25:["$","title",null,{"children":"Political Risks to Offshore Wind | Vekta Group | Observable"}] 49:T547,A visual overview of characteristics of offshore wind projects is presented in the charts below. This information is provided using data from RenewableUK (RUK), the voice of the UK's renewable energy industry. Note that we removed projects that did not contain data for certain factors i.e. development status, so these are not representative of every offshore wind project.

The first chart shows that there is a large number of projects with low capacities (<600 MW). There are notable peaks around 2000 MW and 3000 MW. The second graph shows number of projects per country. China has 250 more projects than the second highest country (Brazil). The third graph shows number of offshore wind projects by development status. Whilst there are nearly 300 operational projects, there are almost 500 in the 'Development' stage. Development timelines vary widely based on policy and regulations, however these earlier stages are considered more risky, as this is when projects are more likely to be delayed or even cancelled.

Each of these graph are linked to the country selection and slider beneath them. Use the country selection to customise which countries are included in the graphs, perhaps to look for geographical trends. The slider below the country selection adjusts the number of countries displayed on the middle graph only. 4a:T17ab,viewof countrySelection = { const countries = Array.from(new Set(data.map(d => d.country))).sort(); const container = html`

Select Countries to Display:

`; const searchBox = container.querySelector('#searchBox'); const countriesContainer = container.querySelector('#countriesContainer'); const selectedCountSpan = container.querySelector('#selectedCount'); const matchCountSpan = container.querySelector('#matchCount'); const selectAllBtn = container.querySelector('#selectAll'); const clearAllBtn = container.querySelector('#clearAll'); let selectedCountries = new Set(countries); let currentFilter = ''; function updateSelectedCount() { selectedCountSpan.textContent = `${selectedCountries.size} of ${countries.length} selected`; } function getVisibleCountries() { if (!currentFilter) return countries; return countries.filter(country => country.toLowerCase().includes(currentFilter.toLowerCase()) ); } function renderCountries() { const visibleCountries = getVisibleCountries(); countriesContainer.innerHTML = ''; if (visibleCountries.length === 0) { countriesContainer.innerHTML = '

No countries match your search

'; matchCountSpan.textContent = 'No matches'; return; } matchCountSpan.textContent = currentFilter ? `Showing ${visibleCountries.length} of ${countries.length}` : ''; visibleCountries.forEach(country => { const isSelected = selectedCountries.has(country); const item = document.createElement('label'); item.style.cssText = ` display: flex; align-items: center; gap: 4px; padding: 4px 6px; border: 1px solid ${isSelected ? '#2196f3' : '#ddd'}; border-radius: 3px; background: ${isSelected ? '#e3f2fd' : 'white'}; cursor: pointer; transition: all 0.2s; user-select: none; font-size: 12px; `; item.addEventListener('mouseenter', () => { if (!isSelected) { item.style.background = '#f5f5f5'; } }); item.addEventListener('mouseleave', () => { item.style.background = isSelected ? '#e3f2fd' : 'white'; }); const checkbox = document.createElement('input'); checkbox.type = 'checkbox'; checkbox.checked = isSelected; checkbox.style.cssText = ` width: 14px; height: 14px; cursor: pointer; flex-shrink: 0; `; checkbox.addEventListener('change', (e) => { e.stopPropagation(); if (checkbox.checked) { selectedCountries.add(country); } else { selectedCountries.delete(country); } renderCountries(); updateSelectedCount(); container.dispatchEvent(new Event('input', { bubbles: true })); }); const text = document.createElement('span'); text.textContent = country; text.style.cssText = ` flex: 1; color: ${isSelected ? '#1976d2' : '#333'}; font-weight: ${isSelected ? '500' : 'normal'}; `; // Highlight matching text if filtered if (currentFilter) { const searchTerm = currentFilter.toLowerCase(); const countryLower = country.toLowerCase(); const matchIndex = countryLower.indexOf(searchTerm); if (matchIndex !== -1) { const before = country.slice(0, matchIndex); const match = country.slice(matchIndex, matchIndex + currentFilter.length); const after = country.slice(matchIndex + currentFilter.length); text.innerHTML = `${before}${match}${after}`; } } item.appendChild(checkbox); item.appendChild(text); countriesContainer.appendChild(item); }); updateSelectedCount(); } searchBox.addEventListener('input', (e) => { currentFilter = e.target.value.trim(); renderCountries(); }); selectAllBtn.onclick = () => { selectedCountries = new Set(countries); renderCountries(); container.dispatchEvent(new Event('input', { bubbles: true })); }; clearAllBtn.onclick = () => { selectedCountries = new Set(); renderCountries(); container.dispatchEvent(new Event('input', { bubbles: true })); }; // Define value property Object.defineProperty(container, 'value', { get() { return Array.from(selectedCountries); }, set(value) { selectedCountries = new Set(value); renderCountries(); } }); // Initial render renderCountries(); return container; }4b:Ta18,To understand political risks to the offshore industry, we create a measure of political riskiness. We first looked at the Renewable Energy Country Attractiveness Index (RECAI) 63 provided by EY in June 2024 [4]. We used RECAI scores specifically for offshore wind, rather than all technologies. Not all countries in the RUK dataset had RECAI scores available, so we used geopolitical averages as proxies for these countries, such as using the average RECAI score of Poland and Romania for Bulgaria, Estonia and Lithuania.

