Evaluation of Environmental and Waste Management Performances of EU Countries: A Co-Application of Data Envelopement Analysis and Artificial Neural Networks

Nazlı Seyhan

Makale detayları

Evaluation of Environmental and Waste Management Performances of EU Countries: A Co-Application of Data Envelopement Analysis and Artificial Neural Networks

Yazarlar:

Nazlı Seyhan

Yayıncı:

Gumushane University

Dergi:

Gümüşhane Üniversitesi Sosyal Bilimler Dergisi Vol.14 Issue 1, pp.343-355

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001283725

pISSN:

1309-7423

Anahtar kelimeler:

Özet:

 With the development of industry, the increase in human needs has led to an increase in production and consumption. The inadequacy of waste recycling in the production-consumption-waste cycle chain brings environmental, climatic, economic and many problems together. Within the framework of the 2015 Circular Economy Action Plan, the European Union has taken many decisions, including waste management. Improvement of recycling capacity and waste management for EU member and candidate countries is at the top of the targeted strategies. In this study, after the 2015 Circular Economy Action Plan, the waste management performances of EU countries for the years 2017, 2018 and 2019 were examined with the Artificial Neural Networks method based on Data Envelopment Analysis DEA. DEA input variables; municipal waste generation per capita, import of waste for recycling, national expenditures for environmental protection, real GDP per capita, human development index, plastic packaging waste production per capita, output variable; taken as the recycling rate of municipal waste. Efficiency scores obtained with DEA were used as output variables in artificial neural networks, and predictions were made for the efficiency scores of countries in 2019 with artificial neural networks feed-forward networks. In the findings, it has been concluded that Slovenia, Latvia, Germany, Ireland, Luxembourg and Belgium are the countries that are active in all years and are frequently included in the reference clusters of other countries. It has been observed that the 2019 prediction values obtained from the data set trained with Feed Forward Artificial Neural Networks are very close to the real efficiency values.

 With the development of industry, the increase in human needs has led to an increase in production and consumption. The inadequacy of waste recycling in the production-consumption-waste cycle chain brings environmental, climatic, economic and many problems together. Within the framework of the 2015 Circular Economy Action Plan, the European Union has taken many decisions, including waste management. Improvement of recycling capacity and waste management for EU member and candidate countries is at the top of the targeted strategies. In this study, after the 2015 Circular Economy Action Plan, the waste management performances of EU countries for the years 2017, 2018 and 2019 were examined with the Artificial Neural Networks method based on Data Envelopment Analysis DEA. DEA input variables; municipal waste generation per capita, import of waste for recycling, national expenditures for environmental protection, real GDP per capita, human development index, plastic packaging waste production per capita, output variable; taken as the recycling rate of municipal waste. Efficiency scores obtained with DEA were used as output variables in artificial neural networks, and predictions were made for the efficiency scores of countries in 2019 with artificial neural networks feed-forward networks. In the findings, it has been concluded that Slovenia, Latvia, Germany, Ireland, Luxembourg and Belgium are the countries that are active in all years and are frequently included in the reference clusters of other countries. It has been observed that the 2019 prediction values obtained from the data set trained with Feed Forward Artificial Neural Networks are very close to the real efficiency values.

 With the development of industry, the increase in human needs has led to an increase in production and consumption. The inadequacy of waste recycling in the production-consumption-waste cycle chain brings environmental, climatic, economic and many problems together. Within the framework of the 2015 Circular Economy Action Plan, the European Union has taken many decisions, including waste management. Improvement of recycling capacity and waste management for EU member and candidate countries is at the top of the targeted strategies. In this study, after the 2015 Circular Economy Action Plan, the waste management performances of EU countries for the years 2017, 2018 and 2019 were examined with the Artificial Neural Networks method based on Data Envelopment Analysis DEA. DEA input variables; municipal waste generation per capita, import of waste for recycling, national expenditures for environmental protection, real GDP per capita, human development index, plastic packaging waste production per capita, output variable; taken as the recycling rate of municipal waste. Efficiency scores obtained with DEA were used as output variables in artificial neural networks, and predictions were made for the efficiency scores of countries in 2019 with artificial neural networks feed-forward networks. In the findings, it has been concluded that Slovenia, Latvia, Germany, Ireland, Luxembourg and Belgium are the countries that are active in all years and are frequently included in the reference clusters of other countries. It has been observed that the 2019 prediction values obtained from the data set trained with Feed Forward Artificial Neural Networks are very close to the real efficiency values.

