Evidence map›Paper›PMID 38047917›Full record

ArticleEnvironmental management2024

Linking Forest Ecosystem Services to the SDGs: Semi-quantitative Mapping of Perceptions towards Integrated Decision-making.

Myriam Pham-Truffert, Jean-Laurent Pfund

Abstract read
In one paragraph

Article in Environmental management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Myriam Pham-TruffertEarth System Science (ESS), Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland. myriam.pham-truffert@geo.uzh.ch.ORCID 0000-0001-5848-8959
Jean-Laurent PfundForest Division, Federal Office for the Environment (FOEN), Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With this study, we test and present the results of a reproducible semi-quantitative methodological approach, which enables us to map perceptions of complex systems, linking the forest ecosystem services (FES) of a given spatial level to the wider policy domains represented by the 2030 Agenda and its Sustainable Development Goals (SDGs). Through a participative process, we used integrated forest management and FES as entry point concepts to support and inform dialog towards a normative desired future as framed by the SDGs, taking into account interdependencies across sectors and policy domains. The scales used in the test were national (Switzerland) and international but it is possible to use the approach at any level of integration, especially the landscape one in the case of forest or other ecosystem issues to be transdisciplinary solved. We stress that the semi-quantitative aspects of the approach - be it the ranking of the importance of FES across the different SDGs, or the positive or negative weighting of interactions among these FES in cross-impact matrices - enable the perceptions held by actors to be more explicit and significant for governance or goal prioritization. The results illustrate the perceptions of selected actors on the effects of integrated forest management and provide a basis for multi-actor deliberation on emerging potential synergies or conflicts, thereby genuinely supporting science-policy-practice dialog, which is crucial to foster integrated decision-making.

Indexed as

Conservation of Natural ResourcesDecision MakingForestsSustainable DevelopmentEcosystemForestryForest ecosystem servicesIntegrated landscape approachesPolicy integrationSustainable development goals

Identifiers

PMID38047917
PMCPMC11208211

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.