اقتصاد کشاورزی و توسعه

اقتصاد کشاورزی و توسعه

بررسی تأثیر مؤلفه­‌های اقتصاد دانش‌‏بنیان بر عملکرد محیط زیست: مقایسه ایران با کشورهای همسایه

نوع مقاله : مقاله پژوهشی

نویسنده
استادیار اقتصاد کشاورزی، مؤسسه پژوهش‌های برنامه‌ریزی، اقتصاد کشاورزی و توسعه روستایی، تهران، ایران
چکیده
اقتصاد دانش‌‏بنیان (KBA) به‏ طور معمول، به ‏عنوان یک راهکار بالقوه برای تحقق رشد اقتصادی پایدار در جامعه مطرح می­‌شود و می‌­تواند در رفع چالش­‌های مختلف زیست‌‏محیطی ناشی از کمبود روزافزون منابع، مؤثر باشد. از این‏رو، هدف اصلی پژوهش حاضر بررسی تأثیر مؤلفه‌­های اقتصاد دانش‌­بنیان بر شاخص عملکرد زیست‏‌محیطی (EPI) در ایران و کشورهای همسایه شامل ترکیه، عراق، پاکستان، جمهوری آذربایجان و ارمنستان بود. بدین منظور، شاخص ترکیبی اقتصاد دانش‌­بنیان برای کشورهای مورد مطالعه با استفاده از روش ارزیابی دانش بانک جهانی طی سال­‌های 2022-2006 محاسبه و سپس، تأثیر مؤلفه‌­های اقتصاد دانش‌‏بنیان بر عملکرد محیط زیست در چارچوب داده­‌های پانل و با به‌‏کارگیری روش حداقل مربعات تعمیم‌‏یافته (GLS) بررسی شد. نتایج پژوهش نشان داد که در شاخص ترکیبی اقتصاد دانش‌بنیان، ترکیه با میانگین 7/27 بیشترین امتیاز را کسب کرده است؛ پس از آن، ارمنستان و ایران، به‏ ترتیب، با میانگین‌های 5/85 و 5/57 در رتبه‌های بعدی قرار دارند. نتایج برآورد مدل نهایی داده‌­های پانل نیز حاکی از آن بود که سهم مخارج آموزش دولتی از تولید ناخالص داخلی (مربوط به رکن آموزش و توسعه منابع انسانی) تنها متغیر دارای اثر مثبت بر شاخص عملکرد زیست‏‌محیطی است، به ‏گونه‌‏ای که با افزایش یک درصدی این سهم، EPI به میزان 0/336 درصد افزایش می‌­یابد؛ افزون بر این، متغیر حجم تجارت بین‌­الملل (از شاخص‌­های رکن نظام انگیزشی و نهادی اقتصادی) و نیز متغیر سهم مخارج تحقیق و توسعه از تولید ناخالص داخلی (متعلق به رکن نوآوری و پذیرش ابداعات) اثر منفی و معنی‌­دار بر عملکرد زیست‌‏محیطی کشورها دارند؛ به ‏طور مشابه، شاخص ترکیبی اقتصاد دانش‌‏بنیان نیز رابطه معکوس با شاخص EPI دارد. با توجه به یافته­‌های پژوهش حاضر، پیشنهاد می‌­شود که در راستای بهبود عملکرد زیست‌محیطی، برای فعالیت‌های واحدهای اقتصادی به‌‏ویژه شرکت‌های خارجی، قوانین زیست‌محیطی دقیق اعمال شود؛ همچنین، توسعه صادرات خدمات فنی و مهندسی و نیز افزایش سهم مخارج آموزش دولتی از تولید ناخالص داخلی به ‏همراه توزیع عادلانه آن از دیگر پیشنهادهای پژوهش حاضر به ‏شمار می‌روند.
کلیدواژه‌ها

عنوان مقاله English

The Impact of Knowledge-Based Economy Components on Performance of Environment: A Comparative Study of Iran and Neighboring Countries

نویسنده English

Mina Salehnia
Assistant Professor, Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran, Iran
چکیده English

