The blueberry industry in China has experienced rapid development over the past 20 years, and the blueberry processing industry has become an important means to extend the blueberry industry chain and increase industrial added value. With the development of agricultural E-commerce, online platforms have become crucial for selling blueberry processed products. Product details and sales information for three main types of blueberry processed products on the JD platform were obtained, including blueberry jam, blueberry wine, and frozen blueberry. Through data preprocessing and analysis, the typical commodity features of these three processed products were extracted, the sales situations were compared and analyzed and the market preferences for blueberry processed products were explored. The results showed that coverage of China on imported blueberry processed products is low, and domestically products dominate in both quantity and sales volume. The blueberry jam market is stable, and the quality and market share of domestic brands have certain advantages. The unit price of products is relatively concentrated, and the packaging forms are diverse, which meets the market demand. Blueberry wine is a niche category but holds market potential. The price differences between different brands are significant, and their packaging forms and bottle capacities are mostly the same. Consumers are more inclined to purchase products with medium or low price. As for frozen blueberry, there are fewer well-known brands. Consumers prefer low-and mid-priced products, and the quality and brand of imported products are more advantageous. Finally, according to the research conclusions, some suggestions are put forward on the development of blueberry industry, in order to achieve sustainable and healthy development of blueberry industry in the future.

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.

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.

The impact of enterprise digital transformation on total factor productivity and its mechanism were empirically tested based on the sample of Shanghai and Shenzhen A-share manufacturing listed companies from 2011 to 2022.It is found that the digital transformation of enterprises can transform data into production factors, optimize production processes, improve the efficiency of resource allocation, and improve total factor productivity.The mechanism analysis shows that digital transformation can enhance innovation efficiency, promote enterprises to accelerate process change and business model innovation, and further improve total factor productivity. Heterogeneity analysis shows that the promotion effect of enterprise digital transformation on total factor productivity varies with the nature of property rights, the nature of high-tech enterprises and the degree of regional marketization. It is suggested that the government should strengthen investment in digital infrastructure and improve innovation policies, and enterprises should actively embrace digital transformation, strengthen data governance and improve the efficiency of resource allocation.

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.

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.

The development of new quality productivity is an effective path to promote the high-quality development of industry in the new era. The development of new quality productivity empowers the industrial development of minority non-legacy resources, which plays a key role in realising the strategic goal of cultural power and promoting rural revitalisation in ethnic areas.The two-way coupling between the new quality productivity and the industrial development is analyzed of minority non-heritage resources, and takes the Yao nationality non-heritage industry in Gongcheng County, Guangxi as a typical case study, investigates the existence of the new quality productivity empowered by the Yao non-heritage resources in the industrial development of the development of resources: insufficient linkage of the development of resources, the weak foundation of the regional industry, the lagging behind in the construction of the standard system, and the lack of power of innovation and development of the realities of the dilemma and puts forward the integration of the non-heritage cultural resources. Optimising the environment for industrial development, promoting the construction of standards and fostering the development of new industries are proposed in order to develop new productivity according to local conditions and comprehensively promote the development of ethnic minority non-legacy-related industries into a new stage of high-quality development.

A comprehensive analysis of air distribution and thermal comfort assessment within large-scale heating, ventilation, and air conditioning(HVAC) systems holds significant value in enhancing indoor thermal comfort levels and reducing energy consumption. In order to improve the use comfort of irregular large space air conditioning and reduce the impact of the installation position of air conditioning indoor unit on the comfort of air conditioning, the construction party carried out software simulation test on the service hall on the second floor of a tourist center in Guangyuan City with the help of computational fluid dynamics (CFD) simulation software Fluent, and deepened the design of the area. In the simulation, it is found that although the comfort level of the service hall meets the standard requirements, there is still a large space for optimization. Therefore, the drawings were further designed according to the simulation results of the CFD software. After the deepening design, the simulation results were improved from local regional level and large area level to local regional level and large area level I. Finally, the actual wind speed and temperature of the service hall are compared with the simulation results. The results show that, except for a small part of the temperature and wind speed affected by the doors and Windows and personnel flow, other areas have less error with the simulation results. With the help of specific examples, the positive feedback idea of the construction party on the comfort control of the air conditioning system is opened, the scientific nature of the construction party in the project construction is improved, which provides some ideas and experience for the construction party to put forward the deepening design.

The geo-economic relationship between China and South Asia is of great significance to its trade development. Using the trade gravity model, the trade data between China and five countries from 2000 to 2022 was selected to analyzes the trade effects of their geo-economic ties. The regression results show that the geo-economic ties between China and South Asian countries have a restraining effect on China’s trade with South Asian countries, while the GDP, geographical distance, electric infrastructure, political stability, government efficiency and rule of law environment have a promoting effect on China’s trade with South Asian countries. Therefore, in the future, on the basis of the “Belt and Road” initiative, China should adjust the structure of import and export commodities to South Asian countries, optimize infrastructure construction, promote economic development, improve national income level, provide policy support, strengthen geo-economic cooperation and trade with South Asian countries, and deepen trade cooperation between China and South Asian countries. It is of great significance to promote regional linkages and development.

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.