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Record W4392617735 · doi:10.1142/s2382624x24300019

Review Paper: A Quarter of a Century of the European Water Framework Directive — The Slow Path Towards Sustainable Water Management

2024· review· en· W4392617735 on OpenAlexaboutno aff
José Albiac, Elena Calvo, Encarna Esteban

Bibliographic record

VenueWater Economics and Policy · 2024
Typereview
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersEuropean Regional Development FundGobierno de AragónEuropean Commission
KeywordsQuarter (Canadian coin)DirectiveWater Framework DirectivePath (computing)BusinessEnvironmental planningEnvironmental scienceGeographyComputer scienceWater quality

Abstract

fetched live from OpenAlex

The European Water Framework Directive (WFD) is one of the most analyzed environmental legislations in the scientific literature, influencing the water management in some non-European countries. The WFD has the strong ambition of achieving a good ecological status of water bodies across all river basins in Europe. However, the advances towards sustainable management are falling far behind the planned schedule. The emphasis of the Directive is focused on water quality rather than on water quantity. The advances during the last quarter of the century since its inception have been strong on urban and industrial point pollution, but not on agricultural non-point pollution that remains high and even increases in major basins. Water quantity aspects have been mostly left aside in the Directive, despite the fact that water scarcity is a serious problem in Southern European countries, and will become more critical with climate change in most basins across Europe. Some policy measures of the WFD need to be reformed, in particular measures for abating agricultural pollution, and new measures for addressing water scarcity. The narrow focus of the WFD on water pricing to solve at the same time issues of financing, water allocation and efficiency, environment, opportunity costs and pollution abatement, should be broadened. The challenge is giving more emphasis to command & control and collective action instruments, and designing combinations of instruments adapted to sectoral and spatial locations in basins. This overhaul of the water policy instruments by the European Commission will be needed to advance in the sustainable management of river basins in Europe.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.218
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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