Influencing Factors and Decoupling Analysis of Carbon Emissions in China's Manufacturing Industry

2021 ◽  
Author(s):  
baoling jin ◽  
ying Han
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Investment Efficiency ◽  
Fixed Asset ◽  
Carbon Productivity ◽  
Fixed Asset Investment

Abstract The manufacturing industry directly reflects national productivity, and it is also an industry with serious carbon emissions, which has attracted wide attention. This study decomposes the influential factors on carbon emissions in China’s manufacturing industry from 1995 to 2018 into industry value added (IVA), energy consumption (E), fixed asset investment (FAI), carbon productivity (CP), energy structure (EC), energy intensity (EI), investment carbon intensity (ICI) and investment efficiency (IE) by Generalized Divisia Index Model (GDIM). The decoupling analysis is carried out to investigate the decoupling states of the manufacturing industry under the pressure of "low carbon" and "economy.” Considering the technological heterogeneity, we study the influential factors and decoupling status of the light industry and the heavy industry. The results show that: (1) Carbon emissions of the manufacturing industry present an upward trend, and the heavy industry is the main contributor. (2) Fixed asset investment (FAI), industry value added (IVA) are the driving forces of carbon emissions. Investment carbon intensity (ICI), carbon productivity (CP), investment efficiency (IE), and energy intensity (EI) have inhibitory effects. The impact of the energy consumption (E) and energy structure (EC) are fluctuating. (3) The decoupling state of the manufacturing industry has improved. Fixed asset investment (FAI), industry value added (IVA) hinder the decoupling; carbon productivity (CP), investment carbon intensity (ICI), investment efficiency (IE), and energy intensity (EI) promote the decoupling.

scholarly journals The log mean divisia index based carbon productivity in the Australian construction industry

2017 ◽  
Vol 17 (3) ◽  
pp. 68-84 ◽  
Author(s):  
Lingfeng Liang ◽  
Xiancun Hu ◽  
Linda Tivendale ◽  
Chunlu Liu
Keyword(s):  
Economic Growth ◽  
Environmental Protection ◽  
Construction Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Influential Factors ◽  
Carbon Intensity ◽  
Divisia Index ◽  
Carbon Productivity ◽  
Regional Adjustment

Environmental protection and economic growth are two indicators of sustainable global development. This study aims to investigate the performance of environmental protection and economic growth by measuring carbon productivity in the construction field. Carbon productivity is the amount of gross domestic product generated by the unit of carbon emissions. The log mean Divisia index method is used to investigate influential factors including carbon intensity, energy intensity and regional adjustment that impact on changes of carbon productivity. The study utilises a range of data from the Australian construction industry during 1995-2004 including energy consumption, industry value added and carbon dioxide equivalent consumption. The research indicates carbon productivity in the Australian construction industry has clearly increased. Energy intensity plays a significant positive role in promoting carbon productivity, whereas carbon intensity and regional adjustment have limited influence. Introducing advanced construction machinery and equipment is a feasible pathway to enhance carbon productivity. The research method is generic and can be used to measure other performance indicators and decomposing them into influential factors.

scholarly journals Evaluation of Greenhouse Gas Emissions and Related Aspects: Case of the Czech Republic

2015 ◽  
Vol 63 (1) ◽  
pp. 281-292 ◽  
Author(s):  
Veronika Solilová ◽  
Danuše Nerudová
Keyword(s):  
Czech Republic ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Energy Intensity ◽  
Greenhouse Gas Emission ◽  
Value Added ◽  
Carbon Intensity ◽  
Gas Emissions ◽  
The Czech Republic ◽  
Carbon Productivity

