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The greenhouse effect of atmospheric pollution is globally concerning. China is transitioning to market-driven emission reduction from policy-driven efforts. In 2021, key power industry emitters were included in the national carbon trading market. However, many companies lack willingness and understanding of carbon assets, hindering progress. Research on power companies in Beijing, a political and carbon market pilot region, is valuable. This study obtained data on the participation of Beijing's power generation companies in the carbon market and the construction of their carbon management systems during the first compliance period through the distribution of surveys. The findings revealed that the implementation and preparation of carbon inventory, Chinese Certified Emission Reduction (CCER) development, the allocation of carbon management personnel, and training are key factors influencing the actual effectiveness of carbon management within companies. Based on the survey results and the impact pathways, this study outlines the preparatory work, system content, and construction steps for power companies to build a carbon management system. It summarizes five key areas of work for power companies in managing carbon assets: carbon inventory, carbon management personnel and mechanisms, carbon trading, carbon emission reduction, and carbon finance. This provides guidance to help power companies fulfill their obligations smoothly, add value to their carbon assets, and achieve low-carbon development goals. Additionally, it offers a reference for other industry enterprises that are about to enter carbon trading. [ABSTRACT FROM AUTHOR]

Competition in China's electric vehicle industry has intensified significantly in recent years. The production mode of power batteries, serving as the pivotal component in these vehicles, has emerged as a critical challenge for electric vehicle manufacturers. We considered a system comprising an electric vehicle (EV) manufacturer with power battery production technology and another EV manufacturer lacking power battery production technology. In the context of carbon trading policy, we constructed and solved Cournot competitive game models and asymmetric Nash negotiation game models in the CC, PC, and WC modes. We examined the decision-making process of electric vehicle manufacturers regarding power battery production modes and carbon emission reduction strategies. Our research indicates the following: (1) The reasonable patent fee for power batteries and the wholesale price of power batteries can not only compensate power battery production technology manufacturers for the losses caused by market competition but can also strengthen the cooperative relationship between manufacturers. (2) EV manufacturers equipped with power battery production technology exhibit higher profitability within the framework of a perfectly competitive power battery production mode. Conversely, manufacturers lacking power cell production technology demonstrate greater profitability when operating under a more collaborative power cell production mode. (3) Refraining from blindly persisting with and advocating for carbon emission reduction measures is advisable for manufacturers amidst rising carbon trading prices. [ABSTRACT FROM AUTHOR]

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

Previous research indicates that it is feasible to incorporate carbon considerations into production and inventory management by making informed decisions based on total costs and other relevant factors. In this article, the inventory model of single-vendor single-buyer with fixed demand is considered and carbon emission costs are added to the model according to the two policies of carbon tax and emission trading, and the model is also discussed through environmental concerns. The objective of this study is to establish an equilibrium between the costs associated with single-vendor single-buyer (SV-SB) and carbon emissions, utilizing carbon tax and carbon trading policies to regulate the quantity of carbon generated. The results show using the carbon trading policy is more economical although it is much more complicated in implementation than carbon tax policy. [ABSTRACT FROM AUTHOR]

Multi-energy virtual power plant (MEVPP) with diversified flexible resources can participate in energy market (EM), frequency regulation market (FRM) and carbon trading market (CM) to obtain extra benefits. However, the complicated market environment provokes grant challenges in the bidding strategy of MEVPP that coordinates heterogenous supply-side devices and demand-side users. To the end, this study proposes a multi-objective bidding strategy for MEVPP comprising various energy flows and multi-energy demand response (MDR) in EM, FRM and CM. Results are delivered that: 1) Compared to the only profit-oriented optimization, the profit of comprise solution under the multi-objective declines by 46% but the satisfaction level improves 8%. Relative to the single satisfaction-oriented optimization, a 5.2% satisfaction loss allows for an approximately 103% growth in profit, realizing a win-win result for MEVPP and users. 2) There were negative profits in the pure EM (Case 1) as well as EM and CM (Case3). Compared with positive profit in EM and FRM (Case 2), the profit in coupled EM, FRM and CM (Case 4) increases by 48.9%. Relative to the CO 2 emission in EM and CM, the emission under the proposed strategy mitigates by 9.36%. 3) A carbon base price at 200 CNY/t CO 2 and an emission interval length of 5–10 t are recommended to maintain the low-carbon economic operation for MEVPP. [Display omitted] • A novel multi-energy virtual power plant (MEVPP) comprising heterogeneous energy flows and multi-energy demand response is proposed. • An optimal bidding strategy for the MEVPP in coupled markets is developed. • The proposed strategy outperforms profit-oriented and satisfaction-oriented optimization by achieving a win-win result for both MEVPP and users satisfaction-oriented optimization. • A base price of 200 CNY/t and interval length of 5–10 t are obtained. [ABSTRACT FROM AUTHOR]

