Trend · rank 229 of 250

Carbon pricing & environmental taxes

More jurisdictions are charging for emissions, waste and pollution through taxes and trading schemes, which shifts investment and revenue while demanding reliable measurement, enforcement and management of unequal adjustment costs.

Governance & IntegrityTrade, industry & supply chainscarbonclimateinstitutions

Second pass: Carried forward from the first pass unchanged. Held by all three panel models.

Sensitivity and network position

Rank
229of 250
Sensitivity
5.0rank 211
Breadth
1of 20 scenarios High or above
PageRank
11.5rank 235
Eigenvector
0.1
Betweenness
7.8
Links
4trends at Moderate similarity or above
Composite
8.8

Attention over three years

How much this trend is read about on Wikipedia and searched for on Google, week by week, set against the typical trend in the list so that what moves every trend at once is taken out. 100 is this trend's own three-year average. The score measures public attention, which is not the same as the strength of the force: a trend can deepen unnoticed, and a news event can lift attention for a fortnight.

Share of attention
0.10%rank 184 of 250 · a year earlier 0.12%
Now
99
Over a year
−18%Easing
Over three years
+14%
Peak
18131 Mar 2024
Sources
Bothagreement -0.37
Coverage
11languages · 56 nations

Trailing 4-week average. 100 is this trend’s three-year average.

By language

Wikipedia reading in each language edition where the trend’s pages are read enough to measure (11 of 28). Share is of all the readers counted.

LanguageShareNowOver a yearThree years
English46%72−13%
German19%88+15%
French12%86−11%
Chinese7%78−6%
Polish5%98−26%
Korean3%70−34%
Japanese3%80−9%
Italian2%137+76%
Spanish2%86−2%
Russian2%82−37%
Vietnamese1%71+8%

By country

Google search interest over the three years, as a share of each country’s searching. 100 is the nation where interest is highest. The search terms are English, which favours countries that search in English.

The 12 highest of 56 nations with a reading. All countries (CSV).

By region and G20 member

Attention to this trend inside each place, from Google searches made in its countries and Wikipedia reading in the languages mostly read there, averaged where both exist. Each line compares the trend with its own past in that place; the lines do not compare places.

Regions

PlaceRead fromNow90 days1 year3 yearsThree years
North AmericaGoogle in 2 countries + Wikipedia in English90−15%+2%−2%
EuropeGoogle in 4 countries + Wikipedia in German, French, Italian, Polish98−18%+19%−11%
Indo-PacificGoogle in 11 countries + Wikipedia in Japanese, Chinese, Korean, Vietnamese104−15%+34%+3%
South AsiaGoogle in 2 countries114+13%+28%+10%
Gulf and Middle EastGoogle in 1 country125−9%+185%+18%
AfricaGoogle in 1 country188+77%+142%+159%
Latin America and CaribbeanWikipedia in Spanish86−20%−3%−13%
Russia and EurasiaWikipedia in Russian82−4%−37%−22%

G20 members

PlaceRead fromNow90 days1 year3 yearsThree years
AustraliaGoogle97−38%+24%+17%
CanadaGoogle19−9%−58%−79%
FranceGoogle + Wikipedia in French74−11%−14%−61%
GermanyGoogle + Wikipedia in German92−48%+43%+10%
IndiaGoogle99−1%+15%−10%
IndonesiaGoogle145+13%+78%+82%
ItalyGoogle + Wikipedia in Italian112+21%+32%+39%
JapanGoogle + Wikipedia in Japanese74−43%+7%−35%
South KoreaGoogle + Wikipedia in Korean80−48%+1%+8%
RussiaWikipedia in Russian82−4%−37%−22%
South AfricaGoogle188+77%+142%+159%
United KingdomGoogle154+17%+119%+80%
United StatesGoogle + Wikipedia in English105−16%+9%+19%

Share of attention by region

GINC Share of Attention: this trend's share of all the public attention the trends in the list draw, worldwide and in each region, week by week (a 4-week average). Each place is read from the Wikipedia languages mostly read there and Google searching in its countries; where a place's language is shared with others, as English is, Google stands alone.

