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Record 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 on OpenAlexaboutno aff
Andrea Renda

Bibliographic record

VenueThe School of Public Policy Publications · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceImpact assessmentPublic administration

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.074
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.072
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.007
Science and technology studies0.0070.041
Scholarly communication0.0380.044
Open science0.0070.019
Research integrity0.0270.026
Insufficient payload (model declined to judge)0.0050.001

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 head0.039
GPT teacher head0.370
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2016
Admission routes1
Has abstractyes

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