25:["$","title",null,{"children":"ETS revision | Meili Vanegas-Hernandez | Observable"}] 4c:T117e,{ const filtered_lrf_scenarios = lrf_scenarios.filter(d => d.type != '3.7% (2031–35) → 2.7% (2036+)'); const filtered_lrf_scenarios_list = lrf_scenarios_list.filter(d => d != '3.7% (2031–35) → 2.7% (2036+)'); const filteredColorScale = d3.scaleOrdinal().domain(['Cap','Current trend','Under the new proposal (with int. credits)']).range(["#54F9DB","#1D9E88","#40B4C9"]) return html`

Weakening the ETS cap: 10 years of delay, 67% more carbon pollution

The European Commission's proposal will add 2GtCO₂e under the cap for power, industry and maritime sectors*.

${Swatches(filteredColorScale)}${Plot.plot( { height: 300, marginRight: 50, x: {}, y: {}, color: { // columns: 6, legend: false, domain: colorScale.domain(), range: ["#54F9DB","#1D9E88","#40B4C9","#40B4C9"], tickFormat: (d) => legendMapping[d] }, marks: [ Plot.axisX({ fontSize: 7, label: "", grid: false, tickFormat: d3.format(".0f"), labelArrow: "none" }), Plot.axisY({ fontSize: 8, label: "MtCO₂e", tickFormat: (d) => d3.format(",.0f")(d / 1000000) }), Plot.gridX({ strokeDasharray: "2", strokeOpacity: 0.1 }), Plot.areaY( filtered_lrf_scenarios.filter((d) => d.year <= 2030), { x: "year", y: "value", stroke: "type", strokeWidth: 5, fill: colorScale("Cap"), opacity: 0.5 } ), Plot.areaY(lrf_scenarios_projections, { x: "year", y: "additional_emissions", fill: "type", opacity: 0.5 }), Plot.lineY(filtered_lrf_scenarios, { x: "year", y: "value", stroke: "type", strokeWidth: (d) => (d.type == "Cap" ? 5 : 3) }), Plot.text(maxValues.filter(d => d.type != '3.7% (2031–35) → 2.7% (2036+)'), { x: (d) => d.year + 1, y: 0, dy: 12, textAnchor: "middle", fontWeight: "bold", fill: (d) => colorScale(d.type), stroke: "white", strokeWidth: 4, paintOrder: "stroke", text: (d) => d3.format(".0f")(d.year + 1) }), Plot.text(filtered_lrf_scenarios_list.slice(1), { x: (d) => parseInt(meanYear.get(d)) + 2, y: (d, i) => 700000000 + 120000000 * i, dx: 15, fill: (d) => colorScale(d), fontWeight: "bold", textAnchor: "end", text: (d, i) => `${i == 0 ? "Allowable emissions" : "Additional "} ${ i == 0 ? "" : `${d3.format(".0f")( (additionalEmissions.get(d) - additionalEmissions.get(filtered_lrf_scenarios_list[1])) / 1000000000 )}GtCO₂e emissions` }` }), Plot.text(filtered_lrf_scenarios_list.slice(1), { x: (d) => parseInt(meanYear.get(d)) + 2, y: (d, i) => 700000000 + 120000000 * i, dx: 15, dy: 10, fill: (d) => colorScale(d), fontWeight: "bold", textAnchor: "end", text: (d, i) => i == 0 ? "under the cap (3GtCO2e)" : `(${d3.format("+.0%")( (additionalEmissions.get(d) - additionalEmissions.get(filtered_lrf_scenarios_list[1])) / additionalEmissions.get(filtered_lrf_scenarios_list[1]) )})` }), Plot.link(filtered_lrf_scenarios_list.slice(1), { x1: (d) => parseInt(meanYear.get(d)) + 2.8, x2: (d) => parseInt(meanYear.get(d)) + 2.8, y1: (d) => scenariosGroups.get(d).get(parseInt(meanYear.get(d)) + 3), y2: (d, i) => 670000000 + 120000000 * i, stroke: (d) => colorScale(d), strokeWidth: 2 }), Plot.dot(filtered_lrf_scenarios_list.slice(1), { x: (d) => parseInt(meanYear.get(d)) + 2.8, y: (d, i) => 670000000 + 120000000 * i, fill: (d) => colorScale(d) }) ] } )}

* Excluding aviation and scope changes.

Source: CMW own calculations based on European Commission proposal of 17 July.

`; }4d:T628,

Weakening the ETS cap: 10 years of delay, 67% more carbon pollution

The European Commission's proposal will add 2GtCO₂e to the "emissions space" for power, industry and maritime sectors*.

${lrfTable.map(d => htl.html``)}

Year	Current trend	EC proposal (with int.credits)	EC proposal (without int.credits)
	${scenarioNames[1]}	${scenarioNames[3]}	${scenarioNames[2]}
${d["year"]}	${d3.format(',.0f')(d[scenarioNames[1]]?d[scenarioNames[1]]/1000000:0)}	${d3.format(',.0f')(d[scenarioNames[3]]?d[scenarioNames[3]]/1000000:0)}	${d3.format(',.0f')(d[scenarioNames[2]]?d[scenarioNames[2]]/1000000:0)}
TOTAL	${d3.format(',.0f')(d3.sum(lrfTable, d => d[scenarioNames[1]]/1000000))}	${d3.format(',.0f')(d3.sum(lrfTable, d => d[scenarioNames[3]]/1000000))}	${d3.format(',.0f')(d3.sum(lrfTable, d => d[scenarioNames[2]]/1000000))}

Source: CMW own calculations based on European Commission proposal of 17 July.

