An assessment of deforestation and forest degradation drivers in developing countries

All 100 non-Annex I countries in this study were grouped into four FT phases (figure 1) based on two factors: percent forest cover and rate of forest area change. The four FT phases are pre-transition, early transition, late transition and post-transition, which generally represent a time sequence of national development. Pre-transition countries have high forest cover and low deforestation rates. In early-transition countries, forest cover is lost at an increasingly rapid rate. Late-transition countries with a rather small fraction of remaining forests exhibit a slowing of the deforestation rate and eventually come into the post-transition phase, where the forest area change rate becomes positive and forest cover increases through reforestation. The FT model reflects a broad-scale typology of tropical developing countries, applicable as a proxy for analyzing the temporal variability of drivers of deforestation and forest degradation.

In general, our methodology followed the one described by da Fonseca et al (2007), where developing countries were stratified into four categories based on remaining forest cover and deforestation rate. A decision tree (figure 2) was developed for categorizing all 100 countries into four FT phases using the percentage forest cover of 2010 and forest area change rates based on the 2010 Global Forest Resources Assessment (FRA) by FAO (FAO 2010). Forest area change rates were calculated based on the amount of annual forest change relative to forest cover in 1990 for four periods: 1990–2000, 2000–05, 2005–10 and 2000–10. An annual forest area change rate of −0.25% was used to separate between pre- and early-transition countries as this is the annual average of 2005–10 for our study area. A forest area change rate of 0% and forest cover of 15% and 50% were selected as additional thresholds.

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The definition of drivers of deforestation and forest degradation in the REDD+ debate are often not clear. In scientific literature, there is a common separation of proximate/direct or underlying/indirect causes. It is often more difficult to establish clear links between underlying (or predisposing) factors and deforestation than between direct causes and deforestation. In this study, we will analyze data on the proximate or direct drivers, i.e. human activities that directly affect the loss of forests, and use the term 'driver' to indicate proximate drivers. This choice is based on available data and the way countries are reporting data on drivers.

The drivers are considered separately for deforestation and forest degradation. Deforestation in this letter denotes the (complete) removal of trees and the conversion from forest into other land uses such as agriculture, mining etc, with the assumption that forest vegetation is not expected to naturally regrow in that area. Forest degradation denotes thinning of the canopy and loss of carbon in remaining forests, where damage is not associated with a change in land use and where, if not hindered, the forest is expected to regrow. In some specific cases multiple proximate drivers work in combination, i.e. forest clearing for timber followed by land use change for agriculture. In this case and to avoid double counting, the land use change (to agriculture) has been identified as the primary cause of deforestation. Five deforestation drivers (table 1) and four forest degradation drivers (table 2) were considered in this study. We use these broad categories to provide a set of driver types for comparative analysis that allow for the variation in detail and quality of information reported by countries.

Since countries have not been obliged to report on drivers, there are no comprehensive, recent and quantitative assessment data available concerning drivers of deforestation and forest degradation on a national level. Thus, this study builds upon new and useful REDD+ readiness related data sources to help fill this gap including 26 Readiness Preparation Proposals (R-PP) and ten Readiness Plan Idea Notes (R-PIN) prepared for the World Bank Forest Carbon Partnership Facility (FCPF 2011) by hosting countries. It is important to note that these data are basically self-reported by countries and they were taken on board independent of what these reports are based on. As another source of data, Matthews et al (2010) describe proximate drivers of deforestation throughout history for 25 tropical countries by reviewing existing literature and data. In addition, we used several CIFOR country profiles (http://www.forestclimatechange.org/) that include driver and activity information for deforestation and forest degradation, and UNFCCC National Communications and other reports that have recently become available. Most of these data sources were developed between 2008 and 2011 and reflect more or less the period of 2005–2010 or 2000–2010 when the report has time series data. In total, driver data were available for 46 of the 100 (sub)tropical non-Annex I countries (appendix). These 46 countries account for 78% of the total forest area (in 2010), and 81% of forest loss (in 2000–2010) of the 100 countries under consideration, according to 2010 FAO FRA data (FAO 2010), and cover a range of FT phases in each continent (table 3). However, for some continent–FT phase combinations there are no or limited data available, namely for post-transition countries in Africa and Latin America and pre-transition countries in Asia.

The different data sources were analyzed and summarized to provide the current 'best' estimate of the relative importances of different drivers. First, all data were categorized given the driver categories listed in tables 1 and 2. The relative importance of a driver within a country is reported in different formats in the different sources, either as a ratio scale (quantitative information), an ordinal scale (ranking) or a nominal scale (listing). The aim was to get as much quantitative information as possible about the relative importance of deforestation and forest degradation drivers as a national fraction (e.g., commercial agriculture was at 40% the most important cause of deforestation on the national level). Table 4 shows how different data scales were processed to allow for comparison. Depending on the scale of the source data, the same approach was used for all countries to ensure consistency. When ratio-scale data were available, this value was directly used. Ordinal data were quantified by assigning ratios (e.g. 3:2:1) in order of decreasing importance and assuming an equal interval. In the case of more than one dominant driver, the estimation procedure was adapted accordingly with the same weight for drivers reported as equally important (see example in table 4). For nominal-scale data the values for attributing ratios were assumed equal. In cases where multiple and different-scale data sources exist for a country we prioritized the most quantitative data, so ratio data were preferred over ordinal data and ordinal data over nominal data. When multiple but same-scale data sources were available for a country, the average values were used. As shown in table 4, countries with the highest quality ratio-scale data reflect 47% of the total forest loss (of 100 countries) and ordinal-scale data are available for countries responsible for 20% of the total forest loss. Although 19 countries have only nominal-scale data, these countries tend to be smaller in size and with lower contributions to forest loss (14% of total forest loss).

