This indicator measures the number of people affected by at least one of the following three forms of poverty or social exclusion: income poverty, severe material and social deprivation and very low work intensity (see the more detailed descriptions of these components below).

Figure 1.1: People at risk of poverty or social exclusion, EU, 2015-2021
(million people)

Note: Break in time series in 2020. The target figures shown for 2030 refer to a reduction of 15 million people at risk of poverty or social exclusion, including 5 million children, compared with 2019 levels.

Source: Eurostat (online data code: sdg_01_10 )

This indicator measures the number of people with an equivalised disposable income below the risk-of-poverty threshold. This is set at 60 % of the national median equivalised disposable income after social transfers.

Figure 1.2: People at risk of income poverty after social transfers, EU, 2010-2021 (million people)

Note: 2010–2018 data are estimated; break in time series in 2020.

Source: Eurostat (online data code: sdg_01_20 )

This indicator is defined as the proportion of the population experiencing an enforced lack
of at least 7 out of 13 deprivation items (6 of these items are related to the individual and 7 to the household).

Figure 1.3: Severe material and social deprivation, EU, 2015-2021
(million people)

Source: Eurostat (online data code: sdg_01_31 )

This indicator describes the share of people aged under 65 living in households where
the working-age adults aged 18 to 64 worked equal or less than 20% of their total combined potential work-time during the previous 12 months.

Figure 1.4: People living in households with very low work intensity, EU, 2015-2021
(million people aged less than 65)

Note: 2019 data are estimated.

Source: Eurostat (online data code: sdg_01_40 )

This indicator refers to the share of employed people aged 18 years or over with an income below the poverty threshold, which is set at 60% of the national median equalised disposable income. People are considered ‘employed’ if they held a job for more than half of the reference year.

Figure 1.5: In work at-risk-of-poverty rate, EU, 2010-2021
(% of population aged 18 or over)

Note: 2010–2019 data are estimated.

Source: Eurostat (online data code: sdg_01_41 )

The indicator reflects the share
of the population living in households where the total housing costs (rental or mortgage payments and the cost of utilities such as water, electricity, gas or heating) represent more than 40% of the disposable income.

Figure 1.6: Housing cost overburden rate, EU, 2010-2021
(% of population)

Note: 2014–2019 and 2021 data are estimated.

Source: Eurostat (online data code: sdg_01_50 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG1 and other SDGs. Synergies indicate that progress of SDG1 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG1 may have negative effects and deteriorate progress towards the other linked SDGs.    

Figure 1.7: Interlinkages of SDG 1 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 1.8: ODA to SDG 1 and interlinkages with other goals

Source: Drawn up by JRC using EU Dashboard data on ODA (2022, commitments). The figure shows interlinkages among SDGs measured by number of projects reported. The bottom (darker) section of the bar shows how projects where SDG1 was marked as the main SDG contribute to other SDGs (right side). The top section (lighter) shows projects where other SDGs were selected as the main target, but that also contribute to SDG1 (left side).

The body mass index (BMI) is defined as the weight in kilograms divided by the square of the height in metres. People aged 18 years or over are considered obese if their BMI is equal to or greater than 30. The category ‘pre-obese’ refers to people with a BMI between 25 and less than 30.

Figure 2.1: Obesity rate, by body mass index (BMI), EU, 2014-2019
(% of population aged 18 or over)

Note: 2017 data are estimated.

Source: Eurostat (online data codes: sdg_02_10 )

Agricultural factor income measures the income generated by farming that is used to remunerate borrowed or rented factors of production (capital, wages and land rents) as well as own production factors (own labour, capital and land). Annual work units (AWUs) correspond to the number of full-time equivalent jobs.

Figure 2.2: Agricultural factor income per annual work unit (AWU), EU, 2010-2022 (index 2010=100)

Note: 2022 data are estimated.

Source: Eurostat (online data code: sdg_02_20 )

This indicator refers to government budget allocations for R&D (GBARD) for agriculture. GBARD data are built up using the guidelines laid out in the standard practice for surveys of research and experimental development, the OECD’s Frascati Manual.

Figure 2.3: Government support to agricultural research and development, EU, 2010-2021 (million EUR)

Note: Estimated data.

Source: Eurostat (online data code: sdg_02_30 )

This indicator is defined as the share of total utilised agricultural area (UAA) occupied by organic farming. It covers both existing organically farmed areas and areas undergoing conversion.

Figure 2.4: Area under organic farming, EU, 2012-2020
(% of utilised agricultural area)

Note: 2017–2020 data are estimated or provisional.

Source: Eurostat (online data code: sdg_02_40 )

This indicator measures ammonia (NH3) emissions from agricultural production. The data come from the EU inventory on air pollution compiled by the EEA under the Convention on Long-range Transboundary Air Pollution (LRTAP). The definition of this indicator is based on the CAP (Common Agricultural Policy) indicator C45 ‘Emissions from agriculture’.

Figure 2.5: Ammonia emissions from agriculture, EU, 2010-2020
(million tonnes)

Source: EEA (Eurostat online data code: sdg_02_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG2 and other SDGs. Synergies indicate that progress of SDG2 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG2 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 2.7: Interlinkages of SDG 2 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 2.8: ODA to SDG 2 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

Healthy life years is a health expectancy indicator which combines information on mortality (death rate) and morbidity (probability of illness).
It measures the number of years at birth that a person can expect to live in a healthy condition.

Figure 3.1: Healthy life years at birth, by sex, EU, 2010-2020
(years)

Note: Breaks in time series in 2015 and 2016.

Source: Eurostat (online data code: sdg_03_11 )

This indicator is a subjective measure of how people judge their health in general on a scale from ‘very good’ to ‘very bad’. Indicators of perceived general health have been found to be a good predictor of people’s future health care use and mortality.

