Forecasting fuel combustion-related CO2 emissions by a novel continuous fractional nonlinear grey Bernoulli model with grey wolf optimizer

The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction

Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model performs well in small-sample prediction. Therefore, a novel multivariate grey model is proposed in this study, called FBNGM (1, N, r), with a fractional order operator, which can increase the impact of new information and background value coefficient to achieve high prediction accuracy. The utilization of an intelligence optimization algorithm to tune the parameters of the multivariate grey model is an improvement over the conventional method, as it leads to superior accuracy. This study conducts two sets of numerical experiments on CO2 emissions to evaluate the effectiveness of the proposed FBNGM (1, N, r) model. The FBNGM (1, N, r) model has been shown through experiments to effectively leverage all available data and avoid the problem of overfitting. Moreover, it can not only obtain higher prediction accuracy than comparison models but also further confirm the indispensable importance of various influencing factors in CO2 emissions prediction. Additionally, the proposed FBNGM (1, N, r) model is employed to forecast CO2 emissions in the future, which can be taken as a reference for relevant departments to formulate policies.

Forecasting the renewable energy consumption of Australia by a novel grey model with conformable fractional opposite-direction accumulation

The accurate prediction of renewable energy consumption (REC) is of great significance to ensure energy security, reduce dependence on fossil energy, and promote sustainable economic and social development. In this paper, a novel grey model with conformable fractional opposite-direction accumulation (CFOA), abbreviated as the CFOGM (1,1) model, is proposed to forecast REC in Australia. The new model is discussed in detail with a new CFOA operation and the GM (1,1) model and can take full advantage of the information carried by the original data. The CFOGM (1,1) model has lower modeling error and better fitting and forecasting accuracy than other grey, Holt, and ARM models and can better capture the change trend of REC and achieve accurate prediction. The forecasting results present that the REC in Australia is 497-581 petajoules in 2021, 596-728 petajoules in 2022, and 715-912 petajoules in 2023, indicating that the REC in Australia is still accelerating.

Forecasting education expenditure with a generalized conformable fractional-order nonlinear grey system model

As an important human capital investment, education is an effective means to improve the comprehensive quality of people. Education expenditure is an important material guarantee for the development of educational undertakings. Education expenditure data is highly susceptible to numerous economic and social factors that complicate its nonlinear structure. In order to model the complex nonlinear problems of the system, this paper proposes a generalized conformable fractional-order nonlinear grey prediction model for the first time by analyzing the traditional time series-based modeling method in a nonlinear grey domain. The proposed model expands on the classical grey Bernoulli model by introducing the generalized conformable fractional accumulation as a new accumulation generator and utilizes error minimization principles in the modeling process. By altering the optimal order of the model and the cumulative generation operator, this model can adapt to various time series and reduce errors. Finally, the model is applied to education expenditure forecasting, and it is proved that the proposed model achieved good results and has higher accuracy than other models.

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition

As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensure the stable operation of natural gas. Considering the complex and non-linear characteristics of natural gas production and consumption data, this paper develops a new hybrid forecasting model (WPD-VMD-LSTM) based on the fuzzy entropy, variational mode decomposition (VMD), wavelet packet decomposition (WPD), and Long Short-Term Memory (LSTM). In this model, WPD and VMD undertake the tasks of primary and secondary decompositions, respectively; fuzzy entropy is used for the preprocessing process before the re-decomposition; and LSTM is used to predict the decomposed time series. In particular, the different criteria set by fuzzy entropy lead to the establishment of two prediction models. Then, two models are used to study monthly natural gas consumption and production in the USA. The results demonstrate that the proposed model performs significantly better than other comparable models and the target model has some practical value. Meanwhile, models may cope with different types of energy data, and models can accurately predict energy transformations with strong applicability, which can be applied to future energy forecasting in various fields. Finally, the constructed models are used to forecast the NGC and NGP in the USA in the next 3 years and make reasonable policy recommendations based on the forecast results.

A classic critique on concrete adsorbing pollutants emitted by automobiles and statistical envision using trend analysis

Globally, road transportation is responsible for about 1/3 of total air pollution, among which Co₂ , No_X and So_X are major by volume which are directly responsible for ozone layer depletion. As these activities continue to rise, the nature faces the threat of an unprecedented environmental catastrophe. In this survey, the adsorbent and the methods used to adsorb the vehicle emission pollutant directly from the ambient air and enhance the air quality are reviewed. There are extensive number of adsorbents available namely titanium oxide, polyethyleneimine (PEI), activated carbon, and other natural admixtures and methods that adsorb these pollutants such as Co₂; specifically, polyethyleneimine (PEI) is a polymer-based product which has a behavior of adsorbing Co₂ directly from ambient air. A carbon-neutral technology for eliminating anthropogenic CO₂ emissions has been proposed, trapping CO₂ from the atmosphere. The major concern including Co₂ adsorption rate and methods to evaluate the volume of adsorption with time has been reviewed. Trend analysis depends on the reason that can foresee what will occur later on by seeing what has happened beforehand. The trend analysis method was used in this study to model the adsorption rate of PEI based on their pore diameter. When it comes to designing regression analysis, R² values of 0.2 to 0.3 indicate that statistical tests have no meaningful impact and further research is needed. For adsorption of No_X, a lot of adsorbents are used namely sodium bentonite, zeolite, activated carbon, and other natural minerals. Among them, activated carbon enhances the adsorption rate of No_x. Vast research has been conducted to analyze the adsorption rate, advantages, disadvantages, and the behavior of different adsorbent with the concrete. Literally for SO_x, the adsorbents that are widely used are coal ash, copper oxide, and silicate. The characterization of different adsorbents with their adsorbate can be analyzed by some test methods as follows: Fourier-transform infrared spectroscopy (FTIR), BET analysis, scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM) analysis, surface area, X-ray diffraction (XRD), porosity analysis, and more according to the requirement. The quantity of adsorbent and adsorbate could be evaluated for facing the real-time constraints through a detailed research by review.

