 | Prof. Jie LiJinan University, China Li Jie is Vice Dean and Professor at the Institute of Industrial Economics, Jinan University, where he also serves as a doctoral supervisor. He is a Leading Talent under the Guangdong Special Support Program and Principal Investigator of a Major Project funded by the Ministry of Education's Key Research Program in Philosophy and Social Sciences. He serves as Associate Editor of Review of International Economics (SSCI), Executive Editor of Industrial Economics Review (CSSCI Extended), and Executive Editorial Board Member of Journal of Liaoning University (Philosophy and Social Sciences) (CSSCI Extended). He is Director of the Guangdong Provincial Key Laboratory for Industrial Big Data Application and Economic Decision-Making Behavior Research, and Director of the Institute of Industrial Organization and Regulation, a key humanities and social sciences platform at Jinan University. His external appointments include Vice President of the Guangdong Innovation Strategy Research Association; Vice Director of the Free Trade Committee, Chinese Business History Society; Council Member of the China Industrial Economics Society; Distinguished Research Fellow at the China (Hainan) Competition Policy Research Center; Expert in the Guangdong Provincial Department of Finance's Foreign Economics Expert Pool; Expert for Major Administrative Decision-Making Consultation in Guangzhou; Member of the Guangzhou Comprehensive Business Environment Optimization Advisory Committee; and External Co-Supervisor for Ph.D. students in Applied Economics at Sun Yat-sen University. His research focuses on industrial economics, international trade, and corporate finance. He has published over 40 papers in leading international SSCI journals, including European Economic Review, Journal of Economic Behavior & Organization, Journal of Comparative Economics, Journal of International Money and Finance, Review of International Economics, Economics Letters, Journal of Regulatory Economics, and China Economic Review. He has also published nearly 30 papers in top domestic journals and newspapers, including Economic Research Journal, China Economic Quarterly, The Journal of World Economy, Journal of Management Sciences in China, China Industrial Economics, and Guangming Daily. He has received funding from the National Natural Science Foundation of China (General Program), the National Social Science Fund of China (General Program and Later-Stage Funding), and the Ministry of Education's Major Project in Philosophy and Social Sciences. He authored a textbook designated under the 11th Five-Year National Textbook Planning Program and serves on the editorial board of China's Open Economy, among the first batch of Chinese economics textbooks. His policy consultation reports have received affirmative feedback from a central principal leader (1 report) and central leaders (3 reports). He is a recipient of the An Zijie International Trade Research Excellent Paper Award and the Guangdong Provincial Philosophy and Social Sciences Outstanding Achievement Award. His undergraduate course, Intermediate Microeconomics, was designated a National Quality Course. Title: Technological Progress in Core Digital Economy Sectors, Newly Added Industry Linkages, and Economic Impact Abstract:The deep integration of digital technology with the real economy is a crucial lever for building a modern industrial system through technological innovation, as well as for stabilizing employment, boosting consumption, and enhancing economic resilience. However, quantitative research on the potential directions of digital technology in promoting industrial integration remains quite scarce. This research extends the production network model of Acemoglu & Azar (2020) from the perspective of industrial linkages, and utilizes the national input-output table to conduct counterfactual simulations of the new industrial linkages generated by technological progress in the core sectors of the digital economy. We analyze the economic effects from four dimensions: GDP, economic resilience, labor allocation, and household consumption, and compare these with the United States. The findings reveal that after technological progress occurs in the core sectors of the digital economy, the newly formed industrial linkages in China are mainly characterized by the digitalization of industries, whereas in the United States they are characterized by the industrialization of digital technologies. The economic effects are manifested in four aspects: First, it promotes GDP growth; second, the core sectors of the digital economy exhibit an employment demand creation effect, while the newly linked industrial sectors show an employment demand substitution effect; third, it enhances the ability of GDP to maintain stability in the face of price increase shocks and accelerates the recovery speed of total output after shocks to digital product prices; fourth, it promotes the consumption of digital products by representative households. Finally, this paper proposes policy recommendations on how to further promote economic growth, enhance economic resilience, stimulate employment and optimize employment structure, and increase household consumption of digital products through the integration of digital and real economies. |
 | Prof. Ming ChenUniversity of South China, China Ph.D., Professor, Vice Dean of the School of Economics, Management and Law. Returned overseas student. Standing Council Member of the Hunan Economic Association. Member of the Joint Council of National Demonstration Centers for Experimental Teaching in Higher Education Institutions. Peer Review Expert for the National Social Science Fund of China, Expert Reviewer for National Social Science Achievements, and Project Review Expert for the Hunan Provincial Department of Education and Department of Science and Technology. Outstanding Graduate Supervisor of Hunan Province, Graduate Supervisor in Applied Economics/Finance/MBA, and Visiting Scholar at University College Dublin. Reviewer for renowned domestic and international academic journals including China Rural Economy, China Rural Survey, Nature portfolio journals, Land Use Policy, and Journal of Cleaner Production. In recent years, he has served as Principal Investigator for several national-level projects including the National Social Science Fund, as well as dozens of provincial/ministerial-level projects including the Ministry of Education Humanities and Social Sciences Fund, and has published three academic monographs. In recent years, he has published dozens of academic papers as first author in authoritative SCI/SSCI/CSSCI journals. Title: The Impact of Digital Ability Enhancement on Farmers' Farmland Transfer Behavior—Empirical Tests Based on Double Machine Learning Abstract: With the rapid development of digital technology, numerous emerging models have significantly influenced farmers' participation in farmland transfer