{ "cells": [ { "cell_type": "markdown", "id": "2dfde7f4-f6d7-4377-9959-e00573f0f636", "metadata": {}, "source": [ "# Uncertainty drilldown\n", "\n", "We have explored various ways to reduce the uncertainty in the emissions data. Now we look at the question: **if we want to obtain more precise estimates, which sector should we focus on?**\n", "\n", "This section presents a simple framework to get started. You will see that the initial suspects are not what you think.\n", "\n", "For now, let's assume that picking each sector in any country is just as easy. In practice of course, the choice of analysis is driven by the data available. Nevertheless, it is a good baseline to understand how hard it would be to reduce this uncertainty.\n", "\n", "The source emissions dataset contains _qualitative confidence_ for each source, based on expert assessment of the data. The values are very low confidence, low confidence, medium, high and very high. In our simple baseline, we are going to assume that each of these confidence assessment correspond to a numerical value of uncertainty. This is more complicated in practice, because humans are not great at assessing uncertainty, and also because when the estimates are wrong, they can be _very_ wrong - sometimes by multiple orders of magnitude! Nevertheless, this is a baseline.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "488a4f87-502c-466e-a32d-a47d1c87dbdb", "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "id": "51a0a8ec-b228-4079-9ba5-316f9b2e3bfb", "metadata": {}, "outputs": [], "source": [ "import polars as pl\n", "import plotly.express as px\n", "\n", "from ctrace.constants import *\n", "import ctrace as ct" ] }, { "cell_type": "markdown", "id": "e0b8df14", "metadata": {}, "source": [ "```{warning}\n", "As data gets updated, the conclusion in this notebook can be upended. Always check the version of the dataset that you are using\n", "```\n", "\n", "Our data version is:" ] }, { "cell_type": "code", "execution_count": 3, "id": "4524bd17", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'v3-2024-ct5'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ct.data.version" ] }, { "cell_type": "markdown", "id": "a12a545e-8909-451e-aa9a-6280aca4757e", "metadata": {}, "source": [ "Let's focus on 2023 and with CO2e_100year." ] }, { "cell_type": "code", "execution_count": 4, "id": "f4936172-3741-4ae4-9b8f-3ef7b3a3c016", "metadata": {}, "outputs": [], "source": [ "year = 2023\n", "gas = CO2E_100YR\n", "gas_conf = \"conf_emissions_quantity\"\n", "c_gas_conf = C(gas_conf)\n", "sdf_gy = ct.read_source_emissions(gas=gas,year=year)" ] }, { "cell_type": "markdown", "id": "30f5bf57-ab6a-49ab-afce-90456e2ef49e", "metadata": {}, "source": [ "The confidence intervals are provided for each source. To reduce the workload, we are aggregating first by confidence interval, country, sector and subsector. This is all we need to consider for this analysis and it makes the analysis significantly faster.\n", "\n", "```{note} Technical\n", "All the aggregation keys are enumerations or categories. This makes all the aggregations very fast because Polars knows precisely how many keys will be aggregated and leverages these statistics.\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "id": "5052b095-fd7b-45df-819a-7c1b13ead32a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iso3_countrysubsectorconf_emissions_quantitysectorcountemissions_quantity
