Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great.

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Decomposition Analysis of Carbon Emissions from Energy Consumption in Beijing-Tianjin-Hebei, China: A Weighted-Combination Model Based on Logarithmic Mean Divisia Index and Shapley Value

Sustainability ◽

10.3390/su10072535 ◽

2018 ◽

Vol 10 (7) ◽

pp. 2535 ◽

Cited By ~ 6

Author(s):

Yi Liang ◽

Dongxiao Niu ◽

Weiwei Zhou ◽

Yingying Fan

Keyword(s):

Economic Development ◽

Energy Consumption ◽

Carbon Emissions ◽

Emission Reduction ◽

Carbon Emission ◽

Industrial Structure ◽

Logarithmic Mean ◽

Consumption Structure ◽

Consumption Intensity ◽

Weighted Combination

The Beijing-Tianjin-Hebei (B-T-H) region, who captures the national strategic highland in China, has drawn a great deal of attention due to the fog and haze condition and other environmental problems. Further, the high carbon emissions generated by energy consumption has restricted its further coordinated development seriously. In order to accurately analyze the potential influencing factors that contribute to the growth of energy consumption carbon emissions in the B-T-H region, this paper uses the carbon emission coefficient method to measure the carbon emissions of energy consumption in the B-T-H region, using a weighted combination based on Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV). The effects affecting carbon emissions during 2001–2013 caused from five aspects, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size, are quantitatively analyzed. The results indicated that: (1) The carbon emissions had shown a sustained growth trend in the B-T-H region on the whole, while the growth rates varied in the three areas. In detail, Hebei Province got the first place in carbon emissions growth, followed by Tianjin and Beijing; (2) economic development was the main driving force for the carbon emissions growth of energy consumption in B-T-H region. Energy consumption structure, population size and industrial structure promoted carbon emissions growth as well, but their effects weakened in turn and were less obvious than that of economic development; (3) energy consumption intensity had played a significant inhibitory role on the carbon emissions growth; (4) it was of great significance to ease the carbon emission-reduction pressure of the B-T-H region from the four aspects of upgrading industrial structure adjustment, making technological progress, optimizing the energy structure and building long-term carbon-emission-reduction mechanisms, so as to promote the coordinated low-carbon development.

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Research on the spatial effect and threshold effect of industrial structure upgrading on carbon emissions in China

Journal of Water and Climate Change ◽

10.2166/wcc.2021.216 ◽

2021 ◽

Author(s):

Zhenqiang Li ◽

Qiuyang Zhou

Keyword(s):

Energy Consumption ◽

Population Size ◽

Carbon Emissions ◽

Carbon Emission ◽

Industrial Structure ◽

Threshold Effect ◽

Local Areas ◽

The Impact ◽

Consumption Intensity ◽

Industrial Structure Upgrading

Abstract Based on panel data from 2000 to 2017 in 29 Chinese provinces, this paper analyzes the impact of industrial structure upgrading on carbon emissions by constructing a spatial panel model and a panel threshold model. The results show that (1) there is a significant spatial correlation between carbon emissions in Chinese provinces, and the carbon emissions of a province are affected by the carbon emissions of surrounding provinces; (2) in China, carbon emissions have a significant time lag feature, and current carbon emissions are largely affected by previous carbon emissions; (3) industrial structure upgrading can effectively promote carbon emission reductions in local areas, and the impact of industrial structure upgrading on carbon emissions has a significant threshold effect. With continued economic development, the promotion effect of industrial structure upgrading on carbon emission reductions will decrease slightly, but this carbon emission reduction effect is still significant. (4) In addition, there is a clear difference between the impact of energy consumption intensity and population size on carbon emissions in short and long terms. In the short term, the increase in energy consumption intensity and the expansion of population size not only increase the carbon emissions of a local area but also increase the carbon emissions of neighboring areas. In the long term, the impact of energy consumption intensity and population size on carbon emissions of neighboring areas will be weakened, but the promotion impact on carbon emissions in local areas will be strengthened.

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Analysis of Impacting Factors of Regional Carbon Emissions Based on the STIRPAT Mode - A Case Study of Shaanxi Province

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.1431 ◽

2014 ◽

Vol 962-965 ◽

pp. 1431-1436

Author(s):

Wei Zhang ◽

Jin Suo Zhang

Keyword(s):

