Decomposition of intensity of energy-related CO 2 emission in Chinese provinces using the LMDI method

Examining the effects of urbanization and industrialization on carbon dioxide emission: Evidence from China's provincial regions

“…First, the largest contribution to regional carbon dioxide emission was made by the energy intensity in the production sector, followed by the contribution of transportation sector and business sector. The dominating role played by the production sector is consistent with the similar studies [26,30], however, another important phenomenon that should be noticed is the rapid changes in transport and business sector during the urbanization process. As the emissions in the transportation and the business sector rose rapidly, the positive effects made by both these sectors to the emissions were also increasing considerably, however, the other three sectors, production, construction, and agriculture experienced a decrease of their impacts on the emissions.…”

“…Most of them confirm that the main influencing factors are energy intensity, energy mix, gross domestic product structure, and GDP itself [3,4,10,22,30,32,33]. Wang et al [19] decomposed the carbon emissions into population, GDP per capita, energy consumption intensity and energy consumption structure and concluded that the total theoretical decrease of CO 2 emission from 1957 to 2000 can be attributed to fossil fuel mix and renewable energy penetration.…”

“…Another major shortcoming of existing literature is that regional heterogeneity has been scarcely taken into consideration in the discussion of urbanization and regional CO 2 emissions [30]. There is a huge difference in population, economic development level, urbanization level, energy efficiency and other indicators in different regions of China [11].…”

Regional-Level Allocation of CO2 Emission Permits in China: Evidence from the Boltzmann Distribution Method

“…On the basis of IPAT, the Japanese scholar Yoichi Kaya proposed the Kaya carbon emission equation. Scholars [11][12][13][14] also investigated LMDI and Kaya identity models to study energy and carbon emission factors. Xu, Shichun et al [15] rewrote the Kaya identity, and analyzed the influencing factors of carbon emissions in china by the decomposition of carbon emissions related to energy consumption.…”

Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China

“…The empirical study also indicated that the decline in the proportion of secondary industries was conducive to lower the carbon intensity. Zhang et al [9] explored the driving factors of carbon intensity of China's 29 provinces from 1995 to 2012 employing the Logarithmic Mean Divisia Index (LMDI) method, and revealed that energy utilization technology progress and energy structure optimization were the two major driving forces for the decline of carbon intensity in the second and tertiary industries. In addition, Yi [10] adopted fixed-effect panel regressions to test the impacts of clean-energy policies on carbon emissions of electricity sector in the U.S. states, and found that supply-side energy policy was conducive to the reduction of carbon intensity and more aggressive policies on the demand side were needed to reduce total carbon emissions in the U.S. electricity sector.…”