2c:null 2d:["$","title",null,{"children":"GROUP 6: Martí Gibert, Mireia Molist, Albert Teixidó | UdG | Master's in Data Science | Observable"}] 32:T524,

Economic development has always been tied to the use of energy with the consequent drawback of carbon footprint. Nevertheless, in recent decades, the rise in global awareness of environmental issues has prompted nations to assess their ecological impact to seek for sustainable alternatives.

The analytical goal of this project is to assess the CO2 emissions between ${textcolor('Italy', 'green')} and ${textcolor('Spain', 'red')} and its position compared to the rest of the EU, Schengen, and associated countries. Making special emphasis on the impact of applying green solutions in each of the studied countries. The Ceteris Paribus analysis pretends to hold the same economic indicators while reducing CO2 emissions. The final purpose is to determine which one is more developed in terms of applying green solutions thus being more CO2 efficient.

The study is based on the dataset Our World In Data, one of the richest datasets for tracking the comparison between GHG emissions and economic variables. Features relative to CO2, CO2 per industry, GDP, and all those computed per capita have been used for all the countries belonging to the EU, Schengen Space, or associated countries in a time period comprised between 1950 and 2022.

33:T498,Plot.plot({ title: "Contrasting Perspectives in 2022 European Carbon Footprint", subtitle: "Revealing the greater contributors in a two sided dilemma. Total Emissions vs Per Capita.", width: 600, projection: { type: "conic-conformal", domain: eu_data_map }, color: { scheme: "YlOrRd", label: selected_plot.legend, legend: true }, style: { padding: 5, color: "black", fontSize:12 }, marks: [ Plot.geo(eu_countries, { fill: (d) => d.properties[selected_plot.variable], opacity: 1 }), Plot.geo(other_eu, { fill: "grey" , stroke: "white", opacity: 0.15 }), Plot.geo(not_esp_ita, {stroke: "black",strokeWidth: 0.5 }), Plot.geo(esp_ita, {stroke: "black",strokeWidth: 2 }), Plot.dot(eu_countries, Plot.centroid({ fill: "white", stroke: "black" })), Plot.tip(eu_countries, Plot.pointer( Plot.centroid({ stroke: "white", strokeWidth: 0.125, tip: true, channels: { "Country": d => d.properties.country, " - CO2": (d) => `${d.properties.co2} M.t.`, " - CO2 per capita": (d) => `${d.properties.co2_per_capita} t.` } }) )) ] });34:T77d,

To reduce CO2 emissions, many factors have been introduced, such as the implementation of policies and regulations, improved energy efficiency, and the EU Emissions Trading Scheme.

One of the key drivers has been the gradual phasing out of polluting coal-fired power plants transitioning towards natural gas and renewable energy sources. This has seen energy supply sector emissions drop more than 40 percent since 1990. And despite coal power CO₂ emissions have increased since 2020, owing to the COVID-19 recovery and the Russia-Ukraine war, they emissions were still almost 40 percent lower than a decade earlier. Nevertheless, the energy supply sector remains the largest source of Europe's GHG emissions, and the most polluting energy production centers in the EU are currently all coal-fired power plants.

Taking an overall look to the graphic below, we can observe that there are countries such as Belgium, the UK, and Germany that in 1950 were fully industrialized, emitting high levels of CO2 emissions. Progressively, most European countries followed them, thus increasing their emission levels. The CO2 release peak was achieved in the 80-90's decade. Since then, and according to multiple conventions and agreements, all the European countries have been trying to decrease their emission levels.

In addition to this Luxembourg, has historically been the largest CO2 per capita releaser, far followed by Estonia and the Czech Republic. This has mainly been due to the coal reliance of these countries. Luxembourg is still the EU Member State with the highest level of GHG emissions per inhabitant, 15.3 tons in 2019. However, it has also delivered one of the steepest reductions in the Union on this indicator, 34% between 2005 and 2019. In the chart, these countries are not displayed to prevent running out of scale.

35:T45c,plot_other_industry_co2_3 = Plot.plot({ title: "Carbon Emissions Evolution in Italy and Spain - " + grafic3.key, subtitle: "Slow convergence of Italy and Spain's CO2 emissions over time", width: 780, height: 500, marginLeft: 80, marginRight: 120, marginBottom: 60, style: { padding: 0, color: "black", fontSize:12 }, x: {grid: true, label: "Year", labelAnchor: "center", labelArrow: false, labelOffset: 35}, y: {grid: true, label: `${grafic3.key} (in million tonnes)` , labelAnchor: "center", labelArrow: false, tickSize: 0, tickPadding: 25, labelOffset: 65}, color:{ type: "categorical", legend: true, domain: ["Italy", "Spain"], range: ["green", "red"], }, marks: [ Plot.ruleY([0]), Plot.lineY(perIndustry, { x: "year", y: grafic3, z: "country", color: { legend: "country"}, stroke: "country" }), Plot.text(perIndustry, {filter: (d) => d.year % 5 === 0, x: "year", y: grafic3, text: (d) => `${d[grafic3.value]}`, dy: -12}), Plot.circle(perIndustry, { x: "year", y: grafic3, fill: "country" }), ] });36:T44f,

When comparing Italy and Spain, their overall trend over time clearly follows a similar pattern, however, it can also be seen that the CO2 emissions of Italy and Spain converge over time, particularly when comparing emissions from the oil and other industries. If all the industries’ emissions are taken into consideration, the study exposes that CO2 emissions increased until around 2005, and then began to decrease. The Kyoto Protocol, which was approved in 1997 did not had any effect until 2005, forcing industrialized countries to limit and reduce greenhouse gases.

