Search Results (15,729)

Motivated by the collaboration of a takeout platform and a crowdsourced delivery platform, we developed a stylized model to explore the interplay between the two platforms’ decisions. We captured the cross-platform network effects of the two complementary platforms, and investigated how the collaboration between the two platforms shapes the optimal prices, platform profits, and social welfare. We found that the takeout platform optimally adopts a subsidy pricing strategy when its commission rate is relatively high. In addition, when the demand-side network effect coefficient increases, the delivery platform optimally raises the shipping fee to trigger a larger supply of drivers. Furthermore, we found that the takeout platform introducing a self-logistics service reduces the subsidy intensity and raises the subsidy threshold. It also reshapes the strategic two-sided pricing to increase the network benefit when the network effect coefficient grows on one side. Specifically, as the supply-side network effect coefficient increases, instead of lowering the delivery price to increase demand and further increase the drivers’ network benefit, the takeout platform optimally raises it under certain conditions. Finally, self-logistics may benefit the takeout platform, while hurting the delivery platform, and it can increase social welfare. Our results, thus, unveil a price regime for platform collaboration and validate the effectiveness of the introduction of self-logistics by takeout platforms. Full article

In the last two decades, the pig market in Poland has been influenced by two key events: accession to the European Union and the spread of African swine fever (ASF). During this time, the pig population in Poland has almost doubled, and the number of farms keeping pigs has fallen almost tenfold. On the other hand, the import of piglets intended for further rearing has increased significantly, which reduces the value added to production retained in the country. The changes taking place in the pig market in Poland in the conditions of ASF have, therefore, prompted the question of which pig-keeping systems are more profitable for pig producers, and identifying this relationship was the main objective of the analysis in this study. This research was conducted using source data from the databases of the Central Statistical Office, the FADN, and the Integrated Agricultural Market Information System of the Ministry of Agriculture and Rural Development. Factors influencing the value of pig production were identified using panel regression, and profitability analysis was performed based on changes in the levels of and relationship between feed costs and live pig prices. Feed consumption was determined based on feed rations for individual utility groups in both products. As the analysis has shown, a closed cycle of pig farming is characterized by better stability and resistance to market shocks compared to an open cycle (over the entire period under review, only the closed system ensured a positive surplus of production value over feed costs), which significantly increases the possibility of obtaining a positive surplus of production value over the value of the main cost, which is the feed cost. However, with the occurrence of extraordinary situations, such as an ASF outbreak in the herd, rebuilding production in a closed cycle may be much more difficult and expensive than production in an open cycle. Full article

China’s implementation of the Comprehensive Commercial Logging Ban in All Natural Forests is deemed as disrupting the stability of both Chinese and international timber markets and has raised widespread concerns about deforestation leakage on a global scale. Clarifying the impact of the logging ban on the Chinese and international timber markets is essential for formulating effective policies and taking collaborative actions to improve the stability of both timber markets and promote the sustainable development of global forest resources. This study examines the causal effects of the logging ban on Chinese and international timber markets by conducting a counterfactual analysis of Chinese domestic and imported timber prices with the synthetic control method. Unlike most previous studies that revealed significant price increases in both markets as a result of the logging ban, our results show that there are no significant causal effects between the logging ban and the price changes in Chinese and international timber markets. As China made extensive efforts in plantation cultivation and harvesting and substantially improved its domestic timber supply capacity, the logging ban has only produced a limited impact on the Chinese domestic timber market and has not disrupted the international timber market through trade. Therefore, China’s logging ban policy has not protected its own forest resources at the expense of deforestation in other countries, and it has provided a practical reference for the formulation of forest protection policies and sustainable forest management. Full article

