Abstract
Emissions Trading Systems (ETSs) with fixed caps lack provisions to address systematic imbalances in the supply and demand of permits due to changes in the state of the regulated economy. We propose a mechanism which adjusts the allocation of permits based on the current bank of permits. The mechanism spans the spectrum between a pure quantity instrument and a pure price instrument. We solve the firms׳ emissions control problem and obtain an explicit dependency between the key policy stringency parameter—the adjustment rate—and the firms׳ abatement and trading strategies. We present an analytical tool for selecting the optimal adjustment rate under both risk-neutrality and risk-aversion, which provides an analytical basis for the regulator׳s choice of a responsive ETS policy.
Highlights
The economic slowdown following the financial crisis has substantially increased the academic interest in how economic shocks interact with climate change policies
In the previous section we showed that by choosing an adjustment rate δ, the regulator can decide on the balance between a low policy responsiveness where the burden of adjustment to shocks is mostly borne by firms, and a high policy responsiveness where the burden of adjustment is shared between the firms and the regulator
Within the setting of the proposed mechanism, we identify a trade-off between the two extremes of the policy stringency spectrum
Summary
The economic slowdown following the financial crisis has substantially increased the academic interest in how economic shocks interact with climate change policies. Carbon pricing instruments can be made responsive to economic shocks by mixing elements of a carbon tax into an ETS, a possibility first recognized by [Roberts and Spence, 1976] Such instruments, known as hybrid, work by imposing a price ceiling and/or floor to an ETS and adjust the stringency of the cap in response to changes in price levels: permits are added to the market when the price crosses the ceiling and are removed when the price crosses the floor. Central to our model is the premise that compliance cost minimization in an ETS is achieved by exploiting intertemporal differences in abatement costs via banking and borrowing With this in mind, we model the responsiveness mechanism such that it adjusts the stringency of the cap in response to changes in the aggregate bank of permits.
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