The collapse of the 'Real State Bubble' occurred in 2008 is also reflected on the plot, by showing a more pronounced decrease. The plot also illustrates the impact of Covid-19: there was a slight reduction in CO2 emissions in 2020, but they quickly returned to the global trend. Since 2014, Spain has been producing more CO2 emissions generated from cement industry, surpassing Italy's emissions, but later on returned to a state in which both are very similar.

37:T61f,{ const svg = d3 .create("svg") .attr("width", 680) .attr("height", 350) .attr("viewBox", [-4500, -2000, 9000, 4000]) svg.append('circle').attr('r', bubblePlot[0].primary_energy_consumption).attr('cx', -bubblePlot[0].primary_energy_consumption).attr('cy', 0).attr("fill", "green").attr("fill-opacity",0.3).attr("stroke-opacity",1).attr("stroke", "black").attr("stroke-width", 10); svg.append('circle').attr('r', bubblePlot[1].primary_energy_consumption).attr('cx', bubblePlot[1].primary_energy_consumption).attr('cy', 0).attr("fill", "red").attr("fill-opacity",0.3).attr("stroke-opacity",1).attr("stroke", "black").attr("stroke-width", 10); svg.append("text") .attr("x", -bubblePlot[0].primary_energy_consumption) .attr("y", 20) .attr("text-anchor", "middle") .style("font-size", "250px") .style("font-weight", "bold") .text("Italy"); svg.append("text") .attr("x", bubblePlot[1].primary_energy_consumption) .attr("y", 20) .attr("text-anchor", "middle") .style("font-size", "250px") .style("font-weight", "bold") .text("Spain"); svg.append("text") .attr("x", -bubblePlot[0].primary_energy_consumption) .attr("y", 250) .attr("text-anchor", "middle") .style("font-size", "200px") .text(bubblePlot[0].primary_energy_consumption.toFixed(2) + " TWh"); svg.append("text") .attr("x", bubblePlot[1].primary_energy_consumption) .attr("y", 250) .attr("text-anchor", "middle") .style("font-size", "200px") .text(bubblePlot[1].primary_energy_consumption.toFixed(2) + " TWh"); return svg.node(); }38:T4d2,Plot.plot({ width: 650, marginLeft: 80, marginBottom: 80, style: { padding: 0, color: "black", fontSize:12 }, x: {label: "Year", labelAnchor: "center", labelArrow: false, labelOffset: 40}, y: {label: "Energy cons difference (kWh)",labelAnchor: "center", labelArrow: false, labelOffset: 60}, marks: [ Plot.ruleY([0]), Plot.ruleX([1965]), Plot.differenceY(energy_diff, {x: "year", y: "diff", positiveFill: "red", negativeFill: "green", fillOpacity: 0.3, strokeWidth: 1}), Plot.tip(energy_diff, Plot.pointerX({x: d => {var yearSelected = d[year]}, y: "diff", channels: {x: "year", y: "diff", EPC_Spain: "epc_spain", EPC_Italy: "epc_italy"}})), Plot.ruleX([year]), Plot.dot(energy_diff, {x: year, y: energy_diff.find(obj => obj.year === year)?.diff, fill: "black", r: 5}) // Plot.tip(energy_diff, {x: year, y: energy_diff.find(obj => obj.year === year)?.diff, channels: {x: year, y: energy_diff.find(obj => obj.year === year)?.diff, EPC_Italy: "epc_italy", EPC_spain: "epc_spain"}, strokeWidth: 1}) ] }) //Plot.tip(selected_plot2.data_source, Plot.pointer({x: selected_plot2.variable_x, y: selected_plot2.variable_y, channels: {year: "year", country: "country"}}))39:T689,

The result is almost the same with the variable of energy consumption per inhabitant, nonetheless, considering that Italy is larger in population, the gap between countries ended up being lower. The year 2000 represented a breakpoint in energy consumption per capita as Spain overcame Italy’s level. Nowadays, the energy per capita used in Spain is about 4700 KWh higher than in Italy.

The energy consumption per capita variable experienced a constant growth until 2004, when, in 2005, countries started applying preventive measures to adapt their situation to the Kyoto Protocol. This first pack of measures made a special emphasis on energy efficiency. The aim was to link the energy output to the energy input, prioritizing the use of devices and maximizing the energy output per unit of energy introduced in the system. Common examples of energy-efficient devices are energy-saving light bulbs that produce the same amount of light as conventional light bulbs but use less energy, and alternative heating systems like active solar heating, among others. The measures to reduce energy consumption have been more solid in Italy than in Spain, thus resulting in a higher yearly energy per capita in the Iberic country.

