Industrial Transformation Research Articles

The impact of artificial intelligence in the global hospitality industry by 2030

Interest in the impact of AI within the hospitality industry by the year 2030 is very high; therefore, this paper will discuss the latest applications and trends that are likely to take over in revolutionizing the whole landscape. It effectively brings into relief the transformative capability of AI technologies-machine learning, natural language processing, and robotics-that sublimely enhance operational efficiencies and attend to the ever-growing need for personalized guest experiences in a profoundly captivating manner. The paper thereby identifies, with full comprehensiveness, a range of applications of AI on hospitality enterprise segments via smart room technologies that perfectly merge state-of-the-art developments to offer an unprecedented level of comfort; automated customer service systems that seamlessly streamline the consumer interaction; and data analytics for revenue management by AI systems to outline unparalleled revenue optimization and organizational success. It goes into detail on what the future of AI in hospitality holds: a tremendous explosion automating tasks to take some of the load off from manual work and increasing productivity while fostering smarter data analytics that will, in turn, enable businesses to make data-driven decisions with the highest degree of precision and accuracy. This foresighted exploration further embraces the idea of seamless integration with Internet of Things devices, therefore making a future possible where smart devices would smoothly cooperate and communicate to create an unparalleled guest experience. The complex challenges and ethical considerations that arise regarding the implementation of AI within the hospitality industry are also explored with minute detail. The act recognizes the very critical aspect of data privacy and gives great emphasis on how important it is to safeguard the personal information of the guests by leveraging its AI capabilities. It goes further by delving into the sensitive issue of job displacement, aware of the potential impact of integration with AI on the workforce. It does so in a manner that aptly demonstrates the essence of achieving a delicate balance between the wonderful advantages gained from AI and the urgent need to observe morality. Lastly, the strategic recommendations to be provided within this paper, targeted at industry stakeholders themselves, really underscore how important it is to consider proactive AI strategies that not only realize the full transformative value of these technologies but also seek ethical conduct. This pioneering research doggedly insists that AI will indeed form an integral part of the reshaping of the hospitality landscape, which emphasizes the critical need for continuous innovation and unrelenting adaptability in today's fast-changing technological environment. In other words, this research plunges into the world of artificial intelligence in the global hospitality industry and outlines its exceptional potential to reform every aspect of the industry as we know it. The agreement unequivocally states that AI should be embraced as a driving force for innovation and progress while ensuring that ethical considerations are placed first and best practices come to the front in the transformation of industries. But as that future comes into view, it is a timely reminder that monumental shift awaiting the hospitality landscape from every corner demands the industry stakeholders take bold moves in reaching out to clasp this technological revolution with open arms and unyielding dedication to remarkable guest experiences.

Convergence of Conversational AI with IoT and AR/VR: A systematic review of emerging applications and industry transformations

Integrating conversational AI with emerging technologies fundamentally transforms human-computer interaction across multiple sectors and applications. This article comprehensively analyzes how conversational AI converges with the Internet of Things (IoT) and Augmented Reality/Virtual Reality (AR/VR) technologies to create more intuitive, responsive, and immersive user experiences. By systematically examining current implementations, we explore the synergistic relationships between these technologies in smart environments, healthcare systems, industrial operations, and educational platforms. The article reveals that while this technological convergence offers unprecedented opportunities for automation, personalization, and enhanced user engagement, it also presents significant challenges regarding system integration, data privacy, and security. The findings indicate that successful implementation requires careful consideration of infrastructure requirements, user experience design, and privacy frameworks. This article contributes to the growing body of knowledge on multi-modal technology integration. It provides insights for researchers, developers, and industry practitioners working towards the next generation of intelligent interactive systems. The article concludes by identifying critical areas for future research and development in this rapidly evolving field.

