Hasil untuk "Revenue. Taxation. Internal revenue"

Yifan Huang, Dong Hao, Zhiyi Fan et al.

Reserve prices are widely used in practice. The problem of designing revenue-optimal auctions based on reserve price has drawn much attention in the auction design community. Although they have been extensively studied, most developments rely on the significant assumption that the target audience of the sale is directly reachable by the auctioneer, while a large portion of bidders in the economic network unaware of the sale are omitted. This work follows the diffusion auction design, which aims to extend the target audience of optimal auction theory to all entities in economic networks. We investigate the design of simple and provably near-optimal network auctions via reserve price. Using Bayesian approximation analysis, we provide a simple and explicit form of the reserve price function tailored to the most representative network auction. We aim to balance setting a sufficiently high reserve price to induce high revenue in a successful sale, and attracting more buyers from the network to increase the probability of a successful sale. This reserve price function preserves incentive compatibility for network auctions, allowing the seller to extract additional revenue beyond that achieved by the Myerson optimal auction. Specifically, if the seller has $ρ$ direct neighbours in a network of size $n$, this reserve price guarantees a $1-{1 \over ρ}$ approximation to the theoretical upper bound, i.e., the maximum possible revenue from any network of size $n$. This result holds for any size and any structure of the networked market.

Emin Ablyatifov, Georgy Lukyanov

We study optimal taxation when citizens hold beliefs about an honest versus opportunistic government and update those beliefs from observed taxes and delivery. In a Ramsey economy with competitive firms, the government privately knows its type: the honest type implements announced taxes and converts revenue into public goods, while the opportunistic type can strategically mimic or divert. Bayesian learning from policy choices and a noisy delivery signal disciplines taxation. We establish a trust cutoff: below it, optimal revenue is zero; above it, the revenue scale is increasing in reputation, with the dynamic cutoff lower than the static one. With broad instruments and symmetric monitoring, dynamic forces act through total revenue while the tax mix is indeterminate along a static equivalence frontier. More informative monitoring (in the Blackwell sense) expands fiscal scale and shrinks the no-tax region.

Masaaki Yoshimori

This study investigates the time-varying effectiveness of central bank interventions in the USDJPY exchange rate market, focusing on their asymmetric impacts under varying market conditions. Quantile regression is employed to analyze intervention outcomes across various segments of the exchange rate distribution, with particular focus on extreme scenarios represented by the 90th quantile. The results reveal that interventions are most effective during periods of lower exchange rate levels, as indicated by the significant negative coefficients at the 10th quantile. However, their influence diminishes at higher quantiles, where extreme market pressures dominate. Key structural factors, such as interest rate differentials and global volatility (proxied by the VIX index), play a dominant role in driving exchange rate dynamics. In particular, the interest rate differential between US and Japanese 10-year bond yields strongly influences the exchange rate, while market volatility reinforces the yen’s status as a safe-haven currency. Although the model’s explanatory power is limited (low pseudo-R² values), the QR analysis highlights the conditional nature of intervention effectiveness. These findings suggest that while interventions can provide short-term stabilization, particularly when the yen faces downward pressure, their overall impact is modest compared to the influence of structural economic factors.The study concludes that while direct central bank interventions are limited in standalone effectiveness, they play a crucial complementary role during market turbulence by tempering extreme fluctuations caused by speculative pressures or external shocks. However, interventions alone cannot mitigate exchange rate volatility under extreme conditions. A broader framework integrating interventions with monetary policy and addressing economic drivers like interest rate differentials and global sentiment is essential for effective exchange rate management.

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

Auction is applied for trade with various mechanisms. A simple but practical question is which mechanism, typically first-price or second-price auctions, is preferred from the perspective of bidders or sellers. A celebrated answer is revenue equivalence, where each bidder's equilibrium payoff is proven to be independent of auction mechanisms (and a seller's revenue, too). In reality, however, auction environments like the value distribution of items would vary over time, and such equilibrium bidding cannot always be achieved. Indeed, bidders must continue to track their equilibrium bidding by learning in first-price auctions, but they can keep their equilibrium bidding in second-price auctions. This study discusses whether and how revenue equivalence is violated in the long run by comparing the time series of non-equilibrium bidding in first-price auctions with those of equilibrium bidding in second-price auctions. We characterize the value distribution by two parameters: its basis value, which means the lowest price to bid, and its value interval, which means the width of possible values. Surprisingly, our theorems and experiments find that revenue equivalence is broken by the correlation between the basis value and the value interval, uncovering a novel phenomenon that could occur in the real world.

