Payments for ecosystem services and farm household behaviour : the case of carbon in Mozambique’s agroforests
Bibliographic record
Abstract
Payments-for-Ecosystem-Services (PES) projects are being developed worldwide to address environmental and economic issues simultaneously. This thesis describes research concerning a PES project in Nhambita Community in Mozambique, where a small agro-forestry based carbon sequestration project is being implemented. The central research question is: Do economic incentives to smallholder farmers result in improved ecosystem services provision and improved household welfare measured by cash income and consumption? Questionnaire-based quarterly household surveys were the main source of data. Data was collected from 290 randomly selected households by eight enumerators who were trained in administering questionnaires in the local language (Sena). The thesis consists of six chapters, two of which are an introduction and conclusion. The remaining four chapters are prepared as manuscripts: the first assesses the contribution of environmental resources to the household economy; the second investigates the miombo woodlands’ use as household safety net against adverse income shocks, using conditional logit analysis; the third investigates socio-economic factors influencing household participation in the PES project, using 3-stage estimation; and, the fourth and final evaluates the impact of the PES-project on household cash income, consumption, forest use and agricultural production, using propensity score matching. It further examines whether there was any discrimination in the flow of benefits, using decomposition analysis. The key results are as follows. 1) Poorer households used miombo resources for subsistence, while richer households used the same for cash income. 2) Women headed households, which had lower level of cash income, used the woodland resources to the same level as did the male headed households. 3) Use of wild products from miombo woodlands was one of the shock coping strategies. 4) Participation in PES-project was influenced by education of household head, length of residence in the community, extent of trust among community members and percentage of cash income derived from sale of forest products. 5) PES-participant households earned higher amount of cash income, had higher consumption and harvested lesser amount of crops, than they would have had they not participated in the project. 6). There were biases in the flow of benefits in favour of richer and male headed households.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".