International Journal of Innovation and Sustainable Development (4 papers in press)

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

• Open Access

  Stability of school-enterprise cooperation in energy education: interaction mechanisms and policy implications ( Free Full-text Access )

  by Chong Li, Yao Xiao 
  Abstract: At present, against the backdrop of deepening reform in vocational education and industry integration in China, the cooperation between schools and enterprises in energy technology research and development characterised by rapid technological iteration, long investment cycles, and strong policy dependence has become a vital pathway for talent cultivation. While short term incentives can enhance cooperation efficiency, they often induce opportunistic behaviour. Long-term collaboration, though conducive to strengthening the stability of cooperation between profit and non-profit organisations, faces a series of challenges such as high initial costs and lengthy implementation cycles. This study expands the application of evolutionary game theory to the field of vocational education, providing a theoretical foundation for advancing school-enterprise cooperation and deepening the integration of industry and education in practice.
  Keywords: school-enterprise cooperation; education policy; evolutionary game; profit organisations; non-profit organisations; opportunism.
  DOI: 10.1504/IJISD.2026.10075596
   
  

• Open Access

  Spatial distribution of geographical indication agricultural product clusters and their impact on the sustainable development of the agricultural energy-economy system ( Free Full-text Access )

  by Hui Yang, Taoran Zou, Xiangda Xu 
  Abstract: Geographical indication agricultural products in Heilongjiang Province play a crucial role in advancing the sustainable development of the agricultural energyeconomic system, with multidimensional impacts on the economy, energy, and ecology. Accordingly, this paper proposes that Heilongjiang Province should optimise and adjust the spatial layout of GI agricultural products, cultivate industrial clusters, promote industrial structure upgrading and the integrated development of the three industries, adhere to the marketisation of agricultural product brands, and strengthen brand marketing and promotion, thereby further leveraging the synergistic driving effect of GI agricultural products on the coordinated development of the agricultural energyeconomicecological system.
  Keywords: GI agricultural products; spatial distribution; agricultural energy economy system; sustainable development; multiple regression models.
  DOI: 10.1504/IJISD.2026.10075597
   
  

• Open Access

  The high-quality development of Chinas green energy economy for promotion of digital finance under deep learning technology ( Free Full-text Access )

  by Ximeng Li, Qiaomeng Sun 
  Abstract: The purpose of this paper is to investigate how digital finance might support the superior growth of Chinas green energy economy and its regional diversity. In order to systematically uncover the mechanism of digital finance in optimising resource allocation, supporting green technological innovation, and promoting green consumption behaviour, this paper uses theoretical analysis and a deep learning model as its research objects. The findings demonstrate the considerable regional variation in the promotion of digital finance. As a result, this paper examines and validates the mechanism of digital finance in a number of areas, including resource optimisation, technical innovation, and consumer guidance. It also demonstrates the policy support and the regulatory impact of regional economic growth level. This paper contributes to the research in the areas of digital finance and the green energy economy and serves as a reference for maximising regional development policies, reducing regional disparities, and fostering sustainable development.
  Keywords: deep learning; green energy economy development; regional heterogeneity; green technology innovation.
  DOI: 10.1504/IJISD.2026.10076287
   
  

• Open Access

  Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder network ( Free Full-text Access )

  by Dong Li, Kuo Liu, Congcong Guo, Qingqing Gong 
  Abstract: This paper combines sparse representation with deep autoencoder (DAE) to propose a method for power transformer sound modelling and fault diagnosis. When a power transformer malfunctions, it is usually accompanied by abnormal sounds. During the operation of transformers, the sound signals generated by structural vibrations such as iron cores and windings contain rich equipment status information. Accurate detection and analysis of sound signals can achieve mechanical fault diagnosis of transformers. Traditional detection methods are no longer able to meet the constantly changing needs of the electrical power system (EPS). This paper constructs a fault sound recognition model that combines sparse representation and DAE. This model optimises the encoding process by introducing sparse constraints, thereby enhancing the ability to extract key fault signal features. The results indicate that the model has advantages in effective feature extraction, diagnostic speed, and fault diagnosis accuracy.
  Keywords: deep learning; sparse representation; DAE; deep autoencoder network; power transformer; sound characteristics; fault diagnosis.
  DOI: 10.1504/IJISD.2026.10076330