Emisija CO2 u Evropskoj uniji: Empirijska analiza demografskih, ekonomskih i tehnoloških faktora
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Razmere i posledice fenomena globalnih klimatskih promena, koji je prevashodno produkovan antropogenim faktorima poput nekontrolisane potrošnje fosilnih goriva i posledične emisije gasova staklene bašte, nameću promenu obrasca ponašanja kao jedan od najvećih izazova sa kojim se civilizacija suočava. Ova studija je posvećena istraživanju najvažnijih demografskih, ekonomskih i tehnoloških determinanti emisije CO2 u 28 zemalja članica Evropske unije u vremenskom periodu 1991-2014. godine. Analiza je sprovedena na osnovu logaritmovanog i postepeno proširivanog STIRPAT modela ocenjivanjem standardnih modela sa komponentama slučajne greške na neizbalansiranom panel uzorku. Dobijeni rezultati pokazuju da je, kratkoročno posmatrano, uticaj populacije, per capita BDP-a i energetske intenzivnosti na emisiju CO2 pozitivan i signifikantan. Parcijalno povećanje stopa rasta populacije, per capita BDP-a i energetske intenzivnosti od 1% dovodi do uvećanja stope rasta emisije CO2 u opsegu između 0,74%-1,02%, 1,10%-1,15% i 1,07%-1,09%, respektivno. Osim toga, analiza nije uspela da potvrdi hipotezu da se elastičnost stope rasta emisije CO2 u odnosu na stopu rasta populacije menja u zavisnosti od veličine stope rasta populacije. Uticaj ostalih demografskih varijabli koje reprezentuju starosnu strukturu stanovništva, kao što su procentualni udeo dece i adolescenata do 14 godina starosti i učešće stanovništva radnog uzrasta u ukupnom stanovništvu, nije ocenjen kao statistički signifikantan. Konačno, rezultati analize sugerišu nesignifikantan uticaj prosečne veličine domaćinstva, što je jedini nalaz čija je validnost upitna, s obzirom da je dobijen na prilično malom uzorku.
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Centar za demografska istraživanja Instituta društvenih nauka
Reference
ADOM, P. K., & KWAKWA, P. A. (2014). Effects of changing trade structure and technical characteristics of the manufacturing sector on energy intensity in Ghana. Renewable and Sustainable Energy Reviews 35(C): 475–483. https://doi.org/10.1016/j.rser.2014.04.014
ADOM, P. K. (2015a). Determinants of energy intensity in South Africa: Testing for structural effects in parameters. Energy 89: 334–346. http://dx.doi.org/10.1016/j.energy.2015.05.125
ADOM, P.K. (2015b). Asymmetric impacts of the determinants of energy inten-sity in Nigeria. Energy Economics 49: 570–580. http://dx.doi.org/10.1016/j.eneco.2015.03.027
BALTAGI, H. B., FENG, Q., & KAO, C. (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics 170: 164–177. https://doi.org/10.1016/j.jeconom.2012.04.004
BECK, N., & KATZ, J.N. (1995). What to do (and not to do) with Time-Series Cross-Section Data. The American Political Science Review 89(3): 634–647. http://www.jstor.org/stable/2082979
BREUSCH, S. T., & PAGAN, R. A. (1980). The Lagrange Multiplier test and its applications to model specification in econometrics. The Review of Economic Studies 47(1): 239-253. https://www.jstor.org/stable/2297111?seq=1#page_scan_tab_contents
COLE, M. A., & NEUMAYER, E. (2004). Examining the impact of demographic factors on air pollution. Population and Environment 26(1): 5–21. https://doi.org/10.1023/B:POEN.0000039950.85422.eb
CRAMER, J. C., & CHENEY, R. P. (2000). Lost in the Ozone: Population Growth and Ozone in California. Population and Environment: A Journal of Interdisciplinary Studies 21(3): 315–338. https://doi.org/10.1007/BF02436134
CRAMER, J. C. (1998). Population growth and air quality in California. Demography 35: 45–56. http://www.jstor.org/stable/3004026
CRAMER, J. C. (2002). Population Growth and Local Air Pollution: Methods, Models, and Results. Population and Development Review 28: 22–52. http://www.jstor.org/stable/3115267
CUMBY, E. R., & HUIZINGA, J. (1992). Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions. Econometrica 60(1): 185–195. https://doi.org/10.2307/2951684
D'AGOSTINO, B. R., BELANGER, A., & D'AGOSTINO, B. R. Jr. (1990). A Suggestion for Using Powerful and Informative Tests of Normality. American Statistician 44(4): 316–321. https://doi.org/10.1080/00031305.1990.10475751
DeHART, J., L., & SOULÉ, P. T. (2000). Does I = PAT work in local places? Professional Geographer 52(1): 1–10. https://doi.org/10.1111/0033-0124.00200
DIETZ, T., & ROSA, E. A. (1994). Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review 1: 277–300. http://www.humanecologyreview.org/pastissues/her12/12dietzosa.pdf
DIETZ, T., & ROSA, E. A. (1997). Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences of the USA 94 (1): 175–179. http://www.pnas.org/content/94/1/175.full
DRISCOLL, C. J., & KRAAY, C. A. (1998). Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. Review of Economics and Statistics 80 (4): 549–560. https://doi.org/10.1162/003465398557825
GREENE, H. W. (2003). Econometric Analysis. 5th ed. New York: Prentice-Hall.
