Introduction

Carrying capacity is the maximum number of visitors that a destination can accommodate without causing unaccpetabale alterations to the physical environment, visitor experience, or local community.

  • Importance in sustainable tourism
  • Relevance to local destinations (e.g., Boracay, Baguio, Siargao, etc.)

Types of Carrying Capacity

Explain that carrying capacity is multidimensional:

Type Description Examples
Physical Carrying Capacity The maximum number of people that can physically fit into a space. Number of tourists per day in a beach area
Environmental/Ecological Capacity The limit beyond which the ecosystem is damaged. Coral reef degradation due to snorkeling
Social Carrying Capacity The level of visitor use that local residents and other visitors can tolerate. Overcrowding leading to complaints
Economic Carrying Capacity The extent tourism can develop without harming other sectors. Tourism inflation affecting local prices
Infrastructure Capacity Limit based on existing facilities (water, waste, transport). Availability of hotel rooms or waste systems

Physical Carrying Capacity (PCC)

\[PCC=\frac{A \times R_f}{A_v} \] where:

  • \(A\): total available area (\(m^2\))
  • \(R_f\): Daily rotation factor (number of visitor shifts per day)
  • \(A_v\): Area required per visitor (\(m^2/person\))

Real Carrying Capacity (RCC)

\[RCC=PCC \times (1-\sum L_f) \] where:

  • \(L_f\): limiting factors (e.g., accessibility, rainfall, wildlife protection, etc.)

Effective Carrying Capacity (ECC)

\[ECC=RCC \times M_f \] where:

  • \(M_f\): Management factors (reflects staff, policy, and budget capacity)

Example 1

To illustrate how carrying capacity is determined for a tourist destination, let us consider a beach site with a total usable area of 20,000 square meters (m²). On average, each visitor requires 10 m² to enjoy the space comfortably without overcrowding.

The beach operates for 10 hours each day, and visitors typically stay for 5 hours. This means that, on average, each space can accommodate two rotations of visitors per day, giving a rotation factor (Rf) = 2.

However, several environmental and management constraints affect the actual number of tourists that the site can sustainably host. For instance:

  • Coastal erosion reduces usable space by 10%,

  • High tide further decreases the available area by 15%, and

  • Wildlife protection zones limit another 5% of the beach area from tourist use.

Together, these limiting factors represent 30% of total area loss, meaning only 70% of the area remains suitable for visitors.

Finally, considering that the management efficiency factor (Mf)—which accounts for enforcement, maintenance, and monitoring capacity—is 0.8, the actual carrying capacity must also be adjusted accordingly.

By integrating all these factors, the Real Carrying Capacity (RCC) can be estimated as:

# Carrying Capacity Calculation
A <- 20000       # total area (m²)
Av <- 10         # area per visitor (m²)
Rf <- 2          # rotation factor
Lf <- c(0.10, 0.15, 0.05)  # limiting factors
Mf <- 0.8        # management factor

# Stepwise computation
PCC <- (A * Rf) / Av
RCC <- PCC * (1 - sum(Lf))
ECC <- RCC * Mf

cat("Physical Carrying Capacity (PCC):", PCC, "\n")
## Physical Carrying Capacity (PCC): 4000
cat("Real Carrying Capacity (RCC):", RCC, "\n")
## Real Carrying Capacity (RCC): 2800
cat("Effective Carrying Capacity (ECC):", ECC, "\n")
## Effective Carrying Capacity (ECC): 2240
library(ggplot2)

df <- data.frame(
  Stage = c("PCC", "RCC", "ECC"),
  Visitors = c(PCC, RCC, ECC)
)

ggplot(df, aes(x = Stage, y = Visitors, fill = Stage)) +
  geom_col() +
  geom_text(aes(label = round(Visitors)), vjust = -0.5) +
  labs(title = "Tourism Carrying Capacity Stages",
       y = "Number of Visitors per Day") +
  theme_minimal()

Interpretation

Thus, although the beach has enough physical space to host 4,000 visitors per day, environmental limitations and management capacity reduce the sustainable level to about 2,240 visitors per day. This figure represents the maximum number of tourists the beach can accommodate without causing ecological degradation or management strain.