Lycium barbarum L. (goji), as an economic crop, has a high added value. However, the tender and fragile fruits are easily damaged during harvesting and transportation, leading to fruit bruising, which can cause rotting or black–brown spots after drying, seriously affecting the quality and price. In this study, two varieties of goji were used to determine and evaluate fruit bruising using a pendulum impact test, and the impact process was recorded using a high-speed camera and impact force sensor. This study discussed the energy changes during the impact process of fruits and conducted a correlation analysis of the impact energy, absorbed energy, restitution coefficient, impact force, and other indicators, analyzing the changes in each indicator with the falling height. The results showed that 0.2 m could be considered a critical height for damaging the fruit of goji. Furthermore, this study calculated the bruise susceptibility of the different varieties at different heights, which can be used for predicting bruising during the harvesting and collection of goji berries and ultimately for estimating the damage caused by mechanical harvesting. Full article

As the global aging trend increases, older adults are placing greater emphasis on their health. Evidence indicates that there is a complex association between house prices and older adults’ health, with their subjective well-being potentially acting as a mediator in this connection. A mediation model, utilizing data from China’s 2018 Labor Dynamics Survey, was employed to examine the impact pathway of house prices, subjective well-being, and self-rated health, while investigating the differences between young-old and old-old groups. The major findings are as follows: (1) House prices negatively affected self-rated health among the older adults. (2) The subjective well-being of older adults mediated the pathway through which house prices affected their self-rated health. (3) For old-old adults, higher house prices were more strongly linked to an increased likelihood of reporting good, very good, or excellent health. Subjective well-being was more significantly associated with reporting better health among the young-old group. Compared with the young-old population, the impact of house prices on self-rated health was stronger among the old-old, and the degree increased with increasing age. Consequently, to improve older adults’ well-being and self-rated health, effective healthy-aging policies should not only consider the influence of the real estate market, but also balance the allocation of elderly service facilities, promote affordable housing, and implement a combination of medical and nursing care from the perspective of urban planning. Full article

The dual-carbon objective aspires to enhance China’s medium- and long-term green power trading and facilitate the low-carbon economic operation of park microgrids from both medium- and long-term and spot market perspectives. First, the integration of medium- and long-term green power trading with spot trading was meticulously analyzed, leading to the formulation of a power purchase strategy for park microgrid operators. Subsequently, a sophisticated Bayesian fuzzy learning method was employed to simulate the interaction between supply and demand, enabling the prediction of the price for bilaterally negotiated green power trading. Finally, a comprehensive multi-objective optimization model was established for the synergistic operation of park microgrid in the medium- and long-term green power and spot markets. This model astutely considers factors such as green power trading, distributed photovoltaic generation, medium- and long-term thermal power decomposition, energy storage systems, and power market dynamics while evaluating both economic and environmental benefits. The Levy-based improved bird-flocking algorithm was utilized to address the multi-faceted problem. Through rigorous computational analysis and simulation of the park’s operational processes, the results demonstrate the potential to optimize user power consumption structures, reduce power purchase costs, and promote the green and low-carbon transformation of the park. Full article

The capital asset pricing model (CAPM) is a foundational asset pricing model that is widely applied and holds particular significance in the globally influential Chinese stock market. This study focuses on the banking sector, enhancing the performance of the CAPM and further assessing its applicability within the Chinese stock market context. This study incorporates a heterogeneous autoregressive (HAR) component into the CAPM framework, developing a CAPM-HAR model with time-varying beta coefficients. Empirical analysis based on high-frequency data demonstrates that the CAPM-HAR model not only enhances the capability of capturing market fluctuations but also significantly improves its applicability and predictive accuracy for stocks in the Chinese banking sector. Full article