Figure 3.2: Share of people with good or very good perceived health, by sex, EU, 2010-2021 (% of population aged 16 or over)

Note: Data for 2010–2016 and for 2020 are estimated.

Source: Eurostat (online data code: sdg_03_20 )

This indicator measures the percentage of the population aged 15 years and over who report that they currently smoke boxed cigarettes, cigars, cigarillos or a pipe. It does not include the use of other tobacco and related products such as electronic cigarettes and snuff.

Figure 3.3: Smoking prevalence, by sex, EU, 2009-2020
(% of population aged 15 or over)

Note: Data for 2009–2017 are estimated; 2012 data excluding Croatia.

Source: European Commission services (Eurostat online data code: sdg_03_30 )

This indicator measures the
age-standardised death rate from selected communicable diseases. The rate is calculated by dividing the number of people dying due to tuberculosis, HIV and hepatitis by the total population. This value is then weighted with the European Standard Population.

Figure 3.4: Standardised death rate due to tuberculosis, HIV and hepatitis, by type of disease, EU, 2010-2020 (number per 100 000 persons)

Note: 2010 are estimated; 2018 and 2019 data are provisional.

Source: Eurostat (online data code: sdg_03_41 )

Avoidable mortality covers mortality that can mainly be prevented through effective public health and primary prevention interventions or avoided through timely and effective healthcare interventions, including secondary prevention and treatment.

Figure 3.5: Standardised avoidable mortality, EU, 2011-2020
(number per 100 000 persons aged less than 75 years)

Note: 2018 and 2019 data are provisional.

Source: Eurostat (online data code: sdg_03_42 )

This indicator measures the share of the population aged
16 and over reporting unmet needs for medical care (dental care is excluded) due to one of the following reasons: ‘financial reasons’, ‘waiting list’ and ‘too far to travel’ (all three categories are cumulated).

Figure 3.6: Self-reported unmet need for medical care, by sex, EU, 2010-2021 (% of population aged 16 and over)

Note: Data for 2010–2020 are estimated.

Source: Eurostat (online data code: sdg_03_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG3 and other SDGs. Synergies indicate that progress of SDG3 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG3 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 3.7: Interlinkages of SDG 3 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 3.8: ODA to SDG 3 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator measures the share of children between the age of three and the starting
age of compulsory primary education who participated in early childhood education.

Figure 4.1: Participation in early childhood education, EU, 2013-2020
(% of children aged 3 and over)

Source: Eurostat (online data code: sdg_04_31 )

The indicator measures the
share of the population aged
18 to 24 with at most lower-secondary education who were not involved in any education or training during the four weeks preceding the survey.

Figure 4.2: Early leavers from education and training, by sex, EU, 2010-2022
(% of population aged 18 to 24)

Note: Breaks in time series in 2014 and 2021.

Source: Eurostat (online data code: sdg_04_10 )

This indicator measures the share of 15-year-old students failing to reach level 2 (‘basic skills level’) in the Programme
for International Student Assessment (PISA) scale for
the three core school subjects of reading, mathematics and science.

Figure 4.3: Low-achieving 15-year-olds in reading, mathematics or science, EU, 2009-2018 (% of 15-year-old students)

Source: OECD (Eurostat online data code: sdg_04_40 )

This indicator measures the share of the population aged 25 to 34 who have successfully completed tertiary studies (for example, at university or a higher technical institution). The data refer to ISCED 1997 levels 5-6 up to 2013 and to ISCED 2011 levels 5-8 from 2014 onwards.

Figure 4.4: Tertiary educational attainment, by sex, EU, 2010-2022
(% of population aged 25 to 34)

Note: Breaks in time series in 2014 and 2021.

Source: Eurostat (online data code: sdg_04_20 )

This indicator refers to people aged 25 to 64 who stated they received formal or non-formal education and training in the four weeks preceding the survey as a share of the total population of this age group.

Figure 4.5: Adult participation in learning, EU, 2010-2022
(% of population aged 25 to 64)

Note: Breaks in time series in 2013 and 2021.

Source: Eurostat (online data code: sdg_04_60 )

This indicator shows the share
of people who have at least
basic digital skills, which comprises the two highest out
of six levels measured. It is a composite indicator based on selected activities performed by individuals on the internet.

Figure 4.6: Share of adults having at least basic digital skills, by sex, EU, 2021 (% of individuals aged 16 to 74)

Source: Eurostat (online data code: sdg_04_70 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG4 and other SDGs. Synergies indicate that progress of SDG4 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG4 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 4.7: Interlinkages of SDG 4 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 4.8: ODA to SDG 4 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator measures the share of women who had experienced physical and/or sexual violence within the 12 months prior to the interview.

Figure 5.1: Physical and sexual violence to women experienced within 12 months prior to the interview, by age group, EU, 2012 (% of women)

Source: European Union Agency for Fundamental Rights (FRA) (Eurostat online data code: sdg_05_10 )

The gender pay gap in unadjusted form represents the difference between average gross hourly earnings of male paid employees and of female paid employees as a percentage of average gross hourly earnings of male paid employees.

Figure 5.2: Gender pay gap in unadjusted form, EU, 2010-2021
(% of average gross hourly earnings of men)

Note: Data for 2019–2021 are provisional.

Source: Eurostat (online data code: sdg_05_20 )

The gender employment gap is defined as the difference between the employment rates
of men and women aged 20 to 64. The employment rate is calculated by dividing the number of people aged 20 to 64 in employment by the total population of the same age group.