The carbon emissions of the power industry account for over 50% of China's total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China's energy transition, economic development, and the power sector's energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China's energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector's generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended. [ABSTRACT FROM AUTHOR]

In the quest to achieve "double carbon" goals, the urgency to develop an efficient Integrated Energy System (IES) is paramount. This study introduces a novel approach to IES by refining the conventional Power-to-Gas (P2G) system. The inability of current P2G systems to operate independently has led to the incorporation of hydrogen fuel cells and the detailed investigation of P2G's dual-phase operation, enhancing the integration of renewable energy sources. Additionally, this paper introduces a carbon trading mechanism with a refined penalty–reward scale and a detailed pricing tier for carbon emissions, compelling energy suppliers to reduce their carbon footprint, thereby accelerating the reduction in system-wide emissions. Furthermore, this research proposes a flexible adjustment mechanism for the heat-to-power ratio in cogeneration, significantly enhancing energy utilization efficiency and further promoting conservation and emission reductions. The proposed optimization model in this study focuses on minimizing the total costs, including those associated with carbon trading and renewable energy integration, within the combined P2G-Hydrogen Fuel Cell (HFC) cogeneration system. Employing a bacterial foraging optimization algorithm tailored to this model's characteristics, the study establishes six operational modes for comparative analysis and validation. The results demonstrate a 19.1% reduction in total operating costs and a 22.2% decrease in carbon emissions, confirming the system's efficacy, low carbon footprint, and economic viability. [ABSTRACT FROM AUTHOR]

Low carbon is the future development trend of new power system, and the simulation of horizontal annual carbon trading volume at the planning level plays an important role in realizing the low-carbon economic operation of future power grid. Based on this understanding, a robust planning method for transmission network considering adaptive decision-making of carbon trading volume is proposed. First, a measurement method of carbon quota trading cost based on the baseline method is constructed, and a two-stage robust programming model considering the uncertainty of wind power and load is established considering the constraint of carbon trading volume. Then, the relax-and-enforce decoupling method is used to decouple the sub-problem into security feasibility detection sub-problem and low carbon feasibility detection sub-problem according to time, and the two-stage robust programming model is solved by using the column and constraint generation algorithm. Based on the cut information feedback from the security constraint and the carbon quota trading volume constraint in each uncertain scenario, the mapping relationship between the feasible solution space of the model and the setting of the threshold parameter of carbon quota trading volume is analyzed, and the upper and lower limits of carbon trading volume are adaptively decided. Finally, the improved IEEE-RTS 24-node system is simulated to analyze the impacts of the low-carbon planning model, carbon quota trading volume, and carbon price on the planning results. The results show that the proposed model realizes the coordinated optimization of the planning scheme in terms of economy and low carbon.

In order to reduce carbon emissions and the impact of source-load uncertainty on system operation, a multi-timescale low-carbon optimization scheduling strategy in day-ahead, intra-day and real-time operations for energy hub (EH) based on a reward-punishment ladder carbon price mechanism and distributed model predictive control (DMPC) is proposed. A reward-punishment ladder carbon price calculation method is introduced and a day-ahead low-carbon optimization scheduling model for EH is constructed. A feedback closed-loop optimization strategy based on DMPC for intra-day rolling and real-time adjustments is formulated. The optimization strategy reduces source-load prediction errors and improves the efficiency of traditional model predictive control (MPC) solving. In the intra-day stage, a rolling optimization model with the objective of minimizing the sum of the ladder carbon price cost, operational cost, and penalty cost for energy storage adjustment is constructed. In the real-time stage, the overall optimization problem is decomposed, and a multi-agent real-time adjustment model based on DMPC is established. The simulation results indicate that the proposed strategy is effective in enhancing the economic efficiency of the system, reducing the uncertainty of source and load, and achieving the low-carbon, economic, stable, and reliable operation for EH.

