Reshaping regional carbon productivity in the Pearl River Delta megaregion: Input-output-based multi-objective optimization to explore low-carbon industrial transitions
Bibliographic record
Abstract
The Pearl River Delta (PRD) megaregion has emerged as China's foremost polycentric area. The region strives to lead China's transition to industrial low-carbon reorientation. Achieving this goal necessitates cross-sectoral strategic development solutions that consider the intricate interconnections between cities within the region. In this study, we developed a novel MRIO-based multi-objective optimization framework to address the challenge of reshaping the region's carbon productivity. This framework explores the optimal planning pathways for multi-city industrial restructuring to achieve a more efficient regional low-carbon transition. Our findings suggest that reshaping the regional industrial structure would propel economic prosperity across all affiliated cities, with advanced manufacturing and modern service sectors having the potential to contribute over 75 % of the region's GDP by 2035. The industrial restructuring would present opportunities for attaining the goal of reaching peak carbon emissions by 2030 and peak energy consumption by 2035 with higher production efficiency. Our study provides valuable insights into the development of industrial restructuring and decarbonization pathways for the PRD and other megaregions. • A novel MRIO-based multi-objective optimization framework is developed. • This framework explores productivity-driven industrial restructuring within the PRD cities. • Reshaping carbon productivity enhances an efficient low-carbon transition. • Promoting industrial restructuring contributes to achieving peak carbon emissions by 2030.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".