Search Results (549)

Social media promotion of harmful products (e.g., combustible tobacco) poses a public health threat. However, strategies that amplify exposure to and engagement with such content remain understudied. This study aims to characterize strategies boosting cigar, little cigar, and cigarillo (CLCC) marketing visibility, referrals, and engagement on Instagram. Using keyword rules, we collected publicly available CLCC-related Instagram posts from CrowdTangle for a six-year period from August 2016 to October 2021. Posts were categorized as commercial (e.g., posts by tobacco brands or vendors) or organic and were coded for consumer engagement (CE) strategies (e.g., presence of prompts to like/share) using a combination of machine learning methods and human coding. Temporal engagement trends were analyzed using metadata. A total of 320,488 CLCC-related public posts were collected, with 44.6% (n = 142,875) identified as overtly commercial. Of these, 33.5% (n = 47,832) contained CE cues, including discounts and giveaways for tagging peers, liking, commenting, or following CLCC brands and spokesperson/influencers accounts, as well as calls to participate in contests and polls. Overtly commercial CE messages consistently garnered more comments per post and likes per post than non-CE commercial posts. There was a significant upward trend in the rate of comments on CE posts, suggesting growing effectiveness in eliciting user interaction. The proliferation of and high level of engagement with cigar-related promotional messages on Instagram demonstrate the need for public health surveillance and regulation of the evolving strategies promoting CLCC marketing exposure, reach, and engagement on social media. Full article

Urbanization-induced ecological problems have affected China’s urban agglomerations since the beginning of rapid economic growth. The InVEST model can be used to study how land use changes affect carbon storage, while land simulation models help project future land use trends and assess the impact of policies on land use, thereby predicting future carbon storage. This study constructs a PLUS-InVEST-MGWR model, corrects carbon storage values in ArcGIS, and thereby analyzes its heterogeneity by MGWR. The economic value of carbon storage is calculated as well. The main findings are as follows: (1) The downward trend of carbon storage in the Chengdu–Chongqing region will continue but slow down to some extent, and only the ecological security scenario can prevent it. (2) In 2015, China’s social cost of carbon (SCC) was CNY 60.83 per ton, with a discount rate of 6.468%, while the economic value of carbon storage (EVCS) in the Chengdu–Chongqing region was CNY 289.516 × 10⁹. (3) Spatial correction of carbon storage is crucial for enhancing the goodness-of-fit and result accuracy of the MGWR model, as the absence of such correction would significantly degrade its performance. The revised InVEST model enables rapid quantification of carbon storage’s spatial heterogeneity. Full article

In the paper, using the three-statement financial modelling methodology as applied to a representative development project, we aim to analyse, ex ante, the industry-level impact of transition to mandatory escrow schemes in residential and mixed-use construction in Tashkent city (due to be implemented in Uzbekistan from 2026). Modelling single-milestone escrow plans against the current steep-discount advance-based system of off-plans as a baseline, the model accounts for salient institutional features of the Tashkent city development market, including land auctioning, full-cycle Value-added tax (VAT) accounting, and Tax loss carryforward provisions. It also incorporates a framework for demand-driven residual valuations for the development land element. Our findings indicate practically unchanged cashflow profitability of developers on the market in question. Around 30% p.a. in nominal Free-cashflow-to-equity based IRRs expressed in the national currency, provided that the transition to the greater use of leverage in funding unfolds as expected. The disappearance of steep off-plan discounts while the transition to escrows unfolds will be countervailed by the reliance on costly loans from escrow banks. Absent the greater use of leverage, the IRR (FCFE) profitability of the developers is expected to decline by some 5%. For the apartment buyers, this is effectively equivalent to increasing property transaction prices on the primary market in line with their headline asking amounts. Thus-generated economic surplus will be partially captured by the developers and partially passed through to escrow banks, increasing their gross profits by up to $50M, p.a. due to their new role in financing Tashkent city residential developments that are still largely equity-driven. Apart from this effect, we find only a moderate financial leverage influence on developers’ profitability due to the high-interest-rate environment prevailing in Uzbekistan. We also find a demand-driven pressure on land auction prices suggested by increasingly back-loaded alterations in project cashflow profiles. This study also purports to make a material contribution to the evolving body of literature on financial modelling of apartment and mixed-use property developments by offering a flexible three-statement modelling framework with innovative endogenised equity management features. Full article

