Trading Market Research Articles

How to Construct a Carbon Asset Management System for Chinese Power Enterprises: A Survey-Based Approach

The greenhouse effect of atmospheric pollution is globally concerning. China is transitioning to market-driven emission reduction from policy-driven efforts. In 2021, key power industry emitters were included in the national carbon trading market. However, many companies lack willingness and understanding of carbon assets, hindering progress. Research on power companies in Beijing, a political and carbon market pilot region, is valuable. This study obtained data on the participation of Beijing’s power generation companies in the carbon market and the construction of their carbon management systems during the first compliance period through the distribution of surveys. The findings revealed that the implementation and preparation of carbon inventory, Chinese Certified Emission Reduction (CCER) development, the allocation of carbon management personnel, and training are key factors influencing the actual effectiveness of carbon management within companies. Based on the survey results and the impact pathways, this study outlines the preparatory work, system content, and construction steps for power companies to build a carbon management system. It summarizes five key areas of work for power companies in managing carbon assets: carbon inventory, carbon management personnel and mechanisms, carbon trading, carbon emission reduction, and carbon finance. This provides guidance to help power companies fulfill their obligations smoothly, add value to their carbon assets, and achieve low-carbon development goals. Additionally, it offers a reference for other industry enterprises that are about to enter carbon trading.

How to maximize the joint benefits of timber production and carbon sequestration for rural areas? A case study of larch plantations in northeast China

BackgroundImplementing large-scale carbon sink afforestation may contribute to carbon neutrality targets and increase the economic benefits of forests in rural areas. However, how to manage planted forests in China to maximize the joint benefits of timber production and carbon sequestration is still unclear. Therefore, the present study quantified the effects of different rotation lengths, thinning treatments, site quality (SCI), stand density (SDI), and management costs on the joint benefits of carbon sequestration and timber production based on a stand-level model system developed for larch plantations in northeast China.ResultsThe performances of the different scenarios on carbon stocks were satisfactory, where the variations in the outcomes of final carbon stocks could be explained by up to 90%. The joint benefits increased significantly with the increases of SDIs and SCIs, regardless of which rotation length and thinning treatments were evaluated. Early thinning treatments decreased the joint benefits significantly by approximately 131.53% and 32.16% of middle- and higher-SDIs, however longer rotations (60 years) could enlarge it by approximately 71.39% and 80.27% in scenarios with and without thinning when compared with a shorter rotation length (40 years). Discount rates and timber prices were the two most important variables affecting joint benefits, while the effects of carbon prices were not as significant as expected in the current trading market in China.ConclusionsThe management plans that promote longer rotations, higher stand densities, and no thinning treatments can maximize the joint benefits of carbon sequestration afforestation and timber production from larch plantations located in northeast China.Graphical

Competition among high-frequency traders and market quality

I study how competition among high-frequency traders (HFTs) affects their trading behavior and market quality. The analysis exploits a unique dataset, which allows comparing environments with and without high-frequency competition, and contains an event - a tick size reform - which I use to separate the effects of high-frequency trading competition from the effects of the rising share of high-frequency trading in the market. I find that when HFTs compete in a stock, their speculative trading increases. Furthermore, market quality in that stock deteriorates. My findings hold for a variety of market quality and high-frequency trading behavior measures.

Dynamic optimisation of carbon allowance considering inter-provincial energy resources trade for emissions reduction: Case of China southern power grid

As the main carrier of natural resources into energy, the power industry is the world's largest carbon emitter, and its sustainable development will have far-reaching impacts on the utilization of natural resources and economic growth. Carbon trading market is a widely used tool to promote proactive emissions reductions in the electricity industry in different regions. On this basis, inter-provincial trading in the power industry creates a hidden carbon transfer between producers and consumers of energy resources. Therefore, it is necessary to construct systematic multi-period emission reduction measures to deal with the conflict and coordination of the operation of the two systems. In this study, to promote orderly emission reductions in the power industry in areas with different natural resource endowments, a two-tier decision-making model for carbon allowances allocation based on interactive model predictive control technique is constructed. The model readjustss the overall allowances by tracking the implied carbon transfer flows between regions and implementing a production-oriented allowance compensation mechanism, forming an optimisation strategy for carbon compliance cost and emission intensity in the power sector. The experimental results show that adjusting the regional allowance distribution in inter-provincial transmission can alleviate the impact of high emission reduction pressure on the overall carbon compliance cost, while the rolling horizon optimisation of the carbon baseline value can approach the expected emission reduction target in stages. Taking the Southern Power Grid as an example, this study ultimately provides policy recommendations for sustainable emission reductions under multi-period inter-provincial trade in energy resources.

