11.3.4 Regional cross-sectoral effects of greenhouse gas mitigation policies to 2025

Various estimates of cross-sectoral mitigation potential for specific regions have been published, usually as reports commissioned by governments. Unfortunately, however, the issue of attributing costs to cross-sectoral effects of greenhouse gas mitigation policies has not been reported extensively since the TAR, and literature on this topic is consequently sparse.

In one of the few studies to examine the sectoral effects of mitigation policies across countries, Meyer and Lutz (2002), using the COMPASS model, carried out a simulation of the effects of carbon taxes or the G7 countries, which include some of the biggest energy users. The authors assumed the introduction of a carbon tax of 1US$ per ton of CO₂ in 2001 in all of these countries, rising linearly to 10 US$ in 2010, with revenues used to lower social security contributions. Table 11.6 shows the effects on output: the decline in petroleum and coal products will be highest, with the effects on construction being mild. The scale of the effects differs substantially between countries, depending on the energy intensities of the economies and the carbon content of this energy, with effects on output being much larger in US and Canadian industries.

One major cross-sectoral study (EU DG Environment, 2001) brings together low-cost mitigation options and shows their effects across sectors and regions. It shows how a Kyoto-style target (8% reduction of EU GHGs below 1990/95 by 2010) can be achieved for the EU-15 member states with options costing less than 20 US$/tCO₂. The study assesses the direct and indirect outcomes using a top-down model (PRIMES) for energy-related CO₂ and a bottom-up model (GENESIS) for all other GHGs. The synthesis of the results is presented in Table 11.7. This multi-gas study considers all GHGs, but assumes that the JI and CDM flexibility instruments are not used. The study shows the wide variations in cost-effective mitigation across sectors. The largest reductions compared to the 1990/95 baselines are in the energy and energy-intensive sectors, whereas there is an increase of 25% in the transport sector compared to 1990/95 emissions. Note also the large reductions in methane and N₂O in the achievement of the overall target as shown in the lower panel of the table. The results are, however, dominated by bottom-up energy-engineering assumptions since PRIMES is a partial-equilibrium model. Consequently, the GDP effects of the options are not provided. These potentials can be compared to those at less than 20 US$/tCO₂ in Table 11.3 above for the sectoral synthesis for the OECD. The EU potentials are similarly concentrated in the buildings sector, but with a larger share for industry, and a lower one for transport, reflecting the high existing taxes on transport fuels in the EU.

Masui et al. (2005) report the effects of a tax and sectoral subsidy regime for Japan to achieve the Kyoto target by 2010, in which carbon tax revenues are used to subsidize additional investments to reduce greenhouse gases. The investment costs are shown in Table 11.8 for each sector. The table shows that about about 9 US$/tCO₂ (3,400 Japanese Yen/tC) will be required as carbon tax and most of the investment will be in energy-saving measures in the buildings sector (Residential and Commercial). The macro-economic effects for this study are reported in Section 11.4.3.4.

Schumacher and Sands (2006) model the response of German GHG emissions to various technology and carbon policy assumptions over the next few decades using the SGM model for Germany. Accounting for advanced technologies such as coal IGCC, NGCC, CCS, and wind power, they show that emission reductions can be achieved at substantially lower marginal abatement costs in the long run with new advanced electricity generating technologies in place. In a scenario assuming a carbon price of 50 US$/tCO₂ giving a 15% reduction of CO₂ below baseline by 2020, they show that, with the new and advanced technologies, the electricity sector would account for the largest share of emissions reductions (around 50% of total emissions reductions), followed by other (non-energy-intensive) industries and households. The effects on gross output are very uneven across sectors: energy transformation is 9% below base, but other industry, services and agriculture (and GDP) are 0.7% below base by 2050.

The effects of different policy mixes on sectoral outcomes are shown in the US EIA (2005) analysis of the National Commission on Energy Policy’s (NCEP) 2004 proposals. These involve reductions in the US emissions in GHGs of about 11% by 2025 below a reference case, including an analysis of the cap-and-trade component, (involving a safety valve limiting the maximum cost of emissions permits to US$ (2003)8.50/tCO₂ through to 2025) and a no-safety-valve case (in which the cost rises to US$(2003) 35/tCO₂ and the GHG reduction to 15% by 2025). The effects on CO₂ emissions by broad sector are shown in Figure 11.4. Note that the NCEP scenario includes the cap-and-trade scheme (with a safety valve) shown separately in the figure and that the no-safety-valve scenario is additional to the NCEP scenario. The NCEP scenario includes substantial energy efficiency policies for transportation and buildings. This explains the relatively large contributions of these sectors in this scenario. The cap-and-trade schemes mainly affect the electricity sector, since the price of coal-fired generation rises relative to other generation technologies. For discussion of macro-economic estimates of mitigation costs for the US from this study and others, see Section 11.4.3.1.

Figure 11.4: Carbon dioxide reductions by sector in the NCEP, Cap-Trade, and No-Safety Cases, 2015 and 2025

Notes: National Energy Modeling System, runs BING-ICE-CAP.D021005C BING-CAP.D021005A, and BING-NOCAP.D020805A. Note that NCEP includes technology mandates, and Cap-Trade is without technology mandates.

Source: US Energy Information Administration (EIA)(2005, p.15).