Economic Growth ◽

Energy Consumption ◽

Carbon Emissions ◽

Industrial Structure ◽

Shaanxi Province ◽

Stirpat Model ◽

Consumption Structure ◽

Carbon Emissions Reduction ◽

Impacting Factors

Studying on the regional carbon emissions impacting factor and its effect will contribute greatly to formulation sound regional carbon emissions reduction policy. As a main province of energy development in the western of China, Shaanxi province is facing growing pressure to reduce carbon emissions. In this paper, carbon emissions impacting factors of Shaanxi were explored from aspects of population,economic growth, urbanization, industrial structure, technological progress and energy consumption structure by STIRPAT model and ridge regression method,then the contribution rate of impacting factors to carbon emissions increment were calculated. The results shows that population, economic growth, urbanization and energy consumption have positive impacting on the growth of carbon emissions in Shaanxi, among them, the economic growth is the decisive factor that pulling carbon emissions growth the economic growth. Industrial structure and technology progress had adverse effect on carbon emissions in Shaanxi, in comparison, the effect of optimizing of industrial structure to inhibit the carbon emissions in Shaanxi is greater than the effect of adjustment of energy consumption structure.

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Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

Sustainability ◽

10.3390/su13031339 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1339

Author(s):

Ziyuan Chai ◽

Zibibula Simayi ◽

Zhihan Yang ◽

Shengtian Yang

Keyword(s):

Energy Consumption ◽

Carbon Emissions ◽

Carbon Emission ◽

Driving Factors ◽

Series Data ◽

Lake Basin ◽

Stirpat Model ◽

Consumption Structure ◽

Ebinur Lake ◽

Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

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Analysis of Impact Factors of China's Carbon Dioxide Emissions

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.616-618.1111 ◽

2012 ◽

Vol 616-618 ◽

pp. 1111-1114

Author(s):

Xiao Yu Ma ◽

Qiang Yi Li ◽

Adili Tuergong

Keyword(s):

Carbon Dioxide Emissions ◽

Industrial Structure ◽

Positive Impact ◽

Dynamic Panel Data ◽

Impact Factors ◽

Dynamic Panel ◽

Consumption Structure ◽

Current Period ◽

Per Capita ◽

The Impact

This paper estimates the quantity of CO2 emissions in 30 provinces of China covering the year from 1999 to 2010, combining static and dynamic panel data model.Meanwhile, we use instruments to control the endogeny of the two models, analyzing the impact factors of China's CO2 emissions comprehensively and objectively. The result shows that a inverted U-shaped relationship is found between per capita GDP and CO2 emissions per capita .And it means that the Environmental Kuznets Hypothesis is verified in China.And energy consumption structure, industrial structure and urbanization have a positive impact on China's CO2 emissions. The CO2 emissions of last period have a crucial impact on the emissions of current period.

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Relationships between Causal Factors Affecting Future Carbon Dioxide Output from Thailand’s Transportation Sector under the Government’s Sustainability Policy: Expanding the SEM-VECM Model

Resources ◽

10.3390/resources7040081 ◽

2018 ◽

Vol 7 (4) ◽

pp. 81 ◽

Cited By ~ 1

Author(s):

Pruethsan Sutthichaimethee ◽

Danupon Ariyasajjakorn

Keyword(s):

Energy Consumption ◽

Confidence Interval ◽

Co2 Emissions ◽

Direct Effect ◽

Industrial Structure ◽

Per Capita Gdp ◽

Net Exports ◽

Transportation Sector ◽

Per Capita ◽

Urbanization Rate

This research aims to analyze the relationships between causal factors likely to affect future CO2 emissions from the Thai transportation sector by developing the Structural Equation Modeling-Vector Autoregressive Error Correction Mechanism Model (SEM-VECM Model). This model was created to fill information gaps of older models. In addition, the model provides the unique feature of viable model application for different sectors in various contexts. The model revealed all exogenous variables that have direct and indirect influences over changes in CO2 emissions. The variables show a direct effect at a confidence interval of 99%, including per capita GDP (), labor growth (), urbanization rate factor (), industrial structure (), energy consumption (), foreign direct investment (), oil price (), and net exports (). In addition, it was found that every variable in the SEM-VECM model has an indirect effect on changes in CO2 emissions at a confidence interval of 99%. The SEM-VECM model has the ability to adjust to the equilibrium equivalent to 39%. However, it also helps to identify the degree of direct effect that each causal factor has on the others. Specifically, labor growth () had a direct effect on per capita GDP () and energy consumption () at a confidence interval of 99%, while urbanization rate () had a direct effect on per capita GDP (), labor growth (), and net exports () at a confidence interval of 99%. Furthermore, industrial structure () had a direct effect on per capita GDP () at a confidence interval of 99%, whereas energy consumption () had a direct effect on per capita GDP () at a confidence interval of 99%. Foreign direct investment () had a direct effect on per capita GDP () at a confidence interval of 99%, while oil price () had a direct effect on industrial structure (), energy consumption (), and net exports () at a confidence interval of 99%. Lastly, net exports () had a direct effect on per capita GDP () at a confidence interval of 99%. The model eliminates the problem of heteroskedasticity, multicollinearity, and autocorrelation. In addition, it was found that the model is white noise. When the SEM-VECM Model was used for 30-year forecasting (2018–2047), it projected that CO2 emissions would increase steadily by 67.04% (2047/2018) or 123.90 Mt CO2 Eq. by 2047. The performance of the SEM-VECM Model was assessed and produced a mean absolute percentage error (MAPE) of 1.21% and root mean square error (RMSE) of 1.02%. When comparing the performance value with the values of other, older models, the SEM-VECM Model was found to be more effective and useful for future research and policy planning for Thailand’s sustainability goals.

