Dynamics of cross-industry low-carbon innovation in energy-intensive industries
Bibliographic record
Abstract
Technological innovations in energy-intensive industries (EIIs) have traditionally emerged within the boundaries of a specific sector. Now that these industries are facing the challenges of deep decarbonisation and a significant reduction in greenhouse gas (GHG) emissions is expected to be achieved across sectors, cross-industry collaboration is becoming increasingly relevant for low-carbon innovation. Accessing knowledge and other resources from other industrial sectors as well as co-developing innovative concepts around industrial symbiosis can be mutually beneficial in the search for fossil-free feedstocks and emissions reductions. In order to harness the potential of this type of innovation, it is important to understand not only the technical innovations themselves, but in particular the non-technical influencing factors that can drive the successful implementation of cross-industry collaborative innovation projects. The scientific state of the art does not provide much insight into this particular area of research. Therefore, this paper builds on three separate strands of innovation theory (cross-industry innovation, low-carbon innovation and innovation in EIIs) and takes an explorative case-study approach to identify key influencing factors for cross-industry collaboration for low-carbon innovation in EIIs. For this purpose, a broad empirical database built within the European joint research project REINVENT is analysed. The results from this project provide deep insights into the dynamics of low-carbon innovation projects of selected EIIs. Furthermore, the paper draws on insights from the research project SCI4Climate.NRW. This project serves as the scientific competence centre for IN4Climate.NRW, a unique initiative formed by politicians, industry and science to promote, among other activities, cross-industry collaboration for the implementation of a climate-neutral industry in the German federal state of North Rhine-Westphalia (NRW). Based on the results of the case study analysis, five key influencing factors are identified that drive the implementation of cross-industry collaboration for low-carbon innovation in EIIs: Cross-industry innovation projects benefit from institutionalised cross-industry exchange and professional project management and coordination. Identifying opportunities for regional integration as well as the mitigation of financial risk can also foster collaboration. Lastly, clear political framework conditions across industrial sectors are a key driver.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".