Market Price Research Articles

Additional public procurements in health services: Lessons from Finnish physiotherapy markets

Private providers are often acquired through public procurements to provide publicly financed health services. The number of contracted providers and their capacity often turns out to be deficient during the contract period. Typically, the purchasers then organize additional procurements or negotiate directly with available uncontracted providers to acquire more capacity. In this study, we examine an unusual additional public procurement in Finnish physiotherapy markets, in which incumbent providers with an existing contract were also eligible to participate. In practice, these contracted providers did not bear any risk when they submitted their bids in the additional procurement. We compare contracted and uncontracted providers’ prices to free market prices using a difference-in-differences approach. The results show that contracted providers increased their prices on average by 9%, while the originally uncontracted providers decreased their prices by 8% compared to the development of free market prices. Although this inefficient procurement led to a waste of public funds, a simple back-of-the-envelope calculation indicates that additional procurement can still be a more efficient way to increase capacity than directly contracting providers that were not contracted in the initial procurement.

Research on the Inhibitory Effect of the EU’s Carbon Border Adjustment Mechanism on Carbon Leakage

Associated with more ambitious targets for reducing emissions, the European Union (EU) plans to implement the Carbon Border Adjustment Mechanism (CBAM) fully in 2026, aiming to reduce carbon leakage and competitiveness concerns by imposing tariffs on carbon-intensive imports, which is expected to significantly impact its trade partners. Existing research has focused on CBAM’s impact on macroeconomic indicators but has insufficiently addressed its effects on global and regional carbon leakage, especially in non-EU countries like China. This research offers a detailed analysis of industry-specific leakage rates and integrates both global and regional impacts by employing the dynamic recursive GTAP-E general equilibrium model to numerically simulate CBAM’s inhibitory effect on carbon leakage under different carbon tariff scenarios, while also exploring the synergistic effects of anti-leakage policies in non-EU countries. Our simulations indicate the following: (1) CBAM effectively inhibits carbon leakage, with greater inhibition observed at higher tax rates and with the expansion of covered industries. (2) Establishing China’s domestic carbon market pricing can further reduce regional carbon leakage rates. Implementing global export carbon tax policies will significantly diminish the risk of global carbon leakage. (3) The implementation of CBAM is projected to reduce China’s total exports to the EU, though this loss will be partly offset by trade diversion effects. Carbon-intensive industries are more adversely affected in the short term, while all industries except fossil fuels face inevitable long-term negative impacts.

Research on Tesla's Pricing Strategy in Chinese Market

As a leading global electric vehicle manufacturer, Tesla's performance in the Chinese market has a significant impact on the entire industry and market. The paper aims to analyze and evaluate Tesla's market pricing strategy in the Chinese market through SWOT analysis. Based on the analysis of pricing strategies, the paper also explores the impact of Tesla on the Chinese automotive economy market. Finally, the paper proposes some suggestions to address the challenges of future development and market expansion in the Chinese automotive market.

Assessment of irrigation performance trends using selected comparative indicators over consecutive periods: A case study of the Koga Irrigation Scheme in the Amhara region, Ethiopia

AbstractThe Koga Irrigation Scheme is a modern irrigation scheme in Ethiopia, and furrow irrigation is a common water application method. The main purpose of this study is to show in which year it performed well and what the problems were and to identify the best alternatives that may be essential for managing the scheme effectively and efficiently. The performance trends of the Koga Irrigation Scheme were evaluated using four selected performance indicators, namely, physical, agricultural, water use and economic performance indicators. Primary and secondary data, including geographic locations, water supply, harvested yield, market prices, irrigated crops, actual irrigated area, planned irrigable area, total management and operation cost, water fees and historical climate data, were collected. Land use performance analysis indicates that the scheme operates below its potential. The maximum irrigated area was 6,327.7 ha less than the designed 7,000 ha in 2015 and 3,619.9 ha in 2016. Water consumption peaked in 2015 and decreased in 2016. The dominant crops were wheat (55%), potato (19%), barley (7.6%), onion (7.4%), maize (5.6%) and other crops (5.4%). This suggests a preference for low‐value, water‐intensive crops. Water use by crops is lower than reservoir release, indicating significant water loss. Economic water productivity is low, primarily due to the dominance of low‐value, water‐intensive crops. To enhance scheme performance, the study recommends adjusting cropping patterns, estimating actual water demand for the respective crops and irrigation scheduling, adopting efficient water application methods and strengthening the market chain.

