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Methodology, Tools and Scenarios to Help Monitor and Evaluate Pathways to the Implementation of the KM GBF
- 1. Using models andscenarios Monitoring progress and evaluating pathways to success Tom Mason, Samantha Hill
- 2. Structure i. COOP4CBDTask 3.3 ii.Background to models and scenarios iii. Pilot methodology iv. Next steps
- 3. COOP4CBD Task 3.3 Developtechnical capacity in the use of models and scenarios to review progress and assess pathways to success Deliverables 1. Piloted assessment methodology to review progress and ambition under the KMGBF 2. Guidance on using models and scenarios to review progress and ambition
- 4. Models and scenarios: yourperception and experience Go to menti.com Input code 5734 2145
- 5. Models and scenarios FromIPBES (2016) Methodological Assessment Report: Scenarios and Models Models Qualitative or quantitative representations of a system and of relationships between components Scenarios Plausible futures for indirect or direct drivers, or to policy interventions targeting these drivers Models + scenarios Consequences of scenarios on systems (e.g., nature) can be evaluated using models
- 6. Relevance to KMGBF Goal/Target Relevanceof models & scenarios Examples Goal A – Biodiversity outcomes Very high Models of state of biodiversity Goal B – Sustainable use High Ecosystem service models Goal C – Benefit sharing Moderate Socio-economic scenarios to explore benefit flows Goal D – Implementation means Moderate Economic models to explore investment needs 1. Monitoring progress towards targets • Are actions being implemented? 2. Evaluating pathways towards goals • Are implemented actions sufficient? Most relevance to: • Goals A and B • Targets 1-12 Target 1–3 (spatial planning, restoration, conservation) Very high Target 7–10 (pollution, climate, invasive species) High Target 14–16 (mainstreaming, sustainable consumption) Moderate Target 18–19 (finance, capacity) Moderate
- 7. Models 1) Correlative • Empiricaldata used to estimate parameters • No predefined ecological meaning 2) Process-based • Relationships explicitly-stated processes • Parameters have predefined interpretation 3) Expert-based • Relationships described using experience of experts and stakeholders e.g., General Circulation Models e.g., Species Distribution Models e.g., Decision support models
- 8. Models and indicators Twotypes of biodiversity indicator: Compiled indicators - Trends determined from data on biodiversity - May incorporate some modelling e.g., gap-filling, correcting biases, smoothing Model-based indicators - Trends estimated from models that incorporate drivers of change - Can project whether a set of proposed actions are expected to be sufficient to achieve goals A.3 Red List Index Biodiversity Intactness Index
- 9. Models as a‘satnav’ for nature i. Models to predict expected future outcomes of today’s choices ii. Rapid feedback from monitoring to enable course corrections and model improvement Without this, we will have to find our way to the goals by looking in the rear- view mirror (eg from periodic review of available headline indicators) Purvis (2025) Phil.Trans. R. Soc. B 380: 20230210
- 10. Models as a‘satnav’ for nature Purvis (2025) Phil.Trans. R. Soc. B 380: 20230210 Models link indicators, goals, targets and policies
- 11. Scenarios Socioeconomic scenariose.g., SSP narratives Broad futures for population, economy, technology, governance that drive demand, land-use, and emissions.
