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In the low-carbon economy, the fresh industry constitutes an “impossible triangle” in products, prices and services. Therefore, based on the idea of cost–benefit, a comprehensive vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer is presented. Then, a hybrid algorithm called local search genetic algorithm (LSGA) is proposed, which amalgamates the destroy-repair operator with GA algorithm. Extensive numerical experiments verify the feasibility and effectiveness of the proposed model and algorithm. Furthermore, the sensitivity analysis of freshness-keeping cost, carbon price and customer satisfaction weights were conducted. The experimental results show that appropriate freshness-keeping effort can reduce total costs and improve customer satisfaction. Increasing carbon price within a certain range can effectively reduce carbon emissions, and there is a trade-off relationship between carbon emissions and customer satisfaction. The results of considering both time satisfaction and freshness satisfaction are better than considering time satisfaction alone. [ABSTRACT FROM AUTHOR]

Forestry carbon sequestration is an economical and effective means of 'carbon absorption' at present, which is of great significance to realize China's carbon peaking and carbon neutrality goals. Based on forestry carbon sink trading, a Stackelberg game model between forest enterprises and emission-controlled enterprises was established. Stochastic differential game theory was used to determine the optimal feedback Nash equilibrium strategy of forest enterprises and emission-controlled enterprises with and without government subsidies. The impact of government subsidies on their decision-making behavior and carbon emission reduction was analyzed, and the validity of the model conclusions was verified by numerical examples. It was found that only when the subsidy ratio given to emission-controlled enterprises exceeded a certain value could the government subsidy effectively improve the willingness of the emission-controlled enterprises to purchase forestry carbon sink. In order to improve environmental quality and realize the Pareto optimality of the forestry carbon sink trading system, the value of the proportion of government subsidies to forest enterprises should be within a suitable range, so as to achieve the harmonic development of pollution control effect and economy. The relevant research results could provide guides for the government to formulate subsidy policies and the game players to decide emission reduction strategies in the forestry carbon sink trading system. [ABSTRACT FROM AUTHOR]

The Congo has long been a site of contestation for global environmental governance strategies, with Indigenous Batwa, Bambuti, and Baaka groups at the center of transnational climate discourse. One such strategy, the REDD+ initiative (Reducing Emissions from Deforestation and Forest Degradation), has come to the fore as a carbon-trading-based solution to the environmental challenges facing the Congo Basin. This article critiques the REDD+ initiative's deterritorialized approach, which favors the voices of international stakeholders over forest-dependent peoples Indigenous to the region. Taking a political ethnographical approach to the area of Mai-Ndombe, this research is based on formal and informal interactions with international actors, local communities, and Indigenous peoples. The strengths and weaknesses of mitigation methods like community forestry, institutionalized participation, and participatory mapping are explored. Ultimately, it is asserted that Indigenous decision-making, rather than institutionally imposed hegemony, must be integrated into the REDD+ initiative. [ABSTRACT FROM AUTHOR]

The framework of the methodology presented in this study is an effort to integrate and optimize the agro-industry sector, especially energy in biogas. In this study, the technique of the system in functional analysis is shown systematically to translate various energy requirements in the factory as criteria for performance and functional design to be integrated, optimized, and energy efficient. The case study results indicated that biogas power plants, with a capacity of 1.5 MW, can produce around 13,140 MWh per year. The annual return on investment (ROI) is around 37.13%. With this ROI value, the payback period is 31 months. The overall reduction of greenhouse gases is approximately 77,826 tons CO2 eq/year. The potential value of carbon trading is about USD 3,113,040 per year. This strategic model presents a novel approach by integrating biogas energy production with a customized wastewater treatment system adapted to biodigesters' effluent characteristics. It offers a sustainable, economically feasible, and scalable solution, combining resource recovery, waste minimization, and potential for carbon trading into a unified system. The novelty of this research lies in maximizing the utility of biogas plants by efficiently treating and reusing wastewater, creating a closed-loop, zero-waste process. Future research on hybrid systems integrating Biogas power plants by focusing on efficiency optimization, economic feasibility, environmental impacts, and innovative approaches like AI and blockchain could make the hybrid system a more robust, scalable, and sustainable solution. Thus, the framework based on the results of this study finds tools that can maximize and integrate energy sources, especially biogas, in the agro-industrial sector. [ABSTRACT FROM AUTHOR]

