Sheng Dai

Personal Details

First Name:	Sheng
Middle Name:
Last Name:	Dai
Suffix:
RePEc Short-ID:	pda998
	[This author has chosen not to make the email address public]
	https://daisheng.org
Terminal Degree:	2022 Kauppakorkeakoulu; Aalto-yliopisto (from RePEc Genealogy)

Affiliation

School of Economics
Zhongnan University of Economics and Law

Wuhan, China
http://jjxy.zuel.edu.cn/
RePEc:edi:seznucn (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

Sorry, no citations of working papers recorded.

Articles

1. Yi, Jiahui & Dai, Sheng & Li, Lin & Cheng, Jinhua, 2024. "How does digital economy development affect renewable energy innovation?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

   Cited by:

   1. Liu, Jiamin & Zhang, Jiaoning & Ma, Xiaoyu & Zhao, Bin & Zhang, Mengyu, 2024. "The road to sustainable development: Can the new energy demonstration city policy promote the industrial structure transformation?," Renewable Energy, Elsevier, vol. 237(PB).
   2. Rabindra Nepal & Yang Liu & Kangyin Dong & Tooraj Jamasb, 2024. "Green Financing, Energy Transformation, and the Moderating Effect of Digital Economy in Developing Countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(12), pages 3357-3386, December.
   3. Hashemizadeh, Ali & Ju, Yanbing & Abadi, Faezeh Zareian Baghdad, 2024. "Policy design for renewable energy development based on government support: A system dynamics model," Applied Energy, Elsevier, vol. 376(PB).
   4. Holzapfel, Peter K.R. & Bánk, János & Bach, Vanessa & Finkbeiner, Matthias, 2024. "Relevance of guarantees of origin for Europe's renewable energy targets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(C).
   5. Hwang, Young Kyu & Sánchez Díez, Ángeles, 2024. "Renewable energy transition and green growth nexus in Latin America," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
   6. Cao, Lihong & Wang, Yueying & Yu, Jinyi & Zhang, Yikai & Yin, Xiaoye, 2024. "The impact of digital economy on low-carbon transition: What is the role of human capital?," Finance Research Letters, Elsevier, vol. 69(PB).
   7. Wang, Haijun & Ge, Chen & Du, Xiance & Feng, Yiqiang & Wang, Weicheng, 2024. "Does digital literacy reduce intergenerational income dependency?," International Review of Financial Analysis, Elsevier, vol. 95(PB).
   8. Sun, Chuanwang & Khan, Anwar & Xue, Juntao & Huang, Xiaoyong, 2024. "Are digital economy and financial structure driving renewable energy technology innovations: A major eight countries perspective," Applied Energy, Elsevier, vol. 362(C).
   9. Shuao Sun & Sheeraz Ahmed, 2024. "Navigating and Overcoming Barriers to Digital Energy Transition for Carbon Neutrality in China," Energies, MDPI, vol. 17(23), pages 1-23, November.

2. Liao, Zhiqiang & Dai, Sheng & Kuosmanen, Timo, 2024. "Convex support vector regression," European Journal of Operational Research, Elsevier, vol. 313(3), pages 858-870.

   Cited by:

   1. Shan, Shan, 2024. "Unveiling Socioeconomic Factors Shaping Global Food Prices and Security: A Machine Learning Approach," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 21(2), December.
   2. Huang, Ling-Wei & Shao, Yuan-Hai & Lv, Xiao-Jing & Li, Chun-Na, 2024. "Large-scale robust regression with truncated loss via majorization-minimization algorithm," European Journal of Operational Research, Elsevier, vol. 319(2), pages 494-504.
   3. Mostafa M. Radwan & Elsaid M. M. Zahran & Osama Dawoud & Ziyad Abunada & Ahmad Mousa, 2024. "Comparative Analysis of Asphalt Pavement Condition Prediction Models," Sustainability, MDPI, vol. 17(1), pages 1-22, December.
   4. Zhiqiang Liao, 2024. "Variable selection in convex nonparametric least squares via structured Lasso: An application to the Swedish electricity distribution networks," Papers 2409.01911, arXiv.org, revised Nov 2024.

3. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Non-crossing convex quantile regression," Economics Letters, Elsevier, vol. 233(C).

   Cited by:

   1. Timo Kuosmanen & Sheng Dai, 2023. "Modeling economies of scope in joint production: Convex regression of input distance function," Papers 2311.11637, arXiv.org.
   2. Kuosmanen, Natalia & Kuosmanen, Timo & Maczulskij, Terhi & Zhou, Xun, 2024. "Least-cost Decarbonization Pathways for Electricity Generation in Finland: A Convex Quantile Regression Approach," ETLA Working Papers 114, The Research Institute of the Finnish Economy.

4. Timo Kuosmanen & Yong Tan & Sheng Dai, 2023. "Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic," Health Care Management Science, Springer, vol. 26(3), pages 447-460, September.

   Cited by:

   1. Kuosmanen, Natalia & Kuosmanen, Timo & Maczulskij, Terhi & Zhou, Xun, 2024. "Least-cost Decarbonization Pathways for Electricity Generation in Finland: A Convex Quantile Regression Approach," ETLA Working Papers 114, The Research Institute of the Finnish Economy.

5. Dai, Sheng, 2023. "Variable selection in convex quantile regression: L1-norm or L0-norm regularization?," European Journal of Operational Research, Elsevier, vol. 305(1), pages 338-355.

   Cited by:

   1. Fu, Saiji & Tian, Yingjie & Tang, Long, 2023. "Robust regression under the general framework of bounded loss functions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1325-1339.
   2. Zhiqiang Liao & Sheng Dai & Eunji Lim & Timo Kuosmanen, 2024. "Overfitting Reduction in Convex Regression," Papers 2404.09528, arXiv.org, revised Oct 2024.
   3. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Non-crossing convex quantile regression," Economics Letters, Elsevier, vol. 233(C).
   4. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
   5. Zhiqiang Liao, 2024. "Variable selection in convex nonparametric least squares via structured Lasso: An application to the Swedish electricity distribution networks," Papers 2409.01911, arXiv.org, revised Nov 2024.
   6. Liao, Zhiqiang & Dai, Sheng & Kuosmanen, Timo, 2024. "Convex support vector regression," European Journal of Operational Research, Elsevier, vol. 313(3), pages 858-870.

6. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.

   Cited by:

   1. Sheng Dai & Natalia Kuosmanen & Timo Kuosmanen & Juuso Liesio, 2023. "Optimal resource allocation: Convex quantile regression approach," Papers 2311.06590, arXiv.org.
   2. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Non-crossing convex quantile regression," Economics Letters, Elsevier, vol. 233(C).
   3. Kuosmanen, Natalia & Kuosmanen, Timo & Maczulskij, Terhi & Zhou, Xun, 2024. "Least-cost Decarbonization Pathways for Electricity Generation in Finland: A Convex Quantile Regression Approach," ETLA Working Papers 114, The Research Institute of the Finnish Economy.

7. Yi, Jiahui & Dai, Sheng & Cheng, Jinhua & Wu, Qiaosheng & Liu, Kailei, 2021. "Production quota policy in China: Implications for sustainable supply capacity of critical minerals," Resources Policy, Elsevier, vol. 72(C).

   Cited by:

   1. Fikru, Mahelet G. & Awuah-Offei, Kwame, 2022. "An economic framework for producing critical minerals as joint products," Resources Policy, Elsevier, vol. 77(C).
   2. Chuandi Fang & Jinhua Cheng & Zhe You & Jiahao Chen & Jing Peng, 2023. "A Detailed Examination of China’s Clean Energy Mineral Consumption: Footprints, Trends, and Drivers," Sustainability, MDPI, vol. 15(23), pages 1-26, November.

8. Dai, Sheng & Zhou, Xun & Kuosmanen, Timo, 2020. "Forward-looking assessment of the GHG abatement cost: Application to China," Energy Economics, Elsevier, vol. 88(C).

