Carbon Trading Research Articles

Complexity and synchronization of carbon and new energy markets based on multiscale entropy

AbstractThis study provides a comprehensive analysis of the dynamic evolution of complexity and synchrony between the carbon market and the new energy market from 2019 to 2023, employing multiscale sample entropy and cross‐sample entropy methods. The study shows that, both in the long term and short term, the complexity of the new energy market consistently exceeds that of the carbon market. Additionally, the carbon and new energy markets exhibit higher synchronization on smaller scales. As the time scale increases, interactions between the carbon and new energy markets become more complex and diverse. According to these findings, it is recommended that policymakers improve the transparency of the new energy market, optimize the operational mechanisms of the carbon market, and ensure that carbon reduction strategies and new energy policies are mutually coordinated. This holds significant importance for both carbon reduction targets and the development of new energy.

Does the Carbon Emission Trading Scheme Truly Enhance Green Innovation Efficiency? From the Perspective of Market Mechanism and Government Intervention

Does the Carbon Emission Trading Scheme Truly Enhance Green Innovation Efficiency? From the Perspective of Market Mechanism and Government Intervention

Cultural, Economic, or Transport Link: Does Carbon Emissions Trading Promote “Good Neighbor” Carbon Emission Reduction?

Carbon emission reduction was a crucial objective for China’s green transformation, yet establishing regional cooperation with neighboring areas posed a significant challenge in carbon governance. We investigated the spillover effects of carbon emissions trading (CET) on carbon emissions among Chinese cities, employing spatial difference-in-differences (DID), Callaway and Sant’Anna DID (CSDID), and other methodologies. Our paper integrated datasets on high-speed rails, dialects, and carbon emissions at the prefecture-level city scale. The findings revealed that (1) CET reduces emissions by 11.55% in pilot regions, although the impact of the second policy pilot was not significant. (2) In terms of spillover effects, CET increased emissions in neighboring cities with economic and geographic ties, creating a “beggar-thy-neighbor” reduction model. Conversely, it reduced emissions in cities with shared cultural ties, fostering a “good neighbor” reduction model. (3) The policy spillover effect exhibited an “inverted N” curve, first decreasing, then increasing, and finally decreasing as economic and geographic distance increased. (4) Technological advancement was identified as the primary mechanism driving the spillover effects of CET. To achieve the dual-carbon goal, avoiding harmful economic competition and promoting low-carbon cultural exchanges were essential.

How Does Digital Economy Influence Green Mobility for Sustainable Development? Moderating Effect of Policy Instruments

The role of green mobility as a low-carbon lifestyle in carbon reduction and sustainable development cannot be ignored. The digital economy effectively promotes green mobility for sustainable energy use in the broader setting of the significant data era and sustainable development. This study utilizes the panel data of 264 cities in China from 2011 to 2021 to construct a two-way fixed-effects regression model to analyze the impact of the digital economy on residents’ green mobility and the indirect impact mechanism of the two policy tools, a low-carbon transportation pilot and carbon emissions trading, from theoretical and empirical aspects. The results show that digital economic development helps promote residents’ green mobility. In addition, the implementation of low-carbon transportation pilots and carbon trading policies has strengthened the role of the digital economy in promoting green mobility. The findings remain after introducing robustness tests such as “smart city” pilots as exogenous shock policies. A heterogeneity study suggests that the effect of the digital economy on green mobility for residents is more significant in economically developed and human capital-rich areas. This study contributes to the literature by providing empirical evidence on the role of the digital economy in promoting sustainable urban transportation and by demonstrating the moderating effects of policy instruments, thereby offering practical insights for policymakers aiming to reduce urban pollution and enhance sustainable development.

Telecoupling cannot be ignored for the forest-based carbon market

Abstract Telecoupling interactions between social–ecological systems across large, often global distances drive negative impacts from the forest-based carbon market. However, these negative impacts have been underreported and, therefore, have likely been vastly underestimated. We identify how these unintended negative impacts may occur and provide recommendations for the forest-based carbon market to better account for externalities by prioritizing positive social impact, expanding comprehensive ecological outcomes, and improving the transparency of investments. We call for a carbon market that is designed to account for its global interconnectedness.

A low-carbon supply chain pricing mechanism considering CSR under carbon cap-and-trade policy.

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare.

