The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models
Abstract
:1. Introduction
2. Review Methodology
3. Results of the Literature Review
3.1. Drivers and Actors
3.2. Models and Decisions
3.2.1. Two-Echelon Level
3.2.2. Multi-Echelon Level
3.2.3. Closed-Loop Level
3.3. Effect of the Carbon Policies
3.3.1. Carbon Tax Policy
3.3.2. Carbon Cap Policy
3.3.3. Cap-and-Trade Policy
4. Conclusions
Further Research Directions
- (1)
- The current research can be extended to assess the stochastic nature of the carbon policy parameters. 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 increasing variability of the stochastic parameters degrades the financial and environmental performance of a SC Osmani and Zhang [72]. As these unstable parameters are the major causes leading to SC risk, they also influence the GSC design. Therefore, uncertainty factors should be used in GSC design.
- (2)
- The current research lacks a comprehensive analysis of international carbon policies on a SC. The carbon policies along a GSC can vary from one region to another (for example, Europe and Asia) or from one country to another [111]. Therefore, when regions or country-specific policies are not considered, it can even lead to a higher level of emission [81] because of carbon leakage. One possible approach to cope with the carbon leakage problem is to impose innovative carbon policies, like carbon tariffs on the goods from unregulated countries [81] or a global cap policy [17]. Another study [83] mentions that a policy of incrementally increasing carbon taxes may help in the coordination of carbon pricing across a SC (that may involve many nations) and it may support a holistic analysis of carbon policies for a GSC. A possibility of linking carbon policies across nations for a coordinated emissions reduction is also mentioned in Wang-Helmreich and Kreibich [10]. Research can, therefore, be extended to analyze the relation between carbon emissions with different carbon policies and their impact on the GSC design.
- (3)
- Carbon taxes and subsidies can promote remanufacturing [2,7,102] and low-carbon technologies [107]. The carbon offset policy provides the firms with opportunities to achieve carbon-neutral SC by engaging in emissions reduction offset projects [112]. However, only limited studies have mentioned the subsidy, offset, and the pricing policies, and so their extensive effects on different levels of a SC deserve further investigation. Furthermore, the implications of carbon policies are higher in SCs [8,68] as the emission reduction activities involve various firms, government, and customers. Existing studies indicate that the effectiveness of carbon policies relies highly on the thresholds of the carbon policy parameters. Research can be extended to study joint carbon policies [39,54,86] and carbon footprints [32]. Such research can provide valuable insights for policymakers to understand and develop a correct level of subsidies.
- (4)
- Emissions can be affected through coordination [42,112,113],) or through collaboration [9,44]. Firms can affect their partners’ emissions by coordination, information sharing, or even simply by leveraging their economic power [38,112]. Most of the studies in a decentralized GSC assume that there is one leader and one follower in decision making. The competition of the actors in a supply system or between different supply systems has also been discussed. However, real-world competition is more complex as it involves multi-faceted actors. For example, two competitors can cooperate in collecting and disposing of the used products. Therefore, the effect of carbon policies on a collaborative GSC design becomes necessary.
- (5)
- The sustainability of SCs under carbon policies is another potential area of research. Sustainability is considered in terms of three main aspects which are economic, environmental and social, and these three aspects are interdependent in the SCs. In particular, it is worthwhile to note that social welfare is not exhaustively considered in existing models. As the relationship between the carbon cost and social welfare also depends on market competition [114], the research can also be extended to the impact of carbon policy on social welfare. In addition, the sustainability of a GSC and customers’ willingness to pay for products can also be explored.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Author | Carbon Regulations | Modeling Techniques | Actors | Decisions |
---|---|---|---|---|
