Multi-country analysis of driving factors to carbon emissions using LMDI decomposition analysis method and rough set modeling

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ID: 286954
2021
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Abstract
Drivers to global carbon emissions have been widely investigated in the scientific literature. However, previous studies had focused mostly on individual countries or regions. In this study, the contribution of drivers to CO2 emissions, particularly economic activity, energy intensity, energy structure, and population of 195 countries grouped by income level are calculated and compared using the spatial logarithmic mean Divisia index (LMDI) method. Moreover, the effects of more specific socio-economic factors such research expenditure, poverty incidence, education level and trading goods, working population etc are examined by using a novel approach Rough Sets theory. This novel approach using rough sets is developed to uncover the effects of detailed socio-economic attributes to the emissions of 195 countries and regions. As significant advantage of rough set theory is its ability to work with incomplete data sets. Global datasets such as World Bank would still have gaps especially in less developed countries. This study aims to quantify macroeconomic, microeconomic and social factors which impacts global carbon dioxide emissions by conducting two different methods: (1) the spatial decomposition analysis (LMDI) method, and (2) Rough set. There is no implemented rough set theory on the evaluation of driving factor of global carbon emission increase. Rough set results present the attributes for low emissions and high emissions countries which focused on the social-economic factors whereas spatial LMDI decomposition focused on primary driving factors such as economic activities, energy intensity, energy structure, and population effect. Based on the result of spatial LMDI reveals that lower-middle-income countries are difficult to reduce energy intensity while the country need to meet the requirement of economic growth. The consumption of fossil fuels has been driving economic growth; therefore, reducing emissions may appear to threaten developing countries ‘progress. According to spatial perspective, China, India, European countries, and the US are the outlier because of their huge economic activities and energy intensity. It can point out that the said countries are the top emitters of carbon emissions, which similar to the study from the World Resources Institute showed that China, European Union, and the US are the top three emitters. Additionally, the rough set model reveals that high GDP per capita, high exports of goods and services, high industrial share in the national GDP, and large working population (population of ages 15-65) are the common drivers of a high-emission countries such as China, India, South Africa, Mexico, etc. However, based on the spatial
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Authors Mouy, Meta
Journal Malay Journal
Year 2021
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_mecheng/1
Keywords Keywords not found

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