Open innovation and digital transformation in the automotive industry: a comparative analysis of South Korea, Japan and Germany

PurposeA research gap exists regarding the impact of digital transformation on automotive open innovation, despite extensive literature on both topics individually. This study aims to fill this.Design/methodology/approachThe study analyzes patent-based open innovation before and after the digital transformation, focusing on US patents filed by automakers from South Korea, Japan and Germany during three periods: 2000–2001, 2010–2011 and 2020–2021. Second, in-depth interviews with selected automotive firms from these countries were conducted to complement the patent analysis and develop grounded theory.FindingsThe common effect of digital transformation on automotive firms’ open innovation channels in all three countries follows a trajectory from R&D collaboration through value chain open innovation to open innovation in other industries. There are notable differences in the primary focus of open innovation among automakers in the three countries, with South Korea emphasizing R&D, Japan focusing on the automotive value chain and Germany engaging in open innovation with other industries.Research limitations/implicationsInvestigating the differences in open innovation channels and structures between electric auto parts and non-electric auto parts through case studies and the statistical research method could be a compelling next research topic as another limitation of this research.Originality/valueFirst, this research demonstrates that firms in the same sectoral innovation system can cultivate different open innovation channels within the knowledge funnel, depending on the regional innovation system (RIS) and national innovation system (NIS). Second, with DT, existing firms’ value chains are evolving and expanding from traditional trajectories to new, information technology-related sub-sectors.

Does digital innovation improve carbon productivity? A new perspective based on industrial upgrading

ABSTRACT In the era of digital economy, digital innovation is an important path to promote the improvement of carbon productivity in developing countries. Based on China’s experience, this paper systematically constructs a digital innovation measurement system, and takes the heterogeneity threshold of industrial transformation upgrading as an entry point, and adopts a non-linear dynamic panel measurement model to empirically explore the mechanism of industrial transformation upgrading threshold of digital innovation on regional carbon productivity. The results show that the current development level of China’s digital innovation is relatively low, with significant regional heterogeneity. In particular, the impact of digital innovation on regional carbon productivity is subject to the heterogeneous threshold effect of industrial transformation upgrading: the low degree of industrial transformation upgrading hinders the role of digital innovation in improving carbon productivity to some extent, and with the further improvement of industrial transformation upgrading, the enabling effect of digital innovation can be effectively brought into play to improve carbon productivity. That is, digital innovation and regional carbon productivity present ‘U-shaped’ relationship. This paper has clarified the differential‘new phenomena’ in the process of promoting carbon productivity, providing new insights for achieving ‘carbon reduction and economic promotion’ in developing countries.

Dynamic mechanism and evolutionary game analysis of sports industry service transformation

Service transformation plays a pivotal role in achieving the sustainable development of the sports industry. This study originates from the interactive relationships among sports enterprises, consumers, and regulatory authorities, proposing a logical framework for the service transformation of the sports industry. Furthermore, a three-party evolutionary game model is constructed to explore the strategic evolution and stability conditions under both single-agent and multi-agent scenarios. The primary findings are as follows: (1) Interactive relationships among sports enterprises, consumers, and regulatory authorities exhibit a game dilemma resembling the "prisoner’s dilemma." (2) A positive promotion relationship conducive to the transformation of the sports industry towards a service model is triggered only when at least two stakeholders’ strategic choices surpass a certain threshold. (3) Fiscal subsidies play a facilitating role in encouraging service transformation for sports enterprises but have limited incentives for consumers. Finally, this paper suggests the introduction of competition mechanisms and the establishment of reward and penalty systems, offering decision-making guidance for the service transformation of the sports industry.

Tripartite Evolutionary Game Analysis for Manufacturing Firms, Service Providers and Consumers in Collaborative Innovation Systems

With global economic growth and intensifying market competition, Chinese manufacturing firms are under pressure to enhance product competitiveness and adapt to changing consumer demands, requiring continuous product innovation and upgrading. This study introduces a tripartite evolutionary game model involving manufacturing firms, service providers and consumers to analyze the strategic stability and the impact of various factors on decision-making within this system. The results indicate that the decisions of these actors are interdependent, with a fair distribution of innovation benefits and costs being key to stimulating innovation enthusiasm and participation. The presence of free-riding behavior affects strategic choices, and a robust system of rewards and penalties can mitigate this problem. The cost of obtaining market information also influences service providers’ participation in innovation, with high costs potentially suppressing their involvement. This research provides insights to guide collaborative innovation efforts during industrial transformation, highlighting the direct impact of service providers and consumers on the success of manufacturing upgrades.

