31:T61f,## Intro The world is in a climate emergency – “a code red for humanity,” according to the UN Secretary-General. The concentration of greenhouse gas (GHG) emissions in the atmosphere is wreaking havoc worldwide and threatening lives, economies, health, and food. The world is far from securing a global temperature rise to below 2°C as promised in the Paris Agreement. Many organizations are tracking every aspect of GHG emissions. Our World In Data has one of the most complete sets, with over 70 years of more than 50 indicators across 200 countries. An analysis of this data could uncover patterns, inform policy, and be used to enlighten the general public. The objective of this analysis is to see how C02 per capita and general GHG per capita emissions evolve over time, comparing four country profiles depending on their Human Development Index, and how global treaties and environmental policies have affected to these emissions, paying special attention to Rio Agreement, Kyoto Protocol and Paris Agreement which are the most accepted by the international community to determine if any of these agreements have had any effect on the activity and development of different groups or are just dead letter. As a sideway we will also put a focus on how CO2 emissions (and GHG) affect to global warming and so, how this global warming can affect the economies of these four country profiles to determine if it is already affecting and detect trends to finally answer a sensitive question: **Are these treats environmentally or economically driven?**32:Ta60,lineDirectionPlot = { //const data = obtenirDadesFiltrades().sort((a, b) => (b.value_end - b.value_start) - (a.value_end - a.value_start)); const data = obtenirDadesFiltrades().sort((a, b) => (a.value_start - b.value_start)); const orderedCountries = data.map(d => d.country); const plot = Plot.plot({ height: 900, width: 1200, marginLeft: 120, grid: true, x: { label: "Value", nice: true }, y: { label: "Country", inset: 5, domain: orderedCountries, }, marks: [ Plot.frame(), Plot.arrow( data, { x1: d => d.value_start, x2: d => d.value_end, y: d => d.country, stroke: d => { const difference = d.value_end - d.value_start; if (difference > 0) { return "red"; } else if (difference < 0) { return "green"; } else { return "gray"; } }, strokeWidth: 2, tip: { fontSize: 15, dy: 0, format: { y: (d) => `${(d)}`, z: false, x: (d) => `${d.toLocaleString("en-US", "numeric")}`, //x: (d) => `Value: ${d.value_start.toLocaleString("en-US", {style: "decimal"})}` }, }, } ) ] }); const plotMap = d3.select(plot); const map = html`

