Price Variation Research Articles

Asset prices, regime switches, and monetary policy: evidence from China

ABSTRACT This paper develops a regime-switching DSGE model that can capture time-varying volatilities in economic activity and monetary policy. The model is then used to study regime switches in macroeconomic dynamics and monetary policy in China. The results suggest a better fitness of the regime-switching model than conventional constant-parameter models. We also find notable differences in macroeconomic dynamics and monetary policy reactions between high and low volatility regimes. Further counterfactual analysis suggests that, at least in an environment with regime-switching volatilities, the optimal reaction of monetary policy involves responses to asset price variations. The findings of the paper shed new light on the regime-switching properties of the economy and the optimal choice of monetary policy within this context.

The effect of hydrocarbon prices and CO2 emission taxes on drainage strategies

Secondary recovery methods, such as water and gas injection, are effective and cost-efficient techniques for enhancing hydrocarbon recovery; however, they are highly energy-intensive, leading to a large environmental footprint. This study provides insights into the challenge of balancing high hydrocarbon demand with the imperative to reduce CO2 emissions from production by estimating how drainage strategies change under varying economic parameters and CO2 tax levels. An operator’s choice of drainage strategy is assumed to be the drainage strategy yielding the highest net present value (NPV). This paper simulates the drainage strategy of an under-saturated oil reservoir, utilizing reservoir simulations and incorporating a mathematical model that represents the production processing plant. This yields hydrocarbon production, the energy required, and the resulting CO2 emissions, all incorporated into an NPV based on hydrocarbon prices and CO2 emission taxes. The drainage strategy is then optimized to maximize NPV for various hydrocarbon prices and several levels of CO2 tax on associated emissions. The findings of this study indicate that increasing gas prices favor reducing reservoir pressure below the bubble point pressure, thereby releasing solution gas and leaving oil in the reservoir. Although lower reservoir pressure reduces the energy required for injection, the resulting increase in gas production necessitates more energy for gas compression, ultimately leading to higher emissions when gas prices rise relative to oil prices. Additionally, this study reveals that for facilities well designed for the present injection and production volumes (greenfield scenarios), the impact of CO2 tax is limited, leading to a single optimal drainage strategy. In contrast, less energy-efficient facilities (brownfield scenarios) exhibit a more pronounced need to optimize drainage strategies in response to variation in CO2 tax, and oil and gas prices. This study indicates that for fields with efficient production facilities, the ratio of gas to oil prices is more important for the drainage strategy than CO2 tax levels, as income from production tends to dominate emission taxes. This implies that hydrocarbon prices are more important for CO2 intensity than CO2 taxes, e.g., a gas price increase relative to oil price promotes higher CO2 intensity due to a shift towards more energy-intensive gas production whereas realistic tax levels for CO2 emissions have only a minor impact. Thus, increasing CO2 tax levels is mainly a measure impacting emissions from fields with inefficient production facilities.

The Link Effect of ESG Score, Stock Price Volatility, and Tax Payment: Doing Well while Doing Good

This study aims to identify how environmental, social and governance (ESG) performance influences stock price volatility, explicitly focusing on the moderating role of tax engagement. ESG performance is measured by an ESG Score calculated from the weighting of three dimensions: environmental, social and governance. Stock price volatility is measured by the degree of price variations over 12 months based on the last 52 weeks’ prices. A sample of Indonesia-listed firms is used, with 770 observations from 2023. The results show that the ESG Score negatively impacts stock price volatility, which is more significant in the social dimension than in the environmental and governance dimensions. In addition, the tax payment variable moderates the relationship and increases the effect of the ESG Score on stock price volatility. These findings suggest that ESG practices and tax transparency are ethical elements and critical components for financial stability, promoting the high-quality development of listed firms. This study is significant for firms, regulators, policymakers and investors. Overall, it underrates the importance of firms adopting ESG activities and engaging in tax management to mitigate risks and maintain viability in the contemporary business environment. This study provides new empirical evidence regarding the factors driving corporate stock price volatility. In addition, it offers pertinent policy recommendations for businesses and governments regarding the significance of ESG investments.

