Impact of abandoned land restoration on ecosystem services: a systematic review
Bibliographic record
Abstract
The degradation of abandoned land has emerged as a global concern, with profound implications for ecosystem services and human well-being. These lands—ranging from agricultural fallows to mining sites and urban derelict areas—commonly experience soil fertility decline, biodiversity loss, and diminished water regulation capacity. Appropriate restoration interventions, including vegetation succession, afforestation, soil amendments, and hydrological rehabilitation, can restore ecological functions and enhance the supply of ecosystem services. However, systematic assessments of these outcomes remain limited. This review synthesizes evidence on the effects of abandoned land restoration on ecosystem services. Empirical evidence demonstrates that restoration enhances provisioning services (e.g., food, timber, biomass energy), regulating services (e.g., carbon sequestration, soil retention, water purification, climate moderation), and cultural services (e.g., recreation, aesthetics, education). Nevertheless, trade-offs emerge, such as reduced water availability from afforestation or biodiversity loss under monoculture bioenergy plantations. These outcomes vary according to spatial heterogeneity, restoration strategies, socioeconomic contexts, and external pressures. Furthermore, some interventions yield short-term benefits but entail long-term uncertainties regarding sustainability and ecological resilience. Our synthesis underscores both opportunities and challenges. While abandoned land restoration has substantial potential to enhance ecosystem services, critical gaps remain, including limited data availability, insufficient long-term monitoring, and weak integration of socioeconomic dimensions. Future research priorities include (i) improving long-term ecological impact assessments; (ii) refining analyses of synergies and trade-offs among multiple services; (iii) integrating socioeconomic and policy dimensions into restoration frameworks; and (iv) advancing modeling and decision-support systems under future climate and land-use scenarios. Ultimately, coordinated monitoring, innovative technologies, and adaptive policy frameworks are indispensable for fully realizing the ecological and social benefits of abandoned land restoration within sustainable land management.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".