Market Price Research Articles

Scenario-Based Planning of Participation of Virtual Power Plants in Storage and Energy Markets in Terms of Load Response and Market Price Uncertainty

Scenario-Based Planning of Participation of Virtual Power Plants in Storage and Energy Markets in Terms of Load Response and Market Price Uncertainty

The Influence of Marketing Mix Strategies for Savings Plus (BNI Taplus) Products on the Level of Customer Satisfaction at Pt. Bank Negara Indonesia (Persero) Tbk Supporting Branch Office (KCP) Wisma Bumiputera Jakarta

Mix marketing is something that can carried out by the company for influence request will its products consist of on four groups, namely: product, price, promotion and place or channel distribution. Research purposes This is For Know the mix strategy marketing product savings plus (BNI Taplus), view factors considered customers in choose BNI Taplus products and see How influence mix marketing to level satisfaction customers. In measuring level satisfaction customers, there is four dimensions mix marketing among them product, price, place and promotion to satisfaction customers. Research methods this, that is study quantitative. Study quantitative is a research analysis in a way general use analysis statistics. Whereas type research used is type study survey, that is research that takes sample from One population and use questionnaire as tool basic data measurements. Sample in research This that is BNI Taplus customers at PT. Bank Negara Indonesia (Persero) Tbk KCP Wisma Bumiputera Jakarta as many as 50 respondents. The design used in study This is method descriptive analysis that is trying method look for description comprehensive about data, facts, events Actually about object research and for research data analysis This using multiple linear regression test, coefficient test determination, f-test, and t-test. Based on results research and f-test results (simultaneous) show that factors mix marketing influential positive to satisfaction customers. The results of the t-test (partial) are influential positive in a way significant to satisfaction customers is variable product, meanwhile variable price, place, promotion influential positive but no significant to satisfaction customers. Meanwhile, contribution variable mix marketing to satisfaction customers indicated by the coefficient determination already customized of 0.223 meaning that mix marketing (product, price, place and promotion) is influential to satisfaction customers amounted to 29.00% while the rest 71.00 % is influenced by other variables outside the model used by the author. Research result This getting R= 0.535 shows R is almost approach number 1, meaning between variable mix marketing (product, price, place and promotion) towards satisfaction customers have influence.

Agent-Based Modeling and Simulation for Farmer Decision Making

Shallots are a highly demanded, perishable commodity whose fluctuating prices pose significant risks to farmers, affecting their income stability. This research aims to model and simulate the selling decisions of shallot farmers using Agent-Based Modeling (ABM). The model represents the individual behaviors of farmers and buyers, focusing on decisions to either delay or immediately sell harvested shallots. Key variables such as market prices, inventory levels, and profit expectations were incorporated into the model. Primary data were collected from observations of shallot farmers in Nganjuk, East Java. In contrast, secondary data were sourced from the Indonesian Central Statistics Agency (BPS). The simulation results indicate farmers maximize their profits by selling shallots when the minimum sales profit margin reaches 20%. Farmers tend to postpone sales when market prices fall by drying the shallots, reducing market supply, and elevating prices. The study concludes that delaying sales can be an effective strategy to stabilize prices and enhance profitability, particularly when dried shallots command higher prices than fresh ones. These findings offer valuable insights for developing agricultural policies to optimize farmers' income while mitigating price volatility risks.

Co-digestion of Sewage Sludge and Biowaste for Biogas Production, GHG Avoided Emissions, and Profitable Carbon Credit Development

In Estonia, where biowaste comprises a significant portion of municipal waste, efficient waste management strategies are essential. Co-digestion presents a promising avenue to enhance biogas yield and reduce greenhouse gas emissions. This study explores the potential of co-digesting sewage sludge and biowaste for biogas production, emphasizing carbon credit calculation and offset project development. Sewage sludge was effectively utilized as a co-feedstock for the co-digestion process, contributing to increased biogas production. In Narva City, 20,401.08037 m3 /year of biogas was produced in 2012, facilitating the generation of electricity from renewable sources and thus reducing GHG emissions, which facilitates the calculation of carbon credits. We developed a robust carbon offset project based on the biogas volume, meeting additionality criteria and demonstrating long-term benefits. The revenue potential from carbon credits ranged from 118 EUR to 41300 EUR, depending on market prices and project attributes. Moreover, the offset project and calculated carbon credits offer tangible benefits to sustainable waste management and the implementation of the circular economy. By valorizing sewage sludge and biowaste through anaerobic digestion, the project contributes to waste diversion from landfills, reduces methane emissions, and promotes renewable energy generation. This integrated approach aligns with principles of sustainable waste management and supports the transition towards a circular economy model. Our findings provide valuable insights for policymakers and stakeholders interested in leveraging anaerobic digestion for renewable energy production and carbon mitigation.

