Can China reach the CO2 peak by 2030? A forecast perspective

With the continuous emission of greenhouse gases, climate issues such as global warming have attracted widespread attention. As the largest CO2 emitter, China proposes the target of reaching the CO2 emissions peak by 2030 at the 75th United Nations General Assembly. To determine whether China can realize the goal, we construct an assessment system consisting of a new discrete grey prediction model on the basis of a rolling mechanism and an improved IPCC method. First, the new grey prediction model is used to predict the CO2 emissions and GDP from 2021 to 2030, and then, the enhanced IPCC method is used to obtain the carbon intensity from 2021 to 2030. In line with the direct judgment based on CO2 emissions and the indirect judgment based on the comparison between the AADR of carbon intensity and the AAIR of GDP, we find that China faces great challenges and difficulties in achieving its carbon peaking target by 2030. Finally, based on the forecast data and China's current situation, some policy recommendations are put forward to accelerate China's CO2 peak goal.

A novel fractional-order grey prediction model: a case study of Chinese carbon emissions

Objective and accurate prediction of carbon emissions can provide a basis for the country to achieve carbon emission reduction targets and can also comprehensively and scientifically predict the peak of carbon emissions effectively, providing valuable reference information for the implementation of specific emission reduction policies and measures at each stage. In this paper, a novel fractional-order grey multivariate forecasting model is established to analyze and forecast China's carbon emissions, reflecting the principle of new information priority. The model adds fractional-order cumulative sequences to the traditional integer-order cumulative sequences, uses the Gamma function to represent the fractional-order sequences and the time-response equation, and uses the particle swarm algorithm to find the optimal order of the cumulative sequence. Finally, the modeling steps of the model are given. Then, the new model is analyzed for its effectiveness from three different perspectives using 21 years of Chinese carbon emission data. The results of the first and second cases show that the newly established particle swarm optimization fractional-order model is superior to the grey multivariate comparison model. The results of the third case show that the new model is superior to the three classical grey prediction comparison models. It has stable characteristics for both simulation and prediction and also shows high accuracy, and all three cases fully illustrate the effectiveness of the new model. Finally, this new model is applied to forecast China's carbon emissions from 2022-2026, analyze the forecast results, and make relevant recommendations.

Predicting of elderly population structure and density by a novel grey fractional-order model with theta residual optimization: a case study of Shanghai City, China

BACKGROUND

Accurately predicting the future development trend of population aging is conducive to accelerating the development of the elderly care industry. This study constructed a combined optimization grey prediction model to predict the structure and density of elderly population.

METHODS

In this paper, a GT-FGM model is proposed, which combines Theta residual optimization with fractional-order accumulation operator. Fractional-order accumulation can effectively weaken the randomness of the original data sequence. Meanwhile, Theta residual optimization can adjust parameter by minimizing the mean absolute error. And the population statistics of Shanghai city from 2006 to 2020 were selected for prediction analysis. By comparing with the other traditional grey prediction methods, three representative error indexes (MAE, MAPE, RMSE) were conducting for error analysis.

RESULTS

Compared with the FGM model, GM (1,1) model, Verhulst model, Logistic model, SES and other classical prediction methods, the GT-FGM model shows significant forecasting advantages, and its multi-step rolling prediction accuracy is superior to other prediction methods. The results show that the elderly population density in nine districts in Shanghai will exceed 0.5 by 2030, among which Huangpu District has the highest elderly population density, reaching 0.6825. There has been a steady increase in the elderly population over the age of 60.

CONCLUSIONS

The GT-FGM model can improve the prediction accuracy effectively. The elderly population in Shanghai shows a steady growth trend on the whole, and the differences between districts are obvious. The government should build a modern pension industry system according to the aging degree of the population in each region, and promote the balanced development of each region.

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application

The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model.

A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

The COVID-19 pandemic has lasted for nearly two years, and the global epidemic situation is still grim and growing. Therefore, it is necessary to make correct predictions about the epidemic to implement appropriate and effective epidemic prevention measures. This paper analyzes the classic Susceptible Infected Recovered Model (SIR) to understand the significance of model characteristics and parameters, and uses the differential and difference information of the grey system to put forward a grey prediction model based on SIR infectious disease model. The Laplace transform is used to calculate the model reduction formula, and finally obtain the modeling steps of the model. It is applied to large and small numerical cases to verify the validity of different orders of magnitude data. Meanwhile, data of different lengths are modeled and predicted to verify the robustness of model. Finally, the new model is compared with three classical grey prediction models. The results show that the model is significantly superior to the comparison model, indicating that the model can effectively predict the COVID-19 epidemic, and is applicable to countries with different population magnitude, can carry out stable and effective simulation and prediction for data of different lengths.