Resources, energy consumption and economic analysis for flame synthesis of ternary cathode materials

Cathode materials account for around one third of the manufacturing cost of lithium-ion battery, which are the major factor preventing the wider applications of power battery packs. Flame synthesis (FS) is a new manufacturing technology of nanostructured materials with few equipment and high efficiency, which potentially can substantially bring down the manufacturing cost of cathode materials. The primary objective of the present study was to quantitatively evaluate the resources, energy consumption and economic feasibility of large-scale production of cathode materials though FS. In the analysis process, the mass flow of reactants and resultants as well as the minimum cathode material selling price (MCSP) of ternary cathode material LiNixCoyMn(1-x-y)O2 (x + y + z = 1) produced by FS were calculated, and compared with that of the conventional hydroxide co-precipitation method. Results show that the production of ternary cathode material through FS can reduce CO2 emissions by ∼60% and water consumption by ∼30%. MCSP of LiNi0.5Co0.2Mn0.3O2 (NCM523), LiNi0.6Co0.2Mn0.2O2 (NCM622), and LiNi0.8Co0.1Mn0.1O2 (NCM811) produced by FS are 224.8 k¥/t, 235.1 k¥/t, 240.0 k¥/t, which respectively are about 2%, 10% and 14% lower than the average selling prices in Chinese market, indicating that FS is economically feasible for producing ternary cathode materials. Particularly, for the production of high-nickel cathode materials, FS has notable potential in simplifying the production process and manufacturing costs. Sensitivity analysis of NCM811 produced by FS shows that when raw material price, fuel and power costs are reduced by 25%, MCSP can be as low as 186.4 k¥/t. When the metal cation concentration of the solution increases to 3.2 mol/L, it enables FS to meet the most rigorous energy consumption limitation standards in China. When the lithium excess is reduced from 10% to 5%, MCSP of NCM811 can be further reduced by 5.6 k¥/t, indicating that lithium loss due to high temperatures in FS is a moderate factor for reducing the manufacturing cost. Finally, this work presents a life cycle assessment (LCA) of NCM materials produced by FS in China. Comparison to the global warming potential (GWP) of the co-precipitation method in the literature indicated that GWP of FS is lower.

A Girsanov transformed Clark-Ocone-Haussmann type formula for $$L^1$$-pure jump additive processes and its application to portfolio optimization

AbstractWe derive a Clark-Ocone-Haussmann (COH) type formula under a change of measure for $$ L^1 $$ L 1 -canonical additive processes, providing a tool for representing financial derivatives under a risk-neutral probability measure. COH formulas are fundamental in stochastic analysis, providing explicit martingale representations of random variables in terms of their Malliavin derivatives. In mathematical finance, the COH formula under a change of measure is crucial for representing financial derivatives under a risk-neutral probability measure. To prove our main results, we use the Malliavin-Skorohod calculus in $$ L^0 $$ L 0 and $$ L^1 $$ L 1 for additive processes, as developed by Di Nunno and Vives (2017). An application of our results is solving the local risk minimization (LRM) problem in financial markets driven by pure jump additive processes. LRM, a prominent hedging approach in incomplete markets, seeks strategies that minimize the conditional variance of the hedging error. By applying our COH formula, we obtain explicit expressions for locally risk-minimizing hedging strategies in terms of Malliavin derivatives under the market model underlying the additive process. These formulas provide practical tools for managing risks in financial market price fluctuations with $$L^1$$ L 1 -additive processes.

Determining the Geographical Authenticity of Sri Lankan Regional Teas with Their Chemical Profile Using Artificial Neural Networks

Characterization of authenticity is a timely requirement in food control and safety. The geographically authenticated food products always realize high market prices and sustainable market share. The present study was conducted to investigate the potential of chemical profile in authenticating geographical differences of Sri Lankan regional black teas using artificial neural networks. Thirteen different geographical origins under eight different basses were evaluated and tea samples were chemically analyzed in monthly intervals for a period of one year. Training of feed-forward back propagation multilayer perceptrons resulted in accuracy rates ranging from 46% - 82%. Out of eleven chemical parameters considered for the study, Thea Rubigin and Total Colour are the most important discriminators of geographical origins. Other than that, Gereniol, Thea Flavin, Trans – 2 – Hexanol, Total Polyphenol and Amino Acids are also potential discriminators. The study concluded that the differentiation and classification of Geographical Origins (GOs) of Sri Lankan regional black teas is possible using the profile of chemical attributes and applying artificial neural networks. Finally, the data obtained demonstrates that it is possible to assign unknown samples according to their region of origin. Further development of this work could lead to a simple interface that could be used by brokers or exporters to identify the geographical origin of an unknown tea sample.

