Evaluating the impact of simulated land use changes under multiple scenarios on ecosystem services in Ji'an, China
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Bibliographic record
Abstract
Ecosystem services (ES) play a crucial role in the sustainable development of human society. It is essential to clarify the impact of land use changes on ES to facilitate sound land spatial planning. This study focuses on the Ji'an area of Jiangxi Province as the research subject. The Future Land Use Simulation (FLUS) model is employed under the 'Production-life-ecological space' (PLES) policy scenario to simulate land use changes in the Ji'an area from 2020 to 2040. A comprehensive evaluation index for ecosystem services (CES) is constructed based on existing policies and ecological conditions in Ji'an City. It is then integrated into the FLUS-InVEST framework to simulate various development scenarios in the future. The results reveal that under the living space priority (LSP) scenario, construction land exhibits the highest growth rate, reaching 59.8%. Under the ecological space priority (ESP) scenario, the forest area is projected to increase by 0.54%. Only under the Productive Space Priority (PSP) scenario will cultivated land experience an increase, at a rate of 3.69%. Notably, CES remains relatively stable only in the ESP scenario, while declining in all other scenarios (ranging from 1.19% to 1.83%). Based on the simulation calculations, a Comprehensive Development (CD) plan for Ji'an is proposed, aiming to optimize the coordination among production, living, and ecological spaces and facilitate the restoration of ecosystem services. Compared to the 'business as usual' (BAU) scenario, the CES value of the CD scenario is expected to increase by 0.61%. In summary, this paper presents an exploratory study that combines the FLUS-InVEST framework with the unique development scenario of the Ji'an region in an innovative way. The proposed planning and recommendations for future sustainable development contribute to a deeper understanding of the impact of socio-economic activities on the ecological environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.001 |
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