This chapter surveys the demographic, economic and environmental trends shaping rural Ireland, providing the basis for policy assessment and recommendations in the following chapters. It begins by defining rural areas according to the OECD and Irish classifications, which offer respective advantages for international comparability and context-specific granularity. It then expands the analysis on key trends by identifying the enabling factors for long-term rural competitiveness: innovation capacity, digital infrastructure, and access to education and services. It concludes by highlighting the need to leverage rural Ireland’s growth potential while addressing structural imbalances.
Rural Policy Review of Ireland 2026
Abstract
Key points
The Central Statistics Office (CSO) six-way urban-rural split offers a more granular picture of rural diversity by distinguishing degrees of urban influence (high, moderate, highly rural/remote). It reveals intra-regional variation invisible in the OECD typology and supports place-based policymaking.
Ireland’s rural regions defy OECD ageing trends, offering a short-term demographic advantage that must be harnessed through labour market and skills policies.
Economic strength remains uneven: proximity to urban centres drives rural success, whereas remote areas require targeted investment and diversification.
Rural skills and education outcomes are improving, but lifelong learning participation gaps persist between accessible and remote regions.
Environmental pressures are acute and rural-centred; while renewable deployment is advancing, sustained investment and continued progress in agriculture are critical to meeting climate goals.
Territorial disparities in healthcare and service access persist despite comparable provider ratios, underscoring the need for improved rural connectivity and service models.
Ireland’s rural areas perform better than many OECD counterparts – economically dynamic, demographically young and benefitting from proximity to urban centres. Yet spatial disparities persist between near-urban and remote rural areas. High emissions, digital lag and ‑ distortions complicate the picture. The evidence underscores the need for spatially differentiated, place based rural policies that leverage urban linkages, innovation capacity and targeted infrastructure investment to sustain balanced growth.
This chapter establishes the analytical foundation for the review. It is organised as follows: the first section examines how rural Ireland is defined and benchmarks its regions against comparable OECD countries. The second explores demographic patterns and population dynamics. The third assesses economic performance and competitiveness. The fourth section reviews environmental pressure, as pertaining to emissions, water, energy and biodiversity. The fifth analyses accessibility of services and social outcomes. The sixth discusses enabling factors.
The final section examines the underlying factors that shape long-term rural competitiveness: innovation, digital infrastructure and skills. These enablers determine how effectively rural regions can adapt to structural change, attract investment and participate in higher-value activities. Together, these sections show that Ireland’s rural areas remain demographically vibrant and economically dynamic, yet spatial inequalities persist, driven by structural dependence on agriculture, uneven digital and service access, and environmental pressures.
Rural regions play an important role in Ireland’s territorial, demographic and economic landscape. With a total land area of 68 655 square kilometres (km²) and an average population density of 76.8 people per km² in 2023, Ireland has a large share of its population living outside major metropolitan areas. According to the OECD regional typology that is applied to Territorial Level 3 (TL3) regions1, approximately 42.5% of Ireland’s 5.3 million residents lived in rural areas in 2023, slightly above the OECD average of 41.5% in 2022. This typology classifies three of Ireland’s eight TL3 regions as non-metropolitan near a small functional urban area (FUA) and two as non-metropolitan remote regions. Unlike many OECD countries, where rural population growth has stagnated, Ireland’s rural population has grown steadily, with a growth rate of 1.19% between 2001 and 2021, the second-highest in the OECD after New Zealand.2 From an economic standpoint, rural regions across Ireland have also recorded above-average gross domestic product (GDP) growth over the past two decades, with those near small FUAs benefitting the most, reflecting the advantages of greater accessibility to urban centres.
Understanding these territorial and economic differences requires appropriate classifications. While international definitions are essential for benchmarking trends across countries, they can overlook important internal variations and more context-specific dynamics. To address this, many countries also apply national definitions of rural areas that offer greater granularity and are better adapted to domestic policy needs. In Ireland, various agencies and policy frameworks use different criteria depending on the purpose, such as census data analysis, spatial planning or the allocation of development funding. The Commission for the Economic Development of Rural Areas, which laid the foundation for the Action Plan for Rural Development, defines rural areas as those outside the administrative boundaries of the five main cities: Cork, Dublin, Galway, Limerick and Waterford. In contrast, the CSO adopts a narrower definition, considering all areas outside towns with fewer than 1 500 inhabitants as rural.
While these national definitions serve specific domestic purposes, cross-country comparisons and the analysis presented in this report rely primarily on the OECD regional typology. This typology remains the main framework for distinguishing “metropolitan” from “non-metropolitan” regions across countries, based on population size, population density and proximity to large urban centres (see Annex Table 2.A.1). It classifies regions according to specific thresholds and their level of access to FUAs (see Annex Box 2.A.1). Definition of a functional urban area. For the purposes of this report, the terms “non-metropolitan” and “rural” are used interchangeably. When applied to Ireland, the typology classifies the following regions (Figure 2.1):
Large metropolitan regions (MR-L): Dublin and Mid-East
Medium-sized metropolitan regions (MR-M): South-West
Rural regions near small FUAs (NMR-S): Mid-West, South-East and West
Rural remote regions (NMR-R): Midland and Border.
The OECD regional typology is used consistently throughout this report, particularly for benchmarking Ireland’s rural regions against those of other OECD Member countries. This approach ensures international comparability and analytical consistency. Where data at the TL3 level are not available, the analysis draws on the degree of urbanisation classification, which classifies smaller areas as cities, towns and semi-dense areas or rural areas (see Annex Box 2.A.2).
In Chapter 3, this analysis is complemented by Ireland’s six-way rural-urban classification, developed by the CSO to better reflect the diversity of rural and small-town dynamics within the country (CSO, 2019[1]). Because this granular framework is specific to Ireland and not easily comparable across countries, it is used here to supplement the analysis with additional insights relevant for Ireland’s policy context. This more granular classification highlights the geographic spread of rural areas and captures variation within urban regions by distinguishing cities, satellite towns and independent towns based on employment and service patterns. It allows for a more nuanced understanding of rurality, considering not only population size but also the degree of urban influence, particularly in terms of access to services and commuting patterns.
The six-way rural-urban split provides a more granular view of rurality in Ireland by focusing on small geographic areas rather than broader administrative regions (Figure 2.2). In contrast, the OECD regional typology classifies entire TL3 regions as metropolitan or non-metropolitan based on population size and proximity to large urban centres. For example, large metropolitan regions (MR-L) are broadly considered urban, as they correspond to regions where at least 50% of the population resides in an FUA with more than 1.5 million inhabitants. However, the six-way split reveals important internal variation within this metropolitan region, identifying 664 rural areas with high urban influence, 304 with moderate urban influence and 37 classified as highly rural (see Table 2.1), areas that would otherwise be overlooked using the OECD typology alone. It also highlights the geographic concentration and variation of rural areas across regions. For example, Dublin contains only two rural areas with moderate urban influence and no rural remote areas, while the Mid-East includes 202 areas with moderate urban influence and 37 classified as highly rural or remote. These distinctions are critical for policymaking, particularly when designing targeted, place-based interventions that respond to varying degrees of rurality.
The OECD consistently encourages Member countries to strengthen and make the most of rural-urban linkages as given the mutual benefits and complementarities (OECD, 2013[2]). While rural areas are widely acknowledged as diverse, the same holds true for urban areas. The six-way classification helps capture this variability by distinguishing between cities, satellite towns and independent towns. This added granularity provides valuable context for understanding and leveraging the functional relationships between rural and urban areas, particularly in terms of access to services, economic activity and local development dynamics.
Note: This table presents the number of Irish small areas falling under each of the six regional types defined by the national classification framework, disaggregated by the eight OECD TL3 regions. In this framework, urban areas comprise Cities with 50 000 or more people, Satellite urban towns with 1 500-49 999 people where at least 20% of workers commute to cities, and Independent urban towns of similar size where fewer than 20% commute to cities. Rural areas are distinguished as those with high urban influence, where at least 15% commute to towns or cities, those with moderate urban influence, where commuting is below 15%, and highly rural or remote areas, defined by low population density and minimal commuting links.
Source: CSO Ireland, DATA.GOV.IE.
Identifying strengths and gaps in Ireland in a regional international context
To support international comparisons, the analysis includes 14 benchmark countries that are broadly comparable to Ireland in terms of the level of development (based on GDP per capita) and population size. These countries are Austria, Belgium, Czechia, Denmark, Estonia, Finland, Iceland, Latvia, Lithuania, the Netherlands, New Zealand, Norway, Slovenia and Sweden. The analysis also benchmarks the performance of rural regions in Ireland to rural regions in all OECD countries across several indicators to provide a broader comparative perspective.
Figure 2.3 compares Irish regions and corresponding average values of the benchmark countries. The figure shows percentage deviations across a range of economic, environmental and digital infrastructure indicators, disaggregated by regional typology. This snapshot helps identify areas where Ireland’s regions are performing above or below the average of its peers and provides insight into persistent regional disparities.