3a:T42c,

After a deep understanding of the situation, it might appear that higher levels of industrialization and production would inevitably lead to higher levels of CO2, suggesting that a robust economy is not compatible with a healthy environment. However, it needs to be mentioned that the source of this energy has not been considered in this analysis. The energy mix plays a crucial role and for this reason, shift towards renewable sources can significantly reduce CO2 emissions in the electricity generation.

In the chart below, can easily be seen that by 2022, Spain was significantly more advanced in the adoption of renewable energies, surpassing Italy’s levels by about 33%, mainly driven by a wide share in wind power. On the other side, regarding fossil fuels, Spain demonstrated again more efficiency, as the multiple nuclear plants contributed to energy production without emitting any GHG, reducing the dependence on gas plants. In the case of Italy, gas represents 56% of their energy mix.

3b:T5cd,

The situation in Spain has experienced a positive transformation after the decision to dismantle 17 coal plants since 2011. The initiative taken has resulted in an 88% reduction in energy derived from coal, currently representing only 2% of Spain's energy mix (REE). Furthermore, the Spanish government pretends to complete the dismantling of all coal units by 2027 and achieve a 63% reduction in combined cycle by 2040. On the side of renewable energies, the increase in the consume of wind power has been significant, thanks to the widespread deployment of wind farms. Spain has now 30,108 installed megawatts (MW), and planning to increase this capacity to 50,333MW by 2050. Hydropower plateaued in 1985 when the last large hydroelectric dams were built. Lastly, solar energy has experienced a relevant increase since 2017.

Italy's energy landscape has undergone notable changes, but challenges persist. The country has reduced coal dependence significantly, nowadays, coal represents only 5% of the energy mix, while a substantial 56% still comes from gas. Unlike Spain, Italy faces limitations in wind energy and compensates it with a larger share of hydropower enery, currently holding/meaning 21,500MW. The absence of nuclear power is noteworthy. Italy has made strides in reducing other fossil fuels, nevertheless, there is still place for further progress in diversifying and decarbonizing its energy sources.

3c:T79b,ratioChart = { return(Plot.plot({ width: 600, height: 500, style: { padding: 5, color: "black", fontSize:12 }, title: "Italy leads GDP/CO2 efficiency over Spain", subtitle: "Sustainable economies constrained by the environmental impact", marginLeft: 80, marginBottom: 80, x: {label: "Year", labelAnchor: "center", labelArrow: false, labelOffset: 35}, y: {label: "Ratio GDP/CO2", labelAnchor: "center", labelArrow: false, labelOffset: 40}, color: {legend:true}, marks: [ Plot.dot(scatterplotData, Plot.pointerX({x: "year", y: "ratio_italy", stroke: "red", strokeWidth:2, r: 5})), Plot.dot(scatterplotData, Plot.pointerX({x: "year", y: "ratio_spain", stroke: "red", strokeWidth: 2, fill: "yellow", r: 5})), Plot.lineY(scatterplotData, {x: "year", y: "ratio_italy", stroke: "green"}), Plot.lineY(scatterplotData, {x: "year", y: "ratio_spain", stroke: "red"}), Plot.ruleX(scatterplotData, Plot.pointerX({x: "year", py: "ratio_italy", stroke: "grey"})), Plot.text(scatterplotData, Plot.pointerX({px: "year", py: "ratio_italy", dx: 30, dy: 20, frameAnchor: "top-left", fontVariant: "tabular-nums", fontSize: 14, fontWeight: "bold", text: (d) => [` Year ${d.year.toFixed(0)}`].join(" ")})), Plot.text(scatterplotData, Plot.pointerX({px: "year", py: "ratio_italy", dx: 30, dy: 28, frameAnchor: "top-left", fontVariant: "tabular-nums", fontSize: 14, text: (d) => [`\n`, ` GDP ($B)`, `\n`, `Italy ${d.gdp_italy.toFixed(1)}`, `\n`,`Spain ${d.gdp_spain.toFixed(1)}`].join(" ")})), Plot.text(scatterplotData, Plot.pointerX({px: "year", py: "ratio_italy", dx: 170, dy: 28, frameAnchor: "top-left", fontVariant: "tabular-nums", fontSize: 14, text: (d) => [`\n`, `CO2 (Mt)`, `\n`, `${d.co2_italy.toFixed(2)}`, `\n`, `${d.co2_spain.toFixed(2)}`].join(" ")})), ] })) } 3d:[{"id":"25104fb370e71fb6eba7cad27779645e9980f4f851ab64da185ba9587a255f250e48bd7384c0eee4917467e5850215415c4dc99d584985ddfc3aa3d0190430a9","href":"https://static.observableusercontent.com/files/25104fb370e71fb6eba7cad27779645e9980f4f851ab64da185ba9587a255f250e48bd7384c0eee4917467e5850215415c4dc99d584985ddfc3aa3d0190430a9","name":"eu_map_data.geojson","type":"application/geo+json","lastModified":1705941456002.335,"size":1605430}] 30:{"id":"0f029b566d53540c","slug":null,"title":"GROUP 6: Martí Gibert, Mireia Molist, Albert Teixidó","thumbnail":"cc2ddd8686d19721f7d232e7d239b09648efaf62e43bf021b6b34075bdddaea1","access_level":"public","create_time":"$D2024-01-22T16:37:36.002Z","update_time":"$D2024-04-26T12:57:16.172Z","view_time":null,"first_public_time":"$D2024-01-22T16:37:36.002Z","has_unpublished_edits":null,"fork":{"id":"7c40e50d2297fbdc","slug":null,"title":"GROUP 6: Martí Gibert, Mireia Molist, Albert Teixidó","owner":{"id":"65f6c248a0776fe8","login":"treballfinal","name":"Treball Final"}},"stars":0,"starred":false,"owner":{"type":"team","id":"f6d094e91910d678","login":"udg-master-data-science","name":"UdG | Master's in Data Science","avatar_url":"https://avatars-next.observableusercontent.com/43ba1b8a69dd1722287c44e61dfee202393e63b350f5814991b6b0af9418435e","tier":"starter_2024"},"version":3400,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"5276b422101e1a12","login":"albert-teixido","name":"Albert Teixidó","avatar_url":"https://avatars-next.observableusercontent.com/3a403d40b3fff1559ad975290add47cbd2e6f663f1e9f6bd4ac407036721cc4f","tier":null}],"members":[{"type":"individual","id":"37cab874ceedc4fa","login":"xaquingv","name":"","avatar_url":"https://avatars-next.observableusercontent.com/964250a396a5c55f0dcee877939a870e04f9741dead22595f1d385b85883f4d6","tier":null,"role":"editor"},{"type":"team","id":"f6d094e91910d678","login":"udg-master-data-science","name":"UdG | Master's in Data Science","avatar_url":"https://avatars-next.observableusercontent.com/43ba1b8a69dd1722287c44e61dfee202393e63b350f5814991b6b0af9418435e","tier":"starter_2024","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"GROUP 6: Martí Gibert, Mireia Molist, Albert Teixidó","cells":[{"id":3364,"value":"# GROUP 6: Martí Gibert, Mireia Molist, Albert Teixidó","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3129,"value":"

