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Carbon trading price is the core component of the carbon emission trading market. Accurate carbon trading price prediction is of great significance for forming an effective carbon trading market and achieving the "dual carbon" goal. Based on the carbon trading price data in China Emissions Exchange, this paper predicts the carbon trading price through the proposed hybrid model of SSA+LSTM. After comparing with the classical methods based on econometrics, machine learning and deep learning, it is found that the prediction model proposed in this paper achieves the optimal values of RMSE and MAE, and is a relatively effective carbon trading price prediction model. [ABSTRACT FROM AUTHOR]

• Taking carbon in RIES as energy flow, which is equivalent to electricity-heat-gas to ensure the carbon quota requirements of the system. • Proposing a new demand-side response mechanism to meet the purpose of reducing carbon emissions. • Scheduling RIES with comprehensive consideration of economy and low carbon. • Proposing a new RIES ladder carbon price trading mechanism to match the purpose of demand side management. Effective mitigation and reduction of carbon emissions is consistently underscored in Regional Integrated Energy Systems (RIES), aligning with the goals of carbon-neutralization. However, the inherent trade-off between carbon emissions reduction and economic operation is an obvious challenge not only in power systems but especially in the coupled electricity-gas-heat energy systems. This paper develops a bi-level low-carbon optimal scheduling model, highlighting reliable operation and the balance of supply and demand of RIESs with demand-response uncertainty. In this paper we firstly redefined the carbon emission of a RIES as a carbon flow and proposed a reward and punishment carbon trading mechanism based on the ladder carbon price. Secondly, we designed an incentive-based demand-side response policy within the subsystem encompassing electricity, gas, and heat. Concurrently, the probability density distribution functions of transferable, replaceable, and interruptible loads are computed separately. Based on the above, the deviation of the load reduction and the actual load is recalculated to delimit the constraint of the demand-side response. Finally, the mechanism of carbon trading and the demand-response is leveraged to address the economic scheduling incorporating operation cost, carbon transaction cost, imbalance costs of supply and demand, and load loss. Simulations demonstrated in the paper prove that the optimal solution with demand-response uncertain may effectively increase the economic performance and reduce carbon emissions of a RIES. [ABSTRACT FROM AUTHOR]

Combined heat and power is an inevitable trend in the development of waste-to-power industry. Many waste incineration power generation projects that have already put into operation in China are waiting for transition and upgrading to combined heat and power projects. From a growth option perspective, this paper considers the irreversible process and uncertainty in economic benefits, introduces carbon emission trading mechanism and develops the growth option theory to design the expansion decision making model of waste-to-energy combined heat and power projects, so as to obtain the project value and find the optimal expansion timing. In this paper, a case study is carried out on the expansion decision making of a waste-to-energy combined heat and power project in Shandong Province in China. And two scenarios with specific and flexible expansion times are developed from European and American options separately, as European options require investors to expand at a certain time but American options not. The results indicate that 1) real option approach successfully improves the project value through considering uncertainty, and American option can achieve the optimal expansion time, 2) under the carbon emission trading mechanism, the carbon price affects the expansion index, which has a positive effect on the project value, 3) the linear nature of the income and cost expansion index can help investors make judgments on expansion investments. The effectiveness of the case study implies the practicality of the expansion option method for similar projects to reach dual carbon targets in cost and benefit analysis. • Real option approach is applied in energy transition in WTECHP projects. • European and American options are proposed for certain and flexible decision times. • Expansion time and triggers of WTECHP are incorporated into life cycle management. • CCER is introduced as an environmental element to evaluate expansion decision making. [ABSTRACT FROM AUTHOR]

Carbon pricing is a crucial mechanism for achieving carbon peaking and carbon neutrality through the carbon trading market. Recently, the price of carbon emissions allowances in China's national carbon market has shown a gradual upward trend. The limitations on carbon emissions allowances have also become increasingly stringent. Power generators, which primarily rely on thermal power generation, have a substantial demand for carbon emissions allowances. The rising prices and stricter carbon emissions constraints have increased operational costs. Therefore, exploring the carbon pricing mechanism under more stringent carbon emissions allowance constraints is vital. It encourages thermal power generators to engage in carbon trading and reduce carbon emissions. This paper focuses on an energy trading platform supply chain comprising three power generators, one energy trading platform, and one power retailer. We consider stringent carbon emissions constraints and power generation rights trading. Through the analysis of three scenarios, the study explores optimal pricing decisions for carbon emissions allowances to facilitate carbon emission reduction and promote renewable energy generation and consumption. The findings indicate that, under certain conditions (i.e., the carbon quotas allocation coefficient b 1 ∈ [ 0.73 , 0.8 ]), scenario 3, which excludes generation rights trading, yields the highest optimal carbon price. The energy trading platform can adjust various commissions to influence the carbon price, as well as the price and quantity of generation rights trading. Introducing power generation rights trading proves beneficial in reducing carbon emissions (nearly 400 tons less than that in scenario 3 without generation rights trading, when the realized total power demand is around 8600 MWh) and fostering renewable power generation and consumption (the actual renewable power consumption weight is 3% higher than that without generation rights trading). When considering generation rights trading, the high-emission generator and renewable power generator achieve the highest power profits, while for the low-emission generator, the profit in this case is the second best among these scenarios. Moreover, by decreasing the coefficient of carbon quotas allocation and increasing the reduction rate simultaneously, the carbon cost will pass through to the electricity price more sufficiently (e.g., when b 1 = 0.76 , the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient. • Based on the energy platform supply chain, the optimal carbon price is explored. • Carbon cost pass-through is improved when the government sets harsher constraints. • The total carbon emissions will drop when considering generation rights trading. • Renewable energy power consumption can be promoted with generation rights trading. [ABSTRACT FROM AUTHOR]

This study investigates the carbon compensation mechanism as a tool to promote coordinated economic and environmental development in China's Jing-Jin-Ji region (Beijing, Tianjin, and Hebei). A novel approach for calculating carbon compensation cost that integrate emission responsibility as well as carbon abatement cost through a bi-level programming model is proposed. Shared producer and consumer responsibility is established for carbon emissions under the multi-regional input-output model. Marginal abatement costs of Beijing, Tianjin, and Hebei are further calculated through the input-output and linear programming method. Results highlight that the emission responsibility allocation method significantly affects the inter-regional carbon compensation amount. The production-based approach leads to a 77% reduction of carbon compensation amount from Beijing to Hebei, and a 170% overestimation from Tianjin to Hebei compared with the shared responsibility approach in 2060. The optimal carbon compensation unit cost ranges from 1539 to 6438 yuan/ton when the carbon reduction ratio ranges from 10% to 20%. Beijing would compensate Hebei 3.56 and 29.74 billion yuan for achieving 10% and 20% emission reduction, respectively. Similarly, Tianjin's compensation for Hebei would be 0.23 and 1.87 billion yuan in the two scenarios. Results further show that implementing the proposed carbon compensation mechanism can reduce total abatement cost by 43% and 84% in Jing-Jin-Ji under the two scenarios. The methods and results of this paper could help inform future carbon compensation implementation in China and other countries. • Develop a bi-level programming model to determine optimal carbon compensation cost. • Consider emission responsibility and carbon abatement cost in Jing-Jin-Ji. • Resulting mechanism reduces abatement cost by 43% for 10% emission reduction. • Production-based allocation underestimates carbon compensation amount by 62%. • Optimal carbon compensation unit cost changes with marginal abatement cost. [ABSTRACT FROM AUTHOR]