${plotMap.node()}

`; function highlight(e) { console.log("HIGHLIIIIGHT"); if (focus !== null) return; const index = getTargetIndex(e); d3.select(this).style("cursor", index >= 0 ? "pointer" : "default"); fill(index, true); if (timeout) clearTimeout(timeout); } function restore(e) { if (focus !== null) return; const index = getTargetIndex(e); fill(index); } function fill(index, highlight, lowlight, darker) { if (index >= 0) { d3.select(line.nodes()[index]) .attr("stroke-width", highlight ? 5 : 2); } } function getTargetIndex(e) { let tar = e.target; return tar instanceof SVGPathElement ? [...tar.parentElement.children].indexOf(tar) : -1; } let timeout, focus = null; const line = plotMap.selectAll("[aria-label='arrow']").selectChildren(); plotMap .on("pointerover", highlight) .on("pointerout", restore) .style("cursor", "default"); return map; }33:T4eb,Here are some of the findings: The general trends we have seen in the previous graphs are corroborated. Analyzing the Co2 per capita: - The countries in the Very High index that increase emissions the most are Qatar, Oman and Lithuania. The ones that have decreased the most are Estonia, Luxembourg, the Czech Republic and Slovakia. - The countries in the High index that increase emissions the most are Mongolia, China and Iran. Those that have decreased the most are Moldova, Bulgaria, Palau and Azerbaijan. - The countries in the Medium index that increase emissions the most are Equatorial Guinea, India and Iraq. Those that have decreased the most are Kyrgyzstan and Syria. - The Low index countries that increase emissions the most are Benin, Pakistan and Mali. Those that have decreased the most are Djibouti. - If we filter by Very High and from 1990 to 1991, we clearly see the reason for the drastic rise in heart per capita that we saw in the previous graph. The country responsible is Kuwait, the country where the Gulf War took place. Analyzing the Ghg per capita: - We can analyze the drastic increase in the "Medium" category in 2015. If we filter by these conditions, we see that the only country responsible is the Solomon Islands.34:T58b,Greenhouse gas emissions, including carbon dioxide (CO2) have a singnificant impact on global temperature rise. Here's how: - **Greenhouse Effect**: Solar radiation reaches Earth, and a portion of it is absorbed by the planet's surface. Earth, in turn, emits thermal radiation back into space. Greenhouse gases in the atmosphere absorb and re-emit some of this thermal radiation, contributing to retaining heat in the atmosphere. Without this natural greenhouse effect, Earth would be much colder. - **Increased GHG concentrations**: Human activities such as burning fossil fuels, deforestation, and industrialization have increased the concentration of greenhouse gases in the atmosphere. CO2 is a major contributor due to the burning of fossil fuels. - **Enhanced Heat Retention**: As GHG concentrations rise, more thermal radiation is absorbed and re-emitted, increasing heat retention in the atmosphere. This leads to additional warming of the Earth, known as the "enhanced greenhouse effect." - **Climate Changes**: Rising global temperatures have various consequences, including changes in climate patterns, more frequent extreme weather events, melting glaciers and ice caps, and **rising sea levels**, among others. - **Positive Feedback Loops**: Global warming can also trigger positive feedback loops, such as the release of methane trapped in permafrost or ocean sediments, further contributing to warming.35:T402,As we can see, the three metrics (emissions, temperature rise, and sea-level rise) are closely interconnected, and the worst part is that globally, it is not perceived that these international treaties and agreements have any effect on the overall trends of temperature or sea-level rise. As observed, not even the worldwide lockdown caused by the Covid-19 pandemic has been able to restrain the upward trends in emissions (they practically rebounded once the pandemic passed) or in temperature and sea-level rise. This poses a major challenge for future generations because if we want to reduce the collateral effects of emissions, we should resort to radical global changes that are likely to be difficult for the emerging economies of the world to assume, as well as the implementation of very strict legislation regarding emissions and greenhouse gases. After witnessing the effects of international treaties, are these truly driven by a desire for environmental recovery? Or are there other significant factors at play?36:T48c,It is evident that environmental degradation is a key element for the survival of the species; nevertheless, it is often manipulated for the interests of the political and economic elite. This raises the question of whether all aspects of environmental policies and treaties solely focus on the environment or if there are also economic factors at play. To delve into this, we find it pertinent to explore how an impact resulting from greenhouse gas (GHG) and CO2 emissions can affect the global economy. One such impact is the rise in sea levels (SLR). In an article published by Bachner, G., Lincke, D. & Hinkel, J. titled "The macroeconomic effects of adapting to high-end sea-level rise via protection and migration" on nature.com (https://doi.org/10.1038/s41467-022-33043-z), the study examined the potential effects of rising sea levels on global economies by making more and less pessimistic assumptions about polar ice melting. The relationship was analyzed in relation to the Human Development Index (HDI) of the affected countries, and the percentage presented represents the average percentage of the involved countries. The details are outlined below:37:T1fb1,linePlot = { const hdiCategories = ["Very High", "High", "Medium", "Low"]; // Lista de categorías HDI const plot = Plot.plot({ width: 1500, height: 1250, margin: 80, marginTop: 50, marginBottom: 30,marginRight: 110, grid: true, x: { label: "Year", //type: "utc", // Especifica que el eje X es de tipo temporal (utc) //tickFormat: "%Y" // Formatea los ticks para mostrar solo el año }, y: { label: radioCO2GHG == "CO2"?"Co2 per capita":"GhG per capita", tickFormat: d => d % 10 === 0 ? d : "" }, symbol: {legend: true}, color: { legend: true, range: ["#F7E425", "#EA7457", "#BF3984", "#081D58"], domain: ["Low", "Medium", "High", "Very High"], }, marks: [ // ---------------------------------- LES 4 CATEGORIES ---------------------------------------------------------- Plot.dot(meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter((d) => d.year.getUTCFullYear() >= 1990 && (hdi_filtre.includes(d.hdi_category) || hdi_filtre.length == 0)),{ x: "year", interval: "year", // yearly data y: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", //r:"sumCo2", fill: "hdi_category", tip: { fontSize: 45, format: { y: (d) => `${d3.format(".1f")(d)}`, z: false, x: (d) => d.getUTCFullYear(), }, }, }), Plot.line(meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter((d) => d.year.getUTCFullYear() >= 1990 && (hdi_filtre.includes(d.hdi_category) || hdi_filtre.length == 0)), { x: "year", interval: "year", // yearly data y: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", z: "hdi_category", stroke: "hdi_category", opacity: 0.4, strokeWidth: 13, curve: "bump-x", }), // ------------------ LÍNIES VERTICALS TRACTATS --------------------------------------------------- Plot.tickX(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { x: "year", strokeWidth: 3, filter: (d) => d.year.getUTCFullYear() == 2017, }), Plot.tickX(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { x: "year", strokeWidth: 3, filter: (d) => d.year.getUTCFullYear() == 2005, dy: 15, }), Plot.tickX(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { x: "year", strokeWidth: 3, filter: (d) => d.year.getUTCFullYear() == 1992, }), Plot.text(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { frameAnchor: "top", x: "year", filter: (d) => d.year.getUTCFullYear() == 1992, text: d => `Rio Agreement`, textAnchor: "end", rotate:-90, fontSize: 40, fontWeight: "bold", dx: -35, dy: 30, y1: d => d.y - 15, }), Plot.text(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { frameAnchor: "top", x: "year", filter: (d) => d.year.getUTCFullYear() == 2005, text: (d) => [2005].includes(d.year.getUTCFullYear()) ? `${ tractats_ambientals.get(d.year.getUTCFullYear())}` : "", textAnchor: "end", fontSize: 40, rotate: -90, fontWeight: "bold", dx: -35, //-5, dy: 30, y1: d => d.y - 15, }), Plot.text(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { frameAnchor: "top", x: "year", filter: (d) => d.year.getUTCFullYear() == 2017, text: (d) => [2017].includes(d.year.getUTCFullYear()) ? `${ tractats_ambientals.get(d.year.getUTCFullYear())}` : "", textAnchor: "end", fontSize: 40, //30 rotate: -90, fontWeight: "bold", dx: -35, //-5, dy: 30 }), // TIPS DE CADA LINIA-------------- Plot.text( meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter(d => d.year.getUTCFullYear() >= 1990 && d.hdi_category == "Very High" && (hdi_filtre.includes(d.hdi_category) || hdi_filtre.length == 0)), Plot.selectLast({ x: "year", y: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", text: d => d.hdi_category, lineAnchor: "middle", fontSize: 30, dx: 41, dy: 10, fill: "#0D0887", // Color del texto fontWeight: "bold", // Negrita }) ), Plot.text( meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter(d => d.year.getUTCFullYear() >= 1990 && d.hdi_category == "High" && (hdi_filtre.includes(d.hdi_category) || hdi_filtre.length == 0)), Plot.selectLast({ x: "year", y: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", text: d => d.hdi_category, lineAnchor: "middle", fontSize: 30, dx: 41, dy: 10, fill: "#BF3984", // Color del texto fontWeight: "bold", // Negrita }) ), Plot.text( meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter(d => d.year.getUTCFullYear() >= 1990 && d.hdi_category == "Medium" && (hdi_filtre.includes(d.hdi_category) || hdi_filtre.length == 0)), Plot.selectLast({ x: "year", y: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", text: d => d.hdi_category, lineAnchor: "middle", fontSize: 30, dx: 41, dy: 10, fill: "#EA7457", // Color del texto fontWeight: "bold", // Negrita }) ), Plot.text( meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter(d => d.year.getUTCFullYear() >= 1990 && d.hdi_category == "Low" && (hdi_filtre.includes(d.hdi_category) || hdi_filtre.length == 0)), Plot.selectLast({ x: "year", y: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", text: d => d.hdi_category, lineAnchor: "middle", fontSize: 30, dx: 41, dy: 10, fill: "#F7E425", // Color del texto fontWeight: "bold", // Negrita }) ), // -------------- LÍNIA VERTICAL I TEXT DE LA LÍNIA ------------------------------------------------------------ Plot.tickX(years.filter((d) => d.year.getUTCFullYear() == any_filtre_2 && ![2017, 2005].includes(d.year.getUTCFullYear())), { x: "year", strokeWidth: 2, stroke: "red", //filter: (d) => d.year.getUTCFullYear() == 2017 || d.year.getUTCFullYear() == 2005 }), Plot.text(years.filter((d) => d.year.getUTCFullYear() == any_filtre_2 && ![2017, 2005].includes(d.year.getUTCFullYear())), { frameAnchor: "top", x: "year", text: (d) => [2017, 2005].includes(d.year.getUTCFullYear()) ? `${ d.year.getUTCFullYear() + ", " + tractats_ambientals.get(d.year)}` : d.year.getUTCFullYear(), textAnchor: "end", fontSize: 30, fontWeight: "bold", dx: -5, dy: -10, fill: "red", }), Plot.axisX({fontSize: 30}), Plot.axisY({fontSize: 30}) ] }); return plot; }38:Tf52,temperaturePlot = { return Plot.plot({ width: 1525, height: 250, margin: 0, marginTop: 25, marginBottom: 30, x: { label: "Year", //type: "utc", // Especifica que el eje X es de tipo temporal (utc) //tickFormat: "%Y" // Formatea los ticks para mostrar solo el año }, color: { //scheme: radioCO2GHG == "CO2"?"reds": "grays", range: radioCO2GHG == "CO2"?co2colorRange:ghgcolorRange, //jfc ho he canviat per les variables, així tocant-les es canviarà tot type: "sequential", // o "diverging" o "sequential", dependiendo de tus datos legend: true, // Habilita la leyenda label: radioCO2GHG == "CO2"?"Total of Co2":"Total of GhG", // Título de la leyenda }, marks: [ Plot.barX(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { x: "year", fill: radioCO2GHG == "CO2"?"sumCo2":"sumGhG", interval: "year", // yearly data insetRight: -0.5, insetTop: 11, tip: { fontSize: 25, format: { y: (d) => `${d3.format(".1f")(d)}`, z: false, x: (d) => d.getUTCFullYear(), }, }, }), Plot.tickX(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { x: "year", strokeWidth:3, stroke:"red", filter: (d) => d.year.getUTCFullYear() == 2017 || d.year.getUTCFullYear() == 2005 || d.year.getUTCFullYear() == 1992 }), Plot.text(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { frameAnchor: "top", x: "year", filter: (d) => d.year.getUTCFullYear() == 2005, text: (d) => [2005].includes(d.year.getUTCFullYear()) ? `${ tractats_ambientals.get(d.year.getUTCFullYear())}` : "", textAnchor: "start", fontSize: 16, fill:"red", fontWeight: "bold", dx: 5, dy: -5 }), Plot.text(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { frameAnchor: "top", x: "year", filter: (d) => d.year.getUTCFullYear() == 2017, text: (d) => [2017].includes(d.year.getUTCFullYear()) ? `${ tractats_ambientals.get(d.year.getUTCFullYear())}` : "", textAnchor: "start", fontSize: 16, fill:"red", fontWeight: "bold", dx: 5, dy: -5 }), Plot.text(totalCo2EmissionsPerYear.filter((d) => d.year.getUTCFullYear() >= 1990), { frameAnchor: "top", x: "year", filter: (d) => d.year.getUTCFullYear() == 1992, text: (d) => [1992].includes(d.year.getUTCFullYear()) ? `${ tractats_ambientals.get(d.year.getUTCFullYear())}` : "", textAnchor: "start", fontSize: 16, fill:"red", fontWeight: "bold", dx: 5, dy: -5 }), // -------------- LÍNIA VERTICAL I TEXT DE LA LÍNIA ------------------------------------------------------------ Plot.tickX(years.filter((d) => d.year.getUTCFullYear() == any_filtre_2 && ![2017, 2005, 1992].includes(d.year.getUTCFullYear())), { x: "year", strokeWidth: 3, stroke: "red", //filter: (d) => d.year.getUTCFullYear() == 2017 || d.year.getUTCFullYear() == 2005 }), Plot.text(years.filter((d) => d.year.getUTCFullYear() == any_filtre_2 && ![2017, 2005, 1992].includes(d.year.getUTCFullYear())), { frameAnchor: "top", x: "year", text: (d) => [2017, 2005, 1992].includes(d.year.getUTCFullYear()) ? `${ d.year.getUTCFullYear() + ", " + tractats_ambientals.get(d.year)}` : d.year.getUTCFullYear(), textAnchor: "end", fontSize: 30, fontWeight: "bold", dx: -5, dy: -25, fill: "red", }), ] }) }39:T60b,mapaEmissions = { const plot = Plot.plot({ width: 1200, projection: { type: "equal-earth", rotate: [-10, 0] }, color: { range: ["#000000", "#F7E425", "#EA7457", "#BF3984", "#0D0887"], domain: ["NA", "Low (<0.55)", "Mid (0.55-0.69)", "High (0.70-0.79)", "Very High (>0.80)"], legend: false, width: 360 }, marks: [ //Plot.sphere({stroke: "currentColor" }), //Plot.graticule({stroke: "#101010"}), Plot.geo(world, { fill: (d) => getHDI(d.properties.iso_a3, d.properties.name, any_filtre_2, true), fillOpacity: 0.6, stroke: "white", strokeWidth : 0.25 }), //aplicar filtre hdi de hdi_filter Plot.dot(world, Plot.geoCentroid( { r: (d) => radioCO2GHG == 'GHG' ? getGHG(d.properties.iso_a3, d.properties.iso_a3_eh, d.properties.name, any_filtre_2) : getCO2(d.properties.iso_a3, d.properties.iso_a3_eh, d.properties.name, any_filtre_2), fill: (radioCO2GHG == 'GHG' ? ghgcolor : co2color), fillOpacity: 0.2, stroke: (radioCO2GHG == 'GHG' ? ghgcolor : co2color), title: (d) => { let valor; if (radioCO2GHG == 'GHG') { valor = getGHG(d.properties.iso_a3, d.properties.iso_a3_eh, d.properties.name, any_filtre_2); return d.properties.name + '\nGHG:' + valor; }else{ valor = getCO2(d.properties.iso_a3, d.properties.iso_a3_eh, d.properties.name, any_filtre_2); return d.properties.name + '\nCO2:' + valor; } } }) ) ] }) return plot; } 3a:Tc43,totalEmissionsPlot = { const myColor = { domain: [0, 100000], scheme: "greys", legend: true, ticks: 5, label: "Total global emissions of CO2 and GHG (in Tons)" } const data = co2_amb_hdi_4; const myPlot = Plot.plot({ marginBottom: 50, marginLeft: 50, y: { ticks: 7, nice: true, grid: true, domain: [0,100000] }, x: { ticks: 30, tickRotate: -90, label: '', domain:[1990,2023] }, color: myColor, marks: [ Plot.areaY(data.filter(d=> parseInt(d.year) >= 1990), Plot.groupX({y:"Sum"},{ x: d=> parseInt(d.year), y: d => (isNaN(d.total_ghg)?parseFloat(d.co2):parseFloat(d.co2) + parseFloat(d.total_ghg)), curve: "bump-x", stroke:"url(#gradient)", fill:"url(#gradient)" })), Plot.line(data.filter(d=> parseInt(d.year) >= 1990), Plot.groupX({y:"Sum"},{ x: d=> parseInt(d.year), y: d => (isNaN(d.total_ghg)?parseFloat(d.co2):parseFloat(d.co2) + parseFloat(d.total_ghg)), curve: "bump-x", stroke:"#303030", strokeWidth: 3, tip: true })), //marques Plot.tickX(data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "red", filter: (d) => d.year == 1992}), Plot.text(data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 1992, text: d => `Rio Agreement`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 200, y1: d => d.y - 15, }), Plot.tickX(data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "red", filter: (d) => d.year == 2005}), Plot.text(data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 2005, text: d => `Kyoto Protocol`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 200, y1: d => d.y - 15, }), Plot.tickX(data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "red", filter: (d) => d.year == 2017}), Plot.text(data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 2017, text: d => `Paris Agreement`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 200, y1: d => d.y - 15, }), Plot.tip( [`Gulf War`], {x: 1991, y: 52769, dy: -2, anchor: "bottom"} ), Plot.tip( [`SubPrime Crisis`], {x: 2009, y: 70040, dy: 2, anchor: "top"} ), Plot.tip( [`CoVid-19`], {x: 2020, y: 77663, dy: 2, anchor: "top"} ), Plot.tip( [`No GHG data`], {x: 2021, y: 34888, dy: 2, anchor: "top"} ), (_index, { y, color }) => htl.svg` ${d3.ticks(0, 1, 10).map( (t) => htl.svg`` )}` ] }); return myPlot; }3b:Tba5,temperaturAnomalyPlot = { const myTempAnomalyPlot = Plot.plot({ marginBottom: 50, marginLeft: 50, y: { nice:true, domain: [0,1.5] }, x: { ticks:30, tickRotate: -90, label:'', domain:[1990,2023] }, color: { domain: [0, 1.5], scheme: "reds", legend: true, ticks: 5, label: "Global Temperature Anomaly (°C)" }, marks: [ Plot.ruleY([0]), //Plot.lineY(meanCo2PerCapitaEmissionsPerHDICategoryAndYear, // Plot.groupX({y:"Mean"},{x: "year", y: "sumCo2", curve: "bump-x"})), Plot.areaY(temperature.filter(d=> d.Year>=1990), { x: d=> parseInt(d.Year), y: "Anomaly", curve: "bump-x", fill: "url(#gradient3)", stroke: "url(#gradient3)" }), Plot.line(temperature.filter(d=> d.Year>=1990), { x: d=> parseInt(d.Year), y: "Anomaly", curve: "bump-x", stroke: "red", strokeWidth:3, channels: { Year: { value: "Year", label: "Year:" }, Anomaly: { value: "Anomaly", label: "TºC anomaly: +" } }, tip: { format: { Year: true, Anomaly: true, x: false, y: false } } }), //Plot.lineY(temperature.filter(d=> d.Year>=1990), Plot.groupX({y:"Mean"}, { //x: "Year", //y: "Anomaly", //curve: "bump-x", //stroke: "orange", //interval: 10})), //marques Plot.tickX(global_data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "green", filter: (d) => d.year == 1992}), Plot.text(global_data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 1992, text: d => `Rio Agreement`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 100, y1: d => d.y - 15, }), Plot.tickX(global_data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "green", filter: (d) => d.year == 2005}), Plot.text(global_data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 2005, text: d => `Kyoto Protocol`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 100, y1: d => d.y - 15, }), Plot.tickX(global_data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "green", filter: (d) => d.year == 2017}), Plot.text(global_data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 2017, text: d => `Paris Agreement`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 100, y1: d => d.y - 15, }), (_index, { y, color }) => htl.svg` ${d3.ticks(0, 1, 5).map( (t) => htl.svg`` )}` ] }); return myTempAnomalyPlot; }3c:Ta39,totalSeaLevelRaisePlot = { const mySLRPlot = Plot.plot({ marginBottom: 50, marginLeft: 50, y: { grid:true, label: "SLR mean (mm)", domain:[-20,80] }, x: { ticks: 30, tickRotate: -90, label : '', domain:[1990,2023] }, color: { domain: [-20, 80], scheme: "blues", legend: true, ticks: 5, label: "Sea Level Raise Global Mean (in mm)" }, marks: [ Plot.ruleY([0]), Plot.areaY(slr_clean, { x: d=> parseInt(d.year), y: "mediana_slr", curve: "bump-x", fill: "url(#gradient2)" }), Plot.line(slr_clean, { x: d=> parseInt(d.year), y: "mediana_slr", curve: "bump-x", stroke: "blue", //"url(#gradient2)", strokeWidth: 3, channels: { year: { value: "year", label: "Year:" }, mediana_slr: { value: "mediana_slr", label: "SLR mean (mm):" } }, tip: { format: { year: true, mediana_slr: true, x: false, y: false } } }), //marques Plot.tickX(global_data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "red", filter: (d) => d.year == 1992}), Plot.text(global_data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 1992, text: d => `Rio Agreement`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 200, y1: d => d.y - 15, }), Plot.tickX(global_data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "red", filter: (d) => d.year == 2005}), Plot.text(global_data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 2005, text: d => `Kyoto Protocol`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 200, y1: d => d.y - 15, }), Plot.tickX(global_data.filter(d=> d.year >= 1990), {x: d=> parseInt(d.year), stroke: "red", filter: (d) => d.year == 2017}), Plot.text(global_data.filter(d=> d.year >= 1990), {frameAnchor: "top", x: d=> parseInt(d.year), filter: (d) => d.year == 2017, text: d => `Paris Agreement`, textAnchor: "end", rotate:-90, fontSize: 10, dx: -10, dy: 200, y1: d => d.y - 15, }), (_index, { y, color }) => htl.svg` ${d3.ticks(0, 1, 10).map( (t) => htl.svg`` )}` ] }); return mySLRPlot; } 3d:T606,slrhdi_veryhigh = Plot.plot({ margin: 30, title: html`

