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The Carbon Footprint of Traditional Woodfuels

Authors:
Robert Bailis at Stockholm Environment Institute (SEI)
Robert Bailis
Rudi Drigo
Rudi Drigo
Adrian Ghilardi at National Autonomous University of Mexico
Adrian Ghilardi
Omar Masera at National Autonomous University of Mexico
Omar Masera
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Abstract

Over half of all wood harvested worldwide is used as fuel, supplying ~9% of global primary energy. By depleting stocks of woody biomass, unsustainable harvesting can contribute to forest degradation, deforestation and climate change. However, past efforts to quantify woodfuel sustainability failed to provide credible results. We present a spatially explicit assessment of pan-tropical woodfuel supply and demand, calculate the degree to which woodfuel demand exceeds regrowth, and estimate woodfuel-related greenhouse-gas emissions for the year 2009. We estimate 27–34% of woodfuel harvested was unsustainable, with large geographic variations. Our estimates are lower than estimates from carbon offset projects, which are probably overstating the climate benefits of improved stoves. Approximately 275 million people live in woodfuel depletion ‘hotspots’—concentrated in South Asia and East Africa—where most demand is unsustainable. Emissions from woodfuels are 1.0–1.2 Gt CO2e yr−1 (1.9–2.3% of global emissions). Successful deployment and utilization of 100 million improved stoves could reduce this by 11–17%. At US11pertCO2e,thesereductionswouldbeworthoverUS11 per tCO2e, these reductions would be worth over US1 billion yr−1 in avoided greenhouse-gas emissions if black carbon were integrated into carbon markets. By identifying potential areas of woodfuel-driven degradation or deforestation, we inform the ongoing discussion about REDD-based approaches to climate change mitigation.
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Title: The Global Footprint of Traditional Woodfuels
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Authors: Robert Bailis1*, Rudi Drigo2, Adrian Ghilardi3, Omar Masera4
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Affiliations:
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1 Yale School of Forestry and Environmental Studies
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2 Independent Consultant
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3 Center for Environmental Geography Research, National Autonomous University of Mexico (UNAM)
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4 Center for Ecosystems Research, National Autonomous University of Mexico (UNAM)
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*Correspondence to: robert.bailis@yale.edu
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Summary
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Over half of all wood harvested worldwide is used as fuel, supplying ~9 percent of global primary energy.
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By depleting stocks of aboveground woody biomass, unsustainable harvesting can contribute to forest
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degradation, deforestation, and climate change. However, past efforts to describe woodfuel sustainability
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failed to provide credible results. We present a spatially explicit assessment of pan-tropical woodfuel
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supply and demand, calculate the degree to which woodfuel demand exceeds regrowth, and estimate
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GHG emissions resulting from woodfuels. We find 27-34% of the woodfuel harvested in 2009 was
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unsustainable, with large geographic variations. Our estimates are lower than current assessments used
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in carbon markets. Approximately 275 million people live in “hotspots” - regions where the majority of
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demand is unsustainable - concentrated in South Asia and East Africa. Emissions from woodfuels are
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1.0-1.2 Gt CO2e/yr (1.9-2.3% of global emissions). In 12 nations, woodfuels are responsible for the
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majority of emissions. Successful deployment of 100 million improved stoves could reduce this by 11-17%
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and generate over $1 billion annually. By identifying potential areas of woodfuel-driven degradation or
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deforestation and mitigation potential, this assessment informs the ongoing discussion about REDD-
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based approaches to climate change mitigation and global efforts to promote “Sustainable Energy for All”.
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Traditional woodfuels, which include both firewood or charcoal used for cooking and heating, represent
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approximately 55% of global wood harvest and 9% of primary energy supply1,2. The current extent and
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future evolution of traditional woodfuel consumption is closely related to several key challenges to
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sustainable development. Roughly 2.8 billion people worldwide,3 including the world’s poorest and most
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marginalized, burn wood to satisfy their basic energy needs. Woodfuels can impact public health,4 cause
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deforestation or forest degradation5, and contribute to climate change6-8. Climate impacts arise from two
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pollutant flows: CO2 is emitted because a fraction of woodfuel is harvested unsustainably; methane (CH4)
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and short-lived climate forcers (SLCFs) are emitted because of incomplete combustion, which also emits
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health-damaging pollutants. Thus, woodfuels present society with two important links between local and
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global impacts; incomplete combustion releases pollutants that damage health and warm the
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atmosphere, while unsustainable harvesting drives both forest degradation and climate change.
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Risks to public health are increasingly well characterized,4 while impacts on deforestation, degradation,
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and global climate remain highly uncertain. Historically, woodfuel demand was considered a major driver
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of land cover change9,10. However, early research failed to account for regrowth, consumers’ response to
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scarcity, and use of trees outside forests11,12. More recent local or regional assessments find conflicting
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results,13-17 suggesting that geography is an important determinant of woodfuel sustainability. However,
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few systematic studies of woodfuel sustainability and GHG emissions have been conducted18. The
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IPCC’s 4th Assessment claimed that 10% of global woodfuel is harvested unsustainably,19,20 while the 5th
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Assessment stresses that net emissions from woodfuels are unknown17. Better understanding of the
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contribution of woodfuels to deforestation, forest degradation, and climate change is needed to evaluate
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the impact of the growing wave of interventions in the household energy sector and inform emerging
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REDD (Reducing Emissions from Deforestation and Degradation) methodologies21,22.