The key findings are summarised below:
Rural Ireland performs well on the main economic indicators such as GDP per capita, with rural areas near small FUAs standing out. Irish regions, particularly Dublin, the South-West and rural regions close to small FUAs, exhibit strong performance in GDP per capita and labour productivity relative to the average of similar region types in the benchmark countries.3 However, this strong performance is not evenly shared. The Mid-East falls slightly below its benchmark despite its proximity to Dublin, while rural remote regions like the Midland and Border also underperform. In contrast, rural areas near small FUAs show the strongest relative performance in disposable income per capita, likely benefitting from a mix of urban access and relative affordability.
Unemployment is substantially lower across rural Ireland, while employment rates are also strong in rural areas near small FUAs. In recent years, both indicators have improved markedly (Annex Figure 2.A.1), with employment rates in most cases surpassing those of the benchmark regions and, in the case of rural remote regions, closing earlier gaps. These gains reflect the mobilisation of underutilised segments of the workforce, yet further opportunities remain, especially for women and those with below upper secondary education.
Greenhouse gas (GHG) emissions per capita are high in rural regions, while electricity production shows low carbon intensity. Across nearly all Irish regions, GHG emissions per capita are higher than in the benchmark group, except in Dublin, which performs better than its metropolitan peers. This reflects Ireland’s dispersed rural settlement pattern, with higher-than-average car dependency in less urbanised areas, as well as above-average emissions from its large agricultural sector. Despite this, several regions – including the Mid-East, South-East and Border – stand out for their strong performance in green electricity generation compared to similar regions abroad, indicating progress in transitioning to renewable energy sources. On the digital front, Internet broadband speed remains below the average for comparable regions, particularly in rural remote areas and the South-West.
Internet broadband speed lags in rural Ireland, while the share of employment in public administration is higher in rural areas near small FUAs. Mean fixed download speeds are lower in rural regions than in the benchmark countries, with the exception of the Mid-West.4 Remote regions such as the Midland and Border record particularly slow broadband, highlighting persistent digital infrastructure gaps. In addition, rural areas near small FUAs show a higher share of employment in public administration compared to rural remote areas, pointing to a comparatively stronger dependency on the public sector.
Comparison of key indicators (latest year), as percentage deviation from benchmark countries (blue = better, red = worse)
Note: Indicators refer to 2021 (Internet broadband speed in Fourth Quarter (Q4) of 2021), except for carbon intensity of electricity generation (2019), unemployment rate (2023) and employment rate (2024). Values for Irish regions are compared to a country-weighted average of benchmark regions within the same regional typology. For example, Dublin’s GDP per capita is compared to the average GDP per capita of MR‑L regions across benchmark countries, with each country contributing equally to the average regardless of size. Regional typology values are generally aggregated using indicator-specific weights: for instance, GDP per capita is weighted by each region’s GDP and population to produce a weighted average. Exceptions include employment rate, unemployment rate and carbon intensity, for which simple regional averages are used. GDP per capita, labour productivity and disposable income are measured in USD in constant prices and purchasing price parity (PPP) with 2015 as the base year. The benchmark includes 13 countries (excluding Iceland) for GDP per capita, labour productivity, and Internet broadband speed; 12 countries (excluding Austria and Iceland) for disposable income, share of employment in public administration and economic diversification (Herfindahl-Hirschman Index); 11 countries for employment rate (excluding Finland, Iceland and the Netherlands); 8 countries (excluding Austria, Denmark, Finland, Iceland, the Netherlands and Norway) for unemployment rate; and all 14 countries for GHG emissions per capita and carbon intensity of electricity generation. Since not all benchmark countries include all regional typologies, the comparison group may vary by indicator – for example, only 6 of the 13 countries in the GDP per capita benchmark have MR-L regions. For interpretative consistency, the direction of deviation was reversed for unemployment rate, share of employment in public administration, economic diversification, GHG emissions per capita and carbon intensity of electricity generation.
Source: Based on the OECD Regional Database.
To summarise, rural Ireland performs strongly on core economic indicators, with rural areas near small FUAs standing out in particular. Both unemployment and employment rates have improved substantially in recent years and now surpass those of benchmark regions, with rural remote areas in particular closing earlier gaps in employment. These gains highlight Ireland’s recent success in mobilising more of its potential workforce, though further scope remains among women and those with below upper secondary education. On the environmental side, per capita GHG emissions are high in rural regions, reflecting car dependency and the large role of agriculture, although progress in renewable electricity generation is notable. Digital infrastructure continues to lag, especially in rural remote areas, and rural areas near small FUAs show an unusually high reliance on public administration compared to rural remote regions.
According to the OECD regional typology, 42.5% of Ireland’s population lived in rural regions in 2022 (Figure 2.4). Within this group, 28.1% lived in rural regions near small FUAs (NMR-S), nearly double the share (14.3%) living in rural remote regions (NMR-R). The six-way rural-urban split estimates a lower share (36.4%) of the national population living in rural areas. Of these, 15.9% live in areas with high urban influence, 11.9% in areas with moderate urban influence and 8.6% in highly rural or remote areas. This difference reflects the fact that even regions classified as rural under the OECD typology can include small urban areas, a nuance better captured by the six-way split.
Population distribution according to OECD typology and Irish six-way split in 2022
Sources: Based on the OECD Regional Database and CSO (2022[3]), Census of Population 2022, https://www.cso.ie/en/statistics/population/censusofpopulation2022/.
Population growth and youth premium
Ireland is amongst OECD countries recording the highest population growth in both urban (1.4%) and rural (1.2%) regions between 2001 and 2021. When compared to the 14 benchmark countries, Ireland stands out for its strong population growth in rural regions (Figure 2.5), only surpassed by New Zealand. It shows a very small gap in the rate of population growth between rural and urban regions, similarly to Belgium and New Zealand. Ireland also has the lowest old-age dependency ratio among the benchmark countries in 2021 (Figure 2.6), reflecting a relatively young population in rural areas. This youth premium suggests a strong labour force potential. Notably, the gap in the old-age dependency ratio has remained largely stable over time.
Population change from 2001-2021
Note: Population changes are expressed as the compound annual growth rate (CAGR). Changes were calculated by summing population of TL3 regions within each country and distinguishing between metropolitan and non-metropolitan regions. Data cover 15 OECD countries and measure growth from 2001 to 2021.
Source: Based on the OECD Regional Database.
Old-age dependency ratio in 2021, in absolute values, and change from 2001-2021
Note: The graph displays the old-age dependency ratio in 2021 across 14 OECD countries (left axis), alongside the CAGR over the period 2001‑2021 (right axis). The old-age dependency ratio is defined as the number of people aged 65 or older per 100 people of working age (20‑64). Values were calculated using a population-weighted approach by summing the population by age across TL3 regions within each country.
Source: Based OECD Regional Database.
The strong population growth in Ireland’s regions was driven primarily by natural increase, with fertility rates among the highest (Annex Figure 2.A.2) and mortality rates among the lowest (Annex Figure 2.A.3) across benchmark countries. Life expectancy is also comparatively high, ranking third among the benchmark group (Annex Figure 2.A.4). In Ireland, these patterns are broadly similar across rural and urban regions, helping to explain the small differences in overall population growth. Migration has generally contributed to population growth, though its impact has fluctuated over time. Between 2011 and 2016, inflows slowed sharply, with rural regions even experiencing a net outflow. Since 2016, however, migration has rebounded, bringing strong net inflows across both rural and urban regions (Figure 2.7).
Population change broken down in natural increase and net migration between 2006-2022
Note: Natural increase is the difference between the number of births and deaths, while net migration is the difference between people moving into and out of a region. Together, these two components account for the total change in population. Figures were aggregated across TL3 regions to reflect a population-weighted trend for each regional type. The values shown for each period represent the percentage change in population relative to the base year. For example, for the period 2006‑2011, the overall population increased by X%, of which Y percentage points (p.p.) were due to natural increase and Z p.p. due to net migration.
Sources: Based on the CSO Census 2011, 2016 and CSO (2022[3]), Census of Population 2022, https://www.cso.ie/en/statistics/population/censusofpopulation2022/. .
In recent years Ireland has experienced strong inflows of both domestic and immigrant individuals reversing a very long period of rural outmigration. This can be largely attributed first to the effects of the Coronavirus disease 2019 (COVID-19) pandemic that caused urban dwellers to relocate to rural areas, especially those with high amenities when teleworking was encouraged. Second, the war in Ukraine led to an influx of refugees to rural areas where there was relatively more underutilised housing than in the larger urban centres. In both instances this influx was largely composed of younger people, which reduced the old-age dependency ratio.
