Enhancing the effectiveness of industry-academia collaborative development in the region is a common concern of schools, local enterprises, and the prerequisite for promoting this work is to find out the current situation of patent utilization and technology supply and demand between enterprises and universities. Existing patent analysis is mainly concerned about the transfer or licenses, lacking the attention of university-industry cooperation and non-technical factors driving the transfer of patents, whose problem led to a need to improve the identification of the technological needs of enterprises. With the research object of enterprises and universities in Pidu District of Chengdu City, comprehensive patent data and industrial and commercial data reveal the patent utilization status quo and patent technology demand of schools and enterprises in the region, the technological crossroads of schools and enterprises were sort out. Suggestions on regional patent management are put forward based on the comprehensive enterprise demand and regional development orientation.

The location entropy method, super efficiency SBM-UN model, and panel Tobit model were adopted to explore the impact mechanism of county-level industrial agglomeration on land use efficiency. The research results indicate that the agglomeration degree of the secondary industry in Inner Mongolia counties first increased and then decreased, and the agglomeration degree of the tertiary industry first decreased and then increased. The overall trend of land use efficiency in Inner Mongolia’s counties shows a decrease followed by an increase, with high-value areas gradually concentrating towards the center. The agglomeration of county-level industries in Inner Mongolia has a positive driving effect on land use efficiency, and its impact mechanism is significantly heterogeneous. The level of economic development and population density have a significant promoting effect on the improvement of land use efficiency, while land use structure, consumption level, and enterprise size have a negative hindering effect on the improvement of land use efficiency. Due to differences in resource endowments and economic development levels, various counties (districts) have significant differences in industrial agglomeration levels and land use efficiency. In the future, it is necessary to continue to strengthen the guidance and planning of industrial agglomeration, and optimize the allocation of land resources.

The ongoing implementation of digital rural development actions and the development of smart agriculture are aimed at narrowing the “digital divide” between urban and rural areas through digital rural construction. In the context of uneven distribution of factor resources, digital village construction is an important way to bridge the digital divide between urban and rural areas. Based on the multiple streams framework, the path of digital village construction was analysed. Through in-depth research on the history and current situation of the construction of three typical digital villages in Henan Province, the entry point of digital village construction in different villages was specifically analysed, and the feasible measures for the construction of rural digital villages was summarized in the context of different regions and different characteristics.

Based on the incoPat patent index database, a study was conducted on the global competition landscape of AI chips and lithography technology, focusing on four key dimensions: year, region, applicant, and patent value. The results indicate that from 1990 to 2023, patent applications for global AI chips and lithography technologies have been rising annually. China plays a significant role in terms of patent applications and the sources of patent inventors for AI chips and lithography technology. However, in terms of applicant distribution and patent value, it has not yet ranked among the leaders. There is a gap between China and the international advanced level in patent concentration and core technological competitiveness. In order to improve its position in global competition, it is suggested to strengthen its patent strategy and technological innovation.

Using the emission factor method, the carbon emissions of Jiangsu Province as a whole, as well as those of southern, central, and northern Jiangsu from 2001 to 2019 were calculated. The variation coefficient, Gini coefficient, and Theil index were employed to measure the disparities in carbon emissions among the three major regions of Jiangsu. Based on the STIRPAT model and using ridge regression, the driving mechanisms of carbon emissions in Jiangsu province and its subregions were explored. Furthermore, scenario analysis was applied to predict the carbon emissions and peak carbon year of Jiangsu province for the period 2020—2050.The results show that significant disparities in carbon emissions exist among the three regions of Jiangsu province during the study period, with overall differences fluctuating and increasing over time; Population size, per capita GDP, urbanization rate, industrial structure, and energy intensity all significantly impact carbon emissions in Jiangsu and its subregions. Population size is identified as the primary driver of carbon emissions growth in Jiangsu province and southern Jiangsu, while the urbanization rate is the main driver in central and northern Jiangsu; Under scenarios of economic slowdown and industrial upgrading, Jiangsu province is projected to reach its carbon emissions peak as early as 2030.

