Deforestation: Drivers, Implications, and Policy Responses
Summary and Keywords
Over the last 8,000 years, cumulative forest loss amounted to approximately 2.2 billion hectares, reducing forest cover from about 47% of Earth’s land surface to roughly 30% in 2015. These losses mostly occurred in tropical forests (58%), followed by boreal (27%) and temperate forests (8%). The rate of loss has slowed from 7.3 Mha/year between 1990–2000 to 3.3 Mha/year between 2010–2015. Globally since the 1980s, the net loss in the tropics has been outweighed by a net gain in the subtropical, temperate, and boreal climate zones. Deforestation is driven by a number of complex direct and indirect factors. Agricultural expansion (both commercial and subsistence) is the primary driver, followed by mining, infrastructure extension, and urban expansion. In turn, population and economic growth drive the demand for agricultural, mining, and timber products as well as supporting infrastructure. Population growth and changing consumer preferences, for instance, will increase global food demand 50% by 2050, possibly requiring a net increase of approximately 70 million ha of arable land under cultivation. This increase is unlikely to be offset entirely by agricultural intensification due to limits on yield increases and land quality. Deforestation is also affected by other factors such as land tenure uncertainties, poor governance, low capacity of public forestry agencies, and inadequate planning and monitoring. Forest loss has a number of environmental, economic, and social implications. Forests provide an expansive range of environmental benefits across local, regional, and global scales, including: hydrological benefits (e.g., regulating water supply and river discharge), climate benefits (e.g., precipitation recycling, regulating local and global temperature, and carbon sequestration), biogeochemical benefits (e.g., enhancing nutrient availability and reducing nutrient losses), biodiversity benefits, and the support of ecosystem stability and resiliency. The long-term loss of forest resources also negatively affects societies and economies. The forest sector in 2011 contributed roughly 0.9% of global GDP or USD 600 billion. About 850 million people globally live in forest ecosystems, with an estimated 350 million people entirely dependent on forest ecosystems for their livelihoods. Understanding how to best manage remaining forest resources in order to preserve their unique qualities will be a challenge that requires an integrated set of policy responses. Developing and implementing effective policies will require a better understanding of the socio-ecological dynamics of forests, a more accurate and timely ability to measure and monitor forest resources, sound methodologies to assess the effectiveness of policies, and more efficacious methodologies for valuing trade-offs between competing objectives.
Forests play a fundamental role in Earth dynamics and provide services that are of significant environmental, societal, and economic value. They exert substantial control over the regional and global climate, modulate water and nutrient cycling, and provide invaluable resources and services that have played a crucial role for the socioeconomic and cultural development of numerous civilizations throughout history (Runyan & D’Odorico, 2016).
Forests provide a wide range of environmental benefits that can have local to regional to global relevance (Foley et al., 2007; Runyan & D’Odorico, 2016). Hydrological benefits of forests include regulating water supply and river discharge via increased transpiration, water storage beneath the forest and increasing the travel time for water to reach streams/rivers (e.g., Lee, 1980; Bosch & Hewlett, 1982). Climatological benefits of forests include maintaining precipitation via precipitation recycling (e.g., Eltahir & Bras, 1996) and regulating local and global temperature both directly—by reducing diurnal sensible heat fluxes (Loarie et al., 2011) and nocturnal radiative cooling (D’Odorico et al., 2013)—and indirectly—by taking up atmospheric CO2 during photosynthesis. The presence of forests reduces soil erosion and enhances soil formation (Runyan & D’Odorico, 2014). Biogeochemical benefits of forests include enhancing nutrient availability and reducing nutrient losses, thereby increasing the amount of nutrients available for plant uptake and aiding in sustaining forest growth (Runyan et al., 2012b; Runyan & D’Odorico, 2013). Forests also provide many ecological services such as maintaining biodiversity and regulating a range of dynamical trophic relationships (Pereira et al., 2010). Forests, by maintaining their own habitat, can possibly reduce the occurrence of disturbances such as fires (for those tree species that do not rely on disturbances such as wildfires) or landslides (Runyan & D’Odorico, 2014).
Forests also provide many economic benefits to societies. About 850 million people globally (FAO, 2018a) live in forest ecosystems, with an estimated 350 million people entirely dependent on forest ecosystems for their livelihoods. The forest sector in 2011 contributed roughly 0.9% of global GDP or USD 600 billion (FAO, 2014b). Forests contribute both timber and non-timber forest products (NTFP) as well as a variety of recreational and culture services. The import/export value of timber products (e.g., paper, pulp, roundwood, lumber) is estimated at about USD 265 billion in 2018 (Source: FAOSTAT-Forestry Production and Trade). NTFPs include food (e.g., nuts, honey, bushmeat, and fruits), medicine, construction materials (e.g., rubber), bioprospecting (i.e., value for new pharmaceutical products) and agricultural products such as fodder for livestock (Belcher & Schreckenberg, 2007). The value of NTFPs has been estimated at $88 billion USD (FAO, 2014a). Forests also contribute recreational, cultural, intellectual, aesthetic, and spiritual values to society, but these services are difficult to value in monetary terms (Box 1).
Distribution and Characteristics of Different Forest Biomes
In 2015, about 4 billion hectares (31% of global land area) is forest, most of which consists of tropical (1.77 billion hectares) and boreal (1.22 billion hectares) forests. The remainder includes 684 million hectares of temperate forests and 329 million hectares of subtropical forests. (Keenan et al., 2015).
Tropical forests occupy a broad band across the Earth’s warm, moist equatorial regions, including Central Africa (the Congo Basin), the Amazon, and Southeast Asia (Lieth & Werger, 1989). They are dominated by evergreen or semi-deciduous broadleaf species that are characterized by tall stature (usually exceeding 30 m), a tightly closed canopy, and very high diversity (200–300 tree species per hectare) (Malhi et al., 1999). The amount of aboveground biomass reported in tropical forests varies substantially across location, ranging from a mean aboveground biomass of 395.7±14.3 Mg dry biomass ha−1 in tropical African forests to 289 Mg ha−1 reported for Amazonia, and 445 Mg ha−1 reported for Borneo (Malhi et al., 2013). Temperate forests are found in midlatitude regions with a well-defined winter season, at least four to six frost-free months, relatively long growing seasons, and mean annual precipitation that is greater than average annual evapotranspiration. Boreal forests occupy a circumpolar belt in high northern latitudes, between circumpolar tundra and temperate forests and grasslands (Larsen, 1980). The transition between temperate and boreal forests is where cold-tolerant conifers replace hardwoods as the dominant forest species (Perry et al., 2008). Thus, this transition is determined by climate conditions too cold for angiosperms (i.e., hardwoods) with minimum winter temperatures dropping below −40o C, which occurs at roughly 50 to 60° Lat. The northernmost limit of the boreal forest is found at about 70o N, which is where the growing season becomes too short for conifer growth.
A number of themes, controversies, and debates have informed and shaped the literature on deforestation. Published papers on deforestation grew from 30 before 1980 to 637 in the 2011–2015 period (Aleixandre-Benavent et al., 2018). During the 1990s, deforestation literature was strongly influenced by the traditional disciplines of environmental science, ecology, agronomy, and botany. Throughout the 2000s, the literature increasingly focused on issues such as land-use change, climate change (Alkama & Cescatti, 2016; Bonan, 2008b), biodiversity loss (Vieira et al., 2008), and economic development (Cuaresma et al., 2017; Runyan et al., 2015). Increasingly, the literature is integrating these topics and placing them in a broader context of resiliency, sustainability, ecosystem services (e.g., REDD), and socioeconomic dynamics. Aleixandre-Benavent et al. (2018) found that socioeconomic topics in relation to deforestation were underrepresented in the literature prior to 2015. Articles dealing with the socioeconomic aspects of deforestation, however, have been increasing since around 2014 (Aguilar & Song, 2018; Ashraf et al., 2017; Cuaresma et al., 2017; Dezecache et al., 2017; Liu et al., 2016; Runyan et al., 2012b; Tritsch & Arvor, 2016).
The deforestation-related literature falls into four broad strands related to (1) gaining a better understanding of ecological and socioeconomic drivers of deforestation; (2) understanding the social, economic, ecological, and climate implications stemming from deforestation; (3) informing the design of effective policies to address deforestation; and (4) evaluating the effects and outcomes of various policies and practices on deforestation levels and rates.
A wide-ranging debate has emerged in the literature around the challenges, trade-offs, and valuation of multiple and competing social-economic-environmental objectives (e.g., timber and agricultural production versus forest and biodiversity protection, the climate impacts of deforestation, and the nature and practice of sustainable management) (Kube et al., 2018). Within this broader debate are controversies over the relative roles of corporations, communities, markets, and governments as well as what governance approaches can effectively address deforestation from local to global scales. A struggle is also evident in the literature in generalizing broader principles from local, context-specific studies to national, regional, and global approaches to deforestation.
Methods to Quantify Deforestation Rates
Being able to quantify the amount of forest biomass globally is important to improve our understanding of where deforestation, reforestation, and forest regrowth is occurring and how forests respond following disturbance, as well as to assess the effectiveness of policies and guide policymakers’ decisions (Runyan & D’Odorico, 2016). There are two broad types of sensors used to map forest biomass and quantify changes in the areal extent of forests: passive and active sensors. Passive sensors depend on an external energy source such as the sun, while active sensors send out a known quantity of energy and measure the amount of that energy reflected back to the sensor. The amount of energy reflected back to the sensor is related to characteristics of the forest canopy by calibrating the satellite measurements to field-based estimates of aboveground biomass (AGB). Methods based on radar or laser are examples of active remote sensing, while methods relying on remote sensing in the reflected solar portion of the spectrum (often known as “optical remote sensing”) are passive. Optical satellite data classifies forest change as occurring when a pixel or polygon changes from one landcover class to another. Optical methods have been widely used to estimate and map deforestation and work well when large areas of forest are cleared but can perform poorly when cloud cover, small-scale deforestation or forest degradation is present (Mitchard et al., 2013; Hoekman, 1997; Asner, 2001; Hansen et al., 2008, 2009). Such satellite systems are operational at the global scale and provide globally consistent records for over 30 years (e.g., MODIS, Landsat, and AVHRR) (Gibbs et al., 2007). Active systems such as synthetic aperture radar (SAR) have been widely used to map AGB (Goetz et al., 2009; Kasischke et al., 1997; Tatem et al., 2008) and can operate day or night and penetrate through haze, smoke, and clouds. The transmitted microwave energy penetrates into forest canopies and the reflected radar signal can then be converted to forest carbon stock estimates using allometric relationships between plot-level measurements of canopy characteristics and AGB (Goetz et al., 2009).