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.

A novel central bank digital currency framework design for offline and foreign transactions based on blockchain

Purpose Fiat money production necessitates physical commodities, increasing costs and its flow is challenging to monitor, making it vulnerable to criminal exploitation. Cryptocurrencies offer decentralized solutions, but their decentralization has led to illegal activities. Current cross-border transactions face high costs, resource intensity and lack of instant currency transfers. Offline transactions are essential in unreliable networks. Design/methodology/approach Here, the authors proposed the methodology to perform offline transactions based on card, quick response (QR) code and a foreign transaction framework with universal identification (UID) to perform cross-border transactions using blockchain-dependent central bank digital currencies (CBDCs). Implications for the financial system are also analyzed. Findings The proposed CBDC framework reduces illegal transactions, corruption and the cost of producing fiat money; eases overseas transactions; and eventually increases international tourism, trade and business between countries. It also reduces the processing fees. Offline framework found useful for performing retail-level transactions. Research limitations/implications The research methodology may face limitations due to diplomatic relations, political instability, sanctions and the need for robust offline transaction infrastructure. Practical implications The proposed CBDC framework simplifies debt and insurance management, tax collection, international trade, tourism and global stock market participation. However, implementing CBDCs in low-income countries presents challenges like extensive training, infrastructure and user acceptance issues. Social implications The adoption of CBDCs can enhance financial stability by reducing corruption and illegal transactions through improved traceability and monitoring, thereby curbing activities like terrorism. Originality/value Common framework for foreign transactions is based on the UID, and offline transaction framework is based on the sender’s QR code for multiple user applications.

Taiwan Emissions Trading System and Market Risk

Taiwan Emissions Trading System and Market Risk

An Intelligent Vehicle Price Estimation Approach Using a Deep Neural Network Model

In recent years, the market for used-vehicle trade in the Kingdom of Saudi Arabia has grown significantly. This is due to the high cost of new vehicles that are not affordable by most buyers and lifting the ban on women drivers. Recently, several online websites for selling vehicles are available with different functions. However, estimating the vehicle price is based on traditional calculation methods, and this is inaccurate in several selling situations, as there are many factors that may affect the vehicle price, and these factors must be taken into consideration when estimating the vehicle’s price. Therefore, there is high demand to develop an automated vehicle price estimation system through adopting artificial intelligence (AI) technologies. Hence, this paper proposes an efficient vehicle price estimation system through developing an efficient deep neural network (DNN) model. The developed DNN model has been trained using a recent collected dataset for used-vehicle prices in the Kingdom of Saudi Arabia. The developed system has been validated using a recent vehicle price dataset, and the obtained results are compared with seven different machine learning models and showed a promising regression accuracy. In addition, we developed a reliable graphical user interface (GUI) for the purpose of allowing the user to estimate the price of any vehicle using the pre-trained DNN model.

Optimal Bidding Scheduling of Virtual Power Plants Using a Dual-MILP (Mixed-Integer Linear Programming) Approach under a Real-Time Energy Market