Population projections and shifting age structures
With life expectancy, fertility rates and mortality rates in Ireland outperforming those of benchmark countries, population projections for Ireland point to several key trends:
Rural population growth is expected to continue in the coming decades, with particularly strong growth anticipated in rural remote regions (Annex Figure 2.A.5).
The old-age dependency ratio is projected to rise further, especially in large metropolitan areas (Annex Figure 2.A.6).
Ireland’s youth premium is expected to gradually decline, as the working-age population continues to age over time (Annex Figure 2.A.7).
Ireland’s urban system is highly centralised, with the Dublin region home to almost one-third of the population and acting as the main centre of economic, cultural and political activity. Other cities such as Cork, Galway, Limerick and Waterford remain much smaller in scale, so the country lacks a polycentric city structure. As a result, population growth has been concentrated in and around Dublin, although rural areas continue to represent a significant share of the population.
In sum, population trends in Irish rural regions go counter to the trends in other OECD countries, with a youth and demographic premium. This represents an important asset in the form of a potential labour force if mobilised correctly. The premium however will gradually disappear, since population projections predict an increase in the old-age dependency ratio. This increase, however, will be much smaller when compared to other OECD countries’ projections (Figure 2.8).
Share of population aged 65 and over, 2040 projection
Note: Metropolitan regions include both MR-L and MR-M. Regions near metropolitan areas correspond to NMR-M, while regions far from metropolitan areas include both NMR-S and NMR-R.
Source: OECD (2022[4]), OECD Regions and Cities at a Glance 2022, https://doi.org/10.1787/14108660-en.
Benchmarking the economic performance of Irish rural regions is essential for assessing territorial imbalances and identification areas of growth potential. Unlike many OECD countries facing rural population decline and accelerated ageing, Ireland’s rural regions currently benefit from a youth and demographic premium, with comparatively younger populations that represent an important potential labour force if mobilised effectively. This advantage, while expected to diminish gradually as the old-age dependency ratio rises, remains stronger than in most OECD countries and could play a key role in driving rural and national economic development. At the same time, structural trends – such as automation, digitalisation and the green transition – are opening new rural opportunities, that can support a broader rural economy that is currently specialised in agriculture, other natural resources and tourism, but has new opportunities in manufacturing and services.
Currently, economic activity remains heavily concentrated in metropolitan regions. Ireland’s unique FDI structure, dominated by large multinational branch plants, disproportionally benefits larger cities, leading to a concentration of recorded economic activity in urban areas relative to their population share. This underscores the importance of enabling rural areas to better capture investment and strengthen their contribution to national growth.
Rural regions in Ireland account for 42.5% of the population but contribute only 20.3% of national GDP, highlighting a notable gap between their demographic and economic weight. Among the 14 benchmarked countries, Ireland records the lowest rural GDP share and ranks in the bottom third for the share of national territory covered by rural regions (Figure 2.9).
Share of GDP, population, and land area by type of region in 2021
Note: Values were aggregated by regional typology to produce weighted averages for each country. GDP is measured in USD in constant prices and PPP with 2015 as the base year.
Source: Based on the OECD Regional Database.
The low share of GDP contributed by rural Ireland reflects the distorting effect from the oversize role of foreign multinational branch manufacturing and service entities that mostly locate in larger cities and suburban areas. This effect is well recognised by statistical offices, which has led to the use of alternative indicators to account for the resulting bias (see Box 2.1). From an analytical perspective, it is important to remain aware of these distortions, particularly when assessing indicators such as GDP, productivity or patent activity.
Ireland’s national economic statistics are heavily influenced by the activities of large multinational enterprises, many of which have their European headquarters located in urban centres. These headquarter effects – such as profits being booked in cities where little actual production occurs – can distort regional economic indicators and contribute to the relatively low recorded GDP share of rural regions in Ireland when compared to benchmark countries (Figure 2.10).
Notes:
1. Flash GDP estimate for Q4 2024. Modified domestic demand excludes large transactions of foreign corporations that do not have a big impact on the domestic economy.
2. Exports refer to both goods and services.
Source: OECD (2025[5]), OECD Economic Surveys: Ireland 2025, https://doi.org/10.1787/9a368560-en.
Different measures to capture the underlying dynamics of the domestic Irish economy
To better capture the underlying dynamics of the Irish economy, the CSO publishes some alternative metrics, such as modified domestic demand (MDD) and modified gross national income (GNI*). The former measures domestic demand, but excludes volatile components of investment spending by multinationals, namely on-shored intellectual property assets and investment in aircraft for leasing, which have very little impact on the domestic economy. GNI* goes further by taking into account the modified current account (CA*), which reflects the import content of domestic expenditure, and the contribution to growth from domestic trade and cross-border flows (excluding multinational-dominated trade), thereby allowing for a comprehensive assessment of productivity trends. In particular, relative to the standard GNI, GNI* excludes the factor income of firms that have re-domiciled their headquarters to Ireland, as well as the depreciation of trade in on-shored intellectual property assets, research and development (R&D) service imports and aircraft owned by aircraft-leasing companies (OECD, 2025[5]).
Because the GNI* and MDD are not reported at a regional level, this report makes use of alternative indicators – such as net disposable income – to assess regional performance. This allows for a more grounded comparison across Irish regions and helps to mitigate the distorting effects of multinational-dominated GDP figures.
Source: OECD (2025[5]), OECD Economic Surveys: Ireland 2025, https://doi.org/10.1787/9a368560-en.
Economic performance and rural disparities
The performance of Irish rural regions in terms of GDP per capita and labour productivity from 2001 to 2021 shows that rural regions near small FUAs (NMR-S) outperformed peers in the 14 benchmark countries for both indicators. In contrast, Ireland’s rural remote regions (NMR-R) were outpaced, particularly in terms of labour productivity growth (Figure 2.11).
At the same time, both types of rural regions in Ireland experienced stronger population growth (and associated increases in labour input) than those in the 14 benchmark countries. However, rural regions near small FUAs have been more successful in translating this growth into higher economic output and productivity than their rural remote counterparts. Moreover, Ireland’s rural remote regions were also outperformed in GDP per capita and labour productivity growth by rural remote regions in comparable OECD countries.
Indicator growth from 2001-2021
Note: Changes are expressed as the CAGR. For each country, the growth rate is calculated by aggregating the values of all TL3 regions according to their regional typology. The benchmark average is a simple country-weighted average. The benchmark group includes: Austria, Belgium, Czechia, Denmark, Estonia, Finland, Latvia, Lithuania, the Netherlands, New Zealand, Norway, Slovenia and Sweden. Due to data availability, 2003 is used as the starting year for Belgium and 2008 for Norway for the indicators GDP per capita and labour productivity. GDP per capita and labour productivity are both measured in USD in constant prices and PPP with 2015 as the base year.
Source: Based on the OECD Regional Database.
Irish rural regions near small FUAs recorded the second-highest GDP growth rate among all OECD countries over the past two decades – trailing only Türkiye – while rural remote regions ranked eighth. Ireland also shows the widest gap in GDP growth performance between these two rural subtypes, underscoring the divergent economic trajectories within its rural areas (Figure 2.12). This divergence reflects the advantages of proximity to urban centres, including stronger infrastructure links, access to labour markets and spillover effects from higher-value economic activity. When compared to the benchmark countries, rural regions close to urban centres in Ireland appear particularly competitive, while rural remote regions lag more strongly, highlighting sharper contrasts in economic performance than elsewhere in the group.
GDP change from 2001-2021
Note: Changes are expressed as the CAGR. The average figures inside countries are weighted by the respective regions’ GDP. For Korea, GDP growth is measured from 2012 onward due to data availability. GDP is measured in constant prices and PPP with 2015 as the base year.
Source: Based on the OECD Regional Database.
Economic perceptions and income gaps across regions
The strong economic performance in Ireland’s rural regions – particularly those near small FUAs – is also reflected in how residents perceive their local economies. According to Gallup’s Local Economic Confidence Index, which combines responses about current local economic conditions and expectations for future improvements, Ireland ranks 7th among 38 OECD countries in terms of rural economic optimism (Figure 2.13). Notably, the perception gap between urban and rural areas is relatively small in Ireland, with rural residents reporting levels of confidence similar to those in cities, while towns and semi-dense areas trail slightly. These results suggest that the economic gains observed in rural areas, especially those with better access to urban centres, are not only measurable in terms of GDP but also felt by residents on the ground.
While not uniformly strong, income data partly reflect this positive perception. Rural regions in Ireland rank 6th highest in disposable income among the 13 benchmark countries, while metropolitan regions rank 4th (Figure 2.14). This indicates that, while rural income levels are relatively high, they underperform slightly if compared to Ireland’s metropolitan regions in an international context. Within Ireland, disposable income has increased across all regions, with the most pronounced growth observed in Dublin, widening the gap between the capital and rural remote regions, such as Midland and Border (Figure 2.15). In contrast, rural regions near small FUAs report income levels that are broadly in line with metropolitan regions, excluding Dublin. This income pattern mirrors the earlier divergence in GDP growth, further reinforcing the economic benefits associated with proximity to urban centres.
