126.96.36.199 Economic potential for GHG mitigation in agriculture
US-EPA (2006b) provided estimates of the agricultural mitigation potential (global and regional) at various assumed
carbon prices, for N2O and CH4, but not for soil carbon sequestration. Manne & Richels (2004) estimated the economic mitigation potential (at 27 US$/tCO2-eq) for soil carbon sequestration only.
In the IPCC Third Assessment Report (TAR; IPCC, 2001b), estimates of agricultural mitigation potential by 2020 were 350-750 MtC/yr (~1300-2750 MtCO2/yr). The range was mainly caused by large uncertainties about CH4, N2O, and soil-related CO2 emissions. Most reductions will cost between 0 and 100 US$/tC-eq (~0-27 US$/tCO2-eq) with limited opportunities for negative net direct cost options. The analysis of agriculture included only conservation tillage, soil carbon sequestration, nitrogen fertilizer management, enteric methane reduction and rice paddy irrigation and fertilizers. The estimate for global mitigation potential was not broken down by region or practice.
Smith et al. (2007a) estimated the GHG mitigation potential in agriculture for all GHGs, for four IPCC SRES scenarios, at a range of carbon prices, globally and for all world regions. Using methods similar to McCarl and Schneider (2001), Smith et al. (2007a) used marginal abatement cost (MAC) curves given in US-EPA (2006b) for either region-specific MACs where available for a given practice and region, or global MACs where these were unavailable from US-EPA (2006b).
Recent bottom-up estimates of agricultural mitigation potential of CH4 and N2O from US-EPA (2006b) and DeAngelo et al. (2006) have allowed inclusion of agricultural abatement into top-down global modelling of long-term climate stabilization scenario pathways. In the top-down framework, a dynamic cost-effective portfolio of abatement strategies is identified. The portfolio includes the least-cost combination of mitigation strategies from across all sectors of the economy, including agriculture. Initial implementations of agricultural abatement into top-down models have employed a variety of alternative approaches resulting in different decision modelling of agricultural abatement (Rose et al., 2007). Currently, only non-CO2 GHG crop (soil and paddy rice) and livestock (enteric and manure) abatement options are considered by top-down models. In addition, some models also consider emissions from burning of agricultural residues and waste, and fossil fuel combustion CO2 emissions. Top-down estimates of global CH4 and N2O mitigation potential, expressed in CO2 equivalents, are given in Table 8.6 and Figure 8.7.
Table 8.6: Global agricultural mitigation potential in 2030 from top-down models
|Carbon price ||Mitigation (MtCO2-eq/yr) ||Number of scenarios |
|US$/tCO2-eq ||CH4 || N2O ||CH4+N2O |
| 0-20 ||0-1116 ||89-402 ||267-1518 ||6 |
| 20-50 ||348-1750 ||116-1169 ||643-1866 ||6 |
|50-100 ||388 ||217 ||604 ||1 |
| >100 ||733 ||475 ||1208 ||1 |
Figure 8.7: Global agricultural mitigation potential in 2030 from top-down models by carbon price and stabilisation target
Note: Dashed lines connect results from scenarios where tighter stabilization targets were modelled with the same model and identical baseline characterization and mitigation technologies. From Chapter 3, Sections 3.3.5 and 3.6.2.
Source: Data assembled from USCCSP, 2006; Rose et al., 2007; Fawcett and Sands, 2006; Smith and Wigley, 2006; Fujino et al., 2006; Kemfert et al., 2006.
Comparing mitigation estimates from top-down and bottom-up modelling is not straightforward. Bottom-up mitigation responses are typically constrained to input management (e.g., fertilizer quantity, livestock feed type) and cost estimates are partial equilibrium in that input and output market prices are fixed as can be key input quantities such as acreage or production. Top-down mitigation responses include more generic input management responses and changes in output (e.g., shifts from cropland to forest) as well as changes in market prices (e.g., decreases in land prices with increasing production costs due to a carbon tax). Global estimates of economic mitigation potential from different studies at different assumed carbon prices are presented in Figure 8.8.
Figure 8.8: Global economic potentials for agricultural mitigation arising from various practices shown for comparable carbon prices at 2030.
Notes: US-EPA (2006b) figures are for 2020 rather than 2030. Values for top-down models are taken from ranges given in Figure 8.7.
The top-down 2030 carbon prices, as well as the agricultural mitigation response, reflect the confluence of multiple forces, including differences in implementation of agricultural emissions and mitigation, as well as the stabilization target used, the magnitude of baseline emissions, baseline energy technology options, the eligible set of mitigation options, and the solution algorithm. As a result, the opportunity cost of agricultural mitigation in 2030 is very different across scenarios (i.e., model/baseline/mitigation option combinations). As illustrated by the connecting lines in Figure 8.7, agricultural abatement is projected to increase with the tightness of the stabilization target. On-going model development in top-down land-use modelling is expected to yield more refined characterizations of agricultural alternatives and mitigation potential in the future.
Smith et al. (2007a) estimated global economic mitigation potentials for 2030 of 1500-1600, 2500-2700, and 4000-4300 MtCO2-eq/yr at carbon prices of up to 20, 50 and 100 US$/tCO2-eq., respectively shown for OECD versus EIT versus non-OECD/EIT (Table 8.7). The change in global mitigation potential with increasing carbon price for each practice is shown in Figure 8.9.
Table 8.7: Estimates of the global agricultural economic GHG mitigation potential (MtCO2-eq/yr) by 2030 under different assumed prices of CO2-equivalents
| || ||Price of CO2-eq (US$/tCO2-eq) |
|SRES Scenario || ||Up to 20 ||Up to 50 ||Up to 100 |
|B1 ||OECD ||310 (60-450) ||510 (290-740) ||810 (440-1180) |
| ||EIT ||150 (30-220) ||250 (140-370) ||410 (220-590) |
| ||Non-OECD/EIT ||1080 (210-1560) ||1780 (1000-2580) ||2830 (1540-4120) |
|A1b ||OECD ||320 (60-460) ||520 (290-760) ||840 (450-1230) |
| ||EIT ||160 (30-230) ||260 (150-380) ||410 (220-610) |
| ||Non-OECD/EIT ||1110 (210-1610) ||1820 (1020-2660) ||2930 (1570-4290) |
|B2 ||OECD ||330 (60-470) ||540 (300-780) ||870 (460-1280) |
| ||EIT ||160 (30-240) ||270 (150-390) ||440 (230-640) |
| ||Non-OECD/EIT ||1140 (210-1660) ||1880 (1040-2740) ||3050 (1610-4480) |
|A2 ||OECD ||330 (60-480) ||540 (300-790) ||870 (460-1280) |
| ||EIT ||165 (30-240) ||270 (150-400) ||440 (230-640) |
| ||Non-OECD/EIT ||1150 (210-1670) ||1890 (1050-2760) ||3050 (1620-4480) |
Figure 8.9: Economic potential for GHG agricultural mitigation by 2030 at a range of prices of CO2-eq
Note: Based on B2 scenario, although the pattern is similar for all SRES scenarios.
Source: Drawn from data in Smith et al., 2007a.