International Journal of Innovation and Sustainable Development (3 papers in press)

Special Issue on: OA Interdisciplinary Research of Energy Application, Governance, and Policy for Sustainability

• Open Access

  The 3D digital revitalisation of industrial heritage in energy sector for the third front construction in Southwest China ( Free Full-text Access )

  by Yanlong Liu, Peiyun Cheng, Jie Li, Seokchool Kim 
  Abstract: This study proposes a deep learning (DL)-based multimodal three dimensional (3D) digital revitalisation and regeneration method: DL-based heritage activation and reconstruction (DL-HAR). It aims to address the complexity and conservation needs of the Industrial heritage in energy sector from the Third Front Construction in Southwest China. First, the method constructs a 3D dataset based on laser scanning and drone imagery while implementing a dual-path DL model. Second, this method combines geometric feature extraction (PointNet++) with texture semantic understanding (ResNet 50) to align and dynamically repair cross-modal data. These results indicate that the DL-HAR method outperforms traditional methods and mainstream baseline models. Therefore, this study provides a theoretical basis and technical framework for the digital protection and revitalisation of Industrial heritage in energy sector, promoting a shift from static to dynamic cultural heritage conservation.
  Keywords: industrial heritage in energy sector; DL-HAR; DL-based heritage activation and reconstruction; deep learning model; feature extraction; texture semantics.
  DOI: 10.1504/IJISD.2026.10076668
   
  

• Open Access

  The innovation of deep learning-based entrepreneurship capabilities of green tourism students under the Internet of Things ( Free Full-text Access )

  by He Wang, Chunyun Wang 
  Abstract: This work intends to enhance the energy-related innovation and entrepreneurship capabilities of Green Tourism major students by leveraging Internet of Things (IoT) technology and deep learning models. As the global tourism industry transitions toward low-carbon development, students need traditional management skills and competencies in scenic area energy management, energy-saving solution design, and the construction of green business models. This work proposes a personalised education-based innovation and entrepreneurship training model, integrating real-time energy consumption data collected via IoT with student learning behaviour data to provide targeted green cultivation strategies for diverse learners. These results indicate that the model effectively strengthens students capacity to address energy challenges and design sustainable development solutions, holding significant implications for the education of Green Tourism major students.
  Keywords: Internet of Things technology; deep learning models; innovation and entrepreneurship capability development; green tourism major students; personalised education.
  DOI: 10.1504/IJISD.2026.10076958
   
  

• Open Access

  Fast identification algorithm for cascading failures of AC-DC hybrid green power grid considering heterogeneous characteristics ( Free Full-text Access )

  by Yu Nan, Jinggang Wang, Yafei Wu, Zhe Liu, Bo Liu, Xifeng Wang 
  Abstract: Aiming at the severe hazards caused by frequent cascading failures in AC-DC hybrid green power grids with high renewable energy penetration such as fast propagation speed, complex paths, and the difficulty of traditional homogeneous models in describing nonlinear evolution a fast identification algorithm of cascading failures of AC-DC hybrid green power grid considering heterogeneous characteristics is proposed. Firstly, the line non-uniformity index (LNI) and DC system instability index (DCSI) indices are constructed to quantify the spatial differences in component bearing capacity and AC-DC coupling characteristics. Finally, the cooperative prevention and control strategy is generated with control cost, power flow balance and system stability as multi-objectives. The effectiveness of the heterogeneous quality index and the advantages of high precision and quick response of the method are verified, which provides a feasible technical way for online security defence of AC/DC hybrid power grid with high proportion of new energy.
  Keywords: fast identification; cascading failures; AC-DC hybrid green power grid; heterogeneous characteristics; multi-objective optimisation; line non-uniformity index; DCSI; DC system instability index.
  DOI: 10.1504/IJISD.2026.10076971