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Is there a niche for finance of energy projects in REDD+?

Is there a niche for finance of energy projects in REDD+?. Presented at the ICREPE Lustrum conference ‘’IMPROVING ENERGY ACCESS THROUGH CLIMATE FINANCE: PICKING THE WINNERS’’ University of Twente , Enschede , the Netherlands, 26-28 March 2013 Margaret Skutsch Adrian Ghilardi.

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Is there a niche for finance of energy projects in REDD+?

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  1. Isthere a nicheforfinance of energyprojects in REDD+? Presented at the ICREPE Lustrum conference ‘’IMPROVING ENERGY ACCESS THROUGH CLIMATE FINANCE: PICKING THE WINNERS’’ University of Twente, Enschede, the Netherlands, 26-28 March 2013 Margaret Skutsch Adrian Ghilardi

  2. Acknowledgements The work presented here is based in part on a study carried out for UNDP´s Bureau for Development Policy , Environment and Energy Group in 2009, entitled Energy Access in REDD+ It also reflects work being done now in connection with a study for the Global Alliance for Clean Cookstoves

  3. There are currently a number of different REDD+ institutionalarrangements at differentscales • UNFCCC compliant REDD+ (notyetagreed): paymentsviafundormarketto a country onbasis of carbonachievementsrelativetoanagreednationalbaseline (REL), MRV methodologywillbefixed • Bi-lateral arrangements (e.g.StateGovernors´ programme): MRV and baselinemethodologies (at level of states) stillunderdevelopment • Voluntarycarbonmarket REDD+ projects: usuallymuchsmallerareas, viaNGOsorbrokers, MRV methodologiesvary

  4. All of these are basedonprinciples of additionality and all are output based (i.e.thefunds are received in directproportiontothereduction in carbonemissionsorincrease in carbonsequestrationacheived). • If a fuelwood/energytechnologyprojectisselected as theinstrumenttoachievereductions in emissions, itwouldhavetodemonstratethatit has achievedsuchreductionsagainst a baseline, and wouldhavetomeasure/quantifythem • Carboncredits (i.e.payment) willonlybeissued in proportiontoachievements, and onlywhenthesecarbonacheivementshavebeenverified • Evenifthefuelwood/energytechnologycomponentwereonlypart of a package of REDD+ activities, itislikelythattheproject manager wouldneedtoquantifytheimpacts of thiscomponent in orderto share thereturnsamongthedifferentstakeholders

  5. Fromthepoint of view of project manager: • Giventhatrewards are ex-post (paidonthebasis of actual reduction) theprojectorganiserwillonlybewillingtoinvest in thefuelwoodsubsititutionefforttotheextentthat he/sheissurethatthisintervention can beshowntobethe cause of reduceddegradation/deforestation • Otherwise, fundswouldbebeingdeductedfromotheractivitieswhich DO result in reducedemissions • Thisisone of thedifficultieswith programes in whichrewards/payments are basedentirelyonverifiable outputs

  6. Preliminarylogic • One of the causes of forestdegradation, and possibly of deforestion, isunsustainablefuelwoodgathering (> than natural regrowthrate) • REDD+ is a systembywhichfinancial incentives are givenforreductions in rates of forestdegradation/deforestation • So logicallyitshould be possibletofundprojectswhichmakefuelwoodgatheringsustainable (< regrowthrate), under of REDD+ • E.g.Improvedcharcoalkilns, improvedstoves, subsistutiton of fuelwoodby use of alternativeenergytechnologies • In fact, thisismuch more difficultthanitwouldappear

  7. Thereasons are mainlymethodological • Scale: REDD+ isoftenevaluated at broadgeographicalscales (national, state). Iftheenergyinterventions are notrolledout at thesamescale, theresultsmaybe ´lost´ (becomeinsignificantcomparedtothewhole) • Type of impact: unsustainablewoodfuelharvestingusuallyresults in degradation, notdeforestation, at least in the short term (and REDD+ ismeasured and credited in the short term). Degradationisdifficulttodetect and measure • Additionality: under REDD+ thereisalwaystheneedtodemonstrateadditionality (in this case, that a reduction in theannualrate of degradation has occurred as a result of theenergyintervention). Thisisdifficultbecause: • Thereisusually no baselinefordegradationbecausewehaveverylittlehistorical data onearlierrates of degradation • Changes in rates of degradationmaybecausedbyotherinterventions

  8. Scale • Woodfuelenergyprojectstendto be targeted at and implemented in specificprojectareas, ratherthancoveringwholecountriesorstates • Theprobabilitythattheireffectsonbiomass stocks willbe of significantsizecomparedtothose of other REDD+ activitiesisthereforemuchhigher in smallscale REDD+ projects (e.g.those in thevoluntarysectors). Suchprojectsmaythereforeofferthemost probable niche in REDD+

  9. Typeof impact:deforestation vs degradation

  10. Difficulties in assessingbiomass stocks • Degradation in thecontext of REDD+ impliesbiomass stock levelsbelow ´normal´ as a result of human activity. Stock changemethodsare recommendedby UNFCCC formeasurethis • Remotesensing can in some cases detecthigher and lowerlevels of canopycoverbutitisverydifficult (a) toquantify stocks and (b) toknowwhetherlowlevels are natural ortheresult of human influence • Theminimumareathat can be ´seen´ dependsontheresolution of the RS and anywaymuchdamagemay be belowcanopy (invisible in satelliteimages) • Remotelysensed data needstobesupplementedwithgroundlevelforestinventory data forbetteridentification of degradation, to determine whatiscausingit and toquantifystanding stocks • Note that ´secondaryforest´ cannotbeequatedwith ´degradedforest´, sincesecondaryforestusuallyrepresentsforestwhichisgrowing (increasingits stock) afterclearfelling has taken place

