.png){kind=icon}
.png){kind=icon}
Return
Driving factors of urban reclaimed water development in water-environment-sensitive regions: Coupled causal inference with double machine learning and causal forest
.png){kind=icon}
PRE
AI
王
F
X
W
X
DOI：10.1016/j.jhydrol.2026.135586.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Built a six-dimension indicator system for urban reclaimed water development. • Coupled DML and causal forest with fixed effects for robust causal inference. • Identified nine core causal drivers of reclaimed water development. • Found spatial and temporal heterogeneity in city responses and effect patterns. • Proposed differentiated strategies for high-quality reclaimed water development.
Keywords:
urban reclaimed water
causal inference
double machine learning
causal forest
spatial heterogeneity
Journal
IF:
6.3
Papers:
2.3W
Citations:
9.8W
Organization
Cited Papers