Abstract Due to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling evaluation, multidisciplinary coupling mathematical model and international engineering case analysis. The innovation of this project's research lies in the comprehensive analysis of the complex coupling effects of various discrete data and uncertainty indicators on the research model across international projects and how to model and evaluate interactive effects accurately. This article provides scientific measurement standards and data support for governments worldwide to formulate carbon tariffs and carbon emission policies. Case analysis data shows that the carbon emission ratio of exporting and importing countries is 0.577:100; the carbon trading quota ratio is 32.50:100.

To enhance the interaction among multiple agents in carbon trading mechanisms， a leader-follower game method is proposed， with carbon trading JMO （joint market operator） as the leader and multiple IES （integrated energy systems） as followers. This method aims to achieve low-carbon economic operation for different IES. Firstly， under the tiered penalty mechanism for carbon emission， the operational benefits of various agents’ IES are modeled and analyzed. A dual-mode operation strategy for IES， which includes options for low carbon emission and low cost， is proposed. Secondly， based on the dynamic non-cooperative Stackelberg game optimization framework， a carbon trading model involving multiple agents is established according to the operational modes of each agent. Finally， the decision-making model of the leader-follower game is solved using both adaptive differential evolution （DE） algorithm and the Gurobi optimizer. Case analysis results demonstrate that this carbon trading mechanism can effectively achieve both low-carbon and economic operation for IES.

Due to the rapid development of the clean energy sector against the global backdrop of carbon emission reduction, the availability of rare earths is becoming scarce. The strategic recycling of rare earth resources serves the dual purpose of alleviating supply shortages and aligning with the global quest for carbon reduction and environmental preservation. This study establishes a second-level, closed-loop supply chain that includes a rare earth group (REG) that synergizes rare earth mining and smelting companies with rare earth material manufacturing companies, as well as a specialized rare earth recycler. The study considers three different recycling scenarios: the recycler only recycles oil sludge scrap (S1), the recycler only recycles end-of-life products (S2), and the recycler both recycles oil sludge scrap and end-of-life products (S3). In addition, the study examines the impact of government subsidies, carbon trading mechanisms, and corporate research and development (R&D) initiatives on closed-loop supply chains. The findings are summarized below. (1) Rare earth recycling does not impact the rare earth market downstream. (2) The profits of both REG and the recycler have a positive correlation with the quantity of recycling. However, recycling sludge waste can generate higher revenues for the recycler compared to recycling end-of-life products. (3) Government subsidies exert a positive effect on the recycler's profits and the production of secondary rare earths. However, the REG redirects a segment of the recycler's profits in its direction. Elevated carbon trading prices encroach upon the REG's profitability, prompting increased procurement of secondary supply rare earths. (4) The benefits of increasing rare earth recycling rates outweigh government subsidies and carbon trading valuations in the supply chain. Therefore, enterprise-focused research and development initiatives play a critical role in improving the efficiency of the closed-loop rare earth supply chain. [ABSTRACT FROM AUTHOR]

Climate change imposes tighter limits on carbon emissions, which require the development of more green electricity. Deep sea wind power has the advantages of high wind energy density, high power generation utilization hours, no land occupation, and near to power load centers for easy on-site consumption, which lead to a broad market prospect. In the foreseeable future, deep sea wind power will usher in large-scale construction and development. The initial investment and construction cost of deep sea wind power is high, subsidies are gradually decreasing, and there is a lack of full life cycle economic analysis combined with various market policies, resulting in a lack of guidance for its investment and construction. In order to promote the development of deep sea wind power, relevant cost recovery mechanisms must be found. This paper proposes a system cost allocation method based on the comparative difference method and load similarity with consideration of the transformation and operation costs of thermal power, demand-side response, and energy storage; a cost allocation method based on carbon trading and green certificate trading income is proposed; and a time sequence optimization operation simulation model is established to maximize the deep sea wind power income. A case study is proposed for a 300 MW deep sea wind farm, the results show that the cost allocation method and cost allocation model proposed in this paper have a significant effect on wind power cost recovery, and the effectiveness of the proposed method and model is verified. [ABSTRACT FROM AUTHOR]

