In his inaugural address in January 2025, President Donald Trump promised that “the American Dream will soon be back and thriving like never before.” Apparently, that required undoing policies to tackle climate change: The Trump administration has invoked “the American Dream” as a justification to propose ending mandatory reporting of greenhouse gas emissions, reverse rules to cut emissions from cars, and deregulate coal.
Voters, on the other hand, view climate change itself as a problem for their aspirations. Exclusive polling for Grist from the think tank Data for Progress shows that 58 percent of likely U.S. voters believe that the warming climate will hinder their ability to achieve or maintain the American Dream over the next 20 years — including 42 percent of Republicans.
“We identify a strong concern about climate change as a threat to achieving the American Dream,” said Grace Adcox, a senior climate strategist at the progressive think tank.
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A majority of likely voters think climate change will get in the way of the American Dream
Percentage of respondents who say climate change will affect their ability to achieve or maintain the American Dream over the next 20 years, by how much
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// Values: a great deal, somewhat, not very much, not at all, don’t know (percent).
const ROWS = [
{ group: ‘All’, key: ‘All likely voters’, v: [25, 33, 20, 17, 6], n: 1167 },
{ group: ‘Party’, key: ‘Democrat’, v: [31, 43, 12, 6, 8], n: 461 },
{ group: ‘Party’, key: ‘Independent / third party’, short: ‘Independent’, v: [27, 28, 21, 17, 8], n: 241 },
{ group: ‘Party’, key: ‘Republican’, v: [18, 24, 27, 28, 3], n: 465 },
{ group: ‘Gender’, key: ‘Female’, v: [25, 31, 22, 15, 7], n: 625 },
{ group: ‘Gender’, key: ‘Male’, v: [24, 34, 17, 19, 5], n: 542 },
{ group: ‘Age’, key: ‘Under 45’, v: [25, 41, 18, 9, 7], n: 375 },
{ group: ‘Age’, key: ’45 and older’, short: ’45+’, v: [24, 29, 21, 21, 6], n: 792 },
{ group: ‘Education’, key: ‘No college’, v: [22, 32, 22, 17, 7], n: 735 },
{ group: ‘Education’, key: ‘College’, v: [29, 34, 16, 16, 5], n: 432 },
{ group: ‘Race’, key: ‘Black’, v: [44, 35, 11, 5, 5], n: 135 },
{ group: ‘Race’, key: ‘White’, v: [23, 28, 21, 21, 6], n: 812 },
{ group: ‘Race’, key: ‘Latino’, v: [21, 47, 21, 8, 3], n: 168 },
];
const ANSWERS = [‘A great deal’, ‘Somewhat’, ‘Not very much’, ‘Not at all’, “Don’t know”];
// Project palette (#FF5134, #FFAEC6, #8AB6E6) plus a deeper blue for the far end of the scale.
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const PEBBLE = ‘M11.3,4.9Q12.4,6.0 12.5,7.7Q12.5,9.5 10.5,10.1Q8.4,10.7 6.7,10.3Q4.9,9.8 3.5,9.2Q2.1,8.6 1.3,7.3Q0.6,6.0 0.9,4.5Q1.3,3.0 2.9,1.9Q4.4,0.8 6.3,1.2Q8.2,1.7 9.2,2.8Q10.1,3.9 11.3,4.9Z’;
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const affected = (r) => r.v[0] + r.v[1];
const pebble = (c) => `x3csvg width=”13″ height=”12″ viewBox=”0 0 13 12″ aria-hidden=”true”>x3cpath d=”${PEBBLE}” fill=”${c}”/>x3c/svg>`;
// Option A keys its legend and tooltip with round dots; B and C keep the pebbles.
