As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO₂ equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.

Spatially explicit information on carbon fluxes related to land use and land cover change (LULCC) is of value for the implementation of local climate change mitigation strategies. However, estimates of these carbon fluxes are often aggregated to larger areas. We estimated committed gross carbon fluxes related to LULCC in Baden-Württemberg, Germany, using different emission factors. In doing so, we compared four different data sources regarding their suitability for estimating the fluxes: (a) a land cover dataset derived from OpenStreetMap (OSMlanduse); (b) OSMlanduse with removal of sliver polygons (OSMlanduse cleaned), (c) OSMlanduse enhanced with a remote sensing time series analysis (OSMlanduse+); (d) the LULCC product of Landschaftsveränderungsdienst (LaVerDi) from the German Federal Agency of Cartography and Geodesy. We produced a high range of carbon flux estimates, mostly caused by differences in the area of the LULCC detected by the different change methods. Except for the OSMlanduse change method, all LULCC methods achieved results that are comparable to other gross emission estimates. The carbon flux estimates of the most plausible change methods, OSMlanduse cleaned and OSMlanduse+, were 291,710 Mg C yr^-1 and 93,591 Mg C yr^-1, respectively. Uncertainties were mainly caused by incomplete spatial coverage of OSMlanduse, false positive LULCC due to changes and corrections made in OpenStreetMap during the study period, and a high number of sliver polygons in the OSMlanduse changes. Overall, the results showed that OSM can be successfully used to estimate LULCC carbon fluxes if data preprocessing is performed with the suggested methods.

Carbon satellites, as the most direct means of observing carbon dioxide globally, offer credible and scientifically robust methods for estimating carbon emissions. To enhance the accuracy and timeliness of urban-scale carbon emission estimates, this study proposes an innovative model that integrates top-down carbon satellite data with high-resolution spatial proxies, including points of interest, road networks, and population distribution. The K-means clustering method was employed to study the relationship between carbon emissions and XCO₂ anomalies. Based on this, the local adaptive carbon emission estimation model was constructed. Further, by integrating the spatial distribution and weights of proxy data, carbon emissions were reallocated to generate a high-resolution urban carbon emission map at a 1 km × 1 km resolution. Taking Urumqi, the XCO₂ background concentration ranged from approximately 408 ppm to 415 ppm in 2020, and the corresponding ${∆\mathrm{XCO}}_2$ ranged from −1.58 ppm to 1.13 ppm. The total carbon emission estimated by the local adaptive model amounted to approximately 58.26718 million tons in 2020, close to the EDGAR dataset, with most monthly relative error within ±10%. The Pearson correlation coefficient between the ODIAC dataset and spatially redistributed carbon emission was 0.192, and their comparison showed that high carbon emission areas in the spatially redistributed carbon emission aligned closely with urban industrial parks and commercial centers, offering a more detailed representation of urban carbon emission spatial characteristics. This method contributed to exploring the potential of carbon satellites for quantitatively measuring anthropogenic emissions and offers improved insights into monitoring urban-scale carbon dioxide emissions.

Accurately targeting and tracking climate change mitigation efforts requires detailed bottom-up greenhouse gas (GHG) emission inventories verified by independent atmospheric measurements. While policy decisions often rely on annual national or city-scale inventories, higher-resolution data, both spatially (sub-city) and temporally (daily to hourly), though more uncertain, provide key advantages. First, they are input to inverse modelling of emission sources from atmospheric measurements, which offers a semi-independent approach to verify bottom-up estimates. Second, they can enable simulations to evaluate the impact of interventions, such as changes of policies, industrial standards or household behaviour, on emissions and atmospheric concentrations. Third, by providing localized, near real-time emissions data that can enhance the communication and tracking of climate actions, they can motivate both behavioural and policy shifts.This study presents a straightforward method to create high-resolution carbon dioxide emission inventories for road traffic (street level) and heating (building level) using publicly available data such as OpenStreetMap. Scalable across Germany and adaptable to diverse contexts and applications, the method is demonstrated in Mannheim and Heidelberg, part of the Rhine-Neckar Metropolitan Area.For road traffic, emissions are derived by estimating daily traffic volumes using road type, lane count, and population density, combined with speed- and fuel-specific emission factors and national vehicle fleet data. Heating emission estimates combine building data with gridded census data on heating energy sources and building age.We validate our traffic volume against independent traffic count data and compare our emissions with existing inventories. Road traffic emissions in the Rhine-Neckar region exceeded the regional estimates of TNO (Super et al., 2021), a widely used European inventory, by 1.6%. Building heating emissions were 12% and 8% lower than inventory estimates for Mannheim and Heidelberg, respectively, primarily due to differences in emission factor assumptions.

