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Energies
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Sustainable Energy, Environment and Low-Carbon Development
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Sustainable Energy, Environment and Low-Carbon Development

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: 18 June 2025 | Viewed by 2068

Special Issue Editors

Dr. Hongyu Ren

E-Mail Website
Guest Editor
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
Interests: algae; anaerobic fermentation/digestion; energy and resource recovery; sludge treatment; biological treatment processes; advanced oxidation process
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fanying Kong

E-Mail Website
Guest Editor
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: biological wastewater treatment; bioelectrochemical system; emerging containments; anaerobic fermentation/digestion; energy and resource recovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental problems such as industrial, domestic and agricultural pollution are becoming more and more serious around the world, causing a series of ecological problems and threatening human health. On the other hand, the insufficient supply of energy and the greenhouse gas emissions caused by fossil fuels also restrict the sustainable development of the economy and society. We hope to contribute to the construction of a low-carbon, green and sustainable society.

Here, we will continue to work in this direction. This Special Issue focuses on how green energy technologies can help reduce environmental pollution and achieve low-carbon sustainable development. The purpose of this Special Issue is to bring together innovative academicians and industrial experts in related fields and to establish an academic platform on the communication of the latest research and developmental activities.

Dr. Hongyu Ren
Prof. Dr. Fanying Kong
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. Energies 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

  • environmental problems
  • environment pollution
  • green energy technologies
  • sustainable development
  • bioenergy
  • energy conversion and management
  • water–energy–food nexus
  • recycling technologies
  • low-carbon development

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Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 6791 KiB  
Article
Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems
by Bartosz Przysucha, Monika Kulisz, Justyna Kujawska, Michał Cioch, Adam Gawryluk and Rafał Garbacz
Energies 2025, 18(9), 2329; https://doi.org/10.3390/en18092329 (registering DOI) - 2 May 2025
Abstract
Sediment accumulation in dam reservoirs significantly impacts hydropower efficiency and infrastructure sustainability. Bottom sediments often contain heavy metals such as Cr, Ni, Cu, Zn, Cd, and Pb, which can pose ecological risks and affect water quality. Moreover, excessive sedimentation reduces reservoir capacity, increases [...] Read more.
Sediment accumulation in dam reservoirs significantly impacts hydropower efficiency and infrastructure sustainability. Bottom sediments often contain heavy metals such as Cr, Ni, Cu, Zn, Cd, and Pb, which can pose ecological risks and affect water quality. Moreover, excessive sedimentation reduces reservoir capacity, increases turbine wear, and raises operational costs, ultimately hindering energy production. This study examined the ecological risk of heavy metals in bottom sediments and explored predictive approaches to support sediment management. Using 27 sediment samples from Zemborzyce Lake, the concentrations of selected heavy metals were measured at two depths (5 cm and 30 cm). Ecological risk index (ERI) values for the deep layer were predicted based on surface data using artificial neural networks (ANNs) and multiple linear regression (MLR). Both models showed a high predictive accuracy, demonstrating the potential of data-driven methods in sediment quality assessment. The early identification of high-risk areas allows for targeted dredging and optimized maintenance planning, minimizing disruption to dam operations. Integrating predictive analytics into hydropower management enhances system resilience, environmental protection, and long-term energy efficiency. Full article
(This article belongs to the Special Issue Sustainable Energy, Environment and Low-Carbon Development)
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19 pages, 2262 KiB  
Article
Development of Advanced Machine Learning Models for Predicting CO2 Solubility in Brine
by Xuejia Du and Ganesh C. Thakur
Energies 2025, 18(5), 1202; https://doi.org/10.3390/en18051202 - 28 Feb 2025
Viewed by 558
Abstract
This study explores the application of advanced machine learning (ML) models to predict CO2 solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). Using a comprehensive database of 1404 experimental data points spanning temperature (−10 to [...] Read more.
This study explores the application of advanced machine learning (ML) models to predict CO2 solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). Using a comprehensive database of 1404 experimental data points spanning temperature (−10 to 450 °C), pressure (0.098 to 140 MPa), and salinity (0.017 to 6.5 mol/kg), the research evaluates the predictive capabilities of five ML algorithms: Decision Tree, Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Regression with a radial basis function kernel. Among these, XGBoost demonstrated the highest overall accuracy, achieving an R2 value of 0.9926, with low root mean square error (RMSE) and mean absolute error (MAE) of 0.0655 and 0.0191, respectively. A feature importance analysis revealed that pressure has the most impactful effect and positively correlates with CO2 solubility, while temperature generally exhibits a negative effect. A higher accuracy was found when the developed model was compared with one well-established empirical model and one ML-based model from the literature. The results underscore the potential of ML models to significantly enhance prediction accuracy over a wide data range, reduce computational costs, and improve the efficiency of CCUS operations. This work demonstrates the robustness and adaptability of ML approaches for modeling complex subsurface conditions, paving the way for optimized carbon sequestration strategies. Full article
(This article belongs to the Special Issue Sustainable Energy, Environment and Low-Carbon Development)
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22 pages, 1660 KiB  
Article
Evaluating the Environmental Impact of Heat Pump Systems: An Integrated Approach to Sustainable Building Operations
by Mahdiyeh Zafaranchi, William T. Riddell, Nicholas B. Chan, Elizabeth Saliba and Luke Leung
Energies 2025, 18(2), 388; https://doi.org/10.3390/en18020388 - 17 Jan 2025
Cited by 1 | Viewed by 1157
Abstract
This study evaluates the energy consumption and embodied carbon emissions of various heat pump systems for an office building in Chicago, IL, U.S., over a 50-year lifespan, including the operation, manufacturing, and construction phases. The analyzed systems include air source heat pumps (ASHP) [...] Read more.
This study evaluates the energy consumption and embodied carbon emissions of various heat pump systems for an office building in Chicago, IL, U.S., over a 50-year lifespan, including the operation, manufacturing, and construction phases. The analyzed systems include air source heat pumps (ASHP) in Air to Air and Air to Water configurations, and ground source heat pumps (GSHP) in Soil to Air and Soil to Water configurations. A traditional HVAC system serves as the baseline for comparison. Advanced simulation tools, including Rhino, Grasshopper, TRACE 700, and One Click LCA, were used to identify the optimal HVAC system for sustainable building operations. Unlike prior studies focusing on GSHP versus traditional HVAC systems, this research directly compares GSHP and ASHP configurations, addressing a significant gap in the sustainable HVAC system design literature. The GSHP (Soil to Water) system demonstrated the lowest energy intensity at 100.8 kWh/m2·yr, a 41.8% improvement over the baseline, and the lowest total embodied carbon emissions at 3,882,164 kg CO2e. In contrast, the ASHP (Air to Air) system, while reducing energy consumption relative to the baseline, exhibited the highest embodied carbon emissions among the heat pump configurations due to its higher operational energy demands. The study highlights the significance of the operating phase in embodied carbon contributions. These findings emphasize the importance of a holistic design approach that considers both operational and embodied impacts to achieve sustainable building designs. Full article
(This article belongs to the Special Issue Sustainable Energy, Environment and Low-Carbon Development)
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