Low-carbon transition or low-carbon trap? — An instrumental variable regression analysis based on carbon decoupling and energy equity

ABSTRACT Although the low-carbon transition has enormous potential for improving economic and environmental benefits, it may also lead to social equity issues, trapping certain regions in a low-carbon dilemma. Based on panel data from the Yangtze River Economic Belt for the period 2007–2019, this study empirically analyzes the impact of carbon decoupling on energy equity, employing the carbon emission trading policy and natural wetlands as joint instrumental variables. The findings reveal that: (1) from 2007 to 2019, the average level of carbon decoupling in the Yangtze River Economic Belt was recorded at 0.415, maintaining a weak decoupling state overall and nearly forming an “inverted V” shape; (2) in the instrumental variable regression results, for every 1% increase in the level of carbon decoupling, the energy equity in the Yangtze River Economic Belt decreased by an average of 0.095% (p < .01); (3) environmental burden played a partial mediating role between carbon decoupling and energy equity, with the mediation effect value being −0.007 (p < .05); (4) green energy consumption and marketization can variably mitigate the adverse effects of carbon decoupling. Examining the sustainability of the economic-environmental system from the perspective of energy equity contributes to shaping a low-carbon economy that maintains social justice and sustainability, thereby avoiding falling into a low-carbon trap.

Carbon trading pilot and low-carbon development of China’s textile industry

To explore whether a carbon trading pilot (CTP) can promote the low-carbon development of China's textile industry (CTI), this paper used a difference-in-difference model to study the impact of CTP on the carbon emissions in CTI and further analysed its regional heterogeneity. The research results showed that the impact coefficient of CTP on the carbon emissions in CTI was significantly negative, indicating that CTP could reduce the carbon emissions in CTI. Robustness tests such as the Placebo test, replacing the explained variable and the estimation method and excluding the interference of other policies verified the robustness of the research results. The dynamic regression results revealed the carbon reduction effect of CTP became stronger as the time of its implementation increased. At the regional level, CTP could also significantly lower the carbon emissions in the textile industry both in the eastern and western regions, and the reduction effect in the western region was greater than that in the eastern.

Tokenized carbon credits in voluntary carbon markets: the case of KlimaDAO

The voluntary carbon market offers a flexible and cost-effective way to reduce greenhouse gas emissions, which has led to increased interest in these markets. Within the decentralized finance ecosystem, Decentralized Autonomous Organizations leverage the tokenization of carbon credits to enhance efficiency and transparency. KlimaDAO, established in August 2021, aims to accelerate the adoption of carbon markets by integrating blockchain technology to facilitate transparent, secure, and accessible carbon trading. This study analyzes the evolution of KlimaDAO by evaluating its market capitalization, token prices, staking participation, carbon credit retirements, market participation, and concentration. The analysis reveals that while KlimaDAO initially achieved significant engagement and activity, it now faces challenges associated with market maturity and participant retention. Finally, the study highlights the importance of standardization and regulatory frameworks to enhance interoperability, transparency, and legitimacy within the tokenized carbon market.

Carbon price prediction in China based on ensemble empirical mode decomposition and machine learning algorithms.

The carbon emission trading market is crucial for reducing emissions, conserving energy, and enhancing the climate and environment. Studying carbon price forecasting can encourage China's involvement in international carbon financial instruments trading and promote the development of China's pilot carbon markets. This study employs ensemble empirical mode decomposition (EEDM) to convert the initial carbon price, which is a non-stationary signal, into several intrinsic mode functions and residual terms. Subsequently, hybrid machine learning methods are used to estimate the carbon price. For empirical analysis, the three main carbon markets with large trading volumes, Guangdong, Hubei, and Shenzhen, are selected from the eight pilot carbon markets. To achieve short-term carbon price prediction, a hybrid model combining a genetic algorithm (GA) and back propagation (BP) neural network is used. This model effectively addresses the issue of neural networks falling into local optimization. The results indicate that the hybrid algorithm is significantly superior to other algorithms for short-term prediction. Additionally, another hybrid model, combining the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms, is employed to reduce forecast error while minimizing search parameters, which is not possible with traditional neural network models.