A Problem as Much of Economic Theory as of Statistical Technique? The Walsh-Edgeworth Debate on Index Numbers, 1921–25

Abstract Correa Moylan Walsh (1862–1936) was a monetary economist of the early twentieth century, mostly remembered for his contributions to the theory of index numbers. Francis Ysidro Edgeworth (1845–1926) was a renowned mathematical economist, remembered as one of the great masters of the discipline. Walsh was an adamant defender of a deterministic, test approach to the theory of index numbers, whereas Edgeworth favored probabilistic approaches and thought index-number theorists should not close themselves to alternative methods. Holding contrasting views about the making of index numbers, they engaged in a fierce debate in the first half of the 1920s—a period marked by several quarrels on the subject. The main points in dispute between Walsh and Edgeworth were the existence of an ideal formula for index numbers and the use of probability in the measurement of price variations. The goal of this article is to explore this debate and its underpinnings. It is argued that the main points of contention between them relate to their conflicting perspectives on the nature of index numbers.

Challenges and Opportunities in the Specialization of Maize Cultivation

Objective: To identify current scientific information on maize (Zea mays L.) in relation to the challenges and opportunities associated with the specialization of this crop. Design/methodology/approach: A bibliographic search was conducted in high-impact journals focusing on the difficulties and opportunities in maize cultivation. Results: Several key challenges in maize cultivation were identified, including the impact of climate change, limited access to technologies for small and medium producers, variability in grain price, and the availability of improved seed to increase yields in their reproductive zones. One potential solution involves access to improved short-cycle varieties with tolerance to adverse factors (both biotic and abiotic), combined with sustainable agricultural techniques such as conservation agriculture. Limitations on study/implications: Despite existing research on maize cultivation, its composition, nutritional contribution and economic importance, the profitability of this crop is affected by factors such as price variation, climate change, and the incidence of pests and diseases. Therefore, further research is needed to identify varieties with more competitive markets to enhance sustainability. Findings/conclusions: Maize is a globally important crop with industrial applications and uses for both human and livestock consumption. The increasing demand underscores the need to improve its performance and profitability. A strategy to increase production involves promoting diversification through the specialization of varieties with more competitive markets and the adoption of sustainable agricultural techniques.

Time-Varying Drivers of Stock Prices

This paper provides novel evidence of the time-varying roles of subjective expectations in explaining stock price variations. Cash flow expectations matter more during times of financial uncertainty and recessions, especially among the hardest-hit industries such as Telecommunications during the dot-com bubble, Financials during the Great Recession, and Healthcare during the COVID-19 pandemic. Conversely, discount rates explain more price variations during expansionary periods. Inflation expectations, while accounting for more than half of price fluctuations in high-inflation environments, play a negligible role otherwise. Finally, factor returns tend to move against earnings growth expectations under low financial uncertainty but move in sync with earnings growth expectations when financial uncertainty is high.

UMBU TREE (Spondias tuberosa Arr. Cam. - Anacardiaceae): FROM EXTRACTIVE TO PLANTED CULTURE IN BRAZIL

Umbu tree (Spondias tuberosa Arr. Cam.) is botanical species native to the Caatinga that plays significant cultural and economic role in Northeast Brazil. The aims of this study to evaluate the trend in umbu fruit production over a period of 21 years and to discuss the cultivation of the species, associated with its socioeconomic and environmental advantages in the semiarid region of Northeast Brazil. Data on extractive production and the monetary value of umbu fruit were obtained from the official platform of the Brazilian Institute of Geography and Statistics. Silvicultural aspects cultivation, management, and its market was obtained from specialized literature. Descriptive statistics on the data set and a trend analysis through regression model fitting was carried out using JMP® software. A downward trend in umbu tree fruit was estimated for most states, except for Alagoas, Paraíba, and Rio Grande do Norte. Alagoas was the only one with the best average variation (74.6%) in production and price (US$ 0.83) per kg of production via extractivism. The cultivating through plantations for commercial fruit umbu production is an alternative to overcome the declining trend in extractivism, ensuring continuation of family income and environmental protection of umbu tree populations in their natural habitats.

