Trade Price Research Articles

Price Premiums for Single‐Name and Compound‐Name Geographical Indications in Swiss Cheese Trade

ABSTRACTGeographical indications (GIs) have become increasingly important in agri‐food markets, especially in Europe. For Swiss cheese imports and exports, we analyze whether GIs are associated with higher trade prices. We find that price premiums can be obtained for both exports and imports. However, this is only the case for cheeses with single name GIs where the whole name is protected (e.g., Gruyère, Roquefort). There is no price premium for cheeses with compound names, consisting of a generic part and a geographical part (e.g., Gouda Holland, Raclette du Valais). As the generic part of the name (here Gouda, Raclette) can be used by competitors from other regions, such GI products may have difficulties in differentiating themselves. We conclude that higher prices in international cheese trade are not a matter of having a GI label or not. Rather, having a compound name seems to offset the benefit of the GI protection.

Identifying and Analysing Constraints in the Branded Rice Market: A Comprehensive Study of Stakeholders in Kerala, India

The study assessed the various constraints faced by the stakeholders in branded rice market in Kerala. These stakeholders include farmers, exporters, non-exporting private rice manufacturers, co-operative society, wholesalers, retailers and consumers. By examining the constraints across the branded rice market from production to consumption, the study aimed to identify the critical issues like, production constraints, procurement constraints, marketing constraints, institutional constraints export constraint and financial constraints. A sample of 30 farmers and one agent, five exporters and 10 non-exporters 20 wholesalers and 30 retailers and 120 consumers from Ernakulam and Palakkad districts were selected for the study. The study identified the major problems faced by the stakeholders of branded rice market by using Garret’s ranking method. Farmers were facing difficulties in rice production due to high input costs, high labour costs and untimely availability of water. Delay in procurement from SUPPLYCO and adequate facilities for drying and cleaning compel them to sell their produce in the open market. There was a communication gap between institutions and farmers. Lack of transportation facilities, high maintenance cost of machinery and financial difficulties were the major constraints faced by PADDICO. High transportation cost, high international trade barriers and price volatility were the major constraints faced by private manufacturers of branded rice. Heavy documentation & procedures was the major constraint faced by exporters. Lack of demand of branded rice was the major constraint faced by resellers. High price was the major constraint faced by the consumers. The high cost of production faced by farmers can be reduced by improved mechanisation in the rice cultivation. The study also recommended the suggestions for the constraints of other market stakeholders of branded rice market.

Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory

To tackle the issues of cooperative energy and reserve trading as well as fair cooperative benefit allocation among multiple integrated energy systems (IESs), this paper proposes a two-stage cooperative energy and reserve trading model for multiple integrated energy systems (MIESs). Specifically, at day-ahead stage, MIESs aim to maximize their overall profit through cooperative electricity and heat trading. At real-time stage, MIESs trade demand response (DR) reserve to minimize the overall wind power deviation compensation cost. To reduce the complexity in model solution, we transform the model into two sub-problems. In sub-problem 1, we determine the energy and DR reserve trading volumes. Here, distributionally robust optimization (DRO) is utilized to manage the severe uncertainties in wind power distribution. In sub-problem 2, based on the outcomes from sub-problem 1, we settle the energy and DR reserve trading prices. To ensure the fairness of benefit allocation, asymmetric Nash bargaining theory is applied to assess each IES's contributions in trading volumes and profit growth. Interval adaptive alternating direction method of multipliers (IA-ADMM) is used to preserve each IES's privacy. Finally, simulation results demonstrate that, compared with independent operation, cooperative trading among MIESs increases profits for all IESs, thereby motivating their participation in cooperative trading.