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Here we present a spatially explicit snapshot of woodfuel supply and demand (Supplementary Information
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section 1) throughout the world’s tropical regions, where traditional woodfuel consumption is
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concentrated. Using 2009 as a base year, we quantify the extent to which woodfuel demand exceeds
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supply, identify specific “hotspots” where harvesting rates are likely to cause degradation or deforestation,
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quantify the carbon emissions that result from current woodfuel exploitation, and estimate the emission
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reductions that could be achieved from large-scale interventions23.
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Nearly all landscapes produce a measurable increment of woody biomass either as new growth or as re-
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growth from previous disturbances. This assessment considers supply/demand balance over one year. If
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an area is harvested for woodfuel below the annual growth rate, then woody biomass stocks are not
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depleted and harvesting is sustainable. However, if annual harvesting exceeds incremental growth, it is
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unsustainable, leading to a decline of woody biomass, forest degradation, and net carbon emissions. In
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this assessment, we define the wood harvested in excess of the incremental growth rate as non-
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renewable biomass (NRB)24.
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Pan-tropical woodfuel supply and demand
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We treat woodfuel demand as an exogenous factor derived from a mix of national and sub-national
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studies supplemented by data from the Food and Agriculture Organization (FAO), International Energy
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Agency (IEA), and United Nations (UN)1,25,26. Woodfuel demand has subsistence and commercial
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components. Subsistence demand occurs primarily in rural areas, where people collect their own fuel
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using simple non-motorized forms of transportation from within few hours’ of their homes. Commercial
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demand originates in urban and some densely populated rural locations are conveyed by motorized
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transport over much longer distances.
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We develop a map of supply-demand balance by estimating harvesting pressure, first from subsistence
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and then commercial harvesters (Figure 1a and b). Areas exploited to satisfy commercial demand form a
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“woodshed”, which represents the region that would satisfy demand if the full MAI is utilized27 (Figure 1c
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shows commercial woodsheds for a high-demand area of East Africa; Extended Data Fig. 5 shows the
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entire pan-tropics).
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Woodfuels and Land Cover Change
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Many woodfuel-dependent regions are characterized by high rates of deforestation. Others, particularly
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parts of China and India, have experienced recent afforestation. Though not directly linked to woodfuel
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demand, these processes, which we define collectively as land cover change (LCC), impact woodfuel
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supplies. Deforestation creates large volumes of non-renewable woodfuel28,29, and afforestation
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augments renewable woodfuel supplies by adding to growing stock of “dendro-energy biomass” (DEB).
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Neither process has been explicitly accounted for in previous woodfuel assessments. When deforestation
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occurs in regions accessible to woodfuel users, the cleared woody biomass may be utilized as timber and
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woodfuel. Similarly, afforestation adds DEB equivalent to the mean annual increment (MAI) of the
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surrounding land class. However, the degree to which LCC by-products are actually used as woodfuel is
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unknown. To accommodate this uncertainty, we explore two scenarios, described in Table 1. In Scenario
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A, we assume LCC by-products are not used. In Scenario B, we assume they are used, yielding two NRB
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components (NRBB1 and NRBB2): NRBB1 quantifies the use of LCC by-products; NRBB2 quantifies the use
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of NRB among the wood harvested to meet whatever demand remains after LCC by-products are utilized.
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In populated regions experiencing high rates of deforestation, large volumes of DEB are accessible, and
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NRBB2 may be zero (Supplementary Information section 5).
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By combining woodshed mapping of commercial demand with localized supply-demand balances, we
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define the minimum quantity of NRB that would be required to meet existing demand (Supplementary
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Information section 5). In this approach, we assume woodfuel consumers manage their resources
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sustainably to the greatest extent possible so that unsustainable harvesting occurs only after the
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sustainable supply in a given location has been fully exploited. Thus, minimum NRB indicates the degree
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to which a given region can sustainably meet woodfuel demand under ideal management. However, ideal
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management is unlikely. To simulate suboptimal harvesting, we assume harvesting sometimes exceeds
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sustainable levels in some areas even if the sustainable supply in an adjacent accessible area has not
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been fully exploited. To estimate the extent of this deviation, we use a proxy defined by the fraction of
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each country’s forested area under formal management plans (methods). From this we derive an
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“expected” quantity of NRB, which we also express as a fraction of the total harvest (fNRB). Both
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minimum and expected NRB are expressed in absolute terms and as a fraction of the total harvest for a
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given region. We report expected NRB below; minimum NRB is given in supplementary information.
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Woodfuel sustainability
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Woodfuel demand in 2009 was ~1.36 Gt. If by-products of LCC were not utilized (Scenario A), pan-
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tropical expected fNRBA was 27-30% (367-413 Mton). If by-products of LCC were utilized (Scenario B),
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we estimate they contributed 8.3% (113 Mton) of pan-tropical woodfuel supply (fNRBB1). We also find 22-
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25% (296-340 Mt) of the remaining demand was harvested unsustainably (fNRBB2). Adding fNRBB1 and
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fNRBB2, the total fraction of NRB using LCC by-products is 30-34%. The uncertainty results from
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uncertain productivity and contribution of plantations (Supplementary Information section 6). This is
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largest in Asia, where forest plantations may be a substantial source of supply, and smallest in sub-
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Saharan Africa, which has few plantations30. Figure 2 shows a global map of fNRBB2 (maps of fNRBA and
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fNRBB1+B2 are shown in Extended Data Fig. 7).
