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Jinchao Li
School of Economics and Management, North China Electric Power University, Beijing 102206, China

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Journal article
Published: 18 March 2019 in Sustainability
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With the consumption of energy, blackouts, and a series of social development problems, the discussion of energy security has become the focus of international attention. This paper aims to construct a universal multidimensional index system from four dimensions, and compare the energy security systems of different countries by measuring the Energy Security Index (ESI) of 19 countries of G20 and analyzing their evolution characteristics. The results of this paper show that the ESI of the G20 countries is increasing, the number of dangerous countries is decreasing, mainly concentrated in the Asian and African regions, and environmental sustainability and safety use are the main factors affecting their energy security. The security countries are mainly concentrated in the developed countries of the Americas and Europe. Since 1995, the ESI of China has continued to rise and now China is a generally safe country, which reflects China’s continuous optimization of energy structure and continuous improvement of the relationship of energy systems and economic, population, and environmental systems.

ACS Style

Jinchao Li; Lina Wang; Tianzhi Li; Shaowen Zhu. Energy Security Pattern Spatiotemporal Evolution and Strategic Analysis of G20 Countries. Sustainability 2019, 11, 1629 .

AMA Style

Jinchao Li, Lina Wang, Tianzhi Li, Shaowen Zhu. Energy Security Pattern Spatiotemporal Evolution and Strategic Analysis of G20 Countries. Sustainability. 2019; 11 (6):1629.

Chicago/Turabian Style

Jinchao Li; Lina Wang; Tianzhi Li; Shaowen Zhu. 2019. "Energy Security Pattern Spatiotemporal Evolution and Strategic Analysis of G20 Countries." Sustainability 11, no. 6: 1629.

Journal article
Published: 05 November 2018 in Sustainability
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In order to eliminate the impact of inter-regional differentiation of development demand on the objective evaluation of the development level of smart grid, this paper establishes the evaluation model of weight modification, transmission mechanism and combination of subjective and objective weights. Firstly, the Analytic Hierarchy Process method is used to calculate the weights of evaluation indices of effect layer and then the indices of development demand are used to modify the weights of them. The association analysis and the correlation coefficient are used to establish the weights conduction coefficient between the effect level and the base level. Then the subjective weights of the indices of the base layer are calculated. The objective weights of the indices of the base layer are obtained by using the entropy method. The subjective weights of the base layer and the objective weights obtained by the entropy method are averagely calculated, and the comprehensive weights of the evaluation indices of the base layer are obtained. Then each index is scored according to the weights and index values. Finally, the model is used to quantitatively inspect the level of development of smart grid in specific regions and make a horizontal comparison, which provides a useful reference for the development of smart grids. The relevant examples verify the correctness and validity of the model.

ACS Style

Jinchao Li; Tianzhi Li; Liu Han. Research on the Evaluation Model of a Smart Grid Development Level Based on Differentiation of Development Demand. Sustainability 2018, 10, 4047 .

AMA Style

Jinchao Li, Tianzhi Li, Liu Han. Research on the Evaluation Model of a Smart Grid Development Level Based on Differentiation of Development Demand. Sustainability. 2018; 10 (11):4047.

Chicago/Turabian Style

Jinchao Li; Tianzhi Li; Liu Han. 2018. "Research on the Evaluation Model of a Smart Grid Development Level Based on Differentiation of Development Demand." Sustainability 10, no. 11: 4047.

Journal article
Published: 19 January 2018 in Sustainability
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Electric grid investment demand analysis is significant to reasonably arranging construction funds for the electric grid and reduce costs. This paper used the panel data of electric grid investment from 23 provinces of China between 2004 and 2016 as samples to analyze the influence between electric grid investment demand and GDP, population scale, social electricity consumption, installed electrical capacity, and peak load based on co-integration tests. We find that GDP and peak load have positive influences on electric grid investment demand, but the impact of population scale, social electricity consumption, and installed electrical capacity on electric grid investment is not remarkable. We divide different regions in China into the eastern region, central region, and western region to analyze influence factors of electric grid investment, finally obtaining key factors in the eastern, central, and western regions. In the end, according to the analysis of key factors, we make a prediction about China’s electric grid investment for 2020 in different scenarios. The results offer a certain understanding for the development trend of China’s electric grid investment and contribute to the future development of electric grid investment.

ACS Style

Jinchao Li; Lin Chen; Yuwei Xiang; Jinying Li; Dong Peng. Influencing Factors and Development Trend Analysis of China Electric Grid Investment Demand Based on a Panel Co-Integration Model. Sustainability 2018, 10, 256 .

AMA Style

Jinchao Li, Lin Chen, Yuwei Xiang, Jinying Li, Dong Peng. Influencing Factors and Development Trend Analysis of China Electric Grid Investment Demand Based on a Panel Co-Integration Model. Sustainability. 2018; 10 (1):256.