Sustainable economies: a comprehensive approach comparing Spain vs Italy

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2122,"value":"---\n# Introduction and objectives","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2109,"value":"$32","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":179,"value":"---\n# Summary","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"toc({\n// title: null,\n headers: \"h1,h2\",\n// headers: 'h3',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2131,"value":"---\n# From Past to Present: Tracing the Trajectory of CO2 Emissions in Europe","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2135,"value":"

Europe has historically been one of the biggest contributors to global greenhouse gas emissions. As of the end of 2021, the countries that now make up the European Union had produced just over 17 percent of cumulative global carbon dioxide emissions since the Industrial Revolution began. As one of the most responsible regions for historical emissions, the EU has set numerous emissions-reduction targets. And while the EU has made great progress in recent decades, having achieved its 20% reduction target by 2020, it was still the joint third-largest emitter of greenhouse gases in 2021.

\n \n

The main contributor to CO₂ emissions in Europe is Germany, considerably discerning from other CO2 emitter, such as France, Italy, and Poland. This is mostly driven by Germany's energy mix being reliant on coal, the dirtiest of all fossil fuels, discharging approximately 50% more CO₂ than natural gas.

\n\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1622,"value":"viewof selected_plot = Inputs.select(map_plots, {label: \"Choose a plot\", format: x => x.name, value: map_plots.find(t => t.variable === \"co2\")})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1625,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2140,"value":"

Diving deeper into a comparison between the Total CO2 and CO2 Per capita, can be easily seen that some countries that might not seem remarkable in terms of total CO2 emissions, are the ones more intensive in per capita terms emitting CO2. Some of those could be Iceland, the Czech Republic, or Norway, which gain a significant importanmce if we evaluate them in terms of emissions per capita.

\n \n

On the other side, countries such as Germany, the UK, or Italy leading in terms of total emissions, have a lower impact if the assessment is performed in terms of emissions per capita.

