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Leaked Draft Shows Most EU Industries to Face Minimal Carbon Costs

According to a leaked draft document from the European Commission, most industries in the European Union (EU) are expected to face negligible carbon costs between 2020 and 2030. The document reveals that this is primarily due to extensive free allocations of carbon allowances and the ability of industries to pass on costs to their customers. The impact assessment of the EU Emissions Trading System (EU ETS) review indicates that regardless of the six options being considered by the Commission for determining free allocations, operators of installations are likely to bear minimal carbon costs, and there may even be a risk of windfall profits in some cases. The document further states that free allowances are estimated to cover a significant portion of emissions for many sectors, leading to a substantial reduction in compliance costs.

While the leaked document is undated, it is expected to undergo revisions as requested by a Commission review board. The full proposal, scheduled for publication by August, will require approval from EU lawmakers, a process that could take at least two years. The review will govern post-2020 rules, including a deeper annual reduction in the EU ETS cap to 2.2% from the current rate of 1.74%, and the establishment of rules for allocating free allowances to industries vulnerable to carbon leakage.

Regarding allocation methods, the Commission dismisses the concept of dynamic allocation of free carbon allowances, which has been favoured by certain industry groups and countries such as Poland and the Netherlands. The Commission argues that dynamic allocation would undermine the incentive to reduce emissions in certain sectors by penalizing installations acting with fewer allowances. The administrative burden of increased data collection and concerns about business confidentiality are cited as additional reasons for rejecting dynamic allocation. Instead, the Commission suggests an ex-ante system with more frequent production data adjustments to address the need for stability, predictability, and flexibility.


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