GHG emission in cities, towns and rural areas

GHG emission in cities, towns and rural areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article GHG emission in cities, towns and rural areas Monica Crippa, Diego Guizzardi, Enrico Pisoni, Sara Ciarlantini, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8405392/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In a rapidly urbanising world facing increasingly stringent climate constraints, understanding where and how greenhouse gases (GHG) are emitted by degree of urbanisation is crucial. This study examines how sector-specific GHG emissions have evolved over 35 years (1990–2024) in cities, towns and semi-dense areas, and rural areas, using the latest release of the Emissions Database for Global Atmospheric Research (EDGAR). For the first time, EDGAR GHG emissions are distributed at 1 km resolution using high-resolution spatial proxies, enabling a detailed characterisation of territorial emissions by degree of urbanisation globally. Results indicate that cities account for around one fifth of global GHG emissions and 45% of the world’s population. Yet per capita emissions are generally lower in cities than in rural areas in particular in high-income countries. Larger and denser cities tend to exhibit lower GHG emissions per capita than smaller and less dense urban areas, underscoring the mitigation potential of compact urban development and targeted, place-based climate policies. Earth and environmental sciences/Climate sciences Scientific community and society Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Cities have become central to the global climate agenda, as they are home to a growing share of the world’s population and act as hotspots of economic activity, resource consumption, and greenhouse gas (GHG) emissions. Between 1990 and 2024, the urban population increased across all continents, in 90% of all the world’s countries, and by 2024 nearly half (45%) of the global population lived in cities, as defined by the degree of urbanisation 1 . The sharpest population growth is found in Africa (+241%), followed by Oceania (+108%), Asia (+95%), North America (+91%) and South America (+83%). A much-reduced growth is found in Europe, where population increased only by 13% over the same time period. Cities concentrate economic activities, which can generate high local concentrations of air pollution 2 . Although per capita emissions in cities are usually lower than national averages 3 , the aggregate contribution of cities remains dominant, with our estimates suggesting that around 21% of global emissions originate in cities (excluding international shipping and aviation). As cities continue to expand and concentrate people, wealth, and consumption, total emissions are expected to increase further 4,5 . At the same time, while they concentrate activities that drive emissions, cities also serve as key sites for climate action 6 , holding significant potential to advance inclusive and effective mitigation. Given their scale, they represent a unique opportunity for interventions that support sustainable development, improve air quality, and reduce climate-related risks. Climate change is a global problem, but it has distinctive urban impacts, as it is closely intertwined with air pollution, a more local issue that must be addressed to reduce population exposure to harmful pollutants, responsible for 8.1 million deaths in 2021 7 . The specific role of urban areas in the climate challenge has been increasingly recognised by science and governance. The 2030 Agenda for Sustainable Development highlights sustainable cities as a central goal, emphasising the need to balance economic growth, social inclusion, and environmental protection 8 . Within this framework, the eleventh Sustainable Development Goal (SDG11, Sustainable cities and communities) stresses that “cities and metropolitan areas are powerhouses of economic growth—contributing about 60% of global GDP. However, they also account for about 70% of global carbon emissions and over 60% of resource use. The rapid urbanisation is resulting into worsening air pollution” 9 . In addition, SDG13 (“Climate Action”) reinforces the central role of cities in addressing climate change. In addition to these goals, global commitments such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction, and the New Urban Agenda further consolidate the role of cities in advancing mitigation and adaptation 6 . While these agreements are negotiated internationally, their successful implementation requires strong local action. Cities act as catalysts for positive change, supported by networks such as the Global Covenant of Mayors for Climate and Energy, which involves more than 13,000 signatories worldwide 10 . Moreover, the IPCC has established a dedicated Special Report on Climate Change and Cities, to be delivered in the seventh assessment cycle. This report will provide an assessment of the latest science on climate change and cities, including impacts, risks, and potential adaptation and mitigation solutions. The earlier IPCC Special Report on Global Warming of 1.5°C already identified urban and infrastructure systems as one of the four systems needing transformative change to limit global warming 11 . Despite challenges such as limited political authority or lack of access to adequate financing 6 , cities often pursue climate goals that are more ambitious than those of their national government, demonstrating their potential to lead on climate action. In this context, cities emerge as indispensable actors in the global effort to reduce GHG emissions. Global and national mitigation cannot be achieved independently from urban action. This study investigates the evolution of sector-specific GHG emissions by degree of urbanisation, (a UN endorsed method that defines cities, towns and semi-dense areas, and rural areas) over a 35-year period (1990–2024), from a local to a global level. The analysis relies on the Emissions Database for Global Atmospheric Research (EDGAR), using its latest release, EDGAR_2025_GHG 13,14 . EDGAR is a global bottom-up emission inventory that provides consistent time series of anthropogenic GHG and air pollutant emissions for all countries and sectors from 1970 to 2024. For the first time, global anthropogenic GHG emissions are reported with a spatial resolution of 0.01° x 0.01° (approximately 1 km). This high level of detail enables the investigation of where emissions happen, supporting the development of place-based mitigation measures from global to local level. To increase both scientific and policy relevance, EDGAR implements high-resolution spatial proxies to distribute emissions over the globe. Combined with the most recent population statistics 15,16 , this approach allows a detailed characterisation of emissions by degree of urbanisation and promotes EDGAR as a reference inventory for mapping emissions from different activities. Compared with previous EDGAR city emission estimates 2 (Crippa et al., 2021), the main novelties of this work are: i) temporal extension of the dataset to 2024 (previously limited to 2015), providing the most up to date (t-1) figure of emissions in global cities; ii) increased spatial resolution, from ~10 km to ~1 km grids, enhancing the granularity of emission mapping; iii) improved spatial proxies to disaggregate national emissions over the global gridmap. In this work we focus on GHG emissions in absolute and per capita terms to inform global climate action and foster a sustainable transition across multiple degrees of urbanisation. 2 Results 2.1 A fifth of global GHG emissions occur in cities Cities represent very localized hotspots in terms of population, activities and consequently also of pollution emitting sources. We estimate that 21% of global GHG emissions occurred in cities, which host 45% of the global population (see Fig. 1). Between 1990 and 2024, the amount of GHG emissions emitted by cities grew from 5.4 billion to 11 billion tonnes CO 2eq in 2024. This increase in emissions, however, was closely matched by the growth of population from 1.8 to 3.7 billion. Towns and semi-dense areas, which host a third of the global population, also saw a doubling of their emissions over this time period, from 5.7 to 12.5 billion. These areas, however, experienced less population growth (+44%). Finally, rural area, which host only 22% of the global population, have seen the lowest growth in emissions (+38%), but their population barely grew (+14%). The development and implementation of effective mitigation measures is a priority to improve people quality of life and health, guarantee a sustainable future for next generations and preserve our environment and resources. High income countries, covering 14% of the world’s population, are responsible for around a quarter of global GHG emissions and contribute the lowest share of emissions in urban areas, compared to other income countries. Conversely, low income countries, representing around 13% global population and 2.5% of global GHG emissions, show the highest share of emissions in urban areas compared to other income-level countries. Table S1 provides more details on the share of emissions by degree of urbanisation and geographical region following the IPCC AR6 definition (see Table S2 for the classification of regions). 2.2 Big emitting sectors are located outside cities As expected, agricultural emissions are heavily skewed with 83% in rural areas and only 3% in cities. Transport, waste, industry and energy emissions also occur in rural areas (see Fig. 1), while the share of these emissions in cities is low (between 12% and 31%). Given that industry requires a lot of land and may also cause local noise or air pollution, these factories are less likely to be located in a city. Also, energy plants are more likely to be located outside a city, in proximity of country borders and the presence of energy resources (e.g. rivers). Transport, especially of goods, is concentrated on major roads and highways, which are located outside city centres. Residential emissions, in contrast, follow the population distribution more closely with 38% of emissions in cities compared to 45% of the population. 2.3 Emissions per capita are lower in cities than in other areas Per capita emissions are a useful metric to compare cities and areas with different size and population levels. In this study, per capita emissions are calculated considering only territorial emissions (excluding international shipping and aviation), that is, emissions physically released within the boundaries of each degree of urbanisation and income group (classified according to the Sustainable Development Index, SDI), regardless of where the associated goods and services are ultimately consumed. Though urbanisation continues to be a major source of GHG emissions, urban emissions per capita are typically much lower than in towns, semi-dense and rural areas (see Fig. 2). In 2024, cities per capita emissions are a third lower than in towns and semi-dense areas and 80% lower than in rural areas. GHG per capita are lower in cities in part because people tend to live in smaller and more compact dwellings, which reduces energy use for heating energy, and they tend to drive less. In addition, as agriculture, industry, transport and energy production occur largely outside cities, this reduces the emissions within cities, as discussed below. 