const dot = (c) => `x3csvg width=”12″ height=”12″ viewBox=”0 0 12 12″ aria-hidden=”true”>x3ccircle cx=”6″ cy=”6″ r=”6″ fill=”${c}”/>x3c/svg>`;
const narrow = () => (600)>(window.innerWidth);
function legend(root, idx, key = pebble) {
root.select(‘.amdream-q5__legend’).html(idx.map((i) => `x3cspan>${key(COLOR[i])}${ANSWERS[i]}x3c/span>`).join(”));
}
function tooltip(root, html, event) {
const tip = root.select(‘.amdream-q5__tooltip’);
if (!html) { tip.style(‘display’, ‘none’); return; }
const box = root.node().getBoundingClientRect();
tip.html(html).style(‘display’, ‘block’);
const w = tip.node().offsetWidth;
let x = event.clientX – box.left + 14;
if (x + w > box.width) x = event.clientX – box.left – w – 14;
tip.style(‘left’, Math.max(0, x) + ‘px’).style(‘top’, (event.clientY – box.top + 14) + ‘px’);
}
function rowTip(r, key = pebble) {
return `x3ch4>${r.key}x3c/h4>` + r.v.map((x, i) => `x3cdiv>${key(COLOR[i])}${ANSWERS[i]}x3cb>${x}%x3c/b>x3c/div>`).join(”) +
`x3cdiv style=”color:${MUTED};margin-top:4px”>Weighted N = ${d3.format(‘,’)(r.n)}x3c/div>`;
}
// Option A: diverging stacked bars grouped by demographic split.
function drawA() {
const root = d3.select(‘#amdream-q5-a’);
legend(root, [3, 2, 1, 0], dot);
const plot = root.select(‘.amdream-q5__plot’).html(”);
const W = plot.node().clientWidth;
const labelW = narrow() ? 92 : 168, dkW = narrow() ? 34 : 54, rowH = 26, groupH = 24, top = 26;
const x = d3.scaleLinear().domain([-60, 85]).range([labelW, W – dkW – 8]);
let y = top;
const layout = [];
let last = null;
ROWS.forEach((r) => {
if ((r.group !== last ? r.group !== ‘All’:!1)) { layout.push({ header: r.group, y: y + groupH – 7 }); y += groupH; }
layout.push({ row: r, y }); y += rowH; last = r.group;
if (r.group === ‘All’) y += 4;
});
const H = y + 4;
const svg = plot.append(‘svg’).attr(‘height’, H + 18).attr(‘viewBox’, `0 0 ${W} ${H + 18}`);
[-50, -25, 25, 50, 75].forEach((t) => {
svg.append(‘line’).attr(‘x1’, x(t)).attr(‘x2’, x(t)).attr(‘y1’, top – 4).attr(‘y2’, H).attr(‘stroke’, GRID);
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, x(t)).attr(‘y’, H + 14).attr(‘text-anchor’, ‘middle’).text(Math.abs(t) + ‘%’);
});
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, x(0) – 6).attr(‘y’, 12).attr(‘text-anchor’, ‘end’).text(narrow() ? ‘← Not’ : ‘← Not much or not at all’);
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, x(0) + 6).attr(‘y’, 12).text(narrow() ? ‘Affected →’ : ‘A great deal or somewhat →’);
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, W – dkW / 2).attr(‘y’, 12).attr(‘text-anchor’, ‘middle’).text(narrow() ? ‘DK’ : “Don’t know”);
layout.forEach((d) => {
if (d.header) { svg.append(‘text’).attr(‘class’, ‘group’).attr(‘x’, 0).attr(‘y’, d.y).text(d.header); return; }
const r = d.row, g = svg.append(‘g’).attr(‘transform’, `translate(0,${d.y})`);
const bh = rowH – 6;
g.append(‘text’).attr(‘x’, 0).attr(‘y’, bh / 2 + 4).style(‘font-weight’, r.group === ‘All’ ? 700 : 500)
.text(narrow() ? (__gvOr(r.short ,()=>( r.key))) : r.key);
// Right of center: somewhat, then a great deal. Left of center: not very much, then not at all.
