CoChat
Sign Up
Cited by 81Efficiency Analysis Using DEAAdvanced Aircraft Design and TechnologiesAviation Industry Analysis and Trends

The DOI matches its Crossref record.

Exploring the impacts of the EU ETS emission limits on airline performance via the Dynamic Environmental DEA approach

Cui, Qiang, Wei, Yi-Ming, Li, Ye·2016·Applied Energy
DOI 10.1016/j.apenergy.2016.09.048
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.apenergy.2016.09.048

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • It is difficult to control the effects of factors beyond managerial controls, such as geographic locations of hubs, regulations, airport slot control, government assistance, emission control policy, and airport-airline vertical arrangements.

    “It is also difficult to control the effects of factors beyond managerial controls, such as geographic locations of an airline's hubs, regulations such as open-skies policy, airport slot control, government assistance including subsidies received during the COVID-19 pandemic, emission control policy, airport-airline vertical arrangements ( Cui et al., 2016; Fu et al., 2011b; Yang et al., 2015 ).”

    From Status quo and challenges in air transport management research (2024) · cites this paper as Cui et al., 2016

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 57

  1. Anger, 2010, Including aviation emissions in the EU ETS: much ado about nothing? A review, Transp Policy, 17, 38, 10.1016/j.tranpol.2009.10.010 · DOI
  2. Albers, 2009, Will the EU-ETS instigate airline network reconfigurations?, J Air Trans Manage, 15, 1, 10.1016/j.jairtraman.2008.09.013 · DOI
  3. Anger, 2010, Including aviation in the European emissions trading scheme: impacts on the industry, CO2 emissions and macroeconomic activity in the EU, J Air Trans Manage, 16, 100, 10.1016/j.jairtraman.2009.10.009 · DOI
  4. Mi, 2011, Estimating the ’value at risk’ of EUA futures prices based on the extreme value theory, Int J Global Energy Issues, 35, 145, 10.1504/IJGEI.2011.045027 · DOI
  5. Malina, 2012, The impact of the European Union emissions trading scheme on US aviation, J Air Trans Manage, 2012, 36, 10.1016/j.jairtraman.2011.12.004 · DOI
  6. Derigs, 2013, Does EU ETS instigate Air Cargo network reconfiguration? A model-based analysis, Eur J Oper Res, 225, 518, 10.1016/j.ejor.2012.10.016 · DOI
  7. Scheelhaase, 2010, The inclusion of aviation into the EU emission trading scheme–impacts on competition between European and non-European network airlines, Transp Res Part D, 15, 14, 10.1016/j.trd.2009.07.003 · DOI
  8. Li, 2016, Has airline efficiency affected by the inclusion of aviation into European Union Emission Trading Scheme? Evidences from 22 airlines during 2008–2012, Energy, 96c, 8, 10.1016/j.energy.2015.12.039 · DOI
  9. Zhang, 2010, An overview of current research on EU ETS: evidence from its operating mechanism and economic effect, Appl Energy, 87, 1804, 10.1016/j.apenergy.2009.12.019 · DOI
  10. Vespermann, 2011, Much Ado about Nothing? An analysis of economic impacts and ecologic effects of the EU-emission trading scheme in the aviation industry, Transp Res Part A, 45, 1066
  11. Buhr, 2012, The inclusion of aviation in the EU emissions trading scheme: temporal conditions for institutional entrepreneurship, Organiz Stud, 33, 1565, 10.1177/0170840612463324 · DOI
  12. Tsai, 2012, A mixed activity-based costing decision model for green airline fleet planning under the constraints of the European Union Emissions Trading Scheme, Energy, 39, 218, 10.1016/j.energy.2012.01.027 · DOI
  13. Kalayci, 2014, A multi-period stochastic portfolio optimization model applied for an airline company in the EU ETS, Optimization, 63, 1817, 10.1080/02331934.2014.895900 · DOI
  14. Miyoshi, 2014, Assessing the equity impact of the European Union Emission Trading Scheme on an African airline, Transp Policy, 33, 56, 10.1016/j.tranpol.2014.02.010 · DOI
  15. Hofer, 2010, The environmental effects of airline carbon emissions taxation in the US, Transp Res Part D, 15, 37, 10.1016/j.trd.2009.07.001 · DOI · Paper page
  16. Sgouridis, 2011, Air transportation for a carbon constrained world: long-term dynamics of policies and strategies for mitigating the carbon footprint of commercial aviation, Transp Res Part A, 45, 1077
  17. Winchester, 2013, Economic and emissions impacts of renewable fuel goals for aviation in the US, Transp Res Part A, 58, 116
  18. Hileman, 2013, The carbon dioxide challenge facing aviation, Prog Aerosp Sci, 63, 84, 10.1016/j.paerosci.2013.07.003 · DOI · Paper page
  19. Wei, 2015, Climate policy modeling: an online SCI-E and SSCI based literature review, OMEGA-Int J Manage Sci, 57, 70, 10.1016/j.omega.2014.10.011 · DOI
  20. Zhou, 2016, Scenario analysis of CO2, emissions from China’s civil aviation industry through 2030, Appl Energy, 175, 100, 10.1016/j.apenergy.2016.05.004 · DOI
  21. Edwards, 2016, Aircraft cost index and the future of carbon emissions from air travel, Appl Energy, 164, 553, 10.1016/j.apenergy.2015.11.058 · DOI
  22. Schefczyk, 1993, Operational performance of airlines: an extension of traditional measurement paradigms, Strateg Manage J, 14, 301, 10.1002/smj.4250140406 · DOI
  23. Capobianco, 2004, Capital structure in the world airline industry, Transp Res Part A, 38, 421
  24. Bhadra, 2009, Race to the bottom or swimming upstream: performance analysis of US airlines, J Air Transp Manage, 15, 227, 10.1016/j.jairtraman.2008.09.014 · DOI
