The simulations in this study analyze the situation after all regions have introduced emissions regulation. The regulation takes a cap-and-trade form, and we assume that permit markets are perfectly competitive. In addition, we assume that the government auctions emissions permits and transfers permit revenue to the household in a lump-sum way. To ensure that the results are not excessively dependent on any specific scenario, we consider three abatement scenarios, shown in Table 2. The figures in the table denote the reduction rates in each region.Footnote 18 Regions for which no value is given have no obligation to reduce emissions. In S_ANN, only ANNEX I regions (excluding Russia) have an obligation to reduce emissions. In S_RC, we add Russia and China to the regions included in S_ANN. Finally, in S_WORLD, all regions reduce emissions.
Under each abatement scenario, we perform calculations when the reducing regions do (TR) and do not (NTR) engage in IET and compare the difference in the two sets of results. When the regions do not engage in IET, the price of emissions permits (i.e., MAC) varies from region to region. On the other hand, when the regions engage in IET, they establish a common market, and therefore a common price, for emissions permits.
Our concept of emissions trading assumes that individual firms trade emissions permits across borders, except for the initial allocation which is made through government auction. This assumption resembles the type of linkage between California, Quebec and Ontario. Trade among firms means that there is no market power in the emissions market. In addition, we assume that emissions trading covers all CO2 emissions including, for example, emissions from agricultural sectors and the household. When IET is possible, we can consider the scenario in which governments (not firms) trade emissions permits internationally.Footnote 19 Note that our simulation does not consider such a scenario and always assumes that emissions permits are traded among firms.
IET and Welfare Effects
Here, we consider the simulation results.Footnote 20 Table 3 shows the volume of permits traded, i.e., net imports of permits in millions of tons of carbon dioxide equivalents (MtCO2) and the effect on welfare (percentage change from the benchmark value) for each country under each abatement scenario. We provide the welfare effects with and without IET (TR and NTR). Welfare effects are measured as the change in utility level of the representative household.Footnote 21 We exclude regions without emissions regulations from the table because the focus of our analysis is only on IET participants. The “World” row indicates world welfare change including non-participating regions. Let us examine the results for each scenario.
Under S_ANN, only Japan, USA, EU27, and other OECD countries have emissions regulations, and in all four models Japan and EU27 are importers of permits and USA and other OECD countries are exporters. In other words, the type of model does not affect the pattern of IET. In FLAB, IET reduces the loss of welfare from emissions regulation for all participating regions. In other words, IET is a policy that benefits all participants in FLAB. This is consistent with the findings of the numerous CGE analyses that fail to account for labor market distortion that IET is desirable. However, the results from the other models are different.
In VLAB, permit importers also benefit from IET. However, the loss of welfare by the exporting regions (i.e., USA and other OECD countries) is larger with IET. Similarly, with MWAGE and WCURVE, permit exporters are disadvantaged under IET. In Sect. 3.5, we saw that when labor market distortions are considered, permit exporters suffer indirect negative effects. Our simulation results show that these negative effects are sufficiently large to outweigh the direct positive benefits.
Next, we examine scenario S_RC. Under scenario S_RC, Russia and China are included among the regulated regions, and both are exporters of permits. China, in particular, is a huge exporter. In FLAB, all participants benefit from IET, as was the case with S_ANN. Moreover, with S_RC, all participants also benefit from IET in VLAB. In MWAGE, however, China (an exporting region) is disadvantaged by IET. Similarly, with WCURVE, China suffers from IET. Although the abatement scenario has changed, the result that some exporters suffer from IET remains unchanged. Finally, with S_WORLD, Russia, China, India, and rest of the world are exporters of permits, and it is better for China and India not to engage in IET, which is essentially a similar result to S_RC.
In addition to impacts on participants, let us examine impacts on the world as a whole. Table 3 shows that welfare costs for the world shrink with IET in all models and scenarios. For example, in the MWAGE model in scenario S_WORLD, the world welfare decreases by 3.95% without IET. However, the loss becomes 1.44% with IET. Thus, the world welfare improves by 2.5% point with IET. Although under IET, several countries may lose, IET is desirable from the viewpoint of world welfare.
