Can Machine Learning be Applied to Carbon Emissions Analysis: An Application to the CO2 Emissions Analysis Using Gaussian Process Regression

Ma, Ning; Shum, Wai Yan; Han, Tingting; Lai, Fujun

doi:10.3389/fenrg.2021.756311

Cited by 21 publications

(5 citation statements)

References 29 publications

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“…Thirdly, the rise of artificial intelligence and machine learning has enabled more sophisticated analysis of large and complex datasets related to carbon emissions [18], [19]. These technologies have proven instrumental in enhancing understanding, prediction, and decision-making in the realm of environmental impact.…”

Section: ) Overview Of Selected Researchmentioning

confidence: 99%

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Somantri,

Surendro

2024

IEEE Access

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Computer Science Architecture (CSA), encompassing data, application, technology, and business architecture, is a vital tool for addressing climate change challenges. It aims to reduce greenhouse gas emissions from sectors such as stationary energy, transportation, industry, product use, waste, and land use. CSA principles extend to advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), Machine Learning (ML), data centers, blockchain, multi-agent systems, sensors, and smart grids. This review explores CSA's role in mitigating greenhouse gas emissions for a sustainable environment. It analyzes implications, indicators, and methodologies for data, application, technology, and business architecture, highlighting their direct impact on creating an environmentally friendly technological landscape. The study also delves into emerging trends and suggestions for future research, contributing to the discourse on leveraging technology for a greener future.INDEX TERMS Greenhouse gas emissions, computer science architecture, sustainable environment, review.

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Section: ) Overview Of Selected Researchmentioning

confidence: 99%

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Somantri,

Surendro

2024

IEEE Access

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“…In this regard, artificial intelligence (AI) and machine learning (ML) techniques occupy a substantial and competitive position among other tools. They are considered the best-nominated approaches that can perfectly handle data modelling and forecasting tasks [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

2023

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Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the “Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models’ outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.

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“…The massive burning of mineral energy has caused global environmental pollution and ecological destruction, including the extreme overheating of the environment (Ma et al, 2021;Arsalan et al, 2022;Zakari et al, 2022). Solar energy is a free and environmentally friendly supply of power that has a negligible impact on the environment (Almutairi, et al, 2021) and has tremendous potential to replace fossil fuels with secure, clean, and sustainable energy (Trancik et al, 2015;Navothna and Thotakura, 2022).…”

Section: Introductionmentioning

confidence: 99%

Radical innovation detection in the solar energy domain based on patent analysis

Feng

Fang

2023

Front. Energy Res.

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Introduction: Detecting radical innovations in the solar energy domain could offer innovation references and support the promotion of solar energy. However, relevant studies in the solar energy domain are lacking, and the related methods need to be improved.Methods: In this paper, a new framework to identify radical innovations in the solar energy domain is proposed by combining a technological convergence study and scientific relation analysis, and the link prediction method is utilized to detect potential radical innovations in this domain.Results: 1) The distributions of both the technological classes and scientific categories are uneven in the solar energy domain. The top 15 technological classes account for nearly 75.46% of all classifications. Fifteen scientific categories are cited by all the patents, and applied physics, multidisciplinary material science, energy and fuels play important roles in this domain. 2) The relationships among technological classes have evolved over time and have mainly focused on neighbouring disciplines. 3) A total of 130 patents containing new convergence relationships and/or closely related to science are identified as radical innovations. Radical innovative topics are related to the subdomains of solar photovoltaic (solar PV), heat storage, heat exchangers, and solar collectors. 4) Five potential radical innovative topics are identified. Automatic plants for producing electric energy, solar energy ecology houses, and so on are considered to have great potential in the future.Discussion: The results are consistent with the authoritative report and previous studies, which verify the viability of our methods. And the findings have important implications for scientists, policy-makers, and investors in this domain.

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Can Machine Learning be Applied to Carbon Emissions Analysis: An Application to the CO2 Emissions Analysis Using Gaussian Process Regression

Cited by 21 publications

References 29 publications

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Radical innovation detection in the solar energy domain based on patent analysis

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