enumenumenumenumu32f64
"CAN""removals""very low""forestry-and-land-use"4104-4.7205e9
"BRA""removals""very low""forestry-and-land-use"70812-4.4673e9
"COD""removals""very low""forestry-and-land-use"4704-3.5941e9
"AGO""removals""very low""forestry-and-land-use"2736-2.1069e9
"RUS""removals""very low""forestry-and-land-use"32448-1.9461e9
………………
"COD""forest-land-fires""high""forestry-and-land-use"16922.1585e9
"CHN""residential-onsite-fuel-usage""very low""buildings"230762.3382e9
"BRA""forest-land-clearing""high""forestry-and-land-use"278282.8755e9
"USA""road-transportation""low""transportation"383882.9109e9
"CHN""electricity-generation""medium""power"169904.9841e9
" ], "text/plain": [ "shape: (10_538, 6)\n", "┌──────────────┬──────────────────┬──────────────────┬──────────────────┬───────┬──────────────────┐\n", "│ iso3_country ┆ subsector ┆ conf_emissions_q ┆ sector ┆ count ┆ emissions_quanti │\n", "│ --- ┆ --- ┆ uantity ┆ --- ┆ --- ┆ ty │\n", "│ enum ┆ enum ┆ --- ┆ enum ┆ u32 ┆ --- │\n", "│ ┆ ┆ enum ┆ ┆ ┆ f64 │\n", "╞══════════════╪══════════════════╪══════════════════╪══════════════════╪═══════╪══════════════════╡\n", "│ CAN ┆ removals ┆ very low ┆ forestry-and-lan ┆ 4104 ┆ -4.7205e9 │\n", "│ ┆ ┆ ┆ d-use ┆ ┆ │\n", "│ BRA ┆ removals ┆ very low ┆ forestry-and-lan ┆ 70812 ┆ -4.4673e9 │\n", "│ ┆ ┆ ┆ d-use ┆ ┆ │\n", "│ COD ┆ removals ┆ very low ┆ forestry-and-lan ┆ 4704 ┆ -3.5941e9 │\n", "│ ┆ ┆ ┆ d-use ┆ ┆ │\n", "│ AGO ┆ removals ┆ very low ┆ forestry-and-lan ┆ 2736 ┆ -2.1069e9 │\n", "│ ┆ ┆ ┆ d-use ┆ ┆ │\n", "│ RUS ┆ removals ┆ very low ┆ forestry-and-lan ┆ 32448 ┆ -1.9461e9 │\n", "│ ┆ ┆ ┆ d-use ┆ ┆ │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", "│ COD ┆ forest-land-fire ┆ high ┆ forestry-and-lan ┆ 1692 ┆ 2.1585e9 │\n", "│ ┆ s ┆ ┆ d-use ┆ ┆ │\n", "│ CHN ┆ residential-onsi ┆ very low ┆ buildings ┆ 23076 ┆ 2.3382e9 │\n", "│ ┆ te-fuel-usage ┆ ┆ ┆ ┆ │\n", "│ BRA ┆ forest-land-clea ┆ high ┆ forestry-and-lan ┆ 27828 ┆ 2.8755e9 │\n", "│ ┆ ring ┆ ┆ d-use ┆ ┆ │\n", "│ USA ┆ road-transportat ┆ low ┆ transportation ┆ 38388 ┆ 2.9109e9 │\n", "│ ┆ ion ┆ ┆ ┆ ┆ │\n", "│ CHN ┆ electricity-gene ┆ medium ┆ power ┆ 16990 ┆ 4.9841e9 │\n", "│ ┆ ration ┆ ┆ ┆ ┆ │\n", "└──────────────┴──────────────────┴──────────────────┴──────────────────┴───────┴──────────────────┘" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = (sdf_gy\n", " .group_by([c_iso3_country, c_subsector, c_gas_conf, c_sector])\n", " .agg(pl.len().alias(\"count\"), c_emissions_quantity.sum())\n", " .sort(by=c_emissions_quantity)\n", " .collect()\n", ")\n", "df" ] }, { "cell_type": "markdown", "id": "d4195754-d956-43f0-99fb-ea19bf87835c", "metadata": {}, "source": [ "We are going to use a very simple mapping from qualitative confidence assessments to quantitative error bounds. We are going to assume that a very high confidence is around 1% standard deviation and that a very low confidence is around 30-50% standard deviation, with a geometric progression in between.\n", "\n", "By default, if no confidence is provided, we will assume it is very low. We will err on the side of caution.\n", "\n", "```{note}\n", "Try different numbers. The results of this ananlysis are rather stable to different choices.