Environment Carrying Capacity of Ecotourism in Aek Nauli Research Forest, Simalungun Regency, North Sumatera

Abstract

Currently, ecotourism has become an important industry because of its rapid development. Many tourism practices have adverse environmental impacts. Due to the increasingly destructive commercialization of the natural resources on which we depend, there are several negative impacts. Aek Nauli Research Forest (ANRF), with an area of 1,900 hectares, is one of the natural tourist destinations around the Lake Toba Tourism area managed by the Aek Nauli Research Institute for Environmental and Forestry Development (BP2LHK). The location of the study is in Girsang District, Sipangan Bolon, Simalungun Regency, North Sumatera Province. The tourist objects are natural panorama, elephant conservation education tour, and siamang animal ape park. On average, the number of visitors of ANRF on regular days is 100-300 visitors/day and on holidays reaches 300-1,700 visitors/day. The increase in the number of visitors is perceived to have an impact on environmental sustainability. This study aims to determine the capacity of the ANRF ecotourism area to accommodate the number of tourists per day simultaneously. The method used is the effective carrying capacity by Cifuentes method based on several stages of analysis, namely Physical Capacity (PCC), Real Capacity (RCC), Management Capacity (MC), and Effective Capacity (ECC). The environmental carrying capacity analysis results showed that the PCC, RCC, ECC were 26,106 visitors/day, 3,007 visitors/day, 2,505 visitors/day respectively while MC was 0.83. This value can be used to advise managers to limit visitors, particularly during peak season, in order to preserve objects and the quality of visits.

Spatially Informed Insights: Modeling Percentage Poverty in East Java Province Using Sem With Spatial Weight Variations

“…The Generalized Spatial Nested (GNS) model can be formally expressed through Equation (8) [30]. 𝒚 = 𝜌𝑾 𝟏 𝒚 + 𝑿 * 𝜷 * + 𝑾 𝟐 𝑿𝜸 + 𝒖, where 𝒖 = 𝑾 𝟑 𝒖 + 𝜺 (8) Assuming 𝜺 follows a normal distribution with a mean (μ) equal to 0 and a variance (𝜎 2 ) equal to 𝜎 2 𝑰, where 𝑰 is the identity matrix of size 𝑛 × 𝑛, then 𝜀 is a random variable distributed as 𝑁(0, 𝜎 2 𝑰), y is a dependent variable vector of size 𝑛 × 1, 𝜌 is the autoregressive coefficient of the lagged dependent variable, 𝑾 𝟏 is the spatial weighting matrix for the dependent variable of size 𝑛 × 𝑛, 𝑿 * = (𝒊 𝑛 , 𝑥 1 , ⋯ , 𝑥 𝑝 ) is a matrix of constants and predictors of size 𝑛 × (𝑝 + 1), 𝒊 𝑛 is a vector with elements valued as one of size 𝑛 × 1, 𝜷 * = (𝛽 0 , 𝛽 1 , ⋯ , 𝛽 𝑝 )′ is the coefficient vector of regression parameters of size (𝑝 + 1) × 1, 𝑾 𝟐 is the spatial weighting matrix for predictors of size 𝑛 × 𝑛, 𝑿 = (𝑥 1 , ⋯ , 𝑥 𝑝 ) is the matrix of predictors of size 𝑛 × 𝑝, 𝜸 is the autoregressive coefficient vector of size 𝑝 × 1, 𝑾 𝟑 is the spatial weighting matrix for errors of size 𝑛 × 𝑛, u is the assumed autocorrelated error vector of size 𝑛 × 1, 𝜆 is the autoregressive coefficient of errors, and I is the identity matrix of size 𝑛 × 𝑛. The determination of the spatial regression model can be observed in Figure 1.…”

“…Several studies that use spatial analysis, including Sihombing [8], are conducting research on variables that are factors of poverty, such as income levels, consumption, health, education, and relationships in society using the SAR model. Tumanggor dan Simamora [9] identified factors that influence the Human Development Index using the SAR model.…”

Mangrove ecotourism development at Karimunjawa National Park, Indonesia

“…The current condition of the KJNP mangrove track requires some repairs, including damaged tracking lines and species interpretation boards. Repairing and improving the quality of facilities and infrastructure is critical so that visitors get a good impression and want to revisit the area (Sihombing et al 2022).…”