Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach
The passenger transportation sector is notoriously sticky to decarbonize because it is interlinked with urban form, individual choice, and economic growth. As the urgency to respond to climate change increases and the transport sector disproportionally increases its contributions to global GHG emiss...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/19/6905 |
| _version_ | 1797479634957238272 |
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| author | Anastasia Soukhov Ahmed Foda Moataz Mohamed |
| author_facet | Anastasia Soukhov Ahmed Foda Moataz Mohamed |
| author_sort | Anastasia Soukhov |
| collection | DOAJ |
| description | The passenger transportation sector is notoriously sticky to decarbonize because it is interlinked with urban form, individual choice, and economic growth. As the urgency to respond to climate change increases and the transport sector disproportionally increases its contributions to global GHG emissions, there is a need for a more meaningful and transparent application of tools to cost GHG emission reduction. This study presents a multi-objective integer optimization (MIO) model to support the costing and GHG reduction estimation of electric mobility road passenger transportation policies. The model considers both cost and GHG emission minimization under resource constraints and background changes in policy interventions within interval ranges for the province of Ontario’s (Canada) in year 2030. All Pareto optimal solutions are included but results that indicate the optimal policy allocation for two discrete targets are discussed in detail; one scenario where $3 billion spending over ten years is the target and another scenario where the target is 40% GHG reduction in year 2030 (relative to 2005 levels). The MIO approach offers an out-of-the-box solution to support the GHG-reducing decision-making process at all levels of government by implementing optimal policy combinations to achieve GHG emission reductions under a target GHG emission reduction target and/or budget. |
| first_indexed | 2024-03-09T21:48:37Z |
| format | Article |
| id | doaj.art-121439fcd4bf460d9ccc0d72dde505c7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:48:37Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-121439fcd4bf460d9ccc0d72dde505c72023-11-23T20:09:51ZengMDPI AGEnergies1996-10732022-09-011519690510.3390/en15196905Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment ApproachAnastasia Soukhov0Ahmed Foda1Moataz Mohamed2School of Earth, Environment and Society, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaThe passenger transportation sector is notoriously sticky to decarbonize because it is interlinked with urban form, individual choice, and economic growth. As the urgency to respond to climate change increases and the transport sector disproportionally increases its contributions to global GHG emissions, there is a need for a more meaningful and transparent application of tools to cost GHG emission reduction. This study presents a multi-objective integer optimization (MIO) model to support the costing and GHG reduction estimation of electric mobility road passenger transportation policies. The model considers both cost and GHG emission minimization under resource constraints and background changes in policy interventions within interval ranges for the province of Ontario’s (Canada) in year 2030. All Pareto optimal solutions are included but results that indicate the optimal policy allocation for two discrete targets are discussed in detail; one scenario where $3 billion spending over ten years is the target and another scenario where the target is 40% GHG reduction in year 2030 (relative to 2005 levels). The MIO approach offers an out-of-the-box solution to support the GHG-reducing decision-making process at all levels of government by implementing optimal policy combinations to achieve GHG emission reductions under a target GHG emission reduction target and/or budget.https://www.mdpi.com/1996-1073/15/19/6905life cycle GHG emissiontransport policymulti-objective optimization |
| spellingShingle | Anastasia Soukhov Ahmed Foda Moataz Mohamed Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach Energies life cycle GHG emission transport policy multi-objective optimization |
| title | Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach |
| title_full | Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach |
| title_fullStr | Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach |
| title_full_unstemmed | Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach |
| title_short | Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach |
| title_sort | electric mobility emission reduction policies a multi objective optimization assessment approach |
| topic | life cycle GHG emission transport policy multi-objective optimization |
| url | https://www.mdpi.com/1996-1073/15/19/6905 |
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