Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

The significance of remittances to the Vietnamese economy necessitates investigating how they affect the value of the Vietnamese currency and other macroeconomic factors. Macroeconomic articles struggle to discover their impact on economic development, but measured remittances by migrant workers have recently soared. There is no academic study that has examined this phenomenon in Vietnam. This study uses wavelet frameworks to analyze the lead-lag nexus between exchange rates, remittances, and economic growth in Vietnam in time-frequency domains from 1995 to 2020. Overall, we find that: (i) remittances enhance economic growth in the short and medium run; (ii) exchange rates boost remittances in the short and medium run; (iii) exchange rates promote GDP in all frequency and time domains. Moreover, the partial wavelet coherence and multiple wavelet coherence frameworks also offered evidence supporting the wavelet coherence approach. More importantly, the outcomes of wavelet-based Granger causality unveil that there is two-way causality between the selected indicators, which means that all the indicators can predict each other at different frequencies. Our empirical results provide meaningful information for market participants and policymakers.

This study explores the role of intercultural communicative competence (ICC) and STEM education in building the soft infrastructure necessary for economic development within Kazakhstan’s transforming education system. The authors conducted an interdisciplinary analysis, emphasizing the cognitive and communicative aspects of foreign language education in secondary schools, proposing a model for integrating ICC through the use of information and analytical technologies. The research focuses on personalized education, teacher competencies, and student engagement, with experimental methods applied in a Karaganda-based school. The study aims to identify mechanisms and principles that enhance ICC development, contributing to Kazakhstan’s modernization efforts in fostering globally competitive graduates prepared for the demands of the international arena. This research lays the foundation for further practical experimentation in profiled schools, aligning education with national development goals.

This paper analyzes the relevance of social accounting information for managing financial institutions, using Banca Transilvania Financial Group (BTFG) as a case study. It explores how social accounting data can enhance decision-making processes within these institutions. Social information from BTFG’s annual integrated reports was used to construct a social balance sheet, and financial data was collected to calculate economic value added (EVA) and social value added (SVA). Research question include: Does social accounting represent a lever for substantiating the managerial decision in financial institutions? Results show that SVA is a valuable indicator for financial institution managers, reflecting the institution’s contributions to social well-being, environmental impact, and community support. Policy implications suggest regulatory bodies should mandate the inclusion of social accounting metrics in financial reporting standards to encourage socially responsible practices, enhance transparency, and incentivize institutions achieving high SVA. This paper contributes to the literature by demonstrating the practical application of social accounting in financial institutions and highlighting the importance of SVA as a managerial tool. It aligns with existing research on integrating corporate social responsibility (CSR) metrics into financial decision-making, enhancing the understanding of combining social and economic indicators for comprehensive performance assessment The abstract covers motivation, methodology, results, policy implications, and contributions to the literature.

This study examines the impact of education quality and innovative activities on economic growth in Shanghai through international trade and fixed asset formation. The study examines how higher education quality and innovation activities drive regional economic growth, with a focus on the mediating effects of international trade and fixed asset formation in Shanghai. The study adopts a quantitative approach utilizing panel data from 31 provinces in China covering the period from 1999 to 2022. The study incorporates variables such as education quality, innovation capacity, and GDP per capita, as well as control variables like labor, capital, and infrastructure. The methodology involves multiple regression models and robustness tests to verify the relationships between and effects of education quality and innovation with regard to economic growth. This study analyzes the direct and indirect effects of university R&D expenditure and innovation on economic growth using a regression model, based on data from 2014 to 2022 in relation to Shanghai. The model introduces variables such as international trade, capital formation, and urbanization to analyze the relationship between higher education quality and economic growth.