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With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM2.5 (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM2.5 concentrations are mainly caused by anthropogenic activities, this paper selected economic growth, economic structure, urbanization, and the number of civil vehicles as the primary factors and then explored the nexus between those variables and PM2.5 concentrations by employing a panel data model for 31 Chinese provinces. The estimated model showed that: (1) the coefficients of the variables for provinces located in North, Central, and East China were larger than that of other provinces; (2) GDP per capita made the largest contribution to PM2.5 concentrations, while the number of civil vehicles made the least contribution; and (3) the higher the development level of a factor, the greater the contribution it makes to PM2.5 concentrations. It was also found that a bi-directional Granger causal nexus exists between PM2.5 concentrations and economic progress as well as between PM2.5 concentrations and the urbanization process for all provinces. Policy recommendations were finally obtained through empirical discussions, which include that provincial governments should adjust the economic and industrial development patterns, restrict immigration to intensive urban areas, decrease the successful proportion of vehicle licenses, and promote electric vehicles as a substitute to petrol vehicles.
Haoran Zhao; Sen Guo; Huiru Zhao. Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM2.5 Concentration: A Provincial Panel Data Model Analysis of China. International Journal of Environmental Research and Public Health 2019, 16, 2926 .
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM2.5 Concentration: A Provincial Panel Data Model Analysis of China. International Journal of Environmental Research and Public Health. 2019; 16 (16):2926.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2019. "Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM2.5 Concentration: A Provincial Panel Data Model Analysis of China." International Journal of Environmental Research and Public Health 16, no. 16: 2926.
With the increasing development of renewable resources-based electricity generation and the construction of wind-photovoltaic-energy storage combination exemplary projects, the intermittent and fluctuating nature of renewable resources exert great challenges for the power grid to supply electricity reliably and stably. An energy storage system (ESS) is deemed to be the most valid solution to deal with these challenges. Considering the various types of ESSs, it is necessary to develop a comprehensive assessment framework for selecting appropriate energy storage techniques in establishing exemplary projects combining renewable resources-based electricity generation and an ESS. This paper proposes a multi-criteria decision making (MCDM) model combining a fuzzy-Delphi approach to establish the comprehensive assessment indicator system, the entropy weight determination method, and the best-worst method (BWM) to calculate weights of all sub-criteria, and a Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) comprehensive evaluation model to choose the optimal battery ESS. In accordance with the comprehensive evaluation results, the Li-ion battery is the optimal battery ESS to apply to wind-photovoltaic-energy storage combination exemplary projects. Based on the discussion on the comprehensive evaluation results, policy implications are suggested to improve the applicability of battery ESSs and provide some references for decision makers in related fields.
Haoran Zhao; Sen Guo; Huiru Zhao. Comprehensive Performance Assessment on Various Battery Energy Storage Systems. Energies 2018, 11, 2841 .
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. Comprehensive Performance Assessment on Various Battery Energy Storage Systems. Energies. 2018; 11 (10):2841.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2018. "Comprehensive Performance Assessment on Various Battery Energy Storage Systems." Energies 11, no. 10: 2841.
The current society is confronting with the crisis of fossil energy resources scarcity and environment deterioration caused by the accelerating development of economy in China. Since the improvement of energy efficiency has been deemed as the most effective way to decrease energy consumption and pollutant emissions, energy efficiency evaluation has been attached great importance in policy formulating. This investigation employed three-stage data envelopment analysis model to evaluate China’s provincial energy efficiency during 2008-2016 excluding the impacts of exterior environmental factors. The empirical results illustrate that the provincial energy efficiencies in China are significantly affected by economic and energy consumption structure, urbanization process, and technical innovation level. Generally, the exterior environmental values and statistical noises result in the underestimation of China’s provincial energy efficiencies. The exclusion of exterior environmental factors has provincial-specific impacts. Additionally, energy efficiency can be disintegrated into scale efficiency and pure energy efficiency, which is mainly dominated by scale efficiency. Based on empirical results, provincial specific strategies can be provided to enhance energy efficiency, such as taking the influences of exterior environmental factors into consideration when formulating policies, optimizing the exterior environment to improve provincial energy efficiency, and pertinently improving scale efficiency or pure energy efficiency according to their categorizations.