 Sanayinin gelişmesiyle insan ihtiyaçlarındaki artış üretimin ve tüketimin artmasına neden olmuştur. Üretim-tüketim- atık döngü zincirinde atık geri dönüşümünün yeterli olmayışı çevresel, iklimsel, ekonomik ve çoğu sorunları da birlikte getirmektedir. 2015 Dongusel Ekonomi Eylem Planı çerçevesinde Avrupa Birliği, içerisinde atık yönetiminin de yer aldığı birçok karar almıştır. AB üye ve aday ülkeleri için geri donuşum kapasitesi ve atık yönetiminin iyileştirilmesi hedeflenen stratejilerde ilk sıralarda yer almaktadır. Bu çalışmada da 2015 Dongusel Ekonomi Eylem Planı sonrasında 2017, 2018 ve 2019 yılları için AB ülkelerinin atık yönetimi performansları Veri Zarflama Analizi VZA ve Yapay Sinir Ağları yöntemleriyle birlikte incelenmiştir. VZA girdi değişkenleri; kişi başı belediye atık üretimi, geri kazanım için atık ithalatı, çevre korumaya yönelik ulusal harcamalar, kişi başına reel GSYİH, insani gelişmişlik indeksi, kişi başına düşen plastik ambalaj atığı üretimi, çıktı değişkeni; belediye atıklarının geri dönüşüm oranı olarak alınmıştır. VZA ile elde edilen etkinlik skorları yapay sinir ağlarında çıktı değişkeni olarak kullanılmış ve yapay sinir ağları ileri beslemeli ağlarla 2019 yılı ülkelerin etkinlik skorları için öngörüde bulunulmuştur. Elde edilen bulgularda, Slovenya, Letonya, Almanya, İrlanda, Lüksemburg ve Belçika tüm yıllarda etkin olan ve diğer ülkelerin referans kümelerinde sıkça yer alan ülkeler olduğu sonucuna ulaşılmıştır. İleri Beslemeli Yapay Sinir Ağları ile eğitilen veri setinden elde edilen 2019 tahmin değerlerinin gerçek etkinlik değerlerine oldukça yakın değerler aldığı görülmüştür.

Makale detayları

Evaluation of Environmental and Waste Management Performances of EU Countries: A Co-Application of Data Envelopement Analysis and Artificial Neural Networks

Yazarlar:

Nazlı Seyhan

Yayıncı:

Gumushane University

Dergi:

Gümüşhane Üniversitesi Sosyal Bilimler Dergisi Vol.14 Issue 1, pp.343-355

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001283725

pISSN:

1309-7423

Environmental Pollution

Waste Management

Circular Economy

Artificial Neural Networks

Data Envelopment Analysis

Çevre Kirliliği

Atık Yönetimi

Döngüsel Ekonomi

Yapay Sinir Ağları

Veri Zarflama Analizi

Environmental Pollution

Waste Management

Circular Economy

Artificial Neural Networks

Data Envelopment Analysis

Sayı

Makale detayları

Yazarlar: Nazlı Seyhan

Yayıncı: Gumushane University

Dergi: Gümüşhane Üniversitesi Sosyal Bilimler Dergisi Vol.14 Issue 1, pp.343-355

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001283725

pISSN: 1309-7423

Sayı

Yazarlar:

Nazlı Seyhan

Yayıncı:

Gumushane University

Dergi:

Gümüşhane Üniversitesi Sosyal Bilimler Dergisi Vol.14 Issue 1, pp.343-355

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001283725

pISSN:

1309-7423