Figure 4 depicts the co-occurrence network graph of keywords in the field of carbon finance. To further investigate the themes of carbon finance research, we conducted keyword clustering analysis using Citespace and selected the top 8 clusters for presentation. Table 2 provides the main keywords contained in these 8 clusters for analysis purposes.

In addition, Figure 5 displays the clustering results of carbon finance and carbon market research. To analyze the topics related to carbon finance and carbon markets in a more reasonable and accurate manner, we utilized co-occurrence and clustering graphs generated by software. Based on these graphs, relevant literature was reviewed and manually screened, leading to the identification of the following categories: research on carbon market policies and carbon trading mechanisms, research on carbon finance and carbon market risks, research on carbon market spillover effects, and research on the design, pricing, and price forecasting of carbon financial instruments.

Firstly, the design of carbon trading market mechanisms is essential in reducing the overall cost of achieving emission targets. The Kyoto Protocol established three mechanisms, namely Joint Implementation (JI), Clean Development Mechanism (CDM), and Emissions Trading (ET), which have been extensively researched by scholars both domestically and internationally. The focus of this research includes carbon quota supply and demand mechanisms, price mechanisms, risk mechanisms, and regulatory mechanisms.

Secondly, the measurement and prevention of carbon finance and carbon market risks are crucial due to the continuous expansion of carbon financial transactions globally. Researchers have studied the measurement and prevention of carbon finance and carbon market risks, including price risks, policy risks, and market risks.

Thirdly, the spillover effects of carbon markets have attracted significant attention from researchers. The carbon finance market is closely linked to the energy market and the financial market. Researchers have explored the spillover effects between carbon markets, carbon markets and energy markets, and carbon markets and financial markets using various research methods such as the Granger causality test, wavelet analysis method, vector autoregressive (VAR) model, GARCH model, and DY spillover index.

Lastly, the design, pricing, and price forecasting of carbon financial instruments are essential for the healthy and sustainable development of carbon finance. Carbon pricing mechanisms, including carbon taxes, carbon emission trading systems (ETS), carbon credit mechanisms, result-based climate finance (RBCF), and internal carbon pricing, play a critical role in the carbon financial market. Additionally, researchers have proposed models such as ARIMA, LSTM, SVR, XGBoost, GRU, and RBFNN for carbon price forecasting.

In conclusion, this paper employed a bibliometric approach to analyze the research hotspots and topics in the field of carbon finance and carbon markets. The major research hotspots identified include the design of carbon trading market mechanisms, measurement and prevention of risks in carbon finance and carbon markets, research on spillover effects in carbon markets, and research on the design, pricing, and price forecasting of carbon financial instruments. We offer three recommendations for future research, including focusing on innovation in carbon financial instruments, addressing knowledge gaps in carbon finance research, and exploring the role of carbon finance in the transition to a low-carbon economy.

CRediT authorship contribution statement: S.L. contributed to the methodology and project administration. W.Y. and S.L. contributed to data curation, writing, review, and editing. W.Y. contributed to the original draft preparation. All authors have read and agreed to the published version of the manuscript

请以学术风格对下列语句进行润色要求没有语法错误并且符合学术规范 Figure 4 The co-occurrence network graph of keywords32 Hot TopicsIn order to further explore the themes of carbon finance research we conducted keyword clustering analy

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