In recent years, the energy industry has increased the proportion of renewable energy sources, which are sustainable and carbon-free. However, the increase in renewable energy sources has led to grid instability due to factors such as the intermittent power generation of renewable sources, forecasting inaccuracies, and the lack of metering for small-scale power sources. Various studies have been carried out to address these issues. Among these, research on Virtual Power Plants (VPP) has focused on integrating unmanaged renewable energy sources into a unified system to improve their visibility. This research is now being applied in the energy trading market. However, the purpose of VPP aggregators has been to maximize profits. As a result, they have not considered the impact on distribution networks and have bid all available distributed resources into the energy market. While this approach has increased the visibility of renewables, an additional method is needed to deal with the grid instability caused by the increase in renewables. Consequently, grid operators have tried to address these issues by diversifying the energy market. As regulatory method, they have introduced real-time energy markets, imbalance penalty fees, and limitations on the output of distributed energy resources (DERs), in addition to the existing day-ahead market. In response, this paper proposes an optimal scheduling method for VPP aggregators that adapts to the diversifying energy market and enhances the operational benefits of VPPs by using two Mixed-Integer Linear Programming (MILP) models. The validity of the proposed model and algorithm is verified through a case study analysis.

Supply and demand of lithium in China based on dynamic material flow analysis

Lithium demand in China has grown rapidly with the popularization of computer, communication and consumer electronic (3C) products, electric vehicles (EVs), and other products. As an important part of the global processing of lithium products, China still relies on importing a vast majority of raw lithium materials, which significantly affects the supply fulfillment of China's lithium market. Therefore, this study used MFA to analyze the supply and demand characteristics of China's lithium trading market from the trade material flow between 2000 and 2021 and industrial material flow between 2017 and 2021 by quantizing trade and industry data. The results showed that the import of lithium in China is mainly concentrated on lithium carbonate, which is the raw material for power batteries, and the import of lithium accounts for 72.5 % of the consumption in China, about 75.2 % of carbonate lithium is used for batteries. Given the expanding EV market, the demand for lithium in China increases rapidly. In addition to a stable supply of lithium imports, secondary lithium recovered from EoL batteries is expected to become an important lithium supply source.

Impacts of carbon emissions allowance limitations on carbon price with power generation rights trading in China

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 b1∈[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 b1=0.76, the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient.

Study on the Effectiveness of CCER Mechanism in Serving the Carbon Emissions Trading Market

Study on the Effectiveness of CCER Mechanism in Serving the Carbon Emissions Trading Market

Optimal operation of energy-intensive load considering electricity carbon market

Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

The Reform of the EU Carbon Emission Right Trading Legal System and Its Enlightenment to Our Country

The EU carbon emissions trading mechanism is the most mature, the most demonstration effect of emissions trading mechanism, its legal system in ten years of operation, the third stage of reform is particularly significant, including regulatory mechanism, total limit and initial distribution mechanism, registration system, monitoring, reporting and verification mechanism, etc. At present, the legislative level of carbon trading in China is not high, and it lacks operability. In-depth analysis of the reasons, mechanism and main system of EU reform can provide important enlightenment for China. When building a national carbon trading market, China should learn from the experience of the European Union, improve the legislative system, and promote the construction of key systems.

An Examination of the Relationship between Volatility, Liquidity and Trading Activity

We investigate the influence of the firm value on the relationship between equity trading activity, market liquidity and return volatility at the portfolio level. For the different firmvalue portfolio, we find that trading activity is negatively associated with liquidity but it is positively associated with volatility so higher trading activity leads to more volatile returns. For the very largest firm-value portfolio, the volatility-liquidity relationship is negative. However, this relationship is positive for other portfolios.

Assessment of PV excess generation savings in Ghana considering peer-to-peer energy trading

ABSTRACT Solar is one of the Renewable (RE) sources with a high electricity generation potential. However, the challenge with using individual stand-alone Photovoltaic (PV) systems is the mismatch between load demands and electricity generated such that surplus and insufficient energy are experienced by prosumers. This study used the Net Present Value (NPV), and levelized cost of energy (LCOE) economic models to assess the impact of modern (peer-to-peer, P2P) trading and applicable PV system cost reduction strategy in Ghana, considering a cluster of stand-alone systems. The energy demand and generations of a total of 7 prosumer and consumer buildings in the Don Bosco Technical Institute, Ashaiman, were used. The results, considering a mini-grid connection between the buildings, showed an LCOE of GH₵ 1.358 over 10 years and NPV of about GH₵ 5 million for 25 years when excess generation is traded as compared to GH₵ 2.423 for LCOE and about GH₵ 180,000 NPV when excess generation is disregarded. A proposed shared investment community-owned system design showed further LCOE reduction to GH₵ 1.122 because the capital cost decreased. This shows that P2P energy trading can make PV generation attractive amidst other benefits like job creation, revenue generation and emissions reduction if RE is used. Lastly, a suitable energy trading market mechanism was proposed for implementing P2P in the context of Ghana. Though the research revealed that more benefits can be obtained from PV systems in Ghana, future technical analysis regarding transmission losses, the Internet of Things (IoTs), and blockchain need to be done.