SLR GDP impact on Very High HDI countries

`, color: { domain: [0, 10], scheme: newBlueRange, legend: false, ticks: 5 }, y : { domain: [0,10], label: "% of impact on GDP", transform: (p) => (p*100*-1) }, x : { domain: [2015, 2050], //tickRotate: 90 }, style: { //padding: 10, color: "black", background: `url(${await FileAttachment("very_high@2.png").url()})`, backgroundSize: "cover" }, marks: [ Plot.frame(), Plot.areaY(slr_gdp_impact_highice.filter(d=> d.HDI=="Very High") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", fill: "#00007D", curve: "bump-x", opacity: 0.4 })), Plot.line(slr_gdp_impact_highice.filter(d=> d.HDI=="Very High") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", stroke: "#00007D", strokeWidth: 3, curve: "bump-x", tip: true })), Plot.image([0], { x: 2050, y: 0.02, width: 100, src: "https://udg-master-data-science.static.observableusercontent.com/3b18eb33-2183-4a09-8ab7-fed6fe7249dc" }), Plot.ruleX([0]), (_index, { y, color }) => htl.svg` ${d3.ticks(0, 1, 10).map( (t) => htl.svg`` )}` ] });3e:T443,slrhdi_high = Plot.plot({ title: html`

SLR GDP impact on High HDI countries`, margin: 30, y : { domain: [0,10], label: "% impact on GDP", transform: (p) => (p100-1) }, x : { domain: [2015, 2050], //tickRotate: 90 }, style: { //padding: 10, color: "black", background: `url(${await FileAttachment("high@1.png").url()})`, backgroundSize: "cover" }, marks: [ Plot.frame(), Plot.areaY(slr_gdp_impact_highice.filter(d=> d.HDI=="High") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", fill: "#00007D", curve: "bump-x", opacity: 0.4 })), Plot.line(slr_gdp_impact_highice.filter(d=> d.HDI=="High") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", stroke: "#00007D", strokeWidth: 3, curve: "bump-x", tip: true })), Plot.image([0], { x: 2050, y: 0.02, width: 100, src: `url("https://observablehq.com/d/ba54650d510f35b6#mywave")` //`url(${await FileAttachment("wave.jpg").url()})` }), Plot.ruleX([0]) ] })3f:T44b,slrhdi_med = Plot.plot({ title: html`

SLR GDP impact on Medium HDI countries`, margin: 30, y : { domain: [0,10], label: "% impact on GDP", transform: (p) => (p100-1) }, x : { domain: [2015, 2050], //tickRotate: 90 }, style: { //padding: 10, color: "black", background: `url(${await FileAttachment("medium@1.png").url()})`, backgroundSize: "cover" }, marks: [ Plot.frame(), Plot.areaY(slr_gdp_impact_highice.filter(d=> d.HDI=="Medium") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", fill: "#00007D", curve: "bump-x", opacity: 0.4 })), Plot.line(slr_gdp_impact_highice.filter(d=> d.HDI=="Medium") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", stroke: "#00007D", strokeWidth: 3, curve: "bump-x", tip: true })), Plot.image([0], { x: 2050, y: 0.02, width: 100, src: `url("https://observablehq.com/d/ba54650d510f35b6#mywave")` //`url(${await FileAttachment("wave.jpg").url()})` }), Plot.ruleX([0]) ] })40:T453,slrhdi_low = Plot.plot({ title: html`