The RECAI uses various criteria such as magnitude of development pipeline to reflect renewable investment opportunity, which means they are naturally higher in larger economies. To take this into account, we calculated a 0-100 scaled RECAI value and then divided this by the log of Gross Domestic Product (GDP) values. GDP information was taken from the World Bank, International Monetary Fund (IMF) and Office for National Statistics (ONS). This normalised index helps show renewable developments in the context of country's economic size. The five risk categories (Very High, High, Moderate, Low and Very Low Political Risk) were assigned using quintiles of the normalised scores.

Of the over 1,000 wind projects in the dataset, most are located in countries with low political risk: - 570 projects with Very Low Political Risk e.g. in China, Denmark, Japan - 270 projects with Low Political Risk e.g. in Ireland, Poland, South Korea - 184 projects with Moderate Political Risk e.g. in Greece, Australia, Brazil - 58 projects with High Political Risk e.g. in USA, India, Saudi Arabia (Note that the USA risk value was manually adjusted to account for political changes since the publication of the RECAI report) - 3 projects with Very High Political Risk e.g. in South Africa, Bahrain, Egypt
Note that this chart is also linked to the country selection in the first section, so ensure that you have cleared all filters to see the overall trends. As with before, this allows for more detailed exploration of data shown in the third graph. From this, we can see some interesting differences between the 'Development' and 'Operational' stages. Operational projects have a much higher proportion of very low political risk countries, and notably lower countries that are either high or moderate risk. This could indicate increased issues with getting projects through to the later stages. Of the seven inactive projects, 3 of these are in high risk countries, which may have played a role in their inactivity (extreme delays or cancellation).4c:T418,Plot.plot({ title: "Projects by Development Status and Political Risk", width: 800, height: 500, marginBottom: 45, x: {label: "Development Status", domain: developmentOrder}, y: {label: "Number of Projects"}, style: {fontSize: "12px"}, color: { legend: true, domain: ["Very High Political Risk", "High Political Risk", "Moderate Political Risk", "Low Political Risk", "Very Low Political Risk", "Other"], range: ["black", "#e8384f", "#eecc36", "#a4c61a", "lightblue", "grey"] }, marks: [ Plot.barY( statusRiskData, Plot.groupX( {y: "count", sort: d => ["Very High Political Risk", "High Political Risk", "Moderate_Political_Risk", "Low Political Risk", "Very Low Political Risk", "Other"].indexOf(d.riskCategory)}, { x: "status", fill: "riskCategory", tip: { channels: { "Development Status": "x", "Number of Projects": "y", "Country Risk": "fill" }, } } )) ] })4d:T47d,The following graph indicates political risk, but from the perspective of developers rather than projects. Individual developers have been anonymised and numbered for sensitivity purposes, so this graph is to explore general distribution. Use the slider below the graph to view different numbers of developers. The number of developers has been capped at 42 for this graph (there are up to 450 in the total dataset), to show those with over 5,000 MW total project capacity.

