In recent years, China has begun to regulate real estate prices based on monetary policy. In order to study the impact of monetary policy on real estate prices, the QVAR model was used to explore the impact of monetary policy factors on real estate price changes by using quantile impulse response graph and variance decomposition. The monetary policy used the quantitative monetary policy, the money supply M2 and the price-based monetary policy, the interbank interest rate as the standard, and the real estate price from 2003 to 2023 as the impact variables, and the M2 growth rate and the interbank interest rate were used to make quantile impulse response function graphs and variance decomposition analysis based on the QVAR model for real estate prices. The results show that quantitative monetary policy is negatively correlated with real estate prices in the short term and positively correlated in the long term, and price-based monetary policy is negatively correlated with real estate prices, and the influence of money supply on real estate prices accounts for a larger weight.

In construction projects, procurement suppliers not only affect the quality and progress of the project, but also directly affect the cost of the project. How to select high-quality suppliers that meet project requirements from numerous suppliers has become an urgent problem to be solved. Based on this, a research on the selection of construction project procurement suppliers based on ANP+VIKOR algorithm was proposed. Taking into account multiple factors, a multi-dimensional supplier selection indicator system was constructed, and the indicator data was standardized. The ANP algorithm was used to clarify the relationship between each indicator and the weights of the indicators were calculated. The VIKOR algorithm was used to calculate the group utility value and individual regret value of each supplier, and the compromise value of each supplier was obtained, and the suppliers were ranked. Case analysis shows that the studied method can stably select the optimal supplier and exhibits relative insensitivity to changes in decision mechanism coefficients, demonstrating its reliability and practicality in practical applications.

In order to explore the relationship between education, science and technology and talent in China, the evaluation indicators of the development level of education, science and technology and talent were constructed respectively. Factor analysis method was used to calculate the development indexes of education, science and technology and talent in each province from 2018 to 2022, and the coupling coordination degree of the three systems was calculated based on the coupling coordination model. The results show that the coupling coordination degree of education, science and technology and talent systems has increased year by year, but the imbalance is still the main tone of the country. Further, the Tobit model was used to analyze the factors affecting the coupling coordination degree. It is found that the degree of opening up and industrial structure have a significant positive effect on the coupling coordination degree, and the government regulation has a significant negative effect. In order to improve the coordination level of education, science and technology and talent system, it is necessary to further expand foreign exchange and cooperation, vigorously support the development of strategic emerging industries, and enhance the systematicness of government regulation.

The state of health(SOH) and remaining useful life(RUL) of a battery are core indicators for evaluating battery performance degradation and potential lifespan. Accurately predicting the SOH and RUL of batteries is crucial in practical applications. To capture changes in battery performance and make predictions, operational data of the battery is typically relied on to train machine learning algorithms, such as neural networks or deep learning methods. However, traditional machine learning models often adopt a single architecture to adapt to the entire dataset, which is insufficient when dealing with complex and highly heterogeneous big data. Such models generally have the risk of insufficient generalization ability and overfitting, and are inefficient in big data processing. Therefore, Hierarchical sparse mixture of experts(HS-MoE) and multi head mixture of experts(MH-MoE) models were used to construct predictive models for battery State of Health(SOH) and Remaining Useful Life(RUL), respectively. Comparative experiments were conducted on publicly available datasets from NASA and EIS, and the results showed that the MH-MoE model outperformed the HS-MoE model in predicting SOH and RUL on both datasets.

In order to solve the problems of weak internal motivation and insufficient resource capabilities in the ESG(environmental, social, and governance)development of Chinese enterprises, grounded theory was used to conduct a textual analysis of ESG reports from 102 A-share listed companies between 2020 and 2023. A dual-dimensional analysis model was constructed encompassing management attitude (“words”) and capital investment (“deeds”), with manual coding of the above ESG reports via Nvivo12 software, and statistical analysis of their patterns and trends. Key findings include from the perspective of management attitude, the sample companies currently focus on compliance ESG construction and institutional safeguards instead of strategic ESG development. From the viewpoint of capital investment, the sample firms’ inputs in the three ESG dimensions are significantly unbalanced, especially the environmental and governance dimensions that are under-invested. Through dual-dimensional cross-analysis, it is found that the “words” of enterprises do not match their “deeds” at this stage, but the current compliance management can be transformed into future strategic development. Industry comparative analysis shows significant differences between industries with high and low ESG ratings in both “Words” and “Deeds” dimensions, necessitating coordinated efforts to promote integrated collaboration between emerging and traditional industries. Accordingly, tripartite improvement pathways of corporate ESG construction are proposed for enterprises, government, and capital market.

