Carbon pricing in dynamic regulation and changing economic environment - agent based model
Bibliographic record
Abstract
Like many industries, the financial services sector increasingly confronts a market environment in which both consumers and regulators are anxious to see organizations develop green products and services that hold the promise of mitigating environmental degradation and encouraging sustainable use of resources. Industry players must therefore be adept at reading demand signals from each of the primary financial services sectors (retail banking, corporate and investment banking, asset management, and insurance) while also keeping a sharp eye on evolving changes in these highly regulated businesses driven by proactive government policies (Porter and Kramer, 2006). Weather derivatives, energy trading and natural resource exploration are only few of the sustainability topics vital for the economic future of our province (Alberta), and the entire world. Our paper presents an overview of complex adaptive systems and the basics of building a model of CAS. We consider CAS to be well suited to the modeling of market behavior because it is robust to micro-level behavioral influences and allows the inclusion of heterogeneous agents. CAS also offers the possibility of capturing the dynamics of agents experience through features such as learning and memory. We present an agent-based model for pricing carbon emissions and results of simulations based on Alberta's current data of demand, supply, and regulation of carbon emissions. Pricing carbon drives innovation in technologies that improve efficiency, reduce pollution and recognize the social cost of business. We analyze the results of simulations in a dynamic framework of changing parameters and input variables. Keywords: Agent Based Modeling, Carbon Market, Alberta Carbon Trading Scheme, Swarm software
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".