سنجش ردپای بوم شناختی به منظور پیش بینی کاربری زمین در رویکرد داده-ستانده پویا (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
پیشی گرفتن تقاضای انسان از ظرفیت زیستی زمین باعث تخریب روزافزون محیط زیست شده و همین امر ض رورت انج ام پژوهش دقیق درباره این تغییرات و پیش بینی آن را دوچندان می کند. ردپای بوم شناختی شاخص مناسبی برای پی گیری تقاضای انسان، ظرفیت احیای منابع و جذب زباله در محیط زیست است. مفهوم آن به دنبال ارائه معیاری زمین محور است که اثر مصرف گذشته را محاسبه کند تا تمام فشار بر محیط زیست را در ناحیه زمین لازم برای تأمین مصرف نشان دهد. در نسخه جدید روش های محاسبه این شاخص تلاش شده با استفاده از جدول داده- ستانده پویا، کاربری زمین با توجه به نرخ رشد اقتصادی بالقوه و نرخ رشد اقتصادی واقعی پیش بینی شود. به این منظور در این پژوهش برای نخستین بار ردپای بوم شناختی برای پیش بینی کاربردی زمین در رویکرد داده- ستانده پویا براساس داده های سال 1395، با استخراج جدول داده-ستانده پویا از جدول سال 1395 بانک مرکزی و با توجه به داده های موجودی سرمایه و اطلاعات زمین در سه بخش کشاورزی، صنعت و خدمات انجام گرفت. براساس نتایج، طبق نرخ رشد برنامه ریزی شده در برنامه ششم توسعه، سهم هر فرد ساکن ایران از زمین های داخلی بالغ بر 0/42هکتار است. اگر ردپای بوم شناختی با همین نرخ رشد کند تا 17/5 سال بعد، یعنی حدود سال 1412 با اتمام ظرفیت زیستی زمین مواجه می شود. این سال با در نظر گرفتن رشد اقتصادی بدون احتساب نفت 6/4 درصد در سال 1395 به سال 1417 می رسد.Ecological Footprint Calculation for Land-Use Prediction: A Dynamic Input–Output Approach
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.
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