4e:Tefa,{ return Plot.plot( { height: 300, marginRight: 50, x: {}, y: {}, color: { // columns: 6, legend: true, domain: colorScale_input.domain(), range: colorScale_input.range(), tickFormat: (d) => legendMappingInput[d] }, title: inputScenarioName || 'Input scenario', caption:"* Excluding aviation and other scope changes. Source: CMW own calculations on the EU COM.", marks: [ Plot.axisX({ fontSize: 7, label: "", grid: false, tickFormat: d3.format(".0f"), labelArrow: "none" }), Plot.axisY({ fontSize: 8, label: "MtCO₂e", tickFormat: (d) => d3.format(",.0f")(d / 1000000) }), Plot.gridX({ strokeDasharray: "2", strokeOpacity: 0.1 }), Plot.areaY( lrf_scenarios_input.filter((d) => d.year <= 2030), { x: "year", y: "value", stroke: "type", strokeWidth: 5, fill: colorScale_input("Cap"), opacity: 0.5 } ), Plot.areaY(lrf_scenarios_projections_input.filter(d => d.year <= 2040), { x: "year", y: d => Math.max(0, d.additional_emissions), fill: "type", opacity: 0.5 }), Plot.lineY(lrf_scenarios_input, { x: "year", y: "value", stroke: "type", strokeWidth: (d) => (d.type == "Cap" ? 5 : 3) }), Plot.text(maxValuesInput, { x: (d) => d.year + 1, y: 0, dy: 18, textAnchor: "middle", fontWeight: "bold", fill: (d) => colorScale_input(d.type), stroke: "white", strokeWidth: 4, paintOrder: "stroke", text: (d) => d3.format(".0f")(d.year + 1) }), Plot.text(lrf_scenarios_list_input.slice(1), { x: (d,i) => (parseInt(meanYearInput.get(d)) + 2) - ((i>0)?2:0), y: (d, i) => 700000000 + 120000000 * i, dx: 15, fill: (d) => colorScale_input(d), fontWeight: "bold", textAnchor: "end", text: (d, i) => `${i == 0 ? "Allowable emissions" : "Additional "} ${ i == 0 ? "" : `${d3.format(".2f")( (additionalEmissionsInput.get(d) - additionalEmissionsInput.get(lrf_scenarios_list_input[1])) / 1000000000 )}GtCO₂e emissions` }` }), Plot.text(lrf_scenarios_list_input.slice(1), { x: (d,i) => (parseInt(meanYearInput.get(d)) + 2) - ((i>0)?2:0), y: (d, i) => 700000000 + 120000000 * i, dx: 15, dy: 10, fill: (d) => colorScale_input(d), fontWeight: "bold", textAnchor: "end", text: (d, i) => i == 0 ? "under the cap (3GtCO2e)" : `(${d3.format("+.0%")( (additionalEmissionsInput.get(d) - additionalEmissionsInput.get(lrf_scenarios_list_input[1])) / additionalEmissionsInput.get(lrf_scenarios_list_input[1]) )})` }), Plot.link(lrf_scenarios_list_input.slice(1), { x1: (d,i) => 2040-(2-i), x2: (d,i) => 2040-(2-i), y1: (d,i) => scenariosGroupsInput.get(d).get(2040-(2-i)), y2: (d, i) => 670000000 + 120000000 * i, stroke: (d) => colorScale_input(d), strokeWidth: 2 }), Plot.dot(lrf_scenarios_list_input.slice(1), { x: (d,i) => 2040-(2-i), y: (d, i) => 670000000 + 120000000 * i, fill: (d) => colorScale_input(d) }) ] } ) }4f:T50c, ${lrfTableInput.map(d => htl.html``)}

Year	Current trend	EC proposal (with int.credits)	${inputScenarioName}
	${scenarioNamesInput[1]}	${scenarioNamesInput[3]}	${scenarioNamesInput[2]}
${d["year"]}	${d3.format(',.0f')(d[scenarioNamesInput[1]]?d[scenarioNamesInput[1]]/1000000:0)}	${d3.format(',.0f')(d[scenarioNamesInput[3]]?d[scenarioNamesInput[3]]/1000000:0)}	${d3.format(',.0f')(d[scenarioNamesInput[2]]?d[scenarioNamesInput[2]]/1000000:0)}
TOTAL	${d3.format(',.0f')(d3.sum(lrfTableInput, d => d[scenarioNamesInput[1]]/1000000))}	${d3.format(',.0f')(d3.sum(lrfTableInput, d => d[scenarioNamesInput[3]]/1000000))}	${d3.format(',.0f')(d3.sum(lrfTableInput, d => d[scenarioNamesInput[2]]/1000000))}

