Optimal reduction and equilibrium carbon allowance price for the thermal power industry under China’s peak carbon emissions target

As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation technologies is also an important means of reducing CO_(2)emissions and achieving the carbon peak and carbon neutral commitments.This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance(CA)trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated.We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional It?’s formula.In this manner,we obtained the models for optimal emission reduction and equilibrium CA price.Our numerical analysis revealed that,within a compliance period of 2021–2030,the optimal reductions and desired equilibrium prices of CAs changed concurrently,with an increasing trend annually in different peak-year scenarios.Furthermore,sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent,the depreciation rate positively impacted the CA price,and increasing the initial CA reduced the optimal reduction and the CA price.Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

Carbon Price Forecasting Approach Based on Multi-Scale Decomposition and Transfer Learning

Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Carbon price impacts on sector cost:based on an input-output model of Beijing

Under the pressure of sustained growth in energy consumption in China,the implementation of a carbon pricing mechanism is an effective economic policy measure for promoting emission reduction,as well as a hotspot of research among scholars and policy makers.In this paper,the effects of carbon prices on Beijing's economy are analyzed using input-output tables.The carbon price costs are levied in accordance with the products'embodied carbon emission.By calculation,given the carbon price rate of 10 RMB/t-CO_2,the total carbon costs of Beijing account for approximately 0.22-0.40%of its gross revenue the same year.Among all industries,construction bears the largest carbon cost Among export sectors,the coal mining and washing industry has much higher export carbon price intensity than other industries.Apart from traditional energy-intensive industries,tertiary industry,which accounts for more than 70%of Beijing's economy,also bears a major carbon cost because of its large economic size.However,from 2007 to 2010,adjustment of the investment structure has reduced the emission intensity in investment sectors,contributing to the reduction of overall emissions and carbon price intensity.

Low Carbon Beijing:Research on the Influencing Factors of Carbon Emission Trading Price

The international community has taken extensive actions to achieve carbon neutrality and sustainable development with the intensification of global warming and climate change.China has also carried out a long-term layout,setting the goal of achieving a carbon peak by 2030 and carbon neutrality by 2060.In 2021,with the official launch of a unified national carbon emissions trading market,China’s nationwide carbon emissions trading kicked off.Carbon emission trading is an important policy tool for China’s carbon peak and carbon-neutral action and an essential part of the country’s promotion of a comprehensive green transformation of the economy and society.This study uses a VAR(Vector Autoregressive)model to analyze the influencing factors of the Beijing carbon emissions trading price from January 2014 to December 2019.The study found that coal prices have the most significant impact on Beijing’s carbon emissions trading prices.Oil prices,industrial development indexes,and AQI(Air Quality Index)impacted Beijing’s carbon emissions trading prices.In contrast,natural gas prices and economic indexes have the most negligible impact.These findings will help decision-makers determine a reasonable price for carbon emissions trading and contribute to the market’s healthy development.

Effect of Carbon Price Floor on Levelised Cost of Gas-Fired Generation Technology in the UK

The UK government implements carbon price floor to provide long-term incentive to invest in low-carbon technology, thus, fossil-fuel power plants have to face increasing carbon price. This report addresses the effect of carbon price floor on levelised cost of gas-fired generation technology through the levelised cost of electricity (LCOE) ap-proach with the estimation of carbon price floor. Finally, the comparison of levelised cost of electricity for all generation technology in the UK will be shown and discussed.

Daily rolling estimation of carbon emission cost of coal-fired units considering long-cycle interactive operation simulation of carbon-electricity market

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets.The carbon emission cost(CEC)of coal-fired units becomes part of the power generation cost through market coupling.The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market.Study of carbon–electricity market interaction and CEC calculations is still in its initial stages.This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets.A long-period interactive operation simulation mechanism for the carbon–electricity market is established,and operation and trading models of the carbon emissions trading market and electric power market are established.A daily rolling estimation method for the CEC of coal-fired units is proposed,along with the CEC per unit electric quantity of the coal-fired units.The feasibility and effectiveness of the proposed method are verified through an example simulation,and the factors influencing the CEC are analyzed.

2023年国内外油气行业发展及2024年展望

2023年,全球能源格局深刻调整,绿色低碳转型稳步推进;国际油价波动下跌,天然气价高位回落;油气勘探开发投资保持增长,油气产量继续双增;炼油乙烯产能继续增长,毛利总体下滑;大石油公司重新强化油气上游,降碳和盈利并重;碳减排共识进一步凝聚,碳市场影响力不断增强。中国油气行业立足安全清洁发展,能源行业政策体系加快完善;新型能源体系建设有序推进;成品油和天然气消费恢复向好,“全国一张网”加速完善;油气产储量保持增长,“两深一非”成增储上产新阵地;炼油能力继续小幅增长;大宗石化产品产能呈扩张态势;碳市场建设步伐加快。2024年,预计国际石油市场基本面由紧平衡转为供应过剩,天然气市场延续脆弱平衡,地缘政治紧张局势及外溢影响等将继续推动油气价格大幅波动;能源转型更加理性务实,基于安全效益发展建设能源韧性体系渐成共识。国内经济景气度回升势头向好,将进一步带动能源行业回暖;深化能源体制机制改革、加快建设新型能源体系、保障能源供应安全稳定仍是能源行业的重要使命任务。