Figure 5.3: Gender employment gap, EU, 2009-2022
(percentage points)

Source: Eurostat (online data code: sdg_05_30 )

This indicator measures the proportion of the population that is outside the labour force due to ‘care of adults with disabilities or children’ and ‘other family or personal reasons’. These people are not working or actively seeking work, nor are they available to work even if they have found a job.

Figure 5.4: People outside the labour force due to caring responsibilities, by sex, EU, 2010-2022 (% of population aged 20 to 64)

Source: Eurostat (online data code: sdg_05_41 )

This indicator refers to the proportion of women in national parliaments in both chambers (lower house and upper house, where relevant).

Figure 5.5: Seats held by women in national parliaments, EU, 2010-2022
(% of seats)

Source: European Institute for Gender Equality (EIGE) (Eurostat online data code: sdg_05_50 )

This indicator measures the share of female board members in the largest publicly listed companies.

Figure 5.6: Positions held by women in senior management, EU, 2010-2022
(% of board members)

Source: European Institute for Gender Equality (EIGE) (Eurostat online data code: sdg_05_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG5 and other SDGs. Synergies indicate that progress of SDG5 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG5 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 5.7: Interlinkages of SDG 5 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 5.8: ODA to SDG 5 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator reflects the share of total population having neither a bath, nor a shower, nor an indoor flushing toilet in their household.

Figure 6.1: Population having neither a bath, nor a shower, nor indoor flushing toilet in their household, EU, 2010-2020 (% of population)

Note: Data for 2010–2019 are estimated. The frequency of the data collection has been changed from annually to every three years, meaning no data were collected for 2021 and 2022.

Source: Eurostat (online data code: sdg_06_10 )

Waste water treatment systems with at least secondary treatment involve biological treatment with a secondary settlement or other process that removes organic material and reduces its biochemical oxygen demand (BOD) by at least 70% and chemical oxygen demand (COD) by at least 75%.

Figure 6.2: Population connected to at least secondary waste water treatment, EU, 2010-2020 (% of population)

Note: Eurostat estimates.

Source: Eurostat (online data code: sdg_06_20 )

The mean annual 5-day biochemical oxygen demand (BOD5) in rivers is a measure of the amount of oxygen that aerobic microorganisms need to decompose organic substances in a water sample over a 5-day period in the dark at 20°C. The cleanest rivers have a 5-day BOD of less than 1 milligram per litre (mg/L).

Figure 6.3: Biochemical oxygen demand in rivers, EU, 2010-2020
(mg O2 per litre)

Note: ‘EU’ refers to an aggregate based on 18 Member States.

Source: EEA (Eurostat online data code: sdg_06_30 )

Data on the concentration of nitrate (NO3) in groundwater are taken from well samples and aggregated to annual average concentrations for groundwater bodies in Europe. While the indicator is relatively robust in presenting the overall trend, the distribution of measuring stations might mask exceedances of nitrate levels in certain polluted areas.

Figure 6.4: Nitrate in groundwater, EU, 2010-2020
(mg NO3 per litre)

Note: ‘EU’ refers to an aggregate based on 18 Member States.

Source: EEA (Eurostat online data code: sdg_06_40 )

This indicator measures the concentration of phosphate
(PO4) per litre in the dissolved phase from water samples from river stations and aggregated to annual average values.

Figure 6.5: Phosphate in rivers, EU, 2010-2020
(mg PO4 per litre)

Note: ‘EU’ refers to an aggregate based on 18 Member States.

Source: EEA (Eurostat online data code: sdg_06_50 )

The regionalised water exploitation index (WEI+) measures total freshwater use as a percentage of the long-term annual average available water (LTAA) from renewable fresh water resources (groundwater and surface water) at a given time and place. It quantifies how much water is abstracted and how much is returned after use
to the environment via basins.

Figure 6.6: Water exploitation index (WEI+), EU, 2010-2019
(% of renewable water resources)

Source: EEA (Eurostat online data code: sdg_06_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG6 and other SDGs. Synergies indicate that progress of SDG6 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG6 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 6.7: Interlinkages of SDG 6 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 6.8: ODA to SDG 6 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

Primary energy consumption represents a country’s total energy demand before any transformation, excluding energy carriers used for non-energy purposes. Final energy consumption covers the energy consumed by end users, such as industry, transport, households, services and agriculture.

Figure 7.1: Primary and final energy consumption, EU, 2010-2021
(million tonnes of oil equivalent (Mtoe))

Source: Eurostat (online data codes: sdg_07_10 and sdg_07_11 )

This indicator measures how much energy each citizen consumes at home, excluding transport. Data are not temperature-adjusted, so variations from year to year are due in part to weather.

Figure 7.2: Final energy consumption in households per capita, EU, 2010-2021 (kg of oil equivalent)

Note: Multiple breaks in population data time series; 2018–2021 population data are provisional estimates.

Source: Eurostat (online data code: sdg_07_20 )

This indicator measures the amount of economic output produced per unit of gross available energy, which represents the quantity of energy products needed to satisfy all demand of bodies in the geographical area under consideration.

Figure 7.3: Energy productivity, EU, 2010-2021
(EUR per kgoe)

Source: Eurostat (online data code: sdg_07_30 )

This indicator is defined as the share of renewable energy consumption in gross final energy consumption. The gross final energy consumption is the energy used by end consumers plus grid losses and power plants’ own consumption.

Figure 7.4: Share of renewable energy in gross final energy consumption, by sector, EU, 2010-2021 (%)

Source: Eurostat (online data code: sdg_07_40 )

Energy import dependency shows the share of a country’s total energy needs that are met by imports from other countries.
It is calculated as net imports (i.e. imports minus exports) divided by the gross available energy.