   Cited by:

   1. Yunfei An & Xunpeng Shi & Qunwei Wang & Jian Yu & Dequn Zhou & Xiaoyong Zhou, 2023. "China's manufacturing firms' willingness to pay for carbon abatement: A cost perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5477-5486, December.
   2. Bei Gao & Zuoren Sun, 2023. "Marginal CO 2 and SO 2 Abatement Costs and Determinants of Coal-Fired Power Plants in China: Considering a Two-Stage Production System with Different Emission Reduction Approaches," Energies, MDPI, vol. 16(8), pages 1-26, April.
   3. Xian, Yujiao & Hu, Zhihui & Wang, Ke, 2023. "The least-cost abatement measure of carbon emissions for China's glass manufacturing industry based on the marginal abatement costs," Energy, Elsevier, vol. 284(C).
   4. Sheng Dai & Natalia Kuosmanen & Timo Kuosmanen & Juuso Liesio, 2023. "Optimal resource allocation: Convex quantile regression approach," Papers 2311.06590, arXiv.org.
   5. Jingyan Fu & Artie W. Ng, 2021. "Scaling up Renewable Energy Assets: Issuing Green Bond via Structured Public-Private Collaboration for Managing Risk in an Emerging Economy," Energies, MDPI, vol. 14(11), pages 1-16, May.
   6. Kuosmanen, Natalia & Kuosmanen, Timo & Maczulskij, Terhi & Zhou, Xun, 2024. "Least-cost Decarbonization Pathways for Electricity Generation in Finland: A Convex Quantile Regression Approach," ETLA Working Papers 114, The Research Institute of the Finnish Economy.
   7. Kuosmanen, Timo & Zhou, Xun, 2021. "Shadow prices and marginal abatement costs: Convex quantile regression approach," European Journal of Operational Research, Elsevier, vol. 289(2), pages 666-675.
   8. Dai, Sheng, 2023. "Variable selection in convex quantile regression: L1-norm or L0-norm regularization?," European Journal of Operational Research, Elsevier, vol. 305(1), pages 338-355.
   9. Shirong Zhao & Guangshun Qiao, 2022. "The shadow prices of CO2, SO2 and NOx for U.S. coal power industry 2010–2017: a convex quantile regression method," Journal of Productivity Analysis, Springer, vol. 57(3), pages 243-253, June.
   10. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
   11. Wu, F. & Wang, S.Y. & Zhou, P., 2023. "Marginal abatement cost of carbon dioxide emissions: The role of abatement options," European Journal of Operational Research, Elsevier, vol. 310(2), pages 891-901.
   12. Quinn, Barry & Gallagher, Ronan & Kuosmanen, Timo, 2023. "Lurking in the shadows: The impact of CO2 emissions target setting on carbon pricing in the Kyoto agreement period," Energy Economics, Elsevier, vol. 118(C).
   13. Wen, Xiaojie & Yao, Shunbo & Sauer, Johannes, 2022. "Shadow prices and abatement cost of soil erosion in Shaanxi Province, China: Convex expectile regression approach," Ecological Economics, Elsevier, vol. 201(C).

9. Kuosmanen, Timo & Zhou, Xun & Dai, Sheng, 2020. "How much climate policy has cost for OECD countries?," World Development, Elsevier, vol. 125(C).

   Cited by:

