Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach

Nilashi, Mehrbakhsh; Rupani, Parveen Fatemeh; Rupani, Mohammad Mobin; Kamyab, Hesam; Shao, Weilan; Ahmadi, Hossein; Rashid, Tarik A.; Aljojo, Nahla

doi:10.1016/j.jclepro.2019.118162

Cited by 113 publications

(51 citation statements)

References 55 publications

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“…Carbon dioxide (CO 2 ) emissions are leading to a change in the worldwide climate (Nilashi et al 2019;Yadegaridehkordi et al 2020;Zhang and Zhang 2018). The increasing global temperature has reached high levels that cannot be neglected easily.…”

Section: Covid-19 Air Quality Improvements and Health Benefitsmentioning

confidence: 99%

Coronavirus pandemic (COVID-19) and its natural environmental impacts

Rupani

Nilashi

Abumalloh

et al. 2020

Int. J. Environ. Sci. Technol.

166

129

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Coronavirus Disease 2019 (COVID-19) is the official name of a respiratory infectious disease caused by a new coronavirus that started first in Wuhan, China, and outspread worldwide with an unexpectedly fast speed. Flights have been canceled worldwide and transportation has been closed nationwide and across international borders. As a consequence, the economic activity has been stopped and stock markets have been dropped. The COVID-19 lockdown has several social and economic effects. Additionally, COVID-19 has caused several impacts on global migration. On the other hand, such lockdown, along with minimal human mobility, has impacted the natural environment somewhat positively. Overall carbon emissions have dropped, and the COVID-19 lockdown has led to an improvement in air quality and a reduction in water pollution in many cities around the globe. A summary of the existing reports of the environmental impacts of COVID-19 pandemic are discussed and the important findings are presented focusing on several aspects: air pollution, waste management, air quality improvements, waste fires, wildlife, global migration, and sustainability.

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Section: Covid-19 Air Quality Improvements and Health Benefitsmentioning

confidence: 99%

Coronavirus pandemic (COVID-19) and its natural environmental impacts

Rupani

Nilashi

Abumalloh

et al. 2020

Int. J. Environ. Sci. Technol.

166

129

View full text Add to dashboard Cite

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“…Patterson et al [ 5 ] used the ecological footprint method to explore the sustainable development of the coupling and coordination of regional tourism and ecological environment. Nilashi et al [ 6 ], for the first time, applied the technology of fuzzy clustering and supervised machine learning to the evaluation of national sustainability to expand the sustainability assessment system.…”

Section: Literature Reviewmentioning

confidence: 99%

Temporal–Spatial Evolution and Influencing Factors of Coordinated Development of the Population, Resources, Economy and Environment (PREE) System: Evidence from 31 Provinces in China

Cao

Zhang

Wei

et al. 2021

IJERPH

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Facing the increasingly severe friction among the domains of population, resources, economy and environment (PREE) in a system, theoretical guidance for the sustainable development of a PREE system can be obtained by exploring the coordinated development of a PREE system during its temporal–spatial evolution process. Based on the PREE data of 31 provinces in China from 2010 to 2019, this study uses a spatial measurement method to analyze the temporal and spatial evolution characteristics of the PREE systems of China’s provinces. The results show that the overall coordination level of China’s provincial PREE systems fluctuated but improved from moderate imbalance to moderate coordination. However, the differences in the regional coordination level first decreased and then increased. The distribution characteristics of the system coordination level changed from “high in the east and low in the west” to “high in the west and low in the east”, resulting in the “inversion” phenomenon of the system coordination level. The spatial correlation of the coordination level of the PREE system among provinces and cities gradually increased. The coordination level of the PREE system in the eastern, central and western regions was noticeably different, accompanied by different degrees of polarization and showing different dynamic evolution trends. In the analysis of influencing factors, it was found that seven factors, such as per capita GDP, the proportion of environmental pollution control investment to GDP and per capita energy production, promoted the coordinated development of China’s PREE system to varying degrees. The coordinated and stable development of China’s PREE system should be adjusted and optimized from the perspectives of different regions, scales and systems.

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“…Each of these prediction models was used to predict the output and finally, the linear combination of these predictions was used as the final prediction result. The results for SOM+Ensembles of CART, CART [32], Adaptive Neuro-Fuzzy Inference System (ANFIS) [32], Neural Network (NN) [32], Multiple Linear Regression (MLR) [32], Fuzzy C-Means + CART [32], and Fuzzy C-Means + ANFIS [32] techniques are shown in Table 2. The results of this study's analysis revealed that the combination of SOM and CART techniques with the aid of ensemble learning resulted in a more superior performance compared to CART, ANFIS, NN, MLR, Fuzzy C-Means + CART, and Fuzzy C-Means + ANFIS in the measurement of country sustainability performance.…”

Section: Data Analysis and Methods Evaluationmentioning

confidence: 99%

“…They used SAFE model criteria to assess sustainability performance. The authors in [32] used the SAFE model to assess the sustainability performance of 128 countries. The study investigated the link between ecological sustainability, human sustainability, and overall sustainability performance by utilizing the decision rules.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

et al. 2021

Self Cite

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This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

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Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach

Cited by 113 publications

References 55 publications

Coronavirus pandemic (COVID-19) and its natural environmental impacts

Coronavirus pandemic (COVID-19) and its natural environmental impacts

Temporal–Spatial Evolution and Influencing Factors of Coordinated Development of the Population, Resources, Economy and Environment (PREE) System: Evidence from 31 Provinces in China

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

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