Modelling Social-Economic-Climatic Feedbacks for Policy Development
Bibliographic record
Abstract
Simple models of the social-economic-climatic system offer an alternative to the standard GCM-driving scenario modelling approach, which focuses on the climate system and neglects important feedback-effects from socio-economic systems. This failure to represent the whole system is clearly problematic, because natural and socio-economic systems exhibit complex, nonlinear behaviour, and each certainly affects the other. We therefore offer an alternative approach, based on explicit modelling of the feedbacks within and between components of the system. The system dynamics simulation methodology used here facilitates representation of feedback processes, time delays, and nonlinearities, and encourages an understanding of the interconnections within a system that fundamentally determine its behaviour. As a working example of the system dynamics approach, our paper describes a simple climate-carbon cycle-water cycle-population closed-loop model. A set of three experiments compare a "business-as-usual" case with model modifications, including exogenous technology change, and endogenous equation and parameter changes. Analysis of the experimental results demonstrates model sensitivities and shortcomings: exponential growth patterns may indicate the necessity for additional model sectors and feedbacks, and sensitive relationships suggest the need for further study. Future work will improve the representation of socio-economic sectors of the model.
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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".