This paper focuses on the measurement of carbon finance level and exploration of its influencing factors in the context of increasingly severe environmental problems and the booming carbon trading markets. The authors emphasize the need to construct an effective evaluation index system for carbon markets due to their complexity and urgency. They propose using a composite indicator called the Carbon Finance Index (CFI) and apply multiple criteria decision analysis (MCDA) to analyze this indicator. To calculate the CFI, the authors selected Beijing, Shanghai, and Guangdong as pilot areas and extensively screened potential impact factors. These factors were then normalized, weighted by coefficient of variation, and aggregated using different methods. The authors used the Shannon-Spearman Measure to measure information loss and determine the method with least loss of information for obtaining the CFI. Through panel model analysis, they found that company size, number of patents per 10,000 people, and proportion of new energy generation were significant influencing factors on carbon finance level. Based on their research findings, they provide corresponding suggestions for different market entities. The authors acknowledge that improvement and generalization could be conducted better with sufficient data from all pilot areas. They also highlight the potential to extend their model to a large-scale carbon financial evaluation system considering that the establishment of a national carbon market has already started. In terms of data sources, trading volume of carbon emission quotas and standard deviation of daily trading days are obtained from CSMAR while trading volume of CCER is sourced from pilot regional exchanges. The total number of enterprises controlling carbon emissions is derived from data published by Local Development and Reform Commission. Data related to sectors such as "Energy Saving and Environmental Cleaning," "Green Protection," "Environmental Protection Concept," "New Energy Vehicles," and others are obtained from WIND database. Overall, this research contributes to understanding how to measure carbon finance level effectively in order to support steady development in national carbon markets.
This is a very complicated topic, but I will try to explain it in a simple way.
- People are trying to measure how much money is being made from carbon trading and what affects it.
- They need a good way to evaluate the carbon market and see how well it is doing.
- They came up with something called the Carbon Finance Index (CFI) to help them do this evaluation.
- They chose three places (Beijing, Shanghai, Guangdong) to test their ideas and looked at different things that might affect the carbon market.
- They used some math methods to calculate the CFI and find out which method was best.
Important words- Measurement: Finding out how much of something there is.
- Evaluation: Looking at something carefully to see how well it is doing.
- Criteria: Things that are important for making a decision or judgment.
- Pilot areas: Places where they test new ideas before using them everywhere.
- Normalization: Making things fair by adjusting them in a certain way.
- Aggregation: Putting things together to get a total or overall result."
Measuring Carbon Finance Level and Exploring Its Influencing Factors
As environmental problems become increasingly severe, the booming carbon trading markets have attracted more attention. To effectively evaluate the level of carbon finance, an effective evaluation index system needs to be constructed. In this research paper, the authors propose using a composite indicator called the Carbon Finance Index (CFI) and apply multiple criteria decision analysis (MCDA) to analyze this indicator.
Calculating CFI
To calculate the CFI, Beijing, Shanghai, and Guangdong were selected as pilot areas and potential impact factors were extensively screened. These factors were then normalized, weighted by coefficient of variation, and aggregated using different methods. The Shannon-Spearman Measure was used to measure information loss in order to determine which method had least loss of information for obtaining the CFI.
Analysis Results
Through panel model analysis, it was found that company size, number of patents per 10,000 people, and proportion of new energy generation were significant influencing factors on carbon finance level. Based on their research findings they provided corresponding suggestions for different market entities such as government policies or corporate strategies.
Data Sources
The trading volume of carbon emission quotas and standard deviation of daily trading days are obtained from CSMAR while trading volume of CCER is sourced from pilot regional exchanges. The total number of enterprises controlling carbon emissions is derived from data published by Local Development and Reform Commission while data related to sectors such as "Energy Saving and Environmental Cleaning," "Green Protection," "Environmental Protection Concept," "New Energy Vehicles," etc., are obtained from WIND database.
Conclusion & Future Work
Overall this research contributes to understanding how to measure carbon finance level effectively in order to support steady development in national carbon markets with improvement possible with sufficient data from all pilot areas . The authors also highlighted potential for extending their model into a large-scale financial evaluation system considering that establishment of a national carbon market has already started .
Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming. Considering the complexity and urgency of carbon market, it is necessary to construct an effective evaluation index system. This paper selected carbon finance index as a composite indicator. Taking Beijing, Shanghai, and Guangdong as examples, we adopted the classic method of multiple criteria decision analysis (MCDA) to analyze the composite indicator. Potential impact factors were screened extensively and calculated through normalization, weighting by coefficient of variation and different aggregation methods. Under the measurement of Shannon-Spearman Measure, the method with the least loss of information was used to obtain the carbon finance index (CFI) of the pilot areas. Through panel model analysis, we found that company size, the number of patents per 10,000 people and the proportion of new energy generation were the factors with significant influence. Based on the research, corresponding suggestions were put forward for different market entities. Hopefully, this research will contribute to the steady development of the national carbon market.
