Socio-economic methods for evaluating decisions in coastal erosion management: State-of-the-art
Bibliographic record
Abstract
It is obvious that across Europe several, different approaches are applied for coastal and particularly erosion management, with economic assessment playing only a minor role. The Defra approach (including the point system) but also the OEEI guidelines (2000) could be a good starting point to introduce and develop more systematic and rigorous procedures to support the decision process. Balanced choices and accepted decisions can best be taken if all economic, ecological and social project impacts are taken into account. Integrated impact assessment and stakeholder participation will lead to more sustainable and satisfactory solutions. The choice of the extent of integrated assessment will depend on the information needs, the complexity of the decision and the available resources. The public carries costs of erosion basically, which may not be sustainable in the long term. Authorities and decision makers, entrepreneurs and initiators must be aware of the erosion (and flooding) risks. Then it will be possible to take the right priorities, procedures and distribution for funding and to internalise the erosion (flooding) costs appropriately. It is essential to monitor the economic impacts of completed projects during its lifetime and to review systematically the approaches and methods used for the valuation of economic, ecological and social impacts. Future climate change and sea level rise will increase the risk of erosion (and flooding) in Europe and appropriate measures for protection and defence have to be decided now. A more comprehensive and harmonised EU approach to Integrated Coastal Zone Management (ICZM) from centralised data collection (but understanding of the local natural processes), strategy and policy setting (holding the line vs. realignment, soft and hard engineering) to project planning, assessment and monitoring would be useful. This could start with a more efficient and coordinated exchange of data and experiences and the introduction of agreed procedures and instruments to support the making of sustainable decisions. The assessment methods presented in this report, Cost-Benefit analysis, Cost-Effectiveness analysis and Multi-Criteria analysis, constitute the main tools to evaluate and validate the spending of public funds on coastal erosion projects. The usefulness of these methods is demonstrated by the many examples included in this report.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".