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How does a scarcer allowance remake the carbon market? An evolutionary game analysis from the perspective of stakeholders

Hao, Xinyu, Sun, Wen, Zhang, Xiaoling·2023·Energy
DOI 10.1016/j.energy.2023.128150
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Research works printed but not linked: 55

  1. Stavins, 1989, Harnessing market forces to protect the environment, Environment, 31, 5
  2. Calel, 2013, Carbon markets: a historical overview, Wiley Interdiscip. Rev.-Clim. Chang., 4, 107, 10.1002/wcc.208 · DOI
  3. Lohmann, 2008, Carbon trading, climate justice and the production of ignorance: ten examples, Development, 51, 359, 10.1057/dev.2008.27 · DOI
  4. Schiermeier, 2009, Prices plummet on carbon market, Nature, 457, 365
  5. Reyes, 2010, Carbon trading: how it works and why it fails, Soundings, 45, 89, 10.3898/136266210792307050 · DOI
  6. Hepburn, 2017, Make carbon pricing a priority, Nat Clim Change, 7, 389, 10.1038/nclimate3302 · DOI
  7. Nogrady, 2021, China launches world's largest carbon market: but is it ambitious enough?, Nature, 595, 637, 10.1038/d41586-021-01989-7 · DOI
  8. Liu, 2015, China's carbon-emissions trading: overview, challenges and future, Renew Sustain Energy Rev, 49, 254, 10.1016/j.rser.2015.04.076 · DOI
  9. Guo, 2020, Assessing the impact of ETS trading profit on emission abatements based on firm-level transactions, Nat Commun, 11, 1
  10. Rosendahl, 2019, EU ETS and the waterbed effect, Nat Clim Change, 9, 734, 10.1038/s41558-019-0579-5 · DOI · Paper page
  11. Hintermann, 2020, Price and market behavior in Phase II of the EU ETS: a review of the literature, Rev Environ Econ Pol, 10, 108, 10.1093/reep/rev015 · DOI
  12. Hintermayer, 2020, A carbon price floor in the reformed EU ETS: design matters!, Energy Pol, 147, 10.1016/j.enpol.2020.111905 · DOI
  13. Wu, 2019, An integrated approach for allocating carbon emission quotas in China's emissions trading system, Resour Conserv Recycl, 143, 291, 10.1016/j.resconrec.2019.01.001 · DOI
  14. 2021, Revision for phase 4 (2021-2030), Climate action
  15. Teixidó, 2019, The impact of the EU Emissions Trading System on low-carbon technological change: the empirical evidence, Ecol Econ, 164, 10.1016/j.ecolecon.2019.06.002 · DOI
  16. Parry, 2020, Increasing carbon pricing in the EU: evaluating the options, Eur Econ Rev, 121, 10.1016/j.euroecorev.2019.103341 · DOI
  17. Song, 2019, Improving the liquidity of China's carbon market: insight from the effect of carbon price transmission under the policy release, J Clean Prod, 239, 10.1016/j.jclepro.2019.118049 · DOI
  18. Zhang, 2015, The impact of China's carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises, Energy Pol, 86, 176, 10.1016/j.enpol.2015.07.004 · DOI
  19. Nabavi-Pelesaraei, 2016, Determination of efficient and inefficient units for watermelon production-a case study: guilan province of Iran, J Saudi Soc Agric Sci, 15, 162
  20. Mostashari-Rad, 2020, Data supporting midpoint-weighting life cycle assessment and energy forms of cumulative exergy demand for horticultural crops, Data Brief, 33, 10.1016/j.dib.2020.106490 · DOI
  21. Nabavi-Pelesaraei, 2017, Modeling of energy consumption and environmental life cycle assessment for incineration and landfill systems of municipal solid waste management-A case study in Tehran Metropolis of Iran, J Clean Prod, 148, 427, 10.1016/j.jclepro.2017.01.172 · DOI
  22. Nabavi-Pelesaraei, 2022, Principal of environmental life cycle assessment for medical waste during COVID-19 outbreak to support sustainable development goals, Sci Total Environ, 827, 10.1016/j.scitotenv.2022.154416 · DOI
  23. Liu, 2019, Measuring the maturity of carbon market in China: an entropy-based TOPSIS approach, J Clean Prod, 229, 94, 10.1016/j.jclepro.2019.04.380 · DOI
  24. Zhu, 2020, Exploring the risk spillover effects among China's pilot carbon markets: a regular vine copula-CoES approach, J Clean Prod, 242, 10.1016/j.jclepro.2019.118455 · DOI
  25. Zhang, 2020, Risk measurement of international carbon market based on multiple risk factors heterogeneous dependence, Finance Res Lett, 32, 10.1016/j.frl.2018.12.031 · DOI
  26. Yang, 2022, A new risk measurement method for China's carbon market, Int J Financ Econ, 27, 1280, 10.1002/ijfe.2214 · DOI
  27. Li, 2022, Spatial spillover effect of carbon emission trading on carbon emission reduction: empirical data from pilot regions in China, Energy, 251, 10.1016/j.energy.2022.123906 · DOI