Chicago/Turabian Style

Jinchao Li; Lin Chen; Yuwei Xiang; Jinying Li; Dong Peng. 2018. "Influencing Factors and Development Trend Analysis of China Electric Grid Investment Demand Based on a Panel Co-Integration Model." Sustainability 10, no. 1: 256.

Journal article
Published: 05 January 2018 in Sustainability
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In order to realize the synergistic optimization management of energy efficiency in the key energy-intensive industries of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region, this paper calculates the total factor energy efficiency (TFEE) of 27 industries in the Jing-Jin-Ji region. We discover that the manufacturing of raw chemical materials and chemical products, the smelting and processing of ferrous metals, and the production and supply of electric power and heat power are key industries, considering their economic output ratio, energy consumption ratio, and energy efficiency. Then, the Malmquist index is used to decompose the TFEE of key energy-intensive industries. The results show that the TFEE changes in the three major industries in the Jing-Jin-Ji region are caused by technological progress. Hebei has the highest total factor average energy efficiency in the production and supply of electric power and heat power industry, the main reason for this being the spillover effect from Beijing enterprises that have led to significant technological changes in Hebei. Due to similar technological advancements, Tianjin has the highest total factor average energy efficiency in the manufacturing of raw chemical materials and chemical products and the smelting and processing of ferrous metals. Therefore, the Jing-Jin-Ji region should work to increase its technological innovation and enhance its core competitiveness. We should optimize the allocation of resources in specific industries to improve the scale efficiency.

ACS Style

Jinchao Li; Yuwei Xiang; Huanyu Jia; Lin Chen. Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region. Sustainability 2018, 10, 111 .

AMA Style

Jinchao Li, Yuwei Xiang, Huanyu Jia, Lin Chen. Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region. Sustainability. 2018; 10 (2):111.

Chicago/Turabian Style

Jinchao Li; Yuwei Xiang; Huanyu Jia; Lin Chen. 2018. "Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region." Sustainability 10, no. 2: 111.

Journal article
Published: 07 December 2016 in Sustainability
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Planning for electricity generation systems is a very important task and should take environmental and economic factors into account. This paper reviews the existing metrics and methods in evaluating energy sustainability, and we propose a sustainability assessment index system. The input indexes include generation capacity, generation cost, and land use. The output indexes include desirable and undesirable parts. The desirable outputs are total electricity generation and job creation. The undesirable outputs are external supply risk and external costs associated with the environment and health. The super-efficiency data envelopment analysis method is used to calculate the sustainability of electricity generation systems of 23 countries from 2005 to 2014. The three input indexes and three undesirable output indexes are used as the input variables. The two desirable outputs are used as the output variables. The results show that most countries’ electricity generation sustainability values have decreasing trends. In addition, nuclear and hydro generation have positive effects. Solar, wind, and fossil fuel generation have negative effects on sustainability.

ACS Style

Jinchao Li; Xian Geng; Jinying Li. A Comparison of Electricity Generation System Sustainability among G20 Countries. Sustainability 2016, 8, 1276 .

AMA Style

Jinchao Li, Xian Geng, Jinying Li. A Comparison of Electricity Generation System Sustainability among G20 Countries. Sustainability. 2016; 8 (12):1276.

Chicago/Turabian Style

Jinchao Li; Xian Geng; Jinying Li. 2016. "A Comparison of Electricity Generation System Sustainability among G20 Countries." Sustainability 8, no. 12: 1276.

Journal article
Published: 10 February 2014 in Sustainability
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This paper measured the economic and unified efficiency of 24 electric power supply companies in China. With the development of a low carbon economy, further requirements for energy-saving and emission-reducing have been put forward for electric power supply companies. In this case, we considered the desirable (good) outputs (e.g., electricity sale amount) and undesirable (bad) outputs (e.g., line loss) in evaluating the performance of power supply companies. We combined the unified efficiency data envelopment analysis (DEA) model and the super-efficiency DEA model to create tge USDEA model, calculating companies’ unified efficiency. The unified efficiency DEA model can easily incorporate both desirable and undesirable outputs in a unified analytical structure. The super-efficiency data envelopment analysis model can make a comparison among various efficient decision making units (DMUs). Our results showed that the electric power supply companies of Hebei, Qinghai, Ningxia, Beijing and Shanghai achieved the highest levels of unified efficiency during the observed period (2003–2010), which differed from the economic efficiency results. The results meant that using unified efficiency to evaluate power supply companies will motivate them to care more about social and environmental benefit besides economic benefit.

ACS Style

Jinchao Li; Jinying Li; Fengting Zheng. Unified Efficiency Measurement of Electric Power Supply Companies in China. Sustainability 2014, 6, 779 -793.

AMA Style

Jinchao Li, Jinying Li, Fengting Zheng. Unified Efficiency Measurement of Electric Power Supply Companies in China. Sustainability. 2014; 6 (2):779-793.

Chicago/Turabian Style

Jinchao Li; Jinying Li; Fengting Zheng. 2014. "Unified Efficiency Measurement of Electric Power Supply Companies in China." Sustainability 6, no. 2: 779-793.