50:Tea3,{ return Plot.plot( { height: 300, marginRight: 50, x: {}, y: {}, color: { // columns: 6, legend: true, domain: shadows_scenarios['colors'].domain(), range: shadows_scenarios['colors'].range(), // tickFormat: (d) => shadows_scenarios['datasets'][d]['name'] }, title: 'Shadows scenario', caption:"* Excluding aviation and other scope changes. Source: CMW own calculations on the EU COM.", marks: [ Plot.axisX({ fontSize: 7, label: "", grid: false, tickFormat: d3.format(".0f"), labelArrow: "none" }), Plot.axisY({ fontSize: 8, label: "MtCO₂e", tickFormat: (d) => d3.format(",.0f")(d / 1000000) }), Plot.gridX({ strokeDasharray: "2", strokeOpacity: 0.1 }), Plot.areaY(shadows_scenarios['projections'].filter(d => d.year <= 2040), { x: "year", y: d => Math.max(0, d.additional_emissions), fill: "type", opacity: 0.5 }), Plot.lineY(shadows_scenarios['data'], { x: "year", y: "value", stroke: "type", strokeWidth: 3 }), Plot.text(maxValuesInput, { x: (d) => d.year + 1, y: 0, dy: 18, textAnchor: "middle", fontWeight: "bold", fill: (d) => shadows_scenarios['colors'](d.type), stroke: "white", strokeWidth: 4, paintOrder: "stroke", text: (d) => d3.format(".0f")(d.year + 1) }), Plot.text([...Object.keys(shadows_scenarios['datasets'])], { x: (d,i) => (parseInt(meanYearInput.get(d)) + 2) - ((i>0)?2:0), y: (d, i) => 700000000 + 120000000 * i, dx: 15, fill: (d) => shadows_scenarios['colors'](d), fontWeight: "bold", textAnchor: "end", text: (d, i) => `${i == 0 ? "Allowable emissions" : "Additional "} ${ i == 0 ? "" : `${d3.format(".2f")( (additionalEmissionsInput.get(d) - additionalEmissionsInput.get([...Object.keys(shadows_scenarios['datasets'])][1])) / 1000000000 )}GtCO₂e emissions` }` }), Plot.text([...Object.keys(shadows_scenarios['datasets'])], { x: (d,i) => (parseInt(shadows_scenarios['datasets'][d]) + 2) - ((i>0)?2:0), y: (d, i) => 700000000 + 120000000 * i, dx: 15, dy: 10, fill: (d) => shadows_scenarios['colors'](d), fontWeight: "bold", textAnchor: "end", text: (d, i) => i == 0 ? "under the cap (3GtCO2e)" : `(${d3.format("+.0%")( (additionalEmissionsInput.get(d) - additionalEmissionsInput.get([...Object.keys(shadows_scenarios['datasets'])][1])) / additionalEmissionsInput.get([...Object.keys(shadows_scenarios['datasets'])][1]) )})` }), Plot.link([...Object.keys(shadows_scenarios['datasets'])], { x1: (d,i) => 2040-(2-i), x2: (d,i) => 2040-(2-i), y1: (d,i) => shadows_scenarios['rolled'].get(d).get(2040-(2-i)), y2: (d, i) => 670000000 + 120000000 * i, stroke: (d) => shadows_scenarios['colors'](d), strokeWidth: 2 }), Plot.dot([...Object.keys(shadows_scenarios['datasets'])], { x: (d,i) => 2040-(2-i), y: (d, i) => 670000000 + 120000000 * i, fill: (d) => shadows_scenarios['colors'](d) }) ] } ) }51:T6aa,{ let legends = { free_allowances: "Free allowances", emissions: "Emissions" }; return Plot.plot({ width: 600, x: { tickFormat: d3.format(".0f"), label: null }, y: { tickFormat: d3.format(",.0f"), label: "MtCO₂e" }, title: "The era of free lunches needs to stop", subtitle: "The Commission proposal would lead to an exorbitant 3.2 billion emissions awarded for free, covering 74% of total industrial emissions until 2040, according to Climact.", caption: "Source: Climact model, processed by CMW.", color: { legend: true, domain: ["free_allowances", "emissions"], range: [colors["free_allowances"], colors["emissions"]], tickFormat: (d) => legends[d] }, marks: [ Plot.ruleY([0]), Plot.areaY(fa_vs_emi, { x: "year", y: "emissions", fill: colors["emissions"] // stroke: colors["emissions"], // strokeWidth: 5 }), Plot.areaY(fa_vs_emi, { x: "year", y: "free_allowances", fill: colors["free_allowances"] }), Plot.text([`${d3.format(',.1f')((d3.sum(fa_vs_emi, d=>d.free_allowances))/1000)} billion`], { x: 2026, y: 150, dx: 15, frameAnchor: "right", textAnchor: "start", fill: "white", fontWeight: "bold", fontSize: 30 }), Plot.text([`${d3.format(',.0%')((d3.sum(fa_vs_emi, d=>d.free_allowances))/d3.sum(fa_vs_emi, d=>d.emissions))} of emissions`], { x: 2026, y: 120, dx: 15, frameAnchor: "right", textAnchor: "start", fill: "white", fontWeight: "bold", fontSize: 15 }) ] }); }52:Tb4c,shadows_scenarios = { let datasetDict = { "data": [], "datasets": { "3.4% (2031–35) → 2.3% (2036+)": { "name": "EPP", "dataset": cap_epp, "description": "3.4% (2031–35) → 2.3% (2036+)", "sum": d3.sum(cap_epp, d => d.value) }, "3.7% (2031–35) → 1.7% (2036+)": { "name": "EU COM", "dataset": lrf_scenario_1, "description": "3.7% (2031–35) → 1.7% (2036+)", "sum": d3.sum(lrf_scenario_1, d => d.value) }, "4.4% (2031–35) → 2.7% (2036+)": { "name": "S&D, Greens/EFA, The Left", "dataset": cap_s_and_d__greens__left, "description": "4.4% (2031–35) → 2.7% (2036+)", "sum": d3.sum(cap_s_and_d__greens__left, d => d.value) }, "2.4% (2031–35) → 1.7% (2036+)": { "name": "ECR", "dataset": cap_ecr, "description": "2.4% (2031–35) → 1.7% (2036+)", "sum": d3.sum(cap_ecr, d => d.value) }, "4.4% (2031–33) → 4.0% (2034–36) → 3.2% (2037–38) → 2.7% (2039+)": { "name": "Renew", "dataset": cap_renew, "description": "4.4% (2031–33) → 4.0% (2034–36) → 3.2% (2037–38) → 2.7% (2039+)", "sum": d3.sum(cap_renew, d => d.value) } } }; const scenarioList = Object.keys(datasetDict['datasets']).sort((a, b) => datasetDict['datasets'][a].sum - datasetDict['datasets'][b].sum); scenarioList.map((scenario, i) => { const length = datasetDict['datasets'][scenario]['dataset'].length; datasetDict['data'] = [...datasetDict['data'], ...datasetDict['datasets'][scenario]['dataset']]; datasetDict['datasets'][scenario]['sort'] = i; datasetDict['datasets'][scenario]['yearZero'] = datasetDict['datasets'][scenario]['dataset'][length-1].year; datasetDict['datasets'][scenario]['meanYear'] = (datasetDict['datasets'][scenario]['dataset'][length-1].year - 2031) / 2; }) const scenariosRolled = d3.rollup( datasetDict['data'], (v, d) => d3.sum(v, (d) => d.value), (d) => d.type, (d) => d.year ) let projections = []; let prevScenario = undefined; for (const scenario of scenarioList) { for (const year of years) { let additionalEmissions = !prevScenario ? scenariosRolled.get(scenario).get(year) : scenariosRolled.get(scenario).get(year) - (scenariosRolled.get(prevScenario).get(year) ? scenariosRolled.get(prevScenario).get(year) : 0); projections.push({ year: year, type: scenario, additional_emissions: additionalEmissions }); } prevScenario = scenario; } datasetDict['projections'] = projections; datasetDict['rolled'] = scenariosRolled; datasetDict['colors'] = d3.scaleOrdinal(lrf_scenarios_list_input, [...categoricalScaleNames1.range()]); return