  28. Zhu, 2019, Low-carbon innovation induced by emissions trading in China, Nat Commun, 10, 1, 10.1038/s41467-019-12213-6 · DOI · Paper page
  29. Lin, 2019, What will China's carbon emission trading market affect with only electricity sector involvement? A CGE based study, Energy Econ, 78, 301, 10.1016/j.eneco.2018.11.030 · DOI
  30. Jung, 2021, Effects of emission trading schemes on corporate carbon productivity and implications for firm-level responses, Sci Rep, 11, 1
  31. Wang, 2015, Achieving Copenhagen target through carbon emission trading: economic impacts assessment in Guangdong Province of China, Energy, 79, 212, 10.1016/j.energy.2014.11.009 · DOI
  32. Ji, 2018, Information linkage, dynamic spillovers in prices and volatility between the carbon and energy markets, J Clean Prod, 198, 972, 10.1016/j.jclepro.2018.07.126 · DOI
  33. Wang, 2018, The dynamic spillover between carbon and energy markets: new evidence, Energy, 149, 24, 10.1016/j.energy.2018.01.145 · DOI
  34. Wu, 2022, A systematic analysis of dynamic frequency spillovers among carbon emissions trading (CET), fossil energy and sectoral stock markets: evidence from China, Energy, 10.1016/j.energy.2022.124176 · DOI
  35. Wen, 2020, China's carbon emissions trading and stock returns, Energy Econ, 86, 10.1016/j.eneco.2019.104627 · DOI
  36. Kaufman, 2020, A near-term to net zero alternative to the social cost of carbon for setting carbon prices, Nat Clim Change, 10, 1010, 10.1038/s41558-020-0880-3 · DOI
  37. Rode, 2021, Estimating a social cost of carbon for global energy consumption, Nature, 598, 308, 10.1038/s41586-021-03883-8 · DOI
  38. Bressler, 2021, The mortality cost of carbon, Nat Commun, 12, 1, 10.1038/s41467-021-24487-w · DOI
  39. Sun, 2020, A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network, J Clean Prod, 243, 10.1016/j.jclepro.2019.118671 · DOI
  40. Lu, 2020, Carbon trading volume and price forecasting in China using multiple machine learning models, J Clean Prod, 249, 10.1016/j.jclepro.2019.119386 · DOI
  41. Sun, 2022, A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction, Energy
  42. Gao, 2022, A novel interval decomposition ensemble model for interval carbon price forecasting, Energy, 243, 10.1016/j.energy.2021.123006 · DOI
  43. Li, 2022, A new carbon price prediction model, Energy, 239, 10.1016/j.energy.2021.122324 · DOI
  44. Zeng, 2017, The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices, Energy Pol, 106, 111, 10.1016/j.enpol.2017.03.046 · DOI
  45. Zhu, 2019, A multiscale analysis for carbon price drivers, Energy Econ, 78, 202, 10.1016/j.eneco.2018.11.007 · DOI
  46. Ji, 2021, Price drivers in the carbon emissions trading scheme: evidence from Chinese emissions trading scheme pilots, J Clean Prod, 278, 10.1016/j.jclepro.2020.123469 · DOI
  47. Ji, 2018, Research on carbon market price mechanism and influencing factors: a literature review, Nat Hazards, 92, 761, 10.1007/s11069-018-3223-1 · DOI
  48. Engström, 2020, Carbon pricing and planetary boundaries, Nat Commun, 11, 1, 10.1038/s41467-020-18342-7 · DOI
  49. Warren-Thomas, 2018, Protecting tropical forests from the rapid expansion of rubber using carbon payments, Nat Commun, 9, 1, 10.1038/s41467-018-03287-9 · DOI
  50. Koh, 2021, Carbon prospecting in tropical forests for climate change mitigation, Nat Commun, 12, 1, 10.1038/s41467-021-21560-2 · DOI
  51. Wilson, 2018, Rapid fuel switching from coal to natural gas through effective carbon pricing, Nat Energy, 3, 365, 10.1038/s41560-018-0109-0 · DOI
  52. Xu, 2017, Supply chain coordination with green technology under cap-and-trade regulation, Int J Prod Econ, 183, 433, 10.1016/j.ijpe.2016.08.029 · DOI
  53. Lyapunov, 1992, The general problem of the stability of motion, Int J Control, 55, 531, 10.1080/00207179208934253 · DOI
  54. Ritzberger, 1995, Evolutionary selection in normal-form games, Econometrica, 63, 1371, 10.2307/2171774 · DOI
  55. Zhao, 2019, A novel analysis of carbon capture and storage (CCS) technology adoption: an evolutionary game model between stakeholders, Energy, 189, 10.1016/j.energy.2019.116352 · DOI

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Metadata

Authors
Hao, X., Sun, W., & Zhang, X.
Venue
Energy
Publisher
Elsevier BV
Published
2023-10
Volume
280
Pages
128150
ISSN
0360-5442
DOI
10.1016/j.energy.2023.128150
Cited by
19
References
4
Language
en
Licence
Funders
  • National Natural Science Foundation of China (71834005)
  • Research Grants Council, University Grants Committee (CityU 11271716)

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