Carbon Emission Trends and Their Economic Implications: A Heuristic Approach to Information-Poor Environments

Carbon emission models are essential tools for analysing and predicting emission trends. However, the development of such models is often limited by a lack of sufficient data, making traditional statistical approaches difficult to apply. This study proposes a novel, qualitative, trend-based modelling framework that utilizes equation-less heuristics as an alternative to conventional, data-intensive carbon emission models.

The model employs a trend reasoning method based on expert knowledge and simplified indicators (increasing, constant, decreasing), applied to qualitative variables such as carbon strategy and profitability. Verbal knowledge statements are formalized without numerical values, allowing modelling in information-poor environments.

The resulting model generated 29 internally consistent future scenarios with defined trend-based transitions between them. The structure allows integration of interdisciplinary insights from economics, environmental science, engineering, and policy domains.

The proposed model enables structured analysis of emission scenarios without the need for precise data. It is flexible but relies on expert judgment and does not quantify scenario probabilities. Still, it offers valuable support for decision-making under uncertainty.

Trend-based models using qualitative reasoning provide a low-data, high-flexibility alternative for exploring carbon emission dynamics, supporting decision-making processes even without formal training in modeling theory.