Provincial differences on CO2 emissions in China’s power industry: a quantile regression approach

• Autorzy: Wen L. , Zhang X. , Zhang Z. , Shao H.
• Języki publikacji: EN

Abstrakty

EN

As the world’s largest energy consumer today, China is causing increasing pressure on the global environment, and the power industry might bear the primary responsibility for producing nearly 50% of China’s CO₂ emissions. Investigating the main drivers of CO₂ emissions in China’s power industry is of vital importance for developing effective environmental policies. Based on the panel data of 30 provinces in China, the quantile regression approach was applied in the present paper in order to find out which provinces should pay more attention to mitigating CO₂ emissions in the power industry. Results show that the upper 90th quantile provinces (Guangdong, Jiangsu and Shandong) have to spend more efforts on carbon reduction, and the influences of economic growth, industrialization level and energy efficiency in these provinces are more significant than in others. These findings are extremely helpful for related departments in the power industry to develop appropriate policies pertaining to energy savings and emissions reduction.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2019
• Tom: 28
• Numer: 5
• Opis fizyczny: p.3929-3939,fig.,ref.

Twórcy

autor

Wen L.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China

autor

Zhang X.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China

autor

Zhang Z.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China

autor

Shao H.

• Department of Economics and Management, North China Electric Power University, Baoding, Hebei, China

