7.5.2 Non-CO2 gases
Table 7.9 shows mitigation potential for non-CO2 gases in 2030 based on a global study conducted by the US EPA (2006a,b), which projected emission and mitigation costs to 2020. Emissions in 2030 were projected by linear extrapolation by region using 2010 and 2020 data. Mitigation costs were assumed to be constant between 2020 and 2030, and interpolated from US EPA data, which used different cost categories. The analysis uses US EPA’s technical adoption scenario, which assumes that industry will continue meeting its voluntary commitments. The SRES A1B and B2 scenarios used as the base case for the rest of this chapter do not include sufficient detail on non-CO2 gases to allow a comparison of the two approaches. IPCC/TEAP (2005) contains significantly different estimates of 2015 baseline emissions for HFCs and PFCs in some sectors compared to Table 7.9. We note that these emissions are reported by end-use, not by the sectoral approach used in this report, and that insufficient information is provided to extrapolate to 2030. Caprolactam projections were not found in the literature. They were estimated based on historical data from a variety of industry sources. Mitigation costs and potentials were estimated by applying costs and potential from nitric acid production.
Table 7.9: Global mitigation potential in 2030 for non-CO2 gases
| Source | 2030 Baseline emissions (MtCO2-eq) | Mitigation potential by cost category (US$) |
|---|
| <0 | <20 | <50 | <100 |
|---|
| N2O from adipic and nitric acid production | 190 | 158 | 158 | 158 | 174 |
| N2O from caprolactam production | 20 | 16 | 16 | 16 | 16 |
| PFC from aluminium production | 51 | 1.6 | 7.6 | 8.2 | 8.2 |
| PFC and SF6 from semiconductor manufacture | 20 | 9.6 | 9.6 | 10 | 10 |
| SF6 from use of electrical equipment (excluding manufacture) | 74 | 32 | 39 | 39 | 39 |
| SF6 from magnesium production | 9.3 | 9.2 | 9.2 | 9.2 | 9.2 |
| HFC-23 from HCFC-22 production | 106 | 0 | 86 | 86 | 86 |
| ODSa substitutes: aerosols | 88 | 27 | 27 | 27 | 27 |
| ODS substitutes: industrial refrigeration and cooling | 80 | 3.5 | 3.5 | 3.5 | 3.5 |
| ODS substitutes: fire extinguishing | 27 | 0 | 0 | 6.3 | 6.7 |
| ODS substitutes: solvents | 4.0 | 1.2 | 2.0 | 2.0 | 2.0 |
| Total: | Global | 668 | 249 | 357 | 364 | 380 |
| | OECDb | 305 | 135 | 154 | 157 | 158 |
| | Economies in Transition | 53 | 27 | 28 | 29 | 29 |
| | Developing Nations | 309 | 87 | 182 | 187 | 187 |
| a ODS = Ozone-Depleting Substances b Regional information given in references. Source: Extrapolated from US EPA 2006a,b. |
7.5.3 Summary and comparison with other studies
Using the SRES B2 as a baseline (see Section 11.3.1), Table 7.10 summarizes the mitigation potential for the different cost categories. To avoid double counting, the total mitigation potential as given in Table 7.8 has been corrected for changes in emission factors of the transformation sectors to arrive at the figures included in Table 7.10 (see also Chapter 11, table 11.3).
Table 7.10: Estimated economic potentials for GHG mitigation in industry in 2030 for different cost categories using the SRES B2 baseline
| Mitigation option | Region | Economic potential <100 US$/tCO2-eq | Economic potential in different cost categories |
|---|
| Cost category (US$/tCO2-eq) | <0 | 0-20 | 20-50 | 50-100 |
|---|
| Cost category (US$/tC-eq) | <0 | 0-73 | 73-183 | 183-367 |
|---|
| Low | High | |
|---|
| | | (MtCO2-eq) |
|---|
| Electricity savings | OECD | 300 | 70 | 70 | 150 |
| EIT | 80 | 20 | 20 | 40 |
| Non-OECD/EIT | 450 | 100 | 100 | 250 |
| Other savings, including non-CO2 GHG | OECD | 350 | 900 | 300 | 250 | 50 |
| EIT | 200 | 450 | 80 | 250 | 20 |
| Non-OECD/EIT | 1,200 | 3,300 | 500 | 1,700 | 80 |
| Total | OECD | 600 | 1,200 | 350 | 350 | 200 |
| EIT | 250 | 550 | 100 | 250 | 60 |
| Non-OECD/EIT | 1,600 | 3,800 | 600 | 1,800 | 300 |
| Global | 2,500 | 5,500 | 1,100 | 2,400 | 550 |
Two recent studies provide bottom-up, global estimates of GHG mitigation potential in the industrial sector in 2030. IEA (2006a) used its Energy Technology Perspectives Model (ETP), which belongs to the MARKAL family of bottom-up modelling tools, to estimate mitigation potential for CO2 from energy use in the industrial sector to be 5.4 Gt/yr (1.5 GtC/yr) in 2050. IEA’s base case was an extrapolation of its World Energy Outlook 2005 Reference Scenario, which projected energy use to 2030. IEA provides ranges for mitigation potential in 2030 for nine groups of technologies totalling about 2.5 to 3.0 GtCO2/yr (0.68 to 0.82 GtC/yr). Mitigation cost is estimated at <25 US$/tCO2 (<92 US$/tC) (2004 US$). While IEA’s estimate of mitigation potential is in the range found in this assessment, their estimate of mitigation cost is significantly lower.
ABARE (Matysek et al., 2006) used its general equilibrium model of the world economy (GTEM) to estimate the emission reduction potential associated with widespread adoption of advanced technologies in five key industries: iron and steel, cement, aluminium, pulp and paper, and mining. In the most optimistic ABARE scenario, industrial sector emissions across all gases are reduced by an average of about 1.54 GtCO2-eq/yr) (0.42GtC-eq/yr) over the 2001 to 2050 time frame and 2.8 GtCO2-eq/yr (0.77 GtC-eq/yr) over the 2030-2050 time frame, relative to the GTEM reference case, which assumes energy efficiency improvements and continuation of current or announced future government policy. The ABARE carbon dioxide only industry mitigation potential for the period 2030–2050 of approximately 1.94 GtCO2-eq/yr (0.53GtC/yr) falls below the range developed in this assessment. This outcome is the likely result of differences in the modelling approaches used – ABARE’s GTEM model is a top down model whereas the mitigation potentials in this assessment are developed using detailed bottom-up methodologies. ABARE did not estimate the cost of these reductions.
The TAR (IPCC, 2001a) developed a bottom up estimate of mitigation potential in 2020 for the industrial sector of 1.4 to 1.6 GtC (5.1 to 5.9 GtCO2) based an SRES B2 scenario baseline and on the evaluation of specific technologies. Extrapolating the TAR estimate to 2030 would give values above the upper end of the range developed in this assessment. The newer studies used in this assessment take industry-specific conditions into account, which reduces the risk of double counting.