2.4 High SDI countries have the highest emissions per capita, but they are shrinking Since 2005, high SDI countries have reduced their emissions in all three types of areas. Although this is partially due because high-income countries are importing more from low and middle-income countries, it also due to investments in energy efficiency and renewable energy. Apart from this SDI category, all the other SDI categories’ emissions have been mostly growing over time. From 2010, emissions per capita in towns and semi-dense areas in high-middle SDI countries even exceed those in high SDI (in 2024: 8.1 vs 6.1 tCO2eq/cap). This is likely to be due to growth of manufacturing in those areas (see Fig. 2). In low SDI countries, per capita emissions have not changed since 1990 in rural areas, and towns and semi-dense areas (3 and 0.6 tCO 2eq /cap, respectively). In cities, it even dropped from 1.6 in 1990 to 0.7 in 2024. These low emissions per capita are not the result of investments in energy efficiency and renewable energy, they are simply the consequence of high levels of poverty and low economic growth. Looking by world regions shows that between 1990 to 2024 in Europe, North America, Australia, Japan, New Zealand, Eastern Europe and West-Central Asia, GHG emissions per capita decreased (see Fig. 3). In all the other regions, emissions per capita grew or remained stable. The biggest increase in emissions per capita occurred in rural areas in the Middle East and Eastern Asia and (+17.4 and +16.4 t CO2 eq/cap). The third biggest increase also happened in Eastern Asia, but in its towns and semi-dense areas (+8.7). This is largely due to the rapid growth of manufacturing in Eastern Asia. Conversely, the biggest reductions also occurred in rural areas with North America in the lead (-8.3) followed by rural Europe, Eastern Europe and West-Central Asia (-5.3). In these regions, the emissions per capita also dropped in cities, towns and semi-dense areas, but reductions were smaller (-1.9 to -4.9). The world average per capita emissions increased for all three degrees of urbanisation, with the biggest increase in rural areas (+2.7 t CO2 eq/cap), followed by towns and semi-dense areas (+1.6) and with very little change in cities (+0.08). During this period, cities have consistently shown the lowest emissions per capita, which stood at 3 tCO2eq per inhabitant in 2024. In the same year, towns and semi-dense areas emitted 4.5 tCO2eq/cap, and rural areas reached 15.4 tCO2eq/cap globally. In 2024, Eastern Asia, Eastern Europe and West-Central Asia, the Middle East, Australia, Japan and New Zealand, and North America show emissions per capita greater than the world average across all three degrees of urbanisation. This is especially true for Australia, Japan and New Zealand, and North America, where rural per capita emissions are more than three times higher than the world average for that type of area. Only around 7.5% of the cities in the world have higher per capita emissions than the country average. Cities with GHG/cap higher than the national average are mostly associated with the presence of point sources (see Fig. S2). GHG per capita emissions sectoral composition remains broadly similar over time (see Fig. S1), overall increasing in all the three degrees of urbanisation over the 35 years’ period. In cities, GHG/cap emissions increase only modestly between 1990 and 2024, from roughly 2.9 to just above 3 t CO2eq per person. Industry and energy together account for the largest share of urban emissions, both sectors’ emissions increasing by 12%. Residential emissions also remain an important contributor, although their share decrease by 40% in 2024, while waste decreases by only 3%. By contrast, transport and especially agriculture emissions remain very low in cities, even though their shares increase over time (+48% and +39%, respectively). The Sankey diagram (Fig. 4) further shows that these sectoral patterns are strongly stratified by income group. High- and high-middle SDI countries dominate flows into the energy and industry sectors. Middle-SDI countries contribute substantially to industry, agriculture and energy-related emissions. Emissions in low-middle SDI countries are mainly split fairly evenly between industry, agriculture and energy. Low-SDI countries contribute proportionally more to agriculture-related emissions, with contributions to other sectors being around three to eight times lower than to agriculture, even though their overall per capita emissions remain much lower than those of high-, high-middle- and middle SDI countries. As a result, the majority of GHG/cap emissions across all sectors and degrees of urbanisation is concentrated in high-SDI areas, while low-SDI regions account for only a small share of the total. 2.5 Big cities have lower GHG emissions per capita than small cities As a general pattern, more populous cities tend to exhibit lower GHG/cap emissions than cities with fewer inhabitants (see Fig. 5) 17 . The world GHG/cap average shows that cities with less than 100,000 inhabitants have 1.23 times higher GHG emissions per capita compared to cities with over 5 million inhabitants (3.7 t vs 2.8 t CO 2 eq/cap in 2024). African, Middle East, and Eastern Europe and West-Central Asia cities are an exception to this pattern, where bigger cities (especially those with over 5 million inhabitants) show higher GHG/cap compared to smaller ones. Latin America and Caribbean and South-East Asia and Pacific display limited variations between cities of different sizes. In particular, this latter region presents a peak for the 250,000–1 million inhabitants class, which is driven by the Indonesian city of Cilegon, considered one of Indonesia's largest industrial hubs due to its heavy industry and fossil-fuel combustion within and around the city 18 , with GHG/cap levels three times higher than the next city in descending order by emissions per capita. Comparing cities of different sizes across regions, overall the highest GHG emissions per capita in cities with less than 100,000 inhabitants occur in Australia, Japan and New Zealand (8.9 t CO 2 eq/cap), mainly occurring in Japan, with only Kamisu city having 239 t CO 2 eq/cap in 2024. Eastern Asian cities follow: in this region’s cities below 100,000 inhabitants, 92% of which are in China, GHG emissions per capita increased over the past two decades, reaching 8.1 t CO 2 eq/cap in 2024. Altogether, cities in Eastern Asia with less than 1 million inhabitants account for 25% of global urban emissions. High GHG/cap emissions are also found in cities with 100.000 to 250.000 inhabitants of Australia, Japan and New Zealand (11.5 t CO 2 eq/cap in 2024), which have remained approximately constant during the last 20 years. Many of these small, high-emission cities are affected by the presence of point sources (see Fig. S2), especially from the energy and industry sectors. Therefore, high emission levels are not linked to consumer habits in individual cities, but rather highlight the impacts of global and national consumption patterns in smaller urban areas of specific regions. Among big cities (above 5 million inhabitants), the highest GHG emissions per capita are found in North America, Eastern Europe/West-Central Asia (Moscow only with 5.8 t CO 2 eq/cap) and the Middle East. Riyad (Saudi Arabia) emerges as the big city with higher per capita emissions (13 t CO 2 eq/cap in 2024), 72% of which stem from the industrial sector. Per capita GHG emissions are also high in Nanjing (China) and Nagoya (Japan), both surpassing 9 t CO 2 eq/cap in 2024. In both cities, GHG emissions mainly arise from the energy and industrial sectors (91% for Nanjing and 84% for Nagoya). Within North America, Toronto (Canada) and Chicago (United States) lead in GHG emissions per capita (above 7.7 t CO 2 eq/cap in 2024), despite a decrease over the last two decades. At the global scale (see Fig. 6), larger cities, particularly those with more than 5 million inhabitants, are among the greatest GHG emitters (Fig. 6b, left). However, the opposite pattern emerges when GHG/cap are examined: cities with fewer inhabitants tend to exhibit the highest per capita emissions (Fig. 6b, right). Apart from Namakkal (India), which has a population of 193,000 people, the GHG/cap ranking is clearly differentiated by city size, with larger cities showing systematically lower per capita emissions. For example, when comparing the cities with the highest GHG/cap emissions in the smallest and largest city size classes, Jinfeng (China) records per capita emissions that are 41 times higher than those of Riyadh. A further distinction emerges between how density and city area relate to GHG emissions. Holding constant country-specific conditions, denser urban areas tend to have substantially lower emissions per resident: a 10% higher density is associated with 8.8% lower GHG emissions per capita. This pattern is consistent with the idea that compact cities make more efficient use of infrastructure, transport systems, and building energy. By contrast, both overall population size and the extent of urbanised land are strongly associated with higher total emissions: larger cities in terms of inhabitants, and those that occupy more land surface (km 2 ), generate substantially more GHG emissions in absolute terms. In line with this, denser cities that rely more heavily on public transport, walking and cycling generally exhibit lower energy use and GHG emissions from transport, reflecting the well-documented inverse relationship between urban population density and transport-related energy consumption and emissions 19 . Beyond the transport sector, cross-country evidence also indicates that higher urban density is associated with lower per capita electricity demand, partly because compact urban forms can more effectively integrate energy-efficient systems such as district heating and cooling, combined heat and power, and smart grids 20 . 