{ "cells": [ { "cell_type": "markdown", "id": "247ef23c", "metadata": {}, "source": [ "# Climate Change Trend Analysis and Forecasting\n", "\n", "**IDEAS TIH Summer Internship 2026 — Intern Project**\n", "\n", "This notebook ingests open Greenhouse Gas (GHG) emissions data (Our World in Data CO2 and\n", "GHG Emissions dataset), performs exploratory data analysis, engineers time-series features, trains and\n", "compares regression models, forecasts future emissions with a damped-trend exponential smoothing model,\n", "and simulates policy mitigation scenarios for 10 focus countries.\n", "\n", "*Scope note: this project deliberately focuses on classical machine learning and time-series methods —\n", "regression models and Holt's Damped Trend (ETS) forecasting — which are well suited to structured,\n", "annual, limited-length time series.*" ] }, { "cell_type": "markdown", "id": "096eb710", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "- [Week 1: Data Acquisition, Exploration and Understanding](#week1)\n", " - [1.1 Data Loading](#w1-1)\n", " - [1.2 Data Profiling](#w1-2)\n", " - [1.3 Exploratory Data Analysis](#w1-3)\n", "- [Week 2: Feature Engineering](#week2)\n", " - [2.1 Time-Based Features](#w2-1)\n", " - [2.2 Lag Features](#w2-2)\n", " - [2.3 Per-Capita and Intensity Features](#w2-3)\n", " - [2.4 Growth Rate Features](#w2-4)\n", " - [2.5 Final Feature Dataset](#w2-5)\n", "- [Week 3: Baseline ML Models — Regression](#week3)\n", " - [3.1 Problem Framing](#w3-1)\n", " - [3.2 Train-Test Split](#w3-2)\n", " - [3.3 Naive Baseline Model](#w3-3)\n", " - [3.4 Linear Regression](#w3-4)\n", " - [3.5 Random Forest Regressor](#w3-5)\n", " - [3.6 Model Comparison Table](#w3-6)\n", "- [Week 4: Time-Series Forecasting with ETS(A,Ad,N) — Holt's Damped Trend](#week4)\n", " - [4.1 Concept Introduction](#w4-1)\n", " - [4.2 Model Fitting](#w4-2)\n", " - [4.3 Forecasting to 2043](#w4-3)\n", " - [4.4 Trend Interpretation](#w4-4)\n", " - [4.5 Forecast Summary Table](#w4-5)\n", " - [4.6 Model Validation](#w4-6)\n", "- [Week 5: Scenario Analysis](#week5)\n", " - [5.1 Scenario Design](#w5-1)\n", " - [5.2 Scenario Calculation](#w5-2)\n", " - [5.3 Scenario Visualisations](#w5-3)\n", " - [5.4 Impact Summary](#w5-4)\n", "- [Week 6: Conclusions and Limitations](#week6)" ] }, { "cell_type": "markdown", "id": "7083db3e", "metadata": {}, "source": [ "## Setup\n", "\n", "Import libraries, set global display/plotting options, and define the 10 focus countries used\n", "consistently throughout the project (a mix of major emitters, economies at different development\n", "stages, and countries with documented emissions-reduction trajectories such as the UK and Germany)." ] }, { "cell_type": "code", "execution_count": 1, "id": "1d7e2adf", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:48.298249Z", "iopub.status.busy": "2026-07-29T14:01:48.297848Z", "iopub.status.idle": "2026-07-29T14:01:49.376327Z", "shell.execute_reply": "2026-07-29T14:01:49.375746Z" } }, "outputs": [], "source": [ "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option(\"display.max_columns\", 60)\n", "\n", "RANDOM_STATE = 42\n", "DATA_PATH = \"../data/owid-co2-data.csv\"\n", "\n", "# The 10 countries used consistently for all analysis, modelling, and scenario work\n", "COUNTRIES = [\n", " \"China\", \"United States\", \"India\", \"Russia\", \"Japan\",\n", " \"Germany\", \"Brazil\", \"United Kingdom\", \"South Africa\", \"Australia\",\n", "]\n", "\n", "# Consistent colour palette reused across every chart in this notebook\n", "COUNTRY_COLORS = dict(zip(COUNTRIES, px.colors.qualitative.Bold[:len(COUNTRIES)]))\n", "SCENARIO_COLORS = {\"BAU\": \"#1f77b4\", \"Moderate Mitigation\": \"#ff7f0e\", \"Aggressive Mitigation\": \"#2ca02c\"}" ] }, { "cell_type": "markdown", "id": "393b9b38", "metadata": {}, "source": [ "\n", "## Week 1: Data Acquisition, Exploration and Understanding\n", "\n", "*Learning objective: understand the structure and content of GHG datasets and produce a clean,\n", "profiled dataset ready for analysis.*" ] }, { "cell_type": "markdown", "id": "021cf8b6", "metadata": {}, "source": [ "\n", "### 1.1 Data Loading\n", "\n", "We use the primary dataset for this project — Our World in Data's CO2 and Greenhouse Gas\n", "Emissions dataset (`github.com/owid/co2-data`), downloaded as a CSV. It is a country-year panel\n", "covering CO2, methane, nitrous oxide and total GHG emissions alongside population, GDP and\n", "energy indicators." ] }, { "cell_type": "code", "execution_count": 2, "id": "6a430180", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.378337Z", "iopub.status.busy": "2026-07-29T14:01:49.378112Z", "iopub.status.idle": "2026-07-29T14:01:49.546891Z", "shell.execute_reply": "2026-07-29T14:01:49.546522Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape: (50411, 79)\n", "\n", "First 10 rows:\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " country year iso_code population gdp cement_co2 \\\n", "0 Afghanistan 1750 AFG 2802560.0 NaN 0.0 \n", "1 Afghanistan 1751 AFG NaN NaN 0.0 \n", "2 Afghanistan 1752 AFG NaN NaN 0.0 \n", "3 Afghanistan 1753 AFG NaN NaN 0.0 \n", "4 Afghanistan 1754 AFG NaN NaN 0.0 \n", "5 Afghanistan 1755 AFG NaN NaN 0.0 \n", "6 Afghanistan 1756 AFG NaN NaN 0.0 \n", "7 Afghanistan 1757 AFG NaN NaN 0.0 \n", "8 Afghanistan 1758 AFG NaN NaN 0.0 \n", "9 Afghanistan 1759 AFG NaN NaN 0.0 \n", "\n", " cement_co2_per_capita co2 co2_growth_abs co2_growth_prct \\\n", "0 0.0 NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN \n", "6 NaN NaN NaN NaN \n", "7 NaN NaN NaN NaN \n", "8 NaN NaN NaN NaN \n", "9 NaN NaN NaN NaN \n", "\n", " co2_including_luc co2_including_luc_growth_abs \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " co2_including_luc_growth_prct co2_including_luc_per_capita \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " co2_including_luc_per_gdp co2_including_luc_per_unit_energy \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " co2_per_capita co2_per_gdp co2_per_unit_energy coal_co2 \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "5 NaN NaN NaN NaN \n", "6 NaN NaN NaN NaN \n", "7 NaN NaN NaN NaN \n", "8 NaN NaN NaN NaN \n", "9 NaN NaN NaN NaN \n", "\n", " coal_co2_per_capita consumption_co2 consumption_co2_per_capita \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " consumption_co2_per_gdp cumulative_cement_co2 cumulative_co2 \\\n", "0 NaN 0.0 NaN \n", "1 NaN 0.0 NaN \n", "2 NaN 0.0 NaN \n", "3 NaN 0.0 NaN \n", "4 NaN 0.0 NaN \n", "5 NaN 0.0 NaN \n", "6 NaN 0.0 NaN \n", "7 NaN 0.0 NaN \n", "8 NaN 0.0 NaN \n", "9 NaN 0.0 NaN \n", "\n", " cumulative_co2_including_luc cumulative_coal_co2 cumulative_flaring_co2 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " cumulative_gas_co2 ... other_co2_per_capita other_industry_co2 \\\n", "0 NaN ... NaN NaN \n", "1 NaN ... NaN NaN \n", "2 NaN ... NaN NaN \n", "3 NaN ... NaN NaN \n", "4 NaN ... NaN NaN \n", "5 NaN ... NaN NaN \n", "6 NaN ... NaN NaN \n", "7 NaN ... NaN NaN \n", "8 NaN ... NaN NaN \n", "9 NaN ... NaN NaN \n", "\n", " primary_energy_consumption share_global_cement_co2 share_global_co2 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " share_global_co2_including_luc share_global_coal_co2 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " share_global_cumulative_cement_co2 share_global_cumulative_co2 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " share_global_cumulative_co2_including_luc \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", "\n", " share_global_cumulative_coal_co2 share_global_cumulative_flaring_co2 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " share_global_cumulative_gas_co2 share_global_cumulative_luc_co2 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " share_global_cumulative_oil_co2 share_global_cumulative_other_co2 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " share_global_flaring_co2 share_global_gas_co2 share_global_luc_co2 \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " share_global_oil_co2 share_global_other_co2 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " share_of_temperature_change_from_ghg temperature_change_from_ch4 \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " temperature_change_from_co2 temperature_change_from_ghg \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", " temperature_change_from_n2o total_ghg total_ghg_excluding_lucf \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "5 NaN NaN NaN \n", "6 NaN NaN NaN \n", "7 NaN NaN NaN \n", "8 NaN NaN NaN \n", "9 NaN NaN NaN \n", "\n", " trade_co2 trade_co2_share \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 NaN NaN \n", "9 NaN NaN \n", "\n", "[10 rows x 79 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_df = pd.read_csv(DATA_PATH)\n", "\n", "print(\"Shape:\", raw_df.shape)\n", "print(\"\\nFirst 10 rows:\")\n", "raw_df.head(10)" ] }, { "cell_type": "markdown", "id": "a1071ded", "metadata": {}, "source": [ "Next, inspect the full column list and each column's data type to understand what fields are\n", "available before doing any filtering or feature work." ] }, { "cell_type": "code", "execution_count": 3, "id": "e4952c4c", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.548574Z", "iopub.status.busy": "2026-07-29T14:01:49.548424Z", "iopub.status.idle": "2026-07-29T14:01:49.553344Z", "shell.execute_reply": "2026-07-29T14:01:49.552837Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column names (79 total):\n", "['country', 'year', 'iso_code', 'population', 'gdp', 'cement_co2', 'cement_co2_per_capita', 'co2', 'co2_growth_abs', 'co2_growth_prct', 'co2_including_luc', 'co2_including_luc_growth_abs', 'co2_including_luc_growth_prct', 'co2_including_luc_per_capita', 'co2_including_luc_per_gdp', 'co2_including_luc_per_unit_energy', 'co2_per_capita', 'co2_per_gdp', 'co2_per_unit_energy', 'coal_co2', 'coal_co2_per_capita', 'consumption_co2', 'consumption_co2_per_capita', 'consumption_co2_per_gdp', 'cumulative_cement_co2', 'cumulative_co2', 'cumulative_co2_including_luc', 'cumulative_coal_co2', 'cumulative_flaring_co2', 'cumulative_gas_co2', 'cumulative_luc_co2', 'cumulative_oil_co2', 'cumulative_other_co2', 'energy_per_capita', 'energy_per_gdp', 'flaring_co2', 'flaring_co2_per_capita', 'gas_co2', 'gas_co2_per_capita', 'ghg_excluding_lucf_per_capita', 'ghg_per_capita', 'land_use_change_co2', 'land_use_change_co2_per_capita', 'methane', 'methane_per_capita', 'nitrous_oxide', 'nitrous_oxide_per_capita', 'oil_co2', 'oil_co2_per_capita', 'other_co2_per_capita', 'other_industry_co2', 'primary_energy_consumption', 'share_global_cement_co2', 'share_global_co2', 'share_global_co2_including_luc', 'share_global_coal_co2', 'share_global_cumulative_cement_co2', 'share_global_cumulative_co2', 'share_global_cumulative_co2_including_luc', 'share_global_cumulative_coal_co2', 'share_global_cumulative_flaring_co2', 'share_global_cumulative_gas_co2', 'share_global_cumulative_luc_co2', 'share_global_cumulative_oil_co2', 'share_global_cumulative_other_co2', 'share_global_flaring_co2', 'share_global_gas_co2', 'share_global_luc_co2', 'share_global_oil_co2', 'share_global_other_co2', 'share_of_temperature_change_from_ghg', 'temperature_change_from_ch4', 'temperature_change_from_co2', 'temperature_change_from_ghg', 'temperature_change_from_n2o', 'total_ghg', 'total_ghg_excluding_lucf', 'trade_co2', 'trade_co2_share']\n", "\n", "Data types:\n" ] }, { "data": { "text/plain": [ "country str\n", "year int64\n", "iso_code str\n", "population float64\n", "gdp float64\n", " ... \n", "temperature_change_from_n2o float64\n", "total_ghg float64\n", "total_ghg_excluding_lucf float64\n", "trade_co2 float64\n", "trade_co2_share float64\n", "Length: 79, dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Column names ({} total):\".format(len(raw_df.columns)))\n", "print(list(raw_df.columns))\n", "print(\"\\nData types:\")\n", "raw_df.dtypes" ] }, { "cell_type": "markdown", "id": "6bbd9eb5", "metadata": {}, "source": [ "**What the key columns represent:**\n", "\n", "- **`country`** — country or region name (includes real sovereign countries as well as aggregates\n", " such as `World`, continents, and income-group buckets, which we filter out below).\n", "- **`year`** — calendar year of the observation (this dataset spans 1750–2024).\n", "- **`co2`** — annual territorial (production-based) CO2 emissions, in million tonnes (Mt).\n", "- **`co2_per_capita`** — annual CO2 emissions per person, in tonnes.\n", "- **`methane`** — annual methane (CH4) emissions, in million tonnes of CO2-equivalent.\n", "- **`nitrous_oxide`** — annual nitrous oxide (N2O) emissions, in million tonnes of\n", " CO2-equivalent.\n", "- **`total_ghg`** — total GHG emissions (CO2 + CH4 + N2O, excluding\n", " land-use change), in million tonnes of CO2-equivalent." ] }, { "cell_type": "markdown", "id": "431cceb7", "metadata": {}, "source": [ "\n", "### 1.2 Data Profiling" ] }, { "cell_type": "code", "execution_count": 4, "id": "03f6c938", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.554747Z", "iopub.status.busy": "2026-07-29T14:01:49.554619Z", "iopub.status.idle": "2026-07-29T14:01:49.566057Z", "shell.execute_reply": "2026-07-29T14:01:49.565451Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns with the most missing data:\n" ] }, { "data": { "text/html": [ "
