IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v68y2014icp498-507.html
   My bibliography  Save this article

Government control or low carbon lifestyle? – Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system

Author

Listed:
  • Fang, Guochang
  • Tian, Lixin
  • Fu, Min
  • Sun, Mei

Abstract

This paper explores a novel selective-constrained energy-saving and emission-reduction (ESER) dynamic evolution system, analyzing the impact of cost of conserved energy (CCE), government control, low carbon lifestyle and investment in new technology of ESER on energy intensity and economic growth. Based on artificial neural network, the quantitative coefficients of the actual system are identified. Taking the real situation in China for instance, an empirical study is undertaken by adjusting the parameters of the actual system. The dynamic evolution behavior of energy intensity and economic growth in reality are observed, with the results in perfect agreement with actual situation. The research shows that the introduction of CCE into ESER system will have certain restrictive effect on energy intensity in the earlier period. However, with the further development of the actual system, carbon emissions could be better controlled and energy intensity would decline. In the long run, the impacts of CCE on economic growth are positive. Government control and low carbon lifestyle play a decisive role in controlling ESER system and declining energy intensity. But the influence of government control on economic growth should be considered at the same time and the controlling effect of low carbon lifestyle on energy intensity should be strengthened gradually, while the investment in new technology of ESER can be neglected. Two different cases of ESER are proposed after a comprehensive analysis. The relations between variables and constraint conditions in the ESER system are harmonized remarkably. A better solution to carry out ESER is put forward at last, with numerical simulations being carried out to demonstrate the results.

