Search Results (365)

The deepening implementation of the energy and carbon market imposes trading requirements on multiple regional integrated energy system participants, including power generation plants, industrial users, and carbon capture, utilization, and storage (CCUS) facilities. Their diverse roles in different markets strengthen the interconnections among these subsystems. On the other hand, the operation of CCUS, containing carbon capture (CS), power-to-hydrogen (P2H), and power-to-gas (P2G), results in the coupling of regional carbon reduction costs with the operation of electricity and hythane networks. In this paper, we propose a regional economic dispatching model of an integrated energy system. The markets are organized in a centralized form, and their clearing conditions are considered. CCUS is designed to inject hydrogen or natural gas into hythane networks, operating more flexibly. A generalized Nash game is applied to analyze the multiple trading equilibria of different entities. Simulations are carried out to derive a different market equilibrium regarding network scales, seasonal load shifts, and the ownership of CCUS. Simulation results in a 39-bus/20-node coupled network indicate that the regional average carbon prices fluctuate from ¥1078.82 to ¥1519.03, and the organization of independent CCUS is more preferred under the proposed market structure. Full article

This research uses advanced statistical methods to examine climate change mitigation policies’ economic and environmental impacts. The primary objective is to assess the effectiveness of carbon pricing, renewable energy subsidies, emission trading schemes, and regulatory standards in reducing CO₂ emissions, fostering economic growth, and promoting employment. A mixed-methods approach was employed, combining regression analysis, cost–benefit analysis (CBA), and computable general equilibrium (CGE) models. Data were collected from national and global databases, and sensitivity analyses were conducted to ensure the robustness of the findings. Key findings revealed a statistically significant reduction in CO₂ emissions by 0.45% for each unit increase in carbon pricing (p < 0.01). Renewable energy subsidies were positively correlated with a 3.5% increase in employment in the green sector (p < 0.05). Emission trading schemes were projected to increase GDP by 1.2% over a decade (p < 0.05). However, chi-square tests indicated that carbon pricing disproportionately affects low-income households (p < 0.05), highlighting the need for compensatory policies. The study concluded that a balanced policy mix, tailored to national contexts, can optimise economic and environmental outcomes while addressing social equity concerns. Error margins in GDP projections remained below ±0.3%, confirming the models’ reliability. Full article

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices. Full article

To realize a carbon-efficient and economically optimized dispatch of the integrated energy system (IES), this paper introduces a highly efficient dispatch strategy that integrates demand response within a tiered carbon trading mechanism. Firstly, an efficient dispatch model making use of CHP and P2G technologies is developed to strengthen the flexibility of the IES. Secondly, an improved demand response model based on the price elasticity matrix and the capacity for the substitution of energy supply modes is constructed, taking into account three different kinds of loads: heat, gas, and electricity. Subsequently, the implementation of a reward and penalty-based tiered carbon trading mechanism regulates the system’s carbon trading costs and emissions. Ultimately, the goal of the objective function is to minimize the overall costs, encompassing energy purchase, operation and maintenance, carbon trading, and compensation. The original problem is reformulated into a mixed-integer linear programming problem, which is solved using CPLEX. The simulation results from four example scenarios demonstrate that, compared with the conventional carbon trading approach, the aggregate system costs are reduced by 2.44% and carbon emissions are reduced by 3.93% when incorporating the tiered carbon trading mechanism. Subsequent to the adoption of demand response, there is a 2.47% decrease in the total system cost. The proposed scheduling strategy is validated as valuable to ensure the low-carbon and economically efficient functioning of the integrated energy system. Full article