activities. This study aims to explore the impact of enhanced digital competence on farmers' participation in farmland transfer, providing insights to improve farmland utilization efficiency. [Methods] Based on data from 5,243 households collected through the China Family Panel Studies (CFPS) from 2016 to 2020, this paper employs the range entropy-weight method to comprehensively measure farmers' digital competence. Utilizing techniques such as double machine learning, the LASSO algorithm, and LASSO residual regression, the study investigates the effects and underlying mechanisms of enhanced digital competence on farmland transfer at the household level. [Results] ①Overall, the enhancement of farmers' digital competence has a significant impact on rural farmland transfer, facilitating greater farmer participation. Compared to digital technology access, improvements in digital platform usage and digital information acquisition exert a stronger promoting effect on farmers' engagement in farmland transfer. ②At the micro-sample level, the promoting effect of enhanced digital competence on farmers' participation in farmland transfer is stronger among middle-aged and young farmers, those with higher education levels, and those with higher income levels. At the regional sample level, significant differences in participation willingness are observed between farmers on either side of the Aihui-Tengchong Line. Farmers on the eastern side experience a more pronounced positive impact of improved digital competence on their engagement in farmland transfer. ③The enhancement of farmers' digital competence primarily promotes their participation in farmland transfer through two channels: facilitating diversified employment opportunities and expanding their social networks. However, it simultaneously inhibits participation by reducing farmers' trust in government. Specifically, the exposure to negative policy-related information on digital platforms weakens farmers' trust in government regulatory capacity and institutional guarantees. This negative effect, however, is not universal; it exhibits heterogeneity across age and education levels, being observed only among older farmers and those with lower levels of education. [Conclusion] While advancing rural digitalization, it is crucial to enhance farmers' digital literacy, strengthen government image management, and mitigate regional "digital lag." These efforts will contribute to continuously improving the efficiency and effectiveness of farmland transfer. |
 | Prof. Hua WangZhejiang University, China Professor Hua Wang, PhD CFA, Haina Research Scientist, at Zhejiang University International Business School (ZIBS). Professor Wang has worked at both academic field and financial institutions such as investment banks with near 20 years of industrial and academic experiences in Japan, United Kingdom and China, etc. Title: Research on CPP-GNN: A Temporal Hypergraph Neural Network for Carbon Price Abstract:This paper proposes CPP-GNN, a carbon price prediction model based on Temporal Hypergraph Graph Neural Networks. The core innovation lies in using a hypergraph structure to represent complex relationships and employing temporal GNNs to capture the dynamic evolution of carbon prices. Experimental results demonstrate that CPP-GNN performs excellently across multiple carbon emission trading datasets from different regions, significantly outperforming the best benchmark models. CPP-GNN consistently outperforms baselines, achieving an average improvement of 9.3% in mean absolute error, 7.5% in root mean squared error, and 2.6% in directional accuracy. Notably, the model achieves the largest gains in highly dynamic markets such as GDEA and SHEA, highlighting its robustness and adaptability. Therefore, decisionmaking in the electricity market can be further supported by CPP-GNN. |
 | Prof. Tong LiGuangdong Peizheng College, China Dr. Li Tong, male, Professor，is the Dean of the School of Innovation and Entrepreneurship at Guangdong Peizheng College. He previously served as the Director of the Mobile Internet Industrialization Research Institute at Shenzhen University, an expert in the Guangdong Province Philosophy and Social Sciences Expert Database, and an expert for the Shenzhen Science and Technology Innovation Committee. He is also an adjunct professor for EDP training at Tsinghua University, Huazhong University of Science and Technology, and South China Normal University, as well as a reviewer for SCI journals including IEEE Access. He has served as the Director of the National Supercomputing Shenzhen Center - Shenzhen ZhihuiLin Network Technology Co., Ltd. Facial Recognition Application Innovation Joint Laboratory (2020-2022), Senior Advisor at Hainan Huike International Information Industry Park (2013-2015), and a member of the Nanshan Entrepreneurial Star Investment Alliance in Shenzhen (2014-2016). He provides consulting on digital transformation and upgrading for listed companies and traditional small and medium-sized enterprises in the internet era. He has led or participated in over 30 national, provincial, and large enterprise research projects and has published more than 50 papers in domestic and international journals, over 10 of which have been indexed by SCI, EI, or ISTP. Title: LLM and Innovative Thinking Abstract: With the explosive development of generative artificial intelligence, large language models (LLMs) are evolving from mere information retrieval tools into key variables reshaping human innovative thinking. Based on cognitive psychology and innovation management theories, this paper constructs the theoretical model of "Human-Machine Collaborative Innovation: Cognitive Symbiosis and Dynamic Evolution," aiming to reveal the deep mechanisms through which LLMs intervene in human innovation processes. The study indicates that LLMs enhance innovation efficiency through dual pathways of "knowledge augmentation" and "cognitive offloading," while also bringing the risk of homogenized thinking caused by "algorithmic convergence." By introducing a "Socratic" adversarial interaction mechanism, this paper proposes a new human-machine collaboration paradigm that stimulates human metacognition and critical thinking. The study argues that in the AI era, the focus of innovation management should shift from "process control" to "cognitive empowerment," and humans need to establish the principal role of "substantive intellectual input" to avoidethical and legal risks associated with technological dependence. This research not only expands the boundaries ofinterdisciplinary studies between artificial intelligence and management science but also provides theoretical support and practical pathways for enterprises to reconstruct innovation ecosystems in the intelligent era. Keywords: Large language models; human-machine collaborative innovation; cognitive symbiosis; Socratic interaction; innovation management paradigm; algorithmic ethics. |