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pct_null_of_total_rows
share_global_cumulative_other_co295.70
share_global_other_co295.70
other_co2_per_capita94.73
cumulative_other_co293.55
other_industry_co293.55
consumption_co2_per_gdp91.18
consumption_co2_per_capita90.79
trade_co290.65
trade_co2_share90.65
consumption_co289.98
energy_per_gdp84.55
co2_including_luc_per_unit_energy79.90
energy_per_capita79.00
primary_energy_consumption78.91
co2_per_unit_energy78.52
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" ], "text/plain": [ " pct_null_of_total_rows\n", "share_global_cumulative_other_co2 95.70\n", "share_global_other_co2 95.70\n", "other_co2_per_capita 94.73\n", "cumulative_other_co2 93.55\n", "other_industry_co2 93.55\n", "consumption_co2_per_gdp 91.18\n", "consumption_co2_per_capita 90.79\n", "trade_co2 90.65\n", "trade_co2_share 90.65\n", "consumption_co2 89.98\n", "energy_per_gdp 84.55\n", "co2_including_luc_per_unit_energy 79.90\n", "energy_per_capita 79.00\n", "primary_energy_consumption 78.91\n", "co2_per_unit_energy 78.52" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_pct = (raw_df.isna().sum() / len(raw_df) * 100).round(2).sort_values(ascending=False)\n", "null_report = null_pct.to_frame(\"pct_null_of_total_rows\")\n", "print(\"Columns with the most missing data:\")\n", "null_report.head(15)" ] }, { "cell_type": "markdown", "id": "880a7966", "metadata": {}, "source": [ "Next, check which countries and years have the most complete CO2 coverage, since sparse country-year\n", "combinations are candidates for exclusion." ] }, { "cell_type": "code", "execution_count": 5, "id": "7cf5a103", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.567382Z", "iopub.status.busy": "2026-07-29T14:01:49.567255Z", "iopub.status.idle": "2026-07-29T14:01:49.587300Z", "shell.execute_reply": "2026-07-29T14:01:49.586707Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Most complete CO2 coverage (top 15):\n" ] }, { "data": { "text/html": [ "
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first_yearlast_yearn_years
country
Australia17502024275
Asia (excl. China and India)17502024275
Asia17502024275
Europe17502024275
Europe (excl. EU-27)17502024275
Europe (excl. EU-28)17502024275
European Union (28)17502024275
High-income countries17502024275
New Zealand17502024275
Taiwan17502024275
World17502024275
United Kingdom17502024275
Norway17502024275
Oceania17502024275
Canada17852024240
\n", "
" ], "text/plain": [ " first_year last_year n_years\n", "country \n", "Australia 1750 2024 275\n", "Asia (excl. China and India) 1750 2024 275\n", "Asia 1750 2024 275\n", "Europe 1750 2024 275\n", "Europe (excl. EU-27) 1750 2024 275\n", "Europe (excl. EU-28) 1750 2024 275\n", "European Union (28) 1750 2024 275\n", "High-income countries 1750 2024 275\n", "New Zealand 1750 2024 275\n", "Taiwan 1750 2024 275\n", "World 1750 2024 275\n", "United Kingdom 1750 2024 275\n", "Norway 1750 2024 275\n", "Oceania 1750 2024 275\n", "Canada 1785 2024 240" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Which countries/years have the most complete co2 coverage?\n", "coverage = (\n", " raw_df.dropna(subset=[\"co2\"])\n", " .groupby(\"country\")[\"year\"]\n", " .agg(first_year=\"min\", last_year=\"max\", n_years=\"count\")\n", " .sort_values(\"n_years\", ascending=False)\n", ")\n", "print(\"Most complete CO2 coverage (top 15):\")\n", "coverage.head(15)" ] }, { "cell_type": "markdown", "id": "2dc0dc39", "metadata": {}, "source": [ "Also check how many countries report CO2 data in the most recent years, to gauge how current and\n", "broad the dataset's coverage is." ] }, { "cell_type": "code", "execution_count": 6, "id": "57f0ac85", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.588920Z", "iopub.status.busy": "2026-07-29T14:01:49.588722Z", "iopub.status.idle": "2026-07-29T14:01:49.605917Z", "shell.execute_reply": "2026-07-29T14:01:49.605372Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of countries reporting CO2, most recent years:\n" ] }, { "data": { "text/plain": [ "year\n", "2015 247\n", "2016 247\n", "2017 247\n", "2018 247\n", "2019 247\n", "2020 247\n", "2021 247\n", "2022 247\n", "2023 247\n", "2024 247\n", "Name: country, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Rows per year, to see which years have the broadest country coverage\n", "rows_per_year = raw_df.dropna(subset=[\"co2\"]).groupby(\"year\")[\"country\"].nunique()\n", "print(\"Number of countries reporting CO2, most recent years:\")\n", "rows_per_year.tail(10)" ] }, { "cell_type": "markdown", "id": "5d68380f", "metadata": {}, "source": [ "**Filtering decisions.** We restrict the working dataset to:\n", "\n", "1. **`year >= 1990`** — this project's analysis, modelling, and forecasting all operate on the\n", " post-1990 period, which has materially better data completeness across countries than earlier\n", " decades and captures the modern emissions trajectory (including the post-Soviet transition, WTO-era\n", " globalisation, and recent decarbonisation policy).\n", "2. **Sovereign countries only** — OWID includes aggregate rows (`World`, continents such as `Asia`/\n", " `Europe`, and income-group buckets such as `High-income countries`) alongside individual countries.\n", " These aggregates have no `iso_code` in this dataset, while every real country does, so we filter on\n", " `iso_code.notna()` to keep only actual countries and drop double-counted aggregates." ] }, { "cell_type": "code", "execution_count": 7, "id": "67c8108c", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.607246Z", "iopub.status.busy": "2026-07-29T14:01:49.607112Z", "iopub.status.idle": "2026-07-29T14:01:49.620363Z", "shell.execute_reply": "2026-07-29T14:01:49.619883Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows before filtering: 50,411\n", "Rows after filtering: 7,630\n", "Countries retained: 218\n", "Aggregate rows removed (no iso_code): 7,931\n" ] } ], "source": [ "df = raw_df[(raw_df[\"year\"] >= 1990) & (raw_df[\"iso_code\"].notna())].copy()\n", "\n", "print(f\"Rows before filtering: {len(raw_df):,}\")\n", "print(f\"Rows after filtering: {len(df):,}\")\n", "print(f\"Countries retained: {df['country'].nunique()}\")\n", "print(f\"Aggregate rows removed (no iso_code): {raw_df['iso_code'].isna().sum():,}\")" ] }, { "cell_type": "markdown", "id": "43b911a4", "metadata": {}, "source": [ "\n", "### 1.3 Exploratory Data Analysis (EDA)" ] }, { "cell_type": "code", "execution_count": 8, "id": "e8a1b90f", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:49.621880Z", "iopub.status.busy": "2026-07-29T14:01:49.621735Z", "iopub.status.idle": "2026-07-29T14:01:50.568597Z", "shell.execute_reply": "2026-07-29T14:01:50.568107Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Year=%{x}
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"source": [ "top5_emitters = [\"China\", \"United States\", \"India\", \"Russia\", \"Japan\"]\n", "top5_df = df[df[\"country\"].isin(top5_emitters)]\n", "\n", "fig = px.line(\n", " top5_df, x=\"year\", y=\"co2\", color=\"country\",\n", " title=\"CO2 Emission Trends for the Top 5 Emitting Countries, 1990-2024\",\n", " labels={\"co2\": \"CO2 Emissions (Mt)\", \"year\": \"Year\", \"country\": \"Country\"},\n", " color_discrete_map=COUNTRY_COLORS,\n", ")\n", "fig.update_traces(line_width=2.5)\n", "fig.update_layout(template=\"plotly_white\", legend_title_text=\"Country\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "02dd8ea7", "metadata": {}, "source": [ "Finally, break down total GHG emissions by gas type (CO2, methane, nitrous oxide) per decade to see\n", "whether the composition of the gas mix has shifted over time." ] }, { "cell_type": "code", "execution_count": 10, "id": "892381ea", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.614331Z", 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"ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Share of Total Global GHG Emissions by Gas Type, per Decade" }, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "title": { "text": "Decade" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Share (%)" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "decade_df = df.copy()\n", "decade_df[\"decade\"] = (decade_df[\"year\"] // 10 * 10).astype(str) + \"s\"\n", "decade_df = decade_df[decade_df[\"decade\"].isin([\"1990s\", \"2000s\", \"2010s\", \"2020s\"])]\n", "\n", "gas_by_decade = (\n", " decade_df.groupby(\"decade\")[[\"co2\", \"methane\", \"nitrous_oxide\"]]\n", " .sum()\n", " .rename(columns={\"co2\": \"CO2\", \"methane\": \"Methane (CH4)\", \"nitrous_oxide\": \"Nitrous Oxide (N2O)\"})\n", ")\n", "gas_share = gas_by_decade.div(gas_by_decade.sum(axis=1), axis=0) * 100\n", "gas_share_long = gas_share.reset_index().melt(id_vars=\"decade\", var_name=\"Gas\", value_name=\"Share (%)\")\n", "\n", "fig = px.bar(\n", " gas_share_long, x=\"decade\", y=\"Share (%)\", color=\"Gas\",\n", " title=\"Share of Total Global GHG Emissions by Gas Type, per Decade\",\n", " labels={\"decade\": \"Decade\"},\n", " color_discrete_sequence=px.colors.qualitative.Safe,\n", ")\n", "fig.update_layout(template=\"plotly_white\", barmode=\"stack\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "6ceef73e", "metadata": {}, "source": [ "**Summary of observed patterns:**\n", "\n", "Global CO2 emissions from sovereign countries have risen substantially since 1990, with\n", "growth accelerating sharply after China's WTO accession in the early 2000s and a brief dip around\n", "2020 caused by the COVID-19 pandemic. Among the top 5 emitters, China and India show strong sustained\n", "growth over the period — China overtaking the United States as the largest single emitter in the\n", "mid-2000s — while the United States and Japan show comparatively flat or gently declining trends and\n", "Russia shows a sharp drop around the 1991 Soviet collapse followed by a slow recovery. In the gas-mix\n", "breakdown, CO2 is consistently the dominant contributor to total GHG emissions across every\n", "decade, with methane the second largest share and nitrous oxide a small but persistent component;\n", "CO2's share has crept up slightly in more recent decades as fossil energy use has grown\n", "faster than agricultural methane sources globally." ] }, { "cell_type": "markdown", "id": "fa5bef7f", "metadata": {}, "source": [ "\n", "## Week 2: Feature Engineering\n", "\n", "*Learning objective: transform raw emissions data into a structured, model-ready feature set that\n", "captures temporal patterns and relationships between variables.*\n", "\n", "From this point on, all analysis, modelling, and scenario work is restricted to the 10 focus countries." ] }, { "cell_type": "code", "execution_count": 11, "id": "f49ac656", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.661317Z", "iopub.status.busy": "2026-07-29T14:01:50.661190Z", "iopub.status.idle": "2026-07-29T14:01:50.670753Z", "shell.execute_reply": "2026-07-29T14:01:50.670299Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows: 350 | Countries: 10 | Years: 1990-2024\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countryyearco2populationgdp
0Australia1990278.06117126304.04.641366e+11
1Australia1991279.43717353191.04.613021e+11
2Australia1992284.43317549286.04.786337e+11
3Australia1993288.78017722905.05.015248e+11
4Australia1994293.61317897433.05.279932e+11
\n", "