\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "id": "c7f7bca7-e8fb-4eca-8e1c-2e5e8fd6140a", "metadata": {}, "outputs": [], "source": [ "margins = {\"very high\": 0.01, \"high\": 0.03,\"medium\":0.07, \"low\": 0.15, \"very low\": 0.3}" ] }, { "cell_type": "code", "execution_count": 7, "id": "a2e28a4f-ede7-4ee1-8ed1-9c9e0ac4fd48", "metadata": {}, "outputs": [], "source": [ "ERR_MARGIN = \"err_margin\"\n", "\n", "df = (df.with_columns(\n", " (c_gas_conf.replace_strict(margins, return_dtype=pl.Float32, default=margins[\"very low\"])\n", " * c_emissions_quantity).alias(ERR_MARGIN)\n", "))" ] }, { "cell_type": "markdown", "id": "2142c5b9-0534-4ecc-bed8-4cbfe3de14d4", "metadata": {}, "source": [ "What is the total error? It is around 18%, which is reasonable (the IPCC reports provide a 10% standard deviation).\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "7c350c61-0550-44fa-8396-1528db3ab538", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (1, 3)
emissions_quantityerr_margincount
f64f64u32
8.6073e105.8014e915184500
" ], "text/plain": [ "shape: (1, 3)\n", "┌────────────────────┬────────────┬──────────┐\n", "│ emissions_quantity ┆ err_margin ┆ count │\n", "│ --- ┆ --- ┆ --- │\n", "│ f64 ┆ f64 ┆ u32 │\n", "╞════════════════════╪════════════╪══════════╡\n", "│ 8.6073e10 ┆ 5.8014e9 ┆ 15184500 │\n", "└────────────────────┴────────────┴──────────┘" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.select(c_emissions_quantity.sum(), C(ERR_MARGIN).sum(), C(\"count\").sum())" ] }, { "cell_type": "markdown", "id": "3de3fc99-8cdc-42ad-81f3-8fe6a8d84e07", "metadata": {}, "source": [ "Let us look at the amount of uncertainy by sector. It is interesting to compare this graph with the attribution of total emissions by sector.\n", "\n", "First, carbon intake from forest is the biggest source of unknown, as we have seen already. The impact of climate change on forests is far from being fully understood.\n", "\n", "Second, house cooking and warming: there is a lot of uncertainty there because houses are very diverse, and the fuels used to cook and warm houses are also very different (gas, wood, ...). Each country has its own specifities, climate and culture, which makes it hard to generalize.\n", "\n", "Third, transportation: there are many vehicles on the road, each of them with different age, engine, ... As for houses, it is very hard to understand how they emit in aggregate.\n", "\n", "On the good news, notice the switch in positions: electrical power generation, the first post in emissions, is ranked _seventh_ from the perspective of uncertainties. This makes sense: there are only a few thousands power plants around the world, of which many are in highly regulated countries and have been already monitored for other pollutants such as NOx.\n", "\n", "\n", "Now, for the surprises: coal mining and gas extractions. This is the opposite of the canary in the coal mine. Satellites have found that coal mines and gas sites leak many potent gases such as methane through the ground. This was underestimated in the original inventory assessments. This is an example of nasty surprise in climate accounting, as there is not much we can do to prevent a mine to vent through the ground.\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "31444db2-d5c9-4881-9e48-364cf1151c8c", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "%{hovertext}

