Aiming at a moving target: economic evaluation of adaptation strategies under the uncertainty of climate change and CO2 fertilization of European beech (Fagus sylvatica L.) and Silver fir (Abies alba Mill.)
Bibliographic record
Abstract
Abstract Key message Drought severely worsened till 2100 and eventually outplayed growth-enhancing CO 2 fertilization turning productivity gains into losses for beech and fir. Most scenarios generated notable losses in profitability but economic tipping points were later than for productivity due to lag effects related to discounting. Time mixture of fir and shortening rotation can counteract economic risks under climate change, but requires early admixture and moderate establishment costs. Context Adaptation strategies to climate change (CC) such as establishing mixed forests are often based on ecological understanding while economic rationale is often disregarded. Aims This paper studies CC uncertainty on productivity and profitability of European beech ( Fagus sylvatica L.) and Silver fir ( Abies alba Mill.). Besides, the economic consequences to actively adapt beech forests by admixing Silver fir are investigated. Methods We used the process-based forest growth model GOTILWA + to simulate RCP2.6, RCP4.5 and RCP8.5 climatic projection by the MPI-ESM-LR global circulation model (MPI-ESM-LR) with the CO 2 fertilization effect (eCO 2 ) switched on and off. We analysed the sensitivity of the land expectation value (LEV) on CC and economic parameters. Results CC initially increased productivity, but declined after a tipping point (2040–2070) and later also profitability (2045–2100). RCP8.5 had positive, RCP2.6 negative and RCP4.5 neutral effects on LEV. Switching off eCO 2 turned RCP8.5 from the most profitable to the least profitable scenario and the opposite for RCP2.6. CC generally reduced optimal rotation ( R opt ) being scenario dependant, but comparatively more for fir than beech. Admixing fir created an economic benefit when implemented before stand age 50 of beech. This benefit was nullified with protection costs for browsing control (fencing or tree shelters). Conclusions Economic parameters (not CC) were the major source of uncertainty stemming from discounting factors and establishment costs. Admixture of fir and shortening rotation can provide a solution to tackle economic and climate uncertainties, but requires early admixture and browsing control.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".