The Environmental Crisis has been creeping up on us for years. Arguably this crisis arises from the abuse of God's creation, particularly from Western overconsumption and our consequently broken relationship with our neighbours worldwide and especially the poor and those in less developed parts of the world who are already suffering most from its consequences. Over the last five decades, prophetic voices have spoken of the dangers of environmental degradation and climate change. It is however only in the last few years that the narrower subject of climate change has become a prominent element of the discourse in the Western Church, though the situation is perhaps rather different in the Eastern Orthodox Church. Many organisations and indeed governments have set targets for Net Zero, but does this represent an appropriate and sufficient response for Churches? Is there a sound basis for such an approach, guided by our faith and theology? [ABSTRACT FROM AUTHOR]

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system's operating cost, and the introduction of P2G-CCS can effectively help the park's integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system's operating costs and improve the economic benefits of the park. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Offshore wind energy entering the grid in coastal areas creates issues with the safe and stable operation of power systems. To control the carbon emission of power systems and increase the proportion of offshore wind consumption, a microgrid optimization model considering offshore wind power and carbon trading is proposed in this paper. To avoid the defect of Particle Swarm Optimization (PSO) falling into the local optimum prematurely, the PSO algorithm is improved by dynamically decreasing inertia weights and chaos factors. Combined with the powerful optimization capability of the genetic algorithm (GA), the improved PSO-GA algorithm is used to solve the model. The simulation results show that the improved algorithm iterates 11 times before the parameters reach the optimal value, with high convergence accuracy. The proposed approach can increase the proportion of offshore wind consumption and ensure the optimal economic performance of the system while reducing the carbon emission. [ABSTRACT FROM AUTHOR]

Green innovation has become a key strategy for reducing emissions. However, existing research mainly examines this phenomenon through the resource and institutional perspectives, often ignoring the changes in corporate green innovation behavior under industry peer pressure. Therefore, this study draws on the policy framework of China's carbon trading pilot and uses a multi-period difference-in-difference (DID) fixed effects model to explore how carbon trading shapes enterprises' green innovation strategies. The survey used data from pilot enterprises from 2008 to 2019 and found that carbon trading policies are conducive to green innovation, and both exploratory green innovation and exploitative green innovation have been reflected. It is worth noting that under the influence of peer pressure, this positive effect is more prominent in exploratory green innovation. Furthermore, it was found that firms facing carbon pressure can skillfully find an equilibrium between exploratory green innovation and exploitative green innovation. The research results demonstrate the green innovation strategies and trade-offs of Chinese enterprises facing the impact of carbon trading policies, with the hope that the research conclusions will have certain theoretical reference significance for future corporate green transformation and increased investment in green innovation. [ABSTRACT FROM AUTHOR]

The visitor economy is responsible for a substantial percentage of the global carbon footprint. The mechanisms used to decarbonize it are insufficient, and the industry is relying on carbon trading with substandard credits that allow businesses to outsource the responsibility to decarbonize. We aim to transform carbon markets, help finance climate investments, and support decarbonization strategies. We identify and define the problem, outline the components and their interactions, and develop a conceptual model to transform carbon markets. The new, blockchain-based Carbon Tokenomics Model rolls out a decentralized database to store, trade, and manage carbon credits, with the goal of enabling sustainable climate finance investment. We outline the criteria needed for an industry-wide carbon calculator. We explain the process needed to increase rigor in climate investments in the visitor economy and introduce a delegated Proof of Commitment consensus mechanism. Our inclusive and transparent model illustrates how to reduce transaction costs and how to build consumer and industry trust, generating much-needed investments for decarbonization. [ABSTRACT FROM AUTHOR]