Business Intelligence Implementation for Groceries Commodity Price Data Analytics

As a startup company specializing in agricultural technology, currently PT XYZ does not have any effective and integrated tools to analyze groceries commodity for purchase and sale data-driven decision making. This research will integrating multiple data sources for accurate analysis, tailoring the business intelligence (BI) system to PT XYZ’s specific needs, and developing a robust solution that supports comprehensive decision-making. BI implementation solution will be developed using Pentaho Data Integration & Apache Superset. Applied research used as its method, that contains processes to gather user requirements to gather user needs from each information, design the solution, and dashboard development. The study focused on analyzing data from both internal sources, such as purchase and sales transactions, and external sources, including market price data, using these BI tools to provide comprehensive insights. The developed dashboard contains several sections that allow users to see price recommendation price and get the insight for the last seven days trend, monitor the market position, see team performance, and find the summary about estimated and actual margin comparison. Pentaho successfully handled ETL and data modeling, while Apache Superset enabled straightforward dashboard setup, though chart customization is limited.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Exploring the diffusion of low-carbon power generation and energy storage technologies under electricity market reform in China: An agent-based modeling framework for power sector

Promoting the widespread adoption of multi-complementary low carbon power generation technologies is a fundamental strategy for attaining carbon neutrality within the electricity sector. In the context of electricity market reform, this study develops an agent-based modeling framework integrated simulation with optimization. The model uses agent-based simulation to analyze annual market dynamics and low-carbon technology diffusion, with a two-stage optimization for energy storage and spot market simulation. The research findings indicate: (1) For different power generation technologies, the spot market can establish differentiated average prices, making the market price of carbon-captured coal power higher than that of conventional coal power, which are advantageous in reducing the subsidies required to promote the diffusion of carbon capture technology. (2) Expanding the spot market scale and implementing differentiated capacity compensation mechanisms both promote carbon capture technology diffusion. Nevertheless, with the marginal clearing mechanism, thermal power holds a average price advantage than renewable energy. Failing to control the growth of thermal power capacity will result in increased carbon emissions. (3) After 2030, energy storage's role in balancing supply and demand grows. Storage capacity should align with renewable energy scale and the regional characteristics of wind and solar resources to prevent overbuilding and stranded assets.

Stock Market Prediction using Machine Learning Techniques

Generally, the stock market index oscillating over the time periodand influencingmany factors. Different stock market indices determined from various mixture of stock may share similar trend in certain. The purpose of this research to predict market price index of Bombay Stock Exchange (BSE) on day wise closing using machine learning technique. The machine learning techniques ANN (Artificial Neural Network)including feedforward with lagged values of the as input, and Support Vector Machine (SVM) are compared. The price index was found to be most relevant and influenced the market performance. The results showed that performance of BSE index can be predicted with machine learningapproach. The machine learning approach shown ANN model better performance than the SVM model. The Mean Absolute Percentage Error (MAPE) of ANN is 0.5790 and the Root Mean Square Error (RMSE) is 472.12.

Multivariate Deep Learning Long Short-Term Memory-Based Forecasting for Microgrid Energy Management Systems

In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynamic relationship between load demand variations and electricity price fluctuations within the microgrid EMSs. The investigation involves a comparative analysis and evaluation of recurrent neural networks’ performance to recognize the most effective technique for the forecasting module of microgrid EMSs. This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked LSTM, Bi-directional LSTM, and Convolution LSTM to attention-based models. The empirical study involves analyzing real-world time-series data sourced from the Australian Energy Market (AEM), specifically focusing on historical data from the NSW state. The findings indicate that while the Triple-Stacked LSTM demonstrates superior performance for this application, it does not necessarily lead to more optimal operational costs, with forecast inaccuracies potentially causing deviations of up to forty percent from the optimal cost.