Local Economic Confidence Index in 2022, as an index score from -100 to 100
Note: Gallup’s Local Economic Confidence Index is based on the combined responses to two questions asking respondents, first, to rate economic conditions in their city today and, second, whether they think economic conditions in their city as a whole are getting better or getting worse. The average figures inside countries are weighted by design weights calibrated to age, gender and education or socio-economic status at the national level.
Source: Based on Gallup data and interim result from the project Regional Development Along the Settlement Network.
Disposable income per capita in 2021 or latest year
Note: The graph displays disposable income per capita for metropolitan and non-metropolitan regions in 13 OECD countries for 2021 or the latest available year (2018 for Estonia, Latvia and Lithuania). Disposable income per capita is measured in USD using constant prices and PPP with 2015 as the base year.
Source: Based on the OECD Regional Database.
Disposable income per capita for 2021
Note: Disposable income per capita is measured in USD using constant prices and PPP with 2015 as the base year.
Source: Based on the OECD Regional Database.
Economic structure and sectoral specialisation
As in all rural areas, Ireland’s rural economy largely lacks economies of agglomeration, leaving it more reliant on primary activities (including agriculture), tourism and small and medium-sized enterprises (see Chapter 3 for further discussion). A comparison of the specialisation profile of rural Ireland with that of other OECD countries reveals the following trends:
In line with patterns observed across benchmark countries, rural regions in Ireland display a higher employment share in agriculture, forestry and fishing compared to metropolitan areas. Rural remote regions are particularly specialised in this sector, with 8.8% of employment in agriculture, close to the OECD average of 9.1%. This places Ireland 5th among benchmark countries in terms of agricultural employment in both rural remote regions and those near FUAs (Figure 2.16).
Compared to the OECD average, Ireland’s rural regions have a lower share of employment in manufacturing. In rural regions near FUAs, 14.7% of the workforce is employed in the sector, compared to 16.7% across the OECD. The share falls to 13.2% in rural remote regions, slightly below the OECD average of 14.3% (Figure 2.17). Among benchmark countries, Ireland ranks in the lower half for manufacturing employment in rural areas. Rural regions near FUAs exhibit a higher degree of manufacturing specialisation than rural remote regions.
In public administration, Ireland’s rural regions report slightly higher employment shares than the OECD average for rural areas. The share reaches 28.8% in rural remote regions and 27.7% in rural regions near FUAs (Figure 2.18). Overall, Ireland ranks near the middle among benchmark countries in terms of rural employment in this sector.
Share relative to total employment in 2021
Note: The average figures inside countries are weighted by the respective regions’ population. The OECD average is a simple average across 30 OECD countries with available data. Metropolitan regions include both MR-L and MR-M. Rural close to an FUA corresponds to NMR-M and NMR-S, while rural remote include NMR-R. Agriculture, forestry and fishing refers to section A of Statistical Classification of Economic Activities in the European Community (NACE) Rev.2.
Source: Based on the OECD Regional Database.
Share relative to total employment in 2021
Note: The average figures inside countries are weighted by the respective regions’ population. The OECD average is a simple average across 29 OECD countries with available data. Metropolitan regions include both MR-L and MR-M. Rural close to an FUA corresponds to NMR-M and NMR-S, while rural remote include NMR-R. Manufacturing refers to Section C of NACE Rev.2.
Source: Based on the OECD Regional Database.
Share relative to total employment in 2021
Note: The average figures inside countries are weighted by the respective regions’ population. The OECD average is a simple average across 27 OECD countries with available data. Metropolitan regions include both MR-L and MR-M. Rural close to an FUA corresponds to NMR-M and NMR-S, while rural remote include NMR-R. Public administration refers to activities in Sections O-Q of NACE Rev.2, covering public administration, defence, education, human health and social work.
Source: Based on the OECD Regional Database.
Regional patterns of foreign direct investment
These sectoral patterns are also reflected in the spatial distribution of FDI, which plays an important role in shaping regional employment opportunities and industrial capacity. Between 2003 and 2022, the majority of FDI was concentrated in large metropolitan regions, accounting for around 60% of total capital expenditure. However, rural regions near FUAs and rural remote regions have also attracted notable FDI flows, particularly in manufacturing, construction and R&D (Figure 2.19). Around two-thirds of these investments originated from the United Kingdom and the United States, with the most common sectors, including information and communication technology (ICT) and electronics, financial services, life sciences and professional services. In some cases, FDI figures are driven by large-scale investments from single companies, as seen in the Mid-East, which recorded USD 13 billion in FDI in 2022 alone, entirely due to Intel’s investment in semiconductor manufacturing.
In sum, this section finds that foreign subsidiary operations play a dual role in Ireland: they contribute to economic activity through investment, jobs and tax revenues, while at the same time distorting key performance indicators. In particular, the profits and intellectual property of multinational firms are often recorded in Ireland even though the underlying production or research activity takes place elsewhere, which inflates measures such as GDP, GDP per capita and productivity, and makes them appear stronger than the domestic economy experienced by households and local firms. When comparing performances, rural regions near FUAs generally appear to perform better than rural remote regions; however, such comparisons should be interpreted with caution, as headquarter effects may inflate economic indicators in certain areas. Manufacturing remains a key component of the rural economy, and rural Irish regions have attracted substantial FDI in manufacturing, construction and R&D. Rural regions also remain more specialised in agriculture, forestry and fishing than in other OECD countries, with rural remote regions showing particularly high specialisation in these sectors, as well as in public administration.
Distribution of FDI by sector and region in Ireland from 2003-2022
Note: FDI data from 2003-2022 were aggregated at the TL3 regional level, and sectoral shares were then calculated based on this total.
Source: Based on the OECD Regional Database.
In 2023, Ireland’s total GHG emissions stood at approximately 57 megatonnes of carbon dioxide equivalent (MtCO2eq), roughly the same level as in 1990. Emissions rose steadily to a peak in 2001, driven mainly by rapid growth in transport and agriculture, before declining through 2014. A short rebound in 2015-2016, linked to post-recession recovery, contributed to Ireland missing its 2020 climate target under the EU Effort Sharing Decision, which required a 20% reduction in non-EU-emissions-trading (ETS) emissions (transport, agriculture, buildings and waste) relative to 2005. Instead, non-ETS emissions were only around 9% lower.
Sectoral shifts have reshaped the emissions profile. Agriculture remains the largest source (around 37% in 2023), followed by transport and energy, though emissions from power generation have fallen sharply with the expansion of renewables. Population growth has helped reduce per capita emissions from 16.3 tonnes in 1990 to 11.4 in 2023, a decline of about 30%. Despite this progress, Ireland continues to rank among the highest emitters in Europe: in 2022, emissions stood at 11.7 tCO2eq per capita, the second-highest in the EU Member countries and about 56% above the EU average of 7.5 tonnes (CSO, 2025[6]).
Looking ahead, the 2019 Climate Action Plan sets a 25% cut in non-ETS emissions by 2030, relative to 2005. Achieving this will require accelerating renewable deployment, decarbonising transport and agriculture and ensuring consistent implementation of carbon pricing and investment measures. Delays to date highlight the challenge of aligning sectoral policies with long-term targets.
GHG emissions at the subnational level were estimated using the using the Emissions Database for Global Atmospheric Research (EDGAR) version 8.0 developed by the European Commission Joint Research Centre and International Energy Agency. EDGAR provides annual sector-specific grid maps for the four GHGs (carbon dioxide [CO2], methane [CH4], nitrous oxide [N2O] and fluorinated gases [F‑gases]) at a 0.1 degree spatial resolution (approximately 11 km). The different sectors and subsectors covered are:
Energy: Power generation.
Industry: Combustion in manufacturing industry, oil refineries and transformation industry, chemical processes, fuel exploitation, iron and steel production, non-energy use of fuels, non‑ferrous metals and non-metallic minerals production, solvents and products use.
Transport: Ground transport: road, trains and off-road transport. Shipping and aviation are excluded in the subnational GHG estimates for the transport sector.
Building: Energy for buildings.
Agriculture: Agricultural soils, agricultural waste burning, enteric fermentation, manure management, indirect N2O emissions from agriculture.
Waste: Solid waste incineration, landfills, wastewater handling.
Emissions from land use and land cover change are not included. National GHG emissions are disaggregated by using subsector-specific geospatial proxies. GHG emissions are expressed in CO2 equivalents using 100-year global warming potential from the Intergovernmental Panel on Climate Change 5th Assessment Report (AR5), i.e. 28 for CH4 and 265 for N2O.
While emissions data provide valuable insight to production-based activities contributing to climate change, they do not account for the usage or consumption of goods or services that may occur outside of where the goods are produced. However, emissions-based data are widely available both geographically and historically, and nevertheless provide insight on attainment of climate change reduction goals.
