A Study on the Impact of Digital Finance on Regional Productivity Growth Based on Artificial Neural Networks

Abstract

The relationship between financial development and economic growth has become a hot topic in recent years and for China, which is undergoing financial liberalisation and policy reform, the efficiency of the use of digital finance and the deepening of the balance between quality and quantity in financial development are particularly important for economic growth. This paper investigates the utility of digital finance and financial development on total factor productivity in China using interprovincial panel data decomposing financial development into financial scale and financial efficiency; an interprovincial panel data model is used to explore the utility of digital finance on total factor productivity. This involves the collection and preprocessing of financial data, including feature engineering, and the development of an optimised predictive model. We preprocess the original dataset to remove anomalous information and improve data quality. This work uses feature engineering to select relevant features for fitting and training the model. In this process, the random forest algorithm is used to effectively avoid overfitting problems and to facilitate the dimensionality reduction of the relevant features. In determining the model to be used, the random forest regression model was chosen for training. The empirical results show that digital finance has contributed to productivity growth but is not efficiently utilised; China should give high priority to improving financial efficiency while promoting financial expansion; rapid expansion of finance without a focus on financial efficiency will not be conducive to productivity growth.

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

“…It proves that eliminating factors with lower contribution rates can simplify the model, reduce unnecessary complexity, reduce the risk of overfitting, improve the generalization ability of the model, and reduce the dimensionality of the feature space. The feature optimization can improve the computational efficiency and training speed of the model, as well as improves stability and prediction accuracy 46 . The remaining 15 features were input into the random forest model for an operation to obtain the predicted spatial distribution of station sites in Lanzhou City.…”

Regional Wealth Data Acquisition and Modeling: Innovations Needed for Advancement in Sustainable Wealth in Energy-Rich Regions

“…Previous studies explored the use of artificial intelligence in regional wealth model development. For example, Chernov et al [116] and Li et al [117] highlighted the potential of AI in scenario modeling and financial efficiency analysis, respectively. Ma [118] further demonstrated the application of AI in analyzing the impact of environmental pollution on income and in clustering and ranking regions based on economic indicators.…”

“…For example, machine learning and big data analytics in scenario modeling and economic potential analysis have been recommended [116]. Other authors have suggested using cognitive informatics and simulation modeling for human-centric systems and sustainable development [119,120], while others proposed feature selection and ensemble decision frameworks for GDP prediction and financial development index construction [108,117]. Ultimately, while progress has been made, there is much work to do.…”

Retracted: A Study on the Impact of Digital Finance on Regional Productivity Growth Based on Artificial Neural Networks

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”