An LCA-based Spatial Matching Scheme Aids the De-carbonization from Plastic Waste Recycling in Japan

Main Presenter:    Richao Cong 

Co-Authors:   Atsushi Fujiyama     Toru Matsumoto                                          

Increasing global ocean plastic waste (PW) has become a major concern in recent years. This study provided an LCA-based spatial matching scheme to support the low-carbon style recycling system, demonstrated using polyethylene (PE) waste generated from Mie Prefecture, Japan. The objective function was defined as the maximum carbon dioxide (CO2) emission reductions from the whole lifecycle of the PE waste recycling e.g., transportation, pretreatment, recycling, and residue disposal processes. The major constraints include the total generation amounts of PE waste, the capacities of recycling facilities, and the distance matrix between facilities (obtained by using ArcGIS). Then, the optimal solutions for the amount of PE waste to be disposed of were detected by a linear programming approach.
As the matching results, only a few PE waste was allocated for mechanical/material recycling (MR) facility, however, the energy recovery (ER), and chemical recycling (CR) facilities were all allocated with full capacity to achieve the maximum objective. The capacity of the allocated MR facilities accounted for about 0.1%, ER 91.5%, and CR 8.9% of the totals. Meanwhile, the maximum emission reductions from the whole lifecycle take up MR: 0.1%, ER: 89.8%, and CR: 10.1% of the totals (157.9 kt-CO2). As the analysis, the averaged emission reductions per ton of PE waste disposed were estimated to be 2.8 t-CO2 t-1. Thus, we concluded that this optimization under the current technical level was effective in making the recycling system with more emission reductions (low-carbon environmental effect) and requiring fewer disposal facilities (cost-saving economic effect). Meanwhile, certain long-distance routes found in solutions implied that technical parameters were more important than
geolocation parameters to achieve maximum emission reductions. In the future, some complex cases (constraints) and ones with other objectives (economic perspective) will be explored.

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