Evaluating the impact of water pricing on macroeconomic variables in Iran using dynamic computable general equilibrium models

Document Type : Research/Original/Regular Article

Authors

1 Graduated Ph.D. Student/ Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran

2 Professor/ Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran

3 Associate Professor/ Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

Introduction
But by the end of the 20th century, most of the water resources have been exploited, and increasing the use of resources increases financial costs and environmental costs. Currently, water demand management is becoming important. The task of demand management is the physical storage of water and economic savings by increasing each product unit with less water and less water pollution. This management is possible through various policy measures. For example, we can refer to economic incentives to preserve water resources, price reform, and reduction of subsidies. Water prices transfer production costs to consumers, and setting appropriate tariffs is a powerful tool to manage consumption, improve allocation, and encourage the conservation of water resources. Pricing is recognized as an important tool to solve water shortage problems. It necessary to modify the pricing of agricultural water for developing countries and move towards sustainable agriculture. Pricing was considered to be one of the most important tools for demand management and it was suggested that the effects of implementing this policy should be investigated by authorities and policymakers in different regions. The results show that before implementing water policies, there is a need for a technical, economic, social, and environmental study based on sustainable development. As studies show, concerns about water scarcity are global and water price reform is essential. Because water is a basic input for production, modifying the price of water affects production costs and, as a result, the amount of production and economic variables. According to global concerns about water shortage and the geographical location of Iran, in this article, with the help of dynamic calculable general equilibrium models, the effect of water pricing in agriculture and industry sectors on macroeconomic variables has been seen.
 
Materials and Methods
In this study, dynamic general equilibrium models have been used. The data required to simulate the scenario proposed in this research is taken from the ninth version of GTAP-E. According to the research objectives, the regions are divided into Iran and the rest of the world. Economic sectors include agriculture, coal, oil, gas, industry, petrochemicals, electricity, water and services. Factors of production include skilled labor, unskilled labor, land, natural resources, and capital. In this study, two scenarios are defined. In the first scenario, an impact of 30 % on the price of water in the industrial sector is considered. In the second scenario, an effect of 30 % on the cost of water in the agricultural sector is considered. Due to the structure of the policy patterns, momentum from 2022 has been of interest for the next 10 years.
 
Results and Discussion
Pricing policy, like other forms of policy, seeks to achieve specific goals, the most important of which is economic welfare, which includes a number of different variables. Certainly, one of the main features of computable general equilibrium models is to specify the effect of shocks in economic models. Therefore, the achievement of the models estimated in this research is to determine the reaction rate of the targeted variables to the change in water price. Based on this, two scenarios have been defined in this research. In the first scenario, a 30 % increase in the price of water in the industrial sector, and in the second scenario, a 30 % increase in the price of water in the agricultural sector is considered. The obtained results showed that in the coming years, the effects of realizing the price of water in the industry and agriculture sectors on the economic welfare from 2022, from the numerical value of -87.11 to -1158.03 in 2032. Also, economic growth and investment also have negative effects. Changes in GDP growth in the country are almost equally affected by the price of water in two sectors. But gradually over time, the impact of the agricultural sector on the growth of GDP has increased. The change in the price of water affects all economic sectors and has caused a decrease in the production of these sectors. The production in the oil and gas sectors is such that when the production of the industry and agriculture sectors is affected due to the price of water, the oil and gas sectors will have the opportunity to produce more. Water pricing policy has an adverse effect on investment changes.
 
Conclusion
In this study, the 30 % water price shock in the agriculture and industry sectors has been considered. The results of the estimation of the model show that the effect of the increase in the price of water has strongly affected the growth of GDP and welfare and has significant negative effects on investment. The important point is that the negative effects of realizing the price of water are more in the agricultural sector than in the industrial sector. This means that by implementing the scenario of a 30 % increase in water price in the agricultural sector, economic welfare, production value and the amount of investment have had more negative effects than the increase in water price in the industry. This issue shows that in Iran's economy, the agricultural sector has a decisive role in the country's economy, regardless of its share in the total added value. Therefore, paying attention to the issue of pricing and inter-sectoral imbalances can provide a suitable basis for water policies.

Keywords

Main Subjects


References
 
Asadi, H., Soltani, G., & Torkamaani, J. (2007). Irrigation water pricing in Iran (a case study on land downstream of Taleghan Dam). Agricultural Economics and Development, 15(2), 61-91. doi:10.30490/aead.2007.58892 [In Persian]
Berck, P., Robinson, S., & Goldman, G. (1991). The use of computable general equilibrium models to assess water policies. Pp. 489-509, In: The economics and management of water and drainage in agriculture, Springer, Boston, MA.
Burniaux, J.M., & Truong, T.P. (2002). GTAP-E: an energy-environmental version of the GTAP model. GTAP Technical Papers, 18.
Chu, L., & Grafton, R.Q. (2020). Water pricing and the value-add of irrigation water in Vietnam: Insights from a crop choice model fitted to a national household survey. Agricultural Water Management228, 105881. doi:10.1016/j.agwat.2019.105881
Currais Monteiro, H.P. (2005). Water pricing models: a survey. DINAMIA-Research Centre on Socioeconomic Change Working Paper, (2005/45).
Duan, Y., & Liu, G. (2016). Water resource pricing study based on water quality fuzzy evaluation: a case study of Hefei City. Computational Water, Energy, and Environmental Engineering5(4), 99-111. doi: 10.4236/cweee.2016.54010
Esmaeili Moakhar Fordoei, M.A., Ebrahimi, K., Araghinejad, S., & Fazlolahi, H. (2018). Economic value determination of the agricultural water based on crop-type in Markazi Province, Iran. Water and Irrigation Management8(1), 149-163. doi:10.22059/jwim.2018.254828.602 [In Persian]
Ghazali, M., Jalal, A., Ahmad, S., & Md, A.H. (2009). Review of water pricing theories and related models. African Journal of Agricultural Research4(11), 1536-1544.
Golub, A. (2013). Analysis of climate policies with GDyn-E, GTAP Technical Papers 4292. Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
Grafton, R.Q., Chu, L., & Wyrwoll, P. (2020). The paradox of water pricing: dichotomies, dilemmas, and decisions. Oxford Review of Economic Policy36(1), 86-107. doi:10.1093/oxrep/grz030
Hassani, Y., & Shahdany, S.M.H. (2021). Implementing agricultural water pricing policy in irrigation districts without a market mechanism: comparing the conventional and automatic water distribution systems. Computers and Electronics in Agriculture185, 106121. doi:10.1016/j.compag.2021.106121
He, J., & Chen, X. (2004). A dynamic computable general equilibrium model to calculate shadow prices of water resources: implications for China. Water Resources Management. 21(9), 1517-1533.
He, J., Chen, X., & Shi, Y. (2006). A dynamic approach to calculate shadow prices of water resources for nine major rivers in China. Journal of Systems Science and Complexity19(1), 76-87. doi:10.1007/s11424-006-0076-6
Hertel, T., & Huff, K. (2001). Decomposing welfare changes in the Gtap model. doi: 10.22004/ag.econ.28708
Jun, X., Qun, D., & Yangbo, S. (2010). Integrated water and CGE model of the impacts of water policy on the Beijing’s economy and output. Chinese Journal of Population Resources and Environment8(2), 61-67. doi:10.1080/10042857.2010.10684978
Khiabani, N., Bagheri, S., & Bashiripour, A. (2017). Economic requirements of water resources management. Journal of Water and Wastewater; Ab va Fazilab28(1), 42-56. doi:10.22093/wwj.2017.39473 [In Persian]
Lika, A., Galioto, F., & Viaggi, D. (2017). Water authorities’ pricing strategies to recover supply costs in the absence of water metering for irrigated agriculture. Sustainability9(12), 2210. doi:10.3390/su9122210
Marques, C.A.F., & De Sousa Fragoso, R.M. (2015). Alternative irrigation water pricing policies: an econometric mathematical programming model. New Medit: Mediterranean Journal of Economics, Agriculture and Environment= Revue Méditerranéenne dʹEconomie Agriculture et Environment14(4), 42-49.
Marston, L., & Cai, X. (2016). An overview of water reallocation and the barriers to its implementation. Wiley Interdisciplinary Reviews. WIREs Water3(5), 658-677. doi:org/10.1002/wat2.1159
Marzano, R., Rouge, C., Garrone, P., Grilli, L., Harou, J.J., & Pulido-Velazquez, M. (2018). Determinants of the price response to residential water tariffs: Meta-analysis and beyond. Environmental Modelling & Software101, 236-248. doi:10.1016/j.envsoft.2017.12.017
Meinzen-Dick, R. (2006). Water reallocation: Challenges, threats, and solutions for the poor (No. HDOCPA-2006-41). Human Development Report Office (HDRO), United Nations Development Programme (UNDP).
Molinos-Senante, M. (2014). Water rate to manage residential water demand with seasonality: peak-load pricing and increasing block rates approach. Water policy16(5), 930-944. doi: 10.2166/wp.2014.180
Reyndaud, A. (2003). An econometric estimation of industrial water demand in France. Environmental and Resource Economics, 25, 213–232. doi:10.1023/A:1023992322236
Ruijs, A., Zimmermann, A., & Van Den Berg, M. (2008). Demand and distributional effects of water pricing policies. Ecological Economics66(2-3), 506-516. doi:10.1016/j.ecolecon.2007.10.015
Suárez-Varela, M., Martinez-Espineira, R., & González-Gómez, F. (2015). An analysis of the price escalation of non-linear water tariffs for domestic uses in Spain. Utilities Policy34, 82-93. doi:10.1016/j.jup.2015.01.005
Tahami Pour Zarandi, M., Khazaei, A., & Kolivand, F. (2020). Analyzing the Tariff System and Economic Value of Water in Iran’s Industry Sector. Journal of Water and Sustainable Development, 6(3), 19-30. doi:10.22067/jwsd.v6i3.76788 [In Persian]
Tian, G.L., Wu, Z., & Hu, Y.C. (2021). Calculation of optimal tax rate of water resources and analysis of social welfare based on CGE model: A case study in Hebei Province, China. Water Policy23(1), 96-113. doi:10.2166/wp.2020.118
Tsur, Y., & Zemel, A. (2016). The management of fragile resources: A long term perspective. Environmental and Resource Economics, 65, 639–655. doi:10.1007/s10640-016-0005-7 
Wheeler, S.A., Loch, A., Crase, L., Young, M., & Grafton, R.Q. (2017). Developing a water market readiness assessment framework. Journal of Hydrology552, 807-820. doi:10.1016/j.jhydrol.2017.07.010
Zhang, C.Y., & Oki, T. (2023). Water pricing reform for sustainable water resources management in China’s agricultural sector. Agricultural Water Management, 275, 108045. doi:10.1016/j.agwat.2022.108045