History of Deforestation
Over the last 8,000 years, there has been a cumulative loss of roughly 2.2 billion hectares of forest—about 15% of the Earth’s land surface (Billington et al., 1996). Most of this loss occurred between 1700 and 1990 when forest area decreased by roughly 1.7 billion hectares (Lambin et al., 2003; FAOSTAT; Houghton, 1996). Between 1990 and 2015, there was a net reduction in global forest area of 282 million ha (Mha) (FAOSTAT; FAO, 2015a), almost all the loss being from tropical forests (Figure 1). Notably, some of this forest gain is attributable to monocultured tree plantations (e.g., Zhai et al., 2014), which can differently impact biodiversity and ecosystem services in comparison to an undisturbed forest (Hartley, 2002).
Until the early 20th century, the highest rates of deforestation occurred in temperate forests in Asia, Europe, and North America (FAO, 2012; Figure 2). This pattern changed during the 20th century with deforestation essentially coming to a stop in the world’s temperate forests (Runyan & D’Odorico, 2016; Houghton, 1996). In contrast, deforestation increased rapidly in the tropics during this period, reaching a peak of almost 13 Mha year–1 in the early to mid-1990s (Malhi & Grace, 2000). Much of the net forest loss (i.e., gross forest loss minus gross forest gain) between 1990 and 2005 was concentrated in South America, followed by Africa (Hansen et al., 2013). Over the period from 2000 to 2012, Hansen et al. (2013) found that tropical forests accounted for 58% (86 Mha) of net global forest loss, followed by boreal (27%; 40 Mha), temperate (8%; 12 Mha) and subtropical (8%; 11 Mha) biomes. In 2018, the tropics lost 12 Mha of forest with Brazil and Indonesia accounting for 46% of primary rainforest loss, and countries such as Colombia, Côte d’Ivoire, Ghana, and the Democratic Republic of Congo experiencing a considerable rise in loss rates (Global Forest Watch, 2018).
Drivers of Deforestation
Drivers of deforestation include proximate causes and underlying causes (Angelsen & Kaimowitz, 1999) (Figure 3). The choices made by deforestation agents (i.e., individuals, households, or companies) are influenced by proximate causes such as prices, market outlets, technologies, and agroecological conditions (Angelsen & Kaimowitz, 1999). These actions are in turn affected by broader national and international macro-level and policy instruments, known as the underlying causes. Oftentimes, deforestation can be explained by multiple proximate and underlying causes (Geist & Lambin, 2002).
Proximate causes are human activities or immediate actions that originate locally from intended land use and directly impact forest cover (Geist & Lambin, 2002). Proximate causes of deforestation include infrastructure expansion, agricultural expansion, and wood extraction. The relative importance of these different causes as a driver of deforestation can vary both in time and depending on geographic location.
Infrastructure Extension. One of the primary proximate causes cited as driving deforestation in rural areas is infrastructure extension. Establishing new or improving existing roads opens up new areas, reduces transport costs, provides market access, and thereby makes forest removal more profitable (Pfaff, 1999; Angelsen, 2010). Roads are among the most powerful factors contributing to deforestation across the tropics (Geist & Lambin, 2002) as well as in temperate regions (Burton et al., 2003) with highways having considerably larger scale impacts than roads because they provide efficient, year-round access to forests and reduce transportation costs (Laurance et al., 2002).
Agricultural Expansion. Clearing of forests and woodlands has historically been driven by the demand for crops and agricultural production. Between 1700 and 2015, global cropland and pasture area grew by about 4.24 billion hectares (Bha) (FAOSTAT), doubling almost every century (Goldewijk et al., 2011). During this same period, forest area decreased by 2.28 Bha x (Figure 4). The net increase in the area of cropland was 50% in the 20th century alone (Ramankutty & Foley, 1999; Goldewijk, 2001), with much of this expansion occurring during the period from 1900 to 1960. Between 1961 and 2009 global cropland only grew by 12% despite agricultural production expanding by 150% (FAO, 2009).
Agricultural expansion includes forest conversion for permanent cropping, cattle ranching, shifting cultivation, and frontier colonization. An analysis of 152 cases of deforestation by Geist and Lambin (2002) spanning tropical forests in Asia, Africa, and the Americas showed that agricultural expansion drives 96% of all deforestation cases. Hosonuma et al. (2012) found that agricultural expansion drives ~80% of deforestation across 46 developing countries worldwide. Agricultural expansion is also a cause of forest decline in temperate regions (e.g., Hobson et al., 2002). Commercial agriculture is associated with 68% of deforestation cases in Latin America and about 35% of deforestation cases Africa and Asia (Geist & Lambin, 2002); subsistence agriculture drives about 27% to 40% of deforestation across the continents (Hosonuma et al., 2012). Higher rates of forest loss in the tropics have been strongly associated with global trade (i.e., demands for agricultural products in distant urban and international locations) (DeFries et al., 2010).
Environmental factors can also influence an area’s susceptibility to deforestation. In the tropics, drier, deciduous forests are most vulnerable to deforestation because they are easier to burn, thereby reducing the effort needed to clear and maintain the cleared area (Laurance et al., 2002; Steininger et al., 2001). However, in semi-arid areas such as the Chaco of Argentina, relatively higher deforestation rates have been documented in areas receiving more than 600 mm of precipitation per year (Grau et al., 2005). Grau et al. (2005) found that 40% of forests have been removed in areas with more than 600 mm of rainfall annually, while sectors with less than 600 mm annually, have lost less than 20% of their forest cover. Other environmental factors driving deforestation rates may include soil fertility (e.g., Pfaff, 1999) because more fertile and higher-quality soils could lead to increased productivity of cleared land. Thus, farmers on soils with low fertility would need to clear larger areas of forest than those on better soils in order to remain viable (Laurance et al., 2002). Historical analyses of environmental factors influencing deforestation (and continued forest removal) suggest that relatively low rainfall, temperature, and soil nutrient content all lead to a permanent reduction in forest cover (Rolett & Diamond, 2004). Steep land slope is another environmental factor that may lead to reduced deforestation rates (e.g., Rolett & Diambond, 2004; Mas et al., 1996). Rolett and Diamond (2004) suggested that agriculture (hence land clearance) decreases with elevation because of low temperatures that may be unfavorable for crops, steep slopes, and difficult access. Apart from environmental characteristics of the forest, ecological attributes such as the presence of a positive feedback can affect the value of the standing forest and thus, potentially make forest conservation more economically attractive (Runyan et al., 2015).
Timber Extraction. Historically, deforestation due to logging is driven by the demand for wood and paper products as well as land conversion to agriculture. Commercially logged forests are often left to recover either spontaneously (secondary forest) or by planting trees (Malhi et al., 1999). During logging there is habitat destruction and a loss of forest biodiversity with the severity of this disturbance dependent on the logging technique (e.g., clear cutting versus selective logging). Most deforestation as a result of logging activities over the past century has occurred in temperate and boreal zones. Rates of logging in boreal forests steadily increased from about 1 Mha year–1 in 1850 to 3.5 Mha year–1 in 1980, and in temperate forests from 3 Mha year–1 in 1850 to 6 Mha year–1 in 1980 (Houghton, 1996). In contrast, logging of tropical forests was below 0.5 Mha year–1 in 1850, less than 2 Mha year–1 in 1950, but accelerated to 8 Mha year–1 in 1980, overtaking temperate forest logging in the mid-1970’s (Houghton, 1996). Overall, 1,069 Mha of forests were logged between 1850 and 1990, an area that was 77% larger than the area of forest converted to agriculture (Houghton, 1996).
Timber extraction involves both commercial timber harvesting as well as the harvesting of fuelwood for domestic purposes. Tropical timber plays a relatively minor role in the global timber market; accounting for approximately 15% of the total volume of global timber production (Burgess, 1993). Only 17% of the total tropical timber production is used for industrial purposes with the remainder being consumed for fuelwood and other nonindustrial uses (Burgess, 1993). In contrast, commercial timber harvesting plays a greater role in boreal forest extraction. For instance, China, Russia, Germany, the United States and Canada accounted for 45% of the global export of wood and wood pulp in 2013 (UN Comtrade Database, 2014).
Underlying causes of deforestation are fundamental social processes, such as human population dynamics or agricultural policies, that drive the proximate causes of deforestation and either occur at the local level or have an indirect impact at the national or global level (Geist & Lambin, 2002; Figure 1). Underlying causes of deforestation include: demographic factors, technological factors, economic factors, policy and institutional factors, and environmental factors.
Demographic Factors. Population growth has frequently been cited as an underlying cause influencing deforestation (e.g., Myers, 1980). Deforestation may increase following population growth because more land is needed for food, fibers, fuelwood, timber, or other forest products (Carr, 2004). In many rural areas in recent decades, deforestation has accelerated despite a deceleration in rural population increase, suggesting that agriculturalists in areas of lower population density may be increasingly responsible for greater forest clearing (Carr, 2009).
DeFries et al. (2010) found that urban population growth was positively associated with forest loss in tropical forests located within Africa, Asia and Latin America because urbanization increases consumption levels and thus so does the demand for agricultural products (DeFries et al., 2010; Godfray et al., 2010). Urban consumers tend to eat more processed foods and animal products than rural consumers, thereby increasing the demand for commercially produced crops and livestock (Kennedy et al., 2004; Mendez & Popkin, 2004; Kastner et al., 2012). Thus, although the initial forest clearing may be conducted by a relatively small number of rural agriculturalists at the forest frontier, it may be driven by demand for timber and agricultural products in urban areas.
Technological Factors. A common type of policy for reducing deforestation is the development and dissemination of technologies that allow farmers to increase production on land that is already deforested. Agricultural intensification has been identified as a viable alternative to agricultural expansion (e.g., Foley et al., 2011; Godfray, 2011) because technological progress lowers the farmers’ average costs leading to increased output of the agricultural good and lower prices. This argument is referred to as the Borlaug hypothesis, named after Norman Borlaug (2007), who claimed that the intensification of cereal production between 1950 and 2000, partly as a result of Green Revolution technologies, saved over one billion hectares of land from being brought into agricultural production. Rudel et al. (2009) examined this hypothesis and concluded that for the great majority of crops, yield growth did not lead to land sparing because increases in productivity from new technologies increase the profitability of agriculture in comparison with alternative land uses (such as forests), thereby encouraging expansion of the agricultural land frontier.