Information acquisition, market professional and discretionary liquidity trading

We analyze an insider trading strategy and market professionals’ information acquisition strategies, and explore their impact on market liquidity and price efficiency in endogenous noise trading. Our findings highlight the negative precision effect on market professionals’ information acquisition. With an increase in market professionals, two opposing effects emerge on market liquidity and price efficiency: the strategic complementarity effect and the precision effect. However, the negative precision effect predominates, which impairs market liquidity and price efficiency. Furthermore, enhancing the ex-ante precision of the risky asset or adjusting liquidity compensation reduces market professionals’ involvement in information acquisition, leading to diminished price efficiency.

The Use of Social Marketing in Community-Wide Physical Activity Programs: A Scoping Review.

Previous community-wide physical activity trials have been criticized for methodological limitations, lack of population-level changes, and insufficient reach among underserved communities. Social marketing is an effective technique for community-wide behavior change and can coincide with principles of community-based participatory research (CBPR). A systematic scoping review of community-wide interventions (system-level) targeting physical activity and/or weight loss was conducted to (i) describe and critically discuss how social marketing strategies are implemented; (ii) identify which populations have been targeted, including underserved communities; (iii) evaluate the use of CBPR frameworks; (iv) assess retention rates; and (v) identify gaps in the literature and formulate future recommendations. Eligible studies included those that: aimed to improve physical activity and/or weight loss on a community-wide level, used social marketing strategies, and were published between 2007 and 2022. Approximately 56% of the studies reported a positive impact on physical activity. All studies described social marketing details in alignment with the five principles of social marketing (product, promotion, place, price, and people). Only two studies explicitly identified CBPR as a guiding framework, but most studies used one (k = 8, 32%) or two (k = 12, 48%) community engagement strategies. Few studies included at least 50% representation of African American (k = 2) or Hispanic (k = 3) participants. This review highlights key gaps in the literature (e.g., lack of fully-developed CBPR frameworks, reach among underserved communities, randomized designs, use of theory), highlights examples of successful interventions, and opportunities for refining community-wide interventions using social marketing strategies.

Multi-Regression Modeling of Harvest Income in Smallholder Long Coriander Production: Insights from Battambang, Cambodia

The target of this study is to promote Long Coriander production; the most widely recognized leafy vegetable in Southeast Asia and Cambodia. As a substitute for regular coriander, it is valued for its taste and health benefits. The market price for Long Coriander seeds typically goes up and down depending on the season, similar to other fluctuations observed in other agricultural products. In an effort to fulfill the study's expectations, researchers intend to identify the key factors affecting the profitability of Long Coriander production for smallholders in Battambang, Cambodia. Using multi-regression models, the analysis will determine the statistical significance of specific cost categories and their influence on incomes from both single and annual harvests of Long Coriander. Two regression analyses will be applied: ordinary least squares (OLS) and stepwise regression. Understanding the factors influencing Long Coriander harvest income is crucial for developing targeted strategies to improve the livelihoods of rural producers. With the knowledge gained from this study, smallholders will be able to make strategies that will maximize profitability and maintain the long-term sustainability of Long Coriander production within the context of agriculture. Additionally, the study also defines variables that can be controlled by producers, besides the variables that have a significant effect by chance.

How are green stocks and monetary policy related?

We propose and test the hypothesis that green stocks and monetary policies are interdependent. Using time-series data for two large economies (India and Indonesia), that have adopted inflation targeting regimes, we develop a structural vector autoregressive model of monetary policy and green stocks that combines with output. We find that green stock prices fall (rise) in response to inflation with a lagged effect and policy rate shocks for India (Indonesia). Currency depreciation, on the other hand, increases green stock prices for India but has a muted effect for Indonesia. We also discover that, for India, monetary policies hardly respond to green stock price shocks while, for Indonesia, green stock prices do influence the evolution of monetary price variables. Our main contribution is that we decipher the effects of green stocks from aggregate market shocks and show each of these market price shocks is related to the monetary aggregates.

“The principles of political economy, though often quoted, are little understood.” Fleeming Jenkin on trade unions and the law of supply and demand

The paper proposes a rational reconstruction of Fleeming Jenkin’s 1868 analysis of wage determination in labour markets characterised by the presence of trade unions. Jenkin intended to show that the supply and demand theory of market price determination did not support the thesis sustained by the opponents of trade unions that the latter are unable to permanently achieve a market-clearing wage higher than that which would be obtained in their absence. Starting with a definition of “demand” and “supply” of a given commodity as functions of a few economic variables, Jenkin elaborated a fresh critique of the wages fund theory and highlighted the impact of trade unions on both supply and demand for labour.