Haoran Zhao; Sen Guo; Huiru Zhao. Provincial energy efficiency of China quantified by three-stage data envelopment analysis. Energy 2018, 166, 96 -107.
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. Provincial energy efficiency of China quantified by three-stage data envelopment analysis. Energy. 2018; 166 ():96-107.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2018. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis." Energy 166, no. : 96-107.
The speeding-up of economic development and industrialization processes in China have brought about serious atmospheric pollution issues, especially in terms of particulate matter harmful to health. However, impact mechanisms of socio-economic forces on PM2.5 (the particle matter with diameter less than 2.5 μm) have rarely been further investigated. This paper selected GDP (gross domestic product) per capita, energy consumption, urbanization process, industrialization structure, and the amount of possession of civil vehicles as the significant factors, and researched the relationship between these factors and PM2.5 concentrations from 1998 to 2016, employing auto-regressive distributed lag (ARDL) methodology and environmental Kuznets curve (EKC) theory. Empirical results illustrated that a long-term equilibrium nexus exists among these variables. Granger causality results indicate that bi-directional causality exist between PM2.5 concentrations and GDP per capita, the squared component of GDP per capita, energy consumption and urbanization process. An inverse U-shape nexus exists between PM2.5 concentrations and GDP per capita. When the real GDP per capita reaches 5942.44 dollars, PM2.5 concentrations achieve the peak. Results indicate that Chinese governments should explore a novel pathway to resolve the close relationship between socio-economic factors and PM2.5, such as accelerating the adjustment of economic development mode, converting the critical industrial development driving forces, and adjusting the economic structure.
Haoran Zhao; Sen Guo; Huiru Zhao. Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China. Sustainability 2018, 10, 2574 .
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China. Sustainability. 2018; 10 (7):2574.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2018. "Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China." Sustainability 10, no. 7: 2574.
As useful supplements and effective support for large-scale electric power networks, micro-grid systems are the development tendency of future electric power systems. The planning performance of a micro-grid not only affects its security, reliability and economy, but also has a profound influence on the stable operation of large-scale electric power networks with the increasing penetration of micro-grids. Hence, studies related to micro-grid planning program evaluation are of great significance. This paper established a novel multi-criteria decision making (MCDM) model combining the best-worst method (BWM), the entropy weighting approach, and grey cumulative prospect theory for optimum selection of micro-grid planning programs. Firstly, an evaluation index system containing 18 sub-criteria was built from the perspectives of economy, electricity supply reliability and environmental protection. Secondly, the weights of sub-criteria were calculated integrating the subjective weights judged by the BWM and the objective weights computed by the entropy weighting method. Then, the cumulative prospect theory (CPT) combined with grey theory was employed to select the optimal micro-grid planning program. The empirical result indicates that the program with 100 kWp photovoltaic power generation unit, 200 kW wind power generation unit and 600 kWh NaS battery energy storage system is the optimal micro-grid planning program. To verify the robustness of obtained result, a sensitivity analysis related to values change of parameters under different risk preferences was conducted, and the result indicates that the selected optimal micro-grid planning program will not be influenced by various risk preferences of decision makers (DMs) and investors. The novel MCDM proposed in this paper is applicable and feasible in the micro-grid planning programs evaluation and selection.
Haoran Zhao; Sen Guo; Huiru Zhao. Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory. Energies 2018, 11, 1840 .
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory. Energies. 2018; 11 (7):1840.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2018. "Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory." Energies 11, no. 7: 1840.
Accurate electricity price prediction is key to the orderly operation of the electricity market. However, the uncertain, stochastic and fluctuant characteristics of electricity pricees make prediction difficult. With the aim of solving this issue, this investigation proposed a multi-stage intelligent model integrating the Beveridge–Nelson decomposition (B-N-D) model, the least square support vector machine (LSSVM), and a nature-inspired optimization model named the whale optimization algorithm (WOA). Firstly, the B-N-D model was utilized to decompose the hourly electricity price time series into determinacy component, periodic trend, and stochastic item. Secondly, the WOA–LSSVM model was proposed to forecast the future hourly data of three components respectively, of which the optimal parameters of LSSVM were determined by using WOA. Finally, the future hourly electricity price data were computed by multiplying the forecasted data of those terms. To verify the validity of the proposed electricity price prediction model in this paper, five comparison approaches based on the B-N-D approach were selected, which are auto-regressive integrated moving average (ARIMA), single LSSVM, LSSVM optimized by the fruit-fly optimization algorithm (FOA), LSSVM optimized by particle swarm optimization (PSO) models, and WOA–LSSVM without B-N-D. By comparatively analyzing the error criteria values of the above models through testing on the objective data of the Pennsylvania–New Jersey–Maryland (PJM) electricity market collected from 11 December 2017 to 18 December 2017, from 15 January 2018 to 22 January 2018, and from 1 February 2018 to 25 February 2018, we conclude that the constructed intelligent model in this paper can greatly enhance the prediction precision of electricity prices.
