Carbon emissions and electricity generation modeling in Saudi Arabia

Alajmi, Reema Ghazi

doi:10.1007/s11356-021-17354-0

Cited by 22 publications

(13 citation statements)

References 41 publications

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“…For example, Hu et al analyzed the carbon emission factors for 57 Belt and Road Initiative countries based on the LMDI model [ 51 ]. Alajmi et al used the LMDI model in their analysis of the growth factors of carbon emission in Saudi Arabia [ 52 ]. The factors influencing carbon emissions, such as economic development, industrial structure, carbon emission intensity, intensive rate of land use, the rate of urbanization, energy structure, and the size of the population, have been selected for decomposition research [ 53 , 54 , 55 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the Decoupling between Net Carbon Emissions and Construction Land and Its Driving Factors: Evidence from Shandong Province, China

Liu

et al. 2022

IJERPH

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Human activities and land transformation are important factors in the growth of carbon emissions. In recent years, construction land for urban use in China has expanded rapidly. At the same time, carbon emissions in China are among the highest in the world. However, little is known about the relationship between the two factors. This study seeks to estimate the carbon emissions and carbon sequestrations of various types of land based on the land cover data of 137 county-level administrative regions in Shandong Province, China, from 2000 to 2020.The study estimated the carbon emissions for energy consumption using energy consumption data and night-time light images, hence, net carbon emissions. The Tapio decoupling coefficient was used to analyze the decoupling between the net carbon emissions and construction land, and where the model for the decoupling effort was constructed to explore the driving factors of decoupling. The results showed that net carbon emissions in Shandong Province continued to increase, and the areas with high carbon emissions were concentrated primarily in specific districts of the province. The relationship between net carbon emissions and construction land evolved from an expansive negative decoupling type to a strong negative decoupling type. Spatially, most areas in the province featured an expansive negative decoupling, but the areas with a strong negative decoupling have gradually increased. The intensive rate of land use and efficiencies in technological innovation have restrained carbon emissions, and they have contributed to an ideal decoupling situation. Although the intensity of carbon emission and the size of the population have restrained carbon emissions, efforts towards decoupling have faded. The degree of land use has facilitated carbon emissions, and in recent years, efforts have been made to achieve an ideal decoupling. The method of estimation of net carbon emissions devised in this research can lend itself to studies on other regions, and the conclusions provide a reference for China, going forward, to balance urbanization and carbon emissions.

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Section: Literature Reviewmentioning

confidence: 99%

Estimating the Decoupling between Net Carbon Emissions and Construction Land and Its Driving Factors: Evidence from Shandong Province, China

Liu

et al. 2022

IJERPH

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“…Their conclusion was to use the ANN model for forecasting the CO 2 in Gulf countries, as it produced the best results relative to the ARIMA and the HWES models. Alajmi studied the modelling of carbon emissions and electricity generation in SA [29]. This study used a structural time-series model (STSM) and logarithmic mean Divisia index (LMDI) for estimating the long-run elasticities.…”

Section: Introductionmentioning

confidence: 99%

“…The paper showed that the ANN was the best, but the paper does not show the value of the training/testing ratio to assess the model's reliability. In [29], the authors built a structural time series model (STSM) to study the effect of gross domestic product (GDP), electricity generation and population on CO 2 emissions in Saudi Arabia between 1980 and 2017. The paper does not show any data-forecasting strategy.…”

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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“…However, over the past few decades, atmospheric CO 2 concentrations have increased dramatically, leading to a growing problem of global warming with increasingly obvious negative impacts on humans, animals and plants. [1] The combustion of coal and fossil fuels produces large amounts of CO 2 and N 2 in the air, so it is important to have an effective method of removing CO 2 and N 2 from the flue gas by adsorption. [2] Physical adsorption is based on the intermolecular attractive force between the guest molecule and the active site on the surface of the porous solid adsorbent to achieve adsorption, and porous adsorbent materials are the core of the adsorption method, which has attracted extensive and intensive research.…”

Section: Introductionmentioning

confidence: 99%

“…Terrestrial ecosystems rely on the carbon‐nitrogen‐water cycle to maintain the balance of the natural environment. However, over the past few decades, atmospheric CO 2 concentrations have increased dramatically, leading to a growing problem of global warming with increasingly obvious negative impacts on humans, animals and plants [1] . The combustion of coal and fossil fuels produces large amounts of CO 2 and N 2 in the air, so it is important to have an effective method of removing CO 2 and N 2 from the flue gas by adsorption [2] …”

Section: Introductionmentioning

confidence: 99%

Porphyrinic Porous Aromatic Frameworks for Carbon Dioxide Adsorption and Separation

et al. 2023

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The capture of carbon dioxide (CO2) from industrial process emissions is increasingly important for the mitigation and prevention of the disruptive effects of global warming. In this study, PAF (porous aromatic frameworks)‐TPB(1,3,5‐triphenylbenzene) and three‐dimensional PAF‐TPM (tetraphenylmethane) porphyrin‐based aromatic porous materials were synthesized through the Scholl reaction. The CO2 and N2 adsorption isotherms at 273 K and 298 K were studied to determine the performance in carbon dioxide capture at flue gas conditions. There is a significant difference in the adsorption capacity of the two materials for CO2 and N2, so they can be used for CO2/N2 adsorption separation. PAF‐TPM has better CO2/N2 separation at low pressure (150 mbar), while PAF‐TPB has the advantage of greater CO2/N2 separation at high pressure (1 bar). It can be applied to CO2 adsorption separation at low and high pressure, respectively. In particular, PAF‐TPB has a CO2/N2 separation efficiency of up to 100.9 at 1 bar and 273 K. This work provides ideas for the design and synthesis of organic porous materials for the adsorption separation of CO2 and N2.

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Carbon emissions and electricity generation modeling in Saudi Arabia

Cited by 22 publications

References 41 publications

Estimating the Decoupling between Net Carbon Emissions and Construction Land and Its Driving Factors: Evidence from Shandong Province, China

Estimating the Decoupling between Net Carbon Emissions and Construction Land and Its Driving Factors: Evidence from Shandong Province, China

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

Porphyrinic Porous Aromatic Frameworks for Carbon Dioxide Adsorption and Separation

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