Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses

Abstract

Low-carbon economic development is a strategy that is emerging in response to global climate change. Being the third-largest energy base in the world, Central Asia should adopt rational and efficient energy utilization to achieve the sustainable economic development. In this study, the logarithmic mean Divisia index (LMDI) decomposition method was used to explore the influence factors of CO 2 emissions in Central Asia (including Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan and Turkmenistan) during the period 1992-2014. Moreover, decoupling elasticity and decoupling index based on the LMDI decomposition results were employed to explore the relationship between economic growth and CO 2 emissions during the study period. Our results show that the total CO 2 emissions decreased during the period 1992-1998, influenced by the collapse of the Soviet Union in 1991 and the subsequent financial crisis. After 1998, the total CO 2 emissions started to increase slowly along with the economic growth after the market economic reform. Energy-related CO 2 emissions increased in Central Asia, mainly driven by economic activity effect and population effect, while energy intensity effect and energy carbon structure effect were the primary factors inhibiting CO 2 emissions. The contribution percentages of these four factors (economic activity effect, population effect, energy intensity effect and energy carbon structure effect) to the total CO 2 emissions were 11.80%, 39.08%,-44.82% and-4.32%, respectively, during the study period. Kazakhstan, Uzbekistan and Turkmenistan released great quantities of CO 2 with the annual average emissions of 189.69×10 6 , 45.55×10 6 and 115.38×10 6 t, respectively. In fact, their economic developments depended on high-carbon energies. The decoupling indices clarified the relationship between CO 2 emissions and economic growth, highlighting the occurrence of a ''weak decoupling'' between these two variables in Central Asia. In conclusion, our results indicate that CO 2 emissions are still not completely decoupled from economic growth in Central Asia. Based on these results, we suggest four key policy suggestions in this paper to help Central Asia to reduce CO 2 emissions and build a resource-conserving and environment-friendly society.

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

“…Among various IDA methods, LMDI is the preferred one because it can perfectly handle zero value and conduct without residuals ( Ang, 2004 ; Ang and Choi, 1997 ). Scholars have done enormous researches on carbon emission decomposition analysis, which covers a wide area from province ( Wang and Jiang, 2020 ), country ( Jiang et al, 2019 ; Ma et al, 2019 ; Yasmeen et al, 2020 ), region ( Li et al, 2019 ; Li et al, 2020 ), to even globe ( Chang et al, 2019 ). Li and Qin (2019) examined challenges for China to peak carbon emission in 2030 from both historical and future perspective.…”

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

“…For example, Etem et al [29] evaluated the decoupling process of Turkey and found that Turkey did not make or had made weak decoupling efforts in most cases, and that the biggest factor that had made decoupling efforts was energy intensity. Li et al [30] also found that energy intensity had made the biggest contribution to decoupling through an analysis of Central Asia. Wang et al [31] studied the status and efforts of urban carbon emission decoupling in different industrial stages in China, and concluded that cities in different industrial stages showed different decoupling states, driving forces and effects.…”

“…Engo et al [15] Xie et al [17] Etem et al [29] Li et al [30] Yan et al [32] LMDI and Tapio index Energy structure, energy intensity, economic structure, population economic activity [15,30], transmission and distribution loss [17] electrification [17], carbon intensity [29], fossil fuel intensity [29], conversion efficiency [29], carbon coefficient [32], fossil energy share [32], energy efficiency [32], transport activity [32] Wang et al [16] C-D production function, LMDI and Tapio [26], investment structure [26], investment dependence [26], emission reduction [28], energy saving [28], transport share [28] Shi et al [24] Decoupling index Per capita GDP, energy intensity, per capita carbon Zheng et al [27] LMDI and OECD decoupling model Carbon emission coefficient, population energy structure, industrial structure, energy intensity, economic growth…”

Differences of Carbon Emission Efficiency in the Belt and Road Initiative Countries

“…Studies reveal that energy consumption, carbon emission efficiency and economic development are closely linked [31][32][33][34]. The representative research method to measure the factors affecting carbon emission efficiency includes the Structural Decomposition Analysis (SDA) [23], the Index Decomposition Analysis (IDA) [34,35] and the Logarithmic Mean Divisia index (LMDI) [36][37][38]. LMDI is extensively applied in the studies, and solves the residual error and zero value problems [37].…”