  25. Wang, 2011, The relationship between airline performance and corporate governance amongst US Listed companies, J. Air Transp Manage, 17, 148, 10.1016/j.jairtraman.2010.06.005 · DOI
  26. Distexhe, 1994, Technical efficiency and productivity growth in an era of deregulation: the case of airlines, Swiss J Econ Statist, 130, 669
  27. Sickles, 2002, Specification of distance functions using semi-and nonparametric methods with an application to the dynamic performance of eastern and western European air carriers, J Prod Anal, 17, 133, 10.1023/A:1013592506555 · DOI
  28. Greer, 2009, Is it the labor unions’ fault?: dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States, Transp Res Part A, 43, 779
  29. Wu, 2014, A balanced scorecard envelopment approach to assess airlines’ performance, Ind Manage Data Syst, 114, 123, 10.1108/IMDS-03-2013-0135 · DOI
  30. Adler, 2013, Measuring the environmental efficiency of the global aviation fleet, Transp Res Part B, 53, 82, 10.1016/j.trb.2013.03.009 · DOI
  31. Arjomandi, 2014, An evaluation of the world’s major airlines’ technical and environmental performance, Econ Model, 41, 133, 10.1016/j.econmod.2014.05.002 · DOI
  32. Cui, 2015, Evaluating energy efficiency for airlines: an application of VFB-DEA, J Air Transp Manage, 44–45, 34, 10.1016/j.jairtraman.2015.02.008 · DOI
  33. Mallikarjun, 2015, Efficiency of US airlines: a strategic operating model, J Air Transp Manage, 43, 46, 10.1016/j.jairtraman.2014.12.004 · DOI
  34. Li, 2016, Energy efficiency measures for airlines: an application of Virtual Frontier Dynamic RAM, J Renew Sustain Energy, 8, 1
  35. Li, 2015, Evaluating airline efficiency: an application of Virtual Frontier Network SBM, Transp Res Part E, 81, 1, 10.1016/j.tre.2015.06.006 · DOI
  36. Cui, 2016, Evaluating energy efficiency for airlines: an application of Virtual Frontier Dynamic Slacks Based Measure, Energy, 113, 1231, 10.1016/j.energy.2016.07.141 · DOI
  37. Tone, 2010, Dynamic DEA: a slacks-based measure approach, Omega, 38, 145, 10.1016/j.omega.2009.07.003 · DOI
  38. Färe, 2007, Environmental production functions and environmental directional distance functions, Energy, 32, 1055, 10.1016/j.energy.2006.09.005 · DOI
  39. Charnes, 1978, Measuring the efficiency of decision making units, Eur J Oper Res, 2, 429, 10.1016/0377-2217(78)90138-8 · DOI · Paper page
  40. Hailu, 2001, Non-parametric productivity analysis with undesirable outputs: an application to the Canadian pulp and paper industry, Am J Agric Econ, 83, 605, 10.1111/0002-9092.00181 · DOI
  41. Färe, 2003, Nonparametric productivity analysis with undesirable outputs: comment, Am J Agric Econ, 85, 1070, 10.1111/1467-8276.00510 · DOI
  42. Hailu, 2003, Non-parametric productivity analysis with undesirable outputs: reply, Am J Agric Econ, 85, 1075, 10.1111/1467-8276.00511 · DOI
  43. Yang, 2010, The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: environmental performance of Chinese coal-fired power plants, Energy Policy, 38, 4440, 10.1016/j.enpol.2010.03.075 · DOI
  44. Färe, 2009, A comment on weak disposability in nonparametric production analysis, Am J Agric Econ, 91, 535, 10.1111/j.1467-8276.2008.01237.x · DOI
  45. Hoang, 2011, Measurement of agricultural total factor productivity growth incorporating environmental factors: a nutrients balance approach, J Environ Econ Manage, 62, 462, 10.1016/j.jeem.2011.05.009 · DOI
  46. Hampf, 2015, Carbon dioxide emission standards for U.S. power plants: an efficiency analysis perspective, Energy Econ, 50, 140, 10.1016/j.eneco.2015.04.001 · DOI
  47. Rødseth, 2013, Environmental regulations, producer responses, and secondary benefits: carbon dioxide reductions under the acid rain program, Environ Resource Econ, 59, 111, 10.1007/s10640-013-9720-5 · DOI
  48. Murty, 2012, On modeling pollution-generating technologies, J Environ Econ Manage, 64, 117, 10.1016/j.jeem.2012.02.005 · DOI
  49. Sueyoshi, 2012, Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: comparison between Japanese electric power industry and manufacturing industries, Energy Econ, 34, 686, 10.1016/j.eneco.2011.10.018 · DOI
  50. Sueyoshi, 2014, DEA radial measurement for environmental assessment: a comparative study between Japanese chemical and pharmaceutical firms, Appl Energy, 115, 502, 10.1016/j.apenergy.2013.10.014 · DOI
  51. Dakpo, 2016, Modelling pollution-generating technologies in performance benchmarking: recent developments, limits and future prospects in the nonparametric framework, Eur J Oper Res, 250, 347, 10.1016/j.ejor.2015.07.024 · DOI
  52. Lee, 2002, Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach, J Environ Manage, 64, 365, 10.1006/jema.2001.0480 · DOI
  53. Zhou, 2014, On estimating shadow prices of undesirable outputs with efficiency models: a literature review, Appl Energy, 130, 799, 10.1016/j.apenergy.2014.02.049 · DOI
  54. Sausen, 2005, Aviation radiative forcing in 2000: an update on IPCC (1999), Meteorol Z, 14, 555, 10.1127/0941-2948/2005/0049 · DOI · Paper page
  55. Pearson, 2002, SPECIES: a spatial evaluation of climate impact on the envelope of species, Ecol Model, 154, 289, 10.1016/S0304-3800(02)00056-X · DOI
  56. Cui, 2013, Dynamic formation mechanism of airport competitiveness: the case of China, Transp Res Part A, 47, 10
  57. Cui, 2014, The changing trend and influencing factors of energy efficiency: the case of nine countries, Energy, 64, 1026, 10.1016/j.energy.2013.11.060 · DOI

