Carbon balance dynamic evolution and simulation coupling economic development and ecological protection: a case study of Jiangxi Province at county scale from 2000–2030
Bibliographic record
Abstract
In addressing global climate change and promoting economic growth, achieving a comprehensive carbon balance at the county level is vital for sustainable development. However, most studies focus on the balance between carbon emissions and sequestration (CESB), neglecting the intricate ecological and economic carbon balance (EECB) at this scale. This study introduces an analytical framework that couples economic development with ecological protection by using coupling coordination degree analysis, carbon balance zoning, and the Markov-PLUS model. Taking Jiangxi Province as a case study, we evaluate the spatio-temporal dynamics of carbon balance from 2000–2020 and predict future trends for 2030 under four potential development scenarios. Results reveal significant regional variations in CESB over the two decades, which primarily exhibit net carbon emission. Meanwhile, the continuous decline in EECB highlights the need for balanced economic and ecological development. By 2030, the land use's carbon sequestration capacity is expected to increase under different scenarios, leading to a ‘middle-high, sides-low’ spatial pattern in CESB. These findings are crucial for policymakers in devising strategies for sustainable regional 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".