Important Explanatory Variables Research Articles

Inbound Australian tourism demand from Asia: a panel gravity model

PurposeThis study attempts to explore the determinants of tourism demand that impact tourist arrivals in Australia from Asia using an augmented panel gravity model.Design/methodology/approachThe augmented panel gravity model was utilised to analyse the demand for Australian tourism from 15 major countries of Asia over the period 1991 to 2018. Tourist arrivals were the dependent variable while per capita gross domestic product (GDP) and weighted distance were important explanatory variables. Further other indicators like population, money supply, globalisation, price index, exchange rate, uncertainty and two dummy variables were added as control variables.FindingsThe results demonstrate based on the novel methodology of Pesaran (2006), namely CCE (common correlated effects) that tourist arrivals are impacted positively and significantly by per capita GDP of both the country of origin and destination country, globalisation also impacts tourist flows positively. However, tourist arrivals are adversely affected by distance and prices confirming the economic theory.Originality/valueGravity models have been intensively used in the recent literature on tourism; however, this study has attempted to explore tourism demand from Asia into Australia which is indeed an unexplored area further the study has used the CCE methodology which takes care of the problems of cross-sectional dependence unlike the earlier methods widely used in the literature like the DOLS and the FMOLS. Last by utilising a wide-ranging set of macro factors the study contributes a novel assessment to the recent literature on tourism demand model.

Does energy-industry investment drive economic performance in regional China: Implications for sustainable development

Abstract The economic giant China, enjoying miraculously high economic performance since the 1980s, is currently facing an economic performance slowdown, along with environmental sustainability challenges. Energy-industry investments might serve as a potential driver of economic performance, indirectly inducing environmental sustainability through investments in the energy-industry transition from old technology to a renewable one. This research provides an empirical assessment of the causal bond between energy-industry investment and economic performance across the regional development levels, employing a dynamic longitudinal data modeling on China’s 27 provincial and municipality units from 1997 through 2017. It developed an economic performance model to incorporate energy-industry investment as an aggregate production growth input. The core empirical findings are as follows. Firstly, the energy-industry investment and economic performance induced a positive and statistically significant mutual influence, manifested from their parameter estimates. Secondly, a bilateral causal bond is unveiled between energy-industry investment and economic performance based on the Dumitrescu-Hurlin technique, confirming the feedback hypothesis of a causal bond between the two variables. The results are consistent across the under-analysis panels in terms of the nature of the influence; however, the induced degrees of their influence significantly differed across the regional development levels. The intensity of the direct influence of energy-industry investment (0.049) on economic performance lags behind the feedback influence of economic performance (1.069) on energy-industry investment. Additionally, considerable heterogeneities are detected in influence intensities across the regional panels. Eastern China (region of high development) displayed the most substantial direct (0.058) and feedback influence (1.193), while western China manifested the least substantial intensity of direct (0.030) and feedback influence (0.836). After that, the central China (region of medium development) demonstrated a medium intensity of direct (0.037) and feedback influence (0.996). Finally, among other regressors, human capital, physical capital, inflation rate, and trade presented a positive and significant influence on economic performance. In contrast, the foreign direct investment significantly influenced the economic performance only in the eastern China. The empirical findings implied that energy-industry investment might serve as a vital driver of economic performance, with the most significant role in the highly developed regions and the least significant in the low developed regions, given the important explanatory variables. Based on empirical findings, policies are suggested.

Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil.