Cited by

Sorted by citations

  • Status quo and challenges in air transport management research

    Wandelt, S., Antoniou, C., Birolini, S., Delahaye, D., Dresner, M., Fu, X., Gössling, S., Hong, S.-J., Odoni, A. R., Zanin, M., et al. (2024) · Journal of the Air Transport Research Society · doi:10.1016/j.jatrs.2024.100014 · Cited by 67

    “…locations of an airline’s hubs, regulations such as open-skies policy, airport slot control, government assistance including subsidies received during the COVID-19 pandemic, emission control policy, airport-airline vertical arrangements ( Cui et al., 2016; Fu et al., 2011b; Yang et al., 2015 ).”
    Background

Metadata

Authors
Cui, Q., Wei, Y.-M., & Li, Y.
Affiliations
  • Beijing Institute of Technology (CN)
  • Nanjing University of Finance and Economics (CN)
Corresponding author
  • Cui, Qiang
  • Li, Ye
ORCID
Venue
Applied Energy
Publisher
Elsevier BV
Published
2016-12
Volume
183
Pages
984-994
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2016.09.048
OpenAlex
W2525567316
Cited by
81
References
0
Citations by year
2026: 3 · 2025: 1 · 2024: 3 · 2023: 7 · 2022: 3 · 2021: 15 · 2020: 10 · 2019: 7 · 2018: 13 · 2017: 19
FWCI
14.24
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Applied Energy (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
European Commission
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Efficiency Analysis Using DEA
  • Physical Sciences › Engineering › Aerospace Engineering › Advanced Aircraft Design and Technologies
  • Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Aviation Industry Analysis and Trends
Keywords
EU Emissions Trading System, airline performance, carbon pricing, aviation emissions, regulatory impact, Air China

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review