IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group III: Mitigation of Climate Change Land use

Changes in land-use practices are regarded as an important component of long-term strategies to mitigate climate change. Modifications to land-use activities can reduce emissions of both CO2 and non-CO2 gases (CH4 and N2O), increase sequestration of atmospheric CO2 into plant biomass and soils, and produce biomass fuel substitutes for fossil fuels (see Chapters 4, 8, and 9 of this report for discussions of detailed land-related mitigation alternatives). Available information before the TAR suggested that land has the technical potential to sequester up to an additional 319 billion tonnes of CO2 (GtCO2) by 2050 in global forests alone (IPCC, 1996a; IPCC, 2000; IPCC, 2001a). In addition, current technologies are capable of substantially reducing CH4 and N2O emissions from agriculture (see Chapter 8). A number of global biomass energy potential assessments have also been conducted (see Berndes et al. 2003 for an overview).[16]

The explicit modelling of land-based climate change mitigation in long-term global scenarios is relatively new and rapidly developing. As a result, assessment of the long-term role of global land-based mitigation was not formally addressed by the Special Report on Land use, Land-use Change, and Forestry (IPCC, 2000) or the TAR. This section assesses the modelling of land in long-term climate stabilization and the relationship to detailed global forestry mitigation estimates from partial equilibrium sectoral models that model 100-year carbon price trajectories.

Development of, among other things, global sectoral land mitigation models (e.g. Sohngen and Sedjo, 2006), bottom-up agricultural mitigation costs for specific technologies (e.g. USEPA, 2006b), and biomass technical potential studies (e.g. Hoogwijk et al., 2005) has facilitated the formal incorpo-ration of land mitigation in long-term integrated assessment of climate change stabilization strategies. Hoogwijk et al. (2005), for example, estimated the potential of abandoned agricultural lands for providing biomass for primary energy demand and identified the technical biomass supply limits of this land type (e.g. under the SRES A2 scenario, abandoned agricultural lands could provide for 20% of 2001 total energy demand). Sands and Leimbach (2003) conducted one of the first studies to explicitly explore land-based mitigation in stabilization, suggesting that the total cost of stabilization could be reduced by including land strategies in the set of eligible mitigation options (energy crops in this case). The Energy Modelling Forum Study-21 (EMF-21; De la Chesnaye and Weyant, 2006) was the first coordinated stabilization modelling effort to include an explicit evaluation of the relative role of land in stabilization; however, only a few models participated. Building on their EMF-21 efforts, some modelling teams have also generated even more recent stabilization scenarios with revised land modelling. These studies are conspicuously different in the specifics of their modelling of land and land-based mitigation (Rose et al., 2007). Differences in the types of land considered, emissions sources, and mitigation alternatives and implementation imply different opportunities and opportunity costs for land-related mitigation; and, therefore, different outcomes.

Four of the modelling teams in the EMF-21 study directly explored the question of the cost-effectiveness of including land-based mitigation in stabilization solutions and found that including these options (both non-CO2 and CO2) provided greater flexibility and was cost-effective for stabilizing radiative forcing at 4.5 W/m2 (Kurosawa, 2006; Van Vuuren et al., 2006a; Rao and Riahi, 2006; Jakeman and Fisher, 2006). Jakeman and Fisher (2006), for example, found that including land-use change and forestry mitigation options reduced the emissions reduction burden on all other emissions sources such that the projected decline in global real GDP associated with achieving stabilization was reduced to 2.3% at 2050 (3.4 trillion US$), versus losses of around 7.1% (10.6 trillion US$) and 3.3% (4.9 trillion US$) for the CO2-only and multi-gas scenarios, respectively.[17] Unfortunately, none of the EMF-21 papers isolated the GDP effects associated with biomass fuel substitution or agricultural non-CO2 abatement. However, given agriculture’s small estimated share of total abatement (discussed below), the GDP savings associated with agricultural non-CO2 abatement could be expected to be modest overall, though potentially strategically significant to the dynamics of mitigation portfolios. Biomass, on the other hand, may have a substantial abatement role and therefore a large effect on the economic cost of stabilization. Notably, strategies for increasing cropland soil carbon have not been incorporated to date into this class of models (see Chapter 8 for an estimate of the short-term potential for enhancing agricultural soil carbon).