Industrial policy is an important driver of industrial development. Taking the Shuangcheng Economic Circle of Chengdu-Chongqing area as the research area, an in-depth study on the driving effect of industrial policy was conducted based on knowledge graph technology, in order to clarify the important role of industrial policy in promoting industrial structure optimization, regional balanced development and economic transformation and upgrading. The industrial policy knowledge map of the region was constructed based on regional differences, time evolution characteristics, policy orientation and other core factors. Then, combining with the comprehensive industrial development level of the region, the visualization and retrieval functions of the knowledge map were utilized. The PMC I(policy modeling consistency) index was used to evaluate 496 policy samples, and the driving effect and driving mechanism of industrial policies were further analyzed. The results show that the industrial development trend in the study area is stable and good on the whole, and the PMC composite index scores of industrial policy samples in all regions have reached an acceptable level or above. Among them, the essence of policy content, the ability of forward-looking planning and the construction of guarantee and incentive mechanism are the core elements of the driving effect of industrial policy. Accordingly, countermeasures and suggestions are put forward from the perspective of policy optimization and perfection, evaluation and adjustment.

In order to make the virtual power plant(VPP) better integrate resources, and then realize product packaging and market transactions, using the natural geographic space attributes of energy resources to combine the demand-side energy of the virtual power plant with GIS(geographic information system) combined, a GIS-based evaluation model for demand-side energy is proposed. The composition of demand-side energy was analyzed, and the index system of the evaluation model was established. The index system of the evaluation model was refined, the common index and difference index were proposed, and the quantification method was given.AHP, entropy weight method and weighted sum are used to realize the calculation method of the standardization of evaluation indicators, the weight of common indicators, the weight of difference indicators and the evaluation model. The evaluation model method was analyzed through the data of 10 regions to provide quantitative methods and scientific support for the construction of virtual power plants for resource optimization aggregation.

Quantitative models are one of the core challenges for investors in stock dynamic prediction. The original LSTM(long short-term memory) stock prediction model was affected by noise in the input data, which interfered with the prediction effect. In this paper, there are 259 indicators that affect stock prices. Firstly, the input data was reduced in dimensionality using dimensionality reduction methods to preserve key information, and then input into LSTM to form an improved prediction model, namely PCA-LSTM model, ISOMAP-LSTM model, and PCA-ISOMAP-LSTM model. Through empirical comparison, compared with the original LSTM prediction model and the attention mechanism model MHA-LSTM, the PCA-LSTM model and ISOMAP-LSTM model reduce training time. The average absolute error (MAE), average relative error (MAPE), and root mean square error (RMSE) in the prediction error evaluation indicators are significantly reduced, and the average rise and fall accuracy (ARRF) is significantly improved. However, the PCA-ISOMAP-LSTM model has an increase in error rate and a certain decrease in accuracy. The Diebold Mariano test also showed that the PCA-LSTM model and ISOMAP-LSTM model have stronger stock prediction abilities than the original LSTM model and MHA-LSTM model, while the PCA-ISOMAP-LSTM model and MHA-LSTM model have weaker prediction abilities than the original LSTM model. The difference in prediction accuracy between the PCA-LSTM and ISOMAP-LSTM models is not significant, and both can be used as a new technical support for quantitative stock investment.

Under the “dual carbon” goals, the awareness of social sustainable development has been enhanced, and ESG(environmental, social, and governance)responsibility has received increasing attention, with an increasingly prominent impact on corporate green technology innovation. Selecting 2656 A-share listed manufacturing companies from 2013 to 2022 as samples, a multidimensional fixed effects model and a difference in differences model were constructed to explore the impact of ESG responsibility on green technology innovation in manufacturing companies. It is found that firstly, fulfilling ESG responsibilities in manufacturing enterprises can promote green technology innovation, especially in state-owned and technology intensive enterprises. Secondly, ESG accountability enhances the ability of enterprises to absorb knowledge and skills by promoting human education and skill upgrading, thereby improving green technology innovation. This mechanism is particularly significant in state-owned and technology intensive enterprises. Finally, the impact of the “dual carbon” goals on green technology innovation is that low-carbon enterprises have better implementation effects than high carbon enterprises.

With the booming development of digital economy, as the core force to promote the transformation of the insurance industry, insurance technology has become the focus of attention in academic circles. Based on CiteSpace software,the literature in the field of insurtech on China Knowledge Network was visualised and analyzed to reveal its research evolution, core hotspots and future directions. It is found that the number of publications in this field shows a significant growth from 2019 to 2023 and reaches the annual peak in 2023. Representing research institutions include Liaoning University and Southwestern University of Finance and Economics, and the research hotspots are highly focused on “big data” and “artificial intelligence”, “application of blockchain technology” and “innovation of insurance products”.The current research network has not yet formed a systematic and holistic cooperation pattern. The current research network has not yet formed a systematic and holistic cooperation pattern, and the closeness of cooperation between institutions and scholars still needs to be further improved.