Regional Price Research Articles

Vehicle-To-Grid (V2G) Charging and Discharging Strategies of an Integrated Supply–Demand Mechanism and User Behavior: A Recurrent Proximal Policy Optimization Approach

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.

Inter-regional rail travel and housing markets connectedness between London and other regions

Taking London as a location with which to measure the ripple effect in the UK housing market, this study aims to explain and verify the high degree of correlation between inter-regional transportation and the regional correlation of the housing market. Based on the literature on the relationship between short-term mobility and long-term migration, this paper illustrates that the extent to which people use trains for travel across regions will be related to the ripple effect in the regional housing markets. Frequent railway transport behavior, whether for commuting or traveling, might increase people's desire to relocate between regions, and thus leading to information transmission effects across regional housing prices and transaction volume. First, we estimate a dynamic indicator for the ripple effect. Then, the empirical tests use panel data, including the ripple indicator and passenger number data across time (1996–2022) and regions (nine regions). It is found that if London house prices drive other regional house prices to rise, inter-regional transportation demand will increase, and in turn, the increase of house prices in other housing markets will again drive up London house prices. The number of passengers will affect the information transmitted by the housing market transaction volume in other regions to the London housing market. This implies that higher inter-regional transport needs may lead to migration between London and other property markets, causing their transaction volumes to change in the same direction. The results of this paper verify that travel behavior between regions is a crucial factor in the leading/lagging behavior of regional housing market performance, implying a relationship between short-term travel and long-term migration. The results also indicate that incorporating variables of housing market correlations may help in the prediction of passenger numbers or transportation demand.

An Empirical Study of the Economic Net-Zero Energy Mix in Industrial Complexes

This study examines the optimal energy mix for industrial complexes by incorporating renewable energy systems, decarbonization strategies, and sector coupling technologies. Using data from the Balan Industrial Complex in Korea, five energy scenarios were evaluated, ranging from conventional systems (Scenario 1) to advanced renewable configurations (Scenario 5). The results show that Scenario 5, which integrates sector coupling systems and decarbonization technologies, is the most cost-effective and environmentally sustainable. Scenario 5 achieves the lowest Net Present Cost (NPC), and significantly reduces CO2 emissions. Furthermore, an analysis of electricity prices and CO2 costs from Korea, the United States, and Germany highlights the critical role of regional electricity tariffs and carbon pricing in determining the economic feasibility of energy systems. While renewable setups require higher initial investments, Scenario 5 proves to be the most economically viable over time, offering both cost savings and environmental benefits. These findings provide valuable insights for policymakers and industry leaders, emphasizing the importance of customized strategies to optimize energy systems in industrial applications.

Overcoming Product Catalog Challenges in Telecom : A Technical Perspective

This comprehensive article explores the intricate challenges telecom providers face in managing complex product catalogs across multiple customer touchpoints. With the global telecom market projected to reach $2.47 trillion by 2028, efficient catalog management is crucial for maintaining competitiveness and customer satisfaction. The article delves into the multifaceted complexities of telecom product catalogs, including regional pricing variations, dynamic promotional offers, and the need for real-time synchronization across platforms. It outlines key challenges such as catalog complexity, slow data retrieval, frequent updates, third-party integration latencies, high traffic spikes, and campaign management inconsistencies. The article then presents innovative technical solutions and best practices, including the adoption of industry-leading catalog solutions, leveraging ElasticSearch for faster data retrieval, implementing differential processing for updates, preprocessing third-party integrations, utilizing dynamic UI templates, employing advanced campaign management tools, and implementing inventory prefetching. These solutions aim to enhance operational efficiency, improve customer experiences, and accelerate time-to-market in the rapidly evolving telecom industry.