A recent review by Villoria, Byerlee, and Stevenson (2014) shed light on this paradox. They found that deforestation may be reduced by some forms of improved technology including: more profitable techniques for sustainable forest management, infrastructure such as irrigation equipment that can lead to a boom in the labor market (i.e., employment), while sustaining higher cropping intensities, alleviating land pressure on the forest frontier and reducing cropland expansion (Shively & Pagiola, 2004). In general, technologies that use more labor will constrain deforestation in the short term (for rural areas that are more remotely located and have smaller populations), although over the long term, they may attract more labor to the region (e.g., through migration). In contrast, yield increasing and labor-saving technology may increase deforestation, especially if the crop was planted extensively (e.g., maize, wheat, and soybean) rather than intensively (e.g., coffee and fruits) where extensive refers to crops planted in the upland or frontier and intensive refers to those crops planted in the lowland (Angelsen & Kaimowitz, 2001).
Economic Factors. Economic factors can be prominent underlying drivers of tropical deforestation (Geist & Lambin, 2002). Higher national income and economic growth may reduce the pressure on domestic forests by improving off-farm employment opportunities, but at the same time, increase it (possibly through international trade) by stimulating demand for agricultural and forest products and improving access to previously unharvested forests (Angelsen & Kaimowitz, 1999). This trend of an initial increase in income accelerating the rate of deforestation, followed by a reduction in the rate of deforestation beyond a certain level of income is referred to as the environmental Kuznet’s curve (Lopez, 1994). Subsequently, the transition from prevalent deforestation to reforestation as countries become more economically developed is known as a “forest transition” (Mather, 1992), and this trajectory is termed the “forest transition curve.” This phenomenon has been explained as an effect of industrialization, agricultural intensification, and international trade. The latter allows for an international displacement of land use whereby reforestation in more affluent countries occurs at the expense of forests in developing countries (Meyfroidt et al., 2010).
Economic variables such as low domestic costs (for land, labor, fuel, or timber), and an increase in the price of agricultural crops stimulate deforestation rates (Geist & Lambin, 2002). In contrast, an increase in the price of rural wages (Angelsen & Kaimowitz, 1999), shortage of off-farm employment opportunities and an increase in the price of agricultural inputs reduce forest clearing rates (Ruben et al., 1994; de Almeida & Campari, 1995; Monela, 1995). Another important economic factor affecting deforestation rates is access to international markets, which often results in more stable demand and prices. Commercialization and the growth of timber markets (as driven by national and international demands) are frequently reported as factors that increase the profitability of logging and therefore increase deforestation rates.
Institutional Factors. The potential impact of the property rights regime, policies, and political stability may play a substantial role in driving deforestation rates. For instance, in their analysis of 152 cases of tropical deforestation, Geist and Lambin (2002) found that institutional factors drive 78% of deforestation cases. These factors included measures such as policies on land use and economic development that are related to colonization, transportation, or subsidies for land-based activities. Land tenure arrangements and policy failures such as corruption or mismanagement in the forestry sector are other institutional factors affecting deforestation rates (Geist & Lambin, 2002). Land tenure arrangements are important because the world’s most carbon-rich forests are often found in regions where ownership is insecure and thus understanding the effect of property rights on deforestation rights is of critical importance (Robinson et al., 2013).
The impact of property rights on deforestation rates is unclear because property rights could affect forest cover in two different ways with opposite implications (Liscow, 2013). Property rights could lead landholders to discount the future less (i.e., place greater emphasis on the future stream of benefits) and reap the long-term benefits of forestry instead of the short-term benefits of agriculture. These property rights do not necessarily need to be private rights. Ostrom (1990) cites case studies of forest resources managed collectively, although Ostrom indicates that minimal recognition by governments of rights to organize common-pool resource arrangements is important. On the other hand, more secure property rights increase investment in land because farmers are more motivated to improve the land and can use these rights as collateral for borrowing money for land investments. As a result, the value of agriculture increases relative to that of forest, thereby leading to a decrease in forest cover.
Policies. Policies aimed at agricultural development such as credits, low taxation, incentives for cash cropping, and legal land titling can increase deforestation rates and lead to the expansion of commercial crops and pastures (Geist & Lambin, 2002). Deforestation rates are also driven by national development policies (Klepeis, 2003). For instance, Klepeis (2003) did a historical analysis of the impact of centralized (i.e., national) versus decentralized (i.e., local) approaches to development in the southern Yucatan peninsula, Mexico. He found that the rates of deforestation tend to be greater, the patterns of forest clearing more pronounced, and land-use decision making less democratic under systems of centralized control. Deforestation can also be favored by policies allowing large-scale land acquisitions by domestic or foreign investors, as has been observed in a number of cases where agribusiness corporations buy forested areas and convert them into farmland (Naylor, 2011), as reported in the case of Papua New Guinea (Nelson et al., 2014), Myanmar (Webb et al., 2014), and Brazil (Oliveira, 2013; Hermele, 2014).
Implications of Deforestation
A number of environmental, social, and economic implications stem from deforestation. Understanding these broad implications is important to the sustainable management of forest resources and the design of effect policies to address the drivers of deforestation but requires an integrated approach across complex ecological, social, and economic systems. In this section, we examine the wide range of environmental processes that deforestation implicates such as climate and temperature, hydrological, biodiversity, biogeochemical, and irreversible ecosystem dynamics. We also consider the social and economic implications of deforestation.
Environmental Implications of Deforestation
Changes to the forest ecosystem from deforestation impacts a continuum from the soil to the atmosphere. Within this continuum, deforestation impacts the physical to biological to chemical conditions within these different compartments (e.g., atmosphere, soil, surface water, and groundwater).
Climate and Temperature Impacts. The effect of deforestation on regional climate varies geographically (Bonan et al., 1992, Bonan, 2008a; Pielke et al., 2011). Much of the research on the effect of deforestation on regional climate has focused on the Amazon basin and the Boreal forest (Bonan et al., 1992; Foley et al., 1994; Chapin et al., 2000a, 2000b). In the Amazon, replacement of forest with pasture increases albedo (i.e., the ratio of reflected shortwave radiation to incoming shortwave radiation) from 0.13 to 0.18 with a subsequent 11% decrease in net radiation (Gash & Nobre, 1997). The change in albedo indicates that more shortwave radiation is reflected back to the atmosphere. Despite this, dry season surface temperatures are approximately 1.5° C higher in pastures than forest (Loarie et al., 2011) because of the higher evapotranspiration of trees versus grasses (Pielke et al., 2011). The lower latent and greater sensible heat fluxes in pastures cause an increase in temperature that outweighs the effect of the increase in albedo. Pastures also exhibit enhanced planetary boundary layer (PBL) development and—depending on the size of the clearing—canopy breezes and near-surface convergence of relatively moist air from the surrounding forest (Souza et al., 2000). Deforestation also affects cloud climatology and the onset of the rainy season (Wang et al., 2009; Butt et al., 2011).
Forest vegetation modifies the microclimate within the canopy (e.g., Geiger, 1965; Raynor, 1971; Lee, 1978; Germino & Smith, 1999; Davies-Colley et al., 2000; Newmark, 2001; Bonan, 2008a); it maintains lower maximum temperatures and higher minimum temperatures than those observed in adjacent areas with no forest cover (e.g., Chen et al., 1993; Renaud & Rebetez, 2009; Villegas et al., 2010; Royer et al., 2011), while the opposite effect is observed at night (e.g., D’Odorico et al., 2013). This pattern of diurnal cooling and nocturnal warming has been reported in boreal, temperate, alpine, and tropical forests (Young & Mitchell, 1994; Newmark, 2001; Renaud & Rebetez, 2009, Maher et al., 2005; Bader et al., 2007). In these forests, the nocturnal warming induced by the forest canopy is generally due to a reduction in radiative cooling that may reduce the exposure to frost stress and improve woody plant survival and growth (Örlander, 1993; Groot & Carlson, 1996; Langvall & Örlander, 2001; Voicu & Comeau, 2006; Langvall & Ottosson Löfvenius, 2002). In the absence of forest cover, nocturnal radiative cooling is more intense because a greater amount of longwave radiation is lost to the atmosphere. Tree canopies slow down nocturnal cooling by absorbing part of the radiation emitted by the ground and re-radiating it back toward the ground (Chen et al., 1993; Grimmond et al., 2000). Thus, forest vegetation increases nocturnal temperatures under conditions with and without snow cover. This warming effect improves seedling survival particularly in areas close to the altitudinal and latitudinal limits of woody plants (D’Odorico et al., 2013).
Land use and land cover change forcings are important to the regional climate, yet, their effect on the global climate system remains poorly understood. Most research has concentrated on the impact of large-scale forest removal on the climatology of the deforested region, or on the combined effect of land atmosphere feedbacks and large-scale forcings on regional climatology (e.g., Wang & Eltahir, 2000a, 2000b; Zeng & Neelin, 2000). Teleconnections between land use/land cover change in one region and climatic changes in another have also been studied (Ray et al., 2006; Pielke et al., 2011).
In summary, forests affect the temperature regime: at the global scale, forests maintain a warmer planetary temperature because of their lower albedo, however, at smaller scales (ranging from forest gaps to entire regions) they can cause diurnal warming and nocturnal cooling, thereby enhancing the exposure to extreme cold events. Thus, forest removal could impact these climatic processes at a range of different spatial scales.
