Towards Reference Levels for the Democratic Republic of Congo

Transcription

1 Towards Reference Levels for the Democratic Republic of Congo Jean-Paul Kibambe Democratic Republic of Congo WWF-FCPF Technical Workshop Washington, January FOREST CARBON PARTNERSHIP FACILITY

2 Before we start! Many decision remain to be taken on RL in DRC The views expressed here are only those of the presenter in a Chatham House setting. I am just throwing ideas out there! 2

3 Outline: question we will try to answer Where do we stand? How we are planning to adjust for national circumstances? Projected national trend adjusted for subnational trends (what is that?) How does that influence the RL for an ER- Program? 3

4 Many things need to be done before a country adjust for national circumstances 1. Determine Scope of Activities 2. Finalize Forest Definition 3. Determine Scale (National or Summed Subnational) 4. Determine Which Pools/Gases to Include 5. Link REDD+ to a National Forest Inventory? 6. Adjust for National Circumstances? 7. Should a Location Analysis Be Included?

5 Forest definition matters! Simulations from 2005 UCL classification (Verheggen et al. 2012) Ideally the forest defintion is something free satellite images can pick-up 10% crown cover as threshold= 189 M/ha or 81% of territory 30% crown cover as threshold= 145 M/ha or 62% of territory No official forest classification map at the national level (NFMS) Available products are a good starting point: FACET data, UCL maps, etc. FAO is working on an official forest stratification with gov. 5

6 Forest cover figures (State of Forest report 2008) HFLD: 2.3% of gross forest cover loss between (0 But deforestation is increasing: Forest cover loss area increased of about 13.8% btw and intervals (FACET - Potapov et al. 2012) 6

7 National Forest Monitoring System Using Terra Amazon software now called Terra Congo (joint work of FAO and INPE) For now FACET data is used to compute historical activity data statistics for any polygon for the period Opens the option to cross verify project scale activity data for deforestation with FACET data (National) 7

8 Emission factors Emissions factors and selection of pools, gases: undergoing process (DRC Gvt and FAO) NFI : has been at the planning stages for years!!!!. The NFI is expected to do a pre-sampling before final forest stratification is agreed 8

9 So where do we stand in DRC? Task to be completed Determine Scope of Activities Status Deforestation is the only thing we have historical data for at scale. But then again it depends on the definition! Finalize Forest Definition Determine which pools and gases to include Determine Scale (National or Summed Subnational) Link REDD+ to a National Forest Inventory? Hugely important (current forest definition submission to UNFCCC for CDM not appropriate All five pool according to FAO methodology for NFI. Historical data should be national but geographically explicit projection of deforestation will be sub-national base on drivers When we have the national forest inventory we will use it to determine emission factors 9

10 Adjusting for National Circumstances: key points Adjustment to national circumstances: crucial for the DRC which is a high forested and low deforested country (HFLD)! However circumstances vary widdely across this gigantic country!!! Very difficult to have a national geographically explicit Reference Level for deforestation in DRC Perhaps subnational RLs are better, but which scale is appropriate 10

11 Rate of deforestation Typical trajectory of a HFLD country such as DRC Past Future Reference Level adjusted to national circumstances REDD+ Credits Level actually reached Historical emissions & absorptions Reference levels based on historical data only Engagement period Time 11

12 Spatial patterns and scale of deforestation vary across 16/01/ /03/

13 Proximate causes Underlying causes National consensus about driving forces (facilitated by FAO and UCLouvain) Agriculture : S&B agriculture Permanent agriculture Use of wood: Charcoal Illegal logging activities Industrial logging Fuelwood Mining Fires Grazing Biophysical factors Forêts dégradées Agriculture Infrastructure Fragmentation Other factors Rural complex Distance to rivers Agricult. areas PA Augmentation de la population Demographic Croissance démographique growth Villages density Institutional aspects Policies Poor governance Wars Roads Infrastructure Economic aspects Crisis Unemployement Povery (Adapted from Geist and Lambin, 2002) 13

14 Sub-national multivariate analyis: Spatial variation and spatial influence of variables 2005

15 However: very high stability of driving forces btw and inside homogeneous zone % Agriculture Socio-econ. Transportation Demographic factors Socio-pol. factors Biophysical fact No high inter-annual variation in rates (very different from Brazil for example) because drivers are not related to commodity markets

16 So what do we do? Use historical data from national activity data analysis Sub-national RLs based on coherent units from a driver analysis For each unit: geographically explicit projections using trends in the different drivers This seems like a relatively robust approach, but how does it apply in the field?

17 Where do we start? Towards a subnational RL for an ER- Program Future Mai Ndombe Province 12 million hectares that seem to be a good compromise between consistency in drivers and administrative boundaries Consistency across drivers from the national analysis

18 Simulation Zone Mai-Ndombe

19 Simulation Zone Mai-Ndombe

20 Simulation Zone Mai-Ndombe

21 Simulation Zone Mai-Ndombe

22 Simulation Zone Mai-Ndombe

23 What about this modelling? 23

24 Simulated forest cover trends up to 2035 Prepared by the Forestry Ministry and DRC CN-REDD team for Doha 24

25 Forest cover at +/- 60% of the country at 2035 Niveau de Référence de la Déforestation en RDC taux de déforestation (%, moyenne annuelle) déforestation nette (M.ha, surface totale) superficie forestière nationale (M.ha, en fin de période) taux de couvert forestier (%, en fin de période) émissions de CO2 (M.tonnes, total période) 0,35% 0,38% 0,39% 0,43% 0,46% 0,41% 2,71 2,93 2,95 3,14 3,33 15,06 152,98 150,04 147,09 143,95 140,61 140,61 65,1% 63,8% 62,6% 61,3% 59,8% 59,8% 994, , , , , ,02 Models (IIASA, MI, UCL) simulate deforestation ranging from Mha up to 2035, corresponding to a loss of 15Mha That corresponds to 5,5G.t of CO2e (conservative hyp. Of 100t.C/ha) with a mean annual deforestation rate ~ 0.41% in moderate progression along the period 25

26 AKSANTI SANA! MERCI! 26