Hua, Cheng and Wang [35] | Cap-and-trade | E-EOQ | Single manufacturer-retailer | Order quantity |
Barari, Agarwal, Zhang, Mahanty and Tiwari [9] | Tax | Evolutionary game theory | Single supplier-manufacturer | Emission cost and marketing cost |
Choi [53] | Tax | Integral equation | Single manufacturer-retailer | Order quantity |
Du, Zhu, Liang and Ma [40] | Cap-and-trade | SG | Single emission permit supplier and single emission-dependent manufacturer | Production quantity and carbon price |
Jaber, et al. [54] | Tax, cap-and-trade | NLP | Single manufacturer-retailer | Production rate and joint lot size |
Xia and Zhi [11] | Cap-and-trade | SG | Single manufacturer-retailer | Retailer’s promotion level, emission reduction of per unit product |
Yang, et al. [55] | Tax, cap, cap-and-trade | SG | Single manufacturer-retailer | Order quantity |
Hafezalkotob [43] | Joint tax and subsidy | SG | Single manufacturer-retailer | Retail price, tariff for products |
He, Zhao and Xia [15] | Tax | SG | Single supplier-manufacturer | Emission decrements |
Hovelaque and Bironneau [34] | Tax | E-EOQ | Single supplier-manufacturer | Order quantity |
Bazan, Jaber and Zanoni [36] | Tax, cap | Classical coordination model and VMI-CS | Single manufacturer-retailer | Production rate, number and size of shipments(truck) |
Li [50] | Tax | NLP | Single distributor, multiple retailers | Delivery cycle, vehicle type |
Ren, Bian, Xu and He [44] | Cap | SG | Single manufacturer-retailer | Wholesale price, retail price, quota allocation |
Jiang, et al. [56] | Cap-and-trade | E-EOQ | Single supplier-manufacturer | Order quantity |
Qi, Wang and Bai [45] | Cap | SG | Single manufacturer, two retailers | Retail and wholesale prices |
Li, Su and Ma [39] | Joint tax and cap-and-trade | E-EOQ | Single manufacturer-retailer | Production rate, order quantity and number of shipments(truck) |
Qiu, Qiao and Pardalos [37] | Cap-and-trade | MIP | Single manufacturer, multiple retailers | Production quantity, inventory, vehicle route, customer satisfaction |
Xu, He, Xu and Zhang [46] | Cap-and-trade | SG | Single supplier-manufacturer | Wholesale price, order quantities |
Xu, et al. [57] | Cap-and-trade | SG | Single manufacturer-retailer | Wholesale prices, production quantities |
Ghosh, et al. [58] | Cap | E-EOQ | Single manufacturer-retailer | Order quantity, reorder point, number of shipments |
Ji, et al. [59] | Cap-and-trade | SG | Single manufacturer-retailer | Wholesale price, marginal profit, promotion degree, emission reduction rate |
Hafezalkotob [60] | tariff | SG | Single manufacturer-retailer | Wholesale price, retail price, tariff |
Yang, Zhang and Ji [49] | Cap-and-trade | SG | Single manufacturer-retailer | Wholesale price, retail price |
Yi and Li [42] | Joint tax and subsidy | SG | Single manufacturer-retailer | Wholesale price, retail price, energy-saving level, carbon-emission level |
Zhou, Hu and Zhou [51] | Tax | Dynamic game model | Single manufacturer, multiple retailers | Retail price, wholesale price, carbon tax |
Yuan, Gu, Guo, Xia and Xu [38] | Cap-and-trade | SG | Single manufacturer-retailer | Wholesale price, order quantities |
Yuyin and Jinxi [8] | Joint tax and subsidy | SG | Single manufacturer-retailer | Wholesale price, retail price, energy-saving level |
Bai, Gong, Jin and Xu [52] | Cap-and-trade | SG | Single manufacturer, multiple retailers | Wholesale price, retail price, green technology level |
Author | Carbon Regulations | Constraints | Modeling Techniques | Decisions |
---|---|---|---|---|
Nagurney, et al. [64] | Tax | / | Variational inequality theory | Carbon tax rate |
Ramudhin, Chaabane and Paquet [26] | Cap-and-trade | Handling capacity, BOM | MILP | Logistics, facility location, supplier selection, transportation mode |
Giarola, et al. [65] | Cap-and-trade | Handling capacity | MILP | Selecting the best biomass and technologies options |
Abdallah, et al. [66] | Cap-and-trade | Handling capacity, Material supply | MILP | Logistics, facility location, supplier selection |
Ortiz-Gutiérrez, et al. [67] | Cap-and-trade | Handling capacity | MILP | Logistics, manufacturing technology, facility location |
Jin, et al. [68] | Tax, cap, cap-and-trade | / | MILP | Logistics, transportation mode |
He, et al. [69] | Tax and cap | / | Input-output | Inventory level, production quantity |
Luo and Tang [70] | Cap-and-trade | Changeover variable | MILP model | Inventory level, production quantity |
