Is smart city low-carbon? Evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smart cities were originally conceived to address a myriad of urban challenges arising from rapid urbanization, including energy scarcity, congestion, and environmental degradation. The Chinese government has made substantial efforts to advance smart city initiatives. However, the extent to which the integration of smart technologies contributes to urban sustainability, especially within a high-carbon urbanization paradigm, poses a critical question in light of escalating extreme weather events and worsening global challenges. Urgency is underscored in prioritizing low-carbon strategies within smart city frameworks. This paper presents a Multicriteria Decision Making Network (MCDN) approach to assess and rank the low-carbon levels (LCL) of 36 pilot smart cities in China. Findings reveal that overall LCL among these cities remains relatively modest, with significant disparities attributed to varying economic, social, institutional, cultural, and environmental contexts. The study also delves into the nexus between urban intelligence and LCL, highlighting a discernible positive correlation between a city's smartness and its low-carbon profile. Moreover, empirical evidence suggests that advancements in smart technologies are conducive, albeit to varying degrees, to enhancing urban LCL. In light of these findings, recommendations are made to fortify economic and social advancement, bolster management practices, and foster multi-stakeholder collaboration to propel the coordinated development of smart and low-carbon initiatives in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it