Print Email Facebook Twitter Strategies towards effective emission reduction of the inland shipping industry in the port of Rotterdam Title Strategies towards effective emission reduction of the inland shipping industry in the port of Rotterdam: Using a mixed-integer linear programming model Author Baas, Daniël (TU Delft Civil Engineering and Geosciences) Contributor Bakker, H.L.M. (mentor) Stikkelman, R.M. (graduation committee) Binnekamp, R. (graduation committee) Degree granting institution Delft University of Technology Programme Civil Engineering | Construction Management and Engineering Date 2019-12-17 Abstract Currently, the inland shipping industry is not sufficiently incentivised to invest in sustainable technologies in order to reduce their emissions. The industry itself wants to be more sustainable, but there is insufficient financial room to invest in alternative technologies. Besides, public authorities try to achieve a reduction using command-and-control policy instruments. Therefore, this study aims to gain insights into the relationship between policy instruments and incentivising technical alternatives. In this study, emission-reducing strategies have been identified using a mixed-integer linear programming (MILP) model based on the fleet owned by the Port of Rotterdam. As a result, this study has identified two effective strategies – environmentally differentiated port dues and environmental fees. The effectiveness of the environmentally differentiated port dues can be allocated to the degree of pollution control. The implication of an environmental fee on carbon dioxide (CO2) emissions, has led to a reduction in either greenhouse gas and pollutant emission. Subject Linear Programming modelMixed Integer Linear ProgrammingEmissionsGreenhouse gasPollutant emissionsEmission reductionPortsRotterdamInland ShippingPolicy InstrumentsEconomic IncentivesEnvironmental feeEnvironmentally differentiated port dues To reference this document use: http://resolver.tudelft.nl/uuid:93a5ab3e-8406-4f42-96a6-194a1ac72a38 Part of collection Student theses Document type master thesis Rights © 2019 Daniël Baas Files PDF Public_Baas_D.pdf 23.81 MB Close viewer /islandora/object/uuid:93a5ab3e-8406-4f42-96a6-194a1ac72a38/datastream/OBJ/view