Belediyelerin Çevre Koruma Harcamaları Üzerinde Sanayileşmenin Etkilerinin Mekânsal Analizi: Türkiye Örneği

Year 2023, Volume: 31 Issue: 55, 397 - 416, 31.01.2023

Mustafa Kızıltan , Ahmet Burçin Yereli

https://doi.org/10.17233/sosyoekonomi.2023.01.20

Cited By: 1

Abstract

Belediyelerin yapmış oldukları çevre koruma harcamaları başta sanayileşme düzeyi olmak üzere çeşitli faktörlerden etkilenmektedir. Bu çalışma 2007-2016 yılları arasında Türkiye’de belediyelerin kişi başı çevre koruma harcamaları üzerinde sanayileşmenin etkilerini inceleyen ilk çalışmadır. Komşu il belediyeleri arasındaki mekânsal bağımlılık ve saçılım etkilerini dikkate almak için çalışmada Mekânsal Durbin Modeli kullanılmaktadır. Bu çalışmanın temel bulguları şu şekildedir: (1) Sanayileşme düzeyinin artması daha fazla çevre koruma harcaması yapılmasını gerektirmektedir. (2) Mekânsal modelin anlamlı fakat negatif bir etkiye sahip olması kişi başı çevre koruma harcamalarının belli bölgelerde yoğunlaştığını ve bedavacılık problemine yol açtığını göstermektedir. (3) Kişi başı çevre gelirlerinin artması kişi başı çevre koruma harcamalarını artırmaktadır. (4) Nüfus yoğunluğu ve yüzölçümü daha fazla kişi başı çevre korumasını beraberinde getirmektedir. Bu çalışmanın sonuçları karar vericilere çevre koruma harcamalarının planlanmasında ve koordine edilmesinde farklı bir bakış açısı sağlayabilir.

Keywords

Çevre Koruma Harcamaları, Bölgesel Kamu Harcamaları, Belediyeler, Mekansal Bağımlılık, Sanayileşme

Spatial Analysis of Industrialization Effects on Municipalities’ Environmental Protection Expenditures: The Case of Turkey

Abstract

Environmental protection expenditures made by municipalities are affected by various factors, especially the level of industrialisation. This is the first study to examine the effects of industrialisation on the per capita environmental protection expenditures of municipalities in Turkey between 2007-2016. The Spatial Durbin Model is used to account for the spatial dependence and spillover effects between neighbouring municipalities. The main findings of this study are as follows: (1) The increase in industrialisation requires more environmental protection expenditures. (2) Spatial model has a significant but negative effect, showing that per capita environmental protection expenditures are concentrated in specific regions and cause the free-rider problem. (3) The increase in per capita environmental revenues increases per capita environmental protection expenditures. (4) As expected, population density and land area increase environmental protection per capita. The results of this study may provide decision-makers with a different perspective in planning and coordinating environmental protection expenditures.

Keywords

Environmental Protection Expenditures, Regional Public Expenditures, Municipalities, Spatial Dependence, Industrialization

