• Amir Dadres Moghadam 1

  • Mahdi Safdari 2

  • Reza Dehvari 3

  1. 1 Assist. Professor, Department of Agricultural Economics, Faculty of Economics, University of Sistan and Baluchestan, Zahedan, Iran
  2. 2 Assoc. Professor, Department of Agricultural Economics, Faculty of f Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran
  3. 3 Ph.D. Scholar, Department of Agricultural Economics, Faculty of f Management and Economics, University of Sistan and Baluchestan, Zahedan, Iran

Abstract

The quality of water resources is essential for the protection of the environment and human public health. Decreasing water quality has several environmental effects. In addition to creating environmental problems, it increases the cost of governments. The aim of this research is to empirically examine the relationship between water quality and income in selected Asian countries, taking into account spatial dependencies between countries. A spatial econometric framework was applied using panel data for nine Asian countries from 2000 to 2020. The required data was obtained from the World Bank. First, the Moran test was performed to prove the existence of spatial dependence. Using the Wald test, the spatial Durbin model was determined as the optimal model. Then, using Stata17 software, the model was estimated. The results showed that there is significant spatial spillover in the model. GDP has the most positive effect on water quality in Asian countries, and population density has the most negative effect. The spatial spillover effect of real GDP per capita and foreign direct investment on water quality has been positive and significant. Spatial spillover of legality and effectiveness of the government has had a positive and significant effect on water quality.

Keywords

Subjects

 Water Pollution

Aklin, M. (2016). Re-exploring the trade and environment nexus through the diffusion of pollution. Environ. Resour. Econom., 64, 663-682. DOI: 10.1007/s10640-015-9893-1
Almeida, E. (2012). Econometria espacial. Campinas. 351 pp
Anselin, L. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. Int. Region. Sci. Rev., 26(2), 153-166. DOI: 10.1177/0160017602250972
Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media. 255 pp. DOI: 10.1007/978-94-015-7799-1
Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. Statistics textbooks and monographs, 155, 237-290. DOI:  10.1201/9781482269901
Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. Spatial analytical perspectives on GIS. Taylor and Francis, London. 268 pp. DOI: 10.1201/9780203739051
Antweiler, W., Copeland, B. R., & Taylor, M. S. (2001). Is free trade good for the environment? Am. Econom. Rev., 91(4), 877-908. DOI: 10.1257/aer.91.4.877
Blanc-Brude, F., Cookson, G., Piesse, J., & Strange, R. (2014). The FDI location decision: Distance and the effects of spatial dependence. Int. Bus. Rev., 23(4), 797-810. DOI: 10.1016/j.ibusrev.2013.12.002
Bradshaw, C. J., Giam, X., & Sodhi, N. S. (2010). Evaluating the relative environmental impact of countries. PloS One, 5(5), e10440. DOI: 10.1371/ journal. pone.0010440
Brockwell, E., Elofsson, K., Marbuah, G., & Nordmark, S. (2021). Spatial analysis of water quality and income in Europe. Water Resour. Econom., 35, 121-137 100182. DOI: 10.1016/j.wre.2021.100182 
Brown, J. (2013). Can participation change the geography of water? Lessons from South Africa. Annal. Assoc. Am. Geogra., 103(2), 271-279. DOI:  10.1080/00045608.2013.754685
Chen, X., Yi, G., Liu, J., Liu, X., & Chen, Y. (2018). Evaluating economic growth, industrial structure, and water quality of the Xiangjiang river basin in China based on a spatial econometric approach. Int. J. Environ. Res. Public Health, 15(10), 20-35. DOI: 10.3390/ijerph15102095
Farzin, Y. H., & Grogan, K. A. (2013). Socioeconomic factors and water quality in California. Environ. Econom. Policy Stud., 15, 1-37. DOI: 10.1007/s10018-012-0040-8
Fazekas, M. (2017). Assessing the quality of government at the regional level using public procurement data. European Commission, Directorate-General for Regional Policy: Working Papers No. WP, 12, 2017.
Fredriksson, P. G., List, J. A., & Millimet, D. L. (2003). Bureaucratic corruption, environmental policy and inbound US FDI: theory and evidence. J. Public Econom., 87(7-8), 1407-1430. DOI:  10.1016/s0047-2727(02)00016-6 
Gassebner, M., Lamla, M. J., & Sturm, J. E. (2011). Determinants of pollution: what do we really know? Oxford Econom. Paper., 63(3), 568-595. DOI:10.1093/oep/gpq029
Hosseini, H. M., & Kaneko, S. (2013). Can environmental quality spread through institutions? Energy Policy, 56, 312-321. DOI: 10.1016/j.enpol.2012.12.067
Kelejian, H. H., & Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. J. Econom., 157(1), 53-67. DOI: 10.1016/j.jeconom.2009.10.025 
Kelejian, H. H., & Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances.  J. Real Estate Finan. Econom., 17, 99-121. DOI: 10.1023/A:1007707430416
Kustanto, A. (2020). Water quality in Indonesia: The role of socioeconomic indicators. PhD dissertation, University of Indonesia, West Java. Indonesia. 143 pp
Li, H., Cohen, A., Li, Z., & Zhang, M. (2018). The impacts of socioeconomic development on rural drinking water safety in China: a provincial-level comparative analysis. Sustain., 11(1), 85-113. DOI: 10.3390/su11010085
Li, Q., & Reuveny, R. (2006). Democracy and environmental degradation. Int. Stud. Quart., 50(4), 935-956. DOI: 10.1111/j.1468-2478.2006. 00432.x
Liddle, B. (2004). Demographic dynamics and per capita environmental impact: Using panel regressions and household decompositions to examine population and transport. Pop. Environ., 31, 23-39. DOI: 10.1023/B: POEN.0000039951. 37276.f3
Volk, M., Liersch, S., & Schmidt, G. (2009). Towards the implementation of the European Water Framework Directive? Lessons learned from water quality simulations in an agricultural watershed. Land Use Policy, 26(3), 580-588. DOI: 10.1016/j.landusepol.2008.08.005
Wang, J., Da, L., Song, K., & Li, B. L. (2008). Temporal variations of surface water quality in urban, suburban and rural areas during rapid urbanization in Shanghai, China. Environ. Pollut., 152(2), 387-393. DOI: 10.1016/j.envpol.2007.06.050
Wen, Y., Schoups, G., & Van De Giesen, N. (2018). Global impacts of the meat trade on in-stream organic river pollution: the importance of spatially distributed hydrological conditions. Environ. Res. Lett., 13(1), 014013. DOI: 10.1088/1748-9326/aa94f6
Zhou, K., Liu, H., & Wang, Q. (2019). The impact of economic agglomeration on water pollutant emissions from the perspective of spatial spillover effects. J. Geogra. Sci., 29, 2015-2030. DOI: 10.1007/s11442-019-1702-2