The impact of economic policy uncertainty on M&A performance was examined. Taking the M&A data of A-share listed companies from 2007 to 2023 as the study sample, it is found that the impact is negative and statistically significant, and the moderating effect factor investor sentiment exacerbates this impact. Further analysis shows that during economic upturns, economic policy uncertainty has the greater significant negative effect on M&A performance compared with the economic downturns. The similar condition happens in the enterprises with non-concentrated institutional investor holdings. But economic policy uncertainty does not seem to influence the M&A performance of the enterprises with green M&A type or located in first-tier or new first-tier cities.

With the development of cross-border e-commerce, enterprises often face institutional pressure from external sources when promoting cross-channel integration. 298 data through Smartpls software was analyzed to explore the mechanism of institutional pressures on cross-channel integration, and verify the moderating role of organizational learning in it. It is found that normative, coercive and imitative pressures all positively affect cross-channel integration in firms. Organizational learning moderates the positive relationship between coercive and imitative pressures and cross-channel integration, while the moderating effect of normative pressure is not significant. The article reveals the complex interaction between institutional pressure and cross-channel integration, emphasizes the key moderating role of organizational learning in this process, and provides a theoretical basis for cross-border e-commerce firms to cope with institutional pressure and optimize cross-channel integration strategies.

Agriculture is the cornerstone of building a modern country. As the impact of global climate change on agricultural production intensifies, studying how agricultural enterprises can respond to challenges such as extreme weather and resource scarcity is crucial for improving agricultural adaptability and mitigating the negative effects of climate change. Based on empirical data of A-share agricultural listed companies from 2013 to 2022, fixed effect and mediation effect models were selected to test the impact of climate risk on business performance. The research results indicate that climate risk has a negative impact on the operational performance of agricultural enterprises, and green innovation plays a mediating role in it. Heterogeneity results show that non-state-owned enterprises are more significantly impacted by climate risk than state-owned enterprises. Important theoretical basis and practical guidance are provided for agricultural enterprises in addressing climate risks.

Digital resilience, as the ability of enterprises using digital technology to respond to major shocks, has received extensive attention from society. Through a review of domestic and foreign literature, it is found that the digital resilience of enterprises mainly consists of the ability to absorb shocks, the ability to adapt to shocks, and the ability to recover to a steady state. The multi-case study method was used to identify the typical practices adopted by chemical enterprises in the process of building digital resilience. On this basis, these scattered typical practices were further summarized and integrated to construct a universal model of the digital resilience building path for chemical enterprises. Through dynamic management and control based on digital innovation, organizational collaboration based on digital platforms, resource aggregation based on industrial Internet platforms, and agile response based on business model innovation, it helps enterprises effectively defuse major shocks and promotes their transition to a new steady state.

Abnormal events involving potential safety hazards and near misses are used as early warnings and signs for the escalation of minor accidents to major accidents, which can be used to establish accident models to identify source events and correct unsafe factors in the protection system. Tailored to the process characteristics and accident features of liquefied natural gas(LNG) storage areas, the system hazard identification, prediction and prevention(SHIPP) model was improved, and a novel risk assessment modeling method integrating fault trees, Bayesian networks, and the A-star algorithm was proposed. Firstly, based on expert experience and abnormal events in the accident alarm database, a safety barrier model and fault tree were established. Then, following the chain rule, the fault tree was mapped to a Bayesian network. Finally, the improved A-star algorithm was integrated to determine the accident occurrence pathways. Research based on the LNG accident alarm database indicates that this method, compared to the traditional SHIPP model, can achieve dynamic forward risk assessment and quantify the conditional probabilities between accidents, as well as simulate the accident occurrence process when safety barriers fail in reverse. The research results can provide reasonable design and decision-making for the system safety and risk avoidance of LNG storage areas.