3 Discussion Cities host almost half the global population and this share is projected to grow 1 . As cities emit only 21% of global emissions, the main actions to reduce global emissions will have to be taken elsewhere. Cities can further reduce emissions from their already low rate, by promoting more energy efficient heating, cooling and lighting and by encouraging more people to walk, cycle and use public transport. Even small changes in per capita emissions in cities can translate into sizable global effects as so many people live in cities. City emissions mitigation strategies should therefore be a part of national and global climate strategies. The distinct emission profiles of cities, towns and rural areas underline the need for coordinated climate action across governance levels. Interactions between local areas, regions and countries are therefore two-way: local choices shape national trajectories, and national decisions condition what is feasible in cities, towns and rural areas. More populous and denser cities tend to have lower GHG/cap emissions than smaller and more dispersed areas. Population density makes it more efficient to provide public transport. Density also reduces trip lengths, making walking and cycling more attractive options. Finally, density also encourages more compact buildings which are easier to heat and cool. Dense neighbourhoods, however, are not automatically attractive. These neighbourhoods need sufficient transport investment, public space and urban green to make it an attractive place to live. In some isolated cases, a dense city will have high emissions per capita because it has a large industrial or energy plant within its boundaries. Globally, per capita emissions are five times higher in rural areas than in cities. This is mostly because high emitting sectors, such as agriculture, industry and energy, are more likely to be located in towns, semi-dense and rural areas. Cities also produce less GHG emissions per capita because its residents can heat their homes with less energy and tend to drive less. Over the last two decades, high-income countries reduced emissions per capita in all three classes of the degree of urbanisation, emissions rose most strongly in middle-income countries. The biggest increases and decreases in emissions per capita occurred in rural areas. A main strength of this work is its global coverage and methodological consistency, combining gridded emissions with a harmonised degree-of-urbanisation framework over a 35-year period. This enables systematic comparison across different degrees of urbanisation, regions and income groups. At the same time, uncertainties are related with the provision of city emission estimates through the downscaling process of national emissions over the global gridmap. This study adopts a consistent territorial perspective that overcomes biases introduced by city definitions or approaches to attribute emissions to specific cities, focusing on emissions released within the boundaries of cities, towns and semi-dense areas, and rural areas; which aligns with the responsibilities and instruments of local and national authorities. A consumption accounting of GHG emissions would yield a different perspective. For example, a large share of the agricultural emissions would shift to cities and towns. The emissions from a power plant in a town would be distributed to all the users of that energy, who may live in the same town or in neighbouring city or rural area. Territorial and consumption-based accounts are thus complementary: the former identifies where emissions need to be directly reduced, while the latter highlights the demand, trade and behavioural patterns that shape emissions along supply chains. 4 Methods 4.1 The EDGAR database 4.1.1 EDGAR emissions calculation methodology The Emissions Database for Global Atmospheric Research (EDGAR, https://edgar.jrc.ec.europa.eu/) is a global emission inventory of greenhouse gases (GHGs) and air pollutants emitted by all anthropogenic sectors, covering historic time series (1970 up to most recent years) and all countries. EDGAR emissions are computed at national level following a consistent Intergovernmental Panel on Climate Change (IPCC) methodology, using international activity data and default emission factors, allowing comparability among country estimates. The detailed EDGAR emission calculation methodology is described in several scientific publications 13,21,22 . In addition to country specific estimates, EDGAR downscales national emission totals over the global gridmap, making use of sector specific high-spatial resolution proxies 2,23 . This downscaling procedure guarantees consistency between country emission values and the gridded once, supporting intercomparison analyses between country and local scale emission patterns. In this work we address emissions by degree of urbanisation and at city level, thus requiring the use of higher spatial resolution proxies than those typically used for EDGAR gridmaps, i.e. 0.1° × 0.1° resolution proxies (~ 10km x 10km). For this purpose, 0.01° × 0.01° spatial proxies (~ 1km x 1km) have been developed using high spatial resolution information for all the available data. In particular, Global Human Settlements Layer 24 (GHSL, https://human-settlement.emergency.copernicus.eu/) products (i.e. population by degree of urbanisation and non-residential built-up surface) and point source location, mostly for power plants from the Global Energy Monitor 25 , are here used at 1km x 1km resolution to downscale national emissions and then to extract emissions by degree of urbanization and city domain. 4.1.2 EDGAR emissions by city and degree of urbanisation EDGAR emissions by degree of urbanization and by cities are available on the EDGAR website (https://edgar.jrc.ec.europa.eu/edgar_smod and https://edgar.jrc.ec.europa.eu/edgar_cities) and on the GHSL Global Urban Centre Database (GHS-UCDB, https://edgar.jrc.ec.europa.eu/dataset_ucdb and http://data.europa.eu/89h/1a338be6-7eaf-480c-9664-3a8ade88cbcd). Grid cells are classified according with the Degree of Urbanisation definition 26–29 . Specifically, urban centres correspond to the ensemble of grid cells having a population density of at least 1,500 people/km 2 on land grouped in clusters by 4-connectivity with at least 50,000 people and are outlined in 2025. In addition to GHG emissions discussed in this publication, the Urban Centre Database (UCDB) 27 provides 471 indicators over 15 thematic areas for each city in the world. Within these indicators, emissions by sector for fossil CO2, GHGs, NOx and PM2.5 are also provided. The datasets released within this work have been obtained working at 1 km x 1km resolution which ensures full consistency and accuracy between EDGAR gridded emissions and GHSL products. It also aims at reducing uncertainties related with spatial re-projection (i.e. from Mollweide to WGS84), cells alignments and cross-settlement cutting methodologies. GHG emissions by degree of urbanisation are available from 1990 until 2024 by country and aggregated sectors (https://edgar.jrc.ec.europa.eu/edgar_smod), while emissions from cities by sector and country are available from 2000 to 2024, with 5 year time step and considers the city shape of 2025 accordingly with UCDB (https://edgar.jrc.ec.europa.eu/edgar_cities). The use of high-spatial resolution data in the downscaling procedure of national emission totals over the global gridmap help reducing the uncertainty of the quantification of emissions e.g. over cities which however may not exactly correspond to the emission values calculated independently at city level 30 . On the other hand, the strength and richness of this work is the capability to provide a comprehensive picture of global emissions at various scales, from country level, degree of urbanisation and cities using a harmonised and internationally recognised methodology which allows comparability analysis and a complete assessment (coverage). The uniqueness of this work and in particular of the Urban Centre Database is the provision of emission estimates for 11421 cities in the world that is invaluable compared to exercises collating together local and city level inventories 31 using different methodologies for emission estimation 32 , data extraction, sectoral coverage, which can support local studies but not a global assessment. 4.1.3 Methodological guidance for emissions extraction over cities Extracting emissions (or other globally distributed indicators) over user-defined polygons from spatially resolved global grid maps at relatively coarse spatial resolution (e.g. 10 km × 10 km, which is the typical resolution of EDGAR products) requires particular attention to data consistency and allocation methods. As a first step, users should verify the consistency of projection systems between gridded data and shape files, as well as the alignment and referencing of grid cells (e.g. whether coordinates refer to the lower-left corner or the cell centre). To extract emissions for a single city (or another domain), a shape file defining the target boundary is applied to a gridded emission map (e.g. at 10 km × 10 km resolution). For grid cells entirely contained within the shape file, emissions can be fully allocated to the city. However, for grid cells that only partially overlap with the city boundary, only a fraction of the emissions should be assigned to the city. Several allocation approaches can be adopted, including area-based and population-based methods (as done by Crippa et al. 2 ). The latter relies on the fraction of population within each 10km × 10km cell, derived from higher-resolution population proxies (e.g. 1km × 1km). These two approaches are compared with a full downscaling procedure performed at 1km × 1km resolution. The results show that area-based allocation leads to a substantial underestimation of city-level emissions, whereas population-based allocation produces estimates that closely match those obtained from high-resolution downscaling. This difference arises because cities are typically characterised by high population density while occupying a relatively small fraction of the grid cell area. This validation analysis highlights the importance of accounting for input data resolution and zonal statistics methods when extracting emissions over sub-domains, and cautions against the use of simple area-weighted clipping of coarse-resolution grids with shape files for urban-scale analyses. Finally, EDGAR provides emission estimates from a territorial based perspective (i.e. where emissions are happening) and does not include information on trade. Therefore, in this work when emissions per capita are presented, they should not be considered as an indicator of consumption based estimates 33 . Depending on the scope, GHG emissions from cities can vary significantly, as presented by Luqman et al. 3 , but their assessment is beyond the purpose of this work. 4.2 The Urban Centre Database or other GHSL products 4.2.1 The GHSL built-up surface (GHS-BUILT-S) and its non-residential component The GHS-BUILT-S spatial raster dataset 34 depicts the distribution of the built-up (BU) surfaces estimates between 1975 and 2030 in 5-year intervals and two functional use components a) the total BU surface and b) the non-residential (NRES) BU surface. The data is made by spatial-temporal interpolation of five observed collections of multiple-sensor, multiple-platform satellite imageries: Landsat (MSS, TM, ETM sensor) data supports the 1975, 1990, 2000, and 2014 epochs, while a Sentinel-2 (S2) image composite (GHS-composite-S2 R2020A 35 ) supports the 2018 epoch. The non-residential (NRES) built-up surface domain 24 , characterized by uses not compatible with the human residence, is predicted from S2 data by observation of radiometric, textural, and morphological features in a multi-faceted image processing framework merging global unsupervised rule-based reasoning and inductive locally-adaptive methods leveraging on pixel-wise spectral indexes, textural assessments, and object-oriented shape analysis . 4.2.2 The GHSL population distribution grid (GHS-POP) This GHS-POP spatial raster dataset 15 depicts the distribution of human population expressed as the number of people per pixel. It represents the residential population estimates in 5-year interval between 1975 and 2030 derived from the raw global census data harmonized by CIESIN for the Gridded Population of the World, version 4.11 (GPWv4.11 36 ) at polygon level, the UN World Population Prospects (UNWPP 2022 37 ) population time series at country level, and the UN World Urbanisation Prospects (UNWUP 2018 38 ) population time series at urban agglomeration level. These estimates are disaggregated from census or administrative units to grid cells, informed by the distribution, classification and volume of built-up as mapped in the GHSL global layers per corresponding epoch 24 . 