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Does change of industrial structure affect energy consumption structure: A study based on the perspective of energy grade calculation

Energy Exploration & Exploitation ◽

10.1177/0144598718784032 ◽

2018 ◽

Vol 37 (1) ◽

pp. 579-592 ◽

Cited By ~ 3

Author(s):

Yang Hong ◽

Peng Can ◽

Yang Xiaona ◽

Li Ruixue

Keyword(s):

Energy Consumption ◽

Calculation Method ◽

Technological Progress ◽

Industrial Structure ◽

Energy Resources ◽

Energy Sources ◽

Energy Structure ◽

Consumption Structure ◽

The Difference ◽

The Impact

In this article, the grades of different kinds of energy sources are distinguished. Thus, we put forward an equivalent electric calculation method, which is compliant with the calculation of various energy resources that have different grades. Based on this aspect, we empirically analyzed the influence of industrial structure changing on energy consumption structure by analyzing panel data in 30 provinces of China from 2003 to 2013. Results showed that the calculated results of equivalent electric calculation method were more accurate because it considered the difference in grades between various energy sources. Industrial structure changing had a significant impact on energy consumption structure. The upgrading and rationalization of the industrial structure had a significant promotion on energy structure cleaning. In addition, technological progress was conducive to the clean development of energy structure, the decrease in energy price boosted energy structure cleaning, and the impact of economic level on energy consumption structure was not significant.

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Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix

International Journal of Climate Change Strategies and Management ◽

10.1108/ijccsm-05-2017-0116 ◽

2019 ◽

Vol 11 (3) ◽

pp. 406-423 ◽

Cited By ~ 2

Author(s):

Lei Wen ◽

Linlin Huang

Keyword(s):

Carbon Emissions ◽

Energy Intensity ◽

Industrial Structure ◽

Projection Pursuit ◽

Energy Structure ◽

Per Capita Gdp ◽

Content Type ◽

Factors Affecting ◽

Optimal Projection ◽

Per Capita

Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

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Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Energies ◽

10.3390/en12163054 ◽

2019 ◽

Vol 12 (16) ◽

pp. 3054 ◽

Cited By ~ 1

Author(s):

Zhen Li ◽

Yanbin Li ◽

Shuangshuang Shao

Keyword(s):

Energy Consumption ◽

Influencing Factors ◽

Carbon Emissions ◽

Total Population ◽

Industrial Structure ◽

Global Climate ◽

Primary Energy ◽

Urbanization Level ◽

Per Capita ◽

Technology Level

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

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Trends and Driving Forces of Carbon Emissions from Energy Consumption: A Case Study of Nanjing, China

Sustainability ◽

10.3390/su10124348 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4348 ◽

Cited By ~ 2

Author(s):

Kong-Qing Li ◽

Ran Lu ◽

Rui-Wen Chu ◽

Dou-Dou Ma ◽

Li-Qun Zhu

Keyword(s):

Growth Rate ◽

Energy Consumption ◽

Carbon Emissions ◽

Driving Forces ◽

Positive Impact ◽

High Growth ◽

Future Trend ◽

Gdp Per Capita ◽

Stirpat Model ◽

Per Capita

Based on the scientific calculation of carbon emissions from energy consumption in Nanjing, this paper analyzed the driving forces of carbon emissions from 2000 to 2016 by using the stochastic impacts by regression on population, affluence and technology (STIRPAT) model. The results show that from 2000 to 2016, the energy carbon emissions of Nanjing were on the rise; the urbanization rate, population, GDP per capita, and energy intensity had a significant positive impact on the growth of carbon emissions in Nanjing, China. Based on this, we presented five development scenarios to analyze the future trend of carbon emissions of the city. By contrast, the growth rate of carbon emissions from energy consumption is the slowest when the population maintains a low growth rate and the GDP per capita and technical level maintain high growth. This indicates a better urban development strategy in which industrial restructuring must be associated with talent structure adjustment to decarbonize the urban economy, and the extensive urban sprawl development approach might need to be changed.

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