HOECHLE, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. The Stata Journal 7(3): 281–312. https://www.stata-journal.com/article.html?article=st0128
HOLDREN, J. P. (1991). Population and the energy problem. Population and Environment 12(3): 231–255. https://doi.org/10.1007/BF01357916
HONDA, Y. (1985). Testing the Error Components Model with Non-Normal Disturbances. Review of Economic Studies 52(4): 681-690. https://doi.org/10.2307/2297739
HÜBLER, M., & KELLER, A. (2009). Energy savings via FDI? Empirical evidence from developing countries. Environment and Development Economics 15: 59–80. https://doi.org/10.1017/S1355770X09990088
IM, K.S., PESARAN, M.H., & SHIN, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics 115: 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7
JARQUE, M. C., & BERA, K. A. (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters 6(3): 255‒259. https://doi.org/10.1016/0165-1765(80)90024-5
JARQUE, M. C., & BERA, K. A. (1987). A Test for Normality of Observations and Regression Residuals. International Statistical Review/Revue Internationale de Statistique 55(2): 163‒172. https://doi.org/10.2307/1403192
JONES, D. W. (1991). How urbanization affects energy use in developing countries. Energy Policy 19(7): 621-630. https://doi.org/10.1016/0301-4215(91)90094-5
KOENKER, R. (1981). A note on studentizing a test for heteroscedasticity. Journal of Econometrics 17(1): 107–112. https://doi.org/10.1016/0304-4076(81)90062-2
LEVIN, A., LIN, C. F., & CHU, C. (2002). Unit root test in panel data: Asymptotic and finite sample properties. Journal of Econometrics 108: 1–25. https://doi.org/10.1016/S0304-4076(01)00098-7
LI, K., & LIN, B. (2014). The nonlinear impacts of industrial structure on China’s energy Intensity. Energy 69: 258-265. http://dx.doi.org/10.1016/j.energy.2014.02.106
NEWEY, K. W., & WEST, D. K. (1987). A Simple, Positive Semi-Definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica 55(3): 703‒708. https://doi.org/10.2307/1913610
PACIONE, M. (2009). Urban Geography ‒ A Global Perspective. New York: Routledge.