Optimizing vertical farm cultivation of Cichorium spinosum L.: White Light's influence and nutrition management

The objective of this study was to examine the integration of a wild leafy vegetable, Cichorium spinosum L., in vertical farms. This research comprises two experiments focusing on different “white” light products and nutrient solutions. During both experiments, the temperature varied between 25 and 28 °C, relative humidity ranged from 50 to 70 %, carbon dioxide was at 450 ppm, and light intensity was set at 300 μmol m−2 s−1 respectively. In the lighting experiment, the three spectra used had the commercial names Neutral, Full and a SunLike™, and their spectral composition (blue:green:red:far-red) were 14:32:43:10, 16:36:40:8, and 21:34:36:7 respectively. The photoperiod was set to 12 h and the plant density was 50 plants m−2. Results showed no significant impact on agronomical parameters and leaf anatomy. The stomatal length and width decreased as the red:blue ratio of the light sources decreased, being greater in the Neutral treatment (red:blue ratio of 3.1) compared to the Full and SunLike™ (red:blue ratios of 2.5 and 1.7 respectively). Based on these results the preferable “white light” product was the one with the highest efficiency and lowest market price at the time of the experiment. In the nutrient solution experiment, the agronomical and nutritional attributes of stamnagathi plants supplied with a control nutrient solution, “N10-Fe15” were compared to plants cultivated under limited nitrogen, “N4-Fe15” and elevated iron, “N10-Fe48”, EC was 1.5 ds m−1, and pH was 5.6–6.5. The experiment simulated commercial practices by increasing the photoperiod to 15 h and plant density to 100 plants per square meter. The results did not demonstrate significant effect of the nutrient solution differences on the agronomical characteristics except from a decrease in total Kjeldahl nitrogen under limited nitrogen conditions. Notably, leaf tissue phosphorus content increased under elevated iron conditions. The nitrate content remained within safe for consumption thresholds for all treatments. Based on these results, stamnagathi can be integrated in vertical farms under limited nitrogen conditions. Stamnagathi's resilience to elevated iron in the nutrient solution demonstrated its potential for future biofortification experiments.

Additional public procurements in health services: Lessons from Finnish physiotherapy markets

Private providers are often acquired through public procurements to provide publicly financed health services. The number of contracted providers and their capacity often turns out to be deficient during the contract period. Typically, the purchasers then organize additional procurements or negotiate directly with available uncontracted providers to acquire more capacity. In this study, we examine an unusual additional public procurement in Finnish physiotherapy markets, in which incumbent providers with an existing contract were also eligible to participate. In practice, these contracted providers did not bear any risk when they submitted their bids in the additional procurement. We compare contracted and uncontracted providers’ prices to free market prices using a difference-in-differences approach. The results show that contracted providers increased their prices on average by 9%, while the originally uncontracted providers decreased their prices by 8% compared to the development of free market prices. Although this inefficient procurement led to a waste of public funds, a simple back-of-the-envelope calculation indicates that additional procurement can still be a more efficient way to increase capacity than directly contracting providers that were not contracted in the initial procurement.

Research on the Inhibitory Effect of the EU’s Carbon Border Adjustment Mechanism on Carbon Leakage

Associated with more ambitious targets for reducing emissions, the European Union (EU) plans to implement the Carbon Border Adjustment Mechanism (CBAM) fully in 2026, aiming to reduce carbon leakage and competitiveness concerns by imposing tariffs on carbon-intensive imports, which is expected to significantly impact its trade partners. Existing research has focused on CBAM’s impact on macroeconomic indicators but has insufficiently addressed its effects on global and regional carbon leakage, especially in non-EU countries like China. This research offers a detailed analysis of industry-specific leakage rates and integrates both global and regional impacts by employing the dynamic recursive GTAP-E general equilibrium model to numerically simulate CBAM’s inhibitory effect on carbon leakage under different carbon tariff scenarios, while also exploring the synergistic effects of anti-leakage policies in non-EU countries. Our simulations indicate the following: (1) CBAM effectively inhibits carbon leakage, with greater inhibition observed at higher tax rates and with the expansion of covered industries. (2) Establishing China’s domestic carbon market pricing can further reduce regional carbon leakage rates. Implementing global export carbon tax policies will significantly diminish the risk of global carbon leakage. (3) The implementation of CBAM is projected to reduce China’s total exports to the EU, though this loss will be partly offset by trade diversion effects. Carbon-intensive industries are more adversely affected in the short term, while all industries except fossil fuels face inevitable long-term negative impacts.

