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This research investigates the dynamic interplay between financial technology, information and communication technology, energy consumption, and economic growth on environmental sustainability within Emerging and Growth-Leading Economies (EAGLEs) from 2005 to 2020. Utilizing advanced econometric techniques, such as Fully Modified Least Squares (FMOLS) and Vector Autoregressive Error Correction Model (VECM), the investigation scrutinizes the hypothesized relationships among these variables. Panel unit root tests were deployed to assess stationarity, while panel least squares methodology was employed to determine the presence of co-integration among the variables under study. The analysis reveals that internet usage, GDP, and renewable energy consumption exhibit a notable influence in diminishing CO2 emissions within EAGLE economies. Additionally, the findings substantiate the existence of long-term causality originating from these variables and impacting CO2 emissions. Conversely, the role of ATM networks in CO2 emissions remains ambiguous, implying that financial technology’s influence on environmental sustainability is inconclusive. Consequently, the research posits that environmental sustainability in EAGLE economies is chiefly determined by factors such as internet usage, economic expansion, and renewable energy consumption, with financial technology demonstrating no discernable impact. In light of these findings, the study advocates for the reevaluation and adaptation of existing policies and strategies to account for shifting climatic conditions. By doing so, decision-makers can better align their efforts with the pursuit of environmental sustainability in the context of rapidly evolving economies.

The current research investigates the impact of the COVID-19 Pandemic on Indian Sustainable Investment. For this purpose, the study selected three sustainable indices, namely, BSE Greenex, BSE Carbonex, and BSE 100 ESG index of Bombay Stock Exchange of India. The Descriptive Statistics, ADF Test and GARCH (1,1) Model were used to found that all the sample sustainable indices were gained positive return with the high volatility throughout the COVID-19 Pandemic and the results confirmed that COVID-19 influenced the Indian sustainable investments.

Air pollution issue has become an important environmental problem in India. This paper proposes to examine the influence of Delhi Air Pollution on the two Indian stock indices, using Descriptive Statistics, Unit Root, and OLS regression. The analysis of the study found that Delhi Air Pollution did create a statistically significant effect on Nifty. This is the first study of this type to look into the effects of air quality issue on stock market indices in India

The present study investigated the relationship between airline stock price and crude oil price. For this study, six airlines such as Air India, Go Air, IndiGo, Jet Lite, Jet Airways and Spice Jet and three crude oil markets, WTI-West Texas Intermediate and Texas Light Sweet, Brent -North Sea Brent Crude, and Dubai-Dubai Crude, were selected Based on their market capitalization. According to empirical results, a Crude oil price triggered fluctuations in most of the airline stock returns. Moreover, Air India, IndiGo, Jet Airways and Spice Jet experienced statistically significant relationships between their stock returns and crude oil price but did not correlate with them during the study period from 1st January 2007 to 30th November 2018. The findings of present study would be useful for individual and institutional investors and policy makers.Keywords: Crude Oil Price, Airline Stock Return, Descriptive Statistics, Unit Root Test, Correlation Matrix, Granger Causality Test.JEL Classifications: G11, G14, G15, Q43, R40DOI: https://doi.org/10.32479/ijeep.7963

The behavioral finance studies generally analyse the relationship between investor moods, caused by weather factors and indices returns via investor moods. The present study examines the relationship between three weather factors (temperature, humidity, and wind speed) in capital cities (namely Beijing for China, Tokyo for Japan, Victoria City for Hong Kong, New Delhi for India and Singapore for Singapore), and sample stock indices (Shanghai Stock Exchange from China, Nikkei 225 from Japan, Hang Seng Index from Hong Kong, BSE Sensex from India and Singapore Exchange Limited from Singapore) of selected Asian countries. This study applied statistical tools like Descriptive Statistics, ADF Test, VAR Module and adopted the Granger Causality Approach. The found that among the three weather variables, temperature recorded a statistically significant influence on the returns of Shanghai Stock Exchange (Unidirectional Linkage), HANG SENG Index (Unidirectional Linkage) and also BSE Sensex (Bi-Directional Linkage). Wind speed recorded Bi-Directional Linkages with HANG SENG Index. The findings of this study could help the investors in making better investment management decisions. It is found that there are valuable opportunities to international investors for diversifying their stocks. The study does contribute to the behavioral finance literature.

This paper proposes to investigate the Co Movement and Causal Relationship, among the three weather factors (temperature, humidity, and wind speed) and the returns of the Agriculture Commodity Index called Dhaanya, in India. The study employed the secondary daily data of weather in five sample cities (Chennai, Mumbai, Delhi, Kolkata and Hyderabad), and Agriculture Commodity Index called Dhaanya, in India. Statistical tools like Descriptive Statistics, Unit Root, Correlation Matrix, and Granger Causality Test were employed. This study found that the temperature and wind speed influenced the investors’ mood in Chennai and Mumbai, in respect of Agriculture Commodity Index, namely Dhaanya. The findings of this study would help the investors in making investment decisions rationally, on the basis of weather condition.

This study proposes to investigate the dynamic relationships between the three weather factors (temperature, humidity, and wind speed) in New York City of USA and Coinbase Index from Federal Reserve Bank of St. Louis, in the USA. Statistical tools like Descriptive Statistics, Unit Root, Granger Causality Test and Johansen Co-Integration test were employed. This study clearly found that the temperature influenced the investors’ mood and their investment decision in respect of Cryptocurrency index (Coinbase Index) and also found that there was long run equilibrium between the sample variables during the study period. The results of study provided strong evidence against the Efficient Market Hypothesis (EMH).

This study investigated the effect of three weather factors (temperature, humidity and wind speed), on the returns of the Sri Lanka stock index (CSE-Colombo Stock Exchange). The study used the daily weather data of Sri Lanka capital city, namely, Colombo. Econometric models like Descriptive Statistics, ADF Test and OLS regression Test were used for the analysis. This paper provides significant evidences for correlation between weather factor and stock market returns in Sri Lanka. The findings of this study will be useful for the investing community, Government and Policy Regulators in the sample country. The study results indicate that only one weather factor, namely, wind speed influenced the Colombo stock market index.