Introduction: Knowledge-Based Economy (KBE) is often explained as a possible way for society to achieve sustainable economic growth, and solve various environmental challenges caused by the increasing scarcity of resources. Today, in most countries, the process of sustainable development is well aligned with the development process of KBE. To achieve appropriate changes, these two processes need to be coordinated. Based on this, this study aimed mainly at focusing on the factors related to KBE, that improves the environmental situation, increases competitiveness, and achieves sustainability in Iran. Despite all the advantages of KBE and its role in the development of countries, according to the latest ranking of KBE in 2012 by the World Bank, Iran’s economy was not in a good place among other countries. Also, currently one of the most important issues at the global and national level in many countries of the world is environmental issues, and for this reason, several environmental indicators were proposed to monitor the processes of environmental destruction. One of the most important indicators, which is currently widely used as a benchmark for comparing countries and is published biannually regarding environmental protection, is the Environmental Performance Index (EPI). In this research, an attempt was made to investigate the effect of KBE components on Environmental Performance Index (EPI) of Iran and neighboring countries. 
Materials and Methods: The World Bank identified four pillars in the framework of KBE, including economic and institutional regime, education and skills, information and communication infrastructure, and innovation system. Each of these pillars consists of indicators. The Knowledge Economy Index (KEI), developed by the World Bank for the Knowledge Assessment Method (KAM), examines whether the environment is suitable for the effective use of knowledge for economic development. KEI is a comprehensive index equivalent to the average normalized scores of the performance of a country or region in each of the four pillars, indicating the overall progress of that country or region from the perspective of the knowledge economy. On the other hand, the environmental performance index is also a composite index that summarizes country-level data on 40 specific indicators. In this study, while knowing the components of the Environmental Performance Index (EPI) and the Knowledge-Based Economy (KBE), the performance of EPI was evaluated against each of the dimensions of the knowledge-based economy. For this purpose, the components of the KBE were included in the modeling once in the form of four pillars in a separate form, and again in the form of the KEI composite index. The studied countries included Iran, Türkiye, Iraq, Pakistan, Azerbaijan and Armenia.
Results and Discussion: According to the study results, the highest score obtained in the composite index of KBE belongs to Türkiye with a value of 7.27, and after that, Armenia and Iran are in almost the same rank with an average of 5.85 and 5.57, respectively. In terms of the constituent elements of the KBE, Armenia performed better than other countries in providing an economic and institutional regime, and the same was true for Türkiye in education and skills. Azerbaijan and Iran countries were leaders in the field of information and communication infrastructure, and innovation system, respectively. The estimation of the final model of the panel data with the Generalized Least Squares (GLS) method indicated that the share of government education expenditures in the GDP (related to the education and skills component) was the only variable that had a positive effect on the Environmental Performance Index (EPI). In such a way that with a one percent increase in the share of education expenses, EPI would increase by 0.336 percent. The variable of the volume of international trade (one of the indicators of the pillar of the economic and institutional regime) and the variable of the share of research and development expenses in GDP (belonging to the pillar of the innovation system) showed a significant negative effect on the environmental performance of countries. Similarly, the composite index of the KBE also had an inverse relationship with EPI. 
Conclusion and Suggestions: Based on the results, it is necessary to adopt strict environmental laws for the activities of relevant units, especially foreign companies, in order to prevent pollution and resource depletion. Avoiding the sale of raw materials and exporting products with higher value-added is recommended in this regard. In the case of Iran, with a high rank in the pillar of the innovation system, the development of the export of technical and engineering services can be considered. Keeping in mind the positive effect of investing in education and human resource development, it is recommended to pay attention to increasing the share of government education in the GDP. It should also be ensured that the resources needed to finance education are distributed fairly in educational levels, programs and regions to improve the enrollment rate and quality of education

