Carbon financial trading risk based on multidimensional analysis of data flow from the perspective of low-carbon economy

Abstract

AbstractCurrently, carbon trading provides financial incentives for buying and selling savings to generate a certain quantity of energy gases with a market-based mechanism. Trade in renewable energy and breakthroughs in energy efficiency can be enhanced by managing either the obstacles to the business or economic risks associated with trade facilitation, making is challenging to implement a low-carbon economy in developing financial systems. Reducing greenhouse gas emissions is likely perceived as in contradiction with the combat for poverty in developing nations, and rising real incomes are often connected with better-increased energy production. To maintain carbon option trading, the analysis begins to predict future carbon option prices using the generalized auto-regressive conditional heteroskedasticity model and fractional brownian motion. Predicting carbon option prices using fractional brownian motion makes sense, given their fractal nature. Data envelopment analysis to better understand the countermeasures for utilizing a low-carbon economy need to further analytical and economic improvement of the marketing function and development. Hence, this research GARCH-DEA has been designed to strengthen carbon financial trading using multidimensional data flow analysis from the perspective of the varying nature of returns and the implications for a low-carbon economy; distribution features are enormous theoretical and practical relevance for the monitoring and management of financial risks. Reducing greenhouse gas emissions, resulting in carbon dioxide is vital in the battle against climate change. Products and services that require carbon-intensive inputs, like electricity and transportation, can be more expensive due to the rising cost of burning fossil fuels.