C. Fuel Sources & Energy Systems

Deconstructing carbon emissions by fuel type, assessing the efficiency of national energy systems, and examining gas flaring and land-use impacts.

Deconstructing the Fuel Mix

While total emissions reveal how much a country pollutes, breaking down those emissions by fuel source shows why they pollute. The structural dependence on specific energy sources fundamentally shapes a nation’s decarbonization pathway.

Plot C1 breaks down the absolute emissions and proportional fuel mixes of the world’s top ten emitters.

Fuel-Source CO₂ Breakdown

fuels_c1   = ["coal_co2","oil_co2","gas_co2","cement_co2","flaring_co2"]
colours_c1 = ["#264653","#E9C46A","#F4A261","#E76F51","#A8DADC"]
labels_c1  = ["Coal","Oil","Gas","Cement","Flaring"]

df_c1_all = country_df.dropna(subset=fuels_c1)
latest_c1 = df_c1_all["year"].max()
df_c1     = df_c1_all[df_c1_all["year"] == latest_c1][["country"] + fuels_c1].copy()
df_c1["total"] = df_c1[fuels_c1].sum(axis=1)
top10_c1   = df_c1.nlargest(10, "total").reset_index(drop=True)

fig, (ax_main, ax_pct) = plt.subplots(1, 2, figsize=(18, 7))
fig.suptitle(f"Plot C1 — Fuel-Source CO₂ Breakdown — Top 10 Emitters ({latest_c1})", fontsize=14, fontweight="bold")

# Absolute stacked bar
bottom = np.zeros(len(top10_c1))
for fuel, colour, label in zip(fuels_c1, colours_c1, labels_c1):
    vals = top10_c1[fuel].fillna(0).values
    ax_main.bar(top10_c1["country"], vals, bottom=bottom, color=colour, label=label, alpha=0.92)
    bottom += vals
ax_main.set_xticklabels(top10_c1["country"], rotation=25, ha="right")
ax_main.set_ylabel("CO₂ Emissions (Mt)")
ax_main.set_title("Absolute (Mt CO₂)")
ax_main.legend(title="Fuel source", bbox_to_anchor=(1.01, 1), loc="upper left", fontsize=9)
ax_main.spines["top"].set_visible(False); ax_main.spines["right"].set_visible(False)

# 100% stacked bar
df_pct = top10_c1[fuels_c1].div(top10_c1["total"], axis=0) * 100
bottom_pct = np.zeros(len(df_pct))
for fuel, colour, label in zip(fuels_c1, colours_c1, labels_c1):
    vals = df_pct[fuel].fillna(0).values
    ax_pct.bar(top10_c1["country"], vals, bottom=bottom_pct, color=colour, label=label, alpha=0.92)
    bottom_pct += vals
ax_pct.set_xticklabels(top10_c1["country"], rotation=25, ha="right")
ax_pct.set_ylabel("Share of Emissions (%)")
ax_pct.set_title("Proportional Mix (%)")
ax_pct.set_ylim(0, 100)
ax_pct.spines["top"].set_visible(False); ax_pct.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig(OUT / "C1_fuel_breakdown_stacked.png")
plt.close()

The Dominance of Coal in Asia

China’s massive emissions (exceeding 12,000 Mt CO₂) are overwhelmingly driven by coal combustion, representing roughly three-quarters of its total emissions. This reflects a deep structural reliance on coal for both electricity generation and heavy industry (steel, cement). India exhibits a nearly identical proportional reliance on coal, though at a smaller absolute scale. For both nations, transitioning away from coal poses the single most significant decarbonization challenge.

Diversified Energy and Oil Reliance

The United States demonstrates a more diversified footprint, with natural gas edging out coal and oil as the primary emission source, largely due to the post-2008 shale gas boom. Meanwhile, Middle Eastern producers like Iran and Saudi Arabia show almost no coal consumption; their emissions are entirely dominated by oil and gas, reflecting domestic resource abundance.

Evaluating Energy Efficiency

Is all energy created equal? Plot C4 evaluates the Carbon Intensity of Energy (kg CO₂/kWh)—measuring how much carbon is emitted per unit of energy generated.

Carbon Intensity of the Energy Mix Over Time

countries_c4 = ["United States","China","India","France","Germany","United Kingdom","Japan","Brazil","South Africa","South Korea"]
palette_c4   = sns.color_palette("tab10", len(countries_c4))

fig, ax = plt.subplots(figsize=(13, 6))
for country, colour in zip(countries_c4, palette_c4):
    sub = (country_df[(country_df["country"] == country) & (country_df["year"] >= 1970)]
           [["year","co2_per_unit_energy"]].dropna())
    if sub.empty: continue
    ax.plot(sub["year"], sub["co2_per_unit_energy"], label=country, color=colour, linewidth=2.0)
    last = sub.iloc[-1]
    ax.annotate(f"  {country}", (last["year"], last["co2_per_unit_energy"]),
                fontsize=7.5, color=colour, va="center")

ax.set_xlabel("Year")
ax.set_ylabel("CO₂ per Unit Energy (kg CO₂ / kWh)")
ax.set_title("Plot C4 — Carbon Intensity of the Energy Mix over Time\n(Lower = cleaner energy grid)")
ax.legend(fontsize=8.5, ncol=2)
ax.set_xlim(1970, country_df["year"].max() + 8)

plt.tight_layout()
plt.savefig(OUT / "C4_co2_per_unit_energy.png")
plt.close()

