Data-driven sustainability : advancing electric vehicle adoption and carbon accounting using artificial intelligence and geospatial analytics
Abstract/Contents
- Abstract
- This dissertation explores data-driven, computational techniques for sustainability decision-making, particularly focusing on the transportation sector and the carbon accounting landscape. Given the urgency of large-scale decarbonization efforts underscored by the Intergovernmental Panel on Climate Change, this work proposes and evaluates methods to help decision-makers navigate complex interactions between humans, engineering systems, and ecological systems, taking into account factors such as data accessibility and uncertainty. The dissertation is divided into two parts. The first, encompassing chapters two and three, delves into how computational techniques and data can expedite the decarbonization of the transportation sector. The second part, which includes chapters four and five, emphasizes elevating societal understanding and accounting of carbon emissions in the context of sustainable decision-making. Altogether, this dissertation underscores the importance of comprehensive, computationally-enhanced, data-driven, and uncertainty-aware approaches to sustainability decision-making, thus providing practical pathways for effective climate change mitigation.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Oladeji, Olamide |
|---|---|
| Degree supervisor | Weyant, John |
| Thesis advisor | Weyant, John |
| Thesis advisor | Heller, Thomas |
| Thesis advisor | Rajagopal, Ram |
| Degree committee member | Heller, Thomas |
| Degree committee member | Rajagopal, Ram |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Olamide Oladeji. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/km067yh5603 |
Access conditions
- Copyright
- © 2023 by Olamide Oladeji
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...