Lu-Tao Zhao

Personal Details

First Name:	Lu-Tao
Middle Name:
Last Name:	Zhao
Suffix:
RePEc Short-ID:	pzh1113
	[This author has chosen not to make the email address public]

Affiliation

Center for Energy and Environmental Policy Research (CEEP)
Beijing Institute of Technology

Beijing, China
http://ceep.bit.edu.cn/
RePEc:edi:cebitcn (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Ce Wang & Hua Liao & Su-Yan Pan & Lu-Tao Zhao & Yi-Ming Wei, 2014. "The fluctuations of China's energy intensity: Biased technical change," CEEP-BIT Working Papers 56, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
   • Wang, Ce & Liao, Hua & Pan, Su-Yan & Zhao, Lu-Tao & Wei, Yi-Ming, 2014. "The fluctuations of China’s energy intensity: Biased technical change," Applied Energy, Elsevier, vol. 135(C), pages 407-414.

   Cited by:

   1. Zhang, Dayong & Li, Jun & Ji, Qiang, 2020. "Does better access to credit help reduce energy intensity in China? Evidence from manufacturing firms," Energy Policy, Elsevier, vol. 145(C).
   2. Song, Yan & Zhu, Jing & Yue, Qian & Zhang, Ming & Wang, Longke, 2023. "Industrial agglomeration, technological innovation and air pollution: Empirical evidence from 277 prefecture-level cities in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 240-252.
   3. Jin, Yi & Gao, Xiaoyan & Wang, Min, 2021. "The financing efficiency of listed energy conservation and environmental protection firms: Evidence and implications for green finance in China," Energy Policy, Elsevier, vol. 153(C).
   4. Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
   5. Chen, Yu & Lin, Boqiang, 2021. "Understanding the green total factor energy efficiency gap between regional manufacturing—insight from infrastructure development," Energy, Elsevier, vol. 237(C).
   6. Zhou, Yang & Liu, Yansui, 2016. "Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China," Applied Energy, Elsevier, vol. 180(C), pages 800-809.
   7. Zhang, Wei & Zhang, Ting & Li, Hangyu & Zhang, Han, 2022. "Dynamic spillover capacity of R&D and digital investments in China's manufacturing industry under long-term technological progress based on the industry chain perspective," Technology in Society, Elsevier, vol. 71(C).
   8. Li, Meng & Gao, Yuning & Liu, Shenglong, 2020. "China’s energy intensity change in 1997–2015: Non-vertical adjusted structural decomposition analysis based on input-output tables," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 222-236.
   9. Azlina Abdullah & Hussain Ali Bekhet, 2019. "Investigating the Driving Forces of Energy Intensity Change in Malaysia 1991-2010: A Structural Decomposition Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 121-130.
   10. Yan, Junna & Su, Bin, 2020. "What drive the changes in China's energy consumption and intensity during 12th Five-Year Plan period?," Energy Policy, Elsevier, vol. 140(C).
   11. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2018. "Energy-biased technical change in the Chinese industrial sector with CES production functions," Energy, Elsevier, vol. 148(C), pages 896-903.
   12. Ai, Hongshan & Wang, Mengyuan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2022. "How does air pollution affect urban innovation capability? Evidence from 281 cities in China," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 166-178.
   13. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
   14. Chao Bi & Minna Jia & Jingjing Zeng, 2019. "Nonlinear Effect of Public Infrastructure on Energy Intensity in China: A Panel Smooth Transition Regression Approach," Sustainability, MDPI, vol. 11(3), pages 1-21, January.
   15. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2017. "Energy biased technology change: Focused on Chinese energy-intensive industries," Applied Energy, Elsevier, vol. 190(C), pages 1081-1089.
   16. Zhang, Dayong & Cao, Hong & Wei, Yi-Ming, 2016. "Identifying the determinants of energy intensity in China: A Bayesian averaging approach," Applied Energy, Elsevier, vol. 168(C), pages 672-682.

2. Qiao-Mei Liang & Yun-Fei Yao & Lu-Tao Zhao & Ce Wang & Rui-Guang Yang & Yi-Ming Wei, 2013. "Platform for China Energy & Environmental Policy Analysis: A general design and its application," CEEP-BIT Working Papers 43, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.