Figure 7.5: Energy import dependency, by product, EU, 2010-2021
(% of imports in gross available energy)

Note: ‘Total’ is not the average of the three fuel categories shown but includes other sources, such as renewables or nuclear energy, which are treated as domestic sources.

Source: Eurostat (online data code: sdg_07_50 )

This indicator measures the share of people unable to afford to keep their home adequately warm. Data collection is based on a survey, which means that indicator values are self-reported.

Figure 7.6: Population unable to keep home adequately warm, EU, 2010-2021 (% of population)

Note: 2010–2019 data are estimated.

Source: Eurostat (online data code: sdg_07_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG7 and other SDGs. Synergies indicate that progress of SDG7 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG7 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 7.7: Interlinkages of SDG 7 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 7.8: ODA to SDG 7 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

Gross domestic product (GDP)
is a measure of economic activity and is often used as a proxy for changes in a country’s material living standards. It refers to the value of total final output of goods and services produced by an economy within a certain period.

Figure 8.1: Real GDP per capita, EU, 2010-2022
(EUR per capita, chain-linked volumes, 2010)

Source: Eurostat (online data code: sdg_08_10 )

The investment share of GDP measures gross fixed capital formation (GFCF) for the total economy, government and business, as well as household sectors as a percentage of GDP.

Figure 8.2: Investment share of GDP, by institutional sector, EU, 2010-2022
(% of GDP)

Source: Eurostat (online data code: sdg_08_11 )

This indicator measures the proportion of young people aged 15 to 29 who are neither in employment (i.e. outside of the labour force or unemployed) nor participating in education and training.

Figure 8.3: Young people neither in employment nor in education and training (NEET), by sex, EU, 2010-2021
(% of population aged 15 to 29)

Source: Eurostat (online data code: sdg_08_20 )

This indicator shows the percentage of employed persons aged 20 to 64 in relation to the total population of this age group. Employed persons are defined as all persons who, during a reference week, worked at least one hour for pay or profit or were temporarily absent from such work.

Figure 8.4: Employment rate, by sex, EU, 2010-2022
(% of population aged 20 to 64)

Source: Eurostat (online data code: sdg_08_30 )

This indicator measures the share of the population in the labour force (which includes both employed and unemployed people) aged 15 to 74 who have been unemployed for 12 months or more.

Figure 8.5: Long-term unemployment rate, by sex, EU, 2010-2022
(% of population in the labour force)

Source: Eurostat (online data code: sdg_08_40 )

Fatal accidents at work are those occurring during the course of employment and leading to the death of the victim within one year; commuting accidents occurring between the home and the workplace are excluded. The incidence rate refers to the number of accidents per 100 000 persons in employment.

Figure 8.6: Fatal accidents at work, EU, 2010-2020
(number per 100 000 workers)

Note: Break in time series in 2020.

Source: Eurostat (online data code: sdg_08_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG8 and other SDGs. Synergies indicate that progress of SDG81 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG8 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 8.7: Interlinkages of SDG 8 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 8.8: ODA to SDG 8 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator measures gross domestic expenditure on R&D (GERD) as a percentage of gross domestic product (GDP) – also called R&D intensity. R&D refers to creative and systematic work undertaken to increase the stock of knowledge.

Figure 9.1: Gross domestic expenditure on R&D, EU, 2010-2021
(% of GDP)

Note: Data for 2010 to 2020 are estimated; 2021 data are provisional.

Source: Eurostat (online data code: sdg_09_10 )

R&D personnel consists of persons engaged directly in the creative and systematic work undertaken to increase the stock of knowledge. In addition, it includes those providing direct services for R&D activities, such as managers, administrators, technicians and clerical staff.

Figure 9.2: R&D personnel, EU, 2010-2021
(% of population in the labour force)

Note: Data for 2010–2020 are estimated; 2021 data are provisional.

Source: Eurostat (online data code: sdg_09_30 )

This indicator measures
requests for the protection of an invention filed with the European Patent Office (EPO) regardless
of whether they are granted or not. Applications are allocated according to the country of residence of the first applicant.

Figure 9.3: Patent applications to the European Patent Office (EPO), by country of applicant, EU, 2010-2022 (number)

Note: 2022 data are provisional.

Source: EPO (Eurostat online data code: sdg_09_40 )

This indicator measures the emissions intensity of particulate matter (PM10 and PM2.5) from the manufacturing sector (NACE Rev. 2 sector ‘C’). Emission intensity is calculated by dividing the sector’s PM emissions by its gross value added (GVA).

Figure 9.4: Air emissions intensity of industry for particulate matter, EU, 2010-2020 (grams per euro, chain-linked volumes, 2010)

Source: Eurostat (online data code: sdg_09_70 )

This indicator measures the share of buses, including coaches and trolley-buses, and trains in inland passenger transport. It excludes inland waterways, air and sea transport. All data are based on movements within national territories, regardless of the vehicle’s nationality.

Figure 9.5: Share of buses and trains in inland passenger transport, EU, 2010-2020 (% of passenger-km)

Note: Estimated data.

Source: Eurostat (online data code: sdg_09_50 )

This indicator measures the share of rail and inland waterways in inland freight transport. Neither sea nor air freight transport are included. All data are based on movements
on national territory, regardless
of the nationality of the train or vessel.

Figure 9.6: Share of rail and inland waterways in inland freight transport, EU, 2010-2021 (% of freight tonne-km)

Note: Data for 2012–2021 are estimated.

Source: Eurostat (online data code: sdg_09_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG9 and other SDGs. Synergies indicate that progress of SDG9 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG9 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 9.7: Interlinkages of SDG 9 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 9.8: ODA to SDG 9 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator measures the
ratio of total equivalised disposable income received by the 20% of the population with the highest income (top quintile) to that received by the 20% of the population with the lowest income (lowest quintile).