- 12. Scenarios Socioeconomic scenariose.g., SSP narratives Broad futures for population, economy, technology, governance that drive demand, land-use, and emissions. Integrated Assessment Model Quantified parameters e.g., population trajectories, land-use constraints, policy settings Emissions Land-use Categories or % cover Climate e.g., temperature, precipitation, extremes Climate model Model of biodiversity or ecosystem services Modelled trajectories in biodiversity indicator Overlayed policy changes e.g., protected-area expansion, restoration targets Land-use and climate scenarios
- 13. Scenarios Different roles depending onphase in policy cycle From IPBES (2016) Methodological Assessment Report: Scenarios and Models
- 14. Scenarios Exploratory scenarios • Examinerange of plausible futures based on potential trajectories of drivers • Indirect (e.g. sociopolitical) or direct drivers (e.g. habitat conversion) “What could happen to nature and society under different possible futures?” Examples: Millennium Ecosystem Assessment, Global Environment Outlooks IPBES Nature Futures Framework
- 15. Scenarios Nature Futures Frameworkscenarios for Europe e.g., Dou et al. (2023) Global Environmental Change Exploratory scenarios • Examine range of plausible futures based on potential trajectories of drivers • Indirect (e.g. sociopolitical) or direct drivers (e.g. habitat conversion) “What could happen to nature and society under different possible futures?” Examples: Millennium Ecosystem Assessment, Global Environment Outlooks
- 16. Scenarios Intervention scenarios • Evaluatealternative policy or management options • “What should we do to achieve a desired target or goal?” Policy screening “Where are we heading under current policies?” Target seeking “What additional actions are needed to get where we want to go?” From IPBES (2016) Methodological Assessment Report: Scenarios and Models
- 17. Scenarios Intervention scenarios • Evaluatealternative policy or management options • “What should we do to achieve a desired target or goal?” Policy screening “Where are we heading under current policies?” Target seeking “What additional actions are needed to get where we want to go?” Kok et al. (2023) Prospective evaluation of the ambition of the KM-GBF Incorporates KM-GBFTargets 1, 2, 3, 7, 8, 10, 11, 16 Examples Target 1: total agricultural land is not allowed to increase after 2030 Target 2: restoration is achieved by natural regeneration of abandoned agricultural areas and by reducing pressures
- 18. Scenarios Intervention scenarios • Evaluatealternative policy or management options • “What should we do to achieve a desired target or goal?” Policy screening “Where are we heading under current policies?” Target seeking “What additional actions are needed to get where we want to go?” Kok et al. (2023) Prospective evaluation of the ambition of the KM-GBF
- 19. Case study: Ecosystem IntegrityIndex The extent to which the composition, structure, and function of ecosystems fall within their natural range of variation Model-based indicator; 1km2 across global terrestrial ecosystems Component indicator of Goal A Strong links to Targets 2 & 3: condition and effectiveness Hill et al. In review https://www.biorxiv.org/content/1 0.1101/2022.08.21.504707v2
- 20. Ecosystem Integrity Index Case study: Ecosystem IntegrityIndex PREDICTS biodiversity models Information on land-use, intensity Composition Human modification of landscapes Landscape structural intactness model Structure Functional intactness model Modeled potential and observed primary productivity Function
- 21. Stable Case study: Ecosystem IntegrityIndex 1. Monitoring progress Monitoring condition of protected areas using historic EII time series Understanding effectiveness: comparison with surrounding landscape Stable-ish Improving + Improving -
- 22. Case study: Ecosystem IntegrityIndex 1. Monitoring progress Monitoring condition of protected areas using historic EII time series Understanding effectiveness: comparison with surrounding landscape Importance of ground truthing: site- level assessments Change in PA – Change in buffer 2010-2020
- 23. Case study: Ecosystem IntegrityIndex 2. Evaluating pathways Data pipeline to feed in scenario outputs
- 24. PREDICTS biodiversity models Information on land-use, intensity Ecosystem Integrity Index Case study: EcosystemIntegrity Index Human modification of landscapes Landscape structural intactness model Functional intactness model Modeled potential and observed primary productivity Land-use scenarios Climate scenarios
- 25. Case study: Ecosystem IntegrityIndex 2. Evaluating pathways Data pipeline to feed in scenario outputs Scenario projections in progress NFF i. Exploratory Europe KM-GBF PBL ii. Intervention Europe iii. Intervention National Restoration
- 26. COOP4CBD Task 3.3 Deliverables 1.Piloted assessment methodology to review progress and ambition under the KMGBF 2. Guidance on using models and scenarios to review progress and ambition • Guidance document, including examples using methodology • Webinars
- 27. Useful resources Guidance materials IPBES(2016) The methodological assessment report on scenarios and models of biodiversity and ecosystem services https://files.ipbes.net/ipbes-web-prod-public-files/downloads/pdf/2016.methodological_assessment_re port_scenarios_models.pdf Biodiversa (2020) Handbook on biodiversity scenarios for decision-making https://www.biodiversa.eu/2023/07/13/handbook-on-biodiversity-scenarios-for-decision-making/
- 28. Useful resources Scenarios Dou etal. (2023) Using the Nature Futures Framework as a lens for developing plural land use scenarios for Europe for 2050 Global Environmental Change https://www.sciencedirect.com/science/article/pii/S0959378023001322 Kok et al. (2024) A prospective evaluation of the ambition of the Kunming-Montreal Global Biodiversity Framework goals and targets https://www.pbl.nl/en/publications/a-prospective-evaluation-of-the-ambition-of-the-kunming-montreal -global-biodiversity-framework-goals-and-targets Ecosystem Integrity Index Hill et al. In review https://www.biorxiv.org/content/10.1101/2022.08.21.504707v2
- 29.
