Current Carbon Price Research Articles

Biomass, Carbon Stock and Economic Value of Carbon Sequestration Resulting from Forest and Landscape Restoration Actions in Semi-arid Ecosystems of Burkina Faso

Plants contribute to mitigate global warming by capturing CO2 from the atmosphere to produce biomass. This study aims to quantify the carbon stock sequestered in woody biomass of managed ecosystems in the semi-arid part of Burkina Faso (West Africa), and assess the economic value of additional carbon sequestered. Study sites include one managed forest and three agricultural lands restored from degraded soil. Forest and landscape restoration activities have been implemented over several decades and include zaï, stone barriers, natural regeneration and tree planting. Woody plant biomass is estimated using allometric equations which have been adapted to the case study site species and ecological conditions. The managed forest of the case study has an estimated 19.3-40.8 t/ha of woody biomass after 45 years. This corresponds to 0.7-1.5 tonne of CO2 per hectare per year stored on average, i.e. a value of $575-7,243 per hectare per year at current carbon prices. Payments to land users for carbon storage services may help provide alternative livelihoods and incentives for reforestation effort.

Incorporating carbon capture and storage in decarbonizing China's cement sector

China's target of carbon neutrality by 2060 has prompted the hard-to-abate cement sector to seriously consider the deep deployment of carbon capture and storage (CCS) technologies. However, the extent to which CCS should be integrated into the decarbonization pathways of China's cement sector, within a nexus of supply- and demand-side mitigation efforts, is not yet well understood. This study integrates supply- and demand-side transition dynamics to systematically assess the role of CCS in these decarbonization pathways. The results indicate that annual cement demand can be reduced from 1.4 gigatons (Gt) per year to 0.5 Gt per year by 2060 through a series of material efficiency improvements on the demand side. Furthermore, total carbon dioxide emissions from the cement sector could decrease from 0.2 to 0.8 Gt CO2 per year to approximately 0.1 Gt CO2 per year by 2060, with large-scale CCS deployments and other supply-side measures. The required CCS capacity would decrease from 901 to 152 million tons of clinker production per year, depending on the combined efforts from both demand- and supply-side strategies. Additionally, total economic costs are projected to be 9.7–12.8 trillion Chinese yuan (CNY), with mitigation costs ranging from 156 to 228 CNY per ton of CO2 avoided, which is higher than current carbon prices in China. These findings clearly demonstrate that reliance on CCS can significantly reduce carbon emissions if mitigation potentials are fully capitalized from both the demand- and supply-side efforts.

Carbon price point–interval forecasting based on two-layer decomposition and deep learning combined model using weight assignment

With the intensification of global warming, the demand for carbon emissions reduction has gradually increased in various countries. Carbon price is crucial for promoting the activation of the carbon trading market and facilitating emissions reduction. However, the current carbon price has non-linear characteristics, large fluctuations, and high complexity, making accurate predictions challenging. To effectively predict the trends and the change of carbon price, this study proposed a hybrid deep learning point–interval prediction model. First, an improved variational mode decomposition–symplectic geometry mode decomposition (IVMD–SGMD) two-layer decomposition model was constructed to decompose the carbon prices into regular subsequences. Then, attention–temporal convolutional network–bidirectional gated recurrent unit (Attention-TCN-BiGRU) and Encoder–Decoder long short-term memory (LSTM) combined prediction models were constructed for the prediction of subsequences. The entropy method (EM) was used to assign weights to the predictions of two models to achieve model complementarity and a linear reconstruction of the models' results. Then the error correction was performed to obtain the final prediction results. This study conducted experiments on carbon prices in the Guangdong and Shenzhen markets. The mean absolute error (MAE) of the proposed model for the two datasets was reduced by 89.69% and 87.43% respectively lower than that for LSTM. To demonstrate the model's adaptability, prediction experiments conducted on natural gas and crude oil prices were employed, confirming its strong predictive accuracy in energy price forecasting. Based on the point prediction error, the interval prediction using the improved kernel density estimation (IKDE) provides more carbon market information for decision makers. The proposed model aids government energy policy formulation and fosters ongoing efforts to reduce carbon emissions.