\n\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1986,"value":"## CO2 emissions in Europe after regulations implemented to decrease GHG emissions. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2807,"value":"$34","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":782,"value":"viewof includeOutliers = Inputs.toggle({label: \"Considering Luxembourg, Estonia and the Czech Republic\", value: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2502,"value":"Plot.plot({\n title: \"Evolution of CO2 Emissions Per Capita in Europe\",\n subtitle: \"Generalised reduction over time in CO2 emissions across Europe\",\n width: 650,\n marginLeft: 120,\n marginBottom: 80,\n padding: 0,\n\n style: {\n padding: 0,\n color: \"black\",\n fontSize:12\n },\n\n \n \n x: {label: \"Year\", labelAnchor: \"center\", labelOffset: 50, ticks: [1950, 1960, 1970, 1980, 1990, 2000, 2010, 2020], tickSize: 7, tickPadding: 10},\n fy: {labelOffset: 100, label: \"Country\"},\n\n color: {\n legend: true,\n scheme: \"Oranges\",\n label: \"CO2 per capita (in million tonnes)\"\n },\n marks: [\n \n Plot.cell(\n cellChartDataSource,\n {x: \"year\", fy: \"country\", inset: 0.1, fill: \"co2_per_capita\",sort: {fy: \"fill\"}}\n ),\n Plot.frame({fy: \"Italy\", strokeWidth: 2.5}),\n Plot.frame({fy: \"Spain\", strokeWidth: 2.5})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2096,"value":"# Comparison of CO2 Emissions Between two Similar Countries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2817,"value":"

\n\n

From now on, this analysis gives a focus on the two highlighted countries in the previous graphics, Italy and Spain. Historically, those two countries have been compared, due to their similar area, population, geographical situation, economy, etc.

\n \n

On the first years taken into account, between 1960-1975 Italy experienced a faster increase in emissions in comparison to Spain. This can be attributed to facing an earlier industrialization of the country. The slower increase in emissions in Spain can be a direct consequence of the end of the dictatorship in Spain in 1975. Since then, CO2 emissions have been steadily increasing, reaching Italy's levels, and reached a maximum in 2005 when, due to the Kyoto Protocol, both have been forced to decrease it until today.

\n \n

In the following graphs, we are going to evaluate Spain and Italy by comparing the evolution of their emissions originated in different types of industries.

\n\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1370,"value":"viewof grafic3 = Inputs.select(tipusco2, {label: \"Select a CO2 plot\", format: x => x.key, value: tipusco2.find(t => t.key === \"CO2\")})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1450,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2011,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2825,"value":"---\n# Energy as a variable to assess similarities","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2890,"value":"

\n \n

CO2 release cannot be fully compared in quantity emitted or per capita units, it is also necessary to consider how its relationship with the economy of the studied country. Since the key driver for economic development is the usage of energy, this can be a valuable feature to consider in the comparison between countries to assess CO2 emissions.

\n \n

As previously mentioned the Spanish transition from a dictatorial regime to a democracy severely affected many indicators. In this case can be observable that the energy consumption in the 60s in Italy was thrice the one in Spain, and this distance was steadily reduced once the Spanish transition to democracy was fully completed. In the last couple of decades that have been analyzed, the energy consumption has been quite similar and stable in both countries.

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2832,"value":"## Shift Towards Spain in the Largest Energy Consumption\n#### Yearly energy consumption","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1409,"value":"viewof year = Scrubber(Array.from({ length: 2023 - 1965 }, (_, index) => 1965 + index), {loop: false, delay: 150, autoplay: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1656,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2838,"value":"#### Evolution of per capita energy consumption difference between Spain and Italy","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1361,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2895,"value":"$39","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3394,"value":"Plot.plot({\n marks: [\n Plot.barX(olympians, Plot.groupY({x: \"count\"}, {y: \"nationality\", sort: {y: \"x\", reverse: true, limit: 10}})),\n Plot.ruleX([0])\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3396,"value":"Plot.plot({\n marks: [\n Plot.cell(weather.slice(-365), {\n x: d => d.date.getUTCDate(),\n y: d => d.date.getUTCMonth(),\n fill: \"temp_max\"\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3398,"value":"pizza","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3399,"value":"map = {\n const container = yield htl.html`

`;\n const map = L.map(container).setView([37.774, -122.423], 13);\n L.tileLayer(\"https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\", {\n attribution: \"© OpenStreetMap contributors\"\n }).addTo(map);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2858,"value":"---\n# Energy mix as a key to relate energy production with CO2 emissions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3016,"value":"$3a","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2255,"value":"

${[em_italy]}

${[em_spain]}

\n Source: Ember climate, energy mix\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3124,"value":"$3b","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3281,"value":"

\n \n

The previous reasoning might lead us to think that fully shifting towards renewable energies would completely solve the stated problem. However, neither the economy nor CO2 emissions are solely based on energy production. To better assess this, we will consider various variables to have indicators of performance on a global economic scale.

\n\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2867,"value":"---\n# Energy efficiency, the only reasonable path to achieve CO2 reduction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1907,"value":"viewof selected_plot2 = Inputs.select(map_scatter, {label: \"Choose a visualization\", format: x => x.select, value: map_plots.find(t => t.variable_X === \"co2\")})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1271,"value":"

${[ratioChart]}

${[scatterChart]}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3283,"value":"

\n \n

The left chart represents a ratio between GDP and CO2 emissions over time. It is evident that Italy has predominantly excelled in economic efficiency relative to pollution. Additionally, it can be observed that after 2005 (when the Kyoto Protocol was applied), efficiency notably increased.