Efficient and secure electronic evidence exchange scheme for internet of things

With the rapid development of information technology, Internet of Things (IoT) is profoundly impacting various fields of the socio-economic landscape, driving the transformation of traditional industries towards intelligence. However, the widespread application of IoT has also led to a surge in electronic evidence, whose importance in the judicial field is increasingly prominent, but its characteristics such as ease of replication and leakage have posed new challenges for privacy protection. This paper focuses on the security and privacy issues of electronic evidence in IoT and proposes an efficient and secure interaction scheme based on chebyshev chaotic map, hash functions and XOR operations. Through a secure two-factor authentication mechanism, this scheme achieves identity verification between the user and the storage center, as well as confidentiality during the data transmission process. This has significant implications for the healthy development of IoT, judicial fairness and personal privacy protection. Experimental and theoretical analysis shows that the proposed scheme not only effectively resists various known attacks, but also performs excellently in terms of communication and computation costs, making it well-suited for IoT.

Population aging, technological innovation and industrial differentiation

As China’s aging population deepens and its pace accelerates, it is particularly crucial to rely on technological innovation to drive industrial differentiation. Is there a connection between population aging, technological innovation, and industrial differentiation? Does technological innovation have a moderating effect? Based on the panel data of 31 provinces in China from 2006 to 2022, this paper constructs the entropy index to measure the overall industrial differentiation and tertiary industrial differentiation in China, and subsequently investigates the relationship among the three using the two-way fixed effect model. The results indicate that population aging has a significant positive impact on the overall industrial differentiation in China, with a regression coefficient of 1.1025. Technological innovation plays a positive moderating role, with an interaction coefficient of 0.3489. The effects of population aging on the differentiation of the three industries differ: the regression coefficient for the primary industry is −0.6437, which is significantly negative; for the secondary industry, the regression coefficient is 0.9252, which is statistically insignificant; and for the tertiary industry, the regression coefficient is 0.1539, which is significantly positive. The government should encourage enterprises to invest in technology research and development through tax cuts and subsidies, and enterprises should absorb high-quality talents, carry out intelligent transformation of traditional industries of enterprises, and improve their competitiveness.

Mechanochemical Synthesis of Type III Porous Liquids from Solid Precursors for the Removal and Conversion of Waste CO2 from CH4.

Porous liquids (PLs) have emerged as a promising class of flow porous materials, offering distinctive benefits for sustainable separation processes coupled with catalytic transformations in the chemical industry. Despite their potential, challenges remain in the realms of synthesis complexity, stability, and the strategic engineering of separation and catalytic sites. In this study, a scalable mechanochemical synthetic approach is reported to fabricate Type III PLs from solid precursors with high stability. In these Type III PLs, ZIF-8 nanocrystals are dispersed in the deep eutectic solvents formed by solid hydrogen-bond donors and acceptors. Owing to the presence of multiple interfacial hydrogen and coordination bonds, these PLs not only maintain porosity and fluidity with high stability, enabling efficient CH4 purification by CO2 removal and sequestration, but also facilitate the catalytic conversion of stored CO2 into valuable products under ambient conditions. This strategy advances the green production of stable and well-dispersed PLs, showing the potential of PLs in the sustainable separation and catalysis coupling system in the chemical industry.

Digital twins: Transforming the chemical process industry—A review

AbstractDigital twin (DT) technology represents a significant advancement in the digital transformation of the chemical process industry (CPI), offering innovative capabilities for real‐time monitoring, predictive maintenance, and process optimization. This review investigates the current deployment status, frameworks, architectures, and applications of DTs within CPI, highlighting their transformative potential in improving operational efficiency, enhancing safety, and promoting sustainability. By examining case studies from industry leaders and analyzing recent advancements, this study elucidates the critical roles of DTs in asset health monitoring, process optimization, and environmental performance. The review identifies key components of DT frameworks, including data integration, hybrid modelling, and real‐time analytics, which are essential for effective implementation. It further explores challenges such as high computational requirements, integration with legacy systems, cybersecurity risks, and the lack of standardization, which impede widespread adoption. Despite these challenges, the paper emphasizes opportunities for leveraging advanced technologies such as artificial intelligence, edge computing, and 5G connectivity to enhance DT capabilities and scalability. In addition, this review underscores the importance of DTs in addressing global sustainability goals, mainly through their ability to optimize energy consumption, reduce emissions, and facilitate circular economy practices. By synthesizing insights from academia and industry, this study provides a comprehensive understanding of DTs' current state and future potential in CPI, offering strategic directions for research and development. The findings contribute to advancing the deployment of DTs as a cornerstone technology in achieving operational excellence, safety, and sustainability in the chemical process industry.