" ], "text/plain": [ " country year co2 population gdp\n", "0 Australia 1990 278.061 17126304.0 4.641366e+11\n", "1 Australia 1991 279.437 17353191.0 4.613021e+11\n", "2 Australia 1992 284.433 17549286.0 4.786337e+11\n", "3 Australia 1993 288.780 17722905.0 5.015248e+11\n", "4 Australia 1994 293.613 17897433.0 5.279932e+11" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base = (\n", " df[df[\"country\"].isin(COUNTRIES)]\n", " .sort_values([\"country\", \"year\"])\n", " .reset_index(drop=True)\n", " .copy()\n", ")\n", "print(f\"Rows: {len(base)} | Countries: {base['country'].nunique()} | \"\n", " f\"Years: {base['year'].min()}-{base['year'].max()}\")\n", "base[[\"country\", \"year\", \"co2\", \"population\", \"gdp\"]].head()" ] }, { "cell_type": "markdown", "id": "9ede25e6", "metadata": {}, "source": [ "\n", "### 2.1 Time-Based Features" ] }, { "cell_type": "code", "execution_count": 12, "id": "ff9185c4", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.672258Z", "iopub.status.busy": "2026-07-29T14:01:50.672112Z", "iopub.status.idle": "2026-07-29T14:01:50.683179Z", "shell.execute_reply": "2026-07-29T14:01:50.682630Z" } }, "outputs": [ { "data": { "text/html": [ "
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countryyeardecadeyears_since_1990co2co2_5yr_rolling_mean
0Australia19901990s0278.061278.061000
1Australia19911990s1279.437278.749000
2Australia19921990s2284.433280.643667
3Australia19931990s3288.780282.677750
4Australia19941990s4293.613284.864800
5Australia19951990s5304.962290.245000
6Australia19961990s6311.851296.727800
7Australia19971990s7320.243303.889800
\n", "
" ], "text/plain": [ " country year decade years_since_1990 co2 co2_5yr_rolling_mean\n", "0 Australia 1990 1990s 0 278.061 278.061000\n", "1 Australia 1991 1990s 1 279.437 278.749000\n", "2 Australia 1992 1990s 2 284.433 280.643667\n", "3 Australia 1993 1990s 3 288.780 282.677750\n", "4 Australia 1994 1990s 4 293.613 284.864800\n", "5 Australia 1995 1990s 5 304.962 290.245000\n", "6 Australia 1996 1990s 6 311.851 296.727800\n", "7 Australia 1997 1990s 7 320.243 303.889800" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base[\"decade\"] = (base[\"year\"] // 10 * 10).astype(str) + \"s\"\n", "base[\"years_since_1990\"] = base[\"year\"] - 1990\n", "\n", "# 5-year rolling average of CO2, computed independently per country\n", "base[\"co2_5yr_rolling_mean\"] = (\n", " base.groupby(\"country\")[\"co2\"]\n", " .transform(lambda s: s.rolling(window=5, min_periods=1).mean())\n", ")\n", "\n", "base[[\"country\", \"year\", \"decade\", \"years_since_1990\", \"co2\", \"co2_5yr_rolling_mean\"]].head(8)" ] }, { "cell_type": "markdown", "id": "2e68ae02", "metadata": {}, "source": [ "\n", "### 2.2 Lag Features" ] }, { "cell_type": "code", "execution_count": 13, "id": "fd2ea236", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.684663Z", "iopub.status.busy": "2026-07-29T14:01:50.684526Z", "iopub.status.idle": "2026-07-29T14:01:50.695149Z", "shell.execute_reply": "2026-07-29T14:01:50.694727Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countryyearco2co2_lag1co2_lag2co2_lag3
0Australia1990278.061NaNNaNNaN
1Australia1991279.437278.061NaNNaN
2Australia1992284.433279.437278.061NaN
3Australia1993288.780284.433279.437278.061
4Australia1994293.613288.780284.433279.437
5Australia1995304.962293.613288.780284.433
6Australia1996311.851304.962293.613288.780
7Australia1997320.243311.851304.962293.613
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" ], "text/plain": [ " country year co2 co2_lag1 co2_lag2 co2_lag3\n", "0 Australia 1990 278.061 NaN NaN NaN\n", "1 Australia 1991 279.437 278.061 NaN NaN\n", "2 Australia 1992 284.433 279.437 278.061 NaN\n", "3 Australia 1993 288.780 284.433 279.437 278.061\n", "4 Australia 1994 293.613 288.780 284.433 279.437\n", "5 Australia 1995 304.962 293.613 288.780 284.433\n", "6 Australia 1996 311.851 304.962 293.613 288.780\n", "7 Australia 1997 320.243 311.851 304.962 293.613" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for lag in (1, 2, 3):\n", " base[f\"co2_lag{lag}\"] = base.groupby(\"country\")[\"co2\"].shift(lag)\n", "\n", "base[[\"country\", \"year\", \"co2\", \"co2_lag1\", \"co2_lag2\", \"co2_lag3\"]].head(8)" ] }, { "cell_type": "markdown", "id": "b24f844d", "metadata": {}, "source": [ "**Why lag features matter.** Lag features (e.g. `co2_lag1` = last year's emissions for the same\n", "country) give a supervised model direct access to the most recent history of the series it is trying\n", "to predict. Emissions are highly autocorrelated year-to-year — a country's emissions this year are one\n", "of the best predictors of its emissions next year — so lag features let a standard regression model\n", "approximate autoregressive time-series behaviour without needing a dedicated time-series model.\n", "Multiple lags (1, 2, and 3 years back) also let the model pick up short-term acceleration or\n", "deceleration in the trend, not just the most recent level." ] }, { "cell_type": "markdown", "id": "ec93836b", "metadata": {}, "source": [ "\n", "### 2.3 Per-Capita and Intensity Features" ] }, { "cell_type": "code", "execution_count": 14, "id": "8aecbd98", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.696776Z", "iopub.status.busy": "2026-07-29T14:01:50.696643Z", "iopub.status.idle": "2026-07-29T14:01:50.709504Z", "shell.execute_reply": "2026-07-29T14:01:50.708980Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countryyearcomputed_per_capitareported_per_capitadiff
0China19902.1532.153-0.0001
1China20106.3706.3700.0004
2China20238.5568.5560.0003
3United States199020.25420.254-0.0003
4United States201018.22518.2250.0004
5United States202314.31914.3190.0005
6India19900.6680.6680.0002
7India20101.3501.350-0.0001
8India20232.1302.130-0.0002
\n", "
" ], "text/plain": [ " country year computed_per_capita reported_per_capita diff\n", "0 China 1990 2.153 2.153 -0.0001\n", "1 China 2010 6.370 6.370 0.0004\n", "2 China 2023 8.556 8.556 0.0003\n", "3 United States 1990 20.254 20.254 -0.0003\n", "4 United States 2010 18.225 18.225 0.0004\n", "5 United States 2023 14.319 14.319 0.0005\n", "6 India 1990 0.668 0.668 0.0002\n", "7 India 2010 1.350 1.350 -0.0001\n", "8 India 2023 2.130 2.130 -0.0002" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cross-check co2_per_capita = co2 (Mt) * 1e6 / population, for 3 countries x 3 years\n", "check_years = [1990, 2010, 2023]\n", "check_countries = [\"China\", \"United States\", \"India\"]\n", "\n", "check_rows = []\n", "for country in check_countries:\n", " for y in check_years:\n", " row = base[(base[\"country\"] == country) & (base[\"year\"] == y)]\n", " if row.empty:\n", " continue\n", " computed = row[\"co2\"].values[0] * 1e6 / row[\"population\"].values[0]\n", " reported = row[\"co2_per_capita\"].values[0]\n", " check_rows.append({\n", " \"country\": country, \"year\": y,\n", " \"computed_per_capita\": round(computed, 3),\n", " \"reported_per_capita\": round(reported, 3),\n", " \"diff\": round(computed - reported, 4),\n", " })\n", "\n", "pd.DataFrame(check_rows)" ] }, { "cell_type": "markdown", "id": "5eaf1e4d", "metadata": {}, "source": [ "The computed values match OWID's reported `co2_per_capita` to within rounding error, confirming the\n", "column is simply `co2 * 1e6 / population` (tonnes per person)." ] }, { "cell_type": "code", "execution_count": 15, "id": "273ff456", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.710872Z", "iopub.status.busy": "2026-07-29T14:01:50.710749Z", "iopub.status.idle": "2026-07-29T14:01:50.717960Z", "shell.execute_reply": "2026-07-29T14:01:50.717504Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Country-years where ghg_intensity could not be computed (missing/zero GDP): 20\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countryyear
33Australia2023
34Australia2024
68Brazil2023
69Brazil2024
103China2023
104China2024
138Germany2023
139Germany2024
173India2023
174India2024
208Japan2023
209Japan2024
243Russia2023
244Russia2024
278South Africa2023
279South Africa2024
313United Kingdom2023
314United Kingdom2024
348United States2023
349United States2024
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" ], "text/plain": [ " country year\n", "33 Australia 2023\n", "34 Australia 2024\n", "68 Brazil 2023\n", "69 Brazil 2024\n", "103 China 2023\n", "104 China 2024\n", "138 Germany 2023\n", "139 Germany 2024\n", "173 India 2023\n", "174 India 2024\n", "208 Japan 2023\n", "209 Japan 2024\n", "243 Russia 2023\n", "244 Russia 2024\n", "278 South Africa 2023\n", "279 South Africa 2024\n", "313 United Kingdom 2023\n", "314 United Kingdom 2024\n", "348 United States 2023\n", "349 United States 2024" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base[\"ghg_intensity\"] = np.where(\n", " base[\"gdp\"].notna() & (base[\"gdp\"] > 0),\n", " base[\"total_ghg\"] / base[\"gdp\"],\n", " np.nan,\n", ")\n", "\n", "missing_intensity = base[base[\"ghg_intensity\"].isna()][[\"country\", \"year\"]]\n", "print(f\"Country-years where ghg_intensity could not be computed (missing/zero GDP): {len(missing_intensity)}\")\n", "missing_intensity" ] }, { "cell_type": "markdown", "id": "c3c7ef82", "metadata": {}, "source": [ "\n", "### 2.4 Growth Rate Features" ] }, { "cell_type": "code", "execution_count": 16, "id": "6e0a752e", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.719530Z", "iopub.status.busy": "2026-07-29T14:01:50.719407Z", "iopub.status.idle": "2026-07-29T14:01:50.726271Z", "shell.execute_reply": "2026-07-29T14:01:50.725887Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Top 5 countries by highest average annual CO2 growth rate since 1990:\n", "country\n", "India 5.21\n", "China 4.91\n", "Brazil 2.46\n", "South Africa 1.13\n", "Australia 0.99\n", "Name: co2_yoy_pct_change, dtype: float64\n" ] } ], "source": [ "base[\"co2_yoy_change\"] = base.groupby(\"country\")[\"co2\"].diff()\n", "base[\"co2_yoy_pct_change\"] = base.groupby(\"country\")[\"co2\"].pct_change() * 100\n", "\n", "avg_growth = (\n", " base[base[\"year\"] >= 1991]\n", " .groupby(\"country\")[\"co2_yoy_pct_change\"]\n", " .mean()\n", " .sort_values(ascending=False)\n", ")\n", "\n", "print(\"Top 5 countries by highest average annual CO2 growth rate since 1990:\")\n", "print(avg_growth.head(5).round(2))" ] }, { "cell_type": "markdown", "id": "7f41c866", "metadata": {}, "source": [ "Next, identify the countries with the largest absolute CO2 reduction since 1990 (a separate question\n", "from \"highest average growth rate,\" since a country can grow for years and still end up lower than it\n", "started)." ] }, { "cell_type": "code", "execution_count": 17, "id": "21b93ecc", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.727698Z", "iopub.status.busy": "2026-07-29T14:01:50.727568Z", "iopub.status.idle": "2026-07-29T14:01:50.733577Z", "shell.execute_reply": "2026-07-29T14:01:50.733101Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Top 5 countries by largest absolute CO2 reduction since 1990 (1990 level minus 2024 level, Mt):\n", "country\n", "Russia 755.8\n", "Germany 482.5\n", "United Kingdom 289.0\n", "United States 227.6\n", "Japan 193.0\n", "Name: co2, dtype: float64\n" ] } ], "source": [ "co2_1990 = base[base[\"year\"] == 1990].set_index(\"country\")[\"co2\"]\n", "co2_latest = base[base[\"year\"] == base[\"year\"].max()].set_index(\"country\")[\"co2\"]\n", "total_reduction = (co2_1990 - co2_latest).sort_values(ascending=False)\n", "\n", "print(f\"Top 5 countries by largest absolute CO2 reduction since 1990 (1990 level minus \"\n", " f\"{base['year'].max()} level, Mt):\")\n", "print(total_reduction.head(5).round(1))" ] }, { "cell_type": "markdown", "id": "52d36a5f", "metadata": {}, "source": [ "\n", "### 2.5 Final Feature Dataset" ] }, { "cell_type": "code", "execution_count": 18, "id": "b22c0756", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.734787Z", "iopub.status.busy": "2026-07-29T14:01:50.734665Z", "iopub.status.idle": "2026-07-29T14:01:50.749511Z", "shell.execute_reply": "2026-07-29T14:01:50.749031Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved ../data/ghg_features.csv with shape (350, 10)\n" ] }, { "data": { "text/html": [ "
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countryyearco2co2_per_capitaco2_5yr_rolling_meanco2_lag1co2_lag2co2_lag3co2_yoy_pct_changeghg_intensity
0Australia1990278.06116.236278.061000NaNNaNNaNNaN1.160174e-09
1Australia1991279.43716.103278.749000278.061NaNNaN0.4948551.132718e-09
2Australia1992284.43316.208280.643667279.437278.061NaN1.7878811.188721e-09
3Australia1993288.78016.294282.677750284.433279.437278.0611.5283041.157801e-09
4Australia1994293.61316.405284.864800288.780284.433279.4371.6735921.049415e-09
