Trade Price Research Articles

Production mode selection for automakers considering product substitution under the dual-credit policy

Purpose This paper aims to compare and analyze the effect of the dual-credit policy and product substitution rate on the automakers’ operational strategies under different production modes (e.g. centralized and independent), and further illustrate which production mode is more conducive to improving new energy vehicle (NEV) development.Design/methodology/approach The decision-making models for a centralized production mode where an integrated automaker produces both NEVs and fuel vehicles (FVs) and for independent production mode where an NEV automaker faces competition from a traditional FV automaker were formulated. The equilibrium solutions of each production mode were obtained by extreme value and game theory methods. The conclusions of the theoretical analysis were further verified with numerical analyses using IBM-MATLAB R2019a. Some management insights could be obtained by comparison analysis.Findings Under the dual-credit policy, an increase in the NEV credit trading price will always raise production quantity of NEVs, but only in an independent production mode where a higher trading price will also bring higher total profits to NEV automakers. In addition, only when the NEV credit trading price is high enough, a rising product substitution rate will be more favorable to NEV production and restrain FV production. Furthermore, an independent production mode is more favorable for the initial production of NEVs, but as each of the two vehicle types captures a certain amount of market share, a centralized production mode will be more conducive to the full replacement of FVs by NEVs.Originality/value The main contributions of this study include the formulation of decision-making models for FVs and NEVs in not only a centralized production mode but also an independent production mode. Moreover, this paper comprehensively analyzes how the dual-credit policy and product substitution relationship affect automakers’ production and pricing decisions. Then, the specific conditions under which each production mode is more conducive to NEV production and sales are summarized. The results proposed in this study provide scientific managerial insights for automakers and policy makers.

Application of Digital Economy Machine Learning Algorithm for Predicting Carbon Trading Prices Under Carbon Reduction Trends

Due to the increasing demand for fossil fuels, excessive emissions of greenhouse gases such as CO2 have been caused. With the intensification of global climate anomalies and warming, how to reduce greenhouse gas emissions is an important issue currently facing the international community. The influencing factors of carbon price are complex, and accurate prediction of carbon price is a difficult problem. There are still some problems in the existing carbon trading price prediction models, such as insufficient understanding of the enormous potential of machine learning models to ilift the performance. The study will use two machine learning models that can address the shortcomings of traditional artificial intelligence models as the basic prediction models. The specific content includes machine learning prediction models that extend to extreme learning machine theory and fuzzy inference system theory. By integrating data preprocessing algorithms, artificial intelligence optimization algorithms, feature selection algorithms, etc., this study constructs and applies a carbon trading price prediction model from multiple perspectives to compensate for the shortcomings in current research. The corresponding values for each indicator in the algorithm are 5.6214E-12 (maximum), 2.8546E-12 (minimum), 4.0239E-12 (mean), and 5.4402E-13 (variance). Compared with other comparative optimization algorithms, this indicates that the hybrid optimization algorithm is an efficient optimization method for the model, which can effectively optimize different problems. In theory, the proposed multiple improved carbon trading price prediction models can theoretically compensate for the shortcomings in existing carbon trading price predictions.

A novel machine learning ensemble forecasting model based on mixed frequency technology and multi-objective optimization for carbon trading price

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.

Complex dynamics of dual oligopoly demand uncertainty under carbon emission trading mechanism

The international community's growing focus on energy conservation and emission reduction has led businesses to prioritize low-carbon operations and the marketing of environmentally friendly products during their production processes. On the one hand, with the reform of carbon emissions trading, carbon emission trading has become a potential profit point for enterprises. On the contrary, the consumer market is influenced by the uncertain external environment. And consumers' environmental awareness increases the uncertainty of product demand. Therefore, the intricate dynamics of dual oligopoly demand uncertainty within the framework of a carbon emission trading mechanism are explored in this scholarly analysis. The best market Porter's generic strategies, including emission reduction investment and product pricing how can carbon quotas, uncertainty in market demand, and consumer environmental preferences improve expected profits? The results show that technology emission reduction costs, carbon emission trading prices and consumer preferences for low-carbon products. It is an important factor that affects the competitive strategy of the two enterprises. The different values of the above factors will change the relative intensity of emission reduction investment and the relative profitability of the two. Complete company statement in a balanced state. This article also investigates the complexity of adjusting the dynamic evolution path of competitive strategies between two organizations. Feedback controllers can effectively suppress unstable equilibrium states and provide effective references for their stable operation.