There doesn't appear to be a noticeable relationship between political risk of developer's projects and overall developer project capacity. It is interesting to note that developers with highest capacities have a large proportion of projects in countries with Moderate Political Risk. The top 4 developers with highest capacity also show projects with a range of political risk profiles. This could indicate that well-established developers can operate across a range of markets, including those with higher risks. The only developers that operate exclusively within areas of Very High Political Risk have small capacities (<5,000 MW, so not visible on this graph). 4e:T6d3,topDevs = Plot.plot({ title: `Top ${topNDevs} Developers: Risk Exposure by Capacity`, width: 800, height: 600, marginLeft: 120, marginBottom: 40, style: { fontSize: "12px" }, x: { label: "Total Capacity (MW)" }, y: { label: "Developer", tickSize: 0 }, color: { legend: true, domain: [ "Very High Political Risk", "High Political Risk", "Moderate Political Risk", "Low Political Risk", "Very Low Political Risk", "Other" ], range: ["black", "#e8384f", "#eecc36", "#a4c61a", "lightblue", "grey"] }, marks: [ Plot.barX( developerRiskArray.slice(0, topNDevs).flatMap(d => Object.entries({ "Very High Political Risk": d["Very High Political Risk"], "High Political Risk": d["High Political Risk"], "Moderate Political Risk": d["Moderate Political Risk"], "Low Political Risk": d["Low Political Risk"], "Very Low Political Risk": d["Very Low Political Risk"], Other: d.Other }).map(([risk, capacity]) => ({ developer: d.developer, capacity, risk, projectCount: d.projectCount, totalMW: d.totalMW })) ), { x: "capacity", y: "developer", fill: "risk", sort: { y: "x", reverse: true }, tip: { format: { fill: false, x: false, y: false }, channels: { "Developer": d => d.developer, "Risk Level": d => d.risk, "Capacity (MW)": d => `${d.capacity.toLocaleString()} MW`, "Total Capacity": d => `${d.totalMW.toLocaleString()} MW`, "Total Projects": d => d.projectCount } } } ) ] })4f:T7b3,Whilst this Insight is focused on political risks, it is important to note that other factors influencing offshore wind developments. Projects also differ in turbine numbers, capacity, area and distance from shore, each affecting engineering, logistics and risk profiles.

To explore these relationships, we used a scatterplot matrix. Each square compares two project variables. Use the axes names to see how the variables interact e.g. the second graph down in the first row (below turbines and to the left of capacity) is a scatterplot where project capacity is along the y axis and turbines are on the x axis. Notice how some of the graphs appear to be mirrored on either side of the variable graphs. These show the same relationships but with flipped axes.

Interactive brushing allows you to highlight data subsets (e.g. low capacity projects) to see how they behave across other dimensions. This is done by drawing a box on a graph which can be moved and re-sized to select different points. You can also filter the matrix by risk level, although there are no projects of Very High risk as these projects do not containing data about the above characteristics.

It was predicted that more of the variables would have a stronger positive linear relationship, whereas some combinations are loosely correlated. The turbines by capacity graph shows a subset of projects with a steeper relationship between the two variables, with mostly Very Low risk levels. This could indicate a relationship with higher power turbines in areas of lower political risk, but this is uncertain and exploratory. More investigation is needed to fully untangle and understand these complex relationships.

Whilst engineering and economics shape the feasibility of offshore wind, it is worth noting that political risk can redefine its trajectory, making diversification, vigilance and adaptability essential for developers navigating this evolving landscape. 50:Te4b,chart = { const width = 1000; const height = width; const padding = 20; const marginLeft = 50; const marginBottom = 30; const columns = Object.keys(dataNum?.[0] || {}).filter(c => typeof dataNum[0][c] === "number"); if (!cols.length) return html`

No numeric columns to plot.

