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«Abstract With federal policies to curb carbon emissions stagnating in the US, California is taking action alone. Sub-national policies can lead to ...»

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Fossil fuel production levels are determined by the price of fuel relative to the price of domestic output. The production of fuel f requires inputs of domestic supply (e.g., labor and intermediate inputs) and a fuel-specific resource. Given the form of the production function in Eq. (2), the elasticity of substitution between the resource and the rest of inputs in the top nest determines the price elasticity of supply (ζf ) at the reference point

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from the rental value of capital input in the database. Price elasticities of supply are taken from Paltsev et al. (2005). We employ ζCOL = ζGAS = 1 and ζCRU = 0.5. In a similar fashion, we calibrate the substitution elasticity between the value-added composite and the sector-specific resource factor for generation from nuclear sources (ζNUC = 0.25). We set ζNUC = 0 for all US regions reflecting our assumption that nuclear cannot expand above current levels, which we believe is consistent with current political realities and with the 10-year horizon of our analysis.

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Note: Substitution elasticity for fossil fuel, and nuclear resource factors are calibrated according to Eq. (11) using the following estimates for price elasticities of supply: zetaCOL = ζGAS = 1, ζCRU = 0.5, and ζNUC = 0.25. σl is calibrated assuming that the compensated and uncompensated labor supply elasticity is 0.05 and 0.3, respectively.

The supply response of our renewable electricity is calibrated by setting ζRNW equal to the generation-weighted average of own-price supply elasticities for hydro and renewable electricity, where weights for generation by source are derived from EIA (2009). Following Paltsev et al. (2005) and Johnson (2010), we set the own-price elasticities of supply from hydro electricity and other renewable electricity equal to 0.5 and 2.7, respectively.

Labor supply is determined by the household choice between leisure and labor. We calibrate compensated and uncompensated labor supply elasticities following the approach described in Ballard (2000), and assume that the uncompensated (compensated) labor supply elasticity is 0.05 (0.3).

3.3 Descriptive analysis of the data Figure 4 displays, for each region exporting to California, the CO2 intensity of output, the share of California’s imports of embodied CO2 emissions attributable to that region, and total CO2 emissions (represented by bubble size). In aggregate, US regions account for 23% of global CO2 emissions. The next largest emitters are China (17%) and the EU (14%). Californian emissions (not shown in Figure 4) are 5.5% of total US emissions (and 2% of global emissions). The largest sources of US emissions are the North East (27% of US emissions and 6% of global emissions), the South East (17% and 4%) and Texas (13% and 3%). As Californian emissions are a small proportion of global emissions, large leakage rates can be consistent with small proportional changes in emissions in other regions. Regions that export electricity to California (Arizona, Nevada, Utah and the Pacific region) account for a small proportion of total emissions.

China accounts for the largest share of emissions embodied in California’s imports, followed by Arizona and Texas. Electricity accounts for one-quarter of California’s total imported emissions, mostly from Arizona (45%) Utah (21%), and Nevada (19%). Other major sources of imported emissions include Other manufacturing from China; and Chemical, rubber and plastic products from China and Texas.

Electricity is a significant source of emissions in all regions. We calculate the average carbon intensity of electricity in each region by dividing the quantity of electricity in kilowatt hours (kWh) by emissions from fossil fuels used in electricity generation. Kilograms

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Figure 4: Scatter plot of share of California’s imports of embodied carbon and carbon intensity of trading partner. Size of bubbles denotes benchmark CO2 emissions.

to electricity generated in California, electricity from Utah is six times as carbon-intensive, electricity from Arizona and Nevada twice as carbon intensive, and electricity from the Pacific region is less carbon-intensive. In other regions, electricity in the Mountain, North Central and North East regions are relatively carbon-intensive. High carbon intensities in these (and other) regions are due to large shares of coal-fired generation in total electricity production. In contrast, emissions from natural gas account for 92% of total electricity emissions in California.

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Figure 5: Kilograms of CO2 emissions per dollar of electricity production.

4 Modeling results

4.1 Scenarios We evaluate leakage from California’s cap-and-trade program by considering six scenarios.

Our first scenario, which we label "EU-ETS", simulates a cap-and-trade program in the EU. The EU-ETS aims to reduce 2020 emissions by 21% relative to 2005 emissions. The reduction in EU emissions in 2020 due to the ETS will be influenced by, among other factors, regulations regarding the use of offsets, the banking of allowances for use in phase three of the EU-ETS, development of the EU’s renewable portfolio standard, and whether or not the EU proceeds with plans to implement a more ambitious 2020 cap. We evaluate the impact of EU-ETS, net of complementary measures, by imposing a cap that reduces EU emissions by 20% relative to benchmark emissions in our model. Reflecting current legislation, we apply the cap to emissions from Electricity; Oil refining; Chemical, rubber and plastic products; Ferrous metals; Metals nec; Mineral products; and Paper products and publishing. The EU emissions cap is imposed in all other scenarios and changes in other simulations are expressed relative to values in the the EU-ETS scenario.





