Assessing Four Decades of Land Use and Land Cover Change: Policy Impacts and Environmental Dynamics in the Min River Basin, Fujian, China
Bibliographic record
Abstract
Land use and land cover change (LULCC) is crucial in sustainable land management. Over the past four decades, the Min River Basin (MRB) has experienced significant LULCC. This study investigated the dynamics of LULCC over these decades (1980–2020) and discusses the key drivers of land use change in different stages. First, we mapped and quantified changes (i.e., LULCC and landscape indices) in forests, croplands, urban areas, and water bodies from 1980 to 2020 using the China National Land Use/Cover Change (CNLUCC) and ArcGIS Pro 2.3. Second, by analyzing existing policies, we categorized four decades of LULCC trends from 1980 to 2020, delineating three distinct stages: (1) the Economic Restoration (ER) stage (1978–1989), when the ecological impacts of LULCC on forests, croplands, and water bodies received limited policy attention; (2) the Construction of Ecological Protection and Economic Development (EPED) stage (1989–2012), which saw a significant increase in forest coverage, primarily driven by various central and provincial environmental conservation policies, such as the Green for Grain and the “Three-Five-Seven Reforestation Project” in Fujian; and (3) the Ecological Civilization (EC) stage (2012–2020), in which policy focus shifted from expanding forest land areas to enhancing the quality of these areas. However, the cropland area has decreased due to urbanization policies and population migration from rural to urban areas, including the above-mentioned forest policies. Thus, this study highlights the complex relationship between different land use land cover policies, as some policies had synergistic effects between the policies and positive outcomes, while other policies showed conflicting outcomes. Our results emphasize the importance of integrated land and water resource management and provide insights for policymakers to balance development and environmental conservation policies in the MRB.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".