Steps toward forest-related mitigation analyses of REDD+ activities and harvested wood products in Mexico

Transcription

1 Steps toward forest-related mitigation analyses of REDD+ activities and harvested wood products in Mexico Marcela Olguín-Alvarez 8th Forestry and Agriculture Greenhouse Gas Modeling Forum September 25-28, National Conservation Training Center Shepherdstown, West Virginia

2 Outline Background Modeling approach First results and lessons learned Next steps

3 1. Background Mexico is the largest fossil fuel consuming country in Latin America CO 2 e emissions derived from fossil fuel burning by country (Mt CO 2 ) In 2010, ranked 12 th in the world for emissions Interested in reducing GHG emissions and enhancing carbon sinks related to forests: - Special Program on Climate Change (PECC in Spanish) Climate Change General Law Source: PECC

4 1. Background Expected that with current GHGs emissions trend, Mexico could reach 1,000 Mt CO 2 e Climate Change General Law includes: - Measuring, Reporting, Verification (MRV) system in Mitigation goals by 2030 and 2050 (all sectors) Signatory of the Paris Agreement. - NDC includes a net zero deforestation goal by 2030 Cited in: PECC, 2014

5 - Mexico s MRV National System LC + LC Change products (Landsat/RapidEye ) National Forest & Soils Inventory ( , ) National System based on stock-difference (AD x EF), Tier 2 level Potential for extensive data generation and compilation on forest cover, forest growth and forest disturbances. Need methods and tools that can harmonize, integrate and process these data into estimates of GHG emissions and removals, both historic and into the future (baseline and mitigation)

6 1. Background Since 2011, with collaboration of CEC, CFS, CONAFOR, and, USFS have developed tools and methods to help understand forest carbon dynamics in pilot studies in North America. In Mexico, cooperation with additional partners (Mexico-Norway Project, SilvaCarbon, COLPOS, Mex-SMIC network) have been key in providing modeling/analysis support to enhance the MRV system and REDD+ programs: - National Forestry Program GHG emissions reduction goal - GHG emissions baseline submitted to R-PIN/FCPF

7 1. Background Objective: To demonstrate the use of modeling tools and methods for monitoring GHG emissions and removals, while being able to understand, assess and project the effect that policy decisions can have on GHG emissions in the future. - Forest ecosystems (Phase I, II) - Harvested wood products (Phase II)

8 2. Approach A. Modeling framework: Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) Used by the forestry & C modeling working groups of the Canada-Mexico partnership and the North American Commission on Environmental Cooperation (CEC). Consistent with IPCC-GPG, estimates of forest GHG Emissions and Removals by combining empirical data with a process modeling approach (

9 A. Modeling framework: CBM-CFS3 (1) National Forest Inventory + LU/LC start year * (1), (2) same data as with National/Tier 2 Detailed Forest Inventory Forest Growth Volume / Age Curves Volume to Biomass Conversion Activity Data Harvesting, planting, fires, land use/land cover changes C Accounting Model CBM-CFS3 Model parameters Litterfall Decomposition Results database (2) AD Monitoring system + National statistics (3) Intensive C Monitoring Sites (Mex-SMIC Network)

10 B. Spatial Framework 94 Spatial Units (SPU) Eco-regions level I + Politicaladministrative boundaries units - Forest with highest C density are located in Temperate Sierras and Tropical Humid ecoregions * The CBM is spatially independent; it can be used to simulate from pixel, polygons, regions, states, country

11 B. Spatial Framework - Strategic Landscapes: Phase I - Main interest on early-action for REDD+ * 16 SPU selected Five classifiers by Spatial Unit - Forest types - Ecoregions (L-IV) - Forest condition - REDD+ status - Municipal boundaries

12 C. Baseline and mitigation assumptions Baseline: Expectations about future GHG emissions development trends, and on different perspectives on the trade-offs between climate change mitigation policies and other policies For example: Business-as-usual (BAU); future development trends follow those of the past and no changes in policies will take place GHG emissions BAU Mitigation: An anthropogenic intervention to reduce the sources or enhance the sinks of greenhouse gases Mitigation scenario 1 Time Source: IPCC 2001 * Mitigation estimates may use a conservative approach

13 C. Baseline and mitigation assumptions - Special Program on Climate Change (PECC): Action Avoid GHGs emissions derived from deforestation and the degradation of wood by means of early actions in the territory. Simulation run Baseline: Business as usual Comments Land use changes continue at the same rate as Scenario 1: Reduction of CO 2 emissions from deforestation Scenario 2: Reduction of CO 2 emissions from deforestation and increase in removals from regeneration Conversions of Forest Land to Other Lands (FL->OL) reduced by 2.5% per year from 2011 to 2020 Same as above plus conversions from Other Lands to Forest Lands (OL->FL) increased by 2.5% per year from 2011 to 2020 *Other Land: any other non-forest land use class

14 C. Baseline and mitigation assumptions - Mitigation activities based on changes in AD For example: Land Use/Land Cover change matrices for the Yucatan Peninsula, Calculate historic ( ) rates of gross deforestation/regeneration Annualize information on Land Use/Land Cover (LU/LC) changes to: - Project the average rate of gross LU/LC changes (baseline), or - Modify the rate of gross LU/LC changes (mitigation scenarios)