Cap-and-trade under a dual-channel setting in the presence of information asymmetry

Cap-and-trade, a widely used carbon regulation policy, encourages firms to adopt carbon abatement technologies to reduce emissions. Traditional supply-chain literature on this policy assumes symmetrical information, overlooking the fact that carbon abatement efforts and costs are often private and vary significantly across geographies, industries, and pollutants. In this paper we explore a dual-channel setting involving a manufacturer and a retailer, where the manufacturer, subject to cap-and-trade regulations, has undisclosed information about its carbon abatement costs. Our findings reveal that high abatement costs can paradoxically benefit the manufacturer, the environment, consumers, and overall social welfare. Our result also cautions that a higher carbon trading price (e.g., due to more ambitious emission reduction targets) can disincentivize the manufacturer from investing in carbon abatement. Moreover, a higher production cost, while resulting in lower market output, can increase pollution generation. We contribute the following to the practitioner debate about the impact of carbon policies: for an industry with a large market size, our findings lend support to governments to implement a cap-and-trade policy, because the manufacturer, customers and social welfare can be better off under a cap-and-trade policy than under a tax policy or no carbon policy. Additionally, we suggest that in such industries, governments need not enforce information transparency within the supply chain.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Analysis of the Impact of Sino-US Poultry Trade on China's Chicken Prices

Abstract: The trade relationship between China and the United States, two of the world's largest economies, is of significant global importance, particularly in the agricultural sector. This sector plays a crucial role in both nations' economies, contributing substantially to their GDPs and employment rates.This paper delves into the intricate dynamics of poultry trade between China and the United States, focusing on its impact on chicken prices in China. Utilizing comprehensive datasets spanning from 2007 to 2022 sourced from the United States Department of Agriculture (USDA) and the National Bureau of Statistics of China, the study employs static economic analysis and regression techniques to explore the factors influencing poultry prices. The findings substantiated the hypothesis posited: a rise in poultry trade volume between the U.S. and China correlates with a decrease in chicken prices in China. Findings reveal a complex relationship between trade dynamics and price movements, providing insights into the broader economic and trade policies shaping agricultural trade between these two economic powerhouses.

Influencing Factors and Formation Mechanism of Carbon Emission Rights Prices in Shanghai, China

With the intensification of global climate change issues and increasingly stringent carbon emission controls, carbon emissions trading has become a crucial market-based mechanism for reducing carbon emissions. A deeper understanding of the factors influencing carbon emission rights prices and their formation mechanisms can enhance market transparency and effectiveness and promote the healthy development of the carbon trading market. This paper identifies the main factors affecting carbon emission rights prices and explores their formation mechanisms from both the internal and external perspectives of enterprises. Using a vector autoregression (VAR) model, it quantifies the impacts of energy prices, the climate, and technology levels on carbon trading prices in Shanghai, China. The results show that, in the short term, enterprise technology levels and energy prices significantly impact carbon emission rights prices, while the climate becomes more critical in the medium to long term. These findings propose specific policy recommendations for improving the carbon emissions trading market, with a focus on these three factors.

The synergetic competition of cross-regional integrated energy systems with low-carbon technology heterogeneity under carbon emission trading mechanism to achieve carbon reduction target

The integration of renewable and fossil energy in a Region Integrated Energy System (RIES) is recognized as an effective measure for carbon emission reduction. The emergence of carbon emission trading (CET) markets worldwide has raised the need to combine RIES optimal planning with CET to achieve carbon reduction targets. This study focuses on the synergetic competition of a cross-RIES system with low-carbon technology heterogeneity under the CET mechanism. A cross-RIES model is constructed, incorporating fossil Carbon Capture System (fossil-CCS), renewable Combined Cooling Heating and Power (renewable-CCHP), and cleaner production paradigms. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to propose a bi-level multi-objective optimal planning framework for the cross-RIES model, employing a grandfather permit allocation method. Comparative analysis is conducted between cross-RIES optimal planning with and without considering the CET mechanism, with a focus on environmental indexes such as carbon emission, carbon intensity, and marginal abatement cost. Sensitivity studies are also performed to explore the impact of key CET design parameters on the system's environmental performance. The simulation results demonstrate that the CET mechanism effectively reduces carbon emission, primary energy consumption, and carbon intensity by promoting synergetic competition among the low-carbon technology heterogeneity of RIESs. Furthermore, the environmental performance varies among the three types of RIES. In terms of CET parameter design, lowering the cap constraint, reducing the allowance allocation ratio of fossil-CCS RIES, and increasing the trading price contribute significantly to reducing carbon emission and carbon intensity, albeit at the expense of higher marginal abatement cost.