کلیدواژه‌ها English

Environmental Performance Index (EPI)
Generalized Least Squares (GLS)
Knowledge-Based Economy (KBE)
Panel Data
1.      Afzal, M. N. I. (2014). Knowledge-Based Economy (KBE): an investigation of theorical frameworks and measurement techniques in the South East Asian Region. PhD Dissertation in University of Southern Queensland.
2.      Alaeddine, S. (2023). The relationship between knowledge economy and environmental pollution: evidence from Arab countries. BAU Journal - Creative Sustainable Development, 4(2-Article 9), 1-27. DOI: 10.54729/2789-8334.1109.
3.      Alghazali, T., Al-Sudani, A. Q. A. S., Alabass, H. S. H., Hawash, M. K., Talib, S. G., Ali, M. H., Mohammad, T. A., Yousif, Y. D., & Al-Muttar, M. Y. O. (2022). The impact of knowledge-based economy on the economic growth of middle eastern countries. Cuadernos de Economía, 45(127), 163-170.
4.      Al-Qahtani, A. S. S. A., & Shirazi, N. S. (2023). What are the binding constraints for a knowledge-based economy in Qatar? Sustainability, 15(5), 3871. DOI: 10.3390/su15053871.
5.      Al‐Roubaie, A. (2013). Building knowledge capacity for sustainable development in the Arab World. International Journal of Innovation and Knowledge Management in Middle East & North Africa, 2(1), 7-20.
6.      Amini Milani, M., & Jalili, N. (2015). The impacts of knowledge-based economy’ components on economic growth in Iran (1975-2012). Iranian Economic Development Analyses3(4), 73-116. DOI: 10.22051/edp.2017.13059.1065.
7.      APEC (2000). Towards knowledge-based economies in APEC. A Report of APEC Economic Committee. Asia-Pacific Economic Cooperation (APEC). Available at https://apec.org/docs/default-source/Publications/2000/11/Towards-KnowledgeBased-Economies-in-APEC-2000/00_ec_knowledgebased.pdf.
8.      Asadpourkordi, M., Amirnejad, H., & Eshghi, F. (2023). The role of a knowledge-based economy in maintaining and improving the quality of the environment. Agricultural Market and Economics1(1), 30-42. [In Persian]
9.      Ashrafzadeh, S. H. R., & Mehreghan, N. (2014). Panel data econometrics. University of Tehran, Cooperative Research Institute, Tehran. [In Persian]
10.   Azizi, F., & Moradi, F. (2018). Calculating the index and sub-indices of knowledge-based economy for Iran (1996-2014). Journal of Economic Research and Policies, 26(85), 243-270. [In Persian]
11.   Baltagi, B. (2008). Econometric analysis of panel data. John Wiley & Sons.
12.   Chen, D. H. C., & Dahlman, C. J. (2006). The knowledge economy, KAM methodology and World Bank operations. Paper No. 35867, World Bank, Washington, DC.
13.   Dincă, G., Bărbuță, M., Negri, C., Dincă, D., & Model (Săndulescu) L.-S. (2022). The impact of governance quality and educational level on environmental performance. Frontiers in Environmental Science. 10, 950683. DOI: 10.3389/fenvs.2022.950683.
14.   Dinda, S. (2004). Environmental Kuznets Curve hypothesis: a survey. Ecological Economics, 49(4), 431-455. 
15.   Eghbal-zadeh, H., Henkel, F., & Widmer, G. (2021). Learning to infer unseen contexts in causal contextual reinforcement learning. Self-Supervision for Reinforcement Learning. ICLR 2021 SSL-RL Workshop, pp. 1-11. Self-Supervised Learning within Reinforcement Learning  (SSL-RL), Causal Contextual Reinforcement Learning (ICLR). Available at https://openreview.net/pdf?id=gPZP5ha9LpX.
16.   Gojarati, D. (2013). Basics of econometrics 2. Translated By H. Abrishami. The 9th Edition, Tehran University Publishing Institute. [In Persian]
17.   Grossman, G.  M., & Krueger, A. B. (1991). Environmental impacts of a North American Free Trade Agreement (No. w3914). National Bureau of Economic Research.
18.   Jednak, S., & Kragulj, D. (2015). Achieving sustainable development and knowledge-based economy in Serbia. Management Journal of Sustainable Business and Management Solutions in Emerging Economies, 20(75), 01-12. DOI: 10.7595/management.fon.2015.0015.
19.   Kalim, R., Ul-Durar, S., Iqbal, M., Arshed, N., & Shahbaz, M. (2024). Role of knowledge economy in managing demand-based environmental Kuznets Curve. Geoscience Frontiers, 15(4), 101594. DOI: 10.1016/j.gsf.2023.101594.
20.   Keykhosravi, M., Dehyouri, S., & Mirdamadi, S. M. (2023). Modeling the environmental performance by focusing on environmental behavior [of] rural farmers. Environmental and Sustainability Indicators. 20, 100309. https://doi.org/10.1016/j.indic.2023.100309.
21.   Mehreghan, N., & Teimouri, Y. (2020). Panel data (cross-sectional-time series data). Encyclopedia of Economics, 3(1), 1-3.
