Bubbles are usually chaotic but can be predictable, provided their formation matches the log periodic power law (LPPL) with unique stylized facts. We investigated Green Bubble behaviour in the stock prices of a selection of stocks during the COVID-19 pandemic, namely, those with the highest market capitalization from a basket of North American and European green energy or clean tech companies and the S&P Global Clean Energy Index. Moreover, the biggest Exchange Traded Fund (TAN) by market capitalization was also considered. The examined period is from 31 December 2019 to 11 October 2021, during which we detected 35 Green Bubbles. All of these followed the LPPL signature while calibrated through the 2013 reformulated LPPL model. In addition, the average drawdown emerged as four times that of the regular S&P-500 stock index (108% vs. 27%) under stressed conditions, such as the COVID-19 pandemic (stylized fact). Finally, the aftermaths of Green Bubbles, unlike regular bubbles, are not destructive, as these bubbles increase economic activity and infrastructure spending and are hence beneficial for holistic growth (described as Social Bubble Hypothesis). We document that there are benefits in adapting greener and more sustainable business models in energy production. Green and sustainable finance offers benefits and opportunities for stock exchanges, especially for energy stocks. As a result, many businesses are focusing on sustainability and adopting an eco-friendly business model, which helps the environment, helps sustainability and attracts investors.

India and other developing economies are receiving more attention in the context of climate change due to their rapid rates of economic expansion and large populations. In terms of absolute emissions, India surpassed China and the U.S. in 2018 to become the third-largest emitter. Solving this wicked problem calls for climate action across the stakeholder spectrum involving governments, business communities, and citizens. While extant literature has focused significantly on the role of governments and individual perceptions, the business sector needs to be more represented. In this study, we consider business news media as a platform that reflects the industry engagement in climate change and as a source of information on climate change for business decision-makers. Hence, understanding the topic and themes in the nexus of climate and business is important to evaluate the business sector’s stance towards climate change and how it has evolved. This work explores business news related to climate change using natural language techniques. We first experiment with three topic-modeling techniques, such as LDA, NMF, and BERTopic, on the business news and two more benchmark news datasets. Our test data is derived from digital news archives of ’The Economic Times – India’s leading business news daily. We evaluate the performance based on quantitative metrics commonly used for topic models. We choose the algorithm that provides the highest precision for climate-specific information represented by the test dataset. We then apply the algorithm with the best performance, as evaluated by the experiment, to a large corpus of Indian climate news from E.T. spanning from 2008 - 2021. We present how different themes, including industry engagement, evolved over the last two decades. The results suggest that climate cooperation has the highest contribution in the corpus, with other themes on resource management, energy and business gaining traction in recent years.

Incentivizing businesses to lower carbon emissions and trade back excess carbon allowances paved the way for rapid growth in carbon credit ETFs. The use of carbon allowances as a hedging alternative fueled this rally further, causing a shift to speculation and forming repetitive bubbles. Speculative bubbles are born from euphoria, yet, they are relatively predictable, provided their pattern matches the log periodic power law (LPPL) with specific stylized facts. A “Minsky moment” identifies a clear speculative bubble as a signal of financial system instability, while a “Social bubble” is regarded as relatively positive, increasing in the long run, infrastructure spending and development. The aim of this paper is to investigate whether various carbon credit bubbles during the pandemic period caused financial instability or had a positive impact (“Minsky” or “Social”). Particularly, we investigate the carbon credit bubble behavior in the ETF prices of KRBN, GRN (Global Carbon Credit tracking ETFs), and the SOLCARBT index during the COVID-19 pandemic period by adopting the log-periodic power law model (LPPL) methodology, which has been widely used, over the past decade, for detecting bubbles and crashes in various markets. In conclusion, these bubbles are social and propelled by the newfound interest in carbon credit trading, for obvious reasons.

Social welfare systems across the developing world have witnessed a significant transformation in the last 20 years with direct cash transfers becoming an increasingly preferred means of tackling poverty and inequality. While this movement finds its roots in Latin America, a large number of developing countries including India have since modified their welfare systems, incorporating direct cash transfers as an alternative to in kind benefits and subsidies. India’s Direct Benefit Transfer (DBT) system, which has been in existence since the past five years, has 316 schemes under its aegis now. While it would be premature to evaluate its success or failure, it would definitely be an interesting and worthwhile exercise to see how Indian states fare in terms of DBT. With Amartya Sen’s pathbreaking Capability Approach as the premise, this research is a unique attempt to explore and identify variables that determine and impact DBT transfers at the Indian state level using multiple regression. We find that literacy and the JAM (Jan-Dhan, Aadhaar, Mobile) Index emerge as significant variables impacting DBT transfers in states. This study contributes to the current literature on social welfare systems and highlights the importance of development architecture in emerging economies.

The agenda-setting and attitude-forming role of media has been proven and endorsed over time. Media has played an instrumental role in the way the issue of climate change is perceived by various stakeholders in society. Although studies on media coverage of climate issues have been gaining prominence in recent years, there is a gap when we consider the Global South. Moreover, although the business sector is a critical stakeholder in climate change policy and action, studies that focus on how business media projects and highlights climate change are relatively sparse. This vacuum is even more pronounced in developing countries. This research is an attempt to address this gap. We have conducted a longitudinal analysis of climate change reporting in a leading Indian business newspaper, using automated content analysis. Results provide us with valuable insights about how climate frames and climate themes have evolved over time in business media. Our findings suggest that while climate cooperation is a prominent topic in business media, however, it has been declining in recent years. On the other hand, the share of domestic news covering sector specific issues is increasing, mirroring India’s change in stance. The insights derived will help in building consensus across stakeholders involving business decision-makers, media houses, policy makers, and civil society.