Suggested Citation

  • Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2014. "Government control or low carbon lifestyle? – Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system," Energy Policy, Elsevier, vol. 68(C), pages 498-507.
    • Handle: RePEc:eee:enepol:v:68:y:2014:i:c:p:498-507
    DOI: 10.1016/j.enpol.2014.01.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421514000184
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2014.01.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Mei & Wang, Xiaofang & Chen, Ying & Tian, Lixin, 2011. "Energy resources demand-supply system analysis and empirical research based on non-linear approach," Energy, Elsevier, vol. 36(9), pages 5460-5465.
    2. Aghion, Philippe & David, Paul A. & Foray, Dominique, 2009. "Science, technology and innovation for economic growth: Linking policy research and practice in 'STIG Systems'," Research Policy, Elsevier, vol. 38(4), pages 681-693, May.
    3. Fourcroy, Charlotte & Gallouj, Faiz & Decellas, Fabrice, 2012. "Energy consumption in service industries: Challenging the myth of non-materiality," Ecological Economics, Elsevier, vol. 81(C), pages 155-164.
    4. Garg, Amit & Maheshwari, Jyoti & Mahapatra, Diptiranjan & Kumar, Satish, 2011. "Economic and environmental implications of demand-side management options," Energy Policy, Elsevier, vol. 39(6), pages 3076-3085, June.
    5. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2013. "The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China," Applied Energy, Elsevier, vol. 110(C), pages 17-28.
    6. Liu, Li-qun & Liu, Chun-xia & Sun, Zhi-yi, 2011. "A survey of China's low-carbon application practice--Opportunity goes with challenge," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2895-2903, August.
    7. Wang, Qiang & Chen, Yong, 2010. "Energy saving and emission reduction revolutionizing China's environmental protection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 535-539, January.
    8. Rezessy, Silvia & Bertoldi, Paolo, 2011. "Voluntary agreements in the field of energy efficiency and emission reduction: Review and analysis of experiences in the European Union," Energy Policy, Elsevier, vol. 39(11), pages 7121-7129.
    9. Zhang, Jianjun & Fu, Meichen & Geng, Yuhuan & Tao, Jin, 2011. "Energy saving and emission reduction: A project of coal-resource integration in Shanxi Province, China," Energy Policy, Elsevier, vol. 39(6), pages 3029-3032, June.
    10. Abdelaziz, E.A. & Saidur, R. & Mekhilef, S., 2011. "A review on energy saving strategies in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 150-168, January.
    11. Hamit-Haggar, Mahamat, 2012. "Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective," Energy Economics, Elsevier, vol. 34(1), pages 358-364.
    12. Streimikiene, Dalia & Volochovic, Andzej, 2011. "The impact of household behavioral changes on GHG emission reduction in Lithuania," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 4118-4124.
    13. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    14. Fang, Guochang & Tian, Lixin & Sun, Mei & Fu, Min, 2012. "Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system," Energy, Elsevier, vol. 40(1), pages 291-299.
    15. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    16. Foxon, Timothy J., 2011. "A coevolutionary framework for analysing a transition to a sustainable low carbon economy," Ecological Economics, Elsevier, vol. 70(12), pages 2258-2267.
    17. Heiskanen, Eva & Johnson, Mikael & Robinson, Simon & Vadovics, Edina & Saastamoinen, Mika, 2010. "Low-carbon communities as a context for individual behavioural change," Energy Policy, Elsevier, vol. 38(12), pages 7586-7595, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & Du, Ruijin & Lu, Longxi & He, Yu, 2017. "The effect of energy construction adjustment on the dynamical evolution of energy-saving and emission-reduction system in China," Applied Energy, Elsevier, vol. 196(C), pages 180-189.
    2. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & Du, Ruijin & Liu, Menghe, 2017. "Investigating carbon tax pilot in YRD urban agglomerations—Analysis of a novel ESER system with carbon tax constraints and its application," Applied Energy, Elsevier, vol. 194(C), pages 635-647.
    3. Xinghua Fan & Ying Zhang & Jiuli Yin, 2018. "Evolutionary Analysis of a Three-Dimensional Carbon Price Dynamic System," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
    4. Zhang, Jian & Zhang, Wei & Song, Qi & Li, Xin & Ye, Xuanting & Liu, Yu & Xue, Yawei, 2020. "Can energy saving policies drive firm innovation behaviors? - Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    5. Fang, Guochang & Tian, Lixin & Liu, Menghe & Fu, Min & Sun, Mei, 2018. "How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price," Applied Energy, Elsevier, vol. 211(C), pages 1039-1049.
    6. Cheng, Xiu & Long, Ruyin & Chen, Hong, 2020. "A policy utility dislocation model based on prospect theory: A case study of promoting policies with low-carbon lifestyle," Energy Policy, Elsevier, vol. 137(C).
    7. Jianfeng Liu & Liguo Zhou & Yuyan Wang, 2021. "Altruistic Preference Models of Low-Carbon E-Commerce Supply Chain," Mathematics, MDPI, vol. 9(14), pages 1-20, July.
    8. Lei Gao & Zhen-Yu Zhao, 2018. "System Dynamics Analysis of Evolutionary Game Strategies between the Government and Investors Based on New Energy Power Construction Public-Private-Partnership (PPP) Project," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    9. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & He, Yu & Lu, Longxi, 2018. "How to promote the development of energy-saving and emission-reduction with changing economic growth rate—A case study of China," Energy, Elsevier, vol. 143(C), pages 732-745.
    10. Xiangsheng Dou & Huanying Cui, 2017. "Low-carbon society creation and socio-economic structural transition in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(5), pages 1577-1599, October.
    11. Xin-gang Zhao & Yu-zhuo Zhang, 2018. "The System Dynamics (SD) Analysis of the Government and Power Producers’ Evolutionary Game Strategies Based on Carbon Trading (CT) Mechanism: A Case of China," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    12. LiuWei Zhao & Charles Oduro Acheampong Otoo, 2019. "Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System," Complexity, Hindawi, vol. 2019, pages 1-11, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    2. Yang, Honglin & Wang, Lin & Tian, Lixin, 2015. "Evolution of competition in energy alternative pathway and the influence of energy policy on economic growth," Energy, Elsevier, vol. 88(C), pages 223-233.
    3. Fang, Guochang & Tian, Lixin & Sun, Mei & Fu, Min, 2012. "Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system," Energy, Elsevier, vol. 40(1), pages 291-299.
    4. Guoxing Zhang & Zhenhua Zhang & Xiulin Gao & Lean Yu & Shouyang Wang & Yingluo Wang, 2017. "Impact of Energy Conservation and Emissions Reduction Policy Means Coordination on Economic Growth: Quantitative Evidence from China," Sustainability, MDPI, vol. 9(5), pages 1-19, April.
    5. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2013. "The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China," Applied Energy, Elsevier, vol. 110(C), pages 17-28.
    6. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    7. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    8. Paramonova, Svetlana & Thollander, Patrik & Ottosson, Mikael, 2015. "Quantifying the extended energy efficiency gap-evidence from Swedish electricity-intensive industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 472-483.
    9. Xu Liu & Bo Shen & Lynn Price & Ali Hasanbeigi & Hongyou Lu & Cong Yu & Guanyun Fu, 2019. "A review of international practices for energy efficiency and carbon emissions reduction and lessons learned for China," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    10. LiuWei Zhao & Charles Oduro Acheampong Otoo, 2019. "Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System," Complexity, Hindawi, vol. 2019, pages 1-11, April.
    11. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & Du, Ruijin & Lu, Longxi & He, Yu, 2017. "The effect of energy construction adjustment on the dynamical evolution of energy-saving and emission-reduction system in China," Applied Energy, Elsevier, vol. 196(C), pages 180-189.
    12. Wu, Zhibin & Xu, Jiuping, 2013. "Predicting and optimization of energy consumption using system dynamics-fuzzy multiple objective programming in world heritage areas," Energy, Elsevier, vol. 49(C), pages 19-31.
    13. Li, J.S. & Alsaed, A. & Hayat, T. & Chen, G.Q., 2014. "Energy and carbon emission review for Macao's gaming industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 744-753.
    14. Irshaid, Jenan & Mochizuki, Junko & Schinko, Thomas, 2021. "Challenges to local innovation and implementation of low-carbon energy-transition measures: A tale of two Austrian regions," Energy Policy, Elsevier, vol. 156(C).
    15. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & Du, Ruijin & Liu, Menghe, 2017. "Investigating carbon tax pilot in YRD urban agglomerations—Analysis of a novel ESER system with carbon tax constraints and its application," Applied Energy, Elsevier, vol. 194(C), pages 635-647.
    16. Mansoor, Muhammad & Mariun, Norman & Ismail, Napsiah & Wahab, Noor Izzri Abdul, 2013. "A guidance chart for most probable solution directions in sustainable energy developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 306-313.
    17. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & He, Yu & Lu, Longxi, 2018. "How to promote the development of energy-saving and emission-reduction with changing economic growth rate—A case study of China," Energy, Elsevier, vol. 143(C), pages 732-745.
    18. Fang, Guochang & Tian, Lixin & Liu, Menghe & Fu, Min & Sun, Mei, 2018. "How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price," Applied Energy, Elsevier, vol. 211(C), pages 1039-1049.
    19. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    20. Burchell, Kevin & Rettie, Ruth & Roberts, Tom C., 2016. "Householder engagement with energy consumption feedback: the role of community action and communications," Energy Policy, Elsevier, vol. 88(C), pages 178-186.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:68:y:2014:i:c:p:498-507. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.