22.   Mohamed, M. M. A., Liu, P., & Nie, G. (2022). Do knowledge economy indicators affect economic growth? Evidence from developing countries. Sustainability, 14(8), 4774. DOI: 10.3390/su14084774. 
23.   Naghavi, S. (2019). The role of knowledge-based economy in the agricultural growth of selected countries with an emphasis on Iran. Agricultural Economics13(2), 83-105. DOI: 10.22034/iaes.2019.105813.1686. [In Persian]
24.   Raei, S. S., & Dahmardeh, N. (2021). The impact of the knowledge-based economy on Iran non-oil export. Quarterly Journal of Quantitative Economics (JQE)18(2), 43-55. DOI: 10.22055/jqe.2020.26777.1922. [In Persian]
25.   Raihan, A., Voumik, L. C., Mohajan, B., Rahman, S., & Zaman, R. (2023). Economy-energy-environment nexus: the potential of agricultural value-added toward achieving China’s dream of carbon neutrality. Carbon Research. 2, Article No. 43. DOI: 10.1007/s44246-023-00077-x.
26.   Režný, L., Buchanan White, J., & Marešová, P. (2019). The knowledge economy: key to sustainable development? Structural Change and Economic Dynamics, Elsevier, 51, 291-300. DOI: 10.1016/j.strueco.2019.02.003.
27.   Sarkodie, S. A., & Strezov, V. (2018). Empirical study of the Environmental Kuznets Curve and environmental sustainability curve hypothesis for Australia, China, Ghana and USA. Journal of Cleaner Production, 201, 98‐110.
28.   Sarkodie, S. A., & Strezov, V. (2019a). A review on Environmental Kuznets Curve hypothesis using bibliometric and meta‐analysis. Science of the Total Environment, 649, 128‐145.
29.   Sarkodie, S. A., & Strezov, V. (2019b). Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Science of the Total Environment, 646, 862‐871.
30.   Shiri, H., & Okhrati, S. (2019). A study on the relationship between education and environmental performance (with an emphasis on Iran’s status). Environmental Researches9(18), 263-274. DOR: 20.1001.1.20089597.1397.9.18.22.1. [In Persian]
31.   Širá, E., Vavrek, R., Vozárová, I. K., & Kotulic, R. (2020). Knowledge economy indicators and their impact on the sustainable competitiveness of the EU countries. Sustainability, 12(10), 4172. DOI: 10.3390/su12104172.
32.   Soleimani, A., & Cheraghi, M. (2021). Comparative analysis of the structure of Environmental Performance Index (EPI) in 2020 with emphasis on Iran’s position. Islamic Council Research Center, Basic Studies, Environment Department, No 17622. [In Persian]
33.   Sorroche-del-Rey, Y., Piedra-Muñoz, L., & Galdeano-Gómez, E. (2023). Interrelationship between international trade and environmental performance: theoretical approaches and indicators for sustainable development. MPRA Paper 119918, University Library of Munich, Germany.
34.   Sukharev, O. (2021). Measuring the contribution of the knowledge economy to the economic growth rate: comparative analysis. Journal of the Knowledge Economy, 12, 1809-1829. DOI: 10.1007/s13132-020-00690-w.
35.   Torres-Reyna, O. (2007). Linear regression using Stata. Available at https://www.princeton.edu/~otorres/Regression101.pdf.
36.   Uralovich K. S., Toshmamatovich, T. U., Kubayevich, K. F., Sapaev, I. B., Saylaubaevna, S. S., Beknazarova, Z. F., & Khurramov, A. (2023). A primary factor in sustainable development and environmental sustainability is environmental education. Caspian Journal of Environmental Sciences, 21(4), 965-975.
37.   Vargas-Merino, J. A., Rios-Lama, C. A., & Panez-Bendezu, M. H. (2024). Critical implications of education for sustainable development in HEIs — A systematic review through the lens of the business science literature. The International Journal of Management Education, 22, 100904. DOI: 10.1016/j.ijme.2023.100904.
38.   Wolf, M. J., Emerson, J. W., Esty, D. C., de Sherbinin, A., & Wendling, Z. A. (2022). 2022 Environmental Performance Index. Yale Center for Environmental Law & Policy, New Haven, CT, epi.yale.edu.
39.   World Bank (2007). Building knowledge economies, advanced strategies for development. DOI: 10.1596/978-0-8213-6957-9.
40.   YCELP-CIESIN (2022). 2022 Environmental Performance Index (EPI)  (Version 2022.00) [Data set]. Yale Center for Environmental Law and Policy (YCELP), Yale University, and Center for International Earth Science Information Network (CIESIN), Columbia University. NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, New York. DOI: 10.7927/dwt2-9k25. 
41.   Yuan, F., Tang, K., & Shi, Q. (2021). Does Internet use reduce chemical fertilizer use? Evidence from rural households in China. Environmental Science and Pollution Research, 28, 6005-6017.