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We define woodfuel “hotspots” as regions in which expected fNRB exceeds 50%, i.e. regions in which the
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majority of harvested woodfuel is unsustainable. Hotspots encompass ~4% of pan-tropical areas and are
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inhabited by 6% of the pan-tropical population. The largest hotpot incorporates a swath of East Africa
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extending from Eritrea through western Ethiopia, Kenya, Uganda, Rwanda and Burundi. Expected fNRBB2
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exceeds 50% in 43 sub-national units throughout his region, encompassing 26% of the region’s
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population. Additional hotspots also occur in Western and Southern Africa, but these do not cover large
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contiguous areas (Figure 2). Notably, much of sub-Saharan Africa is characterized by fNRBB2 below 20%
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including provinces of Angola, Cameroon, Central African Republic, Congo, DR Congo, Mali,
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Mozambique, Nigeria, South Africa, Tanzania, Zambia, and Zimbabwe: home to 55% sub-Saharan
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Africa’s population.
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In Asia, hotspots occur in parts of Pakistan, Nepal, Bhutan, Indonesia and Bangladesh. Expected fNRBB2
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in Pakistan is 79%, the highest national value in the entire sample. In two Pakistani divisions, fNRBB2
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exceeds 90%. Notably, Asia’s woodfuel hotspots are distinct from areas of high deforestation. For
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example, deforestation rates in Indonesia, Malaysia, Cambodia and Laos are among the world’s
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highest31, largely as a result of agricultural expansion16. In contrast, China and India, the largest woodfuel
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consuming nations, both experienced net afforestation in recent years30. At a national level fNRBB2 is 10-
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22% in China and 23-24% in India. The wide range observed in China is a result of uncertainty in the
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productivity of plantation forestry, a potentially large source of China’s woodfuel supply (Extended Data
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Fig. 6).
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Latin America hosts the lowest traditional woodfuel consumption; Haiti is the only nation in which
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expected fNRBB2 exceeds 50%. Still, fNRBB2 exceeds 30% in many sub-national units including most of
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Dominican Republic and parts of Bolivia, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Peru, and
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Venezuela. As in Asia, high rates of deforestation are due primarily to agricultural expansion16. By-
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products of LCC in many parts of Belize, Brazil, Ecuador, Honduras, Mexico, Nicaragua, Panama, Peru,
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and Venezuela, are sufficient to meet most or all woodfuel demand (Extended Data Fig. 6).
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Worldwide, over 275 million people live in woodfuel hotspots: nearly 60% in Asia, 34% in Africa, and the
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remaining 6% in Latin America. Figure 3 shows the regional distribution of population by fNRBB2 decile.
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GHG emissions
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Climate impacts arise from emissions of well-mixed greenhouse gases (GHGs), which include CO2 and
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CH4, and short-lived climate forcers (SLCFs), which include black and organic carbon (BC and OC)
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aerosols, CO, and volatile organic compounds (VOCs). Emissions of well-mixed GHGs and SLCFs as a
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result of unsustainable harvesting and incomplete combustion from traditional woodfuels (methods) were
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1.0-1.2 Gt CO2e in 2009: 1.9-2.3% of global emissions and 3.5-4.3% of emissions in the pan-tropical
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region32. National emissions vary widely (Extended Data Table 2). India and China have the largest
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populations of traditional woodfuel users and highest overall emissions, but relatively low per capita
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emissions. In contrast, Kenya, Ethiopia and Uganda, which constitute part of the East African hotspot,
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rank among the highest emitters in absolute and per capita terms.
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There is geographic variation in the mix of pollutants emitted by traditional woodfuels because of
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variations in fNRB and in the extent of charcoal use, which has different emission characteristics than
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fuelwood (methods). Globally, after accounting for uptake by the fraction of woody biomass that is
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sustainably harvested, CO2 contributes 34-45% of total climate forcing. BC has a similar impact,
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contributing 35-42%, and CH4, CO, and VOCs account for the remaining 31-37%. This variation has
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policy implications; currently, carbon markets value reductions of CO2, CH4, and N2O, but do not value BC
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abatement, which favors interventions in regions with high fNRB.
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Mitigation potential of efficient cookstoves
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Interventions in household energy have been implemented for decades with multiple objectives33:
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including forest conservation; health improvements; and climate change mitigation, as well as poverty
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alleviation and economic development. The Global Alliance for Clean Cookstoves (GACC), the largest
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stove program to date, proposes to deploy 100 million improved stoves by 202023. With large spatial
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variation in fNRB, impacts of interventions vary with geographic patterns of stove uptake. We examine
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this variation with four intervention scenarios (methods; Supplementary Information section 7).
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We assume 100 million state-of-the-art improved cookstoves are successfully disseminated according to
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different programmatic priorities. The resulting emission reductions range from 98-161 MtCO2e/yr. The
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largest reductions result from targeting the highest per capita woodfuel consumers. This is followed by
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reductions achieved by targeting consumers in regions with the highest rates of NRB, though
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uncertainties in emission reductions from individual stoves make the difference insignificant. The smallest
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reductions result from dissemination in the most business-friendly countries. The emission reductions
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achieved by prioritizing health improvements falls between these extremes (Figure 4).
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Discussion and implications
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Taken together, unsustainable harvesting and incomplete combustion contributed 1.9-2.3% of global
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emissions of well-mixed GHGs and SLCFs in 2009. Globally, emissions were split evenly between CO2,
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BC, and other SLCFs. In 12 nations, emissions from woodfuels were 50% or more of the country’s total
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emissions, demonstrating the dominant role that traditional woodfuels have in places with few industrial
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emissions (Extended Data Table 2).
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Our estimates of fNRB are considerably lower than estimates utilized by woodfuel projects in the carbon
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market. Project revenues depend directly on fNRB. A review of 191 carbon projects in 39 countries
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reveals a median fNRB of 90% with minimal regional variation (Supplementary Information section 6). We
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identified only four countries in which sub-national fNRB exceeds 80% as a result of woodfuel demand.
