Technology first, sustainability later: a systematic review on the literature on the policy development of China’s smart city strategy
Bibliographic record
Abstract
Abstract In China, like in other countries, smart cities have been proposed to make cities more efficient and, ideally, also more sustainable and low-carbon. Unlike other countries, China pursued a smart city strategy since 2008 with substantial funding and intermediate goals, resulting in high data and computational-intensive digital infrastructures in some cities. However, there is a lack of systematic understanding of how Chinese smart city policies and practices evolved. It is also unclear if and how smart cities achieve sustainability goals. Here, we fill these gaps by conducting a systematic literature review on the timeline of China’s smart city policies during the past three Five-Year Plans. The literature review, based on screening 7995 papers, and analyzing 364 relevant articles, shows that priority research topics are smart city systems and governance, including surveillance, with a more limited focus on policy. China’s net-zero carbon strategy is far less developed than its smart city strategy. The funding and development of large-scale data and AI technology is exemplified in Hangzhou’s ‘Urban Brain’. While sustainability goals are often associated with smart cities, we find few applications with demonstrated sustainability benefits. We suggest that mutual learning is possible by combining the net zero strategy and sustainable city strategy of cities like Copenhagen, Nairobi, Singapore and Toronto with the urban brain strategy of cities like Hangzhou.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".