   1. Sheng-Wei Lin & Wen-Min Lu, 2024. "A chance-constrained network DEA approach based on enhanced Russell-based directional distance measure to evaluate public sector performance: a case study of OECD countries," Annals of Operations Research, Springer, vol. 342(3), pages 1837-1864, November.
   2. Bei Gao & Zuoren Sun, 2023. "Marginal CO 2 and SO 2 Abatement Costs and Determinants of Coal-Fired Power Plants in China: Considering a Two-Stage Production System with Different Emission Reduction Approaches," Energies, MDPI, vol. 16(8), pages 1-26, April.
   3. Xian, Yujiao & Hu, Zhihui & Wang, Ke, 2023. "The least-cost abatement measure of carbon emissions for China's glass manufacturing industry based on the marginal abatement costs," Energy, Elsevier, vol. 284(C).
   4. Sheng Dai & Natalia Kuosmanen & Timo Kuosmanen & Juuso Liesio, 2023. "Optimal resource allocation: Convex quantile regression approach," Papers 2311.06590, arXiv.org.
   5. Dai, Sheng & Zhou, Xun & Kuosmanen, Timo, 2020. "Forward-looking assessment of the GHG abatement cost: Application to China," Energy Economics, Elsevier, vol. 88(C).
   6. Chu, Long & Grafton, R. Quentin & Nguyen, Hai, 2022. "A global analysis of the break-even prices to reduce atmospheric carbon dioxide via forest plantation and avoided deforestation," Forest Policy and Economics, Elsevier, vol. 135(C).
   7. Quinn, Barry & Gallagher, Ronan & Kuosmanen, Timo, 2021. "Lurking in the Shadows: The Impact of Emissions Target Setting on Carbon Pricing and Environmental Efficiency," QBS Working Paper Series 2021/05, Queen's University Belfast, Queen's Business School.
   8. Kuosmanen, Timo & Zhou, Xun, 2021. "Shadow prices and marginal abatement costs: Convex quantile regression approach," European Journal of Operational Research, Elsevier, vol. 289(2), pages 666-675.
   9. Dai, Sheng, 2023. "Variable selection in convex quantile regression: L1-norm or L0-norm regularization?," European Journal of Operational Research, Elsevier, vol. 305(1), pages 338-355.
   10. Núñez, Angie Paola Bernal & Gutiérrez-Montes, Isabel & Hernández-Núñez, Héctor Eduardo & Suárez, David Ricardo Gutiérrez & García, Gustavo Adolfo Gutiérrez & Suárez, Juan Carlos & Casanoves, Fernando , 2023. "Diverse farmer livelihoods increase resilience to climate variability in southern Colombia," Land Use Policy, Elsevier, vol. 131(C).
   11. Shirong Zhao & Guangshun Qiao, 2022. "The shadow prices of CO2, SO2 and NOx for U.S. coal power industry 2010–2017: a convex quantile regression method," Journal of Productivity Analysis, Springer, vol. 57(3), pages 243-253, June.
   12. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
   13. Liao, Zhiqiang & Dai, Sheng & Kuosmanen, Timo, 2024. "Convex support vector regression," European Journal of Operational Research, Elsevier, vol. 313(3), pages 858-870.
   14. Quinn, Barry & Gallagher, Ronan & Kuosmanen, Timo, 2023. "Lurking in the shadows: The impact of CO2 emissions target setting on carbon pricing in the Kyoto agreement period," Energy Economics, Elsevier, vol. 118(C).
   15. Wen, Xiaojie & Yao, Shunbo & Sauer, Johannes, 2022. "Shadow prices and abatement cost of soil erosion in Shaanxi Province, China: Convex expectile regression approach," Ecological Economics, Elsevier, vol. 201(C).

10. Cheng, Jinhua & Dai, Sheng & Ye, Xinyue, 2016. "Spatiotemporal heterogeneity of industrial pollution in China," China Economic Review, Elsevier, vol. 40(C), pages 179-191.

    Cited by:

