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Enregistrement W2595569428 · doi:10.55016/ojs/sppp.v9i1.42606

From Impact Assessment to the Policy Cycle: Drawing Lessons from the EU’S Better-Regulation Agenda

2016· article· en· W2595569428 sur OpenAlexaboutno aff
Andrea Renda

Notice bibliographique

RevueThe School of Public Policy Publications · 2016
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental and Social Impact Assessments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPolitical scienceImpact assessmentPublic administration

Résumé

récupéré en direct d'OpenAlex

The European Union launched its first comprehensive better-regulation agenda in 2002 and has since then been constantly modifying and improving its toolkit aimed at guaranteeing the quality of its legislation. The first better-regulation agenda followed the pioneering experience of some of its member states and introduced a formal procedure of ex ante impact assessment (IA) as well as minimum criteria for stakeholder consultation.1 Different variables explain the rise of EU-level IA, such as reactions to the overuse of the precautionary principle in risk analysis and health policy (especially in chemicals and tobacco);2 pressure from finance ministers in countries such as the U.K. and the Netherlands to introduce evidence-based procedures in policy formulation, thus increasing accountability;3 and organizational developments within the European Commission, with an expansion to regulatory policy of tools originally crafted for sustainable development policies.4 The EU IA model was introduced together with a communication aimed at simplifying and improving the regulatory environment and promoting “a culture of dialogue and participation” within the EU legislative process.5 As a result, the commission decided to integrate all forms of ex ante evaluation and various tests by building an integrated impact-assessment model, to enter into force on Jan. 1, 2003.6 This model was tasked with the heavy responsibility of ensuring that adequate account was taken, at an early stage of the regulatory process, of both the competitiveness and sustainable-development goals, which ranked among the top priorities on the EU agenda. Over the past 14 years, the better-regulation toolkit of the European Commission has been strengthened from a methodological standpoint, and expanded into a more comprehensive system that involves ex ante IAs, ex post evaluations, “fitness checks” focused on clusters of laws, and cumulative cost assessments that address specific industry sectors. At the same time, the system gradually involved other institutions, such as the European Parliament (especially from 2012) and, to a lesser degree, the Council. And in May 2015, the European Commission further re-launched the system with a much stronger emphasis on ex ante political validation of proposals, stakeholder consultation at all phases of the policy process, and comprehensive, well-structured retrospective reviews. The European Commission has completed more than 1,000 IAs since 2003 and this provides a solid basis for observing the main virtues and challenges of the system as it has evolved to date. This paper looks at the lessons that can be drawn from the EU experience and highlights the challenges that have been successfully addressed and the ones that still remain unsolved. In discussing challenges, reference will be made to other legal systems, such as those in the United States, Canada, Australia and the United Kingdom. The paper also discusses the main novelties introduced by the recently adopted new EU Better Regulation Package, as well as the content of the proposed new Inter-Institutional Agreement on Better Lawmaking, both presented by the European Commission on May 19, 2015. Section 1 of the paper analyzes the current role played by major EU institutions in better lawmaking and aspects of the current inter-institutional agreement that would be worth reconsidering. Key issues include the use of ex ante impact assessments in major EU institutions; the frequency, timing and relevance of stakeholder consultation throughout the policy process; problems related to the ex post evaluation, fitness checks and other forms of analyses of the stock of legislation (e.g., cumulative cost assessments). Section 2 focuses on methodology and discusses the taxonomy of costs and benefits that is now the basis for both ex ante impact assessments and ex post evaluations, including fitness checks and cumulative cost assessments. Section 3 briefly summarizes the main activities carried out in the realm of financial regulation and describes the recent consultation launched by the European Commission for a thorough revision of the whole stock of legislation in this domain. Section 4 concludes by briefly comparing the EU experience with the Canadian one.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,074
score de la tête « metaresearch » (Gemma)0,072
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,074
Score d'incertitude au seuil0,392

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0740,072
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0080,007
Études des sciences et des technologies0,0070,041
Communication savante0,0380,044
Science ouverte0,0070,019
Intégrité de la recherche0,0270,026
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,039
Tête enseignante GPT0,370
Écart entre enseignants0,331 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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