IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v49y2015icp1160-1170.html
   My bibliography  Save this article

The failure of China׳s Energy Development Strategy 2050 and its impact on carbon emissions

Author

Listed:
  • Fan, Jie
  • Wang, Qiang
  • Sun, Wei

Abstract

China is the world׳s largest energy consumer and emitter of carbon. China׳s energy development strategy is thus of great significance to the world׳s energy security and global carbon reduction target. In this study, the implementation of “China׳s Energy Development Strategy 2050” (EDS2050), which has been in operation since 1985, is investigated. This study analyzes the causes of the failure of EDS2050 and the impacts of that failure on carbon emissions. The results show the following: (1) EDS2050 basically failed in 2010, and the strategic planning values of primary energy consumption and production were underestimated compared with the actual values by 39% and 67%, respectively. (2) Comparing the predicted values of basic parameters in EDS2050 with the actual values, in the production field, the main causes of failure were the underestimation of the economic growth rate and the proportion of non-agricultural industries, while in the living field, the main causes were the underestimation of urbanization and per capita energy consumption in rural areas. Further analyses of various provinces demonstrated that the major factors leading to EDS2050 failure include the economic growth rate, urbanization, population growth, and the growth in car ownership. (3) EDS2050 failure resulted in an increase in carbon emissions in 2010 of 583.50×106tc, and increases of per capita carbon emissions reached 46.49–47.04%; carbon emissions per unit of output demonstrated a reduction, which was as a result of technological progress. Improving energy efficiency, optimizing energy utilization, and promoting national energy conservation should be highly emphasized in China׳s future sustainable energy development strategy.

Suggested Citation

  • Fan, Jie & Wang, Qiang & Sun, Wei, 2015. "The failure of China׳s Energy Development Strategy 2050 and its impact on carbon emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1160-1170.
    • Handle: RePEc:eee:rensus:v:49:y:2015:i:c:p:1160-1170
    DOI: 10.1016/j.rser.2015.04.096
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115003664
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.04.096?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. Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
    2. Ünler, Alper, 2008. "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, Elsevier, vol. 36(6), pages 1937-1944, June.
    3. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    4. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    5. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    6. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    7. Sudhakara Reddy, B. & Nathan, Hippu Salk Kristle, 2013. "Energy in the development strategy of Indian households—the missing half," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 203-210.
    8. Wang, Qiang, 2014. "Effects of urbanisation on energy consumption in China," Energy Policy, Elsevier, vol. 65(C), pages 332-339.
    9. World Bank, 2014. "World Development Indicators 2014," World Bank Publications - Books, The World Bank Group, number 18237, December.
    10. Menegaki, Angeliki N., 2014. "On energy consumption and GDP studies; A meta-analysis of the last two decades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 31-36.
    11. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.
    12. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    13. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    14. Lo, Kevin, 2014. "A critical review of China's rapidly developing renewable energy and energy efficiency policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 508-516.
    15. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    16. da Graça Carvalho, Maria, 2012. "EU energy and climate change strategy," Energy, Elsevier, vol. 40(1), pages 19-22.
    17. Shabbir, Rabia & Ahmad, Sheikh Saeed, 2010. "Monitoring urban transport air pollution and energy demand in Rawalpindi and Islamabad using leap model," Energy, Elsevier, vol. 35(5), pages 2323-2332.
    18. Chandarasekharam, D. & Aref, Lashin & Nassir, Al Arifi, 2014. "CO2 mitigation strategy through geothermal energy, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 154-163.
    19. Wang, Run & Liu, Wenjuan & Xiao, Lishan & Liu, Jian & Kao, William, 2011. "Path towards achieving of China's 2020 carbon emission reduction target--A discussion of low-carbon energy policies at province level," Energy Policy, Elsevier, vol. 39(5), pages 2740-2747, May.
    20. Ermis, K. & Midilli, A. & Dincer, I. & Rosen, M.A., 2007. "Artificial neural network analysis of world green energy use," Energy Policy, Elsevier, vol. 35(3), pages 1731-1743, March.
    21. Chang, J. & Leung, Dennis Y. C. & Wu, C. Z. & Yuan, Z. H., 2003. "A review on the energy production, consumption, and prospect of renewable energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(5), pages 453-468, October.
    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. Wang, Zhaohua & Rasool, Yasir & Zhang, Bin & Ahmed, Zahoor & Wang, Bo, 2020. "Dynamic linkage among industrialisation, urbanisation, and CO2 emissions in APEC realms: Evidence based on DSUR estimation," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 382-389.
    2. Fan, Xiao-chao & Wang, Wei-qing, 2016. "Spatial patterns and influencing factors of China׳s wind turbine manufacturing industry: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 482-496.
    3. Wenheng Wu & Hongying Zhu & Yinghao Qu & Kaiying Xu, 2017. "Regional Disparities in Emissions of Rural Household Energy Consumption: A Case Study of Northwest China," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    4. Shi, Rui-jing & Fan, Xiao-chao & He, Ying, 2017. "Comprehensive evaluation index system for wind power utilization levels in wind farms in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 461-471.
    5. Wang, Qiang & Wu, Shi-dai & Zeng, Yue-e & Wu, Bo-wei, 2016. "Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1563-1579.
    6. Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Chen, Fu & Li, Weidong, 2018. "Hydropower curtailment in Yunnan Province, southwestern China: Constraint analysis and suggestions," Renewable Energy, Elsevier, vol. 121(C), pages 700-711.
    7. Zhiyu Fang & Ling Jiang & Zhong Fang, 2021. "Does Economic Policy Intervention Inhibit the Efficiency of China’s Green Energy Economy?," Sustainability, MDPI, vol. 13(23), pages 1-20, December.

    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. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    5. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    6. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    7. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    8. Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
    9. Zhang, Pan & Wang, Huan, 2022. "Do provincial energy policies and energy intensity targets help reduce CO2 emissions? Evidence from China," Energy, Elsevier, vol. 245(C).
    10. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    11. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    12. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    13. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    14. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    15. Alireza Pourdaryaei & Mohammad Mohammadi & Mazaher Karimi & Hazlie Mokhlis & Hazlee A. Illias & Seyed Hamidreza Aghay Kaboli & Shameem Ahmad, 2021. "Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market," Energies, MDPI, vol. 14(19), pages 1-28, September.
    16. Emre Yakut & Ezel Özkan, 2020. "Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 59-78, June.
    17. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    18. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    19. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    20. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.

    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:rensus:v:49:y:2015:i:c:p:1160-1170. 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/wps/find/journaldescription.cws_home/600126/description#description .

    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.