Land use strategies for achieving Chile’s nationally determined contributions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Chile’s Nationally determined contributions (NDCs) commit to carbon neutrality by 2050, with measures to reduce emissions and natural hazards while enhancing water security. The Forestry and Other Land Uses (FOLU) sectors are critical to Chile’s goal of carbon neutrality, as they serve as a net carbon sink. In this paper, we conduct policy scenario analysis focusing on FOLU strategies for meeting the NDCs. We implement the Integrated Economic-Environmental Modeling framework linked with spatial Land Use-Land Cover and Ecosystem Services (ES) Modeling (IEEM + ESM) to assess impacts on economic, environmental and social indicators. Our results show that the implementation of Chile’s FOLU strategies would reduce emissions, enhance wealth and economic growth and increase future flows of ES. Carbon dioxide emissions would be reduced (by 151 million tons by 2050) to levels that would be considerably better than current Government expectations. Gross Domestic Product and wealth would be bolstered by US$16 065 million and US$22 731 million, respectively. Water-related ES would improve including the quality of potable water, while more water would be maintained within forested ecosystems, thereby reducing the future risk of natural hazards such as landslides and floods. The FOLU strategies would create 72 800 new jobs and reduce poverty by 15 586 individuals. Analysis with IEEM + ESM demonstrates that reducing wildfire-driven forest loss would have outsized impacts and be the most effective and expedient way to contribute to meeting NDC targets. The IEEM + ESM approach is an example of an analytical framework that is meeting growing demand from Government institutions and multilateral development banks for understanding the effects and transition pathways of NDC strategies on economic, social and environmental outcomes.
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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 | 0.001 | 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) | 0.001 | 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 it