Regional Operation of Electricity-Hythane Integrated Energy System Considering Coupled Energy and Carbon Trading

The deepening implementation of the energy and carbon market imposes trading requirements on multiple regional integrated energy system participants, including power generation plants, industrial users, and carbon capture, utilization, and storage (CCUS) facilities. Their diverse roles in different markets strengthen the interconnections among these subsystems. On the other hand, the operation of CCUS, containing carbon capture (CS), power-to-hydrogen (P2H), and power-to-gas (P2G), results in the coupling of regional carbon reduction costs with the operation of electricity and hythane networks. In this paper, we propose a regional economic dispatching model of an integrated energy system. The markets are organized in a centralized form, and their clearing conditions are considered. CCUS is designed to inject hydrogen or natural gas into hythane networks, operating more flexibly. A generalized Nash game is applied to analyze the multiple trading equilibria of different entities. Simulations are carried out to derive a different market equilibrium regarding network scales, seasonal load shifts, and the ownership of CCUS. Simulation results in a 39-bus/20-node coupled network indicate that the regional average carbon prices fluctuate from ¥1078.82 to ¥1519.03, and the organization of independent CCUS is more preferred under the proposed market structure.

Aviation Discontinuity and Regional House Prices: A Quasi-Experiment in New Zealand

Although there is evidence that more regional airport activities can increase regional house prices, no study has examined the relevant impacts of aviation discontinuity. My unique quasi-experiment data comes from New Zealand where Jetstar withdrew its domestic services from four airports/regions in December 2019. The synthetic control method (SCM)'s results reveal that the withdrawal created different impacts on the house prices of the regions involved, in which the overall effect was negative in the short run but the housing market in New Zealand has recovered in the long run.

Monetary policy and house price heterogeneity: Evidence from the U.K

This paper investigates the effects of monetary policy shocks on regional house prices in the U.K. The empirical results indicate significant heterogeneity in the impact of monetary policy on regional house prices. Focusing on the source of heterogeneity, the paper suggests a demand-side factor to be a major driver. The regions with relatively affordable housing exhibit higher sensitivity to monetary policy than those with less affordable housing. The prevailing view that region-specific housing supply constraints and regional housing supply elasticity are responsible for the heterogeneous response of regional house prices to monetary policy shocks is not confirmed by the empirical findings.

Do regional trusts alleviate bond market risks? Evidence from the Chinese municipal corporate bond pricing

Municipal corporate bonds have attracted significant attention due to their pricing mechanisms and reflect China's distinct financial landscape. Existing research has highlighted the influence of informal institutional factors on bond pricing. This study introduces the social trust index to measure regional trust levels and assesses its correlation with municipal corporate bond issuance pricing. The empirical analysis indicates that higher regional trust levels are significantly associated with reduced issuance credit spreads for municipal corporate bonds. The relationship is moderated by local economic development and the strength of implicit government guarantees. Employing the “Yongmei” event as a case study, this research explores the dynamics of bond pricing mechanisms following a reduction in government implicit guarantees and finds that a weakening of these guarantees intensifies the influence of regional trust on credit spreads. Additionally, default events are observed to have spillover effects, impacting neighboring provinces and the overall market. This research extends the investigation of regional trust's pricing mechanisms to the municipal corporate bond market, thereby enriching the study of informal institutions' impact on financial market pricing. The findings underscore the importance of effectively harnessing market mechanisms by governments to mitigate risks in the municipal corporate bond sector.

Vertical price transmission in agricultural markets in Ghana

The purpose of this study was to investigate the extent of price transmission along the agri-food supply chain in selected regions in Ghana. The extent of transmission was used to assess the efficiency and effectiveness of the agricultural marketing systems in the area. Efficient marketing systems are expected to reduce the issue of regional price fluctuations, encourage fair income distribution throughout the supply chain, and improve food security. The preliminary time series stationarity/unit root and seasonality tests were performed using the Augmented Dickey-Fuller test (ADF)/Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) and Canova-Hansen seasonal stability tests respectively. Following the detection of statistically significant threshold effects by the Supremum Likelihood Ratio (Sup-LR) tests, the extent of price transmission was investigated using two-region threshold vector error correction models. The initial tests found that the monthly wholesale, retail, and producer price series for maize, cowpea, and yam used in the study were first difference stationary rather than seasonally integrated. The findings revealed that price signals in the agri-food markets were not fully transmitted across the supply chain. Thus, the extent of price transmission was generally low, creating unexploited arbitrage opportunities along the chain. The study identified high transfer costs (proxied by the threshold parameter) and actor-specific market power (as revealed by the Granger causality test) as the primary obstacles to effective market functioning in the area. Minimizing transfer costs through infrastructural development such as improvements in road networks linking farming communities and markets, access to price information, and regular stakeholder engagements could help improve the functioning of the agri-food markets.