The most important drivers of increasing greenhouse gas emissions are increasing world’s population, economic development resulting in higher level of productions and consumption, but also unanticipated increases in the energy intensity of GDP and in the carbon intensity of energy. The EU committed to reduce their greenhouse gas emissions by 20% until 2020 or 40% until 2030 compared to 1990 levels of the Kyoto Protocol. The Czech Republic enlarged EU in 2004 as a country from Eastern Europe where usually the heavy industries or agriculture prevail over other sectors. The aim of the paper was an evaluation of the development of greenhouse gas emissions and related aspects in the industry of the Czech Republic. Based on the results was concluded that although greenhouse gas emissions of the Czech Republic are deeply below the Kyoto targets, there are areas for improvements e.g. in case of energy intensities, as well as in case of carbon intensity and carbon productivity, where the Czech Republic reaches worse results than the EU28. Therefore is recommended to decrease greenhouse gas emission and increase gross value added generated by each NACE sector. Both those factors will impact on improvement of energy intensity, carbon productivity as well as greenhouse gas emissions per capita.

An Optimal Model for Low-Carbon Urban Agglomeration Based on Energy Structure Reduction under Uncertainty

2012 ◽  
Vol 599 ◽  
pp. 211-215
Author(s):  
Lun Wang ◽  
Zhao Sun ◽  
Jing Ya Wen ◽  
Zhuang Li ◽  
Wen Jin Zhao ◽  
...  
Keyword(s):  
Regional Planning ◽  
Energy Intensity ◽  
Greenhouse Gas Emission ◽  
Urban Agglomeration ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Low Carbon ◽  
Optimal Model ◽  
The Sustainable Development

This paper developed an optimal model of low-carbon urban agglomeration on the base of energy structure under uncertainty. The case study shows that the carbon intensity was decreased by [32.19, 41.20] (%) and energy intensity was reduced by [34.08, 43.19] (%) compared with those in 2010; meanwhile, the carbon intensity and energy intensity in the core area was reduced by [50.88, 54.11] (%) and [51.24, 54.57] (%) respectively, compared with those in 2010. The optimized scheme could not only meet the requirements of 12th Five-Year Planning Outline of Controlling Greenhouse Gas Emission, but also complied with the requirements of regional planning targets. The established model also provided more decision-making space for the sustainable development of low-carbon urban agglomeration.

scholarly journals Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Qing-Xiang Ou
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Development Path ◽  
Optimal Outcomes ◽  
Emissions Intensity ◽  
Kaya Identity

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.

scholarly journals Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1089
Author(s):  
Jiancheng Qin ◽  
Hui Tao ◽  
Chinhsien Cheng ◽  
Karthikeyan Brindha ◽  
Minjin Zhan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Economic Structure ◽  
Energy Structure ◽  
Intensity Effect ◽  
Entire Energy ◽  
Key Factor ◽  
Main Factors

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990–2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001–2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011–2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry’s economic structure and transportation’s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.

scholarly journals Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

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 CO2/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.

An Analysis on Carbon Emissions and Energy Efficiency of Small Heavy Industrial Enterprises in Shanghai

2013 ◽  
Vol 869-870 ◽  
pp. 997-1000
Author(s):  
Jing Jing Zhang ◽  
Jian Cheng Kang ◽  
Hao Zhang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Correlation Coefficients ◽  
Carbon Intensity ◽  
Small Enterprises ◽  
Growth Trend ◽  
Positive Correlation ◽  
Industrial Enterprises

Based on the energy consumption and the output value data of the 6 small heavy industrial enterprises during 2007-2011 in Shanghai, we calculated comprehensive energy consumption, carbon emissions, carbon intensity and energy intensity of these enterprises. It been found that the comprehensive energy consumption and the carbon emissions of the 6 small enterprises are in a fluctuating growth trend but the energy intensity and the carbon intensity show a trend of fluctuating downward. The energy intensity and the carbon intensity of the small enterprises are much larger than the average of the two whole industries in Shanghai. We analyzed the correlation coefficients between the output value and the energy consumption as well as between the output value and the carbon emissions. The results show that the comprehensive energy consumption and the carbon emissions have positive correlation as well as the carbon emissions and the output value.

