Study on factors affecting energy-related per capita carbon dioxide emission by multi-sectoral of cities: a case study of Tianjin

Chen, Yanan; Lin, S.-W.

doi:10.1007/s11069-015-1632-y

Cited by 10 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decomposition results signal that during the period of 1995-2012, the cumulative economic structure adjustment effect (ΔIS) is negative with a nominal contribution value of 2.46 million tons of CO2 decrease, which only accounted for −6.6% of the total CO2 emission changes, consistent with the results of other studies [35,37,38]. The ΔIS played a positive driving role in the growth of Tianjin's energy-related CO2 emissions during 2002-2008, and over the same period, the proportion of secondary industry rose from 49.7% to 55.2% and the value of heavy industrial output increased rapidly.…”

Section: Structure Effectsupporting

confidence: 89%

“…The percentage of the urban population of Tianjin increased continually throughout the examined period, i.e., it rose from 52.93% in 1995 to 81.55% in 2012, with an annual growth rate of 2.58%. The ΔUR plays a role in facilitating the increase of CO2 emissions but with a relatively smaller contribution compared with other positive driving factors, and the cumulative and relative contribution of which during 1995-2012 is 0.66 million tons and 1.8% respectively, which is in agreement with the study of Tianjin's energy-related per capita CO2 emission [35]. …”

Section: Structure Effectsupporting

confidence: 88%

“…The contribution degree of the four factors above and the energy-related CO2 emission changes in percentage are shown in Figure 2. The energy intensity's decline, expressed as the reduction of energy consumption per unit of GDP, usually due to the energy efficiency improvement and energy saving, leads to the negative contribution and propitious to the reduction of CO2 emissions; therefore, the ΔEI is generally recorded as the most important inhibiting factor in decomposition analysis studies of carbon emissions [16,26,34,35]. Energy efficiency in Tianjin has progressed substantially in recent years, especially during the period of the FYP11 (2006-2010), due to the implementation of a series of energy saving and emission reduction measures, supported by an average annual energy consumption growth rate of 10.8%, Tianjin achieved an average annual economic growth rate of 16.1%; and the energy consumption per GDP of Tianjin fell continuously from 2.58 tce/10 4 yuan in 1995 to 0.67 tce/10 4 yuan in 2012.…”

Section: Energy Intensity Effectmentioning

confidence: 99%

See 2 more Smart Citations

Factor Decomposition Analysis of Energy-Related CO2 Emissions in Tianjin, China

et al. 2015

View full text Add to dashboard Cite

Abstract:Tianjin is the largest coastal city in northern China with rapid economic development and urbanization. Energy-related CO2 emissions from Tianjin's production and household sectors during 1995-2012 were calculated according to the default carbon-emission coefficients provided by the Intergovernmental Panel on Climate Change. We decomposed the changes in CO2 emissions resulting from 12 causal factors based on the method of Logarithmic Mean Divisia Index. The examined factors were divided into four types of effects: energy intensity effect, structure effect, activity intensity effect, scale effect and the various influencing factors imposed differential impacts on CO2 emissions. The decomposition outcomes indicate that per capita GDP and population scale are the dominant positive driving factors behind the growth in CO2 emissions for all sectors, while the energy intensity of the production sector is the main contributor to dampen the CO2 emissions increment, and the contributions from industry structure and energy structure need further enhancement. The analysis results reveal the reasons for CO2 emission changes in Tianjin and provide a solid basis upon which policy makers may propose emission reduction measures and approaches for the implementation of sustainable development strategies.

show abstract

Section: Structure Effectsupporting

confidence: 89%

Section: Structure Effectsupporting

confidence: 88%

Section: Energy Intensity Effectmentioning

confidence: 99%

See 1 more Smart Citation

Factor Decomposition Analysis of Energy-Related CO2 Emissions in Tianjin, China

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The LMDI technique was used for the carbon increment of Beijing, and results showed that economic activity was the most important factor of carbon emissions growth [35,36]. Chen and Lin used LMDI and Kaya extension methods to analyze the factors of carbon emissions in Tianjin during the period of 2005-2011 and concluded that economic output played a positive role in the increase of carbon emissions, while economic structure, energy intensity of the production sectors, and energy mix of the production sectors played negative roles [37].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Wang

Yufang

Zhao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO 2 /energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

show abstract

“…Shao et al investigated the primary factors governing carbon emissions related to energy consumption from industry in Tianjin and determined that the improvement of energy efficiency is the most effective way to achieve energy conservation and emission reductions in industry [15]. In addition, a few scholars have analyzed the specific influence of various factors on carbon emissions based on the regional change [16][17][18][19]. Ang et al and Wang et al have pointed out that carbon emissions are principally caused by economic growth with the LMDI decomposition approach [20][21].…”

mentioning

confidence: 99%