A Fuzzy-based model proposal for forecasting greenhouse gas-free supply chain potential

Year 2024, Volume: 9 Issue: 3, 519 - 542, 18.09.2024

Gürkan Işık , Miraç Tuba Çelik

https://doi.org/10.58559/ijes.1420845

Cited By: 1

Abstract

Various greenhouse gas emission control approaches, such as filtration, require costly audits and are not suitable for creating foresight to scale gains across the supply chain. Thus, these practices are not suitable for building effective policies to reduce greenhouse gas emissions. This study proposes an approach to forecast greenhouse gas-free supply chain potential based on the producible renewable energy certificate amount to be able to build consistent, realistic, effective, and applicable policies to reduce emissions and promote renewable energy production. The greenhouse gas-free supply chain potential of countries and states can be measured and tracked through their total energy consumption certified with renewable energy certificates. By proportioning this value to the total energy consumption of the supply chain, the extent to which the green transformation has been achieved can be measured and scaled. The proposed model is built on fuzzy logic since renewable energy certificates contain uncertainties, and there is not enough data to make machine learning-supported forecasts because it is a developing field and an innovative business. The developed model is applied to the example of Türkiye, and the practical greenhouse gas-free supply chain potential of Türkiye is forecasted as 30.9 million megawatts (equivalent to 221 thousand ten-year trees) for 2024. Even in possible adverse events in the market and climatic conditions, it is not expected to decrease below 22.7 million megawatts. By considering these calculations, more realistic and more applicable obligatory energy policies can be produced without bringing additional audit burdens to the industrialists across the country.

Keywords

Renewable energy certificate, Green supply chain, Greenhouse gas emission, Electricity trading, Fuzzy logic

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