How to Effectively Improve Pesticide Waste Governance: A Perspective of Reverse Logistics
Zhichao Li () and
Jilin Huang ()
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Zhichao Li: School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China
Jilin Huang: College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
Sustainability, 2018, vol. 10, issue 10, 1-19
With the development of modern science and technology, agricultural production and quality have made major breakthroughs, followed by more and more pesticide waste. Pesticide waste refers to the packaging of pesticide residues. Pesticide waste will cause great damage to soil, water and human health, seriously affecting the ecological sustainability. Most of the existing literature is theoretical articles, and few empirical studies are provided on how to improve pesticide waste management. In this paper, a set of reverse logistics network models of pesticide wastes is constructed, and the framework of reverse logistics is used to realize the efficient recovery and treatment of pesticide wastes. The problem of how to collect pesticide wastes and how to optimize the location and flow allocation of facilities such as recycling center and treatment center was solved. The weights of the factors affecting the reverse logistics network model are determined by using the network analytic hierarchy process. Under the conditions of minimizing the cost and minimizing the negative externality, the mixed integer model programming method is used to make the optimal location decision and flow distribution. Finally, taking the JT area of China as an example, the results of the reverse logistics network model of pesticide wastes are demonstrated. The results show that the scheme is feasible.
Keywords: pesticide waste; reverse logistics; empirical model; sustainable development (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:10:p:3622-:d:174743
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