Figure 3.28 presents the projected mitigation from forestry, agriculture, and biomass for the EMF-21 4.5 W/m2 stabilization scenarios, as well as additional scenarios produced by the MESSAGE and IMAGE models – an approximate 3 W/m2 scenario from Rao and Riahi (2006), a 4.5 W/m2 scenario from Riahi et al. (2006), and approximately 4.5, 3.7, and 2.9 W/m2 scenarios from Van Vuuren et al. (2007) (see Rose et al., 2007, for a synthesis). While there are clearly different land-based mitigation pathways being taken across models for the same stabilization target, and across targets with the same model and assumptions, some general observations can be made. First, forestry, agriculture, and biomass are called upon to provide significant cost-effective mitigation contributions (Rose et al., 2007). In the short-term (2000–2030), forest, agriculture, and biomass together could account for cumulative abatement of 10–65 GtCO2-eq, with 15–60% of the total abatement considered by the available studies, and forest/agricultural non-CO2 abatement providing at least three quarters of total land abatement.[18] Over the entire century (2000–2100), cumulative land-based abatement of approximately 345–1260 GtCO2-eq is estimated to be cost-effective, accounting for 15–40% of total cumulative abatement. Forestry, agriculture, and biomass abatement levels are each projected to grow annually with relatively stable annual increases in agricultural mitigation and gradual deployment of biomass mitigation, which accelerates dramatically in the last half of the century to become the dominant land-mitigation strategy.

Figures 3.28 and 3.29 show that additional land-based abatement is expected to be cost-effective with tighter stabilization targets and/or higher baseline emissions (e.g. see the IMAGE 2.3 results for various stabilization targets and the MESSAGE 4.5 W/m2 stabilization results with B2 (EMF-21) and A2r baselines). Biomass is largely responsible for the additional abatement; however, agricultural and forestry abatement are also expected to increase. How they might increase is model and time dependent. In general, the overall mitigation role of agricultural abatement of rice methane, livestock methane, nitrous oxide (enteric and manure) and soil nitrous oxide is projected to be modest throughout the time horizon, with some suggestion of increased importance in early decades.

Figure 3.28

Figure 3.28: Cost-effective agriculture, forest, and commercial biomass annual greenhouse gas emissions abatement from baselines from various 2100 stabilization scenarios (note y-axes have different ranges).

Notes: The colour of the line indicates the 2100 stabilization target modelled: green < 3.25 W/m2 (< 420 CO2 concentration, < 510 CO2-eq concentration), pink 3.25–4 (42–490, 510–590), and dark blue 4–5 (490–570, 59–710). The IMAGE-EMF21 and IMAGE 2.3 forest results are net of deforestation carbon losses induced by bio-energy crop extensification. These carbon losses are accounted for under forestry by the other scenarios. The MESSAGE-EMF21 results are taken from the sensitivity analysis of Rao and Riahi (2006). The GTEM-EMF21 scenarios ran through 2050 and the GTEM agriculture mitigation results include fossil fuel emissions reductions in agriculture (5-7% of the annual agricultural abatement). Scenario references: IMAGE-EMF21 (Van Vuuren et al., 2006a); MESSAGE-EMF21 (Rao and Riahi, 2006); MESSAGE-A2r (Riahi et al., 2006); GRAPE-EMF21 (Kurosawa, 2006); GTEM-EMF21 (Jakeman and Fisher, 2006); and IMAGE 2.3 (Van Vuuren et al., 2007).

Source: Rose et al. (2007)

However, there are substantial uncertainties. There is little agreement about the magnitudes of abatement (Figures 3.28 and 3.29). The scenarios disagree about the role of agricultural strategies targeting CH4 versus N2O, as well as the timing and annual growth of forestry abatement, with some scenarios suggesting substantial early deployment of forest abatement, while others suggest gradual annual growth or increasing annual growth.