Haoran Zhao; Sen Guo; Huiru Zhao. A Multi-Stage Intelligent Model for Electricity Price Prediction Based on the Beveridge–Nelson Disintegration Approach. Sustainability 2018, 10, 1568 .
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. A Multi-Stage Intelligent Model for Electricity Price Prediction Based on the Beveridge–Nelson Disintegration Approach. Sustainability. 2018; 10 (5):1568.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2018. "A Multi-Stage Intelligent Model for Electricity Price Prediction Based on the Beveridge–Nelson Disintegration Approach." Sustainability 10, no. 5: 1568.
As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.
Haoran Zhao; Huiru Zhao; Sen Guo. Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm. Sustainability 2018, 10, 881 .
AMA StyleHaoran Zhao, Huiru Zhao, Sen Guo. Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm. Sustainability. 2018; 10 (3):881.
Chicago/Turabian StyleHaoran Zhao; Huiru Zhao; Sen Guo. 2018. "Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm." Sustainability 10, no. 3: 881.
Atmospheric pollution gradually become a focus of concern all over the world owing to its detrimental influence on human health as well as long range impact on global ecosystem. This paper investigated the relationship among SO2 emissions, GDP, fossil fuel energy consumption, energy consumption intensity, and economic structure of five provinces in China with the highest SO2 emissions spanning from 2002–2015 based on panel data model. Through comparatively analyzing the coefficients in the established panel data model for Hebei, Henan, Inner Mongolia, Shandong, and Shanxi, we can obtain that: (1) fossil fuel energy consumption made the most devotion to SO2 discharge compared with GDP, energy consumption intensity, and economic structure. And the more the fossil fuel energy consumption, the more the devotion made by it to SO2 discharge. (2) GDP devoted less to SO2 emissions than fossil fuel energy consumption, and the larger the scale of the economy, the greater the contribution made by it to SO2 emissions. (3) The higher the proportion of the secondary industry added value accounted in GDP, the more the devotion made by the economic structure and energy consumption intensity to SO2 emissions. Through analyzing the Granger causality examination results, it can be concluded that: (1) there existed a bi-directional causal relationship between fossil fuel energy consumption and SO2 emissions among five selected provinces. (2) There existed uni-directional causal nexus running from GDP to SO2 emissions, from energy consumption intensity to SO2 emissions, and from economic structure to SO2 emissions among five chosen provinces. Based on the empirical analysis, several policy implications were proposed to provide references for policy makers, which were (1) Giving full play to the guiding role of price signals, and improving the price policy for desulfurization. (2) Formulating a new comprehensive evaluation system to measure the regional development level considering economic development and environmental impacts. (3) Exploring renewable and sustainable energy sources to substitute for fossil fuel energy according to regional resources endowment. (4) Developing high value added and low pollution emissions industries and reducing the proportion of secondary industry.
Haoran Zhao; Sen Guo; Huiru Zhao. Impacts of GDP, Fossil Fuel Energy Consumption, Energy Consumption Intensity, and Economic Structure on SO2 Emissions: A Multi-Variate Panel Data Model Analysis on Selected Chinese Provinces. Sustainability 2018, 10, 657 .
AMA StyleHaoran Zhao, Sen Guo, Huiru Zhao. Impacts of GDP, Fossil Fuel Energy Consumption, Energy Consumption Intensity, and Economic Structure on SO2 Emissions: A Multi-Variate Panel Data Model Analysis on Selected Chinese Provinces. Sustainability. 2018; 10 (3):657.