Wang Chen, Guan Huang, Jintao Ke

This study investigates the development dilemma of ride-sharing services using real-world mobility datasets from nine cities and calibrated customers' price and detour elasticity. Through massive numerical experiments, this study reveals that while ride-sharing can benefit social welfare, it may also lead to a loss of revenue for transportation network companies (TNCs) or drivers compared with solo-hailing, which limits TNCs' motivation to develop ride-sharing services. Three key factors contributing to this revenue loss are identified: (1) the low successful sharing ratio for customers choosing ride-sharing in some cases, (2) the limited saved trip distance by pooling two customers, and (3) the potential revenue loss when pooling customers with significantly different trip fares. Furthermore, this study finds that the monetary benefits of carbon emission reductions from ride-sharing are not substantial enough to affect customers' choices between solo-hailing and ride-sharing. The findings provide a valuable reference for TNCs and governments. For TNCs, effective pricing strategies, such as dynamic pricing, should be designed to prevent revenue loss when introducing ride-sharing. Governments are suggested to subsidize ride-sharing services to solve the development dilemma and maintain or even increase social welfare benefits from ride-sharing, including reduced carbon emissions and improved vehicle occupancy rates.

Ioannis Caragiannis, Georgios Kalantzis

We consider the fundamental problem of designing a truthful single-item auction with the challenging objective of extracting a large fraction of the highest agent valuation as revenue. Following a recent trend in algorithm design, we assume that the agent valuations belong to a known interval, and a (possibly erroneous) prediction for the highest valuation is available. Then, auction design aims for high consistency and robustness, meaning that, for appropriate pairs of values $γ$ and $ρ$, the extracted revenue should be at least a $γ$- or $ρ$-fraction of the highest valuation when the prediction is correct for the input instance or not. We characterize all pairs of parameters $γ$ and $ρ$ so that a randomized $γ$-consistent and $ρ$-robust auction exists. Furthermore, for the setting in which robustness can be a function of the prediction error, we give sufficient and necessary conditions for the existence of robust auctions and present randomized auctions that extract a revenue that is only a polylogarithmic (in terms of the prediction error) factor away from the highest agent valuation.

Naoki Nishimura, Ken Kobayashi, Kazuhide Nakata

Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate revenue and exploring alternative policies to collect data for future policy improvement via off-policy evaluation (OPE). To balance this trade-off, we propose a novel approach that combines a model-based revenue maximization policy and a randomized exploration policy for data collection. Our framework enables flexible adjustment of the mixture ratio between these two policies to optimize the balance between short-term revenue and future policy improvement. We formulate the problem of determining the optimal mixture ratio as multi-objective optimization, enabling quantitative evaluation of this trade-off. We empirically verified the effectiveness of the proposed mixed policy using synthetic data. Our main contributions are: (1) Demonstrating a mixed policy combining deterministic and probabilistic policies, flexibly adjusting the data collection vs. revenue trade-off. (2) Formulating the optimal mixture ratio problem as multi-objective optimization, enabling quantitative evaluation of this trade-off.

Shuchi Chawla, Dimitris Christou, Trung Dang et al.

We study multi-buyer multi-item sequential item pricing mechanisms for revenue maximization with the goal of approximating a natural fractional relaxation -- the ex ante optimal revenue. We assume that buyers' values are subadditive but make no assumptions on the value distributions. While the optimal revenue, and therefore also the ex ante benchmark, is inapproximable by any simple mechanism in this context, previous work has shown that a weaker benchmark that optimizes over so-called ``buy-many" mechanisms can be approximable. Approximations are known, in particular, for settings with either a single buyer or many unit-demand buyers. We extend these results to the much broader setting of many subadditive buyers. We show that the ex ante buy-many revenue can be approximated via sequential item pricings to within an $O(\log^2 m)$ factor, where $m$ is the number of items. We also show that a logarithmic dependence on $m$ is necessary. Our approximation is achieved through the construction of a new multi-dimensional Online Contention Resolution Scheme (OCRS), that provides an online rounding of the optimal ex ante solution. Chawla et al. arXiv:2204.01962 previously constructed an OCRS for revenue for unit-demand buyers, but their construction relied heavily on the ``almost single dimensional" nature of unit-demand values. Prior to that work, OCRSes have only been studied in the context of social welfare maximization for single-parameter buyers. For the welfare objective, constant-factor approximations have been demonstrated for a wide range of combinatorial constraints on item allocations and classes of buyer valuation functions. Our work opens up the possibility of a similar success story for revenue maximization.

