The sino-singapore tianjin eco-city: A case study of Chinese experimental regulatory and institutional development
Bibliographic record
Abstract
The Sino-Singapore Tianjin Ecocity (SSTEC) is a large-scale pilot project, trialling innovative approaches in sustainable urban development.To implement its objectives, the SSTEC has been developed as a central element in a matrix of networked institutional relationships.This paper examines the functioning of these institutional interactions, through examination of the development of renewable energy-related initiatives.The salient elements of these interactions relate to consultation and information flows.This is intended to facilitate an empirically based experimental approach to project planning, implementation and review, in terms of the integration of research and implementation experience.Institutional and regulatory development at the SSTEC is evaluated in the context of other Chinese lowcarbon cities and other localised initiatives such as Green Counties and New Energy Demonstration Cities.This evaluation draws on interviews with senior staff members at the SSTEC, and in specialist Chinese renewable energy institutions, as well as scholarly and other literature.While substantial implementation challenges face the SSTEC and other large-scale projects, they are likely to play an instrumental role in scaling-up technologically and financially effective low-carbon solutions in Chinese urban development.Some inefficiencies and failures are to be expected in any experimental approach.These can be put to effective use through an empirical approach to determining best institutional and regulatory practice, in terms of realising China's low-carbon model of sustainable urban development.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".