Calculating carbon: The value of information in precision for blue carbon restoration projects

Coastal wetland restoration projects can receive payments for ecosystem services but often occur in regions with limited data, and additional data collection can be financially prohibitive. Value of Information analysis can quantify the difference between the expected value of an action before and after new information has been collected, aiming to understand how much data is required to make decisions that balance the costs of implementation versus the benefits of the project. The Australian carbon market provides a method that uses reintroduction of tidal flows to restore coastal wetland ecosystems for their carbon sequestration functions. The method requires a hydrological assessment of prospective sites, which is employed to estimate carbon sequestration potential. This research investigates how different amounts of data collection and different levels of complexity in the hydrological assessment influence the carbon abatement emissions estimated using the method. The results indicate that tidal restoration for blue carbon credits on grazing land may not be financially viable. We found that tidal data collected onsite were important for decision-making while complex hydrological models have low value compared to more simplistic approaches. While investing in data collection provides more value than increasing the complexity of modelling approaches, the value of information was still low. Additionally, restoration of coastal wetlands is unlikely to be financially attractive at current carbon prices, and the land would have to be unsuitable for cattle to become profitable for restoration. This work provides a framework for evaluating the financial benefit of collecting on-site data and using robust methods for estimating inundation, that can be used to guide decision-making to achieve optimal income.

Techno-Economic Analysis of Electrofuel as a Shipping Fuel

Abstract In this study, we aim to evaluate the technical and economic requirements of electrofuel production through Fischer–Tropsch synthesis using green hydrogen and CO2. We find that a plant with a 1000-barrel/day capacity necessitates 60 tH2 and 424 tCO2 daily, achieving a 36% hydrogen-to-electrofuel conversion rate. The initial capital expenditure (CAPEX) is projected at 295 million USD, with an additional 139 million USD for electrolyzer replacement. The hydrogen production unit accounts for 61% of the CAPEX, followed by the direct air capture unit at 22%. The annual operating expense (OPEX) is estimated at 49.3 million USD, with hydrogen production and direct air capture constituting 58% and 25% of this expense, respectively, largely due to substantial electricity requirements. The synthesis and refining units comprise 17% of the OPEX. The levelized cost of electrofuel is calculated at 1881 USD/t, which is 2.5 times the cost of marine gas oil and 3.8 times that of very low sulfur fuel oil. However, electrofuels emission factor is found to be significantly lower, nearly 14 times less carbon-intensive than traditional fuels. We conclude that electrofuels cost competitiveness with fossil fuels depends on favorable conditions such as low electricity costs, low discount rates, and high carbon prices. While the European Union's emission trading system inclusion for maritime transport will lessen the cost disparity, it is unlikely to make electrofuels cost-competitive given the current carbon prices.

Close-to-nature management of tropical timber plantations is economically viable and provides biodiversity benefits

Abstract Reforestation of tropical forests is crucial to mitigate the climate crisis and restore ecosystems. However, past efforts have been criticized for establishing monoculture timber plantations with exotic tree species. Close-to-nature (CTN) practices aim to minimize negative forest management impacts on forests ecosystems by mimicking natural dynamics. So far, CTN management practices are rarely applied in tropical plantation forestry. This study evaluates the economic, carbon sequestration, and biodiversity potential of CTN management in tropical mixed-species plantations in Central America using a simulation-optimization approach. To our knowledge, this study is the first to assess the potential of tropical CTN-managed plantations on the basis of detailed process-based forest growth simulations. CTN practices such as selective harvesting, retention forestry, and shelterwood cutting of mixed-species stands were compared to even-aged mixtures and conventional monoculture practices. Results showed that CTN management was economically viable for certain species mixtures and management practices at an 8 % discount rate and had the potential to increase carbon storage and biodiversity in the modeled plantations. At current carbon prices, CTN-managed plantations may only become financially competitive with monocultures, if monocultures are excluded from carbon certification schemes that increasingly aim at co-producing non-carbon benefits like biodiversity conservation. If carbon prices increase, the sale of carbon credits could finance the transformation of monocultures to CTN-managed mixed-species stands. The competitiveness of CTN management could also be improved through performance-based biodiversity payments, such as the sale of biodiversity credits.

Market analysis of coupled biochar and carbon credit production from wildfire fuel reduction projects in the western USA

AbstractThe investment potential for biochar production from low‐value forest biomass in the western USA is over USD 20 billion at current carbon prices. This investment could generate approximately 70 million carbon credits annual – roughly the same number currently generated globally by all forestry and agricultural carbon projects. Biochar production that utilizes woody biomass specifically from wildfire fuel thinning projects as a feedstock can contribute financially to much‐needed fuel thinning projects. Each coupled biochar and carbon credit production system has positive returns in certain scenarios. Light upgrades to existing biopower facilities have the highest returns, with internal rates of return generally between 10–30%. Mobile biochar production often had the lowest returns. However, land managers can subsidize mobile biochar production up to USD 150–300 per tonne of biochar and still save money in comparison with pile‐burning low‐value biomass while also generating approximately one carbon credit for every two bone‐dry tonnes of low‐value biomass turned to biochar. The critical barrier to industry growth is the lack of transparent biomass supply chains that enable long‐term contracting for feedstock, production schedules, and investment. Moving forward, there are three potential pathways for the biochar industry to scale and utilize biomass from forest management and fuel thinning projects. Either (1) the carbon market will need to sustain high carbon prices, (2) a subsidy or other mechanism will need to decrease the cost of feedstock biomass, or (3) production will need to take advantage of economies of scale to bring down biochar prices while increasing production.

Modified biochar reduces the greenhouse gas emission intensity and enhances the net ecosystem economic budget in black soil soybean fields

Long-term high intensity utilization leads to the degradation of black soil in Northeast China and the serious loss of organic carbon. Biochar has great potential for improving the carbon storage capacity of agroforestry ecosystems and reducing greenhouse gas (GHG) emissions. However, the effectiveness of biochar application depends on pedoclimatic factors, and few studies have explored the effects of biochar and its modified biochars on the net ecosystem economic benefit (NEEB) of black soil farmland. To increase the environmental benefits of carbon sequestration and emission reduction and to improve farmer profits, this study modified biochars by physical, chemical and biological methods, and studied the effects of different types of modified biochars on soil properties, crop yield and GHG emissions in black soil farmland through field experiments. The NEEB of different treatments was evaluated by combining carbon cost, yield benefit and biochar cost. A total of 6 treatments were used in the experiment: normal fertilization (CK), fertilization + raw biochar (BC), fertilization + physically modified biochar (PBC), fertilization + acid-modified biochar (HBC), fertilization + magnetically modified biochar (MBC), and fertilization+ biologically modified biochar (BBC) treatments. Compared with the control (CK), the application of biochar increased the content of soil organic matter by 18.76–42.57% and improved the soil fertility. The raw biochar had no significant effect on soybean yield and increased the global warming potential by 41.18%. Soybean yields significantly increased with decreasing GHG emission intensity when modified biochars were applied. Due to the high cost of biochar and the current low carbon price, the effect of biochar on crop yield is crucial for increasing the NEEB. The NEEB in the HBC treatment was the highest, 57.86% higher than that in the CK treatment and was an effective strategy for increasing crop yield while reducing GHG emissions. The results of this study could provide a theoretical basis and technical support for soybean farmland carbon sequestration, emission reduction, soil improvement and yield improvement in the black soil region of Northeast China.

Investment Evaluation of CCUS Retrofitting for Coal-to-Liquid Industry in China

Coal-to-oil (CTL) combined with carbon capture, utilization and storage (CCUS) can significantly reduce the CO2 emissions generated in the production process to achieve clean coal utilization. Taking CTL enterprises as sources and deep saline aquifers and oil fields as sinks, this paper establishes a source–sink matching model, which is combined with a trinomial tree real-option model of carbon price fluctuation, and evaluates the investment decisions of CTL. The results show that 36 pipelines with an average transportation distance of 319.13 km and predominantly small diameters must be constructed for CO2 capture and storage combined with enhanced oil recovery (EOR). Under the current carbon price, 83.33% of enterprises can invest immediately when adopting EOR; when utilizing storage in a deep saline aquifer (DSF), even with a 50% subsidy and a decrease in costs due to the learning rate, enterprises still need to execute the deferred option investment. Government subsidies and technological advances can greatly increase the value of investment. The critical carbon price of CTL-CCUS projects is sensitive to government subsidies, technological advances, and CO2 transportation distances. Therefore, China should reasonably guide the development of the carbon market and give play to the role of the carbon market in emission reduction incentives. In addition, the Chinese government can provide direct financial support for the CTL-CCUS project to increase the enthusiasm of CTL enterprises for CCUS transformation and promote technological progress.

Carbon Trading in China Reduces the Dependence of Household Waste Electrical and Electronic Equipment Recycling on Government Subsidies.

China's enterprises of waste electrical and electronic equipment (WEEE) recycling suffer from low profitability that is highly dependent on government subsidies. This low economic gain impedes the sustainable growth of China's WEEE-recycling sector and also adds to the government's financial burden. Prior life-cycle studies have approved the carbon reduction potentials or net carbon credit of recycling WEEE. However, policymakers fail to know whether the revenue from selling carbon credits can offset the government's financial subsidy. We performed life-cycle and cost-benefit analyses for a case recycling enterprise that processes six categories of household appliances. The results show that the reduction potentials of greenhouse gases range from 930-3450 kgCO2e by recycling per ton of household appliances and materials substitution. The recycling enterprise would gain extra revenue ranging from 32 to 160 RMB per ton of appliance if the carbon credits were sold at China's current carbon price, i.e., 45-60 RMB tCO2e-1. Recycling waste refrigerators exhibits the highest carbon revenue, offsetting 6-17% of the government's financial subsidy. Microcomputers, by contrast, indicate the lowest carbon revenue, equivalent to 1-3% of its highest government subsidy. For each household appliance category, when the carbon price reaches 270-600 RMB tCO2e-1, selling carbon credits can fully offset the government's financial subsidy. Constrained by the processing capacity of the case enterprise, optimizations for appliance-recycling composition contribute a 15-25% profit growth to the current economic gains. Interpreting the specific profit depends on the predefined scenarios of carbon price and the substitution rate of the regenerated materials for the virginal ones. Our findings show that raising the profitability of WEEE recycling enterprises through the carbon trading policy contributes to the sustainable growth of China's WEEE-recycling sector while alleviating the government's financial burden.

Rooftop solar with net metering: An integrated investment appraisal

This paper develops a framework for a financial, economic, and stakeholder analysis of a residential rooftop solar net-metering program. The empirical focus of the paper is the net-metering program in Ontario, Canada, but the methodology is applicable to evaluating other public programs. The results highlight that without the Federal Government's subsidy for the initial investment cost, net-metered solar systems are not financially viable for representative households. Moreover, the stakeholder analysis reveals that for each additional net-metered system installed in Ontario, non-net-metered households experience financial losses of six times the benefits to the net-metered households. The net losses to the Federal Government of Canada and the Canadian economy are five and nine times the benefit to the net-metered households, respectively. The only stakeholder who benefits marginally is the Government of Ontario. In terms of environmental benefits, our estimate of the cost of greenhouse gas abatement by residential net-metered solar is 325 CAD per ton of CO2, which is significantly higher than the current (65 CAD in 2023) and future (170 CAD by 2030) national carbon price set by the Government of Canada.

The relationship between the contaminating industries and the European carbon price, machine learning approach

This study explores the bidirectional relationship between the industrial stock returns of greenhouse gas (GHG) emitting industries in the European Union (EU) and the European Union Allowance (EUA) price for the period of 2015–2022 by applying novel supervised machine learning (ML) models combined with a granger vector auto regression (VAR) test. Research findings present bidirectional causality and factor significance scores for each industry index in addition to performance metrics of the applied ML models. Results identify the chemicals, renewable energy, meat, electricity, iron&steel, transportation, construction, manufacturing, and cement industries as the most significant factors in explaining the EUA price volatility, while the Extra Trees (ET), K-Nearest Neighbor (KNN), Recurrent Neural Networks (RNN), and Random Forest (RF) models are determined to be the top performers. From the political perspective, research outputs conclude that the current carbon price is below the marginal decarbonization cost and fails to stimulate the technological decarbonization investments for the majority of the contaminating industries of the EU.

A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance

The carbon allowance trading mechanism provides an economic basis to facilitate emission reductions for market participants. As a compliance mechanism, allowance-based emission control demands emitter to surrender the same amount of carbon allowances as it emits during the accounting period, the duration of which is often set to one year by the administrators. This requires emitters to determine the upper limit of annual emissions and allocate it to each day based on the current carbon price to minimize their overall operating costs over the longer carbon accounting period. This paper proposes a low-carbon economic dispatch for IES with CCUS considering multi-time-scale allocation of carbon allowance. In the upper level, a scenario-based stochastic optimization is adopted to decide the annual allowance and complete the allocation from year to month and then from month to day. The lower level is a Wasserstein ambiguity set-based two-stage distributionally robust optimization (TSDRO) model to find the optimal dispatch strategy of each day. The uncertainty from the long-term available renewable energy resources and user demands and from the short-term forecasting errors is both considered. Simulation results show that the proposed model can realize reasonable carbon allocation and decrease the annual operation costs.

Investment feasibilities of CCUS technology retrofitting China's coal chemical enterprises with different CO2 geological sequestration and utilization approaches

CO2 capture, utilization, and storage (CCUS) technology provides an efficient alternative for decarbonizing the coal chemical industry under China's carbon neutrality goal. However, uncertainties such as geological conditions, technology level, carbon markets, subsidy policies, etc., will greatly affect investor confidence in the CCUS applicability in this industry. This study develops a trinomial tree real options based CCUS investment decision model to explore CCUS investment feasibilities for China's coal chemical enterprises with different CO2 geological sequestration and utilization approaches, i.e., CO2 enhanced oil recovery (CO2-EOR) projects, CO2 enhanced coalbed methane (CO2-ECBM) projects, and deep saline aquifer (DSA) projects. The findings show that 1) CO2-ECBM projects have a larger investment benefit compared to CO2-EOR and DSA projects, with a critical coalbed methane (CBM) price for the immediate investment of 1.3 CNY/m³ under the current technology and carbon price level. 2) CO2-EOR projects are immediately investable at a critical oil price of 200 CNY/BBL; DSA projects require the execution of a delayed option with a critical carbon price of 361.8 CNY/t. 3) Provincial investment benefits vary substantially depending on CO2 transport distance and product revenue, particularly for CO2-ECBM projects in Henan and Guizhou, which have significant investment benefits due to high local CBM prices and short CO2 transport distances.

Carbon abatement in the European chemical industry: assessing the feasibility of abatement technologies by estimating firm-level marginal abatement costs

As many other industries, the chemical industry has to strongly reduce its' carbon emissions, for which various abatement technologies exist. We study the economic feasibility of these abatement technologies by estimating the Marginal Abatement Cost (MAC) of CO2 emissions for 24 firms in the European chemical sector over the period 2015–2020. We estimate the firm-level MAC by using a quadratic directional output distance function (DDF) model. We find a median MAC of 429 €/t CO2, which is significantly above current carbon prices, indicating that most firms in this industry prefer to use carbon allowances instead of reducing their own emissions. We conclude that carbon abatement in the chemical industry is only likely when the carbon price is significantly higher or when financial support is provided for certain abatement technologies, such as renewable hydrogen and bio-based ammonia.

Accelerate Large-Scale Biomass Residue Utilization via Cofiring to Help China Achieve Its 2030 Carbon-Peaking Goals.

Cofiring biomass with coal for power generation is an affordable and ready-to-deploy technology to help reduce carbon emissions and resolve residual biomass. Cofiring has not been widely applied in China primarily because of some practical limitations, i.e., biomass accessibility, technological and economic constraints, and lack of policy support. We identified the benefits of cofiring with consideration of these practical limitations based on Integrated Assessment Models. We found that China produces 1.82 Bts/year of biomass residues, 45% of which is waste. 48% of the unused biomass can be utilized without fiscal intervention and 70% can be utilized with the subsidized Feed-in-Tariffs for biopower and carbon trading. The average Marginal Abatement Cost of cofiring is twice that of China's current carbon price. Cofiring can help China create 153 billion yuan of farmers' income annually and reduce 5.3 Bts of Committed Cumulative Carbon Emissions (CCCEs, 2023-2030), contributing to the needed CCCE mitigations to China's overall sector and the power sector by 32 and 86%, respectively. About 201 GW of coal-fired fleets are not compliant with China's 2030 carbon-peaking goals, and 127 GW can be saved by implementing cofiring, representing 9.6% of the total fleets in 2030.

Carbon offset as another driver of timberland investment returns in the United States

Timberland investment has three return drivers: biological growth, timber price change and land value appreciation. The interaction of the three drivers determines the total timberland investment returns. Recent public attention to climate change resulting from excessive greenhouse gas emissions, nonetheless, has led to more discussion of forests as a natural carbon sink. With carbon sequestration, landowners should be compensated for keeping trees alive. The cash flows associated with forest carbon present an opportunity for timberland investors to potentially generate extra returns. For an afforestation investment and at the current carbon price of about $20 per metric ton in the voluntary market, forest carbon has a moderate contribution of about 21% to the total timberland investment return with a return premium is about 115 basis points. However, for a regeneration investment in which only additional carbon sequestration beyond the baseline is credited, the impact of forest carbon on total timberland investment return is minor yet positive. Overall, the return contribution of forest carbon is positively related to carbon price, interest rate, and investment horizon. As the pressure from global warming tightens, demand for nature-based carbon storage tends to increase, leading to higher carbon prices. Meanwhile, concerns about additionality often result in longer-term carbon contracts. All these would boost the influence of forest carbon on total timberland investment returns in the future.

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic implication of CDRT if not effectively managed. Hence, a novel deep reinforcement learning agent (DRL), integrated with an automated hyperparameter selection feature, is proposed in this study for the real-time scheduling of a multi-energy system (MES) coupled with CDRT. Post-carbon capture systems (PCCS) and direct-air capture systems (DACS) are considered CDRT. Various possible configurations are evaluated using real-time multi-energy data of a district in Arizona, the United States, and CDRT parameters from manufacturers' catalogues and pilot project documentation. The simulation results validate that an optimised soft-actor critic (SAC) DRL algorithm outperformed the Twin-delayed deep deterministic policy gradient (TD3) algorithm due to its maximum entropy feature. We then trained four (4) SAC DRL agents, equivalent to the number of considered case studies, using optimised hyperparameter values and deployed them in real time for evaluation. The results show that the proposed DRL agent can meet the prosumers' multi-energy demand and schedule the CDRT energy demand economically without specified constraints violation. Also, the proposed DRL agent outperformed rule-based scheduling by 23.65%. However, the configuration with PCCS and solid-sorbent DACS is considered the most suitable configuration with a high CO2 captured-released ratio (CCRR) of 38.54, low CO2 released indicator (CRI) value of 2.53, and a 36.5% reduction in CDR cost due to waste heat utilisation and high absorption capacity of the selected sorbent. However, the adoption of CDRT is not economically viable at the current carbon price. Finally, we showed that CDRT would be attractive at a carbon price of 400-450USD/ton with the provision of tax incentives by the policymakers.

Assessment of the role of hydrogen to produce high-temperature heat in the steel industry

The iron and steel industry is a major source of industrial greenhouse gas emissions, accounting for 7–9% of global energy-related CO2 emissions. Current steel production routes are therefore expected to undergo a profound decarbonisation process in the coming decades.This work aims to shed light on the role of hydrogen in decarbonising the supply of high-temperature heat in the steel sector by means of an optimisation framework. The model includes on-site hydrogen production using a low-temperature electrolyser integrated with a compression and storage system and gas burners, which can be fed with hydrogen and/or natural gas to cover the process heat demand. The assessment also takes advantage of real thermal load profiles of a scrap-based electric arc furnace steel plant.A sensitivity analysis is conducted on the electricity, natural gas and carbon prices. Cost-optimal maps are then derived to unveil the combination of energy and carbon prices at which hydrogen becomes convenient for heat production in the steel industry. A general relationship to define the cost-effectiveness of hydrogen is also given.The results show that, at current carbon prices (about 100 €/tCO2), the use of hydrogen becomes economically convenient when the electricity price is less than 0.4–0.6 times the natural gas price. In scenarios with electricity prices lower than about 0.10 €/kWh, as could occur with on-site renewable electricity generation, hydrogen cost falls below 6.5 €/kgH2, leading to cost savings of up to 60–70% compared to a natural gas-based configuration. Finally, when total CO2 emissions (direct + indirect) are considered, hydrogen becomes environmentally beneficial if the electricity carbon intensity is below 123 gCO2/kWh.

Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode

Accurate carbon price forecasting can provide policymakers with a reasonable basis for carbon pricing. Interval-valued carbon price forecasting could provide sufficient information compared with real-valued carbon price time series prediction. On the other hand, current interval-valued carbon price forecasting has major challenges including data complexity, outliers, and the selection of forecasting methods, which make the forecasting results with great uncertainty and instability. To address these issues, this paper proposes an interval-valued carbon price forecasting method based on new data processing techniques, and discusses the effects of different combinations of interval variables on the forecasting results. We first established interval complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and interval outlier detection method (I-ksigma) to reduce the data complexity and identify outliers. Then, various neural network models include interval multilayer perceptron (IMLP), multi-output support vector regression (MSVR), long short-term memory network (LSTM), gated recurrent unit neural network (GRU), and convolution neural network (CNN) are chosen to conduct combination forecasting on the interval sub-modes produced by ICEEMDAN. The final results are obtained by summing the interval sub-modes. Finally, taking the carbon trading price in Hubei as the research object, the results show that the developed forecasting framework is superior to all comparison models in forecasting precision and stability. Furthermore, different combinations of interval variables (CRM, Minmax, L + 2R, and U-2R) lead to different decomposition results and outlier detection results, which finally affect the prediction results.