Editorial: Economics of the Environment in the Shadow of Coronavirus

Faster or slower decarbonization? Policymaker and stakeholder expectations on the effect of the COVID-19 pandemic on the global energy transition

The COVID-19 pandemic might have tremendous consequences on decarbonization efforts across the globe. Understanding governments' policy action in the short and medium term is key to assess whether the response to the crisis will crowd out or fast-track decarbonization efforts. We surveyed over 200 policymakers and stakeholders from 55 different countries to collect climate policy expectations in various sectors and regions in the next five years. While support for high-emitting sectors is not expected to dissolve completely, commitment to policies supporting the transition to low-carbon energy and transport sectors is expected to increase substantially. This is true for OECD and Asian countries, representing approximately 90% of global emissions. Our results suggest that expectations that the COVID-19 pandemic will accelerate decarbonization efforts are widely shared.

Modelling the spread of SARS-CoV-2 pandemic - Impact of lockdowns & interventions

BACKGROUND & OBJECTIVES

To handle the current COVID-19 pandemic in India, multiple strategies have been applied and implemented to slow down the virus transmission. These included clinical management of active cases, rapid development of treatment strategies, vaccines computational modelling and statistical tools to name a few. This article presents a mathematical model for a time series prediction and analyzes the impact of the lockdown.

METHODS

Several existing mathematical models were not able to account for asymptomatic patients, with limited testing capability at onset and no data on serosurveillance. In this study, a new model was used which was developed on lines of susceptible-asymptomatic-infected-recovered (SAIR) to assess the impact of the lockdown and make predictions on its future course. Four parameters were used, namely β, γ, η and ε. β measures the likelihood of the susceptible person getting infected, and γ denotes recovery rate of patients. The ratio β/γ is denoted by R0 (basic reproduction number).

RESULTS

The disease spread was reduced due to initial lockdown. An increase in γ reflects healthcare and hospital services, medications and protocols put in place. In Delhi, the predictions from the model were corroborated with July and September serosurveys, which showed antibodies in 23.5 and 33 per cent population, respectively.

INTERPRETATION & CONCLUSIONS

The SAIR model has helped understand the disease better. If the model is correct, we may have reached herd immunity with about 380 million people already infected. However, personal protective measures remain crucial. If there was no lockdown, the number of active infections would have peaked at close to 14.7 million, resulted in more than 2.6 million deaths, and the peak would have arrived by June 2020. The number of deaths with the current trends may be less than 0.2 million.