   Cited by:

   1. Cai, Yiyong & Newth, David & Finnigan, John & Gunasekera, Don, 2015. "A hybrid energy-economy model for global integrated assessment of climate change, carbon mitigation and energy transformation," Applied Energy, Elsevier, vol. 148(C), pages 381-395.
   2. Lin Yang & Yunfei Yao & Jiutian Zhang & Xian Zhang & Karl J. McAlinden, 2016. "A CGE analysis of carbon market impact on CO2 emission reduction in China: a technology-led approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 1107-1128, March.
   3. Yinger Zheng & Haixia Zheng & Xinyue Ye, 2016. "Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China," Sustainability, MDPI, vol. 8(11), pages 1-20, November.
   4. Yun-Fei Yao & Qiao-Mei Liang, 2016. "Approaches to carbon allowance allocation in China: a computable general equilibrium analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 333-351, November.
   5. Lin Yang & Yunfei Yao & Jiutian Zhang & Xian Zhang & Karl McAlinden, 2016. "A CGE analysis of carbon market impact on CO 2 emission reduction in China: a technology-led approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 1107-1128, March.
   6. Qian Wang & Qiao-Mei Liang, 2015. "Will a carbon tax hinder China’s efforts to improve its primary income distribution status?," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(8), pages 1407-1436, December.

Articles

1. Zhao, Lu-Tao & Xing, Yue-Yue & Zhao, Qiu-Rong & Chen, Xue-Hui, 2023. "Dynamic impacts of online investor sentiment on international crude oil prices," Resources Policy, Elsevier, vol. 82(C).

   Cited by:

   1. Sarit Maitra & Vivek Mishra & Sukanya Kundu & Manav Chopra, 2023. "Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns," Papers 2309.13096, arXiv.org, revised Oct 2023.

2. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).

   Cited by:

   1. Bhattacherjee, Purba & Mishra, Sibanjan & Kang, Sang Hoon, 2023. "Does market sentiment and global uncertainties influence ESG-oil nexus? A time-frequency analysis," Resources Policy, Elsevier, vol. 86(PA).

3. Liu, Yuan & Chen, Jiahui & Zhao, Lutao & Liao, Hua, 2023. "Rural photovoltaic projects substantially prompt household energy transition: Evidence from China," Energy, Elsevier, vol. 275(C).

   Cited by:

   1. Wang, Chaofan & Zhao, Yujia & Strezov, Vladimir & Shuai, Chuanmin & Cheng, Xin & Shuai, Jing, 2023. "Spatial correlation analysis of comprehensive efficiency of the photovoltaic poverty alleviation policy - Evidence from 110 counties in China," Energy, Elsevier, vol. 282(C).

4. Zhao, Lu-Tao & Liu, Zhao-Ting & Cheng, Lei, 2021. "How will China's coal industry develop in the future? A quantitative analysis with policy implications," Energy, Elsevier, vol. 235(C).

   Cited by:

   1. Ming Zhang & Wensheng Wang & Xialing Sun, 2023. "Measurement and Multiple Decomposition of Total Factor Productivity Growth in China’s Coal Industry," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
   2. Qin, Tao & Lu, Qiuxiang & Xiang, Hao & Luo, Xiulin & Shenfu, Yuan, 2023. "Ca promoted Ni–Co bimetallic catalyzed coal pyrolysis and char steam gasification," Energy, Elsevier, vol. 282(C).
   3. Olga A. Chernova, 2022. "Stressful Factors of Sustainable Development of the Russian Coal Industry," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 21(1), pages 49-78.
   4. Shi, Xueqiang & Wu, Hao & Jin, Penggang & Zhang, Yutao & Zhang, Yuanbo & Jiao, Fengyuan & Zhang, Yun & Cao, Weiguo, 2023. "On the influence of material and shape of the hot particles on the ignition characteristics of coal dust," Energy, Elsevier, vol. 281(C).
   5. Lee, Chien-Chiang & Hussain, Jafar, 2022. "Carbon neutral sustainability and green development during energy consumption," Innovation and Green Development, Elsevier, vol. 1(1).
   6. Yan, Shiyu & Lv, Chengwei & Yao, Liming & Hu, Zhineng & Wang, Fengjuan, 2022. "Hybrid dynamic coal blending method to address multiple environmental objectives under a carbon emissions allocation mechanism," Energy, Elsevier, vol. 254(PB).
   7. He, Qing & Cheng, Chen & Zhang, Xinsha & Guo, Qinghua & Ding, Lu & Raheem, Abdul & Yu, Guangsuo, 2022. "Insight into structural evolution and detailed non-isothermal kinetic analysis for coal pyrolysis," Energy, Elsevier, vol. 244(PB).
   8. Suli Hao & Chongbao Ren & Lu Zhang, 2022. "Research on Performance Evaluation of Coal Enterprises Based on Grounded Theory, Entropy Method and Cloud Model from the Perspective of ESG," Sustainability, MDPI, vol. 14(18), pages 1-54, September.
   9. Zhu, Hongqing & Liao, Qi & Qu, Baolin & Hu, Lintao & Wang, Haoran & Gao, Rongxiang & Zhang, Yilong, 2023. "Relationship between the main functional groups and complex permittivity in pre-oxidised lignite at terahertz frequencies based on grey correlation analysis," Energy, Elsevier, vol. 278(C).
   10. Jiang, Xu & Xu, Jun & He, Qichen & Wang, Cong & Jiang, Long & Xu, Kai & Wang, Yi & Su, Sheng & Hu, Song & Du, Zhenyi & Xiang, Jun, 2023. "A study of the relationships between coal heterogeneous chemical structure and pyrolysis behaviours: Mechanism and predicting model," Energy, Elsevier, vol. 282(C).