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Just 8% of existing projects fall within these areas. Thus, project developers are very likely overstating the
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emission reduction potential of improved stoves.
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Household energy forms a major component of the United Nations’ promotion of “Sustainable Energy for
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All”34. However, costs are a major barrier to implementing sustainable household energy solutions.
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Despite finding lower fRNB values than market actors assume, with our results, 100 million state-of-the-
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art cookstoves could reduce traditional woodfuel emissions by 98-161 MtCO2e yr-1. At $11/tCO2e, the
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average price of offsets from stove projects in 201235, these reductions would be valued at $1.1-1.8 billion
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if BC can be integrated into carbon markets. This far exceeds current investments in household energy in
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the Global South, which do not garner the same level of finance as other major health impacts like
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malaria, tuberculosis, and HIV. In addition, we find policy objectives are important determinants of
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emission reductions, introducing variation of 60%. Countries with high per capita woodfuel use or high
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NRB rates yield the largest emissions reductions. However, neither group overlaps completely with
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countries experiencing the highest disease burden from woodsmoke exposure (Figure 5). Thus, improved
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stove dissemination among populations suffering from the largest disease burden results in fewer
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emission reductions than dissemination in regions with high rates of woodfuel consumption or
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unsustainable harvesting. However, we identified a small group of countries that rank poorly in all
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categories (red text in Figure 5). Others rank poorly in two out of three categories (blue text in Figure 5).
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These countries deserve clear prioritization. The sub-national dataset generated by this research can be
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used to more accurately identify high-priority areas and pinpoint locations where interventions would have
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the greatest impact. Moreover, by identifying areas where woodfuel-driven degradation or deforestation is
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likely to occur, our assessment fills a critical gap in knowledge about the extent to which woodfuel
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demand may contribute deforestation or forest degradation and informs emerging REDD-based
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approaches to climate change mitigation.
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References
5
1 FAOSTAT. Forestry production and trade, <http://faostat3.fao.org/faostat-
6
gateway/go/to/download/F/*/E> (2013).
7
2 Renewable Energy Policy Network for the 21st century (REN21). Renewables Global Status
8
Report: 2013 Update. Report No., 177 (REN21 Secretariat, Paris, 2013).
9
3 Bonjour, S. et al. Solid fuel use for household cooking: country and regional estimates for 1980-
10
2010. Environmental Health Perspectives 121, doi:10.1289/ehp.1205987 (2013).
11
4 Lim, S. S. et al. A comparative risk assessment of burden of disease and injury attributable to 67
12
risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global
13
Burden of Disease Study 2010. The Lancet 380, 2224-2260, doi:http://dx.doi.org/10.1016/S0140-
14
6736(12)61766-8 (2012).
15
5 Rudel, T. K. The national determinants of deforestation in sub-Saharan Africa. Philosophical
16
Transactions of the Royal Society B: Biological Sciences 368, doi:10.1098/rstb.2012.0405 (2013).
17
6 Bailis, R., Ezzati, M. & Kammen, D. M. Mortality and Greenhouse Gas Impacts of Biomass and
18
Petroleum Energy Futures in Africa. Science 308, 98-103 (2005).
19
7 Bond, T. C. et al. Bounding the role of black carbon in the climate system: A scientific
20
assessment. Journal of Geophysical Research: Atmospheres 118, 5380–5552,
21
doi:10.1002/jgrd.50171 (2013).
22
Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
Pre-print version -- this copy may differ from the final publication
10
8 Ramanathan, V. & Carmichael, G. Global and regional climate changes due to black carbon.
1
Nature Geosci 1, 221-227 (2008).
2
9 Eckholm, E. The other energy crisis: Firewood. Report No., 22 (Worldwatch, Washington DC,
3
1975).
4
10 Eckholm, E. Fuelwood: The energy crisis that won't go away. (Earthscan, 1984).
5
11 Leach, G. & Mearns, R. Beyond the Woodfuel Crisis: people, land, and trees in Africa.
6
(Earthscan, 1988).
7
12 Arnold, J. M. & Dewees, P. A. Farms, trees and farmers: responses to agricultural intensification.
8
(Earthscan, 1997).
9
13 Hansfort, S. & Mertz, O. Challenging the Woodfuel Crisis in West African Woodlands. Human
10
Ecology 39, 583-595, doi:10.1007/s10745-011-9417-8 (2011).
11
14 Singh, G., Rawat, G. S. & Verma, D. Comparative study of fuelwood consumption by villagers
12
and seasonal “Dhaba owners” in the tourist affected regions of Garhwal Himalaya, India. Energy
13
Policy 38, 1895-1899, doi:http://dx.doi.org/10.1016/j.enpol.2009.11.069 (2010).
14
15 Mayaux, P. et al. State and evolution of the African rainforests between 1990 and 2010.
15
Philosophical Transactions of the Royal Society B: Biological Sciences 368,
16
doi:10.1098/rstb.2012.0300 (2013).
17
16 Hosonuma, N. et al. An assessment of deforestation and forest degradation drivers in developing
18
countries. Environmental Research Letters 7, 044009 (2012).
19
17 Smith, P. et al. in Working Group III – Mitigation of Climate Change (eds Ottmar Edenhofer,
20
Ramon Pichs-Madruga, & Youba Sokona) 179 (IPCC, 2014).
21
18 Drigo, R. "WISDOM Case Studies", <http://www.wisdomprojects.net/global/cs.asp> (2014).
22
Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
Pre-print version -- this copy may differ from the final publication
11
19 IEA. Statistics & Balances, <http://www.iea.org/stats/index.asp> (2012).
1
20 IPCC. Climate Change 2007: Mitigation Contribution of Working Group III to the Fourth
2
Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson,
3
P.R. Bosch, R. Dave, L.A. Meyer (eds.)]. (Cambridge University Press, 2007).