SLR GDP impact on Low HDI countries`, margin: 30, y : { domain: [0,10], label: "% impact on GDP", transform: (p) => (p100-1) }, x : { domain: [2015, 2050], //tickRotate: 90 }, style: { //padding: 10, color: "black", background: `url(${await FileAttachment("low@2.png").url()})`, backgroundSize: "cover" }, marks: [ Plot.frame(), Plot.areaY(slr_gdp_impact_highice.filter(d=> d.HDI=="Low") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", fill: "#00007D", //"url(#gradient4)", curve: "bump-x", opacity: 0.4 })), Plot.line(slr_gdp_impact_highice.filter(d=> d.HDI=="Low") , Plot.groupX({y:"mean"},{ x: "Year", y: "GDP_perc", stroke: "#00007D", strokeWidth: 3, curve: "bump-x", tip: true })), Plot.image([0], { x: 2050, y: 0.02, width: 100, src: `url("https://observablehq.com/d/ba54650d510f35b6#mywave")` //`url(${await FileAttachment("wave.jpg").url()})` }), Plot.ruleX([0]) ] })41:Ta14,myRedColorScheme = ["#fff5f0","#fff4ef","#fff4ee","#fff3ed","#fff2ec","#fff2eb","#fff1ea","#fff0e9","#fff0e8","#ffefe7","#ffeee6","#ffeee6","#ffede5","#ffece4","#ffece3","#ffebe2","#feeae1","#fee9e0","#fee9de","#fee8dd","#fee7dc","#fee6db","#fee6da","#fee5d9","#fee4d8","#fee3d7","#fee2d6","#fee2d5","#fee1d4","#fee0d2","#fedfd1","#feded0","#feddcf","#fedccd","#fedbcc","#fedacb","#fed9ca","#fed8c8","#fed7c7","#fdd6c6","#fdd5c4","#fdd4c3","#fdd3c1","#fdd2c0","#fdd1bf","#fdd0bd","#fdcfbc","#fdceba","#fdcdb9","#fdccb7","#fdcbb6","#fdc9b4","#fdc8b3","#fdc7b2","#fdc6b0","#fdc5af","#fdc4ad","#fdc2ac","#fdc1aa","#fdc0a8","#fcbfa7","#fcbea5","#fcbca4","#fcbba2","#fcbaa1","#fcb99f","#fcb89e","#fcb69c","#fcb59b","#fcb499","#fcb398","#fcb196","#fcb095","#fcaf94","#fcae92","#fcac91","#fcab8f","#fcaa8e","#fca98c","#fca78b","#fca689","#fca588","#fca486","#fca285","#fca183","#fca082","#fc9e81","#fc9d7f","#fc9c7e","#fc9b7c","#fc997b","#fc987a","#fc9778","#fc9677","#fc9475","#fc9374","#fc9273","#fc9071","#fc8f70","#fc8e6f","#fc8d6d","#fc8b6c","#fc8a6b","#fc8969","#fc8868","#fc8667","#fc8565","#fc8464","#fb8263","#fb8162","#fb8060","#fb7f5f","#fb7d5e","#fb7c5d","#fb7b5b","#fb795a","#fb7859","#fb7758","#fb7657","#fb7455","#fa7354","#fa7253","#fa7052","#fa6f51","#fa6e50","#fa6c4e","#f96b4d","#f96a4c","#f9684b","#f9674a","#f96549","#f86448","#f86347","#f86146","#f86045","#f75e44","#f75d43","#f75c42","#f65a41","#f65940","#f6573f","#f5563e","#f5553d","#f4533c","#f4523b","#f4503a","#f34f39","#f34e38","#f24c37","#f24b37","#f14936","#f14835","#f04734","#ef4533","#ef4433","#ee4332","#ed4131","#ed4030","#ec3f2f","#eb3d2f","#eb3c2e","#ea3b2d","#e93a2d","#e8382c","#e7372b","#e6362b","#e6352a","#e5342a","#e43229","#e33128","#e23028","#e12f27","#e02e27","#df2d26","#de2c26","#dd2b25","#dc2a25","#db2924","#da2824","#d92723","#d72623","#d62522","#d52422","#d42321","#d32221","#d22121","#d12020","#d01f20","#ce1f1f","#cd1e1f","#cc1d1f","#cb1d1e","#ca1c1e","#c91b1e","#c71b1d","#c61a1d","#c5191d","#c4191c","#c3181c","#c2181c","#c0171b","#bf171b","#be161b","#bd161a","#bb151a","#ba151a","#b91419","#b81419","#b61419","#b51319","#b41318","#b21218","#b11218","#b01218","#ae1117","#ad1117","#ac1117","#aa1017","#a91016","#a71016","#a60f16","#a40f16","#a30e15","#a10e15","#a00e15","#9e0d15","#9c0d14","#9b0c14","#990c14","#970c14","#960b13","#940b13","#920a13","#900a13","#8f0a12","#8d0912","#8b0912","#890812","#870811","#860711","#840711","#820711","#800610","#7e0610","#7c0510","#7a0510","#78040f","#76040f","#75030f","#73030f","#71020e","#6f020e","#6d010e","#6b010e","#69000d","#67000d"]42:Ta56,function obtenirDadesFiltrades() { let year1 = interval_years[0]; let year2 = interval_years[1]; let hdiCategory = hdiCategory_filter; let parameter = parameterToAnalize_filter; let country = country_selected; // Filtrar los datos let filteredData = country == "" ? co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && d.hdi_category === hdiCategory) : //co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && getHDI(d.iso_code, d.country, d.year, true, true) === hdiCategory) : co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && d.country === country); //getHDI(isocode, countryname, year =1990, categorized = false let transformedData = {}; filteredData.forEach(d => { if (!transformedData[d.country]) { transformedData[d.country] = { value_start: null, value_end: null}; } if (d.year === year1) { transformedData[d.country].value_start = d[parameter]; } else if (d.year === year2) { transformedData[d.country].value_end = d[parameter]; } }); if(country != "") { // The mean of the hdi_category let filteredDataHDICategory = co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2) && d.hdi_category === hdiCategory); //let filteredDataHDICategory = co2_amb_hdi_5.filter(d => (d.year === year1 || d.year === year2) && getHDI(d.iso_code, d.country, d.year, true, true) === hdiCategory); let valuesStart = filteredDataHDICategory.filter(d => d.year === year1).map(d => d[parameter]); let valuesEnd = filteredDataHDICategory.filter(d => d.year === year2).map(d => d[parameter]); let medianStart = d3.median(valuesStart); let medianEnd = d3.median(valuesEnd); transformedData[hdiCategory] = { value_start: medianStart, value_end: medianEnd }; // The mean of the world let filteredDataWorld = co2_amb_hdi_4.filter(d => (d.year === year1 || d.year === year2)); //let filteredDataWorld = co2_amb_hdi_5.filter(d => (d.year === year1 || d.year === year2)); let valuesStartWorld = filteredDataWorld.filter(d => d.year === year1).map(d => d[parameter]); let valuesEndWorld = filteredDataWorld.filter(d => d.year === year2).map(d => d[parameter]); let medianStartWorld = d3.median(valuesStartWorld); let medianEndWorld = d3.median(valuesEndWorld); transformedData["World"] = { value_start: medianStartWorld, value_end: medianEndWorld }; } let resultArray = Object.keys(transformedData).map(key => ({ country: key, ...transformedData[key] })); return resultArray; }43:Te19,plotLinesComposite2 = { return Plot.plot({ width: 1100, height: 500, marginRight: 100, y: {grid: true}, style: { overflow: "visible" }, marks: [ Plot.ruleY([0]), // -------------------- LÍNIES DE LES CATEGORIES -------------------------------------- !country_selected && hdi_filtre.length > 0 ? Plot.lineY( co2_per_capita_per_country.filter(d => hdi_filtre.includes(d.hdi_category) ), { x: "year", y: "co2_per_capita", z: "country", stroke: d => obtenirColor(d, hdi_filtre), } ) : [], !country_selected && hdi_filtre.length == 0 ? Plot.lineY( co2_per_capita_per_country, { x: "year", y: "co2_per_capita", z: "country", stroke: d => "grey", } ) : [], // -------------- LÍNIA DEL PAÍS SELECCIONAT I MITJANA DEL SEU HDI ------------------------------------------------ country_selected == "" ? []: Plot.lineY( co2_per_capita_per_country.filter(d => d.country === country_selected), { x: "year", y: "co2_per_capita", stroke: "red", // color rojo para el país seleccionado strokeWidth: 3, tip: d => `Año: ${d.year}, CO2 per cápita: ${d.co2_per_capita}` } ), country_selected == "" ? []: Plot.text( co2_per_capita_per_country.filter(d => d.country === country_selected), Plot.selectLast({ x: "year", y: "co2_per_capita", text: () => country_selected, lineAnchor: "middle", fontSize: 16, dx: 65, dy: 10, fill: "red", // Color del texto fontWeight: "bold", // Negrita }) ), // MITJANA DEL SEU HDI ------------------------ country_selected == "" ? []: Plot.lineY( dataTransformed.filter(d => d.hdi_category === getHDICountrySelected() && d.year <= anyMaximPaisSeleccionat && d.year >= anyMinimPaisSeleccionat), { x: "year", y: "sumCo2", stroke: "lightgray", // color rojo para el país seleccionado opacity: 0.3, strokeWidth: 3, tip: d => `Año: ${d.year}, CO2 per cápita: ${d.co2_per_capita}` } ), country_selected == "" ? []: Plot.text( dataTransformed.filter(d => d.hdi_category === getHDICountrySelected() && d.year <= anyMaximPaisSeleccionat && d.year >= anyMinimPaisSeleccionat), Plot.selectLast({ x: "year", y: "sumCo2", text: () => "HDI: " + getHDICountrySelected(), lineAnchor: "middle", fontSize: 16, dx: 65, dy: 10, fill: "lightgrey", // Color del texto fontWeight: "bold", // Negrita }) ), // -------------- LÍNIA VERTICAL I TEXT DE LA LÍNIA ------------------------------------------------------------ Plot.tickX(years.filter((d) => d.year == any_filtre_2), { x: "year", strokeWidth: 2, //filter: (d) => d.year.getUTCFullYear() == 2017 || d.year.getUTCFullYear() == 2005 }), Plot.text(years.filter((d) => d.year == any_filtre_2), { frameAnchor: "top", x: "year", //filter: (d) => d.year.getUTCFullYear() == 2017 || d.year.getUTCFullYear() == 2005, text: (d) => [2017, 2005].includes(d.year) ? `${ d.year + ", " + tractats_ambientals.get(d.year)}` : d.year, textAnchor: "end", fontSize: 30, fontWeight: "bold", dx: -5 }), Plot.axisX({fontSize: 30}), Plot.axisY({fontSize: 30}) ] }) } 44:[{"id":"c5c9b63e14bf976786e6fac1c5d7699b37c9d12772c03d7208b96999a1cc3308ce4cb03d9606fff891c94afeca579cfcbd4c7f0f8a67f82aff2a20614c15d5b2","href":"https://static.observableusercontent.com/files/c5c9b63e14bf976786e6fac1c5d7699b37c9d12772c03d7208b96999a1cc3308ce4cb03d9606fff891c94afeca579cfcbd4c7f0f8a67f82aff2a20614c15d5b2","name":"graficInteractiu1.png","type":"image/png","lastModified":1703231666050.451,"size":173326},{"id":"3b32d552e2f7d74f95d6907696f209b19672eb473f4d6bb5169c955e903b3a5242dc17d328d4e07af12b0e7d0ce956508abd2b7cde2c41af87c1e55657c5f621","href":"https://static.observableusercontent.com/files/3b32d552e2f7d74f95d6907696f209b19672eb473f4d6bb5169c955e903b3a5242dc17d328d4e07af12b0e7d0ce956508abd2b7cde2c41af87c1e55657c5f621","name":"graficInteractiu2.png","type":"image/png","lastModified":1703233133718.75,"size":98941},{"id":"cbae73edf995e022070917c1997480445dbc3bed9be5cade7593c7a764f21ceda097ef4db52099139265d984ae8b958423832229b7d1e3804b0c714031b2b8f5","href":"https://static.observableusercontent.com/files/cbae73edf995e022070917c1997480445dbc3bed9be5cade7593c7a764f21ceda097ef4db52099139265d984ae8b958423832229b7d1e3804b0c714031b2b8f5","name":"taula.JPG","type":"image/jpeg","lastModified":1703332702339.363,"size":30785},{"id":"3de993c2c152ba8d1455f4c8f986ab37289b32888bb63c6116c5667308388f4094e8923936463c1c4601c59c68c0bea1ecc925ed7c9b94c78abb05b1f411ae48","href":"https://static.observableusercontent.com/files/3de993c2c152ba8d1455f4c8f986ab37289b32888bb63c6116c5667308388f4094e8923936463c1c4601c59c68c0bea1ecc925ed7c9b94c78abb05b1f411ae48","name":"co2_amb_hdi.csv","type":"text/csv","lastModified":1703336801810.914,"size":1683206},{"id":"6b0a14511b927e9c4a747d9399ef4dc0b39279603a6079b19bebc937cdba59a110d70d0fcdfb61047bc435b48d153aac81a290840ca79b9d5e0917d96087f68a","href":"https://static.observableusercontent.com/files/6b0a14511b927e9c4a747d9399ef4dc0b39279603a6079b19bebc937cdba59a110d70d0fcdfb61047bc435b48d153aac81a290840ca79b9d5e0917d96087f68a","name":"hdiRoadMap.png","type":"image/png","lastModified":1704223611076.464,"size":138273},{"id":"d5d294af09df2b8187a8fc5c195b274db59d3869743dca53334f20cb26fc3187bcb8b52ebd59bc6023be92a9a15e3be8157b32039426f5c5fa4aefcc263fcb5f","href":"https://static.observableusercontent.com/files/d5d294af09df2b8187a8fc5c195b274db59d3869743dca53334f20cb26fc3187bcb8b52ebd59bc6023be92a9a15e3be8157b32039426f5c5fa4aefcc263fcb5f","name":"co2_amb_hdi_v2.csv","type":"text/csv","lastModified":1703423806845.455,"size":1401533},{"id":"92824302e97fd80be177a533280e149d5d7107e7b5d598482a3b8fc96ed04ad5a80396e8f56012f453d5ae08edab523b784e043caa2000a6534f48fb19ffd35f","href":"https://static.observableusercontent.com/files/92824302e97fd80be177a533280e149d5d7107e7b5d598482a3b8fc96ed04ad5a80396e8f56012f453d5ae08edab523b784e043caa2000a6534f48fb19ffd35f","name":"grafics3.png","type":"image/png","lastModified":1703432961079.612,"size":133163},{"id":"12a46687f8dd40b596930431875c4b8a945141be768289fd5789bb6b20c05bb681680b72f4be2106810ca68c06f4aa0a56c84f72f160eba0623f4d9221abd2f7","href":"https://static.observableusercontent.com/files/12a46687f8dd40b596930431875c4b8a945141be768289fd5789bb6b20c05bb681680b72f4be2106810ca68c06f4