Julia Krause, Iuliia Myroshnychenko, Serhii Tiutiunyk et al.

In the conditions of worsening global energy problems, financial instruments play a key role in reforming the energy market. They provide the necessary financial resources for the development and implementation of environmentally friendly energy technologies, aimed at supporting projects that reduce the negative impact on the environment, and contribute to attracting private capital to finance environmentally friendly projects. The purpose of this study is to analyze trends in research on financial instruments of the green energy transition. The article carries out a comprehensive bibliometric analysis of scientific works devoted to the problem of financing the green energy transition. The object of the research is the publications indexed by the Scopus database for 1954-2023. Research tools are programs for bibliometric data analysis Vosviewer, SciVal and CiteSpace. Based on the trend analysis of publication activity on the issues of financing the green energy transition, the key role of the Paris Agreement of 2015 in the growth of interest in these issues has been proven. Visualization of the most used keywords in scientific works on green energy transition financing allowed us to identify four clusters of keywords that characterize the concepts of green energy transition: the concept of "green construction", the concept of "green technologies and innovations", the concept of renewable energy, and the concept of "green of finance". Analysis of the frequency and number of citations of publications on green energy transition financing issues using the CiteSpace package made it possible to determine the TOP-17 keywords, highlight the period of their greatest citations, and carry out their clustering. Phrases most often used in publications include: finance, alternative economy, green bonds, green innovation, sustainable development, greenhouse gas emissions, sustainable finance, green credit, green economy, climate finance. Based on the analysis of relationships between keywords, seven clusters were identified: sustainable development, financing constraints, green bonds, green credit policy, green bonds, green economic growth, green economic recovery. The obtained results can be used by state institutions to improve financing strategies for green energy transition programs from the point of view of determining modern trends and key vectors of green finance development in the field of energy, analysis of the most effective financing tools, and identification of problems in the field of energy development.

Olena Dobrovolska, Wolfgang Ortmanns, Serhiy Podosynnikov et al.

Advances in renewable energy technologies, particularly in solar, wind, energy storage, and grid integration solutions, are accelerating the growth of in startups in this sector. Despite the promising outlook, renewable energy startups face several risks, including technological, regulatory, financial and market risks. The article carries out a bibliometric analysis of scientific publications related to the problem of investment and risks associated with the development of entrepreneurship and start-ups in the field of renewable energy. After multi-level filtering of the dataset, the research base consists of 82 publications by 232 scientists for 2005-2023, indexed by Scopus, and the analysis tools used are Biblioshiny and Excel. In 2005-2016, the number of publications grew annually at a rate of 10.47%, with peaks in 2006, 2007, and 2016; in 2017-2023, the number of papers increased (the dependence is described by a third-degree polynomial trend). The article examines 3 different types of Three-field plots in the topic of the investments and risks of the startups and entrepreneurship in renewable energy: 1) “References - Authors – Keywords”, 2) “Countries - Institutions – Authors”, 3) “Authors - Sources – Keywords”. A quantitative and qualitative analysis of the scientific journals that make the largest / periodic but noteworthy / minimal contribution to the dissemination of knowledge in this area was carried out, the top 10 authors and papers were analysed by the levels of local and global influence, the most powerful research networks (based on the results of the analysis of joint citations) were identified both in terms of authors and countries, and the leading countries were identified in terms of the volume of research output of their scientists and the intensity of citation of these works. Building clouds of the most used keywords allowed us to identify priority thematic areas of research, as well as to analyse the structural and logical relationships between different thematic blocks in the research landscape. With the help of multiple correspondence analysis (factorial analysis) and longitudinal thematic map analysis, the scientific landscape in this area is clustered and evolutionary changes and interdependence of individual topics are identified.

