Galvanising Stakeholder Support for Carbon Capture and Storage
Bibliographic record
Abstract
Abstract Our lives depend on reliable energy; if we are to prosper and tackle climate change, society must transition its economies and energy system to meet growing demand and emit less CO2. Shell believes carbon capture and storage (CCS) is critical. CCS fitted to power plants could be a real game-changer, removing up to 90% of carbon dioxide emissions from power generation. Engagement and cooperation with different stakeholder groups is key to maturing CCS projects. Shell has worked with stakeholders to help build a strong foundation for some of our CCS projects and this paper will share the approaches applied. Collaboration is critical to achieving recognition of the scope and value of CCS and achieving acceptance for a specific project. It is important to create engaging outreach and educational initiatives that are targeted to the needs of the stakeholders, demonstrate commitment to local communities, address the points of challenge and clarification, make use of best practice and international project experiences and help bring CCS, energy and climate change to life. QUEST Shell began its community consultation program for its Quest Carbon Capture and Storage (CCS) project in 2008 — three years ahead of filing a project application. A large part of our early consultation efforts were focused on explaining what CCS is, and why the technology is important. Consultation efforts focused on landowners and residents living along the proposed pipeline route and in close proximity to the proposed injection wells, along with the local municipal governments. A community advisory panel has been set up to review data from the Measurement, Monitoring and Verification (MMV) program. The program itself has been reviewed by an independent external expert. PETERHEAD The funding for the Peterhead CCS project has been withdrawn, but we are proud of the relationships established in the early phases of the project's development. The team carried out extensive consultations with the public to keep everybody up to date with plans as they progressed. We proactively sought and received feedback which we then endeavoured to build into our plans. In addition, strong relationships were built with local, regional and international organisations to develop education-based initiatives around CCS and to build an effective approach to local content on the project, both with the aim of creating best practice learnings for the future. At Shell, we believe the world will need to find ways to deploy CCS if it is to achieve its ambition to tackle climate change. In order to set CCS projects up for success, we must explore and develop a new model of cross-sector collaboration including: Building an understanding of the local contextEngaging early - being present, responsive and inclusiveMaking communications engaging and relevant Successful engagement, collaboration and community presence can lead to strong, trusting relationships that can be built on over the life of the project.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".