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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sec_rankings = (\n", "df\n", ".group_by(c_sector, c_subsector, c_gas_conf)\n", ".agg(c_emissions_quantity.abs().sum(), C(ERR_MARGIN).abs().sum())\n", ".sort(by=ERR_MARGIN, descending=True) \n", ")\n", "# Building a nice ordre for the graph\n", "_order = (sec_rankings\n", " .group_by(c_sector)\n", " .agg(C(ERR_MARGIN).sum())\n", " .sort(by=ERR_MARGIN, descending=True)[SECTOR].to_list())\n", "\n", "px.bar(sec_rankings,\n", " x=SECTOR,\n", " y=ERR_MARGIN,\n", " color=gas_conf,\n", " category_orders = {SECTOR: _order},\n", " log_y=False,\n", " hover_name=SUBSECTOR,\n", " color_discrete_map={'very low':'black','low': 'darkblue', 'medium': 'royalblue', 'high': 'lightcyan'}\n", ")" ] }, { "cell_type": "markdown", "id": "fbc8fe12-549d-4c42-be7b-1d1370e7bf8c", "metadata": {}, "source": [ "What could we focus on? Here is a list in order of uncertainy. Better understanding house warming in China and the USA would make a significant difference, as well as road usage in the USA.\n", "\n", "Also, focusing on the Chinese electrical power production (both for power generation and mining coal) is very important. Even if its margin of error is relatively low, this sector is so large that any improvement will have a disproportionate impact." ] }, { "cell_type": "code", "execution_count": 10, "id": "7977c1c4-e94e-478b-baf8-ac55c7606d5c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "shape: (10, 6)
indexiso3_countrysubsectorsectoremissions_quantityerr_margin
u32enumenumenumf64f64
0"CHN""residential-onsite-fuel-usage""buildings"2.3382e97.0146e8
1"USA""road-transportation""transportation"3.5297e96.2229e8
2"USA""residential-onsite-fuel-usage""buildings"1.7643e95.2929e8
3"CHN""coal-mining""fossil-fuel-operations"1.3067e93.9200e8
4"CHN""electricity-generation""power"5.0054e93.5528e8
5"CHN""iron-and-steel""manufacturing"1.7387e92.8236e8
6"CHN""road-transportation""transportation"1.5691e92.7792e8
7"IND""residential-onsite-fuel-usage""buildings"8.4234e82.5270e8
8"BRA""enteric-fermentation-cattle-pa…"agriculture"7.7923e82.3377e8
9"CHN""cropland-fires""agriculture"7.3300e82.1990e8
" ], "text/plain": [ "shape: (10, 6)\n", "┌───────┬──────────────┬────────────────────┬────────────────────┬────────────────────┬────────────┐\n", "│ index ┆ iso3_country ┆ subsector ┆ sector ┆ emissions_quantity ┆ err_margin │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ u32 ┆ enum ┆ enum ┆ enum ┆ f64 ┆ f64 │\n", "╞═══════╪══════════════╪════════════════════╪════════════════════╪════════════════════╪════════════╡\n", "│ 0 ┆ CHN ┆ residential-onsite ┆ buildings ┆ 2.3382e9 ┆ 7.0146e8 │\n", "│ ┆ ┆ -fuel-usage ┆ ┆ ┆ │\n", "│ 1 ┆ USA ┆ road-transportatio ┆ transportation ┆ 3.5297e9 ┆ 6.2229e8 │\n", "│ ┆ ┆ n ┆ ┆ ┆ │\n", "│ 2 ┆ USA ┆ residential-onsite ┆ buildings ┆ 1.7643e9 ┆ 5.2929e8 │\n", "│ ┆ ┆ -fuel-usage ┆ ┆ ┆ │\n", "│ 3 ┆ CHN ┆ coal-mining ┆ fossil-fuel-operat ┆ 1.3067e9 ┆ 3.9200e8 │\n", "│ ┆ ┆ ┆ ions ┆ ┆ │\n", "│ 4 ┆ CHN ┆ electricity-genera ┆ power ┆ 5.0054e9 ┆ 3.5528e8 │\n", "│ ┆ ┆ tion ┆ ┆ ┆ │\n", "│ 5 ┆ CHN ┆ iron-and-steel ┆ manufacturing ┆ 1.7387e9 ┆ 2.8236e8 │\n", "│ 6 ┆ CHN ┆ road-transportatio ┆ transportation ┆ 1.5691e9 ┆ 2.7792e8 │\n", "│ ┆ ┆ n ┆ ┆ ┆ │\n", "│ 7 ┆ IND ┆ residential-onsite ┆ buildings ┆ 8.4234e8 ┆ 2.5270e8 │\n", "│ ┆ ┆ -fuel-usage ┆ ┆ ┆ │\n", "│ 8 ┆ BRA ┆ enteric-fermentati ┆ agriculture ┆ 7.7923e8 ┆ 2.3377e8 │\n", "│ ┆ ┆ on-cattle-pa… ┆ ┆ ┆ │\n", "│ 9 ┆ CHN ┆ cropland-fires ┆ agriculture ┆ 7.3300e8 ┆ 2.1990e8 │\n", "└───────┴──────────────┴────────────────────┴────────────────────┴────────────────────┴────────────┘" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rankings = (df.group_by(c_iso3_country, c_subsector, c_sector)\n", " .agg(c_emissions_quantity.sum(), C(ERR_MARGIN).sum())\n", " .sort(by=ERR_MARGIN, descending=True)\n", " .with_row_index()\n", ")\n", "rankings.head(10)" ] }, { "cell_type": "markdown", "id": "e51509ea-1614-4a2a-a8ad-cc02635395cf", "metadata": {}, "source": [ "Grouping by country, we have have a discussion about which sectors are the most uncertain.\n", "\n", "In the US, road transportation is a surprising source of uncertainty that should be relatively easy to address." ] }, { "cell_type": "code", "execution_count": 11, "id": "4f8d9208-d60e-4dc7-bc3e-2445289790a0", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "%{hovertext}

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sector=transportation
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sector=fossil-fuel-operations
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err_margin=%{y}", "hovertext": [ "coal-mining", "oil-and-gas-transport", "oil-and-gas-production", "oil-and-gas-production", "oil-and-gas-production", "oil-and-gas-production", "oil-and-gas-transport", "oil-and-gas-refining", "oil-and-gas-production", "oil-and-gas-production", "oil-and-gas-refining", "coal-mining", "coal-mining", "oil-and-gas-production", "oil-and-gas-production", "oil-and-gas-refining", "coal-mining", "oil-and-gas-refining", "oil-and-gas-transport", "oil-and-gas-transport", "oil-and-gas-transport", "coal-mining", "oil-and-gas-transport", "oil-and-gas-refining", "oil-and-gas-refining", "oil-and-gas-production", "oil-and-gas-transport", "oil-and-gas-refining", "oil-and-gas-transport", "oil-and-gas-transport", "oil-and-gas-refining", "oil-and-gas-refining", "coal-mining", "oil-and-gas-refining", "coal-mining", "coal-mining", "oil-and-gas-transport", "oil-and-gas-production", "coal-mining", "coal-mining" ], "legendgroup": "fossil-fuel-operations", "marker": { "color": "#00cc96", "pattern": { "shape": "" } }, "name": "fossil-fuel-operations", "offsetgroup": "fossil-fuel-operations", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "CHN", "RUS", "USA", "IRN", "RUS", "CAN", "USA", "CHN", "CHN", "IDN", "USA", "USA", "RUS", "MEX", "BRA", "IND", "IND", "RUS", "CAN", "CHN", "IRN", "IDN", "IDN", "IRN", "BRA", "IND", "BRA", "JPN", "IND", "MEX", "MEX", "IDN", "CAN", "CAN", "BRA", "MEX", "JPN", "JPN", "IRN", "JPN" ], "xaxis": "x", "y": [ 392004768.3905299, 208662576.87664244, 182877458.18316442, 140905937.42492497, 133012283.9228399, 107857053.62825088, 83396071.65115598, 54414790.19301894, 51175920.532145314, 36024664.59146225, 32645441.011036806, 28061329.190416526, 24958018.364478193, 22978018.227617193, 21892810.271572597, 21015486.701263685, 19595165.88997619, 19296372.805961933, 17489572.52170627, 15898345.685388854, 14090685.426123159, 12444143.799341267, 8308717.001955342, 7530464.155770822, 7261842.693884811, 7111953.123206379, 6946227.962801538, 5999257.750047879, 4433580.615839585, 3792570.057838709, 2695778.2739952737, 2447987.4084963603, 1917445.9669835968, 1463126.6503219132, 382877.0004722749, 354255.23137942853, 168364.72938684968, 103877.69830226757, 73559.98375746659, 33262.89167214683 ], "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "%{hovertext}

sector=power
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sector=manufacturing