Chicago/Turabian StyleHaoran Zhao; Sen Guo; Huiru Zhao. 2018. "Impacts of GDP, Fossil Fuel Energy Consumption, Energy Consumption Intensity, and Economic Structure on SO2 Emissions: A Multi-Variate Panel Data Model Analysis on Selected Chinese Provinces." Sustainability 10, no. 3: 657.
As one of the most promising kinds of the renewable energy power, wind power has developed rapidly in recent years. However, wind power has the characteristics of intermittency and volatility, so its penetration into electric power systems brings challenges for their safe and stable operation, therefore making accurate wind power forecasting increasingly important, which is also a challenging task. In this paper, a new hybrid wind power forecasting method, named the BND-ALO-RVM forecaster, is proposed. It combines the Beveridge-Nelson decomposition method (BND), relevance vector machine (RVM) and ant lion optimizer (ALO). Considering the nonlinear and non-stationary characteristics of wind power data, the wind power time series were firstly decomposed into deterministic, cyclical and stochastic components using BND. Then, these three decomposed components were respectively forecasted using RVM. Meanwhile, to improve the forecasting performance, the kernel width parameter of RVM was optimally determined by ALO, a new Nature-inspired meta-heuristic algorithm. Finally, the wind power forecasting result was obtained by multiplying the forecasting results of those three components. The proposed BND-ALO-RVM wind power forecaster was tested with real-world hourly wind power data from the Xinjiang Uygur autonomous region in China. To verify the effectiveness and feasibility of the proposed forecaster, it was compared with single RVM without time series decomposition and parameter optimization, RVM with time series decomposition based on BND (BND-RVM), RVM with parameter optimization (ALO-RVM), and Generalized Regression Neural Network with data decomposition based on Wavelet Transform (WT-GRNN) using three forecasting performance criteria, namely MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error). The results indicate the proposed BND-ALO-RVM wind power forecaster has the best forecasting performance of all the tested options, which confirms its validity.
Sen Guo; Haoran Zhao; Huiru Zhao. A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer. Energies 2017, 10, 922 .
AMA StyleSen Guo, Haoran Zhao, Huiru Zhao. A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer. Energies. 2017; 10 (7):922.
Chicago/Turabian StyleSen Guo; Haoran Zhao; Huiru Zhao. 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer." Energies 10, no. 7: 922.
There are still residents without access to electricity in some remote and less developed areas of China, which lead to low living standards and hinder sustainable development for these residents. In order to achieve the strategic targets of solving China’s energy poverty, realizing basic energy service equalization, and comprehensively building up a moderately prosperous society, several policies have been successively promulgated in recent years, which aim to solve the electricity access issue for residents living in remote and less developed areas. It is of great importance to determine the most economical mode of power supply in remote and less developed areas, which directly affects the economic efficiency of public investment projects. Therefore, this paper focuses on how to select the most economical power supply mode for rural electrification in China. Firstly, the primary modes to supply electricity for residents living in the remote and less developed areas are discussed, which include power grid extension mode and micro-grid mode. Secondly, based on the levelized cost of electricity (LCOE) technique, the life cycle economic cost accounting model for different power supply modes are built. Finally, taking a minority nationality village in Yunnan province as an example, the empirical analysis is performed, and the LCOEs of various possible modes for rural electrification are accounted. The results show that the photovoltaic (PV)-based independent micro-grid system is the most economical due to the minimum LCOE, namely 0.658 RMB/kWh. However, other power supply modes have much higher LCOEs. The LCOEs of power grid extension model, wind-based independent micro-grid system and biomass-based independent micro-grid system are 1.078 RMB/kWh, 0.704 RMB/kWh and 0.885 RMB/kWh, respectively. The proposed approach is effective and practical, which can provide reference for rural electrification in China.
Sen Guo; Huiru Zhao; Haoran Zhao. The Most Economical Mode of Power Supply for Remote and Less Developed Areas in China: Power Grid Extension or Micro-Grid? Sustainability 2017, 9, 910 .
AMA StyleSen Guo, Huiru Zhao, Haoran Zhao. The Most Economical Mode of Power Supply for Remote and Less Developed Areas in China: Power Grid Extension or Micro-Grid? Sustainability. 2017; 9 (6):910.
Chicago/Turabian StyleSen Guo; Huiru Zhao; Haoran Zhao. 2017. "The Most Economical Mode of Power Supply for Remote and Less Developed Areas in China: Power Grid Extension or Micro-Grid?" Sustainability 9, no. 6: 910.
Fuzzy best-worst method is proposed to solve the issues under fuzzy environment.A consistency ratio for fuzzy best-worst method is proposed for verification.The results indicate the fuzzy best-worst method outperforms best-worst method.The fuzzy best-worst method has a higher comparison consistency. Considering the vagueness frequently representing in decision data due to the lack of complete information and the ambiguity arising from the qualitative judgment of decision-makers, the crisp values of criteria may be inadequate to model the real-life multi-criteria decision-making (MCDM) issues. In this paper, the latest MCDM method, namely best-worst method (BWM) was extended to the fuzzy environment. The reference comparisons for the best criterion and for the worst criterion were described by linguistic terms of decision-makers, which can be expressed in triangular fuzzy numbers. Then, the graded mean integration representation (GMIR) method was employed to calculate the weights of criteria and alternatives with respect to different criteria under fuzzy environment. According to the concept of BWM, the nonlinearly constrained optimization problem was built for determining the fuzzy weights of criteria and alternatives with respect to different criteria. The fuzzy ranking scores of alternatives can be derived from the fuzzy weights of alternatives with respect to different criteria multiplied by fuzzy weights of the corresponding criteria, and then the crisp ranking score of alternatives can be calculated by employing GMIR method for optimal alternative selection. Meanwhile, the consistency ratio was proposed for fuzzy BWM to check the reliability of fuzzy preference comparisons. Three case studies were performed to illustrate the effectiveness and feasibility of the proposed fuzzy BWM. The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
Sen Guo; Haoran Zhao. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems 2017, 121, 23 -31.
AMA StyleSen Guo, Haoran Zhao. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems. 2017; 121 ():23-31.
Chicago/Turabian StyleSen Guo; Haoran Zhao. 2017. "Fuzzy best-worst multi-criteria decision-making method and its applications." Knowledge-Based Systems 121, no. : 23-31.
The electric universal service policy, which has been implemented for many years in China, aims to meet the basic electricity demands of rural residents. Electricity consumption can facilitate the daily life of rural residents, such as lighting and cooking, which are necessary to their well-being. In practice, the well-being of rural residents due to electricity consumption is influenced by many factors. Therefore, to improve the well-being of rural residents, it is quite necessary to identify and optimize the significant factors that make the electric universal service policy play its prescribed role as well as possible. In this paper, the significant factors influencing rural residents’ well-being obtained from electricity consumption were identified and discussed by employing the Ordered Probit model. The results indicate that: (1) there are six significant factors, of which ‘educational level’, ‘health condition’, ‘each person income of a family per month’, and ‘service time of household appliances’ play positive roles in rural residents’ well-being, while ‘average power interruption times’ and ‘monthly electric charges’ have negative impacts; (2) for significant factors with positive roles, ‘educational level’ and ‘health condition’ show larger marginal effects on rural residents’ well-being; and (3) for significant factors with negative impacts, ‘average power interruption times’ has the greatest marginal effect. Finally, policy implications are proposed for improving rural residents’ well-being, which can also contribute to the effective implementation of the electric universal service policy in China.
Sen Guo; Huiru Zhao; Chunjie Li; Haoran Zhao; Bingkang Li. Significant Factors Influencing Rural Residents’ Well-Being with Regard to Electricity Consumption: An Empirical Analysis in China. Sustainability 2016, 8, 1132 .
AMA StyleSen Guo, Huiru Zhao, Chunjie Li, Haoran Zhao, Bingkang Li. Significant Factors Influencing Rural Residents’ Well-Being with Regard to Electricity Consumption: An Empirical Analysis in China. Sustainability. 2016; 8 (11):1132.
Chicago/Turabian StyleSen Guo; Huiru Zhao; Chunjie Li; Haoran Zhao; Bingkang Li. 2016. "Significant Factors Influencing Rural Residents’ Well-Being with Regard to Electricity Consumption: An Empirical Analysis in China." Sustainability 8, no. 11: 1132.