4
21 GACC. Alliance Mission and Goals, <http://www.cleancookstoves.org/the-alliance/> (2014).
5
22 Angelsen, A. et al. Realising REDD+: national strategy and policy options. (Center for
6
International Forestry Research (CIFOR), 2009).
7
23 Subramanian, M. Global health: Deadly dinners. Nature 509, 548-551 (2014).
8
24 Johnson, M., Edwards, R. & Masera, O. Improved stove programs need robust methods to
9
estimate carbon offsets. Clim. Change 102, 641-649, doi:10.1007/s10584-010-9802-0 (2010).
10
25 International Energy Agency. IEA World Energy Statistics and Balances, <http://www.oecd-
11
ilibrary.org/statistics> (2013).
12
26 UN Statistics Division. Energy Statistics Database, <http://data.un.org/Explorer.aspx> (2013).
13
27 Drigo, R. & Salbitano, F. WISDOM for cities: Analysis of wood energy and urbanization using
14
WISDOM methodology. Report No., (FAO Forestry department report, Rome, 2008).
15
28 Bailis, R. Modeling climate change mitigation from alternative methods of charcoal production in
16
Kenya. Biomass and Bioenergy 33, 1491-1502, doi:DOI: 10.1016/j.biombioe.2009.07.001 (2009).
17
29 Mwampamba, T. H., Ghilardi, A., Sander, K. & Chaix, K. J. Dispelling common misconceptions to
18
improve attitudes and policy outlook on charcoal in developing countries. Energy for Sustainable
19
Development 17, 75-85, doi:http://dx.doi.org/10.1016/j.esd.2013.01.001 (2013).
20
30 FAO. Global Forest Resources Assessment 2010. (UNFAO, 2010).
21
Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
Pre-print version -- this copy may differ from the final publication
12
31 Hansen, M. C. et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science
1
342, 850-853, doi:10.1126/science.1244693 (2013).
2
32 JRC. EDGAR Database, <http://edgar.jrc.ec.europa.eu/overview.php?v=GHGts1990-2010 and
3
http://edgar.jrc.ec.europa.eu/unep/index.php> (2014).
4
33 Simon, G. L., Bailis, R., Baumgartner, J., Hyman, J. & Laurent, A. Current debates and future
5
research needs in the clean cookstove sector. Energy for Sustainable Development 20, 49-57,
6
doi:http://dx.doi.org/10.1016/j.esd.2014.02.006 (2014).
7
34 Bazilian, M., Cordes, L., Nussbaumer, P. & Yager, A. Partnerships for access to modern cooking
8
fuels and technologies. Current Opinion in Environmental Sustainability 3, 254-259,
9
doi:http://dx.doi.org/10.1016/j.cosust.2011.05.004 (2011).
10
35 Peters-Stanley, M., Yin, D., Castillo, S., Gonzalez, G. & Goldstein, A. Maneuvering the Mosaic:
11
State of the Voluntary Carbon Markets 2013. Report No., 105 (Ecosystem Marketplace and
12
Bloomberg New Energy Finance, Washington, DC, 2013).
13
36 Masera, O., Ghilardi, A., Drigo, R. & Trossero, M. A. WISDOM: A GIS-based supply demand
14
mapping tool for woodfuel management. Biomass and Bioenergy 30, 618-637 (2006).
15
37 Wheeler, D., Kraft, R. & Hammer, D. Forest Clearing in the Pantropics: December 2005-August
16
2011. Report No., (Washington DC, 2011).
17
38 Jetter, J. et al. Pollutant Emissions and Energy Efficiency under Controlled Conditions for
18
Household Biomass Cookstoves and Implications for Metrics Useful in Setting International Test
19
Standards. Environmental Science & Technology 46, 10827-10834, doi:10.1021/es301693f
20
(2012).
21
39 ESA. European Space Agency GlobCover Portal, <http://due.esrin.esa.int/globcover/> (2011).
22
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40 FAO. Global Ecological Zones, <http://www.fao.org/geonetwork/srv/en/metadata.show?id=1255>
1
(2011).
2
41 Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by
3
carbon-density maps. Nature Climate Change 2, 182-185 (2012).
4
42 Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three
5
continents. Proceedings of the National Academy of Sciences 108, 9899-9904,
6
doi:10.1073/pnas.1019576108 (2011).
7
43 Duku, M. H., Gu, S. & Hagan, E. B. A comprehensive review of biomass resources and biofuels
8
potential in Ghana. Renewable and Sustainable Energy Reviews 15, 404-415,
9
doi:http://dx.doi.org/10.1016/j.rser.2010.09.033 (2011).
10
44 Ajoku, K. in Bioenergy for Sustainable Development in Africa (eds Rainer Janssen & Dominik
11
Rutz) Ch. 12, 131-146 (Springer Netherlands, 2012).
12
45 Carlson, K. M. et al. Committed carbon emissions, deforestation, and community land conversion
13
from oil palm plantation expansion in West Kalimantan, Indonesia. Proceedings of the National
14
Academy of Sciences 109, 7559-7564, doi:10.1073/pnas.1200452109 (2012).
15
46 Gatti, L. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric
16
measurements. Nature 506, 76-80 (2014).
17
47 An, L., Linderman, M., Qi, J., Shortridge, A. & Liu, J. Exploring complexity in a human–
18
environment system: an agent-based spatial model for multidisciplinary and multiscale
19
integration. Annals of the Association of American Geographers 95, 54-79 (2005).
