Planning to Practice: Impacts of Large-Scale and Rapid Urban Afforestation on Greenspace Patterns in the Beijing Plain Area
Bibliographic record
Abstract
(1) Research Highlights: Afforestation is one of the most effective urban greening practices for mitigating a variety of environmental issues. Globally, municipal governments have launched large-scale afforestation programs in metropolitan areas during the last decades. However, the spatiotemporal dynamics of urban greenspace patterns are seldom studied during such afforestation programs. (2) Background and Objectives: In this study, the Beijing Plain Afforestation Project (BPAP), which planted 70,711 ha of trees in only four years, was examined by integrating spatial and landscape analysis. To evaluate the real-world outcomes of this massive program, we investigated the spatial-temporal dynamics of landscape patterns during the implementation process to identify potential impacts and challenges for future management of new afforestation. (3) Materials and Methods: We analyzed the transition of various patch types and sizes, applied landscape indicators to measure the temporal changes in urban greenspace patterns, and used the landscape expansion index to quantify the rate and extent of greenspace spatial expansion. (4) Results: Our results illustrated that the implementation of afforestation in the Beijing plain area had generally achieved its initial goal of increasing the proportion of land devoted to forest (increased 8.43%) and parks (increased 0.23%). Afforestation also accelerated the conversion of small-size greenspaces to large-size patches. However, the significant discrepancies found between planned and actual afforestation sites, as well as the large conversion of cropland to forest, may present major challenges for project optimization and future management. (5) Conclusions: This study demonstrated that spatial analysis is a useful and potentially replicable method that can rapidly provide new data to support further afforestation ecosystem assessments and provide spatial insights into the optimization of large inner-city afforestation projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".