This research article examines the impact of stock market capitalization on carbon emissions using forty high carbon-emitting countries from 1996 to 2018. This study adopts the Driscoll-Kraay method that simultaneously tackles heteroscedasticity, autocorrelation, and contemporaneous correlation issues. We find an inverted U relationship between stock market capitalization (SMC) and environmental degradation. We propose an extended environmental Kuznets curve based on SMC while energy intensity, industrialization, and urbanization increase emissions in sample countries. The quadratic method, SLM test, and derivative graphing detect the consensus of the inverted U relationship. The weak-negative SMC2 coefficient reveals that the dangerous impact of capitalization declines gradually and finally curbs the environmental degradation challenges. The relationship is strong in highly polluted countries with overvalued stock markets. The study catches no policy synergies between the growing stock market and increased carbon emissions. Stock market capitalization should be integrated into climate change adaptation strategies at national and regional levels, primarily to address the dark effect of environmental degradation.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465146 | PMC |
http://dx.doi.org/10.1007/s11356-022-22885-1 | DOI Listing |
Risk Anal
December 2024
College of Business, Alfaisal University, Riyadh, Saudi Arabia.
Increasing awareness of climate change and its potential consequences on financial markets has led to interest in the impact of climate risk on stock returns and portfolio composition, but few studies have focused on perceived climate risk pricing. This study is the first to introduce perceived climate risk as an additional factor in asset pricing models. The perceived climate risk is measured based on the climate change sentiment of the Twitter dataset with 16 million unique tweets in the years 2010-2019.
View Article and Find Full Text PDFCirculation
January 2025
Division of Cardiology, Department of Medicine, Emory Clinical Cardiovascular Research Institute; and Emory University School of Medicine, Atlanta, GA (L.S.S.).
There is a new awareness of the widespread nature of metabolic dysfunction-associated steatotic liver disease (MASLD) and its connection to cardiovascular disease (CVD). This has catalyzed collaboration between cardiologists, hepatologists, endocrinologists, and the wider multidisciplinary team to address the need for earlier identification of those with MASLD who are at increased risk for CVD. The overlap in the pathophysiologic processes and parallel prevalence of CVD, metabolic syndrome, and MASLD highlight the multisystem consequences of poor cardiovascular-liver-metabolic health.
View Article and Find Full Text PDFHeliyon
December 2024
North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450011, PR China.
In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series.
View Article and Find Full Text PDFBMC Complement Med Ther
December 2024
College of Medicine, University of Florida, Gainesville, FL, USA.
Background: As the primary cause of various preventable illnesses, smoking results in approximately five million premature deaths each year in the US and a multitude of adults living with serious illness. The majority of smokers know the health risks associated with smoking and intend to quit. However, quitting is very difficult partly because of insomnia and stress associated with it.
View Article and Find Full Text PDFJ Environ Manage
December 2024
Lebow College of Business, Drexel University, Philadelphia, USA. Electronic address:
This study investigates the impact of recent Artificial Intelligence (AI)-driven technological innovations on carbon prices across different quantiles, assessing the influence of AI stock prices on energy prices based on European carbon allowances while controlling for other macroeconomic factors. Using robust methods such as quantile-on-quantile regression, wavelet analysis, and transfer entropy, the research quantifies the information flow between the AI market and carbon allowances. Using daily data with four alternative AI stock prices from September 14, 2016, to December 29, 2023, the findings reveal a strong effect of AI returns on carbon prices, with significant fluctuations across price quantiles and consistent long-term average growth in market returns.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!