Preserving environmental quality of ecotourism sites through community participation in Purulia District of West Bengal,India

The importance of valuing environmental resources,especially in ecotourism sites,has become increasingly important over the last two decades.Ecotourism is now considered as an important source of livelihood of local stakeholders in backward regions.Therefore,the preservation of ecotourism sites through community participation seems very important to maintain continued flow of tourists.This study aimed at recognizing the importance of community participation for the preservation of ecotourism sites.For this,this study executed a survey based on non-probability sampling in two ecotourism sites(Garpanchkot and Baranti)covering 100 respondents in Purulia District,West Bengal of India.The central issue of this study was to assess the tendency of community participation for the conservation of ecotourism sites and find the optimum condition for offering participatory labour time.This study showed that the participation of young people is high,and the majority of respondents are aware of the importance in protecting ecotourism sites.Because respondents were too poor to offer money,the contingent valuation method(CVM)was used to elicit their willingness to pay(WTP)participatory labour time for the conservation of ecotourism sites.Respondents’age,income,education level,caste,and their perceived environmental quality had significant relationship with their WTP participatory labour time by applying the ordinary least square(OLS)model.It was found that the mean WTP participatory labour time of each respondent in a month is approximately 3.64 h.The significance of this study is that community participation can improve the sense of belonging,trust,and credibility of ecotourism sites,making them more appreciative of the value and protection of these sites.

Modeling Solid Waste Minimization Performance at Source in Dar es Salaam City, Tanzania

Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective management performance of municipal solid waste management underscores the interdisciplinarity strategies. Such knowledge and skills are paramount to uncover the sources of waste generation as well as means of waste storage, collection, recycling, transportation, handling/treatment, disposal, and monitoring. This study was conducted in Dar es Salaam city. Driven by the curiosity model of the solid waste minimization performance at source, study data was collected using focus group discussion techniques to ward-level local government officers, which was triangulated with literature and documentary review. The main themes of the FGD were situational factors (SFA) and local government by-laws (LGBY). In the FGD session, sub-themes of SFA tricked to understand how MSW minimization is related to the presence and effect of services such as land use planning, availability of landfills, solid waste transfer stations, material recovery facilities, incinerators, solid waste collection bins, solid waste trucks, solid waste management budget and solid waste collection agents. Similarly, FGD on LGBY was extended by sub-themes such as contents of the by-law, community awareness of the by-law, and by-law enforcement mechanisms. While data preparation applied an analytical hierarchy process, data analysis applied an ordinary least square (OLS) regression model for sub-criteria that explain SFA and LGBY;and OLS standard residues as variables into geographically weighted regression with a resolution of 241 × 241 meter in ArcMap v10.5. Results showed that situational factors and local government by-laws have a strong relationship with the rate of minimizing solid waste dumping in water bodies (local R square = 0.94).

The Robust Regression Methods for Estimating of Finite Population Mean Based on SRSWOR in Case of Outliers

The ordinary least square(OLS)method is commonly used in regression analysis.But in the presence of outlier in the data,its results are unreliable.Hence,the robust regression methods have been suggested for a long time as alternatives to the OLS to solve the outliers problem.In the present study,new ratio type estimators of finite population mean are suggested using simple random sampling without replacement(SRSWOR)utilizing the supplementary information in Bowley’s coefficient of skewness with quartiles.For these proposed estimators,we have used the OLS,Huber-M,Mallows GM-estimate,Schweppe GM-estimate,and SIS GM-estimate methods for estimating the population parameters.Theoretically,the mean square error(MSE)equations of various estimators are obtained and compared with the OLS competitor.Simulations for skewed distributions as the Gamma distribution support the results,and an application of real data set containing outliers is considered for illustration.

Generalized Class of Mean Estimators with Known Measures for Outliers Treatment

In estimation theory,the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares(OLS)method or robust regression techniques for estimating regression coefficients.But when the correlation is negative and the outliers are presented,the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates.Hence,this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method.Precisely,we have proposed generalized estimators by using the ancillary information of non-conventional measures of dispersion(Gini’s mean difference,Downton’s method and probabilityweighted moment)using ordinary least squares and then finally adopting the Huber M-estimation technique on the suggested estimators.The proposed estimators are investigated in the presence of outliers in both situations of negative and positive correlation between study and auxiliary variables.Theoretical comparisons and real data application are provided to show the strength of the proposed generalized estimators.It is found that the proposed generalized Huber-M-type estimators are more efficient than the suggested generalized estimators under the OLS estimation method considered in this study.The new proposed estimators will be useful in the future for data analysis and making decisions.

Prediction and Protection of Cultivated Land in China

[Objectives]To protect cultivated land and ensure national food security and to forecast the cultivated land area of China and the provinces(cities)in 2030,2035 and 2050.[Methods]Based on the cultivated land area data of the whole country and provinces(autonomous regions and municipalities)from 2009 to 2017,the OLS model is used to forecast the cultivated land area of China and the provinces(cities)in 2030,2035 and 2050.[Results]The results show that the predicted area of cultivated land in 2030,2035 and 2050 is 134.0886,133.7856 and 132.8764 million ha,respectively,showing an obvious decreasing trend.The national cultivated land scale predicted in this paper can meet the requirements of the 2030 national cultivated land retention target set in the National Land Planning Outline(2016-2030).However,in the total area of existing cultivated land,there is still a certain area of sloping cultivated land(accounting for about 4%of the total cultivated land area,it is appropriate to gradually return farmland to forest).At the same time,nearly 20%of the cultivated land has been polluted,and the low-quality cultivated land accounts for about 22%of the total cultivated land.[Conclusions]The situation of cultivated land protection in the whole country is not rosy.For this reason,some measures and suggestions are put forward:strengthening land consolidation,striving to improve the quality of cultivated land,and appropriately increasing new cultivated land;strengthening the protection of cultivated land resources and strictly controlling the reduction of cultivated land.

