Spatio-Temporal Dynamics, Driving Mechanisms, and Decoupling Evaluation of Farmland Carbon Emissions: A Case Study of Shandong Province, China
Bibliographic record
Abstract
Understanding the spatio-temporal evolution of farmland carbon emissions, disentangling their underlying driving forces, and exploring the decoupling relationship between these emissions and economic development are pivotal to advancing low-carbon and high-quality agricultural development in Shandong Province, China. Using the Logarithmic Mean Divisia Index (LMDI) and Tapio decoupling model, this study conducted a comprehensive analysis of panel data from 16 cities in Shandong Province spanning 2004–2023. This research reveals that the total farmland carbon emissions in Shandong Province followed a trajectory of “initial fluctuating increase and subsequent steady decline” during the study period. The emissions peaked at 29.4 million tons in 2007 and then declined to 20.2 million tons in 2023, representing a 26.0% reduction compared to the 2004 level. Farmland carbon emission intensity in Shandong Province showed an overall downward trend over the period 2004–2023, with the 2023 intensity registering a 68.9% decline compared to 2004. The carbon emission intensity, agricultural structure, and labor effects acted as inhibiting factors on farmland carbon emissions in Shandong Province, while the economic development effect exerted a positive driving impact on the growth of such emissions. Over the 20-year period, these four factors cumulatively contributed to a reduction of 2.1 × 105 tons in farmland carbon emissions. During 2004–2013, the farmland carbon emissions in Zaozhuang, Yantai, Jining, Linyi, Dezhou, Liaocheng, and Heze showed a weak decoupling state, while in 2014–2023, the farmland carbon emissions and economic development in all cities of Shandong Province showed a strong decoupling state. In the future, it is feasible to reduce farmland carbon emissions in Shandong Province by improving agricultural resource utilization efficiency through technological progress, adopting advanced low-carbon technologies, and promoting the transformation of agricultural industrial structures towards “high-value and low-carbon” designs.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".