This study aims to examine the impact of compressor station availability on the techno-economic aspects of natural gas pipeline transportation, using the proposed Trans-Saharan Gas Pipeline (TSGP) project as a case study. A scenario-based technical and economic analysis was conducted to highlight the economic sensitivities of the systems to availability. The economic assessment of the project was performed using a discounted cash flow method, considering lifecycle costs. The techno-economic model was developed using MATLAB R2020b, accounting for variations in ambient temperatures at the compressor station under different flow conditions. Findings indicate an 8.41% increase in project lifecycle cost in one scenario compared to the baseline, assuming a 15% discount rate. However, the baseline case with a 100% compressor station availability assumption appears unrealistic, as shown by its lifecycle cost and net present value estimates. This is because constant operating conditions throughout the project lifecycle are impossible. Additionally, when station availability increases by 7.87% and the cost of standby units rises by 10.24%, avoided income loss due to station unavailability increases by 14.06%. This reveals a trade-off between the extra capital expenditure on standby units and the savings from avoiding income loss. Furthermore, the impact of 2% and 4% escalation rates of fuel and maintenance costs on lifecycle costs results in a rise of 2.70% and 6.15%, respectively, in one scenario compared to the 0% escalation rate. The results demonstrate the significant influence of compressor station availability analysis on pipeline projects, particularly in reducing engine downtime costs and enhancing project revenue. Therefore, the methods presented here help in understanding the importance of compressor station availability in pipeline techno-economics, leading to more effective resource and financial management. Full article

Sustainable investments in construction are essential for the development of communities and for reducing environmental impacts. This study analyzes two scenarios: rehabilitation of an existing building and construction of a new NZEB-compliant building, based on a life cycle cost–benefit analysis. The results show that both scenarios generate negative Net Present Values (NPVs) due to the social nature of the project, but the new NZEB building presents superior performance (NPV: USD –2.61 million vs. USD –3.05 million for rehabilitation) and lower operational costs (USD 1.49 million vs. USD 1.92 million over 30 years). Key financial indicators (IRR, CBR), sensitivity analysis, and discount rate variation support the conclusion that the NZEB scenario ensures greater economic resilience. This study highlights the relevance of extended LCCBA in guiding sustainable investment decisions in social infrastructure. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

The building sector remains one of the largest contributors to global energy consumption and CO₂ emissions, yet selecting optimal retrofit strategies is often hindered by inconsistent evaluation practices and limited integration of environmental and social impacts. This review addresses that gap by systematically analyzing how various assessment methods are applied to building retrofits, particularly from a financial and environmental perspective. A structured literature review was conducted across four major scientific databases using predefined keywords, filters, and inclusion/exclusion criteria, resulting in a final sample of 50 studies (green colored citations of this paper). The review focuses on the application of Life Cycle Cost Analysis (LCCA), Cost–Benefit Analysis (CBA), and Life Cycle Assessment (LCA), as well as additional indicators that quantify energy and sustainability performance. Results show that LCCA is the most frequently used method, applied in over 60% of the studies, often in combination with LCA (particularly for long time horizons). CBA appears in fewer than 25% of cases. More than 50% of studies are based in Europe, and over 60% of case studies involve residential buildings. EnergyPlus and DesignBuilder were the most common simulation tools, used in 28% and 16% of the cases, respectively. Risk and uncertainty were typically addressed through Monte Carlo simulations (22%) and sensitivity analysis. Comfort and social impact indicators were underrepresented, with thermal comfort included in only 12% of studies and no formal use of tools like Social-LCA or SROI. The findings highlight the growing sophistication of retrofit assessments post-2020, but also reveal gaps such as geographic imbalance (absence of African case studies), inconsistent treatment of discount rates, and limited integration of social indicators. The study concludes that future research should develop standardized, multidimensional evaluation frameworks that incorporate social equity, stakeholder values, and long-term resilience alongside cost and carbon metrics. Full article

Hydrogen is increasingly recognized as a key element of the transition to a low-carbon energy system, leading to a growing interest in accurate and sustainable assessment of its economic viability. Levelized Cost of Hydrogen (LCOH) is one of the most widely used metrics for comparing hydrogen production technologies and informing investment decisions. However, traditional LCOH calculation methods apply a single discount rate to all cash flows without distinguishing between the risks associated with outflows and inflows. This approach may yield a systematic overestimation of costs, especially in capital-intensive projects. In this study, we adapt a binary cash flow discounting model, previously proposed in the finance literature, for hydrogen energy systems. The model employs two distinct discount rates, one for costs and one for revenues, with a rate structure based on the required return and the risk-free rate, thereby ensuring that arbitrage conditions are not present. Our approach allows the range of possible LCOH values to be determined, eliminating the methodological errors inherent in traditional formulas. A numerical analysis is performed to assess the impact of a change in the general rate of return on the final LCOH value. The method is tested on five typical hydrogen production technologies with fixed productivity and cost parameters. The results show that the traditional approach consistently overestimates costs, whereas the binary model provides a more balanced and risk-adjusted representation of costs, particularly for projects with high capital expenditures. These findings may be useful for investors, policymakers, and researchers developing tools to support and evaluate hydrogen energy projects. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

