Mathematical Modeling of Energy Transition, Climate Finance, and Sustainable Economic Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 197

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Faculty of Economic Sciences, Department of Finance and Accounting, Lucian Blaga University of Sibiu, 550324 Sibiu, Romania
Interests: corporate finance; portfolio administration; financial markets
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Special Issue Information

Dear Colleagues,

With progress toward achieving the Sustainable Development Goals (SDGs) being made and the number of global initiatives to combat climate change increasing, the need for robust mathematical frameworks to support the transition to sustainable economic systems has become clear. Mathematical modeling is vital for facilitating long-term planning for low-carbon growth, formulating energy policy, and structuring climate finance instruments.

This Special Issue aims to enhance applied mathematics by exploring its capacity to analyze and predict the complex financial, economic, and technological dynamics that underpin the energy transition and environmental sustainability. We invite proposals that tackle the difficulties at the convergence of sustainability, energy, and finance through the application of established techniques or the creation of fresh mathematical methodologies.

Research that combines advanced mathematical and statistical methodologies with insights from economics and finance is especially significant for this Special Issue. Topics of interest include, but are not limited to:

  • Stochastic processes in renewable energy finance and carbon risk;
  • Optimization and control models for sustainable energy systems;
  • Mathematical modeling of ESG metrics and green financial instruments;
  • Risk measures and pricing models for green bonds, carbon credits, and ESG derivatives;
  • Dynamic systems modeling of circular economy and resource efficiency;
  • Real options theory and decision-making under climate-related uncertainty;
  • Game-theoretic approaches to global climate finance cooperation;
  • Network and graph-based models of energy systems and systemic financial risk;
  • Equilibrium and partial equilibrium models of energy markets and emissions trading;
  • Machine learning and artificial intelligence applied to sustainability forecasting;
  • Partial differential equations and numerical methods in environmental economics;
  • Econometric modeling and panel data analysis in sustainable finance and energy policy;
  • Time series models (e.g., ARIMA, GARCH, VAR) for carbon pricing and renewable energy forecasting.

Prof. Dr. Camelia Oprean-Stan
Guest Editor

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Keywords

  • mathematical finance
  • energy transition
  • climate risk
  • sustainable finance
  • carbon markets
  • ESG modeling
  • stochastic processes
  • partial differential equations
  • optimization
  • network theory
  • control systems
  • game theory
  • econometrics
  • time series analysis

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Published Papers (1 paper)

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Research

20 pages, 3636 KiB  
Article
The Prediction of Civil Building Energy Consumption Using a Hybrid Model Combining Wavelet Transform with SVR and ELM: A Case Study of Jiangsu Province
by Xiangxu Chen, Jinjin Mu, Zihan Shang and Xinnan Gao
Mathematics 2025, 13(14), 2293; https://doi.org/10.3390/math13142293 - 17 Jul 2025
Viewed by 57
Abstract
As a pivotal economic province in China, Jiangsu’s efforts in civil building energy conservation are critical to achieving the national “dual carbon” goals. This paper proposes a hybrid model that integrates wavelet transform, support vector regression (SVR), and extreme learning machine (ELM) to [...] Read more.
As a pivotal economic province in China, Jiangsu’s efforts in civil building energy conservation are critical to achieving the national “dual carbon” goals. This paper proposes a hybrid model that integrates wavelet transform, support vector regression (SVR), and extreme learning machine (ELM) to predict the civil building energy consumption of Jiangsu Province. Based on data from statistical yearbooks, the historical energy consumption of civil buildings is calculated. Through a grey relational analysis (GRA), the key factors influencing the civil building energy consumption are identified. The wavelet transform technique is then applied to decompose the energy consumption data into a trend component and a fluctuation component. The SVR model predicts the trend component, while the ELM model captures the fluctuation patterns. The final prediction results are generated by combining these two predictions. The results demonstrate that the hybrid model achieves superior performance with a Mean Absolute Percentage Error (MAPE) of merely 1.37%, outperforming both individual prediction methods and alternative hybrid approaches. Furthermore, we develop three prospective scenarios to analyze civil building energy consumption trends from 2023 to 2030. The analysis reveals that the observed patterns align with the Environmental Kuznets Curve (EKC). These findings provide valuable insights for provincial governments in future policy-making and energy planning. Full article
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