This metric exposes the underlying cleanliness of the power grid: - France maintains the cleanest energy mix due to its extensive nuclear power fleet. - The UK and US show sharp downward slopes over the past decade as they successfully phase out coal in favor of natural gas and offshore wind. - South Africa, China, and India remain at the top of the chart, penalized heavily for their coal-dominated electricity grids.

Plot C5 brings these elements together into a single snapshot, plotting total energy consumption against total emissions, colored by carbon intensity.

Energy Consumption vs CO₂ Emissions

df_c5 = country_df.dropna(subset=["primary_energy_consumption","co2","co2_per_unit_energy"])
yr_c5 = df_c5["year"].max()
d_c5  = df_c5[df_c5["year"] == yr_c5].copy()

fig, ax = plt.subplots(figsize=(12, 7))
sc = ax.scatter(d_c5["primary_energy_consumption"], d_c5["co2"],
                c=d_c5["co2_per_unit_energy"], cmap="YlOrRd",
                s=80, alpha=0.75, edgecolors="white", linewidths=0.4)
cbar = plt.colorbar(sc, ax=ax)
cbar.set_label("CO₂ per Unit Energy (kg/kWh) — colour = grid carbon intensity")

for country in ["United States","China","India","Russia","Germany","France","Brazil","South Africa","Japan"]:
    row = d_c5[d_c5["country"] == country]
    if row.empty: continue
    col = COUNTRY_COLORS.get(country, "#333333")
    ax.scatter(row["primary_energy_consumption"], row["co2"],
               s=200, color=col, edgecolors="white", linewidths=1.3, zorder=6)
    ax.annotate(country, (row["primary_energy_consumption"].values[0], row["co2"].values[0]),
                xytext=(5, 3), textcoords="offset points", fontsize=8.5, color=col, fontweight="bold")

ax.set_xlabel("Primary Energy Consumption (TWh)")
ax.set_ylabel("CO₂ Emissions (Mt)")
ax.set_title(f"Plot C5 — Energy Consumption vs CO₂ Emissions ({yr_c5})\nColour = carbon intensity of energy mix")

plt.tight_layout()
plt.savefig(OUT / "C5_energy_vs_co2.png")
plt.close()

The conclusion is stark: total emissions are a function of both the sheer scale of energy demand and the carbon intensity of the fuel source. Nations with high energy consumption but lower carbon intensities (like the US) emit proportionally less than their energy-hungry, coal-dominated counterparts (like China).

The Waste of Gas Flaring

Not all emissions generate economic value. Plot C2 investigates gas flaring—the practice of burning excess natural gas during oil extraction.

Top 15 Countries by Gas Flaring Emissions

df_flare = country_df.dropna(subset=["flaring_co2"])
latest_fl = df_flare["year"].max()
top_flare = (df_flare[df_flare["year"] == latest_fl][["country","flaring_co2"]]
             .nlargest(15,"flaring_co2").reset_index(drop=True))

fig, ax = plt.subplots(figsize=(13, 6))
colours_fl = plt.cm.get_cmap("OrRd", len(top_flare))
bars = ax.bar(top_flare["country"], top_flare["flaring_co2"],
              color=[colours_fl(i/len(top_flare)) for i in range(len(top_flare))],
              edgecolor="white", linewidth=0.5, alpha=0.9)
for bar, val in zip(bars, top_flare["flaring_co2"]):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,
            f"{val:.1f}", ha="center", va="bottom", fontsize=8)

ax.set_xticklabels(top_flare["country"], rotation=30, ha="right")
ax.set_ylabel("Flaring CO₂ (Mt)")
ax.set_title(f"Plot C2 — Top 15 Countries by Gas Flaring Emissions ({latest_fl})\nFlaring = waste from oil extraction — an indicator of regulatory failure")
ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig(OUT / "C2_flaring_co2.png")
plt.close()

Flaring represents pure economic and environmental waste, often resulting from insufficient pipeline infrastructure or poor regulatory oversight. Surprisingly, advanced economies like Russia and the United States lead global flaring totals simply due to the massive scale of their oil extraction industries. Developing producers (like Nigeria and Algeria) also rank highly, but often due to weaker regulatory enforcement. This metric serves as a unique proxy for evaluating the efficiency and regulation of national fossil fuel sectors. The structural disadvantage of coal is formally tested in H3, confirmed at p=8.1e-06.