Malzeme Ayak İzinin Belirleyicileri Olarak Dijitalleşme ve Finansal Gelişme: Gelişmekte Olan Ülkelerden Kanıtlar

Yazarlar

DOI:

https://doi.org/10.63556/tisej.2026.1881

Anahtar Kelimeler:

Malzeme ayak izi,- STIRPAT- dijitalleşme- finansal gelişme- DCCE-MG- MM-QR

Öz

This study empirically examines the effects of digitalization and financial development on the per capita material footprint across 27 developing countries during the 2000-2023 period, within the framework of the STIRPAT model. The sample consists of countries classified as developing economies according to the World Bank’s classification and for which complete data are available throughout the analysis period. Because the material footprint -a consumption-based indicator reflecting total resource use across supply chains- serves as a more comprehensive measure of environmental pressure in globalizing economies, it was used as the dependent variable instead of carbon emissions. Long-run coefficients were estimated using the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator, which accounts for cross-sectional dependence and slope heterogeneity. Conditional median estimates were obtained using bootstrap MM-QR. Additional analyses based on a parsimonious specification are reported separately. Trade openness and the trade balance, both included as controls, have negative and statistically significant coefficients in both estimators. By contrast, per capita income, energy intensity, urbanization, and financial depth are positive and significant only in the MM-QR estimates. The negative MM-QR coefficient indicates that digitalization is associated with a lower material footprint at the conditional median. This finding suggests that digital infrastructure development may contribute to resource efficiency in developing economies. The results also differ across countries, indicating substantial heterogeneity within the sample. Accordingly, policy priorities concerning digitalization, institutional capacity, and the energy transition may need to be adapted to country-specific conditions.

Referanslar

Anser, M. K., Ahmad, M., Khan, M. A., Zaman, K., Nassani, A. A., Askar, S. E., Abro, M. M. Q., & Kabbani, A. (2021). The role of information and communication technologies in mitigating carbon emissions: Evidence from panel quantile regression. Environmental Science and Pollution Research, 28(17), 21065-21084. https://doi.org/10.1007/s11356-020-12114-y

Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239-253. https://doi.org/10.2307/2297111

Büyüköztürk, Ş. (2002). Sosyal bilimler için veri analizi el kitabı (2th ed.). Ankara: Pegem Akademi Yayıncılık.

Chishti, M. Z., Ahmad, M., Rehman, A., & Khan, M. K. (2021). Mitigations pathways towards sustainable development: Assessing the influence of fiscal and monetary policies on carbon emissions in BRICS economies. Journal of Cleaner Production, 292, 126035. https://doi.org/10.1016/j.jclepro.2021.126035

Çamkaya, S. (2024). Yenilenebilir enerji ve sanayileşmenin çevre üzerindeki etkisinin STIRPAT-Kaya-EKC hipotezi çerçevesinde analizi: AARDL modelinden kanıtlar. Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(1), 107-125. https://doi.org/10.33399/biibfad.1359874

Çelik, M. (2020). Dördü bir arada: Kariyer uyumunun yordayıcıları olarak iyimserlik, umut, bölüm uygunluğu ve istihdam edilebilirlik. Academia Eğitim Araştırmaları Dergisi, 5(2), 293-305. https://izlik.org/JA46NX72KW

Destek, M. A., & Sinha, A. (2020). Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organization for economic co-operation and development countries. Journal of Cleaner Production, 242, 118537. https://doi.org/10.1016/j.jclepro.2019.118537

Dietz, T., & Rosa, E. A. (1994). Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review, 1(2), 277-300. https://www.jstor.org/stable/24706840

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

Ehrlich, P. R., & Holdren, J. P. (1971). Impact of population growth. Science, 171(3977), 1212-1217. https://doi.org/10.1126/science.171.3977.1212

Erdogan, S., & Acaravci, A. (2022). On the nexus between institutions and economic development: An empirical analysis for Sub-Saharan African countries. The European Journal of Development Research, 34, 1857-1892. https://doi.org/10.1057/s41287-021-00445-6

Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). New York, NY: McGraw-Hill/Irwin.

Hao, X., Li, Y., Ren, S., Wu, H., & Hao, Y. (2023). The role of digitalization on green economic growth: Does industrial structure optimization and green innovation matter? Journal of Environmental Management, 325, 116504. https://doi.org/10.1016/j.jenvman.2022.116504

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74. https://doi.org/10.1016/S0304-4076(03)00092-7

International Resource Panel. (2025). Global material flows database. Retrieved from https://www.resourcepanel.org/global-material-flows-database

Kalaycı, Ş. (2010). SPSS uygulamalı çok değişkenli istatistik teknikleri. Ankara: Asil.