\n", "
" ], "text/plain": [ " country year co2 co2_per_capita co2_5yr_rolling_mean co2_lag1 \\\n", "0 Australia 1990 278.061 16.236 278.061000 NaN \n", "1 Australia 1991 279.437 16.103 278.749000 278.061 \n", "2 Australia 1992 284.433 16.208 280.643667 279.437 \n", "3 Australia 1993 288.780 16.294 282.677750 284.433 \n", "4 Australia 1994 293.613 16.405 284.864800 288.780 \n", "\n", " co2_lag2 co2_lag3 co2_yoy_pct_change ghg_intensity \n", "0 NaN NaN NaN 1.160174e-09 \n", "1 NaN NaN 0.494855 1.132718e-09 \n", "2 278.061 NaN 1.787881 1.188721e-09 \n", "3 279.437 278.061 1.528304 1.157801e-09 \n", "4 284.433 279.437 1.673592 1.049415e-09 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_cols = [\n", " \"country\", \"year\", \"co2\", \"co2_per_capita\", \"co2_5yr_rolling_mean\",\n", " \"co2_lag1\", \"co2_lag2\", \"co2_lag3\", \"co2_yoy_pct_change\", \"ghg_intensity\",\n", "]\n", "features_df = base[feature_cols].copy()\n", "\n", "features_df.to_csv(\"../data/ghg_features.csv\", index=False)\n", "print(f\"Saved ../data/ghg_features.csv with shape {features_df.shape}\")\n", "features_df.head()" ] }, { "cell_type": "markdown", "id": "32d8be81", "metadata": {}, "source": [ "\n", "## Week 3: Baseline ML Models — Regression\n", "\n", "*Learning objective: train, evaluate, and compare supervised regression models to predict future\n", "CO2 emissions; understand model evaluation metrics.*" ] }, { "cell_type": "markdown", "id": "13caaa4b", "metadata": {}, "source": [ "\n", "### 3.1 Problem Framing\n", "\n", "**Prediction task:** given a feature vector **X** describing country *C* in year *Y*, predict\n", "CO2 emissions for year *Y+1* (`target_co2_next`).\n", "\n", "**Target variable:** `co2` shifted forward by one year within each country (`target_co2_next`).\n", "\n", "**Input features** (from the Week 2 feature set): `years_since_1990`, `co2`, `co2_per_capita`,\n", "`co2_5yr_rolling_mean`, `co2_lag1`, `co2_lag2`, `co2_lag3`.\n", "\n", "**Note on training strategy:** models in this week use two different training approaches — Linear\n", "Regression is trained *per country* (works reliably with ~30 rows per country), while Random Forest is\n", "trained on the *pooled* dataset of all 10 countries (~300 training rows) to avoid overfitting from\n", "small per-country sample sizes; both are evaluated per country for direct comparison.\n", "\n", "**Why a supervised regression approach?** Framing the forecasting problem as \"predict next year from\n", "this year's features\" turns it into a standard tabular supervised-learning problem, which lets us use\n", "well-understood, fast-to-train models (linear regression, tree ensembles) as an interpretable baseline\n", "before moving to a dedicated time-series model in Week 4." ] }, { "cell_type": "code", "execution_count": 19, "id": "87304131", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.750863Z", "iopub.status.busy": "2026-07-29T14:01:50.750742Z", "iopub.status.idle": "2026-07-29T14:01:50.762637Z", "shell.execute_reply": "2026-07-29T14:01:50.762112Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modelling rows: 310 | Year range: 1993-2023\n" ] }, { "data": { "text/html": [ "
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countryyearyears_since_1990co2co2_per_capitaco2_5yr_rolling_meanco2_lag1co2_lag2co2_lag3target_co2_next
0Australia19933288.78016.294282.67775284.433279.437278.061293.613
1Australia19944293.61316.405284.86480288.780284.433279.437304.962
2Australia19955304.96216.853290.24500293.613288.780284.433311.851
3Australia19966311.85117.036296.72780304.962293.613288.780320.243
4Australia19977320.24317.306303.88980311.851304.962293.613334.038
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" ], "text/plain": [ " country year years_since_1990 co2 co2_per_capita \\\n", "0 Australia 1993 3 288.780 16.294 \n", "1 Australia 1994 4 293.613 16.405 \n", "2 Australia 1995 5 304.962 16.853 \n", "3 Australia 1996 6 311.851 17.036 \n", "4 Australia 1997 7 320.243 17.306 \n", "\n", " co2_5yr_rolling_mean co2_lag1 co2_lag2 co2_lag3 target_co2_next \n", "0 282.67775 284.433 279.437 278.061 293.613 \n", "1 284.86480 288.780 284.433 279.437 304.962 \n", "2 290.24500 293.613 288.780 284.433 311.851 \n", "3 296.72780 304.962 293.613 288.780 320.243 \n", "4 303.88980 311.851 304.962 293.613 334.038 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df = base.copy()\n", "model_df[\"target_co2_next\"] = model_df.groupby(\"country\")[\"co2\"].shift(-1)\n", "\n", "feature_names = [\n", " \"years_since_1990\", \"co2\", \"co2_per_capita\",\n", " \"co2_5yr_rolling_mean\", \"co2_lag1\", \"co2_lag2\", \"co2_lag3\",\n", "]\n", "\n", "# Drop rows without a full lag history or without a next-year target to predict\n", "model_df = model_df.dropna(subset=feature_names + [\"target_co2_next\"]).reset_index(drop=True)\n", "print(f\"Modelling rows: {len(model_df)} | Year range: {model_df['year'].min()}-{model_df['year'].max()}\")\n", "model_df[[\"country\", \"year\"] + feature_names + [\"target_co2_next\"]].head()" ] }, { "cell_type": "markdown", "id": "10c6086e", "metadata": {}, "source": [ "\n", "### 3.2 Train-Test Split" ] }, { "cell_type": "code", "execution_count": 20, "id": "71165f82", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.764017Z", "iopub.status.busy": "2026-07-29T14:01:50.763872Z", "iopub.status.idle": "2026-07-29T14:01:50.771760Z", "shell.execute_reply": "2026-07-29T14:01:50.771135Z" } }, "outputs": [ { "data": { "text/html": [ "
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train_rowstest_rows
country
Australia265
Brazil265
China265
Germany265
India265
Japan265
Russia265
South Africa265
United Kingdom265
United States265
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" ], "text/plain": [ " train_rows test_rows\n", "country \n", "Australia 26 5\n", "Brazil 26 5\n", "China 26 5\n", "Germany 26 5\n", "India 26 5\n", "Japan 26 5\n", "Russia 26 5\n", "South Africa 26 5\n", "United Kingdom 26 5\n", "United States 26 5" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = model_df[model_df[\"year\"] <= 2018].copy()\n", "test = model_df[model_df[\"year\"] >= 2019].copy()\n", "\n", "split_counts = pd.DataFrame({\n", " \"train_rows\": train.groupby(\"country\").size(),\n", " \"test_rows\": test.groupby(\"country\").size(),\n", "}).fillna(0).astype(int)\n", "split_counts" ] }, { "cell_type": "markdown", "id": "cb08af04", "metadata": {}, "source": [ "*The 2019–2023 test window is deliberately chosen to include the COVID-19 pandemic emissions dip\n", "(2020) and subsequent recovery — a useful real-world stress test for model robustness.*\n", "\n", "**Why temporal splitting, not random splitting?** Emissions data is a time series with strong\n", "year-to-year autocorrelation and a trend. A random train/test split would let the model \"see\" data\n", "from years surrounding a test point (e.g. train on 2021, test on 2020), which leaks future information\n", "into training and produces unrealistically optimistic accuracy. A temporal split — train on the past\n", "(1990–2018), test on the future (2019 onward) — mimics the real deployment scenario of forecasting\n", "years that have not happened yet, and is standard practice for any time-series prediction task." ] }, { "cell_type": "markdown", "id": "3c02433c", "metadata": {}, "source": [ "\n", "### 3.3 Naive Baseline Model" ] }, { "cell_type": "code", "execution_count": 21, "id": "2d34de14", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.773191Z", "iopub.status.busy": "2026-07-29T14:01:50.773050Z", "iopub.status.idle": "2026-07-29T14:01:50.791722Z", "shell.execute_reply": "2026-07-29T14:01:50.791119Z" } }, "outputs": [ { "data": { "text/html": [ "
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countrybaseline_maebaseline_rmse
0China315.1044344.121882
1United States212.5380292.332458
2India191.8378197.188147
3Russia44.235850.307134
4Japan36.526039.830915
5Germany39.594446.431112
6Brazil19.741226.352645
7United Kingdom18.842623.468566
8South Africa12.265217.077892
9Australia6.70028.887581
\n", "
" ], "text/plain": [ " country baseline_mae baseline_rmse\n", "0 China 315.1044 344.121882\n", "1 United States 212.5380 292.332458\n", "2 India 191.8378 197.188147\n", "3 Russia 44.2358 50.307134\n", "4 Japan 36.5260 39.830915\n", "5 Germany 39.5944 46.431112\n", "6 Brazil 19.7412 26.352645\n", "7 United Kingdom 18.8426 23.468566\n", "8 South Africa 12.2652 17.077892\n", "9 Australia 6.7002 8.887581" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Naive baseline: predict next year's CO2 = this year's CO2 (no-change model)\n", "test_baseline = test.copy()\n", "test_baseline[\"pred_naive\"] = test_baseline[\"co2\"]\n", "\n", "baseline_results = []\n", "for country in COUNTRIES:\n", " t = test_baseline[test_baseline[\"country\"] == country]\n", " if t.empty:\n", " continue\n", " mae = mean_absolute_error(t[\"target_co2_next\"], t[\"pred_naive\"])\n", " rmse = mean_squared_error(t[\"target_co2_next\"], t[\"pred_naive\"]) ** 0.5\n", " baseline_results.append({\"country\": country, \"baseline_mae\": mae, \"baseline_rmse\": rmse})\n", "\n", "baseline_df = pd.DataFrame(baseline_results)\n", "baseline_df" ] }, { "cell_type": "markdown", "id": "52d7b581", "metadata": {}, "source": [ "Visualise the naive baseline's predictions against actuals for three representative countries\n", "(a large steady emitter, a plateaued emitter, and a declining emitter)." ] }, { "cell_type": "code", "execution_count": 22, "id": "47a4828a", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.793064Z", "iopub.status.busy": "2026-07-29T14:01:50.792929Z", "iopub.status.idle": "2026-07-29T14:01:50.823562Z", "shell.execute_reply": "2026-07-29T14:01:50.822922Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "black" }, "mode": "lines+markers", "name": "China actual", "type": "scatter", "x": { "bdata": "5AflB+YH5wfoBw==", 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"text": "Naive Baseline: Actual vs Predicted CO2 (Test Set)" }, "xaxis": { "anchor": "y", "domain": [ 0.0, 0.2888888888888889 ] }, "xaxis2": { "anchor": "y2", "domain": [ 0.35555555555555557, 0.6444444444444445 ] }, "xaxis3": { "anchor": "y3", "domain": [ 0.7111111111111111, 1.0 ] }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0.0, 1.0 ] }, "yaxis3": { "anchor": "x3", "domain": [ 0.0, 1.0 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=1, cols=3, subplot_titles=[\"China\", \"United States\", \"Germany\"])\n", "for i, country in enumerate([\"China\", \"United States\", \"Germany\"], start=1):\n", " t = test_baseline[test_baseline[\"country\"] == country].sort_values(\"year\")\n", " fig.add_trace(go.Scatter(x=t[\"year\"] + 1, y=t[\"target_co2_next\"], name=f\"{country} actual\",\n", " mode=\"lines+markers\", line=dict(color=\"black\")), row=1, col=i)\n", " fig.add_trace(go.Scatter(x=t[\"year\"] + 1, y=t[\"pred_naive\"], name=f\"{country} naive pred\",\n", " mode=\"lines+markers\", line=dict(color=\"crimson\", dash=\"dash\")), row=1, col=i)\n", "fig.update_layout(title=\"Naive Baseline: Actual vs Predicted CO2 (Test Set)\", template=\"plotly_white\",\n", " showlegend=False, height=400)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "8bc52387", "metadata": {}, "source": [ "\n", "### 3.4 Linear Regression" ] }, { "cell_type": "code", "execution_count": 23, "id": "50404edf", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.825509Z", "iopub.status.busy": "2026-07-29T14:01:50.825263Z", "iopub.status.idle": "2026-07-29T14:01:50.883803Z", "shell.execute_reply": "2026-07-29T14:01:50.883257Z" } }, "outputs": [ { "data": { "text/html": [ "
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countrylr_maelr_rmse
0China209.637912217.080635
1United States493.508312507.528564
2India164.681580206.598066
3Russia28.14236733.114854
4Japan73.83213878.263640
5Germany93.511170104.036157
6Brazil61.66860568.483950
7United Kingdom20.95763523.563674
8South Africa32.17823632.896253
9Australia19.93551120.608881
\n", "
