IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2020i1p148-d468461.html
   My bibliography  Save this article

Life Cycle Assessment of Nitrate and Compound Fertilizers Production—A Case Study

Author

Listed:
  • Georgios Gaidajis

    (Laboratory of Environmental Management and Industrial Ecology, Department of Production Engineering and Management, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Ilias Kakanis

    (Laboratory of Environmental Management and Industrial Ecology, Department of Production Engineering and Management, Democritus University of Thrace, 67100 Xanthi, Greece)

Abstract

The production and utilization of fertilizers are processes with known and noteworthy environmental impacts. Direct greenhouse gas (GHG) emissions and a high contribution to water eutrophication due to the nitrogen (N) and phosphorus (P) derivatives are some of the most crucial impacts derived from the overall life cycle of fertilizer use. The life cycle assessment (LCA) has been reliable and analytical tool for the identification, quantification, and evaluation of potential environmental impacts of fertilizers related to the products, production processes, or activities throughout their lifecycle. In this paper, a gate-to-gate LCA approach was applied in order to identify and evaluate the impacts derived from the production processes of nitrate and compound fertilizers the production industry in Northeastern Greece. The results from this study prove that compound fertilizers have a greater impact compared with nitrate fertilizers, contributing up to 70% of the total production impacts. Furthermore, climate change, freshwater eutrophication, and fossil fuel depletion were identified as the most crucial impact categories. Finally, a comparison with relevant LCA studies was conducted, in order to identify the possibility of a consistency pattern of the fertilizer production impacts in general.

Suggested Citation

  • Georgios Gaidajis & Ilias Kakanis, 2020. "Life Cycle Assessment of Nitrate and Compound Fertilizers Production—A Case Study," Sustainability, MDPI, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:148-:d:468461
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/1/148/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/1/148/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    2. Alireza Taghdisian & Sandra G. F. Bukkens & Mario Giampietro, 2022. "A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin," Sustainability, MDPI, vol. 14(15), pages 1-25, July.
    3. Zhao, Rongqin & Liu, Ying & Tian, Mengmeng & Ding, Minglei & Cao, Lianhai & Zhang, Zhanping & Chuai, Xiaowei & Xiao, Liangang & Yao, Lunguang, 2018. "Impacts of water and land resources exploitation on agricultural carbon emissions: The water-land-energy-carbon nexus," Land Use Policy, Elsevier, vol. 72(C), pages 480-492.
    4. Sara Ilahi & Yongchang Wu & Muhammad Ahsan Ali Raza & Wenshan Wei & Muhammad Imran & Lyankhua Bayasgalankhuu, 2019. "Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    5. Pritpal Singh & Gurdeep Singh & G. P. S. Sodhi, 2022. "Data envelopment analysis based optimization for improving net ecosystem carbon and energy budget in cotton (Gossypium hirsutum L.) cultivation: methods and a case study of north-western India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2079-2119, February.
    6. Bakhshandeh, Esmaeil & Jamali, Mohsen & Emadi, Mostafa & Francaviglia, Rosa, 2022. "Greenhouse gas emissions and financial analysis of rice paddy production scenarios in northern Iran," Agricultural Water Management, Elsevier, vol. 272(C).
    7. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    8. Ye, Wenlian & Wang, Xiaojun & Liu, Yingwen, 2020. "Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine," Energy, Elsevier, vol. 194(C).
    9. Singh, Pritpal & Singh, Gurdeep & Sodhi, G.P.S., 2019. "Energy auditing and optimization approach for improving energy efficiency of rice cultivation in south-western Punjab, India," Energy, Elsevier, vol. 174(C), pages 269-279.
    10. Xiaobo Xue Romeiko & Zhijian Guo & Yulei Pang & Eun Kyung Lee & Xuesong Zhang, 2020. "Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    11. Hossein Kazemi Author- Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Iran, 2016. "Energy Balance in Modern Agroecosystems; Why and How?," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 1(5), pages 101-104, June.
    12. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    13. Zeynep Ceylan, 2020. "Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 944-956, September.
    14. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    15. Heidari, Mohammad Davoud & Turner, Ian & Ardestani-Jaafari, Amir & Pelletier, Nathan, 2021. "Operations research for environmental assessment of crop-livestock production systems," Agricultural Systems, Elsevier, vol. 193(C).
    16. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    17. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein, 2013. "Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production," Energy, Elsevier, vol. 58(C), pages 588-593.
    18. İnayet Özge Aksu & Tuğçe Demirdelen, 2022. "The New Prediction Methodology for CO 2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach," Sustainability, MDPI, vol. 14(23), pages 1-29, November.
    19. Elsoragaby, Suha & Yahya, Azmi & Mahadi, Muhammad Razif & Nawi, Nazmi Mat & Mairghany, Modather, 2019. "Energy utilization in major crop cultivation," Energy, Elsevier, vol. 173(C), pages 1285-1303.
    20. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein & Shamshirband, Shahaboddin & Hamid, Siti Hafizah Ab, 2015. "Developing a fuzzy clustering model for better energy use in farm management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 27-34.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:148-:d:468461. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.