scholarly journals Construction and Application Analysis of Carbon Emission Influence Factor Model of Energy Consumption in Mining Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lili Wei ◽  
Xiwen Feng ◽  
Guangyu Jia
Keyword(s):  
Energy Consumption ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Mining Industry ◽  
Energy Structure ◽  
Low Carbon ◽  
Greenhouse Gas Inventory ◽  
Carbon Emission Intensity ◽  
Low Carbon Development

With the proposal of China’s “double carbon goal,” as a high energy-consuming industry, it is urgent for the mining industry to adopt a low-carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low-carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China's mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

scholarly journals Energy saving and emission reduction of fossil energy based on low carbon economy and its consumption structure optimization

2019 ◽  
Vol 14 (3) ◽  
pp. 381-385 ◽  
Author(s):  
Yan Li ◽  
Guilin Dai
Keyword(s):  
Energy Saving ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Total Carbon ◽  
Energy Structure ◽  
Low Carbon ◽  
Parallel Acquisition ◽  
Low Carbon Economy ◽  
Carbon Emission Intensity

Abstract Energy saving and emission reduction have been not only a slogan but also a policy in this modern society where the phenomenon of greenhouse is exacerbated. In this study, calculation method of carbon emission and integrated parallel acquisition technique (IPAT) scenario prediction model were combined to predict the changes of total carbon emissions, energy structure distribution, and carbon emission intensity under three measures of energy saving and emission reduction in the next ten years in Shandong, China. The results showed that the total carbon emission increased year by year, and the coal ratio and carbon emission intensity decreased under the natural scenario; the total carbon emission in the weakly constrained scenario would increase annually until 2029, the amplitude was smaller than that of the natural scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than that of the natural scenario. Under the strongly constrained scenario, the total carbon emission would increase annually before 2025, and the amplitude was smaller than the weakly constrained scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than the weakly constrained scenario.

Influencing Factors Decomposition of the Carbon Emission of Industrial Energy Consumption in Nanning Downtown Based on LMDI

2015 ◽  
Vol 737 ◽  
pp. 925-934 ◽  
Author(s):  
Jing Yang ◽  
Huan Mei Yao ◽  
Meng Lin Qin
Keyword(s):  
Energy Consumption ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Driving Factor ◽  
Low Carbon ◽  
Industrial Carbon ◽  
Industrial Energy ◽  
Industrial Energy Consumption

According to IPCC carbon emission calculation instruction, the amount of industrial carbon emission of downtown of Nanning from 2003-2012 is evaluated. With LMDI element decomposition method, the carbon emission of industrial energy consumption in Nanning downtown is decomposed into effect of five aspects such as energy structure, energy intensity, industrial structure, economic scale and population size. It turns out that: the energy structure change can promote the increase of carbon emission. The energy consumption structure should be optimized and the proportion of high-carbon energy consumption should be reduced; The energy intensity is the leading driving factor of carbon emission. The energy efficiency should be further improved to control the increase of carbon emission to some degree; The industrial structure restrains the increase of carbon emission in a great degree. Industrial restructuring should be strengthened and low-carbon industry should be developed; The scale of economy is the main driving factor of the increase of carbon emission. The extensive way of economic growth which depends on the large input of production factors should be changed; The population has a promoting function the increase of carbon emission, while the driving effect is weak, and the growth rate of the population should be strictly controlled.

scholarly journals Evolution of Renewable Energy in BRI Countries: A Combined Econometric and Decomposition Approach

2020 ◽  
Vol 17 (22) ◽  
pp. 8668 ◽  
Author(s):  
Feng Dong ◽  
Yuling Pan
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Economic Effect ◽  
Energy Structure ◽  
Low Carbon ◽  
Renewable Energy Consumption ◽  
Intensity Effect ◽  
Emission Effect

