Optimising Decarbonisation Investment for Firms towards Environmental Sustainability

Tran, Trung-Hieu; Mao, Yong; Siebers, Peer-Olaf

Abstract

We develop a mixed-integer non-linear programming model for firms’ decarbonisation investment decision-making towards a sustainable environment. Our model seeks the optimal investment for a firm to achieve maximum profit under constraints derived from its environmental protection awareness and the government’s taxation policy. We use an uncertainty theory to formulate the relationship of a firm’s environmental protection awareness and its investment budget levels. Governments’ taxation policy is modelled by a step-wise linear function, where reduced carbon dioxide emission can help the firm reduce taxation. A linearisation is proposed to solve the non-linear problem efficiently. A case study for a sector of electronic component manufacturers in Nottingham, the United Kingdom, demonstrates the practical implementation of the proposed model. Several large-sized instances, which were randomly generated, were utilised to evaluate the the efficiency of model in terms of computational time. Our model can be used to explore budget options to obtain higher profits under a particular taxation policy.

Citation

Tran, T.-H., Mao, Y., & Siebers, P.-O. (2019). Optimising Decarbonisation Investment for Firms towards Environmental Sustainability. Sustainability, 11(20), Article 5718. https://doi.org/10.3390/su11205718

• Journal Article Type: Article
• Acceptance Date: Oct 11, 2019
• Online Publication Date: Oct 16, 2019
• Publication Date: Oct 16, 2019
• Deposit Date: Oct 16, 2019
• Publicly Available Date: Oct 17, 2019
• Journal: Sustainability
• Electronic ISSN: 2071-1050
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 20
• Article Number: 5718
• DOI: https://doi.org/10.3390/su11205718
• Keywords: Mixed-integer non-linear programming; Linearised model; Decarbonisation investment; Environmental protection awareness; Taxation policy
• Public URL: https://nottingham-repository.worktribe.com/output/2848966
• Publisher URL: https://www.mdpi.com/2071-1050/11/20/5718/htm
• Contract Date: Oct 16, 2019