4.2.3 The implementation of the Degree of Urbanisation in the GHSL Framework (GHS-SMOD) The GHS Settlement Model (GHS-SMOD) 15 spatial raster dataset is the GHSL implementation of the “Degree of Urbanisation” (DEGURBA) 28,29 in a global and multi-temporal domain. It represents the settlement classification grid (at 1-km spatial resolution in World Mollweide, EPSG:54009) between 1975 and 2030 in 5-year intervals. The “Degree of Urbanisation” is the UN recommended methodology for delineation of cities and urban and rural areas for international and regional statistical comparison purposes ; it is a population-based geospatial classification method of the urban-rural continuum developed by the joint work of the EU, the Food and Agriculture Organization of the United Nations (FAO), the International Labour Office (ILO), the Organisation for Economic Co-operation and Development (OECD), UN-Habitat and the World Bank. Based on resident population density, contiguity and population size criteria applied to population grids (in 1-km equal area projection), the DEGURBA identifies the spatial extents of 7 settlement classes, namely: (from the most dense to the least) the Urban Centres, the Dense Urban Clusters, the Semi-Dense Urban Clusters, the Suburban or peri-urban grid cells, the Rural Clusters, the low density grid cells and the very low density rural grid cells 29 . 4.2.4 The GHSL Urban Centres DataBase (GHS-UCDB) The GHS-UCDB 27 is a spatial database providing attributes to characterise 11,422 urban centres as defined by the “Degree of Urbanisation“ in the GHS-SMOD dataset 15 , between 1975 and 2030 using the urban centres as reporting units (spatial entities). The GHS-UCDB harmonises global urban data reporting by addressing semantic clarity and consistency, thematic and geographic consistency and filling data gaps. The dataset is produced by geospatial data integration carried out with GIS techniques between the Areas of Interest or “zones” (the urban centres), and a variety of open geospatial data to obtain specific attributes belonging to an indicator group and to a thematic area for each of the urban centres. For this study, the spatial extent of urban centres is delineated in 2025, and the boundary is kept fixed going back in time (fixed boundaries approach). The GHS-UCDB R2024 includes an internal process of quality control, based on a data-driven decision ensemble method (univariate linear regression). Nine independent datasets were chosen to support the quality control, including population and land use data. References World Urbanization Prospects. https://population.un.org/wup/. Crippa, M. et al. Global anthropogenic emissions in urban areas: patterns, trends, and challenges. Environ. Res. Lett. 16 , 074033 (2021). Luqman, M., Rayner, P. J. & Gurney, K. R. On the impact of urbanisation on CO2 emissions. Npj Urban Sustain. 3 , 6 (2023). Gurney, K. R. et al. Greenhouse gas emissions from global cities under SSP/RCP scenarios, 1990 to 2100. Glob. Environ. Change 73 , 102478 (2022). Sethi, M. & Creutzig, F. Leaders or laggards in climate action? Assessing GHG trends and mitigation targets of global megacities. PLOS Clim. 2 , e0000113 (2023). World Cities Report 2024. https://unhabitat.org/wcr/. Health Effects Institute. State of Global Air 2024: Special Report. State Glob. Air 2024 Spec. Rep. Transforming our world: the 2030 Agenda for Sustainable Development | Department of Economic and Social Affairs. https://sdgs.un.org/2030agenda. — SDG Indicators. https://unstats.un.org/sdgs/report/2019/goal-11/. Lucchitta, B. et al. Are European cities achieving emission reduction commitments? A comparative analysis under the Covenant of Mayors initiative. Heliyon 10 , e23423 (2024). de Coninck, H. et al. Coordinating Lead Authors: Matsumoto, T., Allain-Dupré, D., Crook, J. & Robert, A. An integrated approach to the Paris climate Agreement: The role of regions and cities. OECD Reg. Dev. Work. Pap. 2019 , (2019). European Commission. Joint Research Centre. GHG Emissions of All World Countries: 2025. (Publications Office, LU, 2025). Crippa, M. et al. EDGAR total GHG (CO2eq AR5) by country, sector and settlement type (1990-2024). (2025). Carioli, A., Schiavina, M., MacManus, K. J. & Freire, S. GHS-POP R2023A - GHS population grid multitemporal (1975-2030). (2023) doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE. Schiavina, M., Melchiorri, M. & Pesaresi, M. GHS-SMOD R2023A - GHS settlement layers, application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A, multitemporal (1975-2030). (2023) doi:10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA. Cheng, L., Mi, Z., Sudmant, A. & Coffman, D. Bigger cities better climate? Results from an analysis of urban areas in China. Energy Econ. 107 , 105872 (2022). Prinajati, P. D., Handayani, L. & Astuti, N. PM2.5 and Heavy Metal Concentrations in Ambient Air of a Steel Industrial Zone: Influence of Meteorological Factors in Cilegon, Indonesia. J. Community Based Environ. Eng. Manag. 9 , 91–100 (2025). Kennedy, C. et al. Greenhouse Gas Emissions from Global Cities. Environ. Sci. Technol. 43 , 7297–7302 (2009). OECD. Compact City Policies: A Comparative Assessment. OECD Green Growth Stud. (2012) doi:10.1787/9789264167865-en. Institute for Environment and Sustainability (Joint Research Centre). EDGAR-HTAP: A Harmonized Gridded Air Pollution Emission Dataset Based on National Inventories . (Publications Office of the European Union, 2011). Crippa, M. et al. Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2. Earth Syst. Sci. Data 10 , 1987–2013 (2018). Crippa, M. et al. Insights into the spatial distribution of global, national, and subnational greenhouse gas emissions in the Emissions Database for Global Atmospheric Research (EDGAR v8.0). Earth Syst. Sci. Data 16 , 2811–2830 (2024). Pesaresi, M. et al. Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data. Int. J. Digit. Earth 17 , 2390454 (2024). Home. Global Energy Monitor https://globalenergymonitor.org/. Melchiorri, M. et al. Stats in the City – the GHSL Urban Centre Database 2025. JRC Publications Repository https://publications.jrc.ec.europa.eu/repository/handle/JRC139768 (2024) doi:10.2760/3046391. Rivero, I. M. et al. GHS-UCDB R2024A - GHS Urban Centre Database 2025. (2024) doi:10.2905/1a338be6-7eaf-480c-9664-3a8ade88cbcd. Applying the Degree of Urbanisation — A methodological manual to define cities, towns and rural areas for international comparisons — 2021 edition. https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-02-20-499. Dijkstra, L. et al. Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. J. URBAN Econ. (2021) doi:10.1016/j.jue.2020.103312. Guevara, M. et al. A benchmarking tool to screen and compare bottom-up and top-down atmospheric emission inventories. Air Qual. Atmosphere Health 10 , 627–642 (2017). Kona, A. et al. Global Covenant of Mayors, a dataset of greenhouse gas emissions for 6200 cities in Europe and the Southern Mediterranean countries. Earth Syst. Sci. Data 13 , 3551–3564 (2021). Arioli, M. S., D’Agosto, M. de A., Amaral, F. G. & Cybis, H. B. B. The evolution of city-scale GHG emissions inventory methods: A systematic review. Environ. Impact Assess. Rev. 80 , 106316 (2020). Wiedmann, T. et al. Three-scope carbon emission inventories of global cities. J. Ind. Ecol. 25 , 735–750 (2021). Pesaresi, M. & Politis, P. GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). (2023) doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA. Joint Research Centre Data Catalogue - GHS-composite-S2 R2020A - Sentinel-2 global pixel ... - European Commission. https://data.jrc.ec.europa.eu/dataset/0bd1dfab-e311-4046-8911-c54a8750df79. Earth Science Data Systems, N. Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11 | NASA Earthdata. Earth Science Data Systems, NASA (2025). Nations, U. World Population Prospects 2022: Summary of Results . (United Nations, 2022). doi:10.18356/9789210014380. Affairs, U. N. D. of E. and S. World Urbanization Prospects: The 2018 Revision . (United Nations, 2019). doi:10.18356/b9e995fe-en. Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8405392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":574407168,"identity":"64b1662f-e38c-4904-ab3b-4cedb11f23cf","order_by":0,"name":"Monica Crippa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYJCCA0Asb99//vmHhAoStBhuOMxjJvHhDAk2JYC0SM5sI0KpbnvvwQM/ahgSDIBapHnn2cgzsPeYffi4w46BXyIBqxazM+cSDvYcY0iwb+Z//pl3W5phA88Z45kzzyQzSM7AoeVGjsFhBjaGBDtmHjZm3m2HExgkcoyZeduYGQxu4NPyjyHBGKTl75z/CQzyb0Ba6hns8WlhbGNI2NgMsqXhANAWHpCWwwwGOP1yxuBgb58EKJDZmHmOJRu28aQVM848c5xH4swD7FqO9xh/+PHNRt7g/Bmglho7eX72w5sZPu6oluNvx24LFEggmGwggrGBgQefeiwAqGUUjIJRMApGAQwAAHVxWwOW2cNZAAAAAElFTkSuQmCC","orcid":"","institution":"European Commission, Joint Research Centre (JRC), Ispra, Italy","correspondingAuthor":true,"prefix":"","firstName":"Monica","middleName":"","lastName":"Crippa","suffix":""},{"id":574407169,"identity":"248f8f86-73bf-4dc2-b750-41356fd5e542","order_by":1,"name":"Diego Guizzardi","email":"","orcid":"","institution":"European Commission, Joint Research Centre (JRC), Ispra, Italy","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Guizzardi","suffix":""},{"id":574407170,"identity":"b939fad8-c596-40ec-b3a5-7375dbe3bb20","order_by":2,"name":"Enrico Pisoni","email":"","orcid":"https://orcid.org/0000-0001-5484-5744","institution":"EC, JRC","correspondingAuthor":false,"prefix":"","firstName":"Enrico","middleName":"","lastName":"Pisoni","suffix":""},{"id":574407171,"identity":"95afd1cb-f6bb-4ec1-a494-58262ebc0190","order_by":3,"name":"Sara Ciarlantini","email":"","orcid":"","institution":"Unisystems Milan S.A, Italy","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Ciarlantini","suffix":""},{"id":574407172,"identity":"44be4431-f855-48cc-ac69-bfdc8b936b5b","order_by":4,"name":"Michele Melchiorri","email":"","orcid":"","institution":"European Commission Joint Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Michele","middleName":"","lastName":"Melchiorri","suffix":""},{"id":574407173,"identity":"1027665e-8cbf-4df1-9757-caa59b38c976","order_by":5,"name":"Marcello Schiavina","email":"","orcid":"https://orcid.org/0000-0003-3399-3400","institution":"NTT Data","correspondingAuthor":false,"prefix":"","firstName":"Marcello","middleName":"","lastName":"Schiavina","suffix":""},{"id":574407174,"identity":"57732ffd-10e6-4d49-a1b3-36e673c36134","order_by":6,"name":"Clara Hormigos Feliu","email":"","orcid":"https://orcid.org/0000-0002-1198-4098","institution":"European Commission, Joint Research Centre (JRC), Ispra, Italy","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"Hormigos","lastName":"Feliu","suffix":""},{"id":574407175,"identity":"802fc2ff-34b5-4e45-8b8f-68fcfaca924e","order_by":7,"name":"Lewis Dijkstra","email":"","orcid":"https://orcid.org/0000-0002-4077-8250","institution":"European Commission, Joint Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Lewis","middleName":"","lastName":"Dijkstra","suffix":""}],"badges":[],"createdAt":"2025-12-19 13:41:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8405392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8405392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100548835,"identity":"a919391c-357c-4c5b-99bd-40b70b9acbb5","added_by":"auto","created_at":"2026-01-19 08:21:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105988,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal GHG emissions by sector and degree of urbanisation, 2024. Total emissions in brackets in billion t CO2 eq.