const segs = [
{ i: 1, x0: 0, x1: r.v[1] }, { i: 0, x0: r.v[1], x1: r.v[1] + r.v[0] },
{ i: 2, x0: -r.v[2], x1: 0 }, { i: 3, x0: -r.v[2] – r.v[3], x1: -r.v[2] },
];
segs.forEach((s) => {
g.append(‘rect’).attr(‘x’, x(s.x0)).attr(‘width’, x(s.x1) – x(s.x0)).attr(‘y’, 0).attr(‘height’, bh).attr(‘fill’, COLOR[s.i]);
if (r.v[s.i] >= (narrow() ? 12 : 8)) {
g.append(‘text’).attr(‘class’, ‘seg-label’).attr(‘x’, (x(s.x0) + x(s.x1)) / 2).attr(‘y’, bh / 2 + 4)
.attr(‘text-anchor’, ‘middle’).style(‘fill’, INK[s.i]).text(r.v[s.i]);
}
});
g.append(‘text’).attr(‘class’, ‘seg-label’).attr(‘x’, W – dkW / 2).attr(‘y’, bh / 2 + 4).attr(‘text-anchor’, ‘middle’).style(‘fill’, MUTED).text(r.v[4]);
g.append(‘rect’).attr(‘x’, 0).attr(‘width’, W).attr(‘y’, -3).attr(‘height’, rowH).attr(‘fill’, ‘transparent’)
.on(‘mousemove’, (e) => tooltip(root, rowTip(r, dot), e)).on(‘mouseleave’, () => tooltip(root, null));
});
svg.append(‘line’).attr(‘x1’, x(0)).attr(‘x2’, x(0)).attr(‘y1’, top – 4).attr(‘y2’, H).attr(‘stroke’, EARTH).attr(‘stroke-width’, 1.5);
}
// Option B: ranked two-tone bars of the “affected” share.
function drawB() {
const root = d3.select(‘#amdream-q5-b’);
legend(root, [0, 1]);
const plot = root.select(‘.amdream-q5__plot’).html(”);
const W = plot.node().clientWidth;
const rows = ROWS.slice().sort((a, b) => affected(b) – affected(a));
const labelW = narrow() ? 92 : 168, rowH = 28, top = 20, H = top + rows.length * rowH;
const x = d3.scaleLinear().domain([0, 100]).range([labelW, W – 34]);
const svg = plot.append(‘svg’).attr(‘height’, H).attr(‘viewBox’, `0 0 ${W} ${H}`);
[0, 25, 50, 75, 100].forEach((t) => {
svg.append(‘line’).attr(‘x1’, x(t)).attr(‘x2’, x(t)).attr(‘y1’, top – 4).attr(‘y2’, H).attr(‘stroke’, t === 0 ? EARTH : GRID);
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, x(t)).attr(‘y’, 10).attr(‘text-anchor’, ‘middle’).text(t + ‘%’);
});
rows.forEach((r, k) => {
const all = r.group === ‘All’, bh = rowH – 9;
const g = svg.append(‘g’).attr(‘transform’, `translate(0,${top + k * rowH})`);
g.append(‘text’).attr(‘x’, 0).attr(‘y’, bh / 2 + 5).style(‘font-weight’, all ? 700 : 500).text(narrow() ? (__gvOr(r.short ,()=>( r.key))) : r.key);
g.append(‘rect’).attr(‘x’, x(0)).attr(‘width’, x(affected(r)) – x(0)).attr(‘y’, 0).attr(‘height’, bh).attr(‘rx’, 4).attr(‘fill’, all ? ‘#B9B5AC’ : COLOR[1]);
g.append(‘rect’).attr(‘x’, x(0)).attr(‘width’, x(r.v[0]) – x(0)).attr(‘y’, 0).attr(‘height’, bh).attr(‘rx’, 4).attr(‘fill’, all ? EARTH : COLOR[0]);
g.append(‘text’).attr(‘x’, x(affected(r)) + 6).attr(‘y’, bh / 2 + 5).style(‘font-weight’, 700).style(‘font-variant-numeric’, ‘tabular-nums’).text(affected(r) + ‘%’);
g.append(‘rect’).attr(‘x’, 0).attr(‘width’, W).attr(‘y’, -4).attr(‘height’, rowH).attr(‘fill’, ‘transparent’)
.on(‘mousemove’, (e) => tooltip(root, rowTip(r), e)).on(‘mouseleave’, () => tooltip(root, null));
});
}
// Option C: one dumbbell per demographic split, on the “affected” share.