Sources: EC JRC and IEA (2023), “EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database: IEA-EDGAR CO2, EDGAR CH4, EDGAR N2O, EDGAR F-GASES version 8.0”, European Commission, JRC (Datasets).
GHG emissions trends in Ireland
Regional contributions have played an important role in national emissions reductions. Historically, rural areas – particularly the Mid-West and South-West – have been among the highest emitters (Figure 2.20). The Mid-West has recorded the most substantial progress in recent years, with emissions falling sharply from approximately 16 MtCO2eq in 2016 to just over 10 MtCO2eq in 2023. While most other regions have followed more stable trajectories, many have also achieved gradual reductions since Ireland’s national emissions peaked in 2001. On a per capita basis, rural regions continue to report higher emissions than metropolitan areas, reflecting the strong role of agriculture, energy production and transport. However, rural remote and rural regions close to an FUA follow almost identical trajectories over time (Figure 2.21), suggesting that structural sectoral factors, rather than proximity to urban centres, drive the gap with metropolitan regions. Nonetheless, progress is evident: per capita emissions in the Mid-West fell by 34% between 2016 and 2023, with similar improvements in the Border and West.
GHG emissions from 1990-2023
Note: The figure provides the GHG in MtCO2eq emissions over time from 1990 to 2023.
Source: Based on the OECD Regional Database.
GHG emissions per capita from 1990-2023
Note: The figure provides GHG in tCO2eq emissions per capita over time from 1990 to 2023.
Source: Based on the OECD Regional Database.
Agriculture dominates the emissions profile of Ireland’s rural regions, especially in rural remote areas. In the Border region, agriculture accounts for 60% of total emissions, and in Midland 50% (Figure 2.22), largely reflecting the narrow regional economic base. Among rural regions close to an FUA, South-East shows a similar pattern, with agriculture making up 50% as well. These high shares highlight how rural emissions are structurally tied to livestock farming, leaving limited scope for offsetting reductions through other sectors.
By contrast, metropolitan regions present a different picture. The South-West, despite being classified as metropolitan, still records an unusually high agricultural share (49%), showing how sectoral structures can blur typology lines. Dublin is the clearest outlier: power generation accounts for 41% of its emissions compared with just 1% in the Border region. Yet Dublin’s per capita power emissions are low, thanks to its large population and more efficient infrastructure. In rural regions such as Mid-West and West, per capita energy-related emissions are much higher, reflecting dispersed energy systems and heavier reliance on fossil fuels (Figure 2.23).
Other sectors also reinforce the rural-urban divide. Manufacturing and buildings emissions are concentrated in metropolitan regions, but on a per capita basis, they tend to be higher in rural areas where industry plays a proportionally larger role in the economy. Waste emissions, conversely, are more prominent in urban centres due to population density and the resulting high volumes of municipal waste. Overall, these contrasts show that rural remote and rural regions close to an FUA share a structural dependence on agriculture, while metropolitan regions are more exposed to power generation and waste. This underlines the need for tailored mitigation: livestock and transport in rural regions, and energy and waste management in urban centres (Figure 2.22 and Figure 2.23).
Distribution of GHG emissions by sector and region in 2023
Note: GHG emissions data were aggregated by sector and represent the share of each sector in total emissions for each region.
Source: Based on the OECD Regional Database.
GHG emissions per capita by sector and region in 2023
Note: GHG emissions are expressed in tCO2eq per capita, shown by sector and region.
Source: Based on the OECD Regional Database.
Water quality
Agriculture is also a major contributor to water pollution in Ireland, particularly in terms of nutrient pollution, primarily driven by nutrient runoff from the expansion of dairy herds and intensive agricultural practices. This pollution, compounded by rising GHG and ammonia emissions, places growing pressure on aquatic ecosystems and contributes to biodiversity loss. From recent assessments, 46% of surface waters in Ireland are in poor condition, and only 52.8% meet good ecological status under the River Basin Management Plan.5 Rivers, estuaries and lagoons are most affected, while coastal waters are generally in better shape. Groundwater quality remains mostly good, although nitrate levels are rising. Strengthening the visibility of biodiversity as a distinct policy area, consistent with evolving EU requirements such as the Nature Restoration Law, would help clarify Ireland’s broader environmental response. Inadequate wastewater treatment, underinvestment in infrastructure and failing septic systems also contribute significantly to pollution and health risks.
Water quality trends in Ireland vary by region, with the South-East experiencing the most significant fluctuations and remaining the most affected overall. In 2020, the Mid-West, South-East and West regions saw improvements in water quality, likely due to decreased activity during the COVID-19 pandemic. However, from 2020 to 2022, there was a general rise in the use of boiled water across all three regions. Despite some recent improvements – particularly in the South-East and West between 2021 and 2022, possibly due to new policy measures – the South-East still has the highest proportion of households relying on boiled water. Notably, the West saw a sharp increase in boiled water use during this period, signalling an urgent need for intervention.
While initiatives under the Climate Action Plan, under the National Adaptation Framework, have made important strides, challenges in water infrastructure remain in Ireland. Centralisation under Irish Water has enhanced planning efforts, although ageing infrastructure continues to contribute to significant water losses. Small private supplies, including wells, are still vulnerable to contamination. Progress has been made in wastewater treatment, but further improvements are needed to meet OECD standards, with less than two-thirds of the population currently connected to adequate systems. Some towns continue to experience untreated wastewater discharges. Irish Water is committed to achieving full treatment coverage, but sustained, accelerated investment will be essential to meet both national and international water quality objectives, including those outlined under United Nations Sustainable Development Goal 6 (OECD, 2021[7]).
Intensive agriculture and related water pollution in Ireland have a direct effect on biodiversity loss. The environmental consequences extend beyond water. Over 20% of Ireland’s species are threatened, and most key habitats, particularly grasslands and wetlands, are in poor condition. Biodiversity loss is closely tied to pollution, land use changes and intensive farming, and invasive species programmes like LAWPRO and ASSAP have led to local improvements, but broader, systemic action is urgently needed. Enhanced policy tools – such as pricing mechanisms, environmentally targeted farm subsidies and better land use planning – are essential to reverse degradation trends and ensure long-term ecological and agricultural sustainability (OECD, 2021[7]).
In terms of waste and materials management, Ireland generates high levels of waste with stagnating recycling rates. Material consumption, especially from construction and agriculture, is among the highest per capita in the OECD, yet the circularity rate remains below 2%, the lowest in the European Union. Municipal waste per capita is above the OECD average and, while landfill use has declined, reliance on incineration and waste exports has increased, potentially undermining recycling efforts. Recycling is further hindered by contamination and limited organic waste collection. Although initiatives like CIRCULÉIRE and the Green Business Fund promote circular economy principles, stronger domestic recycling infrastructure and improved waste collection systems are needed (OECD, 2021[7]).
Energy composition
In 2023, Ireland’s energy system remained heavily dependent on imported fossil fuels, which accounted for 82.6% of the national energy supply. Gas continued to be the dominant energy source, followed by a growing contribution from renewables and a smaller share from coal. Despite the continued reliance on fossil fuels, renewable energy reached a record high of 23.38 terawatt hours (TWh) across electricity, transport and heat, helping to avoid an estimated 7.4 MtCO2 emissions. Of the renewable energy used, 67.4% supported electricity generation, while 32.6% was consumed directly by end users. Renewables – primarily sourced from rural areas – contributed 40.7% of Ireland’s electricity supply, up from 38.6% in 2022. Wind energy alone accounted for 33.7%, while solar photovoltaics (PV) contributed 1.9%, enough to power the country for an entire week. In transport, the biofuel blend in road diesel increased to an average of 8.4% in 2023 (up from 6.5% in 2022) and the biofuel share in road petrol rose to 4.2% from 3.2%, reflecting continued progress in decarbonising the sector (SEAI, 2024[8]).
The energy production landscape in Ireland exhibits significant regional variations. Rural remote regions lead in renewable energy production, with 98.1% from renewables, while metropolitan regions rely heavily on gas, with up to 86.1% in large metropolitan areas. Coal usage is more prominent in regions near small FUAs, suggesting the presence of older power plants and industrial activity (Figure 2.24). There is a clear trend that as urbanisation increases, the proportion of renewable energy decreases, and reliance on gas rises. This highlights the challenges of transitioning to cleaner energy in urbanised regions, while remote areas are successfully embracing renewables. Region-specific energy policies are essential to promote renewable energy in remote areas and reduce gas dependence in urban regions. Additionally, infrastructure development and economic factors are crucial in shaping Ireland’s energy mix and achieving sustainability goals.
