Assessing the sustainability of water and agriculture security by analyzing the decoupling of agriculture economics from groundwater withdrawal

Document Type : Research Paper

Authors

1 Department of Agricultural Economics, Faculty of Agriculture, University of Tehran, Karaj, Iran.

2 Department of Water, Wastewater and Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.

10.22059/jwim.2024.370493.1133

Abstract

The important share of agriculture in groundwater resources depletion as well as job creation for some of the most disadvantaged sections of the society has caused hiring any restrictions on this sector with the aim of restoring groundwater resources results in a wide range of socio-economic challenges. In such a situation, a potential solution to overcome the dire situation of groundwater resources without the emergence of socio-economic consequences is to achieve the growth of the agricultural economy along with the decrease of groundwater consumption, which is known as strong decoupling in the research literature. In this study, an attempt was made to evaluate the nature of the relationship between the growth of the agricultural economy and the withdrawal of groundwater resources in 31 provinces of Iran between 2012 and 2019 by applying an approach called the Tapio approach. Then, the effect of being placed in a state of strong decoupling on the depth of groundwater should be estimated by quantile regression model. According to the findings, almost all the main agricultural centers of Iran, such as Fars and Khorasan Razavi, have not only failed to achieve strong decoupling, but in many cases, they have also experienced negative growth in the agricultural economy along with the increase in the withdrawal of groundwater resources, and consequently, they are in unstable conditions in terms of economic and groundwater resources. Further, the results of the quantile model depicted that in the areas with medium and deep groundwater resources, achieving strong decoupling reduces the depth of these resources by about 8%, although the same thing does not affect the depth of these resources in areas with shallow groundwater. 

