Initial report on policy obstacles and opportunities for the integration and uptake of citizen generated data and citizen & community-led actions in environmental compliance assurance - more4nature D1.1
Bibliographic record
Abstract
This report, as a contribution to the overall aim of the more4nature project, provides a sound understanding – based on a representative sample of international and regional policies – of the status quo regarding the role of citizen & community-led actions (CCLA) and citizen generated data (CGD) in environmental policies with regards to environmental compliance assurance (ECA). The concept of ‘citizen & community-led actions’ is being developed as part of the more4nature project and refers to actions that citizens and communities can take to improve environmental protection. CGD is often the product of actions by citizens and can notably be used to complement institutional data. The policy analysis in this report takes place within the context of the three thematic policy areas of the more4nature project, namely zero pollution, biodiversity protection and deforestation prevention (Z/B/D). This report analyses the three types of intervention of ECA in light of possible use of citizen science, and more specifically: Whether CCLA can be used in compliance promotion, i.e. to contribute to preventing and limiting non-compliance with a policy; Whether CGD can be used in compliance monitoring, i.e. to track and identify potential problems with compliance; and Whether CCLA and/or CGD can be used in compliance enforcement, i.e. to address, via legal or official channels, a situation of non-compliance. Twenty policies were selected for analysis in this report. Each of them was reviewed using selected keywords and a template resulting from a multidisciplinary collaboration with project partners. Every step and stages of the iterative methodological approach developed, implemented and refined to conduct the policy analysis is presented in detail: Step 1: Preparations, including the selection of policies, the selection of criteria and keywords, and the development of the template; Step 2: the policy analysis, i.e. the initial data collection, the review process and the collection of completed data; Step 3: the data analysis, including results extraction, identification of trends and preliminary findings, and drafting of the report. The preliminary findings show that ECA is largely a matter of national competence, whether a policy is adopted at EU or international level. Therefore, most policies analysed did not include many provisions or other specifications about how to conduct ECA. Generally, compliance enforcement was the type of intervention that was least present from policy texts. We found that in the policies we analysed, the terms used to refer to some forms of citizen involvement were mostly not those that define citizens science in the literature. Instead, the most relevant terms that we came across included ‘public’, ‘civil society’, ‘third parties’, ‘stakeholders’, ‘consultation’ and ‘participation’. With regards to the use of CCLA in compliance promotion, the most common occurrences referred to the provision of information to citizens, although some also included requirement of consultation or active public participation. The use of CGD in compliance monitoring was rarely directly specified in the policies, but some provisions contained elements that indicated either that such use could be possible or, on the contrary, that it could be restricted. In the latter case, the existence or extent of such restriction would most often depend on the interpretation of the international or EU provisions at the national level. The use of CCLA and CGD in compliance enforcement ranged among the policies, with some having minimal enforcement provisions and others allowing access to the public in judicial procedures (as applicants or as witnesses). Most opportunities for the use of CCLA and CGD in ECA were found in one horizontal policy - the Aarhus Convention on access to information, public participation in decision-making and access to justice in environmental matters, as well as in the most recently adopted Z/B/D policies that we assessed, namely Regulation (EU) 2023/1115 on Deforestation-free Products and the 2022 Kunming-Montreal Global Biodiversity Framework, the Environmental Crime Directive and the Nature Restoration Regulation (also known as ‘Nature Restoration Law’). These policies may show the way in terms of how future policies or revisions of existing ones may better promote the use of citizen science in ECA. However, it should be noted that our findings are based on an analysis of the text of selected policies and may not necessarily translate in practice.This report demonstrates that, on the one hand, there are opportunities for the use of CCLA and CGD in ECA within the EU and international context. Those are quite varied and stem both from horizontal and vertical policies. On the other hand, the report also exposes the difficulty of identifying such opportunities in environmental policies because of the vagueness of the language or the referral to the national (or local) level to decide whether to allow or restrict the use of CCLA and CGD. This may give room for agency through interpretation, but effectively leaves citizen science groups in the dark about how policies enable or even promote their participation in the ECA process.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".