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

Mele, Marco; Magazzino, Cosimo; Schneider, Niklas; Nicolai, Floriana

doi:10.1007/s11356-021-14264-z

Cited by 47 publications

(17 citation statements)

References 49 publications

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“…Data science for social good (DSSG) and artificial intelligence for social good (AISG) are the applications of data science and/or artificial intelligence for improving social outcomes (Magazzino & Mele, 2022 ; Rostami-Tobar et al, 2021; Moore, 2019 ). DSSG core techniques include data science, data visualization, and quantitative models for a wide range of application areas, such as health, sustainability (Magazzino et al, 2021 ; Mele et al, 2021a , 2021b ), economic development (Mele et al, 2021a ), disaster relief operations, public safety, transportation (Magazzino et al, 2022 ), and human services. Catlett and Ghani ( 2015 ) launched a special issue to promote organizations collaborating with data scientists and applying data-driven quantitative tools for the social good.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: Forecasting carbon emissions future prices using the machine learning methods

et al. 2023

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Due to the uncertainty surrounding the coupling and decoupling of natural gas, oil, and energy commodity futures prices, the current study seeks to investigate the interactions between energy commodity futures, oil price futures, and carbon emission futures from a forecasting perspective with implications for environmental sustainability. We employed daily data on natural gas futures prices, crude oil futures prices, carbon futures prices, and Dow Jones energy commodity futures prices from January 2018 to October 2021. For empirical analysis, we applied machine learning tools including traditional multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM). The machine learning analysis provides two key findings. First, the nonlinear frameworks outperform linear models in developing the relationships between future oil prices (crude oil and heating oil) and carbon emission futures prices. Second, the machine learning findings establish that when oil prices and natural gas prices display extreme movement, carbon emission futures prices react nonlinearly. Understanding the nonlinear dynamics of extreme movements can help policymakers design climate and environmental policies, as well as adjust natural gas and oil futures prices. We discuss important implications to sustainable development goals mainly SDG 7 and SDG 12.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED ARTICLE: Forecasting carbon emissions future prices using the machine learning methods

et al. 2023

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“…Early studies introduced environmental pollution as an essential core variable in the health model ( 39 ). Further studies found that extensive economic development caused environmental pollution, especially air pollution, which directly caused much damage to human health ( 10 , 11 ). Diseases caused by environmental pollution mainly include respiratory conditions, heart diseases, etc., ( 40 , 41 ).…”

Section: Policy Background and Literature Reviewmentioning

confidence: 99%

Does environmental regulation improve public health? Evidence from China's Two Control Zones policy

Yang

Liu

Yan

et al. 2023

Front. Public Health

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Improving public health is the premise of sustainable human development and an essential condition of economic growth. However, increasing severe environmental pollution poses a threat to public health. Implementing environmental regulation policy has become a meaningful way to control environmental pollution and the basis and guarantee for achieving public health. This paper aims to study the impact of environmental regulation on public health. The Two Control Zones (TCZ) policy is the earliest and stricter environmental regulation in China. Based on the policy experiment of TCZ, this paper analyzes the role of TCZ policy in improving public health using the DID model and data from 112 cities. The study finds that the TCZ policy can significantly improve public health, and this improvement effect was continuous and lagging. The results of benchmark regression show that the implementation of the TCZ policy has reduced the incidence rate of respiratory diseases in TCZ areas by 5.7%. When considering city heterogeneity in terms of economic and geographical conditions, the study further found that the impact of improvement is largest for cities in more heavily non-provincial capital and central and western regions, respectively. In addition, the results of mediating test show that TCZ policy improves public health by reducing environmental pollution. Our research fills the gap in the literature on the micro effects of environmental regulation policy on public health in developing countries. The government should prioritize environmental pollution control through reasonable environmental regulation policies. The government should strengthen environmental information disclosure to remind the public to deal with air pollution. The government and enterprises also should take various environmental protection measures to reduce air pollution emissions.

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“…e selection of trade partners according to their own development needs and the formulation of trade policies conducive to the country's development before engaging in international trade are of great importance to national economic development and political security [6]. Senturk et al explored the competitive relationship between oilimporting countries based on complex network theory and analyzed the evolution and transmission of oil trade competition patterns in conjunction with measures of competitive intensity [7].…”

Section: Related Workmentioning

confidence: 99%

A Nonlinear Stochastic Matrix Model for the Analysis of the Association between International Trade Growth and Environmental Change

Zhang

2022

Mathematical Problems in Engineering

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This study uses a nonlinear stochastic matrix for an in-depth analysis of the association between international trade growth and environmental change. First, for a class of uncertain stochastic nonlinear time-lag systems, this study investigates its time-lag-related robust stochastic stabilization problem. A memory-free state feedback controller for the system is then obtained to ensure the effectiveness of the system performance. This makes the system more general and the corresponding theoretical results less conservative. Finally, numerical examples are given to further verify the validity and feasibility of the results. The “tariff measure” based on the magnitude of adverse changes in trade policy is not an accurate measure of trade policy uncertainty; the stochastic volatility method measures trade policy uncertainty by including tariff fluctuations that have been communicated in advance in the trade policy uncertainty indicator and fail to identify the different commodities that occur during the current trade friction. The trade policy uncertainty index constructed through textual analysis of newspapers can better reflect the trade policy uncertainty during this trade friction between China and the United States, and the rich time-varying nature can also reflect the alternating changes of tension and détente in economic and trade relations over some time. The analysis of the mechanism reveals that exporters have fewer emission reduction facilities than nonexporters but significantly lower energy consumption intensity for coal and water than nonexporters, reasoning that the mechanism by which exporters are more environmentally friendly than nonexporters lies in their cleaner energy use mix. Following the conclusions of this study, relevant policy recommendations are put forward, specifically the use of more efficient energy sources in the production process and more investment in energy efficiency and emission reduction to combat pollution.

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

Cited by 47 publications

References 49 publications

RETRACTED ARTICLE: Forecasting carbon emissions future prices using the machine learning methods

RETRACTED ARTICLE: Forecasting carbon emissions future prices using the machine learning methods

Does environmental regulation improve public health? Evidence from China's Two Control Zones policy

A Nonlinear Stochastic Matrix Model for the Analysis of the Association between International Trade Growth and Environmental Change

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