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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F26%3A0200343" target="_blank" >RIV/00216305:26510/26:0200343 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/pages/publications/105026786796?origin=resultslist" target="_blank" >https://www.scopus.com/pages/publications/105026786796?origin=resultslist</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2174/0125902776406706250928033400" target="_blank" >10.2174/0125902776406706250928033400</a>

Result language

angličtina

Original language name

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

Original language description

Introduction 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. Methods 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. Results 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. Discussion 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. Conclusion 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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

50204 - Business and management

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

The open environmental research journal

ISSN

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e-ISSN

2590-2776

Volume of the periodical

18

Issue of the periodical within the volume

12

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

9

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105026786796