20
48 Bhatt, B. P. & Sachan, M. S. Firewood consumption along an altitudinal gradient in mountain
21
villages of India. Biomass and Bioenergy 27, 69-75,
22
doi:http://dx.doi.org/10.1016/j.biombioe.2003.10.004 (2004).
23
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1
Supplementary Information: is available in the online version of the paper.
2
Acknowledgements: This research was funded by the Global Alliance for Clean Cookstoves (GACC)
3
through a grant administered by the UN Foundation.
4
Author Contributions: RD, RB, AG and OM designed the study; RD conducted the pan-tropical
5
WISDOM analysis and constructed the NRB model; RB calculated GHG emissions and emission
6
reductions; RD, RB, AG and OM wrote the paper.
7
Author Information: The authors declare no conflicts of interest.
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Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
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Tables
1
Table 1: Different assumptions considering the use of LCC by-products
2
Assumption
Comment
A
LCC by-products generated in accessible regions
are not utilized for woodfuel. Woodfuels are
harvested entirely from other sources. NRBA is
calculated as the quantity of non-renewable
biomass from sources unrelated to LCC.
NRBA is applicable where LCC by-products
are inaccessible to smallholders despite
being physically proximate. This might be the
case if large-scale farming or timber
extraction drives LCC on private land that
smallholders cannot enter.
B
LCC by-products generated in accessible regions
are utilized as woodfuel. Two quantities are
calculated:
NRBB1 refers to the amount of LCC by-products used
to meet woodfuel demand in a given region. By-
products of deforestation are always considered non-
renewable and by-products of afforestation are
considered renewable.
NRBB2 refers to the amount of woodfuel from other
sources required to meet demand after LCC by-
products are exhausted. LCC by-products may
meet 100% of demand so that NRBB2 = 0
The sum of NRBB1 and NRBB2 indicates the
total quantity of unsustainable woodfuel
consumption that occurs when woodfuel
users have access to LCC by-products.
These values are applicable in regions
where LCC is driven by smallholder
agriculture or regions hosting intense
commercial woodfuel extraction. Woodfuel
users may be the primary agents of LCC.
Household energy interventions can mitigate
NRBB2, but it is unclear how they would
affect NRBB1.
3
Figure Legends
4
Figure 1: Pixel-level supply-demand balance (top left), local balance (top right), and commercial
5
woodsheds (bottom right).
6
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Figure 2: Pan-tropical expected fNRBB2 (box shows the region illustrated in Figure 1)
1
2
Figure 3: Distribution of regional population by expected fNRBB2 decile
3
4
Figure 4: Annual emissions and emission reductions resulting from fulfilling GACC’s objective of
5
100 million stoves disseminated via interventions with different priorities (bars indicate GHG
6
emissions/uptake, data points show net emissions, error bars indicate standard deviations, and
7
numbers indicate annual reductions achieved by shifting from baseline to intervention).
8
9
Figure 5: Countries with highest per capita woodfuel demand, highest expected fNRBB2, and
10
highest rate of DALYs attributable to HAP exposure
11
12
13
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Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
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Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
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Methods
1
We use the WISDOM model36 (Supplementary Information section 1) to characterize sustainability and
2
net carbon emissions of traditional woodfuels in 90 developing countries located primarily in tropical
3
regions, using 2009 as a base year. Woodfuel demand was derived from national and sub-national
4
studies (Supplementary Information section 1) supplemented by data from the Food and Agriculture
5
Organization (FAO), International Energy Agency (IEA), and United Nations (UN)1,25,26. From these data,
6
we constructed a map of traditional woodfuel demand separated by subsistence and commercial
7
components (c). Subsistence demand occurs in rural areas, where people use woodfuels they collect
8
themselves or purchase locally. This wood is harvested within few hours’ walking distance. Commercial
9
demand originates in urban and some densely populated rural locations and is carried using motorized
10
transport over longer distances (Supplementary Information section 1).
11
12
Woodfuel supply is defined by the productivity of woody biomass, which we model as a function of
13
aboveground biomass (AGB) stock. We use recent maps of land cover and ecological zones 39,40 to
14
define a broad system of land units, including cropland and crop mosaic, which are often neglected in
15
assessments of woodfuel supply. Each land unit is assigned an AGB stock using three types of sources
16
1) AGB distribution maps, 2) geo-referenced field plots, and 3) forest inventories from known locations for
17
specific forest types (Supplementary Information section 1). AGB distribution was derived from two
18
recently released datasets41,42. To accommodate disagreements in the two datasets, we gathered data
19
from hundreds of geo-referenced field plots and forest inventories. We subtract woody components not
20
typically used for woodfuels (twigs, leaves, and stumps), to build a map of “Dendro-energy” biomass
21
(DEB) stock (Extended Data Fig. 2). We then estimate woodfuel supply as the “mean annual increment”
22
(MAI) of DEB, which we model via a functional relationship between ~2,800 spatially explicit field
23
observations of MAI and corresponding AGB (Supplementary Information section 2).
24
25
We then make adjustments for potential supply from plantations43,44 (Supplementary Information section
26
3) and accessibility. Accessibility has both legal and physical determinants. Legal accessibility is based
27
on IUCN categorization of “Protected Areas” (Supplementary Information section 3). Physical accessibility
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Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
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is a function of the effort required to access woody biomass from a consumption site. We use an inverse
1
functions of friction in geographic space for subsistence and commercial demand (Supplementary
2
Information section 3) and map the spatial distribution of accessible DEB (Extended Data Fig. 3 and
3
Table 1).