Regional and Global Scale Precipitation Patterns. Vegetation affects the rainfall regime by modifying the exchange of energy and water vapor with the atmosphere. The major mechanisms determining the effect of deforestation on regional precipitation are based on: (1) the impact of the reduction in evapotranspiration and precipitation recycling; (2) changes in the surface energy balance and its effect on the stability of the atmospheric boundary layer; (3) the impact of vegetation on the atmospheric concentration of organic aerosols and cloud microphysics; and (4) mesoscale circulations resulting from the heterogeneous forest cover and the opening of forest gaps. Decades of research has shown that large-scale deforestation generally leads to a decrease in regional precipitation (e.g., Henderson-Sellers & Gornitz, 1984; Lean & Warrilow, 1989; Shukla et al.,1990; Hasler et al., 2009) (particularly in continental regions) and an increase in the occurrence of droughts (Lee et al., 2011). By reducing evapotranspiration and the supply of water vapor to the atmosphere, deforestation leads to a reduction in precipitation (e.g., Eltahir & Bras, 1996). Evapotranspiration is reduced by increasing land surface albedo, decreasing roughness, leaf area index, and access to deeper soil moisture by plant roots. In some regions, a relatively important fraction of precipitation is contributed by water evapotranspired within the same region (Salati et al., 1979). Known as precipitation recycling, this phenomenon is stronger in continental than coastal regions, and during the growing season when evapotranspiration is stronger (Eltahir & Bras, 1996). Model simulations have shown that large-scale forest removal reduces precipitation recycling and alters the surface energy balance, thereby reducing regional precipitation. At the continental scale, the fraction of precipitation contributed by regional evapotranspiration generally ranges from a low of 0.22 in Europe to a high of 0.45 in Africa (van der Ent et al., 2010). Conversely, small-scale deforestation (at scales >10 km) can induce mesoscale circulations that enhance local convergence and precipitation. Deforestation also affects cloud climatology and the onset of the rainy season (Wang et al., 2009; Butt et al., 2011). Deforested regions seem to exhibit a stronger seasonal variability of precipitation and fewer rainfall occurrences than in the presence of forest cover (Webb et al., 2014; Lee et al., 2011).
Hydrological Impacts. Forest removal affects the dynamics and rate of hydrologic processes in deforested watersheds. Deforestation reduces evapotranspiration and canopy interception and consequently increases water yields. It also enhances soil compaction and reduces soil bioturbation, thereby leading to a change from a system affected by saturation-excess runoff to a landscape dominated by infiltration-excess runoff. This transition entails a change from relatively slow pathways of rainwater delivery to the stream—mainly by subsurface stormflow—to faster transport by overland flow. As the litter layer is lost, the water storage capacity of the ground surface is also reduced due to the loss of a variety of areas where water can be stored (canopy, snowpack, forest floor, ground surface, pools, topographic and microtopographic depressions, and the soil column) that temporarily retain rainfall water and delay its flow (Hewlett, 1969). In turn, the hydrologic response becomes faster, with shorter concentration times and higher peak flows due to the decrease in the concentration time of the watershed (i.e., the travel time from the most remote point in the watershed to the outlet). The removal of forest cover affects the hydrologic response at different timescales. At the event scale, deforestation results in faster and more peaked hydrographs and flash floods. At the seasonal or annual scale there is an overall increase in runoff and streamflow volumes or water yields (e.g., Lee, 1980; Bosch & Hewlett, 1982). The increase in water yields increases with the reduction in forest cover, reaching a maximum for clear-cut forests. The water yield enhancement is typically stronger during the growing season and increases with mean annual precipitation (e.g., for mean annual precipitation >500 mm/y). Thus, the removal of riparian vegetation can greatly improve water yields, increase baseflow levels and reduce the number of low flow days in forested watersheds (Chang, 2002).
As precipitation reaches the forest canopy (either as rainfall or snowfall), part of it is retained and temporarily stored by the canopy, while the rest moves through the foliage to the forest floor as throughfall or stemflow (Lee, 1980). Precipitation trapped by the canopy eventually evaporates (or sublimates in the case of snow) or drips down to the ground contributing to additional throughfall. After deforestation, interception losses by residual vegetation or by crops and pastures replacing the forest are strongly reduced. Interception typically ranges between 10% and 40% of precipitation, with the higher fractions corresponding to smaller rainfall events and water droplet size (Lee, 1980). Thus, more water is available for infiltration and runoff in deforested watersheds than in their forested counterparts.
In areas with moist air, low clouds or fog, forest canopies may favor either the condensation of moisture (dew formation) or the deposition of fog and cloud droplets onto leaf surfaces. This moisture, often known as horizontal or occult precipitation eventually drips down to the forest floor thereby increasing (unlike interception) the supply of water to forested watersheds (Chang, 2002). Occult precipitation is a major contributor to the water budget of coastal regions, particularly those close to cold ocean surfaces that are typically affected by fog, low clouds or high relative humidity. The removal of these forests could result in a positive feedback where the overall decline in water availability limits forest regeneration that in turn, further perpetuates the decline in water availability and capacity for tree seedlings to regenerate (Wilson & Agnew, 1992).
Biodiversity Impacts. Biodiversity is defined as the sum total of all of the plants, animals, fungi, and microorganisms on Earth; their genetic and phenotypic variation; and the communities and ecosystems of which they are a part (Dirzo & Raven, 2003). There are roughly ~5 ± 3 million species on Earth (Costello et al., 2013). Biodiversity is largely concentrated, yet unevenly distributed in the tropics. Biodiversity may be threatened by a major extinction event (which is defined when the Earth loses more than three-quarters of its species in a geologically short interval) (Barnosky et al., 2011). Land-use change is one of the most significant drivers of changes in biodiversity, with estimates of global losses of intact ecosystems ranging between 0.5 and 1.5% annually (Sala et al., 2000; Jenkins, 2003). One extreme consequence of habitat loss is extinction. Already humans may have caused the extinction of 5%–20% of species in many groups of organisms, and modern rates of extinction are estimated to be 100–1,000 times greater than average pre-human rates (Chapin et al., 2000c).
It is important to understand these changes as globally, the average annual loss of habitats and the populations they support leads to a loss of goods and services worth approximately US$250 billion annually (Balmford et al., 2002). The impact of habitat loss and degradation on biodiversity decline over the next 50 years could be substantial as forest loss is expected to be highest in the tropics, where biodiversity is greatest (Pereira et al., 2010). Gibson et al. (2011) used 2,200 pairwise comparisons of biodiversity in tropical forests from a meta-analysis of 138 studies to assess the impact of disturbance and land conversion on biodiversity. They found that birds were the most sensitive taxonomic group to primary forest removal while mammals were the least sensitive to disturbance. However, the results varied depending on the type of disturbance. Sala et al. (2000) estimated that by the year 2100, land use change will have the greatest impact on biodiversity (in comparison to rising CO2 concentrations, N deposition, rise in atmospheric temperatures and biotic exchanges [i.e., the introduction of new plant and animal species to an ecosystem]) due to effects of land use change on habitat availability and consequent species extinctions. They also found that biomes such as tropical and southern temperate forests were largely influenced by changes in land use and were less sensitive to the effect of other drivers.
Biogeochemical and Nutrient Cycling Impacts. Carbon (C). Following deforestation, carbon losses from an ecosystem vary depending on the method of how deforestation occurs. For instance, carbon losses during clear cut of a forest result from carbon contained in transported timber that is in turn exported from the ecosystem. In contrast, carbon losses during fire lead to an input of C to the atmosphere as well as carbon deposited to the soil in ash and residue (Kauffman et al., 1993). The 2019 IPCC report on climate change and land use estimated that between 2007–2016 emissions due to Forestry and other Land Use (FOLU), most of which is due to deforestation, averaged 5.8 +/- 2.6 GTCO2e/year, which accounts for 11% of total anthropogenic emissions. However, percentages can range from 6% to 18%.
As noted previously, deforestation is largely driven by agricultural expansion. Whether pasture and agricultural soils are a net sink or source of C following deforestation depends substantially on their management. Carbon emissions to the atmosphere can be reduced following deforestation by adopting management practices that increase the soil C content. In the tropics, increasing C inputs to the soil is obtained by improving the fertility and productivity of cropland and pastures using practices such as no-till and for systems practicing shifting cultivation, using planted fallows and cover crops (Paustian et al., 1997). In temperate regions, key management strategies involve increasing cropping frequency and reducing bare fallow, increasing the use of perennial forages (including N-fixing species) in crop rotations, retaining crop residues and reducing or eliminating tillage (i.e., no-till; Paustian et al., 1997).
Nitrogen (N). Following deforestation, there is an increase in leaching and runoff losses of nitrate to groundwater and stream water that increase when the harvesting method is intensified (Sollins & McCorison, 1981). Nitrate export to stream water increases after deforestation because of reduced plant uptake, increased rates of N mineralization and nitrification, and increased leaching through the soil profile. Deforestation can increase soil temperature and moisture, thereby enhancing conditions for mineralization. Subsequently, nitrification may occur so rapidly that uptake by vegetation and immobilization by microbes are insufficient to prevent large losses of NO3- to stream water and groundwater (Schlesinger, 1997).
N is also lost during deforestation due to the removal of organic N contained in logged wood. In contrast to forests that are logged, fire differently alters patterns of N cycling. During burning there is a loss of both N stored in vegetation (via smoke and airborne ash) and soil N lost to wind and water erosion (Kauffman et al., 1993).
Atmospheric losses of N result both during and following deforestation. The quantity of N lost is dependent on the mechanism of forest removal (Kauffman et al., 1993). Fire volatilizes N from vegetation and litter, reducing N stored in the burned ecosystem but often increasing mineralization of the remaining organic matter (e.g., Turner et al., 2007). Increased fluxes of N to the atmosphere also result over relatively short periods due to denitrification (i.e., the reduction of nitrogen oxides to di-nitrogen gas by microorganisms).
Phosphorus (P). In P-limited forests, the availability of P for plant uptake is dependent on mechanisms and symbioses with trees that aid in increasing P availability. For instance, plant roots can produce extracellular phosphatases, microorganisms, and fungi that mineralize unavailable forms of P, maximize P acquisition via cluster roots, develop symbiotic structures such as mycorrhizas and root nodules that increase scavenging volumes. In P-limited forests, P is cycled very conservatively with minimal losses from the system (Runyan et al., 2012a). However, following deforestation, P losses are high, oftentimes leaving the system in an unproductive and highly nutrient limited state (Runyan et al., 2013). If vegetation growth is constrained by nutrient availability, less carbon will be fixed to structural biomass, and larger amounts of CO2 will be respired and released to the atmosphere (Körner, 2006; Canadell et al., 2007).
It has been suggested that 80% of future deforestation will occur in tropical forests that are often P-limited (Gibbs et al., 2010). Thus, if the cleared land is used for agriculture, it will most likely be able to sustain only a few crop cycles. It has been estimated that 30% to 40% of the world’s arable land is primarily P-limited (Runge-Metzger, 1995; von Uexkull & Mutert, 1998). Short-term P limitations can be overcome in agricultural systems by adding fertilizer to stimulate crop yields; however, the supply of rock phosphate reserves used to create P fertilizer could be depleted in as little as 50 to 100 years (Vance et al., 2003, Cordell et al., 2009).