Palak, Ekşioğlu and Geunes [3] | Tax, cap, cap-and-trade, offset | MILP, E-EOQ | Logistics, supplier selection, transport modes | |
Tseng and Hung [71] | Tax | Handling capacity | MILP | Logistics, transport modes |
Osmani and Zhang [72] | Tax | Handling capacity | Stochastic MILP | Logistics, technology, inventory level, facility capacity |
Fahimnia, et al. [73] | Tax, cap-and-trade | Handling capacity | MINLP model | Logistics, inventory level |
Hammami, et al. [74] | Cap, tax | Handling capacity, lead time | Production-inventory | Logistics, facility location |
Ni and Shu [75] | Cap, tax | Storage capacity, service level | MINLP | Bounds of service time |
Martí, et al. [76] | Tax, cap | / | E-EOQ | Facility location, customer allocation, transport modes |
Fahimnia, et al. [77] | Tax | Handling capacity | MINLP | Logistics, vehicle speed |
Wu, et al. [78] | Tax | Handling capacity, transmission constraints | SG | Carbon tax rate |
Zakeri, Dehghanian, Fahimnia and Sarkis [5] | Offset | Material supply, capacity | MILP | Logistics, inventory level, facility location |
Shaw, et al. [79] | Cap-and-trade | Handling capacity | chance constrained programming theory | Logistics, facility location |
Moon, Jeong and Saha [62] | Cap-and-trade | Material supply, capacity | MILP | Logistics, facility location, inventory level |
Sarkar, et al. [80] | multi-level trade credit | / | NLP | Logistics |
Zhou, et al. [81] | Tariff | Handling capacity | Stochastic MINLP | Logistics, facility location, transport modes |
Ma, Ho, Ji and Talluri [13] | Tax | Inventory capacity | SG | Sales price and production rate |
Saxena, Jain and Sharma [4] | Tax | Handling capacity | Fuzzy goal programming | Logistics, facility location |
Author | Carbon Regulations | Constraints | Modeling Techniques | Algorithms and Tool | Decisions |
---|---|---|---|---|---|
Chaabane, Ramudhin and Paquet [87] | Cap-and-trade | Capacity, number of facilities | MILP | LINGO/LINDO | Logistics, inventory level, facility location, technology |
Fahimnia, Sarkis, Dehghanian, Banihashemi and Rahman [2] | Tax | Capacity | MILP | AMPL/CPLEX | Logistics, facility location |
Gao and Ryan [90] | Tax, cap-and-trade | Capacity | Stochastic MILP | Gams/CPLEX | Logistics, facility location, transportation mode |
Li, Du, Yang and Hua [7] | Carbon subsidy | / | Game theory | / | Price of raw materials, wholesale and retail price of unit product |
Zhang, Sun, Hu and Dai [88] | Cap, tax | / | Variational inequality formula theory | Modified project contraction algorithm | Logistics, inventory level, product price |
Choudhary, Sarkar, Settur and Tiwari [12] | Tax, cap, cap-and-trade | Capacity | MILP | Forest data structure algorithm | Logistics, facility location |
Bing, et al. [91] | Cap-and-trade | Capacity enhancement | MILP | LamaSoft/Xpress | Logistics, emission quota, facility location |
Tao, Guang, Hao and Song [89] | Cap, tax | Periodic and global carbon emission constraint | Variational inequality and complement theory | Modified projection and contraction algorithm | Logistics, inventory level, product price |
Mohammed, et al. [92] | Tax, cap, cap-and-trade, offset | Capacity | Stochastic MILP | Gams/CPLEX | Logistics, facility location, transportation mode |
Entezaminia, Heidari and Rahmani [33] | Tax, cap, cap-and-trade, offset | Capacity, technology | MILP | CPLEX | Logistics, facility location, transportation mode, inventory level |
Xu, et al. [93] | Tax, cap, cap-and-trade | Capacity, number of facilities | MILP | CPLEX | Logistics, facility location |
Bazan, Jaber and Zanoni [86] | Tax and cap | Capacity | NLP | / | Batch size for shipment and manufacturing |
Xu, Elomri, Pokharel, Zhang, Ming and Liu [17] | cap | Capacity, number of facilities | Robust MILP | CPLEX | Logistics, facility location |
Haddadsisakht and Ryan [16] | Tax | Capacity | Hybrid robust/stochastic model | CPLEX | Logistics, facility location |
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Xu, Z.; Elomri, A.; Pokharel, S.; Mutlu, F. The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models. Sustainability 2019, 11, 3094. https://doi.org/10.3390/su11113094
Xu Z, Elomri A, Pokharel S, Mutlu F. The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models. Sustainability. 2019; 11(11):3094. https://doi.org/10.3390/su11113094
Chicago/Turabian StyleXu, Zhitao, Adel Elomri, Shaligram Pokharel, and Fatih Mutlu. 2019. "The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models" Sustainability 11, no. 11: 3094. https://doi.org/10.3390/su11113094