Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India

Singh, Pawan Kumar; Pandey, Alok Kumar; Ahuja, Sahil; Kiran, Ravi

doi:10.1007/s11356-021-17487-2

Cited by 12 publications

(5 citation statements)

References 42 publications

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“…In particular, the VIX index, Treasury Yield Spread, the 5-Year Forward Inflation Expectation Rate and the 10-Year Breakeven Inflation Rate are relevant when predicting tail risks. Notwithstanding the limitations due to the peculiar nature of cryptocurrencies, that exhibit attributes of both commodities and money, our methodology could be applied to other areas, including the forecast of the e-commerce sector (Moiseev et al 2023) and CO 2 emissions (Algieri et al 2023;Singh et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?

Lawuobahsumo,

Algieri,

Leccadito

2023

Qual Quant

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This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.

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Section: Discussionmentioning

confidence: 99%

Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?

Lawuobahsumo,

Algieri,

Leccadito

2023

Qual Quant

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“…[32] analyzed total GHGs emission between years 1990 and 2016. [33] forecasted total CO2 emission from paddy crops in India for coming next six years by using prediction methods. [34] predicted the effect on GHGs emissions of the end-of-life vehicles (ELV).…”

Section: Literature Overviewmentioning

confidence: 99%

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

ÖZTÜRK,

EMİR

2024

KONJES

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The way of life, consumption habits, urbanization rate, type of energy production and increasing energy need with growing economies and population progressively promote the GHGs emissions to Earth’s atmosphere. GHGs consisting of CH4, N2O, CO2, H2O and HFCs cause the climate change, disrupting ecological balance, melting glaciers with global warming in the last decades. Therefore, the issues of future prediction and reduction of GHGs emissions became crucial for policy makers of Turkey and other countries under the international protocols and agreements. This article aims to present the prediction and 8-year future forecasting of CH4, N2O and CO2 emissions of Turkey using past annual data between years 1970 and 2018 with grey, autoregressive integrated moving average and double exponential smoothing models. Based on the results, the best prediction performance is reached by DES model followed by ARIMA and GM for all the emissions. MAPEs calculated from the available data and prediction by DES model from 1970 to 2018 are 0.285, 0.355 and 0.408 for CH4, N2O and CO2 in turn. DES future estimations of CH4, N2O and CO2 at 2026 year are determined as 50700 kiloton of CO2 eq., 38100 thousand metric ton of CO2 eq., and 512000 kilotons.

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“…To date, there exist some works that have modeled the global CO 2 emission footprint, including the COVID-19 transmission period, such as [7]. Most of the existing works have either a partial to local context such as [17] in China, [18] in China, [19,20] in wheat fields, [21] in Iran, [22] in the Middle East, or the modeling parameters and methodology are not appropriate for global CO 2 emission prediction, such as [3] for Indian paddy fields, [2] for the Turkish transportation sector.…”

Section: Literature Reviewmentioning

confidence: 99%

“…If one intended to build a CO 2 emission model with option (1), forecasting results will probably not represent the real situation concerning emissions observed due to the COVID-19 pandemic. Option (2) would not be the justified option for the same reason, with regard to the period of the COVID-19 pandemic just beginning to spread over China. Option (3) would be the viable option for building a model to forecast CO 2 emissions because in this time period the COVID-19 pandemic spreads over the world and massive lockdown processes have already shut down a huge number of CO 2 emission sources acriss the world.…”

Section: Data Modelingmentioning

confidence: 99%

“…During and transmission (trans) time of COVID-19, the emission of CO 2 has descended to 34.4 million tons (MT), which is lower than the previous peak (36.1 MT) [1]. There are numerous works that estimate CO 2 emission before the pandemic but there is no suitable work showing how CO 2 emissions will behave in the trans-and post-COVID-19 era, because most of the recent works either use data from before the pandemic, such as [2] (up to 2018), [3] (up to 2018), [4] (up to 2015), or they use data from during pandemic but with a local scope, such as [2] for India, [3] for Turkey, [4] for the UK, [5] for China and [6] for indoor environments, or they use different approaches for only near future (2 years) forecasting [7]. This research focuses on developing a Machine Learning (ML)based Artificial Intelligence (AI) model to predict CO 2 emission in the near and far future considering the reduced CO 2 emissions due to the lockdowns for the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

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Predicting CO2 Emission Footprint Using AI through Machine Learning

Yang

Noman

2022

Atmosphere

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Adequate CO2 is essential for vegetation, but industrial chimneys and land, space and oceanic vehicles exert tons of excessive CO2 and are mostly responsible for the greenhouse effect, global warming and climate change. Due to COVID-19, CO2 emission was in 2020 at its lowest level compared to prior decades. However, it is unknown how long it will take to reduce CO2 emission to a tolerable point. Furthermore, it is also unknown to what extent it can increase or change in the future. Accurate forecasting of CO2 emissions has real significance for choosing the optimum ways of reducing CO2 emissions. Although some existing models have noticeable CO2 emission forecasting accuracy, the models implemented in this work have more efficacy in prediction due to incorporating COVID-19’s effect on CO2 emission. This paper implements four prediction models using SARIMA (SARIMAX) based on ARIMA. The four models are based on the time period of the surge of the COVID-19 pandemic. The main objective of this work is to compare these four models to suggest an effective model to predict the total CO2 emissions for the future. The study forecasts global total CO2 emission from 2022 to 2027 for near future prediction, 2022 to 2054 for future prediction and 2022 to 2072 for far future prediction. Among the various error measures, mean absolute percentage error (MAPE) is chosen for accuracy comparison. The calculation yields different accuracy for the four SARIMAX models. The MAPEs for the four methods are: pre-COV (MAPE: 0.32), start-COV (MAPE: 0.28), trans-COV (MAPE: 0.19), post-COV (MAPE: 0.09). The MAPE value is relatively low for post-COV (MAPE: 0.09). Hence, it can be inferred that post-COV are suitable models to forecast the global total CO2 emission for the future. The post-COV predictions for the global total CO2 emission for the years 2022 to 2027 are: 36,218.59, 36,733.69, 37,238.29, 37,260.88, 37,674.01 and 37,921.47 million tons (MT). This study successfully predicts CO2 emission either for the COVID-19 period or the post-COVID-19 normal periods. The Machine Learning (ML) method used in this study has shown good agreement with the IPCC model in predicting the past emissions, the current emissions due to COVID-19 and the emissions of the upcoming future. These prediction results can be an asset for the decision support system to develop a suitable policy for global CO2 emission reduction. For future research, a number of other external influence variables responsible for CO2 emission can be added for finer forecasts. This research is an original work in predicting COVID-19-affected CO2 emission using AI through the ML methodology.

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Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India

Cited by 12 publications

References 42 publications

Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?

Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?

Estimations of Green House Gases Emissions of Turkey by Statistical Methods

Predicting CO2 Emission Footprint Using AI through Machine Learning

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