Do fossil fuel firms reframe online climate and sustainability communication? A data-driven analysis.

Identifying drivers of climate misinformation on social media is crucial to climate action. Misinformation comes in various forms; however, subtler strategies, such as emphasizing favorable interpretations of events or data or reframing conversations to fit preferred narratives, have received little attention. This data-driven paper examines online climate and sustainability communication behavior over 7 years (2014-2021) across three influential stakeholder groups consisting of eight fossil fuel firms (industry), 14 non-governmental organizations (NGOs), and eight inter-governmental organizations (IGOs).

Social media enables people-centric climate action in the hard-to-decarbonise building sector.

The building and construction sector accounts for around 39% of global carbon dioxide emissions and remains a hard-to-abate sector. We use a data-driven analysis of global high-level climate action on emissions reduction in the building sector using 256,717 English-language tweets across a 13-year time frame (2009-2021). Using natural language processing and network analysis, we show that public sentiments and emotions on social media are reactive to these climate policy actions.

A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model.

This paper develops a threshold-augmented dynamic multi-country model (TGVAR) to quantify the macroeconomic effects of the Covid-19 pandemic. We show that there exist threshold effects in the relationship between output growth and excess global volatility at individual country levels in a significant majority of advanced economies and several emerging markets. We then estimate a more general multi-country model augmented with these threshold effects as well as long term interest rates, oil prices, exchange rates and equity returns to perform counterfactual analyses.

Covid-19 fiscal support and its effectiveness.

This paper uses a threshold-augmented Global VAR model to quantify the macroeconomic effects of countries' discretionary fiscal actions in response to the Covid-19 pandemic and its fallout. Our results are threefold: (1) fiscal policy is playing a key role in mitigating the effects of the pandemic; (2) all else equal, countries that implemented larger fiscal support are expected to experience less output contractions; (3) emerging markets are also benefiting from the synchronized fiscal actions globally through the spillover channel and reduced financial market volatility.

Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research.

Text-based data sources like narratives and stories have become increasingly popular as critical insight generator in energy research and social science. However, their implications in policy application usually remain superficial and fail to fully exploit state-of-the-art resources which digital era holds for text analysis. This paper illustrates the potential of deep-narrative analysis in energy policy research using text analysis tools from the cutting-edge domain of computational social sciences, notably topic modelling.