PARIKH, J., & SHUKLA, V. (1995). Urbanization, energy use and greenhouse effects in economic development. Global Environmental Change 5(2): 87‒103. https://doi.org/10.1016/0959-3780(95)00015-G
PESARAN, M. H. (2004). General diagnostic tests for cross-section dependence in panels. Cambridge Working Papers in Economics (CWPE) (Working Paper 35/04). https://doi.org/10.17863/CAM.5113
PESARAN, M. H. (2007). A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics 22(2): 249–272. https://doi.org/10.1002/jae.951
PETROVIĆ, P., FILIPOVIĆ, S., & RADOVANOVIĆ, M. (2018). Underlying causal factors of the European Union energy intensity: Econometric evidence. Renewable and Sustainable Energy Reviews 89: 216–227. https://doi.org/10.1016/j.rser.2018.03.061
PETROVIĆ, P., NIKOLIĆ, G., & OSTOJIĆ, I. (2017). Demografske determinan-te energetske potrošnje u Evropskoj uniji: rezultati ekonometrijske analize. Stanovništvo 55(1): 1–20. https://doi.org/10.2298/STNV170606003P
POUMANYVONG, P., & KANEKO, S. (2010). Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecological Economics 70: 434–444. https://doi.org/10.1016/j.ecolecon.2010.09.029
ROSA, E. A., & DIETZ, T. (1998). Climate Change and Society: Speculation, Construction and Scientific Investigation. International Sociology 13(4): 421–455. https://doi.org/10.1177%2F026858098013004002
ROSA, E. A., YORK, R., & DIETZ, T. (2004). Tracking the anthropogenic drivers of ecological impacts. Ambio 33 (8): 509–512. https://doi.org/10.1579/0044-7447-33.8.509
ROYSTON, P. (1982). An Extension of Shapiro and Wilk's W Test for Normality to Large Samples. Journal of the Royal Statistical Society. Series C (Applied Statistics) 31(2): 115‒124. https://doi.org/10.2307/2347973
ROYSTON, P. (1983). A Simple Method for Evaluating the Shapiro-Francia W' Test of Non-Normality. Journal of the Royal Statistical Society. Series D (The Statistician) 32(3): 297‒300. https://doi.org/10.2307/2987935
ROYSTON, P. (1991). sg3.5: Comment on sg3.4 and an improved D’Agostino test. Stata Technical Bulletin 3: 23–24.
ROYSTON, P. (1992). Approximating the Shapiro–Wilk W-test for non-normality. Statistics and Computing 2(3): 117–119. https://doi.org/10.1007/bf01891203
ROYSTON, P. (1993a). A pocket-calculator algorithm for the Shapiro–Francia test for non-normality: An application to medicine. Statistics in Medicine, 12 (2): 181‒184. https://doi.org/10.1002/sim.4780120209
ROYSTON, P. (1993b). A Toolkit for Testing for Non-Normality in Complete and Censored Samples. Journal of the Royal Statistical Society. Series D (The Statistician) 42(1): 37‒43. https://doi.org/10.2307/2348109
SAMOUILIDIS, J. E., & MITROPOULOS, C. S. (1984). Energy and economic growth in industrializing countries. The case of Greece. Energy Economics 6 (3): 191‒201. https://doi.org/10.1016/0140-9883(84)90016-1
SHANDRA, J. M., LONDON, B., WHOOLEY, O. P., & WILLIAMSON, J. B. (2004). International Nongovernmental Organizations and Carbon Dioxide Emissions in the Developing World: A Quantitative, Cross-National Analysis. Sociological Inquiry 74(4): 520–545. https://doi.org/10.1111/j.1475-682X.2004.00103.x
SHAPIRO, S. S., & FRANCIA, S. R. (1972). An Approximate Analysis of Variance Test for Normality. Journal of the American Statistical Association 67 (337): 215‒216.
SHAPIRO, S. S., & WILK, B. M. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika 52(3/4): 591‒611. https://doi.org/10.1093/biomet/52.3-4.591
SHI, A. (2003). The impact of population pressure on global carbon dioxide emissions, 1975–1996: evidence from pooled cross-country data. Ecological Economics 44: 29–42. https://doi.org/10.1016/S0921-8009(02)00223-9
SZROETER, J. (1978). A Class of Parametric Tests for Heteroscedasticity in Linear Econometric Models X1-ab. Econometrica 46(6): 1311‒1327. https://doi.org/10.2307/1913831
TONN, B. E., WAIDLEY, G., & PETRICH, C. (2001). The ageing US population and Environmental policy. Journal of Environmental Planning and Management 44(6): 851–876. https://doi.org/10.1080/09640560120087606
WOOLDRIDGE, M. J. (2013). Introductory Econometrics: A Modern Approach. 5th ed. Mason, OH, USA: South-Western.
YORK, R. (2007). Demographic trends and energy consumption in European Union Nations, 1960–2025. Social Science Research 36: 855–872. https://doi.org/10.1016/j.ssresearch.2006.06.007
YORK, R., ROSA, E. A., & DIETZ, T. (2003a). STIRPAT, IPAT, and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics 46(3): 351–365. https://doi.org/10.1016/S0921-8009(03)00188-5
YORK, R., ROSA, E. A., & DIETZ, T. (2003b). A rift in modernity? Assessing the anthropogenic sources of global climate change with the STIRPAT model. International Journal of Sociology and Social Policy 23(10): 31–51. https://doi.org/10.1108/01443330310790291