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err_margin=%{y}", "hovertext": [ "iron-and-steel", "cement", "lime", "chemicals", "other-chemicals", "iron-and-steel", "petrochemical-steam-cracking", "aluminum", "cement", "iron-and-steel", "chemicals", "food-beverage-tobacco", "iron-and-steel", "iron-and-steel", "chemicals", "chemicals", "iron-and-steel", "lime", "petrochemical-steam-cracking", "cement", "cement", "cement", "textiles-leather-apparel", "other-metals", "cement", "cement", "cement", "other-manufacturing", "cement", "chemicals", "iron-and-steel", "petrochemical-steam-cracking", "petrochemical-steam-cracking", "glass", "aluminum", "iron-and-steel", "iron-and-steel", "iron-and-steel", "pulp-and-paper", "aluminum", "lime", "chemicals", "petrochemical-steam-cracking", "petrochemical-steam-cracking", "petrochemical-steam-cracking", "cement", "aluminum", "petrochemical-steam-cracking", "aluminum", "pulp-and-paper", "chemicals", "aluminum", "pulp-and-paper", "chemicals", "petrochemical-steam-cracking", "petrochemical-steam-cracking", "textiles-leather-apparel", "chemicals", "pulp-and-paper", "aluminum", "other-manufacturing", "pulp-and-paper", "textiles-leather-apparel", "textiles-leather-apparel", "chemicals", "pulp-and-paper", "aluminum", "textiles-leather-apparel", "other-metals", "aluminum", "glass", "textiles-leather-apparel", "pulp-and-paper", "pulp-and-paper", "textiles-leather-apparel", "textiles-leather-apparel", "pulp-and-paper", "pulp-and-paper" ], "legendgroup": "manufacturing", "marker": { "color": "#FFA15A", "pattern": { "shape": "" } }, "name": "manufacturing", "offsetgroup": "manufacturing", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "CHN", "CHN", "CHN", "CHN", "USA", "JPN", "CHN", "CHN", "IND", "IND", "RUS", "USA", "RUS", "USA", "IND", "USA", "BRA", "USA", "USA", "RUS", "USA", "IRN", "CHN", "USA", "BRA", "JPN", "IDN", "USA", "MEX", "CAN", "IRN", "IRN", "IND", "USA", "IND", "CAN", "MEX", "IDN", "USA", "RUS", "CAN", "IRN", "RUS", "BRA", "JPN", "CAN", "CAN", "CAN", "IDN", "CHN", "JPN", "BRA", "BRA", "BRA", "MEX", "IDN", "USA", "MEX", "CAN", "USA", "CAN", "RUS", "BRA", "IDN", "IDN", "JPN", "IRN", "IND", "CAN", "JPN", "CAN", "CAN", "IDN", "IND", "MEX", "JPN", "MEX", "IRN" ], "xaxis": "x", "y": [ 282359841.12692106, 161100929.1941834, 110451304.87437865, 81750618.256424, 26260274.290366083, 23850092.582166467, 21775190.59177376, 21256980.00079107, 20220268.45333082, 20034142.326929264, 15505161.905919764, 14897021.878782425, 14563522.760875626, 12718809.76906706, 10453592.794688446, 10076158.156390531, 9389014.471430682, 9365019.925473513, 9161831.584183006, 8915678.285313884, 7646697.9486861965, 6978827.975157445, 6130580.046139351, 6025723.00533208, 5865622.56338577, 5763827.233476546, 5678009.490730967, 4870832.700960544, 4019028.3828533785, 3874112.7303434005, 3866886.069529579, 3547207.242068155, 2855026.4704821683, 2766509.7380886558, 2705935.2988115917, 2494519.9518160066, 2423030.4775740746, 2399570.1790008307, 2197607.264043243, 1884024.759282967, 1861459.1823751372, 1809581.1929062959, 1543295.0192633406, 1409400.1297483372, 1333727.6252302446, 1305429.921021154, 1127297.1712713859, 1053727.669803703, 1032105.3460519026, 814608.1882390713, 794616.5415752231, 776624.210524593, 735655.2138076888, 716537.524472642, 599637.4691013412, 544280.7274288418, 525864.443020068, 522321.1161551757, 478892.97588840075, 431676.3391263766, 266616.8310567071, 182119.46855840328, 143056.92064282863, 132176.95864426368, 126802.66403868496, 117523.99079410757, 114204.84364096924, 109274.48405080565, 101797.46475169405, 77429.29535134864, 62749.049666907995, 37802.192691300836, 35537.01626393253, 30399.818771101494, 27441.58502685507, 11897.28555094454, 7756.350308209658, 6072.072857695555 ], "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "%{hovertext}

