Document Type : Research Paper


1 Ph.D. Student, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran

2 Professor, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran


Human demand for natural resources is surpassing the Earth’s biocapacity and regenerative capacity, leading to environmental degradation. Accurate research is essential to investigate and predict these changes more precisely. The ecological footprint serves as a suitable index for tracking human demand, resource recovery capacity, and waste absorption in the environment. The concept aims to offer a land-based measure that estimates the impact of consumption on the environment in the land area required to fulfill consumption. The dynamic input–output approach represents a novel method for measuring the ecological footprint, predicting land use based on economic growth rates. Pioneering the dynamic ecological footprint calculation using real-world data, the current study calculated Iran’s ecological footprint by relying on 1395/2016 input-output tables from the Central Bank in three sectors: agriculture, industry, and services. The per-capita ecological footprint for Iran was determined to be 0.42 hectares with an 8% planned economic growth rate. If the ecological footprint continues to grow at the same rate, it is estimated that Iran’s land biocapacity will be depleted by the year 1412/2033. Considering a growth rate of 6.4% (excluding oil) in the year 1395/2016, this scenario is anticipated to occur by the year 1417/2038.


Land use has undergone significant changes due to urbanization and the expansion of economic activities, surpassing the Earth’s capacity for regeneration and absorption and resulting in environmental degradation. Exacerbated by population growth, the issue has caused more serious concerns among policymakers and researchers regarding the future of the environment. It is thus necessary to measure human demand and regenerative capacity of natural resources. In this respect, the ecological footprint is considered a useful measure, defined as an environmental index that quantifies natural resource consumption based on land use, and reflects the impact of human demand on nature. The comparison between human consumption and biocapacity aids in assessing the level of sustainability. Existing literature refers to two methods of ecological footprint calculation. Employing a macro perspective, the first method relies on the evident consumption of resources (land or water) involved in producing domestic goods and services—including imported goods but excluding exported goods. Many scholars have used the input–output model to calculate the ecological footprint for resource management at the sectoral level. The versatility of the model has led to its widespread application in recent years, as it can adapt to variations in monetary and physical units at the same time. It proves particularly useful in analyzing a wider range of environmental issues, such as life cycle assessment and ecological footprint calculation. While the ecological footprint is a vital tool for studying sustainable development, its traditional version primarily focuses on static calculations derived from past footprints. Some critics contend that ecological footprint analysis lacks a dynamic approach to the future, but offers more of a snapshot in time. Dobos and Tóth-Bozó (2023) employed a dynamic input–output model to develop a method for ecological footprint calculation. Within this dynamic model, the ecological footprint becomes predictable through the utilization of the capital coefficient matrix (investment matrix) in conjunction with the direct input coefficient matrix. The present study pioneered the dynamic ecological footprint calculation by utilizing real-world data and the dynamic input–output table of the year 1395/2016.

Materials and Methods

The study employed a dynamic input–output model that maintains equilibrium between supply and demand over specific time periods. Investment was taken into account through capital-output coefficients within an intra-sectoral capital coefficient matrix which shows capital exchanges between demand sectors and capital suppliers, proving valuable in predicting crucial economic variables and growth patterns. It also serves as an efficient tool for economic planning. The model proposed by Dobos and Tóth-Bozó (2023) is a function of vectors representing final consumption, exports, and imports of final goods. They had actually used the dynamic model developed by Leontief (1970) to calculate land demand for each period of national production. The present study showed how the index changes by taking into account the investment flow and the equilibrium path of consumption and production growth. The total ecological footprint is predicted in relation to the potential economic growth rate; Iran’s Sixth Five-Year Economic, Cultural and Social Development Plan (1396–1400); and the growth rate excluding oil in 1395/2016. To accomplish this, three sectors (agriculture, industry, and services) were formed within a closed dynamic input–output model, referred to as forward-looking. The data was gathered from the 1395/2016 input–output table from the Central Bank database, capital stock, inventory data (agriculture and industry) from the Statistical Center of Iran. The lands were studied in three sectors: agriculture, industry, and services.

Results and Discussion

In the dynamic input-output model, the potential growth rate is determined by the maximum eigenvalue of the matrix composed of the direct input coefficient matrix and the capital coefficient matrix. The potential growth rate was found to be 41%. Moreover, the planned growth rate of 8% in Iran’s Sixth Five-Year Economic, Cultural and Social Development Plan (1396–1400) was also considered. According the Statistical Center of Iran, the gross domestic product experienced an overall growth of 11.1% in 1395/2016. Excluding oil, this growth rate stands at 6.4%. The per-capita Iranian ecological footprint was measured at 0.42 hectares with an 8% planned economic growth rate. If the ecological footprint continues to grow at the same rate, it is estimated that Iran’s land biocapacity will be depleted by the year 1412/2033. Considering a growth rate of 6.4% (excluding oil) in the year 1395/2016, this scenario is anticipated to occur by the year 1417/2038.