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/88c6170579d4e0a3f59bd28f.jpg"},{"id":100548623,"identity":"00b6261f-f5f6-4642-a246-43c09529b206","added_by":"auto","created_at":"2026-01-19 08:19:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136146,"visible":true,"origin":"","legend":"\u003cp\u003eGHG emissions per capita by degree of urbanisation and income group, 1990-2024\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/9c5ea69cb77b53c748d2cdaa.jpg"},{"id":100498026,"identity":"a53ed939-3dc9-40a6-8439-6c8e970a64e3","added_by":"auto","created_at":"2026-01-18 04:38:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127082,"visible":true,"origin":"","legend":"\u003cp\u003eGHG emissions per capita by degree of urbanisation and world region for 2000 and 2024\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/d7268b39bb19ad1aa13ce16c.jpg"},{"id":100498027,"identity":"baea858d-19b3-45d9-873f-bcf863bb35b4","added_by":"auto","created_at":"2026-01-18 04:38:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145059,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram showing GHG/cap emissions in 2024 divided by income group, sector and degrees of urbanisation. In parenthesis: GHG/cap values in t CO2eq/cap.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/6fd59d8ee0e44ebcde5039f9.jpg"},{"id":100548385,"identity":"e238f9ee-b1c0-4ea6-a961-c995d60ceff2","added_by":"auto","created_at":"2026-01-19 08:18:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117019,"visible":true,"origin":"","legend":"\u003cp\u003eGHG/cap emissions (in t CO2eq/cap) by city size (inhabitants) and world region in 2024. Regions are ranked by increasing GHG/cap in 2024.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/ba18f8e532989d7fefd7b239.jpg"},{"id":100498028,"identity":"284eea31-7e88-4232-92fd-598b33c63677","added_by":"auto","created_at":"2026-01-18 04:38:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360914,"visible":true,"origin":"","legend":"\u003cp\u003eGHG emissions per capita in cities with at least 250 thousand people, 2024 (panel a); Top 50 GHG emitters by city size (inhabitants) for 2024 by total GHG and by GHG/cap (panel b).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/d5838628e5fb8524db037937.jpg"},{"id":101752971,"identity":"547dffea-07c1-44cf-8425-cc76f1242782","added_by":"auto","created_at":"2026-02-03 10:38:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1795364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/17663ec4-33e4-4d55-a5b9-4447ad0505c2.pdf"},{"id":100498031,"identity":"60c72d9f-05ac-4ac5-a86d-62814f780456","added_by":"auto","created_at":"2026-01-18 04:38:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":656309,"visible":true,"origin":"","legend":"GHG emission in cities, towns and rural areas - Supplementary information","description":"","filename":"EDGARpapersupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8405392/v1/56dcf61883eb16c9c0426e7f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"GHG emission in cities, towns and rural areas","fulltext":[{"header":"1\tIntroduction","content":"\u003cp\u003eCities have become central to the global climate agenda, as they are home to a growing share of the world\u0026rsquo;s population and act as hotspots of economic activity, resource consumption, and greenhouse gas (GHG) emissions. Between 1990 and 2024, the urban population increased across all continents, in 90% of all the world\u0026rsquo;s countries, and by 2024 nearly half (45%) of the global population lived in cities, as defined by the degree of urbanisation\u003csup\u003e1\u003c/sup\u003e. The sharpest population growth is found in Africa (+241%), followed by Oceania (+108%), Asia (+95%), North America (+91%) and South America (+83%). A much-reduced growth is found in Europe, where population increased only by 13% over the same time period. Cities concentrate economic activities, which can generate high local concentrations of air pollution\u003csup\u003e2\u003c/sup\u003e. Although per capita emissions in cities are usually lower than national averages\u003csup\u003e3\u003c/sup\u003e, the aggregate contribution of cities remains dominant, with our estimates suggesting that around 21% of global emissions originate in cities (excluding international shipping and aviation). As cities continue to expand and concentrate people, wealth, and consumption, total emissions are expected to increase further\u003csup\u003e4,5\u003c/sup\u003e. At the same time, while they concentrate activities that drive emissions, cities also serve as key sites for climate action\u003csup\u003e6\u003c/sup\u003e, holding significant potential to advance inclusive and effective mitigation. Given their scale, they represent a unique opportunity for interventions that support sustainable development, improve air quality, and reduce climate-related risks.\u003c/p\u003e\n\u003cp\u003eClimate change is a global problem, but it has distinctive urban impacts, as it is closely intertwined with air pollution, a more local issue that must be addressed to reduce population exposure to harmful pollutants, responsible for 8.1 million deaths in 2021\u003csup\u003e7\u003c/sup\u003e. The specific role of urban areas in the climate challenge has been increasingly recognised by science and governance. The 2030 Agenda for Sustainable Development highlights sustainable cities as a central goal, emphasising the need to balance economic growth, social inclusion, and environmental protection\u003csup\u003e8\u003c/sup\u003e. Within this framework, the eleventh Sustainable Development Goal (SDG11, Sustainable cities and communities) stresses that \u0026ldquo;cities and metropolitan areas are powerhouses of economic growth\u0026mdash;contributing about 60% of global GDP. However, they also account for about 70% of global carbon emissions and over 60% of resource use. The rapid urbanisation is resulting into worsening air pollution\u0026rdquo;\u003csup\u003e9\u003c/sup\u003e. In addition, SDG13 (\u0026ldquo;Climate Action\u0026rdquo;) reinforces the central role of cities in addressing climate change.\u003c/p\u003e\n\u003cp\u003eIn addition to these goals, global commitments such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction, and the New Urban Agenda further consolidate the role of cities in advancing mitigation and adaptation\u003csup\u003e6\u003c/sup\u003e. While these agreements are negotiated internationally, their successful implementation requires strong local action. Cities act as catalysts for positive change, supported by networks such as the Global Covenant of Mayors for Climate and Energy, which involves more than 13,000 signatories worldwide\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMoreover, the IPCC has established a dedicated Special Report on Climate Change and Cities, to be delivered in the seventh assessment cycle. This report will provide an assessment of the latest science on climate change and cities, including impacts, risks, and potential adaptation and mitigation solutions. The earlier IPCC Special Report on Global Warming of 1.5\u0026deg;C already identified urban and infrastructure systems as one of the four systems needing transformative\u0026nbsp;change to limit global warming\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite challenges such as limited political authority or lack of access to adequate financing\u003csup\u003e6\u003c/sup\u003e, cities often pursue climate goals that are more ambitious than those of their national government, demonstrating their potential to lead on climate action. In this context, cities emerge as indispensable actors in the global effort to reduce GHG emissions. Global and national mitigation cannot be achieved independently from urban action.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study investigates the evolution of sector-specific GHG emissions by degree of urbanisation, (a UN endorsed method that defines cities, towns and semi-dense areas, and rural areas) over a 35-year period (1990\u0026ndash;2024), from a local to a global level. The analysis relies on the Emissions Database for Global Atmospheric Research (EDGAR), using its latest release, EDGAR_2025_GHG\u003csup\u003e13,14\u003c/sup\u003e. EDGAR is a global bottom-up emission inventory that provides consistent time series of anthropogenic GHG and air pollutant emissions for all countries and sectors from 1970 to 2024. For the first time, global anthropogenic GHG emissions are reported with a spatial resolution of 0.01\u0026deg; x 0.01\u0026deg; (approximately 1 km). This high level of detail enables the investigation of where emissions happen, supporting the development of place-based mitigation measures from global to local level. To increase both scientific and policy relevance, EDGAR implements high-resolution spatial proxies to distribute emissions over the globe. Combined with the most recent population statistics\u003csup\u003e15,16\u003c/sup\u003e, this approach allows a detailed characterisation of emissions by degree of urbanisation and promotes EDGAR as a reference inventory for mapping emissions from different activities.\u003c/p\u003e\n\u003cp\u003eCompared with previous EDGAR city emission estimates\u003csup\u003e2\u003c/sup\u003e (Crippa et al., 2021), the main novelties of this work are: i) temporal extension of the dataset to 2024 (previously limited to 2015), providing the most up to date (t-1) figure of emissions in global cities; ii) increased spatial resolution, from ~10 km to ~1 km grids, enhancing the granularity of emission mapping; iii) improved spatial proxies to disaggregate national emissions over the global gridmap. In this work we focus on GHG emissions in absolute and per capita terms to inform global climate action and foster a sustainable transition across multiple degrees of urbanisation.\u003c/p\u003e"},{"header":"2\tResults ","content":"\u003ch2\u003e2.1 A fifth of global GHG emissions occur in cities\u003c/h2\u003e\n\u003cp\u003eCities represent very localized hotspots in terms of population, activities and consequently also of pollution emitting sources. We estimate that 21% of global GHG emissions occurred in cities, which host 45% of the global population (see Fig. 1). Between 1990 and 2024, the amount of GHG emissions emitted by cities grew from 5.4 billion to 11 billion tonnes CO\u003csub\u003e2eq\u003c/sub\u003e in 2024. This increase in emissions, however, was closely matched by the growth of population from 1.8 to 3.7 billion. Towns and semi-dense areas, which host a third of the global population, also saw a doubling of their emissions over this time period, from 5.7 to 12.5 billion. These areas, however, experienced less population growth (+44%). Finally, rural area, which host only 22% of the global population, have seen the lowest growth in emissions (+38%), but their population barely grew (+14%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe development and implementation of effective mitigation measures is a priority to improve people quality of life and health, guarantee a sustainable future for next generations and preserve our environment and resources. High income countries, covering 14% of the world\u0026rsquo;s population, are responsible for around a quarter of global GHG emissions and contribute the lowest share of emissions in urban areas, compared to other income countries. Conversely, low income countries, representing around 13% global population and 2.5% of global GHG emissions, show the highest share of emissions in urban areas compared to other income-level countries. Table S1 provides more details on the share of emissions by degree of urbanisation and geographical region following the IPCC AR6 definition (see Table S2 for the classification of regions).