A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling

Abstract

Recycling plastic can abate the environmental pollution as well as CO2 emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO2 price is modeled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modeling framework can be easily applied to other sectors with similar characteristics.

Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

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Reverse supply network design for circular economy pathways of wind turbine blades in Europe

“…On the other hand, Kannan, Noorul Haq, and Devika (2009) proposed a genetic algorithm (GA) for the cost optimisation of the design of the closed loop supply chain of plastic goods. Other authors proposed mathematical models to investigate the impact of the carbon price uncertainty on the closed loop supply chain of plastic recycling (Ren et al, 2020).…”

An energy system optimization model accounting for the interrelations of multiple stochastic energy prices

“…To our knowledge, only a few studies are incorporating time-series analytical methods into energy system modeling (Kim et al, 2019 ; Lima et al, 2018 ), whereas a thorough and profound analysis is still absent regarding examining the mutual effect among multiple energy markets in ESM. Smaller-scale ESMs or single-period supply chain optimization models allow for implementation with methods such as Monte Carlo simulation or SP to deal with price uncertainty (Ren et al, 2020 ; Rezaee et al, 2017 ; Waltho et al, 2019 ). On the contrary, large-scale ESMs often build on relatively long timeframes; generating sufficient scenario trees for price over a long time horizon entails the curse of dimensionality.…”