Can Institutional Reforms Promote Sustainable Planning? Integrating Regional Transportation and Land Use in Toronto and Chicago (2001-2014)
Bibliographic record
Abstract
Although governments have implemented several reforms to better integrate or coordinate regional transportation and land use decisions, little is known about the effects of new institutional designs on planning and development outcomes. This study compares the effects of two different types of institutional reforms on the planning process, transportation investments and land use decisions, while assessing their characteristics in terms of accountability, democracy, and effectiveness. Using semi-structured interviews, planning documents, as well as transportation spending and land use decisions, this longitudinal, comparative case study assesses the effects of the centralized, regulatory framework implemented in Toronto in 2005-2006, to the collaborative governance framework adopted in Chicago in 2005. Although each institutional design features different sets of constraints and opportunities, both reforms improved the planning process by establishing a renewed commitment to the exercise of regional planning. However, their impact on transportation investments was limited because the allocation of transportation funds is still primarily controlled by the province and the state governments who continue to control the purse strings and allocate money to advance their own political agendas. Both cases also show how difficult it is to increase densities and curb urban sprawl because local land uses, zoning and development approvals remain the prerogative of local governments and a function of locational preferences of individuals and corporations, which are contingent upon the market and shaped by global economic forces. Besides stronger regional institutions, the evidence presented in this study calls for new political strategies that address the fiscalization of land use and that offer financial incentives for the adoption of smart growth policies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".