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2 Big emitting sectors are located outside cities\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAs expected, agricultural emissions are heavily skewed with 83% in rural areas and only 3% in cities. Transport, waste, industry and energy emissions also occur in rural areas (see Fig. 1), while the share of these emissions in cities is low (between 12% and 31%). Given that industry requires a lot of land and may also cause local noise or air pollution, these factories are less likely to be located in a city. Also, energy plants are more likely to be located outside a city, in proximity of country borders and the presence of energy resources (e.g. rivers). Transport, especially of goods, is concentrated on major roads and highways, which are located outside city centres. Residential emissions, in contrast, follow the population distribution more closely with 38% of emissions in cities compared to 45% of the population.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.3 Emissions per capita are lower in cities than in other areas\u003c/h2\u003e\n\u003cp\u003ePer capita emissions are a useful metric to compare cities and areas with different size and population levels. In this study, per capita emissions are calculated considering only territorial emissions (excluding international shipping and aviation), that is, emissions physically released within the boundaries of each degree of urbanisation and income group (classified according to the Sustainable Development Index, SDI), regardless of where the associated goods and services are ultimately consumed.\u003c/p\u003e\n\u003cp\u003eThough urbanisation continues to be a major source of GHG emissions, urban emissions per capita are typically much lower than in towns, semi-dense and rural areas (see Fig. 2). In 2024, cities per capita emissions are a third lower than in towns and semi-dense areas and 80% lower than in rural areas. GHG per capita are lower in cities in part because people tend to live in smaller and more compact dwellings, which reduces energy use for heating energy, and they tend to drive less. In addition, as agriculture, industry, transport and energy production occur largely outside cities, this reduces the emissions within cities, as discussed below.\u003c/p\u003e\n\u003ch2\u003e2.4 High SDI countries have the highest emissions per capita, but they are shrinking\u003c/h2\u003e\n\u003cp\u003eSince 2005, high SDI countries have reduced their emissions in all three types of areas. Although this is partially due because high-income countries are importing more from low and middle-income countries, it also due to investments in energy efficiency and renewable energy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApart from this SDI category, all the other SDI categories\u0026rsquo; emissions have been mostly growing over time. From 2010, emissions per capita in towns and semi-dense areas in high-middle SDI countries even exceed those in high SDI (in 2024: 8.1 vs 6.1 tCO2eq/cap). This is likely to be due to growth of manufacturing in those areas (see Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn low SDI countries, per capita emissions have not changed since 1990 in rural areas, and towns and semi-dense areas (3 and 0.6 tCO\u003csub\u003e2eq\u003c/sub\u003e/cap, respectively). In cities, it even dropped from 1.6 in 1990 to 0.7 in 2024. These low emissions per capita are not the result of investments in energy efficiency and renewable energy, they are simply the consequence of high levels of poverty and low economic growth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLooking by world regions shows that between 1990 to 2024 in Europe, North America, Australia, Japan, New Zealand, Eastern Europe and West-Central Asia, GHG emissions per capita decreased (see\u0026nbsp;Fig.\u0026nbsp;3). In all the other regions, emissions per capita grew or remained stable. The biggest increase in emissions per capita occurred in rural areas in the Middle East and Eastern Asia and (+17.4 and +16.4\u0026nbsp;t CO2 eq/cap). The third biggest increase also happened in Eastern Asia, but in its towns and semi-dense areas (+8.7). This is largely due to the rapid growth of manufacturing in Eastern Asia. Conversely, the biggest reductions also occurred in rural areas with North America in the lead (-8.3) followed by rural Europe, Eastern Europe and West-Central Asia (-5.3). In these regions, the emissions per capita also dropped in cities, towns and semi-dense areas, but reductions were smaller (-1.9 to -4.9).\u003c/p\u003e\n\u003cp\u003eThe world average per capita emissions increased for all three\u0026nbsp;degrees of urbanisation, with the biggest increase in rural areas (+2.7 t CO2 eq/cap), followed by towns and semi-dense areas (+1.6) and with very little change in cities (+0.08). During this period, cities have consistently shown the lowest emissions per capita, which stood at 3 tCO2eq per inhabitant in 2024. In the same year, towns and semi-dense areas emitted 4.5 tCO2eq/cap, and rural areas reached 15.4 tCO2eq/cap globally.\u003c/p\u003e\n\u003cp\u003eIn 2024, Eastern Asia, Eastern Europe and West-Central Asia, the Middle East, Australia, Japan and New Zealand, and North America show emissions per capita greater than the world average across all three\u0026nbsp;degrees of urbanisation. This is especially true for Australia, Japan and New Zealand, and North America, where rural per capita emissions are more than three times higher than the world average for that type of area.\u003c/p\u003e\n\u003cp\u003eOnly around 7.5% of the cities in the world have higher per capita emissions than the country average. Cities with GHG/cap higher than the national average are mostly associated with the presence of point sources (see Fig. S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGHG per capita emissions sectoral composition remains broadly similar over time (see Fig. S1), overall increasing in all the three\u0026nbsp;degrees of urbanisation\u0026nbsp;over the 35 years\u0026rsquo; period. In cities, GHG/cap emissions increase only modestly between 1990 and 2024, from roughly 2.9 to just above 3 t CO2eq per person. Industry and energy together account for the largest share of urban emissions, both sectors\u0026rsquo; emissions increasing by 12%. Residential emissions also remain an important contributor, although their share decrease by 40% in 2024, while waste decreases by only 3%. By contrast, transport and especially agriculture emissions remain very low in cities, even though their shares increase over time (+48% and +39%, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Sankey diagram (Fig. 4) further shows that these sectoral patterns are strongly stratified by income group. High- and high-middle SDI countries dominate flows into the energy and industry sectors. Middle-SDI countries contribute substantially to industry, agriculture and energy-related emissions. Emissions in low-middle SDI countries are mainly split fairly evenly between industry, agriculture and energy. Low-SDI countries contribute proportionally more to agriculture-related emissions, with contributions to other sectors being around three to eight times lower than to agriculture, even though their overall per capita emissions remain much lower than those of high-, high-middle- and middle SDI countries. As a result, the majority of GHG/cap emissions across all sectors and degrees of urbanisation is concentrated in high-SDI areas, while low-SDI regions account for only a small share of the total.\u003c/p\u003e\n\u003ch2\u003e2.5 Big cities have lower GHG emissions per capita than small cities\u003c/h2\u003e\n\u003cp\u003eAs a general pattern, more populous cities tend to exhibit lower GHG/cap emissions than cities with fewer inhabitants (see Fig. 5)\u003csup\u003e17\u003c/sup\u003e. The world GHG/cap average shows that cities with less than 100,000 inhabitants have 1.23 times higher GHG emissions per capita compared to cities with over 5 million inhabitants (3.7 t vs 2.8 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024). African, Middle East, and Eastern Europe and West-Central Asia cities are an exception to this pattern, where bigger cities (especially those with over 5 million inhabitants) show higher GHG/cap compared to smaller ones. Latin America and Caribbean and South-East Asia and Pacific display limited variations between cities of different sizes. In particular, this latter region presents a peak for the 250,000\u0026ndash;1 million inhabitants class, which is driven by the Indonesian city of Cilegon, considered one of Indonesia\u0026apos;s largest industrial hubs due to its heavy industry and fossil-fuel combustion within and around the city\u003csup\u003e18\u003c/sup\u003e, with GHG/cap levels three times higher than the next city in descending order by emissions per capita.\u003c/p\u003e\n\u003cp\u003eComparing cities of different sizes across regions, overall the highest GHG emissions per capita in cities with less than 100,000 inhabitants occur in Australia, Japan and New Zealand (8.9 t CO\u003csub\u003e2\u003c/sub\u003e eq/cap), mainly occurring in Japan, with only Kamisu city having 239 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024. Eastern Asian cities follow: in this region\u0026rsquo;s cities below 100,000 inhabitants, 92% of which are in China, GHG emissions per capita increased over the past two decades, reaching 8.1 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024. Altogether, cities in Eastern Asia with less than 1 million inhabitants account for 25% of global urban emissions. High GHG/cap emissions are also found in cities with 100.000 to 250.000 inhabitants of Australia, Japan and New Zealand (11.5 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024), which have remained approximately constant during the last 20 years. Many of these small, high-emission cities are affected by the presence of point sources (see Fig. S2), especially from the energy and industry sectors. Therefore, high emission levels are not linked to consumer habits in individual cities, but rather highlight the impacts of global and national consumption patterns in smaller urban areas of specific regions.\u003c/p\u003e\n\u003cp\u003eAmong big cities (above 5 million inhabitants), the highest GHG emissions per capita are found in North America, Eastern Europe/West-Central Asia (Moscow only with 5.8 t CO\u003csub\u003e2\u003c/sub\u003e eq/cap) and the Middle East. Riyad (Saudi Arabia) emerges as the big city with higher per capita emissions (13 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024), 72% of which stem from the industrial sector. Per capita GHG emissions are also high in Nanjing (China) and Nagoya (Japan), both surpassing 9 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024. In both cities, GHG emissions mainly arise from the energy and industrial sectors (91% for Nanjing and 84% for Nagoya). Within North America, Toronto (Canada) and Chicago (United States) lead in GHG emissions per capita (above 7.7 t CO\u003csub\u003e2\u003c/sub\u003eeq/cap in 2024), despite a decrease over the last two decades.