Research on Energy Trading Mechanism Based on Individual Level Carbon Quota

High economic growth is accompanied by substantial consumption of fossil energy and significant negative externalities on the ecological environment. The global warming effect resulting from environmental pollution caused by energy has brought energy carbon emissions into the forefront of social attention. Establishing a carbon trading market is an essential measure to achieve the “double carbon” goal, with individual and household carbon emissions accounting for 70% of China’s total emissions. Constructing an individual-level carbon trading market will facilitate the efficient realization of this goal. However, addressing the challenge of handling vast amounts of data and network congestion in relation to frequent but small-scale individual carbon trading has become an urgent issue that needs to be resolved. In light of this, the present study designs a digital technology-based framework for the carbon market trading system and proposes an individual carbon asset price-based model for carbon market trading, aiming to establish a research framework for the carbon quota market. Furthermore, blockchain technology is employed as the underlying technology in the proposed carbon trading market model to cater to individual-level carbon trading services and achieve optimal matching between carbon quota suppliers, thereby enhancing profitability of the carbon trading platform. The numerical results obtained from the model demonstrate that in absence of government subsidy mechanisms, individual-level carbon trading can effectively reduce total consumer emissions. The present study successfully overcomes the carbon lock-in effect of consumer groups and achieves the generation and trading of individual carbon assets despite capital constraints. This study facilitates accumulation and trade of individual carbon resources, reduces overall consumer emissions, enhances environmental benefits at societal level, and provides a foundation for governmental decision-making.

Lightweight adaptive Byzantine fault tolerant consensus algorithm for distributed energy trading

With the rapid development of smart grid, constructing distributed energy trading market (DETM) based on blockchain to coordinate distributed energy resources (DER) has become a future direction. However, existing consensus algorithms of blockchain face many challenges in large-scale energy trading scenarios, such as high resource overhead, slow transaction procedure. To solve the above crucial problems for wide deployment of distributed energy trading, this study proposes a novel consensus algorithm named Lightweight adaptive Byzantine fault tolerant consensus (LA-BFT), and a reputation calculation method based on behavioral characteristics for selection of consensus nodes. The LA-BFT consists of two parts: (i) weak consensus for normal cases. By introducing threshold signature mechanism, weak consensus simplifies the consensus process to achieve linear communication complexity O(n). (ii) byzantine node detection scheme is enable for malicious cases. With consensus committee, the detection scheme can detect the potential byzantine nodes by cross-validation, which ensures transaction safety. The reputation calculation method is presented to cooperate with LA-BFT to elect leaders and candidates for consensus procedure. Once a round of consensus is completed, the reputation of each node needs to be updated, only nodes with high reputation are eligible to become leaders or committee nodes in the next round. With the reputation calculation method, honest nodes and byzantine nodes can be effectively identified, ensuring the security of the consensus process. Numerical results indicate that LA-BFT exhibits superior performance on communication overhead and bandwidth occupancy in large-scale concurrent energy trading scenarios. When the number of nodes is 50, under normal scenarios, LA-BFT’s communication overhead is notably lower, constituting a mere 6.02% of PBFT and 24.26% of SHBFT, while bandwidth occupancy amounts to merely 5.55% of PBFT and 9.76% of SHBFT.

Blockchain Technology in Carbon Trading Markets: Impacts, Benefits, and Challenges—A Case Study of the Shanghai Environment and Energy Exchange

Blockchain Technology in Carbon Trading Markets: Impacts, Benefits, and Challenges—A Case Study of the Shanghai Environment and Energy Exchange