Given the severity and scope of the current COVID-19 pandemic, it is critical to determine predictive features of COVID-19 mortality and medical resource usage to effectively inform health, risk-based physical distancing, and work accommodation policies. Non-clinical sociodemographic features are important explanatory variables of COVID-19 outcomes, revealing existing disparities in large health care systems. We use nation-wide multicenter data of COVID-19 patients in Brazil to predict mortality and ventilator usage. The dataset contains hospitalized patients who tested positive for COVID-19 and had either recovered or were deceased between March 1 and June 30, 2020. A total of 113,214 patients with 50,387 deceased, were included. Both interpretable (sparse versions of Logistic Regression and Support Vector Machines) and state-of-the-art non-interpretable (Gradient Boosted Decision Trees and Random Forest) classification methods are employed. Death from COVID-19 was strongly associated with demographics, socioeconomic factors, and comorbidities. Variables highly predictive of mortality included geographic location of the hospital (OR = 2.2 for Northeast region, OR = 2.1 for North region); renal (OR = 2.0) and liver (OR = 1.7) chronic disease; immunosuppression (OR = 1.7); obesity (OR = 1.7); neurological (OR = 1.6), cardiovascular (OR = 1.5), and hematologic (OR = 1.2) disease; diabetes (OR = 1.4); chronic pneumopathy (OR = 1.4); immunosuppression (OR = 1.3); respiratory symptoms, ranging from respiratory discomfort (OR = 1.4) and dyspnea (OR = 1.3) to oxygen saturation less than 95% (OR = 1.7); hospitalization in a public hospital (OR = 1.2); and self-reported patient illiteracy (OR = 1.1). Validation accuracies (AUC) for predicting mortality and ventilation need reach 79% and 70%, respectively, when using only pre-admission variables. Models that use post-admission disease progression information reach accuracies (AUC) of 86% and 87% for predicting mortality and ventilation use, respectively. The results highlight the predictive power of socioeconomic information in assessing COVID-19 mortality and medical resource allocation, and shed light on existing disparities in the Brazilian health care system during the COVID-19 pandemic.

KAZANÇ YÖNETİMİ VE KÜLTÜREL SIKILAŞMA-GEVŞEKLİK: ULUSLARARASI KANIT

This cross-national interdisciplinary research paper empirically investigates the association between cultural tightness–looseness and earnings manipulations. The analyses, which are based on panel-corrected standard errors method, indicate that cultural tightness–looseness, together with investor protection, future–time reference, capital market importance and economic development plays a critical role in shaping financial reporting choices. More specifically, this study finds that the opportunistic manipulations of reported accounting figures are less frequent in tight nations than in loose ones. The results of this study suggest that cultural tightness–looseness is an important explanatory variable that should be considered in future international comparative studies on earnings management behavior. Implications of the results for mitigating earnings management are presented.

Role of number of traffic lanes on pedestrian gap acceptance and risk taking behaviour at uncontrolled crosswalk locations

IntroductionPedestrian gap acceptance has been studied by many researchers for evaluating pedestrian facilities. However, these studies could not relate the gap acceptance at uncontrolled mid-block crosswalks have varied roadway features with pedestrian behaviour under mixed traffic conditions. In this context, the goal of the proposed study is to understand the probability of pedestrian gap acceptance while crossing uncontrolled mid-blocks and pedestrian risk taking behaviour considering the effect of pedestrian behaviour and different roadway characteristics. MethodVideo surveys were conducted at selected uncontrolled crosswalk locations with varied roadway characteristics which includes number of lanes, median etc. in Mumbai city, India. The pedestrian individual and behavioural characteristics as well as roadway, traffic and vehicle characteristics were observed and noted from the processed videos. The binary logit models were developed with extracted data at each selected location by using NLOGIT 4 software package, considering pedestrian gap acceptance behaviour (accept or reject) as the response variable and set of remaining data as explanatory variables. Further, the generic model was developed with combined data by cross validation method. ResultsThe results show that the important explanatory variables affecting the pedestrian gap acceptance behaviour are vehicular gap size, frequency of attempt, pedestrian rolling behaviour and type of vehicle, which play a major role in pedestrian gap acceptance and risk taking behaviour while crossing the road. It is also identified that the pedestrian risk taking behaviour increases with increase in number of lanes by usage of pedestrian behavioural aspects. ConclusionsThe study concluded that pedestrian behavioural characteristics needs to be controlled to reduce the pedestrian risky behaviour during road crossing at uncontrolled mid-block crosswalks. Study findings concluded that the increase in number of traffic lanes increases the risk of pedestrian road crossing. The model results from the study may be useful for practitioner, planners and policymakers to formulate suitable traffic management guidelines to control pedestrian-vehicle conflicts at different uncontrolled mid-block crosswalks.