Under the background of global climate change and the “dual carbon” goal, the oil and gas field industry is facing a dual challenge of stabilizing oil and gas production and controlling emissions to reduce carbon emissions. In recent years, Zhongyuan Oilfield has vigorously developed new energy, with the Pusan Transfer Station as a pilot for new energy substitution. Combining the resources and energy consumption characteristics of the transfer station, the oilfield has fully utilized the waste heat resources and solar energy resources of the produced water in this area, forming a “waste heat+photovoltaic” multi energy comprehensive utilization technology, providing a clean, low-carbon and stable heat source for the transfer station. After the implementation of the project, the annual natural gas consumption has been reduced by 1.6 million cubic meters, saving 1 864 tons of standard coal annually and reducing 3 392 tons of carbon dioxide annually, achieving good economic and social benefits. It has been promoted and implemented at various transfer stations in the oilfield, laying the foundation for achieving green and low-carbon development of the oilfield.

With the increasing penetration of new energy such as wind turbine(WT) and photovoltaic(PV) output in the energy system, the consumption of new energy in the energy system has become an urgent problem to be solved. At the same time, shared energy storage continues to participate in the grid connection, and the trading relationship between shared energy storage and other participants in the energy system is also the key to the optimization of power scheduling in the energy system. In this context, taking the energy system with micro-grids(MGs) and shared energy storage (SES) as the research object, and considering the uncertainty of new energy output, the energy system scheduling mechanism was designed based on Stackelberg game. The results of example analysis show that the actual energy capacity and power capacity of SESC have decreased by 11.83% and 31.89% respectively, and the new energy consumption rates of MGS has increased to more than 90%. The Stackelberg game scheduling strategy improves the overall operating income and stability of the energy system.

China is in the stage of stock renewal and ecological civilization construction, and the renewal and optimization of urban waterfront space plays an important role. The CiteSpace metrology software was used to visually and quantitatively analyze 443 representative literatures in the research field of “waterfront space renewal”“waterfront redevelopment”“waterfront vitality” in Chinese National Knowledge Infrastructure(CNKI) database. The current research hotspot and trend of waterfront space were clarified. Based on this, the renewal path of urban waterfront space was briefly discussed from four aspects: “public”(serving the people), “garden”(conserving ecology), “city”(beautifying life), “municipality”(low-carbon and high-quality development).

With the rapid development of science and technology, the cooperation model in which multiple enterprises fund scientific research projects in the form of innovation alliances has gradually become normalized. Focusing on the problem of achievement rights and interests distribution encountered when multiple enterprises jointly fund a project portfolio, the contributions of funders were comprehensively considered in four aspects: funding amount, management contribution, research contribution, and transformation contribution. Using the fuzzy DEMATEL(decision-making trial and evaluation laboratory)-improved Shapley value method, a distribution strategy for achievement rights and interests is proposed. This provides a solution that is fair, scientific, and practical for the distribution of patent rights in multi-party jointly funded project portfolios. Through case analysis, it is confirmed that the distribution results obtained by this method are more reasonable, offering an effective approach to solving the problem of achievement distribution in multi-party funded project portfolios.

Taking 17 cities in Henan Province as the research object, the level and regional differences of digital economy development in Henan Province were analyzed by using the entropy weight method and Dagum Gini coefficient. The influencing factors of digital economy development in Henan Province were analyzed by using the fixed-effect panel model. The results show that the level of digital economy in Henan Province showed an upward trend from 2013 to 2020, and the overall level of digital economy development in 2021 and 2022 declined. There is a digital divide and Matthew effect in the development of digital economy in Henan Province. The level of economic development, government intervention, education, urbanisation rate and openness have a positive impact on the development of digital economy in Henan Province.