TY - JOUR
T1 - Effects of Fiscal Decentralization on Garbage Classifications
AU - Ma, Qiuzhuo
AU - Huang, Diejun
AU - Li, Hua
AU - Hu, Yimei
AU - Paudel, Krishna P.
AU - Zhang, Sijin
AU - Zhang, Jianfeng
PY - 2021
Y1 - 2021
N2 - China has been promoting garbage classification in its rural areas, yet it lacks financial appropriation and fiscal decentralization to support waste processing projects. Though the existing literature has suggested fiscal decentralization strategies between different local government levels, few of the studies ascertain garbage classification efficiency from a quantitative perspective. To bridge the gap, this study examines the optimal fiscal decentralization strategies for garbage classification. It uses an optimization model while considering decision makers’ requirements regarding the fund allocation amounts at different government levels and the classification ratios in villages as constraints and decisions, respectively. A three-stage heuristic algorithm is applied to determine optimal landfill locations and efficient classification ratios for the garbage processing system in rural China, with an analytical discussion on the propositions and properties of the model. Our analytical results suggest that 1) the theoretically optimal solution is conditionally achievable, 2) the applied algorithm can achieve the optimal solution faster when the relationship between governance costs and classification ratios reaches some mathematical conditions, and 3) there is always a potential for increasing the retained funds between different government levels or for reducing the total appropriation from the county government. The numerical experiment on a primary dataset from 12 towns and 143 villages in the Pingyuan county of Guangdong province, China, does not only affirm the qualitative results, but it also provides insights into the difficulties encountered during the implementation of the garbage classification policy in China’s rural areas.
AB - China has been promoting garbage classification in its rural areas, yet it lacks financial appropriation and fiscal decentralization to support waste processing projects. Though the existing literature has suggested fiscal decentralization strategies between different local government levels, few of the studies ascertain garbage classification efficiency from a quantitative perspective. To bridge the gap, this study examines the optimal fiscal decentralization strategies for garbage classification. It uses an optimization model while considering decision makers’ requirements regarding the fund allocation amounts at different government levels and the classification ratios in villages as constraints and decisions, respectively. A three-stage heuristic algorithm is applied to determine optimal landfill locations and efficient classification ratios for the garbage processing system in rural China, with an analytical discussion on the propositions and properties of the model. Our analytical results suggest that 1) the theoretically optimal solution is conditionally achievable, 2) the applied algorithm can achieve the optimal solution faster when the relationship between governance costs and classification ratios reaches some mathematical conditions, and 3) there is always a potential for increasing the retained funds between different government levels or for reducing the total appropriation from the county government. The numerical experiment on a primary dataset from 12 towns and 143 villages in the Pingyuan county of Guangdong province, China, does not only affirm the qualitative results, but it also provides insights into the difficulties encountered during the implementation of the garbage classification policy in China’s rural areas.
KW - fiscal decentralization
KW - garbage classification
KW - optimization
KW - quantitative analysis
KW - rural area
UR - http://www.scopus.com/inward/record.url?scp=85111577064&partnerID=8YFLogxK
U2 - 10.3389/fenrg.2021.686561
DO - 10.3389/fenrg.2021.686561
M3 - Journal article
SN - 2296-598X
VL - 9
JO - Frontiers in Energy Research
JF - Frontiers in Energy Research
M1 - 686561
ER -