Stock Forecasting Based on Linear Regression Model and Nonlinear Machine Learning Regression Model

To enhance the accuracy of stock price prediction for Netflix and provide individuals with a comprehensive understanding of stock trading prices, this study constructs a predictive model by employing three distinct approaches: a linear regression model, a Long Short-term Memory (LSTM) artificial neural network, and a Gated Recursive Unit (GRU) which serves as a component of the LSTM architecture. A prediction scheme is devised, utilizing historical stock data spanning from 2002 to 2022 for Netflix. The primary objective is to forecast the stock price of Netflix for the subsequent 20-day period. To evaluate the efficacy of the three models, a rigorous assessment is conducted employing robust evaluation indices. The outcomes of this analysis will enable a determination of the fitting adequacy of each model, thereby facilitating the identification of the most suitable approach for accurate stock price prediction in the context of Netflix. This research endeavors to contribute to the field of stock market analysis by leveraging advanced predictive modeling techniques for enhanced forecasting accuracy and insightful decision-making.

Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence

Carbon trading prices are considered important reference indicators for policy formulation and enterprise decision-making, and play an important role in low-carbon development. However, predictive approaches that balance the accuracy and interpretability of carbon trading prices remain limited. This study proposes a hybrid approach that integrates group variable selection regularization and uncertainty inference in Bayesian neural networks (BNN) with inherent interpretability to predict carbon trading prices. We also identify effective predictive indicators in an emerging market context. Based on the carbon data of emerging markets, a dataset of price information was constructed for model training. The results indicate that under the same regularized dataset, all BNN results surpass other artificial neural networks (ANNs). The group smoothly clipped absolute deviation-BNN performs best in all models and has interpretability. The model effectively identified new influencing factors for predicting carbon trading prices in emerging countries, including coal prices and blockchain-related information. The proposed new prediction approach provides a new basis for the prediction and evaluation of carbon trading prices and provides the necessary references for policy formulation in the digitalization era of social change.

Corporate income taxation and external balances in the European Union

This study examines the impact of corporate taxation on the external balances of 27 European Union member countries from the late 1990s to 2021. Using an ARDL process and a 2-stage least squares estimation procedure, we find that, in the short term, higher corporate taxation is positively and significantly related to the current account balance and the trade balance for the whole sample. There are considerable differences in the effects in the euro area and non-euro countries, with the latter experiencing a much stronger short-term impact. In the long term, there are no critical differences in the results between the two groups, and the impact of corporate taxation is positive but statistically significant only for the trade balance. The size of the impact of corporate taxation on net exports and current account balances is of similar magnitude, which likely implies that the international profit shifting via manipulating intrafirm prices in international trade does not strongly affect the external balances in our sample. Our results imply that initiatives to increase global tax rates could be justified from an international trade perspective.

Study on the diffusion of CCUS technology under carbon trading mechanism: Based on the perspective of tripartite evolutionary game among thermal power enterprises, government and public

