TY - JOUR
T1 - Impact of food wastage on water resources and GHG emissions in Korea
T2 - A trend-based prediction modeling study
AU - Adelodun, Bashir
AU - Choi, Kyung Sook
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/10/20
Y1 - 2020/10/20
N2 - Unsustainable use of water resources and environmental degradation as related to global food production systems are critical issues of concern. However, reducing food wastage along the supply chain can provide the needed solutions to resources and environmental conservations, while meeting food demand. This study quantified the wastage of common food types at each stage along the supply chain in Korea using top-down mass flow analysis for the period of 2007–2017. The principal component analysis (PCA) was used to rank the food types based on their contribution to the total wastage. The water resources and GHG emissions associated with food wastage were assessed using the production footprint concept, after which prediction models were developed. The estimated food wastage was 14.97 ± 1.2 million tonnes, with production, postharvest, processing, distribution, and consumption representing 14%, 11%, 13%, 15%, and 46%, respectively. Vegetables, maize, and rice were ranked as the highest food types contributing to the total wastage, while mutton and rapeseed were the least. Our results indicated 15.24 ± 1.95 billion m3 and 20.08 ± 6.14 megatonnes CO2eq of water footprint and GHG emissions associated with food wastage, respectively, with substantial variations among the 28 major food commodity types. The prediction models using Bradley-Terry fitted well for the trend analysis of water footprint and GHG emission associated with food wastage. The prediction suggested that the total food supply, total wastage, water footprint, and GHG emission were estimated to reach 54.89 million tonnes, 16.91 million tonnes, 18.63 billion m3, and 27.41 megatonnes CO2eq by 2030, respectively. This study is of utmost importance considering the strong desire of the Korean government to pursue food self-sufficiency in the face of constraint water resources and GHG emission reduction target.
AB - Unsustainable use of water resources and environmental degradation as related to global food production systems are critical issues of concern. However, reducing food wastage along the supply chain can provide the needed solutions to resources and environmental conservations, while meeting food demand. This study quantified the wastage of common food types at each stage along the supply chain in Korea using top-down mass flow analysis for the period of 2007–2017. The principal component analysis (PCA) was used to rank the food types based on their contribution to the total wastage. The water resources and GHG emissions associated with food wastage were assessed using the production footprint concept, after which prediction models were developed. The estimated food wastage was 14.97 ± 1.2 million tonnes, with production, postharvest, processing, distribution, and consumption representing 14%, 11%, 13%, 15%, and 46%, respectively. Vegetables, maize, and rice were ranked as the highest food types contributing to the total wastage, while mutton and rapeseed were the least. Our results indicated 15.24 ± 1.95 billion m3 and 20.08 ± 6.14 megatonnes CO2eq of water footprint and GHG emissions associated with food wastage, respectively, with substantial variations among the 28 major food commodity types. The prediction models using Bradley-Terry fitted well for the trend analysis of water footprint and GHG emission associated with food wastage. The prediction suggested that the total food supply, total wastage, water footprint, and GHG emission were estimated to reach 54.89 million tonnes, 16.91 million tonnes, 18.63 billion m3, and 27.41 megatonnes CO2eq by 2030, respectively. This study is of utmost importance considering the strong desire of the Korean government to pursue food self-sufficiency in the face of constraint water resources and GHG emission reduction target.
KW - Food wastage
KW - GHG emission
KW - Korea
KW - Prediction modeling
KW - Trend analysis
KW - Water footprint
UR - http://www.scopus.com/inward/record.url?scp=85086903352&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2020.122562
DO - 10.1016/j.jclepro.2020.122562
M3 - Article
AN - SCOPUS:85086903352
SN - 0959-6526
VL - 271
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 122562
ER -