\u003c/p\u003e\n\u003cp\u003eAt the global scale (see Fig. 6), larger cities, particularly those with more than 5 million inhabitants, are among the greatest GHG emitters (Fig. 6b, left). However, the opposite pattern emerges when GHG/cap are examined: cities with fewer inhabitants tend to exhibit the highest per capita emissions (Fig. 6b, right). Apart from Namakkal (India), which has a population of 193,000 people, the GHG/cap ranking is clearly differentiated by city size, with larger cities showing systematically lower per capita emissions. For example, when comparing the cities with the highest GHG/cap emissions in the smallest and largest city size classes, Jinfeng (China) records per capita emissions that are 41 times higher than those of Riyadh.\u003c/p\u003e\n\u003cp\u003eA further distinction emerges between how density and city area relate to GHG emissions. Holding constant country-specific conditions, denser urban areas tend to have substantially lower emissions per resident: a 10% higher density is associated with 8.8% lower GHG emissions per capita. This pattern is consistent with the idea that compact cities make more efficient use of infrastructure, transport systems, and building energy. By contrast, both overall population size and the extent of urbanised land are strongly associated with higher total emissions: larger cities in terms of inhabitants, and those that occupy more land surface (km\u003csup\u003e2\u003c/sup\u003e), generate substantially more GHG emissions in absolute terms. In line with this, denser cities that rely more heavily on public transport, walking and cycling generally exhibit lower energy use and GHG emissions from transport, reflecting the well-documented inverse relationship between urban population density and transport-related energy consumption and emissions\u003csup\u003e19\u003c/sup\u003e. Beyond the transport sector, cross-country evidence also indicates that higher urban density is associated with lower per capita electricity demand, partly because compact urban forms can more effectively integrate energy-efficient systems such as district heating and cooling, combined heat and power, and smart grids\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e"},{"header":"3\tDiscussion ","content":"\u003cp\u003eCities host almost half the global population and this share is projected to grow\u003csup\u003e1\u003c/sup\u003e. As cities emit only 21% of global emissions, the main actions to reduce global emissions will have to be taken elsewhere. Cities can further reduce emissions from their already low rate, by promoting more energy efficient heating, cooling and lighting and by encouraging more people to walk, cycle and use public transport. Even small changes in per capita emissions in cities can translate into sizable global effects as so many people live in cities. City emissions mitigation strategies should therefore be a part of national and global climate strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distinct emission profiles of cities, towns and rural areas underline the need for coordinated climate action across governance levels. Interactions between local areas, regions and countries are therefore two-way: local choices shape national trajectories, and national decisions condition what is feasible in cities, towns and rural areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore populous and denser cities tend to have lower GHG/cap emissions than smaller and more dispersed areas. Population density makes it more efficient to provide public transport. Density also reduces trip lengths, making walking and cycling more attractive options. Finally, density also encourages more compact buildings which are easier to heat and cool. Dense neighbourhoods, however, are not automatically attractive. These neighbourhoods need sufficient transport investment, public space and urban green to make it an attractive place to live. In some isolated cases, a dense city will have high emissions per capita because it has a large industrial or energy plant within its boundaries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGlobally, per capita emissions are five times higher in rural areas than in cities. This is mostly because high emitting sectors, such as agriculture, industry and energy, are more likely to be located in towns, semi-dense and rural areas. Cities also produce less GHG emissions per capita because its residents can heat their homes with less energy and tend to drive less. Over the last two decades, high-income countries reduced emissions per capita in all three classes of the degree of urbanisation, emissions rose most strongly in middle-income countries. The biggest increases and decreases in emissions per capita occurred in rural areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA main strength of this work is its global coverage and methodological consistency, combining gridded emissions with a harmonised degree-of-urbanisation framework over a 35-year period. This enables systematic comparison across different degrees of urbanisation, regions and income groups. At the same time, uncertainties are related with the provision of city emission estimates through the downscaling process of national emissions over the global gridmap.\u003c/p\u003e\n\u003cp\u003eThis study adopts a consistent territorial perspective that overcomes biases introduced by city definitions or approaches to attribute emissions to specific cities, focusing on emissions released within the boundaries of cities, towns and semi-dense areas, and rural areas; which aligns with the responsibilities and instruments of local and national authorities. A consumption accounting of GHG emissions would yield a different perspective. For example, a large share of the agricultural emissions would shift to cities and towns. The emissions from a power plant in a town would be distributed to all the users of that energy, who may live in the same town or in neighbouring city or rural area. Territorial and consumption-based accounts are thus complementary: the former identifies where emissions need to be directly reduced, while the latter highlights the demand, trade and behavioural patterns that shape emissions along supply chains.\u003c/p\u003e"},{"header":"4\tMethods ","content":"\u003ch2\u003e4.1 The EDGAR database\u003c/h2\u003e\n\u003ch3\u003e4.1.1 EDGAR emissions calculation methodology\u003c/h3\u003e\n\u003cp\u003eThe Emissions Database for Global Atmospheric Research (EDGAR, https://edgar.jrc.ec.europa.eu/) is a global emission inventory of greenhouse gases (GHGs) and air pollutants emitted by all anthropogenic sectors, covering historic time series (1970 up to most recent years) and all countries. EDGAR emissions are computed at national level following a consistent Intergovernmental Panel on Climate Change (IPCC) methodology, using international activity data and default emission factors, allowing comparability among country estimates. The detailed EDGAR emission calculation methodology is described in several scientific publications\u003csup\u003e13,21,22\u003c/sup\u003e. In addition to country specific estimates, EDGAR downscales national emission totals over the global gridmap, making use of sector specific high-spatial resolution proxies\u003csup\u003e2,23\u003c/sup\u003e. This downscaling procedure guarantees consistency between country emission values and the gridded once, supporting intercomparison analyses between country and local scale emission patterns. In this work we address emissions by degree of urbanisation and at city level, thus requiring the use of higher spatial resolution proxies than those typically used for EDGAR gridmaps, i.e. 0.1\u0026deg; \u0026times; 0.1\u0026deg; resolution proxies (~ 10km x 10km). For this purpose, 0.01\u0026deg; \u0026times; 0.01\u0026deg; spatial proxies (~ 1km x 1km) have been developed using high spatial resolution information for all the available data. In particular, Global Human Settlements Layer\u003csup\u003e24\u003c/sup\u003e (GHSL, https://human-settlement.emergency.copernicus.eu/) products (i.e. population by degree of urbanisation and non-residential built-up surface) and point source location, mostly for power plants from the Global Energy Monitor\u003csup\u003e25\u003c/sup\u003e, are here used at 1km x 1km resolution to downscale national emissions and then to extract emissions by degree of urbanization and city domain. \u003c/p\u003e\n\n\u003ch3\u003e4.1.2 EDGAR emissions by city and degree of urbanisation\u003c/h3\u003e\n\u003cp\u003eEDGAR emissions by degree of urbanization and by cities are available on the EDGAR website (https://edgar.jrc.ec.europa.eu/edgar_smod and https://edgar.jrc.ec.europa.eu/edgar_cities) and on the GHSL Global Urban Centre Database (GHS-UCDB, https://edgar.jrc.ec.europa.eu/dataset_ucdb and http://data.europa.eu/89h/1a338be6-7eaf-480c-9664-3a8ade88cbcd). \u003c/p\u003e\n\u003cp\u003eGrid cells are classified according with the Degree of Urbanisation definition\u003csup\u003e26\u0026ndash;29\u003c/sup\u003e. Specifically, urban centres correspond to the ensemble of grid cells having a population density of at least 1,500 people/km\u003csup\u003e2\u003c/sup\u003e on land grouped in clusters by 4-connectivity with at least 50,000 people and are outlined in 2025. In addition to GHG emissions discussed in this publication, the Urban Centre Database (UCDB)\u003csup\u003e27\u003c/sup\u003e provides 471 indicators over 15 thematic areas for each city in the world. Within these indicators, emissions by sector for fossil CO2, GHGs, NOx and PM2.5 are also provided.\u003c/p\u003e\n\u003cp\u003eThe datasets released within this work have been obtained working at 1 km x 1km resolution which ensures full consistency and accuracy between EDGAR gridded emissions and GHSL products. It also aims at reducing uncertainties related with spatial re-projection (i.e. from Mollweide to WGS84), cells alignments and cross-settlement cutting methodologies. GHG emissions by degree of urbanisation are available from 1990 until 2024 by country and aggregated sectors (https://edgar.jrc.ec.europa.eu/edgar_smod), while emissions from cities by sector and country are available from 2000 to 2024, with 5 year time step and considers the city shape of 2025 accordingly with UCDB (https://edgar.jrc.ec.europa.eu/edgar_cities).