PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2019 | 28 | 4 |

Tytuł artykułu

Dynamic and spatial character analysis of regional marginal abatement costs of CO2 emissions from energy consumption: a provincial aspect

Autorzy

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The Chinese government has made a commitment to achieve a 60-65% reduction of CO2 emissions by 2030 compared with that in 2005. Most provinces are assigned differentiated reduction tasks due to different natural resources endowment, energy consumption structure, and economic developments. Marginal abatement cost (MAC) supplies cost information on regional pollutant reduction processes and should be an important evaluation indicator of policies. In this study, we build a quadratic parametric directional distance function (DDF) to estimate provincial MAC of CO2 emissions in China during 2000-2015. Linear programming is used to solve the parameter estimation problem. Results are as follows: 1) LP method supplies efficient parameter estimation results and obtains 98.33% reliable MACs during the research period. 2) MAC keeps a growing trend for most provinces in 2000-2015. Especially when China enters the New Normal stage in 2012, this growing trend has been accelerated. These trends reveal that MAC gradually becomes a more important indicator to evaluate emission reduction measurements. 3) From a spatial distribution aspect, positive cluster feature has experienced such fluctuations as “apparent rise→significant decline→close to zero.” In this stage, their spatial cluster is close to random distribution state. Spatial heterogeneity turns to being enlarged, especially among provinces at higher MAC range. These evolutionary trends will have important influence on their carbon reduction measure implementing process. Eastern regions should turn more focus on low-carbon technology innovation to push their lowcarbon transformation. For middle and western regions, they should promote their production efficiencyand obtain more technology spillovers from eastern provinces in the future to stimulate their economic growth and low-carbon transformation.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

28

Numer

4

Opis fizyczny

p.2501-2511,fig.,ref.

Twórcy

autor
  • School of Economics and Management, North China Electric Power University, Baoding, China
autor
  • School of Economics and Management, North China Electric Power University, Baoding, China
autor
  • State Grid Zhejiang Economy Research Institute, Hangzhou, China
autor
  • School of Economics and Management, North China Electric Power University, Baoding, China

