• Fourth Industrial Review
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• v.3 no.2
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• pp.1-10
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• 2023

Purpose - The main purpose of the paper is to examine the variables affecting carbon emissions in different nations around the world. Research design, data, and methodology - To measure its impact on carbon emissions, secondary data has data of the top 50 Countries have been taken. The stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model have been used to quantify the factors that affect carbon emissions. A modified version using Industry 4.0 and region in fundamental STIRPAT model has been applied with the ordinary least square approach. The outcome has been measured using both the basic and extended STIRPAT models. Result - Technology found a positive determinant as well as statistically significant at the alpha level of 0.001models indicating that technological innovation helps reduce carbon emissions. In total, 4 models have been derived to test the best fit and find the highest explaining capacity of variance. Model 3 is found best fit in explanatory power with the highest adjusted R2 (97.95%). Conclusion - It can be concluded that the selected explanatory variables population and Industry 4.0 are found important indicators and causal factors for carbon emission and found constant with all four models for total CO2 and Co2 per capita.

• Environmental Engineering Research
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• v.21 no.1
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• pp.74-83
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• 2016

The purpose of this study is to identify the driven factors affecting the changes in energy-related $CO_2$ emissions in Hebei Province of China from 1995 to 2013. This study confirmed that energy-related $CO_2$ emissions are correlated with the population, urbanization level, economic development degree, industry structure, foreign trade degree, technology level and energy proportion through an improved STIRPAT model. A reasonable and more reliable outcome of STIRPAT model can be obtained with the introducing of the Ridge Regression, which shows that population is the most important factor for $CO_2$ emissions in Hebei with the coefficient 2.4528. Rely on these discussions about affect abilities of each driven factors, we conclude several proposals to arrive targets for reductions in Hebei's energy-related $CO_2$ emissions. The method improved and relative policy advance improved pointing at empirical results also can be applied by other province to make study about driven factors of the growth of carbon emissions.

• Environmental and Resource Economics Review
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• v.33 no.3
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• pp.241-261
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• 2024

This study analyzes the determinants of greenhouse gas emissions using data from 16 metropolitan municipalities in South Korea. The STIRPAT model, which probabilistically models environmental impacts, was employed for the analysis. Both homogeneous and heterogeneous panel analyses were utilized. Recognizing that results from homogeneous panel analysis could be distorted due to the characteristics of panel data, cross-sectional dependence and slope homogeneity tests were conducted. The tests indicated that it is appropriate to use estimates that consider cross-sectional dependence and reflect slope heterogeneity. Therefore, the results from heterogeneous panel analysis were presented as the main findings. The analysis identified income (per capita GRDP) and energy efficiency (energy intensity) as key determinants of greenhouse gas emissions. Conversely, population was found not to be a key factor, and the industrial structure of the regions (share of the service industry in value-added) was also identified as a potential determinant of greenhouse gas emissions. The hypothesis of the Environmental Kuznets Curve was not statistically significant, suggesting that improving energy efficiency, rather than income growth and economic development, would be the most effective policy tool for reducing greenhouse gases in each municipality.

• Journal of the Economic Geographical Society of Korea
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• v.24 no.4
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• pp.400-410
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• 2021

This study analyzed the determinants of carbon dioxide emissions through the expanded STIRPAT model using panel data from 16 metropolitan cities and provinces in Korea from 2000 to 2019. After testing cross-sectional dependence and coefficient heterogeneity of panel data, we performed analysis using MG, CCEMG, and AMG estimation methods reflected these characteristics. The results of analysis using the AMG estimation method are as follows. The coefficients of income, population, and energy intensity were statistically significant with a positive sign, but urbanization was statistically insignificant. Reduction of carbon dioxide emissions in Korea can be achieved through an increase in energy efficiency and sustainable economic growth. It is necessary to establish a policy that can contribute to sustainable economic growth by inducing productivity improvement through technology innovation reducing carbon dioxide emissions in the long-term as well as building a low-carbon society through active development of carbon dioxide reduction technology.

• East Asian Journal of Business Economics (EAJBE)
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• v.2 no.4
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• pp.1-8
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• 2014

This study examines the relationship between population growth and carbon dioxide emissions in the context of Africa perspective. Population growth and carbon dioxide emissions helped identify the key driving forces of environmental impacts by including other predictors in all the different income levels of all sampled countries in Africa. To explore the role of population growth in the emissions of carbon dioxide, this research employed a panel data set of 52 Africa countries from 1960 to 2012 using fixed effects, random effects and GLS/FGLS estimators to estimate the modified STIRPAT model. The results found that a 1% increase in population growth suggests an increase in carbon dioxide emission loads by about 0.33%, 1.08%, 0.57% and 2.32% on the average, controlling for all other anthropogenic driving forces, for LICA, LMICA, UICA and HICA respectively. There is a significant relationship between population growth and carbon dioxide emissions in all the national income levels in Africa.