\u003c/p\u003e\n\u003cp\u003eThe use of high-spatial resolution data in the downscaling procedure of national emission totals over the global gridmap help reducing the uncertainty of the quantification of emissions e.g. over cities which however may not exactly correspond to the emission values calculated independently at city level\u003csup\u003e30\u003c/sup\u003e. On the other hand, the strength and richness of this work is the capability to provide a comprehensive picture of global emissions at various scales, from country level, degree of urbanisation and cities using a harmonised and internationally recognised methodology which allows comparability analysis and a complete assessment (coverage). The uniqueness of this work and in particular of the Urban Centre Database is the provision of emission estimates for 11421 cities in the world that is invaluable compared to exercises collating together local and city level inventories\u003csup\u003e31\u003c/sup\u003e using different methodologies for emission estimation\u003csup\u003e32\u003c/sup\u003e, data extraction, sectoral coverage, which can support local studies but not a global assessment. \u003c/p\u003e\n\u003ch3\u003e4.1.3 Methodological guidance for emissions extraction over cities\u003c/h3\u003e\n\u003cp\u003eExtracting emissions (or other globally distributed indicators) over user-defined polygons from spatially resolved global grid maps at relatively coarse spatial resolution (e.g. 10 km \u0026times; 10 km, which is the typical resolution of EDGAR products) requires particular attention to data consistency and allocation methods. As a first step, users should verify the consistency of projection systems between gridded data and shape files, as well as the alignment and referencing of grid cells (e.g. whether coordinates refer to the lower-left corner or the cell centre).\u003c/p\u003e\n\u003cp\u003eTo extract emissions for a single city (or another domain), a shape file defining the target boundary is applied to a gridded emission map (e.g. at 10 km \u0026times; 10 km resolution). For grid cells entirely contained within the shape file, emissions can be fully allocated to the city. However, for grid cells that only partially overlap with the city boundary, only a fraction of the emissions should be assigned to the city. Several allocation approaches can be adopted, including area-based and population-based methods (as done by Crippa et al.\u003csup\u003e2\u003c/sup\u003e). The latter relies on the fraction of population within each 10km \u0026times; 10km cell, derived from higher-resolution population proxies (e.g. 1km \u0026times; 1km).\u003c/p\u003e\n\u003cp\u003eThese two approaches are compared with a full downscaling procedure performed at 1km \u0026times; 1km resolution. The results show that area-based allocation leads to a substantial underestimation of city-level emissions, whereas population-based allocation produces estimates that closely match those obtained from high-resolution downscaling. This difference arises because cities are typically characterised by high population density while occupying a relatively small fraction of the grid cell area. This validation analysis highlights the importance of accounting for input data resolution and zonal statistics methods when extracting emissions over sub-domains, and cautions against the use of simple area-weighted clipping of coarse-resolution grids with shape files for urban-scale analyses.\u003c/p\u003e\n\u003cp\u003eFinally, EDGAR provides emission estimates from a territorial based perspective (i.e. where emissions are happening) and does not include information on trade. Therefore, in this work when emissions per capita are presented, they should not be considered as an indicator of consumption based estimates\u003csup\u003e33\u003c/sup\u003e. Depending on the scope, GHG emissions from cities can vary significantly, as presented by Luqman et al.\u003csup\u003e3\u003c/sup\u003e, but their assessment is beyond the purpose of this work.\u003c/p\u003e\n\n\u003ch2\u003e4.2 The Urban Centre Database or other GHSL products\u003c/h2\u003e\n\u003ch3\u003e4.2.1 The GHSL built-up surface (GHS-BUILT-S) and its non-residential component\u003c/h3\u003e\n\u003cp\u003eThe GHS-BUILT-S spatial raster dataset\u003csup\u003e34\u003c/sup\u003e depicts the distribution of the built-up (BU) surfaces estimates between 1975 and 2030 in 5-year intervals and two functional use components a) the total BU surface and b) the non-residential (NRES) BU surface. The data is made by spatial-temporal interpolation of five observed collections of multiple-sensor, multiple-platform satellite imageries: Landsat (MSS, TM, ETM sensor) data supports the 1975, 1990, 2000, and 2014 epochs, while a Sentinel-2 (S2) image composite (GHS-composite-S2 R2020A\u003csup\u003e35\u003c/sup\u003e) supports the 2018 epoch. The non-residential (NRES) built-up surface domain\u003csup\u003e24\u003c/sup\u003e, characterized by uses not compatible with the human residence, is predicted from S2 data by observation of radiometric, textural, and morphological features in a multi-faceted image processing framework merging global unsupervised rule-based reasoning and inductive locally-adaptive methods leveraging on pixel-wise spectral indexes, textural assessments, and object-oriented shape analysis .\u003c/p\u003e\n\u003ch3\u003e4.2.2 The GHSL population distribution grid (GHS-POP)\u003c/h3\u003e\n\u003cp\u003eThis GHS-POP spatial raster dataset\u003csup\u003e15\u003c/sup\u003e depicts the distribution of human population expressed as the number of people per pixel. It represents the residential population estimates in 5-year interval between 1975 and 2030 derived from the raw global census data harmonized by CIESIN for the Gridded Population of the World, version 4.11 (GPWv4.11\u003csup\u003e36\u003c/sup\u003e) at polygon level, the UN World Population Prospects (UNWPP 2022\u003csup\u003e37\u003c/sup\u003e) population time series at country level, and the UN World Urbanisation Prospects (UNWUP 2018\u003csup\u003e38\u003c/sup\u003e) population time series at urban agglomeration level. These estimates are disaggregated from census or administrative units to grid cells, informed by the distribution, classification and volume of built-up as mapped in the GHSL global layers per corresponding epoch\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e4.2.3 The implementation of the Degree of Urbanisation in the GHSL Framework (GHS-SMOD)\u003c/h3\u003e\n\u003cp\u003eThe GHS Settlement Model (GHS-SMOD)\u003csup\u003e15\u003c/sup\u003e spatial raster dataset is the GHSL implementation of the \u0026ldquo;Degree of Urbanisation\u0026rdquo; (DEGURBA)\u003csup\u003e28,29\u003c/sup\u003e in a global and multi-temporal domain. It represents the settlement classification grid (at 1-km spatial resolution in World Mollweide, EPSG:54009) between 1975 and 2030 in 5-year intervals. The \u0026ldquo;Degree of Urbanisation\u0026rdquo; is the UN recommended methodology for delineation of cities and urban and rural areas for international and regional statistical comparison purposes ; it is a population-based geospatial classification method of the urban-rural continuum developed by the joint work of the EU, the Food and Agriculture Organization of the United Nations (FAO), the International Labour Office (ILO), the Organisation for Economic Co-operation and Development (OECD), UN-Habitat and the World Bank. \u003c/p\u003e\n\u003cp\u003eBased on resident population density, contiguity and population size criteria applied to population grids (in 1-km equal area projection), the DEGURBA identifies the spatial extents of 7 settlement classes, namely: (from the most dense to the least) the Urban Centres, the Dense Urban Clusters, the Semi-Dense Urban Clusters, the Suburban or peri-urban grid cells, the Rural Clusters, the low density grid cells and the very low density rural grid cells\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e4.2.4 The GHSL Urban Centres DataBase (GHS-UCDB)\u003c/h3\u003e\n\u003cp\u003eThe GHS-UCDB\u003csup\u003e27\u003c/sup\u003e is a spatial database providing attributes to characterise 11,422 urban centres as defined by the \u0026ldquo;Degree of Urbanisation\u0026ldquo; in the GHS-SMOD dataset\u003csup\u003e15\u003c/sup\u003e, between 1975 and 2030 using the urban centres as reporting units (spatial entities).\u003c/p\u003e\n\u003cp\u003eThe GHS-UCDB harmonises global urban data reporting by addressing semantic clarity and consistency, thematic and geographic consistency and filling data gaps. The dataset is produced by geospatial data integration carried out with GIS techniques between the Areas of Interest or \u0026ldquo;zones\u0026rdquo; (the urban centres), and a variety of open geospatial data to obtain specific attributes belonging to an indicator group and to a thematic area for each of the urban centres. For this study, the spatial extent of urban centres is delineated in 2025, and the boundary is kept fixed going back in time (fixed boundaries approach).\u003c/p\u003e\n\u003cp\u003eThe GHS-UCDB R2024 includes an internal process of quality control, based on a data-driven decision ensemble method (univariate linear regression). Nine independent datasets were chosen to support the quality control, including population and land use data.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eWorld Urbanization Prospects. https://population.un.org/wup/.\u003c/li\u003e\n\u003cli\u003eCrippa, M. \u003cem\u003eet al.\u003c/em\u003e Global anthropogenic emissions in urban areas: patterns, trends, and challenges. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 074033 (2021).\u003c/li\u003e\n\u003cli\u003eLuqman, M., Rayner, P. J. \u0026amp; Gurney, K. R. On the impact of urbanisation on CO2 emissions. \u003cem\u003eNpj Urban Sustain.\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 6 (2023).\u003c/li\u003e\n\u003cli\u003eGurney, K. R. \u003cem\u003eet al.\u003c/em\u003e Greenhouse gas emissions from global cities under SSP/RCP scenarios, 1990 to 2100. \u003cem\u003eGlob. Environ. 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D. of E. and S. \u003cem\u003eWorld Urbanization Prospects: The 2018 Revision\u003c/em\u003e. (United Nations, 2019). doi:10.18356/b9e995fe-en.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8405392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8405392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In a rapidly urbanising world facing increasingly stringent climate constraints, understanding where and how greenhouse gases (GHG) are emitted by degree of urbanisation is crucial. This study examines how sector-specific GHG emissions have evolved over 35 years (1990–2024) in cities, towns and semi-dense areas, and rural areas, using the latest release of the Emissions Database for Global Atmospheric Research (EDGAR). For the first time, EDGAR GHG emissions are distributed at 1 km resolution using high-resolution spatial proxies, enabling a detailed characterisation of territorial emissions by degree of urbanisation globally.\r\nResults indicate that cities account for around one fifth of global GHG emissions and 45% of the world’s population. Yet per capita emissions are generally lower in cities than in rural areas in particular in high-income countries. Larger and denser cities tend to exhibit lower GHG emissions per capita than smaller and less dense urban areas, underscoring the mitigation potential of compact urban development and targeted, place-based climate policies.","manuscriptTitle":"GHG emission in cities, towns and rural areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-18 04:37:57","doi":"10.21203/rs.3.rs-8405392/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90fc4c3c-8b64-4654-9ae7-16fee2bdef26","owner":[],"postedDate":"January 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61118019,"name":"Earth and environmental sciences/Climate sciences"},{"id":61118020,"name":"Scientific community and society"}],"tags":[],"updatedAt":"2026-02-02T02:13:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-18 04:37:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8405392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8405392","identity":"rs-8405392","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}