Keyword Cluster Analysis and Hot Topics in Carbon Finance and Carbon Market Research

3.2 Keyword Cluster Analysis

Keyword cluster analysis is based on the co-occurrence analysis of keywords, and through specific algorithms, it classifies scattered keyword nodes into several closely related keyword clusters (Martinez et al., 2019). In order to further explore the themes of carbon finance research, we conducted keyword cluster analysis in Citespace. Since it is difficult to find specific information from the cluster names alone, it is necessary to combine the main keywords in each cluster to analyze the specific research content more in-depth (Huang et al., 2020). Based on the network structure and the clarity of the clusters, CiteSpace provides module values and average silhouette values to evaluate the effectiveness of the network visualization. Figure 6 depicts the keyword clusters of carbon finance and carbon market, with a Q value of 0.4787 (greater than 0.3) and an S value of 0.7534 (greater than 0.5), indicating that the clusters are significant and have relatively high reference values. According to the size of the nodes in the clusters, we selected the top 8 cluster clusters for presentation, namely #0 supply chain, #1 emissions trading, #2 carbon sequestration, #3 taxation, #4 economic development, #5 carbon market, #6 power market, and #7 forecasting. For the convenience of analysis, Table 3 lists the main keywords included in the top 8 clusters.

Figure 6. Keyword Clusters of Carbon Finance and Carbon Market Research

Table 3. Keyword Cluster Information of Carbon Finance and Carbon Market Research

3.3 Summary of Hot Topics

In order to analyze the carbon finance and carbon market related topics more reasonably and accurately, based on the co-occurrence and cluster maps generated by the software, this study conducted manual screening of the literature and summarized 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
Research on carbon finance instrument design, pricing, and price forecasting, etc.

1. Design of Carbon Trading Market Mechanisms

In order to reduce the total cost of achieving emission targets, the Kyoto Protocol stipulates three mechanisms: Joint Implementation (JI), Clean Development Mechanism (CDM), and Emissions Trading (ET). Since the implementation of this agreement in 2005, governments around the world have taken measures to reduce greenhouse gas emissions. Scholars at home and abroad have conducted extensive research on carbon trading market mechanisms, mainly focusing on carbon quota supply and demand mechanisms (Ji et al., 2017), price mechanisms (Ji et al., 2018a), competition mechanisms (Liu et al., 2022), risk mechanisms, and regulatory mechanisms (Hepburn, 2007). The pricing of carbon emissions rights under the total control and emissions trading mechanism is the core issue for solving the funding problem in the field of carbon finance. For example, based on price theory, Ji et al. (2018b) analyzed the theoretical basis and transmission mechanism of carbon price formation from the perspective of the influencing subjects; Du et al. (2020) discussed the design of emission quota allocation mechanism from the perspective of operation by establishing a Stackelberg model.

2. Measurement and Prevention of Carbon Finance and Carbon Market Risks

With the continuous increase in global carbon finance trading volume, the corresponding risks have also increased, and the management and prevention of carbon finance risks have become significant challenges. Therefore, many researchers have conducted studies on the measurement and prevention of carbon finance and carbon market risks, mainly including price risks (Fehr and Hinz, 2006; Dutta, 2018), policy risks (Song et al., 2018; Lin and Jia, 2019), and market risks (Jiao et al., 2018; Jin et al., 2020). Wang and Yan (2022) measured the comprehensive risk of China's carbon market based on the Copula-EVT-VaR model, and the results showed that using Copula function to measure the integrated risk of the carbon market was more effective than methods that did not consider the interdependence among risk factors. Tang et al. (2015) used the Capital Asset Pricing Model to analyze the market risks of the European Union Emissions Trading System (EU ETS) and the Clean Development Mechanism (CDM), and the research results showed that carbon prices were influenced by market mechanisms and external factors (economic crises and environmental policies) under low return expectations; however, under high return expectations, carbon price changes in the EU ETS market were less stable and riskier compared to the CDM market.

3. Research on Carbon Market Spillover Effects

The carbon finance market is widely recognized as an effective means to reduce carbon emissions and develop a low-carbon economy, and therefore, it is closely linked to the energy market and financial market. In recent years, the spillover effects between carbon markets, carbon markets and energy markets, and carbon markets and financial markets have become hot topics of concern for many researchers. The main research methods include Granger causality test (Yu et al., 2015; Peng et al., 2020), wavelet analysis (Khalfaoui et al., 2015; Ren et al., 2022), vector autoregression (VAR) (Zhang and Sun, 2016; Wang and Guo, 2018), GARCH model (Balcılar et al., 2016; Zhao et al., 2023), and DY spillover index (Zhang and Sun, 2016; Tan et al., 2020), etc.

4. Research on the Design, Pricing, and Price Forecasting of Carbon Finance Instruments

Firstly, carbon finance instruments, similar to other financial instruments, can be divided into primary products and derivative products. The basic trading product for carbon finance is carbon emissions rights, and there are many types of derivative products, such as carbon futures (Chevallier, 2009; Evans and Karvonen, 2014; Creutzig, 2016), carbon forwards (Andrews, 2021), carbon options (Viteva et al., 2014; Liu and Huang, 2021), and carbon swaps (Kanamura, 2016). The healthy and sustainable development of carbon finance requires the support of carbon finance instruments, and the continuous innovation of carbon financial products will contribute to a more complete carbon finance system. Secondly, the carbon pricing mechanism is an important means to influence the supply and demand balance of the carbon finance market and is a key part of the carbon market mechanism. Carbon pricing mechanisms mainly include carbon tax (Klenert et al., 2018; Muhammad, 2022), carbon emissions trading system (ETS) (Narassimhan et al., 2018; Rontard and Hernandez, 2022), carbon credit mechanism (Kale et al., 2009), results-based climate finance (RBCF) (Group et al., 2017), and internal carbon pricing (Zhu et al., 2022b) five forms. Finally, carbon emissions are a major factor contributing to environmental pollution and climate change, and the efficient operation of the carbon emissions trading market effectively promotes the process of carbon reduction. Accurate prediction of carbon prices is crucial for carbon trading market management, related policy formulation, and investor decision-making. In carbon price forecasting, researchers focus on improving the accuracy and stability of carbon price forecasting. They have also proposed many carbon price forecasting model methods, such as the deep neural network model TCN-Seq2Seq (Zhang and Wen), the hybrid carbon price prediction model based on second-order decomposition and improved extreme learning machine (ELM) (Zhou and Wang, 2021a), the GARCH-LSTM hybrid model (Huang et al., 2021), and the second-order decomposition carbon price prediction model based on the Sparrow search algorithm-optimized kernel extreme learning machine (Zhou and Wang, 2021b), etc. related carbon price prediction models.