How Do Urbanization, Urban Agglomeration, and Largest City Ratio Affect Co2 Emissions? Investigating Non-Linearity Dynamic STIRPAT (Stochastic Impacts By Regression on Population, Affluence, and Technology) Model in 10 Asian Economies

  • Lia Agustiana Lubis Universitas Diponegoro, Semarang, Indonesia
  • Wahyu Widodo Universitas Diponegoro, Semarang, Indonesia
Keywords: Urbanization, Urban Agglomeration, Largest City Ratio, CO2 Emissions, Ecological Modernization Theory, Compact City Theory, STIRPAT, DSUR

Abstract

This study investigates how do urbanization, urban agglomeration, and largest city ratio affect CO2 emissions in 10 Asian Economies namely Bangladesh, India, Pakistan, Nepal, China, Indonesia, Malaysia, Thailand, the Philippines and Vietnam over the period 1990- 2020. Based on STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we applied dynamic seemingly unrelated regression (DSUR) to establish long-run term effects. The empirical findings revealed that urbanization and urban agglomeration have inverted U shaped effect, meanwhile largest city ratio have U shaped. Urbanization and urban agglomeration improve environment quality in the long term and supports ecological modernization theory. Urbanization and urban agglomeration improves environmental quality after reaching a significant level of urban development due to efficient energy structures, population awareness, environmentally friendly technologies, and strict urban and environmental policies. However, the finding of largest city ratio revealed U shaped. This result rejects compact city theory. It implies that excessive concentration in the largest cities have severely affected the environmental quality and violates the notion of compact-city efficiencies which could be attributed to extreme population density, overcrowding, traffic congestion and extensive demand for energy consumption. The results of the panel granger causality approach unveil bidirectional causality in urban agglomeration and its quadaratic term of urban agglomeration, largest city ratio, and quadaratic term of largest city ratio on CO2 emissions. Bidirectional causality also found in GDP and CO2 emissions. Meanwhile, unidirectional causality found in energy intensity and CO2 emissions, trade openness and CO2 emissions, financial development and CO2 emissions, as well as urbanization and CO2 emissions. The current study has implications for policymakers and respective governments to green urban infrastructures, eco-friendly dwellings, smart cities, country-specific trade policies, and renewable energy options and to adhere more stringent urban planning to improve the environmental quality.

Downloads

Download data is not yet available.

References

Ahmed, Z., Wang, Z., & Ali, S. (2019). Investigating the non-linear relationship between urbanization and CO2 emissions: An empirical analysis. Air Quality, Atmosphere and Health, 12(8), 945–953. https://doi.org/10.1007/s11869-019- 00711-x

Ali, W., Abdullah, A., & Azam, M. (2017). The dynamic linkage between technological innovation and carbon dioxide emissions in Malaysia: An autoregressive distributed lagged bound approach. International Journal of Energy Economics and Policy, 6(3), 389–400

Bekhet, H. A., & Othman, N. S. (2017). Impact of urbanization growth on Malaysia CO2 emissions: Evidence from the dynamic relationship. Journal of Cleaner Production, 154, 374–388. https://doi.org/10.1016/j.jclepro.2017.03.174

Bibri, S. E., & Krogstie, J. (2019). A scholarly backcasting approach to a novel model for smart sustainable cities of the future: strategic problem orientation. City, Territory and Architecture, 6(1), 1–20. https://doi.org/10.1186/s40410-019-0102-3

Bureau, U. C. (2020). The World Population And The Top Ten Countries With Highest Population. https://www.internetworldstats.com/stats8.html

Charfeddine, L., & Mrabet, Z. (2017). The impact of economic development and social- political factors on ecological footprint: A panel data analysis for 15 MENA countries. Renewable and Sustainable Energy Reviews, 76(August 2015), 138–154. https://doi.org/10.1016/j.rser.2017.03.031

