five

Descriptive Statistics.

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://figshare.com/articles/dataset/Descriptive_Statistics_/29383826
下载链接
链接失效反馈
官方服务:
资源简介:
This study aims to evaluate the existing solid waste collection and management system available to households in Pakistan and to explore the factors affecting households’ cash payments for waste collection and disposal services. Robust least square regression is applied to household-level data from 16,155 households in the Pakistan Social Living Measurement Survey (PSLM) for 2019–2020. This method was chosen for its ability to handle outliers and provide more reliable estimates. On average, households pay PKR 214 (USD 1.01) per month for waste collection and disposal services. Households in Baluchistan and Khyber Pakhtunkhwa pay the highest amounts, while those in Sindh and Punjab pay less. Rural households pay more than urban households. Waste collection is primarily handled by private vans/carts, with doorstep collection being the most common method. The municipality’s role in waste collection at the doorstep is limited. Public bins and waste collection points are accessible to 83 percent of households, but their distant locations and infrequent emptying pose significant problems. These limitations highlight the need for improved municipal involvement and infrastructure. Results indicate that household income, education of the household head, age of the household head, gender of the household head, number of earners in the household, doorstep waste collection via private van/cart, availability of bins or waste collection points, distance from waste disposal facilities, bin or waste collection point clearance duration, house ownership, dwelling type, and number of rooms significantly affect households’ cash payments for waste collection services. To increase cash payments for waste collection services, waste management authorities should provide better and modern solid waste management systems. Upgrading existing systems can enhance households’ willingness to pay for these services.
创建时间:
2025-06-23
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作