Carbon Capture, Utilization, and Storage (CCUS), as a carbon emission reduction technology, is an important way for China to achieve the goal of “carbon peaking and carbon neutrality”. Therefore, it is crucial to explore how to diffuse CCUS technology. Under the guidance of considering the carbon trading mechanism, this paper establishes a tripartite evolutionary game model of thermal power enterprises-government-public, discusses the evolutionary stability of each stakeholder and the dynamic evolution process of the system; then the simulation parameters are determined by the optimal power generation decision-making model based on Gounod's equilibrium; finally, the system dynamics (SD) model based on the evolutionary game is used for the simulation of the system's evolution paths, and the sensitivity analyses of the important parameters are carried out. The conclusions of the study are as follows: (1) Currently, the diffusion of CCUS technology in China is in the development stage, corresponding to the system equilibrium state of {investment, no incentives, no participation}, i.e., investment by thermal power generating enterprises, no incentives from the government, and no participation from the public. (2) The carbon trading market has guidance for the deployment of CCUS technology, and an appropriate reduction of carbon allowances and an increase in the unit price of carbon trading can stimulate the willingness of thermal power enterprises to invest in CCUS technology. (3) The choice of equilibrium strategy for thermal power enterprises is influenced by parameters and other stakeholders' initial strategies. Lower investment costs, higher carton emission reduction efficiency, moderate increases in penalties and subsidies, as well as a higher probability of government incentives and public participation in monitoring will increase the probability of thermal power enterprises investing in CCUS technology, and facilitate the evolution of the developmental stage {Investment, No incentive, No participation} to the ideal stage {Investment, Incentive, Participation}.

FRACTAL MARKET HYPOTHESIS FOR TRADING AND MARKET PRICE FORECAST

The article explores the core principles of FMH and its application in trading and market price forecasting. FMH offers a new perspective for understanding market dynamics, allowing for the detection of patterns that traditional analysis methods often overlook. Special emphasis is placed on the scaling properties of market data, which enables the use of forecasting models across different time intervals, from short-term to long-term predictions. FMH also considers the probability of extreme events, enhancing risk assessment and improving forecasting accuracy. The article discusses the advantages of the Fractal Market Hypothesis compared to traditional financial hypotheses, such as the Random Walk Hypothesis and the Efficient Market Hypothesis, and its potential for integration with machine learning methods to create more accurate and reliable forecasting models.

From Prediction to Profit: A Comprehensive Review of Cryptocurrency Trading Strategies and Price Forecasting Techniques

From Prediction to Profit: A Comprehensive Review of Cryptocurrency Trading Strategies and Price Forecasting Techniques

Contract selection for collaborative innovation in the new energy vehicle supply chain under the dual credit policy: Cost sharing and benefit sharing

The dual point policy is an important policy in the field of China's new energy vehicle industry, various factors such as point trading prices and technological innovation costs were included in the profit game model to explore the effects of cost contract model and revenue contract model on the optimal profit of new energy vehicle supply entities after collaborative decision-making. Research has found that the dual credit policy for China's new energy industry has a promoting effect on collaborative innovation among entities in the new energy vehicle supply chain; Compared with decentralized decision-making situations, the integration of cost sharing contracts or revenue sharing contracts can more effectively stimulate the innovation vitality of new energy battery suppliers and enhance their technological innovation level; Under the cost sharing contract and the benefit sharing contract, the optimal profit after collaborative decision-making between new energy vehicle manufacturers and new energy battery suppliers is greater than the optimal profit during decentralized decision-making, while the optimal profit of new energy vehicle supply chain entities under the benefit sharing contract is slightly higher than the optimal profit of new energy vehicle supply chain entities under the cost sharing contract.

БИРЖЕВАЯ ТОРГОВЛЯ САХАРОМ В РОССИИ: СОВРЕМЕННОЕ СОСТОЯНИЕ И ВОЗМОЖНОСТИ ОБЕСПЕЧЕНИЯ МОНИТОРИНГА ВНУТРЕННИХ ЦЕН

In modern Russia, the role of organized trading, which includes exchange trading, is gradually increasing. As our research has shown, exchange trading in sugar in Russia is developing, as evidenced by the increase in exchange turnover of sales of this product and the expansion of exchange instruments used for trading. In addition, exchange trading can provide up-to-date information on domestic average market prices for commodities. A strong connection was established between average domestic Russian prices for sugar and price quotations for this product on the world’s largest commodity exchanges and on commodity exchanges within Russia. In the course of studying the cause-and-effect relationship between average domestic and average exchange prices for sugar in Russia, it was found that domestic prices are the reason for the establishment of certain exchange prices for sugar and the domestic and exchange Russian prices are very close in size. This allowed us to conclude that it is possible to use exchange trade prices for sugar as an operational information source on the size of domestic prices, which can be used for state regulation of the market and for making business decisions directly by business entities.