\n \n

The right chart delves deeper into the composition of this ratio. Based on the positioning of variables in the chart, we would expect the steepest curve to be the most efficient. In total terms, the impact of the economy's size on the relation is apparent, as Italy exhibits the same shape but on a higher scale. In per capita terms, both shapes match. Examining the evolution over time reveals that Italy reached the same levels earlier on. Furthermore, there is a significant reduction in CO2 emissions without a decrease in GDP after 2005, indicating an evolution towards economic efficiency.

\n\n\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"---\n# Conclusions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3285,"value":"

\n \n

Summarizing, the conclusion is that Italy has a slightly more efficient economy than Spain. This is primarily driven by the composition of the economy, with a higher proportion in the industry and less reliance on services and agriculture, which are less efficient in the production of GDP.

\n \n

The results are fairly accurate concerning our objective. However, if more detailed results are desired, we should utilize additional data sources, including demographic, economic, and environmental data. This approach will enable us to better establish correlations between variables and identify industries that are less advanced in implementing green solutions. It will also help us determine the necessary steps to progress towards a more CO2-efficient economy.

\n\n\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":209,"value":"---\n# Data sources, imports and functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3367,"value":"## CHART SCHEMES","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3370,"value":"// europeMap = FileAttachment(\"1.Europe map.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3374,"value":"// heatmap = FileAttachment(\"2.heatmap.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3377,"value":"// co2Industry = FileAttachment(\"3.co2 industry.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3380,"value":"// dotsEnergyCountry = FileAttachment(\"4.dots energy country.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3383,"value":"// difEnergyPerCapita = FileAttachment(\"5.dif energy per capita.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3386,"value":"// pibCo2 = FileAttachment(\"6.pib-co2.jpg\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1776,"value":"## DATA GENERAL","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":336,"value":"rawData = d3.csv(\"https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"keys = [\"country\",\"year\",\"iso_code\",\"population\",\"gdp\",\"co2\",\"co2_growth_abs\",\"co2_growth_prct\",\"co2_per_capita\",\"co2_per_gdp\",\"co2_per_unit_energy\",\"co2_including_luc\",\"co2_including_luc_growth_abs\",\"co2_including_luc_growth_prct\",\"co2_including_luc_per_capita\",\"co2_including_luc_per_gdp\",\"co2_including_luc_per_unit_energy\",\"cement_co2\",\"cement_co2_per_capita\",\"coal_co2\",\"coal_co2_per_capita\",\"consumption_co2\",\"consumption_co2_per_capita\",\"flaring_co2\",\"flaring_co2_per_capita\",\"gas_co2\",\"gas_co2_per_capita\",\"land_use_change_co2\",\"land_use_change_co2_per_capita\",\"oil_co2\",\"oil_co2_per_capita\",\"other_industry_co2\",\"other_co2_per_capita\",\"trade_co2\",\"primary_energy_consumption\",\"energy_per_capita\",\"energy_per_gdp\",\"temperature_change_from_co2\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":362,"value":"europeanCountries = [\"Austria\", \"Belgium\", \"Bulgaria\", \"Croatia\", \"Cyprus\", \"Czechia\", \"Denmark\", \"Estonia\", \"Finland\", \"France\", \"Germany\", \"Greece\", \"Hungary\", \"Iceland\", \"Ireland\", \"Italy\", \"Latvia\", \"Liechtenstein\", \"Lithuania\", \"Luxembourg\", \"Malta\", \"Netherlands\", \"Norway\", \"Poland\", \"Portugal\", \"Romania\", \"Slovakia\", \"Slovenia\", \"Spain\", \"Sweden\", \"Switzerland\", \"United Kingdom\"]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"europeanDataRaw = rawData.filter(item => europeanCountries.includes(item.country) && item.year >= 1950)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"europeanData = europeanDataRaw.map(obj => filterObject(obj, keys));\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":532,"value":"spainData = europeanData.filter(item => item.country === 'Spain');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":529,"value":"italyData = europeanData.filter(item => item.country === 'Italy');\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1607,"value":"## DATA MARTÍ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1611,"value":"eu_data_map = FileAttachment(\"eu_map_data.