Linking perceptions of climate change impacts with adaptation: Insights from landowners in Southern Chile
Bibliographic record
Abstract
• Most forest-owners perceived climate change (CC), and only 60 % took adaptive actions. • Higher off-farm incomes and risk tolerance reduce CC perception and adaptation. • Higher forest cover appears to offer an adaptive strategy (increase farms' resilience). • Policies to raise water/fodder availability would allow a better adaptive response. • Preparing landowners to deal with risks and uncertainty would improve adaptive actions. Adaptation is recognized as the outcome of a complex mix of individual and institutional factors that shape how society responds to climate change. Adaptation results from a joint decision-making process, where actors simultaneously evaluate the risks associated with climate change into whether or not they should adapt. We develop a model of this joint decision-making process that incorporates risk tolerance to identify what factors influence landowners’ perception and adaptation to climate change in southern Chile. The results are based on 86 in-person interviews, involving the collection of socioeconomic data, risk aversion tests, and semi-structured interviews. We found that while most landowners perceived climate change impacts as a threat, only 60 % had taken any action. Two underlying factors applied to both perception and adaptation: risk tolerance and off-farm incomes. Higher risk tolerance and greater reliance on off-farm incomes reduced people's perception and adaptation to climate change. The presence of climate change-induced impacts positively influenced the implementation of adaptation, while schooling and gender were relevant only in shaping climate change perceptions. Following these results, we suggest developing programs to communicate the real magnitude of climate risks so that landowners better understand the opportunity costs of climate change adaptation, and in that way, avoid/anticipate the need to see impacts on the land in order to act. Along these lines, further investigation of the role off-farm incomes play in adaptation is warranted, where it is simultaneously both a factor in the adaptation process but can also be an adaptation action as well.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".