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Record W3132682880

INNOVATIVE REGULATORY AND FINANCIAL PARAMETERS FOR ADVANCING CARBON CAPTURE AND STORAGE TECHNOLOGIES

2020· article· en· W3132682880 on OpenAlexaboutno aff
Zen Makuch, Slavina Georgieva Behdeen Oraee-Mirzamani

Bibliographic record

VenueSpiral (Imperial College London) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon capture and storage (timeline)BusinessFinanceFinancial systemClimate changeGeology
DOInot available

Abstract

fetched live from OpenAlex

In the post-industrial age, the realisation of inherent technical innovation potentials requires that stakeholders develop flexible, cooperation-based frameworks if first mover opportunities and advantages are to be realised. In the Paris Agreement5 implementation context, carbon capture and storage technologies have emerged as a complementary adjunct to climate change mitigation and a diversified energy mix. However, developing the technology is not without technical and financial risks. The challenge for key stakeholders, primarily (but not exclusively) government and industry counterparts is to develop mutually reinforcing strategies, regulations and policies for testing and commercialising Carbon Capture and Storage (“CCS”)technologies and networks, as that will be determinative of their fate. In the Paris Agreement implementation period, the UK, for example, has indicated a commitment to bold greenhouse gas reductions(57% by 2030),and investment in CCS, as part of the ambitious emissions reductions targets set forth by the European Union, the deployment of which is meant to count for 20% of the greenhouse gas emissions captured by 2030. This has subsequently resulted in plans for several pilot CCS plants on UK soil. The up-scaling of CCS to the demonstration level, however, is dependent not only on the presence of sufficient interest and funding –an ongoing issue in the UK both pre-and post-Brexit-but also on the existence of appropriate regulatory conditions and options for additional private financing by industrial stakeholders. Furthermore, it is important to note that the up-scaling of projects from pilot to demonstration, and further on to a commercial-scale, is materializing in the context of a global financial crisis and a dip in investment trust in high-risk ventures. The development of CCS projects in individual states, is not only influenced by national regulatory regimes, policy developments, and fluctuations in financial markets, but is also dependent upon the legislative signals given from supra-national bodies and binding international agreements. In Europe, the CCS Directive’s approach to long term environmental and related financial risk has led to the current state of regulatory and financial uncertainty, thereby, giving rise to potentially uninsurable liabilities which dis-incentivise private sector investment in CCS technology. This is in contrast with legislation in competing states including the United States, Norway, Canada and Australia. There is every indication that the paramount issue standing in the way of CCS is uncertainty over regulated financial security requirements for site operators and the nature and attribution of liability arising from leakage. This uncertainty could be addressed by a combination of insurance for storage sites and a robust permitting process, which would minimize the likelihood of leakage to virtually zero. There are, therefore, excellent reasons for national and international law and policymakers to seriously consider a more careful and tailored legislative and policy mix, so that regulatory oversight is in balance with innovative financial, insurance and liability mechanisms. In addition to exploring this subject matter, the article offers a number of recommendations for flexible, stakeholder partner-based advancement of CCS technology potentials in climate change and related environmental regulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.245
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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