Due to the rapid economic development of globalization and the intensification of economic and trade exchanges, cross-international and regional carbon emissions have become increasingly severe. Governments worldwide establish laws and regulations to protect their countries' environmental impact. Therefore, selecting robustness evaluation models and metrics is an urgent research topic. This article proves the reliability and scientific of the assessment data through literature coupling evaluation, multidisciplinary coupling mathematical model and international engineering case analysis. The innovation of this project's research lies in the comprehensive analysis of the complex coupling effects of various discrete data and uncertainty indicators on the research model across international projects and how to model and evaluate interactive effects accurately. This article provides scientific measurement standards and data support for governments worldwide to formulate carbon tariffs and carbon emission policies. Case analysis data shows that the carbon emission ratio of exporting and importing countries is 0.577:100; the carbon trading quota ratio is 32.50:100. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the issue of carbon emission mitigation within the automotive supply chain, the cooperation between the vehicle manufacturers and the retailers has been proved to be an efficient measure to enhance emission reduction endeavors. This paper aims to evaluate the effectiveness of the cooperations between a vehicle manufacturer and multiple retailers based on the differential game method. By utilizing the Hamilton–Jacobi–Bellman equation, the equilibrium strategies of the participants under two different cooperation models, i.e., the decentralized model and the Stackelberg leader–follower cooperation model, are analyzed. To be specific, in the decentralized model, each participant independently decides its strategies, whereas the manufacturer cooperates with retailers by offering subsidies in the Stackelberg leader–follower model. Unlike previous studies that solely focused on participants’ decision-making in carbon emission reduction efforts, this paper also examines the retail pricing decisions of the retailers. Additionally, carbon trading is introduced to enhance the realism of our model. Through the theoretical analysis and the numerical experiments on the carbon emission reduction efforts of manufacturers and retailers, as well as the low-carbon reputation of vehicles and the overall system profit under both models, we conclude that the cooperative Stackelberg model outperforms the decentralized model in providing benefits to both parties. Furthermore, such a cooperative approach can foster the long-term development of the automotive supply chain, ultimately contributing to a more sustainable low-carbon future. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of "dual carbon", restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction. [ABSTRACT FROM AUTHOR]

China has proposed a shift from the dual control of energy consumption (control of its total amount and intensity) to the dual control of carbon emissions, demonstrating its firm determination and great responsibility to achieve the goals of reaching peak carbon emissions and carbon neutrality. As the largest carbon-emitting country, China needs to carry out mandatory emission reductions to control carbon emissions and encourage extensive carbon sequestration to achieve socialized negative emissions. As a valuable supplement to the mandatory emission reduction market, the voluntary emission reduction market takes into consideration national participation, citizens' initiative, and market operation. The synergy of these two markets is indispensable for green, low-carbon, and high-quality development in the future. This study focused on the ocean carbon sink ('blue carbon') trading and constructed a DSGE model. Using pulse response, this study determined whether ocean carbon sink trading based on the voluntary emission reduction mechanism could achieve the dual benefits of 'economy development and environmental improvement.' By conducting a sensitivity analysis, the study examined the fluctuation of the dual benefits, and combining with a welfare analysis, it also explored strategies for enhancing the dual benefits and social welfare. The results showed that: 1 Ocean carbon sink trading based on the voluntary emission reduction mechanism could achieve the dual benefits, among which the environmental improvement benefit was more significant. 2 After expanding and strengthening the marine economy and moderately increasing government subsidies, the fluctuation of the dual benefits converged, which helped the smooth operation of the ocean carbon sink market. The efficiency of ocean carbon sinks had a significant impact on the environmental improvement benefit, while the impact of carbon sink prices on the dual benefits was weak. 3 After the implementation of ocean carbon sink trading, social welfare significantly improved, and the welfare improvement effect of the proportion of marine output value was the most significant, followed by government incentive subsidies. The impact of transaction price and carbon sink efficiency on welfare was relatively weak. Finally, this study recommends the further strengthening of the implementation of the strategies of building a 'strong maritime country' and a 'marine community with a shared future' and the acceleration of the high-quality development of the marine economy. It also suggests that we should strengthen the research and technological innovation of ocean carbon sink methodology and continue to improve carbon sink efficiency. The government needs to improve the construction of various systems for voluntary emission reductions, appropriately increase incentive subsidies, and accelerate the implementation of the ocean carbon sink market through the exploration of regional voluntary emission reduction markets. [ABSTRACT FROM AUTHOR]