Radial analysis and scaling of housing prices in French urban areas

Urban scaling laws summarize how attributes evolve with city size. However, one limitation concerns the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is an important issue regarding housing prices, given their significant variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017–2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our approach with rescaled profiles fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than in the periphery. This has strong implications for low-income households seeking homeownership because prohibitive prices in the center may contribute to pushing them out into peripheral locations, especially in large cities.

Hybrid local energy markets: Incorporating utility function-based peer risk attributes, renewable energy integration, and grid constraints analysis

This study proposes a novel hybrid Local Energy Market (LEM) trading framework meticulously designed for efficient energy trading among distribution network participants. The proposed LEM hybrid modeling framework aims to maximize social welfare by considering the behavior of market participants and incorporating network constraints into the transactional framework. To address the consumption behavior of market participants, the concept of the utility function with peer risk attributes is employed, differentiating between flexible and inflexible demands. Additionally, it adeptly manages various generation types, including diesel generators, wind and solar generators, and storage, each characterized by a unique cost function. The investigation extends to the analysis of the Japan Electric Power Exchange market (JEPX), taking into account the residential electricity demand in Tokyo, Japan, by comparing with community-based (CB) and peer-to-peer (P2P) markets and scrutinizing market player behavior across multiple scenarios. These scenarios include comparisons of different market structures, variations in prices, considerations of grid constraints, and diverse weather scenarios. The proposed framework undergoes rigorous evaluation utilizing the IEEE 33-bus and 69-bus standard test systems, with results demonstrating its practicality, superiority of the proposed market compared to the CB and P2P markets, and providing valuable insights into the intricate interplay between market players’ behavior and the dynamics of diverse market structures, price fluctuations, grid constraints, and weather scenarios.

Pricing dynamics of anti-seizure medications in the U.S.

BackgroundThe median cost of anti-seizure medications (ASM) in the United States (U.S.) nearly doubled per person between 2006 and 2021. This increase, combined with shifts in ASM usage and the impact of the COVID-19 pandemic on drug supply chains amid rising inflation, underscored the urgent need to scrutinize ASM pricing dynamics. This study aimed to analyze the complex dynamics of ASM pricing in the U.S. over the past decade (2013–2023); this included how the entry of generic ASMs influenced the pricing of brand-name counterparts and what impacted price variations across different ASM formulations (e.g., significant inflation, the COVID-19 pandemic). MethodsThis study utilized National Average Drug Acquisition Cost (NADAC) data from November 2013 to July 2023. We adjusted ASM prices for inflation using the Consumer Price Index for Medicinal Drugs - Seasonally Adjusted (CPI-MDS). Statistical analyses included fixed effects regressions and multivariable regression analysis to evaluate the impact of inflation, the number of medication labelers, and the COVID-19 pandemic on ASM prices. ResultsOur study analyzed 23 ASMs approved by the U.S. Food and Drug Administration (FDA), which encompassed 223 oral formulations:112 brand-name and 111 generics. From 2013–2016 to 2020–2023, accounting for standard deviations (SD), the average price of brand-name ASMs increased from $8.71 (SD 5.9) to $15.43 (SD 10.7), while generic ASMs saw a slight decrease from $1.39 (SD 1.8) to $1.26 (SD 1.6). Consequently, the price gap between brand-name and generic ASMs surged from 1452.39 % to 3399.26 %. The proportion of matched brand-name and generic ASMs with a price difference of 1000 %–9999 % increased from 32.88 % (2013–2016) to 41.43 % (2020–2023), while those exceeding 10,000 % rose from 16.44 % to 20 % in the same period. Generic immediate-release (IR) formulations were significantly less expensive than extended-release (ER) or delayed-release (DR) counterparts, with cost differences reaching up to 7751.20 %. The number of medication labelers was inversely related to generic ASM prices, which decreased by 5.45 % (p = 0.001) with each additional generic labeler, while brand-name ASM prices increased by 2.46 % (p < 0.001) with each additional generic labeler. The COVID-19 pandemic led to a 24.4 % increase in brand-name ASM prices and a 23.1 % decrease in generic ASM prices. ConclusionsThe findings reveal an expanding price disparity between brand-name and generic oral ASMs. An inverse relationship was observed between the number of medication labelers and generic ASM prices, with additional labelers driving down generic prices. However, introducing more generic labelers led to a significant increase in brand-name ASM prices. Furthermore, following patent expirations, brand-name ASM prices rose—a trend explained by the "generics paradox," where, contrary to expectations, brand prices do not decrease and may even increase when generics enter the market. These findings underscore the need for targeted interventions in drug pricing policies to manage the rising costs associated with epilepsy treatment. To ensure equitable access to ASMs, stakeholders must understand and address the factors driving these pricing dynamics.