The development of renewable energy is an important cooperation theme among countries along the Belt and Road Initiative (BRI countries). Through map description, we first explore the changes in renewable energy consumption in BRI countries. Then, β-convergence is employed to examine the development direction of renewable energy consumption in BRI countries. Finally, based on the expanded Kaya equation, we decompose the factors effecting renewable energy consumption into energy structure effect, energy intensity effect, low-carbon economic effect, carbon emission effect, population distribution effect and population effect. The Logarithmic Mean Divisia Index (LMDI) is utilized to calculate the contribution of each factor to renewable energy consumption in the expanded Kaya equation. Our research reaches the following conclusions: (1) β-convergence exists in renewable energy consumption among BRI countries, indicating that it will converge to a relatively stable level, and countries with low renewable energy consumption will increase their renewable energy consumption with a higher convergence rate to chase the countries with high renewable energy consumption. (2) Energy structure effect makes a positive impact on renewable energy consumption, and is the main contributor to renewable energy consumption. (3) The energy intensity effect makes a negative contribution to renewable energy consumption, and the negative impact has deepened in recent years. (4) Both the low-carbon economic effect and the carbon emission effect have positive impacts on renewable energy consumption. Our research not only provides a description of the experience of developing renewable energy for BRI countries, but also makes reference to other organizations.

Carbon Emission of Mining Industry’s Energy Consumption in Heilongjiang Province

2015 ◽  
Vol 1092-1093 ◽  
pp. 1597-1600
Author(s):  
Zhong Hua Wang ◽  
Xin Ye Chen
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Mining Industry ◽  
Calculation Model ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Heilongjiang Province ◽  
Low Carbon

The need to reduce carbon emission in Heilongjiang Province of China is urgent challenge facing sustainable development. This paper aims to make explicit the problem-solving of carbon emission to find low carbon emission ways. According to domestic and foreign literatures on estimating and calculating carbon emissions and by integrating calculation methods of carbon emissions, it was not possible to consider all of the many contributions to carbon emissions. Calculation model of carbon emissions suitable to this paper is selected. The carbon emissions of energy consumption in mining industry are estimated and calculated from 2005 to 2012, and the characteristics of carbon emission are analyzed at the provincial level. It makes the point that carbon emissions of energy consumption in mining industry can be reduced when we attempt to alter energy consumption structure, adjust industrial structure and improve energy utilization efficiency.

scholarly journals Low-carbon development via greening global value chains: a case study of Belarus

2020 ◽  
Vol 476 (2239) ◽  
pp. 20200024
Author(s):  
Huiqing Wang ◽  
Yixin Hu ◽  
Heran Zheng ◽  
Yuli Shan ◽  
Song Qing ◽  
...  
Keyword(s):  
International Trade ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Clean Energy ◽  
Global Value Chains ◽  
Value Chains ◽  
Energy Structure ◽  
Low Carbon ◽  
Low Carbon Development ◽  
Plastic Products

The rise of global value chains (GCVs) has seen the transfer of carbon emissions embodied in every step of international trade. Building a coordinated, inclusive and green GCV can be an effective and efficient way to achieve carbon emissions mitigation targets for countries that participate highly in GCVs. In this paper, we first describe the energy consumption as well as the territorial and consumption-based carbon emissions of Belarus and its regions from 2010 to 2017. The results show that Belarus has a relatively clean energy structure with 75% of Belarus' energy consumption coming from imported natural gas. The ‘chemical, rubber and plastic products' sector has expanded significantly over the past few years; its territorial-based emissions increased 10-fold from 2011 to 2014, with the ‘food processing' sector displaying the largest increase in consumption-based emissions. An analysis of regional emissions accounts shows that there is significant regional heterogeneity in Belarus with Mogilev, Gomel and Vitebsk having more energy-intensive manufacturing industries. We then analysed the changes in Belarus' international trade as well as its emission impacts. The results show that Belarus has changed from a net carbon exporter in 2011 to a net carbon importer in 2014. Countries along the Belt and Road Initiative, such as Russia, China, Ukraine, Poland and Kazakhstan, are the main trading partners and carbon emission importers/exporters for Belarus. ‘Construction’ and ‘chemical, rubber and plastic products' are two major emission-importing sectors in Belarus, while ‘electricity' and ‘ferrous metals' are the primary emission-exporting sectors. Possible low-carbon development pathways are discussed for Belarus through the perspectives of global supply and the value chain.

