Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
30.04.2024
DOI
https://doi.org/10.7307/ptt.v36i2.436
ISSUE
2024 (Vol 36), Issue 2
LICENSE
Copyright (c) 2024 Zsombor Szabó, Mária Szalmáné Csete, Tibor Sipos
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Spatial Econometric Analysis of Carbon Dioxide Emission – European Case Study

Authors:Zsombor Szabó, Mária Szalmáné Csete, Tibor Sipos

Abstract

The level of greenhouse gas emissions is one of the most important issues today, both professionally and politically, because a lower level of greenhouse gas emission is mandatory for a sustainable economy. Besides industry and households, the transport sector is also responsible for these emissions. For this reason, it may be essential to set up a model with which the amount of CO2 emissions could be estimated or predicted. This article presents a model that examines the extent of economic development and CO2 emissions in European countries. The result is establishing a pattern requiring a longer time series. If the pattern is proven, a clear reassessment of the current relationship between economic development and environmental protection should be made.

Keywords:spatial econometrics, spatial statistics, CO2 emission, transportation geography

References

  1. [1] Surya B, et al. The complexity of space utilization and environmental pollution control in the main corridor of Makassar city, south Sulawesi, Indonesia. Sustainability. 2020;12(21):9244. DOI: 10.3390/su12219244.
  2. [2] Wang N, Choi Y. Comparative analysis of the energy and CO2 emissions performance and technology gaps in the agglomerated cities of China and South Korea. Sustainability. 2019;11(2):475. DOI: 10.3390/su11020475.
  3. [3] Tóth G, Sebestyén Szép T. Spatial evolution of the energy and economic centers of gravity. Resources. 2019;8(2):100. DOI: 10.3390/resources8020100.
  4. [4] Maglić L, Krljan T, Grubišić N, Maglić L. Estimating urban road transport vehicles emissions in the Rijeka city streets. Promet – Traffic&Transportation. 2021;33(2):165–178. DOI: 10.7307/ptt.v33i2.3613.
  5. [5] Strömblad E, Winslott Hiselius L, Smidfelt Rosqvist L, Svensson H. Characteristics of everyday leisure trips by car in Sweden – Implications for sustainability measures. Promet – Traffic&Transportation. 2022;34(4):657–672. DOI: 10.7307/ptt.v34i4.4039.
  6. [6] European Commission. White paper – Roadmap to a single European transport area – Towards a competitive and resource efficient transport system. European Commission. Report No. COM/2011/0144, 2011.
  7. [7] Alhindawi R, Abu Nahleh Y, Kumar A, Shiwakoti N. Projection of greenhouse gas emissions for the road transport sector based on multivariate regression and the double exponential smoothing model. Sustainability. 2020;12(21):9152. DOI: 10.3390/su12219152.
  8. [8] Zefreh MM, Török Ádám, Török Árpád. The role of the vehicle's internal combustion engine type in the environmental external costs of road transport. Proceedings of the Asia-Pacific Conference on Intelligent Medical 2018 & International Conference on Transportation and Traffic Engineering 2018, 21-23 Dec. 2018, Beijing China. 2018. p. 312–318. DOI: 10.1145/3321619.3321675.
  9. [9] Mikušová M, Török Á, Brída P. Technological and economical context of renewable and non-renewable energy in electric mobility in Slovakia and Hungary. In: Nguyen NT, Pimenidis E, Khan Z, Trawiński B. (eds.) Computational Collective Intelligence. Cham, Switzerland: Springer International Publishing; 2018. p. 429–436.
  10. [10] Török Á, Zöldy M, Csefalvay E. Effects of renewable energy sources on air-fuel ratio. Journal of KONES Powertrain and Transport. 2018;25(3):473–477. DOI: 10.5604/01.3001.0012.4370.
  11. [11] Žaglinskis J, Rapalis P, Lazareva N. An overview of natural gas use in ships: Necessity and engine supply. Periodica Polytechnica Transportation Engineering. 2018;46(4):185–193. DOI: 10.3311/PPtr.11708.
  12. [12] Bamrungwong N, Vongmanee V, Rattanawong W. The development of a CO2 emission coefficient for medium- and heavy-duty vehicles with different road slope conditions using multiple linear regression, and considering the health effects. Sustainability. 2020;12(17):6994. DOI: 10.3390/su12176994.
  13. [13] Wang H, Zeng W. Revealing urban carbon dioxide (CO2) emission characteristics and influencing mechanisms from the perspective of commuting. Sustainability. 2019;11(2):385. DOI: 10.3390/su11020385.
  14. [14] Li W, Hilmola OP, Panova Y. Container sea ports and dry ports: Future CO2 emission reduction potential in China. Sustainability. 2019;11(6):1515. DOI: 10.3390/su11061515.
  15. [15] Jadaan K, Khreis H, Török Á. Exposure to traffic-related air pollution and the onset of childhood asthma: A review of the literature and the assessment methods used. Periodica Polytechnica Transportation Engineering. 2018;46(1):21–28. DOI: 10.3311/PPtr.10113.
  16. [16] Xu B, Lin B. Factors affecting carbon dioxide (CO2) emissions in China's transport sector: A dynamic nonparametric additive regression model. Journal of Cleaner Production. 2015;101:311–322. DOI: 10.1016/j.jclepro.2015.03.088.
  17. [17] Xu B, Lin B. Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model. Journal of Cleaner Production. 2018;175:109–122. DOI: 10.1016/j.jclepro.2017.12.022.