The integrated energy microgrid (IEM) plays a crucial role in supporting energy structural transformation and achieving carbon peaking and carbon neutrality goals. However, IEM clusters, which comprise various forms of IEMs, often share connections with the same distribution network operator (DNO), leading to competitive tensions both within the cluster and between the cluster and the DNO. To mitigate these conflicts and promote low-carbon, cost-effective operations, this study introduces a double-layer game model. This model aims to minimize operational costs and maximize carbon emission reductions. The external layer applies the Stackelberg game with the DNO as the leader and the IEM cluster as the follower. The internal layer employs cooperative game theory to model interactions among multiple IEMs. Optimal electricity trading and pricing for each entity are determined using a combination of the bisection method and the alternating direction method of multipliers (ADMM). Additionally, a carbon trading mechanism is integrated into the model to balance economic and environmental objectives. The results indicate that the proposed double-layer game model facilitates a mutually beneficial scenario for both the DNO and the IEMs in terms of low carbon emissions and economic efficiency. This model provides a practical framework for effectively managing and coordinating interactions among multiple IEMs, thereby ensuring simultaneous economic and environmental advantages.

Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.

The rise of prosumers – individuals who both produce and consume energy – presents a significant opportunity to reshape energy markets and achieve carbon neutrality. However, current energy trading models struggle to effectively track emissions and incentivize sustainable consumption behaviors. This study introduces a novel, blockchain-based peer-to-peer (P2P) platform for trading carbon allowances, designed to empower prosumers and revolutionize energy consumption patterns. Utilizing blockchain technology, the platform enables direct, transparent, and secure transactions between prosumers, creating a decentralized market where they can set their own prices for carbon allowances. This dynamic and competitive environment empowers prosumers to take control of their energy consumption and incentivizes the adoption of sustainable practices. The platform also incorporates a decentralized reward system targeting specific consumption habits, promoting behaviors that reduce carbon emissions. Empirical evidence and theoretical justification within the study highlight the platform’s potential to transform energy consumption patterns. The transparent and verifiable nature of blockchain technology addresses the limitations of existing centralized and aggregator-based trading methods. The proposed platform provides a robust framework for tracking carbon emissions, promoting sustainable consumption, and empowering prosumers to actively participate in the energy transition. This innovative solution addresses the challenges faced by prosumers in the energy market, paving the way for a more sustainable and equitable future.

The technology of power to hydrogen (P2H) and natural gas mixed hydrogen is highly regarded in theoretical research and engineering application in promoting the consumption of renewable energy and reducing carbon emissions. Targeting the park integrated energy system with high proportion renewable energy, an optimal configuration method of P2H considering natural gas mixed with hydrogen and cross seasonal storage is proposed. Firstly, the operational framework and energy flow relationship of the park integrated energy system are sorted out, and the mathematical models for the internal energy production, conversion, and storage equipment in the park are established. Secondly, a configuration model of P2H is established with the goal of optimizing the annual investment cost of equipment, the annual operating cost, and the carbon trading cost. Finally, the effectiveness of the proposed model is verified by an example analysis. The impact of changes in investment cost of electrolytic cells, the upper limit of mixed hydrogen volume fraction and cost weight coefficients of economy and low-carbon on the planned operation results are analyzed. The simulation results show that the proposed model can effectively improve the absorption capacity of renewable energy and reduce the overall economic cost and carbon emissions.

Abstract The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem.