A number of the recent scenarios suggest that biomass energy alternatives could be essential for stabilization, especially as a mitigation strategy that combines the terrestrial sequestration mitigation benefits associated with bio-energy CO2 capture and storage (BECCS), where CO2 emissions are captured during biomass energy combustion for storage in geologic formations (e.g. Rao and Riahi, 2006; Riahi et al., 2006; Kurosawa, 2006; Van Vuuren et al., 2007; USCCSP, 2006). BECCS has also been suggested as a potential rapid-response prevention strategy for abrupt climate change. Across stabilization scenarios, absolute emissions reductions from biomass are projected to grow slowly in the first half of the century, and then rapidly in the second half, as new biomass processing and mitigation technologies become available. Figure 3.28 suggests biomass mitigation of up to 7 GtCO2/yr in 2050 and 27 GtCO2/yr in 2100, for cumulative abatement over the century of 115–749 GtCO2 (Figure 3.29). Figure 3.30 shows the amount of commercial biomass primary energy utilized in various stabilization scenarios. For example, in 2050, the additional biomass energy provides approximately 5–55 EJ for a 2100 stabilization target of 4–5 W/m2 and approximately 40–115 EJ for 3.25–4 W/m2, accounting for about 0–10 and 5–20% of 2050 total primary energy respectively (USCCSP, 2006; Rose et al., 2007). Over the century, the additional bio-energy accounts for 500–6,700 EJ for targets of 4–5 W/m2 and 6100–8000 EJ for targets of 3.25–4 W/m2 (1–9% and 9–13% of total primary energy, respectively).

Figure 3.30

Figure 3.30: Commercial biomass primary energy scenarios above baseline from various 2100 stabilization scenarios.

Notes: The colour of the line indicates the 2100 stabilization target modelled: green < 3.25 W/m2 (< 420 CO2 concentration, < 510 CO2-eq concentration), pink 3.25–4 (420–490, 510–590), dark blue 4–5 (490–570, 590–710), and light blue 5–6 (570–660, 710–860). Scenario references: IMAGE-EMF21 (Van Vuuren et al., 2006a); MESSAGE-EMF21 (Rao and Riahi, 2006); MESSAGE-A2r (Riahi et al., 2006); IMAGE 2.3 (Van Vuuren et al., 2007); IGSM and MiniCAM (USCCSP, 2006).

Source: Rose et al. (2007) ; USCCSP (2006)

More biomass energy is supplied with tighter stabilization targets, but how much is required for any particular target depends on the confluence of the many different modelling assumptions. Modelled demands for biomass include electric power and end-use sectors (transportation, buildings, industry, and non-energy uses). Current scenarios suggest that electric power is projected to dominate biomass demand in the initial decades and, in general, with less stringent stabilization targets. Later in the century (and for more stringent targets) transportation is projected to dominate biomass use. When biomass is combined with BECCS, biomass mitigation shifts to the power sector late in the century, to take advantage of the net negative emissions from the combined abatement option, such that BECCS could represent a signifant share of cumulative biomass abatement over the century (e.g. 30–50% of total biomass abatement from MESSAGE in Figure 3.29).

Figure 3.29

Figure 3.29: Cumulative cost-effective agricultural, forestry, and biomass abatement 2000–2100 from various 2100 stabilization scenarios.

Source: Rose et al. (2007)

To date, detailed analyses of large-scale biomass conversion with CO2 capture and storage is scarce. As a result, current integrated assessment BECCS scenarios are based on a limited and uncertain understanding of the technology. In general, further research is necessary to characterize biomass’ long-term mitigation potential, especially in terms of land area and water requirements, constraints, and opportunity costs, infrastructure possibilities, cost estimates (collection, transportation, and processing), conversion and end-use technologies, and ecosystem externalities. In particular, present studies are relatively poor in representing land competition with food supply and timber production, which has a significant influence on the economic potential of bio-energy crops (an exception is Sands and Leimbach, 2003).

Terrestrial mitigation projections are expected to be regionally unique, while still linked across time and space by changes in global physical and economic forces. For example, Rao and Riahi (2006) offer intuitive results on the potential role of agricultural methane and nitrous oxide mitigation across industrialized and developing country groups, finding that agriculture is expected to form a larger share of the developing countries’ total mitigation portfolio; and, developing countries are likely to provide the vast majority of global agricultural mitigation. Some aggregate regional forest mitigation results also are discussed below. However, given the paucity of published regional results from integrated assessment models, it is currently not possible to assess the regional land-use abatement potential in stabilization. Future research should direct attention to this issue in order to more fully characterize mitigation potential.

In addition to the stabilization scenarios discussed thus far from integrated assessment and climate economic models, the literature includes long-term mitigation scenarios from global land sector economic models (e.g. Sohngen and Sedjo, 2006; Sathaye et al., 2006; Sands and Leimbach, 2003). Therefore, a comparison is prudent. The sectoral models use exogenous carbon price paths to simulate different climate policies and assumptions. It is possible to compare the stabilization and sectoral scenarios using these carbon price paths. Stabilization (e.g. EMF-21, discussed above) and ‘optimal’ (e.g. Sohngen and Mendelsohn, 2003) climate abatement policies suggest that carbon prices will rise over time.[19] Table 3.6 compares the forest mitigation outcomes from stabilization and sectoral scenarios that have similar carbon price trajectories (Rose et al., 2007).[20] Rising carbon prices will provide incentives for additional forest area, longer rotations, and more intensive management to increase carbon storage. Higher effective energy prices might also encourage shorter rotations for joint production of forest bioenergy feedstocks.

Table 3.6 shows that the vast majority of forest mitigation is projected to occur in the second half of the century, with tropical regions in all but one scenario in Table 3.6 assuming a larger share of global forest sequestration/mitigation than temperate regions. The IMAGE results from EMF-21 are discussed separately below. Lower initial carbon prices shift early period mitigation to the temperate regions since, at that time, carbon incentives are inadequate for arresting deforestation. The sectoral models project that tropical forest mitigation activities are expected to be heavily dominated by land-use change activities (reduced deforestation and afforestation), while land management activities (increasing inputs, changing rotation length, adjusting age or species composition) are expected to be the slightly dominant strategies in temperate regions. The current stabilization scenarios model more limited and aggregated forestry GHG abatement technologies that do not distinguish the detailed responses seen in the sectoral models.

The sectoral models, in particular, Sohngen and Sedjo (2006), suggest substantially more mitigation in the second half of the century compared to the stabilization scenarios. A number of factors are likely to be contributing to this deviation from the integrated assessment model results. First and foremost, is that Sohngen and Sedjo explicitly model future markets, which none of the integrated assessment models are currently capable of doing. Therefore, a low carbon price that is expected to increase rapidly results in a postponement of additional sequestration actions in Sohngen and Sedjo until the price (benefit) of sequestration is greater. Endogenously modelling forest biophysical and economic dynamics will be a significant future challenge for integrated assessment models. Conversely, the integrated assessment models may be producing a somewhat more muted forest sequestration response given:

(i) Their explicit consideration of competing mitigation alternatives across all sectors and regions, and, in some cases, land-use alternatives.

(ii) Their more limited set of forest-related abatement options, with all integrated assessment models modelling afforestation strategies, but only some considering avoided deforestation, and none modelling forest management options at this point.

(iii) Some integrated assessment models (including those in Table 3.6) sequentially allocate land, satisfying population food and feed-demand growth requirements first.

(iv) Climate feedbacks in integrated assessment models can lead to terrestrial carbon losses relative to the baseline.

The IMAGE results in Table 3.6 provide a dramatic illustration of the potential implications and importance of some of these counterbalancing effects. Despite the planting of additional forest plantations in the IMAGE scenario, net tropical forest carbon stocks decline (relative to the baseline) due to deforestation induced by bioenergy crop extensification, as well as reduced CO2 fertilization that affects forest carbon uptake, especially in tropical forests, and decreases crop productivity, where the latter effect induces greater expansion of food crops onto fallow lands, thereby displacing stored carbon.

Table 3.6: Cumulative forest carbon stock gains above baseline by 2020, 2050 and 2100, from long-term global forestry and stabilization scenarios (GtCO2).

