Regional energy-related carbon emission characteristics and potential mitigation in eco-industrial parks in South Korea: Logarithmic mean Divisia index analysis based on the Kaya identity

Energy ◽  
2012 ◽  
Vol 46 (1) ◽  
pp. 231-241 ◽  
Author(s):  
Seok Jung ◽  
Kyoung-Jin An ◽  
Gjergj Dodbiba ◽  
Toyohisa Fujita
Keyword(s):  
South Korea ◽  
Carbon Emission ◽  
Emission Characteristics ◽  
Logarithmic Mean ◽  
Divisia Index ◽  
Regional Energy ◽  
Index Analysis ◽  
Industrial Parks ◽  
Kaya Identity

scholarly journals Multi-scale decomposition of energy-related industrial carbon emission by an extended logarithmic mean Divisia index: a case study of Jiangxi, China

2019 ◽  
Vol 12 (8) ◽  
pp. 2161-2186 ◽  
Author(s):  
Junsong Jia ◽  
Huiyong Jian ◽  
Dongming Xie ◽  
Zhongyu Gu ◽  
Chundi Chen
Keyword(s):  
Carbon Emission ◽  
Logarithmic Mean ◽  
Divisia Index ◽  
Industrial Carbon ◽  
Multi Scale

Study on Factors Analysis Low-Carbon Development in Hi-Tech Industrial Park − A Case Study in China

2012 ◽  
Vol 610-613 ◽  
pp. 3149-3153
Author(s):  
Juan Wen ◽  
Wen Tao Chang ◽  
Peng Fei Shi ◽  
Tao Sun
Keyword(s):  
High Tech ◽  
Low Carbon ◽  
Logarithmic Mean ◽  
Factors Affecting ◽  
Strategic Option ◽  
Factors Analysis ◽  
Index Analysis ◽  
Industrial Parks ◽  
Low Carbon Development

Low-carbon development has been well recognized as a strategic option for the transformation during the socio-economic development in China. As a symbol of highest level of research and development, high-tech eco-industrial parks are playing an important role in response to climate change and the trend of low-carbon development, to maximize the low-carbon development potentials and bridge a unique and feasible solution to low-carbon development. This paper explored the factors affecting the development of high-tech eco-industrial parks using Logarithmic Mean Weight Division Index (LMDI)index analysis and Kaya equation, and summarized these factors as economic growth, industrial structures, energy efficiency, energy structures and carbon emission coefficients.

scholarly journals Characteristics, Decoupling Effect And Driving Factors of Regional Tourism's Carbon Emissions In China

2021 ◽  
Author(s):  
Guobao Xiong ◽  
Junhong Deng ◽  
Baogen Ding
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Driving Factors ◽  
Logarithmic Mean ◽  
Emission Data ◽  
Divisia Index ◽  
Its Regions ◽  
Decoupling Effect

Abstract Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data suggest weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect on emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

scholarly journals Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Qing-Xiang Ou
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Development Path ◽  
Optimal Outcomes ◽  
Emissions Intensity ◽  
Kaya Identity

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.

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