Carbon Tax Refund System for Recycling in Reverse Supply Chain Network to Minimize GHG Emissions and Costs

Haruto Takeshita, Yuki Kinoshita and Tetsuo Yamada ()
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Haruto Takeshita: Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
Yuki Kinoshita: Department of Informatics, Faculty of Engineering, Kindai University, 1 Takaya Umenobe, Higashi-Hiroshima, Hiroshima 739-2116, Japan
Tetsuo Yamada: Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

Sustainability, 2024, vol. 16, issue 24, 1-17

Abstract: Material recycling is vital for achieving carbon neutrality because using recycled materials helps avoid greenhouse gas (GHG) emissions that result from using virgin material. Carbon tax has been introduced in many countries to reduce GHG emissions. As recycling can prevent additional GHG emissions, the carbon tax should be refunded based on the GHG volume saved by recycling. The incentive of carbon tax refund can help promote recycling as an environment-friendly and economical activity. To retrieve material values from end-of-life (EOL) products, a reverse supply chain network should be designed based on the status and value of EOL products. This study introduces carbon tax refund into the reverse supply chain network for maximizing saved GHG emissions and minimizing cost. The bi-objective model is formulated using ϵ constraint method and integer programming. Numerical experiments were conducted based on the recycling of a vacuum cleaner and a laptop. The monetary rate at which the carbon tax refund became economically attractive differed according to product type. Thus, variable carbon tax refund rates would be needed, based on product type, to incentivize recycling.

Keywords: reverse supply chain network; carbon tax; recycling; EOL product; integer programming; carbon neutrality; circular economy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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