The Digital REadiness Assessment MaturitY (DREAMY) framework to guide manufacturing companies towards a digitalisation roadmap

Digital technologies are considered the main drivers for the transformation of the manufacturing industry. To effectively navigate this wave of transformation, companies need to understand their current maturity state regarding implemented technologies and organisational processes across various functions. However, many organisations struggle to assess their digital status quo. Addressing this gap, this paper proposes a framework to help manufacturing companies become aware of their digital maturity level and guide them toward a digitalisation roadmap. The framework consists of a maturity model, the Digital Readiness Assessment MaturitY (DREAMY), inspired by the Capability Maturity Model Integration (CMMI), and a digitalisation roadmap tool. The research follows the design research methodology, combining a literature review, expert consultations, and validation through multiple industrial cases to develop, test, and refine the framework. The proposed framework enables companies to assess their current digital maturity, identify strengths and weaknesses, and discover opportunities for improvement, supporting a conscious transition toward Industry 4.0 and the forthcoming Industry 5.0 paradigm.

A Deep Learning-Based Method for Measuring Apparent Disease Areas of Sling Sheaths

The sling sheath plays an important protective role in the sling of suspension bridges, effectively preventing accidental damage to the sling caused by wind, fatigue and other impacts. To conduct a quantitative analysis of the apparent disease of suspension bridge slings, a method for segmenting and quantifying the apparent disease of the sling sheath using deep learning and image processing was proposed. A total of 1408 disease images were obtained after image acquisition of a suspension bridge following sling replacement. MATLAB 2021a Image Labeler software was used to establish a disease dataset by manual labelling. Then, the MobileNetV2 model was trained and tested on the dataset to determine disease segmentation; additionally, an area measurement algorithm was proposed based on the images’ projection relationships. Finally, the measurement results were compared with the manually acquired crack area. The results show that the accuracy of background and sheath category pixels in the MobileNetV2 model is above 97%, indicating that the model achieves satisfactory results in these classifications. However, the accuracy of crack category pixels and the intersection over union ratio only reaches 80%, which needs to be improved by setting model correction coefficients. When measuring directly, it was found that the area measurement error of the test image mainly ranged between 8% and 30%, and the measurement error of the crack area after correction mainly ranged between −3% and 15%, indicating that the area measurement method can achieve a higher degree of measurement accuracy. The method for segmenting and quantifying the apparent disease of the sling sheath based on deep learning and image processing fills the research gap in the measurement of the surface damage area caused by apparent disease and has the advantages of high efficiency and high recognition accuracy. Reducing the maintenance costs of suspension bridge slings is crucial for promoting comprehensive intelligent detection of bridges and advancing the smart transformation of the civil engineering industry.

Effective dipole model for electrostatic interactions between polarizable spherical particles in particle scale simulations

Despite their widespread adoption, particle-scale simulation methods, such as the Discrete Element Method (DEM), for electrically charged particles in several natural processes and industrial transformations do not include realistic polarization effects. At close distances, these can dominate the particle motion and are impossible to predict by the commonly adopted Coulomb point-charge approximation. Sophisticated mathematical tools can account for uneven charge distributions, predicting an attractive force between a charged particle and a neutral particle or possible attraction between two like-charged particles. Such approaches are accurate but too complex for implementation in DEM simulations of many interacting particles. We propose a novel, simpler yet realistically accurate effective dipole model. By attributing a net charge and an induced effective dipole to each sphere, the interaction force between charged polarizable particles is computed in closed form. A comparison of a rigorous solution and the proposed dipole approach for two spherical particles reveals significant improvement over the commonly employed Coulomb law. The effects of particle size ratio and charge ratio on the interaction force are discussed. Then, the dynamic DEM simulation of a shaker filled with a binary mixture of differently sized particles that are all positively charged is shown to predict the counterintuitive formation of fine-on-coarse aggregates.

How do digital knowledge resources affect the carbon emission intensity of the pig industry?