Trends in Greenhouse Production Technology with Special Reference to Protected Cultivation of Horticultural Crops

Greenhouse is an important application scenario of modern agriculture. With the improvement of people's life quality, the demand for vegetables, fruits, flowers and has also increased, and the amount of greenhouses has developed rapidly. Greenhouse adopts artificial technical means to change natural light and temperature conditions, and to create environmental factors to optimize the growth of animals and plants, so that greenhouse crops can grow throughout the year and the weather. Compared with traditional planting methods, the greenhouse is more automated, intelligent, and mechanized. Greenhouse farming (Protected cultivation) refers to the practice of growing crops in an enclosed or partially enclosed environment, such as greenhouses, shade houses, or high tunnels. The primary purpose of protected cultivation is to create a controlled microclimate that shields the crops from adverse weather conditions, pests, and diseases, while optimizing environmental factors. It involves the use of structures, materials, and technologies that provide a protective barrier and allow for manipulation of temperature, humidity, light, and other environmental variables. The benefits of greenhouses are high production per unit area because the genetic potential of the crop can be completely exploited, vegetables and nurseries can be grown off-season that obtain high market prices, good quality products and it is easy to protect the crops from pests, diseases and extreme climatic conditions. Greenhouse farming is a highly intensive industry that needs considerable labour and capital inputs. Because of this, all factors important for a successful enterprisers should be carefully considered by potential growers.

The Impact of Stochastic Volatility on Option Pricing Using the Black-Scholes Model: Empirical Evidence from NVIDIA

This paper investigates the impact of volatility on option pricing using the Black-Scholes model. Utilizing historical stock and option data from NVIDIA, this study demonstrates the limitations of the Black-Scholes model in capturing dynamic market conditions. The empirical analysis reveals significant discrepancies between model predictions and actual market prices, highlighting the importance of accurate volatility estimation. The findings suggest that incorporating better volatility measures can improve the accuracy of option pricing models. Additionally, the implications of these findings for traders and risk managers are discussed, emphasizing the need for more sophisticated approaches to volatility estimation in financial markets. This study adds to the body of knowledge by presenting actual data regarding the efficiency of different volatility metrics in option pricing. Moreover, it underscores the necessity of continuous improvement in financial modeling techniques to adapt to changing market dynamics. The study's results align with previous research that has shown the limitations of the Black-Scholes model and the benefits of alternative volatility estimation methods. This paper also discusses the potential application of these findings to improving trading strategies and risk management practices. The practical implications are significant, suggesting that traders and risk managers should consider incorporating advanced volatility measures into their models to enhance decision-making processes and financial outcomes. The conclusions drawn from this study highlight the critical role of accurate volatility estimation in achieving more reliable option pricing and underscore the need for ongoing research and innovation in this field.

The effect of live body condition score of beef cows on carcass characteristics, carcass-cutting yields, processor profitability, and tenderness in the longissimus lumborum and psoas major muscles.

The objective of this study was to investigate the effects of body condition score (BCS) of cull cows on carcass characteristics, carcass-cutting yields, profitability, and tenderness development for the longissimus lumborum and psoas major muscles. During a 5-wk period (May-June 2023), 10 boner cull cows (BCS 4 to 6) and 10 leaner cull cows (BCS 1 to 3) were purchased at a commercial auction market. Live conformation, carcass characteristics, weights of fabricated cuts, tenderness, pH decline, and temperature decline were recorded following slaughter. Carcasses were fabricated into the following cuts: knuckle, inside round, outside round, eye of round, strip loin, top sirloin, bottom sirloin flap, tenderloin, flank, ribeye roll, chuck tender, and brisket, whereas lean was separated into lean and fat components. Beef processor returns for boner cows were calculated as the sum of USDA Carlot Report values for the subprimal cuts, trim, bone, and drop value subtracted by actual live costs at the auction market whereas beef processor returns for leaner cows were calculated as the sum of USDA Carlot Report values for the trim, bone, and drop value subtracted by actual live costs at the auction market. Carcass and merchandizing value parameters were analyzed using a completely randomized design with a fixed effect of classification (leaner or boner) and a random effect of slaughter day. Live weight was used as a covariant for all carcass and merchandizing value parameters due to the pronounced effect of live weight for most parameters. Temperature decline, pH decline, shear force, and cooking loss parameters were analyzed using a completely randomized design with a fixed effect of classification (leaner or boner) and a random effect of slaughter day. Boner cows were found to be heavier for both live and carcass weights when compared with leaner cows. In addition, backfat thickness was 0.39cm greater (P = 0.03), and ribeye area was 9.17cm2 (P = 0.05) greater for boner cows compared with leaner cows. This resulted in boner cows yielding larger subprimal cuts and a greater amount of lean trim, which in turn generated more beef processor revenue. However, specific consideration should be provided for profitability as the ability to market subprimal cuts is highly dependent on muscle size, fat deposition, meat quality, and market prices for both boner and leaner cows.

