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

ABSTRACT: 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.

SUBMITTER: Mele M

PROVIDER: S-EPMC8458215 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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

Mele Marco M Magazzino Cosimo C Schneider Nicolas N Nicolai Floriana F

Environmental science and pollution research international 20210518 37

Although the literature on the relationship between economic growth and CO<sub>2</sub> 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 C ...[more]

PMID: 34008065

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Project description:BackgroundGene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. High-throughput technologies allow studying various aspects of gene regulatory networks on a genome-wide scale and we will discuss recent advances as well as limitations and future challenges for gene network modeling. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.MethodologyIn the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings.ConclusionsWe show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example.

Project description:Environmental quality is a sustainability issue influenced by economic realities. To understand their relationships and influence, a comparative study of economic factors that affect pollution in Iwo and Ibadan metropolia was carried out. Data for the study was generated by administering 1067 structured questionnaires at both locations out of which 215 and 658 were, respectively, retrieved from Iwo and Ibadan. Demographic features of the respondents assessed as female gender, married status, tertiary education, a Christian sect, and household size of two to five, were 52%, 67%,79%, 67%, and 55% in that order for Iwo; while for Ibadan, the figures were 67%, 61%, 86%, 69%, and 58% in the same order. The six economic factors evaluated were; (1) income, (2) accommodation & living standards, (3) waste & noise management regimen, (4) energy utilization (5), inclination toward a green economy, and (6) waste sorting technology and adoption. KMO (74.8%) and Bartlett's Test of Sphericity showed that the data were factorable at p < 0.005. Results considered three of the economic variables as significant enough to explain 59.3% of the pollution state in Iwo and 60.2% in Ibadan. Of major importance for Iwo were waste & noise management regime (22.5%), accommodation & living standards (18.7%), and inclination towards a green economy (18.3%). In Ibadan, the three extracted factors also explained 60.2% of all economic variables affecting pollution. These were accommodation and living standards (24.4%), inclination towards a green economy (18.8%), and waste & noise management regime (17.0%). That means the strong economic influencers of pollution were common to both cities, even though, their order of importance varied. In other words, their degree of influence on pollution may be location dependent. Conclusively, the people's standard of living and inclination towards a green economy are compelling determinants of environmental pollution and so should be considered by stakeholders in formulating associated policies.

Dataset Information

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

Publications

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

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