ارزیابی فضایی استخراج منابع آب زیرزمینی و سطحی بر فقر نسبی شهری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه اقتصاد، دانشگاه آیت ا... بروجردی، لرستان، ایران.

2 دکتری اقتصاد، مدرس دانشکده علوم انسانی، دانشگاه زنجان، زنجان، ایران

10.22034/eiap.2024.191704

چکیده

متغیرهای عینی و ذهنی مختلف بر انواع مختلف تعاریف و شاخص‌های فقر مؤثر است؛ اما در سال‌های اخیر تاثیر متغیرهای محیط‌زیست بر انواع فقر موردتوجه اقتصاددانان محیط‌زیستی قرارگرفته است. هدف این پژوهش ارزیابی تاثیر متغیرهای منابع آب زیرزمینی به‌منظور در نظر گرفتن اثرات غیرخطی و آستانه‌ای آن بر فقر نسبی در بین 30 استان ایران و در دوره زمانی 1385- 1398 است. مدل مورداستفاده مدل پانل پویای تصادفی (DPD) با روش گشتاوری‌های تعمیم‌یافته (GMM) با کاربرد برآوردگرهای دومرحله‌ای آرلانو- باور/ بوندل- بوند با در نظر گرفتن 8 متغیر فضایی دوربین (Durbin) است. وقفه فضایی اول فقر نسبی به‌منظور در نظر گرفتن اثرات فضایی دور باطل فقر نیز وارد مدل شده است. میزان آستانه برآورد شده با استفاده از مدل فضایی دوربین 00493/0 میلیون مترمکعب سرانه سالیانه است. با توجه به اینکه ضریب درجه دوم استخراج 2549/929 است در صورت گذر استخراج آب از آستانه 1804/493 مترمکعب سالیانه سرانه، خط فقر نسبی افزایش خواهد یافت. بر اساس این حد آستانه میزان آستانه مصرف سالیانه آب برای یک جمعیت 80 میلیون نفری 454/39 میلیارد مترمکعب است، درصورتی‌که میزان استخراج منابع آب زیرزمینی به‌طور متوسط در دوره فوق برابر با 927/59 میلیارد مترمکعب است که نشان می‌دهد در کل دوره، ایران از حد آستانه عبور کرده‌ است. با توجه به وجود اثرات فضایی پیشنهاد می‌گردد که سیاست‌های مدیریت آب از مدیریت جغرافیای سیاسی به مدیریت حوضه‌های آبریز تغییر یابد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatial Evaluation of Groundwater and Surface Water Resources Extraction on Relative Urban Poverty

نویسندگان [English]

  • Seyyed Parviz Jalili kamju 1
  • Mobina Zarei 2
  • Darush Hassanvand 1
1 Assistant Professor in Economics, Ayatollah Boroujerdi University, Lorestan, Iran.
2 PhD in Economics, Lecturer, Faculty of Humanities, Zanjan University, Zanjan, Iran.
چکیده [English]

In recent years, the impact of environmental variables on the types of poverty has been considered by environmental economists. The aim of this study was to evaluate the effect of groundwater resources variables, quadratic groundwater power to consider nonlinear and threshold effects, surface water resources on relative poverty among 30 provinces of Iran in the period 2006-2019. The model used is the Stochastic Dynamic Panel (DPD) model with the Generalized Torque (GMM) method using Arellano-Bover / Bundell-Bond two-stage estimators considering 8 Durbin spatial variables (Durbin). The first spatial interval of relative poverty has also been included in the model in order to consider the spatial effects of the vicious cycle of poverty. The estimated threshold using the Durbin space model is 0.00493 million cubic meters per year. Considering that the quadratic coefficient of extraction is 929/2549, if the water extraction passes above the threshold of 493/1804 million cubic meters per year, the relative poverty line will increase. According to this threshold, the annual water consumption threshold for a population of 80 million people is 39.454 billion cubic meters, while the average extraction of groundwater resources in the above period is equal to 59.927 billion cubic meters, which shows that in throughout the period, Iran has crossed the threshold. Due to the spatial effects, it is suggested that water management policies be changed from political geography management to catchment management.

