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Advances in Water–Energy–Carbon–Economy–Health Relationships

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3958

Special Issue Editors

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: energy–water–carbon–food nexus; urban system simulation; complex system analysls; ecological economics; environmental management; industrial economics
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Guest Editor Assistant
School of Economics, Minzu University of China, Beijing 100081, China
Interests: sustainable development; digital economy; energy system simulation; international economic governance; industrial economics

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Guest Editor Assistant
School of Business, Jiangnan University, Wuxi 214122, China
Interests: climate change causality analysis; human activity impacts on climate change; social and economic environment effects of low-carbon energy transition; environmental health risk assessment; food–energy–water nexus

Special Issue Information

Dear Colleagues,

With the rapid economy development, the demand for water resources and energy continues to grow, accompanied by increasing carbon emissions and environmental pollution. These issues have profound impacts on the global economy and human health, and the intricate relationship among water, energy, carbon emissions, economy and health has become increasingly prominent. Previous research has primarily focused on the individual components of this complex system, neglecting the intricate interactions among them.

This Special Issue brings together manuscripts from different fields of water–energy–carbon–economy–health relationships, with the common aspect of understanding policy and practice optimization to achieve environmental sustainability and human well-being simultaneously, and providing insights on promoting the coordinated development of water, energy, carbon, economy and health. The topics covered may include, but are not limited to:

  • The role of water resources in energy production;
  • Energy consumption pattern impact on water pollution;
  • Water issues as drivers of environmental burden and their consequences for human health;
  • Economic development strategies that promote sustainable use of water resources;
  • The relationship between digital economy and water resources;
  • The impact of water resource shortage on energy supply and carbon emission;
  • Water–energy–carbon–economy–health coordinated management;
  • Water–energy–carbon–economy–health system planning;
  • Co-benefits and conflict between water, energy, carbon, economy and health;
  • Corporate responsibility and resource sustainable management;
  • Costs of water resource consumption or pollution on economy and health;
  • Transboundary environmental issue transfer and economic trade.

The goal of this Special Issue is to provide a comprehensive understanding of the complex interactions among water, energy, carbon, economy and health. We hope that our efforts will contribute toward developing effective policies and practices for promoting sustainable development while protecting public health. We look forward to receiving your proposals for papers that align with these objectives!

All papers should have a water resource-related component, and it is suggested to mention a connection to energy, carbon, economy or health management issues. Original research articles, theoretical, field, experimental, numerical and analytical studies, and comprehensive review papers focusing on defining the relationship between water resources and other environmental or economic elements are welcome.

Dr. Saige Wang
Guest Editor

Dr. Chengming Li
Dr. Xu Peng
Guest Editor Assistants

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–carbon–economy–health relationships
  • system analysis
  • water resources
  • energy demand
  • carbon emissions
  • economy development
  • human health
  • nexus and sustainable devleopment
  • coordianted management