As the world pivots to a greener paradigm, Shanghai emerges as an archetype in the sustainable urban transit narrative, particularly through the aggressive expansion and refinement of its electric vehicle (EV) charging infrastructure. This scholarly article provides a comprehensive examination of the current state of charging infrastructure in Shanghai, highlighting the challenges that the existing infrastructure may face in light of the burgeoning electric vehicle market. This paper delves into the strategic development approaches adopted by Shanghai to address these challenges, particularly emphasizing the expansion of high-power charging infrastructure to meet the anticipated increase in future electric vehicle charging demands. It also discusses the implementation of co-construction and sharing models, the enhancement of interconnectivity and standardized management of charging facilities, and the continuous improvement and strengthening of infrastructure construction and operations. Furthermore, this article explores the implementation of time-of-use electricity pricing policies and the ongoing conduct of demand response activities, which are instrumental in creating conditions for vehicle-to-grid interaction. The aim of our presentation is to foster a keen understanding among policymakers, industry stakeholders, and urban planners of the mechanisms necessary to effectively navigate the emerging electric vehicle market, thereby encouraging harmonious development between metropolises and transportation systems. Future research endeavors should delve into the realms of fast-charging technologies, intelligent operation and maintenance of charging infrastructure, and vehicle-to-grid interaction technologies. These areas of study are pivotal in fostering the harmonious development of electric vehicles (EVs) and their charging infrastructure, thereby aligning with the dual objectives of advancing urban transportation systems and sustainable green city development. The findings presented herein offer valuable insights for policymakers, urban planners, and industry leaders, guiding them in crafting informed strategies that not only address the immediate needs of the EV market but also lay the groundwork for a scalable and resilient charging infrastructure, poised to support the long-term vision of sustainable urban mobility. Full article

Background/Objectives: Through phase III clinical trials, PARP inhibitors have demonstrated outcome improvements in mCRPC patients with alterations in BRCA1/2 genes who have progressed on a second-generation androgen receptor pathway inhibitor (ARPI). While improving outcomes, PARP inhibitors contribute to the ever-growing economic burden of PCa. The objective of this project is to evaluate the cost-effectiveness of PARP inhibitors (olaparib, rucaparib, or talazoparib) versus the SOC (docetaxel or androgen receptor pathway inhibitors (ARPI)) for previously progressed mCRPC patients with BRCA1/2 mutations from the Canadian healthcare system perspective. Methods: Partitioned survival models were created to represent mCRPC disease after progression until death. Survival inputs for BRCA1/2-mutated patients were extracted from the PROfound, TRITON3, and TALAPRO-1 clinical trials, while Canadian-specific costs are presented in 2023 dollars. Upon progression, patients were treated with chemotherapy. The considered time horizon was 5 years and outcomes were discounted at 1.5% per year. Results: PARP inhibitors provide an additional survival of 0.19 quality-adjusted life years (QALY) when compared to the current standard of care, with additional costs of CAD 101,679 resulting in an incremental cost-utility ratio (ICUR) of CAD 565,383/QALY. The results were most sensitive to PARP inhibitors’ acquisition costs and health-state utilities. PARP inhibitors required price reductions of up to 83% to meet the CAD 50,000/QALY willingness-to-pay threshold (WTP). Conclusions: While providing survival benefits to previously progressed mCRPC patients presenting deleterious BRCA1/2 gene mutations, PARP inhibitors are not cost-effective and require major price reductions to reach local WTP thresholds. Full article

This paper presents an integrated robotic trading strategy developed for the day-ahead energy market that includes different methods for time series analysis and forecasting, such as Detrended Fluctuation Analysis (DFA), Rescaled Range Analysis (R/S analysis), fractional derivatives, Long Short-Term Memory (LSTM) Networks, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. DFA and R/S analysis may capture the long-range dependencies and fractal features inherited by the nature of the electricity price time series and give information about persistence and variability in their behavior. Given this, fractional derivatives can be used to analyze price movements concerning the minor changes in price and time acceleration for that change, which makes the proposed framework more flexible for quickly changing market conditions. LSTM, from their perspective, may capture complex and non-linear dependencies, while SARIMA models may help handle seasonal trends. This integrated approach improves market signal interpretation and optimizes the market risk through adjustable stop-loss and take-profit levels which could lead to better portfolio performance. The proposed integrated strategy is based on actual data from the Bulgarian electricity market for the years 2017–2024. Findings from this research show how the combination of fractals with statistical and machine learning models can improve complex trading strategies implementation for the energy markets. Full article