PlaceShare, last 13 weeksRank thereA year earlierRead from
World0.10%184 of 2500.12%Wikipedia reading in all 28 languages, and Google searching worldwide
Asia Pacific0.09%195 of 2500.12%Wikipedia reading in the region's own languages, not English; Google searching in 15 countries, not China
Europe0.13%164 of 2500.15%German, French, Italian, Polish, Dutch and Swedish reading; Google searching in 10 countries, the UK among them
Africa0.03%205 of 2500.06%Google searching in 7 countries; Afrikaans and Swahili reading reaches too few trends to count
South America0.01%244 of 2500.02%Spanish and Portuguese reading, Spanish also read in Spain; Google searching in 5 countries
North America0.03%212 of 2490.06%Google searching in the United States, Canada and Mexico; English reading cannot be split by country
Oceania0.11%133 of 2490.13%Google searching in Australia and New Zealand; English reading cannot be split by country
United States0.03%203 of 2490.03%Google searching; English reading cannot be split by country
United Kingdom0.04%178 of 2490.05%Google searching; English reading cannot be split by country
India0.04%172 of 2490.02%Google searching; Hindi, Bengali and Tamil reading reaches too few trends to count, and English cannot be split by country
China···Wikipedia and Google are blocked in China; no open source counts reading there
Russia0.04%197 of 2030.04%Russian reading, which is also done across the former Soviet states; Google's figures for Russia cannot be bought
Japan0.05%214 of 2500.07%Japanese reading and Google searching
Germany0.12%175 of 2500.13%German reading and Google searching
France0.21%108 of 2500.30%French reading, also done in Africa and Canada, and Google searching
Italy0.14%134 of 2500.16%Italian reading and Google searching
South Korea0.16%129 of 2500.32%Korean reading and Google searching
Indonesia0.07%140 of 2500.07%Google searching; Indonesian reading reaches too few trends to count
Brazil0.01%244 of 2500.02%Portuguese reading and Google searching
Türkiye0.00%190 of 2500.00%Google searching; Turkish reading reaches too few trends to count

Relation to each scenario

A trend relates to a scenario when it changes how likely the scenario is, how hard it lands, or is itself sharply changed by it. Each score is the median of three scores given separately by the members of the model panel.

RelationScenarioFamily
4 · HighCheap firm clean power becomes available at scale S15 ↗Energy, climate and resources
1 · Very lowA cross-regional market access compact redirects investment S12 ↗Economic and trade
1 · Very lowA validated farming package raises water-efficient output S16 ↗Energy, climate and resources
0 · Not relatedCritical trade divides into rival blocs S01Economic and trade
0 · Not relatedA global funding seizure reaches sovereign balance sheets S02Economic and trade
0 · Not relatedA regional war closes a globally important sea route S03Geopolitical and security
0 · Not relatedHybrid coercion ends in an infrastructure blackout S04Geopolitical and security
0 · Not relatedA disputed transfer of power breaks effective government S05Geopolitical and security
0 · Not relatedTwo breadbasket failures trigger a food availability crisis S06Energy, climate and resources
0 · Not relatedA major disaster disables the national economic core S07Energy, climate and resources
0 · Not relatedAn AI deployment step causes abrupt labour displacement S08Technology and infrastructure
0 · Not relatedA cryptographic breakthrough invalidates digital trust S09Technology and infrastructure
0 · Not relatedA novel respiratory pathogen exceeds health surge capacity S10Health
0 · Not relatedA creditor agreement releases fiscal capacity S11 ↗Economic and trade
0 · Not relatedA great-power agreement lowers economic security barriers S13 ↗Geopolitical and security
0 · Not relatedA verified Middle East settlement restores access S14 ↗Geopolitical and security
0 · Not relatedAI-directed research produces a reproducible discovery step S17 ↗Technology and infrastructure
0 · Not relatedA public service platform reaches inclusive operation S18 ↗Technology and infrastructure
0 · Not relatedAffordable preventive treatments reduce chronic illness S19 ↗Health
0 · Not relatedA live outbreak proves rapid distributed pandemic defence S20 ↗Health

Similarity = 0.3 × shared tags (Jaccard) + 0.3 × likeness of scenario profiles (cosine) + 0.3 × likeness of names and descriptions (TF-IDF cosine) + 0.1 if in the same capability domain, cut into seven levels at 0.12, 0.22, 0.33, 0.45, 0.58, 0.72. Shown at Moderate and above.

SimilarityTrendThemeRank
4 · HighSustainable finance & environmental riskMoney & debt161
3 · ModerateCarbon border adjustmentsTrade, industry & supply chains186
3 · ModerateHydrogen & low-carbon fuelsTrade, industry & supply chains212
3 · ModerateCarbon capture, utilisation & storageTrade, industry & supply chains218