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

21 pages, 15471 KiB  
Article
An Innovative Deep-Learning Technique for Fuel Demand Estimation in Maritime Transportation: A Step Toward Sustainable Development and Environmental Impact Mitigation
by Ayman F. Alghanmi, Bassam M. Aljahdali, Hussain T. Sulaimani, Osman Turan and Mohammed H. Alshareef
Water 2024, 16(22), 3325; https://doi.org/10.3390/w16223325 - 19 Nov 2024
Viewed by 401
Abstract
This study introduces an innovative deep-learning approach for fuel demand estimation in maritime transportation, leveraging a novel convolutional neural network, bidirectional, and long short-term memory attention as a deep learning model. The input variables studied include vessel characteristics, weather conditions, sea states, the [...] Read more.
This study introduces an innovative deep-learning approach for fuel demand estimation in maritime transportation, leveraging a novel convolutional neural network, bidirectional, and long short-term memory attention as a deep learning model. The input variables studied include vessel characteristics, weather conditions, sea states, the number of ships entering the port, and navigation specifics. This study focused on the ports of Jazan in Saudi Arabia and Fujairah in the United Arab Emirates, analyzing daily and monthly data to capture fuel consumption patterns. The proposed model significantly improves prediction accuracy compared with traditional methods, effectively accounting for the complex, nonlinear interactions influencing fuel demand. The results showed that the proposed model has a mean square error of 0.0199 for the daily scale, which is a significantly higher accuracy than the other models. The model could play an important role in port management with a potential reduction in fuel consumption, enhancing port efficiency and minimizing environmental impacts, such as preserving seawater quality. This advancement supports sustainable development in maritime operations, offering a robust tool for operational cost reduction and regulatory compliance. Full article
(This article belongs to the Special Issue Advances in Water–Energy–Carbon–Economy–Health Relationships)
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14 pages, 2848 KiB  
Article
Exploring the Impact of Financial Development on Water–Energy Efficiency in Western China
by Jianqiang Wang and Zhongyun Zhang
Water 2024, 16(14), 2065; https://doi.org/10.3390/w16142065 - 22 Jul 2024
Viewed by 805
Abstract
The western region of China is a fundamental ecological protection barrier for China. The conflict between regional economic development and ecological environmental protection has always existed in the region. This study first evaluated the water–energy efficiency (WEE) of 11 provinces in western China [...] Read more.
The western region of China is a fundamental ecological protection barrier for China. The conflict between regional economic development and ecological environmental protection has always existed in the region. This study first evaluated the water–energy efficiency (WEE) of 11 provinces in western China from 2011 to 2019 using the super-slacks-based model with undesirable outputs and analyzed their spatiotemporal characteristics. It then investigated the influence of financial development level on WEE. The results indicate that (a) the WEE in Western China was at a relatively low level, showing an upward trend from 2011 to 2016 and fluctuating after 2016; (b) financial development levels had U-shaped impacts on the WEE of Western China, which reduced at first and then rose; (c) increasing technological innovation capabilities and trade openness had positive impacts on WEE in Western China, while urbanization level had a negative effect on WEE; and (d) an examination of robustness using two different methods showed that the test results are consistent with the above conclusions. Therefore, this study has robustness. We also put forward corresponding policy suggestions, such as increasing financial support for clean and low-carbon industries, increasing input in science and technology, vigorously developing cleaner and environmentally friendly foreign trade, and implementing a new type of urbanization strategy, to promote the WEE of Western China. Full article
(This article belongs to the Special Issue Advances in Water–Energy–Carbon–Economy–Health Relationships)
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22 pages, 268 KiB  
Article
The Effects of Ocean Governance on Marine Economic Development from an Environmental Optimization Perspective
by Kuan Hong and Xin Guan
Water 2024, 16(13), 1900; https://doi.org/10.3390/w16131900 - 2 Jul 2024
Viewed by 1284
Abstract
The oceans and seas are vital resources for human life and fundamental to global economic growth. With the expansion of globalization and increasing demand for resource exploitation, the ocean economy has emerged as a critical support for economic growth and a significant concern [...] Read more.
The oceans and seas are vital resources for human life and fundamental to global economic growth. With the expansion of globalization and increasing demand for resource exploitation, the ocean economy has emerged as a critical support for economic growth and a significant concern for many. However, the exploitation of marine resources often leads to ecological damage and environmental pollution, seriously threatening the long-term viability of the marine economy. Measures must be implemented to balance ecological protection and economic development for sustainable growth in the marine sector. This investigation synthesizes over a decade’s worth of data from various coastal cities, incorporating insights from national databases, international reports, and scholarly articles, resulting in an analysis of more than 500 data points. Through the application of a rigorous quantitative approach and a dual fixed-effect model, the study delves into the dynamics between ocean governance and economic development. By analyzing a broad and representative dataset, the research provides an exhaustive overview of global ocean governance frameworks and the current state of the marine economy. The statistical analyses unveil the complexity and diversity of factors influencing the development and governance of marine economies. This paper explores the interplay between marine economic development and ocean governance, evaluating the impact of governance on marine economic progression within the context of environmental optimization. Furthermore, it proposes policy interventions aimed at fostering the sustainable growth of marine economies, ensuring the conservation of marine ecosystems, and pursuing overarching long-term global growth ambitions. Full article
(This article belongs to the Special Issue Advances in Water–Energy–Carbon–Economy–Health Relationships)
24 pages, 4641 KiB  
Article
Priority Setting and Resource Allocation in Coastal Local Government Marine Regulatory Reform: Application of Machine Learning in Resource Optimization
by Yingying Tian and Qi Wang
Water 2024, 16(11), 1544; https://doi.org/10.3390/w16111544 - 27 May 2024
Viewed by 814
Abstract
This study investigates the prioritization and resource allocation strategies adopted by the coastal local governments of Qingdao, Dalian, and Xiamen in the context of marine regulatory reform aimed at enhancing regulatory efficiency. Data on relevant opinions, departmental requirements, and existing resource allocations were [...] Read more.
This study investigates the prioritization and resource allocation strategies adopted by the coastal local governments of Qingdao, Dalian, and Xiamen in the context of marine regulatory reform aimed at enhancing regulatory efficiency. Data on relevant opinions, departmental requirements, and existing resource allocations were collected through a questionnaire survey. A backpropagation (BP) neural network was then applied to analyze the survey data, prioritize regulatory tasks, and propose resource allocation schemes. The findings demonstrate that integrating machine learning into marine regulation can significantly improve resource utilization efficiency, optimize task execution sequences, and enhance the scientific and refined nature of regulatory work. The BP neural network model exhibited strong predictive capabilities on the training set and demonstrated good generalization abilities on the test set. The performance of the BP neural network model varied slightly across different management levels. For the management level, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. For the supervisory level, these metrics were 81%, 83%, and 78%, respectively. At the employee level, the accuracy, precision, and recall rates were 79%, 81%, and 76%, respectively. These results indicate that the BP neural network model can provide differentiated resource allocation recommendations based on the needs of different management levels. Additionally, the model’s performance was assessed based on the employees’ years of experience. For employees with 0–5 years of experience, the accuracy, precision, and recall rates were 82%, 84%, and 79%, respectively. For those with 5–10 years of experience, the metrics were 83%, 86%, and 80%, respectively. For employees with over 10 years of experience, the accuracy, precision, and recall rates were 85%, 88%, and 82%, respectively. These data further confirm the applicability and effectiveness of the BP neural network model across different experience groups. Thus, the adoption of machine learning technologies for optimizing marine regulatory resources holds significant practical value, aiding in the enhancement of regulatory capacity and effectiveness within coastal local governments. Full article
(This article belongs to the Special Issue Advances in Water–Energy–Carbon–Economy–Health Relationships)
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