References

• Akbulut, H. & A.B. Yereli (2016), “Kamu Gelirleri ve Kamu Harcamaları Nedensellik İlişkisi: 2006- 2015 Dönemi İçin Türkiye Örneği”, Sosyoekonomi, 24(1), 103-120.
• Anselin, L. & A.K. Bera (1998), “Spatial dependence in linear regression models with an introduction to spatial econometrics”, in: A. Ullah & D.E.A. Giles (eds.), Handbook of Applied Economic Statistics (237-289), New York: Marcel Dekker.
• Anselin, L. & R.J.G.M. Florax (1995), “Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results”, in: L. Anselin & R.J.G.M. Florax (eds.), New Directions in Spatial Econometrics (21-74), Berlin Heidelberg: Springer Verlag.
• Anselin, L. & S. Rey (1991), “Properties of Tests for Spatial Dependence in Linear Regression Models”, Geographical Analysis, 23(2), 112-131.
• Anselin, L. (1988), Spatial Econometrics: Methods and Models, Dordrecht: Kluwer Academic Publishers.
• Anselin, L. (1992), SpaceStat tutorial: A workbook for using SpaceStat in the analysis of spatial data, Urbana-Champaign: University of Illinois, Urbana, IL.
• Anselin, L. (1995), “Local Indicators of Spatial Association-LISA”, Geographical Analysis, 27(2), 93-115.
• Anselin, L. (1996), “The Moran Scatterplot as an ESDA Tool to Assess Local Instability in Spatial Association”, in: M. Fisher et al. (eds.), Spatial analytical perspectives on GIS (111-125), London: Taylor and Francis.
• Anselin, L. (2002), “Under the hood: Issues in the specification and interpretation of spatial regression models”, Agricultural Economics, 27(3), 247-267.
• Anselin, L. (2003), GeoDaTM 0.9 User’s Guide, Center for Spatially Integrated Social Science, <http://www.unc.edu/~emch/gisph/geoda093.pdf>, 25.12.2020.
• Arbolino, R. et al. (2020), “Who achieves the efficiency? A new approach to measure ‘local energy efficiency’”, Ecological Indicators, 110, 105875.
• Beer, C. & A. Riedl (2012), “Modelling spatial externalities in panel data: The Spatial Durbin model revisited*”, Papers in Regional Science, 91(2), 299-318.
• Blanc-Brude, F. et al. (2014), “The FDI location decision: Distance and the effects of spatial dependence”, International Business Review, 23(4), 797-810.
• Broietti, C. et al. (2018), “Public expenditure and the environmental management of Brazilian municipalities: a panel data model”, International Journal of Sustainable Development & World Ecology, 25(7), 630-641.
• Brueckner, J.K. (2003), “Strategic Interaction Among Governments: An Overview of Empirical Studies”, International Regional Science Review, 26(2), 175-188.
• Burnett, J.W. et al. (2013), “A spatial panel data approach to estimating U.S. state-level energy emissions”, Energy Economics, 40, 396-404.
• Case, A.C. et al. (1993), “Budget spillovers and fiscal policy interdependence: Evidence from the states”, Journal of Public Economics, 52(3), 285-307.
• Conley, T.G. & F. Molinari (2007), “Spatial correlation robust inference with errors in location or distance”, Journal of Econometrics, 140(1), 76-96.
• D’Uva, M. (2017), “Population and industrial pressure on local environmental expenditure in the Italian regions”, Land Use Policy, 69, 386-391.
• De Graaff, T. et al. (2001), “A general misspecification test for spatial regression models: Dependence, heterogeneity, and nonlinearity”, Journal of Regional Science, 41(2), 255-276.
• Deng, H. et al. (2012), “Strategic Interaction in Spending on Environmental Protection: Spatial Evidence from Chinese Cities”, China & World Economy, 20(5), 103-120.
• Elhorst, J.P. (2001), “Dynamic Models in Space and Time”, Geographical Analysis, 33(2), 119-140.
• Elhorst, J.P. (2010), “Applied Spatial Econometrics: Raising the Bar”, Spatial Economic Analysis, 5(1), 9-28.
• Ermini, B. & R. Santolini (2010), “Local Expenditure Interaction in Italian Municipalities: Do Local Council Partnerships Make a Difference?”, Local Government Studies, 36(5), 655-677.
• Facchini, F. et al. (2017), “Who cares about the environment? An empirical analysis of the evolution of political parties’ environmental concern in European countries (1970-2008)”, Land Use Policy, 64, 200-211.
• Fernandez, R.M. (2018), “Interactions of regional and national environmental policies: The case of Spain”, Cogent Economics & Finance, 6(1), 1442092.
• Foucault, M. et al. (2008), “Public spending interactions and local politics. Empirical evidence from French municipalities”, Public Choice, 137(1), 57-80.
• Gallo, J.L. & C. Ertur (2003), “Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980-1995”, Papers in Regional Science, 82, 175-201.
• Ge, T. et al. (2020), “The impact of environmental regulation efficiency loss on inclusive growth: Evidence from China”, Journal of Environmental Management, 268, 110700.
• Getis, A. (2007), “Reflections on spatial autocorrelation”, Regional Science and Urban Economics, 37(4), 491-496.
• Haining, R. (2004), Spatial Data Analysis Theory and Practice, Cambridge, UK: Cambridge University Press.
• Huang, G. et al. (2020), “Impact of transportation infrastructure on industrial pollution in Chinese cities: A spatial econometric analysis”, Energy Economics, 92, 104973.
• Jiang, Y. (2014), “Spatial Strategic Interaction in Environmental Protection: An Empirical Study of The Chinese Provinces”, Review of Urban & Regional Development Studies, 26(3), 203-216.
• Kao, S.Y.-H. & A.K. Bera (2016), Spatial Regression: The Curious Case of Negative Spatial Dependence, University of Illinois, Urbana-Champaign, <http://www.econ.uiuc.edu/~hrtdmrt2/Teaching/SE_2016_19/References/Neg.pdf>, 25.12.2020.
• Konisky, D.M. & N.D. Woods (2012), “Measuring State Environmental Policy”, Review of Policy Research, 29(4), 544-569.
• Lesage, J.P. & R.K. Pace (2009), Introduction to Spatial Econometrics, Boca Raton, FL: Chapman & Hall/CRC Taylor & Francis Group.
• LeSage, J.P. & R.K. Pace (2013), “Interpreting spatial econometric models”, in: M.M. Fischer & P. Nijkamp (eds.), Handbook of Regional Science (1535-1552), Springer Berlin Heidelberg.
• Lesage, J.P. & R.K. Pace (2014), “The Biggest Myth in Spatial Econometrics”, Econometrics, 2(4), 217-249.
• López, F.A. et al. (2017), “Spatial spillovers in public expenditure on a municipal level in Spain”, The Annals of Regional Science, 58, 39-65.
• Morgenstern, R.D. et al. (2001), “The Cost of Environmental Protection”, The Review of Economics and Statistics, 83(4), 732-738.
• Oates, W.E. (2001), “A Reconsideration of Environmental Federalism”, Discussion Paper 01-54, Resources for the Future, Washington, D.C.
• Pacheco, L.M. et al. (2017), “Environmental public expenses: An integrative literature review and future research agenda”, Ambiente & Sociedade, 20(4), 209-228.
• Pařil, V. et al. (2022), “The cost of suburbanization: spending on environmental protection”, European Planning Studies, 30(10), 2002-2021.
• Pearce, D. & C. Palmer (2001), “Public and private spending for environmental protection: a cross-country policy analysis”, Fiscal Studies, 22(4), 403-456.
• Remoundou, K. & P. Koundouri (2009), “Environmental Effects on Public Health: An Economic Perspective”, International Journal of Environmental Research and Public Health, 6(8), 2160-2178.
• Revelli, F. (2002), “Testing the Tax Mimicking versus Expenditure Spill-Over Hypotheses Using English Data”, Applied Economics, 34(14), 1723-1731.
• Solé-Ollé, A. (2006), “Expenditure spillovers and fiscal interactions: Empirical evidence from local governments in Spain”, Journal of Urban Economics, 59(1), 32-53.
• Šťastná, L. (2009), “Spatial Interdependence of Local Public Expenditures: Selected Evidence from the Czech Republic”, IES Working Paper No. 12/2009.
• T. C. Kalkınma Bakanlığı (2018), On Birinci Kalkınma Planı (2019-2023) Çevre ve Doğal Kaynakların Sürdürülebilir Yönetimi Çalışma Grubu Raporu, Ankara.
• Tobler, W.R. (1970), “A Computer Movie Simulating Urban Growth in the Detroit Region”, Economic Geography, 46(Jun), 234-240.
• Toprak, D. (2017), “Türkiye’nin Çevre Politikasında Yerel Yönetimlerin Rolü: Yerel Yönetim Bütçesinin İncelenmesi”, Maliye Araştırmaları Dergisi, 3(2), 173-193.
• TÜİK (2020a), Bölgesel İstatistikler, <https://biruni.tuik.gov.tr/bolgeselistatistik>, 25.12.2020.
• TÜİK (2020b), Merkezi Dağıtım Sistemi Kamu Sektörü Çevresel Harcama İstatistikleri, <https://biruni.tuik.gov.tr/medas/?kn=123&locale=tr>, 25.12.2020.
• WCED World Commission on Environment and Development (1987), Our Common Future: The World Commission on Environment and Development, Oxford: Oxford University Press.
• WHO (2019), “Environmental health inequalities in Europe”, Second Assessment Report, Copenhagen.
• Wu, X. et al. (2019), “Effects of environmental regulation on air pollution control in China: a spatial Durbin econometric analysis”, Journal of Regulatory Economics, 55(3), 307-333.
• Yalçın, A.Z. & M. Gök (2015), “Avrupa Birliği ve Türkiye’de Kamu Çevre Koruma Harcamalarının Analizi”, Uluslararası Yönetim İktisat ve İşletme Dergisi, 11(25), 65-89.
• Yang, S. et al. (2022), “Environmental regulation and high-quality sustainable development of China’s economy-an empirical study based on a Spatial Durbin Model and threshold model”, Economic Research-Ekonomska Istraživanja, 35(1), 5699-5718.
• Zhao, Y. et al. (2022), “Local governments’ environmental emphasis and corporate green innovation: evidence from China”, Economic Change and Restructuring, 55(4), 2577-2603.