Forecasting of CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1011" altimg="si29.svg"><mml:msub><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> emissions in Iran based on time series and regression analysis

Hosseini, Seyed Mohsen; Saifoddin, Amirali; Shirmohammadi, Reza; Aslani, Alireza

doi:10.1016/j.egyr.2019.05.004

Cited by 131 publications

(38 citation statements)

References 46 publications

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“…Since quantile 0.55, the impact has been found to be positive, and thereby exhibiting the need of technological innovation with the rise in environmental degradation. This particular segment of the results addresses the policy gap identified by Hosseini et al (2019) for Iran. Moreover, the EKC analysis of Moghadam and Dehbashi (2018) also demonstrates in policy-level ineffectiveness of Iran in controlling environmental degradation, where the emission levels are high, and thereby revealing unsustainable nature of the economic growth pattern.…”

Section: Analysis Of Resultsmentioning

confidence: 66%

Analyzing technology-emissions association in Top-10 polluted MENA countries: How to ascertain sustainable development by quantile modeling approach

Sinha¹,

Shah

Sengupta³

et al. 2020

Journal of Environmental Management

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This study investigates the relationship between technological progression and ambient air pollution in top-10 polluted Middle East and North African (MENA) countries by using monthly data for the period of 1990-2017. The Quantile cointegration proposed by Xiao ( 2009), Quantile-on-Quantile regression (QQ) proposed by Sim and Zhou (2015), and Quantile Autoregressive Granger causality developed by Troster (2018) are applied. In particular, we examine to which extent, quantiles of technological progression affect the quantiles of ambient air pollution, by developing separate indicators for both the mentioned aspects using Principal Component Analysis (PCA). Our empirical findings unfold mutual dependence between technological progression and ambient air pollution. Furthermore, the results of Quantile Autoregressive Granger causality test conclude a bidirectional causal relationship between technological progression and ambient air pollution.

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Section: Analysis Of Resultsmentioning

confidence: 66%

Analyzing technology-emissions association in Top-10 polluted MENA countries: How to ascertain sustainable development by quantile modeling approach

Sinha¹,

Shah

Sengupta³

et al. 2020

Journal of Environmental Management

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“…2030 using Genetic Algorithm on the basis of (non)linear equations and historical energy consumption. Hosseini et al 62 predict CO 2 emissions for Iran until 2030 with multiple linear regression and multiple polynomial regression models. Mi et al 63 developed an Integrated Model of Economy and Climate on the basis of the input-output model to predict emissions for China until 2035 with constraints of economic growth, energy consumption, employment, industrial structure change and so on.…”

Section: Online Contentmentioning

confidence: 99%

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

et al. 2020

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The global economy is facing a serious recession due to COVID-19, with implications for CO 2 emissions. Here, using a global adaptive multiregional input-output model and scenarios of lockdown and fiscal counter measures, we show that global emissions from economic sectors will decrease by 3.9 to 5.6% in 5 years (2020 to 2024) compared with a no-pandemic baseline scenario (business as usual for economic growth and carbon intensity decline). Global economic interdependency via supply chains means that blocking one country's economic activities causes the emissions of other countries to decrease even without lockdown policies. Supply-chain effects contributed 90.1% of emissions decline from power production in 2020 but only 13.6% of transport sector reductions. Simulations of follow-up fiscal stimuli in 41 major countries increase global 5-yr emissions by −6.6 to 23.2 Gt (−4.7 to 16.4%), depending on the strength and structure of incentives. Therefore, smart policy is needed to turn pandemic-related emission declines into firm climate action.

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“…Many methods, including artificial intelligence techniques such as evolutionary algorithms [ 3 , 4 ], neural networks (NNs) [ 5 , 6 ], and statistical methods such as logistic equations [ 7 ], regression models [ 4 , 7 , 8 ], time series models [ 9 ], and the ARIMA model [ 4 , 10 ], have been frequently applied to forecasting. However, the forecasting accuracy of artificial intelligence techniques can be influenced significantly by the training sample size [ 11 ], and statistical methods usually require a large amount of data that conform to some statistical assumptions [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Jiang

Tsai

et al. 2021

IJERPH

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Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fractional-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.

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Forecasting of CO2 emissions in Iran based on time series and regression analysis

Cited by 131 publications

References 46 publications

Analyzing technology-emissions association in Top-10 polluted MENA countries: How to ascertain sustainable development by quantile modeling approach

Analyzing technology-emissions association in Top-10 polluted MENA countries: How to ascertain sustainable development by quantile modeling approach

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

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