Research on Tesla's Pricing Strategy in Chinese Market

As a leading global electric vehicle manufacturer, Tesla's performance in the Chinese market has a significant impact on the entire industry and market. The paper aims to analyze and evaluate Tesla's market pricing strategy in the Chinese market through SWOT analysis. Based on the analysis of pricing strategies, the paper also explores the impact of Tesla on the Chinese automotive economy market. Finally, the paper proposes some suggestions to address the challenges of future development and market expansion in the Chinese automotive market.

Assessment of irrigation performance trends using selected comparative indicators over consecutive periods: A case study of the Koga Irrigation Scheme in the Amhara region, Ethiopia

AbstractThe Koga Irrigation Scheme is a modern irrigation scheme in Ethiopia, and furrow irrigation is a common water application method. The main purpose of this study is to show in which year it performed well and what the problems were and to identify the best alternatives that may be essential for managing the scheme effectively and efficiently. The performance trends of the Koga Irrigation Scheme were evaluated using four selected performance indicators, namely, physical, agricultural, water use and economic performance indicators. Primary and secondary data, including geographic locations, water supply, harvested yield, market prices, irrigated crops, actual irrigated area, planned irrigable area, total management and operation cost, water fees and historical climate data, were collected. Land use performance analysis indicates that the scheme operates below its potential. The maximum irrigated area was 6,327.7 ha less than the designed 7,000 ha in 2015 and 3,619.9 ha in 2016. Water consumption peaked in 2015 and decreased in 2016. The dominant crops were wheat (55%), potato (19%), barley (7.6%), onion (7.4%), maize (5.6%) and other crops (5.4%). This suggests a preference for low‐value, water‐intensive crops. Water use by crops is lower than reservoir release, indicating significant water loss. Economic water productivity is low, primarily due to the dominance of low‐value, water‐intensive crops. To enhance scheme performance, the study recommends adjusting cropping patterns, estimating actual water demand for the respective crops and irrigation scheduling, adopting efficient water application methods and strengthening the market chain.

Resources, energy consumption and economic analysis for flame synthesis of ternary cathode materials

Cathode materials account for around one third of the manufacturing cost of lithium-ion battery, which are the major factor preventing the wider applications of power battery packs. Flame synthesis (FS) is a new manufacturing technology of nanostructured materials with few equipment and high efficiency, which potentially can substantially bring down the manufacturing cost of cathode materials. The primary objective of the present study was to quantitatively evaluate the resources, energy consumption and economic feasibility of large-scale production of cathode materials though FS. In the analysis process, the mass flow of reactants and resultants as well as the minimum cathode material selling price (MCSP) of ternary cathode material LiNixCoyMn(1-x-y)O2 (x + y + z = 1) produced by FS were calculated, and compared with that of the conventional hydroxide co-precipitation method. Results show that the production of ternary cathode material through FS can reduce CO2 emissions by ∼60% and water consumption by ∼30%. MCSP of LiNi0.5Co0.2Mn0.3O2 (NCM523), LiNi0.6Co0.2Mn0.2O2 (NCM622), and LiNi0.8Co0.1Mn0.1O2 (NCM811) produced by FS are 224.8 k¥/t, 235.1 k¥/t, 240.0 k¥/t, which respectively are about 2%, 10% and 14% lower than the average selling prices in Chinese market, indicating that FS is economically feasible for producing ternary cathode materials. Particularly, for the production of high-nickel cathode materials, FS has notable potential in simplifying the production process and manufacturing costs. Sensitivity analysis of NCM811 produced by FS shows that when raw material price, fuel and power costs are reduced by 25%, MCSP can be as low as 186.4 k¥/t. When the metal cation concentration of the solution increases to 3.2 mol/L, it enables FS to meet the most rigorous energy consumption limitation standards in China. When the lithium excess is reduced from 10% to 5%, MCSP of NCM811 can be further reduced by 5.6 k¥/t, indicating that lithium loss due to high temperatures in FS is a moderate factor for reducing the manufacturing cost. Finally, this work presents a life cycle assessment (LCA) of NCM materials produced by FS in China. Comparison to the global warming potential (GWP) of the co-precipitation method in the literature indicated that GWP of FS is lower.