کلیدواژه‌ها [English]

  • Nonlinear Spatial Effects
  • Groundwater Resources
  • Surface water resources
  • Relative Poverty
  • S-GMM-D Model JEL: I30
  • Q25
  • C21
Adhikari, B. 2013. Poverty reduction through promoting alternative livelihoods: implications for marginal drylands. J. Int. Dev. 25, 947–967.
Anselin, L. 1988. Spatial Econometrics: Methods and Models, Dorddrecht: Kluwer Academic Publishers.
Arellano, M. & Bond, S. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment. Rev. Econ. Stud. 58: 277–297.
Arellano, M. & Bover. O. 1995. Another look at the instrumental variable estimation of error-components models.Journal of Econometrics 68: 29–51.
Balasubramanya, S. & Stifel, D. 2020. Water, agriculture & poverty in an era of climate change: Why do we know so little? Food Policy, 93, 101905.‏
Baltagi, B. H. 2008. Econometric Analysis of panel data, Chichester: John Wiely& Sons Ltd.
Blundell, R. & Bond. S. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87: 115–143.
Boelens, R., Dourojeanni, A., & Hoogendam, P. 2005. Improving water allocation for user communities and platforms in the Andes. Lessons for Institutional Design, 183.
Carter, M.R., Little, P.D., Mogues, T. & Negatu, W. 2007. Poverty traps and natural disasters in Ethiopia and Honduras. Food Policy 35, 835–856.
Cohen, A. & Sullivan, C. A. 2010. Water and poverty in rural China: developing an instrument to assess the multiple dimensions of water and poverty. Ecological Economics, 695, 999-1009.‏
Cremers, L., Ooijevaar, M., & Boelens, R. 2005. Institutional reform in the Andean irrigation sector: Enabling policies for strengthening local rights and water management. In Natural Resources Forum, 29(1): 37-50.
Greene, W. H. 2012, Econometric Analysis, 7th ed, New Jersey, Upper Saddle River: Pearson International.
Hanjra, M.A., Ferede, T. & Gutta, D.G. 2009. Reducing poverty in sub-Saharan Africa through investments in water and other priorities. Agricultural Water Management 96, 1062–1070.
Iran Water Resources Development Company, report on the latest status of underground water resources, 2017-2018. (in Persian)
Iran Water Resources Management Company. 2017. Report on underground water resources and water balance of Iranian provinces. (in Persian)
Jalili Kamjo, S. P. & Nademi, Y. 2018. Evaluation of the relationship between underground water resource extraction and rural poverty. Economic Research, 54(3), 525-550. (in Persian)
Kakwani, M. 1993. Performance in living standards: an international comparison. Journal of Development Economics, 41, 307-336.
Kashi, F. K. & Tash, M. N. S. 2014. Effects of macroeconomic variables on poverty in Iran Application of bootstrap technique. Theoretical and Applied Economics, 215, 594.
Khodadadkashi, F. & Shahiki Tash, M. 2011. The impact of macro variables on poverty in Iran, the bootstrap approach in the analysis of statistical inference, Economic Studies and Policies, 6 (2): 69-94. (in Persian)
Khorani, A; & Khajeh, 2013. Simultaneous study of drought and groundwater level drop (case study: Darab Plain). Space planning and preparation (Humanities teacher), 18(2), 57-79. (in Persian)
Lesage, J. 1999. Spatial Econometrics. Department of Economics University of Toledo.
Lewis, K. & Yacob, L. 2004. Water governance for poverty reduction: key issues and the UNDP response to Millenium Development Goals. In Water governance for poverty reduction: key issues and the UNDP response to Millenium Development Goals. PNUD.
Li, J., Liu, Z., He, C., Tu, W. & Sun, Z. 2016. Are the drylands in northern China sustainable? A perspective from ecological footprint dynamics from 1990 to 2010. Sci. Total Environ.553, 223–231.
Lipton, M., Litchfield, J. & Faurès, J. M. 2003. The effects of irrigation on poverty: a framework for analysis. Water Policy, 5(5-6): 413-427.‏
