Abstract
Historically, global urbanization has been an essential ingredient for national economic growth and beneficial social transformation. However, with the global urban population currently generating two-thirds of all carbon emissions, global policymakers are urging mayors and regional leaders to make difficult decisions to reduce the negative impacts of urbanization on the environment. The authors begin their examination of the implications of local and regional factors by applying the Dynamic Spatial Durbin Panel Data Model to empirically examine aspects of developing low-carbon strategies for the rapidly expanding size and number of the world’s urban areas. The results indicate that the contribution of urbanization to carbon emissions can be positively affected when regional policy makers collaborate to focus on spillover effects to simultaneously manage the scope, diversity, and complexity of economic and environmental issues from the perspective of creating a balance between rapid urbanization and relevant regional factors. Regional leaders can make a difference by creating both short-term goals and long-term strategies for maintaining low-carbon urbanization, nurturing regional coordination, monitoring and managing eco-friendly regional spillover effects, supporting low-carbon technology innovations, and maintaining optimal city size.
References
Anselin, L. (1988). Spatial econometrics: methods and models. Dordrecht: Kluwer Academic Publishers.10.1007/978-94-015-7799-1Search in Google Scholar
Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis 27(2): 93–115. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1538-4632.1995.tb00338.x10.1111/j.1538-4632.1995.tb00338.xSearch in Google Scholar
Anselin, L., Gallo, J.L., and Jayet, H. (2008). Spatial panel econometrics. In: Mátyás L., Sevestre P. (eds), The econometrics of panel data. Advanced studies in theoretical and applied econometrics, vol 46. Springer: Berlin, Heidelberg.Search in Google Scholar
Baiocchi, G., and Minx, J.C. (2010). Understanding changes in the UK’s CO2 emissions: a global perspective. Environmental Science and Technology 44(4): 1177–1184. https://pubs.acs.org/doi/abs/10.1021/es902662h10.1021/es902662hSearch in Google Scholar
Baltagi, B.H. (2005). Econometric analysis of panel data. 3rd edition. Wiley, Chichester.Search in Google Scholar
Bento, A., Franco, S., and Kaffine, D. (2006). The efficiency and distributional impacts ofalternative antisprawl policies. Journal of Urban Economics 59(1): 121–141. https://www.sciencedirect.com/science/article/pii/S009411900500067710.1016/j.jue.2005.09.004Search in Google Scholar
Brownstone, D., and Golob, T.F. (2009). The impact of residential density on vehicle usage andenergy consumption. Journal Urban Economics 65(1): 91–98. https://www.sciencedirect.com/science/article/pii/S0094119008001095Search in Google Scholar
Cole, M.A., and Neumayer, E. (2004). Examining the impact of demographic factors on air pollution. Population and Environment 26(1): 5–21.https://www.jstor.org/stable/2750002210.1023/B:POEN.0000039950.85422.ebSearch in Google Scholar
COP21 (2015). Paris City Hall Declaration: A decisive contribution to COP21. https://www.uclg.org/sites/default/files/climate_summit_final_declaration.pdfSearch in Google Scholar
Dey, C., Berger, C., Foran, B., Foran, M., Joske, R., Lenzen, M., and Wood, R. (2007). Household environmental pressure from consumption: An Australian environmentalatlas. Water 1: 280–315.Search in Google Scholar
Dodman, D. (2009). Blaming cities for climate change? An analysis of urban greenhouse gasemissions inventories. Environment and Urbanization 21(1): 185–201. http://journals.sagepub.com/doi/abs/10.1177/095624780910301610.1177/0956247809103016Search in Google Scholar
Ehrhardt–Martinez, K., Crenshaw, E.M., and Jenkins, J.C. (2002). Deforestation and the environmental Kuznets curve: a cross–national investigation of interveningmechanisms. Social Science Quarterly 83(1): 226–243.https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6237.0008010.1111/1540-6237.00080Search in Google Scholar
Ehrlich, P., and Holden, J. (1971). Impact of population growth. Science 171(3977): 1212–1217. http://science.sciencemag.org/content/171/3977/1212Search in Google Scholar
Elhorst, J.P., Piras G., and Arbia G. (2010). Growth and convergence in a multi-regional model with space-time dynamics. Geographical Analysis 42(3): 338–355. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1538-4632.2010.00796.xSearch in Google Scholar
Elhorst, J.P. (2010). Spatial panel data models.In: M. Fischer, A. Getis (eds.), Handbook of applied spatial analysis. Springer, Berlin.Search in Google Scholar
Fang, C., Wang, S., and Li, G. (2015). Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities. Applied Energy 158: 519–531. https://www.sciencedirect.com/science/article/pii/S030626191501031410.1016/j.apenergy.2015.08.095Search in Google Scholar