Ireland has a very different energy mix compared to the OECD, as it does not rely on nuclear energy and only uses a very small share of coal. Both rely heavily on fossil fuels in urban areas – gas in Ireland and oil in the OECD – while remote regions lead in the adoption of renewables. Coal use is more prominent near small FUAs in Ireland, while in the OECD, coal is used across all regions, both rural and urban, at percentages varying from 21.5% in rural remote regions to 25.2% in regions near large FUAs. There is a need for region-specific energy policies to boost renewables in rural areas and reduce fossil fuel reliance in cities (Figure 2.20).
Share of electricity production by source in 2019
Note: Renewables include geothermal, hydro, solar and wind energy sources. The data for the OECD cover 36 countries, excluding only Costa Rica and Israel, as these two countries are not yet classified in the OECD regional rural typology.
Source: Based on the OECD Regional Database.
Wind energy accounts for the largest share of renewable energy in Ireland, at 49.9%. In 2023, Ireland used 23.38 TWh of renewable energy, up from 21.68 TWh in 2022. Wind energy made up the largest share at 49.9%, followed by biodiesel (13.4%) and biomass (11.0%), with these 3 sources comprising 74.3% of the total (Figure 2.25). Despite progress, including record highs in wind, solar PV, biofuels and heat pump energy, Ireland must significantly accelerate renewable deployment and reduce energy demand to meet its climate goals. Improvements in transmission capacity will be essential for Ireland to take advantage of its underutilised renewable energy potential. Ireland’s Climate Action Plan targets major infrastructure investments to boost renewable energy, especially wind and solar. By the end of 2023, installed wind capacity was 4.74 gigawatt (GW), with a target of 6 GW by 2025 and 15 GW by 2030 (9 GW onshore, 5 GW offshore), requiring annual additions of 0.63 GW (2024-2025) and 1.47 GW (2024-2030). Solar PV capacity reached 0.72 GW in 2023, with a goal of 8 GW by 2030, demanding an average annual increase of 1.04 GW. Most solar growth is expected in the latter half of the decade (SEAI, 2024[8]).
Breakdown of Ireland’s overall renewable energy production in 2023
Source: SEAI (2024[9]), First Look: Renewable Energy in Ireland in 2023, https://www.seai.ie/sites/default/files/data-and-insights/seai-statistics/key-publications/renewable-energy-in-ireland/First-Look-Renewable-Energy-in-Ireland-Report.pdf.
Healthcare provision is inherently a territorial issue, as balancing cost, quality and access requires accounting for population density and geographic distance. Rural populations often face longer travel times to access medical services, while rural facilities themselves struggle with declining patient volumes and persistent challenges in attracting and retaining health professionals (OECD, 2021[10]). For example, only 68% of the rural population in Ireland has access to a hospital within a 20-minute drive, compared to 87% in metropolitan areas, a gap of 19 percentage points (Figure 2.26). This is the 12th widest rural-urban gap among OECD countries, showing that access disparities in Ireland are larger than in most OECD Members. The gap reflects both the challenges of providing services in sparsely populated areas and the concentration of specialised facilities in urban centres, underscoring the need to strengthen rural health infrastructure and connectivity to specialised care.
This pattern is also evident in average drive times to the nearest hospital. Metropolitan regions such as Dublin (5.8 minutes) and the South-West (8.6 minutes) offer significantly shorter average travel times than the EU average of 9.1 minutes (Figure 2.27, Panel A). In contrast, more rural Irish regions – particularly the West (15.4 minutes) and Mid-West (14.1 minutes) – face much longer drive times. In the West, for instance, it takes two to three times as long to reach a hospital as it does in Dublin. A similar pattern is observed for access to primary schools, although differences are smaller due to shorter distances and a wider distribution of facilities (Figure 2.27, Panel B).
Percentage of population within a 20-minute drive from a hospital in 2022
Note: Metropolitan regions include both MR-L and MR-M. Regions near metropolitan areas correspond to NMR-M, while regions far from metropolitan areas include both NMR-S and NMR-R.
Source: OECD (2022[4]), OECD Regions and Cities at a Glance 2022, https://doi.org/10.1787/14108660-en.
Drive time to nearest hospital and primary school across regions in 2023
Note: Assuming car driving at speed limit without congestion. Averages are weighted by population.
Source: Based on GISCO.
While travel times capture the geographic accessibility of services, provider-to-population ratios offer a complementary perspective on service availability. There is little regional variation in the number of pharmacies relative to population, with most regions having between 2 and 3 pharmacies per 10 000 people (Figure 2.28). The number of general practitioners (GPs) also shows a similar pattern, ranging from 7 to 9 per 10 000 inhabitants, with slightly higher figures in metropolitan areas, except in the Mid-East. However, these numbers give only a rough sense of accessibility. In densely populated metropolitan areas, services are typically located closer to where people live, resulting in shorter travel times. In contrast, people in rural regions with similar provider-to-population ratios often face longer distances to care, as seen with hospital and primary school access.
Number of pharmacies (2024) and GPs (2022 or latest year) per 10 000 inhabitants
Note: GP refers to a general practitioner, a primary care medical doctor.
Source: OECD Regional Attractiveness database.
These disparities in drive times appear to influence how people perceive the healthcare system. According to the OECD Trust Survey, Ireland exhibits the largest gap in health system satisfaction between cities and rural areas or towns and semi-dense areas among all OECD countries (OECD, 2023[11]). Notably, rural residents in Ireland report the lowest satisfaction levels across the OECD, with only about 23% of respondents indicating a high level of satisfaction (Figure 2.29). While this dissatisfaction likely reflects challenges in geographic accessibility, it may also signal concerns about the quality or responsiveness of services in rural areas. Although the current data do not allow for a direct assessment of service quality, this pattern highlights a critical area for further investigation.
Satisfaction with healthcare system in 2023
Note: The OECD average is not weighted by the number of respondents (only respondents who said they used these are considered users). The question asked is: "On a scale of 0 to 10, where 0 is not at all and 10 is completely, how much are you satisfied with the healthcare system?" (0-4 = Dissatisfied, 5 = Neutral, 6-10 = Satisfied).
Source: Based on OECD (2023[11]), “2023 OECD Trust Survey”, https://www.oecd.org/en/data/datasets/oecd-trust-survey-data.html.
Long-term rural competitiveness depends not only on economic structure but also on the presence of enabling factors, such as innovation capacity, the presence of financial intermediaries, appropriate skills and digital infrastructure. These foundations shape the ability of rural regions to adapt to structural change, attract investment and participate in higher-value activities. While innovation in rural areas often takes forms not captured by traditional metrics, patent data provide one useful lens to assess the geography of science- and technology-driven activity.
Patent activity and rural innovation capacity
Patent data are often affected by headquarter effects, with activity concentrated in capital cities. While this is also the case with Dublin in Ireland, the West stands out, recording the highest levels of patent intensity and the strongest growth rates (Figure 2.30). Importantly, only this region has a dedicated regional development entity, the Western Development Commission, which makes substantial equity investments in innovative local firms. Even without this major source of innovation funding, other rural regions also display notable patent activity across a broad range of sectors (Figure 2.31), highlighting the innovation potential of rural areas, including in science and technology.
Number of patents per region between 2010-2015 and 2016-2020, per million inhabitants
Note: The figure represents the patent intensity (patents per million persons). To account for year-to-year fluctuations in patent data, the number of patents and population were aggregated across TL3 regions over two time periods: 2010‑2015 and 2016‑2020. Patent statistics, as reported here, often contain a headquarter bias that is due to reporting practices that often place the headquarter as the main address of activity while filing a patent or other legal documentation. As such estimates are likely lower‑end estimates in the region, when headquarters of multi-branch or multi-location firms are not located in the region. Furthermore, patent statistics are often subject to biases when used as a proxy for innovation. These often include the high-technology innovation that is under patent, but not other forms of non-patented product and process innovation. As such, interpreting patent statistics by subnational dimensions should be done with caution.
Source: Based on the OECD Regional Database.
Distribution of patents by sector and region over 2010-2020
Note: Patent data were aggregated by sector over the 2010‑2020 period to represent each sector’s contribution to overall patenting activity.
Source: Based on the OECD Regional Database.
Digital access and rural opportunity
Digital infrastructure is increasingly essential for economic and social development in rural areas. Adequate broadband connectivity enables access to key opportunities such as education, healthcare and teleworking, all of which directly affect quality of life and long-term development prospects. However, access remains uneven. Across the OECD, one-third of rural households still lack access to high-speed broadband, and only 7 out of 26 countries have achieved coverage for at least 80% of rural households (OECD, 2023[12]). Rural remote regions also report the lowest median download speeds (OECD, 2024[13]). In line with broader international patterns, rural areas in Ireland continue to lag behind the national average, despite major investments both in terms of infrastructure and in creating access points for rural users. As of 2024, fixed download speeds in rural areas are 16.4% lower than the national average, the 7th‑largest gap among the 15 benchmark countries, where differences range from 40.5% in Latvia to just 8.2% in Estonia (Figure 2.32). At the same time, improvements are underway: the National Broadband Plan launched in 2020 is steadily extending fibre broadband to rural communities, with more than 400 000 additional premises now able to access high-speed connections, although the full benefits of the rollout are not yet fully realised. In terms of digital skills, Ireland also lags behind the EU average in most regions. The share of individuals with above-basic digital skills is 15.7% lower in Northern and Western Ireland and 6.8% lower in Southern Ireland, while Eastern and Midland Ireland performs slightly above the EU average at 0.6% (Dijkstra et al., 2023[14]).
