Traditional management accounting methods are difficult to provide the necessary information for environmental economic management decisions. In response to the increasingly urgent need for decision-related information, a new branch of accounting, environmental management accounting, has emerged and is receiving increasing attention. Material Flow Cost Accounting (MFCA) is a useful tool for managing complex resource and waste streams. However, MFCA is mainly used for ex-post accounting and reporting and no efforts are made to use it for forecasting. In this study, we introduce MFCA method into the budgeting process of manufacturing firms, and thus construct an MFCA-ABB (Activity-Based Budget) model. This model is applied to JLC Company which is a fragrant liquor manufacturer in China, in order to forecast and plan for its resource consumption, positive product output, and negative product generation. Based on the forecasts of involved material flows, inefficiencies in the company’s liquor production process are identified; scenario analysis is then conducted to determine the optimal process and the technology adopted. The proposed MFCA-ABB model turns a pure operating budget into an environmental-economic budget, thus achieving both environmental and economic benefits for the company. Besides, this study makes an attempt to apply ABB in environmental management accounting, which suggests the possibility of applying the conventional management accounting tools, after modified, to the environmental-economic management of manufacturing firms in the future.

In recent years, the development and iteration of information technology have prompted the financial industry to transform and upgrade to financial technology (FinTech), which has received emerging attention from the global financial industry. While the FinTech industry is growing rapidly around the world, however, few studies have foucusd on the shortage of talent and difficulties in recruiting talent. First, this paper clarifies the shortage of FinTech talent through expert interviews and a questionnaire survey of 112 financial industry enterprises in Shanghai, China. Following, based on role theory, we construct a talent capability evaluation index system using 5 primary and 17 secondary indicators. Based on the exploration above, a gray optimization model is designed to support talent recruitment strategy for FinTech enterprises. The results indicate that Chinese FinTech talent should have composite abilities with outstanding professional technical skills and learning abilities, innovation and teamwork ability, project experience, and international vision. This study provides methodological guidelines for global FinTech talent evaluation and recruitment strategies and broadens the application of role theory and gray clustering theory.

Introduction: This study investigates the effect of recently implemented quote limits policy in China’s A-share Main Board market, a mechanism designed to enhance market stability. We examine whether this policy achieves its goals uniformly across distinct large-cap (HS300) and mid-to-small cap (CSI500) market segments.Methods: Utilizing the Multifractal Detrending Moving Average Cross-Correlation analysis (MF-X-DMA) and the nonlinear Granger causality test, we assess the policy’s potential heterogeneous impact on these two indices.Results: Our findings reveal that the policy decreases market efficiency and increases cross-market co-movement complexity for HS300, while conversely improving market efficiency and information flow for CSI500.Discussion: We offer novel empirical evidence on this policy using nonlinear methods. Our findings highlight the potentially divergent and unintended consequences of “one-size-fits-all” financial regulations across varied market segments, providing valuable insights for policymakers.

Introduction: The development of digital technology and upgrading of consumer service demand have led to profound changes in the supply chain, gradually forming a supply chain ecosystem and transforming inter-enterprise cooperation into ecological cooperation. Amidst complex international landscapes and industrial transformation, the role of government has garnered significant attention. Thus, enhancing ecological cooperation in the supply chain during the new development stage and clarifying the government’s role constitute critical research topics.Methods: We analyze strategies of governments, suppliers, and manufacturers using a three-party evolutionary game model. The ideal event is defined within this model, and key influences on the dynamic evolutionary process are examined. A sensitivity analysis of selected parameters is rigorously conducted.Results: Our findings demonstrate that: (1) The government significantly promotes ecological cooperation among supply chain enterprises and can adjust system equilibrium by modulating the subsidy coefficient and regulatory costs. (2) Greater eco-sensitivity enhances advantages in eco-cooperation. (3) Establishing reasonable compensation fees between enterprises ensures effective eco-cooperation. (4) A company’s cost investment in eco-cooperation critically influences its engagement in such collaboration.Discussion: The study confirms the government’s pivotal role in facilitating supply chain ecological cooperation through policy instruments. Eco-sensitivity, inter-firm compensation mechanisms, and cost investments are identified as decisive factors for successful collaboration. Sensitivity analysis further validates the robustness of these parameters in shaping evolutionary outcomes.

The exponential growth in demand for data storage and computing has led to a rapid expansion in the energy consumption and carbon emissions of data centers (DCs). Hybrid energy systems that integrate renewable energy sources are regarded as a sustainable and low-carbon solution for powering the DCs. This study proposes an optimal cooperation scheduling strategy for the hybrid energy system powering the DC and electric vehicles (EVs). The strategy is based on load transferring and operates within a carbon trading mechanism, explicitly addressing the coupling between computational loads and power consumption. An optimization model is constructed that considers economic objectives, including operational cost and a stepped carbon trading cost, to obtain optimal energy dispatch and computational task allocation strategies. This framework ensures the economic interests of EVs’ owners while satisfying the energy demands of both the DC and the EVs. The results of a case study based in Shanghai demonstrate that the proposed hybrid energy system with multiple sources has significant economic and environmental advantages in spite of operational complexity. Furthermore, the collaborative strategy further enhances the cost reduction and carbon emission reduction. Specifically, the cooperative strategy achieves a 5.21% reduction in total cost compared to Case 1 (without V2G) and a 22.80% reduction compared to Case 2 (without computing task transferring). By adopting the optimal scheduling solution, carbon emissions can be reduced by 16.74% relative to Case 1 while remaining at a level comparable to Case 2. Furthermore, the impact of the carbon trading mechanism on the system’s cost and carbon emissions is analyzed. The results indicate that while a stricter carbon trading mechanism leads to an increase in the total cost, it also results in a reduction in carbon emission from the DC’s hybrid energy system.