According to the research results, changes in the growth rate alter the time horizon for land use. The growth rate is influenced by various factors. Consequently, advocating for short-term planning becomes crucial to either manage its effects in the long run or mitigate its adverse consequences—in case of its deviation from sustainable development goals. This model does not incorporate assumptions about technological progress in the economy. Future research could enhance the economic model by integrating technological progress, allowing for the evolution of model matrices over time. In the contemporary economy, Research and Development (R&D) plays a vital role in developing new technologies to promote environmental preservation. Furthermore, providing ample data can enable the creation of inverse Leontief matrices with larger dimensions, facilitating more practical outcomes, such as dynamic analysis of land-use changes within specific timeframes. The current research exclusively sought to introduce the index alongside its predictability. However, the absence of sufficient data might have resulted in estimates based on unrealistic data, impacting the accuracy and validity of the results. Nonetheless, these findings can aid in large-scale policymaking.


Main Subjects

Akbari, N., & Amini, M. (2023). Estimating the investment required to achieve the goals of the sixth economic development program based on the national dynamic input-output table. Journal of Applied Economics Studies in Iran, 12(47): 9-36.
 https://doi:10.22084/aes.2023.27447.3558 [In Persian]
Andayesh, Y., Sadeghi, S. K., Karimi Takanlou, Z., Motafakker Azad, M. A., & Asgharpour, H. (2016). Measuring the carbon footprint of urban and rural household deciles in Iran: the social accounting matrix (sam) approach. Iranian Journal of Economic Research, 21(68): 163-206. https://doi:  10.22054/ijer.2016.7500 [In Persian]
Baabou, W., Grunewald, N., Ouellet-Plamondon, C., Gressot, M., & Galli, A. (2017). The ecological footprint of mediterranean cities: awareness creation and policy implications. Environmental Science & Policy, 69, 94–104.  https://doi: 10.1016/j.envsci.2016.12.013
Banouei, A. A. (2012). Evaluation of the different treatments and methods of separating imports with emphasis on 1381 iot of Iran. The Journal of Economic Policy. 4(8): 31-74.
https://doi: 20.1001.1.26453967.1391. [In Persian]
Banouei, A.A., Momeni, F. & Azizmohammadi, S. (2013). Measurement of land ecological footprint in different sectors Iranian economy: using input-output approach. Journal of Iranian Economic Development Analyses, 1(1), 35-66. https://doi: 10.22051/EDP.2014.59 [In Persian]
Bazzazan, F. (2002). A dynamic input-output price model with application to Iran. PhD thesis, University of Liverpool.
https://doi: 10.17638/03176295
Bazzazan F. (2011). Input-output analysis: static, dynamic extended quantity and price models (with application to Iran). LAP LAMBERT Academic Publishing. 
Bazzazan, F., Banouei, A. A., & Karami, M. (2007). The modern location quotient function, spatial dimension, and regional input-output coefficients: the case of Tehran province. Iranian Journal of Economic Research, 9(31). 27-53. Available online at:
Bicknell, K., Ball, R., Cullen, R., & Bigsby, H. (1998). New methodology for the ecological footprint with an application to the Newzealand economy. Ecological Economics, 27, 149-160.
https://doi: 10.1016/S0921-8009(97)00136-5
Begum, R. A., Pereira, J. J., Jaafar, A. H., & Al-Amin, A. Q. (2009). An impirical assessment of ecological footprint calculations for Malaysia. Resources, Conservation & Recycling, 53(10), 582–587. https://doi: 10.1016/j.resconrec.2009.04.009
Borucke, M., Moore, D. Cranston, G., Gracey. K., Iha, K. Larson, J., Lazarus, E., Morales, J., Wackernagel, M., & Galli, A. (2013). Accounting for demand & supply of the biosphere's regenerative capacity: the national footprint accounts’ underlying methodology & framework. Ecological Indicators, 24, 518–533. https://doi: 10.1016/j.ecolind.2012.08.005
Dobos, I. & Floriska, A. (2005). A dynamic Leontief model with non-renewable resources. Economic Systems Research, 17, 317-326. https://doi: 10.1080/09535310500221856
Dobos, I., & Floriska, A. (2007). The resource conservation effect of recycling in a dynamic Leontief model. International Journal of Production Economics, 108(1–2), 334–340.  https://doi:10.1016/j.ijpe.2006.12.038
Dobos, I. & Tóth-Bozó, B. (2023). Ecological footprint calculation as a land demand: based on the dynamic Leontief model. Periodica Polytechnica Social & Management Sciences, published online. https://doi: 10.3311/PPso.21257.
Ferng, J. (2001). Using composition of land multiplier to estimate ecological footprints associated with production activity. Ecological Economics, 37, 159-172. https://doi:10.1016/S0921-8009(00)00292-5
Guo, Z., Gao, Z., & Zhang, W. (2023). Accounting & decomposition of energy footprint: evidence from 28 sectors in China. Sustainability, 15, 13148.  https://doi: 10.3390/su151713148
