Companion Policies under Capped Systems and Implications for Efficiency - The North American Experience and Lessons in the EU Context
Bibliographic record
Abstract
Carbon pricing regimes are often preceded and accompanied by companion policies, which can include regulatory standards, subsidies, and additional carbon pricing policies. While carbon pricing programs hold the advantage of identifying least-cost means of reducing carbon emissions, non-price based companion policies provide other advantages, such as addressing other externalities besides the social cost of carbon emissions, targeting specific technologies, addressing impacts on disadvantaged communities, and providing additional incentives for behavioral changes when carbon prices are too low to adequately do so. Companion policies therefore do play an important role in meeting climate goals, but some inefficiencies are expected when carbon pricing and companion policies interact. </BR> </BR> The two North American carbon pricing programs we discuss, the Regional Greenhouse Gas Initiative (RGGI) and the Western Climate Initiative (WCI), are cap-and-trade programs composed of individual states and provinces that pursue their own climate objectives and policies in addition to participating in emissions trading. RGGI affects the electricity sector in nine eastern US states, and virtually all allowances are auctioned in this program. The WCI is an economy-wide program covering California, Quebec and Ontario, where most allowance are auctioned. The companion policies in those jurisdictions are challenged by the waterbed effect, in which emissions reductions by one facility in a capped system are offset by increased emissions by another facility, leaving total emissions unchanged at least in the short run. Both trading programs reduce the waterbed effect by implementing a price floor in allowance auctions, below which emissions allowances are not sold. RGGI also plans to adopt an Emissions Containment Reserve (ECR), an additional price step above the price floor, applying to about 10 percent of allowances that will not sell at prices below this level. The auction price floors have been binding in both programs, and subsequently prices have risen off the price floor. These mechanisms cause the supply of allowances to decrease in response to lower demand, allowing the trading programs to capture some of the emissions reductions from companion policies through price suppression, but also maintain the buoyancy of the programs by supporting the price despite lack of allowance scarcity and guarantee a stream of revenue for programs supported by auction revenue. </BR> </BR> Companion policies have been fundamental to the design of the RGGI and WCI programs. In RGGI, most auction revenues are invested in energy efficiency, which by design pushes down electricity demand and allowance prices. In this context, the price floor and ECR provide guardrails for the allowance price path. In California, the largest jurisdiction in the WCI, over 80 percent of emissions reductions under the cap are attributable to regulatory measures. California estimates that these measures have a cost per ton of avoided carbon emissions that is greater than the cap-and-trade allowance price. However, these companion policies achieve essential ancillary benefits such as air quality improvements and investments in low carbon infrastructure. An important part of California’s policy is the focus of companion policies and spending of auction revenues to address emissions outcomes in disadvantaged communities. </BR> </BR> The European Union’s Emissions Trading System (ETS) faces the same challenge from the waterbed effect. The EU and its member states have pursued companion policies that reduce emissions at specific facilities in their jurisdictions but do not affect the volume of emissions allowances in the market. This effect contributes to the large surplus of allowances and the low allowance price that the EU ETS market has experienced over the past years. The EU has hence adopted a mechanism called a Market Stability Reserve (MSR), in which allowances are temporarily withheld from auction based on the number of (surplus) allowances in circulation. </BR> </BR> Beginning in 2023, when the MSR reaches a certain volume, some allowances can be permanently cancelled. This mechanism provides some responsiveness of allowance supply to reduced demand. We find through our modeling of MSR outcomes from 2018-2030 that the waterbed effect is diminished but still exists to some extent. We also find that additional emissions reductions have a greater impact on allowance supply the sooner they are taken. Our analysis of the North American and European Union cap-and-trade experiences provide a number of insights that are useful to Sweden in achieving its commitment to reach net-zero greenhouse gas emissions by 2045.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".