Covid-19 epidemic and regional carbon emissions: A study based on metabolic multivariate grey model with new information priority

Abstract

The COVID-19 epidemic has had an unexpected impact on global carbon emissions. In this context, carbon emission prediction is very important for policy formulation and implementation. It is worth pondering how the grey model deals with the prediction of unexpected events. Considering the spatial dependence of carbon emissions, this paper improves the multivariate grey model, gives new information a higher reference weight, and constructs a metabolic multivariate grey model -MMGM(1,m|λ) which takes new information as the priority. An optimization algorithm is constructed with the minimum error as the goal to determine the value of the weight adjustment parameter λ. Then we simulate and predict the carbon emissions of three regions: China’s Yangtze River Delta (Shanghai, Jiangsu, Zhejiang, and Anhui), the North American Free Trade Area (Canada, Mexico, and the US), and West Europe (the UK and France). These three cases have different regions, different numbers of behavioural variables, different degrees of influence by COVID-19, and different trends, which are comparative. The results show that the new model has a good fitting effect, and it is still applicable in dealing with unexpected events. The new model can reflect regional carbon emission changes more systematically and accurately. Finally, according to cases and discussions, we get some management enlightenment to promote regional environmental collaborative governance.