As the global environmental pollution problem intensifies, the carbon reduction transformation of the power system is urgent. In order to solve the problem of unclear carbon flow and distribution in the operation of the power grid, as well as the mismatch between static time-of-use electricity prices and peak and valley periods in the scheduling of electric vehicle charging loads, a multiperiod dynamic electricity price guidance strategy based on carbon emission flow theory is proposed. First, based on the accurate power flow results of the power system, a complex power distribution matrix of the power system is constructed to obtain the distribution of the power generated by the generator units in each node of the network. Then, the Monte Carlo random sampling method is used to simulate the load situation of electric vehicles in a disordered charging state. A mathematical model based on the carbon trading model is established to minimize the load difference at the grid end and maximize the cost of charging on the user side. Finally, the proposed ordered charging method with a multiperiod dynamic electricity pricing strategy is compared with unordered charging, and considering the participation of electric vehicles in carbon trading, this strategy effectively reduces the peak valley difference between the power grid and user charging costs. [ABSTRACT FROM AUTHOR]

Against the backdrop of global climate change and sustainable development, carbon emissions within transnational closed-loop supply chains have become a critical area of research. This paper utilizes a Stackelberg game model to analyze the relationship between a single export manufacturer and an import retailer. The study investigates the optimal solutions of the supply chain model—wholesale price, retail price, sales volume, and profit—across three consumer preference scenarios: no obvious preference, pure green preference, and pure new preference. Furthermore, this paper examines the impact of carbon emissions per unit of product on supply chain decision-making under two scenarios: with and without carbon trading. Carbon trading, which significantly increases unit costs, exerts a profound influence on the strategic decisions of both manufacturers and retailers. In addition, this paper incorporates carbon tariffs and taxes into its analysis, providing a theoretical foundation for governments and policymakers to promote sustainable production and consumption practices. The validity of the model is confirmed through numerical simulations, which reveal that under pure green and pure new preference scenarios, original equipment manufacturers (OEMs) are more inclined to invest in emissions reduction to minimize tariff costs. In contrast, under no obvious preference scenarios, OEMs are more likely to adjust product portfolios to evade carbon tariffs. This research advances the understanding of low-carbon production strategies in transnational supply chains, offering both theoretical insights and practical guidance for balancing economic and environmental objectives. [ABSTRACT FROM AUTHOR]

The optimal scheduling of networked microgrids considering the coupled trading of energy and carbon emission allowance (CEA) has been extensively studied. Notably, the scheduling is performed on a daily basis, whereas the CEA is usually checked and determined once a year. The temporal mismatch between the daily scheduling and the yearly CEA should be addressed to realize the dynamic valuation of CEA. In this paper, the optimal scheduling of networked microgrids considering the temporal equilibrium allocation of annual CEA is investigated. Firstly, a CEA decomposition model is developed, which allocates allowance to individual microgrids and further decomposes them temporally using the entropy method. Secondly, a Lyapunov optimization-based low-carbon scheduling model is introduced to manage carbon emissions within each dispatch interval, ensuring annual CEA compliance and daily economic efficiency. Thirdly, a Stackelberg game-based energy–carbon coupling trading model is presented, which considers the uncertainties caused by fluctuations in external electricity and carbon prices to optimize trading prices and strategies of the microgrids. Finally, a test system is used to demonstrate the significant effects of emission reduction and the economic benefits of the proposed methods. [ABSTRACT FROM AUTHOR]