Let us summarize the above findings. First, regardless of the abatement scenarios, all participating regions benefit from IET (i.e., their welfare losses are smaller) in FLAB. Second, even with models that account for labor market distortions, regions that import emissions permits still benefit from IET. However, we found that IET may not be beneficial for exporting countries, although this depends on the model used. More specifically, in VLAB, the impact of IET on the welfare of exporting regions is ambiguous, whereas in MWAGE and WCURVE there is a greater likelihood of them being disadvantaged. In VLAB, exporters of permits lose from IET in S_ANN but they gain in S_RC and S_WORLD. On the other hand, in MWAGE and WCURVE, all exporters lose in S_ANN, and some exporters lose even in S_RC and S_WORLD.
The results also differ from country to country. For instance, China, an exporter of emissions permits, is often disadvantaged, while fellow exporter Russia rarely suffers. The above results tell us that when labor market distortion is considered, IET may confer disadvantages.
Impacts on Individual Regions
To analyze the effects of IET in detail, let us consider the impact on individual regions. As explained in Sect. 3.5, the total welfare effect of IET in our model is the sum of (1) the direct effect, (2) the effect caused by labor market distortions and (3) other indirect effects. This decomposition partially explains why IET has different effects for different regions. For example, even if the second effect has negative impacts on all permit exporters equally, an exporter with large positive impacts from the first and third effects is likely to gain from IET as a whole.
It is desirable to discuss the outcomes for all of the regions under each of the three abatement scenarios, but it is difficult to do so due to space limitations. Thus, we will only focus on other OECD countries and EU27 under S_ANN and on China and Russia under S_RC. Table 4 details the impacts in four regions. We begin by looking at other OECD countries under scenario S_ANN. For other OECD countries, the price of emissions permits rises significantly following the introduction of IET, resulting in other OECD countries exporting permits. In FLAB, GDP falls to enable the export of permits, and labor income therefore declines. However, because the revenue from selling the permits offsets this reduction, the rate of decline in household income is smaller with IET. It follows that the rate of decline in welfare is also less with IET.
In VLAB, there is an additional effect: namely, a fall in employment. In fact, the rate of decline in labor income under this model is even larger. With IET, the rate of decline in labor income increases, but there is also revenue from the sale of emissions permits, which is the same as under FLAB. The difference with VLAB, however, is that the decline in labor income is larger than the revenue from the sale of emissions permits, so the decrease in household income is larger with IET. This is why IET increases the rate of decrease in welfare. Moreover, under MWAGE and WCURVE, unemployment occurs, so the rate of decrease in employment increases further. With these two models, the expansion of labor market distortion by IET is more pronounced, and this causes the welfare loss with IET to increase.
Next, let us consider China under scenario S_RC. China is an exporter of permits, and the qualitative aspects of the impact on China are similar to other OECD countries under S_ANN. For China, however, IET is preferable under VLAB, just as it is with FLAB. In other words, the welfare effect under VLAB is the reverse of what it is for other OECD countries under S_ANN. Moreover, when compared with other OECD countries, there is a substantial difference in the size of effects on welfare with and without IET under MWAGE. In fact, the welfare loss in the presence of IET for China is approximately 3.5 times higher under MWAGE (for other OECD countries in S_ANN, only 1.1 times).
One of the reasons for the quantitative differences described above may be the difference in the price of emissions permits with and without IET. Because both China and other OECD countries are exporters of emissions permits, the price of permits increases with the introduction of IET. However, the nature of the increase is very different for the two regions. In other OECD countries, where the price of permits (MAC) is high to begin with, the increase in permit price is small (37.3 to 42.3US$/MtCO2 in FLAB). In China, however, where the price of permits is low, IET results in a more than six fold increase. Because of this, China exports large quantities of emissions permits (and reduces output at the same time). The above findings indicate that the quantitative effects of IET are quite dissimilar across the regions.
Next, let us consider Russia under scenario S_RC. We have already observed that the impacts of IET on China and Russia are quite different even though they are both exporters of permits. That is, China is often disadvantaged by IET, while Russia rarely suffers. The reason why Russia is likely to obtain large gains from IET is that the terms of trade (TOT) for Russia improve significantly when it participates in IET. For example, Table 4 shows that the TOT for Russia deteriorates by 2.9% without IET but improve by 0.6% with IET. This positive TOT effect generates large gains from IET for Russia and cancels out the negative impacts caused by the labor market distortions.
Finally, we examine EU27 under scenario S_ANN, which shows the impacts on permit importers. In all models, EU27 receives welfare gains from IET. In particular, in MWAGE, the welfare cost is reduced from 4.85% without IET to 3.72% with IET, corresponding to a reduction of 20%. The large welfare gains from IET are caused mainly by the decrease in the unemployment rate (from 11.4% without IET to 10.4% with IET). Although so far, we have focused on the decrease in the welfare of permit exporters, the above result shows that labor market distortions can reinforce the gains from IET for permit importers.