The dynamic relationship between industrialization, urbanization, CO2 emissions, and transportation modes in Korea: empirical evidence from maritime and air transport

This study investigates the causal relationship between logistics efficiency and factors affecting the logistics environment, such as industrialization, urbanization, and CO2 emissions. With the expectation that logistics efficiency will contribute to economic growth and enhance country competitiveness in the near future, it is necessary to confirm the impact of each factor on different transportation modes, such as maritime and air transport. To this end, this study identifies causal relationships between the factors affecting the logistics environment and specific modes of transportation using data from 2010 to 2018. We employed the panel unit root test, panel co-integration test, fully modified OLS (FMOLS), panel dynamic OLS (DOLS), and panel VECM Granger causality tests for the estimations. The results revealed that factors affecting the logistics environment have different effects depending on the modes of transportation. For maritime transportation, long-run bidirectional causal associations were found between port volume, total exports, industrialization, and urbanization. This implies that export promotion and the resulting economic and social environment changes can increase port throughput; this increase can, in turn, develop and improve economic growth and factors affecting the logistics environment. In contrast, for air transport, we detected a long-run, unidirectional causal relationship among these variables and air volume changes with growing exports, urbanization, and industrialization. Thus, this study suggests a theoretical framework for analyzing the causal relationship between the factors affecting the logistics environment and each mode of transportation, providing insights for policymakers to promote logistics efficiency.

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

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,[Formula: see text])). First, this paper introduces the modeling mechanism and characteristics of the FGBM(1,1,[Formula: see text]) 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,[Formula: see text]) model is more feasible and effective than the other six grey models. Finally, this paper applies the FGBM(1,1,[Formula: see text]) 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.

Development and Opportunities of Clean Energy in China

In the context of the energy crisis and global climate deterioration, the sustainable development of clean energy will become a new direction for future energy development. Based on the development process of clean energy in China in the past ten years, this paper expounds on China’s clean energy policy and development plan. The development of hydropower, wind power, and solar power in China in recent years is analyzed. On this basis, the Grey Forecasting Model is used to forecast the development and structure of China’s clean energy in the next 10 years, point out the direction and market opportunities of China’s clean energy development in the future, and put forward the implementation methods for the sustainable development of China’s clean energy. It provides a reference for the policy decision-making of China’s clean energy development.

Forecasting CO2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China

Accurate predictions of CO₂ emissions have important practical significance for determining the best measures for reducing CO₂ emissions and accomplishing the target of reaching a carbon peak. Although some existing models have good modeling accuracy, the improvement of model specifications can provide a more accurate grasp of a system’s future and thus help relevant departments develop more effective targeting measures. Therefore, considering the shortcomings of the existing grey Bernoulli model, in this paper, the traditional model is optimized from the perspectives of the accumulation mode and background value optimization, and the novel grey Bernoulli model NFOGBM(1,1,α,β) is constructed. The effectiveness of the model is verified by using CO₂ emissions data from seven major industries in Shaanxi Province, China, and future trends are predicted. The conclusions are as follows. First, the new fractional opposite-directional accumulation and optimization methods for background value determination are effective and reasonable, and the prediction performance can be enhanced. Second, the prediction accuracy of the NFOGBM(1,1,α,β) is higher than that of the NGBM(1,1) and FANGBM(1,1). Third, the forecasting results show that under the current conditions, the CO₂ emissions generated by the production and supply of electricity and heat are expected to increase by 23.8% by 2030, and the CO₂ emissions of the other six examined industries will decline.

A new multivariate grey prediction model for forecasting China's regional energy consumption

Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.

Estimating per Capita Primary Energy Consumption Using a Novel Fractional Gray Bernoulli Model

Abstract: On the basis of the available gray models, a new fractional gray Bernoulli model (GFGBM (1,1,)) is proposed to predict the per capita primary energy consumption (PPEC) of major economies in the world [...]

How to Predict Energy Consumption in BRICS Countries?

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the FGM1,1 with traditional ones, like standard GM1,1 and ARIMA1,1,1 models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM1,1 for a specific range of order parameters and the ARIMA1,1,1 model and the usefulness of both approaches for energy consumption efficient forecasting.