Connecting microgrids can promote the sharing of multi-energy sources, reduce carbon emissions, and enhance the consumption of renewable energy. However, the uncertainty of renewable energy and the coupling of multiple energy sources makes energy management difficult in connected microgrids. To address the challenges, a dual-layer energy management framework for interconnected microgrids is proposed in this paper. In the bottom layer, a load scheduling problem within one microgrid is formulated to maximize the utilization of renewable energy, which is solved by an improved gray wolf algorithm with fast convergence and effective optimum seeking. In the upper layer, a distributed energy dispatch strategy is proposed to coordinate the energy sources for multiple microgrids to achieve multi-energy sharing with carbon trading. Combining the load scheduling and energy dispatching, the overall energy utilization is improved, and the operation cost and carbon emission are reduced. The simulation results on real-world datasets validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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Carbon trading policy is an essential market-oriented emission reduction tool for achieving China's carbon goals. Developing a reasonable and feasible initial carbon quota allocation plan is the key to the effective operation of carbon trading policy. In this article, a multi-principle indicator system based on fairness, efficiency, feasibility, and sustainable development is constructed using panel data from 2005 to 2019 at the provincial and eight primary economic region levels. The carbon quota allocation scheme uses the Entropy Weight TOPSIS method, the CRITIC-VIKOR, and the combination allocation models. The research results indicate significant differences in carbon emissions among different provinces and regions, and constructing a more objective and comprehensive carbon emission allocation indicator system is the key to whether the carbon emission allocation is reasonable. Then, there are significant differences in the allocation results of different allocation models. Through comparative analysis of model results, it can be seen that the combination allocation model has a good allocation effect. Meanwhile, analyzing the allocation of regional carbon quotas makes it possible to accurately identify the degrees of surplus and deficit in eight regions. This is conducive to improving the rationality of carbon quota allocation and reducing the relative exploitation between regions. This study is conducive to achieving a reasonable allocation of regional carbon quotas. It helps promote the smooth operation of the national unified carbon market and achieve the overall carbon emission reduction target. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) is an excellent approach for mitigating the intermittency and fluctuation of renewable energy sources. The present work proposes an optimal scheduling model for VPPs to leverage the benefits of joint electricity and carbon trading from the perspective of multiple energy-sharing mechanisms. First, the optimal sharing scheduling model of the electric, thermal, and hydrogen energy was established. The model integrates various components, including wind turbines, photovoltaic units, electrolytic cells, combined heat and power units, hydrogen-doped gas boilers, electric energy storage, thermal storage tanks, and hydrogen storage tanks. Then, the model incorporates a tiered carbon trading mechanism to minimize operating and trading costs. Finally, numerical results indicate that, compared with the independent operation of virtual power plants and the lack of joint electricity and carbon trading, the optimal scheduling scheme proposed in this paper reduces the total cost and carbon emissions of the three VPPs by 3.3% and 49.7%, respectively. This demonstrates that the proposed model can effectively reduce the total operating expenses of VPPs by facilitating the allocation of electric, thermal, and hydrogen energy and achieving low-carbon emission operations. [ABSTRACT FROM AUTHOR]

The global power sector faces the critical challenge of balancing rising electricity demand with stringent carbon reduction targets. Taiwan's unique geopolitical and energy import constraints provide an ideal context for exploring advanced grid technologies integrated with carbon-trading mechanisms. This study combines the Adaptive Time-Varying Gravitational Search Algorithm (ATGA) with Static Synchronous Series Compensator (SSSC) technology to optimize power flow and enable carbon transactions between the power generation and transmission sectors. Through a feedback-driven mechanism, power producers acquire carbon credits from transmission operators, maximizing profitability while meeting emission targets. Managed by the transmission companies, the SSSC enhances grid stability, reduces transmission losses, and generates valuable carbon credits. Simulations based on Taiwan's power market demonstrate that this integrated approach achieves a 50% reduction in emissions and increases profitability for power producers by up to 20%. This model has potential applications in other regions, and future work could explore its scalability and adaptability in different economic and regulatory contexts. [ABSTRACT FROM AUTHOR]

Abstract In the low-carbon economy, the fresh industry constitutes an “impossible triangle” in products, prices and services. Therefore, based on the idea of cost–benefit, a comprehensive vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer is presented. Then, a hybrid algorithm called local search genetic algorithm (LSGA) is proposed, which amalgamates the destroy-repair operator with GA algorithm. Extensive numerical experiments verify the feasibility and effectiveness of the proposed model and algorithm. Furthermore, the sensitivity analysis of freshness-keeping cost, carbon price and customer satisfaction weights were conducted. The experimental results show that appropriate freshness-keeping effort can reduce total costs and improve customer satisfaction. Increasing carbon price within a certain range can effectively reduce carbon emissions, and there is a trade-off relationship between carbon emissions and customer satisfaction. The results of considering both time satisfaction and freshness satisfaction are better than considering time satisfaction alone.