Passenger car market and regional price discriminations for new cars: Case study and empirical analysis for Poland 2004–2021

Abstract Poland disposes on a large stock of mostly aged private passenger cars. In contrast younger cars still play minor role in individual mobility. The contribution analyzes reasons of lagging modernization by focusing on new cars prices with respect to regional price discrimination. It finds in 2004-2021 substantially increasing new cars prices and regional price discrimination between Poland and Germany. Price differentials were positive, with lower prices in Poland, and increased during time. Producers discriminated by prices increasingly but also limited arbitrage transactions by controlling excess differentials. Optimal regional price discrimination could support car markets developments in Poland by stock age compositions shifts towards younger cars. However, applied discrimination proves in observed market environments not sufficient to achieve a great car market transition in near future.

VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy

Electricity price prediction is essential for the optimal dispatch in power markets, with accurate prediction models being critical for efficient power system operations and market trading decisions. Deep learning networks, with their powerful nonlinear modeling capabilities, have shown promising results in electricity price forecasting. However, their design techniques, especially the selection of network parameters, remain challenging. This indicates that the optimization and exploration of deep learning networks in electricity price forecasting models require further investigation. This paper innovatively proposes a forecasting model that uniquely integrates Variational Mode Decomposition (VMD), Grey Wolf Optimization (GWO), Attention Mechanism (ATT), and Long Short-Term Memory Network (LSTM), optimizing the model from three different perspectives. First, during the data preprocessing phase, the training set is subjected to VMD to reduce noise, thereby enhancing the capture of multi-scale characteristics inherent in electricity price time series. The ATT layer is integrated to adaptively allocate weights, enhancing the model's focus on significant features. The GWO is applied to optimize hyperparameters of the LSTM, accelerating convergence and improving iteration accuracy, thereby reducing model error. A series of experiments were conducted using multiple regional electricity price datasets, evaluated with several metrics including RMSE. The results validated the effectiveness of the proposed three modules in improving the performance of the time series network, with VMD making the most significant contribution. Among all models, VMD-GWO-ATT-LSTM consistently outperformed others, demonstrating its effectiveness and robustness in electricity price forecasting.

Spatial heterogeneity analysis of biased land resource supply policies on housing prices and innovation efficiency

To rationally allocate land resources, the government has formulated biased land resource supply policies. However, this policy will promote the fluctuation of regional housing prices, thereby affecting regional innovation efficiency. To elucidate the intrinsic logical relationship among these factors, this study investigates the influence of biased land resource supply policies on housing prices and innovation across 31 Chinese provinces over a 16-year period. It places particular emphasis on probing into the spatial disparities in this impact. The innovation of this paper lies in the development of a theoretical analysis model, which creatively utilizes the regional land supply status as an instrumental variable to address the endogeneity problem. At the same time, a double robustness test was conducted by substituting the explanatory variables and employing the fixed effects model. The results of the TSLS regression indicate: (1) The land resource supply policy, which prioritizes the central and western regions, has resulted in an increase in housing prices across the majority of regions in China, with the eastern region experiencing a more rapid rise compared to the central and western regions. (2) The accelerated growth of housing prices in the eastern region is likely to exert a more pronounced inhibitory effect on innovation compared to the central and western regions. In summary, China’s land resource supply policy, which favors the central and western regions, has exacerbated the rise in housing prices in the eastern region, and rising housing prices will inhibit the innovation efficiency of the region. Hence, it is imperative for the government to continuously optimize and adjust land resource supply policies, ensuring their alignment with population migration patterns, stabilizing the real estate market, while also considering the innovation efficiency of regional development.