The dynamic coupling nexus among inclusive green growth: a case study in Anhui province, China

Inclusive green growth (IGG) offers an effective alternative to pursue sustainable development. The core of the IGG system lies in the coordination of inclusive, green, and growth subsystems. However, there is little quantitative assessment on IGG based on subsystem collaboration. This study proposes a holistic scheme of inclusive-green-growth nexus in Anhui province from 2009 to 2018 by using an integrated approach, namely, the entropy weight approach (EWA), coupling coordination degree model (CCDM), grey prediction model (GPM), and obstacle factor diagnostic model (OFDM). The results show that: (1) The proposed integrated approach could be viable to measure the synergistic interactions among internal IGG subsystems; (2) At the provincial level, a relatively high IGG performance but a low coupling coordination degree (CCD) of the IGG nexus are seen. Although the predicted value of CCD will show an upward trend, it will not be able to cross the start stage. The obstacle factors on the coordinated development of IGG can be divided into two stages: (3) At the prefectural level, the cities in which CCD is rising outnumber those it is falling. However, the CCD is also low, and the gap between cities is getting wider. The obstacles that affect the CCD of cities see a dynamic evolution trend from "inclusive obstacle type" to "inclusive and growth obstacle type" then to "green obstacle type" over the decade.

Model framework to quantify the effectiveness of garbage classification in reducing dioxin emissions

Although waste incineration is a promising disposal method, it produces unwanted combustion by-products, such as toxic dioxins, that can be unintentionally emitted. Kitchen scraps can result in incomplete combustion of waste, which accelerates the formation of dioxins, especially for the small-sized incinerators without identical operating temperature. Consequently, garbage classification before waste incineration is critical for dioxin control in the small-sized waste incineration industries. To date, the influence of garbage classification on dioxin emissions has not been quantified. In this study, a model framework integrating the grey prediction model and autoregressive prediction model was established and used to predict future dioxin emissions from small-sized waste incineration. If garbage classification is ideally strictly implemented, annual dioxin emissions could be reduced by up to 1697 g TEQ over the next 10 years. Garbage classification reduced emissions by about 30.7% compared with incineration of mixed municipal solid waste without classification (5534 g TEQ over the next 10 years). The established model framework can effectively assess the influence of garbage classification on dioxin emissions from waste incineration, which could facilitate the widespread adoption of garbage classification.

A novel multivariable grey prediction model and its application in forecasting coal consumption

Coal is an important energy source worldwide. Objectively and accurately predicting coal consumption is conducive to healthy coal industry development, because such predictions can provide references and warnings that are useful in formulating energy strategies and implementing environmental policies. Population size and area economic development are the main factors that affect coal consumption. Considering the above influences, this paper first establishes a differential equation and proposes a novel multivariable Verhulst grey model (MVGM(1,N)) based on grey information differences. MVGM(1,N) extends classical model from single-variable to multivariate and diminishes the characteristics of Verhulst's reliance on saturated S-shaped and single-peak data, making classical model more applicable to real situations. To prove the effectiveness of MVGM(1,N) simulation experiments are carried out in areas with high coal consumption. The result of this proposed model is more precise than that of NLARX, ARIMA and five classical grey models Finally, this novel multivariable model predicates coal consumption of Inner Mongolia and Gansu Provinces in China, the results show that MVGM(1,N) is preferable to other models, indicating that this model can effectively predict coal consumption.

A grey seasonal least square support vector regression model for time series forecasting

Seasonality is a fundamental and common property of most time series in the real world. In this article, we propose a grey seasonal least square support vector regression, abbreviated as GSLSSVR, by combining the dummy variables, framework of the LSSVR model, and grey accumulation generation operation to reflect seasonal variations in functional forms, variables, and parameters. Our framework provides an intuitive and simple set up of arbitrary seasonality in any feature, which considerably enhances model realism. Further, the regulation method is introduced to increase the stability and generalization of the newly proposed model. Using the Lagrange multipliers algorithm, the model parameters are obtained by solving a set of linear equations. In addition, the last block evaluation is developed, which has the same size in the validating and testing data, to identify the hyperparameters of this novel model. For verification purposes, four real seasonal time series having various characteristics are employed in this work, including quarterly electricity consumption, monthly cargo throughput, monthly crude oil production, and monthly gasoline production in China. Experimental results demonstrate that our proposed model can provide for analysis of seasonal regulatory measures and is validated to be superior to other prevalent forecasting models referring to the SGM(1,1), SFGM(1,1), LSSVR, SARIMA-GARCH, and BPNN models. Ultimately, our model is highly recommended for addressing issues with periodic and nonlinear features.

A novel grey model based on traditional Richards model and its application in COVID-19

In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1,1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies.

A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy

and accurate prediction of clean energy can supply an important reference for governments to formulate social and economic development policies. This paper begins with the logistic equation which is the whitening equation of the Verhulst model, introduces the Riccati equation with constant coefficients to optimize the whitening equation, and establishes a grey prediction model (CCRGM(1,1)) based on the Riccati equation. This model organically combines the characteristics of the grey model, and flexibly improves the modelling precision. Furthermore, the nonlinear term is optimized by the simulated annealing algorithm. To illustrate the validation of the new model, two kinds of clean energy consumption in the actual area are selected as the research objects. Compared with six other grey prediction models, CCRGM(1,1) model has the highest accuracy in simulation and prediction. Finally, this model is used to predict the nuclear and hydroelectricity energy consumption in North America from 2019 to 2028. The results predict that nuclear energy consumption will keep rising in the next decade, while hydroelectricity energy consumption will rise to a peak and subsequently fall back, which offers important information for the governments of North America to formulate energy measures.