Figure 10.1: Income distribution, EU, 2010-2021
(income quintile share ratio)

Note: 2014–2019 data are estimated; break in time series in 2020.

Source: Eurostat (online data code: sdg_10_41 )

This indicator measures the income share received by the bottom 40% of the population
(in terms of income). The income concept used is the total disposable household income, which is a households’ total income (after taxes and other deductions) that is available for spending or saving. 

Figure 10.2: Income share of the bottom 40% of the population, EU, 2010-2021 (% of income)

Note: 2014–2019 data are estimated; break in time series in 2020.

Source: Eurostat (online data code: sdg_10_50 )

The relative median at-risk-of-poverty gap shows the distance between the median income of people living below the poverty threshold and the threshold itself, expressed in relation to the poverty threshold. The poverty threshold is set at 60% of the national median equivalised disposable income of all people in a country and not for the EU
as a whole.

Figure 10.3: Relative median at-risk-of-poverty gap, EU, 2010-2021
(% distance to poverty threshold)

Note: 2014–2019 data are estimated.

Source: Eurostat (online data code: sdg_10_30 )

Statistics on the degree of urbanisation classify local administrative units as ‘cities’, ‘towns and suburbs’ or ‘rural areas’ depending on population density and the total number of inhabitants. This classification is used to determine the difference in the shares of people at risk of poverty or social exclusion between cities and rural areas.

Figure 10.4: People at risk of poverty or social exclusion, by degree of urbanisation, EU, 2015-2021 (% of population)

Note: 2015–2018 data are estimated.

Source: Eurostat (online data code: sdg_01_10a )

GDP per capita is calculated as the ratio of GDP to the average population in a specific year.
The disparities indicator for the EU is calculated as the coefficient of variation of the national figures in PPS per capita.

Figure 10.5: Disparities in purchasing power adjusted GDP per capita, EU, 2010-2022 (coefficient of variation, in %)

Note: 2022 data are provisional estimates.

Source: Eurostat (online data code: sdg_10_10 )

This indicator reflects the purchasing power of households and their ability to invest in goods and services or save for the future, after accounting for taxes and social contributions and monetary in-kind social benefits. The disparities indicator for the EU is calculated as the coefficient of variation of the national figures in PPS per capita.

Figure 10.6: Disparities in adjusted gross disposable income of households per capita, EU, 2010-2021 (coefficient of variation, in %)

Note: EU coefficient of variation excluding Malta and Romania (whole time series); 2018–2021 data are provisional estimates.

Source: Eurostat (online data code: sdg_10_20 )

A first-time applicant for international protection is a person who lodged an application for asylum for the first time in a given Member State. First-instance decisions are decisions granted by the respective authority acting as a first instance of the administrative or judicial asylum procedure in the receiving country.

Figure 10.7: Asylum applications, by state of procedure, EU, 2012-2022
(number per million inhabitants)

Note: Multiple breaks in population data time series; 2018–2022 population data are provisional and/or estimated.

Source: Eurostat (online data code: sdg_10_60 )

Figure 10.8: People at risk of income poverty after social transfers, by citizenship, EU, 2010-2021
(% of population aged 18 years or more)

Note: 2010–2019 data are estimated; 2010–2011 data for non-EU citizens have low reliability.

Source: Eurostat (online data code: sdg_01_20a )

Figure 10.9: Early leavers from education and training, by citizenship, EU, 2010-2022
(% of population aged 18 to 24)

Note: Breaks in time series in 2014 and 2021.

Source: Eurostat (online data code: sdg_04_10a )

Figure 10.10: Young people neither in employment nor in education and training (NEET), by citizenship, EU, 2010-2022 (% of population aged 15 to 29)

Note: Break in time series in 2021.

Source: Eurostat (online data code: sdg_08_20a )

Figure 10.11: Employment rate, by citizenship, EU, 2010-2022 
(% of population aged 20 to 64)

Note: Break in time series in 2021.

Source: Eurostat (online data code: sdg_08_30a )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG10 and other SDGs. Synergies indicate that progress of SDG10 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG10 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 10.12: Interlinkages of SDG 10 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 10.13: ODA to SDG 10 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator is defined as the percentage of the population living in a dwelling which is considered as overcrowded, while also exhibiting at least
one of the following housing deprivation measures: (i) a leaking roof, (ii) no bath/shower and no indoor toilet, and (iii) considered too dark.

Figure 11.1: Severe housing deprivation rate, by degree of urbanisation, EU, 2010-2020 (% of population)

Note: Estimated data. The frequency of the data collection has been changed from annually to every three years, meaning no data were collected for 2021 and 2022.

Source: Eurostat (online data codes: sdg_11_11 and ilc_mdho06d )

This indicator measures the share of the population who declare they are affected by noise either from neighbours or
from the street.  

Figure 11.2: Population living in households considering that they suffer from noise, by degree of urbanisation, EU, 2010-2020 (% of population)

Note: Estimated data. The frequency of the data collection has been changed from annually to every three years, meaning no data were collected for 2021 and 2022.

Source: Eurostat (online data codes:  sdg_11_20 and ilc_mddw04 )

The indicator measures the premature deaths due to exposure to air pollution by
fine particulate matter (PM2.5).

Figure 11.3: Premature deaths due to exposure to fine particulate matter (PM2.5), EU, 2010-2020 (number)

Source: EEA (Eurostat online data code: sdg_11_52 )

This indicator measures the number of fatalities caused by road crashes, including drivers and passengers as well as pedestrians.