The reduction in California emissions due to the cap-and-trade program will depend on emissions reduction due to complementary measures, such as California’s Low Carbon Fuel Standard and Renewable Portfolio Standard, and the development of eligible offset programs. An analysis by CARB (2010) indicates that the reduction in California’s emissions due to the cap-and-trade program will be 3.6% when offsets are used and 6.7% when there are no offsets.7 We consider a cap that reduces California’s emissions by 5% relative to the benchmark level. The cap is applied to Electricity; Oil refining; Chemical, rubber and plastic products; Ferrous metals; Mineral products; Paper products and publishing;

and the use of refined oil and natural gas in other sectors and in final demand.

As noted in Section 2, Californian legislation requires permits to be turned in for emissions embodied in imports and is similar to a tariff on out-of-state electricity. The effectiveness of this measure in reducing leakage will depend on how deliverers of electricity respond to the tariff and the application of the bill’s measures to prevent resource shufing. If out-of-state producers can reconfigure transmission so that low-carbon electricity is diverted to California and carbon-intensive electricity to other states, the tariff will have little impact on leakage. On the other hand, if electricity producers are unable to reroute supply and/or resource shuffling legislation is effective, the policy may lead to a large reThese calculations combine results from CARB (2010) Table 14 (p.38) and Table B-1 (p.97). Specifically, the CARB study estimates that policies will reduce California’s emissions decrease by 18% relative to business as usual, and 20% of this decrease is due to the cap-and-trade policy when offsets are used and 37% when there are no offsets.

duction in leakage in states producing (on average) relatively carbon-intensive electricity.

We implement three scenarios to tease out the impact of different aspects of California’s policy. In our CAnoTariff scenario, we consider a cap on Californian emissions without electricity tariffs or legislation to prevent resource shuffling. In our CAShuffling scenario, we assume that there is an electricity tariff but electricity exporters can reduce the incidence of the tariff by reconfiguring supply so that low-carbon electricity is supplied to California (i.e., there is resource shuffling). This is modeled by assuming that, in each exporting state, all available renewable and nuclear electricity is supplied to California followed by, if required, electricity from gas and then electricity from coal. Tariffs are applied to the average carbon intensity of electricity exported from each state. As we do not consider transmission constraints, our CAShuffling scenario represents the upper limit on changes in the composition of California’s electricity imports when there is a tariff and resource shufing is allowed.

In our CAnoShuffling scenario, we calculate emissions embodied in imported electricity using emissions coefficients in exporting regions in the benchmark data and set the elas

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equal to zero. This scenario implicitly assumes that the ban on resource shuffling prevents importers from adjusting the composition of electricity to reduce CO2 liabilities. Our CAnoShuffling scenario includes all aspects of California’s cap-and-trade policy and therefore is the most accurate representation of this legislation. In tariff scenarios, consistent with current legislation, the quantity of permits available for in-state production is reduced by the amount needed to cover emissions embodied in imported electricity.

We execute two additional scenarios to assess the impact of international trading of emission permits. One scenario, CA-TRDnotariff, allows trading of permits between the two systems without a tariff on Californian imports of electricity. The other, CA-TRDnoShuffling, considers trading of permits with Californian electricity tariffs and no resource shuffling.

Finally, in the EU-ETS, CAnoTariff and CAnoShuffling scenarios, we implement a counterfactual exercise to distinguish leakage occurring via the trade channel from that occurring through the fossil fuel price channel. Leakage due to trade is estimated by holding the price of fossils fuels constant in all regions and fossil fuel-price leakage is calculated as total leakage (simulated in our core scenarios) minus leakage due to trade. We choose this method to derive fossil-price leakage as fossil fuel price changes in one region will be driven by changes in excess demand for these commodities in other regions, which makes it difficult to design a simulation to isolate the impact of fossil fuel prices on leakage.8

4.2 Leakage without electricity tariffs Modeling results are summarized in Tables 4 to 7. CO2 allowance prices, in 2004 dollars, are displayed in Table 4, as well as a summary of emissions reductions and leakage rates for leakage (i) to US regions, (ii) to international regions, (iii) due to changes in electricity production, and (iv) total leakage. Leakage to each region is calculated as the increase in emissions in that region divided by the decrease in emissions in the EU-ETS in the EU-ETS scenario, and the decrease in emissions in California in scenarios that consider California’s cap-and-trade program. In our scenarios that consider electricity tariffs, the reduction in Californian emissions depends on quantity of permits used for imported electricity. Consequently, the denominator for leakage calculations varies across scenarios that consider Leakage may also result from the reallocation of capital across US regions. In our modeling framework, results when capital was region specific were similar to those when capital was mobile across US regions.

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California’s cap-and-trade program.

Table 5 disaggregates leakage rates by region for each scenario and Table 6 disaggregates leakage amoung sectors for the CAnoShuffling scenario. To assess the contribution of changes in trade and fossil fuel prices, leakage due to each channel for aggregate regions for selected scenarios is reported in Table 7. By design the last panel of Table 7 replicates aggregate results reported in Table 5.

In the EU-ETS scenario, the allowance price is $17 per metric ton of CO2 (tCO2 ) and the leakage rate to all regions is 21% of the reduction in EU emissions. Leakage rates to all regions are positive and the largest sources of leakage are Africa and China. US emissions increase by 2% of the reduction in EU emissions. Table 7 indicates that around 60% of leakage occurs via the trade channel and 40% is due to changes in fossil fuel prices.



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