Target price forecasts: The roles of the 52‐week high price and recent investor sentiment

AbstractThis paper reveals that in addition to fundamental factors, the 52‐week high price and recent investor sentiment play an important role in analysts’ target price formation. Analysts’ forecasts of short‐term earnings and long‐term earnings growth are shown to be important explanatory variables for target prices; equally, the 52‐week high price and recent investor sentiment are also shown to explain target price levels and especially target price biases. Our analysis additionally reveals that analysts place greater weight on these two non‐fundamental factors in settings with greater task complexity and to some extent in those with greater resource constraints. Conversely, on balance, the results suggest that this increased reliance does not translate into an increased impact per unit of each non‐fundamental factor on forecast bias. Finally, our results show that target prices are useful in predicting future stock returns beyond earnings forecasts and commonly used risk proxies. However, in an internally consistent fashion, the informativeness of target prices for future returns is significantly reduced when greater weight is placed on either the 52‐week high or recent investor sentiment in the target price formation process.

How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data.

Hospital readmission is a main cost driver for healthcare systems, but existing works often had poor or moderate predictive results. Although the available information differs in different studies, improving prediction is different from the search for important explanatory variables. With large sample size and abundant information, this study explores state-of-the-art machine-learning algorithms and shows their performance in prediction. Using administrative data on 1 631 611 hospital stays from Quebec between 1995 and 2012, we predict the probability of 30-day readmission at hospital admission and discharge. We compare the performance between traditional logistic regression, logistic regression with penalization, and more recent machine-learning algorithms such as random forest, deep learning, and extreme gradient boosting. After a 10-fold cross-validation on the training set (80% of the data), machine learning produced very good results on a separate hold-out test set (20% of the data). The importance of explanatory variables is not the same for different algorithms. The area under receiver operating characteristic curve (AUC) reached above 0.79 at hospital admission and above 0.88 at hospital discharge. Diagnostic codes, which include many different categories, are among the most predictive variables. Logistic regression with penalization also produced good results, but a standard logistic regression failed without penalization. The good results are confirmed by calibration curves. Although the identification of those at highest risk of readmission is just 1 step to preventing hospital readmissions, 30-day readmission is highly predictable with machine learning.

India: $5 TN Economy and Role of Infrastructure, Export and Skill Development

In the context of the target of achieving $5 trillion Indian GDP by 2024 the paper points to the imperatives of conducting an explorative study to see where emphasis should be given. Data analysis reveals that in the era of globalization export is the most important explanatory variable which can comfortably help India to achieve the goal. Infrastructure development, skill development and poverty alleviation should also be given due weights.

Family income and insufficient medical care: A prospective study of alternative explanations.

Aims: Equity of access to medical care is a central objective in socialised and national health insurance systems. Based on a national panel survey of Icelandic adults, the study examined the effects of family income on insufficient medical care and whether income-related differences were explained by overall financial strain, health-care cost burden, cultural variables or barriers related to health-system obstacles or experiences. Methods: The study used two-wave panel data from national postal health surveys of Icelandic adults aged 18-75. Insufficient medical care was assessed in terms of both reported delay/cancellation of physician care and estimated underutilisation when comparing actual and professionally recommended physician visits. Results: The study found that individuals with lower family incomes were more likely to delay or cancel a needed physician visit and underutilise medical care compared to their higher-income counterparts. High relative out-of-pocket costs, overall financial strain and negative experiences of medical care fully accounted for the disadvantaged medical access of lower-income individuals. The most important explanatory variable was out-of-pocket costs, as it affected insufficient medical care both directly and indirectly by compounding economic difficulties in the family. Attitudes, beliefs and health-related behaviours had limited effects on insufficient medical care and did not account for income-related differences. Conclusions: Poorer access to needed medical care among lower-income individuals was explained by high relative out-of-pocket costs, overall financial strain and negative medical-care experiences. Efforts to reduce income differences in access to needed medical care should address these factors.

Persistent effects of fire severity on ponderosa pine regeneration niches and seedling growth