15 C. Baseline and mitigation assumptions - Baseline: Using historic data for growth and AD *(+) C sinks (-) C source Example of the contribution of each land class category to the net CO 2 e ecosystem balance: FL FL: Forest Land remaining Forest Land FL OL: Forest Land converted to Other Lands OL FL: Other Lands converted to Forest Land OL OL: Other Lands remaining Other Lands Key drivers: FL FL (forest management) FL OL (deforestation) ** Other Lands includes all non-forest classes Source: Olguin et al. 2015

16 - Historic Net Carbon Emissions - Historic *including the effect on C fluxes from land use changes and fire

17 Mt C 3. Results and Lessons Learned - C emissions reduction by mitigation scenario Quintana Roo Scenario 2 Scenario 1 Baseline (mean AD of the last 10 years) *Importance of reducing deforestation (immediate avoided emissions) relative to increasing reforestation (slow future sink)

18 - Impact of Activity Data Sources Differences in: 1) spatial resolution 2) temporal resolution 3) attribution/non-attribution of land-cover changes by disturbance type Source: Mascorro et al

19 - Net Rates vs. Gross Rates - Activity data derived from relatively coarse spatial and temporal resolution. Changes in the gross rates of land-use changes *Same net deforestation rate, but twice gross deforestation rate, accelerates the switch from net sink to net source *Need to better understand the role of net zero deforestation targets on GHG emissions (e.g. NDC)

20 - Missing data - Effect of historic degradation assumptions to develop state-level baseline scenarios Net C fluxes without harvesting events Net C fluxes with harvesting events For example: Assuming 20% selective harvesting in one fifth of the forest land, can change the historic net C emissions average (from sink to source). * Average of last 10 yrs C fluxes can switch from net sink to net source

21 - Development of baseline scenarios - Potential emissions reduction dependent on baseline selection Assumptions: (a) Average annual rates of activity data from a 10-year period remain constant (b) Average of the net GHG balance of the last 10-year period remains constant *(+) CO 2 e source (-) CO 2 e sink Olguín et al **Using a static trend (b) may neglect to show a successful reduction in emissions from lowered deforestation rates 21

22 4. Next Steps Mexico is exploring the use of modeling frameworks to: Identify/integrate/analyze data from multiple sources (AD, NFI, etc.) into an internally consistent accounting of past GHG emissions and removals (monitoring). Fill in data gaps (using best available scientific information), run sensitivity analyses to identify, quantify and prioritize efforts to reduce uncertainty. Make projections about future emissions, compare and rank alternative mitigation scenarios and to enhance collaboration between the scientific and policy-maker communities in Mexico. Improve information exchange and capacity building through close collaboration with key partners in the Mexico, USA and Canada, potentially impacting the design and assessment of activities in the forestry sector that can contribute to meeting national and regional GHG emissions reductions targets.

23 Harvested Wood Product (Phase II) - National production, imports and exports of roundwood (m 3 ) Forest product commodities National reports on GHG emissions currently assumes instantaneous emissions after harvestings In phase II, included Durango state (30% of national wood production), to analyze potential for C storage in HWP

24 sink What will be the contribution of harvested wood products (HWP) in future C emissions? Carbon Budget Modeling Framework for Harvested Wood Product (CBM- FHWP) sink Historic Projected baseline Hafer et al. in prep.

25 Cum. emissions Cum. sinks 4. Next Steps - Carbon Mass Balance Durango

26 4. Next Steps Under the Commission for Environmental Cooperation (CEC) forest C modeling project: Continue to support other bi-lateral/tri-lateral initiatives in North America (NACP, CarboNa, Canada-Mexico Partnership, etc.). Continue the development of tools and their application to run more complex simulations in strategic regions in all three countries. Help identify the most effective approaches to achieve climate change mitigation and adaptation objectives in the forestry sector. - Forest ecosystems - Harvested wood products - Substitution benefits from HWP

27 Collaborators: Canadian Forest Service Werner Kurz Carolyn Smyth Michael Magnan Scott Morken Max Fellows Byron Smiley CEC Karen Richardson Itzia Sandoval Mihaela Vulpescu Marcela Olguin Consultant Vanessa Mascorro- Consultant CONAFOR Enrique Serrano Armando Alanís Raúl Rodríguez Armando Alanís Carmen Meneses Germánico García Jesús Rangel Jorge Fernández Jorge Morfín USDA Forest Service Rich Birdsey Alexa Dugan Mike Nichols EPA John Shoaff SilvaCarbon Craig Wayson COLPOS Gregorio Ángeles IT El Salto Benedicto Vargas UJED Javier Corral

28 Questions? Gracias! Skype: marcela_olguin_a

29 Stock-Difference vs. Gain-Loss General considerations: - Attribution of C emissions - The effect of single factor versus legacy effects Similar trend and magnitude Similar trend, different magnitude Main differences due to: - Cumulative growth vs. national emission factors - Source of uncertainty considered: stock-difference: NFI data gain-loss: ±50% all parameters