Over the past three decades, China’s economy has witnessed remarkable growth, with an average annual growth rate over 9%. However, China also faces great challenges to balance this spectacular economic growth and continuously increasing energy use like many other economies in the world. With the aim of designing effective energy and environmental policies, policymakers are required to master the relationship between energy consumption and economic growth. Therefore, in the case of North China, a multivariate model employing panel data analysis method based on the Cobb-Douglas production function which introduces electricity consumption as a main factor was established in this paper. The equilibrium relationship and causal relationship between real GDP, electricity consumption, total investment in fixed assets, and the employment were explored using data during the period of 1995–2014 for six provinces in North China, including Beijing City, Tianjin City, Hebei Province, Shanxi Province, Shandong Province and Inner Mongolia. The results of panel co-integration tests clearly state that all variables are co-integrated in the long term. Finally, Granger causality tests were used to examine the causal relationship between economic growth, electricity consumption, labor force and capital. From the Granger causality test results, we can draw the conclusions that: (1) There exist bi-directional causal relationships between electricity consumption and real GDP in six provinces except Hebei; and (2) there is a bi-directional relationship between capital input and economic growth and between labor force input and economic growth except Beijing and Hebei. Therefore, the ways to solve the contradiction of economic growth and energy consumption in North China are to reduce fossil energy consumption, develop renewable and sustainable energy sources, improve energy efficiency, and increase the proportion of the third industry, especially the sectors which hold the characteristics of low energy consumption and high value-added.
Huiru Zhao; Haoran Zhao; Xiaoyu Han; Zhonghua He; Sen Guo. Economic Growth, Electricity Consumption, Labor Force and Capital Input: A More Comprehensive Analysis on North China Using Panel Data. Energies 2016, 9, 891 .
AMA StyleHuiru Zhao, Haoran Zhao, Xiaoyu Han, Zhonghua He, Sen Guo. Economic Growth, Electricity Consumption, Labor Force and Capital Input: A More Comprehensive Analysis on North China Using Panel Data. Energies. 2016; 9 (11):891.
Chicago/Turabian StyleHuiru Zhao; Haoran Zhao; Xiaoyu Han; Zhonghua He; Sen Guo. 2016. "Economic Growth, Electricity Consumption, Labor Force and Capital Input: A More Comprehensive Analysis on North China Using Panel Data." Energies 9, no. 11: 891.
The electricity consumption and economic growth are highly correlated. The financial crisis in 2008 brought a negative effect on China’s economic growth, which also influenced the electricity consumption. The electricity demand of North China region was also greatly influenced by this financial crisis, the whole social electricity consumption growth rate of which decreased by 14.31% in 2008 compared to that in 2007. In order to analyze the random impulse effect of the financial crisis on the demand of electricity in North China, the monthly data is decomposed into deterministic trend, stochastic impact effect, and periodic trend using the Beveridge-Nelson decomposition method. After comparatively analyzing the impulse effect of the financial crisis on electricity consumption of six provinces in North China, we can draw the conclusions: (1) the electricity consumption of the whole society and the secondary industry are under larger negative impacts, and the random impulse effect of the secondary industry is more intense; and (2) the impact of the financial crisis on the tertiary industry, which is mainly influenced by seasonal changes, is smaller. Finally, some policy implications are proposed.
Huiru Zhao; Haoran Zhao; Sen Guo; Fuqiang Li; Yuou Hu. The Impact of Financial Crisis on Electricity Demand: A Case Study of North China. Energies 2016, 9, 250 .
AMA StyleHuiru Zhao, Haoran Zhao, Sen Guo, Fuqiang Li, Yuou Hu. The Impact of Financial Crisis on Electricity Demand: A Case Study of North China. Energies. 2016; 9 (4):250.
Chicago/Turabian StyleHuiru Zhao; Haoran Zhao; Sen Guo; Fuqiang Li; Yuou Hu. 2016. "The Impact of Financial Crisis on Electricity Demand: A Case Study of North China." Energies 9, no. 4: 250.
Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.
Huiru Zhao; Haoran Zhao; Sen Guo. Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia. Applied Sciences 2016, 6, 20 .
AMA StyleHuiru Zhao, Haoran Zhao, Sen Guo. Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia. Applied Sciences. 2016; 6 (1):20.
Chicago/Turabian StyleHuiru Zhao; Haoran Zhao; Sen Guo. 2016. "Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia." Applied Sciences 6, no. 1: 20.