IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v281y2021ics0306261920315348.html
   My bibliography  Save this article

Evolution-based CO2 emission baseline scenarios of Chinese cities in 2025

Author

Listed:
  • Cui, Can
  • Wang, Zhen
  • Cai, Bofeng
  • Peng, Sha
  • Wang, Yang
  • Xu, Chengdong

Abstract

City-level CO2 emission scenarios are important for cities’ policies of emission reduction. However, current studies do not reveal the macro patterns of the evolution of cities. This work uses the evolution-based city emission scenario (ECES) model, which tracks the city evolution patterns by probability methods based on multiple cities’ emissions of different periods, to reveal the underlying evolution rules of cities’ CO2 emissions. By the K-means clustering method, five clusters of cities are divided, and the evolution patterns of the city clusters are analyzed. Based on the maximum evolution probability, we discover the city evolution chains that reflect the common pattern of city development. We also propose two indicators for the estimation of emission intensity in 2025 in the natural evolution scenario. Policy implications are then discussed, including optimizing the low-carbon development pathway of cities, cooperate with similar cities.

Suggested Citation

  • Cui, Can & Wang, Zhen & Cai, Bofeng & Peng, Sha & Wang, Yang & Xu, Chengdong, 2021. "Evolution-based CO2 emission baseline scenarios of Chinese cities in 2025," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920315348
    DOI: 10.1016/j.apenergy.2020.116116
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920315348
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.116116?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. Jerzmanowski, Michal, 2006. "Empirics of hills, plateaus, mountains and plains: A Markov-switching approach to growth," Journal of Development Economics, Elsevier, vol. 81(2), pages 357-385, December.
    2. Michail Fragkias & José Lobo & Deborah Strumsky & Karen C Seto, 2013. "Does Size Matter? Scaling of CO2 Emissions and U.S. Urban Areas," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    3. Cui, Ying & Yan, Da & Hong, Tianzhen & Xiao, Chan & Luo, Xuan & Zhang, Qi, 2017. "Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China," Applied Energy, Elsevier, vol. 195(C), pages 890-904.
    4. Fiaschi, Davide & Lavezzi, Andrea Mario, 2007. "Nonlinear economic growth: Some theory and cross-country evidence," Journal of Development Economics, Elsevier, vol. 84(1), pages 271-290, September.
    5. Cai, Bofeng & Guo, Huanxiu & Ma, Zipeng & Wang, Zhixuan & Dhakal, Shobhakar & Cao, Libin, 2019. "Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data," Applied Energy, Elsevier, vol. 242(C), pages 994-1009.
    6. Xintong Li & Xinran Wang & Jiang Zhang & Lingfei Wu, 2015. "Allometric scaling, size distribution and pattern formation of natural cities," Palgrave Communications, Palgrave Macmillan, vol. 1(palcomms2), pages 15017-15017, August.
    7. Cai, Bofeng & Cui, Can & Zhang, Da & Cao, Libin & Wu, Pengcheng & Pang, Lingyun & Zhang, Jihong & Dai, Chunyan, 2019. "China city-level greenhouse gas emissions inventory in 2015 and uncertainty analysis," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Andersson, Ake E., 1975. "A closed nonlinear growth model for international and interregional trade and location," Regional Science and Urban Economics, Elsevier, vol. 5(4), pages 427-444, December.
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. D' Avignon, Alexander & Carloni, Flávia Azevedo & Rovere, Emilio Lèbre La & Dubeux, Carolina Burle Schmidt, 2010. "Emission inventory: An urban public policy instrument and benchmark," Energy Policy, Elsevier, vol. 38(9), pages 4838-4847, September.
    11. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.
    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. Yelin Wang & Ping Yang & Zan Song & Julien Chevallier & Qingtai Xiao, 2024. "Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 711-740, February.
    2. Luo, Shihua & Hu, Weihao & Liu, Wen & Xu, Xiao & Huang, Qi & Chen, Zhe & Lund, Henrik, 2021. "Transition pathways towards a deep decarbonization energy system—A case study in Sichuan, China," Applied Energy, Elsevier, vol. 302(C).
    3. Lin, Huaxing & Zhou, Ziqian & Chen, Shun & Jiang, Ping, 2023. "Clustering and assessing carbon peak statuses of typical cities in underdeveloped Western China," Applied Energy, Elsevier, vol. 329(C).