In this section, we alter our assumptions concerning the models, parameters, and scenarios, and perform a sensitivity analysis to examine the extent to which this affects the results obtained so far. Scenarios of the sensitivity analysis are shown in Table 5. First, we change the reduction rates. Because there is uncertainty about the future reduction rates of many regions, we consider two scenarios: in Scenario hrd (the case of higher reduction rates), the original reduction rates in Table 2 are multiplied by 1.\(5\), and in Scenario lrd (the case of lower reduction rates), reduction rates are multiplied by 0.5.
With VLAB, the elasticity of the labor supply and the benchmark labor tax rate are important. We therefore double and halve their values (Scenarios elas_l, elas_h, ltax_l and ltax_h). On the other hand, with WCURVE, the wage curve elasticity and the benchmark unemployment rate are important, so we double and halve the values of each (Scenarios phi_l, phi_h, ur_l and ur_h).
From the preceding analysis, we confirmed that labor market distortion makes it possible for IET to confer disadvantage. This means that it should be possible, by simultaneously implementing policies to correct the distortions, to eliminate the indirect negative impact of IET and leave only the positive effects. To confirm whether this is indeed the case, we consider a situation in which policies to curtail the expansion of labor market distortion accompany the introduction of emissions regulation. More specifically, with VLAB we examine a “revenue-recycling” policy under which the revenue from the sale of emissions permits lowers the labor tax, while with MWAGE and WCURVE, we consider a policy of lowering the labor tax to maintain the benchmark level of employment.
Because of the limitation of the space, numerical results from the sensitivity analysis are omitted here.Footnote 22 We summarize the insights from sensitivity analysis. First, under FLAB, welfare improves for all participants as a result of IET in almost all scenarios of sensitivity analysis.Footnote 23 Second, under VLAB, the high values of labor supply elasticity and the labor tax rate tend to make IET disadvantageous for permit exporters. This is because high values of labor supply elasticity and the labor tax rate reinforce negative tax-interaction effects. Third, under WCRUVE, low values of wage curve elasticity and high values of the benchmark unemployment rate tend to make IET disadvantageous for permit exporters. This is because low values of wage curve elasticity reinforce the downward rigidity of wages and high values of the benchmark unemployment rate mean that the existing distortions in the labor market are large. Fourth, changes in reduction rates have ambiguous effects. In some models and abatement scenarios, an increase in reduction rates makes IET more disadvantageous, but in other models and scenarios, this is not the case.
Finally, in labor tax cut scenarios, all participants gain from IET in all models except for one case. That is, the simultaneous adoption of measures to correct distortion will always ensure that IET improves welfare, even for regions that export emissions permits. This means that by imposing emissions regulation and implementing policies to alleviate labor market distortion at the same time, we can reduce the indirect negative effects of IET, and therefore all regions can benefit from its implementation.
By changing assumptions and parameter values, the quantitative impacts of IET often change to a large extent, but almost all qualitative insights derived from the benchmark case remain unchanged. It follows that the analysis of the previous sections has a certain level of robustness.
Our analysis has several policy implications for potential ETS linkages. The first implication is for a developed-developing country linkage. One of the potential links of this type is between EU-ETS and the Chinese ETS. Our analysis (MWAGE and WCURVE under Scenario S_RC and S_WORLD) shows that China may suffer from higher unemployment if it links its domestic ETS to EU-ETS because China will be a net exporter of emissions permits.
However, China can earn revenues from selling permits to the EU. If the Chinese government can use these revenues wisely as a measure to correct the unemployment issue, the negative impact of linking may not matter as much as initially anticipated, as shown in the sensitivity analysis. Moreover, China is one of fastest growing economies in the world. Thus, in the long run, this negative impact from linking may vanish as the Chinese economy continues to grow.
Our analysis in scenario S_ANN also has implications for a developed–developed country link. For example, one can picture the linkage between the North American markets, such as the US and Canada, and the European markets, such as the EU-ETS and Switzerland ETS. In this case, the US and Canada are expected to become exporters of permits. Developed economies tend to have higher labor tax rates, which often entail lower labor supply and larger labor market distortion. Therefore, the distortion brought by IET can have a larger negative impact. This implies that the labor distortion problem is more severe for a developed–developed country link than for a developed-developing country link, which is shown in scenario S_ANN where permit exporters always lose in models with labor market distortions.