Multi-country analysis of driving factors to carbon emissions using LMDI decomposition analysis method and rough set modeling - Mouy, Meta (Animo Repository, 2021)
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 LMDI decomposition method demonstrated that the energy intensity has been decreased while maintaining GDP growth. This means that they reduce carbon emissions without compromising growth for developing countries. From spatial LMDI decomposition outputs reveal that economic activity, energy intensity, and population growth played an increasingly important role in global carbon emissions increasing which is in line with results documented in previous studies. Rough set generates social-economic driving factors such as high export of good and services, high industrial share in the national GDP, high working population, agricultural exports, high pump prices, and research expenditure which contribute to the growth of global carbon emissions. Both methods generated notable results which provides different roles of each driving factors, and these factors can be sited as the main reason of the significant increase in CO2 emissions in the globe. Especially results produces from rough set model is a great potential for policymakers to implement climate changes policies in the globe. This study can provide practical policies to suppress the increase of CO2 emissions; however, there are some limitations to the current study. An investigation on global carbon emissions may be improved by employing more accurate and updated data. More specific drivers of carbon emissions by using spatial LMDI decomposition model such as industrial activities effect, R&D intensity effects may be investigated for future work.