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Water Footprint and Energy Sustainability
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Water Footprint and Energy Sustainability

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water-Energy Nexus".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 2313

Special Issue Editors

Dr. Winnie Gerbens-Leenes

E-Mail Website
Guest Editor
Integrated Research on Energy, Environment and Society (IREES, Groningen, The Netherlands), University of Groningen, 9747 AG Groningen, The Netherlands
Interests: water footprint; energy; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. S.D. Vaca Jimenez

E-Mail Website
Guest Editor Assistant
1.Department of Mechanical Engineering, Escuela Politécnica Nacional, Quito, Ecuador
2.Integrated Research on Energy, Environment and Society (IREES), University of Groningen, 9747 AG Groningen, The Netherlands
Interests: water footprint; energy; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sustainability of the energy supply, its production and demand is an important topic of research. In many basins worldwide, good-quality freshwater is becoming increasingly scarce, while the production of energy requires increasing volumes of freshwater. The use of some types of renewable energy sources also requires the utilization of more freshwater, particulary in the cooling of power plants or the irrigation of crops. In addition, hydropower has a relatively large water footprint per unit of electricity, particularly when hydropower stations are located in areas subject to the extensive evaporation of water. This means that shifting away from fossil fuels in order to reduce greenhouse gas emissions might place additional pressure on freshwater bodies.

This Special Issue welcomes the submission of research that addresses the relationship between water and energy or presents innovative applications. We welcome, for example, papers that analyze enhancements in the efficient use of water or ways in which to minimize water pollution. Research that compares the water footprints of different types of energy is also welcome. We also encourage the submission of papers that address global energy scenarios and water, including the role of new energy sources or the trade-offs between water, land and the carbon footprint of energy production. We also welcome papers that study the trade-offs on a systems level, e.g. analyses of electricity production on a national scale or on isolated islands with a fixed boundary, as well as the related water footprints within the basin boundaries.

Dr. Winnie Gerbens-Leenes
Dr. S.D. Vaca Jimenez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water–energy nexus
  • energy sustainability
  • water resources management
  • water footprint assessment
  • sustainable electricity production
  • water, land and carbon footprint trade-offs

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Published Papers (3 papers)

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Research

24 pages, 3662 KiB  
Article
Optimizing Water Footprint and Energy Use in Industry: A Decision Support Framework for Industrial Wastewater Treatment and Reuse Applied to a Brewery
by Ioanna Nydrioti and Helen Grigoropoulou
Water 2025, 17(8), 1179; https://doi.org/10.3390/w17081179 - 15 Apr 2025
Viewed by 346
Abstract
Water and energy use, along with wastewater reuse, are critical for sustainable industrial production. This study develops a decision support framework (DSF) to assess wastewater treatment and reuse, incorporating Water and Carbon Footprint indicators. The framework is applied to a Greek brewery producing [...] Read more.
Water and energy use, along with wastewater reuse, are critical for sustainable industrial production. This study develops a decision support framework (DSF) to assess wastewater treatment and reuse, incorporating Water and Carbon Footprint indicators. The framework is applied to a Greek brewery producing 1.4 × 106 hL of beer annually, with a total water consumption of 5.6 hL per hL of beer and an in-house wastewater treatment plant (WWTP). The WWTP consumes over 40% more energy than expected, indicating a need for efficiency improvements. An advanced wastewater treatment method is proposed, capable of treating 43% of the total wastewater volume, with 3% covering the brewery’s utility water demand and the rest allocated to restricted irrigation. This reduces the operational Water Footprint by 12% and the supply chain Water Footprint by 1%, while increasing energy use by 3%. The optimal scenario, integrating water reuse and energy efficiency improvements, results in a 35% reduction in the Carbon Footprint, a 10% decrease in the operational Water Footprint, and a 1% reduction in the supply chain Water Footprint. The DSF provides a structured approach for industries to optimize sustainability by balancing water reuse with energy efficiency. Full article
(This article belongs to the Special Issue Water Footprint and Energy Sustainability)
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15 pages, 3115 KiB  
Article
Spatial–Temporal Distribution Characteristics of the Water Footprint and Water-Saving Potential of Fruit Trees in Tarim River Basin
by Xinyuan Lin, Yan Chen, Zheng He, Minghua Li, Baoxia Ci, Yang Liu, Xin Zhang and Fuyu Ma
Water 2025, 17(8), 1158; https://doi.org/10.3390/w17081158 - 13 Apr 2025
Viewed by 191
Abstract
It is of great significance to optimize water resource management and promote sustainable development in the Tarim River Basin (TRB) by using the water footprint (WF) evaluation method to evaluate the water shortage of fruit trees in the TRB and analyse its water-saving [...] Read more.
It is of great significance to optimize water resource management and promote sustainable development in the Tarim River Basin (TRB) by using the water footprint (WF) evaluation method to evaluate the water shortage of fruit trees in the TRB and analyse its water-saving potential. This study aimed to elucidate the WF spatial–temporal distribution characteristics of fruit trees in the water-limited TRB from 2000 to 2020 and evaluate their water-saving potential capability. The WF was calculated using a combination of irrigation technology simulation and water usage assessments for four different fruit trees (apple, pear, date, and walnut). The results indicate that the green WF (WFgreen) initially increased and then decreased, reaching its lowest value of only 175.09 m3/t in 2020, and decreased by 22.71% from 2000 to 2020. WFblue decreased by 47.13% over the same period. In 2020, the WFblue of date and walnut accounted for a higher percentage of WFblue. WFblue significantly exceeded WFgreen, indicating their high water consumption and the limited adoption of water-saving technologies in the study area. Due to the increase in fruit tree planting area and fertilization, WFgrey exhibited an overall upward trend. Meanwhile, the total WF (WFtotal) indicated a general downward trend, though the walnut tree had the highest WFtotal at 2.21 × 105 m3/t, indicating the popularization of water-saving technology. The results show that, taking 2020 as the baseline, the WFblue of the four fruit trees in the TRB was 2.64 × 105 m3/t (accounting for 89.1%), total WFblue decreased by 0.73 × 105 m3/t (a decrease of 48.38%) after drip irrigation, and the water-saving potential in the five prefectures of the TRB was in the range of 38.55–56.18%. Therefore, the promotion of drip irrigation technology plays a key role in alleviating the water pressure of fruit trees and promoting the sustainable utilization of water resources in the TRB. Full article
(This article belongs to the Special Issue Water Footprint and Energy Sustainability)
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34 pages, 17396 KiB  
Article
Predicting Green Water Footprint of Sugarcane Crop Using Multi-Source Data-Based and Hybrid Machine Learning Algorithms in White Nile State, Sudan
by Rogaia H. Al-Taher, Mohamed E. Abuarab, Abd Al-Rahman S. Ahmed, Mohammed Magdy Hamed, Ali Salem, Sara Awad Helalia, Elbashir A. Hammad and Ali Mokhtar
Water 2024, 16(22), 3241; https://doi.org/10.3390/w16223241 - 11 Nov 2024
Cited by 1 | Viewed by 1185
Abstract
Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various [...] Read more.
Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m3 ton−1, followed closely by RF-SVR at 5.05 m3 ton−1, while the minimum MBE was 0.03 m3 ton−1 in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R2 values throughout all scenarios. Notably, the R2 values for dual hybrid models surpassed those of triple hybrid models. The highest Nash–Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP. Full article
(This article belongs to the Special Issue Water Footprint and Energy Sustainability)
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