  18. [18] Kamruzzaman M, Hine J, Yigitcanlar T. Investigating the link between carbon dioxide emissions and transport-related social exclusion in rural Northern Ireland. International Journal of Environmental Science and Technology. 2015;12(11):3463–3478. DOI: 10.1007/s13762-015-0771-8.
  19. [19] De Souza Mendonça AK, et al. An overview of environmental policies for mitigation and adaptation to climate change and application of multilevel regression analysis to investigate the CO2 emissions over the years of 1970 to 2018 in all Brazilian states. Sustainability. 2020;12(21):9175. DOI: 10.3390/su12219175.
  20. [20] Alhendi O, et al. The impact of language and quality education on regional and economic development: A study of 99 countries. Regional Statistics. 2021;11(1):42–57. DOI: 10.15196/RS110101.
  21. [21] Dudek H, Sedefoğlu G. Modelling severe material deprivation rates in EU regions using fractional response regression. Regional Statistics. 2019;9(2):130–147. DOI: 10.15196/RS090210.
  22. [22] Neagu O, Teodoru M. The relationship between economic complexity, energy consumption structure and greenhouse gas emission: Heterogeneous panel evidence from the EU countries. Sustainability. 2019;11(2):497. DOI: 10.3390/su11020497.
  23. [23] Anselin L. Spatial econometrics: Methods and models. Dordrecht, The Netherlands: Kluwer Academic Publishers; 1988.
  24. [24] Varga A. Spatial econometrics [Térökonometria]. Statisztikai szemle. 2002;80(4):354–370.
  25. [25] Bolla M, Krámli A. Theory of statistical inference [Statisztikai következtetések elmélete] Budapest, Hungary: TYPOTEX Kiadó; 2005.
  26. [26] Tobler WR. A computer movie simulating urban growth in the Detroit region. Economic Geography. 1970;46(sup1):234–240. DOI: 10.2307/143141.
  27. [27] Moran PAP. Some theorems on time series: II the significance of the serial correlation coefficient. Biometrika. 1948;35(3–4):255–260. DOI: 10.2307/2332344.
  28. [28] Getis A. Spatial interaction and spatial autocorrelation: A cross- product approach. Environment and Planning A. 1991;23:1269–1277. DOI: 10.1068/a231269.
  29. [29] Sarmiento-Barbieri I. An introduction to spatial econometrics in R. 2016. http://www.econ.uiuc.edu/~lab/workshop/Spatial_in_R.html [Accessed 10th Dec. 2020].
  30. [30] Szabó Z, Török Á. Spatial econometrics – Usage in transportation sciences: A review article. Periodica Polytechnica Transportation Engineering. 2019;48(2):143–149. DOI: 10.3311/PPtr.12047.
  31. [31] Anselin L, Bera AK, Florax R, Yoon MJ. Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics. 1996;26:77–104. DOI: 10.1016/0166-0462(95)02111-6.
  32. [32] Zhukov YM. Applied spatial statistics in R, Section 6. 2010. http://www.people.fas.harvard.edu/~zhukov/Spatial6.pdf [Accessed 10th Aug. 2020].
  33. [33] R Core Team. A language and environment for statistical computing. Wien, Austria: R Foundation for Statistical Computing; 2017.
  34. [34] Bivand RS, Lewin-Koh N. Maptools: Tools for reading and handling spatial objects. 2017. https://CRAN.R-project.org/package=maptools [Accessed 10th Aug. 2020].
  35. [35] Bivand RS, Pebesma EJ, Gómez V. Applied Spatial Data Analysis with R. New York, NY, USA: Springer; 2013.
  36. [36] Pebesma EJ, Bivand RS. Classes and methods for spatial data in R. R News. 2005;5(2):9–13.
  37. [37] Bivand RS, Wong DWS. Comparing implementations of global and local indicators of spatial association. TEST. 2018;27(3):716–748. DOI: 10.1007/s11749-018-0599-x.
  38. [38] Bivand RS, Hauke J, Kossowski T. Computing the Jacobian in Gaussian spatial autoregressive models: An illustrated comparison of available methods. Geographical Analysis. 2013;45(2):150–179. DOI: 10.1111/gean.12008.
  39. [39] Bivand RS, Piras G. Comparing implementations of estimation methods for spatial econometrics. Journal of Statistical Software. 2015;63(18):1–36. DOI: 10.18637/jss.v063.i18.
  40. [40] Zubair AO, Abdul SAR, Dankumo AM. Does gross domestic income, trade integration, FDI inflows, GDP, and capital reduces CO2 emissions? An empirical evidence from Nigeria. Current Research in Environmental Sustainability. 2020;2:100009. DOI: 10.1016/j.crsust.2020.100009.
  41. [41] Kincses Á, Tóth G. Spatial autocorrelation of the foreign affiliated population living in Hungary [A Magyarországon élő külföldi kötődésű népesség térbeli autokorreláltsága]. Területi Statisztika. 2019;59(6):579–606. DOI: 10.15196/TS590601.
  42. [42] Malešević PL, Golem S, Mihaljević KM. The impact of education expenditures on growth in the EU28 – A spatial econometric perspective. Acta Oeconomica. 2018;68(2):271–294. DOI: 10.1556/032.2018.68.2.5.
  43. [43] Szalavetz A. Fifty shades of green – What pales the final colour of green solutions? Acta Oeconomica. 2018;68(4):499–519. DOI: 10.1556/032.2018.68.4.2.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Factors Influencing Sustained Use of Shared E-Scooter Services in Urban TaiwanA Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving AreasCollege Students’ Cognition and Attitude Towards Connected and Autonomous Vehicles in China: an Exploratory StudyOptimising the Economic Feasibility of High-Speed Maglev Systems: A Simulation-Based Approach for Variable and Parameter Analysis

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal