Agriculture, natural resources and environment Economics
Simin Azizmohammadi; Fatemeh Bazzazan
Abstract
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 ...
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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. IntroductionLand 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 MethodsThe 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 DiscussionIn 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.ConclusionAccording 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.
Welfare, poverty and income distribution
Fatemeh Bazzazan
Abstract
Poverty is a global issue of high importance for both developing and developed countries. The first step in tackling poverty is to identify the impact of economic policies on poverty indicators. In this direction, the purpose of this study is to measure the effect of foreign tourism development on poverty ...
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Poverty is a global issue of high importance for both developing and developed countries. The first step in tackling poverty is to identify the impact of economic policies on poverty indicators. In this direction, the purpose of this study is to measure the effect of foreign tourism development on poverty reduction using SAM fixed price multiplier approach. For this purpose, 2011 SAM, 2018 foreign tourist receipts, and three poverty indicators: head count ratio, poverty gap, and (FGT) have been considered. The results indicate that the arrival of foreign tourists through the production growth channel reduces poverty in Iran and reducing poverty of rural households is greater than urban households. Results also show that the highest share in sectoral poverty reduction based on the three poverty indicators is related to the agricultural sector (based on the census poverty index), hotels and restaurants, and manufacturing, and transportation (based on the poverty gap index and the FGT indices). Whereas the least reduction in poverty occurs in the financial, insurance and education activities. Any policy making in the direction of tourism development is considered as a suitable socio-economic achievement.
Fatemeh Bazzazan; Parisa Mohammadi
Abstract
Iran is located on fault lines and due to abundant seismicity as well as being positioned on one of the world's seismic belts Lpa, it is very vulnerable to earthquakes. In addition, in spite of having one percent of the world's population, Iran suffers from more than six percent of all casualties from ...
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Iran is located on fault lines and due to abundant seismicity as well as being positioned on one of the world's seismic belts Lpa, it is very vulnerable to earthquakes. In addition, in spite of having one percent of the world's population, Iran suffers from more than six percent of all casualties from natural disasters in the world. Many major cities including Tehran are located on active faults. In the event of a potential earthquake Tehran's general urban weakness, high population density, and poor dispersion in neighborhoods can cause significant casualties and structural damage. Due to high level of trade with other provinces, the extent of damage would not be limited to Tehran and can cause considerable damage to the economy at a national scale. The main goal of this paper is to predict the regional economic loss from earthquake in Tehran and the rest of the country using a two-region input-output model. The main source of the data is a two-regional input-output table, which is constructed from 2011 national input-output table (Parliament Research Center) and regional accounts (Statistical Center of Iran), using FLQ non-survey based technique. The sectoral vulnerability under the five scenarios: none, little, moderate, major, and extensive are borrowed from Rahimi (2012). The results showed the economic impact of earthquake in Tehran on its GDP would be 81% in none, and up to 103% in the extensive scenario, while for rest of the country it would be 24% to 30% of national GDP.
Fatemeh Bazzazan; Ali Asghar Banooi; Mahdi Karami
Volume 13, Issue 39 , July 2009, , Pages 29-52
Abstract
The importance of spatial economy has been considered recently in Iran and has been investigated in the form of single regional input-output model in a series of articles which is a mutation in the regional studies in Iran. In these studies regional coefficients were calculated and tested statistically. ...
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The importance of spatial economy has been considered recently in Iran and has been investigated in the form of single regional input-output model in a series of articles which is a mutation in the regional studies in Iran. In these studies regional coefficients were calculated and tested statistically. As applications of single regional coefficients have some limitations, overcoming to limitations, inter-regional input-output model has been developed. The main aim of this paper is to introduce a non-survey technique to estimate interregional input-output coefficients for two regions: Tehran and the Rest of Iran which is the first experience in Iran. These coefficients will enable policy makers to capture feedback and spillover effects that are primarily attribute to inter-regional trading model. In this study, regional coefficients are estimated for 10 sectors in 2001. Results show 58% of Tehran import is from the rest of Economy whereas 41% of the Rest of Economy is from Tehran. Other results also show spillover effects in Tehran province are greater than the Rest of Economy. While, small feedback effects are observed in both regions. Moreover, we found the errors of using single input-output coefficients and neglecting the spillover and feedback effects are 20% and 12% for the Rest of Economy and Tehran province respectively, both are considerable and reveals the importance of using two-region input-output model.
Fatemeh Bazzazan; Ali Asghar Banouei; Mehdi Karami
Volume 9, Issue 31 , July 2007, , Pages 27-53
Abstract
Modern location quotient method (MLQM) is used to generate regional input-output coefficients (RIOCs). It has a number of functions which one should take into account in generating RIOCs and for statistical testing. The prerequisite for such statistical testing is the availability of survey- based RIOCs. ...
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Modern location quotient method (MLQM) is used to generate regional input-output coefficients (RIOCs). It has a number of functions which one should take into account in generating RIOCs and for statistical testing. The prerequisite for such statistical testing is the availability of survey- based RIOCs. This then raises the following important question: In the absence of survey based RIOCs, is it possible to apply MLQM for generating RIOCs for country like Iran? If yes, how to test the derived coefficients statistically? To answer that, we propose a new method for statistical testing through minimizing total errors using Ghosh supply side model.