datasetDict; }53:T1ad7,// Copyright 2021-2023 Observable, Inc. // Released under the ISC license. // https://observablehq.com/@d3/treemap function Treemap( data, { // data is either tabular (array of objects) or hierarchy (nested objects) path, // as an alternative to id and parentId, returns an array identifier, imputing internal nodes id = Array.isArray(data) ? (d) => d.id : null, // if tabular data, given a d in data, returns a unique identifier (string) parentId = Array.isArray(data) ? (d) => d.parentId : null, // if tabular data, given a node d, returns its parent’s identifier children, // if hierarchical data, given a d in data, returns its children value, // given a node d, returns a quantitative value (for area encoding; null for count) sort = (a, b) => d3.descending(a.value, b.value), // how to sort nodes prior to layout label, // given a leaf node d, returns the name to display on the rectangle group, // given a leaf node d, returns a categorical value (for color encoding) title, // given a leaf node d, returns its hover text link, // given a leaf node d, its link (if any) linkTarget = "_blank", // the target attribute for links (if any) tile = d3.treemapBinary, // treemap strategy width = 640, // outer width, in pixels height = 400, // outer height, in pixels margin = 0, // shorthand for margins marginTop = margin, // top margin, in pixels marginRight = margin, // right margin, in pixels marginBottom = margin, // bottom margin, in pixels marginLeft = margin, // left margin, in pixels padding = 1, // shorthand for inner and outer padding paddingInner = padding, // to separate a node from its adjacent siblings paddingOuter = padding, // shorthand for top, right, bottom, and left padding paddingTop = paddingOuter, // to separate a node’s top edge from its children paddingRight = paddingOuter, // to separate a node’s right edge from its children paddingBottom = paddingOuter, // to separate a node’s bottom edge from its children paddingLeft = paddingOuter, // to separate a node’s left edge from its children round = true, // whether to round to exact pixels colors = d3.schemeTableau10, // array of colors zDomain, // array of values for the color scale fill = "#ccc", // fill for node rects (if no group color encoding) fillOpacity = group == null ? null : 0.6, // fill opacity for node rects stroke, // stroke for node rects strokeWidth, // stroke width for node rects strokeOpacity, // stroke opacity for node rects strokeLinejoin // stroke line join for node rects } = {} ) { // If id and parentId options are specified, or the path option, use d3.stratify // to convert tabular data to a hierarchy; otherwise we assume that the data is // specified as an object {children} with nested objects (a.k.a. the “flare.json” // format), and use d3.hierarchy. // We take special care of any node that has both a value and children, see // https://observablehq.com/@d3/treemap-parent-with-value. const stratify = (data) => d3 .stratify() .path(path)(data) .each((node) => { if (node.children?.length && node.data != null) { const child = new d3.Node(node.data); node.data = null; child.depth = node.depth + 1; child.height = 0; child.parent = node; child.id = node.id + "/"; node.children.unshift(child); } }); const root = path != null ? stratify(data) : id != null || parentId != null ? d3.stratify().id(id).parentId(parentId)(data) : d3.hierarchy(data, children); // Compute the values of internal nodes by aggregating from the leaves. value == null ? root.count() : root.sum((d) => Math.max(0, d ? value(d) : null)); // Prior to sorting, if a group channel is specified, construct an ordinal color scale. const leaves = root.leaves(); const G = group == null ? null : leaves.map((d) => group(d.data, d)); if (zDomain === undefined) zDomain = G; zDomain = new d3.InternSet(zDomain); const color = group == null ? null : d3.scaleOrdinal(zDomain, colors); // Compute labels and titles. const L = label == null ? null : leaves.map((d) => label(d.data, d)); const T = title === undefined ? L : title == null ? null : leaves.map((d) => title(d.data, d)); // Sort the leaves (typically by descending value for a pleasing layout). if (sort != null) root.sort(sort); // Compute the treemap layout. d3 .treemap() .tile(tile) .size([width - marginLeft - marginRight, height - marginTop - marginBottom]) .paddingInner(paddingInner) .paddingTop(paddingTop) .paddingRight(paddingRight) .paddingBottom(paddingBottom) .paddingLeft(paddingLeft) .round(round)(root); const svg = d3 .create("svg") .attr("viewBox", [-marginLeft, -marginTop, width, height]) .attr("width", width) .attr("height", height) .attr("style", "max-width: 100%; height: auto; height: intrinsic;") .attr("font-family", "sans-serif"); const node = svg .selectAll("a") .data(leaves) .join("a") .attr("xlink:href", link == null ? null : (d, i) => link(d.data, d)) .attr("target", link == null ? null : linkTarget) .attr("transform", (d) => `translate(${d.x0},${d.y0})`); node .append("rect") .attr("fill", color ? (d, i) => color(G[i]) : fill) .attr("fill-opacity", fillOpacity) .attr("stroke", stroke) .attr("stroke-width", strokeWidth) .attr("stroke-opacity", strokeOpacity) .attr("stroke-linejoin", strokeLinejoin) .attr("width", (d) => d.x1 - d.x0) .attr("height", (d) => d.y1 - d.y0) .style("font-size", (d) => { ((d.y1 - d.y0) / 200) * (20 - 10) + 10; return ((d.y1 - d.y0) / 200) * (20 - 10) + 10; }); if (T) { node.append("title").text((d, i) => T[i]); } if (L) { // A unique identifier for clip paths (to avoid conflicts). const uid = `O-${Math.random().toString(16).slice(2)}`; node .append("clipPath") .attr("id", (d, i) => `${uid}-clip-${i}`) .append("rect") .attr("width", (d) => d.x1 - d.x0) .attr("height", (d) => d.y1 - d.y0); node .append("text") .attr( "clip-path", (d, i) => `url(${new URL(`#${uid}-clip-${i}`, location)})` ) .selectAll("tspan") .data((d, i) => `${L[i]}`.split(/\n/g)) .join("tspan") .attr("x", 3) .attr("y", (d, i, D) => `${(i === D.length - 1) * 0.3 + 1.1 + i * 0.9}em`) .attr("fill-opacity", (d, i, D) => (i === D.length - 1 ? 0.7 : null)) .style("font-size", (d) => (d.includes("(") ? 11 : 12)) .text((d) => d); } return Object.assign(svg.node(), { scales: { color } }); }54:T81b,{ let all_data = historical_sector_trend.filter( (d) => d.year > 2012 && d.year < 2025 ); let yTop = d3.max(all_data, (d) => d.verified_emissions); let htmlGraph = html`