aa0a56c84f72f160eba0623f4d9221abd2f7","name":"world_low.json","type":"application/json","lastModified":1703931867319.384,"size":987825},{"id":"c647d5fb4dd45d13c9f775587abbbf81bf735765c70afd50f0d0641c61f6a5129b53475094fa9aab76dbe2b116270bada3dfb0bdd16efc03123fafad4db3aa38","href":"https://static.observableusercontent.com/files/c647d5fb4dd45d13c9f775587abbbf81bf735765c70afd50f0d0641c61f6a5129b53475094fa9aab76dbe2b116270bada3dfb0bdd16efc03123fafad4db3aa38","name":"HDR21-22_Composite_indices_complete_time_series.csv","type":"text/csv","lastModified":1704283182346.235,"size":1781357},{"id":"6e3e2284bb46c7bdd20a22b5d40146e4e110197242c83a482eabebc8f1181ac4667bbf446cca13eee1b4ab185c271a4e72cd72e7d09d0d6fa8f5d6cc6c4c630f","href":"https://static.observableusercontent.com/files/6e3e2284bb46c7bdd20a22b5d40146e4e110197242c83a482eabebc8f1181ac4667bbf446cca13eee1b4ab185c271a4e72cd72e7d09d0d6fa8f5d6cc6c4c630f","name":"Kyoto_v6.csv","type":"text/csv","lastModified":1703670064739.5,"size":8720},{"id":"3f58cd1bdc30ebb5bdbe60bba41ea278b862189e05bb948d2f167afdd7634b6d8b8f75a8f581398f2fbd1ea964e8724ff29db28cc34d6a4a9e1d1d3e12bc77ee","href":"https://static.observableusercontent.com/files/3f58cd1bdc30ebb5bdbe60bba41ea278b862189e05bb948d2f167afdd7634b6d8b8f75a8f581398f2fbd1ea964e8724ff29db28cc34d6a4a9e1d1d3e12bc77ee","name":"SLR_GDP_IMPACT_v2.csv","type":"text/csv","lastModified":1704987325899.528,"size":5702},{"id":"e4e7f0288ad07eb13757a035a97dbea42e0bfbab6665b298ffb3d5fcf87d0043f0c7122f1ea5e799ddb8b3682b6962c747485a31d1f7447c6ff181c3381881fe","href":"https://static.observableusercontent.com/files/e4e7f0288ad07eb13757a035a97dbea42e0bfbab6665b298ffb3d5fcf87d0043f0c7122f1ea5e799ddb8b3682b6962c747485a31d1f7447c6ff181c3381881fe","name":"SLR_1992-2023.csv","type":"text/csv","lastModified":1705402969219.347,"size":31696},{"id":"bfdad2ba0c180a9366ce2b73ab74024bf7cdceb624f09a058a4853a792ab9a6eab359d9b7eb54e13bb12b4e13a26ea4f199cc9d7edc1c9fd3bb0084507a17366","href":"https://static.observableusercontent.com/files/bfdad2ba0c180a9366ce2b73ab74024bf7cdceb624f09a058a4853a792ab9a6eab359d9b7eb54e13bb12b4e13a26ea4f199cc9d7edc1c9fd3bb0084507a17366","name":"temperature_raise.csv","type":"text/csv","lastModified":1705141683722.82,"size":1854},{"id":"f69694beb40ab929f8d68173e8157a00d705e6314b0a042a686cff39697f47a361d2cf339816605e32f1a04f097557122637365b3c19e6e4fbc2fa1e601dc6b9","href":"https://static.observableusercontent.com/files/f69694beb40ab929f8d68173e8157a00d705e6314b0a042a686cff39697f47a361d2cf339816605e32f1a04f097557122637365b3c19e6e4fbc2fa1e601dc6b9","name":"owid-co2-data.csv","type":"text/csv","lastModified":1705145352975.353,"size":13153941},{"id":"3f58cd1bdc30ebb5bdbe60bba41ea278b862189e05bb948d2f167afdd7634b6d8b8f75a8f581398f2fbd1ea964e8724ff29db28cc34d6a4a9e1d1d3e12bc77ee","href":"https://static.observableusercontent.com/files/3f58cd1bdc30ebb5bdbe60bba41ea278b862189e05bb948d2f167afdd7634b6d8b8f75a8f581398f2fbd1ea964e8724ff29db28cc34d6a4a9e1d1d3e12bc77ee","name":"SLR_GDP_IMPACT_v2@1.csv","type":"text/csv","lastModified":1705150808982.4,"size":5702},{"id":"d69b227e76b44c577e15c2e3509b57b6703a89870560a8e6b940052196c1cf5c1d9d00867d374220546a26454693fe7ac6286bb2d4d1c122fea3c7b9d5129854","href":"https://static.observableusercontent.com/files/d69b227e76b44c577e15c2e3509b57b6703a89870560a8e6b940052196c1cf5c1d9d00867d374220546a26454693fe7ac6286bb2d4d1c122fea3c7b9d5129854","name":"co2_amb_hdi_3.csv","type":"text/csv","lastModified":1705153421517.818,"size":455065},{"id":"c40f48fa1349cd13b3626171757fbd0a0ed3b97b9143db3f9273632a975f36e602d83a423afd528af20d0fe4985998f6f2c00f729884d8a4193e61e91971d605","href":"https://static.observableusercontent.com/files/c40f48fa1349cd13b3626171757fbd0a0ed3b97b9143db3f9273632a975f36e602d83a423afd528af20d0fe4985998f6f2c00f729884d8a4193e61e91971d605","name":"owid_corregit.csv","type":"text/csv","lastModified":1705487641283.204,"size":15673791},{"id":"ead7110295ebbff39329c3d58e67f01e4d6e426c8373e5c100c50e05658d89b08babd1ade10eb868dcf02ce0520ebf4ea75d3b664d0b7a76ea38e7b8dfc08e04","href":"https://static.observableusercontent.com/files/ead7110295ebbff39329c3d58e67f01e4d6e426c8373e5c100c50e05658d89b08babd1ade10eb868dcf02ce0520ebf4ea75d3b664d0b7a76ea38e7b8dfc08e04","name":"GDP_SLR_med_Ice_v3.csv","type":"text/csv","lastModified":1705252784233.45,"size":2852},{"id":"a8babc420eeead5677430f449742fd6b239926e8eb41c7fc62987ab57ded6c312965ace6185904d503da69f067b0030b87c64dd0e304bed8ca0f50ba0acb6b49","href":"https://static.observableusercontent.com/files/a8babc420eeead5677430f449742fd6b239926e8eb41c7fc62987ab57ded6c312965ace6185904d503da69f067b0030b87c64dd0e304bed8ca0f50ba0acb6b49","name":"GDP_SLR_high_Ice_v4.csv","type":"text/csv","lastModified":1705254646383.156,"size":8174},{"id":"725cb262d7b9f4878eafc3a28a9c1f19568c6f6961edef52a750368e9190f28942cd6a7491727dfbce42aff7dbb89be78d100953cc330e8db8fd4c6b164cc629","href":"https://static.observableusercontent.com/files/725cb262d7b9f4878eafc3a28a9c1f19568c6f6961edef52a750368e9190f28942cd6a7491727dfbce42aff7dbb89be78d100953cc330e8db8fd4c6b164cc629","name":"wave.jpg","type":"image/jpeg","lastModified":1705258584207.797,"size":198250},{"id":"d322e50fde4cc1a1680b427c4f2a2c73b0da3450ad9bd65a1d0f194ff146f0272e4f3e06cd1e25ec2c5aaf3a6464e4afe6cbe72cb41b194fd2dbb9eb6e760ea7","href":"https://static.observableusercontent.com/files/d322e50fde4cc1a1680b427c4f2a2c73b0da3450ad9bd65a1d0f194ff146f0272e4f3e06cd1e25ec2c5aaf3a6464e4afe6cbe72cb41b194fd2dbb9eb6e760ea7","name":"low@2.png","type":"image/png","lastModified":1705265162433.352,"size":93556},{"id":"a5b085e9fa1fc6dc41a1bc814e9fecffacdfabfe9bd8019cfcb6063c60a406fc94f6e7e59f656831238742e0c0e465e87e88d2bd03467c36d8e381a30e01513c","href":"https://static.observableusercontent.com/files/a5b085e9fa1fc6dc41a1bc814e9fecffacdfabfe9bd8019cfcb6063c60a406fc94f6e7e59f656831238742e0c0e465e87e88d2bd03467c36d8e381a30e01513c","name":"medium@1.png","type":"image/png","lastModified":1705265499724.764,"size":63994},{"id":"0cef01f40f0a042bb85cfb3d4f07e7a8866092c67da1cd6023b6f91285f0676f0b6a08e973fe1683224649d24e36c461d797459f7e4d7e7b80854198e0f4efbb","href":"https://static.observableusercontent.com/files/0cef01f40f0a042bb85cfb3d4f07e7a8866092c67da1cd6023b6f91285f0676f0b6a08e973fe1683224649d24e36c461d797459f7e4d7e7b80854198e0f4efbb","name":"very_high@2.png","type":"image/png","lastModified":1705265499837.777,"size":100333},{"id":"ae343945f8dd6358dea3f1e14f9b82faef4ade45a43bb986e796fb3839c58f1e578c53594cd90ea8c8069a58a2e4e67ac93405b71f9f4af51bac9ab63aca89c7","href":"https://static.observableusercontent.com/files/ae343945f8dd6358dea3f1e14f9b82faef4ade45a43bb986e796fb3839c58f1e578c53594cd90ea8c8069a58a2e4e67ac93405b71f9f4af51bac9ab63aca89c7","name":"high@1.png","type":"image/png","lastModified":1705265499849.298,"size":89294},{"id":"e76e29609c9a0e069cc46cab5e1fefb6a951e4af8f355ff9a9382e8ed8766679728024b5ddbd0942dc0a9d9abfec2119cc34244732f1f5425fdd6688118546c1","href":"https://static.observableusercontent.com/files/e76e29609c9a0e069cc46cab5e1fefb6a951e4af8f355ff9a9382e8ed8766679728024b5ddbd0942dc0a9d9abfec2119cc34244732f1f5425fdd6688118546c1","name":"co2_amb_hdi_4.csv","type":"text/csv","lastModified":1705508769717.384,"size":1721545},{"id":"5bb1642f9cf94a7d20204922223805b974ae497adf7a355f004c0cd23afc6c42b2784abceaa098d5bff438894c3a062a6595c730a9f44e838df6740250b7c04a","href":"https://static.observableusercontent.com/files/5bb1642f9cf94a7d20204922223805b974ae497adf7a355f004c0cd23afc6c42b2784abceaa098d5bff438894c3a062a6595c730a9f44e838df6740250b7c04a","name":"co2_amb_hdi_5.csv","type":"text/csv","lastModified":1705509833491.905,"size":2162387}] 2d:{"title":"FINAL EXERCISE","cells":[{"id":0,"value":"# FINAL EXERCISE","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":324,"value":"","pinned":false,"hidden":false,"mode":"md"},{"id":179,"value":"---\n## Summary","pinned":false,"hidden":false,"mode":"md"},{"id":197,"value":"toc({\n title: null,\n headers: \"h2, h3\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":694,"value":"---\n## Country classification by HDI\n\nFirst of all we need to put a first focus on the Human Development Index for every country. \n\n### What is the HDI?\n\nAccording to the definition of the United Nations, the Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent satardard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.\n\n![hdiRoadMap.png](${await FileAttachment(\"hdiRoadMap.png\").url()})\n\nCountries are divided in four groups based on their Human Development Index. Low group is for countries with an index below 0.55, Medium group is for countries with an index between 0.55 and 0.69, High group for countries with an index between 0.70 and 0.79 and finally, the Very High group if for those with an index equal or greater than 0.80.","pinned":false,"hidden":false,"mode":"md"},{"id":705,"value":"\n---\n### Evolution of HDI from 1990 to 2021\nBelow is a map illustrating the evolution of the HDI index for each country worldwide from 1990 to 2021\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":863,"value":"viewof hdiYear = Scrubber(d3.range(1990, 2022), {loop: false, delay: 200, autoplay: false})","pinned":false,"hidden":false,"mode":"ojs"},{"id":712,"value":"(Press play to see the evolution through the years)
","pinned":false,"hidden":false,"mode":"html"},{"id":723,"value":"Plot.legend({\n label: \"Human Development Index Classification\", \n width: 500, \n color: {\n type: \"categorical\", \n range: [\"#000000\", \"#F7E425\", \"#EA7457\", \"#BF3984\", \"#0D0887\"],\n domain: [\"Unknown\", \"Low (<0.55)\", \"Mid (0.55-0.69)\", \"High (0.70-0.79)\", \"Very High (>0.80)\"]\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":718,"value":"html`${hdimap}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2231,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":2479,"value":"## Emissions","pinned":false,"hidden":false,"mode":"md"},{"id":2220,"value":"### Evolution of Co2 and GhG emissions through the years by HDI index\nHere we are shown in the map and line graphs the co2 per capita or the ghg per capita for each of the HDI indices (map and line graph) over the years. In addition, the totals of these two emissions can also be seen (bar graph).\n\nWith this composition of graphs, what we want to see is what is the general trend regarding CO2 and GHG emissions of the different groups of countries depending on their human development index, with special interest in the periods after the environmental treaties of Kyoto and Paris .","pinned":false,"hidden":false,"mode":"md"},{"id":2072,"value":"html`\n
\n
Filters
\n
\n ${viewof radioCO2GHG}\n
\n
\n ${viewof hdi_filtre}\n
\n
\n ${viewof any_filtre_2}\n
\n
\n`\n//
${viewof co2perCapita}
/","pinned":false,"hidden":false,"mode":"ojs"},{"id":1998,"value":"
\nlinePlot
\n
${mapaEmissions}
\n
${linePlot}
\n
\n
${temperaturePlot}
\n
\n\n","pinned":false,"hidden":false,"mode":"html"},{"id":2245,"value":"As discoveries, we can highlight several:\n\nAnalyzing the Co2 per capita:\n- There is an upward trend in co2 per capita except for countries with a \"Very High\" HDI index. This same category is the only one in which its co2 per capita drops significantly after the environmental treaties.\n- There is a considerable drop in the decade of the 20s, but we have to be careful when relating it to the environmental treaties since that is when the world entered the pandemic due to COVID-19.\n- We see a very high peak of co2 per capita in 1991. This is because this was the Golf War and co2 emissions skyrocketed.\n\nAnalyzing Ghg per capita:\n- Both \"Very High\" and \"Low\" HDI index countries have had a downward trend after the Kyoto treaty.\n- The \"Medium\" countries were on a downward trend but shot up in 2015 (we'll see later because of which country)\n- Countries with a \"High\" index, on the other hand, have an upward trend.","pinned":false,"hidden":false,"mode":"md"},{"id":2848,"value":"

