The Linkage between Natural Gas Consumption and Industrial Output: New Evidence Based on Time Series Analysis

Shaari, Mohd Shahidan; Majekodunmi, Temitayo B.; Zainal, Nor Fadzilah FADZILAH; Harun, Nor Hidayah; RIDZUAN, ABDUL RAHIM

doi:10.2139/ssrn.4383568

Cited by 1 publication

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“…Natural gas, which has a lower carbon footprint than other non-renewable energy sources like coal and oil, is being promoted by laws in other nations as well. Specifically, compared to coal and oil, natural gas produces 20 to 50% less CO2, making it a greener energy source [4].…”

Section: Introductionmentioning

confidence: 99%

Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption

Nalici,

Soylemez,

Ünlü

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Natural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.

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Section: Introductionmentioning

confidence: 99%