Several recent studies have documented how fire severity affects the density and spatial patterns of tree regeneration in western North American ponderosa pine forests. However, less is known about the effects of fire severity on fine-scale tree regeneration niche attributes such as understory plant composition and cover, surface fuel abundance, and soil properties, or how these attributes in turn affect regenerating ponderosa pine growth. Using 1-m2 plots centered on 360 ponderosa pine seedlings that regenerated naturally after the Pumpkin Fire in 2000 in Arizona, we quantified regeneration niche attributes 13 years post-fire and measured their associations with seedling growth 11–16 years post-fire. Plots were established in a) unburned, b) moderate-severity, and two types of high-severity (100% tree mortality) burns, either c) adjacent to residual live forest edges (high-severity edge plots), or d) >200 m from any residual live trees (high-severity interior plots). We found that all burned plots had greater understory plant species richness, percent cover of forbs, exotic plants and coarse wood, as well as higher soil pH, sand and gravel content, and lower soil clay content compared to unburned plots. High-severity burn plots had the greatest total understory plant and shrub cover, the most herbaceous fine fuel biomass, and tended to have the highest soil nitrogen content compared to other burn severity classes. Ponderosa pine growth (i.e., stem diameter at root collar (DRC) and length of terminal leader) was lowest in the unburned compared to burned plots, and ponderosa pine terminal leader growth was consistently greater in the high-severity edge plots compared to other severities. Finally, niche characteristics such as overstory tree canopy cover (−), soil phosphate (+), and cover of coarse wood (+), forbs (+), and the native grasses, mountain muhly (+) and squirreltail (−), were important explanatory variables of ponderosa pine growth. Exotic plant cover did not have a negative association with ponderosa pine growth. These results suggest that if ponderosa pine seeds can disperse and germinate, and if seedlings can survive the first few critical years after germination, then low overstory canopy cover and abundant forbs or coarse wood may be associated with increased growth rates. Alternatively, forbs may be responding to the same site benefits as the seedling; and abundant forbs, coarse wood, and fine fuels might also put seedlings at increased risk of mortality from subsequent fire, at least until they are taller and more fire resistant.

Crown structural properties, wood density, and liana load: influence on growth and mortality in subtropical forests

Wood density (WD) and other wood mechanical and structural properties may have a strong functional relationship with demographic patterns and allometry of trees. We analyzed the influence of WD, structural properties, architectural traits, and community-level attributes on growth rates (GRs) and mortality modes of canopy tree species in a subtropical forest of Argentina. Stem WD and the WD, strength, stiffness, toughness, and hardness of branches were measured in 10 canopy species. Architectural traits and liana load were also determined. Strength and hardness of branches were linearly correlated to branch WD, and GRs were linearly correlated to stem WD across species. At the individual level, trees with greater hardness and toughness in branches died mostly uprooted, and trees with greater branch stiffness and susceptibility to colonization by lianas were mostly broken. At the community level, the suppressed trees died mostly broken. The dominant trees with high local tree density died mostly broken, whereas more isolated trees died mostly uprooted. Mortality modes were determined not only by mechanical properties, but also by community properties such as liana load, crown canopy position, and number of neighboring trees. Other biophysical traits besides WD are important explanatory variables when dry wood is used to describe functional characteristics of trees.

Determinants of Commercial Banks Profitability: Empirical Evidence from Ethiopian Commercial Banks

Commercial Banks play an important role in contributing to a country’s economic development. Accordingly understanding factors affecting commercial banks profitability is essential. The main objective of this study is pointing out the variables that have impact on the determination of Ethiopian commercial banks profitability. The data was collected from National bank of Ethiopia annual reports and from 17 Ethiopian commercial banks, which covers from 2005 to 2019. The explanatory variables covered under this study were Bank Specific (Capital Adequacy, Bank Size, Deposit Ratio, Credit Risk and Fund Cost), Macro-Economic (Inflation Rate, Real Economic Growth Rate & Foreign Exchange Rate) and industry specific (Lending Interest rate). ROA, ROE & NIM was used as dependent variables to measure profitability. Fixed effect model with bootstrap estimation was used to analyze the effect of those explanatory variables on ROA. On the other hand, PSCEs regression and PSCEs regression with transformation of the data to logarithmic value were done to ROE & NIM respectively. The STATA 14 output depicts bank size capital adequacy and deposit ratio has a positive and significant relationship with Ethiopian commercial banks profitability. The other important bank specific explanatory variable is liquidity, which has a negative and significant relationship with banks profitability (measured by NIM). the last significant variable is lending interest rate, which has a negative significant influence on ROA & ROE. Unexpectedly the three macro-economic variable haven’t a significant influence on ROA, ROE &NIM. So it is suggested that Ethiopian commercial banks should focus on bank specific and industry specific variables to increase their profitability. Keywords : Commercial Banks Profitability, Ethiopia, Bank Specific, industry specific and Macroeconomic DOI: 10.7176/RJFA/11-15-04 Publication date: August 31 st 2020

