KNOMAD migration database. The analysis first evaluates spatial clustering through Global Moran’s I and then compares baseline ordinary least squares (OLS) models with spatial autoregressive (SAR), spatial error model (SEM), and spatial Durbin model (SDM) specifications. The dependent variable is transformed as log(Emigration + 1) to reduce skewness, while the TOPSIS-derived indicators are standardised before estimation. The results show significant spatial clustering in both water risk and migration indicators. Baseline OLS models exhibit residual spatial autocorrelation, whereas spatial econometric models substantially reduce this dependence. PRQn and RRR show positive associations with international emigration, while PRQl is less robust across specifications. The findings should be interpreted as cross-sectional spatial associations rather than causal effects. The study contributes to the environmental migration literature by showing how composite water risk indicators and spatial econometric models can be combined to identify geographically uneven patterns of vulnerability and human mobility.