Cystic fibrosis has been transformed by the development of CFTR modulator therapies, with substantial improvements in survival and quality of life. However, access to these therapies remains profoundly unequal worldwide. The greatest benefits have been realized in high-income countries, while people with cystic fibrosis in low- and middle-income countries and underserved populations within high-income settings continue to face limited access and poorer outcomes. Underdiagnosis is a major contributor to these disparities, as limited newborn screening, restricted access to sweat testing, and incomplete genetic characterization directly limit treatment eligibility and registry inclusion. Beyond diagnosis, disparities are driven by differences in genetic variant distribution, pricing and reimbursement policies, regulatory processes, and health system capacity. This review examines how these interrelated factors shape global access to therapies, with particular emphasis on CFTR modulators. Emerging strategies-including differential pricing, licensing mechanisms, regulatory adaptation, international collaboration, and health system strengthening-are discussed. Achieving equitable access will require coordinated action across diagnostic, economic, and policy domains to ensure that advances in cystic fibrosis care benefit patients regardless of geographic or socioeconomic context.

The aim of the study is to reconstruct the role of women economists who have had a significant impact on the development of the national accounts measurement in the post-World War II Era. In particular, the role played by sisters Maria and Vera Cao Pinna in defining household consumption was explored. Their expertise helped to overcome theoretical-definitional and statistical obstacles arising from the complex activity of defining the statistical framework for economic and social accounts, which still contribute to define the aggregate of private consumption consumption as a component of National Income. In the 1940s, private consumption expenditure presented problems related to both the estimation of quantities consumed and prices. These problems were subsequently overcome by applying the criterion of “consistency” between the aggregates that make up the National Accounts after the Second World War.

This article examines how the development of Article 6 of the Paris Agreement is shaping the evolution of carbon markets in the Association of Southeast Asian Nations (ASEAN) and the implications for the region's energy interconnectivity and transition. ASEAN member states are at varying stages of establishing carbon pricing systems, from implementing emissions trading schemes to carbon taxes and voluntary markets. However, the operationalisation of Article 6 introduces a high-integrity framework that addresses persistent institutional and technical barriers. Core features such as corresponding adjustments, measurement, reporting and verification (MRV) protocols and centralised registries provide a structural reference for enhancing market credibility and interoperability. The article analyses how Article 6 could play a role in improving governance, legal infrastructure, price signals and MRV requirements in ASEAN. It further considers the indirect role of carbon pricing in influencing energy transition outcomes, including investment decisions, dispatch patterns and cross-border electricity trade. While Article 6 mechanisms are shaping the design of regional carbon markets, their implementation remains at an early stage due to the complexity of international negotiations and differing levels of domestic readiness. The article highlights that the pace and effectiveness of Article 6 uptake in ASEAN will depend on both international rule-making and countries’ ability to build the institutional infrastructure required for high-integrity carbon trading.

ABSTRACT This study examines personalized algorithmic pricing and consumer protection across three major jurisdictions the United States, European Union, and India analyzing how artificial intelligence‐driven pricing systems challenge traditional regulatory frameworks and threaten consumer autonomy. The research adopts a comparative methodology combining doctrinal legal analysis with empirical examination of enforcement patterns, scrutinizing recent regulatory developments including the EU's Digital Services Act, the US Department of Justice's RealPage litigation, and India's Consumer Protection Act amendments. The central argument demonstrates that transparency‐only approaches prove fundamentally inadequate in addressing algorithmic filter bubbles and market concentration. Evidence from India's fast‐commerce sector reveals sophisticated discrimination patterns, including device‐based pricing differentials and usage‐pattern exploitation, while “hub‐and‐spoke conspiracies” enable algorithmic collusion without explicit coordination between competitors. Key findings of study that existing legal frameworks, designed for pre‐digital markets, cannot effectively address technologically sophisticated forms of consumer harm and market manipulation. The study identifies critical gaps in jurisdictional approaches: India's reactive consumer protection model, the EU's proactive transparency requirements, and the US's antitrust‐centric enforcement. The research proposes moving beyond disclosure paradigms toward “information enrichment” mandates requiring platforms to actively diversify algorithmic recommendations, coupled with user‐controlled choice architectures and structural market reforms. These interventions, aligned with fundamental rights principles requiring states to serve as ultimate guarantors of diversity offering pathways for regulatory frameworks that balance technological innovation with consumer welfare and market competition. This article is categorized under: Commercial, Legal, and Ethical Issues > Legal Issues Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Security and Privacy