","pinned":false,"hidden":false,"mode":"html"},{"id":2657,"value":"

Difference of Co2 and GhG emissions of the countries between two years

\nThis view is intended to complement the previous charts. Here, we don't have as much of an overview as others. Instead, we can see it better for each of the countries in each HDI index. More specifically, what we see is for each country, the difference in the filtered parameter (Co2 per capita, total GhG, population, etc.) between two years. The longer the arrow, the more difference there is (the lines are green if it has decreased and red if it has increased).

\nWhat we were looking for, then, is to have a more personal approach with each country and thus also see in detail the reasons for the trends and peaks that we have seen in the previous graphs.","pinned":false,"hidden":false,"mode":"md"},{"id":2662,"value":"html`\n

Filters

\n ${viewof parameterToAnalize_filter}\n ${viewof hdiCategory_filter}\n

\n\n

\n Country: ${viewof country_selected}\n

\n\n

\n ${viewof interval_years}\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2665,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2856,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":2475,"value":"---\n## Emissions, Temperatures and Sea Level Rise","pinned":false,"hidden":false,"mode":"md"},{"id":2491,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":2497,"value":"Scientific consensus is clear that human activities are significantly contributing to climate change through the emission of greenhouse gases. Reports from the Intergovernmental Panel on Climate Change (IPCC) and other scientific organizations provide overwhelming evidence of the connection between anthropogenic emissions and observed global warming. Mitigating emissions and transitioning to more sustainable energy sources are crucial to addressing this issue and limiting its future impacts.","pinned":false,"hidden":false,"mode":"md"},{"id":2499,"value":"Below we can see a direct relation among some of these causes and effects:","pinned":false,"hidden":false,"mode":"md"},{"id":5597,"value":"

${totalEmissionsPlot}

${temperaturAnomalyPlot}

${totalSeaLevelRaisePlot}

\n\n","pinned":false,"hidden":false,"mode":"html"},{"id":2877,"value":"$35","pinned":false,"hidden":false,"mode":"md"},{"id":2871,"value":"### Are environmental policies economically driven?","pinned":false,"hidden":false,"mode":"md"},{"id":2873,"value":"$36","pinned":false,"hidden":false,"mode":"md"},{"id":5653,"value":"_(The used scale, from zero (0) to ten (10), underlines a main effect: any country with a negative effect in its economy from 8% to 10% of its GDP may fall into a deep crisis. This is why it's showed on the charts below)_","pinned":false,"hidden":false,"mode":"md"},{"id":2795,"value":"

\n ${slrhdi_veryhigh} ${slrhdi_high}\n

\n ${slrhdi_med} ${slrhdi_low}\n

\n\n\n \n","pinned":false,"hidden":false,"mode":"html"},{"id":2875,"value":"As we can see, the main economic effects of sea-level rise disproportionately affect countries with a medium and high Human Development Index. These countries can be considered as developing nations and emerging economies. Naturally, there is no international interest in these economies, which, let's not forget, serve as the factories of the developed world, falling into an economic recession resulting from the collateral effects of human activity.","pinned":false,"hidden":false,"mode":"md"},{"id":5675,"value":"## Conclusions","pinned":false,"hidden":false,"mode":"md"},{"id":5926,"value":"We must believe, and indeed we do believe, that it is very likely that a significant portion of the reasons behind the establishment of laws, treaties, agreements, and international protocols for the protection of the environment are motivated by scientists worldwide who have been studying the effects of human activity on the planet for years. They have filled the scientific community with articles, studies, and information suggesting that if we do not change our way of life and our interaction with our environment, a challenging future awaits us in terms of pollution, natural resources, food availability, and human health. However, we must not forget that organizations, lobbies, and, in general, the powerful leaders of our world have economic interests behind most, if not all, of their actions.\n\nAs we have seen, we cannot assert this with certainty, but we also cannot rule out that one of the significant driving forces behind the creation of environmental treaties is economic.","pinned":false,"hidden":false,"mode":"md"},{"id":2503,"value":"---\n## References","pinned":false,"hidden":false,"mode":"md"},{"id":2505,"value":"1. Data on CO2 and Greenhouse Gas Emissions by Our World in Data\n2. Global Temperature Anomalies - Climate.gov, Climate & Information for a climate-smart nation\n3. Bachner, G., Lincke, D. & Hinkel, J. The macroeconomic effects of adapting to high-end sea-level rise via protection and migration. Nat Commun 13, 5705 (2022). https://doi.org/10.1038/s41467-022-33043-z\n4. Barley Trellis plot with arrows. https://observablehq.com/@observablehq/plot-barley-trellis-arrows?intent=fork","pinned":false,"hidden":false,"mode":"md"},{"id":2515,"value":"---\n## Ethics declarations","pinned":false,"hidden":false,"mode":"md"},{"id":2507,"value":"1. Competing interests: The authors declare no competing interests.\n2. Usage of the data: All data used is from public, open source or under Creative Commons Attribution 4.0 International License.\n3. Personal data: Used data do not contain private, personal or sensible information.\n","pinned":false,"hidden":false,"mode":"md"},{"id":2247,"value":"---\n## Annexes","pinned":false,"hidden":false,"mode":"md"},{"id":2966,"value":"### Plots source codes","pinned":false,"hidden":false,"mode":"md"},{"id":5772,"value":"HDI evolution MAP","pinned":false,"hidden":false,"mode":"md"},{"id":5774,"value":"hdimap = {\n\n const mapPlot = Plot.plot({\n title: html`