Utilization Patterns of Milk and Milk Products among Various End Users in Karnataka, India: A Comparative Analysis

The utilization patterns of milk and milk products in Karnataka were investigated through a study of 100 farmers and 50 consumers. The focus was on how milk was retained, consumed and transformed into various products. It was found that significant variation existed in milk production and consumption across different herd sizes. Small, medium, and large farmers were reported to produce an average of 33.80, 45.18 and 66.26 liters of milk per day, respectively, with family consumption accounting for 19.23 per cent, 17.37 per cent and 15.84 per cent of their production. Larger herds were associated with increased milk production, which allowed for a greater surplus to be sold. Utilization patterns showed that small farmers retained 6.5 liters of milk daily, consuming 32.94 per cent as liquid milk and converting 64.23 per cent into curd, with minimal ghee production. In contrast, large farmers retained 10.5 liters daily, consuming 43.80 per cent as liquid milk and converting 53.12 per cent into curd. This indicated a trend towards higher liquid milk consumption and varying product transformation depending on herd size. Consumer data indicated that rural households spent an average of ₹141.18 on 2.41 kg of milk products daily, while urban households spent ₹142.10 on 2.25 kg. Despite higher costs, urban consumers spent more on liquid milk, whereas rural consumers allocated more to curd and ghee. These findings reflected regional price differences and consumption preferences. Overall, the study highlighted the complex dynamics of milk utilization, with varying consumption patterns and product transformations observed across different farmer categories and consumer demographics. This information was deemed crucial for optimizing milk production and distribution strategies in Karnataka.

Assessing the impact of hydrogen trade towards low-carbon energy transition

Hydrogen is increasingly recognized as a pivotal commodity in the low-carbon energy transition, bridging regional disparities in hydrogen production capacity and facilitating international energy trade. Existing research has yet to delve into the impact of global hydrogen trade on energy transitions, as well as the key factors driving the development of hydrogen trade. This study, utilizing the updated GCAM-TU model, which mainly extends the original Global Change Analysis Model (GCAM) with a hydrogen trade module, provides a multi-scenario outlook on the characteristics of hydrogen trade and its impact on global and regional energy transition. The findings indicate that by 2050, the global hydrogen trade volume is expected to reach 9-19EJ, accounting for a quarter of the global hydrogen consumption in different scenarios, with green hydrogen trade dominating due to its cost advantages. Pipeline hydrogen and ammonia shipping will govern the trade market, representing 33%–61% and 24%–64% of trade volume, respectively, while liquid hydrogen shipping holds an advantage for medium distances. Hydrogen trade is projected to increase global hydrogen demand by 1.2–5.3EJ and reduce hydrogen prices by 11%–36% in the European Union, Japan, and South Korea, thereby expanding their hydrogen consumption by 8%–55% and reducing regional carbon prices by 1%–6%. Moreover, hydrogen trade significantly alters the regional distribution of global hydrogen production, with major exporting regions such as North and East Africa increasing their hydrogen production for export by up to 150%. To facilitate the development of hydrogen and hydrogen trade, it is crucial for potential importing and exporting regions to enhance global financial and technological cooperation, develop key hydrogen production and transportation technologies, and retrofit existing nature gas infrastructure to reduce costs. Additionally, forming stable trade partnerships is essential for maintaining energy security in the hydrogen trade.

Regional price differences of medical services: evidence from China

BackgroundPrice levels of medical services may vary across regions with different income levels, which would raise concerns about the equal access to medical services. This study aimed to estimate the spatial price index of medical services to measure price levels across 31 provincial regions in China.MethodsPrice data were collected from medical service price schedule in each region. Two methods based on the Purchasing Power Parities were used to estimate the spatial price index and measure price differences across regions. The two-way fixed effects models were used to examine the association between medical service price levels and income levels, and further investigate the impacts of price differences on utilization of medical services and medical expenditure.ResultsThe consistent estimation results were given by two methods. Medical service price level in the highest-price region was found to be 74% higher than the lowest. There was a significant negative correlation between price levels and income levels, as well as price levels and the utilization of outpatient services. Moreover, we also found a 1% increase in medical service price level was significantly associated with a 0.34% and 0.24% increase in the medical service expense per outpatient visit and per inpatient respectively.ConclusionsRegions in China had significant gaps in medical service price levels. Policymakers should pay more attention to regional price differences and take great measures such as enhancing financial protection to ensure the equal access to medical services and better achieve the universal health coverage.

Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression

Purpose This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices. Design/methodology/approach The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation. Findings The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker. Originality/value This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

The nonlinear impacts of macroeconomic shocks on the US regional housing markets

ABSTRACT This paper examines the asymmetrical impacts of aggregate macroeconomic shocks on the US regional housing markets by estimating nonlinear Vector Autoregressive models. We show how aggregate output, monetary, inflation, and credit shocks result in heterogenous impulse responses of the housing market volatilities across growing and declining housing market regimes in nine census divisions and 19 metropolitan cities. We find that output and inflation shocks cause qualitatively asymmetrical responses across regimes in majority of regions. A positive output shock and a negative inflation shock reduce (increase) housing price uncertainties when housing markets are in a declining (booming) regime in most regions. The responses of regional housing price volatilities to monetary and credit shocks are quantitatively asymmetrical across regimes in most regions. We also find that aggregate shocks have larger contributions on changing the probabilities of regime switching when a regional housing market is initially expanding than declining. Among aggregate shocks, inflation shock has the most prominent contributions across most regions. It also presents the strongest asymmetrical effects across positive and negative shocks. Our results can help regional decision makers utilize fiscal policies, regulatory measures, and regional planning to mitigate the destabilizing effects of macroeconomic shocks on regional housing markets.

The Impact of Urban Planning on House Prices in China

Urban planning is a major part of urban development and leads the future direction of the city. In different times and social situations, urban planning can change, and this change can have an impact on house prices. This paper analyzes the changes in house prices due to urban planning, with China as the main subject. China's real estate market has changed significantly. Housing prices have changed more dramatically and are more susceptible to external factors. Through the collection of data on house prices in Beijing, Jiangsu, and other regions and the development of urban planning, it summarizes the important factors of urban planning affecting house prices and provides an in-depth analysis of different factors. Through the conclusions obtained from different analyses, this essay summarizes the relationship between urban planning and house prices and provides certain suggestions to the government on plans and improvements to address the issues involved.

Prediction of new housing prices in Changsha urban area based on multiple machine learning algorithms: A comparative analysis

<p>As China’s pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.</p>

Where is my footprint located? Estimating the geographical variance of hybrid LCA footprints

AbstractCurrent implementations of hybrid life cycle assessment (LCA) mostly do not fully exploit valuable information from multi‐regional input–output databases by aggregating regional supply chains to the lower geographical resolution of process LCA databases. We propose a method for sampling the various individual regions within the aggregated regional scope of LCA processes. This sampling maximizes the information content of hybrid LCA footprint results by preserving the regional variance, and it allows for regional price distributions from BACI/UN‐COMTRADE international trade statistics to be used to simultaneously improve the accuracy of the hybrid model. This work makes the previously hidden regional and price variance explicit and analyzes uncertainty of the hybrid carbon (global warming potential 100, GWP100) and land use footprints arising from these variances, both separately and in combination. We find that the median process footprint intensity increases by for the GWP100 due to hybridization, and for the land use footprint. Results show that the magnitude of the footprint uncertainty strongly depends on the product sector of the LCA process and environmental impact considered. In a case study of Swiss household consumption, we find truncation error estimates of for the GWP100 and for the land use footprint. Our results highlight the importance of regionalization of process LCA databases, as it has the potential to significantly improve both the precision and accuracy of derived hybrid LCA models. This article met the requirements for a gold/gold JIE data openness badge described at http://jie.click/badges.