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1634,"value":"eu_countries = {\n return eu_data_map.features.filter(feature => feature.properties.type === 0 ||feature.properties.type === 2);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1647,"value":"other_eu = {\n return eu_data_map.features.filter(feature => feature.properties.type === 1);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1637,"value":"esp_ita = {\n return eu_data_map.features.filter(feature => feature.properties.type === 2);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1640,"value":"not_esp_ita = {\n return eu_data_map.features.filter(feature => feature.properties.type === 0);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1620,"value":"map_plots = [\n {variable: \"co2\", name: \"Total CO2\", legend: \"CO2 Emissions (in million tonnes)\"},\n {variable: \"co2_per_capita\", name: \"CO2 per capita\", legend: \"CO2 per capita (in tonnes)\"},\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":649,"value":"## DATA MIREIA","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1382,"value":"perIndustry = europeanData\n .filter((value) => value.year >= 1980 && (value.country === 'Italy' || value.country === 'Spain'))\n .map((entry) => ({\n year: convertToFloat(entry.year),\n country: entry.country,\n co2: convertToFloat(entry.co2),\n cement_co2: convertToFloat(entry.cement_co2),\n coal_co2: convertToFloat(entry.coal_co2),\n consumption_co2: convertToFloat(entry.consumption_co2),\n oil_co2: convertToFloat(entry.oil_co2),\n other_industry_co2: convertToFloat(entry.other_industry_co2),\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1445,"value":"tipusco2 = [\n {key: \"All industries\", value: \"co2\"},\n {key: \"Cement industry\", value: \"cement_co2\"},\n {key: \"Coal industry\", value: \"coal_co2\"},\n {key: \"Consumption based industry\", value: \"consumption_co2\"},\n {key: \"Oil industry\", value: \"oil_co2\"},\n {key: \"Other industries\", value: \"other_industry_co2\"}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1407,"value":"bubblePlot = europeanData\n .filter((value) => value.year == year && (value.country === 'Italy' || value.country === 'Spain'))\n .map((entry) => ({\n year: entry.year,\n country: entry.country,\n primary_energy_consumption: convertToFloat(entry.primary_energy_consumption),\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1313,"value":"## DATA ALBERT","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":744,"value":"#### Cell chart","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":791,"value":"cellChartDataSource = includeOutliers ? cellChart1 : cellChart0;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1728,"value":"cellChart0 = europeanData\n .filter((value) => value.year >= 1950 && !['Luxembourg', 'Czechia', 'Estonia'].includes(value.country))\n .map((entry) => ({\n year: +entry.year,\n country: entry.country,\n co2_per_capita: convertToFloat(entry.co2_per_capita)\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1740,"value":"cellChart1 = europeanData\n .filter((value) => value.year >= 1950)\n .map((entry) => ({\n year: +entry.year,\n country: entry.country,\n co2_per_capita: convertToFloat(entry.co2_per_capita)\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":829,"value":"#### energy diff","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":823,"value":"SELECT\n CAST (ed1.year AS Float) AS year,\n CAST(ed1.energy_per_capita AS Float) AS epc_italy,\n CAST(ed2.energy_per_capita AS Float) AS epc_spain,\n CAST(ed2.energy_per_capita AS Float) - CAST(ed1.energy_per_capita AS Float) AS diff\nFROM\n europeanData ed1\n \nJOIN\n europeanData ed2 ON ed1.year = ed2.year\n AND ed1.country = 'Italy'\n AND ed2.country = 'Spain'\nWHERE\n ed1.year >= 1965\n\nORDER BY ed1.year\n\n","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"energy_diff","database":"var:europeanData"},{"id":874,"value":"#### scatterplot","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":878,"value":"SELECT\n CAST(ed1.year AS Float) AS year,\n CAST(ed1.gdp AS Float) / CAST(ed1.co2 AS Float) / 1000000000 AS ratio_italy,\n CAST(ed2.gdp AS Float) / CAST(ed2.co2 AS Float) / 1000000000 AS ratio_spain,\n CAST(ed1.gdp AS Float) / 1000000000 AS gdp_italy,\n CAST(ed2.gdp AS Float) / 1000000000 AS gdp_spain,\n CAST(ed1.co2 AS Float) AS co2_italy,\n CAST(ed2.co2 AS Float) AS co2_spain,\n \n\nFROM\n europeanData ed1\n \nJOIN\n europeanData ed2 ON ed1.year = ed2.year\n AND ed1.country = 'Italy'\n AND ed2.country = 'Spain'\nWHERE\n ed1.year >= 1965 AND ed1.year < 2019\n\n\nORDER BY ed1.year\n","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"scatterplotData","database":"var:europeanData"},{"id":2000,"value":"scatterplotData1 = europeanData\n .filter((value) => value.year >= 1965 && value.year < 2019 && (value.country === 'Italy' || value.country === 'Spain'))\n .map((entry) => ({\n year: convertToFloat(entry.year),\n country: entry.country,\n co2_per_capita: convertToFloat(entry.co2_per_capita),\n gdp_per_capita: convertToFloat(entry.gdp/entry.population)\n}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1123,"value":"SELECT\n year AS year,\n country AS country,\n CAST(gdp AS Float) / 1000000000 AS gdp,\n CAST(co2 AS Float) AS co2,\n\nFROM\n europeanData\n \nWHERE\n year >= 1965 AND year < 2019\n AND (country = 'Italy' OR country = 'Spain')\n","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"scatterplotData2","database":"var:europeanData"},{"id":1958,"value":"map_scatter = [\n {variable_x: \"co2_per_capita\", variable_y: \"gdp_per_capita\", select: \"Per capita\", label_x: \"CO2 emissions per capita (t)\", label_y: \"GDP per capita ($)\", data_source: scatterplotData1},\n {variable_x: \"co2\", variable_y: \"gdp\", select: \"Total\", label_x: \"CO2 emissions (Mt)\", label_y: \"GDP ($B)\", data_source: scatterplotData2},\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":905,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1104,"value":"scatterChart = {\n return(Plot.plot({\n width: 600,\n height: 500,\n marginLeft: 80,\n marginBottom: 80,\n\n style: {\n padding: 5,\n color: \"black\",\n fontSize:12\n },\n \n title: \"Evolving towards efficiency\",\n subtitle: \"1980 and 2005 were two inflection years in the co2 emissions reduction\",\n x: {label: selected_plot2.label_x, labelAnchor: \"center\", labelArrow: false, labelOffset: 35},\n y: {label: selected_plot2.label_y, labelAnchor: \"center\", labelArrow: false, labelOffset: 60},\n marks: [\n Plot.dot(selected_plot2.data_source, {\n x: selected_plot2.variable_x,\n y: selected_plot2.variable_y,\n fill: d => d.country === \"Italy\" ? greens(d.year) : reds(d.year),\n }),\n \n Plot.tip(selected_plot2.data_source, Plot.pointer({x: selected_plot2.variable_x, y: selected_plot2.variable_y, channels: {year: \"year\", country: \"country\"}}))\n ]\n }))\n} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2371,"value":"#### Energy mix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2668,"value":"electricityDataEmber","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2694,"value":"\nSELECT\n CAST(SUBSTRING(year, 1, 4) AS INTEGER) AS year,\n country,\n CAST(value AS FLOAT) AS energy,\n source\nFROM\n electricityDataEmber\nWHERE\n country = 'Italy';","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"energy_mix_italy","database":"var:electricityDataEmber"},{"id":2733,"value":"\nSELECT\n CAST(SUBSTRING(year, 1, 4) AS INTEGER) AS year,\n country,\n CAST(value AS FLOAT) AS energy,\n source\nFROM\n electricityDataEmber\nWHERE\n country = 'Spain';","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"energy_mix_spain","database":"var:electricityDataEmber"},{"id":2717,"value":"energySources = [\"Gas\", \"Coal\", \"Other Fossil\", \"Nuclear\", \"Solar\", \"Wind\", \"Hydro\"];\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2724,"value":"colorPalette = ['#454546', '#656565', '#878686', '#aaa9a9', '#3ba29b', '#7abeb8', '#afdad6']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2152,"value":"em_italy = \n{return(Plot.plot({\n style: {\n padding: -10,\n color: \"black\",\n fontSize:12\n },\n x: {label: \"Year\", labelAnchor: \"center\", labelArrow: false, labelOffset: 27},\n y: {\n label: \"Composition of energy generation (%)\",\n percent: true,\n labelAnchor: \"center\", \n labelArrow: false,\n labelOffset: 40\n },\n title: \"Italian energy mix distribution\",\n color: {legend: true, domain: energySources, range: colorPalette},\n marks: [\n Plot.areaY(\n energy_mix_italy,\n Plot.stackY({offset: \"normalize\", reverse: false, order: energySources},\n {x: \"year\", y: \"energy\", z: \"source\", fill: \"source\"})),\n Plot.ruleY([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], {stroke: \"white\", strokeOpacity: 0.4})\n ]\n}))}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2188,"value":"em_spain = \n{return(Plot.plot({\n style: {\n color: \"black\",\n fontSize:12\n },\n x: {label: \"Year\", labelAnchor: \"center\", labelArrow: false, labelOffset: 27},\n y: {\n label: \"Composition of energy generation (%)\",\n percent: true,\n labelAnchor: \"center\", \n labelArrow: false,\n labelOffset: 40\n },\n title: \"Spanish energy mix distribution\",\n color: {legend: true, domain: energySources, range: colorPalette},\n marks: [\n Plot.areaY(\n energy_mix_spain,\n Plot.stackY({offset: \"normalize\", reverse: false, order: energySources},\n {x: \"year\", y: \"energy\", z: \"source\", fill: \"source\"})),\n Plot.ruleY([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], {stroke: \"white\", strokeOpacity: 0.4})\n ]\n}))}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":398,"value":"---\n## FUNCTIONS","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":385,"value":"function filterObject(obj, keysToKeep) {\n return Object.fromEntries(Object.entries(obj).filter(([key]) => keysToKeep.includes(key)));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1384,"value":"function convertToFloat(value){\n return value !== null ? parseFloat(value): null\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1135,"value":"greens = d3.scaleLinear([1965, 2020], [\"#ebf5ec\", \"#008001\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1137,"value":"reds = d3.scaleLinear([1965, 2020], [\"#ffebec\", \"#fe0000\"]);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":710,"value":"## IMPORTS","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2102,"value":"import {textcolor} from \"@observablehq/text-color-annotations-in-markdown\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1419,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"import { toc } from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2657,"value":"import {electricityDataEmber} from \"@udg-master-data-science/data-playground\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3d","stdlib":"1","titling":"auto"}} 31:"$30:body"