Current year: ${hdiYear}

`,\n width: 1200,\n projection: { type: \"equal-earth\", rotate: [-10, 0] },\n color: {\n range: [\"#000000\", \"#F7E425\", \"#EA7457\", \"#BF3984\", \"#0D0887\"],\n domain: [\"NA\", \"Low (<0.55)\", \"Mid (0.55-0.69)\", \"High (0.70-0.79)\", \"Very High (>0.80)\"],\n legend: false,\n width: 360\n },\n marks: [\n //Plot.sphere({stroke: \"currentColor\" }),\n //Plot.graticule({stroke: \"#101010\"}),\n Plot.geo(world, {\n fill: (d) => getHDI(d.properties.iso_a3, d.properties.name, hdiYear, true),\n stroke: \"currentColor\",\n strokeWidth : 0.25,\n title: (d) => `${d.properties.name} (${d.properties.iso_a3})\\nHDI ${hdiYear}: ${getHDI(d.properties.iso_a3, d.properties.name, hdiYear)}`\n })\n ]\n});\n\n return mapPlot;\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2464,"value":"Line plot emissions by HDI - Quim","pinned":false,"hidden":false,"mode":"md"},{"id":2442,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":2466,"value":"Range emissions by HDI (termometer) - Quim","pinned":false,"hidden":false,"mode":"md"},{"id":2454,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":4167,"value":"Emissions map (div embed) - Jordi","pinned":false,"hidden":false,"mode":"md"},{"id":1247,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":5339,"value":"Emissions, temperature & SLR plots","pinned":false,"hidden":false,"mode":"md"},{"id":5430,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":5366,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":5364,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":4367,"value":"SLR effects on GDP (facet like plots with specific background) - Jordi","pinned":false,"hidden":false,"mode":"md"},{"id":2278,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":2766,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":2775,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":2779,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":2970,"value":"### View of's","pinned":false,"hidden":false,"mode":"md"},{"id":2972,"value":"GHG per capita (co2perCapita)","pinned":false,"hidden":false,"mode":"md"},{"id":1390,"value":"viewof co2perCapita = Inputs.toggle({label: radioCO2GHG == 'GHG' ? 'GHG per capita': 'CO2 per capita', value: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2974,"value":"HDI index selectors (hdi_filtre, hdiCategory_filter)","pinned":false,"hidden":false,"mode":"md"},{"id":945,"value":"viewof hdi_filtre = Inputs.checkbox([\"Low\", \"Medium\", \"High\", \"Very High\"], {label: \"HDI Index\", value: [\"Low\", \"Medium\", \"High\", \"Very High\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1752,"value":"viewof hdiCategory_filter = Inputs.select([\"Very High\"].concat([\"High\", \"Medium\", \"Low\"]), {label: \"HDI Category\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2976,"value":"Year range selectors (any_filtre_2 (scrubber), interval_years)","pinned":false,"hidden":false,"mode":"md"},{"id":1009,"value":"viewof any_filtre_2 = Scrubber(d3.range(1990, 2021), {loop: false, delay: 100, autoplay: false})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1726,"value":"viewof interval_years = rangeSlider({\n min: 1990,\n max: 2020,\n step: 1,\n value: [1990, 2020],\n title: 'Interval of years',\n description: 'Select the interval of years to analize.',\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2978,"value":"Emission type selector (CO2 or GHG) (radioCO2GHG)","pinned":false,"hidden":false,"mode":"md"},{"id":2256,"value":"viewof radioCO2GHG = Inputs.radio([\"CO2\", \"GHG\"], {label: \"Select emissions\", value: \"CO2\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3509,"value":"General data selector (emissions, population, GDP, ...) (parameterToAnalize_filter)","pinned":false,"hidden":false,"mode":"md"},{"id":1767,"value":"viewof parameterToAnalize_filter = Inputs.select([\"co2_per_capita\"].concat([\"co2\",\"population\",\"co2_per_gdp\", 'total_ghg','ghg_per_capita']), {label: \"Parameter\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3512,"value":"Country Selector (country_selected)","pinned":false,"hidden":false,"mode":"md"},{"id":2322,"value":"viewof country_selected = autoSelect({\n options: countries,\n placeholder: \"Insert a country . . .\",\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":769,"value":"### General data & variables","pinned":false,"hidden":false,"mode":"md"},{"id":269,"value":"emissions = (\n await fetch(\n \"https://nyc3.digitaloceanspaces.com/owid-public/data/co2/owid-co2-data.json\"\n )\n).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2140,"value":"global_data = FileAttachment(\"owid-co2-data.csv\").csv()\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":365,"value":"co2_amb_hdi = await FileAttachment(\"co2_amb_hdi.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":366,"value":"co2_amb_hdi_v2 = await FileAttachment(\"co2_amb_hdi_v2.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":2290,"value":"co2_amb_hdi_3 = await FileAttachment(\"co2_amb_hdi_3.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":412,"value":"Kyoto = FileAttachment(\"Kyoto_v6.csv\").csv()\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":421,"value":"Kyoto","pinned":true,"hidden":false,"mode":"ojs"},{"id":1446,"value":"meanCO2resumit = meanCo2PerCapitaEmissionsPerHDICategoryAndYear.filter((d) => d.year.getUTCFullYear() >= any_filtre)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2911,"value":"ghgcolor = \"#00072D\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":2920,"value":"//ghgcolorRange = [\"#008DC2\", \"#00567A\" , \"#004D71\",\"#004367\", \"#003A5D\", , \"#003053\" , \"#00264A\",\"#001D40\" , \"#001336\", \"#00072D\"]\nghgcolorRange = [\"#008DC2\", \"#00072D\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":2913,"value":"co2color = \"#314052\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":2922,"value":"co2colorRange = [\"#477AAF\",\"#4574A5\" ,\"#426F9C\" , \"#406992\" , \"#3E638B\",\"#3C5D81\", \"#3A5778\",\"#38516E\", \"#354C65\", \"#33465B\", \"#314052\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":5852,"value":"hdiColorLow = \"#F7E425\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":5869,"value":"hdiColorMedium = \"#EA7457\" ","pinned":true,"hidden":false,"mode":"ojs"},{"id":5871,"value":"hdiColorHigh = \"#BF3984\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":5860,"value":"hdiColorVeryHigh = \"#0D0887\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":5880,"value":"newBlueRange = \"blues\"\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":760,"value":"---\n#### Jordi's data","pinned":false,"hidden":false,"mode":"md"},{"id":1368,"value":"topojson = require(\"topojson-client@3\", \"topojson-server@3\", \"topojson-simplify@3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1381,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"world_raw = FileAttachment(\"world_low.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1396,"value":"world = world_raw.features","pinned":false,"hidden":false,"mode":"ojs"},{"id":2105,"value":"temperature = FileAttachment(\"temperature_raise.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2286,"value":"slr_gdp_impact = FileAttachment(\"SLR_GDP_IMPACT_v2@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2531,"value":"slr_gdp_impact_highice = FileAttachment(\"GDP_SLR_high_Ice_v4.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2533,"value":"slr_gdp_impact_medice = FileAttachment(\"GDP_SLR_med_Ice_v3.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2537,"value":"slr_codes_grouped = [\n {HDI: \"Very High\", \n Countries: ['DEU', 'AUT', 'ITA', 'UKD', 'FRA', 'BLU', 'NLD', 'CEU', 'MEU', 'NAM', 'AUZ', 'TUR']\n },\n {HDI: \"High\", \n Countries: ['ERA', 'CHN']\n },\n {HDI: \"Med\", \n Countries: ['IND', 'ECA', 'SEA', 'LAM', 'OIE']\n },\n {HDI: \"Low\", \n Countries: ['AFR']\n } \n ]\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":5310,"value":"slr_change = FileAttachment(\"SLR_1992-2023.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":5314,"value":"-- Extracción de las primeras cuatro cifras del campo 'year'\nWITH datos_modificados AS (\n SELECT\n SUBSTRING(CAST(year AS VARCHAR), 1, 4) AS year,\n NULLIF(\"TOPEX/Poseidon\", '')::DOUBLE AS TOPEX_Poseidon,\n NULLIF(\"Jason-1\", '')::DOUBLE AS Jason_1,\n NULLIF(\"Jason-2\", '')::DOUBLE AS Jason_2,\n NULLIF(\"Jason-3\", '')::DOUBLE AS Jason_3\n FROM slr_change\n)\n\n-- Cálculo de la mediana por año y por columna\n, mediana_por_columna AS (\nSELECT\n year,\n AVG(TOPEX_Poseidon) AS mediana_TOPEX_Poseidon,\n AVG(Jason_1) AS mediana_Jason_1,\n AVG(Jason_2) AS mediana_Jason_2,\n AVG(Jason_3) AS mediana_Jason_3\nFROM datos_modificados\nGROUP BY year\n)\n\nSELECT\n year,\n AVG(mediana) AS mediana_slr\nFROM (\n SELECT year, mediana_TOPEX_Poseidon AS mediana FROM mediana_por_columna\n UNION\n SELECT year, mediana_Jason_1 FROM mediana_por_columna\n UNION\n SELECT year, mediana_Jason_2 FROM mediana_por_columna\n UNION\n SELECT year, mediana_Jason_3 FROM mediana_por_columna\n) AS mediana_union\nGROUP BY year\nORDER BY year;\n","pinned":false,"hidden":false,"mode":"sql","output":"slr_clean","database":"var:slr_change"},{"id":678,"value":"hdi = FileAttachment(\"HDR21-22_Composite_indices_complete_time_series.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":525,"value":"function getHDI(isocode, countryname, year =1990, categorized = false, reduced = false) {\n let hdiData = hdi.find(v=> v.iso3 == isocode);\n if (!hdiData) {\n hdiData = hdi.find(v=> v.country == countryname);\n }\n\n let HdiScore = 0;\n if (hdiData) {\n HdiScore = hdiData[`hdi_${year}`]\n }else{\n console.log(countryname, isocode, \"not found\")\n }\n\n if (categorized) {\n return hdiCategory(HdiScore, reduced)\n }else {\n return HdiScore\n }\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":570,"value":"function hdiCategory(hdiValue, reduced = false) {\n if (hdiValue==0) {\n return \"NA\"\n }else if (hdiValue<0.55) {\n if (reduced) {\n return \"Low\"\n }else{\n return \"Low (<0.55)\"\n }\n }else if (hdiValue<0.70) {\n if (reduced) {\n return \"Medium\"\n }else{\n return \"Mid (0.55-0.69)\"\n }\n }else if (hdiValue<0.80) {\n if (reduced) {\n return \"High\"\n }else{\n return \"High (0.70-0.79)\"\n }\n }else if (hdiValue>=0.80) {\n if (reduced) {\n return \"Very High\"\n }else{\n return \"Very High (>0.80)\"\n }\n }else {\n if (reduced) {\n return \"Low\"\n }else{\n return \"Low (<0.55)\"\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1251,"value":"function getCO2(isocode1, isocode2, countryname, year) {\n\n let co2data = co2_amb_hdi_4.find(v=> v.iso_code == isocode1 && v.year == year);\n\n if (!co2data) { co2data = co2_amb_hdi_4.find(v=> v.iso_code == isocode2 && v.year == year)};\n \n if (!co2data) { co2data = co2_amb_hdi_4.find(v=> v.country == countryname && v.year == year)};\n\n //const isperCapita = co2perCapita;\n const isperCapita = true;\n\n let totalCO2 = 0;\n if (co2data) {\n if (isperCapita) {\n totalCO2 = co2data['co2_per_capita'].toFixed(2) \n }else{\n totalCO2 = co2data['co2'].toFixed(2)\n }\n\n }else{\n //console.log(\"getCO2\", \"Not found: \", isocode1, isocode2, countryname, year)\n }\n\n return totalCO2;\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1530,"value":"function getCO2hdiyear(tmpHdi, tmpYear) {\n \n console.log(\"GetCO2HDI\",tmpHdi, tmpYear)\n \n let co2data = co2_amb_hdi_4.find(v=> v.hdi_category == tmpHdi && v.year == tmpYear);\n\n if (!co2data) { co2_amb_hdi_4.find(v=> v.hdi_category == tmpHdi && (v.year < tmpYear + 5 && v.year > tmpYear - 5))};\n \n let totalCO2 = 0;\n if (co2data) {\n if (co2perCapita) {\n totalCO2 = co2data['co2_per_capita'].toFixed(2) \n }else{\n totalCO2 = co2data['co2'].toFixed(2)\n }\n\n }else{\n //console.log(\"CO2_HDI\",\"Not found\")\n }\n\n return totalCO2;\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2225,"value":"function valueToNum(value) {\n\n const numericValue = parseFloat(value);\n \n if (isNaN(numericValue)) {\n return 0;\n }else{\n return numericValue.toFixed(3);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2175,"value":"function getGHG(isocode1, isocode2, countryname, year) {\n\n let ghgdata = co2_amb_hdi_4.find(v=> v.iso_code == isocode1 && v.year == year);\n\n if (!ghgdata) { ghgdata = co2_amb_hdi_4.find(v=> v.iso_code == isocode2 && v.year == year)};\n \n if (!ghgdata) { ghgdata = co2_amb_hdi_4.find(v=> v.country == countryname && v.year == year)};\n\n //const isperCapita = co2perCapita;\n const isperCapita = true;\n\n let totalGHG = 0;\n if (ghgdata) {\n if (isperCapita) {\n totalGHG = valueToNum(ghgdata['ghg_per_capita'])\n }else{\n totalGHG = valueToNum(ghgdata['total_ghg'])\n }\n\n }else{\n //console.log(\"totalGHG\", \"Not found: \", isocode1, isocode2, countryname, year)\n }\n\n return totalGHG;\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5422,"value":"function getPopulation(isocode1, isocode2, countryname, year) {\n let popdata = global_data.find(v=> v.iso_code == isocode1 && v.year == year);\n\n if (!popdata) { popdata = global_data.find(v=> v.iso_code == isocode2 && v.year == year)};\n \n if (!popdata) { popdata = global_data.find(v=> v.country == countryname && v.year == year)};\n\n\n let totalPop = 0;\n if (popdata) {\n totalPop = valueToNum(popdata['population']);\n }else{\n //console.log(\"totalPop\", \"Not found: \", isocode1, isocode2, countryname, year)\n }\n\n return totalPop;\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5424,"value":"function getGDP(hdiCategory, year) {\n\n let acumGDP = 0;\n \n hdi.filter(d=>d.hdicode == hdiCategory).forEach(country => {\n \n let gdpdata = global_data.find(v=> v.iso_code == country.iso3 && v.year == year);\n \n if (!gdpdata) { gdpdata = global_data.find(v=> v.country == country.country && v.year == year)};\n \n if (gdpdata) {\n acumGDP += valueToNum(gdpdata['gdp']);\n }else{\n //console.log(\"totalPop\", \"Not found: \", isocode1, isocode2, countryname, year)\n }\n\n });\n\n return acumGDP;\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2752,"value":"mywave = FileAttachment(\"wave.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":736,"value":"---\n#### Quim's data","pinned":false,"hidden":false,"mode":"md"},{"id":738,"value":"meanCo2PerCapitaEmissionsPerHDICategoryAndYear = d3\n .flatRollup(\n co2_amb_hdi_4,\n (v) => ({\n sumCo2: d3.mean(v, (d) => d.co2_per_capita),\n sumGhG: d3.mean(v, (d) => d.ghg_per_capita)\n }),\n //(v) => d3.mean(v, (d) => d.ghg_per_capita),\n (d) => d.hdi_category,\n (d) => new Date(Date.UTC(d.year, 0, 1)),\n )\n .sort((a, b) => d3.ascending(a[1], b[1]))\n .map(([hdi_category, year, values]) => ({\n hdi_category: hdi_category,\n year: year,\n sumCo2: values.sumCo2,\n sumGhG: values.sumGhG\n }));","pinned":false,"hidden":false,"mode":"ojs"},{"id":740,"value":"meanCo2PerCapitaEmissionsPerYear = d3\n .flatRollup(\n meanCo2PerCapitaEmissionsPerHDICategoryAndYear,\n (v) => d3.mean(v, (d) => d.sumCo2),\n (d) => new Date(Date.UTC(d.year.getUTCFullYear(), 0, 1))\n )\n .sort((a, b) => d3.ascending(a[0], b[0]))\n .map(([year, meanCo2]) => ({\n year: year,\n meanCo2: meanCo2,\n }));","pinned":false,"hidden":false,"mode":"ojs"},{"id":2469,"value":"function getHDIsFiltered()\n{\n if(hdi_filtre.length == 0) return [\"Very High\", \"High\", \"Medium\", \"Low\"]\n else return hdi_filtre\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":742,"value":"totalCo2EmissionsPerYear = d3\n .flatRollup(\n co2_amb_hdi_4.filter((d) => getHDIsFiltered().includes(d.hdi_category)),\n (v) => ({\n sumCo2: d3.sum(v, (d) => d.co2),\n sumGhG: d3.sum(v, (d) => d.total_ghg)\n }),\n (d) => new Date(Date.UTC(d.year, 0, 1))\n )\n .sort((a, b) => d3.ascending(a[0], b[0]))\n .map(([year, values]) => ({\n year: year,\n sumCo2: values.sumCo2,\n sumGhG: values.sumGhG\n }));","pinned":false,"hidden":false,"mode":"ojs"},{"id":744,"value":"tractats_ambientals = new Map([\n [1992, \"Rio Agreement\"],\n [2005, \"Kyoto Protocol\"],\n [2017, \"Paris Agreement\"],\n]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":956,"value":"hdi_colors = [\n {name: \"Very High\", color: \"#081D58\"},\n {name: \"High\", color: \"#BF3984\"},\n {name: \"Medium\", color: \"#EA7457\"}, \n {name: \"Low\", color: \"#F7E425\"}, \n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":859,"value":"function obtenirColor(d, hdi_filtre)\n{\n if(obtenirColorPerFiltreCountry().length == 1 && d.country == country_selected) return \"red\";\n else\n {\n if(hdi_filtre.includes(d.hdi_category))\n {\n return hdi_colors.find(element => element.name === d.hdi_category).color;\n }\n else\n {\n return \"lightgrey\";\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1114,"value":"function obtenirColorPerFiltreCountry()\n{\n if(getCountriesToFilter().length == 1) return \"red\";\n else return \"lightgrey\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2296,"value":"radioCO2GHG","pinned":true,"hidden":false,"mode":"ojs"},{"id":1107,"value":"function getCountriesToFilter()\n{\n let countriesToFilter = [];\n if(countries.includes(country_selected))\n {\n countriesToFilter.push(country_selected);\n return countriesToFilter;\n }\n else return countries\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1150,"value":"dataTransformed = meanCo2PerCapitaEmissionsPerHDICategoryAndYear.map(d => ({\n ...d,\n year: d.year.getFullYear() // extrae solo el año del objeto Date\n}));","pinned":true,"hidden":false,"mode":"ojs"},{"id":1217,"value":"hdi_countries = co2_amb_hdi_4.reduce((map, item) => {\n map[item.country] = item.hdi_category;\n return map;\n}, {});","pinned":true,"hidden":false,"mode":"ojs"},{"id":1164,"value":"anyMaximPaisSeleccionat = co2_amb_hdi_4.filter(d => d.country === country_selected).reduce((max, d) => d.year > max ? d.year : max, 0);","pinned":true,"hidden":false,"mode":"ojs"},{"id":1171,"value":"anyMinimPaisSeleccionat = co2_amb_hdi_4.filter(d => d.country === country_selected).reduce((min, d) => d.year < min ? d.year : min, Infinity);","pinned":true,"hidden":false,"mode":"ojs"},{"id":1139,"value":"function getHDICountrySelected()\n{\n let hdi_category = \"\";\n\n const hdiCategory = hdi_countries[country_selected];\n \n if(hdiCategory != null)\n {\n hdi_category = hdiCategory;\n }\n\n return hdi_category;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1189,"value":"country = country_selected","pinned":false,"hidden":false,"mode":"ojs"},{"id":882,"value":"SELECT year, country, co2_per_capita, hdi_category, paris_agreement, kyoto_protocol\nFROM co2_amb_hdi_v2\nWHERE co2_per_capita <= 40\nORDER BY country asc, year asc","pinned":false,"hidden":false,"mode":"sql","output":"co2_per_capita_per_country","database":"var:co2_amb_hdi_v2"},{"id":982,"value":"","pinned":false,"hidden":false,"mode":"sql","output":"country_names","database":"var:co2_amb_hdi_4"},{"id":1209,"value":"","pinned":true,"hidden":false,"mode":"sql","output":"","database":"var:co2_amb_hdi_4"},{"id":997,"value":"countries = Array.from(new Set(co2_amb_hdi_4.map(d => d.country)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":5738,"value":"co2_amb_hdi_5 = await FileAttachment(\"co2_amb_hdi_5.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1020,"value":"years = Array.from(new Set(co2_amb_hdi_4.map(d => d.year)))\n .map(year => ({\n year: new Date(Date.UTC(year, 0, 1)) // Año, mes (0 para enero), día (1)\n }));","pinned":false,"hidden":false,"mode":"ojs"},{"id":843,"value":"objetoMaxCO2 = co2_amb_hdi_4.reduce((max, obj) => obj.co2_per_capita > max.co2_per_capita ? obj : max, co2_amb_hdi_v2[0]);\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":5304,"value":"**Plot de prova**\n\n(https://observablehq.com/@observablehq/plot-barley-trellis-arrows?intent=fork)","pinned":false,"hidden":false,"mode":"md"},{"id":2888,"value":"wetrg = obtenirDadesFiltrades();","pinned":true,"hidden":false,"mode":"ojs"},{"id":2564,"value":"$42","pinned":true,"hidden":false,"mode":"ojs"},{"id":1709,"value":"/*miniPlots3 = html`\n
\n \n
\n
${lineDirectionPlotVeryHigh}
\n
${lineDirectionPlot}
\n
\n \n
\n
${lineDirectionPlotMedium}
\n
${lineDirectionPlotLow}
\n
\n
`*/\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1671,"value":"filteredData = co2_amb_hdi_v2.filter(d => d.year >= 1973 && d.year <= 1994);\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1673,"value":"groupedData = Array.from(d3.group(filteredData, d => d.hdi_category, d => d.country), \n ([hdi_category, countries]) => {\n return Array.from(countries, ([country, values]) => {\n values.sort((a, b) => a.year - b.year); // Ordena por año\n let firstValue = values[0].co2_per_capita;\n let lastValue = values[values.length - 1].co2_per_capita;\n return { country, hdi_category, firstValue, lastValue };\n });\n }).flat();","pinned":false,"hidden":false,"mode":"ojs"},{"id":1349,"value":"viewof co2Year = Scrubber(d3.range(1800, 2023), {loop: false, delay: 100, autoplay: false})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1986,"value":"$43","pinned":false,"hidden":false,"mode":"ojs"},{"id":734,"value":"viewof any_filtre = Inputs.range([1750,2021], {value: 1, step: 1, label: \"A partir de: \"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":209,"value":"---\n#### Addendum: data, libraries, other ...","pinned":false,"hidden":false,"mode":"md"},{"id":2125,"value":"//emissionsCountry = emissions[country].data","pinned":true,"hidden":false,"mode":"ojs"},{"id":19,"value":"import { toc } from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":865,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1102,"value":"import {autoSelect} from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1728,"value":"import {rangeSlider} from '@mootari/range-slider'","pinned":false,"hidden":false,"mode":"ojs"},{"id":338,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":346,"value":"
Planning
","pinned":false,"hidden":false,"mode":"html"},{"id":342,"value":"El treball ha de tenir una coherència i cohesió amb les seves parts. Jo faria:\n\n1. Introducció\n2. Índex (amb enllaços)\n3. Contingut (gràfics amb explicacions, seguint el fil argumental, plantejant preguntes)\n4. Conclusions\n5. Treball futur (a partir d’aquí què es podria fer?)\n6. Afegits (transformacions de dades, preparacions prèvies, planejament, etc)\n7. Referències (webgrafia i/o bibliografia)","pinned":false,"hidden":false,"mode":"md"},{"id":348,"value":"**Coses a tenir presents**","pinned":false,"hidden":false,"mode":"md"},{"id":351,"value":"- Per cumplir aquesta coherència, jo faria:\n - Introduccio del problema\n - Aclariment de les quatre categories\n - Gràfics simples?\n - Aclariment dels tractats i en quin moment es van posar en pràctica i per quin motiu\n - Grafics simples?\n - A partir d'aquí plantejar preguntes i respondre-les amb gràfics (seguint un ordre i coherència, anant cap a un objectiu)\n - Introduir factor temperatura\n - Introduir altres factor si cal (econòmic, canvi climàtic, etc.)\n - Necessitem un o varis gràfics que ens responguin la pregunta general plantejada: si són útils o no els tractats ambientals\n- Colors\n - Hem de triar un color per cada categoria de les 4 i sempre fer servir el mateix\n - Hem de tenir clar si el que volem és destacar, categoritzar, representar una seqüència...\n - Hi ha colors que per lògica són millors que d'altres en certes situacions\n - Seguir el diagrama aquell que ens va ensenyar en xaquín (aquell de si el fons és blanc, tal, si no, tal, etc.)\n ","pinned":false,"hidden":false,"mode":"md"},{"id":358,"value":"---\n### Mockups","pinned":false,"hidden":false,"mode":"md"},{"id":353,"value":"graficinteractiu1 = FileAttachment(\"graficInteractiu1.png\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":362,"value":"graficinteractiu2 = FileAttachment(\"graficInteractiu2.png\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":763,"value":"grafics3 = FileAttachment(\"grafics3.png\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":5715,"value":"co2_amb_hdi_4 = await FileAttachment(\"co2_amb_hdi_4.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W44","stdlib":"1","titling":"auto"}