`; const size = (width - (columns.length + 1) * padding) / columns.length + padding; const labelMap = { turbines: "Number of Turbines", distance: "Distance to Shore (km)", area: "Area (km²)", capacity: "Capacity (MW)" }; // Scales const x = columns.map(c => d3.scaleLinear() .domain(d3.extent(allDataNum, d => d[c])) .range([padding / 2, size - padding / 2])); const y = x.map(s => s.copy().range([size - padding / 2, padding / 2])); const color = d3.scaleOrdinal() .domain([ "Very High Political Risk", "High Political Risk", "Moderate Political Risk", "Low Political Risk", "Very Low Political Risk" ]) .range(["black", "#e8384f", "#eecc36", "#a4c61a", "lightblue"]); // SVG const svg = d3.create("svg") .attr("width", width + marginLeft) .attr("height", height + marginBottom) .attr("viewBox", [-marginLeft, 0, width + marginLeft, height + marginBottom]); svg.append("style").text(`circle.hidden { fill-opacity: 0.1; }`); // Axes const axisx = d3.axisBottom().ticks(5).tickSize(size * columns.length); const xAxis = g => g.selectAll("g").data(x).join("g") .attr("transform", (d,i) => `translate(${i*size},0)`) .each(function(d){ d3.select(this).call(axisx.scale(d)); }) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line").attr("stroke","#ddd")); const axisy = d3.axisLeft().ticks(5).tickSize(-size * columns.length); const yAxis = g => g.selectAll("g").data(y).join("g") .attr("transform", (d,i) => `translate(0,${i*size})`) .each(function(d){ d3.select(this).call(axisy.scale(d)); }) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick line").attr("stroke","#ddd")); svg.append("g").call(xAxis); svg.append("g").call(yAxis); // SPLOM cells const cell = svg.append("g") .selectAll("g") .data(d3.cross(d3.range(columns.length), d3.range(columns.length))) .join("g") .attr("transform", ([i,j]) => `translate(${i*size},${j*size})`); cell.append("rect") .attr("fill","none") .attr("stroke","#aaa") .attr("x", padding/2 + 0.5) .attr("y", padding/2 + 0.5) .attr("width", size - padding) .attr("height", size - padding); // Plot points cell.each(function([i,j]){ const c = d3.select(this); const circles = c.selectAll("circle") .data(dataNum) .join("circle") .attr("cx", d => x[i](d[columns[i]])) .attr("cy", d => y[j](d[columns[j]])) .attr("r", 1.8) .attr("fill", d => color(d.riskCategory)); // 5 categories now brush(c, circles, svg, {padding, size, x, y, columns, dataNum}); }); // X-axis labels svg.append("g").style("font","14px sans-serif").style("pointer-events","none").style("text-anchor","middle") .selectAll("text").data(columns).join("text") .attr("transform", (d,i) => `translate(${i*size+size/2},${columns.length*size+40})`) .text(d => labelMap[d]||d); // Y-axis labels svg.append("g").style("font","14px sans-serif").style("pointer-events","none").style("text-anchor","middle") .selectAll("text").data(columns).join("text") .attr("transform", (d,i) => `translate(-40,${i*size+size/2}) rotate(-90)`) .text(d => labelMap[d]||d); svg.property("value", []); return Object.assign(svg.node(), {scales:{color}}); }51:T189c,getCountryRisk = (country, recaigdp) => { // --- Regional groupings --- const regions = { nordics: { countries: ["Denmark","Finland","Norway","Sweden"], referenceCountries: ["Denmark","Finland","Norway","Sweden"], method: "direct" }, westernEurope: { countries: ["Austria","Belgium","France","Germany","Ireland","Netherlands","Switzerland"], referenceCountries: ["Austria","Belgium","France","Germany","Ireland","Netherlands","Switzerland"], method: "direct" }, southernEurope: { countries: ["Greece","Italy","Portugal","Spain"], referenceCountries: ["Greece","Italy","Portugal","Spain"], method: "direct" }, easternEurope: { countries: ["Bulgaria","Estonia","Lithuania","Poland","Romania"], referenceCountries: ["Poland","Romania"], method: "average" }, angloOceania: { countries: ["Australia","New Zealand"], referenceCountries: ["Australia"], method: "direct" }, ukRegions: { countries: ["England","Scotland","Wales","Isle of Man"], referenceCountries: ["UK"], method: "direct" }, eastAsia: { countries: ["Japan","South Korea","Taiwan"], referenceCountries: ["Japan","South Korea","Taiwan"], method: "direct" }, southeastAsia: { countries: ["Philippines","Vietnam"], referenceCountries: ["Philippines","Vietnam"], method: "direct" }, middleEast: { countries: ["Bahrain","Saudi Arabia","Israel"], referenceCountries: ["Israel","Saudi Arabia"], method: "average" }, northAfrica: { countries: ["Egypt","Morocco"], referenceCountries: ["Egypt","Morocco"], method: "direct" }, subsaharanAfrica: { countries: ["South Africa"], referenceCountries: ["South Africa"], method: "direct" }, latinAmerica: { countries: ["Argentina","Brazil","Chile","Mexico","Peru"], referenceCountries: ["Argentina","Brazil","Chile","Mexico","Peru"], method: "average" }, southAsia: { countries: ["Bangladesh","India"], referenceCountries: ["India"], method: "direct" }, centralAsia: { countries: ["Kazakhstan"], referenceCountries: ["Kazakhstan"], method: "direct" }, eastEuropeTurkey: { countries: ["Turkey"], referenceCountries: ["Turkey"], method: "direct" }, chinaRegion: { countries: ["China"], referenceCountries: ["China"], method: "direct" }, northAmerica: { countries: ["Canada","USA"], referenceCountries: ["Canada","USA"], method: "average" } }; const countryAdjustments = { "USA": -70}; const findRegionalReference = (countryName) => { for (const regionData of Object.values(regions)) { if (regionData.countries.includes(countryName)) return regionData; } return null; }; // --- Normalization helpers --- const recaiValues = recaigdp.map(d => d.RECAI).filter(v => typeof v === "number"); const minScore = Math.min(...recaiValues); const maxScore = Math.max(...recaiValues); // prevent exact 0/100 by forcing range 1–99 const normalizeScore = score => ((score - minScore) / (maxScore - minScore)) * 98 + 1; // --- Step 1: Base score --- let baseScore, sourceMethod, referenceCountries=[], referenceScores=[]; const recaiEntry = recaigdp.find(d => d["Country Name"] === country); if (recaiEntry && recaiEntry.RECAI) { baseScore = normalizeScore(recaiEntry.RECAI); sourceMethod = "direct"; } else { const regionalInfo = findRegionalReference(country); if (regionalInfo) { const refScores = regionalInfo.referenceCountries.map(refCountry => { const refEntry = recaigdp.find(d => d["Country Name"] === refCountry); return refEntry ? { country: refCountry, score: normalizeScore(refEntry.RECAI) } : null; }).filter(Boolean); if (refScores.length > 0) { baseScore = refScores.reduce((sum, r) => sum + r.score, 0) / refScores.length; referenceCountries = refScores.map(r => r.country); referenceScores = refScores; sourceMethod = "regional_proxy"; } else { baseScore = 50; sourceMethod = "median_fallback"; } } else { baseScore = 50; sourceMethod = "median_fallback"; } } // --- Step 2: Adjustment --- const countryAdj = countryAdjustments[country] || 0; const adjustedScore = Math.max(0, Math.min(100, baseScore + countryAdj)); // --- Step 3: GDP normalization --- let gdpValue = recaiEntry ? recaiEntry.mostrecent : null; let gdpNormalized = null; if (gdpValue && gdpValue > 0) { gdpNormalized = adjustedScore / Math.log(gdpValue); // natural log } // --- Step 4: Rescale GDP-normalized scores across dataset --- const allGDPnorm = recaigdp.map(d => { if (!d.RECAI || !d.mostrecent) return null; const rawNorm = normalizeScore(d.RECAI) + (countryAdjustments[d["Country Name"]] || 0); return rawNorm / Math.log(d.mostrecent); }).filter(v => v !== null && isFinite(v)); const minGDPn = Math.min(...allGDPnorm); const maxGDPn = Math.max(...allGDPnorm); const rescaleGDPn = val => ((val - minGDPn) / (maxGDPn - minGDPn)) * 100; let gdpNormRescaled = (gdpNormalized !== null && isFinite(gdpNormalized)) ? rescaleGDPn(gdpNormalized) : null; // --- Step 5: Risk category --- const quintiles = [ d3.quantile(allGDPnorm, 0.2), d3.quantile(allGDPnorm, 0.4), d3.quantile(allGDPnorm, 0.6), d3.quantile(allGDPnorm, 0.8) ]; const getRiskCategory = score => { if (score <= quintiles[0]) return "Very High Political Risk"; if (score <= quintiles[1]) return "High Political Risk"; if (score <= quintiles[2]) return "Moderate Political Risk"; if (score <= quintiles[3]) return "Low Political Risk"; return "Very Low Political Risk"; }; return { country, recaiScore: recaiEntry ? recaiEntry.RECAI : null, normalizedBaseScore: baseScore.toFixed(2), countryAdjustment: countryAdj, adjustedScore: parseFloat(adjustedScore.toFixed(2)), gdp: gdpValue, gdpNormalized: gdpNormRescaled !== null ? gdpNormRescaled.toFixed(2) : null, riskCategory: gdpNormRescaled !