Purpose By exploring the impact of digital knowledge resources (DKR) on the carbon emission intensity of the pig industry (PCEI), this study aims to reveal the role of DKR in reducing PCEI. Design/methodology/approach Based on provincial panel data in China from 2011 to 2021, this study uses the entropy and Intergovernmental Panel on Climate Change coefficient methods to calculate the evaluation index system of DKR and PCEI, respectively. Empirical analysis using a panel fixed-effects model examines the influence of DKR on PCEI and its underlying mechanisms. Findings DKR can significantly reduce PCEI. This conclusion still holds even after undergoing endogeneity treatment and a series of robustness tests. Mechanism test results indicate that DKR can operate indirectly through the mediation mechanism of rural human capital (RHC) and pig breeding technology innovation (PTI), while environmental regulation intensity (ERI) plays a positive moderating role in the relationship between DKR and PCEI. The magnitude of the impact of DKR on PCEI depends on ERI. Further studies found that the impact of DKR on PCEI has obvious heterogeneity characteristics, and the promotion effect is more obvious in regions with good integration degrees and high development potential. Practical implications This paper divides DKR into three dimensions: digital technology knowledge (DTK), digital management knowledge (DMK) and digital application knowledge (DAK), providing a new framework for research and enriching the understanding of the relationship between DKR and PCEI. Furthermore, the research results reveal the application potential of DKR in the pig industry, particularly in terms of resource allocation efficiency. This is of great significance for promoting low-carbon development in the pig industry and provides insights for the low-carbon transformation of other industries. In addition, the study emphasizes the moderating effect of ERI on the mechanism of carbon reduction in the pig industry through DKR. This offers a new perspective for understanding the relationship between knowledge management and environmental governance, providing a reference basis for policy formulation in related fields. Originality/value This paper further enriches the role of DKR in the livestock industry. Integrating DKR with traditional industries promotes knowledge innovation, information distribution and utilization and scientific decision-making. This has significant value in promoting the development and application of carbon reduction technologies, enhancing industrial competitive advantages, and other aspects.

Global Value Chain Embedding and Total Factor Productivity in Carbon Emission Reduction: A Multi-Country Analysis of the Paper Industry

Against the backdrop of carbon reduction and low-carbon economic development, this study takes the global paper industry chain as its research subject and employs the SBM-GML model and input–output model to measure the low-carbon TFP and value chain embedding levels of 42 major global economies from 2001 to 2021. Using fixed-effects and threshold effect models, the study examines both the overall and stage-specific impacts of value chain embedding on low-carbon TFP. The results reveal that between 2001 and 2021, while the low-carbon TFP of major countries in the global paper industry chain steadily increased, significant disparities persisted among them. Most countries experienced low-carbon TFP growth driven by technological progress, whereas only a few high low-carbon TFP nations also demonstrated upward trends in technical efficiency. The division of labour position in the value chain significantly promoted low-carbon TFP at a ratio of 1:0.26. In contrast, the low-carbon TFP effects of participation displayed a nonlinear impact with a threshold of 0.21: it exerted an insignificant inhibitory effect in the initial stage but, upon crossing the threshold, resulted in a significant positive effect with a ratio of 1:0.13. These findings highlight the importance of optimizing GVC strategies to achieve sustainable industrial transformation and growth.

Paving the Ecological Foundation of New Industrialization with Green and Low-Carbon Development

Since its inception, the new industrialization has incorporated green and low-carbon into its characteristics. Against the backdrop of the interweaving of global economic development and environmental protection, green development is an inevitable choice for China to break through resource and environmental constraints and promote the development of new industrialization. This article first analyzes the connotation and characteristics of new industrialization and green low-carbon, and then introduces the practical experience of green development in the European Union, the United States, and China. The European Union focuses on transforming energy structure and promoting innovation in green technologies, the United States focuses on supporting green low-carbon development through legislation and building market mechanisms, while China actively explores from top to bottom through policy guidance, industrial transformation, and energy system optimization. Based on domestic and foreign experiences, this article proposes to strengthen the division of responsibilities, improve supply levels, optimize energy structure, and other aspects to cultivate a green background for new industrialization.

The intelligent brain and the energy heart: Synergistic evolution of artificial intelligence and energy storage technology in China.

In the context of China's ongoing industrial revolution and technological transformation, there is a growing demand for advanced energy management solutions and the increasing role of artificial intelligence in various industries. This paper aims to explore how artificial intelligence (AI) and Energy Storage Technology (EST) interact and co-evolve. Utilizing a full-sample Granger causality test, we identified significant interactions between AI and EST. Afterwards, we further revealed their dynamic influence relationship using advanced sub-sampling techniques. Quantitative analysis indicates that AI positively influences EST by optimizing energy management systems, with an improvement of 15% in efficiency, and by enhancing the intelligence level of energy storage. However, there are also negative effects, such as a 10% increase in operational risks due to over-reliance on AI. Similarly, EST facilitates the development of AI by providing a stable energy supply. AI and EST, like the intelligent brain and the energy heart, require mutual dependence for stable development. In context of the industrial revolution and technical advancement, this study provides meaningful recommendations. Strengthening technological research and innovation support, optimizing energy policies, and enhancing standardization will promote the deep integration and synergistic development of AI and EST. This will provide significant impetus for China's technological advancement and economic development, aiding in the achievement of sustainable development goals.