Editor's Notes
- #3 Aims to increase the understanding on the use of scenarios and models to review implementation over time and evaluate the adequacy of ambition
- #6 Most relevance to those goals and targets concerning state of nature and nature’s contributions to people
- #8 Many of the limitations common to compiled indicators—geographic and taxonomic biases in data, time lags in compilation, synthesis and reporting—can in principle be greatly mitigated through enhanced data collection and aggregation. A more fundamental limitation is that compiled indicators are informative only about what has happened so far, not about what Might happen in the future or how to change future trajectories. they can project whether a set of proposed actions are expected—based on the current understanding of the models embody—to be sufficient to achieve the desired outcome goals. Models vary widely in how they work and how they make predictions: they range from highly mechanistic (i.e. process-based) to purely phenomenological (i.e. pattern-based), and from fully static (modelling net effects on the equilibrium state) to completely dynamic (explicitly considering the time course of changes
- #9 To meet the outcome goals of KMGBF, the GBMF should support adaptive policy responses to the state of biodiversity, which in turn requires a ‘satnav’ for nature.
- #10 To meet the outcome goals of KMGBF, the GBMF should support adaptive policy responses to the state of biodiversity, which in turn requires a ‘satnav’ for nature.
- #11 | SSP | Name | Key Features | Implications for Climate & Adaptation | | ---- | ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- | | SSP1 | Sustainability (“Taking the Green Road”) | Low population growth, high education & health, rapid technological progress, strong environmental policies | Low emissions, high adaptive capacity | | SSP2 | Middle of the Road | Trends continue roughly as today, moderate economic & population growth | Moderate emissions and adaptation challenges | | SSP3 | Regional Rivalry (“Fragmented World”) | High population growth in developing countries, slow economic growth, weak international cooperation | High emissions, low adaptive capacity, fragmented response | | SSP4 | Inequality | High inequality, uneven development, elites thrive while large parts of the population lag | Moderate to high emissions, very uneven adaptation capacity | | SSP5 | Fossil-fueled Development (“Taking the Highway”) | Rapid economic growth, heavy reliance on fossil fuels, high energy demand | High emissions, strong resources for adaptation but high climate risk |
- #12 | SSP | Name | Key Features | Implications for Climate & Adaptation | | ---- | ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- | | SSP1 | Sustainability (“Taking the Green Road”) | Low population growth, high education & health, rapid technological progress, strong environmental policies | Low emissions, high adaptive capacity | | SSP2 | Middle of the Road | Trends continue roughly as today, moderate economic & population growth | Moderate emissions and adaptation challenges | | SSP3 | Regional Rivalry (“Fragmented World”) | High population growth in developing countries, slow economic growth, weak international cooperation | High emissions, low adaptive capacity, fragmented response | | SSP4 | Inequality | High inequality, uneven development, elites thrive while large parts of the population lag | Moderate to high emissions, very uneven adaptation capacity | | SSP5 | Fossil-fueled Development (“Taking the Highway”) | Rapid economic growth, heavy reliance on fossil fuels, high energy demand | High emissions, strong resources for adaptation but high climate risk |
- #13 This figure shows the roles played by different types of scenarios corresponding to the major phases of the policy cycle. Types of scenarios are illustrated by graphs of changes in nature and nature’s benefits over time. The four major phases of the policy cycle are indicated by the labels and grey arrows outside the coloured quarters of the circle. In “exploratory scenarios”, the dashed lines represent different plausible futures, often based on storylines. In “target-seeking scenarios” (also known as “normative scenarios”), the diamond represents an agreed-upon future target and the coloured dashed lines indicate scenarios that provide alternative pathways for reaching this target. In “policy-screening scenarios” (also known as “ex-ante scenarios”), the dashed lines represent various policy options under consideration. In “retrospective policy evaluation” (also known as “ex-post evaluation”), the observed trajectory of a policy implemented in the past (solid black line) is compared to scenarios that would have achieved the intended target (dashed line).
- #26 Aims to increase the understanding on the use of scenarios and models to review implementation over time and evaluate the adequacy of ambition