Application of Multi linear regression (MLR) analysis for determining predictors of illegal dumping in rapidly urbanized rural areas: A case study of Bangkalan District, Indonesia

Abstract

This study investigates the factors influencing illegal waste dumping in Bangkalan District, a rapidly urbanizing rural area in Indonesia. Illegal dumping has become a significant environmental and public health concern due to inefficient waste management systems, irregular collection schedules, and inadequate infrastructure. The research identifies key socio-economic, demographic, and technical factors contributing to illegal dumping behaviors. Through data collected from 387 households, a Multilinear Regression (MLR) analysis reveals that six factors such as low income, lower education levels, larger family sizes, irregular waste collection services, easy accessibility and short distance to illegal dump site (IDS) significantly increase illegal dumping rates. The findings emphasize the need for improving waste management infrastructure, enhancing public awareness, and addressing economic constraints to mitigate illegal dumping. By identifying the primary drivers of illegal dumping, this research contributes to achieving several United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 3 (Good Health and Well-being), and SDG 12 (Responsible Consumption and Production). The study concludes with policy recommendations for improved waste management and enforcement, offering insights for addressing this issue in other rapidly urbanizing rural areas.