The Role of the Digital Economy in Rebuilding and Maintaining Social Governance Mechanisms

The digital transformation has impacted society at different levels, mainly on the economic and governance levels. This paper investigates the impact of the digital economy on social governance mechanisms. Additionally, it captures the indirect effects or mediating forces such as social reforms and a sustainable digital economy. The study followed a positivism philosophy, and it is survey research influencing cross-sectional study. The unit of analysis in the current paper was employees from four different professions as economists, financial analysts, managers, and teachers. The random sampling technique was used as a sampling type, and a questionnaire was used for data collection. Structural equation modeling (SEM) was carried out as a data analysis technique. The research findings revealed that the digital economy has a favorable impact on the social governance mechanism. Likewise, the digital economy positively affects social reforms and a sustainable digital economy. Social reforms also proved to link with a sustainable digital economy positively. The output of the indirect effects and structural model confirmed that social reform played a partial mediation role between the digital economy and sustainable digital economy. Moreover, a sustainable digital economy confirmed a partial mediation between the digital economy and the social governance mechanism. Finally, analysis confirmed a serial mediation among digital economy, social reforms, sustainable digital economy, and social governance mechanism. Therefore, policymakers and government agents should improve the digital economy to have a strong social governance mechanism.

An Austrian Take on ESG

This paper analyzes the contemporary debate about ESG – Environment, Social, Governance – using economic insights from Austrian Economics; particularly, on entrepreneurship, agency, and information asymmetry. These insights are contrasted to similar concepts in “mainstream” economics suggesting that the Austrian insight goes beyond them, first by stressing effectiveness in addition to efficiency and institutions in addition to law-likeliness. When applied to ESG, the Austrian insight portraits ESG as a special case of the socialist, or economic calculation debate causing misalignments between inter- and intrafirm goals, exacerbates agency problems and suffers from serious flaws in its conceptualization as well as methodology. Relying on entrepreneurship, however, could make ESG work. This paper, thus, applies Austrian economics to contemporary debates claiming that its insights provide a unique perspective but at the same time updating its research program.

The Application of Big Data in Modern National Economy and Politics

Big data is a new stage of informatization development. With the convergence and integration of information technology and human production and life, the rapid spread of the Internet, global data showing explosive growth and massive agglomeration, have had a significant impact on economic development, social governance, national management, and people’s lives.Countries around the world regard the promotion of economic digitization as an important driving force for innovation and development, and have made forward-looking layouts in cuttingedge technology research and development, data open sharing, privacy and security protection, and talent training.In-depth understanding of the current situation and trends of big data development, and its impact on economic and social development, analyze the achievements and existing problems of my country’s big data development, summarize and discuss the government’s response strategies, and promote the innovation of government management and social governance models, and realize government decision-making Identification, precise social governance, and efficient public services all have important meanings.

Short-Term IoT Data Forecast of Urban Public Bicycle Based on the DBSCAN-TCN Model for Social Governance

Internet of Things will play a vital role in the public transport systems to achieve the concepts of smart cities, urban brains, etc., by mining continuously generated data from sensors deployed in public transportation. In this sense, smart cities applied artificial intelligence techniques to offload data for social governance. Bicycle sharing is the last mile of urban transport. The number of the bike in the sharing stations, to be rented in future periods, is predicted to get the vehicles ready for deployment. It is an important tool for the implementation of smart cities using artificial intelligence technologies. We propose a DBSCAN-TCN model for predicting the number of rentals at shared bicycle stations. The proposed model first clusters all shared bicycle stations using the DBSCAN clustering algorithm. Based on the results of the clustering, the data on the number of shared bicycle rentals are fed into a TCN neural network. The TCN neural network structure is optimized. The effects of convolution kernel size and Dropout rate on the model performance are discussed. Finally, the proposed DBSCAN-TCN model is compared with the LSTM model, Kalman filtering model, and autoregressive moving average model. Through experimental validation, the proposed DBSCAN-TCN model outperforms the traditional three models in terms of two metrics, root mean squared logarithmic error, and error rate, in terms of prediction performance.