نوع مقاله : مقاله پژوهشی - کاربردی

نویسندگان

1 گروه اقتصاد محیط زیست، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران

2 گروه مهندسی انرژی و اقتصاد، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران.

3 گروه اقتصاد، دانشکده مدیریت و اقتصاد، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران.

4 گروه اقتصاد کشاورزی، دانشکده علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران.

چکیده

هدف: هدف از این مطالعه ایجاد یک شاخص ترکیبی برای کیفیت محیط­زیست می‌­باشد. از آنجایی که نتایج مطالعات تجربی مربوط به بررسی روابط بین متغیرهای اقتصادی و محیط­‌زیستی به واسطه در نظر گرفتن شاخص‌­های محیط­‌زیستی مختلف، همسو با یکدیگر نمی­‌باشند؛ بکارگیری یک شاخص ترکیبی که در بردارنده تمامی ابعاد آلودگی محیط­زیست باشد، امری ضروری به نظر می‌­رسد.
روش‌شناسی پژوهش: در این تحقیق با استفاده از 6 شاخص محیط‌­زیستی مربوط به دو گروه از کشورهای منتخب OPEC و OECD طی دوره زمانی 2010 تا 2019 و با به‌کارگیری سه روش تحلیل مؤلفه­ اصلی، تحلیل مؤلفه اصلی کرنل و تحلیل مؤلفه اصلی مبتنی بر منطق فازی، ایجاد یک شاخص ترکیبی برای کیفیت محیط‌زیست مورد بررسی قرار گرفت.
یافته‌ها: نتایج نشان داد که روش FRPCA نسبت به سایر روش­‌ها به دلیل داشتن کمترین معیارهای خطا، از کارایی و توانایی بیشتری در وزن­دهی شاخص­‌های محیط زیستی برخوردار است. لذا با استفاده از این روش شاخص ترکیبی محاسبه گردید. در پایان، با استفاده از این شاخص ترکیبی روند رشد اقتصادی و کیفیت محیط‌زیست به‌صورت نموداری موردبررسی و تحلیل قرار گرفت. نتایج نشان داد که همگام با روند صعودی رشد اقتصادی در کشورهای منتخب OPEC، کیفیت محیط‌زیست از یک روند نزولی و در کشورهای منتخب OECD از یک روند صعودی تبعیت می­کند.
اصالت/ارزش افزوده علمی: همچنین بر اساس نتایج به‌دست‌آمده، پیشنهادات سیاستی و همچنین دیدگاه‌­ها و پیشنهادات جدیدی برای مطالعات آینده ارائه شده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

New Insight into Development of Environmental - Economic Model Based on a Composite Environmental Quality Index: An Application of Principal Components Analysis

نویسندگان [English]

  • Hossein Ali Fakher 1
  • Mostafa Panahi 2
  • Karim Emami 3
  • Kambiz Peykarjou 3
  • Seyed Yaghoub Zeraatkish 4

1 Department of Environmental Economics, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Energy Engineering and Economics, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Economics, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Agriculture Economics, Faculty of Agricultural Sciences and Food Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده [English]

Purpose: This paper aimed to create a composite index for environmental quality. Since the results of empirical studies on economic - environmental variables nexus, considering different environmental indicators, are not consistent with each other; it seems necessary to use a comprehensive index that includes all dimensions of environmental pollution.
Methodology: Using 6 environmental indicators related to two groups of selected OPEC and OECD countries for the period 2010 to 2019 and using three methods including principal component analysis, kernel-based principal component analysis, fuzzy robust principal component analysis, the creation of a composite environmental quality index is examined.
Findings: The results showed that FRPCA method has more efficiency and ability in weighting environmental indicators than other methods due to having the lowest error criteria. Therefore, using this method, the composite index was calculated. Moreover, the results showed that along with the upward trend of economic growth, the quality of the environment follows a downward trend in OPEC countries and an upward trend in OECD countries.
Originality/Value: Based on the results, policy recommendations as well as new perspectives and suggestions for future studies are presented.

کلیدواژه‌ها [English]

  • Environmental quality indicators
  • Principal component analysis
  • Kernel-based principal component analysis
  • Fuzzy robust principal component analysis
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