Proactive visual prediction auditing the green eco-safety through backcasting approach booster by grey recruitment priority conceptual framework

Climate change is a challenge that endangers societal TBL elements' stability. The countries' economies focus on planning for reducing carbon emissions 'CE' and replacing them with low CE energy. This objective needs accurate prediction for CE till 2030 via recording the most significant variables related to CE causes. The variables ( $L_{iq}$ ) are divided into two types tacking in phase I through two steps. The first step classifying the government policies that tackling by the backcasting approach to ranking them. The second step classifies the nature of the energy source which produces CE in Mega ton by SVM. The second phase is fed by phase I outputs to generate a series of prediction values by the LSTM, which is supported by the grey recruitment technique GRPT ( $1,1$ ) to reduce the forecasting errors. The proposed conceptual framework named (Green Eco-Safety Monitoring; GESM), which considered a methodology gathering the backcasting, SVM, and LSTM provided by GRPT ( $1,1$ ) in phase II. The paper tracks 21 governorates' CE. Proactive monitoring helps take corrective actions, enhancing the reduction in errors gap to less than 2.4%. The paper reveals that the industrial sector extracting CE at (38.67%) to 2020 with hopeful reduction of 1.72% annually if the government's interested in supporting carbon sinks, which drastically decreased to 1% by 2020 and to 0.72% annually by 2030.