scholarly journals Divergent Leading Factors in Energy-Related CO2 Emissions Change Among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

2019 ◽  
Vol 11 (18) ◽  
pp. 4929 ◽  
Author(s):  
Zou ◽  
Tang ◽  
Wu
Keyword(s):  
Co2 Emissions ◽  
Industrial Structure ◽  
Driving Forces ◽  
Low Carbon ◽  
Logarithmic Mean ◽  
Index Method ◽  
Positive Effects ◽  
Energy Demands ◽  
Rapid Economic Growth ◽  
Bth Region

In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

scholarly journals Decomposing the decoupling of water consumption and economic growth in Jiangxi, China

2018 ◽  
Vol 9 (1) ◽  
pp. 94-104 ◽  
Author(s):  
Mianhao Hu ◽  
Yunlin Hu ◽  
Juhong Yuan ◽  
Fucai Lu
Keyword(s):  
Economic Growth ◽  
Water Consumption ◽  
Industrial Structure ◽  
Jiangxi Province ◽  
Key Factors ◽  
Logarithmic Mean ◽  
Primary Industry ◽  
Index Method ◽  
Industrial Growth ◽  
Factors Affecting

Abstract Current population growth coupled with industrial growth has caused water supply to be outstripped by human demand. Understanding water consumption (WC) decoupling patterns and the factors affecting the decoupling status are essential for balancing economic growth and WC. This study determines the decoupling relationship between WC and economic growth in Jiangxi Province, China, and the driving factors were determined by the Tapio decoupling model and the logarithmic mean Divisia index method. Results showed that changes in the industrial structure in Jiangxi Province resulted in corresponding changes in WC structure. Analysis of the decoupling relationship showed that the decoupling state between WC and economic growth for primary industry was very unstable and largely volatile from 1999 to 2015, but showed a good decoupling status for secondary and tertiary industries. The largest cumulative effects on WC were economic development and technology, which were positive and negative drivers of WC changes, contributing 1,406.14% and −902.96% to the total effect of WC, respectively. The findings can help Jiangxi government identify the key factors influencing the decoupling effect, and formulate effective policies to reduce WC, which will benefit the harmonious development of economy, society and water resources in Jiangxi Province.

Decomposition analysis of energy-related CO2 emissions in South Africa

2015 ◽  
Vol 26 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Ming Zhang ◽  
Shuang Dai ◽  
Yan Song
Keyword(s):  
South Africa ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Driving Forces ◽  
Dominant Role ◽  
Decomposition Analysis ◽  
Structure Effect ◽  
Energy Structure ◽  
Index Method ◽  
Intensity Effect

South Africa has become one of the most developing countries in the world, and its economic growth has occurred along with rising energy-related CO2 emission levels. A deeper understanding of the driving forces governing energy-related CO2 emissions is very important in formulating future policies. The LMDI (Log Mean Divisia Index) method is used to analyse the contribution of the factors which influence energy-related CO2 emissions in South Africa over the period 1993-2011. The main conclusions drawn from the present study may be summarized as follows: the energy intensity effect plays the dominant role in decreasing of CO2 emission, followed by fossil energy structure effect and renewable energy structure effect; the economic activity is a critical factor in the growth of energy-related CO2 emission in South Africa.

scholarly journals Industrial Structure Optimization and Low-Carbon Transformation of Chinese Industry Based on the Forcing Mechanism of CO2 Emission Peak Target

2021 ◽  
Vol 13 (8) ◽  
pp. 4417
Author(s):  
Feng Wang ◽  
Changhai Gao ◽  
Wulin Zhang ◽  
Danwen Huang
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Objective Assessment ◽  
Structure Optimization ◽  
Emission Peak ◽  
Low Carbon ◽  
Chinese Industry ◽  
Industrial Structure Optimization

The setting of a CO2 emission peak target (CEPT) will have a profound impact on Chinese industry. An objective assessment of this impact is of great significance, both for understanding/applying the forcing mechanism of CEPT, and for promoting the optimization of China’s industrial structure and the low-carbon transformation of Chinese industry at a lower cost. Based on analysis of the internal logic and operation of the forcing mechanism of CEPT, we employed the STIRPAT model. This enabled us to predict the peak path of China’s CO2 emissions, select the path values that would achieve the CEPT with the year 2030 as the constraint condition, construct a multi-objective and multi-constraint input/output optimization model, employ the genetic algorithm to solve the model, and explore the industrial structure optimization and low-carbon transformation of Chinese industry. The results showed that the setting of CEPT will have a significant suppression effect on high-carbon emission industries and a strong boosting effect on low-carbon emission industries. The intensity of the effect is positively correlated with the target intensity of the CO2 emissions peak. Under the effect of the forcing mechanism of CEPT, Chinese industry can realize a low-carbon transition and the industrial structure can realize optimization. The CEPT is in line with sustainable development goals, but the setting of CEPT may risk causing excessive shrinkage of basic industries—which should be prevented.

