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Calculate Net Energy Burden (NEB)

Source: R/energy_ratios.R
neb_func.Rd

Calculates Net Energy Burden with proper aggregation methodology via the Net Energy Return (Nh) framework. For individual households, NEB = EB = S/G. When aggregating across households (with weights), automatically uses the Nh method to avoid 1-5% aggregation errors.

Usage

neb_func(g, s, se = NULL, weights = NULL, aggregate = FALSE)

Arguments

g

Numeric vector of gross income values

s

Numeric vector of energy spending values

se

Optional numeric vector of effective energy spending (defaults to s)

weights

Optional numeric vector of weights for aggregation (e.g., household counts). When provided, uses Nh method: 1 / (1 + weighted.mean(nh, weights))

aggregate

Logical, if TRUE forces aggregation even without weights (uses unweighted mean). Default FALSE for backwards compatibility.

Value

  • If weights = NULL and aggregate = FALSE: Numeric vector of individual NEB values (S/G)

  • If weights provided or aggregate = TRUE: Single aggregated NEB value via Nh method

Details

Individual Level: NEB = EB = S/G (mathematically identical)

Aggregation Modes:

  1. No aggregation (default): Returns vector of individual NEB values

    neb_func(income, spending)  # Returns vector

  2. Weighted aggregation: Automatically uses Nh method when weights provided

    neb_func(income, spending, weights = households)  # Returns single value

  3. Unweighted aggregation: Use aggregate = TRUE for simple mean

    neb_func(income, spending, aggregate = TRUE)  # Returns single value

Why Nh Method? Avoids 1-5% error from naive averaging:

  • CORRECT: neb_func(g, s, weights = w) → Uses Nh internally

  • WRONG: weighted.mean(s/g, w) → Introduces bias

The Nh method: 1 / (1 + weighted.mean(nh, weights)) where nh = (g-s)/se uses arithmetic mean instead of harmonic mean, providing computational simplicity and numerical stability.

See also

ner_func() for the Net Energy Return (Nh) calculation

energy_burden_func() for simple EB without aggregation support

Examples

# Individual household - returns vector
neb_func(50000, 3000)  # 0.06
#> [1] 0.06
neb_func(c(30000, 50000), c(3000, 3500))  # c(0.10, 0.07)
#> [1] 0.10 0.07

# Aggregation with weights - returns single value (CORRECT method)
incomes <- c(30000, 50000, 75000)
spending <- c(3000, 3500, 4000)
households <- c(100, 150, 200)
neb_func(incomes, spending, weights = households)
#> [1] 0.06528497

# Unweighted aggregation
neb_func(incomes, spending, aggregate = TRUE)
#> [1] 0.06970954

# Comparison: naive mean (WRONG) vs Nh method (CORRECT)
neb_naive <- weighted.mean(spending/incomes, households)  # Biased
neb_correct <- neb_func(incomes, spending, weights = households)  # Correct
abs(neb_naive - neb_correct) / neb_correct  # ~1-5% error
#> [1] 0.06087596