Cities have a great responsibility to mitigate CO2 emissions, as they contribute substantially to global anthropogenic CO2. To assist cities in efficient mitigation planning an independent data-driven approach to monitor emissions within urban areas is required. The Rhine-Neckar area comprises the cities of Mannheim and Heidelberg and is characterized by large emissions due to significant energy production and industry on the one hand, and by ambitious climate goals on the other hand. To monitor and support mitigation efforts of these cities, we are developing an urban monitoring network using mid-cost CO2 and air quality sensors for Heidelberg and Mannheim. The network will consist of 18 sensor nodes provided by the University of California, Berkeley. Each node is identical in construction to the sensors in the Berkeley Air Quality and CO2 Network (BEACO2N) (Shusterman et al., 2016) and measures CO2, CO, PM2.5 and NO2.In conjunction with the measurement network, we use GRAMM/GRAL to model atmospheric transport in the domain on high resolution. GRAMM/GRAL is run following a catalog approach, in which hourly steady-state wind conditions are assumed. This way the computational costs can be reduced enabling the simulation of longer time scales on street canyon resolving spatial resolution (Berchet et al., 2017, May et al., 2024). We feed the model with high-resolution inventories of fossil fuel and biogenic emissions and compare the simulated enhancements to the measurements of the first deployed nodes. We discuss the capability of the conjunction of high-resolution modeling and mid-cost observations to detect emission patterns within the Rhine-Neckar area.Shusterman, A. A., et al., (2016). The BErkeley Atmospheric CO2 Observation Network: initial evaluation. Atmos. Chem. Phys., 16, 13449–13463., https://doi.org/10.5194/acp-16-13449-2016Berchet, A., et al., (2017). A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale. Atmospheric Environment, 158, 181-196., https://doi.org/10.1016/j.atmosenv.2017.03.030May, M., et al., (2024). Evaluation of the GRAMM/GRAL Model for High-Resolution Wind Fields in Heidelberg, Germany. Atmospheric Research, 300, 107207., https://doi.org/10.1016/j.atmosres.2023.107207

Achieving climate neutrality requires a socio-ecological transformation of the mobility sector. Consequently, urban traffic infrastructure must be redesigned to promote and support active mobility. By analysing multiple criteria extracted from open-access geodata via OSM (OpenStreetMap), we can assess the current state of the urban traffic infrastructure and evaluate the walkability and bikeability of streets and neighborhoods. HeiGIT’s walkability and bikeability indices – HiWalk and HiBike – provide traffic planners, decision-makers, NGOs, and the general public with quantitative insights into how well a city's traffic infrastructure supports active mobility.In recent years, numerous walkability and bikeability indices have emerged, focusing primarily on accessibility metrics. However, these anlyses often assume that all streets are equally suitable for active mobility, overlooking the specific needs of groups such as the elderly, young children, people with disabilities, and risk-averse cyclists.It is therefore essential to address two critical gaps: (a) providing detailed information on the suitability of urban environments for active mobility, and (b) ensuring that traffic infrastructure transformation is inclusive. To meet these needs, we collaborate with practitioners and NGOs to co-create street-level indices of walkability (HiWalk) and bikeability (HiBike). Our goal is to offer practical applications that go beyond general index values for cities or neighborhoods. By assessing bikeability and walkability at the street-level, we can better inform routing engines and support accessibility analyses of inclusive “15-minute cities“.HiWalk and HiBike consist of indicators that assess the user-friendliness, attractiveness, and safety of paths and streets. They evaluate factors such as the presence of sidewalks and cycling lanes, surface smoothness, and surface material types. HiBike also incorporates information on street-side parking from OSM to identify streets where cyclists may be at risk of “dooring”. Both indices are entirely based on open data and can be adapted to various urban settings worldwide.Since HiWalk and HiBike are still under development, our presentation will focus on the main challenges we have encountered, including (1) their application for cities with different cultural, socioeconomic, and environmental contexts, and (2) the variability in the quality and completeness of OSM data. These challenges underscore the benefits of our co-creation approach in enhancing the indices' usability and impact on policy.