scholarly journals Analysis of CO2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1212 ◽  
Author(s):  
Yao Qian ◽  
Lang Sun ◽  
Quanyi Qiu ◽  
Lina Tang ◽  
Xiaoqi Shang ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Industrial Structure ◽  
Ghg Emissions ◽  
Energy Structure ◽  
Low Carbon ◽  
Stirpat Model ◽  
Carbon Reduction ◽  
Economic Output ◽  
Low Carbon Development ◽  
And Control

Decomposing main drivers of CO2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

scholarly journals Analysis of Carbon Emission and Emission Reduction Potentialin Shaanxi Province

2021 ◽  
Vol 245 ◽  
pp. 01055
Author(s):  
Li Zhao ◽  
Yu Lulu
Keyword(s):  
Emission Reduction ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Rapid Development ◽  
Critical Role ◽  
Shaanxi Province ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Rapid Economic Growth ◽  
The World

The rapid development of China’ s economy and society is obtained at the cost of the consumption of energy, especially fossil energy. And the coal-based energy consumption structure enables China to become the largest carbon dioxide emitter in the world. With the enhancement of global greenhouse effect, ecological environment deterioration is becoming increasingly serious. Carbon emission reduction has become one of the most important international issues. As one of the fastest growing economies in the world, China has also actively responded to energy conservation and emission reduction.As a major energy province in China, Shaanxi province plays a critical role in the national energy supply, and it was also listed as the first batch of low-carbon pilot areas in 2010. In recent years, relying on its own energy advantages, Shaanxi has witnessed rapid economic growth, but the resource stock and environmental carrying capacity cannot withstand the high intensity of resource consumption and environmental pollution under the traditional resource mode. As the first batch of “low-carbon development” pilot provinces, adjusting the industrial structure, changing the mode of economic development and realizing “low-carbon economy” have become the urgent problems to be solved in Shaanxi Province.

scholarly journals An Analysis of Energy-related Greenhouse Gas Emissions in Turkish Energy-intensive Sectors

2020 ◽  
Author(s):  
Abdulkadir BEKTAŞ
Keyword(s):  
Greenhouse Gas ◽  
Co2 Emissions ◽  
Economic Activity ◽  
Energy Intensity ◽  
Driving Forces ◽  
Ghg Emissions ◽  
Low Carbon ◽  
Activity Effect ◽  
Reduction Of Co2 ◽  
The Impact

In recent decades, greenhouse gas (GHG) emissions have been a critical priority of global environmental policy. The leading cause of the increase in GHG triggering global warming in the atmosphere is the continuously growing demand for universal energy due to population and economic growth. Energy efficiency and reduction of CO2 emissions in highly-energy consuming sectors of Turkey are critical in deciding a low-carbon transition. In this study, the change of energy-related CO2 emissions in Turkey’s energy-intensive four sectors from 1998 to 2017 is analyzed based on the Logarithmic Mean Divisia Index (LMDI) method. It is used to decompose CO2 equivalent emissions changes in these sectors into five driving forces; changes in economic activity, activity mix, energy intensity, energy mix, and emission factors. Analytical results indicate that economic activity is a vital decisive factor in determining the change in CO2 emissions as well as sectoral energy intensity. The activity effect has raised CO2 emissions, while energy intensity has decreased. This method indicates that the impact of the energy intensity could be the first key determinant of GHG emissions. Turkey's efforts to be taken in these sectors in adopting low carbon growth policies and reducing energy-related emissions to tackle climate change are clarified in detail.

scholarly journals Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

2020 ◽  
Vol 12 (3) ◽  
pp. 791
Author(s):  
Zhiyuan Duan ◽  
Xian’en Wang ◽  
Xize Dong ◽  
Haiyan Duan ◽  
Junnian Song
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Co2 Emissions ◽  
Industrial Structure ◽  
Peak Time ◽  
Low Carbon ◽  
Clean Coal ◽  
Industrial Energy ◽  
Industrial Energy Consumption ◽  
Peak Value

Reducing CO2 emissions of industrial energy consumption plays a significant role in achieving the goal of CO2 emissions peak and decreasing total CO2 emissions in northeast China. This study proposed an extended STIRPAT model to predict CO2 emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO2 emissions and explore the reduction path and mechanism to achieve CO2 emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO2 emissions efficiency of energy consumption > industrialized rate > GDP > urbanization rate > industrial energy intensity > the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.

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