4.7 Article

Estimating CO2 emissions using a fractional grey Bernoulli model with time power term

期刊

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
卷 29, 期 31, 页码 47050-47069

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-022-18803-0

关键词

CO2 emissions; Grey Bernoulli model; Grey wolf optimizer; Particle swarm optimizer; Quantum genetic algorithm; Forecasting

资金

  1. Shaanxi Province Education Department Philosophy and Social Science Key Institute Base Project [19JZ048]
  2. Social Science Project of Shaanxi [2021D062]
  3. Youth Innovation Team of Shaanxi Universities [21JP044]
  4. Scientific Research Project of China (Xi'an) Institute for Silk Road Research [2019YA08]

向作者/读者索取更多资源

Global warming caused by CO2 emissions has a direct impact on human health and quality of life. Accurate prediction of CO2 emissions is crucial for formulating scientific and reasonable low-carbon environmental policies. This paper proposes a new fractional grey Bernoulli model (FGBM(1,1,t(alpha))) for predicting CO2 emissions, which demonstrates high adaptability and accuracy compared to other models. The study also predicts the future trends of CO2 emissions in different regions over the next 5 years.
Global warming caused by CO2 emissions will directly harm the health and quality of life of people. Accurate prediction of CO2 emissions is highly important for policy-makers to formulate scientific and reasonable low-carbon environmental protection policies. To accurately predict the CO2 emissions of the world's major economies, this paper proposes a new fractional grey Bernoulli model (FGBM(1,1,t(alpha))). First, this paper introduces the modeling mechanism and characteristics of the FGBM(1,1,t(alpha)) model. The new model can be transformed into other grey prediction models through parameter adjustment, so the new model exhibits high adaptability. Second, this paper employs four carbon emission datasets to establish a grey prediction model, calculates model parameters with three optimization algorithms, adopts two evaluation criteria to evaluate the accuracy of the model results, and selects the optimization algorithm and model results that yield the highest model accuracy, which verifies that the FGBM(1,1,t(alpha)) model is more feasible and effective than the other six grey models. Finally, this paper applies the FGBM(1,1,t(alpha)) model to predict the CO2 emissions of the USA, India, Asia Pacific, and the world over the next 5 years. The forecast results reveal that from 2020 to 2024, the CO2 emissions of India, the Asia Pacific region, and the world will gradually rise, but that in USA will slowly decline over the next 5 years.

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