Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

Abstract

We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optimization algorithm to obtain joint optimal production quantities combination for maximizing overall profit under regulatory policies, respectively. Furthermore, numerical studies by featuring exponentially distributed demand compare systemwide performances in various scenarios. We build the “carbon emission elasticity of profit (CEEP)” index as a metric to evaluate the impact of regulatory policies on both chainwide emissions and profit. Our results manifest that by facilitating the mandatory emission cap in proper installation within the network one can balance well effective emission reduction and associated acceptable profit loss. The outcome that CEEP index when implementing Carbon emission tax is elastic implies that the scale of profit loss is greater than that of emission reduction, which shows that this policy is less effective than mandatory cap from industry standpoint at least.

The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models

“…One of the important aspects of GSCs is the uncertainty in demand over the long term. Demand uncertainty has been considered by He, Xu and Niu [69], Osmani and Zhang [72] through a probability density function based on an input-output model for carbon policy and the loss of profit for the firms. Shaw, Irfan, Shankar and Yadav [79] incorporate the uncertainties in capacities of the supplier, manufacturer and warehouse, and demand in order to develop a chance-constrained model of a GSC network.…”

“…Therefore, the knowledge on a range of carbon cap becomes important for policymakers to design different levels of carbon cap [45]. Between carbon tax and carbon cap policies, the latter is generally less flexible in terms of emission reduction cost [33] but it usually leads to a lower total cost [69,76].…”

“…Tao et al [76] mention that the global cap has an advantage when the emission limit is high, while the periodic cap is superior when the emission limit is low. He, Xu and Niu [69] introduce the carbon emission elasticity of profit measure to evaluate the impact of carbon policies and show that the mandatory cap is better than the carbon tax from an industry standpoint. Martí, Tancrez and Seifert [76] mention that the optimal network structure for the carbon tax and carbon cap policies are similar.…”

“…Although the uncertainty in the GSC is mentioned in the literature, it is limited to the customer demand aspects [38,40,41,69,72,90,109]. The uncertainties can also be seen in various carbon policies, such as the shift of the carbon tax policy to a carbon trading scheme in Australia [110], and the parameters of a particular carbon policy, such as the unstable carbon price on the market, the carbon tax rate and the carbon price on the carbon market.…”

The Impact of Transport Mode and Carbon Policy on Low-Carbon Retailer

“…Chang et al [19] consider a monopolist manufacturer under carbon cap-andtrade mechanism that makes new products in the first period and makes new and refabricated products simultaneously in the next period and the manufacturer has to make the decision on the optimal production quantities for the first and the second period. He et al [20] focus on how to get the optimal production planning confronting two typical carbon Discrete Dynamics in Nature and Society 3 emission policies for a complex supply chain facing stochastic market demands and design optimization algorithm to solve the joint optimal production problem.…”