Influencing Factors and Formation Mechanism of Carbon Emission Rights Prices in Shanghai, China

With the intensification of global climate change issues and increasingly stringent carbon emission controls, carbon emissions trading has become a crucial market-based mechanism for reducing carbon emissions. A deeper understanding of the factors influencing carbon emission rights prices and their formation mechanisms can enhance market transparency and effectiveness and promote the healthy development of the carbon trading market. This paper identifies the main factors affecting carbon emission rights prices and explores their formation mechanisms from both the internal and external perspectives of enterprises. Using a vector autoregression (VAR) model, it quantifies the impacts of energy prices, the climate, and technology levels on carbon trading prices in Shanghai, China. The results show that, in the short term, enterprise technology levels and energy prices significantly impact carbon emission rights prices, while the climate becomes more critical in the medium to long term. These findings propose specific policy recommendations for improving the carbon emissions trading market, with a focus on these three factors.

Strategic behaviors of renewable energy generation companies participating in the electricity and carbon coupled markets based on non-cooperative game theory

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos.

Carbon farming in India: are the existing projects inclusive, additional, and permanent?

ABSTRACT As voluntary carbon markets gain popularity, carbon farming emerges as a win-win strategy to mitigate climate change and improve farmers’ income in developing countries. India, with over 50 active carbon farming projects and an impending domestic carbon market, forms an ideal case to explore the early-stage challenges of these initiatives. Using survey data from 841 farmers from seven carbon farming project villages in Haryana and Madhya Pradesh, this study focused on socio-economic inclusion and adherence to additionality and permanence principles, which are underexplored in the existing literature. We found that carbon farmers were predominantly large-holders and from non-marginalized castes, showing patterns of systematic exclusion. Only 4% of participants were women. Around 99% of farmers had not received any monetary benefit. While certain agricultural practices predated carbon projects, raising concerns about additionality, practices like no-tillage, alternate wetting and drying, intercropping, reduced chemical fertilizers, micro-irrigation, and tree planting aligned with additionality principles. Nonetheless, a high disadoption rate (28%) raises concerns about the permanence of emissions reduction. Our findings also indicated that companies that exclusively focus on carbon credits, termed ‘Carbon Core’ companies in this study, were more efficient in spreading regenerative agricultural practices than subsidiaries or offshoots of larger corporations whose primary businesses are unrelated to carbon credits.

Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

A Novel Day-Ahead Optimization-Oriented Low-Carbon Economic Scheduling Scheme for Integrated Energy Systems

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system.

Energy, environmental, economic, and social assessment of photovoltaic potential on expressway slopes: A case in Fujian Province, China

Expressway slopes within the boundary of expressway construction land have the potential to be transformed from conventional land use to valuable utilization through the installation of photovoltaic (PV) systems. Considering the continuous increase in expressway mileage, the PV potential could be enormous; therefore, a method to quantify such potential is urgently needed. This study proposes a potential assessment method for expressway slope photovoltaics. First, the suitable locations of expressway slopes for installing PV systems are identified on the basis of publicly available digital maps, combined with the principles of PV site selection and geographic information systems. Then, the PVsyst model is used to simulate the power generation efficiency and power output of the PV systems and to evaluate their PV potential, contribution, and economic benefits. The implementation of the proposed method in Fujian Province, China, indicates that the installed capacity of PV systems could reach 16.3 GW, with an annual power output of 17,940 GWh, contributing to the peak shaving of power grids and regional energy supply. The reductions in CO2, SOx, and NOx emissions are 16.65 Mt, 8970 t, and 3947 t per year, respectively. Considering carbon trading, the payback period is shortened to 11.7 years, with the return on investment of 65.38 % and the net present value of 52.6 billion RMB over a 25-year operational period. Additionally, this project could create 19.88 % of jobs in the energy sector in Fujian Province, providing 27,861 jobs during the construction phase and 18,248 jobs during the operation and maintenance phase. Thus, this project is economically viable and generates positive energy, environmental, and social benefits, contributing to the promotion of local energy transition and sustainable development.