Abstract The rational utilization of energy is an important issue for sustainable development. Electrically coupled integrated energy systems can enhance energy utilization efficiency and reduce energy costs. However, traditional integrated energy system optimization has problems with local optima and slow convergence speed, which cannot fully utilize energy resources. Therefore, this study proposes an improved electrical coupling integrated energy system on the ground of particle swarm optimization algorithm. In response to the problems of local optima and slow convergence speed in traditional optimization algorithms, particle swarm optimization algorithm is introduced for system optimization. By combining PSO with simulated annealing algorithm, the possibility of PSO in global optimization is reduced. The local search ability of PSO and the global search ability of simulated annealing algorithm are used to find the optimal solution. The particle swarm optimization algorithm is used for preliminary search. When the particle falls into the local optimal, the simulated annealing algorithm is introduced for global search, and the particle is guided to jump out of the local optimal and continue searching. The experiment demonstrates that the improved algorithm has certain advantages in solving test functions. The variance, mean, and optimal values are 0.00125, 0.13874, and 0.105531, respectively, which are all better than the particle swarm optimization algorithm. The simulated annealing algorithm improved the particle swarm optimization algorithm with a high accuracy index, which eventually stabilized above 0.9, and the recall index also remained above 0.8. After 100 iterations, it had already fallen into a local optimal solution. By applying the improved hybrid optimization algorithm to the electrically-coupled integrated energy system, the distribution of various energy sources can be managed and optimized more effectively, and the overall performance of the system can be improved. Especially when dealing with complex energy scheduling and distribution problems, the algorithm can provide more stable, efficient and reliable solutions. This study can achieve efficient operation and optimized scheduling of integrated energy systems, reduce energy consumption and environmental pollution, and reduce energy costs. And it can improve the reliability and stability of energy supply, which has important application value and research significance.

Climate change is driven by a combination of natural fluctuations and human activities, particularly the widespread use of fossil fuels (such as coal, oil, and natural gas) and alterations in land use practices such as logging, farming, and clearing land for agriculture. This global phenomenon encompasses various end-of-use activities, including agriculture, forestry, and consumerism. An inherent challenge in the global carbon dioxide (CO2) trading landscape lies in the competition between developed and developing countries, stemming from inconsistent CO2 prices. This competition manifests in three primary areas. Firstly, in the context of internationally transmitted mitigation outcomes, involving international trade aimed at surpassing Nationally Determined Contributions (NDC) targets. Secondly, it pertains to sustainable development mechanisms, specifically the use of carbon offsets derived from projects implemented by both public and private entities worldwide. The third aspect revolves around non-market approaches, encompassing emission reduction through mitigation and adaptation efforts, financial support, technology transfer, and capacity building, which may involve tools like carbon taxes and Carbon Border Adjustment Mechanism (CBAM). This research adopts a legal norm perspective, delving into methods that investigate, describe, synthesize, interpret, evaluate, and analyze positive approaches. The findings signify a paradigm shift aligning with the "polluter pays principle," recognizing that entities and individuals responsible for environmental pollution should bear the associated costs. In the quest for a new paradigm of sustainable development, a carbon market ecosystem assumes a pivotal role. This ecosystem contributes to enhancing sustainability by curbing greenhouse gas emissions and offering economic incentives to address climate change. It acts as a cornerstone in constructing a fresh paradigm for potential development.

With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on a case study with 10 units. The ICS algorithm obtained higher quality solutions, with a total cost of $ 632 719 and a calculation time of 0.51 minutes, which was superior to the solutions obtained by the particle swarm optimization and CS algorithms. Fluctuations in the confidence level of system rotation reserve capacity could lead to variations in the final system cost, which needs to be adjusted according to actual conditions. The dispatch scheme obtained by the ICS algorithm showed reduced carbon emissions, total costs, and better performance when compared with the optimal dispatch algorithm in different scenarios. The results show that the proposed methods are reliable and practical.

