Climate Report

Additional time series data about climate change come in all the time. Here are some plots that I like to track.

I think that what is essential for this problem is a global consciousness,
a view that transcends our exclusive identifications with the generational
and political groupings into which by accident we have been born.

The solution to these problems requires a perspective that embraces the
planet and the future, because we are all in this greenhouse together. 

Carl Sagan, https://youtu.be/Wp-WiNXH6hI?t=985

EIA Monthly: Electric Power Sector

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

url = 'https://www.eia.gov/totalenergy/data/browser/csv.php?tbl=T07.02B'
df = pd.read_csv(url)
df = df.loc[~df['Description'].str.contains('Generation Total')]
df = df.loc[~df['YYYYMM'].astype(str).str.endswith('13')]
df['source'] = df['Description'].str.split(",").str[0].str.replace('Electricity Net Generation From ', '')
df['date'] = pd.to_datetime(df['YYYYMM'], format='%Y%m')
df2 = df.pivot(index='date', columns='source', values='Value')
df2 = df2.rename(columns={'Conventional Hydroelectric Power': 'Hydroelectric',
                          'Nuclear Electric Power': 'Nuclear'})
df2 = df2.replace('Not Available', np.nan).replace('Not Meaningful', np.nan).astype(float)
df2 = df2.loc[:, df2.max() > 5000]  # only keep the main players

fractions = 100 * df2.divide(df2.sum(axis=1), axis=0).clip(lower=0)

fig, ax = plt.subplots(figsize=(10, 6))
df2.rolling(12, center=True).mean().plot(ax=ax, colormap='tab10')
ax.legend()
ax.set_ylabel('Million kWh')
fig.tight_layout()
df2.plot(ax=ax, alpha=0.2, colormap='tab10', legend=False)
plt.show()

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fig, axes = plt.subplots(3, 1, sharex=True, figsize=(6, 7))
fig.suptitle('Utility-Scale Solar Monthly Generation')

df2['Solar'].dropna().loc['2009':].plot(ax=axes[0])
axes[0].set_ylabel('Million kWh')

fractions['Solar'].loc['2009':].plot(ax=axes[1])
moving_average = fractions['Solar'].rolling(12).mean()
moving_average.loc['2009':].plot(ax=axes[1])
axes[1].set_ylabel('Fraction of total [%]')

yoy_change = moving_average - moving_average.shift(12)
yoy_change.loc['2009':].plot(ax=axes[2])
axes[2].set_ylabel('Year-on-year change [% of total]')

for ax in axes:
    ax.axvline('2017-02-01', ls='--', c='k')
    ax.grid(which='both')

fig.tight_layout()

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fig, axes = plt.subplots(3, 1, sharex=True, figsize=(6, 7))
fig.suptitle('Utility-Scale Wind Monthly Generation')

df2['Wind'].dropna().loc['2000':].plot(ax=axes[0])
axes[0].set_ylabel('Million kWh')

fractions['Wind'].loc['2000':].plot(ax=axes[1])
moving_average = fractions['Wind'].rolling(12).mean()
moving_average.loc['2000':].plot(ax=axes[1])
axes[1].set_ylabel('Fraction of total [%]')

yoy_change = moving_average - moving_average.shift(12)
yoy_change.loc['2000':].plot(ax=axes[2])
axes[2].set_ylabel('Year-on-year change [% of total]')

for ax in axes:
    ax.grid(which='both')

fig.tight_layout()

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# reorder for plotting purposes
col_order = ['Nuclear', 'Hydroelectric', 'Wind', 'Solar',
             'Coal', 'Natural Gas', 'Petroleum']
fractions = fractions[col_order]
fractions.iloc[-12*10:].plot.area(lw=0, figsize=(10, 4))
plt.ylabel('Share of total generation [%]')

handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(reversed(handles), reversed(labels),
           loc='center right', bbox_to_anchor=(1.2, 0.5))
plt.tight_layout()
plt.grid()