A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction

ABSTRACTIn response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF‐CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF‐CPPM consists of a feature‐extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF‐CPPM outperformed baseline models in terms of carbon price‐prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF‐CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.

How to combine different types of prefabricated components in a building to reduce construction costs and carbon emissions?

In the context of industrialized construction and carbon emission reduction globally, prefabrication has gained considerable attention. However, most studies cared the carbon emissions and construction costs from the perspective of a whole prefabricated building rather than the prefabricated components. It makes it difficult to understand the role of prefabricated components play and make improvements from the prefabricated components level. To fill this gap, this study proposes a model to optimize the selection and combination of prefabricated components in a building project to simultaneously reduce the construction costs and carbon emissions. Six common component types were considered: shear wall, beam, floor slab, stair, balcony, and air-conditioning slab. Their costs and carbon emissions were quantified when adopting prefabrication and cast-in-situ technologies. To obtain the optimal solution, we developed an improved multi-objective mayfly algorithm (IMOMA), which incorporates enhanced circle chaos mapping and a global best guiding strategy to generate Pareto solution sets. The performance of IMOMA was evaluated using the Zitzler-Deb-Thiele series test functions, demonstrating its advantages in convergence, diversity, and comprehensive performance through metrics such as generation distance, diversity metric Δ, and inverse generational distance. A case study validated the effectiveness of this proposed method, demonstrating an average reduction of 9.05% in total construction costs and 8.04% in carbon emissions when compared to the initial scheme. Further study revealed that different prefabrication rates correspond to different optimal components combination schemes, with different trends in the costs and carbon emission curves as the prefabrication rate increases. Additionally, carbon trading policies influence the components combination, with construction costs reduced by 10.88% at a carbon trading price of 42.80 CNY/t CO2eq. This study provides valuable guidance for general contractors to design optimal component combinations and for governments to promote the sustainable development of prefabricated construction.

A Study on Remanufacturing Supply Chain Decision-Making Considering Greenness Under Carbon Trading Policy

The aim of this paper is to study the impact of carbon trading system and product greenness on remanufacturing strategy, so a Stackelberg game model is constructed to consider a remanufacturing supply chain consisting of a manufacturer and a third-party remanufacturer under carbon trading policy. Through theoretical analysis and numerical simulation, the relationship between decision variables such as product greenness, price of new products and remanufactured products is analyzed. It is found that: the level of carbon emission reduction is conducive to increase the price of new products and remanufactured products; under certain conditions, a stronger green market demand is conducive to increase the greenness of the products and the profits and prices of the remanufacturing supply chain members; an increase in the investment in green technology will lead to a decrease in the manufacturer's profit and the greenness of the products, and even reduce the price of the products under certain conditions; and under certain conditions, the carbon trading price is effective in regulating the price of new products and the price of the products. Under certain conditions, the carbon trading price can effectively regulate the price of new products and remanufactured products, while the greenness of products will decrease with the increase of carbon trading price.

Designing a carbon-trading incentive scheme for mode shifts in multi-modal transport systems

The pressing need to reduce greenhouse gas emissions triggers the imperative for efficient travel demand management. Previous studies have explored budget-based and aggregated incentive programs, which place a significant financial burden on governments and tend to be limited in contributing to effective behavior change in practice due to budget issues. This study proposes a personal carbon trading travel incentive (PCTTI) mechanism, to encourage private car commuters shifting to using public transit. The incentive budget for PCTTI is sourced from the revenue generated through selling carbon emission reductions resulting from commuters’ travel mode shifts. To determine the optimal incentives, we developed an incentive scheme optimization model based on the Stackelberg game model. Numerical analysis reveals the significant potential of the PCTTI to reduce carbon emissions and travel costs across various scenarios within a multi-modal transportation system. This potential is evident amidst changes in the fixed costs of car travel, carbon trading prices, the use of different travel modes, the value of time, and the prevalence of electric vehicles. The advantages are most pronounced when the carbon trading price exceeds 40 CNY/ton, and when the usage of public transit, the value of time, and the proportion of electric vehicles each fall below 0.4, 50 CNY/hour, and 0.4, respectively.

