Identifying the major players behind increasing global carbon dioxide emissions: a decomposition analysis

Abstract

The paper using data on 114 countries during 1992-2004 identifies the major perpetrators of escalating global emissions. Using the LMDI decomposition technique, we examine the contribution of the major factors in changing the level of emissions. The effect of GDP on emission is found to be substantially more than that of population. However, the income effect shows high fluctuation over time, while the population effect has been roughly constant.

Variance analysis of global CO2 emission – A management accounting approach for decomposition study

“…At the regional level, North America had the highest correlation between emission in 1993 and the change in emission, followed by Africa, while for Eurasia and Europe there had been negative correlation. The high emitting countries in 1993 of North America 1 However, a few studies have been made on large sample (See World Bank [29] and Pani and Mukhopadhyay [30]). 2 For detailed comments on the IPAT model see Gans and Jost [33].…”

“…A common contention among these studies is that structural change of industries has the most dominating role, while inter-fuel substitution has very little contribution. However, although emission intensity has considerable role in reducing emission (World Bank [29] and Pani and Mukhopadhyay [30]), its decomposition and detail analysis has eluded the attention of researchers.…”

Decomposition Analysis of Energy-Related Industrial CO2 Emissions in China

“…At the global level, the driving forces for the changes of energy consumption and carbon emissions are decomposed in multiple scales [12][13][14]. On top of this, the secondary decomposition is further conducted for intensity factors of CO 2 emissions from sectors like manufacturing and transportation [15,16].…”

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

“…Hasiao (2012) applied the improved nonlinear gray model (Bernoulli) to analyze the characteristics of carbon dioxide emissions, energy consumption and actual output in China and to establish a predictive model of numerical iteration [20]. Pani (2010) applied correlation analysis to study the correlation of energy consumption, GDP and carbon emissions [21]. Wenying (2015) conducted a bottom-up analysis of energy consumption and carbon dioxide emissions from the Chinese steel industry [22].…”