A Study on the Determinants of Location Choice in Inward Foreign Direct Investment （FDI）： Focusing on the Incheon Free Economic Zone

Inward foreign direct investment （FDI） is expected to grow further by virtue of economic globalization. A thorough understanding of the locational determinants of inward FDI will be conducive to enhanced efficiency in attracting direct and SOC-related investments from foreign entities. This study analyzes 51 cases of inward direct foreign investment made in the Incheon free economic zone （IFEZ） from 2002 to 2009 to determine the factors influencing FDI volume, the relevance of locations and the correlation between investment size and location. First, the relationship between the loeational determinants of FDI and the total investment size （total expected project cost） is analyzed. Second, the relationship between the locational determinants of FDI and the FDI is analyzed. Third, the relationship between the locational determinants of FDI and the location choice is analyzed. The results indicate the determinants that influence locations and investment size of FDI entities; whether these factors exercise influence in the zone; and the factors that have relatively significant effects. Ultimately, based on the analytical findings, a few implications for policy and practice are derived.

Factors Affecting Teff (Eragrostis tef) Market Supply in Woliso and Becho Districts of South West Shoa Zone Oromia Regional State, Ethiopia

This study was aimed to analyze teff (Eragrostis tef) market chain in south west Shoa zone with objective of factors affecting teff market supply using two stage ordinary least square approaches. The majority of Ethiopia’s population earns its livelihood primarily from agriculture. Cereals teff is the first in Ethiopia area coverage and production. Teff (Eragrostis tef) is a major staple food crop in Ethiopia. Both primary and secondary data were used in this study. Primary data was collected from 138 sampled farmers and 38 traders from both districts by using semi-structured interview. The OLS (ordinary least square) model results showed that seven explanatory variables significantly affected the quantity of teff supplied to the market supplied by smallholder producers. Age, education level and current market price were negatively and significantly affecting teff market supply. Distance to the nearest market, farm size, perception and quantity produced were positively and significantly influencing marketed supply of teff. Policy implications that were to take place highly recommendation those are relevant to improve teff marketing system in the study area which indicated production and market orientation were set based on the significant variables and raised problems by the stakeholders. To improve market supply of teff in the study area resolving the prevailing production problems deems a necessary condition.

Modeling the dynamic performance of transportation infrastructure using panel data model in state-space specifications

In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation.The models could simultaneously capture the heterogeneity and update forecast through inspections.PDMs are applied to tackle the cross-section heterogeneity of longitudinal data,and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data.To illustrate the methodology,three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China.Estimation results obtained by ordinary least square(OLS)estimator and system generalized method of moments(SGMM)are compared for two dynamic instances.The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time.There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time.In addition,SGMM does not obtain higher prediction accuracy than OLS in this case.Finally,it is recommended to specify the inspection intervals as several constants with integer multiples.

Spatial study of particulate matter distribution, based on climatic indicators during major dust storms in the State of Arizona

Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate.The state of Arizona is vulnerable to dust stomas,especially in die monsoon season because of the anomalies in wind direction and magnitude.In this study,a high-resolution Weather Research and Forecasting(WRF)model coupled with a chemistry module(WRF-Chem)was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona.Subsequently,Ordinary Least Square(OLS),spatial lag,spatial error,and Geographically Weighted Regression(GWR)techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms.Census tracts were adopted to create local spatial averages for the chosen variables.Terrain height,temperature,wind speed,and vegetation fraction were designated as the most significant variables,whereas base state and perturbation pressures,planetary boundary layer height and soil moisture were adopted as supplementary variables.The determination coefficient for OLS,spatial lag,spatial error,and GWR models peaked at 0.92,0.93,0.96,and 0.97,respectively.These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.

Partial Dynamic Dimension Reduction for Conditional Mean in Regression

In many regression analysis,the authors are interested in regression mean of response variate given predictors,not its the conditional distribution.This paper is concerned with dimension reduction of predictors in sense of mean function of response conditioning on predictors.The authors introduce the notion of partial dynamic central mean dimension reduction subspace,different from central mean dimension reduction subspace,it has varying subspace in the domain of predictors,and its structural dimensionality may not be the same point by point.The authors study the property of partial dynamic central mean dimension reduction subspace,and develop estimated methods called dynamic ordinary least squares and dynamic principal Hessian directions,which are extension of ordinary least squares and principal Hessian directions based on central mean dimension reduction subspace.The kernel estimate methods for dynamic ordinary least squares and dynamic Principal Hessian Directions are employed,and large sample properties of estimators are given under the regular conditions.Simulations and real data analysis demonstrate that they are effective.