An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration?

Mele, Marco; Nieddu, Luciano; Abbafati, Cristiana; Quarto, Alessandro

doi:10.1007/s11356-021-13182-4

Cited by 6 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, numerous scientific research studies have focused on sustainable development. The results of AI analysis in one study showed the impact of air pollution change on the acceleration of economic growth in India [ 24 ]. AI can also facilitate the efficient monitoring of resource allocation of governments, donors, and development agencies involved in achieving Sustainable Development Goals (SDGs).…”

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence in health financing: a scoping review

Ramezani,

Takian,

Bakhtiari

et al. 2023

Cost Eff Resour Alloc

View full text Add to dashboard Cite

Introduction Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing. Method We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023. Results We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI. Conclusions To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.

show abstract

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence in health financing: a scoping review

Ramezani,

Takian,

Bakhtiari

et al. 2023

Cost Eff Resour Alloc

View full text Add to dashboard Cite

show abstract

“…e results in Table 3 show that compared with the prediction results of ARMA, logistic method, exponential regression method, and moving average method, the deep neural network prediction method has smaller prediction error and higher prediction accuracy and precision, which makes the effectiveness of prediction based on the deep neural network significantly improved. ere have been studies on the free combination of various models to form a new prediction model after the analysis of the prediction value, which can provide a good basis for the assessment of social development forms [14][15][16][17]. Nevertheless, the forecasting approach also has some limitations, all the forecasting models are linear forecasting, which is an important way to forecast GDP, and although it has higher accuracy compared with the traditional forecasting approach, the validity has to continue to be improved [18].…”

Section: Analysis Of Prediction Accuracymentioning

confidence: 99%

Socioeconomic Development Trend Prediction Model and Research Which is Based on Deep Neural Network

Zhang

Wei²,

Wang³

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In recent years, my country’s economic development has entered a new stage, and economic development has entered a new height. Gross Domestic Product (GDP) is the most important and commonly used measure to evaluate social and economic development. It can comprehensively reflect the operating conditions of the economy. At the 2014 APEC summit, Xi Jinping focused on the characteristics of the new normal of China’s economy. The economic growth has undergone major changes, and the economic structure has become more and more complete and has been continuously optimized. Therefore, the driving force of innovation plays an important role in the future social and economic development. The high-tech demonstration zone is the main hub of my country’s economic innovation and scientific and technological progress, and the development of the high-tech demonstration zone is particularly important in the development of the innovative economy. By predicting the economic growth, the development structure can be adjusted in time, and the coordinated allocation of resources can be achieved, which is conducive to the stable and rapid development of the economy. Therefore, it is very necessary to predict my country’s economic growth. At present, more and more scholars have invested more energy in the establishment of economic forecasting models. Previously, the prediction effect of traditional models was not satisfactory. At present, the field of target camera machine learning has been transferred. The application of the powerful nonlinear fitting ability of the machine learning model can improve the prediction effect. Based on this, this paper studies the use of artificial neural network with improved BP algorithm to predict social and economic development, and analyzes social and economic development trends and changing laws. The results show that the forecasting model constructed in this paper has a good forecasting effect, and the research results can provide a theoretical basis for scientifically formulating macro-control policies.

show abstract

“…Upon them, we can highlight Magazzino (2012), Magazzino and Giolli (2014), and Magazzino (2015) for the total energy-GDP relationship; Magazzino (2017) and Brady and Magazzino (2018) for the renewable energy consumption-income nexus; and Magazzino (2018) for the energy consumption-financial development nexus in Italy. Overall, recent assessments of the energy-GDP, energy-CO 2 , waste-GDP, waste-CO 2 nexuses using ML tools and for various case studies can be found in Mele and Magazzino (2020), Magazzino et al (2020aMagazzino et al ( , 2020bMagazzino et al ( , 2021aMagazzino et al ( , 2021bMagazzino et al ( , 2021cMagazzino et al ( , 2021d, and Mele et al (2021).…”

Section: The Relationship Between Economic Activity and Environmental Pollution In Italymentioning

confidence: 99%

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Mele

Magazzino

Schneider

et al. 2021

Environ Sci Pollut Res

Self Cite

View full text Add to dashboard Cite

Although the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

show abstract

An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration?

Cited by 6 publications

References 42 publications

The application of artificial intelligence in health financing: a scoping review

The application of artificial intelligence in health financing: a scoping review

Socioeconomic Development Trend Prediction Model and Research Which is Based on Deep Neural Network

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

Contact Info

Product

Resources

About