The EU has established the world's first cross-border emission-trading systems (ETS) for greenhouse gas (GHG) emissions, currently covering aviation, emission-intensive sectors, and electricity (EITE). The EU Commission has offered to apply emissions trading in new sectors where emissions from maritime transport will be incorporated into the current EU ETS, while a separated emissions trading system will cover emissions from road transport and the building sector. This paper employs a multi-regional multi-sectoral computable general equilibrium (CGE) model with two simultaneous international emission permit markets. After examining the abatement costs for the EU regions, various policy scenarios are implemented to study the welfare effects of forming an ETS covering the sectors other than EITE (NEIT) and its linking with the EITE sectors under two different baselines and four emission reduction targets. The results provide several important insights: (i) Marginal abatement costs in Germany and the Eastern European Union region (EEU) are significantly lower than in the rest of the EU regions. (ii) The carbon price in the emission permit market covering NEIT is significantly higher than the carbon price in the emission permit market covering EITE. (iii) Germany and EEU appear as notable suppliers of emission permits in both markets. (iv) There is a significant aggregate welfare gain under the scenario in which the ETS covering NEIT co-exists parallel with the ETS covering EITE. (v) The aggregate welfare in the EU under the full integration of EITE and NEIT may fall below its value under the scenario with two parallel emission permit markets. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

This paper is the first to investigate empirically the link between carbon prices and macro risks in China’s cap-and-trade pilot scheme. Using data from four pilot markets in Beijing, Guangdong, Hubei, and Shenzhen from 2014 to 2016, we demonstrate that the carbon price in Hubei is weakly linked to international prices of natural gas. Our results also indicate that energy, utilities, industrial and materials sector indices are positively related to the allowance prices in Shenzhen and Guangdong, suggesting that higher emitters in the region may have factored the carbon price into their production mix. We find no statistically significant relationship in the Beijing pilot. Overall, the findings suggest that China’s carbon market is currently in an early stage of development, as the carbon price fundamentals are weak and the markets are comparatively less efficient than the European trading scheme in an informational sense. The findings of the paper have policy implications for the upcoming integration of regional markets into the national carbon market. [ABSTRACT FROM AUTHOR]

This paper studies the carbon price fluctuation in China through the adaptive Fourier decomposition (AFD). Apart from the transient time-frequency distribution of the original AFD model, we also reconstruct the mono-components of this model to obtain the components in different time-frequency scales. Our empirical results based on the carbon price in Hubei Province demonstrate that there are three periods when the price fluctuates dramatically, mainly affected by the governmental policies about carbon emission and the development of clean energies, as well as the outbreak of COVID-19. Furthermore, the fluctuations of the price in the three identified periods are reflected in different scales. The comparison of the decomposition results and those of EMD and VMD shows that the AFD performs best in absorbing the price's useful information extracted through all these methods. [ABSTRACT FROM AUTHOR]

Equilibrium capacity expansion is an effective tool to help regulators predict the generation evolution of power systems by analyzing the decision-making of generation companies (Gencos). With the development of carbon markets (CMs) and electricity markets (EMs), Gencos' decisions in these two markets (encompassing investment and bidding decisions related to carbon emission allowances and electricity) are deeply intertwined through market clearing results. However, existing studies mainly focus on equilibrium capacity expansion with fixed carbon prices and usually ignore the realistic clearing process of CMs, which cannot simulate Gencos' coupled decision-making and accurately predict generation evolution under the CMs and EMs. This paper proposes a two-level equilibrium capacity expansion model for power systems which considers the coupled decisions of Gencos between CMs and EMs. At the upper level, multiple Gencos' coupled decision models are proposed based on their interaction with market clearing results. At the lower levels, CMs and EMs are respectively cleared to determine the varying carbon and electricity prices. Furthermore, the developed equilibrium capacity expansion model is reformulated as an equilibrium problem with equilibrium constraints (EPEC), which is solved by a diagonalization-based solution procedure. Finally, a provincial power system of southeast China is used to validate the performance of the proposed framework. The results illustrate that under CMs and EMs, non-fossil energy would account for 81.3% of total capacity and 73.88% of total generation in 2050. Compared to the existing model using fixed carbon prices, the consideration of realistic carbon market clearing will promote the installation of renewable energies and improve social welfare. The proposed model can provide beneficial references for regulators to perceive the evolution of power systems in CMs and EMs. • A two-level equilibrium capacity expansion model for power systems is proposed. • A novel coupled decision model for Gencos in CMs and EMs is developed. • The proposed model is solved by a diagonalization based solution procedure. • The results can guide the transition of power systems under the CMs and EMs. [ABSTRACT FROM AUTHOR]

This study assesses alternative energy technologies (i.e., PV and battery systems, electric heat pumps, hybrid gas heating with solar thermal energy) in terms of profitability and CO 2 emissions, for the case of two simulated typical households living in detached houses in Germany. Under the status-quo regulatory framework, the energy transition in the heating sector is fostered through grants for replacing old heating systems, whereas PV generation is fostered by feed-in tariffs and indirect subsidies for self-consumption. This study considers an alternative regulatory scenario with a more market-oriented approach, finding that a CO 2 -oriented reform of energy surcharges and taxes, as well as a reform of network charges, can support a more cost-efficient energy transition in the residential sector. The paper concludes with a discussion of the consistency, cost efficiency and effectiveness of past and current policies underpinning the energy transition in the residential sector. • Assessment of home energy systems across 2 households and 2 regulatory scenarios. • Consideration of PV, batteries, heat pumps and hybrid gas-solar heating systems. • Comparison of subsidies and flat energy tariffs vs. cost-reflective, dynamic tariffs. • Market-oriented approach incentivizes far more cost-efficient CO 2 emission reduction. • The German residential energy sector needs a consistent regulatory framework. [ABSTRACT FROM AUTHOR]

Carbon trading, one of the key policy instruments for reducing carbon emissions, can significantly contribute to the government's goal of achieving "peak carbon" and "carbon neutrality". Thus, under pressure from the government to implement carbon trading policies, enterprises often seek new low-carbon production technologies or business models. Meanwhile, outsourcing remanufacturing is often considered an effective means of promoting carbon reduction. Therefore, based on an outsourced remanufacturing model, we investigated the impact of carbon trading on the manufacturing/remanufacturing supply chain (SC) by constructing a game model involving an original equipment manufacturer (OEM) and an outsourced remanufacturer (OR) under decentralized versus centralized decision-making conditions. Furthermore, we designed a coordination mechanism to address the loss of benefits for the overall SC under decentralized decision-making conditions. We drew the following conclusions: (1) Whether in a decentralized or centralized decision-making model, there is a feasibility boundary condition when a government implements the carbon trading policy. Namely, the policy fails when the carbon trading price is greater than a certain threshold. (2) The decentralized decision-making approach reduces the sales of remanufactured goods and the recycling rate for used goods compared to the centralized decision-making paradigm, resulting in a marginal loss of profit. And, as the price of carbon trading rises, carbon trading regulations tend to expand the room for coordination of interests in the decentralized decision-making paradigm. (3) A single cost-sharing contract, while increasing revenues for OEM and SC, reduces revenues for OR. Profit-sharing contracts and fixed-cost contracts increase OEM and OR revenues, but overall SC profits are still lower than in the centralized decision-making model. Therefore, this paper combines revenue-sharing, cost-sharing, and fixed-fee outsourcing contracts to effectively achieve SC coordination. [ABSTRACT FROM AUTHOR]

In order to probe collaborative environmental governance (CEG) in China's "1 + N" policy framework for carbon peak and carbon neutrality, this paper applies the Actor-network theory (ANT) as an analytical framework to explore carbon reduction policy coordination. We then model ANT's relations with an evolutionary game theory (EGT) model to examine the evolutionarily stable strategies in CO 2 emission reduction by various actors including the central government, local governments, commercial banks, and polluting enterprises. Our objective is to examine policy coordination as the key enabler for governing success via an ANT-based mechanism design in China's institutional context. Combining the strength of ANT with a four-party EGT model, we investigate how to improve policy coordination to mobilize the actor-network for promoting CEG. Simulations are conducted to examine the effectiveness of policy coordination involving multiple policy instruments for controlling carbon emissions. The central government fulfills leadership in translating the interests of other actors and shaping the desired collaborative network. Without proper coordination, environmental policies may counteract each other. Government transfers and subsidies have direct impacts, but in the long run enhanced competitiveness and profitability of enterprises represent the intrinsic key to CEG success. From a methodology perspective, policy simulation in an EGT framework represents a novel addition to the research toolbox that expands ANT. [ABSTRACT FROM AUTHOR]

As an efficient technique to manage massive distributed energy resources (DER), the virtual power plant (VPP) can leverage the aggregated flexibility to enhance the economy and security of the system. However, few efforts have been reported on the carbon emission reduction benefit of the VPP. In this paper, a novel incentive mechanism for low-carbon demand response is designed, which is based on dynamic pricing and carbon intensity. Considering the uncertainty of renewable energy output characterized by the chance-constrained approach, a coordinated optimization model in the joint day-ahead energy, reserve and carbon market for the VPP operator and flexible consumers is proposed, including the pre-dispatch model, the energy storage systems (ESSs) incorporated carbon emission flow calculation model, and the bi-level final-dispatch model. Numerical simulations of both small-scale and large-scale cases demonstrate that the proposed low-carbon dispatch strategy can boost the payoff of the VPP operator, reduce carbon emissions in the joint energy-reserve-carbon market, and improve the robustness of the power system against uncertainty. • Low-carbon demand response (DR) to fully utilize the potential of the demand side. • Carbon emission flow model integrating energy storage systems. • Chance constrained approach to compensate for the uncertainties based on the versatile distribution. • The VPP engages in the joint energy-reserve-carbon market considering renewable energy uncertainties. [ABSTRACT FROM AUTHOR]

Due to the proliferation of distributed renewable energy, bidirectional electricity and carbon interaction appears in the coordinated transmission and distribution network and increases complexity in entities' decision-making. To address the issues, this paper develops a hierarchical market framework integrated with electricity and carbon trading. Specifically, an electricity market which combines locational marginal price (LMP) and peer-to-peer (P2P) trading is established considering transmission and distribution coordination. Meanwhile, based on carbon emission flow and P2P trading, a hierarchical coordinated carbon market is also designed to precisely measure and price carbon responsibility and contribution from prosumers. In addition, a hierarchical distributed algorithm is developed to clear the proposed market with privacy protection. Numerical results authenticate the necessity for transmission and distribution coordination, benefits of distribution electricity P2P trading as well as superiority in carbon measurement and pricing method. • An LMP-based electricity P2P market is designed for transmission and distribution network. • A systematic method is proposed to precisely price prosumers' carbon responsibility and contribution. • A hierarchical distributed algorithm is proposed to clear the P2P market under TSO-DSO-prosumer coordination. [ABSTRACT FROM AUTHOR]

Hydrogen produced from renewables-fuelled electrolysis has the potential to provide deep decarbonisation for sectors that are the most difficult to decarbonise, but the intermittency of renewable sources may result in low electrolyser utilisation and, consequently, high costs. This study determines the greenhouse gas (GHG) emissions and levelised cost of hydrogen (LCOH) associated with blending wind electricity with the grid to increase electrolyser utilisation. A novel cost model was developed to determine the production performance, costs and embodied CO 2 emissions of an electrolyser with hybrid fuel supply (wind and grid) considering dynamic load constraints of alkaline (ALK) and Proton exchange membrane (PEM) electrolysers and using high-resolution historical weather data to determine wind production profiles in two case study regions: northern (Bayannur) and southern (Pingtan) China. The results show that using 52% of grid electricity in an ALK electrolyser in Bayannur reduces LCOH by 11%, increasing hydrogen production by 74% but with 17.4 kg CO 2 / kg H 2 generated in CO 2 emissions. When a carbon price is accounted for, the optimal electrolyser utilisation is reduced to 40%, with the LCE 44% reduced. Emissions of 9.8 kg CO 2 /kg H 2 are lower than for stream methane reforming with carbon capture. In Southern China (Pingtan), LCOH is higher than Bayanur because of the high electricity cost. The economic benefits of increasing grid power depend primarily on the total CAPEX cost, grid power cost, and carbon tax. By altering the carbon tax, the optimal blend of wind/ grid differs. The grid contribution to the optimal blend diminishes as a carbon tax approaches 35 USD/ t CO 2. • This paper analyses the value of adding grid electricity to a pure wind-powered H 2 production plant. • The LCOH and CO 2 life-cycle emissions are obtained for Alkaline and PEM electrolysers. • We are introducing the novel ICOH metric to optimise the trade-off of LCOH and CO 2 emission on H 2 production. • Using only 52% of grid electricity in an ALK electrolyser, H 2 production can increase by 75%. [ABSTRACT FROM AUTHOR]

As an important means to reduce CO 2 emissions from coal-fired power plants, CCUS project has received more and more attention. In this paper, the comprehensive benefits of CCUS project in coal-fired power plants are defined as economic benefits, environmental benefits and energy benefits, and comprehensive evaluation and prediction analysis of CCUS project in coal-fired power plants are made from these three aspects. The goodness-of-fit of the constructed comprehensive evaluation model can reach 0.99, and the ratio of economic, environmental and energy benefits is about 5: 9: 1. Among them, the best scenario of policy influence, economic development and technological progress can promote CCUS project with benefits of 6.51%, 6.56% and 13.07% respectively. Through case analysis, it can be calculated that when the service life of CCUS project reaches 21.7 years, the investment cost can be recovered from the economic benefits. In the prediction model, the goodness of fit of the economic benefit prediction model is up to 0.95, in which the carbon capture cost of CCUS technology, operation and maintenance cost and carbon price fluctuation play a major role. The goodness of fit of the environmental benefit prediction model can reach 0.90, in which the carbon capture capacity of CCUS project and the world environmental situation are the main influencing factors, and the goodness of fit of the energy benefit prediction model can reach up to 0.95, which is mainly affected by the energy consumption level of CCUS project. The research results show that CCUS project of coal-fired power plants at home and abroad has a good development prospect and remarkable comprehensive benefits. This paper puts forward relevant policy suggestions that are beneficial to the development of CCUS project in coal-fired power plants, with a view to providing empirical evidence for the evaluation and decision-making of CCUS project in coal-fired power plants. • A comprehensive evaluation and prediction system of CCUS projects in coal-fired power plants is established. • In this paper, neural network is used for benefit accounting and evaluation. Meanwhile, machine learning is used for benefit prediction, with a good degree of fitting and high accuracy of the model.Neural network and machine learning are used to forecast and estimate the benefits. • The analysis method is universal and can be used to study CCUS projects in other industries. [ABSTRACT FROM AUTHOR]

The sustained growth of global aviation emissions has highlighted the urgent need for using Sustainable Aviation Fuels (SAFs) to replace traditional aviation fuels to mitigate emissions. SAFs can potentially reduce greenhouse gas emissions by 50%–90% compared to conventional jet fuels. This paper collected aviation emissions data from South American routes between 2019 and 2021, and the GWP100 method was used to convert CH 4 and N 2 O emissions into CO 2 equivalents. Additionally, emissions from the South American route between 2023 and 2070 were predicted, as well as the amount of subsidy required to promote the use of SAFs under four different scenarios. The result shows that adopting SAFs could reduce approximately 69.3 million to 113.53 million tons of CO 2 -equivalent emissions in South America by 2070. However, the South American aviation industry will likely embrace using SAFs only if the carbon trading price exceeds 0.273 times the difference between SAFs and jet fuel prices. • This study calculates the emission reduction effects under four scenarios. • The GWP100 method is used to convert non-CO 2 emissions into CO 2 equivalent emissions. • SAFs can reduce 69.3–113.53 million tons of CO 2 -equivalent emissions by 2070. • Using SAFs is profitable when carbon price exceeds 0.273 times SAFs-kerosene price gap. • Using SAFs earlier will increase subsidies by $3.65–4.56 billion every five years. [ABSTRACT FROM AUTHOR]

As the financialization of carbon emission right, developing effective carbon asset trading strategy is important for both investors and regulators. Traditional trading strategy based on technical analysis is hardly profitable due to high complexity inherent in carbon markets. In order to increase investment returns and reduce risk in carbon market, this paper introduces PSO-VMD-DRL, an innovative carbon asset trading strategy that integrates signal decomposition technology and deep reinforcement learning (DRL). Firstly, the carbon price is decomposed into multiple components by variational mode decomposition (VMD) optimized through particle swarm optimization (PSO) to extract frequency features and reduce noise. Then state representation of the environment (state space) is constructed based on the components and transaction data. Finally, deep reinforcement learning (PPO, A2C, DDPG) are performed separately based on state space to learn optimal trading strategy. Empirical studies based on the data from carbon markets of Hubei and Guangzhou validate that PSO-VMD-DRL can adapt to carbon asset with the better profitability and risk resistance. The PSO-VMD-DRL outperform the other comparison strategies with superiority in all evaluation indices, achieving annual return of 21.39%, 17.67%, 25.50% and 46.34%, 44.55%, 43.03% in carbon market of Hubei and Guangzhou. [ABSTRACT FROM AUTHOR]

Under the background that China will achieve the goal of carbon neutrality by 2060, analyzing the impact of carbon neutral target constraints on the welfare of urban and rural residents is an important part of the design of a carbon emission reduction policy mechanism, in this paper, the dynamic recursive CGE(Computable general equilibrium) model is used to simulate the changes in welfare level, income consumption, and CO2 emissions of residents and their urban-rural heterogeneity under the policy of imposing a carbon tax on the fossil energy industry from 2022 to 2060. The results show that: compared with the BAU scenario, by 2060, first the income and consumption of residents will decrease slightly. Second, the target constraint of carbon neutrality makes the equivalent variation and compensate variation of urban and rural residents' welfare decrease slightly, and the equivalent variation and compensate variation of urban residents decrease more than those of rural residents. The following conclusions are drawn: first, the carbon-neutral target constraint makes the welfare of urban and rural residents decline slightly, but it will not have a strong impact on residents' income and consumption. Second, this constraint has a greater impact on the income, consumption and savings of urban residents than those of rural residents. [Display omitted] • A five-layer nested dynamic recursive CGE model is established to analyze residents' welfare. • Carbon taxes are incorporated into production decisions as a policy cost for energy companies. • The heterogeneity of the impact of carbon tax policy constraints on the welfare of urban and rural residents was analyzed. • The government needs to improve the intensive and diversified energy supply in rural areas to reduce the urban-rural gap. [ABSTRACT FROM AUTHOR]

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing. • A carbon-embedded multilateral energy interaction mechanism in a PIES is discussed. • A time-varying carbon emission measurement model is established. • An energy-carbon integrated pricing strategy based on the dynamic carbon factor and virtual carbon emission flow is designed. • The impacts of energy-carbon integrated pricing on the scheduling of the PIES and carbon emission are investigated. [ABSTRACT FROM AUTHOR]

Sustainable Aviation Fuels (SAFs) are considered one of the potential ways for airlines to reduce emissions. However, African airlines may need more support using SAFs in underdeveloped regions. This paper calculates the carbon emissions from the international routes in Africa from 2019 to 2021 through the Modified Fuel Percentage Method (MFPM) and the ICAO standard method. The error rate is about 4.10%. Then, we predict the carbon emissions for each route and airline under different epidemic recovery paths and analyze the potential subsidies, the impact of SAFs price adjustment, and carbon trading price changes. It can be found that the more extensive the overall carbon emission base, the higher the cost of using SAFs. In addition, the higher the SAFs mixing ratio, the more the cost will increase. The average subsidy to promote SAFs will be 3492.82–3784.74 million dollars at a 50% mixed ratio. By 2025, if only the carbon price is not lower than 12.84 times the current price, or the SAF price is 1.412 times lower than the current aviation kerosene price, the African airlines may promote the SAFs. • Cost-benefit analysis of Sustainable Aviation Fuels (SAFs) in Africa aviation industry is done. • The average subsidy to promote SAFs will be 3492.82–3784.74 million dollars at a 50% mixed ratio. • Only the carbon price is not lower than 12.84 times, or the SAF price is 1.412 times lower, the African airlines may promote the SAFs. [ABSTRACT FROM AUTHOR]

Towards the target of carbon neutrality, the need for deep decarbonization in iron and steel industry is increasingly recognized. Existing studies rarely mention the effects of collaboration with its source industries for carbon emission reduction in ISI. Furthermore, the fundamental laws of CO 2 emission reduction capacity with cost change of source substitutions for various steel production routes under market economics are unclear. To avoid the blind substitution of clean sources for meeting the urgent requirements for decarbonization in China's steel industry, which may result in increased carbon emissions and uneconomical carbon reduction from a lifecycle perspective. This paper integrates the production and utilization processes of source resources into the steel production process, establishes coupling models of carbon and value flows for four main types of steel production routes, and aims to quantitatively analyze the specific CO 2 emission reduction potential and costs. Three economic market development scenarios from 2021 to 2060 are set, and the marginal carbon prices of various source substitutions are determined. Subsequently, decarbonization pathways with the goal of minimizing production costs are developed. Finally. The future resource demand and the carbon emission reduction contributions of source industries collaborated are predicted. The results show that (1) about 81.7–89.9 % of CO 2 emissions could be reduced in the scenarios; (2) the demands of electric power and hydrogen energy will reach 54.48–511.90 BkWh and 7.47–10.31 Gt in 2060, respectively; and (3) collaboration with source industries could make 18.3–18.3% of CO 2 emission reduction contributions. All in all, this study could provide scientific support for the low-carbon transformation of China's steel industry. • Carbon and value flows of BF-BOF, Scrap-EAF, DRI-EAF, MOE-EAF routes are analyzed. • CO 2 emission reduction potential with marginal carbon price is determined. • Decarbonization paths during 2021–2060 for China's steel industry are developed. • Quantify the contribution of collaborative carbon reduction with source industries. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of carbon pricing policies on firms' greenhouse gas (GHG) emissions. We include two carbon pricing policies: carbon tax and emission trading scheme (ETS) at national and sub-national (province) levels, measured as policy existence (binary variable) and carbon price (continuous variable). Firms' GHG emissions are measured as total emissions and emission intensity. Using unique recent firm-level data from 2010 to 2021 from three East Asia countries (Japan, the Republic of Korea (ROK), and the People's Republic of China (PRC)), we obtain the following key findings. First, the national carbon pricing policy significantly reduced firms' GHG emissions and GHG emission intensity in Japan and the ROK. The greater the carbon price, the greater the impact. Second, the subnational carbon pricing policies implemented in two provinces of Japan (Saitama and Tokyo) decreased firms' GHG emissions. Third, heavy industry sectors such as energy, utilities, and industrials experienced a substantial decrease in GHG emission intensity due to the carbon pricing policy. Finally, our main results are robust to different model specifications and various measures of firms' GHG emissions. Although literature studying the ex-post impact of carbon pricing on emissions at country and sector-level is abundant, this paper contributes to the existing literature providing empirical evidence of the impact of carbon pricing policies on firms' emissions using firm-level data, which is highly limited especially in Asia. [ABSTRACT FROM AUTHOR]

This study examines the predictive impact of weather conditions and electricity demand on hourly spot prices of emissions allowances during the first three phases of the European Union Emissions Trading System (EU ETS) (2005–2019). We propose an original methodology for constructing European-scale electricity demand and weather indices and characterize the relationship between those indices and emissions allowances prices by means of an advanced predictive modeling technique (Extreme Gradient Boosting). Empirical findings assert that electricity demand and the weather variables under study were of importance for estimating EUA prices during the first three phases of the EU ETS, with air temperature and electricity demand being most relevant to emissions allowances prices. Conversely, total precipitation and relative humidity proved to be the least relevant variables to the outcome. The results also indicate that the relationship between emissions allowances prices and their weather-related predictors was not linear in the studied period. The paper contributes to the growing body of literature on the structural determinants of carbon prices in the EU ETS and enhances our understanding of the impact of climate and weather variability – in the provision of renewable energy production – on the most prominent market-based measure to reduce CO 2 emissions in Europe. [ABSTRACT FROM AUTHOR]

• A Stackelberg game model is developed to coordinate the energy transactions between the operator and MVPP. • A dynamic update algorithm of sampling points to correct the Kriging metamodel is proposed. • The Stackelberg game strategy can significantly decrease the operation costs and fully exploit the carbon reduction potential of MVPP. • A dual-compensated DR mechanism can provide a better motivation to reduce carbon emissions. With the improvement of the electricity-heat-carbon trading mechanism, it has been a trend for multiple virtual power plants (MVPP) to participate in the market competition. Firstly, in order to mitigate the burden of environmental pollution and balance the conflicting market interests of MVPP, this paper proposes a Stackelberg game strategy for the operator and MVPP considering a ladder-type carbon price mechanism and a dual-compensated demand response (DR) mechanism, which consists of an optimal pricing strategy for the operator and an optimal energy scheduling strategy for each VPP. During the game process, the operator serves as the leader, guiding the energy transactions of MVPP through price optimization, while each VPP acts as the follower, optimizing the internal aggregated units and loads based on the price issued. To solve the Stackelberg game equilibrium, a Kriging metamodel is then adopted to fit the energy scheduling model of the VPP. Finally, several cases are analyzed to verify that the proposed strategy can effectively contribute to the energy complementation and carbon reduction of MVPP. [ABSTRACT FROM AUTHOR]

Carbon emission trading mechanism is an important market-based instrument to address global climate change. The auction ratio and price of carbon emission allowances (CEA) are cores of the carbon emission trading mechanism. What combination of CEA auction ratio and auction price is most conducive to carbon emission reduction for emission control enterprises? There are still relatively few studies conducted on this issue, and there is no clear answer from the views of theory and practice. In this paper, the AIM/Enduse model was used to analyze the changes and differences of installed power capacity, power supply, energy consumption and carbon emissions in the Guangdong-Hong Kong-Macao Great Bay Area (GBA) with different combinations of CEA auction ratios and prices. Moreover, the best pathway of energy transition was explored in the power industry of the GBA, and the best combination of CEA auction ratios and prices was identified in the GBA. The results show that: (1)The carbon emission constraint starts to work only when the auction ratio of CEA is increased to 50% and the auction price is increased to 60 CNY/ton (Scenario p 3). When the auction ratio of CEA is 100% and the auction price reaches 120 CNY/ton (Scenario p 5), a low-carbon transition of the power sector in the GBA can be achieved. (2) Under scenario p 5 , the total cost of power transition rises and then falls. And the cost of carbon in the power sector increases exponentially as the auction ratio of CEA and the auction price rise. However, the growth in the cost of power supply is relatively flat, which shows that appropriate carbon trading mechanism can improve the economic benefits of power sectors. With the increase of installed capacity of clean low-carbon energy generation and power supply, as well as the decrease of local power cost in the GBA, the proportion of outsourcing power can be appropriately reduced in the future, with an aim to reduce the external dependence of electricity to ensure the security of energy supply. In the end, it suggests that cities in the GBA should give full play to their regional advantages and establish a unified carbon emission trading market in the GBA as soon as possible. This study provides a theoretical basis for the supplementation and improvement of China's carbon emission trading mechanism. • What is the best combinations of CEA auction ratios and auction prices in the GBA? This paper gives a clear answer. • The best transition pathway of the power industry under technological progress and carbon trading policy was suggested. • With the decrease of local power cost, the proportion of outsourcing power can be appropriately reduced in the future. • Provides a theoretical basis for the supplementation and improvement of China's carbon emission trading mechanism. [ABSTRACT FROM AUTHOR]

This paper presents an energy-sharing market coupled with carbon emission trading to quantify peer-to-peer energy-sharing markets' economic and environmental influences. We establish a bi-level peer-to-peer energy sharing market considering the interaction between energy prosumers and the system operator. An interaction algorithm and generalized Nash game are thus applied in this work. With the simulation results, we find that the daily disutility of provincial systems is reduced by about 7 ¥ when time-of-use electricity rates are applied. This pricing strategy can encourage prosumers to participate in energy-sharing transactions. The subsidies of electricity rates, carbon prices, and renewable energy investments can improve the social welfare of the whole system. Considering the development of power generation technology and emission reduction targets, the change of optimization parameters in the next 30 years is analyzed in the example. It can be concluded that the total CO2 emission in 2050 will be reduced to almost half of that in 2020 in the energy-sharing markets. The subsidy of renewable energy generation and stricter CO2 emission constraints may accelerate the reduction of carbon emissions. [Display omitted] • It is the first time to assess the environmental effect of peer-to-peer energy trading with actual data. • We analyze the peer-to-peer energy trading impacts on the carbon emission amount in China. • We compare the market participants' revenue and CO2 emission considering the different scenarios. • We develop a bi-level coupled emission right trading market to verify whether total emission is reduced. • The development of technologies and various energy structures are considered quantitatively. [ABSTRACT FROM AUTHOR]

Strict climate goals require a rapid reduction of carbon emissions across society. It is a considerable challenge to design decarbonization pathways considering co-operation in energy and carbon emissions across multiple sectors simultaneously. In order to obtain the decarbonization pathway for the whole society, this paper establishes a long-term multi-sector low-carbon transition pathway planning model. Besides inter-sectoral energy supply and demand, this study fully considers the allocation of carbon emissions quotas among different sectors by modified-grandfathering method, which takes into account both equity and efficiency principles. The decarbonization targets of different sectors, corresponding mitigation pathways and energy consumption can be calculated. China is taken as a case study. Results show that China's total energy consumption peaks at 5 billion tces around 2030, then falls below 4 billion in 2060, with the proportion of primary electricity and other clean energy reaching over 50 percent. Besides, the realisation of carbon neutralisation in China depends heavily on the improvement of carbon capture and storage (CCS) and end-use electrification rate. Moreover, the study of different quota allocation ways shows that the increase in the contribution rate of power system mitigation leads to the reduction of the total low-carbon transition costs, which also aggravates administrative difficulties. • A multi-sector model is set up to plan the long-term low-carbon transition pathway. • Modified-grandfathering allocation method can consider multiple indicator factors. • The clean energy consumption of China accounts for about 55% in 2060. • The end-use electrification rate in China is close to 70% in 2060. • The transition pathway with lower cost increases administrative difficulties. [ABSTRACT FROM AUTHOR]

• Gauging the carbon shadow prices across the world. • Nonparametric meta-frontier is used. • OECD, ASEAN, and BRICS countries are treated as groups. • Increasing carbon shadow prices are observed for 1991–2017. • The group-wise differences are evident. Unintended outputs, as non-market goods, may have no exchange value and require shadow pricing to investigate the abatement costs. Unlike previous research, which assumes homogeneous production technology for decision-making units in the shadow price analysis, this paper considers group and global production frontiers. The ratio of group to global shadow prices is derived to indicate the position with respect to the tangent point (i.e., equal shadow prices) of the group and global technologies. This departs from the conventional technology gap approach. The nonparametric meta-frontier approach is used to evaluate the shadow price of carbon emission for several groups of countries (OECD, ASEAN, BRICS) that are likely to face homogenous technologies. The results show that carbon shadow prices for the countries differ across group and non-meta technologies. Therefore, the traditional approach towards shadow pricing may render a biased economic interpretation. This paper also compares the inefficiency scores with and without considering carbon emissions in countries. Therefore, the role of the carbon emission is assessed from different perspectives. [ABSTRACT FROM AUTHOR]

China has implemented a national carbon emission trading scheme (Carbon ETS) to mitigate climate change and has set a goal to reach the peak of carbon emissions by 2030. The potential performance of the carbon tax as a supplement to the carbon ETS may contribute to achieving the carbon peaking target by reducing income inequality among the various labour forces. Nevertheless, carbon pricing places a greater burden on lower-income households than on wealthier households. In this paper, we construct a multisectoral dynamic computable general equilibrium (CGE) model and incorporate the carbon ETS and carbon tax modules with 20 labour income distributions (ID-CGE model). The results indicate that the carbon pricing mechanism has a progressive effect on income distribution and a heterogeneous effect on the income of rural and urban workers. In rural areas, the middle- and high-income labours have witnessed a higher loss in income. In contrast, in urban areas, the low-income labours have experienced a greater decrease in income. Combining carbon tax and ETS policies to achieve carbon peaking goals by 2029 is a superior strategy. • This study introduces carbon ETS and tax modules with income distribution. • The dynamic ID-CGE model analyzes the heterogeneous effect of carbon pricing on 20 different income groups in China. • Results show that achieving carbon peaking goals by 2029 is the superior strategy. [ABSTRACT FROM AUTHOR]

Curbing carbon emissions has been a hot topic of public concern around the world. Although the Cap-and-Trade (C&T) system has excellent environmental performance, its effectiveness in achieving low-carbon emissions objectives is not enough. Practices show that carbon emission reduction technology (CERT) investment by micro-manufacturers can make up this limitation. In the context of C&T system, this paper develops a dual-objective non-linear programing model of CERT investment strategies based on matching game between the oligopolistic manufacturers (oligo-manufacturers) and suppliers. Maximum production capacity (MPC) of oligo-manufacturer is considered. A matching game mechanism of bilateral stakeholders is proposed with consideration of vertical and horizontal aspects. From the vertical aspect, there is a matching analysis between a finite number of oligo-manufacturers (offering investment funds) on one side and a finite number of suppliers (providing CERTs) on the other side. From the horizontal aspect, there is Cournot game among the oligo-manufacturers. The model proposed in this paper enriches the literature on the selection process of CERT investment strategies. The impacts of oligo-manufacturer amount, green investment coefficient and carbon price on the investment-production decision-makings are finally analyzed by numerical methods. The results show that stable matching outcomes (i.e., the optimal joint production quantity, CERT investment intensity, emission reduction and emission trading quantity) can be obtained by constructing the Lagrange function and solving Karush-Kuhn-Tucker (KKT) conditions. This means that the matching game mechanism helps optimize the CERT investment and production mode. In addition, intense competition among oligo-manufacturers is conducive to reducing carbon emissions. A higher carbon price leads to a higher economic profit, and a higher green investment coefficient leads to a lower carbon emission objective. MPC acts as a drag on development of the oligo-manufacturer, which can be used to improve the profit structure of stakeholders within a certain scope. • A dual-objective nonlinear programing model of CERT investment strategy based on matching game in a supply chain is proposed. • A matching analysis between multiple oligo-manufacturers and multiple suppliers is conducted from the vertical aspect. • A Cournot game among the oligo-manufacturers is analyzed from the horizontal aspect. • Intense competition among oligo-manufacturers is conducive to reducing carbon emissions. • Maximum production capacity can be used to improve the profit structure of stakeholders within a certain scope. [ABSTRACT FROM AUTHOR]

The paper presents a cleaner production and distribution model for global facility network that offers reverse logistics services under the warranty period. Hybrid manufacturing and storage facilities with flexible capacity are considered to save the extra setup and maintenance cost linked with establishing new reverse logistics facilities. The carbon emissions are accounted for the various activities related to product manufacturing and return. The carbon cap and trade policy is considered for regulating emissions. The dynamic mixed-integer non-linear programming (MINLP) model discussed in the paper optimizes the total cost of the network including costs associated with reverse logistics. The model is analysed on two hypothetical data sets. The results for the smaller data set reveal that the inventory holding is a better alternative to capacity expansion to deal with fluctuations in market demand. However, for the bigger data set, capacity expansion is recommended to satisfy the increased demand instead of relying completely on inventory management. The results are further analysed for varying carbon price and it is observed that increase in carbon price initially causes an increase in the carbon emissions but later it starts decreasing. This indicates that the increase in carbon price does not provide quick results and is more effective in controlling the emissions in the longer run. Furthermore, the proposed model investigates how different trade policies viz. free trade agreement (FTA), FTA with cap, and an increase in tariff barriers influence the total carbon emissions and the total cost. The results indicate that the total cost and the total emissions are relatively less under FTA with cap, however, there is a slight increase in the emissions of one country. [ABSTRACT FROM AUTHOR]

The regulatory framework influences households' decisions in the context of the energy transition, affecting the potential for CO 2 emissions savings and the operation of the electrical network infrastructure. In this paper, the profitability and optimal operation of alternative home energy systems (HESs) consisting of photovoltaics (PV), battery energy storage (BES), and either a gas condensing boiler (GB) or an electrical air-to-water heat pump (HP) is investigated for the case of a German single-family house and across alternative regulatory scenarios. Two policy reforms are considered: (i) an alternative design of network tariffs, the objectives of which are the financial sustainability and the efficient operation of the power grid, as well as the cost reflectivity of such charges; and (ii) a CO 2 -oriented reform of energy taxes and surcharges on retail energy prices. For the latter, the real-time carbon intensity of grid electricity is estimated and priced in dynamic retail electricity rates. After the optimization of the operation of each alternative HES under such alternative sets of price signals, a simulation over a 20-year planning horizon is carried out in order to evaluate each option in terms of profitability, impact on CO 2 emissions and grid integration. The findings show how a change of regulatory framework can foster a low-carbon-oriented and grid-friendly adoption and operation of energy technologies. In the case under analysis, a regulatory shift: (i) results in a decrease of up to 17% in the discounted lifetime costs of the HP heating, thereby steering the household's adoption decision towards a reduction of up to 63% in CO 2 emissions; (ii) induces a grid-oriented operation of the HP and the BES, reducing coincident peak demand by up to 62%. The implications of such a regulatory shift are discussed in relation to the effectiveness, cost efficiency and distributional fairness of the energy transition in the residential sector. • Assessment of home energy systems under alternative regulatory scenarios. • Consideration of photovoltaics, battery storage, heat pumps and gas boilers. • Shift from flat, volumetric network charges to dynamic, capacity-based charges. • Dynamic power prices reflecting real-time carbon intensity of grid electricity. • A regulatory shift may be the key to the decarbonization of the residential sector. [ABSTRACT FROM AUTHOR]

Emission trading scheme (ETS) is considered as a tool to reduce emissions at a low cost, which can effectively promote the carbon peak and carbon neutrality. As the main participants in the carbon emission trading (CET) market, the behavior and strategies for governments and different types of enterprises affect the final emission reduction performance. Taking the national carbon market as an example, this paper constructs a tripartite evolutionary game model including governments and power generation enterprises with different emission reduction capacity to analyze the possible game situations in the development process of the CET market. The results show that with the development of the CET market, the governments and power generation enterprises will have different game results. In order to achieve the optimal stable equilibrium and maximize carbon emission reduction, governments should improve regulatory efficiency and formulate appropriate incentive strategies for excess emission reduction and punishment strategies for insufficient emission reduction. In addition, the initial willingness will not affect the final strategy choice of the three parties. Reasonable free carbon quota ratio, lower technology emission reduction cost and higher carbon price can stimulate the enthusiasm of governments and enterprises to reduce emissions. The above results provide a reference for coordinating the relationships between governments and power generation enterprises with different emission reduction capacity in the construction of the CET market in the future. [ABSTRACT FROM AUTHOR]

A new logistics mode has emerged in online retailing, which ships products before customers place orders online to reduce the waiting time for customers to receive them. The existing research on this logistics mode mainly focuses on the shipping quantity (i.e., the quantity of products to be shipped in advance from the order fulfillment center to the distribution center nearby customers before the order is placed online), while the product pricing problem and carbon emissions have not been studied comprehensively. Moreover, the risk attitudes of decision-makers have been ignored in the studies related to this new logistics mode. This paper builds an extended newsvendor model to explore the optimal joint decision problem of shipping quantity and product pricing under this new logistics mode and analyzes the carbon emissions of this new logistics mode compared with the traditional logistics mode. We consider decision-makers to be risk averse and take Conditional Value-at-Risk (CVaR) as the risk measure criterion. For both multiplicative and additive demand models, we obtain the closed-form solution of the optimal strategy. The differences between the optimal strategies of risk-averse and risk-neutral decision-makers and between the optimal strategies of this logistics mode and the traditional one have also been explored. Based on the optimal shipping quantity and product pricing strategies, we analyze the carbon emission variation of this logistics mode compared with the traditional one and provide sufficient conditions for reducing carbon emissions. Numerical experiments verify the theoretical conclusions and analyze the effect of some parameters on the optimal strategy, profit and carbon emission variation. The results show that online retailers should reduce product price in this new logistics mode compared with the traditional one. The risk-aversion degree slightly affects the optimal price but significantly affects the shipping quantity. Moreover, implementing this new logistics mode contributes to reducing carbon emissions compared with the traditional logistics mode in most cases. This study helps support online retailers in developing shipping quantity and product pricing strategies under this new logistics mode and helps the Environmental Protection Association to understand the carbon emissions under this new logistics mode so that relevant strategies can be developed. • We explore the shipping quantity, pricing and carbon emission in a new logistics mode. • We have considered the risk attitude, which has been ignored in the relevant studies. • We find the price should be reduced in the new mode compared with the traditional one. • We find the risk aversion degree significantly affects the optimal shipping quantity. • We find that implementing the new mode helps to reduce carbon emissions in most cases. [ABSTRACT FROM AUTHOR]

Sharing resources and curbing emissions are critical for promoting the adoption of cleaner logistics technologies and improving environmental sustainability. However, little research has examined reducing the impact of carbon emissions on the environment by considering transfer between depots. Hence, this study investigated the impact of carbon emissions on a multi-depot collaborative pickup and delivery network with time windows and transfers (MDCPDPTW-Ts) and proposed a carbon emissions measurement to describe the relationship between fuel consumption and carbon emissions. Then, a two-stage heuristic framework was proposed to simplify MDCPDPTW-Ts and generate high-quality delivery routes. Finally, a case study from Dalian, China, was used to verify the effectiveness of the research methodology. The results demonstrated the following: (1) The multi-depot transfer network constructed in this paper is significantly better than the traditional no-transfer scheme, reducing the cost by 26.64%. (2) Sharing transfer facilities among different depots is the most attractive mode of realizing clean logistics, reducing the emission cost by 59.2% compared with the traditional depot-based transfer scheme. (3) Reasonable carbon prices and carbon quotas are highly significant to establishing a sustainable carbon trading market. As carbon prices increase, carbon emissions first show a sharp reduction trend and then gradually tend to remain unchanged. Therefore, this work can provide practical and theoretical implications for the sustainable development of multi-depot collaborative logistics. [Display omitted] • Transfer locations reduce complexity of the multi-depot collaboration system. • Adaptive iterated local search improves quality of solutions. • Minimizing transportation cost simultaneously reduces fuel and carbon emissions. • The proposed transfer scheme has lower emissions than the traditional scheme. • Carbon quotas and prices to conserve energy and curb emissions are discussed. [ABSTRACT FROM AUTHOR]

• The influencing factors of carbon prices in China's carbon market are analysed. • The influencing factors and historical data are combined to predict the carbon price. • The EEMD-BP-ELM model and the CHINAGEM-E model are constructed and combined. Carbon price is one of the core indicators of the carbon market. Making accurate predictions of carbon prices based on insight into the volatility characteristics of the carbon market will not only provide a scientific basis for investors and regulators to make decisions and greatly promote emission reduction, but also effectively promote the healthy development of the carbon financial market. Based on the historical carbon price data of the national carbon market, this paper constructs the EEMD-BP-ELM model to predict the high and low frequency components of China's future carbon price. On this basis, according to China's goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, the CHINAGEM-E model is used to predict the trend component of the carbon price under the baseline and the 2060 carbon neutral scenarios. Finally, the trend component is combined with the high and low frequency components to obtain the price range from 2022 to 2060. We find that under the baseline scenario, China's carbon price range in 2060 is [343, 785] CNY/tCO2, while under the 2060 carbon neutral scenario, this price range is [1543, 3531] CNY/tCO2. [ABSTRACT FROM AUTHOR]

Accurate carbon price forecasting is essential to reduce carbon dioxide emissions and slow down global warming. However, a key issue in the carbon trading market is the diversity and uncertainty of external factors. Some studies began to focus on the impact of a single external factor, but few of them considered the application of multi-source information on carbon prices. In addition, the selection of the decomposition method is still controversial, making carbon price forecasting inefficient and unstable. Therefore, this paper proposes a carbon price forecasting method based on multi-source information fusion (MSIF) and hybrid multi-scale decomposition (HMSD). First, MSIF can provide complete, interactive, and timely information for raw carbon prices, including historical data, influencing factors (coal prices, oil prices), and unstructured data (Baidu index, social media sentiment). Second, HMSD is used to completely extract the internal features of multi-source information and avoid the problem of decomposition method selection. Third, due to the linear and nonlinear characteristics of carbon prices, a combination strategy based on Holt, ARIMA, SVR, BPNN, and LSTM can achieve satisfactory results. Finally, to evaluate the effectiveness of the proposed framework, seven types of comparative experiments (based on historical data, influencing factors, Baidu index, and sentiment analysis) are carried out. The results show that MSIF is superior to single-source information in improving carbon price forecasting performance. Furthermore, the HMSD is stronger than the single multi-scale decomposition method in information extraction. Therefore, the proposed hybrid framework is a state-of-the-art carbon price forecasting approach. [ABSTRACT FROM AUTHOR]

Carbon emissions in air travel have attracted widespread attention with the fast development of the aviation sector. Currently, biofuel usage has the potential to curb these emissions due to the bottleneck of other aircraft emission reduction technologies. This paper studies carbon tax policy-induced air travel carbon emission reduction and biofuel usage in China considering the interactive relationship between an authority and multiple airlines by a bi-level model and an interactive solution approach. Then a case study based on airlines from southern China's Sichuan Province is conducted to explore useful insights, with scenario analyses being performed. The computational results indicate that the policy could be useful for air travel carbon emission reduction and biofuel usage improvement in China. These results and analyses provide insights to the authority for the tradeoffs between policy equity and carbon emissions reduction effectiveness, and airlines for profitability. • Carbon tax policy-induced air travel carbon emission reduction in China is studied. • A bi-level model considering interaction of an authority and airlines is developed. • Trade-off between the effectiveness and equity of the carbon tax policy is analyzed. • A case study based on airlines in China is provided to explore useful insights. • The policy plays a vital role in carbon emission reduction and biofuel usage. [ABSTRACT FROM AUTHOR]

• Carbon emissions trading inhibits green technology innovation in the short run. • Carbon emissions trading will reduce the carbon emissions and carbon intensity greatly at this stage. • Carbon emissions trading has a crowding-out effect on R&D investment and increases the carbon trading price, which inhibited green technology innovation. • It is necessary to control the scale of carbon emission quota and diversify the subjects of carbon trading. Carbon emissions trading is an important measure to promote high-quality economic development. Based on the panel data of 30 provincial administrative regions in China from 2008 to 2017, this paper uses the difference-in-differences method to analyze the impact of carbon emissions trading on green technology innovation. The results show that: (1) Carbon emissions trading inhibits green technology innovation in the current stage, but greatly reduces carbon emissions and carbon intensity; (2) Carbon price and R&D investment are the mainly working channels, carbon emissions trading has a crowding-out effect on corporates' R&D investment and increases the carbon trading price, which in turn inhibits green technology innovation; (3) Carbon emissions trading has a stronger inhibitory effect on green technology innovation in eastern regions and regions with low emission intensity. Local government competition positively moderates the green technology innovation effect of carbon emissions trading. However, the command-controlled environmental regulations and the scale of the carbon emission quota have opposite effects, with the compatibility of environmental regulation tools remaining to be resolved. Therefore, it is necessary to control the scale of carbon emission quota to diversify the subjects of carbon trading and strengthen the impact of market-incentive environmental regulation tools. [ABSTRACT FROM AUTHOR]

Alberta operates one of the few energy-only electricity markets. This paper highlights the benefits of Alberta's market design for the incorporation of a carbon price which efficiently transmits incentives to market participants. Specific attention is paid to how carbon pricing has contributed to the shift away from coal towards natural gas and wind. The incentives created by carbon pricing have resulted in substantial new investment in wind, solar, and natural gas-fired generation capacity. • Alberta's competitive energy-only electricity market provides immediate flow-through of carbon pricing to purchasers. • The merit order and dispatch has changed dramatically, reducing coal use and increasing the frequency that it is marginal. • The price signals have led to rapid improvements in carbon intensity and total carbon emissions. • High value monetized environmental attributes have created strong incentives for expansion of wind generation. [ABSTRACT FROM AUTHOR]

Carbon emission allowances are issued to limit the carbon emission by enterprises, but some unforeseen variables may cause their demand unpredictable. Together with the high volatility of carbon price dynamic, management of carbon asset is of great significance and difficulty for those enterprises with heavy carbon consumption. To cope with the demand and price uncertainty of carbon emission right, carbon derivative provides a feasible solution for those enterprises. This paper proposes applying the carbon option for carbon management. For better applying this tool, carbon option is evaluated through a geometric Brownian motion model with regime-switching. Based on the value estimation and analysis of carbon option, decision making and strategy design for carbon asset management are achieved by the quantitative results of option pricing and digital detection during the operation. Development of the modeling and algorithm for carbon option pricing is the main theoretical contribution, also it enriches the theory of real option property of carbon. Moreover, it provides a detailed schema and an inspiring case study of applying the options for carbon asset management and facilitating the decision-making digitally in line with the market information constantly updated, and it is applicable and believed to optimize the risk control for all enterprises with carbon-consumption. • Carbon option is priced and applied for digital decision-making of the transactions. [ABSTRACT FROM AUTHOR]

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%. [Display omitted] • A novel consumer-centric and negotiation-based framework to decarbonize power systems. • A Blockchain based platform to integrate the exchanged information and controlling functions of the power system scheduling into data structures, nodes, and smart contracts of Blockchain networks. • How Blockchain based platform can prevent cyber attacks is discussed. • Case studies demonstrate the effectiveness of the proposed framework in the context of Great Britain power systems. [ABSTRACT FROM AUTHOR]

• The theoretical model connecting the energy and carbon markets is developed. • The model can be used as a monitoring tool for carbon price dynamics. • Identification of fundamental and market-specific shocks in a SVAR is proposed. • Time-varying short, medium, and long-run connectedness indices are computed. • A 65% on average of the variation in the carbon price is due to fundamental factors. In 2005, the European Union launched its Emissions Trading System (ETS), the first and one of the largest international carbon markets aimed at reducing member states' CO2 emissions. Policymakers tend to use the carbon price as an indicator of the "health" and effectiveness of the ETS mechanism, although this measure is influenced by many other energy and climate policies, energy market fundamentals, and speculative shocks. This paper develops a model that links the energy sector (oil, natural gas, coal, electricity prices, and the share of fossil fuels in electricity generation), economic activity, and the carbon price. The model can be used as a monitoring tool for carbon price dynamics. We represent the model empirically through a Structural Vector Autoregression and use frequency-domain analysis to distinguish the effects of changes in fundamental factors from shocks to market microstructure. Our empirical results show that up to 90% (65% on average) of the fluctuations in the carbon price, adjusted for supply effects, are explained by fluctuations in fundamental market variables; however, the individual contributions are not stable. Overall, our results suggest that the ETS has started to work well. [ABSTRACT FROM AUTHOR]

Carbon emission right allowance is a double-edged sword, one edge is to reduce emission as its original design intention, another edge has in practice slain many less developed coal-consuming enterprises, especially for those in thermal power industry. Partially governed on the hilt in hands of the authority, body of this sword is the prices of carbon emission right. How should the thermal power plants dance on the blade motivates this research. Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion. Under the overall goal of maximizing the ultimate profitability, the optimal stopping indicates the timing of suspend or halt of production, hence the optimal stopping boundary curve implies the edge of life and death with regard to this enterprise. Applying this methodology, real cases of failure and survival of several Chinese representative thermal power plants were analyzed to explore the industry ecotope, which leads to the findings that: 1) The survival environment of existed thermal power plants becomes severer when facing more pressure from the newborn carbon-finance market. 2) Boundaries of survival environment is mainly drawn by the technical improvements for rising the utilization rate of carbon emission. Based on the same optimal stopping model, outlook of this industry is drawn with a demarcation surface defining the vivosphere of thermal power plants with different levels of profitability. This finding provides benchmarks for those enterprises struggling for survival and policy makers scheming better supervision and necessary intervene, and the methodology developed in this paper provides a research paradigm for enterprise survival analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a new prediction method incorporating a set of measuring models applicable to the financing efficiency of China's carbon market to quantify such market's maturity, trading risk coefficient, and financing income. Our theoretical analysis indicates that the price of carbon emission quota essentially affects the financing efficiency of carbon market. While a robust safeguard measure to obtain a respectable income from carbon market financing is the long-term average quota price exceeding the initial quota price. Empirical findings derived from China's emissions trading scheme pilots reveal that these pilots can be divided into the growth-oriented market (i.e. Guangdong whose financing capacity is always significant), the balance-oriented market (i.e. Shanghai and Hubei whose quota pricing mechanisms and the financing level of carbon markets both maturely develop), and the risk-oriented market (i.e. Beijing whose quota price runs at a high-level with an intense financing income volatility). It is therefore achieved that carbon market's maturity level and quota price volatility are both robustly explicable for different financing effects among these pilots. Our key findings show that expanding the coverage of quota trading parties, stabilizing carbon price, and promoting carbon asset management help to improve the financing efficiency of carbon market. • The financing mechanism is analyzed with clarifying its main influence factors. • A stochastic process is used to build a price simulation equation that meets the operation law of China's carbon market. • An income model is built and thus evaluate market financing effect based on price expression and financing mechanism. • The financing effect of China's 8 pilot markets is measured, and thus 3 kinds of development modes are proposed. [ABSTRACT FROM AUTHOR]

The global economy is consistently concerned with what can accurately predict the price of carbon allowance, a trading concept that resulted from the need to mitigate carbon emissions. This motivates this paper to examine the predictive role of energy prices in the forecast of European Union carbon allowance prices. Using the novel Feasible Quasi Generalized Least Squares estimator which is able to account for the effects of conditional heteroskedasticity, serial correlation, persistence and endogeneity in the predictors, the symmetric predictive model is compared with the asymmetric predictive model to determine if the latter outperforms the former in the forecast performance. The results show that the carbon allowance prices are significantly predicted by all the energy prices considered. However, the asymmetric predictive models for oil and coal prices offer a better forecast performance than their symmetric versions. For natural gas, the results appear mixed, as asymmetries only matter when lower sample size, i.e. 50% of the full sample size, is used. Comparison of the proposed models with traditional models reveals that the former outperform the latter. Robustness is provided for these findings through different sample sizes, various forecast horizons and alternative forecast test. Policy inferences for carbon emissions mitigation and investment purposes are drawn accordingly. • Predicting the EU carbon allowance prices with energy prices FQGLS estimator. • Energy prices are found to be good predictors of the EU carbon allowance prices. • Accounting for asymmetries improve the forecast performance of oil and coal prices, but inconclusive for natural gas price. • Forecast graphs show that proposed models track carbon allowance prices better than the traditional models. • Findings have policy implications for emissions mitigation and investments. [ABSTRACT FROM AUTHOR]