Strategic investments and portfolio management in interdependent low-carbon electricity and natural gas markets

Addressing global warming necessitates carbon emissions reduction and renewable energy integration within the energy sector. Gas-Fired Power Plants (GFPP) are appealing to investors due to their low emissions and operational flexibility, which are considered necessary characteristics within low-carbon power systems with increasing renewable energy uncertainty. Investing in GFPPs presents intricate challenges due to the increasingly interdependent electricity and natural gas markets, especially in the light of a low-carbon economy. This work addresses these challenges for a strategic agent by proposing a bi-level optimization framework. The upper-level model derives the optimal electricity portfolio management regarding new investments and strategic biddings, while in the lower-level model, the electricity and gas markets are cleared sequentially under a Carbon Emission Trading Scheme (CETS). Case studies on a Pennsylvania-New Jersey- Maryland (PJM) 5-bus power system and an IEEE 24-bus test system demonstrate the applicability and efficacy of the proposed model in capturing the impact of a transitional integrated market framework on GFPPs investments. Also, introducing stochasticity to the model provides a better insight into the contrasting effects of emission allowance trading and gas prices on investment and bidding strategies.

A Sustainable Model for Forecasting Carbon Emission Trading Prices

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Analysis of the influencing factors of emission trading price in China

China established pilot carbon markets in 2013. In 2020, it set targets for carbon peaking in 2030 and carbon neutrality by 2050. China’s national carbon market officially commenced operations in 2021. Based on the national market and seven pilot markets, this study established the factors influencing carbon trading prices by examining market participants, macroeconomics, energy prices, carbon prices in other markets, etc. Asymmetrical development among the seven pilot cities, for which the study employed a mixed-effects model, was the primary factor impacting carbon prices. The carbon prices in the pilot cities cannot be extrapolated to the entire country. In the national carbon market, where the study employed a multiple regression lag model, the SSE index was positively correlated with carbon prices, whereas the Dow Jones index had no significant effect on carbon prices in terms of macroeconomics. Coal and natural gas prices were negatively correlated with carbon prices, whereas oil prices were positively correlated with energy prices. The EU market prices have a positive correlation with prices in other markets. The significance of this study is that it covers the largest national Emissions Trading System (ETS) in the world and allows for comparing the characteristics of the Chinese market with those of other ETS markets. Additional studies, including more sectors, should be conducted as China’s ETS coverage increases.

Assessment of the broader applicability of a smart agent in peer-to-peer energy trading: A full factorial analysis of a multi-agent reinforcement learning solution

To realise the clean energy transition, peer-to-peer (P2P) renewable energy sharing markets have been proposed as one possible solution for achieving such a goal and are recognised as a potential path to achieving other goals such as affordable and reliable energy. Existing studies have shown that coordination at the micro level can be achieved by employing such P2P market structures. A pressing question concerns how to set the trade price such that the community coordinates in a way that maximises social welfare. A solution to this question based on multi-agent reinforcement learning (MARL) has been provided as a proof-of-concept in a single environment. However, various factors such as climate and community scale have been shown to affect the collective performance in such energy-sharing communities. In this work, to test the wider applicability of the proposed solution, a full factorial experiment based on the factors of climate, community scale, and price mechanism, is conducted to ascertain the response of the community w.r.t. the outputs: community self-sufficiency, total net-loss, and income equality. In short, we find that a community stands an odds of 2 to 1 in higher savings by adopting a smart agent.