Meshki, M. 2018. Determining the effective factors on the performance of listed companies using the method of generalized moments and estimated generalized least squares, Journal of Accounting Advances of Shiraz University, 3(1): 91-119. (in Persian)
Mokhtari Hashi, H. 2012. Hydropolitics of Iran; The geography of the water crisis in the horizon of 1404, International Quarterly of Geopolitics, 9 (31: 8-49). (in Persian)
Molden, D., Frenken, K., Barker, R., De Fraiture, C., Mati, B., Svendsen, M., Sadoff, C. & Finlayson, C.M. 2007. Trends in water and agricultural development. In: Molden, D. Ed., Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture. Earthscan/International Water Management Institute, London/Colombo.
Mu, L., Liu, Y., & Chen, S. 2022. Alleviating water scarcity and poverty through water rights trading pilot policy: A quasi-natural experiment based approach. Science of The Total Environment, 153318.‏
Nadami, Y. & Jalili Kamjo, S. P. 2019. Water crisis and inter-provincial migration in Iran: application of spatial stochastic dynamic panel model of Dorbin generalized moments. Majlis and Strategy, (101) 27, 5-32. (in Persian)
Nadifar, M. & Poursafoui, S.M. 2016. The effect of reducing the level of underground water on rural migrations (case example: Qazvin city), 4th National Conference on Architecture and Urban Planning "Sustainability and Resilience from Ideal to Reality", Qazvin. (in Persian)
Nadiri, M. & Mohammadi, T. 2018. Investigating the effect of institutional structures on economic growth with dynamic panel data GMM method, Economic Modeling Quarterly, 5th year, (3): 1-24. (in Persian)
Oki, T. & Kanae, S. 2006. Global hydrological cycles and world water resources. Science 313, 1068–1072.
Panahi, F. & Malek Mohammadi, A. 2016. The effects of agricultural water resource management on livelihood poverty alleviation in rural areas, Village and Development, 16(4):1-17.
Parvin, S., Banoui, A. & Abbasian Nigjeh, S. 2013. Identifying the growth of economic sectors in reducing poverty using the constant price increasing coefficients approach, economic growth and development researches, 3 (10): 27-40. (in Persian)
Pedroni, P. 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61: 653-670.
Rezaei, A. & Chabkero, G. 2018. Drought and management of agricultural water resources, National Water Crisis Management Conference, Iran Water Resources Development Company course, report on the latest status of underground water resources, 2018-2019. (in Persian)
Sekhri, S. 2014. Wells, water, and welfare: the impact of access to groundwater on rural poverty and conflict. American Economic Journal: Applied Economics, 63, 76-102.
Upadhyay, B., Samad, M. & Giordano, M. 2005. Livelihoods and gender roles in drip-irrigation technology: A case of Nepal (87): IWMI.‏
WescoatJr, J. L., Headington, L. & Theobald, R. 2007 .Water and poverty in the United States.Geoforum, 385, 801-814.‏
Westmore, B. 2018. Do government transfers reduce poverty in China? Micro evidence from five regions. China Economic Review, 51, 59-69.‏
World Bank Institute (2005). Poverty Manual.
Xu, Z., Chau, S.N., Chen, X., Zhang, J., Li, Y., Dietz, T. & et al., 2020. Assessing progress towards sustainable development over space.
Yao, Y., Sun, J., Tian, Y., Zheng, C. & Liu, J. 2020. Alleviating water scarcity and poverty in drylands through telecouplings: Vegetable trade and tourism in northwest China. Science of the Total Environment, 741, 140387.‏
Yu, J., Jong, R. & Fei, L. L. 2008. Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large, Journal of Econometrics, vol. 146, issue 1, 118-134.
Yavari, K. & Ashraf-zadeh, H. 2014. Economic integration of developing countries; Application of gravity model with combined data using GMM and convergence method, Bazargani Research Quarterly, (36): 1-28. (in Persian)