基于季节性碳交易机制的园区综合能源系统低碳经济调度

为有效提高碳排放配额分配的合理性,并且避免年度结算时碳排放量超标导致环境污染加剧问题,提出基于奖惩因子的季节性碳交易机制,以园区综合能源系统(park integrated energy system,PIES)为对象进行低碳经济调度。首先,构建包含能量层–碳流层–管理层的综合能源系统(integrated energy system,IES)运行框架,建立电气热多能流供需动态一致性模型;其次,分析系统内“日–季节–年度”碳排放特性,打破传统应用指标法的配额分配方法,采用灰色关联分析法建立碳排放配额分配模型,并基于奖惩阶梯碳价制定季节性碳交易机制;最后,以系统内全寿命周期运行成本及碳交易成本最小为目标,对执行季节性碳交易机制的PIES进行低碳经济调度,分析长时间尺度下季节性储能参与调度的减碳量。搭建IEEE 33节点电网5节点气网7节点热网的PIES,并基于多场景进行算例分析,验证此调度方法能够实现零碳经济运行,保证系统供能可靠性,为建立零碳园区奠定理论基础。

绿色金融:破解“碳诅咒”困境的有效策略

本文立足资源型地区经济增长过度依赖化石能源的特征,探讨利用金融手段破解“碳诅咒”困境问题的路径。首先,将绿色金融嵌入包含二氧化碳排放的经济增长理论分析框架,通过数理模型,阐释绿色金融通过缓解资本价格扭曲、加速碳排放与经济增长脱钩的作用机理,证实绿色金融发展应当以资本配置达到最优为上限,避免由于绿色金融发展过度导致绿色产业领域产能过剩,从而制约其他产业的发展。其次,运用空间计量经济模型,对理论模型的现实解释力进行实证检验。主要研究结论包括,我国资源型省份过度依赖化石能源产业,更倾向于碳密集型发展,迈入“碳诅咒”困境;资源型地区的绿色金融发展可以通过缓解资本价格扭曲加速碳排放同经济增长的脱钩,进而破解资源型地区由“资源诅咒”引致的“碳诅咒”困境;资源型地区的绿色金融发展能够加强邻居地区的能源脱钩与碳脱钩。研究结论为有序推进绿色金融高质量发展提供有益指导,为破解资源型地区“碳诅咒”困境提供合理可行的政策思路。

中国碳排放权交易市场发展现状与展望

碳排放权交易机制作为全球各国控制温室气体排放的重要手段之一,由于其良好的政策兼容性、金融衍生性,在全球发展势头强劲。从政策制度的完善、全国性及区域性碳市场交易现状、自愿减排市场现状三方面对中国碳市场发展现状进行分析,并综合考虑国际成熟碳市场发展经验、国家政策动向等因素,对中国碳市场未来发展趋势进行展望:中国碳市场扩容是必然趋势,电解铝、水泥和钢铁将依次纳入碳市场;配额总量将不断下降,拍卖比例预计将达到5%;预期碳价“十四五”升至80~100元/吨左右,“十五五”升至130~200元/吨;电力市场与碳市场协同统筹发展是必然选择;碳市场和碳税联合使用将是未来政府控排管理的主要方式。从宏观和微观两个层面提出适时稳步扩大碳市场覆盖范围、不断提高配额拍卖比例、系统构建企业内部碳核查与信息披露机制以强化碳管理效能与透明度等发展建议。

中国碳市场建设成效与展望(2024)

碳交易通过显性碳定价原则,将排放的负外部效应内部成本化,为处理经济发展与减排关系提供了一种解决方案。截至2023年,全球共28个碳交易体系投入运行,覆盖的温室气体排放量超过全球排放总量的17%,实施区域从发达国家逐步拓展至发展中国家,从欧洲、北美等地逐步拓展至拉美、东亚等地,更多的区域选择将碳市场减排目标与宏观减排目标绑定,并通过不断优化制度提高市场有效性,碳市场影响力逐步提高。2023年中国全国碳市场核算、核查、配额分配、数据管理、自愿减排等制度方法取得重要突破,全年配额成交2.12亿吨,是2022年的4.2倍,成交均价68.15元/吨,较2022年上涨23.24%,较第一个履约周期上涨59.04%。经过两个履约周期的建设,全国碳市场已形成要素完整的全流程制度框架,责任主体分工明确,支撑平台安全运转,碳排放数据质量大幅提高,碳价格发现机制初步形成,碳减排激励约束效果初显,推动碳市场成为中国实现“双碳”战略目标的重要政策工具。下一步全国碳市场将扩大行业覆盖范围,梯次纳入水泥、民航、电解铝、钢铁等行业;优化配额分配方法,适时引入有偿分配机制;调整履约机制,明确结余配额结转规定;出台多项中国核证自愿减排方法学,完善强制减排与自愿减排市场的衔接;加快推进与国际碳市场的连接,促进技术、方法、标准、数据互认互通,协助积极应对欧盟碳边境调节机制。

国际碳税政策实践发展与经验借鉴

碳税是应对全球气候危机的有效碳减排政策工具之一。尽管国际上碳税的发展取得了一定的减排成效,但是各国碳税实施路径与政策调整逻辑却有显著差异,仍存在参与碳定价机制不成熟、发展缓慢和国际争议调整机制缺失等问题。借鉴当前国际碳税制度的具体税收制度、政策设计和税收关系的实践经验,我国碳税的开征应循序渐进,与碳交易协同互补,采取“逐步拓宽”的税制设计,利用税收优惠平衡税收负担,充分参与国际碳税协调,致力于推动绿色经济的发展和促进碳达峰、碳中和目标的达成。

2023年中国成品油行业运行特点与近期展望

2023年,中国炼油产能再创新高,炼厂开工率回升,成品油产量大幅增长;成品油消费恢复至疫情前水平,不同品种恢复节奏存在差异,航空煤油消费明显恢复;成品油消费结构变化明显,供需差量扩大;在国际原油价格高位宽幅震荡背景下,成品油批发价格小幅增长;成品油商业库存“汽跌柴涨”,油库建设需求旺盛;成品油进口大幅下降,俄罗斯成为中国成品油进口主要来源国,成品油出口量继续增加,主要出口至新加坡;多项市场专项整治方案出台,规范成品油市场运行,推动行业高质量发展。2024年,中国成品油市场监管仍将持续完善,炼油行业将继续扩能,成品油产量将继续增长,成品油消费将回归低速增长通道,国际原油价格将宽幅震荡加剧成品油市场价格波动风险。

考虑动态定价和碳交易的多园区综合能源服务商低碳合作优化策略

为解决多园区综合能源服务商参与综合能源市场和碳交易市场带来的利益分配和动态定价问题,该文提出一种新的多园区服务商合作交易框架。首先,设计阶梯式碳交易下多园区综合能源服务商合作运行交易机制,针对区域零售市场制定动态价格,以指导各园区服务商购能行为;其次,在新型电力系统的逐步构建下,根据新能源占比变化,完善碳配额与碳排放模型,并通过市场下阶梯式碳交易机制约束各园区的碳排放;最后,建立多园区服务商纳什议价模型,将所建模型分解为联盟成本最小化与电热交易支付谈判子问题,通过算例验证所提模型和方法能提高各园区参与联盟主动性,并有效降低各园区碳排放和总运行成本。

欧美碳关税对我国的挑战及对策探究——基于“碳边境调节机制”与“清洁竞争法案”的比较

简要阐述了欧盟“碳边境调节机制”与美国“清洁竞争法案”的发展历程及现状,并对两者进行了比较。欧美碳关税的实施将对我国的贸易体系产生一定的冲击。在“双碳”目标的指引下,我国政府和企业应该积极应对:主动开展国际交流合作、坚决抵制单边贸易保护,发展低碳技术、大力降低碳含量,增加碳排放成本、提升碳减排效率,加强碳足迹管理、构建产品碳排放核算体系,锚定“双碳”目标,坚定不移走绿色低碳的高质量发展道路。

碳交易机制下双渠道供应链定价与协调研究

文中运用博弈论求得了集中决策和分散决策下双渠道供应链最优定价决策和单位碳减排决策,分析了碳交易价格、消费者渠道偏好和低碳偏好对最优决策的影响,采用了收益共享-成本分担契约协调供应链,并通过数值算例验证了模型和结论的有效性.结果表明:在一定的条件下,两种渠道的定价均随碳交易价格的增加而降低;线上直销渠道价格与线上渠道偏好成正相关,线下零售渠道价格则相反;合理的碳交易价格、渠道偏好和低碳偏好有助于碳减排和供应链整体利润的提高;集中决策下单位产品碳减排量和供应链整体利润均高于分散决策时;收益共享-成本分担契约能使供应链成员的利润实现Pareto改进.

电力用户侧能源优化碳普惠体系设计及应用

碳普惠是一种引导广大公众践行节能减碳行为的新型激励机制。针对电力客户能源优化节碳潜力大但缺少常态化激励的问题,以碳普惠底层运行逻辑为指导开展电能用户能源优化碳普惠体系设计。首先面向碳减排方法学开发,从电力用户能源优化的减排机理分析出发,梳理了4类10种低碳行为,接着设计了政府监管下电网企业主导且多方参与下的碳普惠机制,并进行碳普惠服务平台技术架构设计;然后针对电动汽车能源替代场景构建了碳减排和碳积分计算模型;最后以江苏电动汽车碳普惠实践为例进行碳普惠应用效果分析,碳普惠实施后充电量增长2 123 k W·h,合计减碳量37 068 kg,站均碳积分激励系数为7.36%。算例表明碳普惠体系设计合理且对电动汽车移峰填谷起到较好的激励效果。