sector=agriculture
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err_margin=%{y}", "hovertext": [ "enteric-fermentation-cattle-pasture", "cropland-fires", "cropland-fires", "cropland-fires", "rice-cultivation", "rice-cultivation", "synthetic-fertilizer-application", "enteric-fermentation-cattle-pasture", "cropland-fires", "synthetic-fertilizer-application", "rice-cultivation", "enteric-fermentation-cattle-pasture", "manure-left-on-pasture-cattle", "synthetic-fertilizer-application", "enteric-fermentation-cattle-pasture", "synthetic-fertilizer-application", "rice-cultivation", "cropland-fires", "enteric-fermentation-cattle-operation", "manure-left-on-pasture-cattle", "manure-left-on-pasture-cattle", "cropland-fires", "enteric-fermentation-cattle-pasture", "synthetic-fertilizer-application", "rice-cultivation", "synthetic-fertilizer-application", "synthetic-fertilizer-application", "manure-left-on-pasture-cattle", "enteric-fermentation-cattle-pasture", "synthetic-fertilizer-application", "manure-left-on-pasture-cattle", "manure-management-cattle-operation", "rice-cultivation", "enteric-fermentation-cattle-pasture", "cropland-fires", "synthetic-fertilizer-application", "manure-left-on-pasture-cattle", "enteric-fermentation-cattle-pasture", "manure-left-on-pasture-cattle", "synthetic-fertilizer-application", "rice-cultivation", "enteric-fermentation-cattle-pasture", "enteric-fermentation-cattle-operation", "manure-left-on-pasture-cattle", "enteric-fermentation-cattle-pasture", "enteric-fermentation-cattle-operation", "cropland-fires", "cropland-fires", "enteric-fermentation-cattle-operation", "enteric-fermentation-cattle-operation", "cropland-fires", "manure-left-on-pasture-cattle", "enteric-fermentation-cattle-operation", "manure-left-on-pasture-cattle", "rice-cultivation", "rice-cultivation", "manure-management-cattle-operation", "manure-management-cattle-operation", "manure-management-cattle-operation", "manure-management-cattle-operation", "manure-management-cattle-operation", "rice-cultivation" ], "legendgroup": "agriculture", "marker": { "color": "#19d3f3", "pattern": { "shape": "" } }, "name": "agriculture", "offsetgroup": "agriculture", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "BRA", "CHN", "BRA", "IND", "CHN", "IND", "CHN", "IND", "IDN", "IND", "IDN", "USA", "BRA", "USA", "MEX", "BRA", "JPN", "RUS", "USA", "USA", "IND", "MEX", "IDN", "IDN", "BRA", "RUS", "CAN", "MEX", "CAN", "MEX", "IDN", "USA", "USA", "RUS", "USA", "IRN", "CAN", "JPN", "RUS", "JPN", "IRN", "IRN", "BRA", "JPN", "CHN", "CAN", "JPN", "CAN", "CHN", "RUS", "IRN", "IRN", "MEX", "CHN", "RUS", "MEX", "CHN", "RUS", "BRA", "CAN", "MEX", "CAN" ], "xaxis": "x", "y": [ 233769209.22321343, 219900047.55174842, 218649602.17762208, 213991623.080494, 122032984.39419046, 110078069.99902405, 110062559.96155344, 92958436.55571808, 91361995.02807467, 90199863.330854, 78017450.95454398, 66761415.62741627, 