Bibliografia

  • 1. HAN F., XIE R. Does the agglomeration of producer services reduce carbon emissions?. The Journal of Quantitative & Technical Economics. 3, 40, 2017.
  • 2. ZHAO Q.Z., YAN Q.Y., He Y.G. A study on simulation effects of industrial carbon reduction in China based on input-output method. Statistical Research. 34, 8, 71, 2017.
  • 3. XU S.C, Zhang W.W. Analysis of impacts of carbon taxes on China’s economy and emissions reductions under different refunds: based on dynamic CGE model. China Population, Resources and Environment. 2, 46, 2016.
  • 4. Wu Q.L., Peng C.Y. Scenarios analysis of carbon emissions of China’s electric power industry up to 2030. Energies. 9, 988, 2016.
  • 5. Nnaemeka Vincent Emodi , Chinenye Comfort Emodi , Grish Panchakshara Murthy , Adaeze Saratu Augusta Emodi Energy policy for low carbon development in Nigeria: A LEAP model application. Renewable and Sustainable Energy Reviews. 68, 247, 2017.
  • 6. Felix Pretis , Max Roser Carbon dioxide emission intensity in climate projections: comparing the observational record to socio-economic scenarios. Energy. 135, 718, 2017.
  • 7. CUI L.B., FAN Z.L., ZHU L., BI Q.H. How wil the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target?. Applied Energy. 136, 1043, 2014.
  • 8. LIU W., LI H. Research on coal subsidies reform and CO2 emissions reduction in China. Economic Research Journal. 8, 146, 2014.
  • 9. CHEN S.Y. Evaluation of low carbon transformation process for Chinese provinces. Economic Research Journal. 8, 32, 2012.
  • 10. ZHOU P. The spatial differentiation of regional low carbon efficiency based on super efficiency DEA model. Economic Geography. 3, 188, 2017.
  • 11. Maethee Mekaroonreung , Andrew L. Johnson . A nonparametric method to estimate a technical change effect on marginal abatement costs of U.S. coal power plants. Energy Economics. 46, 45, 2014.
  • 12. TAO Y. J., CHAN P. Marginal abatement cost of CO2 mitigation options for the residential sector in Korea. Korea and the World Economy. 1, 27, 2017.
  • 13. GARY A., SHUKLA P.R., MAHESHWARI J., UPADHYAY J. An assessment of household electricity load curves and corresponding CO2 marginal abatement cost curves for Gujarat state, India. Energy Policy. 66, 568, 2014.
  • 14. Vogt -Schilb A., Hellegatte S., De Gouvello C. Marginal abatement cost curves and the quality of emission reductions: a case study on Brazil. Climate Policy. 6, 703, 2015.
  • 15. Govinda R. Timilsina , Anna Sikharulidze , Eduard K., Suren S. Development of marginal abatement cost curves for the building sector in Armenia and Georgia. Energy Policy. 108, 29, 2017.
  • 16. WANG Y.Z, WANG Q.W., HANG Y., ZHAO Z.Y., GE S.L. CO2 emission abatement cost and its decomposition: A directional distance function approach. Journal of Cleaner Production. 170, 205, 2018.
  • 17. WU X.R., ZHANG J.B., CHENG W.N. The efficiency and reduction cost of carbon emission in China’s planting industry. Journal of Environmental Economics. 1, 57, 2017.
  • 18. LIU N.F., FAN L.L., CHEN X.L. Marginal abatement cost curve of technology oriented under carbon-trading mechanism – Taking cement, thermal power, coal and iron and steel sectors as an example. Forum on Science and Technology in China. 7, 57, 2017.
  • 19. YUAN P., CHENG S. Estimating shadow pricing of industrial pollutions in China. Statistical Research. 9, 66, 2011.
  • 20. CHEN S.Y. Industrial carbon dioxide emissions’ shadow prices: Parametric and nonparametric methods. The Journal of World Economy. 8, 93, 2010.
  • 21. PENG J., YU B.Y., LIAO H., WEI Y.M. Marginal abatement costs of CO2 emissions in the thermal power sector: A regional empirical analysis from China, Journal of Cleaner Production. 171, 163, 2018.
  • 22. JI D.J. CO2 marginal abatement cost estimations of Chinese provinces: A parametric approach. Journal of ChangZhou University (Social Science Edition). 1, 52, 2017.
  • 23. CHEN D.H., PAN Y.C., WU C.Y. Marginal abatement costs of CO2 emission in China and its regional differences. China Population, Resources and Environment. 10, 86, 2016.
  • 24. CHEN L.Y., YANF Q. The marginal carbon cost forecast for Chinese provinces. Journal of Arid Land and Resources and Environment. 5, 1, 2015.
  • 25. WU L.B., QIAN H.Q. TANG W.Q. Selection mechanism between emission trading and carbon tax based on simulation of dynamic marginal abatement cost. Economic Research Journal. 9, 48, 2014.
  • 26. LIU M.L., ZHU L., FAN Y. Evaluation of carbon emission performance and estimation of marginal CO2 abatement costs for provinces of China: A non-parametric distance function approach. China Software Science. 3, 106, 2011.
  • 27. WEI C. Urban CO2 marginal abatement costs and their influential factors in China. The Journal of World Economy. 7, 115, 2014.
  • 28. HUANG J. Toward characteristics of development of wind power and carbon capture technology in China based on technological learning curves. Resource Science. 1, 20, 2012.
  • 29. FAN M.T., WEI T.Y., ZHANG X.G., ZHANG Y.M. The composite effects of policy mix for low-carbon development: a dynamic CGE modeling and cost effective analysis for Beijing case. Industrial Economy Review. 1, 31, 2015.
  • 30. YAO Y.F., LIANG Q.M., WEI Y.M. The impacts of international energy price volatility on China’s marginal abatement cost: a CEEPA-based analysis. China Soft Science. 2, 156, 2012.
  • 31. Färe R., Grosskopf S., Knox Lovell C.A., Yaisawarng Suthathip Derivation of shadow prices for undesirable ouputs: a distance function approach. The Review of Economics and Statistics. 2, 374, 1993.
  • 32. Lee M.. The shadow price of subsititutable sulfur in the US electric power plant: a distance function approach. Journal of Environmental Management. 2, 104, 2005.
  • 33. Lee M., Zhang N. Technical efficiency, shadow price of carbon dioxide emissions, and substitutability for energy in the Chinese manufacturing industries. Energy Economics. 5, 1492, 2012.
  • 34. XIE H.L., YU Y.N., WANG W., LIU Y.C. The substitutability of non-fossil energy, potential carbon emission reduction and energy shadow prices in China. Energy Policy. 107, 63, 2017.
  • 35. TU Z.G. The shadow price of industrial SO2 emission: A new analysis framework. China Economic Quarterly. 1, 259, 2009.
  • 36. Fukuyamaa H., Weber W.L. Japanese banking inefficiency and shadow pricing. Mathematical and Computer Modelling. 48, 1854, 2008.
  • 37. Tobler W.R. Philosophy in Geography. Theory and Decision Library, 20, 379, 1979.
  • 38. Färe R., Grosskopf S., Noh D., et al. Characteristics of a polluting technology: theory and practice. Journal of Econometrics. 2, 469, 2005.
  • 39. GAO T.M. Econometric analysis method and modeling: Eviews application and samples (3rd edition). Beijing: Qinghua University Press. 2016 [In China].
  • 40. CHEN Q. Advanced econometrics and Stata applications (2nd edition). Beijing: Higher Education Press, 2014 [In China].
  • 41. ZHANG J., WU G.Y., ZHANG J.P. The estimation of China’s provincial capital stock: 1952-2000. Economic Research Journal. 10, 35, 2004.
  • 42. Intergovernmental Panel on Climate Change. IPCC Guidelines for national greenhouse gas inventories. Japan: IGES. 2006 [In Japan].

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-e5867c78-a7bc-42ac-812c-5aa851c40d77
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.