Olena Dobrovolska, Nazar Fenenko

The real estate market in Ukraine, particularly in Kyiv, has experienced significant fluctuations due to the ongoing conflict and associated economic challenges. This research focuses on forecasting trends within the market, with a specific emphasis on price dynamics and the factors influencing these shifts. The actuality of this study stems from the pressing need to understand how the war, macroeconomic instability, and demographic changes impact real estate demand, investment patterns, and property prices. Given the rapid shifts in market dynamics and the complex interplay between economic variables, accurate forecasting is crucial for stakeholders such as investors, policymakers, and analysts. The methodology of this study combines traditional and advanced analytical tools to provide comprehensive and accurate forecasts. A dual-approach forecasting model was employed, using both the Brown-Mayer method implemented in Excel and advanced functionality provided by Statgraphics software. The Brown-Mayer method, which relies on exponential smoothing, allowed for the analysis of basic trends and seasonality in the time series data. Statgraphics, with its capacity to consider complex changes in market dynamics, provided more precise forecasts. The study involved data preparation, anomaly detection, correction, and checks for stationarity to ensure that the forecasts were not distorted by irregularities or missing data. Data were derived from key indicators like the Ukrainian Index of Retail Deposit Rates (UIRD3M), price changes in construction, the National Bank of Ukraine's key interest rate, and the average prices of primary real estate in Kyiv from 2018 to 2023. The findings indicate that Statgraphics was more accurate in forecasting real estate trends in Kyiv than the Brown-Mayer method. The confidence intervals generated by Statgraphics aligned closely with observed price trends, while the Brown-Mayer method showed a larger deviation due to its simpler approach to trend smoothing. The study revealed that external factors such as war-induced economic instability, high interest rates, inflation, and currency devaluation slowed the growth rates of real estate prices, particularly in urban centers like Kyiv. Moreover, the demand for real estate shifted towards rental properties and commercial spaces, particularly warehousing, reflecting changes in business operations and population migration to safer regions. The discussion emphasizes the importance of employing multiple methodologies to enhance forecast reliability. The research underlines that while simpler methods like the Brown-Mayer model are useful for general trends, advanced software tools like Statgraphics are more effective for accurate market prediction in volatile environments. Additionally, the study recommends a holistic approach to future forecasting models by incorporating a wider range of variables and indicators. This would improve model robustness and predictive power, particularly in the context of geopolitical and macroeconomic disruptions. This study contributes to the understanding of real estate market forecasting, providing valuable insights for stakeholders aiming to navigate the complexities of a rapidly changing market landscape in Ukraine. The research highlights the need for adaptive forecasting methods that can account for market volatility and external shocks.

Alla Moroz, Serhiy Lyeonov

The transition to renewable energy sources is imperative for addressing global challenges such as climate change, energy security, and sustainable economic development. Financial-fiscal instruments, including subsidies, carbon pricing, green bonds, and tradable green certificates, have emerged as critical mechanisms to overcome economic and market barriers that impede renewable energy adoption. This study provides a comprehensive bibliometric analysis to explore the evolution, thematic focus, and global collaboration patterns in research on financial-fiscal mechanisms supporting renewable energy development, covering the period from 1972 to 2025. The aim of this research is to identify key trends, influential contributions, and emerging themes within the field, offering actionable insights for policymakers and researchers. The study employs bibliometric analysis, using the Scopus database as the primary data source. Analytical techniques, including keyword co-occurrence networks, thematic mapping, and collaboration trend analysis, were performed using R Studio’s Biblioshiny tool. The dataset comprised publications spanning five decades, enabling a robust exploration of the research landscape. The results highlight a steady annual growth rate of over 7% in publications, reflecting heightened academic and policy interest in financial-fiscal tools for renewable energy. Dominant research themes identified include carbon pricing, subsidies, green bonds, and technological innovation, which collectively underscore the integrative role of fiscal mechanisms in driving renewable energy transitions. Regional analysis reveals Europe and North America as leading contributors to the research field, with increasing participation from developing economies, particularly in Asia and Africa. Emerging financial mechanisms, such as green bonds and climate funds, were noted for their critical role in mobilizing private capital and reducing investment risks in renewable energy projects. The study also emphasizes the persistent challenges faced by developing nations, including limited access to financing, grid infrastructure constraints, and policy instability, and highlights the effectiveness of tailored fiscal measures like concessional loans and grants in addressing these barriers. This research contributes to the understanding of the dynamic evolution of financial-fiscal instruments in renewable energy development. It provides empirical evidence supporting their critical role in fostering sustainable energy systems and highlights areas for future research and policy refinement to accelerate the global energy transition. By leveraging these insights, policymakers and researchers can enhance the design and implementation of fiscal tools to achieve global climate and sustainability goals.

