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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

• We compare a seller's disaster payment period policies to boost demand. • We consider Advance, Cash, Credit or Disaster Grace Period (ACCD) payment options. • The trade-off between default/disaster risk and demand is investigated. • We identify the optimum replenishment cycle and payments period for a seller's profit. Sellers can offer various payment schemes for buyers to boost demand, with the advance, cash, and credit payment policies being popular approaches. To provide further insight into the dynamics of these policies, we consider them in addition to a Disaster Grace Period payment option, which has become increasingly prevalent. The seller's motivation for offering Advance, Cash, Credit or Disaster Grace Period (ACCD) payment options include the desire for a stronger relationship to the buyer, a smoother cash flow throughout the supply chain, and a potential increase in demand. In addition, the disaster grace period payment scheme can help in better navigating worldwide emergencies, such as pandemics, but also natural emergencies like severe storms, floods, tornadoes, and landslides, which have been happening with an increasing frequency over the last decades. However, the increase in the payment period increases the default/disaster risk, rendering a trade-off between risk and demand, making these crucial parameters in influencing the seller's profit. Within this context, we aim to find the optimum replenishment cycle and payments period for a seller to increase their total profit. To do so, we perform numerical analysis to examine the impact of the demand coefficient, interest rate, default risk, disaster risk, and the disaster demand coefficient on profit. The results offer managerial insights on which payment option to choose in a particular setting to maximize profit. We for example find that sellers should offer a disaster grace period when the disaster demand increases and the risk of a disaster decreases. [ABSTRACT FROM AUTHOR]