Installation of charging infrastructure for electric vehicles in multi-unit residential buildings in British Columbia
Bibliographic record
Abstract
Electric Vehicles (EVs) contribute to the mitigation of climate change through reduced greenhouse gas emissions, when powering with sustainable sources of electricity. The province of British Columbia (BC) is an attractive location for EV deployment since most of its electricity is sourced from clean renewable energy sources. Due to their driving range and potential to reduce local emissions, EVs work well in urban contexts, where most residential buildings are located. As a result, residents from Multi-Unit Residential Buildings (MURBs) are among those interested in becoming EV owners, thus requiring access to charging infrastructure, especially overnight home charging, which is the preferred charging alternative. However, most residential buildings are not equipped with charging infrastructure and its installation can have numerous challenges that can turn into barriers. This thesis explores the implications, challenges and decision-making processes of EV charging infrastructure installation in MURBs to identify present and future barriers to infrastructure provision, as well as potential policy-driven interventions to address them. The methods used to conduct the research study include the utilization of conceptual frameworks and the application of systems thinking principles to map the interrelation and causalities of the problem domains as causal loop diagrams. A review of the literature identified the key problem domains. Policy recommendations were then classified based on each problem domain. First, financial or fiscal policy measures include creating incentives for EV owners and extending them to the building owners, as well as programs to incentivize and provide financial aid for building owners to develop building retrofit plans. Second, regulatory policy measures include revising the regulations and addressing the rights and obligations of the stakeholders, as well as making mandatory the installation of charging stations in new MURBs. Third, information and awareness policy measures include expanding the existing guidelines and informing the development of a long-term EV charging infrastructure plan. These policy recommendations are relevant to different stakeholders as they have the potential to inform the decisions and policy programs of the municipal and provincial government of BC, as well as other governmental and non-governmental agencies and associations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".