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clean = ['Solar', 'Wind', 'Hydroelectric', 'Nuclear']
clean_fraction = 100 * df2[clean].sum(axis=1) / df2.sum(axis=1)
clean_fraction.plot()
clean_fraction_rolling = 100 * (
    df2[clean].sum(axis=1).rolling(12, center=True).sum() /
    df2.sum(axis=1).rolling(12, center=True).sum()
)
clean_fraction_rolling.plot()
plt.ylabel('Clean Fraction [%]')
plt.tight_layout()
plt.grid()

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fractions.assign(clean_fraction=clean_fraction).tail(24)

source	Nuclear	Hydroelectric	Wind	Solar	Coal	Natural Gas	Petroleum	clean_fraction
date
2022-09-01	18.986338	5.048766	8.137298	3.932015	19.233835	44.211907	0.449841	36.104417
2022-10-01	19.771396	4.796688	10.975156	3.979908	18.047142	41.933118	0.496591	39.523148
2022-11-01	20.306756	5.832166	13.474568	2.731391	18.322071	38.877355	0.455694	42.344881
2022-12-01	20.111733	5.914083	11.250983	1.960361	21.226943	38.414368	1.121527	39.237161
2023-01-01	21.378713	6.829645	11.564014	2.341649	18.375743	39.117119	0.393117	42.114021
2023-02-01	20.564285	6.712776	13.999658	3.171743	15.554932	39.477606	0.519000	44.448461
2023-03-01	19.916152	6.720322	13.808265	3.848019	15.766925	39.574943	0.365374	44.292758
2023-04-01	19.720321	6.857132	14.867287	5.206729	13.878566	39.084168	0.385796	46.651470
2023-05-01	19.627885	8.839253	10.336556	5.512343	13.938005	41.376049	0.369910	44.316037
2023-06-01	18.900872	6.264731	8.028298	5.170707	16.737549	44.545662	0.352181	38.364608
2023-07-01	17.117925	5.360484	6.857133	4.601842	19.228424	46.455501	0.378691	33.937384
2023-08-01	17.195248	5.229906	6.997361	4.351117	19.165171	46.680475	0.380723	33.773632
2023-09-01	19.046513	4.895612	8.233865	4.503001	17.311023	45.595512	0.414474	36.678991
2023-10-01	19.769502	5.023012	11.584722	4.520814	16.274658	42.433923	0.393369	40.898050
2023-11-01	20.454888	5.572131	11.966668	3.413016	16.707199	41.550830	0.335268	41.406702
2023-12-01	20.715930	5.696206	11.437776	2.728903	16.839782	42.229566	0.351837	40.578815
2024-01-01	19.041106	6.012012	9.594374	2.666135	20.748726	41.448070	0.489578	37.313626
2024-02-01	21.152255	6.549504	13.567146	4.060834	14.308962	40.062440	0.298857	45.329740
2024-03-01	20.520890	7.517278	14.731597	5.086164	12.303900	39.542805	0.297365	47.855930
2024-04-01	19.591850	6.556132	16.050784	6.440374	12.583051	38.404279	0.373529	48.639141
2024-05-01	19.700667	6.824115	11.693433	6.666835	13.814455	40.958703	0.341792	44.885050
2024-06-01	18.203560	5.623397	10.153971	6.422335	16.288155	42.975729	0.332853	40.403262
2024-07-01	16.877150	5.093956	6.735946	5.792282	17.212521	47.954884	0.333261	34.499334
2024-08-01	17.147078	5.225041	7.049227	5.857630	16.821398	47.555633	0.343993	35.278976

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rolling = df2.rolling(12).sum()
rolling['Renewables'] = rolling[['Solar', 'Wind', 'Hydroelectric']].sum(axis=1)
rolling.loc['2015':, ['Coal', 'Nuclear', 'Renewables']].plot()
plt.ylim(0, 1.6e6)
plt.ylabel('Million kWh (12-month moving)');

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import datetime
datetime.date.today().strftime('%Y-%m-%d')

'2024-12-04'

df2.loc['2020':, clean].plot(drawstyle='steps-post')
plt.ylim(bottom=0);