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

2018 ◽  
Vol 37 (1) ◽  
pp. 579-592 ◽  
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.

scholarly journals Urban and rural income, residents’ consumption structure and energy consumption

2020 ◽  
Vol 218 ◽  
pp. 01034
Author(s):  
Yan Li ◽  
Zhi-wei Liu ◽  
Nan-nan Li ◽  
Jia-li Zhang ◽  
Ya-chen Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Rural Areas ◽  
Emission Reduction ◽  
Rural Residents ◽  
Consumption Structure ◽  
Urban And Rural Residents ◽  
The Difference ◽  
The Government ◽  
The Impact

As an internal driving force to promote economic growth, residents’ consumption also has an important impact on energy consumption. Based on the difference of consumption structure between urban and rural residents, this paper introduces the income gap variable of urban and rural residents, analyzes the impact of urban and rural residents’ consumption on energy consumption, and provides reference for the implementation of energy conservation and emission reduction policies. On this basis, the author believes that the government should take flexible measures to control energy consumption according to the differences between urban and rural areas, make accurate efforts, and steadily promote the implementation of energy conservation and emission reduction.

scholarly journals Can Green Finance Optimize Energy Structure: A Spatial Econometric Analysis based on China's Traditional and Renewable Energy Consumption

2021 ◽  
Author(s):  
Hui Wang ◽  
Lili Jiang ◽  
Hongjun Duan ◽  
Yifeng Wang ◽  
Yichen Jiang
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Spillover Effect ◽  
Energy Structure ◽  
Renewable Energy Consumption ◽  
Spatial Spillover ◽  
Consumption Structure ◽  
Green Finance ◽  
The Impact ◽  
Spatial Spillover Effect

Abstract This paper studies the impact of the development of green finance on China’s energy consumption structure. In terms of the construction of the green finance index (GFI), this paper selects 17 basic indexes from the three aspects of economy, finance, and environment, uses the improved entropy weight method to construct the GFI, and studies the spatial spillover effect of the GFI of China's provinces. This paper further studies the impact of green finance on traditional and renewable energy consumption. We first uses panel regression to determine that the development of green finance has a positive effect on the slowdown of traditional energy consumption and acceleration of renewable energy consumption, and then further studies the spatial characteristics of green finance development on energy consumption by using spatial Durbin model. The results show that there is a positive spatial spillover effect in the development of green finance among provinces in China. The development of green finance contributes to the conversion of traditional to renewable energy consumption. The effect of green finance on the transformation of energy consumption structure is mainly reflected in the direct effect. Therefore, the government should support the green finance, reduce traditional energy consumption and increase renewable energy consumption.

scholarly journals The Impact of Green Finance Development on China’s Energy Structure Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Lili Jiang ◽  
Hongjun Duan ◽  
Yifeng Wang ◽  
Yichen Jiang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Local Development ◽  
Clean Energy ◽  
Green Energy ◽  
Energy Structure ◽  
Renewable Energy Consumption ◽  
Consumption Structure ◽  
Green Finance ◽  
The Impact

This paper studies the impact of the development of green finance on China’s energy consumption structure. 17 basic indexes and the improved entropy weight method are used to construct the green finance index (GFI). Multiple regression, panel regression, and spatial regression are used to study the impact of green finance on China’s traditional energy and renewable energy consumption. The results show that there is a positive spatial spillover effect in the development of green finance among provinces in China. The development of green finance contributes to the conversion of traditional to renewable energy consumption. The effect of green finance on the transformation of energy consumption structure is mainly reflected in the direct effect. The green finance in each province not only helps the local development of green energy but also plays a good role in the production and utilization of clean energy consumption in surrounding provinces. Therefore, the government should support the green finance, reduce traditional energy consumption, and increase renewable energy consumption.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiekun Song ◽  
Qing Song ◽  
Dong Zhang ◽  
Youyou Lu ◽  
Long Luan
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Shandong Province ◽  
Stirpat Model ◽  
Per Capita Gdp ◽  
Consumption Structure ◽  
Per Capita ◽  
The Impact ◽  
Consumption Intensity

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.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

scholarly journals 8.6. Analysis of the development trend of energy strategies of the BRICS member countries in the field of energy resources use

2020 ◽  
Author(s):  
Ц. Чжоу
Keyword(s):  
Resource Use ◽  
Renewable Energy Sources ◽  
Development Trend ◽  
Energy Resources ◽  
Added Value ◽  
Fossil Energy ◽  
Consumption Structure ◽  
The Difference ◽  
The Development Trend ◽  
Energy Strategies

В данной статье авторы обозначили, что страны – члены БРИКС определили повышение эффективности использования ресурсов как одно из своих приоритетных направлений развития. Различие между странами состоит в том, что Российская Федерация как основной поставщик и экспортер энергии уделяет больше внимания увеличению использования ископаемой энергии за счет увеличения ее добавленной стоимости, остальные же страны – члены БРИКС (с дефицитом энергоресурсов) трансформируют свою структуру потребления энергии, сокращая использование угля за счет увеличения использования возобновляемых источников энергии. In this article, the authors indicated that the BRICS member countries have identified improving the efficiency of resource use as one of their priority areas of development. The difference between the countries is that Russia, as the main supplier and exporter of energy, pays more attention to increasing the use of fossil energy by increasing the added value of energy, while the rest of the BRICS member countries (with a shortage of energy resources) transform their energy consumption structure by reducing the use of coal by increasing the use of renewable energy sources.