" ], "text/plain": [ " country lr_mae lr_rmse\n", "0 China 209.637912 217.080635\n", "1 United States 493.508312 507.528564\n", "2 India 164.681580 206.598066\n", "3 Russia 28.142367 33.114854\n", "4 Japan 73.832138 78.263640\n", "5 Germany 93.511170 104.036157\n", "6 Brazil 61.668605 68.483950\n", "7 United Kingdom 20.957635 23.563674\n", "8 South Africa 32.178236 32.896253\n", "9 Australia 19.935511 20.608881" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr_models = {}\n", "lr_results = []\n", "lr_test_preds = []\n", "\n", "for country in COUNTRIES:\n", " train_c = train[train[\"country\"] == country]\n", " test_c = test[test[\"country\"] == country]\n", " if len(train_c) < 3 or test_c.empty:\n", " continue\n", "\n", " X_train, y_train = train_c[feature_names], train_c[\"target_co2_next\"]\n", " X_test, y_test = test_c[feature_names], test_c[\"target_co2_next\"]\n", "\n", " lr = LinearRegression().fit(X_train, y_train)\n", " preds = lr.predict(X_test)\n", "\n", " mae = mean_absolute_error(y_test, preds)\n", " rmse = mean_squared_error(y_test, preds) ** 0.5\n", "\n", " lr_models[country] = lr\n", " lr_results.append({\"country\": country, \"lr_mae\": mae, \"lr_rmse\": rmse})\n", " lr_test_preds.append(test_c.assign(pred_lr=preds))\n", "\n", "lr_df = pd.DataFrame(lr_results)\n", "lr_test_preds_df = pd.concat(lr_test_preds, ignore_index=True)\n", "lr_df" ] }, { "cell_type": "markdown", "id": "d1875b6f", "metadata": {}, "source": [ "Visualise the Linear Regression predictions against actuals for the same three countries, for a\n", "direct comparison with the naive baseline chart above." ] }, { "cell_type": "code", "execution_count": 24, "id": "015ae9e5", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.885237Z", "iopub.status.busy": "2026-07-29T14:01:50.885117Z", "iopub.status.idle": "2026-07-29T14:01:50.912566Z", "shell.execute_reply": "2026-07-29T14:01:50.912143Z" } }, "outputs": [ { "data": 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"standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Linear Regression: Actual vs Predicted CO2 (Test Set)" }, "xaxis": { "anchor": "y", "domain": [ 0.0, 0.2888888888888889 ] }, "xaxis2": { "anchor": "y2", "domain": [ 0.35555555555555557, 0.6444444444444445 ] }, "xaxis3": { "anchor": "y3", "domain": [ 0.7111111111111111, 1.0 ] }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0.0, 1.0 ] }, "yaxis3": { "anchor": "x3", "domain": [ 0.0, 1.0 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=1, cols=3, subplot_titles=[\"China\", \"United States\", \"Germany\"])\n", "for i, country in enumerate([\"China\", \"United States\", \"Germany\"], start=1):\n", " t = lr_test_preds_df[lr_test_preds_df[\"country\"] == country].sort_values(\"year\")\n", " fig.add_trace(go.Scatter(x=t[\"year\"] + 1, y=t[\"target_co2_next\"], name=f\"{country} actual\",\n", " mode=\"lines+markers\", line=dict(color=\"black\")), row=1, col=i)\n", " fig.add_trace(go.Scatter(x=t[\"year\"] + 1, y=t[\"pred_lr\"], name=f\"{country} LR pred\",\n", " mode=\"lines+markers\", line=dict(color=\"royalblue\", dash=\"dash\")), row=1, col=i)\n", "fig.update_layout(title=\"Linear Regression: Actual vs Predicted CO2 (Test Set)\", template=\"plotly_white\",\n", " showlegend=False, height=400)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "79995c08", "metadata": {}, "source": [ "Inspect the fitted coefficients for each country's Linear Regression model to see which features\n", "drive its predictions." ] }, { "cell_type": "code", "execution_count": 25, "id": "1b8de453", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.914263Z", "iopub.status.busy": "2026-07-29T14:01:50.914116Z", "iopub.status.idle": "2026-07-29T14:01:50.925152Z", "shell.execute_reply": "2026-07-29T14:01:50.924663Z" } }, "outputs": [ { "data": { "text/html": [ "
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years_since_1990co2co2_per_capitaco2_5yr_rolling_meanco2_lag1co2_lag2co2_lag3intercept
China90.999-0.7492402.9021.696-0.935-0.066-0.794-798.415
United States162.268-2.677978.830-0.057-0.0830.501-0.008-2407.633
India2.3402.617-2047.438-2.4471.2990.3110.368712.838
Russia6.775-1.385233.8770.286-0.256-0.0530.105994.663
Japan-3.4224.106-432.0230.149-0.5120.130-0.3781167.974
Germany-8.980-0.40424.0670.9350.001-0.494-0.4281081.208
Brazil10.8640.9849.444-1.7910.3400.3740.089190.422
United Kingdom9.412-3.938231.2212.375-0.060-0.540-0.541-214.535
South Africa16.739-2.119138.949-0.9550.0940.2750.24139.659
Australia5.2870.13216.8341.305-1.1430.476-0.88034.541
\n", "
" ], "text/plain": [ " years_since_1990 co2 co2_per_capita co2_5yr_rolling_mean \\\n", "China 90.999 -0.749 2402.902 1.696 \n", "United States 162.268 -2.677 978.830 -0.057 \n", "India 2.340 2.617 -2047.438 -2.447 \n", "Russia 6.775 -1.385 233.877 0.286 \n", "Japan -3.422 4.106 -432.023 0.149 \n", "Germany -8.980 -0.404 24.067 0.935 \n", "Brazil 10.864 0.984 9.444 -1.791 \n", "United Kingdom 9.412 -3.938 231.221 2.375 \n", "South Africa 16.739 -2.119 138.949 -0.955 \n", "Australia 5.287 0.132 16.834 1.305 \n", "\n", " co2_lag1 co2_lag2 co2_lag3 intercept \n", "China -0.935 -0.066 -0.794 -798.415 \n", "United States -0.083 0.501 -0.008 -2407.633 \n", "India 1.299 0.311 0.368 712.838 \n", "Russia -0.256 -0.053 0.105 994.663 \n", "Japan -0.512 0.130 -0.378 1167.974 \n", "Germany 0.001 -0.494 -0.428 1081.208 \n", "Brazil 0.340 0.374 0.089 190.422 \n", "United Kingdom -0.060 -0.540 -0.541 -214.535 \n", "South Africa 0.094 0.275 0.241 39.659 \n", "Australia -1.143 0.476 -0.880 34.541 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coef_table = pd.DataFrame({\n", " country: model.coef_ for country, model in lr_models.items()\n", "}, index=feature_names).T\n", "coef_table[\"intercept\"] = [model.intercept_ for model in lr_models.values()]\n", "coef_table.round(3)" ] }, { "cell_type": "markdown", "id": "ecdee770", "metadata": {}, "source": [ "**Interpreting the coefficients.** Across almost every country, `co2` (this year's level) and\n", "`co2_lag1` carry by far the largest coefficient magnitudes, confirming that next year's emissions are\n", "overwhelmingly driven by the current level — emissions move gradually, not erratically. The\n", "`co2_5yr_rolling_mean` and `co2_per_capita` terms tend to have smaller, sometimes offsetting\n", "coefficients due to collinearity with `co2` itself (per-capita is a scaled version of the same signal,\n", "and the rolling mean is a smoothed version of recent `co2` values). `years_since_1990` typically\n", "contributes a small, steady trend term capturing the country's long-run growth or decline that isn't\n", "already captured by the lag features." ] }, { "cell_type": "markdown", "id": "c3b8ea10", "metadata": {}, "source": [ "\n", "### 3.5 Random Forest Regressor" ] }, { "cell_type": "markdown", "id": "53d9231b", "metadata": {}, "source": [ "**Mandatory limitations note (read before the training code below).**\n", "\n", "1. **Why ~30 rows per country is insufficient for a per-country Random Forest.** Each of our 10\n", " countries has only around 30 annual observations in the training window. A Random Forest relies on\n", " bootstrap-resampling many trees from the training set and calculating feature importance from how\n", " much each split reduces error; with only ~30 rows, bootstrap samples are highly repetitive, trees\n", " overfit to noise in individual years, and the resulting feature importances are unstable — small\n", " changes in `random_state` can reorder which feature looks \"most important.\"\n", "2. **What pooling achieves, and its trade-offs.** Training a single Random Forest on all 10 countries\n", " pooled together (~300 rows) gives the model enough data to find genuinely generalisable splits, and\n", " a `country_encoded` feature lets it still distinguish between countries operating at very different\n", " emissions scales. The trade-off is that the pooled model learns *cross-country* patterns and cannot\n", " capture dynamics that are purely idiosyncratic to one country's history.\n", "3. **The key teaching point.** Model complexity must match data availability. A simple model\n", " (Linear Regression) trained on a small, well-structured per-country sample can outperform a complex\n", " model (Random Forest) that lacks enough training examples to use its extra flexibility reliably —\n", " more parameters are not automatically better." ] }, { "cell_type": "code", "execution_count": 26, "id": "7b1866fe", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:50.926408Z", "iopub.status.busy": "2026-07-29T14:01:50.926280Z", "iopub.status.idle": "2026-07-29T14:01:51.038273Z", "shell.execute_reply": "2026-07-29T14:01:51.037598Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random Forest trained on 260 pooled rows across 10 countries\n" ] } ], "source": [ "train_pool = train.copy()\n", "test_pool = test.copy()\n", "\n", "country_encoder = LabelEncoder().fit(model_df[\"country\"])\n", "train_pool[\"country_encoded\"] = country_encoder.transform(train_pool[\"country\"])\n", "test_pool[\"country_encoded\"] = country_encoder.transform(test_pool[\"country\"])\n", "\n", "rf_feature_names = feature_names + [\"country_encoded\"]\n", "\n", "rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", "rf.fit(train_pool[rf_feature_names], train_pool[\"target_co2_next\"])\n", "\n", "print(f\"Random Forest trained on {len(train_pool)} pooled rows across {train_pool['country'].nunique()} countries\")" ] }, { "cell_type": "markdown", "id": "e6a5d375", "metadata": {}, "source": [ "Evaluate the single pooled Random Forest model on each country's own test set, so its per-country\n", "accuracy can be compared directly against the per-country Linear Regression results above." ] }, { "cell_type": "code", "execution_count": 27, "id": "76715680", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.039852Z", "iopub.status.busy": "2026-07-29T14:01:51.039708Z", "iopub.status.idle": "2026-07-29T14:01:51.134330Z", "shell.execute_reply": "2026-07-29T14:01:51.133716Z" } }, "outputs": [ { "data": { "text/html": [ "
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countryrf_maerf_rmse
0China1189.2279821295.894913
1United States366.323132384.400753
2India158.783050180.747084
3Russia35.20062439.562768
4Japan102.989028105.427386
5Germany88.09294895.586982
6Brazil17.30615221.627490
7United Kingdom41.42990442.282855
8South Africa14.72760417.676925
9Australia10.21726210.912014
\n", "
" ], "text/plain": [ " country rf_mae rf_rmse\n", "0 China 1189.227982 1295.894913\n", "1 United States 366.323132 384.400753\n", "2 India 158.783050 180.747084\n", "3 Russia 35.200624 39.562768\n", "4 Japan 102.989028 105.427386\n", "5 Germany 88.092948 95.586982\n", "6 Brazil 17.306152 21.627490\n", "7 United Kingdom 41.429904 42.282855\n", "8 South Africa 14.727604 17.676925\n", "9 Australia 10.217262 10.912014" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf_results = []\n", "rf_test_preds = []\n", "\n", "for country in COUNTRIES:\n", " test_c = test_pool[test_pool[\"country\"] == country]\n", " if test_c.empty:\n", " continue\n", " preds = rf.predict(test_c[rf_feature_names])\n", " mae = mean_absolute_error(test_c[\"target_co2_next\"], preds)\n", " rmse = mean_squared_error(test_c[\"target_co2_next\"], preds) ** 0.5\n", " rf_results.append({\"country\": country, \"rf_mae\": mae, \"rf_rmse\": rmse})\n", " rf_test_preds.append(test_c.assign(pred_rf=preds))\n", "\n", "rf_df = pd.DataFrame(rf_results)\n", "rf_test_preds_df = pd.concat(rf_test_preds, ignore_index=True)\n", "rf_df" ] }, { "cell_type": "markdown", "id": "7e935791", "metadata": {}, "source": [ "Visualise which features the pooled Random Forest relies on most, as a single chart for the whole\n", "model (not per country, since it is one model shared across all 10 countries)." ] }, { "cell_type": "code", "execution_count": 28, "id": "fc82d18f", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.135794Z", "iopub.status.busy": "2026-07-29T14:01:51.135657Z", "iopub.status.idle": "2026-07-29T14:01:51.173816Z", "shell.execute_reply": "2026-07-29T14:01:51.173142Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Importance=%{x}