Background: This study aims to evaluate the cost-effectiveness of providing the 23-valent pneumococcal polysaccharide vaccine (PPV23) free of charge versus self-paying vaccination among adults aged 60 years and older in Nanning, Guangxi, China. Methods: A decision tree–Markov model was developed to compare three strategies (government-funded free vaccination, self-funded vaccination, and no vaccination) over a 5-year time horizon. The model incorporated local epidemiological data and cost parameters, applying a 3% discount rate. Sensitivity analyses were conducted on key parameters, including vaccine effectiveness against pneumonia and pneumonia treatment costs. Results: The benefit–cost ratios for free and self-funded vaccination were 0.075 and 0.015, respectively, both below the cost-effectiveness threshold of 1. However, the free vaccination strategy resulted in a higher net benefit (USD 399,651.32) compared to the self-funded strategy (USD 222,594.14), along with a lower Incremental Cost-Effectiveness Ratio (ICER) (USD 1.47 per USD 0.14 of avoided disease cost). Although both strategies yielded benefit–cost ratios far below the conventional threshold of 1, the free strategy demonstrated relatively greater economic efficiency. Sensitivity analyses confirmed that vaccine effectiveness against pneumonia and treatment costs were key drivers of economic outcomes. Conclusions: While neither vaccination strategy achieved conventional cost-effectiveness benchmarks in this setting, the free PPV23 vaccination program demonstrated relatively greater economic efficiency compared to the self-funded approach; although neither strategy met the conventional cost-effectiveness thresholds, they should be considered for inclusion in regional health policy for older adults. Full article

With rising energy costs and the need for sustainable power solutions in urban South African settings, grid-tied renewable energy systems have become viable alternatives for reducing dependence on traditional grid supply. This study investigates the techno-economic feasibility of a grid-connected hybrid photovoltaic (PV) and battery storage system designed for a commercial facility located in Johannesburg, South Africa—an area characterized by a subtropical highland climate. We conducted the analysis using the HOMER Grid software and evaluated the performance of the proposed PV/battery system against the baseline grid-only configuration. Simulation results indicate that the optimal systems, comprising 337 kW of flat-plate PV and 901 kWh of lithium-ion battery storage, offers a significant reduction in electricity expenditure, lowering the annual utility cost from $39,229 to $897. The system demonstrates a simple payback period of less than two years and achieves a net present value (NPV) of approximately $449,491 over a 25-year project lifespan. In addition to delivering substantial cost savings, the proposed configuration also enhances energy resilience. Sensitivity analyses were conducted to assess the impact of variables such as inflation rate, discount rate, and load profile fluctuations on system performance and economic returns. The results affirm the suitability of hybrid grid-tied PV/battery systems for cost-effective, sustainable urban energy solutions in climates with high solar potential. Full article

Background/Objectives: European healthcare systems are increasingly adopting automation technologies to improve efficiency. This study evaluates the economic viability of hospital automation and medication management digitalization. Methods: An economic evaluation was based on a standardized hospital model comprising 561 beds, representative of an average acute care hospital across EU27 + UK. For each technology, several cost items were estimated using country-specific parameters such as labor costs, medication error rates, healthcare expenditure, and money discount rate. The financial metrics (Return On Investment—ROI, Net Present Value—NPV, Payback Time—PBT) were first calculated at the hospital level. These results were then extrapolated to the national level by scaling the per-hospital estimates according to the total number of hospital beds reported in each country. Finally, national results were aggregated to derive the overall European impact. Results: The analysis estimated a total European investment of EUR 3.55 billion, with an average PBT of 4.46 years and annual savings of 1,96 billion. ROI averaged 167%, and the total NPV was 8.21 billion. A major saving driver was the reduction in Medication Administration Errors that has an impact of 37.2% on the total savings. Payback times ranged from 3 years in high-GDP countries, to 7 years in lower-GDP nations. Conclusions: These findings demonstrate how providing structured data on hospital automation benefits could support decision-making processes, highlighting the organizational and economic feasibility of the investment across different European national contexts. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article