A joint production and carbon trading policy for unreliable manufacturing systems under cap-and-trade regulation

2021 ◽  
Vol 293 ◽  
pp. 125973
Author(s):  
Arezou Entezaminia ◽  
Ali Gharbi ◽  
Mustapha Ouhimmou
2019 ◽  
Vol 11 (2) ◽  
pp. 474
Author(s):  
Wen Tong ◽  
Jianbang Du ◽  
Fu Zhao ◽  
Dong Mu ◽  
John Sutherland

Carbon cap-and-trade mechanism is a government-mandated, market-based scheme to reduce emissions, which has a significant effect on manufacturers’ operation decisions. Based on the cap-and-trade mechanism, this paper studies the joint production and emission reduction problem of a manufacturer. The manufacturer faces emissions-sensitive demand impacted by consumers’ environmental preferences (CEP). An extended newsvendor model is used to find the optimal production quantity and emissions reduction quantity. We explore the impacts of market price of carbon credits, emission reduction investment coefficient and CEP on the optimal strategies. Numerical examples are provided to illustrate the theoretical results and orthogonal experimental design technique was applied to find robust system parameters. It is concluded that among all parameters, emissions cap has the greater impact on the expected profit, which is followed by than the market price of carbon credits. This means that the government plays a major role in economic development. The total carbon emissions are mainly affected by the carbon trading price and the product’s sale price, which indicates the carbon trading market and product market play a larger role in controlling environmental benefits. Several valuable managerial insights on helping governments and industries understand how market conditions change and make better long-term decisions are further concluded.


ICLEM 2014 ◽  
2014 ◽  
Author(s):  
Changsong Ma ◽  
Li Tan ◽  
Shucheng Zhou ◽  
Xiang Wang ◽  
Xinyi Zhang

Author(s):  
Yong Wang ◽  
Lin Li

This paper proposes a framework for addressing challenges of joint production and energy modeling of sustainable manufacturing systems. The knowledge generated is used to improve the technological readiness of manufacturing enterprises for the transition towards sustainable manufacturing. Detailed research tasks of the framework are on the modeling of production, energy efficiency, electricity demand, cost, and demand response decision making. Specifically, the dynamics and performance measures of general manufacturing systems with multiple machines and buffers are modeled to integrate energy use into system modeling. The expressions of electrical energy efficiency and cost are then established based on the electricity pricing profile. Finally, joint production and energy scheduling problem formulations and the solution technique are discussed. New insights are acquired based on the applications of the established model in system parameter selection, rate plan switching decision making, and demand response scheduling. Appropriate implementation of this research outcome may lead to energy-efficient, demand-responsive, and cost-effective operations and thus improve the sustainability of modern manufacturing systems.


2019 ◽  
Vol 51 (4) ◽  
pp. 406-421 ◽  
Author(s):  
Jin Xu ◽  
Hoang M. Tran ◽  
Natarajan Gautam ◽  
Satish T. S. Bukkapatnam

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Bin Chen ◽  
Man Yu

In an uncertainty market, social learning plays a significant role in obtaining information to make better decisions. Under cap-and-trade regulation, this paper aims to investigate firms’ pricing and carbon emission abatement issues considering the impact of social learning. This paper establishes a two-period model in a market consisting of a manufacturer and heterogeneous consumers. The manufacturer produces two alternatives (ordinary product and low-carbon product) and makes decisions on sales prices and carbon emission abatement levels. Consumers make decisions on whether and which product to buy. Consumers are not sure about their valuations of products and have the opportunity to discover their true valuation by social learning. The results show that the emission abatement level on ordinary product is affected by the pricing strategy for both types of products. However, the emission abatement level on low-carbon product is only affected by its own pricing strategy. It also shows that social learning lowers the emission abatement level on ordinary product, whereas it improves the emission abatement level on low-carbon product when charging a high price for low-carbon product. Moreover, the price of ordinary product in period 1 is no less than that in period 2. In contrast, the price of low-carbon product in period 2 is higher than that in period 1.


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