Land, energy and water resource management and its impact on GHG emissions, electricity supply and food production- Insights from a Ugandan case study
Bibliographic record
Abstract
Abstract Despite the excitement around the nexus between land, energy and water resource systems, policies enacted to govern and use these resources are still formulated in isolation, without considering the interdependencies. Using a Ugandan case study, we highlight the impact that one policy change in the energy system will have on other resource systems. We focus on deforestation, long term electricity supply planning, crop production, water consumption, land-use change and climate impacting greenhouse gas (GHG) trajectories. In this study, an open-source integrated modelling framework is used to map the ripple effects of a policy change related to reducing biomass consumption. We find that, despite the reduction in deforestation of woodlands and forests, the GHG emissions in the power sector are expected to increase in between 2040–2050, owing to higher fossil fuel usage. This policy change is also likely to increase the cost of electricity generation, which in turn affects the agricultural land types. There is an unforeseen shift from irrigated to rainfed type land due to higher electricity costs. With this integrated model setup for Uganda, we highlight the need for integrated policy planning that takes into consideration the interlinkages between the resource systems and cross propagation effects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".