4
5
Land cover change is accommodated by estimating the amount of DEB produced by deforestation and
6
afforestation processes based on data from FAO1 which we distribution spatially using data from Forest
7
Monitoring for Action (FORMA)37. Biomass from large-scale deforestation in remote areas of the Amazon
8
or Indonesian rain forests is often burned on site45,46. Only LCC occurring in areas that are accessible (as
9
defined above) contribute to NRB. The actual quantity of LCC by-products used as fuel is unknown. Even
10
in accessible areas, some materials may be burned in situ or left to decay. To accommodate this
11
uncertainty, we explore two variants of LCC by-product utilization (Table 1, Extended data Fig. 5).
12
13
We combine the commercial and subsistence supply-demand maps to define the minimum quantity of
14
NRB that would be required to meet existing demand (Supplementary Information section 4). This
15
assumes unsustainable harvesting occurs only after the sustainable supply in a given location has been
16
fully exploited. However, ideal management is unlikely. To simulate more realistic harvesting, we assume
17
harvesting exceeds sustainable levels in some areas even if the sustainable supply in an adjacent area
18
has not been fully exploited. To estimate the extent of this deviation, we use a proxy defined by the
19
fraction of each country’s forested area under formal management plans30 (Supplementary Information
20
section 5).
21
22
We then define local balance assuming subsistence users do not travel more than a few kilometers to
23
access woodfuels (Supplementary Information section 4)47,48 (Figure 1a and Extended Data Fig. 4). Then
24
we assess the commercial supply-demand balance in urban centers and rural regions with large deficits
25
by defining a “woodshed”, which represents the region that a commercial demand center needs to exploit
26
in order to satisfy demand assuming that the full MAI is utilized27. We assume a threshold of 12-hour one-
27
way travel. When several consumption sites are considered simultaneously, the woodshed is determined
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19
by the aggregate demand from all sites (Supplementary Information section 4, Figure 1b, and Extended
1
Data Fig. 5).
2
3
Annual GHG emissions from traditional woodfuels are estimated by accounting for two flows of GHGs.
4
The first flow consists of combustion emissions including well-mixed GHGs (CO2, CH4, and N2O) and
5
SLCFs (BC, OC, CO, and VOCs). The second flow consists of CO2 sequestered by the renewable
6
fraction of harvested woodfuel. We utilize 100-yr global warming potentials (GWPs) to estimate climate
7
impacts and we derive emissions from published analyses of woodfuel combustion and charcoal
8
pyrolysis38. Sequestered CO2 comes from results of this study (Supplementary Information section 4).
9
10
To investigate the implications of GACC’s 100 million stove objective, we define scenarios representing
11
broad goals of cookstove dissemination: climate change mitigation, decreasing dependence on NRB;
12
reducing exposure to HAP and economic development. We examine the outcome of focusing specifically
13
on these objectives by targeting stove dissemination at the locations that rank among the highest in one
14
of four categories described in Supplementary Information section 6.
15
16
Nature Climate Change 5, 266–272 (2015) doi:10.1038/nclimate2491
Pre-print version -- this copy may differ from the final publication
Extended data: The Carbon Footprint of Traditional Woodfuels
Authors: Robert Bailis1*, Rudi Drigo2, Adrian Ghilardi3, Omar Masera4
Affiliations:
1 Yale School of Forestry and Environmental Studies
2 Independent Consultant
3 Center for Environmental Geography Research, National Autonomous University of Mexico
(UNAM)
4 Center for Ecosystems Research, National Autonomous University of Mexico (UNAM)
*Correspondence to: robert.bailis@yale.edu
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%
Fig. 7: efNRBA (top) efNRBB1+B2 (bottom)
In these maps, the impact of LCC by-products is apparent, particularly in regions subject to high rates of
deforestation like Central America, the Amazon Basin, and SE Asia. If by-products are not used (Scenario
A – top of Fig. 7), then few hotspots occur in those regions. However, by definition, woodfuel supplied by
deforestation is non-renewable. Thus, if LCC by-products are used as woodfuel, then those areas emerge
as major hotspots because the DEB obtained through LCC processes is sufficient to meet the majority of
the woodfuel demand.
The lower map in Fig. 7 may overemphasize the impact of NRB in deforestation hotspots. While it shows
woodfuel as 100% non-renewable, the underlying drivers of LCC are unrelated to energy demand and
would likely be unaffected by measures taken to reduce demand. In addition, if we normalize NRB by
population as in Fig. 8, or by area, as in Fig. 9, a different picture emerges. Conditions in the Amazon
Basin and SE Asia are less extreme. It is particularly noteworthy that considering NRB per unit area
causes large parts of India, which had relatively low levels of fNRB, to stand out among the worse off
regions.
Fig. 8: per capita NRBA (top) and NRBB1+B2 (bottom) using sub-national administrative units
Fig. 9: NRBA (top) and NRBB1+B2 (bottom) per area using sub-national administrative units
There is regional variation in outcome, as discussed in the main text. This is demonstrated in Fig. 10,
which shows the regional distribution of efNRB estimates at subnational levels showing different
assumptions about the use of LCC by-products.
Fig. 10: Regional distribution of efNRB estimates at subnational levels showing different
assumptions about the use of LCC by-products.
The main text gives results of our assessment assuming woodfuel harvesters did not harvest precisely
according to sustainable yields, which we defined as the “expected” or efNRB. We also carried out an
assessment assuming harvesters acted such that sustainable yields are fully exploited before any
unsustainable harvest occurred. This provides estimates of minimum or mfNRB. In addition, in the main
text, we focus primarily on national-level results and only refer to a handful of subnational outcomes. In
the following pages, we provide tables showing all subnational, national, and regional results. Table 3 lists
results of m- and efNRB in all subnational units of the pan-tropics for all scenarios.