Temporal Coherence Between Lake and Landscape Primary Productivity

Understanding the patterns and drivers of primary productivity is a major goal of ecology, but little is known about whether the primary productivities of different types of ecosystems—here, lakes and the landscapes in which they are embedded—fluctuate in related ways through time. Due to shared climatic variation and well-known connections between lake and terrestrial ecosystems, such as nutrient and resource subsidies, we hypothesized that interannual fluctuations in aquatic and terrestrial primary productivity indices could be coherent. We also expected that lake and watershed characteristics could modify the strength and nature of primary productivity relationships. We applied wavelet coherence analyses to time series of lake chlorophyll-a and satellite-derived NDVI to examine coherence between lakes and land, and used random forest regression and generalized additive models to evaluate why coherence varies among lakes. There can be substantial coherence between lake and terrestrial primary productivity, but the strength and phase (direction and time lag) of this relationship vary widely, and there were marked differences between short (2–4-year periods of oscillation) and long (> 4-year periods of oscillation) timescales. Across all timescales, variables associated with the connectedness of lakes to their watersheds were consistently the important explanatory variables of the strength and phase of coherence. The patterns observed in this study suggest the importance of cross-ecosystem flows, as opposed to shared climatic variation, in determining temporal coherence between lakes and the landscape.

Penalized profile quasi-maximum likelihood method of partially linear spatial autoregressive model

In this paper, we develop a class of penalized likelihood method to identify important explanatory variables in parametric component of partially linear spatial autoregressive model. Compared to existing estimation methods, the proposed method can simultaneously select the significant explanatory variables and estimate the nonzero parameters in the parametric component of partially linear spatial autoregressive model. Under appropriate conditions, we establish the consistency, sparsity and asymptotic normality properties of the resulting penalized likelihood estimator. Especially, with proper choice of the penalty function and the regularization parameter, the estimator of the nonzero parameter vector is shown to enjoy the oracle property, in the sense that it is asymptotically normal with the same mean vector and covariance matrix as those it would have if the zero parameters were known in advance. Furthermore, we propose a computationally feasible algorithm to obtain the penalized likelihood estimator. The finite sample performance of the proposed variable selection method is evaluated through extensive simulation studies and illustrated with a real data set.

Spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May–September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.

Variable selection of partially linear varying coefficient spatial autoregressive model

The partially linear varying coefficient spatial autoregressive model is a recently proposed semi-parametric spatial autoregressive model, in which some of the explanatory variables have varying coefficients while the remained explanatory variables possess constant ones. Although some estimation methods have been proposed for the partially linear varying coefficient spatial autoregressive model, the problem of selecting important explanatory variables in the parametric component of such model has not been addressed to date. In this paper, we propose a penalized profile least squares method to address this problem. Different from the existing estimation methods, the proposed method can simultaneously select the significant explanatory variables in the parametric component and estimate the corresponding nonzero regression coefficients. Furthermore, we provide a computationally feasible algorithm to obtain the penalized profile least squares estimator. The finite sample performance of the proposed variable selection method is evaluated through some simulation studies and illustrated by a real data example.

Fine root C:N:P stoichiometry and its driving factors across forest ecosystems in northwestern China

Fine roots (≤2 mm in diameter) play a more significant role in regulating the biogeochemical cycles of forest ecosystems, but our current knowledge of fine root stoichiometry and its driving factors is extremely limited. In this study, fine root biomass (FRB) and their carbon (C), nitrogen (N) and phosphorus (P) concentrations were measured from dominant forests along environmental gradients in Northwestern China. The results showed that forest type (coniferous vs. broadleaved, and plantation vs. secondary forest) and climatic factors had no effects on FRB. FRB was only correlated with soil P, C:P and N:P in coniferous forests and N:P in secondary forests. Thus, forest type, soil C:N:P stoichiometry and climatic factors were less important to FRB. The fine root C and C:N and C:P were higher, and N and P were lower in coniferous than in broadleaved forests. Only fine root N concentration was higher in plantations than in secondary forests. The fine root C was positively correlated with soil C, N and C:N, C:P and N:P except in coniferous forests. The fine root N was negatively correlated soil C:N, C:P and N:P in plantations and C:N in broadleaved forests, but positively correlated with soil C, N, C:P and N:P in secondary forests. The fine root P was positively correlated with soil P in plantations and in coniferous forests, but negatively correlated with soil C:N, C:P and N:P in all forest types. The fine root C in broadleaved and in secondary forests was positively correlated with mean annual precipitation (MAP) and fine root N and N:P in plantations were negatively correlated with MAP. Only the fine root P and C:P in broadleaved forests were correlated with mean annual temperature (MAT). Collectively, forest type, soil C:N:P stoichiometry and climatic factors explained 29, 13 and 12% of the variation in the fine root C, N and P, and their most important explanatory variables were leaf form, soil C:N and soil C:P, respectively. These results advance our knowledge about the regional fine root stoichiometry and its driving factors and provide basic data for improving the key below-ground parameters for biogeochemical models.