== null ? getRiskCategory(gdpNormalized) : "No GDP data", sourceMethod, referenceCountries: referenceCountries.length > 0 ? referenceCountries : null, referenceScores: referenceScores.length > 0 ? referenceScores.map(r => `${r.country}: ${r.score.toFixed(1)}`) : null, confidence: sourceMethod === "direct" ? "high" : sourceMethod === "regional_proxy" ? "medium" : "low" }; };52:T5d8,function brush(cell, circle, svg, {padding, size, x, y, columns, dataNum}) { const b = d3.brush() .extent([[padding / 2, padding / 2], [size - padding / 2, size - padding / 2]]) .on("start", brushstarted) .on("brush", brushed) .on("end", brushended); cell.call(b); function brushstarted() { if (svg._activeBrushCell && svg._activeBrushCell !== this) { d3.select(svg._activeBrushCell).call(b.move, null); // clear previous brush } svg._activeBrushCell = this; } function brushed({selection}, [i, j]) { if (!selection) return; const [[x0, y0], [x1, y1]] = selection; // compute selected points in this cell const selected = dataNum.filter( d => x0 <= x[i](d[columns[i]]) && x1 >= x[i](d[columns[i]]) && y0 <= y[j](d[columns[j]]) && y1 >= y[j](d[columns[j]]) ); svg._selection = selected; // store globally on svg // update **all circles in all cells** svg.selectAll("circle").classed("hidden", d => !svg._selection.includes(d)); // style the brush rectangle cell.selectAll(".selection") .attr("stroke", "black") .attr("stroke-width", 1) .attr("fill", "none") .attr("fill-opacity", 0.1); svg.property("value", selected).dispatch("input"); } function brushended({selection}) { if (!selection) { svg._selection = []; svg.selectAll("circle").classed("hidden", false); svg.property("value", []).dispatch("input"); } } }53:[{"id":"415bfbb4eef3006506abec82e6935f3be15c43266e205b7e1482c1733d6be898e907bc25375ed60cb811a7dd29cd3f28f8ad9c755ef6bbc8d2cd4b39b0b75c58","href":"https://static.observableusercontent.com/files/415bfbb4eef3006506abec82e6935f3be15c43266e205b7e1482c1733d6be898e907bc25375ed60cb811a7dd29cd3f28f8ad9c755ef6bbc8d2cd4b39b0b75c58","name":"RUKdataPrep2.json","type":"application/json","lastModified":1758560113601.221,"size":417814},{"id":"4cde9ce767495567dea1864ee1b475be79e950b1f9957451e239a1d2e1536dec08c2b55ff61c6db2ace8a6ae1bf89c09caf373dcd617bf6daf9a0fdf2fe122ad","href":"https://static.observableusercontent.com/files/4cde9ce767495567dea1864ee1b475be79e950b1f9957451e239a1d2e1536dec08c2b55ff61c6db2ace8a6ae1bf89c09caf373dcd617bf6daf9a0fdf2fe122ad","name":"RECAIandGDP.csv","type":"text/csv","lastModified":1759319393793.651,"size":4865}] 29:{"id":"852675751d9a4406","slug":null,"title":"Political Risks to Offshore Wind","thumbnail":"92374cc55c85ae4a39eefaf4e2bce900d7e1614167ba52b27c69082f84158f23","access_level":"public","create_time":"$D2025-09-10T15:30:34.719Z","update_time":"$D2025-10-03T14:17:20.876Z","view_time":null,"first_public_time":"$D2025-10-02T12:15:52.230Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"3594fe6877d14711","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":2154,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"353739170940576d","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"353739170940576d","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":null,"role":"editor"},{"type":"team","id":"3594fe6877d14711","login":"vekta-group","name":"Vekta Group","avatar_url":"https://avatars-next.observableusercontent.com/13f6934c06a2c847905f92615bfe639e05a96b39badbe0036f3139b0c24ef101","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":{"id":"a5603c9dcf7492ca","slug":"vekta-digital-insights","title":"Vekta Digital Insights ","owner":{"id":"3594fe6877d14711","login":"vekta-group","name":"Vekta Group"}},"type":"notebook","body":{"titling":"auto","title":"Political Risks to Offshore Wind","cells":[{"id":0,"value":"# Political Risks to Offshore Wind","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"Offshore wind is expanding globally, with total offshore capacity now totalling 83 GW [1], but progress across countries is uneven. Although renewable energy initially started developing due to lower environmental impact than fossil based power, the recent increase in wind energy can also be attributed to the economic and political independence opportunities it can provide. \n