Khan, K., Luo, T., Ullah, S., Rasheed, H. M. W., & Li, P. H. (2023). Does digital financial inclusion affect CO2 emissions? Evidence from 76 emerging markets and developing economies (EMDE's). Journal of Cleaner Production, 420, 138313. https://doi.org/10.1016/j.jclepro.2023.138313

Le, T. H., Le, H. C., & Taghizadeh-Hesary, F. (2020). Does financial inclusion impact CO2 emissions? Evidence from Asia. Finance Research Letters, 34, 101451. https://doi.org/10.1016/j.frl.2020.101451

Machado, J. A., & Silva, J. M. C. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145-173. https://doi.org/10.1016/j.jeconom.2019.04.009

Majeed, M. T., & Tauqir, A. (2020). Effects of urbanization, industrialization, economic growth, energy consumption, financial development on carbon emissions: An extended STIRPAT model for heterogeneous income groups. Pakistan Journal of Commerce and Social Sciences, 14(3), 652-681. https://hdl.handle.net/10419/224955

Nathaniel, S. P. (2020). Modelling urbanization, trade flow, economic growth and energy consumption with regards to the environment in Nigeria. GeoJournal, 85(6), 1499-1513. https://doi.org/10.1007/s10708-019-10034-0

Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels (Cambridge Working Papers in Economics No. 0435). https://doi.org/10.2139/ssrn.572504

Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312. https://doi.org/10.1002/jae.951

Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias‐adjusted LM test of error cross-section independence. The Econometrics Journal, 11(1), 105-127. https://doi.org/10.1111/j.1368-423X.2007.00227.x

Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010

Regueiro-Ferreira, R. M., & Alonso-Fernández, P. (2023). Interaction between renewable energy consumption and dematerialization: Insights based on the material footprint and the Environmental Kuznets Curve. Energy, 266, 126477. https://doi.org/10.1016/j.energy.2022.126477

Sun, G., Fang, J., Li, J., & Wang, X. (2024). Research on the impact of the integration of digital economy and real economy on enterprise green innovation. Technological Forecasting and Social Change, 200, 123097. https://doi.org/10.1016/j.techfore.2023.123097

Swamy, P. A. V. B. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38(2), 311-323. https://doi.org/10.2307/1913012

Şentürk, C. (2023). Döngüsel bir ekonomiye doğru Türkiye: Düşük karbonlu bir ekonomi için genişletilmiş STIRPAT modeline dayalı analiz. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 14(100. Yıl Özel Sayısı), 91-107. https://doi.org/10.21076/vizyoner.1334488

Tatoğlu, F. Y. (2018). Panel zaman serileri analizi (2th ed). İstanbul: Beta Yayınları.

Tatoğlu, F. Y. (2020). İleri panel veri analizi (4th ed). İstanbul: Beta Yayınları.

Topdağ, D., Acar, T., & Çelik, İ. E. (2020). Estimation of the global-scale ecological footprint within the framework of STIRPAT models: The quantile regression approach. İstanbul İktisat Dergisi, 70(2), 339-358. https://doi.org/10.26650/ISTJECON2020-815891

Usman, M., & Hammar, N. (2021). Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: Fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries. Environmental Science and Pollution Research, 28, 15519-15536. https://doi.org/10.1007/s11356-020-11640-z

Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748. https://doi.org/10.1111/j.1468-0084.2007.00477.x

Wiedmann, T. O., Schandl, H., Lenzen, M., Moran, D., Suh, S., West, J., & Kanemoto, K. (2015). The material footprint of nations. Proceedings of the National Academy of Sciences, 112(20), 6271-6276. https://doi.org/10.1073/pnas.1220362110

World Bank. (2024). World development indicators. Retrieved from https://databank.worldbank.org/

Yıldırım, K., & Akın, T. (2023). OECD ülkelerinde enerji kaynakları ve CO2 emisyonu arasındaki ilişkinin STIRPAT modeli ile incelenmesi. Yaşar Üniversitesi E-Dergisi, 18(71), 316-341. https://izlik.org/JA86RJ64UL

Yılmazer, M. (2024). Türkiye’de ekolojik ayak izinin belirleyicileri: STIRPAT modeli. İzmir İktisat Dergisi, 39(3), 637-657. https://doi.org/10.24988/ije.1373210

York, R., Rosa, E. A., & Dietz, T. (2003). 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

Yayınlanmış

22-09-2026

Sayı

Bölüm

Araştırma Makalesi

Nasıl Atıf Yapılır

YILMAZ, H. (2026). Malzeme Ayak İzinin Belirleyicileri Olarak Dijitalleşme ve Finansal Gelişme: Gelişmekte Olan Ülkelerden Kanıtlar. Üçüncü Sektör Sosyal Ekonomi Dergisi, 61(3), 3190-3210. https://doi.org/10.63556/tisej.2026.1881