---
title: "Rebuild the UCLA carbon tracker"
subtitle: "Carbon Trackers · R · UCLA Emissions Reports"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```

# Rebuild the UCLA carbon tracker

**Carbon Trackers · R · UCLA Emissions Reports**

Recreate the live carbon tracker: verified UCLA greenhouse gas totals (Scopes 1+2+3), a trend from the 2019 baseline, and UC's **90% reduction by 2045** commitment.

**You will build:** a reproducible emissions chart plus interpretation of progress vs. pace.

---

## Learning objectives

- Read a verified campus GHG inventory time series
- Compute a policy target line (90% cut from 2019 by 2045)
- Fit and interpret a simple linear trend
- Explain why extrapolation ≠ forecast

**Time:** ~1.5 hr · Compare with [live tracker](../index.html) · Data: [ucla_emissions.csv](../data/ucla_emissions.csv)

---

## For mentors

- **Prerequisites:** One beginner project; basic comfort with plots.
- **Key vocabulary:** *Scope 1* = on-campus fuels · *Scope 2* = purchased electricity · *Scope 3* = commute, travel, etc.
- **Watch for:** Students mixing up the old 2025 net-zero pledge with the current **2045 / 90%** policy.
- **Tip:** Open the [live tracker](../index.html) side-by-side and compare lines.

---

## 1. Setup

**Mentor check-in:** *What does "metric tons CO₂e" mean — why the "e"?*

```{r packages}
required <- c("dplyr", "ggplot2", "readr", "scales", "broom")
missing <- required[!vapply(required, requireNamespace, quietly = TRUE, FUN.VALUE = logical(1))]
if (length(missing)) install.packages(missing, repos = "https://cloud.r-project.org")
library(dplyr)
library(ggplot2)
library(readr)
library(scales)
library(broom)
```

---

## 2. Load verified UCLA emissions

**Goal:** Import the same annual totals used on the live tracker.

**Concept:** Each row is one calendar year of **Scopes 1+2+3** from UC Annual Sustainability Reports.

**You should see:** A table from 2009–2023 with `year` and `emissions`.

```{r load}
load_emissions <- function() {
  paths <- c(
    "data/ucla_emissions.csv",
    "../data/ucla_emissions.csv",
    "notebooks/data/ucla_emissions.csv"
  )
  for (p in paths) if (file.exists(p)) return(read_csv(p, show_col_types = FALSE))
  stop("Could not find data/ucla_emissions.csv")
}

emissions <- load_emissions()
emissions
```

**Goal:** Define UC policy constants.

**You should see:** Baseline **335,331 t** and 2045 target **~33,533 t** (90% reduction).

**Mentor check-in:** *Why use 2019 as baseline instead of the lowest year on the chart?*

```{r meta}
BASELINE_YEAR <- 2019L
BASELINE_EMISSIONS <- 335331
TARGET_YEAR <- 2045L
REDUCTION_PCT <- 0.90
target_emissions <- round(BASELINE_EMISSIONS * (1 - REDUCTION_PCT))

commitment_pace <- function(year) {
  if (year <= BASELINE_YEAR) return(BASELINE_EMISSIONS)
  if (year >= TARGET_YEAR) return(target_emissions)
  frac <- (year - BASELINE_YEAR) / (TARGET_YEAR - BASELINE_YEAR)
  round(BASELINE_EMISSIONS - (BASELINE_EMISSIONS - target_emissions) * frac)
}

cat("2019 baseline:", comma(BASELINE_EMISSIONS), "t CO2e\n")
cat("2045 target (90% cut):", comma(target_emissions), "t CO2e\n")
```

---

## 3. Fit a trend from 2019

**Goal:** Quantify recent direction with `lm()` — same idea as the orange trend on the live chart.

**Concept:** We fit only **2019 onward** because COVID distorts 2020 and policy baseline is 2019.

**You should see:** Regression table with slope (tons per year) — negative slope = declining emissions.

**Mentor check-in:** *If slope were positive, what would that imply about reaching 2045?*

```{r trend}
recent <- emissions |> filter(year >= BASELINE_YEAR)
fit <- lm(emissions ~ year, data = recent)
fit_tidy <- tidy(fit)
fit_tidy

slope <- coef(fit)[["year"]]
intercept <- coef(fit)[["(Intercept)"]]

reach_year <- if (slope >= 0) {
  NA_integer_
} else {
  yr <- ceiling((target_emissions - intercept) / slope)
  if (yr > TARGET_YEAR) yr else NA_integer_
}

if (!is.na(reach_year)) {
  message("Linear trend from 2019 reaches the 2045 target around ", reach_year,
          " (simple extrapolation — not a forecast).")
} else {
  message("At the current post-2019 slope, emissions are not declining toward the 2045 target.")
}
```

---

## 4. Build the chart

**Goal:** Layer three stories on one plot: **reported** (dots), **trend** (dashed orange), **commitment pace** (dashed blue).

**You should see:** Chart matching the structure of the [live tracker](../index.html).

**Mentor check-in:** *Is the latest year above or below the dashed commitment line?*

```{r chart, fig.width=9, fig.height=5}
chart_years <- seq(min(emissions$year), TARGET_YEAR)
commitment_df <- tibble(
  year = chart_years,
  commitment = vapply(chart_years, commitment_pace, numeric(1))
)

trend_df <- tibble(
  year = chart_years,
  trend = if_else(year >= BASELINE_YEAR, slope * year + intercept, NA_real_)
)

latest <- emissions |> slice_tail(n = 1)
pace_latest <- commitment_pace(latest$year)
gap <- latest$emissions - pace_latest

ggplot() +
  geom_line(data = commitment_df, aes(x = year, y = commitment),
            color = "#5B8BB0", linewidth = 1, linetype = "dashed") +
  geom_line(data = trend_df, aes(x = year, y = trend),
            color = "#D98E5A", linewidth = 1, linetype = "dashed") +
  geom_point(data = emissions, aes(x = year, y = emissions),
             color = "#2F6B4F", size = 2.2) +
  geom_vline(xintercept = TARGET_YEAR, color = "#4A5A54", linetype = "dotted", alpha = 0.6) +
  scale_y_continuous(labels = label_comma()) +
  labs(
    title = "UCLA greenhouse gas emissions vs. UC commitment",
    subtitle = paste0("Scopes 1+2+3 · latest ", latest$year, ": ",
                      comma(latest$emissions), " t CO₂e · ",
                      if (gap > 0) paste0(comma(gap), " t above linear pace to 2045") else paste0(comma(abs(gap)), " t below pace")),
    x = "Calendar year",
    y = "Metric tons CO₂e",
    caption = "Sources: UC Annual Sustainability Reports · UC Sustainable Practices Policy (90% by 2045)"
  ) +
  theme_minimal(base_size = 13) +
  theme(plot.title = element_text(face = "bold"))
```

---

## 5. Final takeaway

**Mentor prompts:**

1. How far is the latest total from the **2019 baseline** and **2045 target**?
2. Above or below the **commitment pace** this year?
3. Why is the trend line illustrative, not a prediction?

**Extension:** Write a 150-word memo a campus administrator could read.

**Next:** [Live tracker](../index.html)