Figure 11.4: Road traffic deaths, EU, 2010-2021
(number of people killed)

Note: 2021 data are provisional.

Source: European Commission services (Eurostat online data code: sdg_11_40 )

This indicator captures the amount of settlement area such as for buildings, industrial and commercial areas, infrastructure and sports grounds, and includes both sealed and non-sealed surfaces.

Figure 11.5: Settlement area per capita, EU, 2009-2018
(m²)

Note: EU-23* refers to an aggregate including the UK but excluding Bulgaria, Croatia, Cyprus, Malta and Romania.

Source: Eurostat (online data code: sdg_11_31 )

This indicator measures the percentage of recycled
municipal waste. Recycling includes material recycling, preparing for re-use, composting and anaerobic digestion.

Figure 11.6: Recycling rate of municipal waste, EU, 2010-2021
(% of total municipal waste generated)

Note: 2019–2021 data are Eurostat estimates.

Source: Eurostat (online data code:  sdg_11_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG11 and other SDGs. Synergies indicate that progress of SDG11 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG11 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 11.7: Interlinkages of SDG 11 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 11.8: ODA to SDG 11 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

The material footprint of the EU, also referred to as raw material consumption (RMC), quantifies the global extraction of materials needed to satisfy consumption of goods and services within the EU. For comparability reasons, raw material equivalents are estimated for imports and exports.

Figure 12.1: Raw material consumption, EU, 2000-2020
(billion tonnes)

Note: Estimated data.

Source: Eurostat (online data code: sdg_12_21 )

This indicator measures the volume of consumption of hazardous chemicals. Consumption is calculated as follows: production + imports – exports.

Figure 12.2: Consumption of hazardous chemicals, EU, 2010-2021
(million tonnes)

Source: Eurostat (online data code: sdg_12_10 )

This indicator is defined as the average carbon dioxide (CO2) emissions per km from new passenger cars in a given year. The reported emissions are based on type-approval and can deviate from the actual CO2 emissions of new cars.

Figure 12.3: Average CO2 emissions per km from new passenger cars, EU, 2010-2021 (g CO2 per km)

Note: 2010-2012 data are estimated; break in time series in 2021; 2021 data are provisional.

Source: EEA, European Commission services, Eurostat (online data code: sdg_12_30 )

The environmental goods and services sector (EGSS) is engaged in producing goods
and services used in environmental protection and resource management activities. Gross value added in EGSS is defined as the difference between the value of the sector’s output and intermediate consumption.

Figure 12.4: Gross value added in the environmental goods and services sector, EU, 2010-2020 (million EUR, chain-linked volumes, 2010)

Note: Eurostat estimates.

Source: Eurostat (online data codes: sdg_12_61 )

The circular material use rate (CMU) measures the share of material recovered and fed back into the economy in overall material use. The circular use
of materials is approximated by the amount of waste recycled in domestic recovery plants minus imported waste destined for recovery plus exported waste destined for recovery abroad.

Figure 12.5: Circular material use rate, EU, 2010-2021
(% of material input for domestic use)

Note: Data for odd years (2011, 2013, etc.) and for 2020 are estimated; 2020 and 2021 data are provisional.

Source: Eurostat (online data code: sdg_12_41 )

This indicator is defined as all waste generated in a country.
It covers waste generated by industrial production (including the waste-management sector itself) and by households.

Figure 12.6: Generation of waste, by hazardousness, EU, 2010-2020 (kg per capita)

Source: Eurostat (online data code: sdg_12_51 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG12 and other SDGs. Synergies indicate that progress of SDG12 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG12 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 12.7: Interlinkages of SDG 12 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 12.8: ODA to SDG 12 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator measures man-made greenhouse gas (GHG) emissions as well as carbon removals. At present, carbon removals are accounted for only in the land use, land-use change and forestry (LULUCF) sector. The net GHG emissions shown here include international aviation, indirect CO2 and
natural carbon removals from LULUCF.

Figure 13.1: Net greenhouse gas emissions, EU, 2010-2021
(index 1990 = 100)

Note: Data for 2021 are provisional estimates based on the EEA approximated GHG inventory for the year 2021.

Source: EEA, Eurostat (online data code: sdg_13_10 )

This indicator measures net carbon removals from the land use, land-use change and forestry (LULUCF) sector, considering both emissions
and removals from the sector.

Figure 13.2: Net greenhouse gas emissions from land use and forestry, EU, 2010-2021 (million tonnes of CO2 equivalent)

Note: Data for 2021 are provisional estimates based on the EEA approximated GHG inventory for the year 2021.

Source: EEA, Eurostat (online data code: sdg_13_21 )

This indicator includes the
overall monetary losses from weather- and climate-related events. Due to the variability of the annual figures, the data are presented as a 30-year moving average.

Figure 13.3: Climate-related economic losses (30-year moving average), EU, 2010-2021 (EUR billion, constant prices)

Note: Data are shown as 30-year moving average (annual data points refer to the 30-year period up to that year).

Source: EEA, Eurostat (online data code: sdg_13_40 )

The Covenant of Mayors for Climate and Energy in Europe, now part of the Global Covenant of Mayors for Climate and Energy, represents a climate initiative at multiple levels of governance with actors all
across the globe pledging to deliver comprehensive climate-change mitigation and adaptation and energy action plans and establish a regular monitoring process.

Figure 13.4: Population covered by the Covenant of Mayors for Climate and Energy signatories, EU, 2010-2022 (million people)

Note: break in time series in 2019.

Source: Covenant of Mayors for Climate and Energy (Eurostat online data code: sdg_13_60 )

The intention of the international commitment on climate finance under the United Nations Framework Convention on Climate Change (UNFCCC) is to enable and support enhanced action by developing countries to advance low-emission and climate-resilient development.