Chen, J., Wang, S., Zhou, C., & Li, M. (2019). Does the path of technological progress matter in mitigating China’s PM2.5 concentrations? Evidence from three urban agglomerations in China. Environmental Pollution, 254, 113012. https://doi.org/10.1016/j.envpol.2019.113012

Danish, Baloch, M. A., & Wang, B. (2019). Analyzing the role of governance in CO2 emissions mitigation: The BRICS experience. Structural Change and Economic Dynamics, 51, 119–125. https://doi.org/10.1016/j.strueco.2019.08.007

Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America, 94(1), 175–179. https://doi.org/10.1073/pnas.94.1.175

Du, W. C., & Xia, X. H. (2018). How does urbanization affect GHG emissions? A cross-country panel threshold data analysis. Applied Energy, 229(August), 872– 883. https://doi.org/10.1016/j.apenergy.2018.08.050

Ehrlich, P. R., & Holdren, J. P. (n.d.). Impact of Population Growth in Nepal. SCIENCE, 171, 1212–1217.

Emadodin, I., Taravat, A., & Rajaei, M. (2016). Effects of urban sprawl on local climate: A case study, north central Iran. Urban Climate, 17, 230–247 https://doi.org/10.1016/j.uclim.2016.08.008

Fan, H., Hasmi, S. H., Habib, Y., & Ali, M. (2020). How Do Urbanization and Urban Agglomeration Affect CO2 Emissions in South Asia? Testing Non-Linearity Puzzle with Dynamic STIRPAT Model. Chinese Journal of Urban and Environtmental Studies (CJUES, 8(1), 1–37.

Gren, Å., Colding, J., Berghauser-Pont, M., & Marcus, L. (2019). How smart is smart growth? Examining the environmental validation behind city compaction. Ambio, 48(6), 580–589. https://doi.org/10.1007/s13280-018-1087-y

Hackett, C. (2018). Which 7 Countries Hold Half the World’sPopulation?

https://www.pewresearch.org/fac t-tank/2018/07/11/world- population-day

Hashmi, S. H., Fan, H., Habib, Y., & Riaz, A. (2021).Non-linear relationship between urbanization paths and CO2 emissions: A case of South, South-East and East Asian economies. Urban Climate, 37 (September 2020),100814. https://doi.org/10.1016/j.uclim.202 1.100814

He, Z., Xu, S., Shen, W., Long, R., & 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 (2016), 1719–1730. https://doi.org/10.1016/j.jclepro.2016.08.155

Huo, T., Cao, R., Du, H., Zhang, J., Cai, W., & Liu, B. (2021). Nonlinear influence of urbanization on China’s urban residential building carbon emissions: New evidence from panel threshold model. Science of the Total Environment, 772, 145058. https://doi.org/10.1016/j.scitotenv.2021.145058

Khan, A. Q., Saleem, N., & Fatima, S. T. (2018). Financial development, income inequality, and CO2 emissions in Asian countries using STIRPAT model. Environmental Science and Pollution Research, 25(7), 6308–6319. https://doi.org/10.1007/s11356-017-0719-2

Khoshnevis Yazdi, S., & Dariani, A. G. (2019). CO2 emissions, urbanisation and economic growth: evidence from Asian countries. Economic Research-Ekonomska Istrazivanja , 32(1), 510–530.

Koçak, E., Ulucak, R., Dedeoğlu, M., & Ulucak, Z. Ş. (2019). Is there a trade-off between sustainable society targets in Sub-Saharan Africa? Sustainable Cities and Society, 51, 1–9. https://doi.org/10.1080/1331677X.2018.1556107

Makido, Y., Dhakal, S., & Yamagata, Y. (2012). Relationship between urban form and CO 2 emissions: Evidence from fifty Japanese cities. Urban Climate, 2, 55–67. https://doi.org/10.1016/j.uclim.2012.10.006

Mark, N. C., Ogaki, M., & Sul, D. (2005). Dynamic Seemingly Unrelated Cointegrating Regressions. Review of Economic Studies, 72(3), 797–820. https://doi.org/10.1111/j.1467-937X.2005.00352.x