Keywords

References

1. Ashraf, S., Nazemi, A., & AghaKouchak, A. (2021). Anthropogenic drought dominates groundwater depletion in Iran. Scientific Reports, 11(1).
2. Caetano, R. V., Marques, A. C., & Afonso, T. L. (2023). Can sustainable development induce foreign direct investment? Analysis of the complex inward and outward flows of investment in European Union countries. Journal of the Knowledge Economy.
3. Canay, I. A. (2011). A simple approach to quantile regression for panel data. The Econometrics Journal, 14(3), 368-386.
4. Cao, S., He, Z., Wang, S., & Niu, J. (2023). Decoupling analysis of water consumption and economic growth in tourism in arid areas: Case of Xinjiang, China. Sustainability, 15(13), 10379.
5. Conte Grand, M. (2016). Carbon emission targets and decoupling indicators. Ecological Indicators, 67, 649-656.
6. Dasgupta, S., Laplante, B., Wang, H., & Wheeler, D. (2002). Confronting the environmental kuznets curve. The Journal of Economic Perspectives, 16(1).
7. Dogulu, N., López López, P., Solomatine, D. P., Weerts, A. H., & Shrestha, D. L. (2015). Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments. Hydrology and Earth System Sciences, 19(7), 3181-3201.
8. Gerber, J. S., Ray, D. K., Makowski, D., Butler, E. E., Mueller, N. D., West, P. C., Johnson, J. A., Polasky, S., Samberg, L. H., Siebert, S., & Sloat, L. (2024). Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation. Nature Food, 1-11.
9. Ghorbani, Z., Khosravi, A., Maghsoudi, Y., Mojtahedi, F. F., Javadnia, E., & Nazari, A. (2022). Use of InSAR data for measuring land subsidence induced by groundwater withdrawal and climate change in Ardabil Plain, Iran. Scientific Reports, 12(1), 1-22.
10. Gnangnon, S. K. (2023). Effect of the Shadow Economy on Tax Reform in Developing Countries. Economies, 11(3). 96.
11. He, C., Li, Y., Wang, T., & Shah, S. A. (2024). Is cryptocurrency a hedging tool during economic policy uncertainty? An empirical investigation. Humanities and Social Sciences Communications, 11(1), 1-10.
12. Hossein Hamzeh, N., Shukurov, K., Mohammadpour, K., Kaskaoutis, D. G., Ranjbar Saadatabadi, A., & Shahabi, H. (2023). A comprehensive investigation of the causes of drying and increasing saline dust in the Urmia Lake, northwest Iran, via ground and satellite observations, synoptic analysis and machine learning models. Ecological Informatics, 78, 102355.
13. Hosseinzadeh, M., Saghaian, S. H., Nematollahi, Z., & Shahnoushi Foroushani, N. (2022). Water consumption and economic growth: evidence for the environmental Kuznets curve. Water International, 47(8), 1333-1348.
14. Hu, M., Hu, Y., Yuan, J., & Lu, F. (2019). Decomposing the decoupling of water consumption and economic growth in Jiangxi, China. Journal of Water Reuse and Desalination, 9(1), 94-104.
15. Huang, K., Wang, M., Zhou, Z., Yu, Y., & Bi, Y. (2021). A Decoupling Analysis of the Crop Water Footprint Versus Economic Growth in Beijing, China. Frontiers in Environmental Science, 9.
16. Ike, G. N., Usman, O., & Sarkodie, S. A. (2020). Testing the role of oil production in the environmental Kuznets curve of oil producing countries: New insights from Method of Moments Quantile Regression. Science of The Total Environment, 711, 135208.
17. Kim, H. R., Yu, S., Oh, J., Kim, K. H., Oh, Y. Y., Kim, H. K., Park, S., & Yun, S. T. (2019). Assessment of nitrogen application limits in agro-livestock farming areas using quantile regression between nitrogen loadings and groundwater nitrate levels. Agriculture, Ecosystems & Environment, 286, 106660.
18. Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89.
19. Kumar, S., & Khanna, M. (2023). Distributional heterogeneity in climate change impacts and adaptation: Evidence from Indian agriculture. Agricultural Economics, 54(2), 147-160.
20. Lamarche, C. (2010). Robust penalized quantile regression estimation for panel data. Journal of Econometrics, 157(2), 396-408.
21. Li, F., Wei, W., Zhao, Y., & Qiao, J. (2017). Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model. Hydrogeology Journal, 25(1), 191-202.
22. López López, P., Verkade, J. S., Weerts, A. H., & Solomatine, D. P. (2014). Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison. Hydrology and Earth System Sciences, 18(9), 3411-3428.
23. Machado, J. A. F., & Santos Silva, J. M. C. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145-173.
24. Madani, K. (2014). Water management in Iran: what is causing the looming crisis? Journal of Environmental Studies and Sciences, 4(4), 315-328.
25. Mesgaran, M., & Azadi, P. (2018). A national adaptation plan for water scarcity in Iran. Working Paper 6, Stanford Iran 2040 Project, Stanford University.
26. Najafabadi, M. M., Mirzaei, A., Laskookalayeh, S. S., & Azarm, H. (2022). An investigation of the relationship among economic growth, agricultural expansion and chemical pollution in Iran through decoupling index analysis. Environmental Science and Pollution Research, 29(50), 76101-76118.
27. Nouri, M., Homaee, M., Pereira, L. S., & Bybordi, M. (2023). Water management dilemma in the agricultural sector of Iran: A review focusing on water governance. Agricultural Water Management, 288, 108480.
28. OECD. (2002). Indicators to measure decoupling of environmental pressure from economic growth. Paris: OECD.
29. Qiu, L., Huang, J., & Niu, W. (2018). Decoupling and driving factors of economic growth and groundwater consumption in the coastal areas of the Yellow Sea and the Bohai Sea. Sustainability, 10(11), 4158.
30. Raifu, I. A., & Aminu, A. (2023). The effect of military spending on economic growth in MENA: evidence from method of moments quantile regression. Future Business Journal, 9(1), 7.
31. Rinne, H. (2011). Location-Scale Distributions. In M. Lovric (Ed.), International Encyclopedia of Statistical Science (pp. 752-754). Heidelberg: Springer.
32. Rios-Avila, F., & Maroto, M. L. (2022). Moving beyond linear regression: Implementing and interpreting quantile regression models with fixed effects. Sociological Methods & Research.
33. Saemian, P., Tourian, M. J., AghaKouchak, A., Madani, K., & Sneeuw, N. (2022). How much water did Iran lose over the last two decades? Journal of Hydrology: Regional Studies, 41, 101095.
34. Safdari, Z., Nahavandchi, H., & Joodaki, G. (2022). Estimation of groundwater depletion in Iran’s catchments using well data. Water, 14(1), 131.
35. Shahi, A. (2019). Drought: The Achilles heel of the Islamic republic of Iran. Asian Affairs, 50(1), 18-39.
36. Shi, C., Yuan, H., Pang, Q., & Zhang, Y. (2020). Research on the decoupling of water resources utilization and agricultural economic development in Gansu province from the perspective of water footprint. International Journal of Environmental Research and Public Health, 17(16), 5758.
37. Shi, J. (2022). Study on the decoupling relationship and rebound effect between agricultural economic growth and water footprint: A case of Yangling agricultural demonstration zone, China. Water, 14(6), 991.
38. Simionescu, M., & Gavurová, B. (2023). Pollution, income inequality and green finance in the new EU member states. Humanities and Social Sciences Communications, 10(1), 677.
39. Sinha, A., Sedai, A. K., Kumar, A., & Nepal, R. (2021). Are autocracies bad for the environment? Global evidence from two centuries of data. CAMA Working Paper No. 24.
40. Tapio, P. (2005). Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transport Policy, 12(2), 137–151.
41. United Nations. (2018). Sustainable Development Goal 6 Synthesis Report 2018 on Water and Sanitation. New York: United Nations Publications
42. Uribe, J. M., & Guillen, M. (2020). Why and when should quantile regression be used? In quantile regression for cross-sectional and time series data (pp. 1-5). Cham: Springer.
43. Vaheddoost, B., & Aksoy, H. (2018). Interaction of groundwater with Lake Urmia in Iran. Hydrological Processes, 32(21), 3283-3295.
44. Waldmann, E. (2018). Quantile regression: A short story on how and why. Statistical Modelling, 18(3-4), 203-218.
45. Wang, Q., & Wang, S. (2019). Decoupling economic growth from carbon emissions growth in the United States: The role of research and development. Journal of Cleaner Production, 234, 702-713.
46. Wang, Q., & Wang, X. (2020). Moving to economic growth without water demand growth - a decomposition analysis of decoupling from economic growth and water use in 31 provinces of China. Science of The Total Environment, 726, 138362.
47. Wang, S. X., Fu, Y. B., & Zhang, Z. G. (2015). Population growth and the environmental Kuznets curve. China Economic Review, 36, 146-165.
48. Weerts, A. H., Winsemius, H. C., & Verkade, J. S. (2011). Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrology and Earth System Sciences, 15(1), 255-265.
49. Zhang, C., & Zhang, Z. (2023). Novel research methods to examine renewable energy and energy related greenhouse gases: evidence from novel panel methods. Economic Research, 36(1), 1187-1204.
50. Zhao, X., Zhang, X., & Shao, S. (2016). Decoupling CO2 emissions and industrial growth in China over 1993-2013: The role of investment. Energy Economics, 60, 275-292.
Water and Irrigation Management
Volume 14, Issue 3
December 2024
Pages 543-565

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