Figure 13.5: Contribution to the international USD 100 bn commitment on climate-related expenditure, EU, 2014-2021 (EUR billion, current prices)

Note: Break in time series in 2020.

Source: European Commission services and European Environment Information and Observation Network (Eionet) (Eurostat online data code: sdg_13_50 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG13 and other SDGs. Synergies indicate that progress of SDG13 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG13 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 13.6: Interlinkages of SDG 13 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 13.7: ODA to SDG 13 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator shows the global yearly mean surface seawater acidity expressed as pH value. A decline in pH corresponds to an increase in the acidity of seawater and vice versa.

Figure 14.1: Global mean surface seawater acidity, 2010-2021
(pH value)

Source: EEA, Copernicus Marine Service (Eurostat online data code: sdg_14_50 )

This indicator shows the extent of eutrophic marine waters in the Exclusive Economic Zone. An area is classified as eutrophic if for more than 25% of the observation days of a given year the chlorophyll concentrations are above the 90th percentile of the 1998-2017 reference base line. Due to the variability of the annual figures, the data are also presented as a 4-year moving average.

Figure 14.2: Marine waters affected by eutrophication, EU, 2010-2022
(km²)

Source: Mercator Ocean International, Copernicus Marine Service (Eurostat online data code: sdg_14_60 ) 

This indicator shows the share of bathing sites with excellent water quality. Bathing water quality is assessed according to standards for microbiological parameters.

Figure 14.3: Bathing sites with excellent water quality, by locality, EU, 2011-2021 (% of bathing sites)

Note: EU data refer to 22 Member States for coastal water (no data for landlocked countries) and 25 Member States for inland water (no data for Cyprus and Malta).

Source: EEA (Eurostat online data code: sdg_14_40 ) 

This indicator measures the surface of marine protected areas in EU marine waters. Marine protected areas comprise nationally designated protected areas and Natura 2000 sites.

Figure 14.4: Marine protected areas, EU, 2012-2021
(% of marine area)

Note: Break in time series in 2021.

Source: EEA (Eurostat online data code: sdg_14_10 )

Fish stock biomass is a function of biological characteristics such as abundance and weight and can indicate the status of a fish stock. This is a model-based indicator.

Figure 14.5: Estimated trends in fish stock biomass, 2010-2021
(index 2003 = 100)

Note: Estimated data; Mediterranean and Black Sea data are only available until 2020.

Source: JRC/STECF (Eurostat online data code: sdg_14_21 )

The indicator shows the model-based value of current fishing mortality (F) relative to the estimated maximum sustainable yield (FMSY), expressed with the term F/FMSY. The maximum sustainable yield (MSY) is determined by the long-term average stock size that allows fishing at this level. Values below 1 indicate sustainable fishing levels (F ≤ FMSY).

Figure 14.6: Estimated trends in fishing pressure, 2010-2021
(model-based median value of fishing pressure (F/FMSY))

Note: Estimated data; Mediterranean and Black Sea data are only available until 2020.

Source: JRC/STECF (Eurostat online data code: sdg_14_30 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG14 and other SDGs. Synergies indicate that progress of SDG14 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG14 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 14.7: Interlinkages of SDG 14 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 14.8: ODA to SDG 14 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator measures the proportion of forest in comparison to the total land area. Data used for this indicator is derived from the Land Use and Cover Area frame Survey (LUCAS) but have been adapted to the FAO forest definitions.

Figure 15.1: Share of forest area, EU, 2009-2018 (% of total area)

Note: EU-23* refers to an aggregate including the UK but excluding Bulgaria, Croatia, Cyprus, Malta and Romania.

Source: Eurostat (online data code: sdg_15_10 )

This indicator estimates the increase in soil surfaces sealed with impervious materials due to development and construction.

Figure 15.2: Soil sealing index, EU, 2006-2018 (index 2006 = 100)

Note: Break in time series in 2018.

Source: EEA (Eurostat online data code: sdg_15_41 )

This indicator estimates the
area potentially affected by severe erosion by water such
as rain splash, sheet-wash and rills (soil loss > 10 tonnes per hectare per year).

Figure 15.3: Estimated severe soil erosion by water, EU, 2000, 2010 and 2016 (km²)

Source: Joint Research Centre (Eurostat online data code: sdg_15_50 )

This indicator measures the surface of terrestrial protected areas. The indicator comprises both nationally designated protected areas and Natura 2000 sites.

Figure 15.4: Terrestrial protected areas, EU, 2013-2021
(% of total area)

Note: Break in time series in 2021. 

Source: EEA (Eurostat online data code: sdg_15_20 )

This index integrates the abundance and the diversity of
a selection of common bird species associated with specific habitats. Rare species are excluded. Three groups of bird species are represented: common farmland species (39 species), common forest species (34 species) and all common bird species (167 species; including farmland and forest species).

Figure 15.5: Common bird index, by type of species, EU, 2010-2021
(index 2000 = 100)

Note: The composition of the EU aggregate changes depending on when countries joined the Pan-European Common Birds Monitoring Scheme.

Source: European Bird Census Council (EBCC)/BirdLife/Statistics Netherlands (Eurostat online data code: sdg_15_60 )

This indicator measures the population trends of 17 butterfly species at EU level. It is based on data from 18 EU Member States.