Example 2

Typical constraints in a university carrying capacity: program

Faculty capacity: total full-time equivalent (FTE) teaching staff × acceptable students-per-faculty ratio. Data: number of FTEs; target supervision/teaching load (e.g., 1 faculty → 25 students).

Classroom/lecture space: sum of usable seats × number of distinct class periods per week / weekly contact requirement. Data: seats per room, available periods, required contact hours per student cohort.

Laboratory / specialized facilities: capacity determined by stations, sessions per week, equipment.

Clinical/internship placements (if applicable): number of placement slots × turnover per year.

Budget / recurrent funding: available teaching/support budget ÷ per-student cost.

Library/IT resources: concurrent access limits or licensing.

Accreditation or regulatory caps: explicit maximum intake.

Support services (counseling, admin) and housing: similar modelling.

# Example R: compute program carrying capacity
constraints <- data.frame(
  name = c("faculty","classroom","clinical","budget","accreditation"),
  capacity = c(8*30,    # faculty: 8 FTE * 30 students each
               200,     # classroom seats available per cohort
               180,     # clinical placement slots/year
               220,     # budget-limited students
               9999)    # accreditation cap (9999 = no cap)
)

# Bottleneck approach
program_capacity <- min(constraints$capacity)
bottleneck <- constraints[which.min(constraints$capacity), ]

# Weighted index example (weights sum to 1)
weights <- c(0.25, 0.20, 0.30, 0.15, 0.10) # example weights by importance
desired_target <- 250  # managerial target for cohort size
score <- sum(weights * (constraints$capacity / desired_target))
effective_capacity_weighted <- desired_target * score

list(
  constraints = constraints,
  program_capacity_bottleneck = program_capacity,
  bottleneck = bottleneck,
  weighted_effective_capacity = effective_capacity_weighted
)
## $constraints
##            name capacity
## 1       faculty      240
## 2     classroom      200
## 3      clinical      180
## 4        budget      220
## 5 accreditation     9999
## 
## $program_capacity_bottleneck
## [1] 180
## 
## $bottleneck
##       name capacity
## 3 clinical      180
## 
## $weighted_effective_capacity
## [1] 1186.9

  • The overall program capacity is 180 students. This result occurs because the clinical training component has the lowest available capacity among all constraints. In carrying capacity analysis, the binding constraint is always the minimum value, since it represents the maximum number of students that the program can support without compromising quality or resource availability.

  • The output correctly identifies clinical capacity (180) as the bottleneck constraint. This means: Faculty resources could support 240 students, Physical facilities could support 200 students, Budget could support 220 students, BUT clinical facilities can support only 180, Thus, clinical resources determine the program’s true, sustainable enrollment capacity.

  • The weighted effective capacity equals 1,186.9.This number comes from using a weighted scoring method (with user-defined weights) and represents a scaled index, not the actual number of students. Its magnitude indicates the strength of resource availability relative to a target cohort size (e.g., 250 in your example), but it does not replace the bottleneck result.

Interpretation:The weighted capacity index suggests that, in aggregate, the program has strong resources relative to the target size, but this overall strength does not eliminate the clinical bottleneck. The index is useful for strategic planning and prioritization, but the enforceable capacity remains 180 students.

Factors Influencing Carrying Capacity

Categories and Examples of Factors Affecting Carrying Capacity
Category Examples
Environmental Soil erosion, wildlife disturbance, water quality
Socio-cultural Local tolerance, cultural integrity
Managerial Staff size, budget, monitoring system
Infrastructure Access roads, parking, waste disposal

Extensions and Applications

  • Tourism Zoning: Designating high-, medium-, and low-use zones.

  • Dynamic Modeling: Using time-series or agent-based simulations to assess seasonal variation.

  • Integration with GIS: Mapping spatial distribution of capacity and stress areas.

  • Economic Linkages: Combining with Cost-Benefit or Cost-Effectiveness Analysis for sustainable tourism planning.