5. Lu-Tao Zhao & Zi-Jie Wang & Shu-Ping Wang & Ling-Yun He, 2021. "Predicting Oil Prices: An Analysis of Oil Price Volatility Cycle and Financial Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(4), pages 1068-1087, March.

   Cited by:

   1. Nan, Yu & Sun, Renjin & Zhen, Zhao & Fangjing, Chu, 2022. "Measurement of international crude oil price cyclical fluctuations and correlation with the world economic cyclical changes," Energy, Elsevier, vol. 260(C).
   2. Jakub Horák & Michaela Jannová, 2023. "Predicting the Oil Price Movement in Commodity Markets in Global Economic Meltdowns," Forecasting, MDPI, vol. 5(2), pages 1-16, March.

6. Zhang, Zhi-Gang & Hu, Xiao & Liu, Zhao-Ting & Zhao, Lu-Tao, 2021. "Multi-attribute decision making: An innovative method based on the dynamic credibility of experts," Applied Mathematics and Computation, Elsevier, vol. 393(C).

   Cited by:

   1. Zhao, Lu-Tao & Liu, Zhao-Ting & Cheng, Lei, 2021. "How will China's coal industry develop in the future? A quantitative analysis with policy implications," Energy, Elsevier, vol. 235(C).

7. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.

   Cited by:

   1. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
   2. Gharib, Cheima & Mefteh-Wali, Salma & Serret, Vanessa & Ben Jabeur, Sami, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Resources Policy, Elsevier, vol. 74(C).

8. Lu-Tao Zhao & Shun-Gang Wang & Zhi-Gang Zhang, 2020. "Oil Price Forecasting Using a Time-Varying Approach," Energies, MDPI, vol. 13(6), pages 1-16, March.

   Cited by:

   1. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
   2. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
   3. Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.

9. Liu, Lan-Cui & Cheng, Lei & Zhao, Lu-Tao & Cao, Ying & Wang, Ce, 2020. "Investigating the significant variation of coal consumption in China in 2002-2017," Energy, Elsevier, vol. 207(C).

   Cited by:

   1. Nicholas Ngepah & Charles Raoul Tchuinkam Djemo & Charles Shaaba Saba, 2022. "Forecasting the Economic Growth Impacts of Climate Change in South Africa in the 2030 and 2050 Horizons," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
   2. Jiang, Wei & Sun, Yifei, 2023. "Which is the more important factor of carbon emission, coal consumption or industrial structure?," Energy Policy, Elsevier, vol. 176(C).
   3. Lin, Boqiang & Teng, Yuqiang, 2022. "Structural path and decomposition analysis of sectoral carbon emission changes in China," Energy, Elsevier, vol. 261(PB).
   4. Liu, Jixiang & Tian, Shu & Wang, Qingsong & Xu, Yue & Zhang, Yujie & Yuan, Xueliang & Ma, Qiao & Ma, Haichao & Liu, Chengqing, 2023. "The regulation path of coal consumption based on the total reduction index—a case study in Shandong Province, China," Energy, Elsevier, vol. 262(PB).

10. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.

    Cited by:

    1. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Jiangwei Liu & Xiaohong Huang, 2021. "Forecasting Crude Oil Price Using Event Extraction," Papers 2111.09111, arXiv.org.
    3. Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.
    4. Lucey, Brian & Ren, Boru, 2021. "Does news tone help forecast oil?," Economic Modelling, Elsevier, vol. 104(C).
    5. Kai Chen & Rabea Jamil Mahfoud & Yonghui Sun & Dongliang Nan & Kaike Wang & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM," Energies, MDPI, vol. 13(17), pages 1-17, September.
    6. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Ousama Ben-Salha & Lamia Ben Amor, 2022. "Does Uncertainty Forecast Crude Oil Volatility before and during the COVID-19 Outbreak? Fresh Evidence Using Machine Learning Models," Energies, MDPI, vol. 15(15), pages 1-20, August.

11. Zhao, Lu-Tao & Liu, Kun & Duan, Xin-Lei & Li, Ming-Fang, 2019. "Oil price risk evaluation using a novel hybrid model based on time-varying long memory," Energy Economics, Elsevier, vol. 81(C), pages 70-78.

    Cited by:

    1. Wen, Jun & Zhao, Xin-Xin & Chang, Chun-Ping, 2021. "The impact of extreme events on energy price risk," Energy Economics, Elsevier, vol. 99(C).
    2. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    3. Pushpa Dissanayake & Teresa Flock & Johanna Meier & Philipp Sibbertsen, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Mathematics, MDPI, vol. 9(21), pages 1-33, November.
       • Dissanayake, Pushpa & Flock, Teresa & Meier, Johanna & Sibbertsen, Philipp, 2021. "Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights," Hannover Economic Papers (HEP) dp-690, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
       • Hibiki Kaibuchi & Yoshinori Kawasaki & Gilles Stupfler, 2021. "GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Papers 2104.09879, arXiv.org.
    5. Paulo F. Marschner & Paulo Sergio Ceretta, 2021. "The impact of oil price shocks on latin american stock markets: a behavioral approach," Economics Bulletin, AccessEcon, vol. 41(2), pages 457-467.
    6. Ra l De Jes s Guti rrez & Lidia E. Carvajal Guti rrez & Oswaldo Garcia Salgado, 2023. "Value at Risk and Expected Shortfall Estimation for Mexico s Isthmus Crude Oil Using Long-Memory GARCH-EVT Combined Approaches," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 467-480, July.
    7. Lu, Linna & Lei, Yalin & Yang, Yang & Zheng, Haoqi & Wang, Wen & Meng, Yan & Meng, Chunhong & Zha, Liqiang, 2023. "Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018–2022," Resources Policy, Elsevier, vol. 82(C).
    8. Lahmiri, Salim & Bekiros, Stelios, 2021. "The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    9. Chen, Xuehui & Zhu, Hongli & Zhang, Xinru & Zhao, Lutao, 2022. "A novel time-varying FIGARCH model for improving volatility predictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    10. Jingjian, Si & Xiangyun, Gao & Jinsheng, Zhou & Anjian, Wang & Xiaotian, Sun & Yiran, Zhao & Hongyu, Wei, 2023. "The impact of oil price shocks on energy stocks from the perspective of investor attention," Energy, Elsevier, vol. 278(PB).
    11. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
    12. Yanqiong Liu & Zhenghui Li & Yanyan Yao & Hao Dong, 2021. "Asymmetry of Risk Evolution in Crude Oil Market: From the Perspective of Dual Attributes of Oil," Energies, MDPI, vol. 14(13), pages 1-22, July.
    13. Yonghong Jiang & Jinqi Mu & He Nie & Lanxin Wu, 2022. "Time‐frequency analysis of risk spillovers from oil to BRICS stock markets: A long‐memory Copula‐CoVaR‐MODWT method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3386-3404, July.
    14. Lu Yang & Shigeyuki Hamori, 2020. "Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach," Energies, MDPI, vol. 13(14), pages 1-27, July.
    15. Liu, Siyao & Fang, Wei & Gao, Xiangyun & Wang, Ze & An, Feng & Wen, Shaobo, 2020. "Self-similar behaviors in the crude oil market," Energy, Elsevier, vol. 211(C).

12. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.

    Cited by:

    1. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Working Papers 202009, University of Pretoria, Department of Economics.
       • Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    2. Shahriyar Mukhtarov & Sugra Humbatova & Mubariz Mammadli & Natig Gadim‒Oglu Hajiyev, 2021. "The Impact of Oil Price Shocks on National Income: Evidence from Azerbaijan," Energies, MDPI, vol. 14(6), pages 1-11, March.
    3. Jiangwei Liu & Xiaohong Huang, 2021. "Forecasting Crude Oil Price Using Event Extraction," Papers 2111.09111, arXiv.org.
    4. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    5. Casandra Okogwu & Mercy Odochi Agho & Mojisola Abimbola Adeyinka & Bukola A. Odulaja & Obinna Arize Ufoaro & Sodrudeen Abolore Ayodeji & Chibuike Daraojimba, 2023. "Adapting To Oil Price Volatility: A Strategic Review Of Supply Chain Responses Over Two Decades," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(10), pages 68-87, October.
    6. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.
    7. Lu-Tao Zhao & Shun-Gang Wang & Zhi-Gang Zhang, 2020. "Oil Price Forecasting Using a Time-Varying Approach," Energies, MDPI, vol. 13(6), pages 1-16, March.
    8. Pruethsan Sutthichaimethee & Sthianrapab Naluang, 2019. "The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model," Energies, MDPI, vol. 12(16), pages 1-21, August.
    9. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).

13. Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.

    Cited by:

    1. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    2. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
    3. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    4. Qingqing Hu & Tinghui Li & Xue Li & Hao Dong, 2021. "Dynamic Characteristics of Oil Attributes and Their Market Effects," Energies, MDPI, vol. 14(13), pages 1-22, June.
    5. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    6. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    7. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    8. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    9. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
    10. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    11. Yu, Hongchu & Fang, Zhixiang & Lu, Feng & Murray, Alan T. & Zhang, Hengcai & Peng, Peng & Mei, Qiang & Chen, Jinhai, 2019. "Impact of oil price fluctuations on tanker maritime network structure and traffic flow changes," Applied Energy, Elsevier, vol. 237(C), pages 390-403.
    12. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    13. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).
    14. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
    15. Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
    16. Lu-Tao Zhao & Shun-Gang Wang & Zhi-Gang Zhang, 2020. "Oil Price Forecasting Using a Time-Varying Approach," Energies, MDPI, vol. 13(6), pages 1-16, March.
    17. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    18. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    19. Miljkovic, Dragan & Goetz, Cole, 2020. "The effects of futures markets on oil spot price volatility in regional US markets," Applied Energy, Elsevier, vol. 273(C).

14. Lutao Zhao & Lei Cheng & Yongtao Wan & Hao Zhang & Zhigang Zhang, 2015. "A VAR-SVM model for crude oil price forecasting," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 126-144.

    Cited by:

    1. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
    2. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    3. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    4. Jianguo Zhou & Xuechao Yu & Xiaolei Yuan, 2018. "Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition," Energies, MDPI, vol. 11(7), pages 1-17, July.
    5. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
    6. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
    7. Wang, Delu & Ma, Gang & Song, Xuefeng & Liu, Yun, 2017. "Energy price slump and policy response in the coal-chemical industry district: A case study of Ordos with a system dynamics model," Energy Policy, Elsevier, vol. 104(C), pages 325-339.
    8. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    9. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.

15. Wang, Ce & Liao, Hua & Pan, Su-Yan & Zhao, Lu-Tao & Wei, Yi-Ming, 2014. "The fluctuations of China’s energy intensity: Biased technical change," Applied Energy, Elsevier, vol. 135(C), pages 407-414.
    • Ce Wang & Hua Liao & Su-Yan Pan & Lu-Tao Zhao & Yi-Ming Wei, 2014. "The fluctuations of China's energy intensity: Biased technical change," CEEP-BIT Working Papers 56, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    See citations under working paper version above.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ENE: Energy Economics (2) 2014-08-28 2014-12-08
2. NEP-ENV: Environmental Economics (1) 2014-08-28
3. NEP-HME: Heterodox Microeconomics (1) 2014-12-08
4. NEP-TRA: Transition Economics (1) 2014-12-08

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