As a pivotal element in market mechanisms, carbon trading is increasingly recognized as crucial for achieving China's Carbon Peaking and Carbon Neutrality Goals. This study introduces a comprehensive dynamic model, integrating carbon trading, emissions, economic growth, and green technology innovation, to offer a holistic understanding of the interplay between these domains. Utilizing principles from nonlinear dynamics and chaos theory, the model is adept at simulating various scenarios and assessing the effectiveness of government policies in stabilizing these complex systems. In-depth analysis provided by this research sheds light on the nuanced impact of carbon trading policies on sustainable development. Key findings highlight (1) Carbon trading's essential role as a catalyst in propelling sustainable and high-quality growth. (2) A strong positive relationship is observed between the sophistication of the carbon trading mechanism and its effectiveness in stimulating green technology innovation and fostering high-quality green development. Notably, carbon trading's influence on green technology innovation markedly enhances the efficacy of carbon emission reduction strategies. (3) Government regulations are instrumental in augmenting carbon prices, thus incentivizing increased corporate participation in emission reduction and enhancing the overall impact of carbon emission reduction. Nevertheless, the study identifies a critical threshold in regulatory intensity, beyond which there is a risk of system destabilization ( a 3 ≥ 0.032 ). These findings underscore the imperative for developing an integrated national carbon emission trading market, prioritizing sustainable growth strategies and diligently pursuing China's environmental objectives. [ABSTRACT FROM AUTHOR]

This paper studies when and under what conditions the actions undertaken by the power plants involved in China's emission trading scheme (ETS) pilot became cost effective. Based on unique plant-level panel data and the difference-in-differences strategy, we identify that an insignificant initial reduction in cost efficiency occurred at the announcement stage for power plants in the pilot provinces; however, the cost efficiency of the pilot plants increased significantly following formal policy implementation. Additionally, the by-stage treatment effects differed across the pilot provinces due to localized market and non-market variations. Localized conditions of higher marketization, stricter policy enforcement, and lower carbon dependence enhanced this positive effect. The synthetic control results confirmed this variation in the policy effects. The carbon trading pilots resulted in improved efficiency in power plants in Shanghai, Guangdong, and Tianjin during the period 2013--2017, with an associated total cost saving of approximately 29.75 million RMB. To enhance the efficacy of the ETS policy, our findings suggest that the design of the policy should consider localized external factors. [ABSTRACT FROM AUTHOR]

The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem. [ABSTRACT FROM AUTHOR]

This study proposes a cooperative fuzzy optimal scheduling method for multi-energy hubs considering carbon trading. The traditional integrated energy system has difficulty integrating system economy and environment, as well as allocating benefits to multiple subjects. To address the problem, a multi-energy hub cooperative fuzzy optimization dispatching model, considering carbon trading, was developed to promote low carbon emissions and high renewable energy penetration. The model optimizes the dispatch of the integrated energy system while reducing CO2 emissions, also reducing the impact of renewable energy output uncertainty. Moreover, decision-makers can adjust the risk tolerance of the system according to their own risk appetite. To ensure a fair method of benefit distribution to achieve overall optimum through cooperation, this study introduced and examined three methods of secondary benefit distribution in cooperative games: Shapley value method, Kernel method, and Equal DP (Disruption Propensity) method. Finally, the model's economic, environmental protection, and stability were verified by an example of cooperative optimal scheduling and collaborative residual benefit secondary allocation of three energy hubs. Moreover it was found that in the secondary allocation,the Equal DP method was more stable and acceptable to all subjects compared to the other two methods. [ABSTRACT FROM AUTHOR]

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To meet carbon peak and neutrality targets, accurate carbon trading price forecasting is very important for enterprises making emission reduction decisions. By fusing convolutional neural network (CNN) and long short-term memory network (LSTM), the CNN–LSTM model is constructed. After variational mode decomposition (VMD), several intrinsic mode functions (IMFs) components are obtained and input into the CNN–LSTM model, thus constructing the combined sooty tern optimization algorithm (STOA)–VMD–CNN–LSTM forecasting model. To test this model, the carbon trading prices of the carbon emission trading markets of Hubei, Guangdong and Shenzhen were forecast. The prediction performance of the STOA–VMD–CNN–LSTM model is compared with ARIMA, BP, CNN and LSTM benchmark models and models combining different decomposition technologies. The international carbon trading price (EUR and CER) is used for prediction. Compared with other methods, the developed model makes fewer errors and achieves superior performance. Several important implications are provided for investors and risk managers involved in carbon financial products. [ABSTRACT FROM AUTHOR]