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

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  • 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
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    References listed on IDEAS

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    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.
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