    1. Xingwei Li & Jianguo Du & Hongyu Long, 2019. "Green Development Behavior and Performance of Industrial Enterprises Based on Grounded Theory Study: Evidence from China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    2. Bo Chen & Han Wang & Jishun Zhou, 2022. "Producer service foreign direct investment and pollution in China," The World Economy, Wiley Blackwell, vol. 45(10), pages 3294-3311, October.
    3. Jing Wu & Chee Yoong Liew, 2024. "Revisiting the nexus between corporate social responsibility and corporate value: Evidence from China," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2066-2085, May.
    4. Yi Ren & Yuan Tian & Chengqiu Zhang, 2022. "Investigating the mechanisms among industrial agglomeration, environmental pollution and sustainable industrial efficiency: a case study in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12467-12493, November.
    5. Li, Hao & Guo, Huanxiu, 2021. "Spatial spillovers of pollution via high-speed rail network in China," Transport Policy, Elsevier, vol. 111(C), pages 138-152.
    6. Feng Dong & Yue Wang & Xiaojie Zhang, 2018. "Can Environmental Quality Improvement and Emission Reduction Targets Be Realized Simultaneously? Evidence from China and A Geographically and Temporally Weighted Regression Model," IJERPH, MDPI, vol. 15(11), pages 1-22, October.
    7. Kaizheng Xiang & Anzhou Zhao & Haixin Liu & Xiangrui Zhang & Anbing Zhang & Xinle Tian & Zihan Jin, 2022. "Spatiotemporal Evolution and Coupling Pattern Analysis of Urbanization and Ecological Environmental Quality of the Chinese Loess Plateau," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    8. Daikun Wang & Victor Jing Li & Huayi Yu, 2020. "Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area," Land, MDPI, vol. 9(5), pages 1-18, May.
    9. Junsong Wang & Xinyue Ye & Yehua Dennis Wei, 2019. "Effects of Agglomeration, Environmental Regulations, and Technology on Pollutant Emissions in China: Integrating Spatial, Social, and Economic Network Analyses," Sustainability, MDPI, vol. 11(2), pages 1-20, January.
    10. Fang, Debin & Yu, Bolin, 2021. "Driving mechanism and decoupling effect of PM2.5 emissions: Empirical evidence from China’s industrial sector," Energy Policy, Elsevier, vol. 149(C).
    11. Zaman, Qamar uz & Zhao, Yuhuan & Zaman, Shah & Shah, Aadil Hameed, 2023. "Examining the symmetrical effect of traditional energy resources, industrial production, and poverty lessening on ecological sustainability: Policy track in the milieu of five neighboring Asian econom," Resources Policy, Elsevier, vol. 83(C).
    12. Tian Tian & Li Li & Jing Wang, 2022. "The Effect and Mechanism of Agricultural Informatization on Economic Development: Based on a Spatial Heterogeneity Perspective," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    13. Chenglin Qin & Xinyue Ye & Yingxia Liu, 2017. "Spatial Club Convergence of Regional Economic Growth in Inland China," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    14. Hongbo Li & Yali Liu & Anlu Zhang, 2018. "Spatially varying associations between creative worker concentrations and social diversity in Shenzhen, China," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 85-99, January.
    15. Jin, Gang & Shen, Kunrong & Li, Jian, 2020. "Interjurisdiction political competition and green total factor productivity in China: An inverted-U relationship," China Economic Review, Elsevier, vol. 61(C).
    16. Da Gao & Yanjun Cao & Chang Liu, 2023. "The Low-Carbon Policy and Urban Green Total Factor Energy Efficiency: Evidence from a Spatial Difference-in-Difference Method," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    17. Hanxiao Wei & Huiqin Yao, 2022. "Environmental Regulation, Roundabout Production, and Industrial Structure Transformation and Upgrading: Evidence from China," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    18. Zhengsong Lin & Xinyue Ye & Qian Wei & Fan Xin & Zhang Lu & Sonali Kudva & Qiwen Dai, 2017. "Ecosystem Services Value Assessment and Uneven Development of the Qingjiang River Basin in China," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    19. Siwen Ji & Shuhua Ma, 2022. "The effects of industrial pollution on ecosystem service value: a case study in a heavy industrial area, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6804-6833, May.
    20. Zhaohui Chong & Chenglin Qin & Xinyue Ye, 2017. "Environmental Regulation and Industrial Structure Change in China: Integrating Spatial and Social Network Analysis," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    21. Wei Wang & Hualin Xie & Fucai Lu & Xinmin Zhang, 2017. "Measuring the Performance of Industrial Green Development Using a Non-Radial Directional Distance Function Approach: A Case Study of Jiangxi Province in China," Sustainability, MDPI, vol. 9(10), pages 1-17, September.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 4 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-ECM: Econometrics (2) 2023-12-18 2024-05-20. Author is listed
2. NEP-EFF: Efficiency and Productivity (2) 2023-12-18 2023-12-18. Author is listed
3. NEP-BAN: Banking (1) 2023-12-18. Author is listed
4. NEP-CNA: China (1) 2024-10-28. Author is listed
5. NEP-ENE: Energy Economics (1) 2023-12-18. Author is listed
6. NEP-URE: Urban and Real Estate Economics (1) 2024-10-28. Author is listed

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