NON-OPERATING PROFITS IN THE BRAZILIAN HIGHER EDUCATION SECTOR: INTERFERENCE IN DIVIDEND POLICY?

The aim of this work is to seek a correlation between non-operating profits and the dividend distribution policy by publicly traded companies in the Brazilian education sector through their respective payouts. Publicly traded companies listed on the B3 education sector were selected and analyzed with the other companies listed on the B3 and among them. The time period covered was from January 2, 2017 to December 31, 2020. The variables studied were: payout and non-operating gross profit, in addition to yield and share price variation. The results of this study, by showing that publicly traded companies in the educational sector have higher non-operating profits than other companies listed on B3, can be justified by asset sales, given the mergers and acquisitions in the sector. Furthermore, this realignment could also justify the intra-sector analysis, where the payout of these companies was lower in those where the non-operating profit was higher. These results, in addition to expressing the rejection of null hypotheses, show how similar private higher education companies have become to other publicly traded companies registered at B3 in terms of payout, yield and share price variation. This may show that university autonomy also meant the commodification of education.

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

Similar accuracy and patient experience with different one-step ovulation predictor kits

To examine the accuracy of five different at home ovulation predictor kits (OPKs), and to understand patient experience with the use of those kits. Prospective cohort study. Single academic fertility practice. Patients with regular menses undergoing monitored natural cycle frozen embryo transfer, timed intercourse, and intrauterine insemination cycles involving daily blood luteinizing hormone (LH) level monitoring between 2022 and 2024. Use of five commercially available OPKs for the first five days of blood LH monitoring with a daily experience survey. The primary outcome was accuracy of the ovulation predictor kits defined as concordance between test result (positive or negative) and blood LH level (above or below 25mIU/ml). Secondary outcomes included positive predictive value, negative predictive value, sensitivity, and specificity of OPK surge detection. We also examined patient report regarding clarity of kit instructions, confidence in kit results, and likelihood of future purchase and use. 23 patients completed 97 total days of ovulation predictor kit testing and 13 patients had a documented blood LH surge during their testing. OPK surge detection accuracy when compared to blood LH surge was similar across the five kits (Easy@Home 95.88%, Wondfo 94.85%, Pregmate 96.90%, Clearblue 91.75%, Clinical Guard 91.75%, p-value 0.06). Sensitivity was slightly better for Easy@ Home (75.00%), Wondfo (69.23%) and Pregmate (76.92%) than for Clearblue (61.54%) and Clinical Guard (38.46%). There were no clinically significant differences between the five OPKs regarding positive predictive value, negative predictive value, or specificity of surge detection. Participant experience was qualitatively similar across all five OPKs with exception of slightly fewer patients reporting that they were likely to purchase and use Clinical Guard again in the future. All five tested at home one step OPKs were highly accurate and performed similarly regarding patient experience despite variation in price.