In the branch banking system, branches with surplus funds cannot be profitable without an internal fund transfer pricing (FTP) system. Therefore, a dynamic FTP model is critical to enhance a bank’s sustainability. This article has designed a market-oriented multiple FTP model by factoring in liquidity risk, market interest rate, credit demand, and portfolio diversification. The FTP rates are estimated using the 151 branch-level daily data of a bank in India. We reduced the endogeneity issue related to variable selection with the 2SLS system equation. The findings suggest that dual-functioning branches are profitable if they reduce their fund borrowings from the corporate head office (HO). Corporate treasury provides liquidity support to banks, but their profit gets impacted by the increase in the policy and HO-transfer rate. The study showed that corporate spread is significantly affected by increased liquidity cost and market borrowing rate. It is also empirically established that internal FTP pricing may hamper branches’ earnings when branch managers mobilize funds beyond the cost of lending as per FTP rates. The dynamic FTP model is linked to the yield curve, making it market oriented and enabling managers to price products effectively. JEL Codes: G21, D02

Aim: Dynamic-pricing services rely on good demand indicators, but the retail and logistics sites continue to be affected by price bots. These automated agents interact with traffic volumes and corrupt the input on price optimization. This study aims to develop and evaluate a hybrid artificial intelligence–based bot-detection framework that identifies automated sessions in retail and logistics platforms and provides explainable traffic-quality signals for dynamic pricing systems. Methods: The study adopts a system-based experimental design that integrates graph-based modeling, sequence learning, Agentic AI-generated synthetic traces, and ensemble classification. Data were drawn from anonymized web logs, honeypot captures, and multi-agent adversarial simulations. Models are tested in terms of precision, recall, AUC, and stability of the pricing engine. Results: The proposed pipeline achieved high detection performance (AUC ≈ 0.94) and showed that graph metrics and timing distribution characteristics were the most prominent predictors of automated behavior. Conclusion: The use of AI-based synthetic traces enhanced the model’s strength, aligning with BotChase and other research studies. Recommendation: This study suggests that a hybrid AI/ML-based bot detection approach can enhance the reliability of demand signals used in dynamic pricing systems, providing a viable alternative to traditional binary blocking methods. This helps significantly in preventing pricing distortion and enhancing market resilience.

The exponential growth of e-commerce has fundamentally transformed warehousing and distribution operations, creating unprecedented pricing strategies and inventory management complexity that traditional static models cannot adequately address. This comprehensive article explores the revolutionary impact of machine learning and conversational artificial intelligence technologies in optimizing fee structures and enhancing operational efficiency for online retail sellers across diverse market segments. It demonstrates how advanced algorithmic approaches, including Long Short-Term Memory networks, reinforcement learning algorithms, and sophisticated natural language processing systems, can transform complex multi-dimensional cost datasets into actionable strategic insights that drive informed decision-making throughout the e-commerce supply chain. The article reveals that AI-driven pricing systems deliver substantial improvements in operational efficiency through automated decision-making frameworks that eliminate time-intensive manual processes while significantly enhancing cost transparency for sellers through comprehensive analytical frameworks that provide granular visibility into previously opaque cost structures. It examines critical implementation challenges, including data quality considerations, model interpretability requirements, and organizational barriers. It proposes solutions through robust data infrastructure architectures, cross-functional collaboration frameworks, and advanced user experience design methodologies. Furthermore, the article explores emerging technological opportunities, including deep reinforcement learning, federated learning approaches, Internet of Things integration, and blockchain technologies that promise to enhance pricing optimization capabilities further. It provides compelling evidence for the transformative potential of AI technologies in creating more intelligent, responsive, and efficient pricing strategies that enable organizations to achieve sustainable competitive advantages in increasingly complex e-commerce environments.

