Ecosystem services concept: Challenges to its integration in government organizations
Bibliographic record
Abstract
• We interviewed government organizations’ actors to investigate the barriers to ES concept implementation. • Integration into organizations is limited by confusion in terminology and difficulties in ES evaluation. • Operationalization across fragmented organizations, sectors and scales is considered complex to resolve. • Organizational prioritization of ES associated with wealth creation is a challenge due to related trade-offs. • A common cognitive and technical framework could bridge organizations towards a shared understanding of ES. The ecosystem services (ES) concept is well-established in the scientific community but remains underutilized and poorly understood within government organizations. In this article, we analyze the barriers to integrating ES into relevant governmental operations in Quebec, Canada. According to the perspective expressed by eight governmental organizations’ representatives, we analyze factors constraining the integration of the ES concept at the government level while suggesting ways forward. We found that despite the relevance of this concept in many areas ranging from planning to environment management, its operationalization faces challenges due to 1) confusion of terminology; 2) difficulties in evaluating and prioritizing ES; and 3) the complexity of operationalization in a context of interdependent practices. Moreover, the adoption of guidelines to implement the ES concept in governmental policies and programs is insufficient. Along with institutional and operational limitations, government organizations face structural constraints preventing the concept’s full use, namely the fundamental of State and market. Our study shows that although the ES concept is apprehended in a utilitarian way for short-term goals within government organizations, it nonetheless holds the potential to raise awareness and promote enlightened decision-making on environmental benefits.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".