Balancing livestock production and environmental outcomes in northern Australia’s tropical savanna under global change

Abstract Livestock production is an integral part of the global food system and the livelihoods of local people, but it also raises questions of environmental sustainability due to issues such as greenhouse gas (GHG) emissions, biodiversity decline, land degradation, and water use. Further challenges to extensive livestock systems may arise from changes in climate and the global economy (particularly variation in prices for livestock and carbon). However, significant potential exists for both mitigating these impacts and adapting to change via altering stocking rates, managing fire, and supplementing cattle diets to reduce methane emissions. We developed an integrated, spatio-temporal modelling approach to assess the effectiveness of these options for land management in northern Australia’s tropical savanna under different global change scenarios. Performance was measured against a range of sustainability indicators, including environmental (GHG emissions, biodiversity, water intake, and land condition) and agricultural (profit, beef production) outcomes. Our model shows that maintaining historical stocking rates is not environmentally sustainable due to the accelerated land degradation exacerbated by a changing climate. However, planned early dry season burning substantially reduced emissions, and in our simulations was profitable under all global change scenarios that included a carbon price. Overall, the balance between production and environmental outcomes could be improved by stocking below modelled carrying capacity and implementing fire management. This management scenario was the most profitable (more than double the profit from maintaining historical stocking rates), prevented land degradation, and reduced GHG emissions by 23%. By integrating the cumulative impacts of climate change, external economic drivers, and management actions across a range of sustainability indicators, we show that the future of rangelands in Australia’s savannas has the potential to balance livestock production and environmental outcomes.

The Macroeconomic Impact of Crude Oil Price Fluctuations in Nigeria

The main objective of the study was to determine the way fluctuations in the price of crude oil affected macroeconomic variables in Nigeria from 2000 Q1 to 2022 Q4. The data was analysed in the paper using Structural Vector Autoregressive (SVAR) models. According to the findings, Nigeria's current economic growth has been significantly impacted by lag GDP values. This indicates that economic growth in Nigeria was significantly impacted by one-period lag GDP by 3,791.7 units in the short-run and 7,430.4 units in the long-run. Also in the short-run, the current broad money supply was significantly impacted by both the one-period lag general price level (0.445 units) and the one-quarter lag broad money supply (757.7 units). According to the crude oil price fluctuation model, current crude oil price variations in Nigeria are significantly influenced by one-period lags in general price level (-64.9 units), crude oil price fluctuation (1,114.6 units), and fiscal budget deficits (0.65 units). All of the one-period lagged coefficients of macroeconomic variables, including real GDP, broad money supply, interest rates, general price level, inflation rates, and fiscal budget deficits, had a significant impact on current economic growth and broad money supply, respectively, as demonstrated by the long-run economic growth and broad money supply models. The real GDP's impulse response to fluctuations in crude oil prices fell during the first period, peaked during the second, and then increased to show a positive trend during the third. This study came to the conclusion that shocks or fluctuations in the price of crude oil are the main cause of macroeconomic policy instability in Nigeria. The study recommended that the government should control foreign exchange rate regimes, pursue expansionary monetary policy, general price level control and stabilisation measures.

Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models

Crude oil is one of the most vital products that ever existed and variation in prices affects all sectors of the economy and variation in its prices is very crucial. Therefore, without an accurate and appropriate predictive model for crude oil prices, it has proven difficult to predict future oil prices. Therefore, appropriate modeling is crucial for the oil companies to adjust strategies used in the production and supply as well as structural optimization. This study sought to fit several time series forecasting models namely; ARIMA, Naïve, Seasonal Naïve, Time Series Linear Model (TSLM), and Exponential Smoothing (ETS) to model and forecast crude oil prices. The study used Kenya’s monthly crude oil prices from Jan 2003 to Dec 2023, giving a sample of 252 observations. The selection of the best model was based on the minimization of the information criteria, where ARIMA, ETS, and Seasonal Naïve attained an AIC of 1294.193, 1846.780, and 1403.821, respectively. Similarly, the models attained the BIC of 1304.991, 1863.071, and 1450.489, respectively. Since the Naïve and TSLM could not provide the AIC and BIC, model selection was entirely based on the forecast accuracy measures. From the results, the Naive model reported the lowest RMSE (19.6329), indicating that it has the smallest average squared error, with the lowest MAE (15.5212), suggesting it has the smallest average absolute error. Besides, the Naive model reported an ME (1.9819) which is relatively low but not the lowest. The automatic ETS model has a slightly lower ME (3.478211). The naive model reported lower MPE and MAPE values (-3.9525 and 26.8022, respectively) compared to most models, indicating less percentage error. Similarly, a lower MASE and RMSSE were reported by the naïve model, (0.7343624) and RMSSE (0.7228792), respectively, indicating that the model performs well relative to forecasting crude oil prices in Kenya. The naïve model demonstrated a higher consistency and reliability in forecasting crude oil prices in Kenya.