Analysis of the Dynamic Changes and Driving Mechanism of Land Green Utilization Efficiency in the Context of Beijing–Tianjin–Hebei Synergistic Development

Studying the development of land green utilization efficiency and the factors that influence it in the Beijing–Tianjin–Hebei region can improve the distribution of land resources among regions and reinforce interregional integrated planning. By constructing a super-efficiency SBM model, calculating the Malmquist–Luenberger index, and constructing a Tobit model, this study explores the spatial features and temporal variations of land green use efficiency in the Beijing–Tianjin–Hebei region from 2010 to 2022. It also examines the mechanism that drives land green use efficiency in the context of the Beijing–Tianjin–Hebei synergistic development. According to this research, Beijing has consistently had the highest land green usage efficiency and a strong green development strength, whereas Baoding, Xingtai, Handan, and other cities in Hebei Province have lower land green utilization efficiency. According to the geographical dimension, the research area’s land green use efficiency exhibits a pattern of “high in the middle and low in the surroundings”, with Cangzhou, Langfang, and Tangshan standing out in terms of both industrial transformation and ecological building. Based on the results of the driving mechanism of land green use efficiency, it is evident that while the degree of urbanization and population concentration has a negative effect on land green use efficiency, the degree of economic development, industrial synergy, opening up to the outside world, environmental regulation, and ecological output all have positive and promoting associations with it. In summary, increasing the optimization of the economic and industrial structure, bolstering technological innovation and policy coordination, and attaining a harmonious coexistence of the economy and ecology are all essential steps in the process to increase the land green use efficiency in the research area when attempting to achieve the goal of sustainable development in the region.

Pelatihan Digital Marketing pada Kelompok Industri Jamu Madura Berbasis Focus Group Discussion (FGD) di Kabupaten Pamekasan

The traditional herbal medicine (jamu) industry in Pamekasan, Madura, which has been passed down through generations, faces significant challenges in marketing in the digital era. Most jamu entrepreneurs still rely on conventional marketing methods, such as direct sales and word-of-mouth, which limit their market reach. Therefore, a community service program based on digital marketing training was conducted to help these entrepreneurs utilize digital technology to expand their market. The training employed a Focus Group Discussion (FGD) method and involved six jamu business owners from Pamekasan. The material covered the basics of digital marketing, strategies for using social media platforms like Instagram and Facebook, as well as marketplace optimization for platforms such as Tokopedia and Shopee. The results of the training showed an increase in participants' understanding of the importance of digital marketing in business development. However, there were still challenges, including low digital literacy, limited technological infrastructure, and complex regulatory requirements for product licensing. Overall, this training had a positive impact, helping the entrepreneurs enhance their competitiveness in broader markets. Continued mentoring and regulatory simplification are needed to ensure the success of the digital transformation of the jamu industry in Pamekasan.

Can green finance exorcize the resource curse in China's resource-based cities? A geographically and temporally weighted regression (GTWR) analysis.

Resource-dependent regions often face the challenge of the resource curse, which hinders sustainable development. This study investigates how green finance can address this issue, using panel data from 115 resource-based cities in China and employing a two-way fixed effects model and a Geographically and Temporally Weighted Regression (GTWR) model. The findings reveal that: First, resource-based cities in China exhibit the resource curse, characterized by significant spatiotemporal non-stationarity. Second, green finance demonstrates a nonlinear, inverted U-shaped effect on the resource curse, initially exacerbating it due to short-term disruptions but alleviating it over time by promoting industrial transformation. Third, mechanism tests show that green finance mitigates the resource curse by fostering green technology innovation. Based on these findings, this study recommends that resource-based cities adopt region-specific green finance strategies, strengthen financial infrastructure, promote green technology innovation, and implement transitional support measures to ensure a sustainable transition away from resource dependency.