scholarly journals A Study on The Driving Factors and Spatial Spillover of Carbon Emission Intensity in The Yangtze River Economic Belt under Double Control Action

2019 ◽  
Vol 16 (22) ◽  
pp. 4452 ◽  
Author(s):  
Xuhui Ding ◽  
Zhongyao Cai ◽  
Qianqian Xiao ◽  
Suhui Gao
Keyword(s):  
Technological Innovation ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Spillover Effects ◽  
The Yangtze River ◽  
Low Carbon ◽  
Spatial Spillover ◽  
Carbon Emission Intensity

It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investion (FDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.

scholarly journals Influential Factors Regarding Carbon Emission Intensity in China: A Spatial Econometric Analysis from a Provincial Perspective

2020 ◽  
Vol 12 (19) ◽  
pp. 8097
Author(s):  
Li-Ming Xue ◽  
Shuo Meng ◽  
Jia-Xing Wang ◽  
Lei Liu ◽  
Zhi-Xue Zheng
Keyword(s):  
Spatial Econometrics ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Spillover Effect ◽  
Influential Factors ◽  
Energy Structure ◽  
Driving Mechanism ◽  
Emission Mitigation ◽  
Carbon Emission Intensity

Emission reduction strategies based on provinces are key for China to mitigate its carbon emission intensity (CEI). As such, it is valuable to analyze the driving mechanism of CEI from a provincial view, and to explore a coordinated emission mitigation mechanism. Based on spatial econometrics, this study conducts a spatial-temporal effect analysis on CEI, and constructs a Spatial Durbin Model on the Panel data (SDPM) of CEI and its eight influential factors: GDP, urbanization rate (URB), industrial structure (INS), energy structure (ENS), energy intensity (ENI), technological innovation (TEL), openness level (OPL), and foreign direct investment (FDI). The main findings are as follows: (1) overall, there is a significant and upward trend of the spatial autocorrelation of CEI on 30 provinces in China. (2) The spatial spillover effect of CEI is positive, with a coefficient of 0.083. (3) The direct effects of ENI, ENS and TEL are significantly positive in descending order, while INS and GDP are significantly negative. The indirect effects of URB and ENS are significantly positive, while GDP, ENI, OPL and FDI are significantly negative in descending order. Economic and energy-related emission reduction measures are still crucial to the achievement of CEI reduction targets for provinces in China.

Research on the Regional Low-Carbon Industrial Development: A Case of Tianjin Binhai New Area

2011 ◽  
Vol 219-220 ◽  
pp. 250-253 ◽  
Author(s):  
Xiao Ying Cui ◽  
Hui Ming Li ◽  
Lei Wang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Industrial Development ◽  
Carbon Emission ◽  
Large Scale ◽  
Industrial Structure ◽  
Industrial Sector ◽  
Energy Structure ◽  
Low Carbon ◽  
Tianjin Binhai New Area

Energy consumption induced by industry sector is the main source of carbon emission. So it is important to the policy making that research on the low-carbon industrial development, which is aiming to establish an industry system with low-carbon character. There are four restrictive factors on low-carbon industrial development in Tianjin Binhai New Area of China: the rather large scale of the secondary industry, the heavy industrial structure which strongly caused the increasing energy consumption, the high-carbon energy structure of industrial sector, and the lower industrial energy efficiency which has certain gap compared with other regions. Several countermeasures are proposed to reduce carbon emission induced by industrial sector, such as improving energy efficiency, optimizing energy structure, establishing multiple-access financing mechanism to encourage R&D on low-carbon technology, enhancing the development of low carbon industry, participating in the international cooperation actively, and making strategic plan of low-carbon industrial development.

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