The Impact of Sufficient Infrastructure Investment on Regional Macro-economy – Taking California High-Speed Rail Project vs LaGuardia Airport Reconstruction Project as Examples
Bibliographic record
Abstract
This paper employs case study methodologies to scrutinize the macroeconomic ramifications of the California High-Speed Railway project and the LaGuardia Airport reconstruction project on the regional economy and explore the multidimensional impact of infrastructure initiatives on their regional economies. It delves into the multifaceted impacts of infrastructure investments in fostering regional economic expansion, augmenting employment opportunities, and bolstering regional competitiveness. The research approach integrates qualitative analysis, invoking pivotal economic theories to substantiate the comprehensive assessment of these two monumental projects' effects on the regional economy, encompassing social responsibility, sustainability, and governmental governance. The California High-Speed Railway Project facilitates the circulation of economic resources within California and the connectivity and commercial development of small and medium-sized cities by building a convenient high-speed transportation network that reduces dependence on car travel. Conversely, the LaGuardia Airport Redevelopment Project strengthens New York's function as a global transportation hub and an important port on the East Coast by upgrading the airport's aviation infrastructure and logistics clearance efficiency. Studies have shown that these infrastructure investment projects create many jobs, improve the labor market's employment flexibility and social inclusiveness, and promote sustainable development that reduces costs and increases efficiency through green building and energy-saving and emission-reduction technologies. This paper comprehensively verifies the endogenous growth theory and regional competition theory as a strategy for future investment in the construction of metropolitan infrastructure. It provides empirical support and scientific optimization suggestions for policy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".