Predicting ecological footprint based on global macro indicators in G-20 countries using machine learning approaches

Roumiani, Ahmad; Mofidi, Abbas

doi:10.1007/s11356-021-16515-5

Cited by 10 publications

(7 citation statements)

References 75 publications

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“…These records reveal that problems related to the global environmental footprint are intensifying, posing an increasing concern among managers, economists, and environmentalists. This concern is particularly pronounced in the G20 countries 18 , 19 .…”

Section: Introductionmentioning

confidence: 99%

Forecasting the ecological footprint of G20 countries in the next 30 years

Eufrasio Espinosa,

Lenny Koh

2024

Sci Rep

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The Ecological Footprint evaluates the difference between the availability of renewable resources and the extent of human consumption of these resources. Over the past few decades, historical records have shown an accelerated decline in the availability of resources. Based on national footprint and biocapacity accounts, this analysis aims to advance the forecasting of the G20 countries' ecological footprints over a 30-year time frame. We employed a time series forecasting approach implemented in Python, which included-modular regression (Prophet) and Autoregressive Integrated Moving Average (ARIMA & Auto-ARIMA) methods. We evaluated and combined the performance of these three methods. The results indicated that among the largest economies of the G20, only four countries are projected to have a positive ecological footprint balance by 2050. These countries share the common denominator of large land areas and a moderate population growth projection. However, the overall trend of the indicator suggests that it will continue to decline.

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Section: Introductionmentioning

confidence: 99%

Forecasting the ecological footprint of G20 countries in the next 30 years

Eufrasio Espinosa,

Lenny Koh

2024

Sci Rep

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“…In ecology, machine learning has been used to predict ecological footprints, which is an important metric that provides insight into the impact of human activities on the environment. For example, one study utilized machine learning to predict ecological footprints in China, which helped identify key factors that contribute to the ecological footprint, such as population density, urbanization, and industrialization [41,42].…”

Section: No Unitmentioning

confidence: 99%

Assessing Human Influence on Ecological Dynamics: A Study of Anthropogenic Ecology and Impact Measurement

Ding¹

2023

Preprint

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The current epoch, known as the Anthropocene, is characterized by significant human impact on the global ecological system, including altered species survival and distribution patterns due to global warming resulting from industrialization. Accurately quantifying and assessing the extent of human impact on the environment is of utmost importance. To this end, four primary methods have been developed to measure human impact: (1) ecological footprint, (2) Human appropriation of net primary production (HANPP), (3) Planetary boundary, and (4) Living Planet Index. In this paper, we aim to provide a comprehensive understanding of these methods, including their strengths and weaknesses, to evaluate their efficacy in accurately assessing the impact of human activities on the global ecosystem. Furthermore, we explore the potential of utilizing novel machine learning modelling for future predictions in this area. By analyzing and comparing these methods, we can gain insight into the ways in which human activities impact the environment and use this knowledge to inform future policies and practices to mitigate negative effects.

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“…However, OLS has several weaknesses, including sensitivity to outliers and multicollinearity, and is prone to overfitting (Roumiani & Mofidi, 2022). Given the Gauss–Markov theorem, the OLS has the lowest variance among all linear unbiased estimates of