Forestry Mitigation Options for Mexico: Finding Synergies between National Sustainable Development Priorities and Global Concerns

We examine carbon (C) reference and mitigation scenarios for the Mexicanforest sector between the year 2000 and 2030. Estimates are presentedseparately for the period 2008–2012.Future C emissions and capture are estimated using a simulation modelthat: a) allocates the country land use/land cover classes among differentfuture uses and categories using demand-based scenarios for forestryproducts; b) estimates the total C densities associated to each land usecategory, and c) determines the net carbon implications of the process ofland use/cover change according to the different scenarios.The options analyzed include both afforestation/reforestation, such ascommercial, bionenergy and restoration plantations, and agroforestrysystems, and forest conservation, through the sustainable management ofnative forests and forest protection.The total mitigation potential, estimated as the difference between the totallong-term carbon stock in the reference and the mitigation scenario reaches300 × 10⁶ Mg C in the year 2012 and increases to 1,382 × 10⁶ Mg C in 2030. The average net sequestration in the 30 year period is 46 × 10⁶ Mg C yr^-1, or 12.5 × 10⁶ Mg C yr^-1 within the period 2008 to 2012. The costs of selected mitigation options range from 0.7–3.5 Mg C^-1 to 35 Mg C^-1. Some options are cost effective.     

Carbon mitigation potential and costs of forestry options in Brazil,China, India,Indonesia, Mexico,the Philippines and Tanzania

This paper summarizes studies of carbon (C) mitigation potential and costs of about 40 forestry options in seven developing countries. Each study uses the same methodological approach – Comprehensive Mitigation Assessment Process (COMAP) – to estimate the above parameters between 2000 and 2030. The approach requires the projection of baseline and mitigation land-use scenarios. Coupled with data on a per ha basis on C sequestration or avoidance, and costs and benefits, it allows the estimation of monetary benefit per Mg C, and the total costs and carbon potential. The results show that about half (3.0 Pg C) the cumulative mitigation potential of 6.2 Petagram (Pg) C between 2000 and 2030 in the seven countries (about 200× 10⁶ Mg C yr^-1) could be achieved at a negative cost and the remainder at costs ranging up to $100 Mg C^-1. About 5 Pg C could be achieved, at a cost less than $20 per Mg C. Negative cost potential indicates that non-carbon revenue is sufficient to offset direct costs of these options. The achievable potential is likely to be smaller, however, due to market, institutional, and sociocultural barriers that can delay or prevent the implementation of the analyzed options.     

Baselines for land-use change in the tropics: application to avoided deforestation projects

Although forest conservation activities, particularly in the tropics, offer significant potential for mitigating carbon (C) emissions, these types of activities have faced obstacles in the policy arena caused by the difficulty in determining key elements of the project cycle, particularly the baseline. A baseline for forest conservation has two main components: the projected land-use change and the corresponding carbon stocks in applicable pools in vegetation and soil, with land-use change being the most difficult to address analytically. In this paper we focus on developing and comparing three models, ranging from relatively simple extrapolations of past trends in land use based on simple drivers such as population growth to more complex extrapolations of past trends using spatially explicit models of land-use change driven by biophysical and socioeconomic factors. The three models used for making baseline projections of tropical deforestation at the regional scale are: the Forest Area Change (FAC) model, the Land Use and Carbon Sequestration (LUCS) model, and the Geographical Modeling (GEOMOD) model. The models were used to project deforestation in six tropical regions that featured different ecological and socioeconomic conditions, population dynamics, and uses of the land: (1) northern Belize; (2) Santa Cruz State, Bolivia; (3) Paraná State, Brazil; (4) Campeche, Mexico; (5) Chiapas, Mexico; and (6) Michoacán, Mexico. A comparison of all model outputs across all six regions shows that each model produced quite different deforestation baselines. In general, the simplest FAC model, applied at the national administrative-unit scale, projected the highest amount of forest loss (four out of six regions) and the LUCS model the least amount of loss (four out of five regions). Based on simulations of GEOMOD, we found that readily observable physical and biological factors as well as distance to areas of past disturbance were each about twice as important as either sociological/demographic or economic/infrastructure factors (less observable) in explaining empirical land-use patterns. We propose from the lessons learned, a methodology comprised of three main steps and six tasks can be used to begin developing credible baselines. We also propose that the baselines be projected over a 10-year period because, although projections beyond 10 years are feasible, they are likely to be unrealistic for policy purposes. In the first step, an historic land-use change and deforestation estimate is made by determining the analytic domain (size of the region relative to the size of proposed project), obtaining historic data, analyzing candidate baseline drivers, and identifying three to four major drivers. In the second step, a baseline of where deforestation is likely to occur–a potential land-use change (PLUC) map—is produced using a spatial model such as GEOMOD that uses the key drivers from step one. Then rates of deforestation are projected over a 10-year baseline period based on one of the three models. Using the PLUC maps, projected rates of deforestation, and carbon stock estimates, baseline projections are developed that can be used for project GHG accounting and crediting purposes: The final step proposes that, at agreed interval (e.g., about 10 years), the baseline assumptions about baseline drivers be re-assessed. This step reviews the viability of the 10-year baseline in light of changes in one or more key baseline drivers (e.g., new roads, new communities, new protected area, etc.). The potential land-use change map and estimates of rates of deforestation could be re-done at the agreed interval, allowing the deforestation rates and changes in spatial drivers to be incorporated into a defense of the existing baseline, or the derivation of a new baseline projection.     

Indoor particle size distributions in homes with open fires and improved Patsari cook stoves

Particulate pollution has been clearly linked with adverse health impacts from open fire cookstoves, and indoor air concentrations are frequently used as a proxy for exposures in health studies. Implicit are the assumptions that the size distributions for the open fire and improved stove are not significantly different, and that the relationship between indoor concentrations and personal exposures is the same between stoves. To evaluate the impact of these assumptions size distributions of particulate matter in indoor air were measured with the Sioutas cascade impactor in homes using open fires and improved Patsari stoves in a rural Purepecha community in Michoacan, Mexico. On average indoor concentrations of particles less than 0.25 μm were 72% reduced in homes with improved Patsari stoves, reflecting a reduced contribution of this size fraction to PM_2.5 mass concentrations from 68% to 48%. As a result the mass median diameter of indoor PM_2.5 particulate matter was increased by 29% with the Patsari improved stove compared to the open fire (from 0.42 μm to 0.59 μm, respectively). Personal PM_2.5 exposure concentrations for women in homes using open fires were approximately 61% of indoor concentration levels (156 μg m^?3 and 257 μg m^?3 respectively). In contrast personal exposure concentrations were 77% times indoor air concentration levels for women in homes using improved Patsari stoves (78 μg m^?3and 101 μg m^?3 respectively). Thus, if indoor air concentrations are used in health and epidemiologic studies significant bias may result if the shift in size distribution and the change in relationship between indoor air concentrations and personal exposure concentrations are not accounted for between different stove types.