A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of EtVaRαand EtCVaRα to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic.

Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis

This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs.

Wage Growth, Labor Market Tightness, and Inflation: A Service Sector Analysis

This Economic Commentary explores the connections among labor market tightness, wage inflation, and price inflation at the service sector level. Across most service sectors, sector-specific labor market tightness and nominal wage growth have been above prepandemic averages since 2022. The data suggest that a stronger positive relationship between labor market tightness and wage growth has emerged in the aftermath of the pandemic. The relationship between sector-specific wage growth and inflation is more varied. In the education and health services sector, higher wage growth is associated with higher inflation after about one year; in the leisure and hospitality services sector, the positive relationship is contemporaneous. I do not find a significant relationship between wage growth and inflation in the other service sectors, such as transportation or financial and business services.

Original Research Article Analyzing Price Behaviour and Constraints in Marketing of Small Onion in Tamil Nadu, India

This study examines market trends and seasonal patterns in small onion Markets in Tamil Nadu, while also investigating challenges faced by various stakeholders in the onion supply chain. The various analyses have made use of secondary data that has been gathered from different sources. The NHRDF office in Coimbatore, NHB website and AGMARKNET website are the sources of the monthly data on wholesale market prices and arrivals of small onions f markets of Dindigul, Chennai, Coimbatore and Idukki from 2014 to 2023. Primary data also obtained to fulfill the one of the research objectives by surveying the sample of respondents. Two districts were selected purposefully: Perambalur and Trichy districts, based on the top onion producing districts in Tamil Nadu. The sample size constitutes 180 respondents (80 farmers, 40 retailers, 20 wholesalers and 40 consumers) were randomly interviewed with help of pre structured interview schedule. Trend analysis revealed increasing price trends across all markets, with Idukki experiencing the highest rate of increase. Seasonal index analysis indicated that prices in Tamil Nadu markets are influenced by arrivals, with lower prices during peak arrival periods., while Idukki market in Kerala prices less influenced by arrivals. Farmers struggle primarily with high labor costs (78%) and high cost of fertilizers pesticides and machineries (68.7%), while consumers face problems with price fluctuations (68.8%) and quality concerns (68.6%), retailers face problems with lack of storage facilities (76.27%) and absence of facilities for grading (64.85%), and wholesalers struggle with financial risks (79.2%). The study's findings will aid policymakers to focus on improving market infrastructure, regulating intermediaries, and supporting farmers in managing costs. Leveraging existing labor schemes could help alleviate workforce shortages. Furthermore, customers would gain from initiatives to maintain price stability and raise quality standards.

An analysis of the Indian Economy during the three COVID-19 pandemic waves

The objective of the study was to examine the effects of the COVID-19 pandemic on India’s economy. The analysis focused on several economic metrics, including stock market prices, the rupee’s value in relation to the US dollar, economic activity, the unemployment rate, and the rate of inflation. Contrary to popular belief, the results demonstrate that during the first wave (25 March 2020 to 16 September 2020), the increasing number of cases had a beneficial influence on economic activity and a negative impact on the unemployment rate. The second wave, which lasted from 15 March 2021 to 17 July 2021, was considerably stronger and demonstrated how confirmed instances had a significant detrimental impact on inflation rates and stock values. Contrary to expectations, the third wave (December 28, 2021, to January 30, 2022) was found to be less intense. Overall, the report shows how the pandemic affected India’s economy during each of the three waves and notes that there have been encouraging signs of recovery during the return to normalcy phase. The government, scholars, policymakers, and economists will find this study useful in understanding how the COVID-19 Pandemic affected the Indian economy and in coming up with ideas for future risk mitigation measures.

Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning Techniques

The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.

Unraveling the Shift: Exploring Factors Influencing Protein Preference and Consumption Patterns in Ga South Municipality of Ghana

This study endeavors to elucidate the factors influencing protein preference and consumption patterns in the Ga South Municipality of Ghana. Understanding these determinants is pivotal for promoting environmentally sustainable and healthy dietary habits. Utilizing a random sampling approach, the study collected 150 meticulously structured questionnaires from a representative sample of municipality residents. It delved into the socio-cultural and economic factors shaping protein preferences and consumption behaviors, alongside investigating the impact of awareness, knowledge, and availability of protein sources on dietary choices. The findings unveiled that respondents partake of protein daily, albeit with variations in patterns based on sources: plant protein consumption occurred weekly, while animal protein was consumed daily. Moreover, respondents exhibited awareness of the nutritional benefits associated with protein consumption. While awareness for protein nutrition is high, the price remains a deterrent to accessing protein in Ga South Municipality. Calling on policymakers to support local food production and supply, invest in food literacy, and develop good transportation to reduce logistics bottlenecks that often create a massive difference between farm gate and market prices.

Pricing or Prizing? The Valuation of Need in a Crisis of Housing Affordability

ABSTRACT The concept of affordability in housing policy signalled a critical shift away from the priorities of housing need. This displacement of need by affordability can be understood as an act of economic valuation in which market price becomes the standard against which housing policies are measured. In this article I draw on John Dewey’s neglected theory of value to examine the relation between price and need. I apply this theory to an investigation of the assessments of housing need carried out by municipal authorities to establish quotas for the delivery of affordable housing in England. I argue that the shift from need to affordability has separated the concept of value from the process of valuation and resulted in a displacement of housing policy goals. I conclude that the systemic failure to address affordability problems in global housing markets should direct our attention to the failure of price to adequately value a prized goal of public policy.

The Impact of Tourism on the Economy in the Destination of Ubud After the Lifting of Travel Restrictions

This study aims to assess the impact of tourism on the economy in Ubud after travel restrictions were lifted, focusing on people working in the tourism sector. The study involved 150 respondents selected through purposive sampling and accidental sampling. Data analysis was conducted using descriptive statistics and confirmatory factor analysis. The results show that the impact of tourism in the Ubud destination after the lifting of travel restrictions which has the highest average value in each variable begins with an increase in souvenir sales with an average of 4.53, increasing market prices for local handicraft products with an average of 4.44, and increasing employment opportunities for local workers with an average of 4.60. The most felt economic impact in the direct impact section is an increase in the availability of local crafts with a CFA value of 0.751, then in the indirect impact there is the most felt impact of increased government spending on promotion with a CFA value of 0.898, and in the induced impact is an increase in the use of local labor with a CFA value of 0.875.

Assessing exploitation vulnerability risk of marine ornamental reef fish in Sri Lanka: A productivity susceptibility analysis

We conducted a Productivity Susceptibility Analysis (PSA) to empirically assess the vulnerability risk of marine ornamental reef fish in Sri Lanka for exploitation. We considered distribution, accessibility, ecological niche, and market price as susceptibility attributes, while fecundity, larval development, maximum length, and longevity were productivity attributes. The vulnerability score, demand, and market price were used to assess species prioritization, and then compositions of the prioritized species were computed. The veracity of primary and secondary data collected on susceptibility and productivity attributes was confirmed by interviewing traditional fishers in situ. The IUCN and present PSA classifications for the medium susceptibility risk species were comparatively assessed. Four species out of 17 medium susceptibility risk species were encountered as the medium vulnerability risk species for exploitation. The Pacific cleaner shrimp (Lysmata amboinensis) emerged as the highest prioritized species. In addition, we observed significant differences in the species’ status assessed through the PSA approach compared to the IUCN status. The study affirms that demand and market prices substantially influence the exploitation vulnerability of species, and the PSA offers a promising multidisciplinary approach to evaluating intrinsic and extrinsic factors in conservation ecology compared to widely used IUCN status for assessing extinction risk. Furthermore, the present findings have practical implications, as they warrant essential policy changes for conserving the prioritized species and the medium vulnerability risk species encountered in the present research to extinction risk.