Ecological footprint simulation and prediction by ARIMA model—A case study in Henan Province of China

Jia, Junsong; Zhao, Jingzhu; Deng, Hongbing; Duan, Jing

doi:10.1016/j.ecolind.2009.06.007

Cited by 48 publications

(11 citation statements)

References 23 publications

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“…To have a better understanding of the study area’s future EC and to provide a decision-making basis for sustainable development, scholars began to establish an evaluation and prediction model of the EC [ 31 ] and combined the EF method with the Autoregressive Integrated Moving Average (ARIMA) model [ 32 ] or the Grey model [ 33 ] to forecast the regional future EC s, and also combined it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model [ 34 , 35 , 36 ] to study the drivers of EF changes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction of the Ecological Footprint and Ecological Carrying Capacity for Yangtze River Urban Agglomeration Based on the Grey Model

Peng

Wang

Elahi

et al. 2018

IJERPH

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The conflict between economic development and environmental protection has become increasingly prominent in the urbanization process of the Yangtze River urban agglomeration, the most economically developed region in Jiangsu Province in China. In order to investigate the sustainable development status, and thus provide decision support for the sustainable development of this region, the ecological footprint model was utilized to evaluate and analyze the ecological footprint per capita, the ecological carrying capacity per capita, and the ecological deficit per capita for the period from 2013 to 2017. Furthermore, the Grey model is employed to predict the development trend of the ecological footprint for 2018 to 2022. The evaluation results show that the ecological footprint per capita has been increasing year by year since 2013, reaching a peak of 2.3897 hm2 in 2015 before declining again. In the same period, the available ecological carrying capacity per capita and the ecological footprint per capita basically developed in the same direction, resulting in an ecological deficit per capita and gradually increasing from 2013 to a peak of 2.0303 hm2 in 2015 before declining. It is also found that the change of ecological carrying capacity is not substantial, and the change of the ecological deficit is mainly caused by a huge change of the ecological footprint. The forecast results show that the ecological deficit per capita will reach 1.1713 hm2 in 2018, which will be another deficit peak after 2015. However, in the later period until 2022, the ecological deficit per capita will begin to decline year by year. These results can provide effective inspirations for reducing the ecological deficit of the Yangtze River urban agglomeration, thus promoting the coordinated development of the economy and environment in this area.

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Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction of the Ecological Footprint and Ecological Carrying Capacity for Yangtze River Urban Agglomeration Based on the Grey Model

Peng

Wang

Elahi

et al. 2018

IJERPH

View full text Add to dashboard Cite

show abstract

“…An ARIMA model involves estimation of the parameters which account for the trends and autoregressive (AR) and moving averages (MA) processes. The typical ARIMA (p,d,q) comprises three types of parameters: the AR parameters (p), the number of differencing induced (d), and the MA parameters (q) 33 . When dealing with seasonal time series, seasonal parameters must be incorporated into the model.…”

Section: Time Series Modelsmentioning

confidence: 99%

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

Eufrasio-Espinosa,

Koh

2023

Preprint

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The Ecological Footprint evaluates the difference between the availability of renewable resources and how much of these resources humans consume. During the last few decades, historical records show an accelerated decline in resource availability. Based on the national footprint and biocapacity accounts, this analysis aims to further develop the forecasting of the G20 countries' ecological footprint over a 30-year time frame. We used a time series forecasting approach implemented in Python, considering modular regression (Prophet) and Autoregressive Integrated Moving Average (ARIMA & Auto-ARIMA) methods, and evaluated and combined the performance of the three methods. The results showed that within the largest economies of the G20, only four countries will have a positive footprint balance by 2050, with a common denominator of large land areas and a moderate projection of population growth. However, the general trend of the indicator shows that it will continue to decline.

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“…They have used the methods not only to create a baseline prediction model but also use them for future forecasting. Besides this study, there exist various researches using statistical methods such as ARIMA and Seasonal ARIMA [8][9][10]. In addition to those specific papers, more detailed information can be found in the survey paper proposed by [11].…”

Section: R üNlü / Time Series Analysis With Deep Learning and Traditmentioning

confidence: 99%

A Comparative Study of Machine Learning and Deep Learning for Time Series Forecasting: A Case Study of Choosing the Best Prediction Model for Turkey Electricity Production

Ünlü

2019

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Over the last decades, Turkey pays special attention to electricity production to afford its needs. Researchers applied different methodologies including statisticalbased and artificial intelligence-based to correctly predict the future amount of electricity production, consumption, and demand. However,limited researchers focused on Turkey's electricity production prediction problem as a time series analysis. For this reason, we tackle this problem by considering it as a time series analysis in this study. We have used different methods including traditional machine learning algorithms Support Vector Regression (SVR) and Multilayer Perceptrons (MLP) and a deep learning algorithm Long Short-Term Memory (LSTM) to create a better model for Turkey monthly electricity production dataset. Based on our findings LSTM outperforms SVR and MLP approaches in terms of commonly used statistical error evaluation metrics

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Ecological footprint simulation and prediction by ARIMA model—A case study in Henan Province of China

Cited by 48 publications

References 23 publications

Evaluation and Prediction of the Ecological Footprint and Ecological Carrying Capacity for Yangtze River Urban Agglomeration Based on the Grey Model

Evaluation and Prediction of the Ecological Footprint and Ecological Carrying Capacity for Yangtze River Urban Agglomeration Based on the Grey Model

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

A Comparative Study of Machine Learning and Deep Learning for Time Series Forecasting: A Case Study of Choosing the Best Prediction Model for Turkey Electricity Production

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