Percent deviation from national average by the degree of urbanisation, Q4 2024
Note: This figure shows the deviation of the region type’s mean fixed download speed from the country’s mean fixed download speed for all fixed technologies, measured in megabits per second (mbps). Speeds are user-measured, such that speed tests only are captured when a request has been made from an individual’s computer or mobile device.
Source: OECD (forthcoming[15]), Bridging Connectivity Divides, OECD Publishing, Paris.
Learning outcomes, access and satisfaction with the education system
Education and skills are essential enablers of long-term development and resilience in rural areas. Foundational competencies – particularly in literacy and numeracy – underpin local labour market performance and the ability of rural regions to adapt to technological change. Across the OECD, student outcomes in rural schools often lag behind those in urban areas, reflecting in part underlying socio‑economic disparities. In some countries, the gap in reading performance between urban and rural schools exceeds 40 percentage points, equivalent to more than a year of schooling (OECD, 2021[10]).
In Ireland, rural-urban differences in student performance are more moderate than in many other OECD countries, but they remain notable. In 2022, students in urban schools outperformed their rural peers by 19 percentage points in reading and 11 points in mathematics before controlling for socio-economic background. After accounting for these factors, the gaps narrowed to 13 percentage points in reading and 7 in mathematics (Figure 2.33 and Figure 2.34).
Beyond compulsory education, access to adult learning opportunities plays a critical role in supporting skills development, labour market adaptability and regional resilience. In Ireland, participation among adults aged 25-69 varies widely across regions: between 37% and 55% in non-formal education, 41% to 70% in informal learning, and 40% to 59% in lifelong learning activities (Figure 2.35). In contrast, participation in formal education leading to a recognised qualification remains limited, with only 4% to 13% of adults taking part.
Regional patterns highlight where policy efforts may be most useful. Participation rates in rural remote regions are 10-15 percentage points lower than in rural regions close to an FUA across non-formal, informal and lifelong learning. In formal education, where overall participation is modest, the Border region stands out as an outlier, with rates about 10 percentage points below all other regions. Rural regions close to an FUA, by contrast, generally record participation rates 5-10 percentage points higher than most metropolitan regions, except Dublin where levels are broadly similar. These patterns indicate that access to adult learning may be influenced both by proximity to urban centres and by local conditions, suggesting scope for targeted measures to extend opportunities in remote and Border regions.
Note: Data for 2022 are drawn from the Programme for International Student Assessment (PISA) student questionnaire (reading scores) and the school questionnaire (school location). Using school location, we approximated the degree of urbanisation classification (Annex Table 2.A.2) by assigning villages, hamlets or rural areas (fewer than 3 000 inhabitants) as “rural”, and cities (more than 100 000 inhabitants) as “urban”. Socio-economic conditions are included as a control to assess the robustness of rural-urban differences.
Source: Based on OECD (2022[16]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Note: Data for 2022 are drawn from the PISA student questionnaire (math scores) and the school questionnaire (school location). Using school location, we approximated the degree of urbanisation classification (Annex Table 2.A.2) by assigning villages, hamlets, or rural areas (fewer than 3 000 inhabitants) as “rural”, and cities (more than 100 000 inhabitants) as “urban”. Socio-economic conditions are included as a control to assess the robustness of rural-urban differences.
Source: Based on OECD (2022[16]), PISA 2022 Database, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
Participation in education among adults aged 25-69 in 2022
Note: “Formal education” is provided in schools, colleges, universities or other educational institutions and leads to a certification that is recognised by the national educational classification. “Non-formal education” is defined as any organised and sustained educational activities that do not correspond exactly to the above definition of formal education. This includes courses through distance education, on-the-job training, seminars, workshops or private lessons. “Informal learning” relates to typically unstructured, often unintentional, learning activities that do not lead to certification. In the workplace, this is more or less an automatic byproduct of the regular production process of a firm. “Lifelong learning” in this dataset is defined as the participation in formal and/or non-formal learning activities, unlike the OECD definition of lifelong learning that additionally includes the participation in informal learning activities. Individuals may participate in more than one type of learning activity simultaneously.
Source: CSO Ireland, DATA.GOV.IE.
Beyond current patterns of adult learning participation, longer-term changes in the formal qualifications of adults in rural Ireland reveal substantial progress since 2009. Between 2009 and 2021, the share of adults aged 25-64 with a tertiary degree rose by 14 to 20 percentage points, depending on the region (Figure 2.36). Gains have been especially strong at the highest levels, with the share of adults holding a doctoral or equivalent degree doubling or even tripling across rural regions. At the other end of the spectrum, low attainment has declined steadily: the share of adults whose highest qualification is primary education fell by 15-20 percentage points over the same period. By contrast, the share with an upper secondary degree has remained largely unchanged. These shifts point to a gradual upgrading of skills in rural areas, with strong growth in higher education attainment and a steady reduction in low qualifications.
These improvements in educational attainment are also reflected in how residents perceive the education system. According to the OECD Trust Survey, Ireland ranks 4th highest among OECD countries in terms of rural satisfaction with the education system, with more than 70% of rural respondents expressing a positive view (Figure 2.37). Satisfaction levels are similarly high across urban and semi-dense areas, indicating broad confidence in the quality and accessibility of education nationwide. This widespread positive perception reinforces the progress made in educational outcomes and highlights the importance of maintaining equitable education standards across all regions.
Highest level of education achieved among adults aged 25-64
Note: “Doctoral or equivalent” is included within the broader category of “Tertiary education”.
Source: CSO Ireland, DATA.GOV.IE.
Satisfaction with education system in 2023, in %
Note: The OECD average is not weighted by the number of respondents (only respondents who said they used these are considered users). The question asked is: "On a scale of 0 to 10, where 0 is not at all and 10 is completely, how much are you satisfied with the healthcare system?" (0-4 = Dissatisfied, 5 = Neutral, 6-10 = Satisfied).
Source: Based on OECD (2023[11]), “2023 OECD Trust Survey”, https://www.oecd.org/en/data/datasets/oecd-trust-survey-data.html.
Across all dimensions – demography, economy, environment, education and services – rural Ireland demonstrates substantial human capital and growth potential but also persistent structural imbalances. Areas near-urban centres benefit from connectivity and investment, while more remote regions face higher emissions, weaker infrastructure and limited service access. These disparities highlight the continued need for a place-based policy approach that leverages rural-urban linkages green and digital transitions, to maximise Ireland’s demographic and renewable energy advantages to strengthen rural communities. The enabling factors will help determine how effectively rural regions can adapt to structural change, attract investment and participate in higher-value activities. For example, rural innovation potential exists but depends on decentralised investment mechanisms and more balanced innovation funding beyond metropolitan hubs. Broadband connectivity is good but uneven: some remote areas in the Midland and Border regions continue to experience the slowest speeds.
The rest of this report will explore these aspects and more. Chapter 3 sets the stage with an overview of the key rural development strategy, Our Rural Future, along with the institutions, and multilevel governance arrangements that shape rural policy delivery. Chapter 4 assesses Our Rural Future through the prism of the OECD Principles on Rural Policy (OECD, 2019[17]) and the Well-Being Framework (OECD, 2020[18]). Chapter 5 examines emerging environmental, demographic and institutional vulnerabilities that may undermine rural resilience despite national growth trends, and makes the case that Ireland needs more forward-looking, targeted action.
Source: Adapted from Fadic, M., et al. (2019[19]), "Classifying small (TL3) regions based on metropolitan population, low density and remoteness", https://doi.org/10.1787/b902cc00-en.
An FUA is defined through a systematic, four-step approach that combines densely populated urban centres with surrounding areas linked by commuting patterns:
An urban centre is identified as a contiguous cluster of grid cells with a high population density of at least 1 500 residents per km². This cluster must include a minimum of 50 000 residents across these contiguous cells.
A city is defined as one or more local administrative units (e.g. municipalities) where at least 50% of the population lives within the boundaries of an identified urban centre.
A commuting zone includes contiguous local units surrounding the city, where at least 15% of employed residents commute to the city for work. This establishes the economic and functional connection between the urban centre and its surroundings.
The FUA is created by combining the city with its commuting zone, representing an integrated region that reflects both residential concentration and commuting patterns, indicating a shared socio-economic space.