Figure 15.6: Grassland butterfly index, EU, 2010-2020
(index 2000 = 100)

Source: Butterfly Conservation Europe, European Butterfly Monitoring Scheme partnership, SPRING project (Eurostat online data code: sdg_15_61 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG15 and other SDGs. Synergies indicate that progress of SDG15 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG15 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 15.7: Interlinkages of SDG 15 with other goals

Source: Based on literature review by JRC`- - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 15.8: ODA to SDG 15 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

This indicator tracks deaths due to homicide and injuries inflicted by another person with the intent to injure or kill by any means, including ‘late effects’ from assault. It does not include deaths due to legal interventions or war.

Figure 16.1: Standardised death rate due to homicide, by sex, EU, 2010-2020
(number per 100 000 persons)

Note: 2010 are estimated; 2018 and 2019 data are provisional.

Source: Eurostat (online data code: sdg_16_10 )

This indicator shows the share of the population who reported facing crime, violence or vandalism in their local area.
This describes the situation where the respondent feels these issues to be a problem for the household, although this perception is not necessarily based on personal experience.

Figure 16.2: Population reporting occurrence of crime, violence or vandalism in their area, EU, 2010-2020 (% of population)

Note: Estimated data. The frequency of the data collection has been changed from annually to every three years, meaning no data were collected for 2021 and 2022.

Source: Eurostat (online data code: sdg_16_20 )

This indicator refers to the general government total expenditure on law courts. It includes expenditure on the administration, operation or support of civil and criminal law courts and the judicial system, including enforcement of fines and legal settlements imposed
by the courts.

Figure 16.3: General government total expenditure on law courts, EU, 2010-2021 (million EUR)

Source: Eurostat (online data code: sdg_16_30 )

This indicator is designed to explore respondents’ perceptions about the independence of the judiciary across EU Member States, looking specifically at the perceived independence of the courts and judges in a country.

Figure 16.4: Perceived independence of the justice system, EU, 2016-2022
(% of population)

Note: 2016–2020 data are estimated; break in time series in 2021.

Source: European Commission services, Eurobarometer (Eurostat online data code: sdg_16_40 )

This indicator is a composite index based on a combination
of surveys and assessments of corruption. It ranks countries based on how corrupt their public sector is perceived to be, with a score of 0 representing a very high level of corruption and 100 representing a very clean country.

Figure 16.5: Corruption Perceptions Index, EU, 2012-2022
(score scale of 0 (highly corrupt) to 100 (very clean))

Source: Transparency International (Eurostat online data code: sdg_16_50 )

This indicator measures confidence among EU citizens
in three EU institutions. It is expressed as the share of positive opinions (people who declare that they tend to trust) about the institutions.

Figure 16.6: Population with confidence in EU institutions, by institution, EU, 2010-2023 (% of population)

Note: 2010–2017 data are estimated.

Source: European Commission services, Eurobarometer (Eurostat online data code: sdg_16_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG16 and other SDGs. Synergies indicate that progress of SDG16 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG16 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 16.7: Interlinkages of SDG 16 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 16.8: ODA to SDG 16 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.

Official development assistance (ODA) is provided by governments and their executive agencies to support economic development and welfare in developing countries. ODA must be concessional in character, having a grant element that varies in proportion depending
on the recipient. Data for the EU include the 27 Member States’ ODA and EU institutions’ ODA not imputed to Member States.

Figure 17.1: Official development assistance as share of gross national income, EU, 2010-2021 (% of GNI)

Note: Breaks in time series for total ODA in 2018 and for ODA to least developed countries (LDCs) in 2020.

Source: OECD (Eurostat online data code: sdg_17_10 )

EU financing to developing countries takes a number of forms. These include: ODA,
other official flows, private flows, grants by non-governmental organisations, and officially supported export credits.

Figure 17.2: EU financing to developing countries, by financing source, EU, 2010-2021 (EUR billion, constant prices)

Source: OECD (Eurostat online data code: sdg_17_20 )

This indicator is defined as the value (at current prices) of EU imports from the countries on the Development Assistance Committee (DAC) list of ODA beneficiaries. It indicates to what extent products from these countries access the EU market.

Figure 17.3: EU Imports from developing countries, by country income group, EU, 2010-2022 (EUR billion, current prices)

Source: Eurostat (online data code: sdg_17_30 )

This indicator measures the
ratio of government debt at the end of the year to gross domestic product at current market prices. Government debt is defined as the total consolidated gross debt at nominal value in the following categories: currency and deposits, debt securities and loans.

Figure 17.4: General government gross debt, EU, 2010-2022
(% of GDP)

Source: Eurostat (online data code: sdg_17_40 )

Environmental taxes are defined as taxes that are based on a physical unit (or proxy of it) of something that has a proven, specific negative impact on the environment. The indicator includes taxes on energy, transport, pollution and resources.

Figure 17.5: Share of environmental taxes in total tax revenues, EU, 2010-2021 (%)

Source: Eurostat (online data code: sdg_17_50 )

The indicator measures the
share of households with a fixed very high capacity network (VHCN) connection. VHCN means either a network that consists entirely of optical fibre elements, or a network capable of delivering similar performance.

Figure 17.6: High-speed internet coverage, by type of area, EU, 2013-2021
(% of households)

Source: European Commission services, Eurostat (online data code: sdg_17_60 )

The figure shows positive (synergies) and negative (trade-offs) interactions between SDG17 and other SDGs. Synergies indicate that progress of SDG17 may contribute or enable progress on the other connected SDGs. Trade-offs indicate that the achievement of SDG17 may have negative effects and deteriorate progress towards the other linked SDGs.

Figure 17.7: Interlinkages of SDG 17 with other goals

Source: Based on literature review by JRC - Interlinkages - targets | KnowSDGs (europa.eu)

Figure 17.8: ODA to SDG 17 and interlinkages with other goals

The figure shows interlinkages among SDGs measured by number of projects reported, in the terms described in the figure under SDG1.