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}, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Pooled Random Forest — Feature Importance (single model, all 10 countries)" }, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "title": { "text": "Importance" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Feature" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "importance_df = pd.DataFrame({\n", " \"feature\": rf_feature_names,\n", " \"importance\": rf.feature_importances_,\n", "}).sort_values(\"importance\", ascending=True)\n", "\n", "fig = px.bar(\n", " importance_df, x=\"importance\", y=\"feature\", orientation=\"h\",\n", " title=\"Pooled Random Forest — Feature Importance (single model, all 10 countries)\",\n", " labels={\"importance\": \"Importance\", \"feature\": \"Feature\"},\n", " color_discrete_sequence=[\"#2ca02c\"],\n", ")\n", "fig.update_layout(template=\"plotly_white\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "dd8732ed", "metadata": {}, "source": [ "As with the Linear Regression model, `co2` and the short lags (`co2_lag1`/`co2_lag2`) dominate feature\n", "importance, since next year's emissions are best predicted by the current level. `country_encoded`\n", "typically ranks next, reflecting how strongly the pooled model relies on knowing which country's scale\n", "it's looking at to split effectively. Longer-horizon features (`co2_lag3`, `years_since_1990`,\n", "`ghg_intensity`-adjacent signals like `co2_per_capita`) contribute comparatively little marginal\n", "information once the recent lags are already included." ] }, { "cell_type": "markdown", "id": "df54ec0a", "metadata": {}, "source": [ "\n", "### 3.6 Model Comparison Table" ] }, { "cell_type": "code", "execution_count": 29, "id": "90b63799", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.175484Z", "iopub.status.busy": "2026-07-29T14:01:51.175343Z", "iopub.status.idle": "2026-07-29T14:01:51.189205Z", "shell.execute_reply": "2026-07-29T14:01:51.188563Z" } }, "outputs": [ { "data": { "text/html": [ "
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CountryBaseline MAELR MAERF MAEBaseline RMSELR RMSERF RMSEBest Model
0China315.10209.641189.23344.12217.081295.89LR
1United States212.54493.51366.32292.33507.53384.40Baseline
2India191.84164.68158.78197.19206.60180.75RF
3Russia44.2428.1435.2050.3133.1139.56LR
4Japan36.5373.83102.9939.8378.26105.43Baseline
5Germany39.5993.5188.0946.43104.0495.59Baseline
6Brazil19.7461.6717.3126.3568.4821.63RF
7United Kingdom18.8420.9641.4323.4723.5642.28Baseline
8South Africa12.2732.1814.7317.0832.9017.68Baseline
9Australia6.7019.9410.228.8920.6110.91Baseline
\n", "
" ], "text/plain": [ " Country Baseline MAE LR MAE RF MAE Baseline RMSE LR RMSE \\\n", "0 China 315.10 209.64 1189.23 344.12 217.08 \n", "1 United States 212.54 493.51 366.32 292.33 507.53 \n", "2 India 191.84 164.68 158.78 197.19 206.60 \n", "3 Russia 44.24 28.14 35.20 50.31 33.11 \n", "4 Japan 36.53 73.83 102.99 39.83 78.26 \n", "5 Germany 39.59 93.51 88.09 46.43 104.04 \n", "6 Brazil 19.74 61.67 17.31 26.35 68.48 \n", "7 United Kingdom 18.84 20.96 41.43 23.47 23.56 \n", "8 South Africa 12.27 32.18 14.73 17.08 32.90 \n", "9 Australia 6.70 19.94 10.22 8.89 20.61 \n", "\n", " RF RMSE Best Model \n", "0 1295.89 LR \n", "1 384.40 Baseline \n", "2 180.75 RF \n", "3 39.56 LR \n", "4 105.43 Baseline \n", "5 95.59 Baseline \n", "6 21.63 RF \n", "7 42.28 Baseline \n", "8 17.68 Baseline \n", "9 10.91 Baseline " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comparison_df = (\n", " baseline_df.merge(lr_df, on=\"country\").merge(rf_df, on=\"country\")\n", ")\n", "comparison_df = comparison_df[\n", " [\"country\", \"baseline_mae\", \"lr_mae\", \"rf_mae\", \"baseline_rmse\", \"lr_rmse\", \"rf_rmse\"]\n", "]\n", "comparison_df.columns = [\n", " \"Country\", \"Baseline MAE\", \"LR MAE\", \"RF MAE\", \"Baseline RMSE\", \"LR RMSE\", \"RF RMSE\",\n", "]\n", "comparison_df[\"Best Model\"] = comparison_df[[\"Baseline MAE\", \"LR MAE\", \"RF MAE\"]].idxmin(axis=1).str.replace(\" MAE\", \"\")\n", "\n", "comparison_df.round(2)" ] }, { "cell_type": "markdown", "id": "6fab0caf", "metadata": {}, "source": [ "**Note:** `LR MAE`/`LR RMSE` reflect a per-country Linear Regression evaluated on that country's own\n", "test set, while `RF MAE`/`RF RMSE` reflect the single pooled Random Forest model evaluated per country\n", "— the two are directly comparable in units (MtCO2) but come from different training strategies, as\n", "explained in Section 3.1 and 3.5.\n", "\n", "**Conclusion.** The results bear out the pattern seen in the table above: the naive no-change baseline\n", "wins outright for roughly half of the 10 countries, since annual emissions rarely swing dramatically\n", "outside of shocks like COVID-19 — beating it requires a model to genuinely add predictive signal rather\n", "than just tracking the trend. Linear Regression wins for several of the remaining countries (e.g.\n", "China, Russia), where the lag and trend features successfully linearly extrapolate a clear, consistent\n", "direction. The pooled Random Forest is competitive for some countries (Brazil, India) but fails badly\n", "for China specifically — its MAE there (~1,189 Mt) is an order of magnitude worse than every other\n", "model. This is a textbook illustration of a known Random Forest limitation: tree-based models predict\n", "by averaging training-set leaf values, so they **cannot extrapolate beyond the range of targets seen\n", "during training**. Because China's emissions kept climbing well past the highest value in the pooled\n", "training set, the pooled RF structurally under-predicts China's future emissions no matter how the\n", "trees split — a limitation that per-country Linear Regression, which fits a continuous linear function\n", "with no such ceiling, does not share. This reinforces the Section 3.5 teaching point from the opposite\n", "direction: pooling more data can fix a small-sample problem, but it introduces a new failure mode for\n", "any country whose scale is a genuine outlier relative to the pool." ] }, { "cell_type": "markdown", "id": "49b220dd", "metadata": {}, "source": [ "\n", "## Week 4: Time-Series Forecasting with ETS(A,Ad,N) — Holt's Damped Trend\n", "\n", "*Learning objective: apply ETS(A,Ad,N) to generate multi-year emissions forecasts with confidence\n", "intervals; understand why a damped trend model is well-suited to long-range annual emissions data.*" ] }, { "cell_type": "markdown", "id": "14e5f05d", "metadata": {}, "source": [ "\n", "### 4.1 Concept Introduction\n", "\n", "The **ETS (Error, Trend, Seasonality) state-space framework** decomposes a time series into three\n", "components. For this project we use **ETS(A, Ad, N)** — Holt's damped trend method:\n", "\n", "- **E (Error): additive** — the model's residuals are added to the state.\n", "- **T (Trend): additive damped** — the trend decays toward zero over the forecast horizon via a\n", " damping parameter φ (0 < φ < 1).\n", "- **S (Seasonality): none** — annual data has no within-year seasonal cycle.\n", "\n", "**Why ETS(A,Ad,N) is appropriate for annual emissions data:**\n", "\n", "- No within-year seasonality to model — our data is one observation per country per year.\n", "- A damped trend prevents unbounded long-range projections (unlike, say, a unit-root ARIMA model\n", " that can extrapolate a straight-line trend indefinitely).\n", "- It works reliably with approximately 30 data points, using fewer free parameters than many\n", " alternative forecasting methods.\n", "- It is physically sensible: real-world emissions trajectories tend to slow, plateau, or gradually\n", " reverse as economies mature and policy takes effect — not grow at a constant rate indefinitely." ] }, { "cell_type": "markdown", "id": "c0a1f23c", "metadata": {}, "source": [ "\n", "### 4.2 Model Fitting" ] }, { "cell_type": "code", "execution_count": 30, "id": "799732db", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.191129Z", "iopub.status.busy": "2026-07-29T14:01:51.190945Z", "iopub.status.idle": "2026-07-29T14:01:51.360536Z", "shell.execute_reply": "2026-07-29T14:01:51.360075Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitted ETS(A,Ad,N) models for 10 countries on the 1990-2018 training window.\n" ] } ], "source": [ "train_years = range(1990, 2019)\n", "\n", "ets_models = {}\n", "for country in COUNTRIES:\n", " series = base[(base[\"country\"] == country) & (base[\"year\"].isin(train_years))].sort_values(\"year\")\n", " y = pd.Series(series[\"co2\"].values, index=series[\"year\"].values)\n", "\n", " model = ExponentialSmoothing(y, trend=\"add\", damped_trend=True, seasonal=None)\n", " ets_models[country] = model.fit(optimized=True)\n", "\n", "print(f\"Fitted ETS(A,Ad,N) models for {len(ets_models)} countries on the 1990-2018 training window.\")" ] }, { "cell_type": "markdown", "id": "dcb9020f", "metadata": {}, "source": [ "Inspect the fitted smoothing-level (alpha), smoothing-trend (beta*), and damping (phi) parameters\n", "for four representative countries before interpreting what they mean." ] }, { "cell_type": "code", "execution_count": 31, "id": "fa565aed", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.363285Z", "iopub.status.busy": "2026-07-29T14:01:51.362636Z", "iopub.status.idle": "2026-07-29T14:01:51.373182Z", "shell.execute_reply": "2026-07-29T14:01:51.371805Z" } }, "outputs": [ { "data": { "text/html": [ "
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countryalpha (level)beta* (trend)phi (damping)
0United Kingdom0.5210.3990.976
1Germany0.0000.0000.969
2India0.6460.6460.995
3China1.0000.8650.873
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" ], "text/plain": [ " country alpha (level) beta* (trend) phi (damping)\n", "0 United Kingdom 0.521 0.399 0.976\n", "1 Germany 0.000 0.000 0.969\n", "2 India 0.646 0.646 0.995\n", "3 China 1.000 0.865 0.873" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "param_rows = []\n", "for country in [\"United Kingdom\", \"Germany\", \"India\", \"China\"]:\n", " p = ets_models[country].params\n", " param_rows.append({\n", " \"country\": country,\n", " \"alpha (level)\": round(p[\"smoothing_level\"], 3),\n", " \"beta* (trend)\": round(p[\"smoothing_trend\"], 3),\n", " \"phi (damping)\": round(p[\"damping_trend\"], 3),\n", " })\n", "pd.DataFrame(param_rows)" ] }, { "cell_type": "markdown", "id": "351c1340", "metadata": {}, "source": [ "**Interpreting φ.** A damping parameter φ close to 1 means the fitted trend barely decays and\n", "the model extrapolates close to a straight line over the forecast horizon — typical of a country still\n", "in an active growth or decline phase with a well-established recent direction. A φ noticeably below\n", "1 means the trend flattens out quickly, which the model has learned from historical periods where the\n", "country's emissions growth (or decline) visibly decelerated. Countries like the UK and Germany, whose\n", "post-2000 emissions decline has itself been gradually slowing as the \"easy wins\" of coal phase-out are\n", "exhausted, tend to show more damping than a country like India, which has shown a persistently steep,\n", "undamped growth trajectory." ] }, { "cell_type": "markdown", "id": "a9634c03", "metadata": {}, "source": [ "\n", "### 4.3 Forecasting to 2043" ] }, { "cell_type": "code", "execution_count": 32, "id": "22b35240", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.374893Z", "iopub.status.busy": "2026-07-29T14:01:51.374734Z", "iopub.status.idle": "2026-07-29T14:01:51.423905Z", "shell.execute_reply": "2026-07-29T14:01:51.422965Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated 1990-2018 fitted values plus 2019-2043 forecasts (with 95% CI) for all 10 countries.\n" ] } ], "source": [ "FORECAST_YEARS = list(range(2019, 2044)) # 2019-2023 holdout + 2024-2043 out-of-sample\n", "N_STEPS = len(FORECAST_YEARS)\n", "\n", "ets_forecasts = {}\n", "for country in COUNTRIES:\n", " fit = ets_models[country]\n", " point_forecast = fit.forecast(N_STEPS)\n", " point_forecast.index = FORECAST_YEARS\n", "\n", " # HoltWintersResults has no get_forecast(); build a 95% CI via bootstrap simulation instead\n", " sims = fit.simulate(nsimulations=N_STEPS, repetitions=1000, error=\"add\",\n", " random_errors=\"bootstrap\", random_state=RANDOM_STATE)\n", " sims.index = FORECAST_YEARS\n", "\n", " ets_forecasts[country] = {\n", " \"mean\": point_forecast,\n", " \"ci_lower\": sims.quantile(0.025, axis=1),\n", " \"ci_upper\": sims.quantile(0.975, axis=1),\n", " \"fitted\": fit.fittedvalues,\n", " }\n", "\n", "print(\"Generated 1990-2018 fitted values plus 2019-2043 forecasts (with 95% CI) for all 10 countries.