Model Factsheets
| Name | EcoSense |
| Acronym | EcoSense |
| Methodical Focus | Environmental impact assessment , Air quality management , External costs |
| Institution(s) | Institute of Energy Economics and Rational Energy Use; University of Stuttgart |
| Author(s) (institution, working field, active time period) | |
| Current contact person | Dorothea Schmid |
| Contact (e-mail) | dorothea.schmid@ier.uni-stuttgart.de |
| Website | https://www.ier.uni-stuttgart.de/en/research/models/ecosense/ |
| Logo | |
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| Primary Purpose | EcoSense is a Europe-wide integrated atmospheric dispersion and exposure assessment model which calculates external costs related to the exposure to airborne pollutants with a focus on impacts on human health, following the Impact Pathway approach. EcoSense considers impacts of classical air pollutants, such as SO2, NOx, Particulate Matter, NMVOCs and NH3 and assesses impacts due to long-term exposure to particulate matter (PM), nitrogen dioxide (NO2), and ozone (SOMO35). |
| Primary Outputs | EcoSense outputs health impacts with the common metric "Disability Adjusted Life Years" (DALY). DALYs consider both changes in quality of life (due to illness) and losses in life expectancy (due to premature deaths). Impacts can also be monetized, which results in estimates of external costs. |
| Support / Community / Forum | |
| Framework | Impact Pathway Approach |
| Link to User Documentation | https://openenergy-platform.org/dataedit/view/scenario/reeem_ecosenseeva_output |
| Link to Developer/Code Documentation | - |
| Documentation quality | not available |
| Source of funding | Third-party-funded research |
| Number of developers | less than 10 |
| Number of users | - |
| Open Source | |
| License | Creative Commons Attribution 4.0 |
| Source code available | |
| GitHub | |
| Access to source code | - |
| Data provided | all data |
| Collaborative programming |
| Modelling software | PHP, Python, R |
| Internal data processing software | SQL |
| External optimizer | |
| Additional software | |
| GUI |
| Modeled energy sectors (final energy) |
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| Modeled demand sectors |
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| Modeled technologies: components for power generation or conversion |
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| Modeled technologies: components for transfer, infrastructure or grid |
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| Properties electrical grid |
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| Modeled technologies: components for storage |
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| User behaviour and demand side management | |||||||
| Changes in efficiency | |||||||
| Market models | - | ||||||
| Geographical coverage | EU28; Norway; Switzerland | ||||||
| Geographic (spatial) resolution |
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| Time resolution | annual | ||||||
| Comment on geographic (spatial) resolution | spatial resolution can be defined by users, highest resolution currently possible is 25' x 25' (determined by population data) | ||||||
| Observation period | 1 year | ||||||
| Additional dimensions (sector) |
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| Model class (optimisation) | - |
| Model class (simulation) | Top down |
| Other | |
| Short description of mathematical model class | |
| Mathematical objective | costs, external costs of emission reduction scenarios |
| Approach to uncertainty | comperative scenario analysis |
| Suited for many scenarios / monte-carlo | |
| typical computation time | less than an hour |
| Typical computation hardware | - |
| Technical data anchored in the model | - |
| Interfaces | |
| Model file format | Other |
| Input data file format | .csv |
| Output data file format | .csv |
| Integration with other models | Soft-link with energy system optimization models (air pollutants; damage cost factors) |
| Integration of other models | Soft-link with atmospheric dispersion models |
| Citation reference | - |
| Citation DOI | - |
| Reference Studies/Models | https://doi.org/10.5281/zenodo.3368530 |
| Example research questions | - |
| Model usage | - |
| Model validation |
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| Example research questions | - |
| further properties | |
| Model specific properties | - |