Bibliografia

• 1. TASESKA V., MARKOVSKA N., CAUSEVSKI A., BOSEVSKI T., POP-JORDANOV J. Greenhouse gases (ghg) emissions reduction in a power system predominantly based on lignite. Energy. 36 (4), 2266, 2011.
• 2. MARLAND G. Emissions accounting China‘s uncertain CO₂ emissions. Nat. Clim. Change. 2, 645, 2012.
• 3. XU B., LIN B. A quantile regression analysis of china‘s provincial CO₂ emissions: where does the difference lie? Energ. Policy. 98, 328, 2016.
• 4. YAN Q., ZHANG Q., ZOU X. Decomposition analysis of carbon dioxide emissions in China‘s regional thermal electricity generation, 2000-2020. Energy. 112, 788, 2016.
• 5. China‘s power industry is the main force of carbon emission reduction. Available online: http://power.in-en.com/html/power-2256316.shtml.
• 6. GRUBB M., BURLER L., TWOMEY P. Diversity and security in uk electricity generation: the influence of low-carbon objective. Energ. Policy. 34(18), 4050, 2006.
• 7. STRACHAN N., KANNAN R. Hybrid modelling of long-term carbon reduction scenarios for the UK. Energ. Econ. 30 (6), 2947, 2008.
• 8. FOXON T.J., HAMMOND G.P., PEARSON P.J.G. Developing transition pathways for a low carbon electricity system in the UK. Techn. Forec. Social. Change, 77 (8), 1203, 2010.
• 9. LISNIANSKI A., LEVITIN G., BEN-HAIM H., ELMAKIS D. Power system structure optimization subject to reliability constraints. Electr. Pow. Syst. Res. 39 (2), 145, 1996.
• 10. PAI P.F., HONG W.C. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energ. Conv. Manag. 46(17), 2669, 2005.
• 11. SABER A.Y., VENAYAGAMOORTHY G.K. Efficient utilization of renewable energy sources by gridable vehicles in cyber-physical energy systems. Ieee. Syst. J. 4 (3), 285, 2010.
• 12. SATHAYE J., MURTISHAW S., PRICE L., LEFRANC M., ROY J., WINKLER H. Multiproject baselines for evaluation of electric power projects. Energ. Policy. 32 (11), 1303, 2004.
• 13. WISE M., DOOLEY J., DAHOWSKI R., DAVIDSON C. Modeling the impacts of climate policy on the deployment of carbon dioxide capture and geologic storage across electric power regions in the united states. Int. J. Greenh. Gas. Con. 1 (2), 261, 2007.
• 14. ROSE A., STEVENS B., EDMONDS J., WISE M. International equity and differentiation in global warming policy. Environ. Res. Econ. 12 (1), 25, 1998.
• 15. TAMURA, H. and KIMURA, T. Evaluating the effectiveness of carbon tax and emissions trading for resolving social dilemma on global environment. Ieee. Intern. Conf. Syst. Man and Cybernetics. 1746, 2007.
• 16. SODERHOLM P., HILDINGSSON R., JOHANSSON B., KHAN J., WILLELMSSON F. Governing the transition to low-carbon futures: a critical survey of energy scenarios for 2050. Futures. 43 (10), 1105, 2011.
• 17. VIEBAHN P., NITSCH J., FISCHEDICK M., ESKEN A., SCHUWER D., SUPERSBERGER N. Comparison of carbon capture and storage with renewable energy technologies regarding structural, economic, and ecological aspects in germany. Int. J. Greenh. Gas. Con. 1 (1), 121, 2007.
• 18. BRAUN M. The evolution of emissions trading in the european union – the role of policy networks, knowledge and policy entrepreneurs. Acc. Org. Societ. 34 (3-4), 469, 2009.
• 19. ZHANG C., ZHOU K., YANG S., SHAO Z. On electricity consumption and economic growth in China. Renew. Sust. Energ. Rev. 76, 353, 2017.
• 20. TORVANGER A., MEADOWCROFT J. The political economy of technology support: making decisions about carbon capture and storage and low carbon energy technologies. Global. Environ. Chang. 21 (2), 303, 2011.
• 21. NELSON J., JOHNSTON J., MILEVA A., FRIPP M., HOFFMAN I., PETROS-GOOD A. High-resolution modeling of the western north american power system demonstrates low-cost and low-carbon futures. Energ. Policy. 43 (3), 436, 2012.
• 22. LAMPREIA J., ARAUJP M.S.M.D., CAMPOS C.P.D., FREITAS M.A.V., ROSA L.P., SOLARI R. Analyses and perspectives for brazilian low carbon technological development in the energy sector. Renew. Sust. Energ. Rev. 15 (7), 3432, 2011.
• 23. SIRIWARDENA K., WIJAVATUNGA P.D.C., FERNANDO W.J.L.S., SHRESTHA, R.M., ATTALAGE R.A. Economy wide emission impacts of carbon and energy tax in electricity supply industry: a case study on sri lanka. Energ. Conv. Manag. 48 (7), 1975, 2007.
• 24. BRAITSCH J., GALE J., HERZOG H. Editorial. Energ. Procedia. 1 (1), 1, 2009.
• 25. GIOVANNI E., RICHARDS K.R. Determinants of the costs of carbon capture and sequestration for expanding electricity generation capacity. Energ. Policy. 38 (10), 6026, 2010.
• 26. KOENKER R., BASSETT G. Regression quantiles. Econometrica. 46 (1), 33, 1978.
• 27. BATUR D., BEKKI J.M., CHEN X. Quantile regression metamodeling: toward improved responsiveness in the high-tech electronics manufacturing industry. Eur. J. Oper. Res. 264, 2017.
• 28. PRIGENT A., KAMENDJETCHOKOBOU B., CHEVR K. Socio-demographic, clinical characteristics and utilization of mental health care services associated with sf-6d utility scores in patients with mental disorders: contributions of the quantile regression. Qual. Lif. Res. An Int. J. Qual. Lif. Asp. Treat. Care. Rehab. 1, 2017.
• 29. AMEDEE-MANESME C.O., BARONI M., BARTHElEMV F., ROSIERS F.D. Market heterogeneity and the determinants of paris apartment prices: a quantile regression approach. Urb. Stud. 2016.
• 30. YE J., LI Z., LV Y., AN L., YU J., GUO X. Associations of blood pressure with the factors among adults in jilin province: a cross-sectional study using quantile regression analysis. Sci. Rep.-UK. 7 (1), 2017.
• 31. PRADKHAN E. Financial activity in agricultural futures markets: evidence from quantile regressions. Austral. J. Agricult. Res. Econ. 5, 2017.
• 32. DIETZ T., ROSA E.A. Effects of population and affluence on CO₂ emissions. P. NatL. Acad. Sci. USA. 94 (1), 175, 1997.
• 33. XU B., LIN B. What cause a surge in china‘s CO₂, emissions? a dynamic vector autoregression analysis. J. Clean. Prod,143, 2016.

Identyfikatory

DOI

10.15244/pjoes/94620