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Sustainability
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Energy Price Forecasting and Sustainability on Energy Transition
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Energy Price Forecasting and Sustainability on Energy Transition

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 6973

Special Issue Editors

Prof. Dr. Jian Chai

E-Mail Website
Guest Editor
School of Economics and Management, Xidian University, Xi'an 710126, China
Interests: energy economy; energy management; economic forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Quanying Lu

E-Mail Website
Guest Editor
College of Economics and Management, Beijing University of Technology, Beijing 100124, China
Interests: energy economics; environmental economics; economic analysis and forecasting; energy-environment-policy analysis and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current complex and rapidly changing international geopolitical landscape, the global energy market has been significantly impacted. In 2022, energy prices experienced the largest surge since the oil crisis of 1973; prices of major food commodities and fertilizers reached their highest levels since 2008. Influenced by geopolitical conflicts, world trade is shifting toward higher-cost modes, and the surge in energy prices may disrupt global plans for transitioning toward clean energy. On one hand, some countries have announced expansions in fossil fuel production; on the other hand, the relationship between energy prices and sustainable development is multi-faceted and complex. Fluctuating energy prices can have significant implications for various aspects of sustainable development, including environmental, social, and economic dimensions. The energy market is under immense pressure, with the nominal prices of certain energy resources reaching historic highs, likely resulting in enduring ripple effects. The sharp increase in energy prices may have a significant influence on the sustainable development of energy, the environment, and the economy, particularly in developing economies. Hence, employing cutting-edge technologies and novel approaches for the precise forecasting of energy and commodity prices across different time scales is of paramount significance in the current context.

This Special Issue will focus on the precise forecasting of bulk commodity prices, particularly energy prices, across various time scales within the context of a sustainable energy transition. It will also examine the economic costs and implications arising from fluctuations in energy prices for the transition toward cleaner energy sources. Furthermore, there will be a specific emphasis on the impacts of energy price fluctuations on sustainable development. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Energy price forecasts, such as natural gas, oil, and coal price forecasts;
  • The economic costs and implications of energy price fluctuations on low-carbon transition and sustainable development;
  • Energy consumption forecasting at multiple time scales, such as natural gas, oil, and coal consumption;
  • Energy consumption, energy transition, and sustainable development;
  • Carbon emissions forecasting;
  • Machine learning and artificial neural networks;

We look forward to receiving your contributions.

Prof. Dr. Jian Chai
Dr. Quanying Lu
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 250 words) can be sent to the Editorial Office for assessment.

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. Sustainability 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 2400 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

  • energy price forecasts
  • energy consumption forecasts
  • sustainable energy transition
  • sustainable development
  • carbon emissions

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Published Papers (3 papers)