The Impact of the EU Carbon Border Adjustment Mechanism on Chinas Exports and Suggestions on Countermeasures

The European Parliament plans to gradually introduce the Carbon Border Adjustment Mechanism in 2023, with a complete implementation set for 2026. As an essential trading partner of the EU, China will shoulder an additional carbon border tariff due to the lack of low-carbon technology and the dependence on extensive development patterns. These extra costs will undermine the profit margins of Chinas carbon-intensive industry sectors, possibly resulting in decreased exports and a restructuring of production. The latest estimate of the exact costs of Chinas embedded carbon exports to the EU is a problem that has received limited academic attention. Using the IPCC carbon emission accounting methodology and non-competitive single-region input-output analysis, this research precisely measures the embedded carbon in Chinas exports to the EU for the nine most affected industries. Furthermore, this study quantifies the impact of the CBAM on Chinas exports of these nine industry sectors. The findings suggest that China should improve the domestic carbon emission trading system and take the initiative to participate in formulating a carbon pricing consultation and dialogue mechanism, mitigating the negative impact of the EU CBAM on exports.

Optimal scheduling of boiler electrification for process decarbonization

AbstractProcess heat electrification offers the prospect of deep decarbonization of the chemical and allied industries. Replacing fossil fuel‐fired boilers with electric units can reduce carbon emissions if the power mix has a large share of renewables. For multinational firms with plants in multiple locations, the electrification decisions should be scheduled based on grid carbon intensity projections and should also be coordinated among these subsidiaries. In addition, carbon credits can be traded among the multiple sites to allow lagging plants to reduce their carbon footprints. A novel mathematical model has been developed to optimize process heat electrification plans in multinational corporations. The model determines the optimal timing of electrification at each location, and also the necessary level of carbon trading among subsidiaries. An illustrative case study demonstrates how the model can be used to generate electrification plans that are superior to those based on simple heuristics.

A negative-carbon planning method for agricultural rural industrial park integrated energy system considering biomass energy and modern agricultural facilities

Biomass energy, recognized as a “zero-carbon” energy, has gradually become a crucial approach to promoting the low-carbon transformation of agricultural rural industrial parks (ARIP). Meanwhile, modern agricultural facilities (MAF) can play a significant role in adjusting agricultural load. Therefore, this paper proposes a negative-carbon planning method for the ARIP integrated energy system (IES). Firstly, carbon activities occurring during the operation of ARIP are sorted out and modeled based on the life cycle assessment (LCA) method, especially modeling the “zero-carbon” and “negative-carbon” benefits of biomass energy. At the same time, the models of both agricultural irrigation systems and straw bundling direct combustion heating are constructed in the ARIP planning. Then, a negative-carbon planning model for ARIP IES is established with the goal of minimizing the annualized total cost, where carbon emission reduction is integrated into the optimization objectives through carbon trading. On this basis, a two-stage distributionally robust optimization (DRO) planning model is constructed, where a box-like set and comprehensive norm constraints are used to model the seasonal differences of agricultural industry loads and the uncertainty of probability distribution in photovoltaic (PV) scenarios. Especially, a box set with adjustable uncertainty coefficients is introduced to simulate fluctuations of output in PV scenario. Finally, the CPLEX solver is used to solve the planning model. verify the effectiveness of the proposed planning method in enhancing the economic and environmental effects of ARIP. Results indicate that the proposed planning model can reduce the total cost by 49.584 × 104 $. Besides, the annual carbon emissions can be reduced from 37533.98 t CO2-eq to −15834.5 t CO2-eq. Especially, the carbon sequestration effect of biochar is the key link in achieving “negative carbon” in ARIP.

The Impact of Carbon Emissions Trading on Corporate Environmental Performance--A Hybrid Study Based on PSM-DID and Dual Machine Learning

In the context of "dual carbon", the green economy has gradually become the mainstream trend of economic development. As the main force of social production, the environmental governance behaviour of enterprises is crucial to China's economic transformation. Based on the panel data of Chinese A-share listed companies from 2011 to 2021, this study empirically examines, analyses the mechanism of action, and analyses the heterogeneity of enterprises using the PSM-DID model and the dual machine learning DML model. It is found that the carbon trading pilot policy significantly and positively affects firms' environmental performance, especially for non-state-owned enterprises, non-high-tech and manufacturing industries. In terms of spatial distribution, enterprises in the eastern and central regions are more significantly affected by the carbon trading pilot policy. In addition, the study also finds that executives' green awareness plays a partial mediating role in carbon trading's influence on firms' environmental performance through mediation effect analysis. Finally, the article puts forward recommendations for the government, enterprises and executives to promote the further application and effect of the carbon trading pilot policy in the enhancement of corporate environmental performance.