The deepening development of the digital economy has endowed critical data with significant infrastructural properties. However, the "walled gardens" constructed by dominant platforms through "data feedback loops" have increasingly become structural barriers to fair market competition. Against the backdrop of China's "Data Twenty Measures," which established the separation of data property rights, and the amendment of the Anti-Monopoly Law, the mechanical application of the traditional Essential Facilities Doctrine (EFD) faces dilemmas regarding innovation suppression and ambiguity in definition. This paper constructs a regulatory paradigm of "mandatory sharing," predicated on the logic of opening data as a "quasi-public facility." By introducing a tripartite criterion consisting of "ecological non-substitutability," "public necessity," and "technical interoperability," this study reconstructs the constituent elements of essential data. Furthermore, based on the source of value and competitive attributes, it constructs a classified and hierarchical mechanism where "basic operational data" is subject to mandatory sharing in principle, while "derivative value-added data" is exempted in principle. The research demonstrates that establishing a pricing system based on FRAND principles and a "penetrating" algorithmic supervision mechanism can not only break the monopolistic loop of "data feudalism" but also effectively protect innovation incentives through trade secret defenses. This approach offers a distinct institutional scheme with Chinese characteristics for global digital market governance.

By converting to an environmentally friendly energy system, water electrolysis technology based on renewable sources (like solar photovoltaic and wind power) has presented a sustainable route to carbon-neutral hydrogen generation. In this paper, we introduce a complete techno-economic characterization of renewable electric hydrogen production technologies within storage systems. Our review presents existing pricing profiles including electrolysis capital investment costs, renewable energy pricing systems, and operating parameters. We cover various storage forms (compressed gaseous hydrogen, cryogenic liquid hydrogen, geological formations) and three main electrolyzer technology stacks: alkaline electrolyzers (AEL), proton exchange membrane electrolyzers (PEM), and solid oxide electrolysis cells (SOEC). For these reasons, Levelized Cost of Hydrogen (LCOH) is considered the most significant economic indicator of these experiments. With data available that provides a glimpse of the potential cost reductions associated with scale-up of the manufacturing process, technological advancement, and decreasing costs associated with renewable energy, we sought to explore potential cost reductions regarding cost of this process. The total cost of producing low-carbon hydrogen is currently estimated at between $4 and $8 per kilogram, but it is projected to drop to $2 per kilogram by 2040, which is expected to be comparable to the cost of producing hydrogen from fossil fuels. To ensure the success of this technology, it is essential to develop integrated plans that combine, a supportive policy framework and the use of new methods to increase the efficiency of electrolysis, while maintaining reasonable cost-effectiveness, Total costs of producing low-carbon hydrogen.

Abstract—The rapid growth of the automobile resale market has increased the demand for accurate and transparent pricing of used vehicles. However, determining the fair resale value of a used car is a complex task influenced by multiple factors such as brand, model, year of manufacture, mileage, fuel type, transmission type, and ownership history. Traditional pricing methods rely heavily on manual evaluation and subjective judgment, which often results in inconsistent and inaccurate pricing [1], [3]. To address these limitations, this project proposes a Machine Learning–based Used Car Price Prediction System that provides automated and reliable price estimation. The proposed system utilizes historical used car data and applies regression-based machine learning techniques to analyze patterns that affect car prices [5], [9]. Data preprocessing and feature extraction are performed to improve model accuracy, and prediction results are generated through a web-based interface developed using Flask. Experimental results demonstrate that the system achieves high prediction accuracy and responds efficiently to user inputs. This approach reduces human bias, improves transparency, and assists buyers and sellers in making informed decisions in the used car market [7], [14]. Keywords—Used Car Price Prediction, Machine Learning, Supervised Learning Algorithms, Regression Models, Data Preprocessing and Feature Engineering, Automotive Data Analytics, Predictive Modeling in Automobile Industry, Vehicle Resale Value Estimation, Market Price Analysis, Price Forecasting Systems, Machine Learning in Automotive Applications, Data-Driven Decision Support Systems, Real-Time Price Prediction, Web-Based Predictive Systems, Evaluation Metrics for Regression Models, Intelligent Pricing Systems.