To normalize the issuance and trading of green certificates， the National Energy Administration （NEA） recently introduced a new policy. This policy mandates issuing tradable and nontradable green certificates for on-grid renewable energy electricity and spontaneous self-use renewable energy electricity， respectively. Although green certificates contribute in offsetting carbon emissions， maximizing the economic and environmentally friendly aspects of virtual power plants （VPPs） is important. This study proposes an approach to implement both green certificate trading and carbon emissions offsetting in the development of day-ahead operational strategy of the VPP. First， the electricity associated with tradable and nontradable green certificates of the VPP is quantified based on the "use what is generated， and the excess on the grid" mode of renewable energy generation in the VPP. The charging-discharging strategy in electricity storage for the renewable and non-renewable energy electricity is also considered. Second， a method for offsetting carbon emissions using green certificates is proposed， and the total amount of carbon emissions offset by the VPP under the two types of green certificates is calculated. Third， an optimal day-ahead operational strategy model of the VPP under the electricity-carbon-green certificate coupled market is developed to maximize the total revenue in the coupled market. This study demonstrates that the proposed approach enhances the financial performance of the VPP and boosts its renewable energy consumption capacity by engaging in carbon-green certificate trading under the new policy. Moreover， this approach contributes to an overall reduction in carbon emissions within the energy system.

The carbon trading system is a mechanism that employs carbon caps (limits) and taxation to regulate the rate of carbon dioxide emissions produced by human activities. To further strengthen the effectiveness of this mechanism, the United Nations (UN) initiated the Kyoto Protocol in 1997, enabling the trading of carbon emission rights in exchange for financial compensation. As a result, the market value rose to 10.9 billion in 2005, with an annual growth rate of approximately 8%, reaching 95 billion in 2023. Despite this expansion, the system’s management has remained centralised since its inception, leading to issues such as a lack of transparency and openness in the trading process, inefficiencies in the trading mechanisms, and inaccurate or dishonest centralised data recording. The rapid expansion of the market and the accompanying challenges underscore the need for blockchain technology (BCT), which offers a decentralised, secure, and tamper-proof system. Although several research publications have demonstrated the potential enhancements that BCT could contribute to the carbon market, no systematic review has yet examined the optimal implementation of BCT within the context of carbon trading and taxation. This study, therefore, undertakes a systematic review of peer-reviewed research articles published between 2015 and 2023 using the PRISMA methodological framework. Our analysis establishes the feasibility and viability of a blockchain-powered solution for carbon trading, alongside the development of a comprehensive framework for its effective implementation, designed to stand the test of time in the fast-evolving technological landscape. Furthermore, this comprehensive review identifies research gaps, offering future researchers direction in exploring blockchain applications within the Emissions Trading System (ETS) domain. Overall, this review fosters a digitalised and incentivised shift towards renewable energy sources.

The largest emitting countries in the world are predominantly developing countries, including the BRICS countries. The general principle of “climate justice” asserts that the largest emitting countries should take the lead in efforts to reduce greenhouse gas emissions. The legal implications for the implementation of climate change mitigation efforts play an active role in the implementation and establishment of the carbon exchange concept in the context of the carbon trading system. The urgency of listing on the carbon exchange is driven by the precautionary principle of global carbon accounting, which aims to avoid the risk of carbon leakage. The purpose of this research is to examine the obligation of emitting countries to make ambitious efforts towards reducing their greenhouse gas emissions while also upholding the basic principles of accountability and transparency. Offsetting the amount of carbon emitted by each country is largely calculated based on carbon credits purchased. In order to prevent double counting, carbon exchanges have the responsibility of recording the sale of carbon units with certificates issued under a “polluter pays” system.

