Environmental economics essay Case Solution
Appropriateness of valuation model:
The advantage to using the linear regression model is the appropriateness of the data considerations that would perform accuracy in the related results. It means that if there are 3 variables of which two are explanatory, and one is dependent then the model would calculate the impact of the two independent variables over the dependent one. So, the results would indicate the R-square value that has a direct relationship with the certain variables that shows the frequency of the changes between the selected data. Therefore, the model indicates the accuracy of the certain outcome that would analyse as the tool to predict future performance of the selected approach and feasibility of the study. In the case of an urban forest, it is identified that the selected model would deem appropriate to predict the future benefits derived from attracting the people and increasing the economic concerns of the particular country.
The description of some key variable used in the model:
The model is said to be appropriate to use for predicting the urban forest results because it would allow analysing the key variables that might consider having a positive impact on the development of the urban forests in Korea. Again the linear regression model would only be preferable if the selection of the variables have a positive outcome or they would show correct values to determine the results. So, it shows that if variables are selected with correct values, then the model would perform the accuracy of the results that might have a positive impact on the future values.
Under the following conditions, there are different variables incorporated to generate the optimal results in order to determine the impact over each other. The particular variables consist of:
- Urban Forest Area (Dependent Variable)
- InWater (Explanatory)
- ForSubLiv (Explanatory)
- ProdFor (Explanatory)
- SoilWatProt (Explanatory)
- PubRecr (Explanatory)
- Reforest (Explanatory)
B: Net change in Y for each unit change in X
X: Independent variables at various intervals
The compatibility of the discrete choice approach and the model:
In order to assess the variations of different explanatory variables over the single dependent variable, mixed multi nomial log it model would allow predicting the results properly in case of complex changes in the variables over time. However, the certain data indicates that the use of this model would be preferable as compared to other alternative methods to calculate the results. Basically, it is used to predict more than one outcome to determine the changes that would allow adjusting for future aspects. It also means that if there are only one dependent variable and various explanatory variables, then this model would properly calculate the impact of all the selected independent variables over the dependent one
The implementation of the method:
The model is also useful to predict the accuracy of the results based on the complex level of data that might not perform under the single factor model. Therefore, a multi nomial log it model is appropriate to use for identifying various outcomes that might have a major impact on the future considerations. The selected parameters to predict the model are a various level of independent and dependent variables that are not subjected to use for single regression model because the net outcome would change in every condition. Thus, the only way to use the particular model is by identifying the complexity of variables and then calculate the results in order to find out the single and effective outcome that might predict the future performance.Â The nature to use multi nomial log it model is through determining the possibility to predict the data that is unable to generate individual results (Like the use of Linear Regression model). Thus, under the case, it should be considered that the multi-level data is identified and perform predict results into a single outcome based on the use of this model.
Definition of the attributes and level of provision
The mixed multi nomial log it model is used to generate the multi-purpose statistical results to identify the overall impact of the selected attributes. Under the study, the attributes are defined as the resources to contribute towards the growth of urban forestry in Korea. There are six types of attributes that have apositive impact on the expansion of the forest. These attributes are the key to develop an experiment in order to analyze the level of contribution each would be able to make for urban forest area. Therefore, the level of provision is quite sensitive towards the changing in the results as if the attributes are changing or not consider a growth in the particular level. Thus, it would show adirect relationship with the specified area of forestry over time.
The objective to design an experiment is to perform the selected statistical tool to generate the outcome that would show the efficiency of the changing in the forest area based on the selected attributes. However, the design is developed by identifying the key attributes that would consider a positive impact and contribute most towards the increase in the forest area. Then the second step is to analyze the impact of every attribute with the dependent variable. After the analysis, the experiment would be incurred by using the mixed multi nomial log it model generate the multi-functional analysis in order to find out the net value that indicates an overall change as compared to the level of forestry area. Finally, the conclusion would show to the respondents in the form of aquestionnaire. It means that the result would then prove by asking the respondents of whether the outcome would match with their experiences in the urban forest or not………………………..
This is just a sample partial case solution. Please place the order on the website toÂ orderÂ your own originally done case solution