How to use BAPV to alleviate the urban heat island effect: An evolutionary game perspective.

In recent years, the phenomenon of the urban heat island caused by the rapid development of cities is very serious. To solve the problem of the urban heat island, this study proposed a PPP project consisting of the government (GOVT), photovoltaic investment company (PVIC), and residential customers (RS). Based on an evolutionary game model and combined with current policies and industry regulations in China, the evolution process and stable evolution strategies were studied. The result shows that more government subsidies, higher carbon trading prices, and feed-in tariffs will promote the development of the PPP project. For relatively suitable reference value ranges, the installation tilt angle of the BAPV system is 30°, the photovoltaic grid electricity price is 0.1096∼0.1296 $/kWh, the carbon trading is 8.92∼9.42 $/t.

Remanufacturing closed-loop supply chain decision comparison under multiple carbon policies

Abstract This article constructs a closed-loop supply chain system including a single manufacturer and a single retailer and studies the optimal decision problem of enterprises under three different carbon reduction policies. Three supply chain game models were constructed to research the impact of different carbon reduction policies on the decisions of supply chain members. The results revealed that:(1) The carbon emission reduction level of the manufacturer is negatively correlated with the recycling price of used products and the abatement cost factor;(2) When the carbon trading price is equal to the tax rate, the carbon cap and trade policy is more conducive to promoting the manufacturer to improve the level of carbon emission reduction;(3) When the carbon trading price is low, mixed carbon policy is more favorable for the manufacturer to increase its carbon emission reduction. The research results of this study can provide certain suggestions for the government to implement a reasonable carbon reduction policy.

Cost-benefit analysis of using sustainable aviation fuels in South America

The sustained growth of global aviation emissions has highlighted the urgent need for using Sustainable Aviation Fuels (SAFs) to replace traditional aviation fuels to mitigate emissions. SAFs can potentially reduce greenhouse gas emissions by 50%–90% compared to conventional jet fuels. This paper collected aviation emissions data from South American routes between 2019 and 2021, and the GWP100 method was used to convert CH4 and N2O emissions into CO2 equivalents. Additionally, emissions from the South American route between 2023 and 2070 were predicted, as well as the amount of subsidy required to promote the use of SAFs under four different scenarios. The result shows that adopting SAFs could reduce approximately 69.3 million to 113.53 million tons of CO2-equivalent emissions in South America by 2070. However, the South American aviation industry will likely embrace using SAFs only if the carbon trading price exceeds 0.273 times the difference between SAFs and jet fuel prices.

Dynamic interactions of carbon trading, green certificate trading, and electricity markets: Insights from system dynamics modeling.

In the context of the evolving landscape of reduction in carbon emissions and integration of renewable energy, this study uses system dynamics (SD) modeling to explore the interconnected dynamics of carbon trading (CT), tradable green certificate (TGC) trading, and electricity markets. Using differential equations with time delays, the study provides a comprehensive analysis of structural relationships and feedback mechanisms within and between these markets. Key findings reveal the intricate interplay between carbon prices, green certificate prices, and electricity prices under various coupling mechanisms. For example, under the three-market coupling mechanism, carbon trading prices stabilize around 150 Yuan/ton, while green certificate prices reach a peak of 0.45 Yuan/KWH, impacting electricity prices, which fluctuate between 0.33 and 1.09 Yuan / KWH during the simulation period. These quantitative results shed light on nuanced fluctuations in market prices and the dynamics of anticipated purchases and sales volumes within each market. The insights gleaned from this study offer valuable implications for policy makers and market stakeholders in navigating the complexities of carbon emission reduction strategies, the integration of renewable energy and market equilibrium. By understanding the dynamics of multi-market coupling, stakeholders can better formulate policies and strategies to achieve sustainable energy transitions and mitigate impacts of climate change.