Power side energy storage is a powerful tool for promoting the consumption of new energy and implementing the “dual carbon” strategy. In view of the imperfection of the bidding mechanism of the power side energy storage as an emerging entity participating in the competitive joint electricity market, and starting from the game relationship of the power side energy storage as a price maker participating in the spot electricity market, a bidding game two-level optimization model of the power side energy storage participating in the multi scenario application of the electric energy and reserve ancillary service market is proposed, considering the uncertainty of wind power output and the coupling of the electricity market with the introduction of carbon trading. The energy storage at the upper power supply side, as a leader, makes bidding decisions with the goal of maximizing its own comprehensive income, while the lower power trading center as a follower completes market clearing with the minimum cost of social power purchase and balance regulation. The MATLAB simulation verifies the rationality and effectiveness of the proposed model in optimizing the participation of power side energy storage in multi scenario application bidding in the electricity market and improving the comprehensive operating income of the energy storage.

In the context of sustainable energy development to reduce carbon emissions, the application of new energy sources and smart grid technologies in power systems is becoming more widespread. However, current research results on power system technology strategies for carbon emission reduction are not satisfactory. To address this problem, a model for optimal power system operation and scheduling based on the prediction error mechanism and synthetic fuel technology is proposed. The model used the carbon trading mechanism to further reduce carbon emissions and the carnivorous plant algorithm to optimize the scheduling strategy. The results indicate that the model demonstrates significant advantages in terms of carbon emission, total operating cost, prediction accuracy, and energy utilization efficiency, respectively, at 60.8 kg, 2517.5 yuan, 96.5%, and 90.2%, indicating that it utilizes energy more fully and helps to enhance the overall energy efficiency of the system. The calculation time of the optimized power system was only 12.5 s, the stability was as high as 98.7%, and the satisfaction rate was 95.6% in terms of user satisfaction. Compared to other contemporary designs, the proposed model can successfully reduce the system's carbon emissions while increasing energy efficiency. The model has positive implications for smart grid and sustainable development.

ObjectivesTraditional electric energy storage has limitations in scale, duration, and environmental impact.Moreover, the renewable energy absorption capacity in the microgrid is low, and low-carbon and economy cannot be taken into account in planning. In order to solve the above problems, based on the basic working principle of hydrogen energy storage, hydrogen energy storage was incorporated into the microgrid instead of traditional electric energy storage, and a low-carbon and economic synergy bi-level optimization configuration model of microgrid with hydrogen energy storage was established.MethodsThe upper-level planning model aimed at minimizing the comprehensive equivalent annual value of the microgrid, based on the joint operation of electricity and hydrogen. The carbon trading mechanism was introduced to plan the capacity of various power generation equipments in the microgrid, which can enhance the low-carbon of the system. The lower-level operation model aimed to minimize the sum of the absolute values of the difference between the new energy output and the load demand. The model also encouraged users to adopt diversified demand-side response behaviors with the goal of accurately tracking the new energy output curve, and feeded back the user’s energy consumption behavior to the upper-level model to optimize the load curve. On the basis of improving the absorption capacity of new energy, the system economy is further improved, deeply exploring the synergy between the low-carbon and economic characteristics of microgrids.ResultsThe simulation results of microgrid in a certain industrial park show that the proposed method yields a planning scheme with excellent low-carbon and economy. Compared with the traditional planning method, the low-carbon and economy are improved by 53.6% and 37.1%, respectively.ConclusionsThe model presented in this paper not only enhances the capacity for new energy absorption but also further improves the system economic performance. It achieves a synergistic enhancement of the microgrid low-carbon and economy.