US$2.73/tCO2 (in 2010) + 5% per year 
 2020 2050 2100 
Sathaye et al., (2006)  World na 91.3 353.8 
 Temperate  na  25.3  118.8  
 Tropics na 55.1 242.0 
Sohngen and Sedjo (2006) World 0.0 22.7 537.5 
original baseline  Temperate  3.3  8.1  207.9  
 Tropics -3.3 14.7 329.6 
Sohngen and Sedjo (2006) World 1.5 15.0 487.3 
accelerated deforestation baseline  Temperate  1.1  12.1  212.7  
 Tropics 0.7 2.9 275.0 
Stabilization at 4.5 W/m2 (~650 CO2-eq ppmv) by 2100 
 2020 2050 2100 
GRAPE-EMF21 World -0.6 70.3 291.9 
 Temperate  -0.2  10.0  45.2  
 Tropics -0.5 60.3 246.7 
IMAGE-EMF21  World -22.5 -13.4 10.4 
 Temperate  14.1  31.9  78.3  
 Tropics -36.6 -45.3 -67.9 
MESSAGE-EMF21*  World 0.0 3.5 152.5 
 Temperate  0.0  0.1  23.4  
 Tropics 0.0 3.4 129.1 

Notes: * Results based on the 4.5 W/m2 MESSAGE scenario from the sensitivity analysis of Rao and Riahi (2006).

Tropics: Central America, South America, Sub-Saharian Africa, South Asia, Southeast Asia. Temperate: North America, Western and Central Europe, Former Soviet Union, East Asia, Oceania, Japan. Na = data not available.

Source: Stabilization data assembled from Rose et al. (2007)

In addition to reducing uncertainty about the maginitude and timing of land-based mitigation, biomass potential, and regional potential, there are a number of other important outcomes from changes in land that should be tracked and reported in order to properly evaluate long-term land mitigation. Of particular importance to climate stabilization are the albedo implications of land-use change, which can offset emissions reducing land-use change (Betts, 2000; Schaeffer et al., 2006), as well as the potential climate-driven changes in forest disturbance frequency and intensity that could affect the effectiveness of forest mitigation strategies. Non-climate implications should also be considered. As shown in the Millennium Ecosystem Assessment (Carpenter et al., 2005), land use has implications for social welfare (e.g. food security, clean water access), environmental services (water quality, soil retention), and economic welfare (output prices and production).

A number of relevant key baseline land modelling challenges have already been discussed in Sections and Central to future long-term land mitigation modelling are improvements in the dynamic modelling of regional land use and land-use competition and mitigation cost estimates, as well as modelling of the implications of climate change for land-use and land mitigation opportunities. The total cost of any land-based mitigation strategy should include the opportunity costs of land, which are dynamic and regionally unique functions of changing regional biophysical and economic circumstances. In addition, the results presented in this section do not consider climate shifts that could dramatically alter land-use conditions, such as a permanent El-Nino-like state in tropical regions (Cox et al., 1999).

To summarize, recent stabilization studies have found that land-use mitigation options (both non-CO2 and CO2) provide cost-effective abatement flexibility in achieving 2100 stabilization targets, in the order of 345–1260 GtCO2-eq (15–40%) of cumulative abatement over the century. In some scenarios, increased commercial biomass energy (solid and liquid fuel) is significant in stabilization, providing 115–749 GtCO2-eq (5–30%) of cumulative abatement and 500–9500 EJ of additional bio-energy above the baseline over the century (potentially 1–15% of total primary energy), especially as a net negative emissions strategy that combines biomass energy with CO2 capture and storage. Agriculture and forestry mitigation options are projected to be cost effective short-term and long-term abatement strategies. Global forestry models project greater additional forest sequestration than found in stabilization scenarios, a result attributable in part to differences in the modelling of forest dynamics and general economic feedbacks. Overall, the explicit modelling of land-based climate change mitigation in long-term global scenarios is relatively immature, with significant opportunities for improving baseline and mitigation land-use scenarios.

  1. ^  Most of the assessments are conducted with large regional spatial resolutions; exceptions are Fischer and Schrattenholzer (2001), Sørensen (1999), and Hoogwijk et al. (2005).
  2. ^  All values here are given in constant US dollars at 2000 prices.
  3. ^  The high percentage arises because some scenarios project that the required overall abatement from 2000–2030 is modest, and forestry and agricultural abatement options cost-effectively provide the majority of abatement.
  4. ^  Optimal is defined in economic terms as the equating of the marginal benefits and costs of abatement.
  5. ^  Rose et al. (2007) report the carbon price paths from numerous stabilization and sectoral mitigation scenarios.