This paper describes the design and development of an E-commerce Hardware Website that enables customers to purchase computer hardware components through an online platform. The proposed system is designed to simplify the hardware buying process by providing structured product listings, specification- based selection, secure payment processing, and automated order management. The platform integrates essential web technologies to support user registration, product browsing, shopping cart operations, order confirmation, and inventory updates. A key focus of the system is to provide a reliable and user-friendly interface that supports both individual customers and administrators. The website also supports real-time pricing visibility and system customization features to assist users in making informed purchasing decisions. Experimental evaluation of the system shows improved accessibility, reduced manual processing, and faster transaction completion when compared to traditional offline hardware stores. The results confirm that web-based e-commerce solutions can significantly enhance efficiency and customer experience in the hardware retail domain.

Precise forecasting of fluctuations in carbon allowance valuations is critical for shaping environmental policy and for bolstering the effectiveness of market-based regulatory mechanisms. Advanced statistical and machine-learning techniques afford regulators the capacity to fine-tune carbon taxation schemes, enhance the operational efficiency of emissions trading frameworks, and steer financial resources toward low-carbon development projects with greater assurance. This study examines the China Emissions Trading Scheme (CHNTS)-one of China's pioneering carbon markets established under the broader national decarbonization strategy-and presents an innovative predictive model based on Gaussian process regression (GPR) whose hyperparameters are optimized through a Bayesian framework. By dynamically adjusting to latent market behaviors and unobserved structural shifts, this method adapts more responsively to evolving trading patterns. Our empirical investigation utilizes daily settlement data for China Emission Allowances spanning July 16, 2021, through April 9, 2025-a timeframe marked by key regulatory amendments, market maturation phases, and changing participant conduct as the scheme integrated into the wider national carbon pricing system. Model validation is performed on an out-of-sample window from June 28, 2024, to April 9, 2025, yielding notable performance metrics: a relative root-mean-square error (RRMSE) of 1.0771%, root-mean-square error (RMSE) of 1.0212, mean absolute error (MAE) of 0.6773, and a correlation coefficient (CC) reaching 98.604%. To the best of our knowledge, this represents the first deployment of GPR in the context of China's carbon trading exchanges. Beyond enriching theoretical understanding of price discovery in emergent emissions markets, the proposed approach provides a flexible analytical template that could readily be applied to analogous cap-and-trade systems worldwide.

In today’s airline industry, the shift toward dynamic pricing—where prices evolve continuously rather than being fixed in fare classes—has become more than a trend; it represents a pivotal transformation in revenue management (RM). Yet, many existing systems still rely on legacy multi-fare-class reservation frameworks and conventional business logic, leaving them ill-equipped to handle modern demands. These systems often struggle with integrating competitive pricing data, capturing nuanced passenger behavior across web and mobile platforms, and reacting swiftly to real-time market shifts. More critically, they lack the capacity for processing the vast, granular datasets essential for artificial intelligence (AI)-powered revenue strategies. To bridge this gap, we developed a rule-based dynamic pricing algorithm that not only aligns with classic bid-price principles but also leverages both real-time data and the practical expertise of revenue managers. This algorithm is sales-target-driven and designed for responsiveness in live environments. Building on this foundation, we introduced a multi-layered software architecture tailored for airlines. Additionally, we offer practical recommendations for database structuring—both logical and physical—and propose streamlined business processes to enhance responsiveness under high computational loads and complex system integrations. Notably, a major Chinese airline has implemented our system prototype, marking a significant advancement in its RM capabilities.

This paper presents a critical strategic analysis of international carbon pricing and its environmental, economic, and social impacts. This discussion will rely on peer-reviewed articles, policy reports, and empirical studies that have been published between 2007-2024. It examines the effect of carbon taxes and emissions trading systems (ETS) in reducing greenhouse gas (GHG) emissions, technological innovation, and long-term structural change using a systematic literature review and content analysis. Distributional equity, competitiveness, administrative capacity, and risk of carbon leakage are also examined in the study. It also analyzes the complementary tools such as voluntary carbon markets, carbon border adjustments (CBAM), revenue-recycling frameworks and just transition frameworks. It shows that carbon pricing alone cannot be used to achieve the level of decarbonization required to meet international climate targets, but is an important pillar when used in conjunction with more robust regulatory, fiscal and industrial policies. International coordination, better policy design, better revenue utilization and social fairness are important in maximizing the effectiveness and legitimacy of carbon pricing across the globe. This paper provides policy implications to policymakers, scholars, and climate negotiators to develop sustainable and equitable carbon pricing systems.

