Persona: Senior Natural Resource Economist and Environmental Valuation Specialist.
Abstract
This technical presentation details an economic valuation study of the Puente Sopó Ecotourism Park in Colombia using the Travel Cost Method (TCM), a revealed preference technique. The study employs a zonal approach to estimate the recreational value of the park by analyzing the relationship between travel costs and visitation rates across four specific geographic zones (Cajicá, Sopó, Chía, and Zipaquirá).
The analysts define a total travel cost composed of three primary variables: direct transport expenses (fuel), the opportunity cost of time (indexed to the national minimum wage), and the fixed entrance fee. By constructing a demand curve through incremental price simulations, the study calculates the Consumer Surplus, which serves as a proxy for the total economic welfare provided by the site. Additionally, the presentation incorporates a break-even analysis for a proposed trail expansion and utilizes a Poisson distribution model to estimate the probability of daily visitation fluctuations. The findings conclude with a per-capita consumer surplus of 3,448 COP, representing the average individual welfare benefit derived from the park’s recreational services.
Valuation of Recreational Services: Travel Cost Analysis of Puente Sopó Ecotourism Park
0:18 – Theoretical Foundation: The analysts categorize the Travel Cost Method under "revealed preferences," tracing its origins to Harold Hotelling’s 1947 study. The core premise is that visitation frequency decreases as travel costs increase, allowing for the estimation of a demand function for non-market environmental goods.
1:40 – Defining Economic Welfare: A distinction is made between total expenditure and consumer surplus. The valuation aims to measure the "surplus" or welfare benefit provided by the visit, rather than just the costs incurred, as the latter would imply zero net benefit to the visitor.
2:21 – Data Collection and Survey Design: Information was gathered via surveys addressing origin, economic costs, opportunity cost of time, socioeconomic characteristics, and environmental quality perceptions.
2:43 – Zonal Identification: The study focuses on four zones within a 22km radius: Cajicá (7km), Sopó (10.4km), Chía (12.9km), and Zipaquirá (21.8km). The methodology excludes distances exceeding 60km (approx. 1.5 hours) due to topographical constraints affecting travel behavior.
Displacement: Calculated at 213 COP per kilometer based on a fuel efficiency of 12km/liter and a price of 2,563 COP/liter.
Shared Costs: Assumes an average occupancy of four persons per vehicle to distribute fuel costs.
Opportunity Cost of Time: Calculated by converting the current monthly legal minimum wage into a per-minute value multiplied by round-trip travel time.
Entrance Fee: A fixed administrative cost of 10,000 COP.
5:06 – Sampling and Population Dynamics: Based on a weekend population of 955 visitors, a sample size of 127 was determined (95% confidence level). The study notes that visitation density is not strictly linear; the closest zone may have fewer total visitors than mid-distance zones due to population density, but the percentage of inhabitants visiting decreases as distance increases.
6:43 – Demand Curve Construction: Utilizing the Hotelling-Clawson approach, the team simulated increases in the entrance fee to observe the "Estimated Number of Visits." This identifies the "choke price" where visitation from the furthest zones drops to zero.
9:51 – Consumer Surplus Calculation: The analysts used a geometric method (calculating areas of triangles and rectangles under the demand curve) to estimate total welfare.
Total Consumer Surplus: 438,003 COP for the sampled group.
Individual Surplus: 3,448 COP per person, representing the average welfare gain per visit.
13:46 – Project Break-Even Analysis: For the implementation of a new trail, the study established a break-even point of 960 visitors (distributed in 38 groups of 25) to cover fixed maintenance and labor costs.
14:24 – Statistical Modeling (Poisson Distribution): The study concludes by applying a Poisson distribution to predict visitation patterns. Given an average daily arrival of 136 persons, the model calculates a 10.9% probability of receiving 60 visitors and a significantly higher 34.17% probability of receiving 200 visitors in a single day.