Global climate problem has attracted attention from all over the world. How to correctly reflect greenhouse gas emission trend is not only an environmental problem, but also concerns human society's ...sustainable development. Energy consumption is the primary source of greenhouse gas emission. It is necessary to accurately forecasting energy consumption greenhouse gas emission future trend. COVID-19 epidemic has brought respite from global climate change problem by reducing energy consumption through home office, work stoppage, and global travel ban. However, in the post-epidemic period, energy consumption greenhouse gas emission intensity trend has become the center research field. This study takes global energy consumption carbon emission problem as the main research line and focuses on forecasting global energy consumption carbon dioxide emission trend in major regions based on COVID-19 epidemic shock background. The study considering epidemic shock volatility characteristic. Firstly, fractional order grey mode (FGM(1,1))is used as the baseline model to balance time series data weight. Secondly, Median absolute deviation data preprocessing is introduced to reduce data fluctuation. Finally, a novel delaying pulse shock function optimized background grey forecasting model is also proposed to reflect epidemic shock-response characteristic. The proposed model is compared with existing models. It is found that data preprocessing and novel proposed model not only improves historical data's fitting quality by reflecting COVID-19 epidemic's shock characteristic, but also showed excellent forecasting performance for future trend. The novel grey model largely solves existing model underfitting/overfitting problem. In the end, based on forecasted results, we summarize research conclusion and implication.
•Delaying pulse shock function optimized background grey forecasting model is proposed.•The novel grey model solves existing model underfitting/overfitting problem.•Fossil energy's increasing presence in energy structure increased carbon dioxide emission.
Energy emission systems are often influenced by external information and policy shock. However, conventional grey models ignore the existence of the impact of multiple shock events and cumulative ...time-delay effects, which are crucial for achieving accurate forecasts. From a dynamic perspective, we aim to establish a multivariable grey prediction model considering the impact of multiple shock events, namely MSGTDM(1,N) model. Specifically, to accurately describe the impact of different types of external shocks on the system, three kinds of nonlinear dynamic shock functions are designed, including growth dynamic shock function, decline dynamic shock function, and slope dynamic shock function. Based on this, multi-dimensional instantaneous shock utility terms and cumulative time-delay utility terms are constructed to accurately measure shock effects. And Whale Optimization Algorithm is employed to determine the optimal parameters of the shock functions through comprehensive comparative analysis. The findings affirm the MSGTDM(1,N) model's higher predictive accuracy and validity. Consequently, the forecast results of China's carbon emissions from coal and natural gas consumption in 2022–2025 provide a reliable basis for adjusting the energy structure and implementing the “dual-carbon” policy effectively.
We study financial networks where banks are connected by debt contracts. We consider the operation of debt swapping when two creditor banks decide to exchange an incoming payment obligation, thus ...leading to a locally different network structure. We say that a swap is positive if it is beneficial for both of the banks involved; we can interpret this notion either with respect to the amount of assets received by the banks, or their exposure to different shocks that might hit the system. We analyze various properties of these swapping operations in financial networks. We first show that there can be no positive swap for any pair of banks in a static financial system, or when a shock hits each bank in the network proportionally. We then study worst-case shock models, when a shock of given size is distributed in the worst possible way for a specific bank. If the goal of banks is to minimize their losses in such a worst-case setting, then a positive swap can indeed exist. We analyze the effects of such a positive swap on other banks of the system, the computational complexity of finding a swap, and special cases where a swap can be found efficiently. Finally, we also present some results for more complex swapping operations when the banks swap multiple contracts, or when more than two banks participate in the swap.
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