Consumption upgrading is an inevitable trend of national development to a certain extent, and the differentiated research of different regions is the inevitable basis for formulating policies. The framework of residents’ consumption upgrading system was constructed from five dimensions: consumption scale and level, consumption structure, consumption capacity, consumption environment and consumption mode. The results show that the consumption upgrading of residents in the whole country and the three major regions shows a relatively flat upward trend, and the eastern region > the whole country> the central region > the western region. The difference in the level of consumption upgrading of Chinese residents mainly comes from the internal differences in the eastern region and the imbalance in the development of the eastern-central and eastern-western regions. There isσdevelopment in the whole country and the three major regions, there isβconvergence between the whole country and the eastern and western regions, and there is noβconvergence in the central region.

As one of the core policy tools for achieving the “dual-carbon” goals, the carbon market possesses financial, environmental and policy attributes. These characteristic attributes prompt the establishment of an effective regulatory system for carbon emission rights trading. Currently, in China’s carbon emission rights trading and its subsequent supervision, there are problems such as inefficient supervision and opaque trading information. Based on the application requirements of modern environmental governance for new digital technologies, applying technologies such as blockchain’s distributed storage, consensus mechanism, and smart contract encryption to the supervision of carbon emission rights trading will help improve the supervision efficiency and the security of the circulation of carbon trading information, and also alleviate the problems existing in the process of carbon emission monitoring and verification. However, due to the dual nature of technology, while blockchain empowers the supervision of carbon emission rights trading with its technology, there are also certain hidden concerns in aspects such as data security and the protection of the rights and interests of trading entities. In this regard, it is necessary to further improve the smart contract review mechanism and perfect the relief rules, construct a risk prevention and evaluation system, and clarify the main responsibilities of blockchain system development and maintenance to ensure the effective supervision of carbon emission rights trading.

Advanced nuclear energy technology and green computing power technology are both typical representatives of new quality productive forces, and are of great significance for the future development of artificial intelligence and the transformation of green energy. In recent years, the collaborative development model between nuclear energy and computing power centers has been burgeoning. The formation basis of this collaborative development model, as well as the advantages of the business model in the fields of energy conservation and carbon emission reduction, economic profitability, safety and stability, resource saving and market absorption were introduced. The two potential coupling models of electricity purchase-sale model and off-grid direct supply model were also discussed. At the same time, the article also focused on the challenges from the fields of policy, regulations, technology, cost and market, and outlined the typical business experiments abroad. Finally, recommendations are elaborated on in three aspects including technological advancement, commercial innovation and policy support.

Accounting to the goal of achieving the carbon peak by 2030, based on the existing relevant policies, the kaya carbon emission identity, combined with China’s “14th Five-Year Plan” and long-term planning and other relevant policies, the scenario analysis method was used to estimate China’s total carbon emissions in 2030. On the basis of the total amount, taking into account the two principles of fairness and efficiency, entropy method and zero-sum income data Envelopment (ZSG-DEA) were used for empirical testing, and a provincial carbon emission quota allocation scheme under the multi-index system was constructed. The allocation plan that takes into account the two principles of fairness and efficiency is conducive to the establishment of a reasonable cross-regional carbon emission quota allocation mechanism, and has important reference value for promoting China’s cross-provincial collaborative emission reduction strategy, establishing and improving the national carbon trading market, and achieving the goal of carbon peak in 2030.

Innovation-driven development provides new impetus for economic and social development and is the core approach to leading future economic transformation. Government R&D investment reflects the national will, and it is necessary to evaluate its output efficiency. Using panel data from 31 provinces(due to the lack of data, the statistical data mentioned here do not include the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province) in China from 2018 to 2022, 13 specific indicators from the input and output sides were selected to build a performance evaluation system for government R&D investment. Factor analysis was used to study the government R&D investment performance. Combining the model construction and the results analysis, improving government R&D investment performance is proposed by promoting diversification of R&D funding sources, optimizing the execution structure of government R&D funding, and increasing R&D personnel input.