Performance and decomposition analyses of carbon emissions from industrial energy consumption in Tianjin, China

Shao, Chaofeng; Guan, Yang; Wan, Zheng; Guo, Chao; Chu, Chunli; Mei, Ju

doi:10.1016/j.jclepro.2013.08.017

Cited by 114 publications

(51 citation statements)

References 16 publications

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“…Vaninsky [29] expanded the Cara identity and compared the differences in carbon emissions between the US and China from GDP, energy consumption carbon intensity and population. Shao [30] analyzed the time series of carbon emissions from the energy consumption of the industrial sector and divided industries into different types and analyzed combination features of "emissions-efficiency".…”

Section: Introductionmentioning

confidence: 99%

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Fan

Meng

2015

Energies

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Based on the international community's analysis of the present CO2 emissions situation, a Log Mean Divisia Index (LMDI) decomposition model is proposed in this paper, aiming to reflect the decomposition of carbon productivity. The model is designed by analyzing the factors that affect carbon productivity. China's contribution to carbon productivity is analyzed from the dimensions of influencing factors, regional structure and industrial structure. It comes to the conclusions that: (a) economic output, the provincial carbon productivity and energy structure are the most influential factors, which are consistent with China's current actual policy; (b) the distribution patterns of economic output, carbon productivity and energy structure in different regions have nothing to do with the Chinese traditional sense of the regional economic development patterns; (c) considering the regional protectionism, regional actual situation need to be considered at the same time; (d) in the study of the industrial structure, the contribution value of industry is the most prominent factor for China's carbon productivity, while the industrial restructuring has not been done well enough.

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Section: Introductionmentioning

confidence: 99%

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Fan

Meng

2015

Energies

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“…Mishra et al [38] conducted a decomposition analysis on global CO 2 emissions using the LMDI (logarithmic mean Divisia index) approach and scenario planning. Ouyang and Lin [39], and Shao et al [40] also conducted a similar study. Morrow et al [41], Salahuddin and Gow [42], and Wen et al [43] explored the connection between energy consumption and CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 83%

Scenario Prediction of Energy Consumption and CO2 Emissions in China’s Machinery Industry

Lin

Liu

2017

Sustainability

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Abstract:Energy conservation and CO 2 abatement is currently an important development strategy for China. It is significant to analyze how to reduce energy consumption and CO 2 emissions in China's energy-intensive machinery industry. We not only employ a cointegration method and scenario analysis to predict the future energy demand and CO 2 emissions in China's machinery industry, but we also use the Monte Carlo simulation to test the validity of the predictions. The results show that energy demand in the industry will respectively reach 678.759 Mtce (million ton coal equivalent) in 2020 and 865.494 Mtce in 2025 under the baseline scenario. Compared with the baseline scenario, the energy savings in 2020 will respectively be 63.654 Mtce and 120.787 Mtce in the medium and advanced scenarios. Furthermore, we forecast the corresponding CO 2 emissions as well as the reduction potential respectively in 2020 and 2025. In order to achieve energy conservation and emissions reduction, the government should increase energy price, levy environmental taxes based on the emissions level of machinery enterprises, promote mergers and acquisitions of enterprises, and expand the scale of enterprises. This paper provides a reference for energy conservation and CO 2 abatement policy in China's machinery industry.

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“…This method can, for example, directly decompose the variation in pollutant emissions and make a quantitative analysis of the influencing factors (Chen et al, 2004;Fujii et al, 2013). Furthermore, the decomposition method separates the contributions and relative importance of the various mechanisms available for changes in pollution emission, and this provides an empirical basis for establishing and examining the pollution reduction measures used (Selden et al, 1999;Shao et al, 2014). Here a specific index decomposition analysis model is developed.…”

Section: Index Decomposition Analysismentioning

confidence: 99%

An analysis of the driving forces behind pollutant emission reduction in Chinese industry

Yao

Liu

Tao

et al. 2016

Journal of Cleaner Production

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Performance and decomposition analyses of carbon emissions from industrial energy consumption in Tianjin, China

Cited by 114 publications

References 16 publications

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

Scenario Prediction of Energy Consumption and CO2 Emissions in China’s Machinery Industry

An analysis of the driving forces behind pollutant emission reduction in Chinese industry

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