Evidence map›Paper›PMID 37300408›Full record

ArticleGlobal change biology2023

Evaluating the impact of biodiversity offsetting on native vegetation.

Sophus O S E Zu Ermgassen, Katie Devenish, B Alexander Simmons, Ascelin Gordon, Julia P G Jones, Martine Maron, Henrike Schulte To Bühne, Roshan Sharma, Laura J Sonter, Niels Strange and 2 more

Abstract read
In one paragraph

Article in Global change biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

12 authors.

Sophus O S E Zu ErmgassenDepartment of Biology, University of Oxford, Oxford, UK.ORCID 0000-0001-6044-3389
Katie DevenishSchool of Natural Sciences, College of Environmental Science and Engineering, Bangor University, Bangor, UK.
B Alexander SimmonsTampa Bay Estuary Program, St. Petersburg, Florida, USA.
Ascelin GordonSchool of Global Urban and Social Studies, RMIT University, Melbourne, Victoria, Australia.
Julia P G JonesSchool of Natural Sciences, College of Environmental Science and Engineering, Bangor University, Bangor, UK.ORCID 0000-0002-5199-3335
Martine MaronThe University of Queensland, School of Earth & Environmental Sciences, and Centre for Biodiversity and Conservation Science, Brisbane, Queensland, Australia.
Henrike Schulte To BühneInstitute of Zoology, Zoological Society of London, London, UK.ORCID 0000-0003-1505-264X
Roshan SharmaSchool of Global Urban and Social Studies, RMIT University, Melbourne, Victoria, Australia.
Laura J SonterThe University of Queensland, School of Earth & Environmental Sciences, and Centre for Biodiversity and Conservation Science, Brisbane, Queensland, Australia.
Niels StrangeDepartment of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark.
Michelle WardThe University of Queensland, School of Earth & Environmental Sciences, and Centre for Biodiversity and Conservation Science, Brisbane, Queensland, Australia.
Joseph W BullDepartment of Biology, University of Oxford, Oxford, UK.

Funding

Environmental Decision AllianceNERC's EnvEast Doctoral Training Partnership NE/L002582/1
6 · The paper itself

Abstract

Biodiversity offsetting is a globally influential policy mechanism for reconciling trade-offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victoria's Native Vegetation Framework (2002-2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near-complete baseline woody vegetation cover ("avoided loss", 2702 ha) and with incomplete cover ("regeneration", 501 ha), and evaluated impacts on woody vegetation extent from 2008 to 2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in sites that were not offsets for the study duration but were later enrolled as offsets, to partially account for self-selection bias (where landholders enrolling land may have shared characteristics affecting how they manage land). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9%-3.6%/year more than non-offset sites (138-180 ha from 2008 to 2018) but this effect weakened with the second approach (0.3%-1.9%/year more than non-offset sites; 19-97 ha from 2008 to 2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of 'net gain' (NG) was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a NG outcome seems unlikely. The results highlight the importance of considering self-selection bias in the design and evaluation of regulatory biodiversity offsetting policy, and the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies.

Indexed as

BiodiversityConservation of Natural ResourcesEcosystemMotivationVictoriaWoodAustralian native vegetationbiodiversity offsetscounterfactual analysisenvironmental policyimpact evaluationnet gainno net lossregulatory marketsstatistical matching

Identifiers

PMID37300408
PMCPMC10946555

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Registered trials

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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.