Farhani, S., and Ozturk, I. (2015). Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research 22(20): 15663–15676. https://link.springer.com/article/10.1007/s11356-015-4767-110.1007/s11356-015-4767-1Search in Google Scholar
Feng, K.,Hubacek, K, and Guan, D. (2009). Lifestyles, technology and CO2 emissions inChina: a regional comparative analysis. Ecological Economics 69(1): 145–154. https://www.sciencedirect.com/science/article/pii/S092180090900316410.1016/j.ecolecon.2009.08.007Search in Google Scholar
Fragkias, M., Lobo, J., Strumsky, D., and Seto, K.C. (2013). Does size matter? Scaling of CO2 emissions and U.S. urban areas. PLoS ONE 8(6): e64727.http://dx.doi.org/10.1371/journal.pone.006472710.1371/journal.pone.0064727Search in Google Scholar
Freund, R.J., Wilson, W.J., and Sa, P. (2006). Regression analysis. Academic Press.Search in Google Scholar
Glaeser, E.L., and Kahn, M.E. (2010). The greenness of cities: carbon dioxide emissions andurban development. Journal of Urban Economics 67(3): 404–418. https://www.sciencedirect.com/science/article/pii/S009411900900102810.1016/j.jue.2009.11.006Search in Google Scholar
Gibbons, S., and Overman, H.G. (2012). Mostly pointless spatial econometrics? Journal of Regional Science 52(2): 172–191. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9787.2012.00760.xSearch in Google Scholar
Goldstein, G.S., and Gronberg, T.J. (1984). Economies of scope and economies of agglomeration. Journal of Urban Economics 16(1): 91–104. https://www.sciencedirect.com/science/article/pii/009411908490052410.1016/0094-1190(84)90052-4Search in Google Scholar
Gough, I., Adbdallah, S., Johnson, V., Ryan–Collins, J., and Smith, C. (2011). The distribution of total greenhouse gas emissions by households in the UK, and some implications for social policy. LSE STICERD Research Paper No. CASE152. London: Centre for Analysis of Social Exclusion, London School of Economics, and New Economic’s Foundation. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1935761Search in Google Scholar
Han, F., and Xie, R. (2017). Does the agglomeration of producer services reduce carbon emissions? Journal of Quantitative & Technical Economics 3: 40–58 (in Chinese).Search in Google Scholar
He, K.B., Huo, H., Zhang, Q., He, D., An, F., Wang, M., and Walsh, M.P. (2005). Oil consumption and CO2emissions in China’s road transport: current status, future trends, and policy implications. Energy Policy 33(12): 1499–1507. https://www.sciencedirect.com/science/article/pii/S0301421504000151Search in Google Scholar
He, Z., Xu, S., Shen, W., Long, R., and Chen, H. (2017). Impact of urbanization on energy related CO2 emission at different development levels: Regional difference in China based on panel estimation. Journal of Cleaner Production 140: 1719–1730. https://www.sciencedirect.com/science/article/pii/S0959652616313191Search in Google Scholar
Hossain, M.S. (2011). Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy 39(11): 6991–6999. https://www.sciencedirect.com/science/article/pii/S030142151100574XSearch in Google Scholar
Jefferson, M.(1939). The Law of the Primate City. Geographical Review 29(2): 226–232. https://www.jstor.org/stable/20994410.2307/209944Search in Google Scholar
Jones, C.M., and Kammen, D. M. (2011). Quantifying carbon footprint reduction opportunities for U.S. households and communities. Environmental Science & Technology 45(9): 4088–4095. https://pubs.acs.org/doi/abs/10.1021/es102221h10.1021/es102221hSearch in Google Scholar
Jones, D.W. (1991). How urbanization affects energy use in developing countries. Energy Policy 19(7): 621–630. https://www.sciencedirect.com/science/article/pii/030142159190094510.1016/0301-4215(91)90094-5Search in Google Scholar
Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90(1): 1–44. https://www.sciencedirect.com/science/article/pii/S030440769800023210.1016/S0304-4076(98)00023-2Search in Google Scholar
Kasman, A., and Duman, Y.S. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Economic Modelling 44: 97–103. https://www.sciencedirect.com/science/article/pii/S026499931400377010.1016/j.econmod.2014.10.022Search in Google Scholar
Lantz, V., and Feng, Q. (2006). Assessing income, population, and technology impacts on CO2 emissions in Canada: where’s the EKC? Ecological Economics 57(2): 229–238. https://www.sciencedirect.com/science/article/pii/S092180090500203XSearch in Google Scholar
Lee, L.F., and Yu, J. (2010).A spatial dynamic panel data model with both time and individual fixed effects. Econometric Theory 26(2): 564–597. https://www.jstor.org/stable/4066447610.1017/S0266466609100099Search in Google Scholar
LeSage, J., and Pace, R.K. (2009). Introduction to spatial econometrics. CRC Press. Taylor & Francis Group, Boca Raton.10.1201/9781420064254Search in Google Scholar
Liang, Q., Fan, Y., and Wei, Y.(2007). Multi-regional input–output model for regional energy requirements and CO2 emissions in China. Energy Policy 35(3): 1685–1700. https://www.sciencedirect.com/science/article/pii/S030142150600198410.1016/j.enpol.2006.04.018Search in Google Scholar