Emissions and free allocation across EU ETS stationary sectors

Data for Phase 3 (2013-2020) and Phase 4 (2021-2024)

${sortedSectors .slice(1, sortedSectors.length) .reverse() .map((subsector) => { let data = all_data.filter((d) => d.sector == subsector); let emi = d3.sum(data, (d) => d.verified_emissions); let fa = d3.sum(data, (d) => d.free_allowances); return html`

${subsector}

${Plot.plot({ height: 200, x: { axis: false }, y: { axis: false // domain: [0, yTop] }, marks: [ Plot.areaY(data, { x: "year", y: "verified_emissions", fill: categoricalScaleNames1("color_1"), opacity: 0.5 }), Plot.lineX(data, { x: "year", y: "free_allowances", stroke: categoricalScaleNames1("color_6"), strokeWidth: 2 }), Plot.text( data.filter((d) => d.year == 2018), { x: "year", y: (d) => d.verified_emissions / 2, fill: "white", text: (d) => `${d3.format(",.0f")( emi / 1000000 )} MtCO₂e emit · ${d3.format(",.0f")(fa / 1000000)} M FA`, fontSize: 30, fontWeight: "bold" } ) ] })}`; })}

Source: European Commission, processed by CMW. Data extracted on 15 April 2026.

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(2038-40)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1267,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1281,"value":"----","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1290,"value":"

Emissions under the cap

Excluding aviation and other scope changes.

Source: CMW own calculations based on European Commission proposal of 17 July.