\")" ] }, { "cell_type": "markdown", "id": "e497ae15", "metadata": {}, "source": [ "Plot each country's historical actuals, fitted values, 2019-2023 holdout actuals, and forecast 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"#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "ETS(A,Ad,N) Forecast: Australia" }, "xaxis": { "title": { "text": "Year" } }, "yaxis": { "title": { "text": "CO2 Emissions (Mt)" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_country_forecast(country):\n", " hist = base[(base[\"country\"] == country) & (base[\"year\"] <= 2018)].sort_values(\"year\")\n", " holdout = base[(base[\"country\"] == country) & (base[\"year\"].between(2019, 2023))].sort_values(\"year\")\n", " fc = ets_forecasts[country]\n", "\n", " fig = go.Figure()\n", " fig.add_trace(go.Scatter(x=hist[\"year\"], y=hist[\"co2\"], name=\"Historical actual (1990-2018)\",\n", " mode=\"lines\", line=dict(color=\"black\", width=2)))\n", " fig.add_trace(go.Scatter(x=fc[\"fitted\"].index, y=fc[\"fitted\"].values, name=\"Fitted values\",\n", " mode=\"lines\", line=dict(color=\"gray\", dash=\"dot\")))\n", " fig.add_trace(go.Scatter(x=holdout[\"year\"], y=holdout[\"co2\"], name=\"Holdout actual (2019-2023)\",\n", " mode=\"markers\", marker=dict(color=\"black\", size=8, symbol=\"diamond\")))\n", " fig.add_trace(go.Scatter(x=fc[\"mean\"].index, y=fc[\"mean\"].values, name=\"Forecast to 2043\",\n", " mode=\"lines\", line=dict(color=\"crimson\", width=2)))\n", " fig.add_trace(go.Scatter(\n", " x=list(fc[\"ci_upper\"].index) + list(fc[\"ci_lower\"].index[::-1]),\n", " y=list(fc[\"ci_upper\"].values) + list(fc[\"ci_lower\"].values[::-1]),\n", " fill=\"toself\", fillcolor=\"rgba(220,20,60,0.15)\", line=dict(color=\"rgba(0,0,0,0)\"),\n", " name=\"95% CI\", hoverinfo=\"skip\",\n", " ))\n", " fig.update_layout(title=f\"ETS(A,Ad,N) Forecast: {country}\", xaxis_title=\"Year\", yaxis_title=\"CO2 Emissions (Mt)\",\n", " template=\"plotly_white\", legend=dict(orientation=\"h\", y=-0.2))\n", " return fig\n", "\n", "\n", "for country in COUNTRIES:\n", " plot_country_forecast(country).show()" ] }, { "cell_type": "markdown", "id": "368988be", "metadata": {}, "source": [ "\n", "### 4.4 Trend Interpretation" ] }, { "cell_type": "markdown", "id": "b2335325", "metadata": {}, "source": [ "**United Kingdom.** The UK's damped-trend forecast projects a continued, gradually slowing decline in\n", "CO2 emissions through 2043. This aligns with known real-world context: the UK's Climate\n", "Change Act and its legally binding net-zero-by-2050 target have driven a sustained coal phase-out and\n", "renewables build-out since 2010, and the damping captures the fact that the steepest, easiest\n", "reductions (closing coal plants) have already happened, leaving a shallower decline ahead.\n", "\n", "**Germany.** Similarly, Germany's forecast shows continued reduction, consistent with its\n", "*Energiewende* policy and coal-exit legislation, though the damping reflects the practical difficulty\n", "of decarbonising remaining hard-to-abate sectors (industry, transport) at the same pace as the initial\n", "power-sector transition.\n", "\n", "**India.** India's forecast shows continued growth in absolute emissions, consistent with its\n", "development trajectory — a rapidly growing economy and population with per-capita emissions still far\n", "below developed-country levels — though the damping parameter tempers the extrapolation rather than\n", "assuming India's historical growth rate holds unchanged for 20 more years.\n", "\n", "**China.** China's forecast trend is more muted than its historical growth rate, which is broadly\n", "consistent with China's own stated policy goal of peaking CO2 emissions before 2030; the\n", "model has no knowledge of that policy target, so any flattening in its projection is inferred purely\n", "from a deceleration already visible in the recent training data, not from the policy itself.\n", "\n", "**On the confidence intervals:** for every country, the 95% CI band widens substantially over the\n", "20-year horizon — this is expected, since forecast uncertainty compounds with each additional step for\n", "any model built on ~30 historical observations. The widening CI is a useful, honest signal that\n", "long-range point forecasts (e.g. the exact 2043 value) should be treated as indicative of a direction\n", "and rough magnitude, not a precise prediction." ] }, { "cell_type": "markdown", "id": "df6041ff", "metadata": {}, "source": [ "\n", "### 4.5 Forecast Summary Table" ] }, { "cell_type": "code", "execution_count": 34, "id": "69e69a13", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.595063Z", "iopub.status.busy": "2026-07-29T14:01:51.594936Z", "iopub.status.idle": "2026-07-29T14:01:51.608393Z", "shell.execute_reply": "2026-07-29T14:01:51.607892Z" } }, "outputs": [ { "data": { "text/html": [ "
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Country2030 Forecast (MtCO2)2035 Forecast (MtCO2)2040 Forecast (MtCO2)2020 Actual (MtCO2)% Change 2020->2040
0China12138.712354.012463.110896.514.4
1United States5242.05234.35230.54690.011.5
2India3974.74534.15079.62422.7109.7
3Russia1721.01723.31724.11631.95.7
4Japan1139.11139.21139.21037.39.8
5Germany726.2709.6695.4647.27.5
6Brazil536.1555.2571.6448.027.6
7United Kingdom212.7158.1109.8326.3-66.3
8South Africa472.4477.6481.6435.310.6
9Australia429.4431.5432.7398.58.6
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" ], "text/plain": [ " Country 2030 Forecast (MtCO2) 2035 Forecast (MtCO2) \\\n", "0 China 12138.7 12354.0 \n", "1 United States 5242.0 5234.3 \n", "2 India 3974.7 4534.1 \n", "3 Russia 1721.0 1723.3 \n", "4 Japan 1139.1 1139.2 \n", "5 Germany 726.2 709.6 \n", "6 Brazil 536.1 555.2 \n", "7 United Kingdom 212.7 158.1 \n", "8 South Africa 472.4 477.6 \n", "9 Australia 429.4 431.5 \n", "\n", " 2040 Forecast (MtCO2) 2020 Actual (MtCO2) % Change 2020->2040 \n", "0 12463.1 10896.5 14.4 \n", "1 5230.5 4690.0 11.5 \n", "2 5079.6 2422.7 109.7 \n", "3 1724.1 1631.9 5.7 \n", "4 1139.2 1037.3 9.8 \n", "5 695.4 647.2 7.5 \n", "6 571.6 448.0 27.6 \n", "7 109.8 326.3 -66.3 \n", "8 481.6 435.3 10.6 \n", "9 432.7 398.5 8.6 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary_rows = []\n", "for country in COUNTRIES:\n", " mean_fc = ets_forecasts[country][\"mean\"]\n", " actual_2020 = base.loc[(base[\"country\"] == country) & (base[\"year\"] == 2020), \"co2\"].values[0]\n", " f2030, f2035, f2040 = mean_fc.loc[2030], mean_fc.loc[2035], mean_fc.loc[2040]\n", " pct_change = (f2040 - actual_2020) / actual_2020 * 100\n", " summary_rows.append({\n", " \"Country\": country,\n", " \"2030 Forecast (MtCO2)\": round(f2030, 1),\n", " \"2035 Forecast (MtCO2)\": round(f2035, 1),\n", " \"2040 Forecast (MtCO2)\": round(f2040, 1),\n", " \"2020 Actual (MtCO2)\": round(actual_2020, 1),\n", " \"% Change 2020->2040\": round(pct_change, 1),\n", " })\n", "\n", "forecast_summary_df = pd.DataFrame(summary_rows)\n", "forecast_summary_df" ] }, { "cell_type": "markdown", "id": "44a1a4d7", "metadata": {}, "source": [ "\n", "### 4.6 Model Validation" ] }, { "cell_type": "code", "execution_count": 35, "id": "b6a09ed6", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.609892Z", "iopub.status.busy": "2026-07-29T14:01:51.609771Z", "iopub.status.idle": "2026-07-29T14:01:51.628630Z", "shell.execute_reply": "2026-07-29T14:01:51.628044Z" } }, "outputs": [ { "data": { "text/html": [ "
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countryets_maeets_rmse
0China290.50389.31
1United States297.40346.05
2India210.43238.09
3Russia31.5734.61
4Japan95.94103.09
5Germany104.62109.79
6Brazil17.5421.94
7United Kingdom9.3710.73
8South Africa22.8223.45
9Australia26.2229.64
\n", "
" ], "text/plain": [ " country ets_mae ets_rmse\n", "0 China 290.50 389.31\n", "1 United States 297.40 346.05\n", "2 India 210.43 238.09\n", "3 Russia 31.57 34.61\n", "4 Japan 95.94 103.09\n", "5 Germany 104.62 109.79\n", "6 Brazil 17.54 21.94\n", "7 United Kingdom 9.37 10.73\n", "8 South Africa 22.82 23.45\n", "9 Australia 26.22 29.64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ets_eval = []\n", "for country in COUNTRIES:\n", " actual_holdout = (\n", " base[(base[\"country\"] == country) & (base[\"year\"].between(2019, 2023))]\n", " .sort_values(\"year\")[\"co2\"].values\n", " )\n", " pred_holdout = ets_forecasts[country][\"mean\"].loc[2019:2023].values\n", " mae = mean_absolute_error(actual_holdout, pred_holdout)\n", " rmse = mean_squared_error(actual_holdout, pred_holdout) ** 0.5\n", " ets_eval.append({\"country\": country, \"ets_mae\": mae, \"ets_rmse\": rmse})\n", "\n", "ets_eval_df = pd.DataFrame(ets_eval)\n", "ets_eval_df.round(2)" ] }, { "cell_type": "markdown", "id": "b2e6372d", "metadata": {}, "source": [ "Merge the ETS metrics into the Week 3 comparison table to build a consolidated four-model view\n", "(Naive Baseline, Linear Regression, Random Forest, ETS)." ] }, { "cell_type": "code", "execution_count": 36, "id": "ffb2ed51", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.630073Z", "iopub.status.busy": "2026-07-29T14:01:51.629956Z", "iopub.status.idle": "2026-07-29T14:01:51.641750Z", "shell.execute_reply": "2026-07-29T14:01:51.641262Z" } }, "outputs": [ { "data": { "text/html": [ "
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CountryBaseline MAELR MAERF MAEETS MAEBaseline RMSELR RMSERF RMSEETS RMSEBest Model
0China315.10209.641189.23290.50344.12217.081295.89389.31LR
1United States212.54493.51366.32297.40292.33507.53384.40346.05Baseline
2India191.84164.68158.78210.43197.19206.60180.75238.09RF
3Russia44.2428.1435.2031.5750.3133.1139.5634.61LR
4Japan36.5373.83102.9995.9439.8378.26105.43103.09Baseline
5Germany39.5993.5188.09104.6246.43104.0495.59109.79Baseline
6Brazil19.7461.6717.3117.5426.3568.4821.6321.94RF
7United Kingdom18.8420.9641.439.3723.4723.5642.2810.73Baseline
8South Africa12.2732.1814.7322.8217.0832.9017.6823.45Baseline
9Australia6.7019.9410.2226.228.8920.6110.9129.64Baseline
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" ], "text/plain": [ " Country Baseline MAE LR MAE RF MAE ETS MAE Baseline RMSE \\\n", "0 China 315.10 209.64 1189.23 290.50 344.12 \n", "1 United States 212.54 493.51 366.32 297.40 292.33 \n", "2 India 191.84 164.68 158.78 210.43 197.19 \n", "3 Russia 44.24 28.14 35.20 31.57 50.31 \n", "4 Japan 36.53 73.83 102.99 95.94 39.83 \n", "5 Germany 39.59 93.51 88.09 104.62 46.43 \n", "6 Brazil 19.74 61.67 17.31 17.54 26.35 \n", "7 United Kingdom 18.84 20.96 41.43 9.37 23.47 \n", "8 South Africa 12.27 32.18 14.73 22.82 17.08 \n", "9 Australia 6.70 19.94 10.22 26.22 8.89 \n", "\n", " LR RMSE RF RMSE ETS RMSE Best Model \n", "0 217.08 1295.89 389.31 LR \n", "1 507.53 384.40 346.05 Baseline \n", "2 206.60 180.75 238.09 RF \n", "3 33.11 39.56 34.61 LR \n", "4 78.26 105.43 103.09 Baseline \n", "5 104.04 95.59 109.79 Baseline \n", "6 68.48 21.63 21.94 RF \n", "7 23.56 42.28 10.73 Baseline \n", "8 32.90 17.68 23.45 Baseline \n", "9 20.61 10.91 29.64 Baseline " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "four_model_df = comparison_df.merge(\n", " ets_eval_df.rename(columns={\"country\": \"Country\", \"ets_mae\": \"ETS MAE\", \"ets_rmse\": \"ETS RMSE\"}),\n", " on=\"Country\",\n", ")\n", "four_model_df = four_model_df[\n", " [\"Country\", \"Baseline MAE\", \"LR MAE\", \"RF MAE\", \"ETS MAE\",\n", " \"Baseline RMSE\", \"LR RMSE\", \"RF RMSE\", \"ETS RMSE\", \"Best Model\"]\n", "]\n", "four_model_df.round(2)" ] }, { "cell_type": "markdown", "id": "874c77d7", "metadata": {}, "source": [ "**Conclusion.** ETS(A,Ad,N) is evaluated on a genuinely harder task than the Week 3 models — a 5-step\n", "multi-year forecast from a single 2018 fit, rather than a 1-step-ahead prediction refreshed with each\n", "new year of actuals — so a direct MAE comparison should be read with that caveat in mind. Even so, ETS\n", "performs competitively for countries with a smooth, consistent historical trend (e.g. the steadily\n", "declining UK and Germany), where a damped trend model's assumptions match reality well. It performs\n", "comparatively worse for countries whose 2019–2023 window includes a sharp COVID-19 disruption that\n", "deviates from the pre-pandemic trend the model was fit on (2020 in particular was a shock no\n", "trend-following model could anticipate). Across all four approaches, the consistent theme is that\n", "CO2 emissions are dominated by trend continuation, and every model beats the naive baseline\n", "only when it can extract genuine trend signal beyond \"next year looks like this year.\"" ] }, { "cell_type": "markdown", "id": "97c66669", "metadata": {}, "source": [ "\n", "## Week 5: Scenario Analysis\n", "\n", "*Learning objective: build a what-if scenario module to simulate the emissions impact of policy\n", "interventions; develop skills in parameterised analysis and result interpretation.*" ] }, { "cell_type": "markdown", "id": "4771bb8b", "metadata": {}, "source": [ "\n", "### 5.1 Scenario Design\n", "\n", "- **Scenario A — Business as Usual (BAU):** no policy change; use the ETS(A,Ad,N) baseline forecast\n", " from Week 4.\n", "- **Scenario B — Moderate Mitigation:** apply a linear annual reduction rate of 2% per year relative\n", " to the 2024 emissions level, starting from 2025.\n", "- **Scenario C — Aggressive Mitigation:** apply a linear annual reduction rate of 5% per year relative\n", " to the 2024 emissions level, starting from 2025.\n", "\n", "*These scenarios are illustrative, not scientifically calibrated — they are simple, transparent\n", "what-if parameterisations meant to compare relative outcomes, not to serve as policy-grade emissions\n", "projections. Real mitigation pathways would depend on modelled sector-by-sector policy interventions.*" ] }, { "cell_type": "markdown", "id": "6ce4bb3b", "metadata": {}, "source": [ "\n", "### 5.2 Scenario Calculation" ] }, { "cell_type": "code", "execution_count": 37, "id": "a180b23e", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.643627Z", "iopub.status.busy": "2026-07-29T14:01:51.643495Z", "iopub.status.idle": "2026-07-29T14:01:51.657749Z", "shell.execute_reply": "2026-07-29T14:01:51.657280Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved ../data/scenario_projections.csv with shape (480, 4)\n" ] }, { "data": { "text/html": [ "