function drawC() {
const root = d3.select(‘#amdream-q5-c’);
const plot = root.select(‘.amdream-q5__plot’).html(”);
const W = plot.node().clientWidth;
const splits = [‘Race’, ‘Party’, ‘Age’, ‘Education’, ‘Gender’].map((g) => {
const rs = ROWS.filter((r) => r.group === g).sort((a, b) => affected(a) – affected(b));
return { group: g, rows: rs, gap: affected(rs[rs.length – 1]) – affected(rs[0]) };
}).sort((a, b) => b.gap – a.gap);
const labelW = narrow() ? 78 : 104, gapW = 54, rowH = 64, top = 30, H = top + splits.length * rowH;
const x = d3.scaleLinear().domain([35, 85]).range([labelW + 10, W – gapW – 10]);
const svg = plot.append(‘svg’).attr(‘height’, H).attr(‘viewBox’, `0 0 ${W} ${H}`);
[40, 50, 60, 70, 80].forEach((t) => {
svg.append(‘line’).attr(‘x1’, x(t)).attr(‘x2’, x(t)).attr(‘y1’, top – 6).attr(‘y2’, H).attr(‘stroke’, GRID);
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, x(t)).attr(‘y’, H + 16).attr(‘text-anchor’, ‘middle’).text(t + ‘%’);
});
svg.attr(‘height’, H + 20).attr(‘viewBox’, `0 0 ${W} ${H + 20}`);
const all = affected(ROWS[0]);
svg.append(‘line’).attr(‘x1’, x(all)).attr(‘x2’, x(all)).attr(‘y1’, top – 10).attr(‘y2’, H).attr(‘stroke’, EARTH).attr(‘stroke-dasharray’, ‘5,5’);
svg.append(‘text’).attr(‘class’, ‘tick halo’).attr(‘x’, x(all)).attr(‘y’, top – 14).attr(‘text-anchor’, ‘middle’).text(`All likely voters, ${all}%`);
svg.append(‘text’).attr(‘class’, ‘tick’).attr(‘x’, W).attr(‘y’, top – 14).attr(‘text-anchor’, ‘end’).text(‘Gap’);
splits.forEach((s, k) => {
const cy = top + k * rowH + rowH / 2;
const g = svg.append(‘g’);
g.append(‘text’).attr(‘class’, ‘group’).attr(‘x’, 0).attr(‘y’, cy + 4).style(‘fill’, EARTH).text(s.group);
const lo = affected(s.rows[0]), hi = affected(s.rows[s.rows.length – 1]);
g.append(‘line’).attr(‘x1’, x(lo)).attr(‘x2’, x(hi)).attr(‘y1’, cy).attr(‘y2’, cy).attr(‘stroke’, COLOR[0]).attr(‘stroke-width’, 3.5).attr(‘stroke-linecap’, ’round’);
s.rows.forEach((r, i) => {
const above = i % 2 === (s.rows.length === 2 ? 1 : 0);
g.append(‘circle’).attr(‘cx’, x(affected(r))).attr(‘cy’, cy).attr(‘r’, 6.5).attr(‘fill’, EARTH).attr(‘stroke’, ‘#FFFFFF’).attr(‘stroke-width’, 2)
.on(‘mousemove’, (e) => tooltip(root, rowTip(r), e)).on(‘mouseleave’, () => tooltip(root, null));
g.append(‘text’).attr(‘class’, ‘halo’).attr(‘x’, x(affected(r))).attr(‘y’, above ? cy – 13 : cy + 24).attr(‘text-anchor’, ‘middle’)
.style(‘font-size’, ’12px’).text(`${__gvOr(r.short ,()=>( r.key))} ${affected(r)}%`);
});
g.append(‘text’).attr(‘x’, W).attr(‘y’, cy + 5).attr(‘text-anchor’, ‘end’).style(‘font-weight’, 700).style(‘fill’, s.gap >= 10 ? ‘#D63A20’ : MUTED).text(`${s.gap} pts`);
});
}
function render() {
d3.selectAll(‘.amdream-q5__footer’).each(function () {
if (!this.querySelector(‘.amdream-q5__logo’)) this.insertAdjacentHTML(‘beforeend’, LOGO);
});
// Each chart draws only where its markup is on the page (the WordPress embeds carry one chart each).
