TY - JOUR
T1 - Optimizing the number and location of household waste collection sites by multi-maximal covering location model
T2 - An empirical study in Minamata City, Kumamoto Prefecture, Japan
AU - Zhuo, Qiannan
AU - Yan, Wanglin
N1 - Funding Information:
We gratefully acknowledge the support from Minamata local government, the residents' associations, and Prof. Uehara Keisuke, Faculty of Environment and Information Studies Keio University, for data collection. We also thank for the kindly supervision for the methodology part from Prof. Ken-ichi Tanaka, Department of Administration Engineering Keio University. The Graduate School of Media and Governance Research Fund has supported this research financially for the proofreading.
Publisher Copyright:
© 2022
PY - 2022/12/15
Y1 - 2022/12/15
N2 - Convenience has consistently been perceived as one of the crucial determinants of household recycling behavior in source separation systems studied over the last several decades. Although residents' accessibility to collection sites significantly affects household recycling behavior, allocating the collection sites for household waste is complex for policymakers. It requires balancing between residents' needs and the limited budgetary and human resources of local government and consideration of waste management policies. This article proposes the multi-maximal covering location model (MMCLM) to solve the waste collection site allocation problem based on residents' satisfaction while considering the capacity of municipal services. In this study, MMCLM was applied in Minamata City, Kumamoto Prefecture, Japan, a city with more than 25 years of successful experience in implementing household recycling. Six scenarios were developed to allocate recyclable waste collection sites. With 10 new sites, there was a more than 5% improvement in average access distance to drop off waste for all households. 30 new sites could effectively reduce the average access distance by 72.4% for 324 households who currently have to walk more than 10 min, and 8.0% of the average access distance for all residents. Low requirements for input datasets and flexible parameters adjustment make MMCLM more feasible in various contexts, especially for small to medium-sized cities or developing regions.
AB - Convenience has consistently been perceived as one of the crucial determinants of household recycling behavior in source separation systems studied over the last several decades. Although residents' accessibility to collection sites significantly affects household recycling behavior, allocating the collection sites for household waste is complex for policymakers. It requires balancing between residents' needs and the limited budgetary and human resources of local government and consideration of waste management policies. This article proposes the multi-maximal covering location model (MMCLM) to solve the waste collection site allocation problem based on residents' satisfaction while considering the capacity of municipal services. In this study, MMCLM was applied in Minamata City, Kumamoto Prefecture, Japan, a city with more than 25 years of successful experience in implementing household recycling. Six scenarios were developed to allocate recyclable waste collection sites. With 10 new sites, there was a more than 5% improvement in average access distance to drop off waste for all households. 30 new sites could effectively reduce the average access distance by 72.4% for 324 households who currently have to walk more than 10 min, and 8.0% of the average access distance for all residents. Low requirements for input datasets and flexible parameters adjustment make MMCLM more feasible in various contexts, especially for small to medium-sized cities or developing regions.
KW - Household recycling behavior
KW - Multi-maximal covering location model
KW - Residents' satisfaction
KW - Source separation system
KW - Waste collection site accessibility
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U2 - 10.1016/j.jclepro.2022.134644
DO - 10.1016/j.jclepro.2022.134644
M3 - Article
AN - SCOPUS:85143827263
SN - 0959-6526
VL - 379
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 134644
ER -