Abstract
While emission tax and mandatory emission capacity regulations are widely implemented to control greenhouse gas emissions, it remains unclear which will lead to better performance in an uncertain environment. Furthermore, a regulated sustainable manufacturer with only partial demand information may be challenged by more uncertainties than a manufacturer would be in a free market without regulation. These factors motivate us to examine the effects of two types of emission reduction regulations on the optimal production decisions of a manufacturer facing stochastic demand with partial distribution information. Based on distributionally robust newsvendor models, we use the Hurwicz decision criterion to build robust optimization models for a regulated sustainable manufacturer. We first solve the closed-form expressions of the optimal production quantities for two optimization models; then, we compare their expected profits and carbon emissions. The comparisons show that an emission tax may be more favorable than mandatory emission capacity regulation in terms of the manufacturer’s environmental performance. A numerical study with a Taguchi experiment is performed to compare the two policies and to test the robustness. We find interesting insights: For manufacturers, a decision maker with an optimistic attitude to demand uncertainty, can gain higher profits and emit less carbon, which differs from the results of some previous research. For regulators, with a large carbon cap, mandatory emission capacity regulation can lead manufacturers to gain higher profits and emit more carbon than those achieved under emission tax regulation, which provides new insights for policy makers.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.