if (document.getElementById(‘amdream-q5-a’)) drawA();
if (document.getElementById(‘amdream-q5-b’)) drawB();
if (document.getElementById(‘amdream-q5-c’)) drawC();
}
if (document.readyState === ‘loading’) document.addEventListener(‘DOMContentLoaded’, render, { once: true });
else render();
window.addEventListener(‘resize’, render);
})();
}
function fail(){var el=document.getElementById(“amdream-q5-a”);if(el)el.insertAdjacentHTML(“beforeend”,”x3cp>This chart could not load. Try refreshing the page.x3c/p>”);}
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The survey, conducted this spring with 1,167 likely voters, shows that more than half see the cost of living as the biggest threat to reaching the American Dream today. That tracks with other political polls this year as well as reports on rising inflation. A recent analysis from Investopedia found that the typical elements of the American Dream — owning a home and a new car, raising kids, and other milestones — now add up to a price tag of $5.3 million per household. Only 2 percent of voters surveyed named climate change and extreme weather events as the central threat.
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Cost of living tops voters’ worries about the American Dream; climate change ranks last
Share of likely voters who named each as the greatest threat today to their ability to achieve or maintain the American Dream
5 percent said they didn’t know.
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Why do so many people think climate change will hurt the American Dream, if it’s so close to the bottom of the list of their concerns today? One likely reason is that people are making the connection between climate change and the rising cost of living. More than half of voters are very worried about climate change making life more expensive over the next 20 years, according to the Data for Progress poll. That’s higher than the share of voters who are worried about warmer temperatures, extreme weather, crop failures, and degraded natural areas.
Climate change is already showing up in household bills. A study from MIT earlier this year found that families in the United States were paying between $400 and $900 more each year because of the effects of global warming, from higher utility bills to climbing insurance premiums, with costs above $1,000 in the hardest-hit counties.
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Voters in every party worry most about climate change raising the cost of living
Share of likely voters who are very worried about each effect of climate change in the next 20 years, by party
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// Topline percentages from the Data for Progress crosstabs (QT7YZW).
const THREAT = [
[‘Cost of living’, 55],
[‘Domestic political instability and polarization’, 12],
[‘Immigration, crime, and public safety’, 10],
[‘Health care affordability and access’, 9],
[‘Global conflicts and national security’, 5],
[‘Education quality and costs’, 3],
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[‘Increasing cost of living’, 52, [62, 53, 42]],
[‘More severe extreme weather’, 39, [57, 39, 21]],
[‘Crop failures’, 37, [48, 40, 24]],
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Ahead of the midterm elections this week, climate advocacy groups have been talking about high energy costs, a top issue for voters in battleground districts. They have championed wind and solar as cheaper sources of energy and have argued that Trump’s efforts to block offshore wind farms and slow down other renewable energy projects will drive up costs.
While Trump has been talking about the “Green New Scam” and emphasizing the costs of taking climate action, that message doesn’t appear to be gaining much traction with voters. When participants in the Data for Progress poll were asked to choose between phrases that captured their views on climate policies, very few of them — only 6 percent — opted for “an expensive burden on working people.” Even Republicans didn’t pick that characterization, identifying climate action instead as a distraction from more important issues or a misguided approach based on faulty information. The most common phrase respondents chose to describe their views on climate policies was “a critical step to protect our environment.”
“That really is interesting, and suggests that potentially voters are not going to be as susceptible to the sacrifice or expense framing, despite this top-down use of the rhetoric,” Adcox said.
This story was originally published by Grist with the headline Exclusive: Most voters believe climate change threatens the American Dream on Nov 2, 2026.