However, in January 2025, the Trump administration issued a memorandum [2] imposing a temporary moratorium on new offshore wind leasing and permitting reviews in US federal waters, creating uncertainty for developers and prompting delays or reconsideration of planned investments. This notebook explores how political risks in countries may impact offshore wind project development.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":917,"value":"## Characteristics of Offshore Wind Projects","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1135,"value":"$49","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":628,"value":"viewof combinedChart = {\n const container = DOM.element(\"div\");\n\n // Use CSS grid for 2x1 layout\n Object.assign(container.style, {\n display: \"grid\",\n gridTemplateColumns: \"1fr 1fr\", // two equal-width columns\n gridTemplateRows: \"auto auto\", // two rows\n gap: \"0px\"\n });\n\n // Place the charts\n container.appendChild(capacityDist);\n container.appendChild(devStatus);\n\n // For the chart that spans both columns\n const devStatusWrapper = DOM.element(\"div\");\n devStatusWrapper.style.gridColumn = \"1 / span 2\";\n devStatusWrapper.appendChild(topCountries);\n container.appendChild(devStatusWrapper);\n\n return container;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":648,"value":"viewof topN = Inputs.range([1, 40], {value: 20, step: 1, label: \"Number of countries\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"## Assessing Political Risk","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1137,"value":"$4b","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":365,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"## Developers at Risk","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1197,"value":"$4d","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":295,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":830,"value":"viewof topNDevs = Inputs.range([1, 42], {\n value: 20, \n step: 1, \n label: \"Number of developers\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2011,"value":"The above analyses show that political intervention is a powerful driver of risk in offshore wind. While most projects are in countries with 'Low' or 'Very Low' political risk, the US market has recently seen dramatic swings. In September 2025, a Federal Judge partially reversed the memorandum [3], allowing the nearly complete Revolution Wind project to resume. This case demonstrates how rapid legal changes can either heighten or reduce industry uncertainty, showing why developers diversify across geographies to buffer against abrupt political turns. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":973,"value":"## Exploratory Chart of Other Characteristics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1209,"value":"$4f","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1598,"value":"viewof riskSelection = Inputs.select(\n [\"All\", \"High Political Risk\", \"Moderate Political Risk\", \"Low Political Risk\", \"Very Low Political Risk\"],\n {\n label: \"Filter by Political Risk\",\n value: \"All\",\n format: x => x === \"All\" ? \"All Risk Levels\" : x\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":518,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"
\n## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":123,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2129,"value":"### References","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2137,"value":"[1] https://www.gwec.net/gwec-news/offshore-wind-installed-capacity-reaches-83-gw-as-new-report-finds-2024-a-record-year-for-construction-and-auctions ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2139,"value":"[2] Trump administration memorandum https://www.whitehouse.gov/presidential-actions/2025/01/temporary-withdrawal-of-all-areas-on-the-outer-continental-shelf-from-offshore-wind-leasing-and-review-of-the-federal-governments-leasing-and-permitting-practices-for-wind-projects/","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2141,"value":"[3] https://www.reuters.com/sustainability/climate-energy/us-court-weighs-trump-halt-rhode-island-offshore-wind-project-2025-09-22/","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1843,"value":"[4] EY's 2024 RECAI report: https://www.ey.com/content/dam/ey-unified-site/ey-com/en-gl/insights/energy-resources/documents/ey-gl-recai-63-final-07-2024.pdf ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":112,"value":"### Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":131,"value":"rukdataprep = FileAttachment(\"RUKdataPrep2.json\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":186,"value":"allData = rukdataprep.features.map(d => d.properties)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":227,"value":"data = Array.from(\n new Map(allData.map(d => [d.rukid, d])).values()\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":367,"value":"developmentOrder = [\n \"In planning\", \n \"Development\", \n \"Consented\", \n \"Under construction\",\n \"Operational\", \n \"Inactive\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"### Plot 1: Characteristics of Offshore Wind Projects","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":796,"value":"filterData = (data, countryFilter = [], requireStatus = false, requireTurbines = false) => {\n let filtered = data;\n \n // Apply country filter - only filter if some countries are selected\n if (countryFilter && countryFilter.length > 0) {\n filtered = filtered.filter(d => countryFilter.includes(d.country));\n }\n \n if (requireStatus) {\n filtered = filtered.filter(d => d.websitesta);\n }\n \n if (requireTurbines) {\n filtered = filtered.filter(d => d.mw && d.noofturbin && !isNaN(+d.noofturbin));\n }\n \n return filtered;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":666,"value":"filteredData = countrySelection.length > 0 \n ? data.filter(d => countrySelection.includes(d.country))\n : data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"statusData = data.filter(d => d.websitesta)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":668,"value":"filteredStatusData = countrySelection.length > 0\n ? data.filter(d => countrySelection.includes(d.country) && d.websitesta)\n : data.filter(d => d.websitesta)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":135,"value":"capacityDist = Plot.plot({\n title: \"Wind Farm Capacity Distribution\",\n width: 600,\n height: 315,\n marginTop: 25,\n marginBottom: 40,\n x: {label: \"Capacity (MW)\"},\n y: {label: \"Number of Projects\"},\n style: {fontSize: \"12px\"},\n marks: [\n Plot.rectY(filteredData, Plot.binX(\n {y: \"count\"},\n {\n x: \"mw\", \n fill: \"#20bea3\",\n tip: {\n channels: {\n \"Capacity Range (MW)\": \"x\", \n \"Number of Projects\": \"y\"\n },\n format: {\n \"Capacity Range (MW)\": d => Array.isArray(d) ? `${Math.round(d[0])}-${Math.round(d[1])}` : d,\n fill: false\n }\n }\n }\n )),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"devStatus = Plot.plot({\n title: \"Projects by Development Status\",\n width: 600, // Slightly wider\n height: 320,\n marginBottom: 50, // More space for wrapped labels\n x: {\n label: \"Development Status\",\n domain: developmentOrder,\n tickFormat: d => d.replace(/\\s+/g, '\\n'), // Replace spaces with line breaks\n tickRotate: 0\n },\n y: {label: \"Number of Projects\"},\n style: { fontSize: \"12px\" },\n marks: [\n Plot.barY(\n filteredStatusData,\n Plot.groupX(\n {y: \"count\"},\n {\n x: \"websitesta\",\n fill: \"#20bea3\",\n tip: {\n channels: {\n \"Development Status\": \"x\",\n \"Number of Projects\": \"y\"\n }\n }\n }\n )\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"topCountries = Plot.plot({\n title: \"Top Countries by Number of Projects\",\n width: 1200,\n height: 400,\n marginBottom: 80,\n marginTop: 30,\n x: {label: \"Country\", tickRotate: -45},\n y: {label: \"Number of Projects\"},\n style: { fontSize: \"14px\" },\n marks: [\n Plot.barY(\n filteredData,\n Plot.groupX(\n {y: \"count\"}, // Now using groupX instead of groupY\n {\n x: \"country\",\n fill: \"#20bea3\",\n sort: {x: \"-y\", limit: topN},\n tip: true\n }\n )\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"### Plot 2: Assessing Political Risk","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1626,"value":"recaigdp = FileAttachment(\"RECAIandGDP.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":705,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":783,"value":"statusRiskData = filteredStatusData\n .filter(d => d.websitesta)\n .map(d => ({\n status: d.websitesta,\n ...getCountryRisk(d.country, recaigdp), // flatten country risk fields\n project: d.project,\n country: d.country,\n developer: d.developer,\n capacity: d.mw\n }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":927,"value":"### Plot 3: Developers at Risk","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":286,"value":"developerRisk = allData.reduce((acc, d) => {\n const dev = d.developer;\n const riskCat = getCountryRisk(d.country, recaigdp).riskCategory; // flat riskCategory\n const mw = d.mw || 0;\n\n if (!acc[dev]) {\n acc[dev] = {\n developer: dev,\n \"Very High Political Risk\": 0,\n \"High Political Risk\": 0,\n \"Moderate Political Risk\": 0,\n \"Low Political Risk\": 0,\n \"Very Low Political Risk\": 0,\n Other: 0,\n totalMW: 0,\n projectCount: 0\n };\n }\n\n acc[dev][riskCat] += mw;\n acc[dev].totalMW += mw;\n acc[dev].projectCount += 1;\n\n return acc;\n}, {});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1276,"value":"developerRiskArray = Object.values(developerRisk)\n .filter(d => d.totalMW > 0)\n .sort((a, b) => b.totalMW - a.totalMW)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":932,"value":"### Plot 4: Exploratory Chart of Other Characteristics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":268,"value":"projectData = data.map(d => ({\n ...d,\n ...getCountryRisk(d.country, recaigdp)\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":482,"value":"turbineData = projectData.filter(d => d.mw && d.noofturbin && !isNaN(+d.noofturbin))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1605,"value":"allDataNum = turbineData.map(d => ({\n project: d.project,\n turbines: +d.noofturbin,\n capacity: +d.mw,\n area: +d[\"area(km2)\"],\n distance: +d.HubDist,\n riskCategory: d.riskCategory\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"dataNum = {\n let filtered = allDataNum;\n \n // Apply risk selection\n if (riskSelection !== \"All\") {\n filtered = filtered.filter(d => d.riskCategory === riskSelection);\n }\n \n return filtered;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"cols = Object.keys(dataNum?.[0] || {}).filter(c => typeof dataNum[0][c] === \"number\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":441,"value":"selection = Generators.input(chart) // or use viewof selection = chart","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":438,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W53","stdlib":"1"}} 24:null