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This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.

For this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.

The new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.

This article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.

Forecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with grey-Markov method and applied it to the prediction of emergency supplies demand. Therefore, this article aims to establish a novel method for emergency supplies demand forecasting under major epidemics.

Emergency supplies demand is correlated with the number of infected cases in need of relief services. First, a novel method called the Intuitionistic Fuzzy TPGM(1,1)-Markov Method (IFTPGMM) is proposed, and it is utilized for the purpose of forecasting the number of people. Then, the prediction of demand for emergency supplies is calculated using a method based on the safety inventory theory, according to numbers predicted by IFTPGMM. Finally, to demonstrate the effectiveness of the proposed method, a comparative analysis is conducted between IFTPGMM and four other methods.

The results show that IFTPGMM demonstrates superior predictive performance compared to four other methods. The integration of the grey method and intuitionistic fuzzy set has been shown to effectively handle uncertain information and enhance the accuracy of predictions.

The main contribution of this article is to propose a novel method for emergency supplies demand forecasting under major epidemics. The benefits of utilizing the grey method for handling small sample sizes and intuitionistic fuzzy set for handling uncertain information are considered in this proposed method. This method not only enhances existing grey method but also expands the methodologies used for forecasting demand for emergency supplies.

1. An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

2. The safety inventory theory is combined with IFTPGMM to construct a prediction method.

3. Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

The safety inventory theory is combined with IFTPGMM to construct a prediction method.

Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

Globally, the paucity of conventional energy sources has created an unprecedented increase in demand for green energy. Continuous dependency on conventional energy sources has given rise to several undesirable environmental consequences. In the 20th century, the international forum pondered about the development and uses of green energy, which commenced with the realization of global warming and the signing of the Kyoto Protocol agreement. This study aims to divulge the nexus between green energy, carbon emissions and economic prosperity from a global perspective. The study has been conducted by considering panel data of 35 global economies from 1971 to 2019.

To calibrate the uses of green energy, this study dwells upon the ratio between green energy consumption and total energy use. These instrumental variables have been widely acknowledged and accepted by several empirical analysis done in the past (Lin and Moubarak, 2014; Shahbaz et al., 2015). This research specifically uses the emission of carbon dioxide in a million tons as an instrumental variable of environmental degradation, which has been disregarded by all-preceding researchers from a global perspective. Additionally, this study also considers real gross domestic product value in terms of US$ (2010 constant price) as an indicator of economic prosperity. The same has been contemplated by an ample number of empirical research studies conducted previously. Thus, the authors adopted the panel autoregressive distributed lag (ARDL) technique to achieve this research objectives; and to tackle the issue of contemporaneous correlation, the authors applied cross-sectional augmented autoregressive distributed lag (CSARDL) of common correlated effect pooled mean group (CCEPMG).

The results of panel ARDL analysis reveal that in the long-run, real gross domestic product (GDP) leads to carbon emission, whereas green energy uses do not have a substantial effect on the reduction of carbon emission. However, in the short-run, green energy consumption seems definitely helpful for combating carbon emission, while real GDP instigates carbon emission. This study effectively fortifies the notion of a trade-off between ecological pollution and economic prosperity. The empirical results of the Granger Causality test produce evidence of unidirectional causality from carbon emission to green energy uses and from real GDP to carbon emission in the panel countries

First, decisive corollaries of the conclusions drawn above have been made purely on the basis of a comprehensive investigation of 35 global economies. However, there is the scope for inclusive examination by considering more modern economies simultaneously. Second, this paper studied the potential impact of the uses of green energy and real GDP on carbon emission. Notably, the inference of this study has been grounded on three relevant variables, whereas there are possibilities that such an investigation could possibly be extended by considering other instrumental variables of environmental pollution.

A significant number of studies in the past have investigated the connection between renewable energy consumption (REC) and economic growth. To the best of the authors’ knowledge, none have looked to investigate the nexus between REC, economic prosperity and environmental sustainability simultaneously, specifically from the global perspective. Hence, this study intends to widen the prevailing perception of the emerging context above in two ways; first, by reconnoitering the effect of REC on environmental consequences and economic progress simultaneously, which has not been accomplished in extant literature. Second, the authors also strive to gradually augment the comprehensive analysis by expanding the study from a global perspective and by constructing the panel data of developing and advanced economies.