Francisco Lopez-Navarrete, Joaquin Sanchez-Soriano, Oscar M. Bonastre

The consumption of online videos on the Internet grows every year, making it a market that increasingly generates a greater volume of income. This paper deals with a problem of great interest in this context: the allocation of the generated revenues in a video website between the website and the video creators. For this, we consider a dynamic model of the revenues generation. We will consider that revenue can come from two sources: through the pay-per-view system and through the insertion of advertisements in the videos. Then to study how to divide the revenues in a reasonable and fair way between the two parties, we consider a dynamic cooperative game that reflects the importance of each part in generating revenue. From this game, we determine how its Shapley value is and introduce other allocation rules derived from it. We provide a structure of algorithm to calculate the Shapley value and its derived rules. We show that the computational complexity of the algorithms is polynomial. Finally, we provide some illustrative examples and simulations to illustrate how the proposed allocation rules perform.

Iryna Pozovna, Dariusz Krawczyk, Vadym Babenko

Investments, scientific patents, export and import of high-tech goods and services stimulate the country’s technological development, contribute to economic growth, job creation, the formation of a qualified workforce, and the maintenance of social living standards of the population. At the same time, the ecosystem supporting technological innovation is largely dependent on macroeconomic stability in the country, inflationary fluctuations, etc. Based on this, the article examines systemic interrelationships between the factors of technological development (export and import of computer, information, telecommunications and other high-tech goods and services, investments in advanced research and technologies, volumes of transfer of rights to new technological developments, as well as general the level of coverage of the population by information technologies and innovativeness of the country) and macroeconomic development (gross domestic and national product, inflation rate). The research was carried out using the method of Principal component analysis, canonical analysis, panel regression modeling on the data of 11 countries with developed economies for 2011 and 2021 (World Bank and WIPO statistical databases). From 14 indicators of technological development, the 8 most relevant ones were selected using the method of Principal component analysis; by means of canonical analysis, it was found that 32.503% (in 2011) and 37.557% (in 2021) of their variation is due to changes in the studied macroeconomic indicators. On the other hand, the change in macroeconomic indicators by 46.497% (in 2011) and 38.739% (in 2021) is caused by the variation of indicators of investment, transfer, export and import of advanced technologies. Thus, macroeconomic dynamics depend much more on technological development, and not vice versa. Based on the conducted panel regression modeling, a statistically significant dependence of the inflation index on the share of the population that is Internet users and the country's place in the Global Innovation Index was revealed. GDP per capita was found to be dependent on the share of exports of high-tech goods and services, the share of exports of goods in the field of information and communication technologies, the share of the population that are Internet users, the country's place in the Global Innovation Index. State investments in research and technological development turned out to be dependent on the inflation index, the share of imports of computer, information and other services, the share of exports of goods in the field of information and communication technologies, the share of the population that are Internet users, and the country’s place in the Global Innovation Index.

Anatoliy Guley, Artem Koldovskyi

During the Covid-19 pandemic, there has been a rapid shift from offline global transaction models to digital payment models, along with increased interest in the development of Central Bank Digital Currencies (CBDCs) in various countries. Currently, around 114 countries around the world are researching and developing CBDCs, and these countries account for 95% of global GDP. Some countries have already launched CBD’s and it is very likely that CBDCs will become a part of our lives in the near future. The study discusses the essence and features of the digital currency of the central bank, examines the prospects of its implementation in various socio-economic conditions, examines the advantages and disadvantages of using digital currencies. The authors also compared the digital currency of the central bank and decentralized cryptocurrencies. The authors analyzed the global experience of central banks that explored the possibility of issuing their own digital currencies. The authors of the article use a system-structural analysis to determine approaches to understanding the concept of “central bank digital currency”. The authors also identified the potential advantages of introducing digital currencies, which strengthen the transmission mechanism of monetary policy. Attention is focused on the opportunities provided by digital currencies as an innovative payment tool for financial integration in society. It is argued that the creation of retail central bank digital currencies may pose risks to financial stability that can be minimized through the architecture of a central currency system. The authors proved the importance of using CBDC using an econometric model using the Chinese digital yuan as an example.