The study examined the potential changing roles of ports in terms of diversifying their revenue through the expansion of new markets in the Port of Ngqura. This is by means of the production and sales of renewable hydrogen as marine fuel produced from a wavefarm in Nelson Mandela Bay. A key objective of the study was to conduct a comprehensive techno-economic analysis of the feasible hydrogen production technologies based on the analysis performed, including alkaline electrolysis of seawater and renewable-powered electrolysis of seawater. The produced hydrogen aligns with global decarbonisation of ships and ports and will be used to supply the port with electricity, serve to refuel tugboats, and provide green hydrogen bunkering fuel for commercial shipping vessels. The Port of Ngqura is geographically well positioned to lead the production of zero carbon shipping fuel. This work considers the CAPEX and OPEX of a hydrogen plant using electrolysers and evaluates the current cost of production and selling price of hydrogen. The primary aim of this study was to examine the feasibility of hydrogen production through electrolysis of seawater at the Port of Ngqura. Through assessing resource and technological options, determining advantageous economic assumptions, and identifying existing limitations and potential opportunities, a feasibility study was conducted with special consideration of the site characteristics of Ngqura. The output of this study is a model that simulates the production, storage, and transportation of hydrogen gas from the Port of Ngqura, which was further used to analyse different case study scenarios. This approach directly addresses the main goal of the study. The results found showed that with wave energy convertors in a row of three next to each other, the energy produced by the wave farm was 2.973 TJ per month, which is equivalent to 18.58 tons of produced hydrogen when considering the lower heating value of hydrogen and assuming that hydrogen production efficiency is 75%. The anticipated hydrogen fuel will be able to refuel a tugboat with green hydrogen from the energy produced by the wave farm each month. It is predicted that the price of hydrogen is expected to drop, and the price of fossil fuel will gradually increase in the coming years. The fact that coal electricity can be produced on demand and wind and solar energy are weather dependent as a result lacks the ability to achieve a constant supply. There is currently an urgent need for energy storage and the efforts to study the production of hydrogen and ammonia. Hydrogen is still predicted to be more expensive than coal electricity; however, from this, maybe a critical cost for a kg of CO₂ could be calculated, which could make hydrogen competitive. The cost of green hydrogen production from wave energy in the Port of Ngqura was calculated as R96.07/kg (4.88 EUR/kg) of produced hydrogen, which is equivalent to 2.1 times the cost of the same energy supplied as Marine Diesel Oil (MDO) at current prices. Hydrogen from wave energy would thus become competitive with MDO; if a price is set for the emission of CO₂, this may also offset the difference in cost between MDO and hydrogen from wave energy. The carbon price necessary to make green hydrogen competitive would be approximately R6257/tonne CO₂, or 318 EUR/tonne CO₂, which is around 4.5 times the current trading price of carbon in the EU Emissions Trading Scheme. Full article

Prefabricated buildings (PBs) are considered a green way to reduce energy consumption and carbon emissions in the construction industry due to their environmental and social benefits. However, PBs have obstacles such as high construction costs, immature technology, and insufficient policy incentives, and developers’ willingness to develop them needs to be higher. Therefore, it is necessary to explore how to motivate more developers to develop PBs. In this paper, we first discuss the impact of the carbon emissions trading scheme (ETS) on the construction industry and then consider the heterogeneity of construction developers, introduce a collaborative mechanism to establish a three-party evolutionary game model between the government and the heterogeneous developers, and explore the evolution of the three-party dynamic strategies through numerical simulation. The results show that developers’ initial development probability affects the system’s evolutionary trend, and the developer who obtains more low-carbon benefits plays a dominant role. Further analyses show that critical factors such as market profitability, synergistic benefits, and carbon tax price positively influence the development of PBs, and the influence of synergistic cooperation mechanisms should be especially emphasized. This study provides practical insights into the sustainable development of the construction industry and the government’s development of a suitable carbon portfolio policy for it. Including the construction industry in the ETS is recommended when carbon prices reach 110 RMB/t. At this point, the government can remove the subsidy for PBs, but the behaviors of the developers who participate in the ETS still need to be supervised. Full article

Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role played by EV charging operators in providing essential charging infrastructure and services for widespread EV adoption. This paper introduces a blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market with renewable energy sources. We propose a single-leader, multi-follower Stackelberg game between the electricity market and EV charging operators. In the two-stage Stackelberg game, the electricity market acts as the leader, deciding the price of electric energy. The EV charging aggregator leverages blockchain technology to record and verify energy trading transactions securely. The EV charging operators, acting as followers, then decide their demand for electric energy based on the set price. To find the Stackelberg equilibrium, we employ a Deep Reinforcement Learning (DRL) algorithm that tackles non-stationary challenges through policy, action space, and reward function formulation. To optimize efficiency, we propose the integration of pruning techniques into DRL, referred to as Tiny DRL. Numerical results demonstrate that our proposed schemes outperform traditional approaches. Full article

As manufacturing advances swiftly and public consciousness about low-carbon practices rises, eco-friendly supply chains have seen significant expansion. This study investigates a government-driven green supply chain in two phases, involving a producer and a seller. Four scenario game models are established to determine whether the manufacturer engages in green technology innovation or whether the retailer has altruistic preferences. The Stackelberg game was used to analyze changes in government carbon quota regulations, retail prices of retailers, and manufacturers’ carbon reduction efforts in the context of carbon market trading. Research shows that the government will set looser carbon emission limits for manufacturers when retailers have no altruistic preferences. When carbon prices in the market are low, encouraging manufacturers to invest in green technology innovation enhances social welfare. This study offers essential theoretical backing for the government in crafting carbon quota regulations and aids businesses in making prompt technological innovation choices. Full article

Associated with more ambitious targets for reducing emissions, the European Union (EU) plans to implement the Carbon Border Adjustment Mechanism (CBAM) fully in 2026, aiming to reduce carbon leakage and competitiveness concerns by imposing tariffs on carbon-intensive imports, which is expected to significantly impact its trade partners. Existing research has focused on CBAM’s impact on macroeconomic indicators but has insufficiently addressed its effects on global and regional carbon leakage, especially in non-EU countries like China. This research offers a detailed analysis of industry-specific leakage rates and integrates both global and regional impacts by employing the dynamic recursive GTAP-E general equilibrium model to numerically simulate CBAM’s inhibitory effect on carbon leakage under different carbon tariff scenarios, while also exploring the synergistic effects of anti-leakage policies in non-EU countries. Our simulations indicate the following: (1) CBAM effectively inhibits carbon leakage, with greater inhibition observed at higher tax rates and with the expansion of covered industries. (2) Establishing China’s domestic carbon market pricing can further reduce regional carbon leakage rates. Implementing global export carbon tax policies will significantly diminish the risk of global carbon leakage. (3) The implementation of CBAM is projected to reduce China’s total exports to the EU, though this loss will be partly offset by trade diversion effects. Carbon-intensive industries are more adversely affected in the short term, while all industries except fossil fuels face inevitable long-term negative impacts. Full article

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers. Full article

Based on different carbon quota trading mechanisms, the price and emission reduction strategies of oligopoly manufacturers in the low-carbon market and the government carbon quota mechanism are considered. A dynamic game evolution model of the two oligopoly manufacturers with competitive relations is established. The stability of the equilibrium point of the game model, the price adjustment speed of the decision variable, the impact of carbon emission reduction investment, and the government carbon quota on the system are discussed. Through nonlinear dynamics research, it is found that the advantage of the grandfathering method is that it is conducive to maintaining market stability when the government’s carbon quota decision changes; the advantage of the benchmarking method is that when manufacturers formulate price adjustment strategies, the benchmarking method carbon quota mechanism has a stronger stability range for the market, the manufacturer’s profit price adjustment speed is positively correlated, and the government carbon quota decision and emission reduction investment are also positively correlated. Decision makers need to choose appropriate carbon quota mechanisms and manufacturers’ emission reduction strategies according to actual market changes to maintain supply chain stability. Full article

To address the growing demand for green development, governments worldwide have introduced policies to promote a green economy. Among these policies, the carbon cap-and-trade mechanism is adopted as an effective approach to control carbon emissions. Additionally, blockchain may increase transparency in the industrial process. Despite focusing on improving its own green standards, the supply chain needs to establish stable cooperative relationship. Thus, we focus on a supply chain consisting of a dominant manufacturer and a retailer, where the manufacturer opts for implementing blockchain and the retailer selects their stance on fairness. We construct a Stackelberg game model and use backward induction to obtain the equilibrium solutions. In the supply chain, the highest profits can be achieved when the manufacturer adopts blockchain technology, provided that the cost of application is relatively low. For manufacturer and retailer, when the cost of applying blockchain is relatively low, they can both obtain maximized profits without applying blockchain and the retailer does not have fairness concerns. However, as the cost of inducing blockchain and the product’s reduction in carbon emission increase, the optimal strategies for manufacturer and retailer begin to diverge, which may affect the stability of the supply chain. Full article

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. Full article

The present article aims to determine the green economic policies of an inventory model for non-instantaneous deteriorating items under practical scenarios. These scenarios involve specific maximum lifetimes for items with deteriorations controllable through investments in preservation technologies, which can affect the period without deterioration. Additionally, carbon is emitted due to energy-related costs, prompting retailers to invest in green technology investments to reduce carbon emissions concurrently under the carbon tax policy and the carbon cap-and-trade policy simultaneously. Meanwhile, when a retailer is required to make a prepayment, the purchase discount policy is contingent on the number of installments offered. This means that the retailer prepays off the entire purchasing cost with a single installment, thereby receiving a maximum percentage of price discount. Otherwise, the retailer prepays a certain fraction of the purchasing cost with multiple installments, and the percentage of the price discount will be contingent on the number of $n$ identical installments. In this context, we present theoretical results for optimal solutions, and a salient algorithm is presented, which is derived from these theoretical findings within a sustainable inventory system. To better illustrate the proposed mathematical problems, several numerical examples are presented, followed by sensitivity analysis for different scenarios. Full article

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy. Full article