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Research

33 pages, 4409 KB  
Article
An Integrated Framework for Electricity Price Analysis and Forecasting Based on DROI Framework: Application to Spanish Power Markets
by Nuo Chen, Caishan Gao, Luqi Yuan, Jiani Heng and Jianwei Fan
Sustainability 2025, 17(24), 11210; https://doi.org/10.3390/su172411210 - 15 Dec 2025
Cited by 1 | Viewed by 477
Abstract
Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the [...] Read more.
Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the efficacy of traditional forecasting methodologies. To address these challenges, this study proposes a four-stage Decomposition-Reconstruction-Optimization-Integration (DROI) framework, coupled with an econometric breakpoint test, to evaluate forecasting performance across distinct time segments of Spanish electricity price data. The framework employs CEEMDAN for signal decomposition, decomposing complex price sequences into intrinsic mode functions to retain essential features while mitigating noise, followed by frequency-based data reconstruction; integrates the Improved Sparrow Search Algorithm (ISSA) to optimize initial model parameters, minimizing errors induced by subjective factors; and leverages Convolutional Neural Networks (CNN) for frequency-domain feature extraction, enhanced by an attention mechanism to weight channels and prioritize critical attributes, paired with Long Short-Term Memory (LSTMs) for temporal sequence forecasting. Experimental results validate the method’s robustness in both interval forecasting (IPCP = 100% and IPNAW is the smallest, Experiment 1.3) and point forecasting tasks (MAPE = 1.3758%, Experiment 1.1), outperforming naive approaches in processing stationary sequence clusters and demonstrating substantial economic utility to inform sustainable power system management. Full article
(This article belongs to the Special Issue Energy Price Forecasting and Sustainability on Energy Transition)
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37 pages, 6517 KB  
Article
Forecast Natural Gas Price by an Extreme Learning Machine Framework Based on Multi-Strategy Grey Wolf Optimizer and Signal Decomposition
by Zhuolin Wu, Jiaqi Zhou and Xiaobing Yu
Sustainability 2025, 17(12), 5249; https://doi.org/10.3390/su17125249 - 6 Jun 2025
Cited by 2 | Viewed by 2323
Abstract
Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price series makes it difficult to [...] Read more.
Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price series makes it difficult to capture. Therefore, we propose a forecasting framework based on signal decomposition and intelligent optimization algorithms to predict natural gas prices. In this forecasting framework, we implement point, probability interval, and quantile interval forecasting. First, the natural gas price sequence is decomposed into multiple Intrinsic Mode Functions (IMFs) using the Ensemble Empirical Mode Decomposition (EEMD) technique. Each decomposed sequence is then predicted using an optimized Extreme Learning Machine (ELM), and the individual results are aggregated as the final result. To improve the efficiency of the intelligent algorithm, a Multi-Strategy Grey Wolf Optimizer (MSGWO) is developed to optimize the hidden layer matrices of the ELM. The experimental results prove that the proposed framework not only provides more reliable point forecasts with good nonlinear adaptability but also describes the uncertainty of natural gas price series more accurately and completely. Full article
(This article belongs to the Special Issue Energy Price Forecasting and Sustainability on Energy Transition)
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26 pages, 4352 KB  
Article
Knowledge Graph of Low-Carbon Technologies in the Energy Sector and Cost Evolution Based on LDA2Vec: A Case Study in China
by Xingjiu Zhao, Zhiwen Peng and Sibao Fu
Sustainability 2024, 16(17), 7337; https://doi.org/10.3390/su16177337 - 26 Aug 2024
Viewed by 2805
Abstract
Climate change has attracted global attention, highlighting the critical role of low-carbon technologies in addressing environmental challenges. Due to the multidisciplinary nature, complexity, and diversity of research content on low-carbon technologies, a comprehensive overview is still limited. This paper uses bibliometrics analysis to [...] Read more.
Climate change has attracted global attention, highlighting the critical role of low-carbon technologies in addressing environmental challenges. Due to the multidisciplinary nature, complexity, and diversity of research content on low-carbon technologies, a comprehensive overview is still limited. This paper uses bibliometrics analysis to discuss the research status and hotspots of low-carbon technology from a macro-perspective. The LDA2Vec topic recognition model is adopted to identify key technical terms, and CiteSpace software 6.3.1 Advanced Edition is used to conduct in-depth analysis of the development trajectory of low-carbon technology. After checking the frequency of the relevant keywords, four key techniques were identified. In order to further analyze the research results, the learning curve theory is used to predict the cost development trend of key low-carbon technologies. The results show that: (i) low-carbon technologies play a key role in the energy sector and have a potential impact on policy making, and the cost of related technologies will be significantly reduced in the next few years. (ii) Global low-carbon technologies have entered an important period of development, but remaining challenges need to be addressed by optimizing technological performance. (iii) It is very important to strengthen the research on hydrogen production technology and photovoltaic power generation technology; the cost reduction in hydrogen production technology is still significant and there is room for further optimization. (iv) To effectively address the high costs and technical barriers associated with emerging low-carbon technologies, increased funding for research and development is critical. Full article
(This article belongs to the Special Issue Energy Price Forecasting and Sustainability on Energy Transition)
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