Green Technology Investment Strategies Under Cap-and-Trade Policy

This article aims to address methods for reducing carbon emissions by investigating the green technology investment behavior of enterprises under cap-and-trade regulation and governments’ cap-and-trade policy design mechanisms. Considering that investments are long-term, we developed a two-period green technology investment model for a manufacturing firm facing carbon-sensitive demand in a cap-and-trade regulated market. Our findings show that the firm's investment strategy depends on trade-offs between the gains from selling low-carbon products and the costs of investing in technology. Such trade-offs are affected by consumers’ carbon emissions sensitivity, the unit carbon emission trading price, and the firm's investment efficiency across two periods. The first-period optimal free allowance allocated to the manufacturer is unique, but the second-period optimal free proportion is not unique as it is decided by the unit carbon emission trading price. In addition, the firm's chosen investment strategy based on the optimal carbon policies can create a mutually beneficial situation for the manufacturer, consumers, and society, which shows that the government-designed carbon policies, including the first-period free emissions allowance and the second-period free proportion, are effective. Our study results can aid manufacturers in making wise, strategic, and operational decisions about green technology investment and provide guidance on carbon quota allocation policies that promote the development of low-carbon economies.

Research on information collaboration strategy of fresh agricultural products supply chain under carbon cap-and-trade

The information coordination of fresh agricultural products supply chain is conducive to improving the overall efficiency and competitiveness of the supply chain, and reducing carbon emissions. Under the carbon cap-and-trade policy, this paper constructed an evolutionary game model of supply chain information collaboration involving a fresh agricultural products supplier, a cold chain logistics service provider and a fresh agricultural products retailer, explored the selection of information collaboration strategies of each subject, and simulated it with system dynamics. The results show that: The stability strategy of the game system is related to the initial willingness of each subject. When any two parties participate in information collaboration, the other party will also choose to participate in collaboration. In addition, the choice of information collaboration strategy of the subject is also related to the collaborative planning ability, collaborative strategy formulation ability, consumers’ preference, the initial cost of each subject, revenue sharing and cost sharing mechanism, government subsidies, government regulation and carbon cap-and-trade policy. It is worth noting that the collaborative planning capability is more conducive to promoting the subject to choose information collaborative strategy than the collaborative strategy formulation capability; the carbon trading price has a greater impact on the subject’s participation in collaboration, while the total amount of carbon quota has no impact on the choice of the subject’s information collaboration strategy. Furthermore, based on the research results, the corresponding management suggestions are put forward from the two aspects of the government and the participants.

Optimization of intermodal routes for reefer containers under double uncertainty

Aiming at the influence of demand uncertainty and stochastic carbon trading price on the choice of intermodal transportation route for reefer containers, a hybrid robust stochastic optimization model with minimum economic cost is established, and a Monte Carlo sampling-based catastrophic adaptive genetic algorithm is designed to test the reasonableness and validity of the model. The results show that: influenced by the regret value constraint, the robust optimization of demand uncertainty will increase the total cost of reefer container multimodal transportation, while the increase of carbon trading price stochasticity doesn’t necessarily mean the increase of cost, and the reasonable setting of carbon trading price stochasticity and the trade-off between the maximum regret value and the economic cost, so that the cold-chain logistics and transportation enterprises can easily deal with the fluctuation of the demand of the freight transportation market, and also respond to the low-carbon requirements.

Diffusion of ESG Behavior Improvement Strategy for New Energy Enterprises under Weighted Benefit Risk Mechanism ——Based on Complex Network Evolution Game

In order to explore the impact of the enterprise's right, responsibility and profit risk mechanism on the enterprise's ESG behavior improvement strategy in the context of carbon emission reduction, this study uses Matlab to build a new energy enterprise cooperation game network model and establish the NW small world initial network. According to the process and result of multi-agent complex network game, this paper analyzes the strategic choice and development factors of ESG behavior in new energy enterprises. It is found that when the awareness of executives increases, the profits of enterprises will increase, but the spread of ESG behavior improvement is not significant; When the competitive intensity of enterprises increases, the profits of enterprises with ESG behavior improvement will decrease, and the diffusion of ESG behavior improvement strategy will be slow. When the price of carbon trading increases, the income of enterprises adopting ESG behavior will increase, but the sensitivity of carbon trading price to the expansion of enterprise network is not significant.