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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

The open environmental research journal

ISSN

—

e-ISSN

2590-2776

Svazek periodika

18

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105026786796