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This section deals with the most important aspects that could be considered in designing climate change policy.
Optimal climate change policy–irrespective of whether it is national or international–under uncertainty and/or asymmetric information deviates from more typical analyses with best-guess parameter values and/or information symmetry, not only in terms of the stringency of policies, but also in terms of policy design (Weitzman, 1974). Depending on the degree of uncertainty and correlation between the marginal damage and MAC curves, taxes could be a better or inferior alternative to tradable permits (Watson and Ridker, 1984; Stavins, 1996).104 Recent literature shows that taxes dominate quotas for the control of GHGs when the environmental damage function is rather flat (Hoel and Karp, 1998). Hoel (1998) and Pizer (1997b) point out that the lack of a clear, short-term threshold for severe climate damages favours the use of market-based policies, like taxes, that limit cost uncertainty. In addition, there is mounting evidence that rigid emission limits are not appropriate in the short run under a weak emissions reduction regime (Newell and Pizer, 1998).
Recently, Pizer (1997a) argued that excluding uncertainty might lead to policy recommendations that are too lax. Ebert (1996) has argued that improving the information of the regulator is crucial, because decision makers always overestimate abatement costs if they neglect that firms possess an abatement option other than decreasing output–additional abatement technology.
To increase the effectiveness and efficiency of domestic GHG emissions reduction policies, it is argued that governments could adopt policies that take a comprehensive approach, stimulating the development of all kinds of new materials, materials substitution, product re-design, resource productivity, and waste management strategies that can reduce GHG emissions. Moreover, governments could set long-term GHG emissions reduction targets, since the optimal set of technical options at low GHG mitigation levels may not include options that are efficient at high GHG emissions reduction levels.
It is important to consider how the domestic policy instruments examined in this chapter may interact with existing fiscal systems, because such interactions can have significant effects on the overall costs of achieving specified GHG emissions reduction targets. A growing literature demonstrates theoretically, and with numerical simulation models, that the costs of addressing GHG targets with policy instruments of all kinds–command-and-control as well as market-based approaches–can be greater than anticipated because of the interaction of these policy instruments with existing domestic tax systems.105 Domestic taxes on labour and investment income change the economic returns to labour and capital, and distort the efficient use of these resources.
The cost-increasing interaction reflects the impact that GHG policies can have on the functioning of labour and capital markets through their effects on real wages and the real return to capital (see, e.g., Parry et al., 1999). By restricting the allowable GHG emissions, permits, regulations, or a carbon tax raise the costs of production and the price of output, thus reducing the real return to labour and capital, and exacerbating prior distortions in the labour and capital markets. Thus, to attain a given GHG emissions target, before or after use of IET and other Kyoto mechanisms, all the instruments have a cost-increasing “interaction effect”.
For policies that raise revenue for the government, carbon taxes and auctioned permits, this is only part of the story, however. These revenues can be recycled to reduce existing distortionary taxes. Thus, to attain a given GHG emissions target, revenue-generating policy instruments have the advantage of a potential cost-reducing “revenue-recycling effect” as compared to the alternative, non-auctioned tradable permits or other non-revenue-generating instruments (Bohm, 1998). For a more complete theoretical discussion, see Chapter 7, and see Chapter 8 for the empirical results.
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