Biocapacity—Premise of Sustainable Development in the European Space

Gogonea, Rodica-Manuela; Ghiţă, Simona; Săseanu, Andreea Simona

doi:10.3390/su12031037

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…Calculation of ecological carrying capacity. e total potential ecological carrying capacity corresponding to the input of all renewable natural energy values of a system is defined as the local maximum potential ecological carrying capacity, which represents the maximum ecological carrying capacity when the system potential is fully exploited and utilized in an ideal state [20,21]. e calculation formula of ecological carrying capacity is rewritten as follows:…”

Section: Open System Ecological Footprintmentioning

confidence: 99%

Prediction of Per Capita Ecological Carrying Capacity Based on ARIMA-LSTM in Tourism Ecological Footprint Big Data

2022

Scientific Programming

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Reasonable and effective regional ecological evaluation and analysis methods can be an effective help for urban sustainable development, but there are still some errors in the current ecological prediction and analysis methods. To solve this problem, this paper proposes a prediction method of per capita ecological carrying capacity based on the autoregressive integrated moving average model (ARIMA) and long short-term memory (LSTM). First, the method improves the ecological footprint model based on energy analysis and constructs a comprehensive regional ecological data model; considering the complex characteristics of ecological data set, based on the ARIMA network model and LSTM model, a reliable and efficient big data prediction model of per capita ecological carrying capacity is established by analyzing the linear or nonlinear data sets in the data set. Finally, according to the actual ecological data set collected in Shenzhen, China, the results show that the economic and ecological trend of Shenzhen is generally good.

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Section: Open System Ecological Footprintmentioning

confidence: 99%

Prediction of Per Capita Ecological Carrying Capacity Based on ARIMA-LSTM in Tourism Ecological Footprint Big Data

2022

Scientific Programming

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“…Over the past ten years, the EF in the European Union has increased further, reaching 4.6 gha/person in 2016. The EU ranks second after North America in regions with the highest EF (6.6 gha/capita) (Gogonea et al 2020). The growing gap between EF and biological capacity reduces land production capacity, leading to climate change, food shortages, and biodiversity loss (Rashid et al 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Roumiani

Mofidi

2021

Preprint

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Paying attention to human activities in terms of land grazing infrastructure, crops, forest products and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. In the present study, data from global databases were used. The ability of the penalized regression approach (PR including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 over the past two decades (1999–2018) was depicted and compared. For this purpose, 10-fold cross-validation was used to assess predictive performance and to specify a penalty parameter for PR models. Based on the results, a slight improvement in prediction performance was observed over linear regression. Using the Elastic Net model, more global macro indices were selected than Lasso. Although Lasso included only some indicators, it still had better predictive performance among PR models. Although the findings using PR methods were only slightly better than linear regression, their interest in selecting a subset of controllable indicators by shrinking the coefficients and creating a parsimonious model was apparent. As a result, penalized regression methods would be preferred, using feature selectivity and interpretive considerations rather than predictive performance alone. On the other hand, neural network-based models with higher values of coefficients of determination (R2) and values lower of RMSE than PR and OLS had significant performance and showed that they are more accurate in predicting EF. The results showed that the ANN network could provide considerable and appropriate predictions for EF indicators in the G-20 countries. predictions

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“…In the case of humans, this concept mainly encompasses the provision of resources for human consumption and activities as well as the assimilation of the associated waste. There may be a biocapacity reserve or deficit in a territory [2].…”

Section: Introductionmentioning

confidence: 99%

Environmental Impact of Mobility in Higher-Education Institutions: The Case of the Ecological Footprint at the University of A Coruña (Spain)

2021

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Ecological footprint (EF) determines the area of ecologically productive territory necessary to produce the resources utilised and to assimilate the waste produced by a given population over a year. Knowing your own EF is the first step in planning and implementing policies to reduce it. Considering that transport is a significant component of the impact of human activity on the environment, this study calculates the EF of the CO2 emissions associated with person mobility (EFM) at the University of A Coruña (UDC). The results presented are based on statistical inference from surveys conducted in 2008, 2016, and 2020. The period 2008–2016 coincides with the effects of the economic crisis, and 2020 with those of the COVID-19 pandemic. The EFM of UDC in 2016 was 2177 global hectares (gha), 148 times its biocapacity. Ninety-two percent of it was generated by car trips. Policies to reduce the environmental impact of mobility should focus on reducing car use. This study analyzes various factors of variability. The EFM per capita of the commutes from the place of residence to the UDC campuses grew between 2008 and 2020 by 17%. The results obtained are compared with those of other nearby universities and around the world.

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Biocapacity—Premise of Sustainable Development in the European Space

Cited by 8 publications

References 46 publications

Prediction of Per Capita Ecological Carrying Capacity Based on ARIMA-LSTM in Tourism Ecological Footprint Big Data

Prediction of Per Capita Ecological Carrying Capacity Based on ARIMA-LSTM in Tourism Ecological Footprint Big Data

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Environmental Impact of Mobility in Higher-Education Institutions: The Case of the Ecological Footprint at the University of A Coruña (Spain)

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