Environmental Irreversibility. Positive feedbacks may induce bi-stable ecosystem dynamics, with forest ecosystems occurring as metastable states of a system that has an alternative stable configuration with no forest vegetation (Runyan et al., 2012b). The existence of such dynamics suggests that some forests are prone to abrupt and highly irreversible shifts to a stable and often “degraded” state with no trees. These feedbacks span many different environmental processes and are found across forests globally (Runyan et al., 2012b). Moreover, no tools exist to predict whether or not the forest will be able recover if it undergoes a shift to the degraded state (Runyan & D’Odorico, 2016).
Forests have important recreational, cultural, intellectual, aesthetic, and spiritual values that are important and oftentimes necessary to society. Therefore, deforestation impacts these values in addition to the livelihoods of rural populations. Deforestation also impacts human health. Deforestation can result in the emergence of infectious diseases in wildlife (e.g., Nipah virus, West Nile virus disease, HIV) and human (zoonotic) infections (Runyan & D’Odorico). Some infectious diseases are a major global threat to human health because of their high fatality rates or lack of prevention and treatment therapies (Daszak et al., 2001). Wildlife populations can play a critical role in the emergence of infectious diseases because they provide a reservoir for pathogens that are then transmissible to humans (i.e., zoonotic infection). Land-use change can favor the flow of pathogens to humans (Wilby et al., 2009) who enter in close contact with them (Daszak et al., 2001) and increase the exposure to zoonotic infections. Forest destruction increases interactions with wildlife, due to human encroachment into previously forested areas, bushmeat consumption, and a loss of the buffering effect of biodiversity. Deforestation improves the habitat for reservoir species and increases their populations, thereby increasing the potential for human contact with wildlife and zoonotic pathogens (Myers et al., 2013). Deforestation also increases the exposure to vector-borne diseases such as dengue (Froment, 2009), lishmaniasis (Coimbra, 1991), and malaria, both in Africa (Coluzzi, 1994; Cohuet et al., 2004; Guerra et al., 2006; Yasuoka & Levins, 2007) and South America (de Castro et al., 2006; Singer & de Castro, 2006).
Deforestation has a number of economic implications, yet valuing deforestation presents a number of challenges surrounding monetary, economic, social, and ecological issues (Chen et al., 2016; Fischer et al., 2008; Turner et al., 2003). By accessing timber and other natural resources, deforestation can contribute to economic growth and is typically an important component in the early stages of a nation’s economic development (Cuaresma et al., 2017). But unless timber extraction and competing land uses are managed sustainably, overexploitation of forests can result with attendant downside economic implications. The economic implications of unsustainable deforestation extend beyond the loss of the $600 billion USD global GDP contribution of forest products. Policies promoting or restricting deforestation involve trade-offs between competing land uses, which in turn can affect such factors as the productivity of agriculture, trade, and food security. Deforestation also affects the economic and social values derived from non-timber benefits (e.g., recreation, tourism, non-forest products). Most importantly, deforestation has implications for a number of non-marketable forest benefits that are hard to value (e.g., biodiversity, ecological stability, climate) but upon which broader economic and societal health depends. For example, the impact of deforestation on greenhouse gas emissions is substantial, and the resulting economic implications of climate change are well documented (Houser et al., 2015; Nordhaus, 2013; Stern, 2007; Stern, 2015, Wagner & Weitzman, 2015). Furthermore, the maintenance of important economic resources such as the quantity and quality of a region’s water supply are often dependent on forest ecosystems. These dependencies can extend regionally and globally as in the case of precipitation patterns, potentially affecting agriculture and other economic activity in regions remote from the deforestation (Avissar & Werth, 2005; Devaraju et al., 2015).
Policy Responses to Address Deforestation
Policies and practices to reduce deforestation span a wide range of issues, including land use, commodity supply chains, economic development, and ecosystem services at local to global scales. Understanding appropriate policy designs and policy mixes is important for assessing and improving the effectiveness of individual policies, as well as understanding how individual policies complement and reinforce (or work at cross-purposes with) each other to reduce deforestation.
Types of Policies Addressing Deforestation
The literature on deforestation-related policy falls into four broad groups—forest protection, sustainable management (forest and agriculture), maintenance of ecosystem services, and deforestation-free supply chains (Table 1). Policies in each of these areas vary along a number of dimensions and employ different strategies, instruments, and practices.
Table 1. Types of Policy Responses Generally Used to Address Deforestation
Forest Land Use and Management
Legal designation of protected areas/reserves
Sustainable Use of Forests
Criteria & Indicator Frameworks, Forest Management Certification
Prevention of illegal logging
Legal Instruments (e.g., performance bonds, voluntary agreements, liability standards)
Conditions imposed through logging concessions
Incentives to maintain ecosystem services
Property Rights, including CPR arrangements
Payments for ecosystem services (PES)
Product Supply Chains
Sustainable Resource Use
Voluntary Corporate No Deforestation Commitments
Commitments to deforestation-free supply chains
Certifications, Eco-labeling, and Information Disclosures
Facilitate demand for sustainable products
Sustainable manufacturing or building requirements
Public procurement requirements
Legal Instruments—Voluntary Agreements
Import licensing based on VPA
Import/Export restrictions around illegal logging
Deter Agricultural Expansion
Subsidies, Taxes, Technical Assistance
Encourage and support agricultural intensification and sustainability practices
Changes in conditions for tenure security
Forest Protection Policies
The designation of protected areas—preserves, national forests, and reserves—is one of the oldest and most dominant policy approaches to maintain forests. In 2018, there were 245,449 designated protected areas that protected just over 20 million km2, equivalent to 14.9% of the Earth’s land surface (UN EP, 2018). Protected forests account for about a third of protected areas (6.5 million km2), or about 16.3% of all forested land globally (Schmitt et al., 2009; FAO, 2015a). Over a quarter of tropical forests are protected (Morales-Hidalgo et al., 2015; FAO, 2015a). Protected areas largely exist on public lands, subject to government-promulgated policies. However, private trusts, corporations, individuals, and local communities, among others, also establish and govern protected areas (Watson et al., 2014). Protected areas have been shown to reduce deforestation (Joppa & Pfaff, 2011), but the strength of reduction is dependent on a number of factors, including location and management effectiveness. For instance, protected areas are often established in areas that are unattractive for other land uses due to location (remoteness) and biophysical factors such as soil characteristics, slope, elevation, and wetness, making these areas inherently at lower risk of deforestation (Andam et al., 2008; Ferraro et al., 2011; Ferraro & Pressey, 2015; Joppa & Pfaff, 2009; Nelson & Chomitz, 2009; Pfaff et al., 2009; Pfaff et al., 2010; Pfaff et al., 2014). Several studies have found that the levels of avoided deforestation in protected areas are significantly overestimated if the location and biophysical characteristics are not accounted for (Andam et al., 2008; Busch & Ferretti-Gallon, 2017; Ferraro et al., 2011; Mas, 2005; Nagendra, 2008).
Management also influences the effectiveness of a protected area. Kaimowitz et al. (1998) state that deforestation can be reduced “where protected areas are effectively managed” and Adams et al. (2019) found that management is often the better first investment relative to the expansion of a protected area. A substantial body of literature has looked at management effectiveness in protected areas using a variety of methodologies that collectively is referred to as Protected Areas Management Effectiveness (PAME) assessments (Geldmann et al., 2015; Leverington et al., 2010; Stoll-Kleemann, 2010).
PAME assessments look at conditions and processes in six management areas—context, planning, inputs, processes, outputs, and outcomes (Hockings, 2003; Hockings et al., 2006). However, only about 20% of all protected areas have conducted a PAME assessment (UN EP, 2018). Incentives for conducting PAME and other sustainable forest management assessments largely stem from donor institution requirements (e.g., World Bank, NGOs), international policy requirements (e.g., IUCN certification of protected and conserved areas), national demonstration of progress toward international goals (e.g., Aichi Target 11), and measurement, verification, and reporting around international programs (e.g., REDD+ readiness). Of these assessed protected areas, about 40% have major management deficiencies and another 32% have only a basic level of management effectiveness; sound management practices were apparent in only 22% of the assessed protected areas (Leverington et al., 2010). Key management strengths largely fell in the planning area (e.g., legal establishment, boundary marking, design), while weaknesses were typically in the areas of inputs (e.g., funding, equipment, infrastructure), process (e.g., research, monitoring and evaluation, public programs), and output/outcome measures (Leverington et al., 2010). However, Geldmann et al. (2015) found that some protected areas show management improvement over time. In part, such improvements may be due to the process of evaluation leading to managers sharing information, and in part, to redirecting resources to the most serious issues (Geldmann et al., 2015). At the same time, studies have criticized assessment methods for focusing on inputs and outputs, rather than outcomes, and raised concerns about the accuracy of assessment methods (Cook et al., 2014; Geldmann et al., 2015; Mascia et al., 2014b; UN EP, 2018; Visconti et al., 2019).
Sustainable Forest Management Policies
Sustainable forest management (SFM) is an evolving and dynamic concept of producing sustainable economic, social, and environmental benefits from forests (MacDicken et al., 2015; Siry et al., 2003; FAO, 2000; FAO, 2018d; Wang, 2004). But SFM also can be context specific so no single definition fits all cases (Wang, 2004).
Despite these definitional issues, over 90% of the world’s forests are deemed to be covered by policies and legislation supporting SFM, 78% are subject to periodic national assessments, and 50% are subject to a forest management plan (FAO, 2015a). The literature largely focuses on two groups of SFM policies—policies addressing timber harvesting practices and policies for assessing and monitoring forest management practices.
Policies surrounding timber-harvesting practices include the use of forest concessions to prescribe certain logging and environmental practices, and regulation to prevent illegal logging (Asner et al., 2009; Bicknell et al., 2014; Putz et al., 2008; Putz et al., 2000). Forest concessions—a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber from public forests—are important policy instruments for imposing forest management requirements in developing countries. Through forest concessions, SFM practices can be required as a condition of timber extraction, such as maintaining certain levels of forest cover and biodiversity, protecting watersheds and habitat, and controlling erosion, as well as specifying logging practices that support these objectives such as reduced-impact logging and species and size-class harvesting restrictions (Gray, 2002; Pfaff et al., 2010). Contract design, performance monitoring, and enforcement, however, are noted problems.