How does carbon trading price matter for bank loans? Evidence from Chinese banking sector

This paper investigates the impact of carbon trading prices on Chinese commercial banks. We propose a stress test model to calculate the decline of firms’ liabilities and the climate-driven loss of commercial banks to different industries. The results indicate that State-Owned Banks suffer the highest climate-driven loss among all types of banks. City commercial banks suffer the highest climate-driven loss ratio since they maintain a relatively low level of Tier 1 core capital. Beijing, Shanghai, Jiangsu, and Zhejiang maintain the highest levels of climate-driven loss ratio. Furthermore, the allocation of loans portfolio across different industries also influences climate-driven loss of banks. The climate-driven loss to the electric power, heat, gas, and water production and supply sector is the largest in all industries. It is necessary for banks to incorporate climate factors into the risk assessment framework.

Economic Scheduling Strategy for Multi-Energy-Integrated Highway Service Centers Considering Carbon Trading and Critical Peak Pricing Mechanism

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Optimal recycling model selection in a closed-loop supply chain for electric vehicle batteries under carbon cap-trade and reward-penalty policies using the Stackelberg game

In recent years, a large number of electric vehicle (EV) batteries have retired owing to the significant growth of EVs. Developing an efficient closed-loop recycling system and effective management of retired EV batteries have become crucial. Motivated by this challenge, a closed-loop supply chain for the EV battery is formulated with four mixed-channel recycling models under the carbon cap-trade and reward-penalty mechanism. In the forward supply chain, the retailer purchases EV batteries from the manufacturer and then sells the batteries to the customers. In the reverse supply chain, the collection of retired EV batteries is carried out by either the retailer, third-party recycler, echelon firm, or a combination of these members. These collected EV batteries are classified into echelon utilizable or non-echelon utilizable batteries. The echelon-utilizable batteries are modified into echelon products, while the non-echelon batteries are recycled for extraction of raw materials to manufacture new EV batteries. The findings are derived by using actual market data and Stackelberg game theory. The influence of carbon trading prices, reward penalty intensity, costs associated with the firms, and competition coefficients are studied to provide a detailed and comprehensive analysis. A comparison of optimal profits pertaining to different models is also conducted. The results indicate that (i) different models of recycling EV batteries do not influence the optimal decisions in the forward supply chain; (ii) it is beneficial for the members to participate in the reverse chain; (iii) manufacturer’s profit increases when the carbon quota rises and decreases when the price of carbon trading increases; (iv) the involvement of echelon firms in the recycling sector has a positive effect on reducing the recycling load and increasing the profitability of the manufacturer. Managerial insights and concluding remarks are provided to guide manufacturers and recyclers in selecting the most suitable recycling model based on their specific requirements.

Exploring the first impression of a company: an empirical analysis of IPO prospectuses applying the impression formation model

PurposeThe present study aims to identify how firms positioned their corporate reputation (i.e. impressiveness vs respectability) in their initial public offering (IPO) communication based on the impression formation model. Further, the study examined whether this presentation of corporate reputation was related to IPO success (i.e. stock price and volume of trading).Design/methodology/approachThe present study analyzed 248 IPO prospectuses that were submitted to the major US stock markets. Specifically, various substantive and symbolic information and cues in IPO prospectuses were content analyzed.FindingsThe results suggest that bigger (in terms of revenue) IPO companies featured more “impressiveness” in their IPO prospectus, leading to greater IPO success. Bigger (in terms of both revenue and number of employees) IPO companies featured more “respectability” impressions in the IPO prospectus, although they did not achieve direct IPO success on the first day of IPO. Different types of industry used different information cues to feature “impressiveness” and/or “respectability,” suggesting that different types of firms view different cues to be important to IPO communication.Practical implicationsThe results also suggest some practical guidelines for the strategic use of contents, tables and illustrations. Using more charts, tables and illustrations in IPO prospectus summaries was associated with a higher volume of trading on the first day. The more illustrations included in the IPO prospectus summaries, the less investors were willing to pay for initial stock prices.Originality/valueIPO communication is a generally understudied area in corporate communication and strategic communication scholarship. The results should help to explain which communicative aspects and PR strategies effectively manage the firm’s impression to maximize the chances of an IPO success as well as initially build the financial reputation of a company. By doing so, the findings contribute to the broader advancement of financial communication within the strategic communications domain.