Human behavioral drivers of household food waste: Empirical evidence from Pakistan

This study aimed to analyze human behavioral drivers of household food waste. To obtain the study objective, data were collected from 379 respondents (18 years and above) in four Town Councils of District Peshawar using a multistage stratified random sampling technique. A forward stepwise multiple regression analysis was used to identify key predictors of household food waste, measured as the weekly monetary value of food waste. The model was statistically significant ( p < 0.05) and explained 88.4% of the variation in weekly household food waste ( R 2 = 0.884). The model-based estimations show that awareness of the weekly monetary value of food waste was associated with a reduction of weekly household food waste by PKR 453, followed by having better knowledge of food storage by PKR 260, improved handling of leftovers by PKR 165, using better planning and organizing activities to prevent weekly household food waste by PKR 128, lower socioeconomic status, stigmatized food waste, and Joint family system were associated with a reduction of PKR 63, PKR 49.9, and PKR 35.8, respectively. The study recommended socio-psychological induction of human affection to reduce food waste through appropriate socialization of children and their involvement in meal planning, community mobilization for collective efforts, and balanced use of social stigma to control household food waste.

Authors

Institutions

Publication Details

Journal
Waste Management & Research The Journal for a Sustainable Circular Economy
Published
2026-11-06
DOI
https://doi.org/10.1177/0734242x261499520
Primary Topic
Food Waste Reduction and Sustainability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Human behavioral drivers of household food waste: Empirical evidence from Pakistan

Abid Jan, Shakeel Ahmad, Asad Ullah, Sikandar Khan et al.
3 links
Waste Management & Research The Journal for a Sustainable Circular Economy
Food Waste Reduction and Sustainability
article

Human behavioral drivers of household food waste: Empirical evidence from Pakistan

Abid Jan, Shakeel Ahmad, Asad Ullah, Sikandar Khan, Imran Ullah Khan
article en

Abstract

This study aimed to analyze human behavioral drivers of household food waste. To obtain the study objective, data were collected from 379 respondents (18 years and above) in four Town Councils of District Peshawar using a multistage stratified random sampling technique. A forward stepwise multiple regression analysis was used to identify key predictors of household food waste, measured as the weekly monetary value of food waste. The model was statistically significant ( p < 0.05) and explained 88.4% of the variation in weekly household food waste ( R 2 = 0.884). The model-based estimations show that awareness of the weekly monetary value of food waste was associated with a reduction of weekly household food waste by PKR 453, followed by having better knowledge of food storage by PKR 260, improved handling of leftovers by PKR 165, using better planning and organizing activities to prevent weekly household food waste by PKR 128, lower socioeconomic status, stigmatized food waste, and Joint family system were associated with a reduction of PKR 63, PKR 49.9, and PKR 35.8, respectively. The study recommended socio-psychological induction of human affection to reduce food waste through appropriate socialization of children and their involvement in meal planning, community mobilization for collective efforts, and balanced use of social stigma to control household food waste.

Waste Management & Research The Journal for a Sustainable Circular Economy
The University of Agriculture, Peshawar (PK), Hazara University (PK)
Responsible consumption and production, No poverty, Zero hunger, Good health and well-being
Openalex Percentile: Top 20%
Food Waste Reduction and Sustainability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon