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.

An Empirical Study on the Relationship Between China's Maize Price and Maize Import and Export Trade

Based on the time series of China’s maize trade and domestic maize price,the article analyzes the relationship between domestic maize price and China’s maize international trade by using empirical methods.The results show that there is a connection between domestic maize prices and China's maize international trade,but the link is not very close.Domestic maize price is the cause of China’s maize international trade change,but its impact is very limited;China's maize international trade also can make some difference on the domestic maize price.Based on the results of research,the article brings three suggestion and expectation to adjust China’s maize international trade and the cost control reasonably in order to stabilize domestic maize price and maize yield,and to protect domestic grain security.

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.

Discussion on the Formation and Trading Mechanism of Art Market Capital Price

Under the market economy system,art is a new investment channel.With the improvement of people's living standards,it has a new understanding of art investment.Based on this,this paper takes the price of art as the research object,and elaborates the price formation and transaction of the art capital market from the aspects of the intrinsic elements of art,the investment of art,the supply and demand of art market,people's boastful consumption and social education.The constraints imposed by the mechanism.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Pricing Mechanism of China’s Trans-regional and Trans-provincial Electricity Trading

With the large-scale development of Chinese electric power, the contradiction of China’s energy supply and demand that reverse distributed is very prominent, therefore, promoting electricity trading is one of the important measures to get optimized configuration of energy resources in nationwide. For the two kinds of trading method, the “power point to the grid” trading and the “grid to grid” trading, this paper designed pricing mechanism model, and took one area as an example, we analyzed the impact of the participants by using different pricing mechanism, and put forward reasonable policy proposals for China’s pricing mechanism of trans-regional and trans-provincial electricity trading.

Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.

Study on the investment value and investment opportunity of renewable energies under the carbon trading system

China has promised to start the national carbon trading system in 2017.In the carbon trading system,the renewable energy projects may obtain additional benefits through the Certified Carbon Emission Reduction(CCER) trade.As the carbon price fluctuates along with the market conditions,such fluctuation enables the renewable power projects to acquire the rights of an option,i.e.it may contain an even higher value due to the uncertainties in the future.While making an investment decision,the renewable power companies may choose to make the investment immediately,or postpone the investment and accumulate more information to increase the return of investment;and for immediate investments,the return must be sufficient to exceed the potential value of a waiting option.To study the investment in renewable power projects subject to the fluctuation of carbon price,this paper adopts the trinomial tree model of real options to estimate the net present value(NPV) and real option value(ROV) of three types of renewable power projects;according to the decision-making rules of real options to defer,all the three types of projects will exercise the option to postpone the investment decision.This thesis also calculates the benchmark prices of the three types of renewable projects at different times,in the two situations of having no government subsidy and having the government subsidy,so as to determine the investment opportunity of a project.The benchmark price decreases gradually along with the increase of government subsidy,indicating that the government subsidy will stimulate the investment in renewable projects.The benchmark price also increases gradually along with the lapse of time,indicating that the uncertainty will increase together with the time span and thus requires an even higher carbon price to determine the investment opportunity.This thesis also analyzes the sensitivity of factors affecting the investment in renewable projects and draws the conclusion that the fluctuation of carbon price is positively related with the benchmark price of renewable power projects,which indicates that the fluctuation of carbon price increases the option value of an investment but postpones the time of investment.As the China's carbon trading system improves gradually,the carbon price will reach a stable status,thus stimulate the power companies to invest in the renewable projects.

Old and New Problems: A Probe into Deteriorating Relations between Japan and the ROK

In recent years,relations between Japan and the Republic of Korea(hereafter referred to as South Korea)have become tense,giving rise to economic and trade frictions between them,and plunging their bilateral relationship to an all-time low.The disputes between Japan and South Korea have exhibited the features of endurance,expansion in disputing areas,and a frequent switch from defense to offense or vice versa on both sides.The fast deteriorating relationship between Japan and South Korea has resulted from multiple factors that interact in an entangled way,including mutually enhancing old and new grievances and discontent over historical issues,the adjustment of foreign policies by Japan and South Korea,the rise of nationalist emotions in both countries,US adjustment of its alliance policy towards the Asia-Pacific,and structural competition in forming the regional order.As the structural contradictions between Japan and South Korea appear hard to overcome,the potential for the tense relationship between the two countries to persist is mounting,and this will lead to new repercussions on the regional order.

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.

Price Limit, Superior Information and Investor Behavior:Evidence from China Stock Market

The mechanism of price limit impacts on informed traders’ behavior. To find out what effects related to price limit, three hypotheses caused by price limit are analyzed. Comprehensive method of event study and comparative grouping is used to test the performance of price limit in Chinese stock market. The result of test indicates that price limit policy impedes fulfillment of traders and delays the discovery of stock price so it should be abolished.

Pairs Trading Strategy for A and H Shares Based on Kalman-HMM Approach

Pairs trading is a statistical arbitrage strategy that takes advantage of unbalanced financial markets.A common difficulty for quantitative trading participants is the detection of market institutional changes in financial markets.In order to solve this issue,the hidden Markov model(HMM)is applied for status detection.The research objective is to use Kalman filter to predict and the hidden Markov model(HMM)to identify state transitions on the basis of screening transaction pairs with obvious co-integration relationship.This research would prove the profitability of the strategy and the ability to resist risk through the combination of these two methods with real data.The empirical results showed that compared with the traditional cointegration strategy,the holding yield increased from 1.6%to 16.2%and the maximum pullback reduced to 0.02%.Further research is required to improve trading rules.

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。

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。

关于超计划(定额)累进加价与用水权交易制度竞合的初步思考

超计划(定额)累进加价制度已实行多年,对于强化节约用水管理发挥了重要作用。近年来,随着用水权改革的深入推进,两项制度在对公共供水管网内非居民用水户新增用水需求的管理上形成了竞合。文章基于对用水权权利范围边界的界定,提出了超计划(定额)累进加价与用水权交易制度竞合的破解思路,并从持续深化用水权改革实践探索、深化理论研究方面提出了对策建议。

基于纳什谈判和主从博弈的多园区综合能源系统优化调度

为充分考虑多园区综合能源系统(MCIES)中多利益主体间交易合作与多种能量间的耦合关系,提出了1种基于纳什谈判-主从博弈的点对点分布式交易策略。首先,建立考虑综合需求响应的MCIES主从博弈动态定价模型,以园区综合能源系统(CIES)运营商作为领导者,给下层用户制定动态电价与热价,用户响应电价与热价进行综合需求响应,CIES运营商根据用户的需求响应计划调整机组出力;其次,基于纳什谈判理论,建立包含主从博弈动态定价机制与CIES间合作博弈的MCIES双层优化模型;然后,将原问题等效为MCIES收益最大化子问题和合作收益分配最大化子问题,采用交替方向乘子法(ADMM)对模型进行求解。最后,仿真结果表明所提策略可实现CIES联盟利润的最大化,并合理分配收益,同时最小化用户用能成本。

生态型县域生态系统服务时空格局及其协同关系——以国家全域旅游示范区江西省武宁县为例

[目的]分析生态型县域生态系统服务的时空变化及协同关系,为促进国家生态文明建设,实现生态保护和社会经济协调发展提供理论参考。[方法]以江西省武宁县为例,结合野外实测数据,运用InVEST模型评估了该区2000—2020年水源涵养、土壤保持、碳储存和粮食供给4种典型生态系统服务,在此基础上探讨了生态系统服务间的协同关系及其时空变化特征。[结果]①2000—2020年,武宁县单位栅格年平均水源涵养量从173.14 mm提升至283.92 mm,单位栅格年平均粮食供给量从3.52 kJ提升至5.34 kJ,两者均呈现增长趋势;土壤保持功能呈先减少,后增长,再减少的波动趋势;20 a间土壤保持功能下降了15.71%,而碳储存功能呈现微弱增长的趋势。②武宁县南部和北部的林地是水源涵养、土壤保持和碳储存的高值区;水域、建设用地及未利用地分布区域是水源涵养、土壤保持和碳储存的低值区;4种生态系统服务中,林地和耕地是生态系统服务功能发展的关键用地。③武宁县水源涵养、土壤保持和碳储存3者之间是协同关系,与粮食供给是权衡关系;“高—高”和“低—低”集聚的协同关系主要分布在高海拔的林地及水域,“高—低”和“低—高”集聚的权衡关系主要分布在耕地。[结论]2000—2020年武宁县生态系统服务功能整体呈上升趋势,未来应重点关注生态调节服务功能向资源价值的转化。