The drivers of diatom in subtropical coastal waters: A Bayesian modelling approach

Temporal variability in phytoplankton is driven by a range of biotic and abiotic factors. In this study, we aimed to identify and estimate the important explanatory variables affecting the diatom species and their abundance in the coastal waters of Bangladesh. For this, species interaction and six explanatory variables were taken into consideration to develop a density dependent Bayesian regression model for quantifying the relative importance of different explanatory variables causing the variability in diatom abundance. Thus, three nutrients such as silicate, nitrate and phosphate, and zooplankton were identified as the most important predictors for 19–25 species. Moreover, the effect of three nutrients and salinity was positive for most diatoms, while temperature and zooplankton had a negative effect. Species interaction and environmental variables combinedly explained 77% of the variability in diatom abundance, with the latter being the dominant factor. Specifically, six environmental variables combinedly explained 57% of the variability in diatom abundance and the remaining 20% of the variability was due to species interaction. This is the first modelling study on phytoplankton community describing the dynamics of diatom species in the coastal waters of northern Bay of Bengal.

Acorn Weevil (Coleoptera: Curculionidae) Predation Dynamics in a Mississippi Bottomland Hardwood Forest

Curculio and Conotrachelus weevils (Coleoptera: Curculionidae) can render the majority of North American Quercus spp. acorn crops nonviable, thereby reducing food resources for wildlife and limiting opportunities for seedling establishment. Acorn predation by weevils at the individual tree level can be influenced by many factors, and research specifically investigating acorn predation by weevils in seasonally flooded bottomland oak forests is lacking. We placed cone emergence traps in a periodically flooded forest on the Sam D. Hamilton Noxubee National Wildlife Refuge near Brooksville, Mississippi to obtain weevil population parameter estimates, record emergence phenologies, and identify variables that may aid in understanding tree-to-tree variability in acorn predation rates. Forty-three Curculio weevils representing five species emerged from mid-August through early November, and 56% of those captured emerged over an 11-day period in mid-September. Sixty-four Conotrachelus weevils representing two species emerged from mid-August through late November and occurred at nearly twice the density of Curculio weevils. The exotic Asiatic oak weevil, Crytepistomus castaneus, a minor defoliator, emerged from August through early November. We also identified Quercus density, specifically the proportion of oaks that immediately neighbor the host tree, as a potentially important explanatory variable of acorn predation by weevils at the tree level in bottomland forest habitats.

가상가치평가법(CVM)을 이용한 국립공원 한라산 입장료 지불가치 추정

The purpose of this study was to estimate the payment value of the Hallasan National Park admission fee using a dichotomous choice Contingent Valuation Method(CVM). Particularly, we examined explanatory variables that can determine the willingness to pay the entrance fee of the Hallasan National Park. Using purposeful sampling method, the survey was conducted with domestic tourists who have visited the Hallasan National park from August to September 2018. A total of 237 surveys were collected. The results of this study indicated that the payment value of the Hallasan National Park admission fee was estimated as KRW 6,695 per person/visit and the total amount of economic value was KRW 67 billion per year. The result of the logit model revealed that bid, age, and agreement (or disagreement) of the reservation system are important explanatory variables in predicting the payment value of the Hallasan National Park admission fee. This study confirmed that the agreement of the Hallasan National Park reservation system, which indirectly shows the attitude toward the environment, affects significantly the willingness to pay the park’s admission fee. The findings suggest several practical implications associated with implementing environmental policies.