GHG emissions
In 2009, net emissions of GHGs from traditional woodfuels were 1.0-1.2 Gt CO2e. Here we show how
these emissions break down by type of woodfuel (Fig. 11) and by climate forcing agent (Fig. 12).
Fig. 11: National woodfuel emissions and CO2 uptake among the top-20 emitters accounting for
over 80% of global emissions disaggregated by fuelwood, charcoal production and charcoal use.
Fig. 12: National woodfuel emissions and CO2 uptake among the top-20 emitters accounting for
over 80% of global emissions disaggregated by climate forcing agent
!400$
!200$
0$
200$
400$
India$
China$
Indonesia$
Ethiopia$
Pakistan$
Brazil$
Kenya$
Uganda$
Nepal$
Congo,$Dem.$Rep.$
Sudan$(former)$
Bangladesh$
Nigeria$
South$Africa$
Tanzania,$United$Rep.$
Mexico$
Viet$Nam$
Mozambique$
Thailand$
Philippines$
Mton%CO2e%
Carbon$taken$up$by$biomass$regrowth$under$each$scenario$$$$$$
Emissions$from$charcoal$consumpQon$$
Emissions$from$charcoal$producQon$
Emissions$from$fuelwood$$
Net$emissions$
!400$
!200$
0$
200$
400$
India$
China$
Indonesia$
Ethiopia$
Pakistan$
Brazil$
Kenya$
Uganda$
Nepal$
Congo,$Dem.$Rep.$
Sudan$(former)$
Bangladesh$
Nigeria$
South$Africa$
Tanzania,$United$Rep.$
Mexico$
Viet$Nam$
Mozambique$
Thailand$
Philippines$
Mton%CO2e%
CO2$uptake$ OC$ BC$
N2O$ NOx$$ NMOC$
CH4$ CO$ CO2$
Net$GHG$emissions$
Supplemental Information: The Carbon Footprint of Traditional Woodfuels
Authors: Robert Bailis1*, Rudi Drigo2, Adrian Ghilardi3, Omar Masera4
Affiliations:
1 Yale School of Forestry and Environmental Studies
2 Independent Consultant
3 Center for Environmental Geography Research, National Autonomous University of Mexico (UNAM)
4 Center for Ecosystems Research, National Autonomous University of Mexico (UNAM)
*Correspondence to: robert.bailis@yale.edu
1.!The WISDOM method ........................................................................................................... 2!
Sources of Data and Analysis of Woodfuel Demand .................................................................................................. 9!
Population distribution data sources: ........................................................................................................................... 9!
Woodfuel use data sources: ......................................................................................................................................... 9!
Accounting for non-energy uses of harvested wood .................................................................................................. 13!
Sources of Data and Analysis of Woodfuel Supply ................................................................................................... 14!
Pan-tropical Supply Module ....................................................................................................................................... 14!
Biomass Productivity .................................................................................................................................................... 21!
Contributions from Plantations ................................................................................................................................... 23!
Accessibility .................................................................................................................................................................. 25!
Legal accessibility ...................................................................................................................................................... 25!
Physical accessibility ................................................................................................................................................... 25!
Considering National Borders .................................................................................................................................... 27!
Elevation factor ........................................................................................................................................................... 27!
Slope factor ................................................................................................................................................................ 28!
Cost-distance analysis ............................................................................................................................................... 29!
Accounting for industrial roundwood ......................................................................................................................... 32!
2.!Integrating Supply and Demand Modules ........................................................................ 32!
Pixel-level balance ........................................................................................................................................................ 32!
Local Balance ................................................................................................................................................................ 32!
Non-local or “Commercial” balance ............................................................................................................................ 33!
Woodshed analysis ....................................................................................................................................................... 33!
Transport time threshold ............................................................................................................................................ 34!
3.!Estimating the expected range of subnational and national NRB ................................. 37!
Potential Renewable Biomass fraction (pRBf) ........................................................................................................... 37!
Minimum fraction of Non-Renewable Biomass (mfNRB) .......................................................................................... 37!
Sustainable Increment Exploitation Fraction (SIEF) ................................................................................................. 38!
Expected Renewable Biomass fraction (eRBf) .......................................................................................................... 38!
Expected Fraction of Non-Rene wable Biomass (efNRB) .......................................................................................... 39!
Accounting for woody biomass from deforestation and afforestation ......................................................................... 39!
4.!Determining GHG emissions from traditional woodfuels ............................................... 40!
5.!Results ................................................................................................................................. 41!
Defining GACC scenarios ............................................................................................................................................ 42!
Other Estimates of fNRB .............................................................................................................................................. 42!
6.!Sensitivities ......................................................................................................................... 43!
Minimum vs. Expected values of NRB ........................................................................................................................ 43!
Stove type ...................................................................................................................................................................... 44!
Fuel savings .................................................................................................................................................................. 44!
7.!References for Supplementary Information .................................................................... 44!
1. The WISDOM method
This study is based on “Woodfuels Integrated Supply/Demand Overview Mapping” (WISDOM), a
spatially explicit analytic method developed to identify priority areas of intervention and supporting
biomass energy planning and policy formulation. WISDOM is the fruit of a collaborative effort
between the Wood Energy Program of FAO and the Centro de Investigaciones en Ecosistemas
(CIECO) of the National Autonomous University of Mexico (UNAM)1-4 and has been implemented
in over 25 countries5.
WISDOM presents a flexible approach that is adaptable to local conditions and available
information, which allows it to cope with heterogeneity. The approach utilizes seven steps (Figure