Statistical behavior of the European Energy Exchange-Zero Carbon Freight Index (EEX-ZCFI) assessments in the context of Carbon Emissions Fraction Analysis (CEFA)

• Published in: Sustainable futures 2024-06, Vol.7, p.100164, Article 100164
• Main Authors: Kimothi, Sanjeev, Bhatt, Vyomkesh, Kumar, Somil, Gupta, Ashulekha, Dumka, Umesh Chand
• Format: Article
• Language: English
• Subjects: European Energy Exchange, GARCH; Green house gas emission; Zero carbon freight index
• ISSN: 2666-1888; 2666-1888
• DOI: 10.1016/j.sftr.2024.100164

Greenhouse gas (GHG) emissions through freight transportation have received growing concerns and are one of the critical issues for the United Nation's Sustainability Development Goals (SDGs). The literature survey reveals that freight transportation makes up global GHG emissions and its carbon emissions may double by 2050. In European Union (EU) carbon taxes are a promising way to reduce CO2Carbon di oxide and other GHG emissions. The European Energy Exchange (EEX) recently launched its new Zero Carbon Freight Index (ZCFI). EEX-ZCFI provides the first insight into how much the price of carbon will add to freight costs. This work developed the analysis of a ZCFI using a statistical model (i.e. GARCH and FBM) with the intention of offering a reference for understating the wide-range ramifications of such indices. Preprocessing methods (descriptive statistics, unit root test, and an ARCH effect test) are performed to verify the validity of the GARCH (1,1) model for forecasting volatility. This work employs the EEX-ZCFI time series for January 2020 to August 2022. To further examine the carbon freight indices, a Ljung-Box test method based on the GARCH model was applied. The bootstrapped returns are forming a linear relation with the forecast data; therefore, it concluded that the model designed strongly fits with the time series. With GARCH optimal model parameters we have forecasted the carbon freight index time series data and hypothetically examined the influence on the carbon emission with C5TC time series, which can also be applied in the Asia-pacific region.