Under the 'dual-carbon' goal, industrial parks, as the most important and extensive carriers of industrial systems, must undergo low-carbon transformation. In this context, this paper establishes a bi-level optimal dispatching model of low-carbon industrial park considering flexible scheduling of high-energy-consuming enterprises' production workshops. The model fully exploits the flexible adjustment ability of high-energy-consuming enterprises, obtains the optimal low-carbon scheduling plan of the park by flexibly adjusting the production scheme of high-energy-consuming enterprises to realize the coordinated operation of the industrial park and high-energy-consuming enterprises. Firstly, the upper layer establishes an optimal scheduling model with the objective of minimizing the total operating cost, taking into account carbon trading and tradable green certificate costs. Then, the lower layer focuses on the flexible scheduling problem of the high-energy-consuming enterprise's production workshops within the industrial park, aiming to minimize the maximum completion time and cost. The cost savings achieved by enterprise scheduling at the operational layer are used as subsidies. The upper and lower layers continuously coordinate and schedule plans to achieve the optimal objective. Finally, the feasibility and effectiveness of the proposed model are verified through case studies. The proposed approach not only reduces production costs and improves production efficiency but also effectively promotes load balancing, realizing the low-carbon operation of the industrial park.

A virtual power plant (VPP) is proposed to aggregate various resources to participate as a whole in both electricity and carbon trading markets. As the scale of VPPs continues to expand, they are transitioning from being price takers to price makers. To this end, this paper treats the VPP as a price maker and proposes a bi-level bidding strategy in the day-ahead electricity market, considering the impact of carbon trading. Firstly, an introduction and analysis of the day-ahead electricity market mechanism, considering carbon trading, are provided. Secondly, based on the Stackelberg game theory, a bi-level bidding model in the day-ahead electricity market is established with the VPP as the bidding entity. The upper-level model aims to maximize the anticipated profit of the VPP, while the lower-level model aims to minimize the system's clearing cost. Considering the uncertainty in wind farm output predictions within the VPP, operators are provided with two bidding strategies: risk-averse and opportunity seeker strategies based on the information gap decision theory (IGDT). Then, utilizing the strong duality theory, the Karush-Kuhn-Tucker (KKT) optimality conditions, and the big-M method, the bi-level model is simplified into a mixed-integer linear programming problem for resolution. Finally, an example is provided to illustrate the optimal bidding strategy and operation plan for the VPP, along with an analysis of how uncertainty in wind farm output predictions within the VPP affects the expected profit of the VPP. The example shows that VPPs can influence market prices through strategic bidding decisions. After considering carbon trading, the expected revenue of the VPP increased by 5.1% compared to the scenario without carbon trading.

The carbon trading pilot policy is a key policy to achieve low-carbon development through market mechanism. Studying the impact of carbon trading on resource allocation efficiency can provide an important empirical basis to achieve energy saving and green development. This paper delves into the influence of carbon trading on resource allocation efficiency by utilizing the multiperiod difference-in-differences based on comprehensive provincial data spanning from 2011 to 2020 in China. The findings show that carbon trading can promote the optimization of capital allocation efficiency but negatively affect the labor allocation efficiency in the pilot areas. This conclusion still holds through various robustness tests. Further research on regional heterogeneity shows that there are significant differences in the impact of resource allocation efficiency and labor allocation efficiency between eastern and western regions. Crucially, the limited scope of the carbon market's direct influence on labor allocation efficiency is highlighted, demonstrating the need for government intervention to amplify its optimization effects based on the synergistic effect. It provides vital empirical insights for China's ongoing pursuit of energy-saving and green development strategies, emphasizing the synergies between market mechanisms and government regulation in fostering sustainable economic transformation. [ABSTRACT FROM AUTHOR]

To fully leverage the potential flexibility resources of a source-network-load-storage (SNLS) system and achieve the green transformation of multi-source systems, this paper proposes an economic and low-carbon operation strategy for an SNLS system, considering the joint operation of ladder-type green certificate trading (GCT)–carbon emission trading (CET), and integrated demand response (IDR). Firstly, focusing on the load side of electricity–heat–cooling–gas multi-source coupling, this paper comprehensively considers three types of flexible loads: transferable, replaceable, and reducible. An IDR model is established to tap into the load-side scheduling potential. Secondly, improvements are made to the market mechanisms: as a result of the division into tiered intervals and introduction of reward–penalty coefficients, the traditional GCT mechanism was improved to a more constraining and flexible ladder-type GCT mechanism. Moreover, the carbon offset mechanism behind green certificates serves as a bridge, leading to a GCT-CET joint operation mechanism. Finally, an economic low-carbon operation model is formulated with the objective of minimizing the comprehensive cost consisting of GCT cost, CET cost, energy procurement cost, IDR cost, and system operation cost. Simulation results indicate that by effectively integrating market mechanisms and IDR, the system can enhance its capacity for renewable energy penetration, reduce carbon emissions, and achieve green and sustainable development. [ABSTRACT FROM AUTHOR]

The enhancement of the carbon trading mechanism signifies a gradual transition in China's environmental regulatory framework, shifting from a command and control approach to a market-based incentive model. Despite the significance of this shift, existing research has insufficiently explored the impact of market-oriented environmental policies on consumption-based emissions. This study leverages the carbon trading policies implemented in 2013 as a quasi-natural experiment, combined with a precise measurement of urban and rural household carbon emissions (HCE) during 2005–2021. Employing a difference-in-differences method, we evaluate the heterogeneous impact of these policies on urban and rural HCE. The results demonstrate a significantly negative effect of the policies on indirect HCE, a conclusion that remains robust across various placebo and robustness tests. Furthermore, we identify the transmission mechanisms through which carbon trading policies affect the reduction in HCE. The results indicate that the policy has a significant negative impact on indirect HCE, with a notable urban–rural difference. The effect of the policy is −0.829 for urban areas and −0.365 for rural areas, a conclusion that remains robust across various placebo and robustness checks. Additionally, we identified two transmission mechanisms through which carbon trading policies operate: financial deepening and employment effects. Lastly, we found that carbon trading policies can reduce carbon inequality between urban and rural areas by 46.8%. [ABSTRACT FROM AUTHOR]

This study aims to analyze the temporal and spatial distribution of carbon capture technologies worldwide, examining the economic, social, and political developments reflected in related academic literature. By conducting a comprehensive analysis of over 40,000 related documents from 2004 to June 2024, as well as selecting 108 relevant articles from SSCI and SCI journals, the study explores the development of carbon capture technologies from different perspectives through keyword searches, trend analysis, and relevance ranking. The study finds that, in terms of temporal trends, significant progress has been made in carbon capture technologies since 2009, and their importance has surpassed that of carbon trading, becoming one of the core technologies in addressing climate change. Spatial trend analysis shows that North American and European countries are more inclined to prioritize "carbon capture" technologies, while Asian countries focus more on "carbon trading", reflecting regional differences in economic, policy, and technological development. Although carbon capture technologies hold immense potential for sustainable development, they also face numerous challenges, including balancing technological advancements with economic and policy frameworks. This balance is crucial to ensuring that carbon capture technologies can make a positive contribution to sustainable work, climate action, and environmental sustainability, further transforming the essence of sustainable efforts. To fully realize their benefits, it is essential to recognize and address these challenges. [ABSTRACT FROM AUTHOR]

In the context of sustainable energy development to reduce carbon emissions, the application of new energy sources and smart grid technologies in power systems is becoming more widespread. However, current research results on power system technology strategies for carbon emission reduction are not satisfactory. To address this problem, a model for optimal power system operation and scheduling based on the prediction error mechanism and synthetic fuel technology is proposed. The model used the carbon trading mechanism to further reduce carbon emissions and the carnivorous plant algorithm to optimize the scheduling strategy. The results indicate that the model demonstrates significant advantages in terms of carbon emission, total operating cost, prediction accuracy, and energy utilization efficiency, respectively, at 60.8 kg, 2517.5 yuan, 96.5%, and 90.2%, indicating that it utilizes energy more fully and helps to enhance the overall energy efficiency of the system. The calculation time of the optimized power system was only 12.5 s, the stability was as high as 98.7%, and the satisfaction rate was 95.6% in terms of user satisfaction. Compared to other contemporary designs, the proposed model can successfully reduce the system's carbon emissions while increasing energy efficiency. The model has positive implications for smart grid and sustainable development. [ABSTRACT FROM AUTHOR]

A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions. [ABSTRACT FROM AUTHOR]