This research presents a new solution for optimizing the economics of energy produced by a hybrid power generation plant that converts nuclear, solar, and thermal energy into electricity while operating under load-following conditions. To achieve the benefits of cleaner electricity with minimal production costs, multi-criteria management decisions are applied. The investigation of a hybrid system combining nuclear, solar, and thermal energy generation demonstrates the impact of such technology on the optimal price of generated energy; the introduction of nuclear reactors in hybrid systems reduces the cost of electricity production compared to the equivalent cost of energy produced by solar systems and compared to fossil fuel thermal systems. This method can be applied to hybrid energy systems with nuclear, solar, and thermal power generation plants of various sizes and configurations, making it a useful tool for engineers, researchers, and managers in the energy sector. Full article

This paper reports a comparative analysis of energy transition policies in Hong Kong, London, and Melbourne, highlighting their approaches to achieving carbon neutrality. Utilizing a qualitative research approach, the study combines desktop research and policy analysis to examine secondary data from academic literature and policy reports. A structured policy analysis was developed to compare the strategies of each city, focusing on legislative tools, regulatory mechanisms, and decarbonization goals. The findings reveal that, while all three cities aim to reduce greenhouse gas emissions through energy transition policies, they adopt different strategies shaped by their socio-economic contexts. Hong Kong emphasizes regulatory measures like the Buildings Energy Efficiency Ordinance, London uses market-based instruments such as carbon pricing, and Melbourne prioritizes community engagement and renewable energy integration. Despite progress, challenges remain, including compliance with standards, funding, and public awareness. Recommendations include developing benchmarking strategies, fostering public–private partnerships, and investing in education. This analysis provides actionable insights for future policy development, emphasizing adaptability and innovation in combating climate change and fostering sustainable urban environments. Full article

This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. The results show that while each model possesses distinct characteristics, selecting the most efficient approach heavily depends on the specific data and forecasting objectives. The complexity of advanced models such as XGBoost and GRU is reflected in their overall performance, suggesting that they can be particularly effective at capturing patterns and making accurate predictions in more complex time series, such as stock prices. Full article

Traditional BP neural networks frequently encounter local optima during carbon price forecasts. This study adopts a hybrid approach, combining a genetic algorithm and particle swarm optimization (GA-PSO) to improve the BP neural network, resulting in the creation of a GA-PSO-BP neural network model. Seven critical factors were identified affecting carbon prices, and we utilized data on carbon emission trading prices from China for the analysis. Compared to traditional BP neural network models, including GA-BP neural network models optimized solely with genetic algorithms and PSO-BP neural network models enhanced through particle swarm optimization, the findings reveal that the GA-PSO-BP neural network model demonstrates superior performance in terms of precision and robustness. Furthermore, it demonstrates advancements across various error evaluation metrics, thus delivering more accurate forecasts. Offering precise carbon price predictions, the enhanced GA-PSO-BP neural network model proves to be a valuable tool for analyzing the market and making decisions in the carbon pricing sector. Full article

The development of energy-saving vehicles is an important measure to deal with environmental pollution and the energy crisis. On this basis, more accurate and efficient energy management strategies can further tap into the energy-saving potential and energy sustainability of vehicles. The equivalent consumption minimization strategy (ECMS) has shown the ability to provide a real-time sub-optimal fuel efficiency performance. However, when taking the different market prices of fuel and electricity cost as well as battery longevity cost into account, this method is not very accurate for total operational economic evaluation. So, as an improved scheme, the instantaneous cost minimization strategy is proposed, where a comprehensive cost function, including the market price of the electricity and fuel as well as the cost of battery aging, is applied as the optimization objective. Simulation results show that the proposed control strategy for series-parallel hybrid electric buses can reduce costs by 41.25% when compared with the conventional engine-driven bus. The approach also impressively improves cost performance over the rule-based strategy and the ECMS. As such, the proposed instantaneous cost minimization strategy is a better choice for hybrid electric vehicle economic evaluation than the other main sub-optimal strategies. Full article