Timber harvesting also may occur illegally (i.e., in unauthorized ways or in violation of established laws and regulations) (Pfaff et al., 2010). Estimates of 10% to 15% of global timber supplies and up to 50% in some areas are illegally harvested, mostly from Indonesia, Brazil, and Malaysia (Hoare, 2015; IPBES, 2019; Lawson & MacFaul, 2010). Policy strategies to combat illegal logging include strengthening enforcement and governance of forest laws, improving land tenure and ownership rights, streamlining the legal framework, providing payments for ecosystem services, establishing international agreements to control trade in wood products, and enacting national measures to control imports of illegal forest products (Contreras-Hermosilla et al., 2007). National measures, for example, may prohibit the importation of illegal timber products (e.g., U.S. Lacey Act, EU Timber Regulation, Australian Illegal Logging Prohibition Act), or impose bans on importing wood products without appropriate certification (Bugayong, 2006; Busch et al., 2015; FAO, 2001a).
Illegal logging policies appear to have had a positive effect (Barber & Canby, 2018), with illegal logging peaking between 2006 and 2008 and then declining. From 2006 to 2013, for example, volumes of illegal wood-based products imported by the United States fell by one-third, and by half in France, The Netherlands, and the United Kingdom. Such policies also have benefited from improved detection and tracing technologies, allowing more accurate determination of timber sources (Kaldjian et al., 2015; Mason & Parker-Forney, 2018; Nogueron et al., 2016).
Policies for assessing and monitoring forest management practices generally take the form of criteria and indicator (C&I) frameworks or voluntary SFM certification standards. National governments develop and use C&I frameworks to monitor forest conditions and trends, and as a guide to help define, monitor, assess, and demonstrate progress towards sustainable forest management (Baelemans & Muys, 1998; Brand, 1997; Castaneda, 2000; Lammerts van Bueren & Blom, 1997). About 77% of global forest area is subject to C&I national reporting (FAO, 2015a).
SFM certification standards are voluntary standards developed by nongovernmental standards bodies and used by third parties for point-in-time evaluations of commercial companies’ forest management practices and performance in a given forest area. Two types of certification standards exist. One involves certification of forest management conditions and the other, known as chain-of-custody certification (CoC), tracks and certifies wood and paper products from a certified forest through processing to the point of sale. Certification standards bodies exist at both national and international levels; the two largest are the Programme for Endorsement of Forest Certification (PEFC) and the Forest Stewardship Council (FSC) (Clark & Kozar, 2011; Overdevest, 2009; Pasiecznik & Savenija, 2017). Certification standards typically cover such issues as legal compliance, community relations and indigenous peoples’ rights, management planning, implementation of management activities, and monitoring and assessment. Standards also look at the maintenance of ecosystem services, environmental impacts, carbon sequestration, habitat conservation, biodiversity maintenance, and cultural values (FSC, 2015; PEFC, 2018).
Forest product companies seek to obtain certifications in order to comply with timber harvesting concessions, access markets or to capture market premiums for certified forest products (Bowler et al., 2017; MacDicken et al., 2015; Siry et al., 2003; Tuppura et al., 2016). In turn, markets for certified products are largely driven by demand-oriented deforestation policies, such as building standards, private and public procurement policies, differential import tariffs, and import licensing of certified products under voluntary partnership agreements (Brack, 2014; Brack & Bailey, 2013; European Commission, 2013; van den Berg et al., 2013). Price premiums for certified forest product are estimated at 1.5% to 6.3% in developed markets such as Europe, the United States, and Canada (Yuan & Eastin, 2007), but the market for certified products is small, representing only about 5% to 10% of the overall wood products market (Ozinga, 2004).
Determining the overall impact of SFM policies is challenging (van der Ven & Cashore, 2018). First, determining “good” SFM practices is subjective given different views of SFM, cultural influences, and political conflicts (Ozinga, 2004). Second, there is significant variation in the uptake of certification schemes between countries (e.g., the low uptake in developing countries due to concerns about certification costs, non-tariff trade implications, and unpredictable business environments (Carlsen et al., 2012; Marx & Cuypers, 2010; Ramesteiner & Simula, 2003; Van Kooten et al., 2005). Third, determining impacts faces research design challenges, insufficient data, changing certification standards, and difficulties determining appropriate outcome variables (van der Ven & Cashore, 2018).
In addition, only a limited forest area globally demonstrate application of SFM attributes through C&I frameworks or certification standards. For example, about 2.1 billion ha of forest area (about 53% of all forests) is subject to a management plan (FAO, 2015a) and only 1.1 billion ha of forests (about 28% of all forests) are covered by all four SFM indicators tracked by the FAO—legal frameworks, national reporting, management plans, and stakeholder involvement (MacDicken et al., 2015). Further, these areas are skewed toward certain regions. Forest areas with management plans, for instance, are in the boreal and temperate zones, with just over 20% in the tropical zone (Figure 6) (FAO, 2015a), and most certified areas are located in developed countries in the northern hemisphere that are already being intensely managed. Thus C&I frameworks and certification schemes may not have a substantial effect on management practices in natural forests, especially tropical forests, where deforestation is of most concern.
Finally, C&I frameworks and certification schemes largely focus on inputs, processes, and outputs. Only a limited number of studies looked at outcomes (Ozinga, 2004). Generally, these studies found mixed and inconclusive evidence of positive social and environmental impacts (van der Ven & Cashore, 2018), and limited effectiveness on halting deforestation (Marx & Cuypers, 2010). Blackman et al. (2018), for instance, found no evidence from panel data on FSC certification in Mexico that certification reduced deforestation, findings that comport with other quantitative studies (Barbosa de Lima et al., 2009; Norden et al., 2015; Panlasigui et al., 2015). Miteva et al. (2015), however, found that FSC certification reduced deforestation in Indonesian concessions by 5% over an eight-year period.
Ecosystem Services Policies
Payments for ecosystem services (PES) are policies that seek ways to increase incentives to maintain forest ecosystems, employ (or avoid) certain practices, or offset certain impacts (Pirard, 2012) through better valuation of, and compensation for, ecosystem services obtained from various biomes, including forests (UN EP, 2011). PES policies can be structured in a variety of ways using cash or in-kind compensation (e.g., technical assistance, micro-credits, training, or materials), and targeting different types of ecosystem services or service bundles, such as biodiversity, water, and carbon sequestration (Grima et al., 2016; Salzman et al., 2018). For example, the UN REDD+ policy (Reducing Emissions from Deforestation and Forest Degradation) is intended to incentivize developing countries to reduce carbon emissions from deforestation and forest degradation. The design and implementation of REDD+ is closely studied and debated (see Angelsen, 2009; Corbera, 2012; Corbera & Schroeder, 2011; Duchelle et al., 2018; Gibbs et al., 2007; Goetz et al., 2015; Kanninen et al., 2007; Kanowski et al., 2011; Lubowski & Rose, 2013; Myers, 2007; Obersteiner et al., 2009; Rakatama et al., 2017; Visseren-Hamakers et al., 2012).
Policy and program design are important for the effectiveness of PES (Alix-Garcia & Wolff, 2014; Engel, 2016; Engel et al., 2008; Pattanayak et al., 2010; Vatn, 2010; Wunder, 2006; Wunder et al., 2008; Wunder et al., 2018). Policy variables such as enrollment, conditionality (degree, activity versus outcome based, unit of control), additionality, land use–service linkage, payment details (e.g., amount, mode, timing, differentiation, duration), permanence, and targeting (expected ecosystem benefits and provision costs) are important to policy outcomes (Engel, 2016; Wunder et al., 2008). Other design considerations include the amount of information available, provision of technical assistance, and land tenure conditions.
PES policies ultimately rest on valuing different ecosystem services (FAO, 2018b), and differences in policy approach stem largely from differences in how to calculate and allocate ecosystem value (Parks & Gowdy, 2013). Valuation approaches can be categorized as biophysical, sociocultural, and monetary (economic) (Bishop, 1998; de Groot et al., 2010; Farber et al., 2002; Haines-Young & Potschin, 2010; Martin-Lopez et al., 2014; Pearce, 2001). Evaluating trade-offs between the three valuation approaches, however, raises a number of challenges (Chen et al., 2016; Fischer et al., 2008; Martin-Lopez et al., 2014).
Biophysical and socio-cultural approaches value the capacity of ecosystems to supply provisioning, regulating, and cultural services (Bagstad et al., 2013; Bakhtiari et al., 2014; Bennett et al., 2015; Carrasco et al., 2014; de Groot et al., 2002; Dunford et al., 2018; Neugarten et al., 2018). But a number of these services (e.g., biodiversity, ecological stability, climate) are hard to “value” in relative to other options despite their importance to broader economic and societal health.
Monetary approaches value the contribution of provisioning services to human well-being using economic theory. Neoclassical economic theory argues that valuation and allocation of ecosystem services should be addressed largely through market mechanisms with the goal of achieving greater economic efficiency in the allocation of ecosystem services (Engel et al., 2008; Gomez-Baggethun et al., 2010; Wunder, 2005, 2015; Wunder et al., 2008, 2018). Others argue that neoclassical market-based PES policies apply to only a narrow set of circumstances and services (Farley & Constanza, 2010; Kemkes et al., 2010; Muradian et al., 2010; Spash, 2011; Temel et al., 2018; Vatn & Bromley, 1994). Farley and Constanza (2010) pointed out that “most ecosystem services have physical characteristics that make them ill-suited for market provision,” and that ecosystem services need to be carefully distinguished based on characteristics of rival or non-rival, excludable or non-excludable, and scarce or abundant. Distinguishing ecosystems and ecosystem services in this way allows a better determination of those most amendable to market-based instruments (e.g., rival, excludable, and scarce) (Kemkes et al., 2010).
Do PES policies work? There are only a limited number of evaluations (albeit growing) that assess the outcomes of PES schemes along the dimensions of ecosystem service provision, economic efficiency, and social welfare (Andersson et al., 2018; Borner et al., 2017; Grima et al., 2016; Jayachandran et al., 2017; Samii et al., 2014). Conducting such evaluations is hampered in part by the lack rigorous evaluation criteria in the original policy design (a problem common to a number of deforestation-related policy designs), including a lack of baseline data and control areas to evaluate counterfactuals. As a result, many evaluations rely on case studies with a potential for selection bias (Salzman et al., 2018).
Designing and applying PES policies also faces a number of challenges, including data and technical barriers to measuring ecosystem service values accurately; lack of required expertise; methodological challenges; the practical application of valuation techniques to land management decisions; lack of necessary preconditions; and the interaction of PES policies within existing policy mixes (Carrasco et al., 2016; Daily et al., 2009; De Groot et al., 2010; Engel, 2016; Ruckelshaus et al., 2013). Some studies of PES also note concerns around adverse or unintended outcomes. For example, PES schemes seeking to compensate for carbon sequestration may unintentionally reduce biodiversity (Venter et al., 2009) and payments may be structured in a way that is counterproductive relative to the time profile of the recipient’s opportunity costs, reducing sellers’ incentives to participate (Jayachandran, 2013). Finally, responses to financial incentives also may deviate from those predicted by rational choice models (Anderson, 2006; Engel, 2016).
Deforestation-Free Supply Chain Policies
Another focus of policies to address deforestation are corporate supply chains (Varsei, 2016). These are typically voluntary and seek to ensure that the supply chains for forest and agricultural products are obtained from sustainable sources with no embedded deforestation component (Brack et al., 2016; Lambin et al., 2018). Policies for zero-deforestation supply chains focus primarily on high forest-risk commodities, such as palm oil, soy, beef, and wood products (Nepstad et al., 2014). Policy instruments include corporate “zero-deforestation” commitments in sourcing raw materials (Donofrio et al., 2017; Jopke & Schoneveld, 2018; Newton & Benzeev, 2018), sustainable supply chain standards and certification frameworks (Taylor & Streck, 2018), and information disclosure practices such as corporate sustainability reporting and eco-labeling (van der Ven et al., 2018; Varangis et al., 1993).
Corporate commitments come in several different flavors, including collective commitments organized by various stakeholder groups (e.g., New York Declaration on Forests, Tropical Forest Alliance, Consumer Goods Forum); commodity sector standards (e.g., Roundtable on Responsible Soy, ProTerra-certified soy, Roundtable on Sustainable Palm Oil); and individual company commitments. Over 440 commercial entities have made public no-deforestation commitments, mostly in the palm oil and timber sectors and to a lesser extent in the soy and beef sectors (Donofrio et al., 2017).
Standards and certification frameworks are the instruments for implementing commitments (apart from moratoria). These standards and frameworks (see section on Sustainable Forest Management Policies) provide for sourcing from sustainably certified producers, a chain-of-custody assurance, and traceability through the entire supply chain. The major drawback is that the coverage of certification standards is limited, largely to the palm oil and timber sectors, with lesser coverage of the soy sector, and limited use in the beef sector. For example, the CDP (2017) reports that manufacturers and retailers able to trace some portion of their forest-risk commodities back to the point of origin were 50% or more in the timber and palm oil sectors but only 29% and 12% in the beef and soy sectors, respectively. Even where producer certification standards are applied and followed, results can be mixed for a number of reasons (DeFries et al., 2017).
Finally, global supply chains are complex and opaque, hampering efforts to ensure sustainable sourcing (Gardner et al., 2019). Increased transparency, in the form of corporate information disclosures (D’Amato et al., 2015), certification and chain-of-custody processes, and eco-labeling (van der Ven et al., 2018; Varangis et al., 1993), has the potential to reduce information asymmetries around the origin of commodities and lower costs of monitoring compliance. Several initiatives aimed at increasing transparency include the Universal Mill List for palm oil, the Sustainable Palm Oil Transparency Toolkit (SPOTT), and the Transparency for Sustainable Economies (TRASE) (Taylor & Streck, 2018).
Because they are voluntary, corporate supply chain efforts vary widely in terms of content, making evaluation of their effectiveness in mitigating deforestation difficult (Garrett et al., 2019). Zero-deforestation supply chain commitments, however, may have a limited impact on deforestation due to their small coverage of the global market for the targeted commodities, lack of biome-wide implementation, use of net deforestation targets rather than gross, and lack of specific and immediate implementation dates (Collins, 2019; Garrett et al., 2019; Taylor & Streck, 2018). To be effective, individual commitments need to specify gross deforestation reduction targets and definitive implementation dates, provide clear definitions of key terms, target high-deforestation vulnerable areas, establish implementation and compliance mechanisms, and cover more deforestation-risk commodities (Garrett et al., 2019).
Population growth drives agricultural demand, and demand has driven agricultural expansion. Agricultural expansion is one of the leading causes of deforestation, resulting in forest conversion to pasture, cropland, and tree plantations for products such as beef, soy, palm oil, and cocoa, and pulp (Benhin, 2006; Defries et al., 2010; Graesser et al., 2015; Hosonuma et al. 2012; Laurance et al., 2014; Tilman, 1999). A key policy concern, therefore, is how to preserve forest ecosystems and the services they provide while enhancing food production to meet growing global food demands (Angelsen, 2010; Angelsen & Kaimowitz, 2001; Borlaug, 2007; Lambin & Meyfroidt, 2011; Runyan & Stehm, 2019; Searchinger et al., 2019).
Policies in this area focus on two basic parts of the food-forest equation—agricultural production and food demand. Policies addressing agricultural production focus on the land sharing–land sparing trade-off. Land sharing seeks to integrate the objectives of increased food production and biodiversity conservation on the same land; land sparing seeks to protect natural habitats while implementing sustainable agricultural intensification on existing farmland (i.e., increasing agricultural production per unit land area, per unit fertilizer input, and per unit water consumed in a sustainable manner) (Foley et al., 2005; Garnett et al., 2013; Godfray & Garnett, 2014; Pretty & Bharucha, 2014; Tilman et al., 2002). Phalan et al. (2011) concluded that land sparing is a more promising strategy for minimizing the negative impacts of agriculture, maintaining higher levels of biodiversity, and possibly mitigating greenhouse gas emissions. For farmers, sustainable intensification often requires tailored credit and access to financing for intensification measures, technical and administrative support, and land tenure reform in order to provide the necessary incentives. Production-oriented policies, therefore, typically target adjustments to agricultural supports, technical assistance, inputs (fertilizers, integrated pest management, and management), land ownership, and credit in a manner that discourages agricultural expansion and encourages agricultural sustainability (Anderson et al., 2006). Such policies also may promote the use of genetically modified crops and second-generation biofuels, as well as restoration of degraded land, and preservation of prime agricultural land as other policy targets for sustainability. Policies also need to address ways to increase agricultural productivity through investment in R&D to stimulate technological, biological, and management innovations (Ewers et al., 2009).
Demand-oriented policies target food demand factors that might lessen pressures on agricultural production, such as at changes in diets (more vegetarian), reduced food waste, and increased use of synthetic food, feed and fibers (Lambin & Meyfroidt, 2011; Runyan & Stehm, 2019).
Crafting sustainable agricultural policies, however, faces a number of challenges. First, the limited understanding of the different drivers and causal relationships between expansion and deforestation (see section on the Drivers of Deforestation) hampers policies promoting sustainable agricultural intensification. Byerlee et al. (2014), for example, find that differences between technology-induced and market-induced drivers of intensification may affect expansion differently, with the latter more likely to lead to continued deforestation; location of agricultural intensification (at the forest frontier or away from it) also affected continued deforestation. Another complex set of causal relationships is that between biodiversity and other environmental outcomes, which also must be carefully considered in policy design (Carrasco et al., 2016; Donald et al., 2001; Flynn et al., 2009; Kremen et al., 2002; Matson et al., 1997).Finally, policies around sustainable agricultural intensification may not be effective in isolation. Improving forest and land governance, market certification of sustainable agricultural products along with other sustainability standards, and payments for ecosystem services are important areas for complementary policy intervention (Byerlee et al., 2014; Tayleur et al., 2017; Vorlaufer et al., 2017).
Forest coverage continues to shrink globally—down from 4.1 billion hectares in 2000 (or 31.2% of total land area) to about 4 billion hectares (30.7% of total land area) in 2015. The rate of forest loss, however, has been cut by 50% since the 1990s (Keenan et al., 2015) and 25% since 2000–2005 (UN, 2018; FAO, 2015a). How much of this slowing might be attributed to deforestation-related policies and how much to other non-policy factors is difficult to quantify. Policies regarding protected areas and illegal logging appear to have a positive effect in reducing deforestation, while payments for ecosystem services and sustainable agricultural policies tend to be more mixed. Sustainable forest and supply chain management policies have a more limited impact, largely because of their limited coverage.
Establishing a link between policies that address deforestation and trends in forest cover is difficult for several reasons, including variation among sources and methods of various data sets (Achard et al., 2002; Keenan et al., 2015; Puyavaud, 2003), study design (Joppa & Pfaff, 2009; Nelson & Chomitz, 2009), and different framings of the causal relationships driving deforestation. More fundamentally, socio-ecological systems, such as forests, behave in a complex, dynamic, and non-linear manner (Levin et al., 2013). As a result, policies attempting to address deforestation face the “wicked problem” challenge where a lack of clear definitions, elusive solutions, multiple interpretations, and seemingly intractable problems apply, making policy effectiveness a slippery concept (DeFries & Nagendra, 2017; Schindler & Hilborn, 2015).
Policy effectiveness, therefore, rests on a number of design and execution factors. Policy design is especially important in assuring policy effectiveness. Peters (2018) describes policy design as an attempt “to integrate understandings of the problems being addressed with some ideas of the instruments used for intervention, and the values that are being sought through the policy.” Effective policy design requires (1) a viable causal model of the drivers of deforestation and the appropriate formulation of the policy problem(s) around clear policy goals, targets, and values; (2) the selection of appropriate policy instruments; (3) the establishment of an effective program infrastructure (governance, institutions, management procedures, monitoring protocols, and compliance/enforcement mechanisms); and (4) evaluation of policy outcomes to inform policy adjustment (Bali et al., 2019; Bemelmans-Videc et al., 2003; Howlett, 2005, 2014; Howlett & Lejano, 2013; Howlett & Rayner, 2007; Peters, 2018; and Peters & Reva, 2017; UN EP, 2019). Because of the wicked problem nature of socio-ecological systems, designing and executing effective policies also requires multisector decision making, decision making across administrative boundaries, adaptive management, balancing views of diverse stakeholders, and incremental, partial solutions (DeFries & Nagendra, 2017)
Causation and Problem Framing
Framing deforestation problems and causation is rife with difficulties and challenges due in large part to the non-linear dynamics and complexities involved with socio-ecological systems (see Peters, 2018, for an excellent discussion of defining policy problems and DeFries & Nagendra, 2017, for a discussion of wicked problems in ecosystems). Traditionally, economic theory and analysis is one of the main tools used for identifying underlying drivers, modeling causation, and framing policy problems (Kaimowitz & Angelsen, 1998). Three general economic models appear in the policy literature—the neoclassical economic model and its subfield of environmental economics; the ecological economic model, and the forest transition model.
The neoclassical model views deforestation and related land use changes as driven by land rents (i.e., land will be allocated to the use with the highest economic profit) with deforestation occurring because it is a profitable alternative to forest maintenance; it emphasizes the efficient allocation of resources in the context of market mechanisms, property rights, and consumer utility (Angelsen, 2009; Angelsen & Kaimowitz, 1999; Busch & Ferretti-Gallon, 2017; Carrasco et al., 2016; Kaimowitz et al., 1998). While studies have shown that policies focused on these factors are effective in reducing deforestation in certain situations (Busch & Ferretti-Gallon, 2017), neoclassical methods also are subject to policy, institutional, and market failures that distort economic incentives leading to biases in land use (Barbier & Tesfaw, 2015; Barbier et al., 2010; Kapp, 1971).
In contrast, ecological economics embeds neoclassical concepts of economic efficiency in a larger context of ecological limits, sustainability, and equitable resource distribution (Costanza et al., 1991; Daly & Farley, 2011; Farley, 2012). It diverges from neoclassical economics in its view on what is scarce, what are the appropriate means of allocating resources, and how competing ends are ranked (Costanza & Daly, 1987; Daly & Farley, 2011; Fischer et al., 2008; Gowdy & Erikson, 2005; Illge & Schwarze, 2006; Pearce, 1987, 2001; van den Bergh, 2000). Ecological economics, however, faces challenges and controversies around (1) the subjective nature of value, especially in relation to non-material ecosystem benefits; (2) linkages between ecosystem services and society at different scales; (3) differences in the essential nature of ecosystem services; (4) the potential existence of ecological thresholds and the limits they impose on marginal analysis; and (5) whether ecosystem services ultimately impose limits on economic growth (Anton et al., 2010; Barbier, 2011; Carrasco et al., 2014; Daly, 2007; de Groot et al., 2010; de Groot et al., 2012; Farley, 2012; Farley, 2008; Georgescu-Roegen, 1971; Martin-Lopez et al., 2014; Small et al., 2017; Turner et al., 2003).
Forest transition theory views deforestation (and reforestation) as driven by longer-term socioeconomic and socio-ecological trends, especially economic development and forest scarcity (Barbier & Tesfaw, 2015; Barbier et al., 2010; Calaboni et al., 2018; Cuaresma et al., 2017; Gupta et al., 2012; Li et al., 2017; Mather 1992; Mather & Needle, 1998; Meyfroidt & Lambin, 2011; Meyfroidt et al., 2009, 2010; Rudel et al., 2005, 2010; and Wilson et al., 2017). In forest transition theory, economic development leads to industrialization and urbanization; changes in agricultural productivity, population density, and demand for forest and agricultural products; and increased trade and market access (Robalino & Herrera, 2010; Tsurumi & Managi, 2014). Meyfroidt and Lambin (2011) posited that these trends initially lead to agricultural expansion and forest depletion followed by growing forest scarcity and market incentives to manage forest resources more effectively. However, these trends may play out differently in different social and environmental contexts. Magliocca et al. (2019), for example, conceptualized casual pathways of both direct and indirect land use change stemming from agricultural commodity production and large-scale land acquisitions that depend on the rate of land use change, types of crops, nature of land acquisition, and cascading effects.
Much of the deforestation-related policy literature is focused implicitly or explicitly on selection, use, and evaluation of various policy instruments, such as subsidies, taxes, direct regulation, information and legal instruments, and property rights (Bali et al., 2019; Coria & Sterner, 2011; Howlett, 2005; Sterner & Coria, 2012). Selection of appropriate instruments involves considerations of monitoring and enforcement costs, administrative costs, distribution of costs, political feasibility, and other factors. Peters (2018), however, cautions that policy instruments are not the sole consideration nor can they be seen in a purely technical light. Instead, selection of policy instruments requires judgment, experimentation, and a systemic approach within a broader policy design context (Peters, 2018).
Policies are implemented and administered through programs—the mixture of organizational, managerial, financial, and legal resources and activities used to implement and administer a policy or a portion of a policy (Folke et al., 2005; Peters, 2018). Governance is a key factor in effective programs; it has been shown to affect positively deforestation outcomes (Kaufmann et al., 2009; Umemiya et al., 2010).
Governance is a broad term that refers to the formal and informal rules, organizational structures (including formal and informal institutions), and processes through which public and private actors articulate their interests, negotiate, and make and implement decisions about the management, use, and conservation of forest resources (Rametsteiner, 2009; FAO, 2019).
Program administration and governance can take a number of forms. Theories about the tragedy of the commons (overuse) have typically led to the dichotomy of either top-down governmental (public) administration or private ownership of natural resources. In the 1980s and 1990s, research began to validate the efficacy of collective governance schemes for pools of natural resources held in common by communities and groups. The work of Elinor Ostrom and colleagues laid out a number of case studies and design principles for community-based natural resource management, including forests, and showed their viability in a number of contexts (Cox et al., 2010; Ostrom, 1990; Ostrom, 2009; Wilson et al., 2013).
Designing governance arrangements involves factors such as accountability, effectiveness, efficiency, fairness/equity, participation, and transparency (Kaufmann et al., 2007; FAO, 2011a) around decision-making processes, stakeholder involvement, political processes, power relationships, and lobbying, among other things (Ostrom et al., 1994). Ostrom (1990) provided a set of eight design principles for common-pool resource governance arrangements, and Wilson et al. (2013) generalize these principles to a wider range of governance arrangement.
Policy evaluation, another key aspect of effective policy design, involves both a systematic assessment of results as well as an assessment of the extent to which the policy caused those results. Few studies, however, have looked systematically at evaluating policy performance against intended outcomes (e.g., reduced deforestation, sustainability, biodiversity, carbon sequestration, improved rural livelihoods, etc.). Instead, most studies have focused on policy and program inputs, processes, and outputs.
An effective policy evaluation process has a number of features including (1) a governing evaluation policy (Trochim, 2009), (2) an appropriate evaluation design (Baylis et al., 2016; Ferraro, 2009; Ferraro & Hanauer, 2014), (3) clear evaluation measures, and (4) accountability and organizational learning mechanisms (Gordillo & Andersson, 2004). Policies often have multiple, sometimes conflicting, objectives that make evaluation of outcomes challenging, especially when objectives may be a mix of economic, sociocultural, and ecosystem goals. Evaluation methods need to consider how a policy’s contributions to each objective is to be weighted and aggregated into an overall determination of effectiveness. Given these challenges, impact evaluations are only slowly being applied to deforestation and other conservation policies (Ferraro & Pattanayak, 2006).
Assessing policy effectiveness is also difficult due to the distorting and confounding effects of policy leakage (Atmadja & Verchot, 2012; Aukland et al., 2003; Chomitz, 2002; Delacote & Angelsen, 2015; Ewers & Rodrigues, 2008; Fuller et al., 2019; Gan & McCarl, 2007; Henders & Ostwald, 2014; Lambin & Meyfroidt, 2011; Murray, 2008; Sohngen et al., 1999). For instance, the estimates of policy leakage—where deforestation or degradation is shifted to another location that is not subject to the policy—range from a 5% to 65% shift of logging from protected areas to areas with no protection policy (Lambin & Meyfroidt, 2011; Meyfroidt et al., 2010; Meyfroidt et al., 2009). Demand for forest and agricultural products coupled with economic globalization are the primary drivers deforestation displacement to countries willing to absorb these demands (Lambin & Meyfroidt, 2011). Policy leakage also is a problem with illegal logging policies. Reductions in illegal wood products in the United States and Europe have been offset by increased importation of illegal timber by China and other Asian countries with no import controls (Hoare, 2015; Lawson & MacFaul, 2010). Chomitz (2002), however, suggests that complementary policy mixes, such as agricultural intensification policies applied in conjunction with forest protection policies, could neutralize some leakage, and Aukland et al. (2003) echoed this point by finding that “certain avoided deforestation projects appear to have a low risk of primary leakage as long as alternative livelihood options are implemented and adopted” (e.g., addressing opportunity costs and financial returns caused by the market effects of protection policies). Atmadja and Verchot (2012), however, argue that further research is required on leakage—how it develops in response to policy initiatives, factors influencing leakage, and “practical and accurate working definitions, typologies, and characterizations of leakage” in order to account for leakage and design better policies.
This review assessed the historical and modern drivers of deforestation as well as the numerous ways in which deforestation affects environmental, social, and economic processes. A range of policy responses to deforestation also is reviewed including the challenges to policy effectiveness.
From this review, several aspects arising from the literature are worth highlighting. First, how future drivers of deforestation might differ from historical drivers, as well as the interrelationship and compounding effects among drivers, is important to better understand areas that may susceptible to future loss of forest and how best to manage this change.
Second, considering the difficulties associated with measuring forest biomass, technological progress in this area is important to ensuring more timely and accurate quantification of deforestation rates and evaluating where change is occurring. Understanding these trends is important because the long-term loss of forest resources affects both the functioning of ecosystems as well as the societies, and human livelihoods of those dependent upon them.
Third, given competing land use demands, how to best manage forest resources in order to preserve their unique aspects and qualities for future generations is a challenge that requires an integrated and concentrated effort by the scientific community and civil society across ecological, social, and economic spheres. Important aspects of this effort will involve dialogue and research on the valuation of ecosystem services and the integration of valuation methods across biophysical, sociocultural, and economic domains; such a valuation dialogue will be important in assessing actual and potential impacts as well as trade-offs among policy choices (Fischer et al., 2008; Chen et al., 2016; Turner et al., 2003).
Finally, environmental policy has traditionally been associated with governmental policies and regulations. While strong government policies still anchor much of environmental policy, issues of deforestation are too complex, pervasive, and context/scale dependent to be addressed by government policies alone (Paddock, 2016). In the case of deforestation, a range of actors (governments, corporations, civil society, communities) and a mix of policy approaches are required to structure, guide, and shape decisions toward those actions that are most likely to reduce deforestation and sustain forests (IPCC, 2019; Pfaff et al., 2010; Searchinger et al., 2019). Recognition of the policy challenges arising from the wicked problem nature of the forest socio-ecological system is also necessary (DeFries & Nagendra, 2017). This will require a policy process involving greater participation and coordination across stakeholders, better coherence and integration of policies within a larger policy mix, and greater agility and adaptability in the application of policies in order to achieve desired results (Armitage et al., 2009; Schindler & Hilborn, 2015). Evaluating policy effectiveness will be an important feedback component to adjust and fine-tune policies as part of an ongoing, dynamic, iterative process of feedback and change.
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