scholarly journals Energy Efficiency: The Manufacturing Sector in Indonesia

JEJAK ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 200-217
Author(s):  
Bayu Hariyanto
Keyword(s):  
Energy Efficiency ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Manufacturing Sector ◽  
Empirical Finding ◽  
Value Added ◽  
Industrial Sector ◽  
Improve Energy Efficiency ◽  
Pulp Paper ◽  
Industry Standards

Indonesia is involved in the global effort to alleviate the deterioration of the environment due to climate change. Given that the manufacturing industry accounts for the second-highest share of national energy consumption, efficiency energy in the industrial sector is crucial. This research examines which industrial subsector has to be prioritized to improve energy efficiency and what are the determinant factors that influence energy efficiency in Indonesia manufacturing. This study analyzes energy intensity as an approach to measure energy efficiency. Focusing on the 2010 - 2015 period, this research employs two methods, namely input-output and panel data regression analysis. The empirical finding shows that textiles and textile products; pulp, paper, paper products, printing, and publishing; and rubber and plastics sectors are the first priority subsectors that must implement green industry standards. The next priority is the subsectors at the second level but have no green industrial standards, namely electrical and optical equipment. Furthermore, there were four variables that statistically increase energy intensity, namely lagged energy intensity, technology intensity, lagged value added, and location of plant. However, other two variables, the price of electricity and company size, can reduce energy intensity.

Calculation and Evaluation Methodology of Transport Energy Consumption and Carbon Emissions: A Case Study of Jiangsu Province

2014 ◽  
Vol 962-965 ◽  
pp. 1293-1302 ◽  
Author(s):  
Bin Ouyang ◽  
Zhen Hua Feng ◽  
Qing Hua Bi
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Jiangsu Province ◽  
Policy Implications ◽  
System Structure ◽  
Evaluation Methodology ◽  
Carbon Intensity ◽  
Low Carbon ◽  
Carbon Transport

The calculation methodology of transport carbon emissions, based on the methodology recommended by Intergovernmental Panel on Climate Change (IPCC) and the energy consumption statistics of provincial transport industry in China, is proposed. By using the methodology, the energy consumption and carbon emissions of highway, waterway and urban passenger transport from 2005 to 2012 of Jiangsu Province are calculated and evaluated. And the developing trends and main features from the perspectives of the total amount of transport energy consumption and carbon emissions, the proportional of both various energy types and various transport modes in the energy consumption, the energy intensity and carbon dioxide intensity, are systematically analyzed. Finally, some policy implications of low-carbon transport development were conclusively put forward, including reducing energy intensity and carbon intensity as the core focus, the highway transport as the breakthrough point, optimizing the integrated transport system structure and developing of public transport in priority as the strategic orientation, developing clean and low-carbon energy as an important way, etc. The research methodology and results can provide references for decision-making and management of the relevant provinces and cities on low-carbon transport development.

scholarly journals Development of an Evaluating Method for Carbon Emissions of Manufacturing Process Plans

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yanhong Wang ◽  
Hua Zhang ◽  
Zhiqing Zhang ◽  
Jing Wang
Keyword(s):  
Process Planning ◽  
Carbon Emissions ◽  
Manufacturing Process ◽  
Carbon Emission ◽  
Manufacturing Industry ◽  
Evaluation Method ◽  
Energy Utilization ◽  
Carbon Intensity ◽  
Emission Analysis ◽  
Process Plan

Carbon intensity reduction and energy utilization enhancement in manufacturing industry are becoming a timely topic. In a manufacturing system, the process planning is the combination of all production factors which influences the entail carbon emissions during manufacturing. In order to meet the current low carbon manufacturing requirements, a carbon emission evaluation method for the manufacturing process planning is highly desirable to be developed. This work presents a method to evaluate the carbon emissions of a process plan by aggregating the unit process to form a combined model for evaluating carbon emissions. The evaluating results can be used to decrease the resource and energy consumption and pinpoint detailed breakdown of the influences between manufacturing process plan and carbon emissions. Finally, the carbon emission analysis method is applied to a process plan of an axis to examine its feasibility and validity.

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