Jin, W., Xu, L. & Yang, Z. (2009). Modeling a policy making framework for urban sustainability: incorporating system dynamics into the ecological footprint. Ecological Economics, 68, 2938-2949.  https://doi: 10.1016/j.ecolecon.2009.06.010
Kakaie, J., Faridzad, A., Momeni, F., & banouei, A. A. (2019). Measuring ecological footprint of fossil fuels in economic sectors of Iran: an input-output approach. Economics Research, 19(73), 147-174. https://doi: 10.22054/joer.2019.10766 [In Persian]
Kratena, K. & Wiedmann, T. (2008). A monetary measure for ecological footprints of domestic final demand: the UK example. Presented at International Input-Output Meeting on Managing the Environment, Seville, Spain, July 9–11.
Lenzen, M., Wiedmann, T., Foran, B., Dey, C., Widmer-Copper, A., Williams, M., & Ohlemüller, R. (2007). Forecasting the ecological footprint of nations: a blueprint for a dynamic approach. ISA Research Report 07-01. University of Sydney Centre for Integrated Sustainability Analysis, Stockholm Environment Institute and University of York.
Leontief, W. (1970). Environmental repercussions and the economic structure: an input-output approach. The Review of Economics and Statistics, 52(3), 262–271. https://doi: 10.2307/1926294
Li, Y., Zhan, J., Zhang, F., Zhang, M., & Chen, D. (2017). The study on ecological sustainable development in Chengdu. Physics and Chemistry of the Earth, Parts A/B/C. 101, 112-120.
https://doi: 10.1016/j.pce.2017.03.002
Miller, R., & Blair. P. (2022). Mixed & dynamic models (chapter 14) from book: input-output analysis foundations & extensions. Publisher: Cambridge University Press. 678–721.
Moffatt, I., Wiedmann, T., & Barrett, J. (2005). The impact of Scotland’s economy on the environment: a note on input-output and ecological footprint analysis. Quarterly Economic Commentary, 30(3), 37–44.   
Monfreda, C., Wackernagel, M., & Deumling, D. (2004). Establishing national natural capital accounts based on detailed ecological footprint and biological capacity assessments. Land Use Policy, 21, 231-246. https://doi: 10.1016/j.landusepol.2003.10.009
Najafi, B., Khodadad kasha, F., Souri, A., & Mousavi, Y. (2022). Identification of water footprint in Iran's foreign trade with the approach of the input-output table-2016. Quarterly Journal of New Economic & Trade, 17(1), 169-194. [In Persian]
Nouri, F. (2012). Evaluation of partial investment in the third economic, political and socio-cultural development plan. Master's thesis. Alzahra University Faculty of Social and Economic Sciences. [In Persian]
Okuyama, Y. (2017). Dynamic Input-Output Analysis in handbook of input–output analysis (chapter 13) edited by Thijs Ten Raa. Publisher: Edward Elgar. 464–484. https://doi: 10.4337/9781783476329.00019
Pei, J., Oosterhaven, J., & Dietzenbacher, E. (2012). How much do exports contribute to China's income growth?. Economic Systems Research, 24(3), 275-297. https://doi: 10.1080/09535314.2012.660746
Rees, W. E. (2023). Ecological Footprint, Concept of. Chapter in ‘Encyclopedia of biodiversity’ (2nd Ed) book edited by Samuel M. Scheiner. Published by Academic press, San Diego.   
Ruben, G. B., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis & projection of land-use/land-cover dynamics through scenario-based simulations using the CA-markov model: a case study in guanting reservoir basin, China. Sustainability, 12(9), 3747.
Steengea, A. E., & Reyes, R. C. (2020). Return of the capital coefficients matrix. Economic Systems Research, 32(4), 439-450.
Takayama, A. (1985), Mathematical economics, 2nd edition, New York: Cambridge University press.
Ten Raa, T. (2017). Handbook of input–output analysis II. Edward Elgar Publishing. ISBN: 9781783476312
Tsuchiya, K., Iha, K., Murthy, A., Lin, D., Altiok, S., Rupprecht, C., & McGreevy, S. R. (2021). Decentralization & local food: Japan's regional ecological footprints indicate localized sustainability strategies. Journal of Cleaner Production, 292, 126043.
Van Den Bergh, J., & Grazi, F. (2015). Reply to the first systematic response by the global footprint network to criticism: a real debate finally? Ecological Indicators, 58, 458-463.
Wackernagel, M., & Rees, W. )1997(. Perceptual and structural barriers to investing in natural capital: economics from an ecological footprint perspective. Ecological economics, 20(1), 3–24.
https://doi:10.1016/S0921-8009 (96)00077-8 
Wackernagel, M., Kitzes, J., Moran, D., Goldfinger, S., & Thomas, M. (2006). The ecological footprint of cities & regions: comparing resource availability with resource demand.  Environment & Urbanization, 18(1), 103–112.
Wang, S., Xu, L., Yang, F., & Wang, H. (2014). Assessment of water ecological carrying capacity under the two policies in tieling city on the basis of the integrated system dynamics model. The Science of the total environment, 472, 1070-1081. DOI: 10.1016/j.scitotenv.2013.11.115  
Wei, J., Zeng, W., & Wu, B. (2013). Dynamic analysis of the virtual ecological footprint for sustainable development of the Boao special planning area. Sustainability Science, 8, 595–605.