Examining smart city implementation models in Hong Kong, Macao, and Shenzhen: an analytical review
Bibliographic record
Abstract
Purpose This study aims to review the variety of smart city development projects in Guangdong-Hong Kong, Macao, and Shenzhen of the Greater Bay Area (GBA) of China, based on social, economic, and political factors. Design/methodology/approach A comparative and actor-focused political-economic method is applied to explore project-level smart city implementation models (SCIMs). A framework is first constructed to assess the state-market-community relationships of smart city projects. Subsequently, the array of smart city projects is examined, along with the explanations of how social, economic, and political factors influence these cities against the backdrop of the ‘One Country, Two Systems’ principle. Findings The findings show four varieties of SCIMs that highlighted proactive government support for smart city development, with place-specific strategies and pathways. With the state-market-community background and engagement of mega-technology firms, a variety of smart cities were found to exist and thrive. Originality/value This study reviews the political-economic framework of smart cities under the ‘One Country, Two Systems’ principle. Different SCIMs are examined and investigated, and the locally adopted pathway for smart city development are identified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".