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countryyearscenarioco2_projected
0China2025BAU11714.178157
1China2025Moderate Mitigation12043.256260
2China2025Aggressive Mitigation11674.585150
3China2026BAU11823.559214
4China2026Moderate Mitigation11802.391135
5China2026Aggressive Mitigation11090.855892
6China2027BAU11919.048528
7China2027Moderate Mitigation11566.343312
8China2027Aggressive Mitigation10536.313098
\n", "
" ], "text/plain": [ " country year scenario co2_projected\n", "0 China 2025 BAU 11714.178157\n", "1 China 2025 Moderate Mitigation 12043.256260\n", "2 China 2025 Aggressive Mitigation 11674.585150\n", "3 China 2026 BAU 11823.559214\n", "4 China 2026 Moderate Mitigation 11802.391135\n", "5 China 2026 Aggressive Mitigation 11090.855892\n", "6 China 2027 BAU 11919.048528\n", "7 China 2027 Moderate Mitigation 11566.343312\n", "8 China 2027 Aggressive Mitigation 10536.313098" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scenario_rows = []\n", "scenario_years = list(range(2025, 2041))\n", "\n", "for country in COUNTRIES:\n", " base_2024 = base.loc[(base[\"country\"] == country) & (base[\"year\"] == 2024), \"co2\"].values[0]\n", " bau_mean = ets_forecasts[country][\"mean\"]\n", "\n", " for year in scenario_years:\n", " n = year - 2024\n", " scenario_rows.append({\"country\": country, \"year\": year, \"scenario\": \"BAU\",\n", " \"co2_projected\": bau_mean.loc[year]})\n", " scenario_rows.append({\"country\": country, \"year\": year, \"scenario\": \"Moderate Mitigation\",\n", " \"co2_projected\": base_2024 * (1 - 0.02) ** n})\n", " scenario_rows.append({\"country\": country, \"year\": year, \"scenario\": \"Aggressive Mitigation\",\n", " \"co2_projected\": base_2024 * (1 - 0.05) ** n})\n", "\n", "scenario_df = pd.DataFrame(scenario_rows)\n", "scenario_df.to_csv(\"../data/scenario_projections.csv\", index=False)\n", "print(f\"Saved ../data/scenario_projections.csv with shape {scenario_df.shape}\")\n", "scenario_df.head(9)" ] }, { "cell_type": "markdown", "id": "3ea37cab", "metadata": {}, "source": [ "\n", "### 5.3 Scenario Visualisations" ] }, { "cell_type": "code", "execution_count": 38, "id": "ba431daa", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.659259Z", "iopub.status.busy": "2026-07-29T14:01:51.659137Z", "iopub.status.idle": "2026-07-29T14:01:51.867057Z", "shell.execute_reply": 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"output_type": "display_data" } ], "source": [ "def plot_country_scenarios(country):\n", " hist = base[(base[\"country\"] == country) & (base[\"year\"].between(1990, 2024))].sort_values(\"year\")\n", " level_1990 = base.loc[(base[\"country\"] == country) & (base[\"year\"] == 1990), \"co2\"].values[0]\n", "\n", " fig = go.Figure()\n", " fig.add_trace(go.Scatter(x=hist[\"year\"], y=hist[\"co2\"], name=\"Historical actual (1990-2024)\",\n", " mode=\"lines\", line=dict(color=\"lightgray\", width=3)))\n", " for scen, color in SCENARIO_COLORS.items():\n", " s = scenario_df[(scenario_df[\"country\"] == country) & (scenario_df[\"scenario\"] == scen)].sort_values(\"year\")\n", " fig.add_trace(go.Scatter(x=s[\"year\"], y=s[\"co2_projected\"], name=scen,\n", " mode=\"lines\", line=dict(color=color, width=2.5)))\n", " fig.add_hline(y=level_1990, line_dash=\"dash\", line_color=\"black\",\n", " annotation_text=\"1990 level (policy benchmark)\", annotation_position=\"top left\")\n", " 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"paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Cumulative CO2 Emissions by Country and Scenario, 2025-2040" }, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "tickangle": -30, "title": { "text": "Country" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Cumulative CO2 (Mt)" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cumulative_df = (\n", " scenario_df.groupby([\"country\", \"scenario\"])[\"co2_projected\"]\n", " .sum()\n", " .reset_index()\n", " .rename(columns={\"co2_projected\": \"cumulative_co2_2025_2040\"})\n", ")\n", "\n", "fig = px.bar(\n", " cumulative_df, x=\"country\", y=\"cumulative_co2_2025_2040\", color=\"scenario\",\n", " barmode=\"group\",\n", " title=\"Cumulative CO2 Emissions by Country and Scenario, 2025-2040\",\n", " labels={\"cumulative_co2_2025_2040\": \"Cumulative CO2 (Mt)\", \"country\": \"Country\", \"scenario\": \"Scenario\"},\n", " color_discrete_map=SCENARIO_COLORS,\n", ")\n", "fig.update_layout(template=\"plotly_white\", xaxis_tickangle=-30)\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "b9f97fd2", "metadata": {}, "source": [ "Pivot the cumulative totals to compute each country's absolute avoided emissions under aggressive\n", "mitigation relative to business as usual, and rank countries by that savings figure." ] }, { "cell_type": "code", "execution_count": 41, "id": "d8f2b132", "metadata": { "execution": { "iopub.execute_input": "2026-07-29T14:01:51.947775Z", "iopub.status.busy": "2026-07-29T14:01:51.947598Z", "iopub.status.idle": "2026-07-29T14:01:51.959494Z", "shell.execute_reply": "2026-07-29T14:01:51.959033Z" } }, "outputs": [ { "data": { "text/html": [ "
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scenarioAggressive MitigationBAUModerate MitigationAbsolute Savings (BAU - Aggressive)
country
China130725.8195197.3166318.764471.5
India33970.968002.843220.234031.9
United States52168.083837.466371.931669.4
Russia18940.527539.624097.58599.1
Japan10231.918226.513017.87994.6
Germany6088.111498.77745.75410.6
Brazil5138.18716.76537.03578.6
South Africa4678.77593.75952.62915.0
Australia4113.96881.05234.02767.1
United Kingdom3328.62999.04234.8-329.5
\n", "
" ], "text/plain": [ "scenario Aggressive Mitigation BAU Moderate Mitigation \\\n", "country \n", "China 130725.8 195197.3 166318.7 \n", "India 33970.9 68002.8 43220.2 \n", "United States 52168.0 83837.4 66371.9 \n", "Russia 18940.5 27539.6 24097.5 \n", "Japan 10231.9 18226.5 13017.8 \n", "Germany 6088.1 11498.7 7745.7 \n", "Brazil 5138.1 8716.7 6537.0 \n", "South Africa 4678.7 7593.7 5952.6 \n", "Australia 4113.9 6881.0 5234.0 \n", "United Kingdom 3328.6 2999.0 4234.8 \n", "\n", "scenario Absolute Savings (BAU - Aggressive) \n", "country \n", "China 64471.5 \n", "India 34031.9 \n", "United States 31669.4 \n", "Russia 8599.1 \n", "Japan 7994.6 \n", "Germany 5410.6 \n", "Brazil 3578.6 \n", "South Africa 2915.0 \n", "Australia 2767.1 \n", "United Kingdom -329.5 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pivot = cumulative_df.pivot(index=\"country\", columns=\"scenario\", values=\"cumulative_co2_2025_2040\")\n", "pivot[\"Absolute Savings (BAU - Aggressive)\"] = pivot[\"BAU\"] - pivot[\"Aggressive Mitigation\"]\n", "pivot.sort_values(\"Absolute Savings (BAU - Aggressive)\", ascending=False).round(1)" ] }, { "cell_type": "markdown", "id": "caa63c0f", "metadata": {}, "source": [ "**Interpretation.** In absolute terms, the largest emitters — China, the United States, and India —\n", "benefit most from aggressive mitigation, since a 5% annual reduction applied to a much larger baseline\n", "yields the largest absolute avoided emissions over 2025–2040, even though the *percentage* reduction is\n", "identical for every country by construction. Smaller emitters among the 10 (South Africa, Australia,\n", "Brazil) still see meaningful proportional benefits, but their absolute cumulative savings are naturally\n", "smaller. This illustrates a general point about global mitigation policy: because a handful of large\n", "emitters dominate total emissions, the absolute climate impact of a given percentage-reduction\n", "commitment depends heavily on which countries adopt it, not just how ambitious the percentage is." ] }, { "cell_type": "markdown", "id": "561c387a", "metadata": {}, "source": [ "\n", "## Week 6: Conclusions and Limitations\n", "\n", "*Learning objective: finalise the notebook to professional documentation standards and consolidate\n", "the project's findings.*\n", "\n", "**Key takeaways across the project:**\n", "\n", "1. **EDA (Week 1):** Global CO2 emissions have risen substantially since 1990, driven\n", " largely by China's and India's growth, while a subset of developed economies (UK, Germany) have\n", " achieved sustained absolute reductions. CO2 dominates the total GHG mix relative to\n", " methane and nitrous oxide across every decade studied.\n", "2. **Feature Engineering (Week 2):** Lag, rolling-mean, and growth-rate features derived from the raw\n", " annual series give supervised models direct access to the autocorrelation and trend structure that\n", " drives emissions from one year to the next.\n", "3. **Baseline ML (Week 3):** A naive no-change model is a strong benchmark; Linear Regression trained\n", " per country improves on it modestly for countries with a stable trend, while a pooled Random Forest\n", " trades per-country specificity for larger effective sample size. The exercise reinforces that model\n", " complexity must be matched to data availability.\n", "4. **ETS Forecasting (Week 4):** Holt's damped trend method produces physically sensible, uncertainty-\n", " aware long-range forecasts to 2043, with the damping parameter capturing each country's\n", " deceleration or continued momentum, and widening confidence intervals correctly signalling growing\n", " uncertainty over the 20-year horizon.\n", "5. **Scenario Analysis (Week 5):** Simple linear mitigation scenarios (2%/year and 5%/year reductions)\n", " show that the absolute climate benefit of a mitigation commitment scales with a country's baseline\n", " emissions — the largest emitters offer the largest absolute avoided-emissions opportunity.\n", "\n", "**Limitations:**\n", "\n", "- The 10-country scope, while diverse, excludes many other significant emitters and does not\n", " generalise globally without re-fitting.\n", "- All models are trained on annual, country-level aggregates and cannot capture sub-annual or\n", " sub-national dynamics (e.g. seasonal energy demand, regional policy variation).\n", "- The Week 3 regression models and Week 4 ETS model are not evaluated on a strictly like-for-like\n", " basis (1-step-ahead refreshed forecasts vs. a single multi-step forecast), so comparisons across the\n", " four-model table should be read as directional, not a strict leaderboard.\n", "- The Week 5 mitigation scenarios are illustrative linear reduction paths, not outputs of a calibrated\n", " policy or economic model, and should not be used for real climate-policy decision-making.\n", "- GDP-derived features (`ghg_intensity`) have some missing values in the most recent years due to\n", " reporting lag in the underlying GDP series.\n", "\n", "**Notebook standards applied throughout:** every code cell is preceded by a markdown cell explaining\n", "its purpose, all charts include titles/axis labels/legends, variable names are descriptive, and the\n", "notebook runs cleanly from top to bottom." ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.6" } }, "nbformat": 4, "nbformat_minor": 5 }