    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. Garcia, Rene, 1998. "Asymptotic Null Distribution of the Likelihood Ratio Test in Markov Switching Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 763-788, August.
    2. Chao, Hui-Ping, 1998. "Regime Switching In Us Livestock Cycles," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20824, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Jaehee Kim & Sooyoung Cheon, 2010. "A Bayesian regime‐switching time‐series model," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 365-378, September.
    4. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    5. Mohamed Boutahar & David Gbaguidi, 2009. "Which Econometric Specification to Characterize the U.S. Inflation Rate Process?," Computational Economics, Springer;Society for Computational Economics, vol. 34(2), pages 145-172, September.
    6. Olivier Darné & Laurent Ferrara, 2011. "Identification of Slowdowns and Accelerations for the Euro Area Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(3), pages 335-364, June.
    7. Sen, Rituparna & Hsieh, Fushing, 2009. "A note on testing regime switching assumption based on recurrence times," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2443-2450, December.
    8. Wilfredo L. Maldonado & Octávio A. F. Tourinho & Jorge A. B. M. de Abreu, 2014. "Cointegrated Periodically Collapsing Bubbles in the Exchange Rate of 'BRICS' Countries," CAMA Working Papers 2014-34, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Franses, Philip Hans & Paap, Richard & Vroomen, Bjorn, 2004. "Forecasting unemployment using an autoregression with censored latent effects parameters," International Journal of Forecasting, Elsevier, vol. 20(2), pages 255-271.
    10. Markku Lanne, 2006. "Forecasting Realized Volatility by Decomposition," Economics Working Papers ECO2006/20, European University Institute.
    11. Siregar, Reza Yamora & Nguyen, Thi Kim Cuc, 2013. "Inflationary Implication of Gold Price in Vietnam," MPRA Paper 46157, University Library of Munich, Germany.
    12. Charles, Amélie & Darné, Olivier & Diebolt, Claude & Ferrara, Laurent, 2015. "A new monthly chronology of the US industrial cycles in the prewar economy," Journal of Financial Stability, Elsevier, vol. 17(C), pages 3-9.
    13. Bai, Yan & Zhang, Jing, 2012. "Financial integration and international risk sharing," Journal of International Economics, Elsevier, vol. 86(1), pages 17-32.
    14. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.
    15. Pierre-Yves Hénin & Pape N. Diaye, 2001. "L'effet des politiques budgétaires sur l'activité : une fonction des conditions conjoncturelles et du régime budgétaire ?," Economie & Prévision, La Documentation Française, vol. 147(1), pages 73-88.
    16. Chien-Chung Nieh & Jeng-Bau Lin & Yu-Shan Wang, 2008. "Regime-switching analysis for the impacts of exchange rate volatility on corporate values: a Taiwanese case," Applied Economics, Taylor & Francis Journals, vol. 40(4), pages 491-504.
    17. ZHENG, Tingguo & WANG, Xia & GUO, Huiming, 2012. "Estimating forward-looking rules for China's Monetary Policy: A regime-switching perspective," China Economic Review, Elsevier, vol. 23(1), pages 47-59.
    18. Maddalena Cavicchioli, 2021. "OLS Estimation of Markov switching VAR models: asymptotics and application to energy use," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 431-449, September.
    19. Muhsin Kar & Tayfur Bayat & Selim Kayhan, 2016. "Impacts of Credit Default Swaps on Volatility of the Exchange Rate in Turkey: The Case of Euro," IJFS, MDPI, vol. 4(3), pages 1-18, July.
    20. Aloui, Chaker & Jammazi, Rania, 2009. "The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach," Energy Economics, Elsevier, vol. 31(5), pages 789-799, September.

    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:appene:v:281:y:2021:i:c:s0306261920315348. 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/405891/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.