Niger agricultural sector has experienced changes in the production system and in the economic environment both at national and international level. It has faced cereals price distortions engendered by the recent fiscal policies reforms. Rice production is encouraged by agricultural policies, which invest significant resources with a view to increasing production to meet growing demand. However, local rice production covers only 16.66% of national needs. To fill this gap, large quantities of rice are imported at significant cost and sold at varying prices. In this context, this paper aim to analyze the competitiveness of local rice sector. The Political Analysis Matrix has been used as method to analyze the performance indicators and the differences between economic and financial prices. It also allow to simulate and predict the impact of price system variations on production, farmers' incomes, and society. To do this, data were collected through questionnaires with 1110 rice producers and focus group with relevant stakeholders in the irrigated perimeters along the river Niger. Data were used to determine the financial and economic rice prices exploited in order to calculate policy analysis indicator. The results showed that all cropping systems are characterized by a Domestic Resource Cost less than one (1). Systems have comparative advantages in economy open to international competition. All Nominal Protection Coefficient are greater than one (1). Systems are above the economic realities of the international scale. Production factors are heavily subsidized. In addition, all systems have an Effective Protection Coefficient greater than one (1). Product prices are overvalued. There is income transfer from the social to the private. It’s more interesting to buy rice than to produce local rice under current conditions. It will be interesting to produce local rice when the price of imported rice has been increased by 15%.

Precise forecasting of fluctuations in carbon allowance valuations is critical for shaping environmental policy and for bolstering the effectiveness of market-based regulatory mechanisms. Advanced statistical and machine-learning techniques afford regulators the capacity to fine-tune carbon taxation schemes, enhance the operational efficiency of emissions trading frameworks, and steer financial resources toward low-carbon development projects with greater assurance. This study examines the Fujian Emissions Trading Scheme (FJTS) — one of China’s pioneering provincial carbon markets established under the broader national decarbonization strategy — and presents an innovative predictive model based on Gaussian process regression (GPR) whose hyperparameters are optimized through a Bayesian framework. By dynamically adjusting to latent market behaviors and unobserved structural shifts, this method adapts more responsively to evolving trading patterns. Our empirical investigation utilizes daily settlement data for Fujian Emission Allowances spanning 9 January 2017 through 13 January 2021 — a timeframe marked by key regulatory amendments, market maturation phases, and changing participant conduct as the scheme integrated into the wider national carbon pricing system. Model validation is performed on an out-of-sample window from 19 August 2019 to 13 January 2021, yielding notable performance metrics: a relative root-mean-square error (RRMSE) of 7.9738%, root-mean-square error (RMSE) of 1.2976, mean absolute error (MAE) of 1.0236 and a correlation coefficient (CC) reaching 95.768%. To the best of our knowledge, this represents the first deployment of GPR in the context of China’s carbon trading exchanges. Beyond enriching theoretical understanding of price discovery in emergent emissions markets, the proposed approach provides a flexible analytical template that could readily be applied to analogous cap-and-trade systems worldwide.

This article focuses primarily on the current possibilities of using data and information from BIM models to estimate costs using identified methods and pricing systems for apartment buildings with different construction technologies. The authors analyse buildings with a built-up space of 3600–5300 m3, representing hundreds of projects currently available on the market. The applied methods include Pricing of Buildings Using a Spreadsheet Program, IFC-Supported Pricing Software, Pricing of Buildings in Design Software, and Pricing of Buildings Using a Design/Construction Library to compile cost estimates in the Czech URS, German Baupreislexikon, and British Spon’s Architects’ and Builders’ Price Book pricing systems. The usability of the BIM model with respect to the selected pricing system, construction technology, and methods ranges from 50% to 85%, with labour intensity ranging from 64 to 159 h. The key aspects for a wider application of BIM models include the completion of standardization at the level of graphic and non-graphic requirements related to the intended use of the data and information. The average cost per cubic metre of built-up space is EUR 469 in the Czech Republic, EUR 617 in Germany, and EUR 671 in Great Britain. This study brings new and distinctive insights compared to previous research by providing specific values for labour intensity and extractability, defining the limits of BIM use for cost estimation, and proposing recommendations to increase the applicability of the obtained data in practice.