The country driver data were aggregated for different continents and FT phases and also analyzed in that context (see sections 3.2 and 3.3). The aggregation by continents and forest transition phases can be used as suitable proxies to describe the country circumstances in terms of active deforestation and degradation drivers; i.e., it can be assumed that a country (without current data on drivers) will have a similar situation to other countries on the same continent and the same FT phase where empirical data are available. Thus, building upon the continent and FT model proxies, the study has derived estimates for situations where currently limited country data have been reported (see table 3). For situations with sufficient driver data, the driver data were averaged. Table 5 explains the data estimation procedures for situations with limited or no driver data using proxy information.

The 100 non-Annex I (sub)tropical countries are categorized into four FT phases (see the appendix) using the decision tree (figure 2). Exceptions on the decision tree were made for Thailand and Costa Rica. These countries, while just not fulfilling the criteria, clearly belong in the post-transition phase. Of the 100 non-Annex I countries, 13 countries are in the pre-transition phase, 39 in early transition, 33 in late transition and 15 in post-transition. Thus the majority (72) of the 100 countries are either in early or late transition, which are the phases of rapid deforestation. The spatial distribution of FT phases across the (sub)tropics (figure 3) shows that many pre-transition countries in Africa and America are located around the equator, surrounded by early-transition countries, and with late-transition countries mostly located in sub-tropical regions.

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Forest cover (FAO 2010) and intact forest area values (Potapov et al 2008), both for 2005, were averaged for each FT phase for all 100 countries (figure 4). The forest cover transition follows the conceptual framework shown in figure 1. Intact forest area follows a similar FT curve to forest cover but the change in intact forest cover from the late- to post-transition phase remains quite small, suggesting that a large proportion of forests in post-transition countries remains degraded. The difference between forest cover and intact forest area, i.e. the disturbed forest area fraction, is an important indicator of degraded forest area.

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The driver data are summarized and analyzed for three continents, Africa, America and Asia (including Oceania) (figure 5(A)). Agriculture is the main driver of deforestation, but with differences in geographic distribution of the importance of commercial versus subsistence agriculture. Commercial agriculture is the most important driver in Latin America (68%), while in Africa and Asia it contributes to around 35% of deforestation. Local and subsistence agriculture is quite equally distributed among the continents (27–40%), which makes sense since this type of land use (change) remains widespread in all areas in the tropics and sub-tropics. Overall, agriculture reflects around 80% of deforestation worldwide, which is in line with estimates provided by Geist and Lambin (2002) for the 1980s and 1990s. Mining plays a larger role in Africa and Asia than in Latin America. Urban expansion is most significant in Asia. De Fries et al (2010) state that further urban population growth is expected across the tropics, which will likely be associated with increased pressure on tropical forests.

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Timber extraction and logging account for more than 70% of total degradation in Latin America and Asia (figure 5(C)). Fuelwood collection and charcoal production is the main degradation driver for the African continent, and is of small to moderate importance in Asia and Latin America. Uncontrolled fires are most prominent in Latin America. In terms of absolute net forest area change over the period 2000–10 (figure 5(B)), the largest driver remains commercial agriculture, with the largest deforested area located in Latin America. In Africa and Asia, subsistence and commercial agriculture contribute roughly equally to forest area change.

The driver data are summarized and analyzed for four FT phases (figure 6). The relative area contribution of commercial agriculture rises until the late-transition phase, after which it decreases again (figure 6(A)). The relative importance of subsistence agriculture remains fairly stable throughout the different phases, while the relative importance of urban expansion and infrastructure is largest in the post-transition phase. The total area deforested, however, is largest in the early-transition phase and is driven by agriculture expansion (figure 6(B)). This is in line with the FT model, where forest area change rates level off toward the later transition stages, and so total deforested area decreases as well. Intensification of agriculture and urbanization is expected in the course of economic development and decelerating deforestation, that generally accompanies the FT model (Mustard et al 2004, De Fries et al 2004). Mining seems to play an important role in deforestation in the pre-transition phase, but this is likely due to the presence of some resource-rich countries with large remaining forest cover in this phase (e.g. Guyana, Democratic Republic of the Congo).

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Regarding degradation (figure 6(C)), the relative degraded area caused by timber and logging activities is most pronounced in all phases but decreases in the late-transition phase. In the late-transition phase, fuelwood and charcoal as well as uncontrolled fires are much more prominent. This can be attributed to the fact the forest timber resources maybe largely exploited in the late transition and the remaining forest area receives increasing pressure for wood fuel, in particular in many African woodland countries that are in the late-transition phase. In the post-transition phase, economic development will likely cause a decline in fuelwood collection and charcoal production as other energy sources become available, and timber extraction is usually better managed in this phase, which will cause a decline in the prevalence of fires.

Overall, the patterns of deforestation drivers are quite similar in Africa and Asia, while degradation patterns are more similar in Latin America and Asia (figure 5). Building upon this relationship and the usefulness of the continent and FT model proxies, the study has derived estimates for situations where currently limited country data have been reported, in particular for post-transition countries in Africa and Latin America, and the pre-transition countries in Asia. This provides an approach for incorporating all countries and can provide useful best current estimates for global policy development. The aim of the results presented in table 6 is to estimate the importance of deforestation and degradation drivers, based on currently available data, for all 100 countries and thus to provide a pan-tropical assessment. It also highlights some of the remaining data gaps (estimates with an annotation, see table 6) that will be potentially filled as countries progress in the REDD+ readiness phase. Table 5 in the methodology section indicates the procedures followed for annotated estimates with no or limited data availability.