Xiaofei Wang

The convergence of intelligent computational innovations—exemplified by cognitive intelligence—into the real economy is fundamentally transforming traditional industries and driving high-quality development. As a cornerstone of national economic growth, the energy sector faces mounting pressure to meet demands for green, low-carbon, and sustainable development, particularly under “dual carbon” targets and tightening regulatory frameworks. This study examines how digital transformation in this sector facilitates or impedes carbon emission reduction and green growth. Focusing on five key energy subsectors, including coal mining and processing, a coupling coordination model assesses the interaction between digitalization and greening. Utilizing panel data spanning from 2014 to 2023, the study systematically evaluates the level of digital–green coordination across the sector. The results indicate notable inter-sectoral variation, alongside a consistent upward trend in the overall coupling coordination, reaching moderate to high levels. These findings offer critical strategic insights for policymakers and energy enterprises seeking to harmonize digital innovation with green transition goals. The empirical evidence underscores the potential of next-generation technologies to expedite intelligent system upgrades, embed green development practices, and enhance enterprise-level carbon reduction and sustainability performance.

The Fourth Industrial Revolution, driven by advancements in information technology, has ushered humanity into the age of intelligence. As digital technologies like artificial intelligence and large-scale models continue to evolve and gain traction, the convergence of digital innovation and green development within manufacturing enterprises has emerged as a pivotal trend. This integration not only fosters high-quality, sustainable growth, but also increasingly validates the impact of digital intelligence on advancing low-carbon performance. This study delves into how manufacturing enterprises can attain sustainable and low-carbon growth via digital transformation, employing the entropy TOPSIS evaluation model to assess the effectiveness of various empowerment strategies. Based on the findings, the paper offers actionable recommendations for enhancing sustainable practices in manufacturing during this digital shift. Beyond enriching the theoretical framework on the synergy between digital intelligence and sustainability in manufacturing, this research provides practical insights and guidance for enterprises leveraging next-generation digital technologies to drive their green and low-carbon initiatives more effectively.

The coordinated development of digitalization and greening is essential for economic transformation and upgrading, especially given the pressing global carbon emission challenges. China’s commitment to achieving “dual carbon” goals highlights the need for sustainable solutions, particularly in the manufacturing sector, which is a significant source of energy consumption and emissions; carbon emissions account for more than 30%. Integrating advanced digital technologies with manufacturing is critical for reducing carbon and sustainable growth. According to the research results, more than 70% of scholars believe that digital transformation boosts green innovation and low-carbon development, but the mechanisms still need to be clarified, slowing transformation efforts and reducing efficiency. Taking the intellectualization and green low-carbon development of manufacturing enterprises as latent variables, and taking the nine paths obtained by scholars’ research results and investigation interviews to promote green low-carbon performance as observation variables, this paper constructs a structural equation model and deeply explores the mechanism and paths of the intellectualization transformation of manufacturing enterprises affecting carbon reduction, emission reduction and sustainable development of enterprises. The research results show that the digital intelligent transformation of manufacturing enterprises affects the green and low-carbon performance improvement and sustainable development of enterprises through technological innovation, industrial structure transformation and upgrading, and reshaping resource allocation. These strategies lower energy use and emissions, strengthen sustainability, and improve green performance. The findings offer theoretical and practical insights, providing a roadmap for efficient digital transformation in manufacturing to achieve the “dual carbon” goals and support sustainable development.

Exploring the spatial distribution of intangible cultural heritage (ICH) by geographic information system (GIS) is a new perspective. It can help to identify the laws and characteristics of the formation and evolution of ICH to further support the development of protection. This paper takes Shaanxi Province as the research area to analyse and identify the spatial distribution characteristics by mathematical statistics using the nearest neighbour index (NNI) and kernel density estimation (KDE). The results show 1699 ICH items of Shaanxi distributed with obvious geographic characteristics: the geospatial distribution is not uniform; the aggregate spatial distribution pattern demonstrates that ICH has strong spatial dependence; all the high-density core areas and sub-high-density core areas are centralized around Guanzhong area; and the ICH items are relatively dense in specific geographical conditions (plain and mountainous areas with an elevation between 160 m and 800 m; plains and valleys at the confluence of rivers or areas within 10 km of the river; and semi-humid climate regions). We can conclude that the geographic environment is the most fundamental and important factor affecting ICH, indirectly affecting its generation, inheritance, and distribution through regional history, culture, and humanity for a long time.