This study examines the monthly returns of thirteen companies with renewable energy investments on the Borsa Istanbul at the firm level between 2015 and 2024. The aim is to measure how stock returns are explained by market sentiment within a multi-factor structure. The scope encompasses the entire population and allows comparison of two sub-periods: 2020 to 2022 and 2022 to 2024. The method is based on the single-factor CAPM and multi-factor APT frameworks, with estimates made using the least squares method and Newey West robust standard errors. Diagnostic tests included F-statistics, adjusted R-squared, and Durbin Watson-Breusch-Pagan and Jarque Bera. Outliers were weighted between the first and ninety-ninth percentiles. The CAPM used the market proxy as the equal-weighted monthly sector return. The APT extracted principal components from the panel returns, and firm-based factorial regressions were run. Our findings indicate that the CAPM's overall explanatory power is limited, but some stocks exhibit significant sensitivity to market volatility. Our firm-based analysis reveals that Zorlu, SAY, and Pamukova exhibit high beta values, Odaş exhibits significant co-movement in the mid-high range, Aksa, Enerjisa, and Orge remain marginal, and market sentiment is weak for Alarko Doğan Global Koç and Ak Enerji. The APT increases its explanatory power and reveals that the common sector component dominates both subperiods. Our periodic analysis found that AKSA, IŞIKLAR, ODAŞ, PAMUKOVA, SAY, and ZORLU exhibited strong bonds between 2020 and 2022, while the bond strengthened further between 2022 and 2024 for AKSA, ENERJİSA, ODAŞ, ORGE, SAY, and ZORLU. Conversely, ALARKO, AK ENERJİ, and GLOBAL's returns were better explained by the company's news feed, project schedule, and balance sheet dynamics. When a two-tiered pricing system is established for energy stocks, the first tier sees common sector volatility become the primary driver during certain periods, while the second tier, driven by company-specific sentiment, drives differentiation. It has been concluded that these two risks should be managed together in portfolio decisions.

Indeed, in e-commerce as an evolving industry, applying generative AI has been regarded as the key to the revolution of the entire pricing strategy. With the increase in the amount of available data and computational resources, the application of dynamic pricing strategy is more characteristic for e-commerce organizations. This article will concentrate on the comparison of the generative AI-based dynamic pricing and the traditional price techniques for instances specifically based on their effects on the efficiencies of revenue management and customer experience. The appearance of the term dynamic pricing in the sphere of e-commerce was possible due to the fact that large amount of data could be analyzed to find such sources of steady and predictable revenue and demand forecast was being made. However, in earlier research on these models, the most used variable was the Price Elasticity of Demand constructed from historical information; however, emerging complexities of consumers and markets required more studies on pricing models incorporating generative AI. This theoretical as well as practical research paper aims to underlie extended knowledge about the generative AI dynamic pricing strategies and assess their efficiency in comparison with the traditional price strategies. Such elements of costbenefit analysis as cost efficiencies, customer satisfaction levels, opportunities for increasing revenues etc for changing conditions are pinpointed. This research uses literature review and the analysis of cases to examine the key concepts and practical implementation of the generative AI-driven dynamic pricing in e-commerce. It analyses a critical set of factors specifically, personalization strategies, sensitivity to price changes, and perceived value regarding consumer behavior whilst making a purchase in dynamic price context. The study also compares the effectiveness of dynamic pricing systems based on generative AI with traditional models, including fixed, time-based, and competition-based pricing schemes. Further, in this work, it has been illustrated that e-commerce companies can gain significant benefits by integrating generative AI into dynamic pricing frameworks, such as higher revenue, enhanced customer experience.

Purpose This study aims to examine how pricing transparency affects organizational boundaries in agricultural markets. The authors use the introduction of the Global Dairy Trade auction (2008) and Farmgate Milk Price system (2010) as exogenous shocks that dramatically improved price discovery for dairy commodities in New Zealand. Design/methodology/approach Using difference-in-differences methodology, the authors compare regions with both cooperative and investor-owned firm (IOF) processing facilities against cooperative-only regions from 2002 to 2015. Findings The results reveal that cooperatives lost approximately 8% of market share following these pricing transparency improvements in regions where IOFs were active. Originality/value This study contributes to organizational economics by showing how external market infrastructure changes can shift optimal firm boundaries, with implications for agricultural policy and cooperative governance.