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Assessing the effectiveness of subnational REDD+ initiatives by tree cover change analysis

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Given the key role of forests in mitigating climate change, it becomes increasingly important to monitor the carbon effectiveness of policies and programmes for reducing emissions from deforestation and forest degradation (REDD+). Performance assessment is essential to check progress, verify accountability, and learn from REDD+ implementation in general, with important bearings on funding for REDD+ in the long term. This study presents a new framework to assess the effectiveness of subnational REDD+ initiatives from 2000 to 2014 using tree cover change trajectories with and without REDD+ since its implementation.

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Assessing the effectiveness of subnational REDD+ initiatives by tree cover change analysis

  1. 1. Session 66: Evaluating the impacts of REDD+ interventions on forests and people ATBC 23 June 2016 Astrid Bos Valerio Avitabile, Martin Herold, Amy Duchelle, Shijo Joseph, Claudio de Sassi, William Sunderlin, Erin Sills, Arild Angelsen, Sven Wunder Assessing the effectiveness of subnational REDD+ initiatives by tree cover change analysis
  2. 2. CIFOR Global Comparative Study on REDD+ Module 2: subnational initiatives in 6 countries 2
  3. 3. Performance assessment Reference levels vs. Before-After/Control-Intervention B A C I C IB A B A B A 𝐵𝐴𝐶𝐼 𝑟𝑎𝑡𝑖𝑜 𝛽 = 𝑥 𝐴𝐼 − 𝑥 𝐵𝐼 − 𝑥 𝐴𝐶 − 𝑥 𝐵𝐶 𝑤𝑖𝑡ℎ 𝑥 𝐴𝐼 = 1 𝑛 𝑎 𝑖=1 𝑛 𝑎 𝑥𝑖 𝑤ℎ𝑒𝑟𝑒 𝑥 𝐴𝐼 𝑟𝑒𝑝𝑟𝑒𝑠𝑒𝑛𝑡𝑠 𝑡ℎ𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑓𝑜𝑟𝑒𝑠𝑡𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑒𝑟𝑖𝑜𝑑 𝑎𝑓𝑡𝑒𝑟 𝑡ℎ𝑒 𝑖𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑠𝑡𝑎𝑟𝑡𝑒𝑑; 𝑎𝑛𝑑 𝑛 𝑎 𝑖𝑠 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑒𝑎𝑟𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑒𝑟𝑖𝑜𝑑 𝑎𝑓𝑡𝑒𝑟 𝑡ℎ𝑒 𝑖𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑠𝑡𝑎𝑟𝑡𝑒𝑑 3
  4. 4. • Global Forest Change 2000–’14 (Hansen et al., Science 2013) • Forest definition 10% tree cover (FAO) • Relative change focus Input data Tree cover and tree cover change 4
  5. 5. Results difference Before-After & Before-After/Control-Intervention ratio good 7 30.4% neutral 7 30.4% poor 9 39.1% good 8 34.8% neutral 9 39.1% poor 6 26.1% good 9 40.9% neutral 4 18.2% poor 9 40.9% good 11 50.0% neutral 8 36.4% poor 3 13.6% 5
  6. 6. Results explained (1) Bias in before period Intervention < control Conservation area (Indonesia_4) Average annual deforestation rate in intervention area (initiative) Average annual deforestation rate in control area (district) bias Intervention > control Deforestation frontier (Brazil_3) Average annual deforestation rate in intervention area (initiative) Average annual deforestation rate in control area (district) bias B A B A B A B A bias bias B A C I C IB A B A B A 6
  7. 7. Results explained (2) Low absolute deforestation small differences  high uncertainty  big influence on score (e.g. Tanzania_1) B A C I C IB A B A B A 7
  8. 8. Results explained (3) Peak years Tanzania_1 control area (district) • In before period (in control area)  “better” Before-After score for control  “poorer” BACI (e.g. Brazil_1/Tanzania_1/Tanzania_6) Tanzania_5 intervention area (initiative) • In after period (in intervention area)  Poor performance?  REDD+ not addressing big event drivers (e.g. Tanzania_5) B A C I C IB A B A B A 8
  9. 9. Results explained (4) Limited additionality Decrease in deforestation, but limited additionality (control area performs even better than intervention villages) Brazil_2 intervention (villages) Brazil_2 control (villages) B A C I C IB A B A B A 9
  10. 10. Results explained (5a) good performance Reduced deforestation e.g. Brazil_3 & Indonesia_3 Increased but avoided deforestation e.g. Indonesia_6 (both site & village level) B A C I C IB A B A B A 10
  11. 11. Results explained (5b) poor performance High deforestation in 3 consecutive years in after period (e.g.Vietnam_1, Tanzania_06) Vietnam_1 ceased project in 2012 B A C I C IB A B A B A 11
  12. 12. Conclusions • Performance measure itself has implications on results • For result-based finance, it is important to understand causes of change • Which measure is more “climate-friendly”? • Overall, most REDD+ sites perform relatively well when compared to control units, especially on village level (here: only relative change is analysed) • Causes of “poor” & “good” BACI scores vary widely – Random/contextual factors o Bias o Low absolute deforestation o Peaks (is REDD+ influencing big drivers?) – Additionality – Poor/good performance • Next: link to specific REDD+ interventions 12
  13. 13. Credits photographs in this presentation: CIFOR & WUR Contact Astrid Bos astrid.bos@wur.nl More info www.cifor.org/gcs Literature Sills et. al (2014) www.cifor.org/redd-case-book Financial support for GCS REDD+ Norwegian Agency for Development Cooperation, Australian Agency for International Development, European Commission, UK Department for International Development, German International Climate Initiative, CGIAR Forests, Trees and Agroforestry (FTA) Programme Thank you 13

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