Source: Adapted from Dijkstra, L., H. Poelman and P. Veneri (2019[20]), "The EU-OECD definition of a functional urban area", https://doi.org/10.1787/d58cb34d-en.
The DEGURBA definition identifies settlements from clusters of adjacent 1‑km2‑grid cells with medium or high population density. Such clusters meet the criteria for settlements if their total population is also above a certain threshold (see below). The DEGURBA definition also incorporates built-up areas, in addition to population, to avoid the identification of multiple urban centres for a single city. However, with DEGURBA, settlements such as cities are defined by their population density, not including the surrounding commuting areas.
The table below shows the mapping of Level 1 definitions for local area units and Level 2 definitions for grid-based DEGURBA classifications. The Level 2 definition of DEGURBA distinguishes towns and villages, which are settlements, from suburbs and dispersed rural areas, which are not. The minimum population thresholds are shown in the right-most column: villages have at least 500 residents while cities start at 50 000 residents. This report uses the original DEGURBA definition, which defines towns as having at least 5 000 residents.
Source: UNSD (2020[21]), “A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons”, https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf.
Change in employment and unemployment rates, population aged 15-64
Source: Based on the OECD Regional Database.
Fertility rate change in Irish regions (Panel A) and international comparison (Panel B)
Note: The total fertility rate in a specific year is defined as the total number of children that would be born to each woman if she were to live to the end of her child-bearing years and give birth to children in alignment with the prevailing age-specific fertility rates. It is calculated by totalling the age-specific fertility rates as defined over five-year intervals. Assuming no net migration and unchanged mortality, a total fertility rate of 2.1 children per woman ensures a broadly stable population.
Source: Based on the OECD Regional Database.
Age-adjusted death rate change in Irish regions (Panel A) and international comparison (Panel B)
Note: The age-adjusted death rate accounts for differences in population age structures across regions and is expressed as deaths per 1 000 inhabitants.
Source: Based on the OECD Regional Database.
Life expectancy at birth across benchmark countries in 2023
Source: Based on the OECD Regional Database.
Population growth in Irish regions, relative to a 2001 (Panel A) and 2023 (Panel B) baseline (=100)
Note: Panel A illustrates historical population growth in Irish regions based on the OECD regional typology. Panel B presents projected population growth using the same typology, drawing on Eurostat projection data. In both panels, population figures were aggregated across TL3 regions to reflect a population-weighted trend for each regional type.
Source: Based on the OECD Regional Database and Eurostat.
Old-age dependency ratio (OADR) growth in Irish regions, relative to a 2001 (Panel A) and 2023 (Panel B) baseline (=100)
Note: Panel A illustrates historical OADR growth in Irish regions based on the OECD regional typology. Panel B presents projected OADR growth using the same typology, drawing on Eurostat projection data. In both panels, population figures were aggregated by age across TL3 regions to reflect a population-weighted trend for each regional type. The old-age dependency ratio is defined as the number of people aged 65 or older per 100 people of working age (20–64).
Source: Based on the OECD Regional Database and Eurostat.
Age group composition of rural regions in Ireland (Panel A) and change in shares between years (Panel B)
Note: Panel A presents the distribution of the population by age group in rural regions of Ireland (NMR-S and NMR-R) for the years 1996, 2023 and 2100. Panel B shows the corresponding changes in age group shares over time, expressed in percentage points.
Source: Based on the OECD Regional Database and Eurostat.
References
[6] CSO (2025), Environmental Indicators Ireland 2024, Central Statistics Office, https://www.cso.ie/en/releasesandpublications/ep/p-eii/environmentalindicatorsireland2024/.
[3] CSO (2022), Census of Population 2022, Central Statistics Office, https://www.cso.ie/en/statistics/population/censusofpopulation2022/.
[1] CSO (2019), Urban and Rural Life in Ireland, 2019, Central Statistics Office, https://www.cso.ie/en/releasesandpublications/ep/p-urli/urbanandrurallifeinireland2019/introduction/ (accessed on 10 March 2025).
[14] Dijkstra, L. et al. (2023), “The EU Regional Competitiveness Index 2.0, 2022 edition”, DG REGIO Working Paper Series, No. 1/2023, https://ec.europa.eu/regional_policy/information-sources/maps/regional-competitiveness_en.
[20] Dijkstra, L., H. Poelman and P. Veneri (2019), “The EU-OECD definition of a functional urban area”, OECD Regional Development Working Papers, No. 2019/11, OECD Publishing, Paris, https://doi.org/10.1787/d58cb34d-en.
[22] Fadic, M. et al. (2019), Classifying small (TL3) regions based on metropolitan population, low density and remoteness, https://doi.org/10.1787/b902cc00-en.
[19] Fadic, M. et al. (2019), “Classifying small (TL3) regions based on metropolitan population, low density and remoteness”, OECD Regional Development Working Papers, No. 2019/06, OECD Publishing, Paris, https://doi.org/10.1787/b902cc00-en.
[5] OECD (2025), OECD Economic Surveys: Ireland 2025, OECD Publishing, Paris, https://doi.org/10.1787/9a368560-en.
[13] OECD (2024), “Going Digital”, OECD, Paris, https://www.oecd.org/en/about/projects/going-digital.html.
[11] OECD (2023), “2023 OECD Trust Survey”, OECD, Paris, https://www.oecd.org/en/data/datasets/oecd-trust-survey-data.html.
[12] OECD (2023), OECD Regional Outlook 2023: The Longstanding Geography of Inequalities, OECD Publishing, Paris, https://doi.org/10.1787/92cd40a0-en.
[4] OECD (2022), OECD Regions and Cities at a Glance 2022, OECD Publishing, Paris, https://doi.org/10.1787/14108660-en.
[16] OECD (2022), PISA 2022 Database, OECD, Paris, https://www.oecd.org/en/data/datasets/pisa-2022-database.html.
[10] OECD (2021), Delivering Quality Education and Health Care to All: Preparing Regions for Demographic Change, OECD Rural Studies, OECD Publishing, Paris, https://doi.org/10.1787/83025c02-en.
[7] OECD (2021), OECD Environmental Performance Reviews: Ireland 2021, OECD Environmental Performance Reviews, OECD Publishing, Paris, https://doi.org/10.1787/9ef10b4f-en.
[18] OECD (2020), Rural Well-being: Geography of Opportunities, OECD Rural Studies, OECD Publishing, Paris, https://doi.org/10.1787/d25cef80-en.
[17] OECD (2019), OECD Principles on Rural Policy, OECD, Paris, https://www.oecd.org/fr/regional/oecd-principles-rural-policies.htm (accessed on 6 July 2023).
[2] OECD (2013), Rural-Urban Partnerships: An Integrated Approach to Economic Development, OECD Rural Policy Reviews, OECD Publishing, Paris, https://doi.org/10.1787/9789264204812-en.
[15] OECD (forthcoming), Bridging Connectivity Divides, OECD Publishing, Paris.
[8] SEAI (2024), Energy in Ireland 2024 Report, Sustainable Energy Authority of Ireland, https://www.seai.ie/sites/default/files/publications/energy-in-ireland-2024.pdf.
[9] SEAI (2024), First Look: Renewable Energy in Ireland in 2023, Sustainable Energy Authority of Ireland, https://www.seai.ie/sites/default/files/data-and-insights/seai-statistics/key-publications/renewable-energy-in-ireland/First-Look-Renewable-Energy-in-Ireland-Report.pdf.
[21] UNSD (2020), “A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons”, United Nations Statistics Division, https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf.
← 1. The OECD TL3 classification groups small regions based on their population density and distance to dense areas. TL3 regions are designated as ‘metropolitan’ if more than half of their population lives in one or more functional urban area (FUA) of at least 250 thousand inhabitants and as ‘non-metropolitan’ otherwise. The method sub-classifies metropolitan regions into ‘large’ or ‘midsize’ metropolitan regions based on the population size of the FUAs located within those regions. Non-metropolitan TL3 regions are sub-classified into near a midsize/large FUA, near a small FUA, or remote (Fadic et al., 2019[22]).
← 2. The OECD averages refer to the national share of rural population across 36 OECD countries, excluding Costa Rica and Israel. It is calculated as a population-weighted average within each country and then averaged across countries.
← 3. The elevated values in Dublin and the South-West are partly influenced by headquarter effects, which are discussed in more detail later in the chapter (see Box 2.1).
← 4. These results reflect the situation prior to the full rollout of the National Broadband Plan initiated in 2019. While more than 400 000 premises can now access fibre optic broadband, around 150 000 are already connected as of late 2025, with take-up expected to increase as the rollout continues. The River Basin Management Plan 2018-2021 targets improving 726 water bodies in priority areas, aiming to bring 152 to at least good ecological status. It identifies 1 460 water bodies at risk of failing water quality objectives and prioritises 190 areas for action, including 57 where wastewater is the main pressure.