COMMENT: Financing innovation – Filling the gap between the NER300 and the Innovation Fund

Published 20:55 on May 27, 2015  /  Last updated at 13:31 on December 19, 2023  / Carbon Pulse /  Contributed Content, EMEA, EU ETS, Other Content

Sanjeev Kumar of environmental campaigners Change Partnership argues that urgent action is required to maintain Europe’s drive for commercial-scale demonstration and development of essential low-carbon technologies.

By Sanjeev Kumar, founder of environmental campaigners Change Partnership

Urgent action is required to maintain Europe’s drive for commercial-scale demonstration and development of essential low-carbon technologies.

Solutions such as CCS, wind, solar, electric batteries, electricity storage and more provide a critical path to cost-effective GHG emission reduction as well as economic prosperity, jobs, growth and sustainable competition. However, a funding gap between the end of the ground-breaking NER300 and the Innovation Fund, which starts in 2021, could be catastrophic.

Completion of the MSR, which should stabilise the thus far dysfunctional EU ETS, provides both a benefit and a hindrance to innovation policy. The MSR compromise between the European Parliament, Council and Commission, reached on May 5, invited the Commission to come forward, within 6 months of entry into force, with a proposal to use 50 million surplus allowances to “supplement existing resources” to promote “low-carbon industrial innovation projects, with projects in all member states including small-scale projects, before 2021.”

The 50 million sale process may seem small but could create a dangerous precedent where all the hard work gone into correcting the EU ETS is undermined by further re-injections of allowances to finance numerous outcomes.

Maintaining a robust and stringent carbon market through genuine scarcity is of paramount importance. Otherwise, the EU ETS risks constantly being locked in a pointless dance of taking one step forward and two back.

The key lesson from the two rounds of NER300 monetisation is unless attention is paid to when revenue is raised, both the carbon price signal and the value of finance derived from it are undermined. Beating the proverbial golden goose does not provide a long-term market for new technology.

Revenue raising should not come at the expense of undermining the robustness of the carbon price signal. The better solution is to borrow allowances from the 2021 auctioning calendar and use them to finance the period between 2016-2021 thereby preserving the immense achievement of securing a good MSR agreement and safeguarding future stringency of supply.

It also ensures a higher revenue return from monetisation. Such recalibration should govern operation of the Innovation Fund so as to avoid one of the main failings of the NER300.

The rules for the improved NER300 also need to be revised. The time taken to assess projects eligible for funding has to be streamlined as does eligibility criteria to ensure that pre-commercial solutions which will make a significant impact on GHG emissions for a sector or sectors, receive the support they need.

Priority should be given to solutions that cut across sectors. Examples of this are CCS, electric vehicle batteries and electricity storage. Electric vehicle batteries aid decarbonisation in both the power sector and for surface transport, where emissions have remained static for decades. CCS is another example as it applies to both electricity generation and many industrial applications such as blast furnace steel, cement, chemicals and paper. In this context, the eligibility criteria needs to be extended to support development of critical infrastructure that enables these cross-sectoral solutions.

The updated NER300 and Innovation Fund need to be simple, cogent and effective and deliver the biggest sectoral wins. This way public money is used to deliver real outcomes that are not only a step away from investment but provide an invaluable step-up in the race to develop sustainable, low-carbon economies, jobs and business models.

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