Non-monetary narratives motivate businesses to engage with climate change.

The dominant narrative to motivate business actors to take climate actions emphasizes opportunities to increase monetary gains, linking sustainability to the financial goals of these organizations. The prevalence of monetary motivations in sustainability communication among businesses, consultancies, academics and international organizations has made this narrative a truism in the private sector. We conducted an online, real-world, large-n experiment to evaluate the comparative effectiveness of different motivations using narrative communication. We show that non-monetary narratives highlighting prosocial or achievement motivations are 55% more effective in creating responses from businesses than narratives emphasizing monetary gains. These findings are robust across most narrative and audience characteristics, including age and language. Our findings suggest that communication towards business leaders around sustainability can be multi-pronged and should incorporate prosocial and achievement motivations aside from articulating potential financial benefits.

Fine-tuned Forecasting Techniques for COVID-19 Prediction in India

Estimation of statistical quantities plays a cardinal role in handling of convoluted situations such as COVID-19 pandemic and forecasting the number of affected people and fatalities is a major component for such estimations. Past researches have shown that simplistic numerical models fare much better than the complex stochastic and regression-based models when predicting for countries such as India, United States and Brazil where there is no indication of a peak anytime soon. In this research work, we present two models which give most accurate results when compared with other forecasting techniques. We performed both short-term and long-term forecasting based on these models and present the results for two discrete durations.

Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdown

BackgroundCOVID-19 is widely spreading across the globe right now. While some countries have flattened the curve, others are struggling to control the spread of the infection. Precise risk prediction modeling is key to accurate prevention and containment of COVID-19 infection, as well as for the preparation of resources needed to deal with the pandemic in different regions. MethodsGiven the vast differences in approaches and scenarios used by these models to predict future infection rates, in this study, we compared the accuracy among different models such as regression models, ARIMA model, multilayer perceptron, vector autoregression, susceptible exposed infected recovered (SEIR), susceptible infected recovered (SIR), recurrent neural networks (RNNs), long short term memory networks (LSTM) and exponential growth model in prediction of the total COVID-19 confirmed cases. We did so by comparing the predicted rates of these models with actual rates of COVID-19 in India during the nationwide lockdowns. ResultsFew of these models accurately predicted COVID-19 incidence and mortality rates in six weeks, though some provided close results. While advanced warning can help mitigate and prepare for an impending or ongoing epidemic, using poorly fitting models for prediction could lead to substantial adverse outcomes. ImplicationsAs the COVID-19 pandemic continues, accurate risk prediction is key to effective public health interventions. Caution should be taken when choosing different risk prediction models based on specific scenarios and needs. To improve risk prediction of infectious disease such as COVID-19 for policy guidance and recommendations on best practices, both internal (e.g., specific virus characteristics in transmission and mutation) and external factors (e.g., large-scale human behaviors such as school opening, parties, and breaks) should be considered and appropriately weighed.