Mouratidis, K. (2019). Compact city, urban sprawl, and subjective well-being. Cities, 92(November 2018), 261–272.https://doi.org/10.1016/j.cities.2019.04.013

Mrabet, Z., & Alsamara, M. (2017). Testing the Kuznets Curve hypothesis for Qatar: A comparison between carbon dioxide and ecological footprint. Renewable and Sustainable Energy Reviews,70 (December),1366–1375.

https://doi.org/10.1016/j.rser.2016.12.039

Munir, K., & Ameer, A. (2022). Assessing nonlinear impact of urbanization, economic growth, technology, and trade on environment: evidence from African and Asian emerging economies. GeoJournal, 87 (3), 2195–2208. https://doi.org/10.1007/s10708-020-10366-2

Nasreen, S., Saidi, S., & Ozturk, I. (2018). Assessing links between energy consumption, freighttransport, and economic growth: evidence from dynamic simultaneous equationmodels.Environmental Science and Pollution Research,25(17),16825–16841.https://doi.org/10.1007/s11356-018-1760-5

Niu, H., & Lekse, W. (2018). Carbon emission effect of urbanization at regional level: Empirical evidence from China. Economics, 12(1), 1–31. https://doi.org/10.5018/economics-ejournal.ja.2018-44

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3),289–326. https://doi.org/10.1002/jae.616

Poumanyvong, P., & Kaneko, S. (2010).Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecological Economics, 70(2),

Shahbaz, M., Jam, F. A., Bibi, S., & Loganathan, N. (2016). Multivariate Granger causality between CO2 emissions, energy intensity and economic growth in Portugal: evidence from cointegration and causality analysis. Technological and EconomicDevelopment of Economy, 22(1), 47–74. https://doi.org/10.3846/20294913.2014.989932

Shahzad, S. J. H., Kumar, R. R., Zakaria, M., & Hurr, M. (2016). Carbon emission, energy consumption, trade openness and financial development in Pakistan: A revisit. Renewable and Sustainable Energy Reviews, 70, 185–192. https://doi.org/10.1016/j.rser.2016.11.042

Shen, W., Liang, H., Dong, L., Ren, J., & Wang, G. (2021). Synergistic CO2 reduction effects in Chinese urban agglomerations: Perspectives from social network analysis. Science of the Total Environment, 798, 149352. https://doi.org/10.1016/j.scitotenv.2021.149352

Su, W., Liu, Y., Wang, S., Zhao, Y., Su, Y., & Li, S. (2018). Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China. Journal of Geographical Sciences, 28(4), 495–513. https://doi.org/10.1007/s11442-018-1486-9

Wang, Q., & Wang, L. (2021). The nonlinear effects of population aging, industrial structure, and urbanization on carbon emissions: A panel threshold regression analysis of 137 countries. Journal of Cleaner Production, 287, 125381. https://doi.org/10.1016/j.jclepro.2020.125381

WHO. (2019). WHO Global Ambient Air Quality Database (update 2018). https://www.who.int/airpollution/data/cities/en/.

World Population Review. (2020). World City Populations. https://worldpopulationreview.com/world-cities

Zhang, N., Yu, K., & Chen, Z. (2017). How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis. Energy Policy, 107(January), 678–687. https://doi.org/10.1016/j.enpol.2017.03.072

Zhu, H. M., You, W. H., & Zeng, Z. fa. (2012). Urbanization and CO2 emissions: A semi-parametric panel data analysis. Economics Letters, 117(3), 848–850. https://doi.org/10.1016/j.econlet.2012.09.001

Published
2025-12-16
How to Cite
Lubis, L. A., & Widodo, W. (2025). How Do Urbanization, Urban Agglomeration, and Largest City Ratio Affect Co2 Emissions? Investigating Non-Linearity Dynamic STIRPAT (Stochastic Impacts By Regression on Population, Affluence, and Technology) Model in 10 Asian Economies. Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), 8(3), 14237-14262. https://doi.org/10.31538/iijse.v8i3.8872