Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model

Abstract

Quantitative analysis of the impact factors in energy-related CO 2 emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO 2 emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO 2 emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO 2 emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO 2 emissions, which produced 110.86 Mt CO 2 emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO 2 emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused-110.90 Mt CO 2 emissions with a contribution rate of-43.94%, followed by the energy carbon structure effect resulting in-18.76 Mt CO 2 emissions with a contribution rate of-7.43%. In order to provide an in-depth examination of the change response between energy-related CO 2 emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO 2 emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.

Impact of Land Urbanization on Carbon Emissions in Urban Agglomerations of the Middle Reaches of the Yangtze River

“…The Logarithmic Mean Divisia Index (LMDI), one type of IDA which does not produce a residual term, is more suitable for temporal analysis and has been widely used in the decomposition analysis of carbon emissions [ 46 ]. Research that applied the LMDI to decompose CO 2 emissions focused not only on energy-related and industrial sectors but also on sectors such as transportation, land use, and agriculture [ 28 , 31 , 47 , 48 , 49 , 50 , 51 ].…”

Spatial spillover effect and driving forces of carbon emission intensity at the city level in China

“…Current research on the relationship between transportation and carbon emissions mainly includes the influencing factors of transportation carbon emissions (Zhang and Zeng, 2013), the impact of the transportation sector (Glaeser and Kahn, 2010), and transportation infrastructure (Xie et al, 2017) on carbon emissions. Based on existing research results (Li et al, 2018;Su et al, 2018), this study selected economic development level, population density, industrial structure, capital investment intensity, foreign capital intensity, land urbanization, and road density as driving factors, combining the quantile sorting technique in the Markov method and quantile regression method to explore the driving factors of urban CEI.…”

Kazakhstan's CO2 emissions in the post-Kyoto Protocol era: Production- and consumption-based analysis

“…Assembayeva et al (2018) focused on Kazakhstan's electricity system and used a techno-economic model to account for related particularities; Tokbolat et al (2018) evaluated the efficiency of energy consumption of residential buildings in Astana and Kerimray, as well as the decarbonisation of the residential sector (Kerimray, 2018;Kerimray et al, 2018b); Onyusheva et al (2017) researched a similar topic in the transport and energy sectors. For empirical studies, Li et al (2018) adopted the Logarithmic Mean Divisia Index (LMDI) decomposition and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to study major driving factors of CO 2 emissions in Kazakhstan from 1992 to 2013 and Kerimray et al (2018c) used LMDI to analyse energy intensity; Xiong et al (2015) explored the development of Kazakhstan's low-carbon economy by decoupling relationship analysis, reflecting the relationship between energy consumption and economic growth. Besides, Kazakhstan also established the domestic national Emissions Trading Schemes (Gulbrandsen et al, 2017), where an extended GTAP-E model was applied to estimate emissions permits allocation (Nong and Siriwardana, 2017); carbon sequestration as a reduction tool was also discussed to help toward building low-carbon society (Kurganova et al, 2015).…”