Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries

Fan, Tijun; Luo, Ruiling; Xia, Haiyang; Li, Xiaopeng

doi:10.1007/s11069-014-1226-0

Cited by 53 publications

(18 citation statements)

References 13 publications

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“…Most of the previous studies have adopted the LMDI model, as it has a strong theoretical basis and adaptability, is easy to use, and offers better results interpretation. LMDI has been widely used in different countries, sectors, and environmental problems [16][17][18]; however, the factors that can be investigated by the index decomposition method still have some limitations. The decomposition factors are similar, and many other factors cannot be included in the analysis.…”

Section: Research On Driving Factors Behind Power Sectormentioning

confidence: 99%

Driving Factor Analysis of Carbon Emissions in China’s Power Sector for Low‐Carbon Economy

Yan

Lei

2017

Mathematical Problems in Engineering

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The largest percentage of China’s total coal consumption is used for coal-fired power generation, which has resulted in the power sector becoming China’s largest carbon emissions emitter. Most of the previous studies concerning the driving factors of carbon emissions changes lacked considerations of different socioeconomic factors. This study examines the impacts of eight factors from different aspects on carbon emissions within power sector from 1981 to 2013 by using the extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model; in addition, the regression coefficients are effectively determined by a partial least squares regression (PLS) method. The empirical results show that (1) the degree of influence of various factors from strong to weak is urbanization level (UL) > technology level (T1) > population (P) > GDP per capita (A) > line loss (T2) > power generation structure (T3) > energy intensity (T4) > industry structure (IS); (2) economic activity is no longer the most important contributing factor; the strong correlation between electricity consumption and economic growth is weakening; and (3) the coal consumption rate of power generation had the most obvious inhibitory effect, indicating that technological progress is still a vital means of achieving emissions reductions.

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Section: Research On Driving Factors Behind Power Sectormentioning

confidence: 99%

Driving Factor Analysis of Carbon Emissions in China’s Power Sector for Low‐Carbon Economy

Yan

Lei

2017

Mathematical Problems in Engineering

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“…Emissions studies on the petrochemical industry have generally used a direct calculation method: Li and Fan and Fan et al found that direct emissions from 2001 to 2010 increased by 81.3 Mtn, and they decomposed the drivers for those direct CO2 emissions [14,15]. Yet the EIO-LCA model, recent IO tables published by National Bureau of Statistics (NBS), and sectorial energy data provide us with the space in this research to perform an in-depth look at the embodied energy and emissions of petrochemical industry, the contributions of each sectors, and the influence and induction effects among sectors regarding energy consumption and CO2 emissions reduction in China.…”

Section: Review On Methodsmentioning

confidence: 99%

Energy Consumption and Energy-Related CO2 Emissions from China’s Petrochemical Industry Based on an Environmental Input-Output Life Cycle Assessment

Ling

Sager

2017

Energies

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Abstract:The fast-growing petrochemical industry is one of the largest energy consumers and emitters in China, exerting a strong impact on the national economic, energy and environmental systems. We provide a holistic picture of energy consumption and energy-related CO 2 emissions from China's petrochemical industry in 2012 through an environmental input-output life-cycle assessment (EIO-LCA). We combine two perspectives: (1) direct energy consumption and emissions, and (2) the indirect energy and emissions embodied and reallocated from other sectors in the supply chain to satisfy final demand in the petrochemical industry. Results indicate that the total of its direct and indirect energy consumption and CO 2 emissions accounts for approximately 32% and 18% of China's industrial total, respectively, exerting high "influence" and "induction" with regards to the rest of the economic sectors. Most of the petrochemical industry's embodied energy and CO 2 emissions comes from the "Production and Supply of Electric and Heat Power". We also identified five other sectors key to China's energy conservation and CO 2 mitigation efforts due to their high influence and induction effects: "Smelting and Pressing of Ferrous Metals", "Manufacture of Non-metallic Mineral Products", "Smelting and Pressing of Non-ferrous Metals", "Transport, Storage and Post", and "Mining and Washing of Coal". A systematic view of direct and indirect energy, environmental relationships, and the conveying effects among sectors is crucial for policymaking in China to achieve its energy and mitigation goals.

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“…The petrochemical industry is one of the important energy-based industries for the development of the national economy [1]. While ethylene is the foundation of the chemical industry, its production level usually represents the level of development of a country's petrochemical industry [2].…”

Section: Introductionmentioning

confidence: 99%

ABC-ANFIS-CTF: A Method for Diagnosis and Prediction of Coking Degree of Ethylene Cracking Furnace Tube

et al. 2019

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The carburizing and coking of ethylene cracking furnace tubes are the important factors that affect the energy efficiency of ethylene production. To realize the diagnosis and prediction of the different coking degrees of cracking furnace tubes, and then take corresponding treatment measures, is of great significance for improving ethylene production and prolonging the service life of the furnace tube. Therefore, a fusion diagnosis and prediction method based on artificial bee colony (ABC) and adaptive neural fuzzy inference system (ANFIS) is proposed, which also introduces a coking-time factor (CTF). The actual data verification shows that the method not only improves the training efficiency and diagnosis accuracy of the coking diagnosis and inference system of the cracking furnace tube, but also realizes the prediction of the development trend of the coking degree of the furnace tube.

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Using LMDI method to analyze the influencing factors of carbon emissions in China’s petrochemical industries

Cited by 53 publications

References 13 publications

Driving Factor Analysis of Carbon Emissions in China’s Power Sector for Low‐Carbon Economy

Driving Factor Analysis of Carbon Emissions in China’s Power Sector for Low‐Carbon Economy

Energy Consumption and Energy-Related CO2 Emissions from China’s Petrochemical Industry Based on an Environmental Input-Output Life Cycle Assessment

ABC-ANFIS-CTF: A Method for Diagnosis and Prediction of Coking Degree of Ethylene Cracking Furnace Tube

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