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Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":503,"value":"## Cap","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":383,"value":"baselineEmissions = 2044749558","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"cap2026 = 1183203284","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"cap_data = {\n const lrfRanges = [\n { start: 2027, length: 1, lrf: 0.043 },\n { start: 2028, length: 3, lrf: 0.044 }\n ];\n\n const lrfValues = [];\n let cap = cap2026;\n\n for (const range of lrfRanges) {\n const rangeYears = [...Array(range.length).keys()].map(\n (d) => d + range.start\n );\n\n for (const year of rangeYears) {\n lrfValues.push({\n year: year,\n value: cap - range.lrf * baselineEmissions,\n type: \"Cap\"\n });\n cap = cap - range.lrf * baselineEmissions;\n }\n }\n\n return lrfValues;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":374,"value":"cap2030 = cap_data[cap_data.length - 1].value","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":641,"value":"legendMapping = {\n return {\n Cap: \"Cap\",\n \"4.4% Scenario\": \"Current trend\",\n \"3.7% (2031–35) → 1.7% (2036+)\":\n \"Under the new proposal (with int. credits)\",\n \"3.7% (2031–35) → 2.7% (2036+)\":\n \"Under the new proposal (without int. credits)\"\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1277,"value":"legendMappingInput = {\n let inputScenario = `${d3.format(',.1%')(lrf2031/100)} (2031–32) → ${d3.format(',.1%')(lrf2034/100)} (2034–35) → ${d3.format(',.1%')(lrf2036/100)} (2036–37) → ${d3.format(',.1%')(lrf2038/100)} (2038+)`\n return {\n Cap: \"Cap\",\n \"4.4% Scenario\": \"Current trend\",\n \"3.7% (2031–35) → 1.7% (2036+)\":\n \"EU COM proposal (with int. credits)\",\n \"3.7% (2031–35) → 2.7% (2036+)\":\n \"Under the new proposal (without int. credits)\",\n [inputScenario]:\n inputScenarioName?inputScenarioName:'Input scenario'\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":301,"value":"lrf_baseline_scenario = {\n const lrfRanges = [{ start: 2031, length: 21, lrf: 0.044 }];\n\n return calculateLRFScenarios(lrfRanges, `4.4% Scenario`);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":248,"value":"lrf_input_scenario = {\n const lrfRanges = [\n { start: 2031, length: 3, lrf: Math.round(lrf2031 / 100 * 1000) / 1000 },\n { start: 2034, length: 2, lrf: Math.round(lrf2034 / 100 * 1000) / 1000 },\n { start: 2036, length: 2, lrf: Math.round(lrf2036 / 100 * 1000) / 1000 },\n { start: 2038, length: 20, lrf: Math.round(lrf2038 / 100 * 1000) / 1000 }\n ];\n\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(',.1%')(lrf2031/100)} (2031–33) → ${d3.format(',.1%')(lrf2034/100)} (2034–35) → ${d3.format(',.1%')(lrf2036/100)} (2036–37) → ${d3.format(',.1%')(lrf2038/100)} (2038+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":316,"value":"lrf_scenario_1 = {\n const lrfRanges = [\n { start: 2031, length: 5, lrf: 0.037 },\n { start: 2036, length: 15, lrf: 0.017 }\n ];\n\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(\".1%\")(lrfRanges[0].lrf)} (2031–35) → ${d3.format(\".1%\")(\n lrfRanges[1].lrf\n )} (2036+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":321,"value":"lrf_scenario_2 = {\n const lrfRanges = [\n { start: 2031, length: 5, lrf: 0.037 },\n { start: 2036, length: 15, lrf: 0.027 }\n ];\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(\".1%\")(lrfRanges[0].lrf)} (2031–35) → ${d3.format(\".1%\")(\n lrfRanges[1].lrf\n )} (2036+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"lrf_scenarios = {\n let scenariosDatasets = [\n {\n dataset: lrf_baseline_scenario,\n sum: d3.sum(lrf_baseline_scenario, (d) => d.value)\n },\n // {\n // dataset: lrf_input_scenario,\n // sum: d3.sum(lrf_input_scenario, (d) => d.value)\n // }\n { dataset: lrf_scenario_1, sum: d3.sum(lrf_scenario_1, (d) => d.value) },\n { dataset: lrf_scenario_2, sum: d3.sum(lrf_scenario_2, (d) => d.value) }\n ];\n scenariosDatasets = scenariosDatasets.sort((a, b) => a.sum - b.sum);\n let list = [...cap_data];\n for (const scenario of scenariosDatasets) {\n list = [...list, ...scenario.dataset];\n }\n return list;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":489,"value":"years = [...new Set(lrf_scenarios.map((d) => d.year))].filter((d) => d > 2030)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"scenarioNames = [...new Set(lrf_scenarios.map((d) => d.type))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1289,"value":"scenarioNamesInput = [...new Set(lrf_scenarios_input.map((d) => d.type))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1278,"value":"scenariosGroupsInput = d3.rollup(\n lrf_scenarios_input,\n (v, d) => d3.sum(v, (d) => d.value),\n (d) => d.type,\n (d) => d.year\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"scenariosGroups = d3.rollup(\n lrf_scenarios,\n (v, d) => d3.sum(v, (d) => d.value),\n (d) => d.type,\n (d) => d.year\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"lrf_scenarios_list = [...new Set(lrf_scenarios.map((d) => d.type))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1272,"value":"lrf_scenarios_input = {\n let scenariosDatasets = [\n {\n dataset: lrf_baseline_scenario,\n sum: d3.sum(lrf_baseline_scenario, (d) => d.value),\n },\n\n { dataset: lrf_scenario_1, sum: d3.sum(lrf_scenario_1, (d) => d.value) },\n {\n dataset: lrf_input_scenario,\n sum: d3.sum(lrf_input_scenario, (d) => d.value)\n },\n ];\n scenariosDatasets = scenariosDatasets.sort((a, b) => a.sum - b.sum);\n let list = [...cap_data];\n for (const scenario of scenariosDatasets) {\n list = [...list, ...scenario.dataset];\n }\n return list;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1270,"value":"lrf_scenarios_list_input = [...new Set(lrf_scenarios_input.map((d) => d.type))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1269,"value":"lrf_scenarios_projections_input = {\n let projections = [];\n let prevScenario = undefined;\n for (const scenario of lrf_scenarios_list_input) {\n for (const year of years) {\n let additionalEmissions = !prevScenario\n ? scenariosGroupsInput.get(scenario).get(year)\n : scenariosGroupsInput.get(scenario).get(year) -\n (scenariosGroupsInput.get(prevScenario).get(year)\n ? scenariosGroupsInput.get(prevScenario).get(year)\n : 0);\n projections.push({\n year: year,\n type: scenario,\n additional_emissions: additionalEmissions\n });\n }\n prevScenario = scenario;\n }\n return projections;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":33,"value":"lrf_scenarios_projections = {\n let projections = [];\n let prevScenario = undefined;\n for (const scenario of lrf_scenarios_list) {\n for (const year of years) {\n let additionalEmissions = !prevScenario\n ? scenariosGroups.get(scenario).get(year)\n : scenariosGroups.get(scenario).get(year) -\n (scenariosGroups.get(prevScenario).get(year)\n ? scenariosGroups.get(prevScenario).get(year)\n : 0);\n projections.push({\n year: year,\n type: scenario,\n additional_emissions: additionalEmissions\n });\n }\n prevScenario = scenario;\n }\n return projections;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"lrfTable = years.map((year) => {\n let row = { year: year };\n lrf_scenarios_list.slice(1).map((scenario) => {\n row[scenario] = scenariosGroups.get(scenario).get(year);\n });\n return row;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1286,"value":"lrfTableInput = years.map((year) => {\n let row = { year: year };\n lrf_scenarios_list_input.slice(1).map((scenario) => {\n row[scenario] = scenariosGroupsInput.get(scenario).get(year);\n });\n return row;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":396,"value":"typesGroup = d3.group(\n lrf_scenarios.filter((d) => d.value > 0),\n (d) => d.type\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1276,"value":"typesGroupInput = d3.group(\n lrf_scenarios_input.filter((d) => d.value > 0),\n (d) => d.type\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":405,"value":"maxValues = [...typesGroup.keys().filter((d) => d !== \"Cap\")].map((d) => ({\n type: d,\n year: d3.max(typesGroup.get(d), (e) => e.year)\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1275,"value":"maxValuesInput = [...typesGroupInput.keys().filter((d) => d !== \"Cap\")].map((d) => ({\n type: d,\n year: d3.max(typesGroupInput.get(d), (e) => e.year)\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":416,"value":"meanYear = d3.rollup(\n lrf_scenarios.filter((d) => (d.type !== \"Cap\") & (d.value > 0)),\n (v) => (v, d3.mean(v, (d) => d.year)),\n (d) => d.type\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1273,"value":"meanYearInput = d3.rollup(\n lrf_scenarios_input.filter((d) => (d.type !== \"Cap\") & (d.value > 0)),\n (v) => (v, d3.mean(v, (d) => d.year)),\n (d) => d.type\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":412,"value":"additionalEmissions = d3.rollup(\n lrf_scenarios.filter((d) => (d.type !== \"Cap\") & (d.value > 0)),\n (v) => (v, d3.sum(v, (d) => d.value)),\n (d) => d.type\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1268,"value":"additionalEmissionsInput = d3.rollup(\n lrf_scenarios_input.filter((d) => (d.type !== \"Cap\") & (d.value > 0)& (d.year <= 2040)),\n (v) => (v, d3.sum(v, (d) => d.value)),\n (d) => d.type\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":421,"value":"colorScale = d3.scaleOrdinal(lrf_scenarios_list, [\n categoricalScaleNames1(\"color_2\"),\n categoricalScaleNames1(\"color_4\"),\n categoricalScaleNames1(\"color_6\"),\n categoricalScaleNames1(\"color_3\")\n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"colorScale_input = d3.scaleOrdinal(lrf_scenarios_list_input, [...categoricalScaleNames1.range()\n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":875,"value":"eua_avg_price = {\n let data = await FileAttachment(\"eua_price.csv\").csv({ typed: true });\n return d3.rollup(\n data,\n (v, d) => d3.sum(v, (d) => d.avg_price),\n (d) => d.year\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1283,"value":"fa_vs_emi = FileAttachment(\"forecasted_fa_vs_emissions.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1304,"value":"### Shadows\n* EPP: Liese (lead rapporteur)\n* S&D: Chahim\n* ECR: Vondra\n* Greens/EFA: Bloss\n* Renew: Ytterell, Friis, Bosse, Gerbrandy, Stügkh, Kulumni\n* The Left: Andersson","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1311,"value":"cap_epp = {\n const lrfRanges = [\n { start: 2031, length: 5, lrf: 0.034 },\n { start: 2036, length: 20, lrf: 0.023}\n ];\n\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(',.1%')(0.034)} (2031–35) → ${d3.format(',.1%')(0.023)} (2036+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1303,"value":"cap_s_and_d__greens__left = {\n const lrfRanges = [\n { start: 2031, length: 5, lrf: 0.044 },\n { start: 2035, length: 20, lrf: 0.027}\n ];\n\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(',.1%')(0.044)} (2031–35) → ${d3.format(',.1%')(0.027)} (2036+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1307,"value":"cap_ecr = {\n const lrfRanges = [\n { start: 2031, length: 5, lrf: 0.024 },\n { start: 2035, length: 20, lrf: 0.017}\n ];\n\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(',.1%')(0.024)} (2031–35) → ${d3.format(',.1%')(0.017)} (2036+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1306,"value":"cap_renew = {\n const lrfRanges = [\n { start: 2031, length: 3, lrf: 0.044 },\n { start: 2034, length: 3, lrf: 0.040 },\n { start: 2037, length: 2, lrf: 0.032},\n { start: 2039, length: 20, lrf: 0.027}\n ];\n\n return calculateLRFScenarios(\n lrfRanges,\n `${d3.format(',.1%')(0.044)} (2031–33) → ${d3.format(',.1%')(0.04)} (2034–36) → ${d3.format(',.1%')(0.032)} (2037–38) → ${d3.format(',.1%')(0.027)} (2039+)`\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1310,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":519,"value":"## Sectors","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1140,"value":"sectors_combustion = {\n let data = await FileAttachment(\"sectors_combustion@1.csv\").csv({\n typed: true\n });\n return d3.rollup(\n data,\n (v, d) => d3.sum(v, (d) => d.combustion),\n (d) => d.sector\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":521,"value":"stationary_json = FileAttachment(\"industrial_sector_emissions_2024@1.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":567,"value":"stationary_emissions_24 = FileAttachment(\n \"industrial_sector_emissions_2024.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":573,"value":"stationaryGroups = d3.rollup(\n stationary_emissions_24,\n (v, d) => d3.sum(v, (d) => d.value),\n (d) => d.group\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":576,"value":"totalStationary = d3.sum(stationary_emissions_24, (d) => d.value)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":609,"value":"sortedSectors = {\n return [...stationaryGroups.keys()].sort(\n (a, b) => stationaryGroups.get(a) - stationaryGroups.get(b)\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":606,"value":"historical_sector_trend = FileAttachment(\"historical_sector_trend.csv\").csv({\n typed: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":735,"value":"historicalGroupData = d3.rollup(\n historical_sector_trend,\n (v, d) =>\n Object.fromEntries(\n [\"verified_emissions\", \"free_allowances\"].map((col) => [\n col,\n d3.sum(v, (d) => +d[col])\n ])\n ),\n (d) => d.year,\n (d) => d.sector\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":914,"value":"transport = [\"Aviation\", \"Maritime\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":920,"value":"sectors2024 = {\n let data = historical_sector_trend.filter((d) => d.year == 2024);\n return data.sort((a, b) => b.verified_emissions - a.verified_emissions);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":971,"value":"allPhase3 = historical_sector_trend.filter(\n (d) => d.year >= 2013 && d.year < 2021\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"allPhase4 = historical_sector_trend.filter(\n (d) => d.year >= 2021 && d.year < 2025\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":929,"value":"stationaryPhase3 = historical_sector_trend.filter(\n (d) => !transport.includes(d.sector) && d.year >= 2013 && d.year < 2021\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":932,"value":"stationaryPhase4 = historical_sector_trend.filter(\n (d) => !transport.includes(d.sector) && d.year >= 2021 && d.year < 2025\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":949,"value":"stationary2024 = sectors2024.filter((d) => !transport.includes(d.sector))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1017,"value":"totalEm2024 = d3.sum(sectors2024, (d) => d.verified_emissions)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"# Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1296,"value":"style","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"import {\n categoricalScaleNames1,\n strippedDefs,\n colorsEmissionsFA as colors,\n style\n} from \"@meilivh/carbon-market-watch-colour-scales\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1297,"value":"import { Swatches, Legend } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":529,"value":"# Utilities","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1106,"value":"calculateLRFScenarios = (lrfRanges, scenarioName) => {\n const lrfValues = [];\n let cap = cap2030;\n\n for (const range of lrfRanges) {\n const rangeYears = [...Array(range.length).keys()].map(\n (d) => d + range.start\n );\n\n for (const year of rangeYears) {\n const newCap = cap - range.lrf * baselineEmissions;\n if (newCap < 0) {\n lrfValues.push({\n year: year,\n value: 0,\n lrf: range.lrf,\n type: scenarioName,\n });\n break;\n }\n lrfValues.push({\n year: year,\n value: newCap,\n lrf: range.lrf,\n type: scenarioName,\n });\n cap = newCap;\n }\n }\n return lrfValues;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":532,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":510,"value":"{\n let chart = Treemap(stationary_json, {\n value: (d) => d.value,\n group: (d, n) => d.group,\n label: (d, n) =>\n [\n ...d.name.split(\".\"),\n `${\n d.name.includes(\"combustion\")\n ? \"\"\n : d3.format(\",.0f\")(stationaryGroups.get(d.group) / 1000000) +\n \"M (\" +\n d3.format(\",.0%\")(\n stationaryGroups.get(d.group) / totalStationary\n ) +\n \")\"\n }`,\n `${d3.format(\",.0f\")(n.value / 1000000)}M`\n ].join(\"\\n\"),\n colors: [...categoricalScaleNames1.range(), \"lightblue\", \"lightgrey\"],\n width: 1200,\n height: 400\n });\n let htmlChart = html`

2024 stationary emissions by sector

Power 489 MtCO₂e, Refineries 105 MtCO₂e, Iron and steel 96 MtCO₂e,

${chart}

2024 Data.

Source: European Commission, processed by CMW. Data extracted on 15 April 2026.

`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":512,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":634,"value":"0.017 * baselineEmissions","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":742,"value":"historicalGroupData.get(2013).get(\"Aviation\").verified_emissions","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W56","stdlib":"1"}} 24:null