3.5.2. Carbon Dioxide Emissions from Anthropogenic Land-Use Change

A variety of changes in land use can result in anthropogenic CO₂ emission or absorption. These changes most obviously include permanent deforestation or afforestation. However, many changes in land-management practices also contribute to CO₂ fluxes because of changes in standing biomass densities or in soil carbon. Empirical studies of such CO₂ fluxes are rare, so that information on current emissions is very poor. Whereas comprehensive information exists for forests globally, only a few countries have detailed information on forest and agricultural land-management practices. Hence, global estimates of CO₂ emissions from anthropogenic land-use change, including those in the SRES, tend to be based entirely on net deforestation-afforestation and on average figures for carbon storage per hectare in forests.

Emissions of CO₂ from deforestation arise mostly from the burning of trees and other vegetation in tropical forests cleared for agricultural use. These emissions also stem from the decomposition of trees harvested for lumber, the burning of wood for fuel, and soil respiration. If harvested wood is replaced by new seedlings, it is normally assumed that the amount of CO₂ released by decomposition or burning is compensated by the CO₂ taken up during growth of the seedlings and, therefore, that the net emissions of harvested trees is zero. Where net afforestation occurs, net emissions are taken to be negative (i.e. afforestation acts as a sink).

As a consequence of inconsistencies in base-year estimates for net changes in forest biomass, emission estimates are normalized relative to their 1990 value before comparison with each other (Figure 3-14). Figure 3-14 also clearly depicts the relative change of emissions with time (Alcamo, et al., 1995; Nakicenovic et al., 1998b).

The scenarios of CO₂ emissions from land-use change have quite different temporal paths, and show their widest range before the middle of the 21^st century (Figure 3-14). Nearly all the scenarios then converge to very low emissions by the end of the century. At their widest point, the scenarios span about a factor of 14.

The different sets of scenarios can be grouped into roughly two typical paths: One set declines smoothly after 1990, while the other sharply increases for a few decades after 1990. After the middle of the 21st century most scenarios stabilize or continue to decline because either the driving forces of deforestation equilibrate or because forests are depleted. These processes are discussed further below. By 2100, CO₂ scenarios of deforestation range from zero to 30% of their base-year estimates.

As emissions from deforestation are more significant in developing regions than industrial regions, few scenarios of CO₂ from deforestation are available for industrial regions, so the focus here is on developing regions only. For Asia, emissions in all but one scenario (EPA-SCW, 7-1 in Table 3-7) steadily decline after the base year, and reach 25% or less of their 1990 value in 2100 (Figure 3-14b). For Africa, emissions first increase before eventually decreasing in 2100 to a small fraction of their 1990 value (Figure 3-14c). In this scenario, deforestation rates decline in Asia and Africa because of the depletion of their forests. For Latin America, the wide range of scenarios reflects the wide range of views about its future rates of deforestation (Figure 3-14d).

One of the main factors that affects estimates of CO₂ from deforestation is the assumed deforestation rate, which is estimated by a wide variety of methods. For example, the IS92a-IS92f scenarios assume that deforested area is proportional to population, with a time lag of 25 years, and that deforestation continues until 25 years after the population stabilizes or until forests are exhausted (Leggett et al., 1992). The IMAGE 2.1 emission estimates are based on computed changes in global land cover, which take into account changing demand for agricultural commodities. Trexler and Haugen (1995) compute the rate of tropical deforestation on a country-by- country basis, and include information from questionnaires. Jepma (1995) uses a combination of three models (a socio-economic model, a wood demand-supply model, and a land-use model), while Palo et al. (1997) correlate deforestation rates with income levels. To further confound this situation, the factors that affect deforestation vary greatly from place to place, and therefore need to be defined as locally as possible.

Figure 3-15 presents population assumptions of various deforestation scenarios, together with the assumed or implied deforestation rates on a per capita basis. The shape of the emission curves (Fig. 3-14) follows the shape of the deforestation rate curves, rather than that of the population assumptions. However, the range of emissions is much larger than the range of deforestation rates. Some conclusions can be drawn from these data:

• When given on a per capita basis, the range of deforestation rates is perhaps not as great as expected (about a factor of eight) considering the wide range of assumptions and methods used to estimate these rates.
• The temporal trend of emissions arises from the temporal trend of deforestation estimates.
• The range of emissions is strongly, but not only, influenced by the deforestation rates, and at its widest (a factor 14) can be better explained by the combined range of per capita deforestation rates, population, and carbon density.

The role that assumed carbon density of vegetation plays in the estimation of CO₂ emissions must be considered. To estimate CO₂ emissions, it is necessary to estimate the carbon density of vegetation that is burned and decayed when forests are cleared. However, there is wide disagreement in the literature as to typical values of carbon density because of both the many methods used to measure density and the wide variation in the mix of tropical forest vegetation.

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