A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques

Mardani, Abbas; Liao, Huchang; Nilashi, Mehrbakhsh; Alrasheedi, Melfi; Cavallaro, Fausto

doi:10.1016/j.jclepro.2020.122942

Cited by 79 publications

(35 citation statements)

References 58 publications

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“…There are also examples where AI-based approaches can help improve the understanding of, and facilitate effective responses to, climate change—particularly in the policy-making domain. For example, AI can help to predict carbon emissions based on present trends (Mardani et al 2020 ; Wei et al 2018 ), and help monitor the active removal of carbon from the atmosphere through sequestration (Menad et al 2019 ). AI approaches have also been employed to assess the potential viability and impact of large-scale policy changes and other societal shifts.…”

Section: Ai Against Climate Changementioning

confidence: 99%

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

et al. 2021

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In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

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Section: Ai Against Climate Changementioning

confidence: 99%

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

et al. 2021

View full text Add to dashboard Cite

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“…Some scholars have also introduced machine learning methods to predict carbon emissions. Mardani et al ( 2020 ) used self-organizing mapping clustering algorithm, adaptive neural fuzzy reasoning system, and artificial neural network to predict carbon dioxide emissions. The results showed that the average error of prediction results was very small, and the accuracy of the results was much higher than that of the single multi-linear regression model.…”

Section: Literature Reviewmentioning

confidence: 99%

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

Ziyuan

Yibo

Simayi

et al. 2022

Environ Sci Pollut Res

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In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020–2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

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“…Other work has explored the use of AI in electrical grid management (Di Piazza et al 2020), to forecast building energy usage (Fathi et al 2020), and to assess the sustainability of food consumption (Abdella et al 2020). AI can also help to predict carbon emissions based on present trends (Mardani et al 2020;Wei, Yuwei, and Chongchong 2018) and the impact of interventionist policies like a carbon tax (Abrell, Kosch, and Rausch 2019) and carbon trading systems (Lu et al 2020). AI could also be used to help monitor the active removal of carbon from the atmosphere through sequestration (Menad et al 2019).…”

Section: How Ai Is Used Against Climate Changementioning

confidence: 99%

The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

et al. 2021

View full text Add to dashboard Cite

In this article we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change and it contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI's greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

show abstract

A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques

Cited by 79 publications

References 58 publications

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network

The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

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