Reference
Teaching guide: environment and economy
How to use this atlas in an economics course and an environmental management course, how it maps onto the Introduction to Environmental Data Science chapters, and ideas for assessment.
Course mapping
| Chapter | Topic | Where in the atlas | Skill practised |
|---|---|---|---|
| 1–3 | Data, programming basics, data abstraction | Data & sources; every chart’s Table view and CSV | Read a tidy CSV, identify units, missing values and vintages |
| 4 | Visualization | Emissions, Electricity | Stacked versus share views, indexing, why there are no dual axes |
| 5 | Data transformation (joins, pivots) | Emissions (province table), Air (shares vs population) | Join emissions to population and GDP; pivot long ↔ wide |
| 6 | Spatial data and maps | Province maps on Emissions and Electricity | Choropleths, classification breaks, the ecological fallacy |
| 7 | Spatial analysis | Air pollution, environmental justice exercise | Buffers and spatial joins with NPRI facilities and census areas |
| 8 | Raster analysis | Climate (gridded anomalies, extension) | From station anomalies to gridded surfaces |
| 9 | Statistical summaries and tests | Climate trend, Green economy scatter | Trend estimation, correlation, small-sample fragility |
| 10 | Modeling | Stats lab | OLS with logs, quadratic EKC, residuals and omitted variables |
Two tracks
Environmental economics
- Emissions & the economy: externalities, production vs consumption accounting.
- Decoupling: Kaya identity, log decomposition, rebound effect.
- Carbon pricing: Pigouvian taxes, MAC curves, output-based pricing, tax vs cap.
- Electricity: endowments, comparative advantage, public goods.
- Green economy: satellite accounts, Porter hypothesis, just transition.
- Stats lab: EKC estimation and critique.
Environmental management
- Emissions: targets, baselines and tracking progress.
- Air pollution: standards, monitoring, exposure versus emissions.
- Climate change: trends, anomalies, adaptation planning.
- Electricity: provincial grids and regulation.
- Carbon pricing: compliance arithmetic for a facility.
- Spatial project: NPRI facilities and census areas (see the Air exercises).
Running a session
A 75-minute session works well in three parts: ten minutes reading the module question and figures together, thirty minutes on the charts with students predicting before revealing (use the Table view and hints), and the rest on exercises in pairs. Ask students to write their answer before opening “Check your reasoning”.
For projection, the dark theme (bottom of the menu) reads better in dim rooms. Every chart has a Table view and a CSV download, so students on screen readers or without the dashboard can work from the same numbers.
Assessment ideas
- Data brief (individual): pick one chart, download its CSV, recreate it in Python or R, and write 300 words interpreting it for a policymaker.
- Policy memo (pairs): using the OBPS calculator and the price schedules, advise a provincial ministry on the effect of the 2026 price revision on a cement plant.
- Replication (individual): reproduce the Stats lab EKC with robust standard errors; add one control variable from another source and discuss how the turning point changes.
- Spatial project (groups): map NPRI facilities against census income for one metropolitan area and test for an environmental justice gap.
Starter code
Every dataset is available as a CSV at a stable path on this site. In Python:
import pandas as pd
BASE = "https://YOUR-SITE.vercel.app/data/csv/"
sectors = pd.read_csv(BASE + "ghg_by_sector.csv")
wide = sectors.pivot(index="year", columns="sector", values="mt_co2e")
print(wide.loc[[2005, 2024]].T.assign(change=lambda d: d[2024] - d[2005]))In R:
library(tidyverse)
base <- "https://YOUR-SITE.vercel.app/data/csv/"
prov <- read_csv(paste0(base, "ghg_by_province.csv"))
ggplot(prov, aes(t_per_capita_2024, reorder(province, t_per_capita_2024))) + geom_col()