Strategy of Optimizing Energy Consumption Structure Based on Energy Security

2018 ◽  
pp. 185-199
Author(s):  
Jing Cao ◽  
Xinxin Ren
Keyword(s):  
Energy Consumption ◽  
Energy Security ◽  
Consumption Structure

Status Analysis of Energy Consumption, Supply and Conservation in Beijing

2013 ◽  
Vol 724-725 ◽  
pp. 1310-1313
Author(s):  
An Jia Mao ◽  
Han Li
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Security ◽  
Global City ◽  
Consumption Structure ◽  
The Government ◽  
Status Analysis

As Beijing is trying to build a global city, its energy security has become very significant. In recent years, Beijing's energy consumption increases steadily. And the government has taken lots of measures to optimize its energy consumption structure, promote utilization of renewable energy and pay attention to energy conservation. By a series of efforts, Beijing has made some achievements in the field of energy.

scholarly journals Analysis of the Impact of China’s Energy Consumption Structure on Energy Security

2021 ◽  
Vol 245 ◽  
pp. 01054
Author(s):  
Wang Ning ◽  
Li Zhao ◽  
Zhang Bei
Keyword(s):  
Energy Consumption ◽  
Energy Security ◽  
Energy Demand ◽  
Greenhouse Gas Emission ◽  
Energy Utilization ◽  
Practical Significance ◽  
Utilization Rate ◽  
Consumption Structure ◽  
Depth Analysis ◽  
The Impact

Energy, as the guarantee of China’s economic development, plays an important role in it. Nowadays, however, oil production almost reaches its upper limit and is going downhill. The yearly rising output of natural gas fails to meet the increasingly huge energy demand. Although coal reserves are large, it still gets into a bottleneck period due to environmental and technological reasons. Therefore, the adjustment and optimization of energy consumption structure is the primary problem for China’s energy security, and it has become, and will still remain as the core of China’s energy strategy in the future. So, it is of great theoretical and practical significance to delve into the inter relationship between energy consumption structure and energy security, and then to optimize energy consumption structure under the new situation. After an in-depth analysis on how to adjust the energy consumption structure to ensure energy security, the author finds several problems, for instance, an unreasonable energy consumption structure, a serious environmental pollution, a low energy utilization rate, and a recessive role of energy price market. To solve the practical problems, this paper puts forward three measures namely deepening the supply side reform, accelerating the energy technology innovation, and changing the concept in energy use. The improvement in energy consumption structure is not only of general significance to improve the energy security theory, but also of great significance for China to achieve the goal of greenhouse gas emission reduction and ensure energy security.

SMART NETWORKS FOR DIGITALIZATION OF URBAN ECONOMY WITHIN THE FRAMEWORK OF THE “SMART CITY”

2020 ◽  
Vol 3 (8) ◽  
pp. 21-27
Author(s):  
S. V. PROKOPCHINA ◽  
Keyword(s):  
Energy Consumption ◽  
Internal Energy ◽  
Energy Management ◽  
Energy Security ◽  
Smart City ◽  
Urban Economy ◽  
Training Center ◽  
Energy Audit ◽  
Intelligent Networks ◽  
Industrial Energy

The article deals with methodological and practical issues of building Bayesian intelligent networks (BIS) for digitalization of urban economy based on the principles of the “Smart city” concept. The BIS complex as a whole corresponds to the architecture of urban household management complexes for construction and industrial energy purposes for solving the problems of internal energy audit, accounting for energy consumption, ensuring energy security of enterprises and territories, in Addition, the system can become the basis for the implementation of a training center for energy management and housing.

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

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.

scholarly journals A Review on Energy and Renewable Energy Policies in Iran

2021 ◽  
Vol 13 (13) ◽  
pp. 7328
Author(s):  
Saeed Solaymani
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Energy Security ◽  
Fossil Fuels ◽  
Agricultural Products ◽  
Energy Resources ◽  
Causality Analysis ◽  
Renewable Energy Consumption

Iran, endowed with abundant renewable and non-renewable energy resources, particularly non-renewable resources, faces challenges such as air pollution, climate change and energy security. As a leading exporter and consumer of fossil fuels, it is also attempting to use renewable energy as part of its energy mix toward energy security and sustainability. Due to its favorable geographic characteristics, Iran has diverse and accessible renewable sources, which provide appropriate substitutes to reduce dependence on fossil fuels. Therefore, this study aims to examine trends in energy demand, policies and development of renewable energies and the causal relationship between renewable and non-renewable energies and economic growth using two methodologies. This study first reviews the current state of energy and energy policies and then employs Granger causality analysis to test the relationships between the variables considered. Results showed that renewable energy technologies currently do not have a significant and adequate role in the energy supply of Iran. To encourage the use of renewable energy, especially in electricity production, fuel diversification policies and development program goals were introduced in the late 2000s and early 2010s. Diversifying energy resources is a key pillar of Iran’s new plan. In addition to solar and hydropower, biomass from the municipal waste from large cities and other agricultural products, including fruits, can be used to generate energy and renewable sources. While present policies indicate the incorporation of sustainable energy sources, further efforts are needed to offset the use of fossil fuels. Moreover, the study predicts that with the production capacity of agricultural products in 2018, approximately 4.8 billion liters of bioethanol can be obtained from crop residues and about 526 thousand tons of biodiesel from oilseeds annually. Granger’s causality analysis also shows that there is a unidirectional causal relationship between economic growth to renewable and non-renewable energy use. Labor force and gross fixed capital formation cause renewable energy consumption, and nonrenewable energy consumption causes renewable energy consumption.

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.

Sign in / Sign up

Export Citation Format

Share Document