Liu, Y., Liu,Y., Chen, Y., and Long, H. (2010). The process and driving forces of rural hollowing in China under rapid urbanization. Journal of Geographical Sciences 20(6): 876–888. https://link.springer.com/article/10.1007/s11442-010-0817-210.1007/s11442-010-0817-2Search in Google Scholar
Liu, Y., Xiao, H.,Zikhali, P., and Lv, Y. (2014). Carbon emissions in China: aspatial econometric analysisat the regional level. Sustainability 6(9): 1–19. https://ideas.repec.org/a/gam/jsusta/v6y2014i9p6005-6023d39967.html10.3390/su6096005Search in Google Scholar
Martínez–Zarzoso, I., andMaruotti, A. (2011).The impact of urbanization on CO2 emissions: evidence from developing countries. Ecological Economics 70(7): 1344–1353. https://www.sciencedirect.com/science/article/pii/S092180091100081410.1016/j.ecolecon.2011.02.009Search in Google Scholar
Oliveira, E.A., Andrade Jr., J.S., and Makse, H.A. (2014). Large cities are less green. Scientific Reports 4(4235). https://www.nature.com/articles/srep04235Search in Google Scholar
Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61(S1): 653–670. https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0084.0610s165310.1111/1468-0084.61.s1.14Search in Google Scholar
Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled timeseries tests with an application to the PPP hypothesis. Econometric Theory 20(3): 597–625. https://econpapers.repec.org/article/cupetheor/v_3a20_3ay_3a2004_3ai_3a03_3ap_3a597-625_5f20.htm10.1017/S0266466604203073Search in Google Scholar
Poumanyvong, P., and Kaneko, S.(2010). Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecological Economics 70(2): 434–444. https://www.sciencedirect.com/science/article/pii/S092180091000388510.1016/j.ecolecon.2010.09.029Search in Google Scholar
Sharma, S.S. (2011). Determinants of carbon dioxide emissions: empirical evidence from 69 countries. Applied Energy 88(1):376–382. https://www.sciencedirect.com/science/article/pii/S030626191000291610.1016/j.apenergy.2010.07.022Search in Google Scholar
Tukker, A., Cohen, M.J., Hubacek, K., and Mont, O. (2010). The impacts of household consumption and options for change. Journal of Industrial Ecology 14(1): 13–30. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1530-9290.2009.00208.x10.1111/j.1530-9290.2009.00208.xSearch in Google Scholar
United Nations, Department of Economic and Social Affairs, Population Division (2014). World urbanization prospects: The 2014 revision, highlights (ST/ESA/SER.A/352).Search in Google Scholar
Weber, C.L., and Matthews, H.S. (2008). Quantifying the global and distributional aspects of American household carbon footprint. Ecological Economics 66(2–3): 379–391. https://www.sciencedirect.com/science/article/pii/S092180090700493410.1016/j.ecolecon.2007.09.021Search in Google Scholar
Xu, S., He, Z., and Long, R. (2014). Factors that influence carbon emissions due to energy consumption in China: decomposition analysis using LMDI. Applied Energy 127: 182–193. https://www.sciencedirect.com/science/article/pii/S030626191400383310.1016/j.apenergy.2014.03.093Search in Google Scholar
Xu, B., and Lin, B. (2015). How industrialization and urbanization process impacts onCO2 emissions in China: Evidence from nonparametric additive regression models. Energy Economics 48: 188–202. https://www.sciencedirect.com/science/article/pii/S0140988315000195Search in Google Scholar
York, R., Rosa, E.A., and Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics 46(3): 351–365. https://www.sciencedirect.com/science/article/pii/S092180090300188510.1016/S0921-8009(03)00188-5Search in Google Scholar
Yu, J., de Jong, R., and Lee, L. (2008). Quasi–maximum likelihood estimators for spatial dynamicpanel data with fixed effects when both n and T are large. Journal of Econometrics 146(1): 118–134. https://www.sciencedirect.com/science/article/pii/S0304407608000808Search in Google Scholar
Yu, J., de Jong, R., and Lee, L. (2012). Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration. Journal of Econometrics 167(1): 16–37. https://www.sciencedirect.com/science/article/pii/S030440761100241710.1016/j.jeconom.2011.05.014Search in Google Scholar
Zhang, C., and Lin, Y. (2012). Panel estimation for urbanization, energy consumption and CO2 emissions: a regional analysis in China. Energy Policy 49: 488–498. http://dspace.xmu.edu.cn/handle/2288/1452810.1016/j.enpol.2012.06.048Search in Google Scholar
Zhou, Y., Liu, Y., Wu, W., and Li, Y. (2015). Effects of rural-urban development transformation on energy consumption and CO2 emissions: A regional analysis in China. Renewable and Sustainable Energy Reviews 52(12): 863–875. https://www.sciencedirect.com/science/article/pii/S136403211500805910.1016/j.rser.2015.07.158Search in Google Scholar
© 2018 Honglei Niu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.