Decoding the dynamics and disparities of urban carbon intensity under the influence of land use and demographics from both global and local perspectives
Bibliographic record
Abstract
Timely and accurate assessment of how land use and demographics affect carbon intensity (CI) variations matters to effective reduction policies. However, the interdependencies driving these effects are not yet fully understood. This study examines the spatial heterogeneity and dynamics of urban CI in China, focusing on the roles of land use patterns and demographic factors. Results show that despite an overall decline in urban CI, carbon inequality has worsened. Notably, previously narrowing regional disparities have now diverged, driven mainly by growing variations within regional clusters. There are significant spatial differentiation in urban CI, with northern regions exhibiting higher values compared to southern regions. The spatial integration of urban CI is strongly shaped by path dependency and lock-in effects, even amid persistent inter-regional competition. The endogenous effect of urban CI suggests that a 1 % increase in neighboring areas corresponds to at least a 0.5 % increase locally. The global regression demonstrates that urbanization, labor participation, and income positively affect urban CI, while aging, technological progress, industrial upgrading, and R&D investment negatively influence it, with both direct and spillover effects observed. Local regression analysis uncovers pronounced spatial heterogeneity, with land urbanization (accounting for 71.8 %) and population aging (60.9 %) emerging as the two most significant determinants in these relationships. These findings shed light on the need to integrate land use and demographic profiles into carbon abatement strategies, advocating for locally tailored interventions to effectively mitigate urban CI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".