Anthony Olugbenga Adaramola, Yusuf Olatunji Oyedeko

The study examined effect of drawdown on return in the Nigerian stock market. The study covered the period of 2005 to 2020. Purposive sampling was employed and the sample size comprising 90 regularly traded stocks were used for the analysis. Monthly data sourced from the CBN statistical bulletin and Nigeria Stock Exchange on stock prices, market index, risk-free rate ownership shareholdings, market capitalization, book value of equity, earnings before interest and taxes, total assets and drawdown were used for study. The Fama-MacBeth two-step regression method was employed. The study found that the drawdown has a negative and significant effect on stock returns but has a positive and significant effect on risk in the Nigerian stock market over the whole sample period. Findings also revealed that the sub-period are not stable in terms of the magnitude of effect and significance on risk and return. Our findings contradict the a-priori expectation that drawdown could improve performance through risk minimization and return maximization in the Nigerian stock market. Based on the findings investors and other market participant are encouraged to use drawdown as one of the investment performance measures to guide investors’ expectation and their tolerance on the size of stock market disruption or crashes or rallies in Nigeria.

S.N. Singh

Coffee is a bulging commercial crop ever growing up in Ethiopia to export for gaining comparative advantageous of price and income. It also plays a pivotal role to supporting livelihoods of most of the people particularly poor within the territory of the country. It is obvious that the farmers are facing numerous problems encountered with coffee value chain for marketing of their products in marketing channel. The main objective of this research is to analyzing the factors influencing coffee cooperatives effectiveness in coffee value chain of Ilubabor Zone in Oromia Region of Ethiopia. Research is carried out with methodology of data analysis based on descriptive statistics and econometrics model. A logistic regression method is used to analysis the effectiveness of coffee cooperatives in coffee value chain and multi -collinearity regression analysis is employed to determine the correlation between explanatory variables. It is found that despite of inactiveness of cooperatives the coffee value chain is playing an important role to facilitating marketing of coffee in Ethiopia. The research is an important perspective to measuring emerging problems associated with value chain and its solutions with valuable recommendations.

Debesh Bhowmik

The paper basically explains the nature and trends of FDI inflows in agriculture and subsectors of agriculture in India under two broad ways. In the first case, the linear trend was examined utilising linear semi-log regression model. In the second case, the nature of cycle and the cyclical trend were found out by applying H.P. Filter model. The linear trend, cycle and cyclical trend of FDI inflows in India in agriculture during 2000-01-2017-18, agricultural services during 2001-02-2021-22, agricultural machinery, tea and coffee, food processing, sugar and fertilisers respectively during 2005-2018 have been computed. Yet, the paper included the nature of global FDI inflows in agriculture very briefly. The paper observed that the linear trends in FDI in agriculture, agriculture service, food processing have been increasing significantly in which their cycle and cyclical trends are significantly meaningful. On the other hand, the linear trends of FDI in tea and coffee and agricultural machinery have been declining insignificantly in which their cycles and cyclical trends are significant in H.P. Filter model. However, the linear FDI trends in sugar and fertilisers sectors have been stepping up insignificantly. Their cycles and cyclical trends revealed insignificant. In the second part, the paper examined the nexus between the gross value added in agriculture and FDI inflows in agriculture from 2000-01-2017-18 and agricultural service during 2001-02-2021-22 using double-log regression model and found out that there is positive relation between them which indicated a stable model. The paper included some important policy measures for India.

Cao Chen, Fen Zhou, Massimo Tornatore et al.

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today's Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator's revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.

Greta Keliuotytė-Staniulėnienė, Kamilė Daunaravičiūtė

This paper summarizes the relevant researches in the area of the green bond market within the perspective of the performance of the global green bond market in the face of the COVID-19 pandemic. Despite the rapid expansion of the green bond market during the last decade, this market has also experienced the consequences of the COVID-19 pandemic. The researches on the effect of COVID-19 and its induced crisis on the green bond markets are still fragmentary; therefore, the main purpose of this research is to evaluate the impact of the COVID-19 pandemic on the global green bond market. To reach the purpose, the methods of literature analysis, and correlation-regression analysis are used. In the first section of the paper, the research problem is presented; in the second part the analysis of academic literature is conducted; in the third part the design of the research is described, and in the fourth part the results of the assessment of the impact of COVID-19 pandemic on the global green bond market are discussed. The results of the research revealed that the spread of the COVID-19 pandemic appeared to have a negative impact on the performance of the S&P Green Bond Index. The market reaction to deaths caused by COVID-19 infection proved to be stronger than the reaction to confirmed cases of COVID-19 infection. However, after a sufficiently significant negative shift, which was observed in the first quarter of 2020, the S&P Green Bond Index regained its upward trend, which continued for the rest of the year.