World issues

% Annual mean CO2 at Mauna Loa (NOAA, via github.com/datasets/co2-ppm) — live data
T = readtable('https://raw.githubusercontent.com/datasets/co2-ppm/main/data/co2-annmean-mlo.csv');
yr = T.Year; co2 = T.Mean;
% growth rate per decade
rate = diff(co2);
plot(yr, co2, 'LineWidth', 2), hold on
yline(280, '--', 'pre-industrial ≈ 280 ppm'), hold off
xlabel('year'), ylabel('CO₂ (ppm)')
title(sprintf('CO₂ at Mauna Loa: %.1f ppm in %d', co2(end), yr(end)))
subtitle(sprintf('rising %.2f ppm/yr in the 1960s, %.2f ppm/yr in the last decade', mean(rate(1:10)), mean(rate(end-9:end))))

% NASA GISTEMP temperature anomaly and Mauna Loa CO2 (live, via github.com/datasets)
Tt = readtable('https://raw.githubusercontent.com/datasets/global-temp/main/data/annual.csv');
gis = strcmp(Tt.Source, 'GISTEMP');
ty = Tt.Year(gis); temp = Tt.Mean(gis);
[ty, idx] = sort(ty); temp = temp(idx);
C = readtable('https://raw.githubusercontent.com/datasets/co2-ppm/main/data/co2-annmean-mlo.csv');
[both, loc] = ismember(C.Year, ty);
x = log2(C.Mean(both) / 280); y = temp(loc(both));
p = polyfit(x, y, 1);

subplot(1, 2, 1)
plot(ty, temp, 'Color', [0.6 0.6 0.6]), hold on
plot(ty, filter(ones(1, 10)/10, 1, temp), 'LineWidth', 2), hold off
xlabel('year'), ylabel('°C vs 1951–1980'), title('Global temperature (GISTEMP)')
legend('annual', '10-year mean')
subplot(1, 2, 2)
scatter(x, y, 18, C.Year(both), 'filled'), hold on
xx = linspace(min(x), max(x)); plot(xx, polyval(p, xx), 'k--'), hold off
colorbar, xlabel('doublings of CO₂ since 280 ppm'), ylabel('°C')
title(sprintf('%.1f °C per CO₂ doubling (simple fit)', p(1)))
subtitle('Correlation only: real climate sensitivity needs physics and other forcings')

% Share of renewables in final energy use, world and selected countries (World Bank, live)
d = webread('https://api.worldbank.org/v2/country/WLD;DEU;CHN;IND;BRA/indicator/EG.FEC.RNEW.ZS?format=json&per_page=500&date=1990:2024');
rows = d{2};
codes = {'WLD', 'DEU', 'CHN', 'IND', 'BRA'}; names = {'World', 'Germany', 'China', 'India', 'Brazil'};
for c = 1:numel(codes)
  yr = []; v = [];
  for k = 1:numel(rows)
    r = rows{k};
    if strcmp(r.countryiso3code, codes{c}) && ~isempty(r.value), yr(end+1) = str2double(r.date); v(end+1) = r.value; end
  end
  [yr, idx] = sort(yr);
  if c == 1, plot(yr, v(idx), 'k', 'LineWidth', 3), hold on, else, plot(yr, v(idx), 'LineWidth', 1.6), end
end
hold off, legend(names{:}), ylim([0 60])
xlabel('year'), ylabel('% of final energy consumption')
title('Renewable energy share (EG.FEC.RNEW.ZS)')

% The Preston curve: life expectancy against GDP per person, every country (World Bank, live)
le  = webread('https://api.worldbank.org/v2/country/all/indicator/SP.DYN.LE00.IN?format=json&date=2022&per_page=400');
gdp = webread('https://api.worldbank.org/v2/country/all/indicator/NY.GDP.PCAP.CD?format=json&date=2022&per_page=400');
cty = webread('https://api.worldbank.org/v2/country?format=json&per_page=400');

% keep real countries (the API also lists regions and income groups as "Aggregates")
isCountry = {}; regionOf = {};
for k = 1:numel(cty{2})
  c = cty{2}{k};
  if ~strcmp(strtrim(c.region.value), 'Aggregates'), isCountry{end+1} = c.id; regionOf{end+1} = strtrim(c.region.value); end
end
x = []; y = []; reg = [];
regions = unique(regionOf);
for k = 1:numel(le{2})
  a = le{2}{k}; b = gdp{2}{k};
  [ok, at] = ismember(a.countryiso3code, isCountry);
  if ok && ~isempty(a.value) && ~isempty(b.value) && strcmp(a.countryiso3code, b.countryiso3code)
    [~, r] = ismember(regionOf{at}, regions);
    x(end+1) = b.value; y(end+1) = a.value; reg(end+1) = r;
  end
end
scatter(x, y, 22, reg, 'filled'), colormap turbo
set(gca, 'XScale', 'log'), xlabel('GDP per person, US$ (log scale)'), ylabel('life expectancy (years)')
p = polyfit(log10(x), y, 1); xx = logspace(log10(min(x)), log10(max(x)));
hold on, plot(xx, polyval(p, log10(xx)), 'k--'), hold off
title(sprintf('%d countries, 2022: +%.1f years per tenfold income', numel(x), p(1)))
subtitle('Colour = World Bank region')