65701905.45643003, 49507065.97848643, 34838302.43362347, 31524464.47969267, 28988953.805740304, 26062285.37807825, 23502872.84703237, 21650169.055533182, 20727690.029858038, 19842357.022529237, 19383334.764871497, 16947519.768929817, 15363203.191459766, 13400051.116925603, 12648506.014455853, 9791464.232004205, 8564214.994934123, 7017716.154231092, 6379138.14286144, 6184932.497154351, 5800289.794117904, 5738149.355431004, 5236604.285968013, 4375612.8982402915, 2777303.3456423925, 2437681.4487103415, 1916356.4834086755, 1581979.1406566326, 1336883.5378285367, 1274307.6190011238, 952774.0430279087, 802251.3484670959, 753972.557200277, 733012.9715283094, 587187.3289784113, 457778.5965499125, 420307.05478250305, 403368.84465252113, 403040.9172618186, 284142.6110172127, 253167.16661796797, 248135.57449432046, 239452.28114704226, 133064.97954228285, 82073.10978329304, 52000.82148233768, 19254.12441308968, 18421.434777011345, 5116.1225362964815, 0 ], "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "%{hovertext}

sector=waste
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sector=mineral-extraction
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err_margin=%{y}", "hovertext": [ "iron-mining", "copper-mining", "iron-mining", "iron-mining", "copper-mining", "iron-mining", "iron-mining", "copper-mining", "bauxite-mining", "iron-mining", "copper-mining", "copper-mining", "iron-mining", "copper-mining", "copper-mining", "copper-mining", "iron-mining", "bauxite-mining", "bauxite-mining", "bauxite-mining", "bauxite-mining", "bauxite-mining", "copper-mining" ], "legendgroup": "mineral-extraction", "marker": { "color": "#B6E880", "pattern": { "shape": "" } }, "name": "mineral-extraction", "offsetgroup": "mineral-extraction", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "BRA", "CHN", "IRN", "RUS", "IRN", "CAN", "IND", "USA", "BRA", "CHN", "CAN", "RUS", "USA", "MEX", "IDN", "BRA", "MEX", "IND", "IDN", "CHN", "RUS", "IRN", "IND" ], "xaxis": "x", "y": [ 2705551.8111058325, 1940183.7852668958, 1916751.5261648581, 1719150.8708110256, 1558479.7594284185, 1068435.566316153, 876546.9348308444, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_plot_data = (rankings\n", " .filter(~(c_sector == FORESTRY_AND_LAND_USE)))\n", "_country_order = (_plot_data\n", " .group_by(c_iso3_country)\n", " .agg(C(ERR_MARGIN).sum())\n", " .sort(by=ERR_MARGIN, descending=True)[ISO3_COUNTRY].to_list())\n", "px.bar(_plot_data.filter(c_iso3_country.is_in(_country_order[:10])), x=ISO3_COUNTRY,\n", " y=ERR_MARGIN, \n", " color=SECTOR,\n", " hover_name=SUBSECTOR, \n", " category_orders = {ISO3_COUNTRY: _country_order},\n", " log_y=False)" ] }, { "cell_type": "markdown", "id": "9a87f1d3-05c8-43f0-a871-4d1f77c04666", "metadata": {}, "source": [ "## Conclusion\n", "\n", "We saw in this notebook how to define levels of uncertainty from qualitative assessments. This gives us areas of focus: electrical generation in China, transportation in the US, ...\n", "\n", "It also underlines - again - the complexity around forestry and land uses. Vegetation is both the largest sink of carbon at scale and the least understood. There is much to understand in that area." ] }, { "cell_type": "code", "execution_count": null, "id": "55893d8a-0d99-44d1-a8e4-115a8b061d1e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "climate-trace-nO4nxH9g-py3.10", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }