Emissions Trading Research Articles

Policy intervention and stock market stability risks: Evidence from carbon emission trading policy on energy firms in China

Policy intervention and stock market stability risks: Evidence from carbon emission trading policy on energy firms in China

Does the carbon emission trading system facilitate public building carbon dioxide emission reduction in China?

Does the carbon emission trading system facilitate public building carbon dioxide emission reduction in China?

Development of the green finance market, carbon emission trading, and corporate environmental responsibility

Development of the green finance market, carbon emission trading, and corporate environmental responsibility

Green Transformation in Maritime Industry: The Role and Impact of Regulations

As concerns over climate change and environmental sustainability continue to grow, the shipping industry faces increasing pressure to reduce its carbon footprint and adopt greener practices. In alignment with the United Nations Sustainable Development Goals (SDGs)—particularly SDG13 (Climate Action), SDG7 (Affordable and Clean Energy), and SDG9 (Industry, Innovation, and Infrastructure), this study aims to review the role and impact of green shipping regulations on the maritime industry, focusing on their effects on operational efficiency, financial structures, and technological innovation. By examining both target-based (e.g., Energy Efficiency Design Index, Carbon Intensity Indicator) and market-based (e.g., EU Emissions Trading System) regulatory measures, the study evaluates how these policies shape industrial productivity and competitiveness. It also highlights the challenges and opportunities stakeholders encounter while adapting to these regulations. The review provides critical insights for ship operators, policymakers, and researchers in developing effective strategies for a sustainable maritime industry.

Optimum routing for dry bulk voyages with the effect of an emission trading system: NSR vs SCR

Optimum routing for dry bulk voyages with the effect of an emission trading system: NSR vs SCR

Carbon emission trading scheme and green investor entry: Evidence from China

Carbon emission trading scheme and green investor entry: Evidence from China

Empowering energy transition: Revisiting the dynamic impacts of Carbon emissions trading and the crude oil market

Empowering energy transition: Revisiting the dynamic impacts of Carbon emissions trading and the crude oil market

Comparisons of emission trading scheme implementation modes in a low-carbon supply chain

Comparisons of emission trading scheme implementation modes in a low-carbon supply chain

Legal Foundations of Vietnam’s Emission Trading System: A Path toward Climate Commitments

Legal Foundations of Vietnam’s Emission Trading System: A Path toward Climate Commitments

UK Carbon Price Dynamics: Long-Memory Effects and AI-Based Forecasting

This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems.

The Synergistic Effect of the Dual Carbon Reduction Pilot on Corporate Carbon Performance: Empirical Evidence from Listed Manufacturing Companies

In recent years, a pressing global challenge has been that increasingly stringent environmental regulations have failed to prevent global climate change. In this context, exploring the synergistic effects of a policy mix approach has emerged as a promising strategy to turn the tide. Given that companies are the primary sources of carbon emissions, this study adopts a novel micro-level perspective. It employs the difference-in-differences method and establishes a two-way fixed effects model to empirically examine the interactive effects of the Low-Carbon City Pilot (LCCP) and the Carbon Emissions Trading Pilot (CETP) on corporate low-carbon development. Based on data availability and relevance, it uses a sample of Chinese listed industrial companies in 2007–2020. The findings indicate that the CETP enhances corporate carbon performance, whereas the LCCP has no significant impact on its own. However, the combined implementation of the two policies has resulted in a synergistic effect, with green innovation playing a mediating role in this process. The study also identifies the presence of a “green paradox” under heavily polluting industries and a weakening of the policies’ effectiveness in Western China and among non-high-tech firms. For emerging countries undergoing low-carbon transitions, it is essential to design context-specific policy combinations that maximize the effectiveness of environmental regulations.

Assessing the impact of European Union Emissions Trading System on crude oil, maritime transportation, and EU’s carbon markets: a spillover analysis

Assessing the impact of European Union Emissions Trading System on crude oil, maritime transportation, and EU’s carbon markets: a spillover analysis

Decomposition of Driving Factors of Green Innovation in Carbon Emissions Trading Pilot Enterprises

This paper investigates the factors driving green innovation in Chinese listed firms from 2012—when China launched its pilot carbon trading policy—through 2022. Building on the logarithmic mean Divisia index (LMDI) decomposition method, we disaggregate changes in green patent applications into four firm-level effects: economic scale, R&D investment intensity, R&D efficiency and green patent share. Our panel dataset combines green and total patent counts from CNRDS with R&D and revenue data from CSMAR, excluding financial and “ST” firms. Time-series decomposition reveals that both pilot and non-pilot firms achieved substantial growth in green patenting, with non-pilot firms slightly outperforming pilots and both groups experiencing a pandemic-related slowdown in 2022. The green share effect proved the most powerful driver, reflecting a widespread strategic reallocation of R&D toward environmental technologies. Pilot firms saw early gains in R&D efficiency under carbon trading, which reversed after 2018 as they shifted toward fewer, higher-value patents. R&D intensity rose most sharply among pilot firms post-2018, indicating that sustained carbon pricing strengthened innovation budgets. Economic scale consistently supported patent growth across all firms. We conclude that market-based carbon regulation can catalyze initial efficiency and investment responses, but lasting green innovation requires a structural shift in corporate R&D priorities. Policy recommendations include expanding carbon market coverage, enhancing targeted R&D incentives, fostering collaborative innovation ecosystems, and integrating environmental regulation with green finance and intellectual property protection.

Economy or Climate? Impact of Policy Uncertainty on Price Volatility of China’s Carbon Emission Trading Markets

Based on the economic and climate policy uncertainty index and the price data of major carbon emission trading markets from May 2014 to August 2023, this paper uses the generalized autoregressive conditional heteroskedasticity and mixing data sampling (GARCH-MIDAS) model to analyze the impact of policy uncertainty on carbon market price volatility. The results indicate the following: (1) The price volatility in the Hubei carbon market is influenced by both economic and climate policy uncertainties, while the Guangdong market is only affected by climate policy uncertainty, and the Shenzhen carbon market is only affected by economic policy uncertainty. (2) Before the establishment of the national carbon market, the carbon market prices in Hubei were impacted by both policy uncertainties, while Guangdong and Shenzhen carbon markets were only affected by climate policy uncertainties. (3) On the contrary, after the establishment of the national carbon market, only the Shenzhen carbon market was affected by both policy uncertainties, and the price volatility in the Guangdong and Hubei carbon markets was not affected by policy uncertainties. The above research conclusions are helpful for regulatory agencies and policymakers to assess the future direction of the pilot carbon market and provide an empirical basis for preventing and resolving policy risks. At the same time, the proposed GARCH-MIDAS model effectively solves the inconsistent frequency problem of policy uncertainty and carbon price volatility, providing a new perspective for the study of factors affecting carbon market volatility.

Balancing Green and Growth: Exploring the Implementation of Environmental Tax in Asian Context – A Study Case of Vietnam, China, and Malaysia

The case studies of environmental tax reforms in Vietnam, China, and Malaysia provide a comprehensive view of the challenges and opportunities faced by developing nations in implementing environmental fiscal policies to foster green growth. Each of these countries has adopted distinct approaches to environmental taxation, tailored to their specific economic, social, and environmental contexts. Vietnam and Malaysia have focused on targeted sectors such as fuel consumption and natural resource extraction, aiming to curb pollution and over-exploitation while generating revenue for sustainable initiatives. In contrast, China has taken a broader and more integrated approach by establishing a nationwide environmental tax system, complemented by the implementation of an Emissions Trading System (ETS), which seeks to reduce industrial emissions through market-based mechanisms. These case studies highlight several critical lessons that can inform future environmental fiscal reforms. One key takeaway is the importance of comprehensive tax coverage that addresses multiple sources of environmental degradation rather than focusing narrowly on a single sector. Additionally, strong enforcement mechanisms are essential to ensure compliance, as lax regulation or weak penalties can undermine the effectiveness of these taxes. Equally important is the strategic allocation of tax revenues, which should be directed towards sustainability projects such as renewable energy development, pollution control, and environmental restoration efforts. This revenue reinvestment can further bolster green growth and ensure long-term environmental benefits.

Risk Spillover in the Carbon-Stock System and Sustainability Transition: Empirical Evidence from China’s ETS Pilots and A-Share Emission-Regulated Firms

This study employs the TVP-VAR-BK-DY spillover index model to investigate the risk spillover effects between China’s carbon emission trading system (ETS) pilots and A-share listed emission-regulated enterprises. The findings reveal that, due to the nascent stage of China’s carbon market, the overall risk spillover level within the “carbon-stock” system remains low; however, dynamic risk spillovers have shown an upward trend driven by the advancement of ETS pilots. In particular, during compliance periods, enterprises that exceed their emission limits must purchase sufficient allowances on the carbon trading market to avoid high penalties for non-compliance. This creates substantial demand, which drives a rapid increase in the spot prices of carbon allowances, triggering intense short-term price fluctuations and risk spillovers—a pronounced “compliance-driven trading” effect. Frequency domain analysis indicates that long-term shocks have a significantly greater impact on the market than short-term oscillations, reflecting moderate information processing efficiency within the “carbon-stock” system. Directional spillover analysis shows that A-share enterprises initially absorb risks from the carbon market in the short term, but over the long term, they transmit part of these risks back to the carbon market, forming a significant bidirectional risk transmission relationship. Furthermore, heterogeneity analysis reveals marked differences in risk spillover contributions among firms associated with different ETS pilots, as well as between enterprises with polluting behaviors and those with high ESG scores, with the latter contributing considerably higher spillovers to the overall carbon market. These findings offer nuanced insights into the dynamic, structural, and firm-level characteristics of risk spillovers, providing valuable guidance for policymakers and investors to enhance market stability and optimize investment strategies.

A Bidding Strategy for Power Suppliers Based on Multi-Agent Reinforcement Learning in Carbon–Electricity–Coal Coupling Market

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization.

Prediction of Carbon Emission Rights Trading Prices Based on the CNN–LSTM Model in the Context of Carbon Peak: Taking Guangdong Province as an Example

ABSTRACTCarbon emissions are a significant contributor to global warming. As one of the largest carbon emitters in the world, China is committed to establishing a carbon emission trading market to address the challenges posed by climate change. The carbon price is a fundamental component of the carbon financial market. Accurately predicting it can improve environmental quality, reduce energy demand, and promote economic growth. This study uses price data from the Guangdong carbon market as a case study and employs a hybrid model that integrates Convolutional Neural Networks (CNN) and Long Short‐Term Memory (LSTM) networks for carbon price forecasting. The findings indicate that: (1) the CNN–LSTM model exhibits optimal predictive performance when the sliding window is set to a size of 5 on the basis of previous carbon price data. (2) By incorporating significant indicator features from the Guangdong pilot carbon price dataset while maintaining a sliding window size of 5, the model achieves superior predictive accuracy, as evidenced by a Goodness of Fit (R2) of 0.8622 and a mean absolute error (MAE) of 0.0228, resulting in the most favorable comprehensive evaluation index. (3) The integration of one‐dimensional convolutional layers with LSTM layers in the CNN–LSTM model effectively leverages the strengths of CNNs for local feature extraction and the capabilities of LSTMs for modeling time series data. This approach leads to a substantial improvement in predictive performance compared with alternative models such as Support Vector Machine (SVM), Recurrent Neural Network (RNN), and LSTM.

Beyond leading by example: enhanced EU-LAC climate cooperation—the case of Brazil, Chile and Mexico

Abstract This article analyses the impact of the European Green Deal (EGD) on the EU’s claim to climate leadership, the extent to which this affects cooperation between the EU and Latin America and the Caribbean (LAC), and how the EDG could help enable the net-zero transition. The EGD restates the EU’s quest for climate leadership which has heretofore been prominently ideational and exemplary (directional). However, the EGD’s implementation is expected to have a significant impact on partner countries. Both conflict and cooperation could arise amid shifting geopolitical alliances and insufficient climate action. Building on the literature on the EU’s climate and EGD diplomacy, expert analyses, closed-door working groups and elite interviews, this article contends there is scope for the EU to transcend directional climate leadership and deepen entrepreneurial (coalition-led) and structural leadership, both through coercion and assistance, and makes the case for expanding cooperation with Latin America. The main conclusions are: (1) for the EU to retain its climate leadership it needs an overarching green deal diplomacy strategy that helps LAC countries adapt to the European decarbonisation strategy; (2) Enhanced entrepreneurial (diplomatic) and structural leadership (through assistance) can result from strengthening climate governance in areas such as climate laws, scientific advisory boards, citizens participation and policy instruments including taxonomies and emission trading systems; (3) structural leadership through assistance could also be strengthened by ramping up climate finance (e.g. via a revised Global Gateway), furthering climate-proof trade agreements and supporting just transition initiatives.

Do emission trading schemes induce green FDI? Evidence from China

Do emission trading schemes induce green FDI? Evidence from China