Shortstack double machine learning model results.

Access to safe drinking water is essential for public health. In Colombia, Resolution 2115 of 2007 mandates the use of the Water Quality Risk Index for Human Consumption (IRCA) to classify risk levels. However, a significant gap exists between objective IRCA measurements and public perception, which unfolds in the context of water injustice. This study examines the factors influencing perceptions of water quality in regions with high levels of water contamination. Based on a sample of 37,028 household heads from the 2022 Quality of Life Survey by DANE, the study applied advanced machine learning techniques, including logistic regressions with Lasso regularization and double machine learning, combining random forests and logit models. The analysis included sociodemographic factors, environmental awareness, and the environmental conditions experienced by respondents. Findings confirm the presence of water injustice in Colombia, highlighting a significant disconnect between IRCA scores and public perceptions. Additionally, perceptions of water quality are strongly influenced by visible environmental problems, such as air pollution, bad odors, and litter, suggesting that people tend to focus on more evident issues while overlooking water contamination. The results of this study highlight the need to implement policies that inform Colombians about the quality of the water they consume, in order to promote greater environmental commitment.

获取安全饮用水对公共卫生至关重要。在哥伦比亚，2007年第2115号决议强制要求使用人类饮用水水质风险指数（Water Quality Risk Index for Human Consumption, IRCA）对风险等级进行划分。然而，客观IRCA检测结果与公众认知之间存在显著差距，这一现象背后折射出水环境不公问题。本研究聚焦水污染严重地区，探讨影响公众水质认知的各类因素。本研究依托哥伦比亚国家行政统计局（DANE）2022年生活质量调查的37028名户主样本，采用了包括带Lasso正则化的逻辑回归、双重机器学习（结合随机森林与Logit模型）在内的先进机器学习技术。分析涵盖了社会人口学特征、环境认知水平以及受访者所感知到的周边环境状况。研究结果证实了哥伦比亚存在水环境不公问题，凸显出IRCA评分与公众认知之间存在显著脱节。此外，公众的水质认知显著受到可见环境问题的影响，例如空气污染、异味与垃圾乱弃，这表明公众往往更关注直观可见的问题，却忽视了水污染本身。本研究结果凸显出，有必要出台相关政策向哥伦比亚民众普及其所饮用的水质情况，以提升公众的环境责任感。