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WP 4.2. Policy Scenario building - UFMG & CIT

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WP 4.2. Policy Scenario building - UFMG & CIT

  1. 1. WP 4.2.Policy Scenario building UFMG & CIT
  2. 2. Are key forest countries going to meet their NDCs? Copenhagen’s NAMA Paris’ NDC Indicative NDC https://themasites.pbl.nl/o/climate-ndc-policies-tool/#countries
  3. 3. Are key forest countries going to meet their NDCs?
  4. 4. Project structure Work Package 1: Achieving transparency & accountability Work Package 2: Tracking & assessing actions Work Package 3: Bringing out the politics Global Forests & Climate Arena National Policies & Actions Subnat’l Policies & Actions Work Package 4: Linking science, policy & politics • diagnostic framework • policy scenarios • science-policy platforms
  5. 5. General process • Refining LoA • Phased approach - Year 1: Brazil - Year 2: Peru - Year 3: DRC and Indonesia • Submission of a publication describing model specificities for country partners and country coordinators: Rochedo et al. (2018)
  6. 6. Current activities • Compilation of list of demands for spatial data - Focus on LULUCF - AFOLU information important for assessing NDC attainment • Presentation in the GCF meeting in Belém (October, 20th) - Emission scenario platform within CIFOR project
  7. 7. GHG Emissions Scenarios • Business as usual: current deforestation and emissions scenario • NDC scenario: minimum deforestation reduction required to meet NDC • REDD+ scenario: ambitious deforestation reduction
  8. 8. Scenario components • Overall narrative for each scenario • Qualitative description of deforestation drivers • Qualitative description of deforestation control policies • Quantitative deforestation trajectory (total Km2 per year, with or without subdivision) • Data set per country: • Land use map (minimum 2-years, forest / non-forest use) • Above ground/below ground carbon map • BAU GHG for other non-LULUCF sectors (energy, agriculture) • Spatially explicit drivers and policies (eg. built and planned roads, conservation areas) • Fall back datasets: Hansen (land use), Saatchi (carbon), ClimateTracker (BAU GHG for energy and agriculture), OpenStreet Map, WDPA, PBL.NL
  9. 9. Scenarios outputs • Spatially explicit deforestation projections • Improve deforestation policies by indicating priority areas • Allow science-based regional target and benefit-sharing proposals (i.e. avoid barrel/competition pushing between jurisdictions) • GHG Emissions trajectory • Assess if country is on track to meet its NDC • Estimate potential for generating carbon credits / result-based payments via LEAF ART-TREES, ITMOs (Art. 6.2) and REDD+ (Art. 5)
  10. 10. Brazil’s Case Study
  11. 11. Scenario components for Brazil • Data set per country: • Land use map: PRODES and National Inventory • Above ground/below ground carbon map: Brazil’s National Inventory • BAU GHG for other non-LULUCF sectors: Official projections from Min Science & Technology • Overall narrative for each scenario • Deforestation drivers • Deforestation control policies • Deforestation trajectory (total Km2 per year, with or without subdivision) • BAU: historical trajectory • REDD+: original PNMC target (4 thousands km2, down from current 11 thousands km2)
  12. 12. Preliminary results for Brazil REDD+ = SEG BAU = IEG NDC = to be developed
  13. 13. Preliminary results for Brazil
  14. 14. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 GtCO 2 Governança Forte AFOLU Energia Preliminary results for Brazil Strong Governance
  15. 15. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2 0 1 8 2 0 2 0 2 0 2 2 2 0 2 4 2 0 2 6 2 0 2 8 2 0 3 0 2 0 3 2 2 0 3 4 2 0 3 6 2 0 3 8 2 0 4 0 2 0 4 2 2 0 4 4 2 0 4 6 2 0 4 8 2 0 5 0 GtCO 2 Governança Intermediária AFOLU Energia Preliminary results for Brazil Intermediary Governance (BAU)
  16. 16. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 GtCO 2 Governança Fraca AFOLU Energia Preliminary results for Brazil Weak Governance (BAU)
  17. 17. Key challenges • Identity appropriate spatially explicit data sources and Tier 2-3 emission factors • Develop scenario narratives grounded on country’s expectations • Align results with National GHG Inventories, FREL and ART-TREES • Integration with other WPs: CIT&UFMG as final integrator • Avoid missing the forest for the trees

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