A Bayesian theory of market impact

Abstract The available liquidity at any time in financial markets falls largely short of the typical size of the orders that institutional investors would trade. In order to reduce the impact on prices due to the execution of large orders, traders in financial markets split large orders into a series of smaller ones, which are executed sequentially. The resulting sequence of trades is called a meta-order. Empirical studies have revealed a non-trivial set of statistical laws on how meta-orders affect prices, which include (i) the square-root behaviour of the expected price variation with the total volume traded, (ii) its crossover to a linear regime for small volumes and (iii) a reversion of average prices towards its initial value, after the sequence of trades is over. Here we recover this phenomenology within a minimal theoretical framework where the market sets prices by incorporating all information on the direction and speed of trade of the meta-order in a Bayesian manner. The simplicity of this derivation lends further support to the robustness and universality of market impact laws. In particular, it suggests that the square-root impact law originates from over-estimation of order flows originating from meta-orders.

Forward Guidance under Imperfect Information

AbstractI study the effect of forward guidance in a flexible‐price economy in which both the private sector and the central bank are subject to imperfect information about the aggregate state of the economy. When forward guidance is provided, the central bank reveals its current imperfect information and commits to a policy rule that makes future policy conditional on perfect information that is only available in the future. The information provided by forward guidance makes individual prices more responsive to firm‐specific technology shocks, which increases production efficiency at the cost of higher cross‐sectional price variation. The net effect improves social welfare.

Co-movement of Oil and Stock Markets During COVID-19: Evidence from the Gulf Corporation Council

This study aims to document the impact of oil price variations on generating stock returns in Gulf Cooperation Council (GCC) markets during the COVID-19 era. It documents the pandemic era results from January 2020 to October 2022 by employing cointegration, Granger causality, and time-varying coefficient-vector autoregression techniques on daily data. The findings suggest two-way causality between all stock indices and the oil market. Time-dependent relationships were observed during the review period. The findings (based on regression and variance decomposition) indicate that although the reliance of the GCC stock markets was not on oil alone, oil had a significant impact during the study period. It is recommended that investors not consider diversifying portfolios in GCC stocks and oil markets to optimize benefits. The findings are expected to enhance the understanding of academics, market players, regulators, and investors regarding relationships among GCC stocks and oil markets. This study contributes to the literature by documenting the impact of the oil market on stocks during an abnormal period of the COVID-19 pandemic, considering time-varying parameters in a net oil exporting region.

Discarded fishing net pollution in coastal areas of Bangladesh

Discarded fishing nets, a major source of marine litter, significantly threaten the marine environment and contribute to plastic pollution due to the synthetic polymers they contain. Though Bangladesh is a maritime country with 0.5 million of fishers dependent on coastal and marine fishing, there have been no studies to date on the plastic pollution impact of fishing nets. This study demonstrates the fishing nets associated with marine litter in two coastal locations of Bangladesh, Charfesson and Cox's Bazar. Fishing net samples were collected from local net shops and semi-structured interviews were taken of the shop owners to gather information about available fishing nets. This was complemented by photo-quadrat surveys to document waste fishing net materials on the shore in both locations. Among the 17 net samples, there were 12 types of gill nets, which showed a wide range of variation in price, material types, and longevity. Through the FTIR analysis, we identified the presence of Nylon 6, Polyethylene, Polyvinyl chloride, Polypropylene and Polyethylene terephthalate in the collected fishing net samples. Photoquadrat surveys found that fishing nets are related to plastic pollution in coastal areas. This study addressed the knowledge gap regarding the diversity and chemical characteristics of fishing nets and the resulting litter in Bangladesh.