Power System for Beautiful China

2021 ◽  
Vol 144 ◽  
pp. 14-21
Author(s):  
Vladimir P. Polevanov ◽  
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Natural Gas ◽  
Power System ◽  
Renewable Energy Sources ◽  
Primary Energy ◽  
Energy Resources ◽  
Energy Sources ◽  
Primary Energy Consumption ◽  
Leading Position

The growth in primary energy consumption in 2019 by 1.3% was provided by renewable energy sources and natural gas, which together provided 75% of the increase. China in the period 2010–2020 held a leading position in the growth of demand for energy resources, but according to forecasts, India will join it in the current decade.

Study on the industrial structure optimization under constraint of energy intensity

2020 ◽  
pp. 0958305X2092159
Author(s):  
Xiongfeng Pan ◽  
Mengna Li ◽  
Chenxi Pu ◽  
Haitao Xu
Keyword(s):  
Energy Consumption ◽  
Computable General Equilibrium ◽  
Technological Progress ◽  
Energy Intensity ◽  
Industrial Structure ◽  
High Energy ◽  
Industrial Sector ◽  
Added Value ◽  
Tertiary Industry ◽  
Intensity Control

This study establishes a multi-sector dynamic computable general equilibrium framework that integrates energy intensity module to explore the reverse feedback effect of energy intensity control on industry structure. The results indicate that (1) the tightening effect of energy intensity constrains on the Industrial sector is most significant, followed by the Tertiary Industry, with the least impact on Agriculture; (2) when there is no technological progress in the departments, the change of industrial structure is mainly reflected in the sharp decline in the proportion of Industry and the significant increase in the proportion of Tertiary Industry. When technological progress exists in high energy-consumption departments, the tightening effect of energy intensity constraints on the industrial sector will be reduced; when there is technological progress in all departments, the industrial structure will have a smaller change, and the technology progress can alleviate the tightening effect of the energy intensity target on various sectors; (3) under the constraint of energy intensity, the high energy-consuming industry shifts to the Equipment Manufacturing with low energy-consumption and high-added value. The increasing proportion of Tertiary Industry mainly comes from two industries including Wholesale, Retail, Hoteling and Catering, and Transportation, Storage, and Post.

scholarly journals How Much Energy Efficient are Renewable Energy Sources Cooperatives’ Initiatives?

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1136 ◽  
Author(s):  
Nikolaos Sifakis ◽  
Tryfon Daras ◽  
Theocharis Tsoutsos
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Smart Meters ◽  
Green Consumption ◽  
Consumption Behavior ◽  
The Difference ◽  
Actual Energy Consumption

In this paper is provided a systematic, in-depth, behavioral analysis of renewable energy sources cooperatives’ members. The analysis proved that in, on hand, there was a noticeable difference in the portion of affection of each proposed intervention on the actual energy consumption, which may be to even ten times more in some cases, and on the other hand, the difference in energy consumption between the analyzed groups was noticeable as well. So, implementing energy efficiency interventions of various types, such as technical support, special tariffs, energy generation schemes, and smart meters, seems to lead to substantial energy reductions to even more than 10%, cumulatively, and reduces the environmental footprint. Additionally, the majority of energy efficiency interventions applied by the renewable energy sources cooperatives are proved to be effective in achieving their primary goal, sensitizing members, and leading them to a more efficient energy consumption behavior (“greener”). The results of the analysis showed that each proposed intervention had played a different but nonetheless significant role in the diminishing of the energy consumption of the members and that there was a noticeable difference in energy consumption between the analyzed groups. The results of the analysis demonstrated more than 22 GWh totally in green consumption, and almost 4500 tons of CO2 saved.

Research on Energy Efficiency and Environmental Pollution in China: a Simultaneous Equations Approach

2011 ◽  
Vol 361-363 ◽  
pp. 974-977 ◽  
Author(s):  
Ying Nan Dong ◽  
Yu Duo Lu ◽  
Jiao Jiao Yu
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Economic Loss ◽  
Simultaneous Equations ◽  
Consumption Structure ◽  
The Relationship

This paper examined the relationship between the energy efficiency and the environmental pollution. By using the data of energy intensity and economic loss caused by environmental pollution (ELP) in China from 1989-2009, a simultaneous equations was developed. The result of two-stage OLS estimation suggested that the energy had exerted positive influences on the decreasing of the environmental pollutions. By enhancing the energy efficiency and adjusting the industrial structure and energy consumption structure, China is exploring a road for sustainable development in the energy conservation.

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