  11. Theinfluence of resolutiononwhat can beregistered • Landsat (maps at 1:250,000) – minimumarea of land use change visible = around 15 ha (notadquatefor REDD+) • SPOT5 (maps at 1:40,000) – minimumarea of land use change visible = around 1-2 ha, dependingonhowcomplexthelandscapeis • Ifalllandscapeis of onetype, itiseasiertoidentifychangesaccurately. In areaswithmosaictypepatterns , youneed more pixels (i.e.largerarea) tobesurethatthere has beenchange • Degradationisnotland use change! Itmaybepossibletodetect and changefrom 70% canopycoverto 40% canopycoverusingLandsat (iftheextent of thechangecoversanarea of > 15 ha), butdegradationrelatedtofuelwoodextractionis more subtlethanthis (more patchy, lessloss of canopycover).

  12. Adapted from:GOFC-GOLD: Report of the 2nd GOFC-GOLD Workshop on Reducing Emissions from Deforestation. St. Cruz, Bolivia, 17-19 April 2007.

  13. Researchon RS and degradation Studies (e.g. Souza et al 2003, Asner 2005) which have assessed degradation using Landsat and SPOT have been based in the Amazon and have detected degradation due to logging and fires (from drag tracks etc). In these cases, the degradation is highly focused both spatially and in time, thus more visible (the loss is relative large where and when it occurs) For the case of unsustainable fuelwood harvesting, it tends to be spread out both in space and in time, thus much less visible In addition, it often takes place in mosaic landscapes (which are more common in highly populated areas) and in dry forests. And degradation processes for woodfuels (esepcially firewood) are gradual, they may represent a net loss of as little as 1 ton per year per ha from biomass stock of 50 tons per ha, on-going over many years Radar and aerial photos from UAVs (drones) offers possibilities for the future

  14. For REDD+ baselineisrequiredwhichrepresentstherate of degradationthat has beentaking place in thepast • Butbaselinesfordegradation are almostimpossibletoconstruct in most cases because • Wehaveremotesensing (Landsat, SPOT5) forthepastbutcannot ´read´ thepastbiomasslevels and therate of change of these • We do nothave radar and UAV data fromthepast • In most cases there are no on-the-groundforestinventory data availableforthehistoricalperiod • In otherwords, itisverydifficulttoquantifytheimpactthattheenergyprojectishavingontherate of degradationusing stock changemethodology as thebasis

  15. And westillneedtoworkout How can we distinguish whether the observed and measured reductions in degradation are due to the energy project or to other activities?

  16. Withcurrentapproaches: • Providingphysicalevidence of theachievements in emissionsreductionisparticularlydifficultforinterventionswhoseimpacts are likelytobewidelybutthinly spread overlargeareas of forest, such as improvedcookstoves and alternativeenergytechnologies • Impacts of improvedcharcoalkilnsmightbeslightlyeasiertomeasure as they are more likelytobeconcentrated in particular locations, whichcouldbeintensivelymonitored • So althoughweknowmany of theseinterventionsmaybe social valuable and may in fact reduce impactsontheforest, withthecurrent MRV methodologyfor REDD+ they are unlikelytofind a niche

  17. Theothermethodsuggested in GPG for LULUCF isthe • Gain-loss method, based on an assessment of the stock at one point in time and simple arithmetic calculations of gains (estimated natural growth rates, from secondary sources relevant to this type of forest) and losses (estimated off-take for different uses, from records such as logging reports and models) • Needless to say gain-loss methods are very difficult to validate in areas under community uses, as impacts of grazing, fuelwood gathering, shifting cultivation etc all have to be incorporated. Very unreliable method for this situation • Not clear if this method will be accepted for use in REDD+

  18. New possibilitities: modeling • E.g.Rüdger 2006 in cloudforests in Veracruz (Mexico) • Studiedimpacts of repeatedharvestingforfuelwoodforresidential and local market • Even at lowlevels of off-take, foreststructurebecomes more simplified and more homogeneous • Number of treeswithlargediameterdecreases • Number of treeswithsmalldiameterincreases • Annualincrement (growthrate) increases • Fromthis, a model can bebuilt of impacts of differentlevels of off-take

  19. NRBv1.0: geo-spatialmodelquantifies non-renewablefirewoodextraction and impacts • simulates the spatiotemporal effect of fuelwood harvesting on the landscape vegetation • accounts for savings in non-renewable woody biomass from reduced consumption due to an external intervention, such as improved cookstoves • projects harvesting sites in time based on accessibility and preference for fuelwood sources • vegetation responds to harvest at each iteration based on the amount of wood extracted and re-growth functions for trees within and outside forest

  20. Essential drivers of supply Land cover LULC categories Biomass stocks Biomassgrowthcharacteristics • Essentialdrivers of demand Settlements/accessibility Stovesandfuels Market characteristics Self-collection Market distribution • Supply/Demandinteractions Stochasticity Biophysical response

  21. Appliedto field area in Honduras:

  22. This kind of modeling could form the basis for much more accurate gain-loss estimation Software is free-ware and user friendly But you need local data to calibrate Methodology would have to be approved by UNFCCC-REDD+ But stands more chance of justifying woodfuel projects under REDD+

  23. Thank you! www.youtube.com/user/Yayoyutub

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