Monitoring CO2 emissions

Monitoring CO2 emissions

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Lecture 3: Monitoring CO2 emissions

Learning objectives

At the end of this lecture, you are expected to be able to

  • Describe data sources for monitoring of CO2 emissions and content
  • Describe data sources of related sectors (air quality, traffic, ...)
  • Explain pro's and con's of Copernicus Climate Change Services
  • Explain basic principles of forecasting

Why monitoring CO2 emissions?

  • Target group(s) / message
    • Politicians / Effect of policy decisions
    • Politicians / Evaluate efficiency and effectiveness of investments
    • Researchers / Indicate impact of actions (verify impact models or C/B models)
    • General public / Contribute to a factual and broad public debate
    • Schools / Teaching
    • Monitoring should, if possible, be based on open statistical records

  • Quality criteria: Frequency, accuracy, relevance

Global monitoring of CO2 emissions?

IPCC guidelines for national greenhouse gas inventories

Update of UNFCCC statistics

CO2 emissions 2019

  • The European statistics for 2019 published in late May 2021
    • The target for 2019 was a 0.5 % reduction down to 3905 Tg CO2
    • The reported reduction is 4.0 %, down to 3610 Tg CO2
  • The national statistics for Sweden is reported in December each year
    • The target for 2019 was a 7.8 % reduction down to 47.2 Tg CO2
    • The reported reduction is 2.4 %, down to 50.9 Tg CO2
    • Possible actions identified for each emission type, for instance
      • Transport: Other fuels, public transports, unstudded winter tyres, newer vehicles, …
      • Agriculture: Manure, usage of fertilizers, forage additives

  • Local data for 2019 to be published in July 2021

How to find data?

  • We have several different portals, aimed for searching and downloading
    • Geoportals (national, regional, local, sectoral)
    • Open data portals (also national, regional, local, sectoral)
    • Search engines (Google, Bing, …)
    • European portals (EEA, INSPIRE, …)
  • What to look for?
    • CO2 content in atmosphere?
    • Sectoral data?
      • Traffic related data (volume, sales of petrol, air pollutants, …)?
      • Sectoral policies often include additional objectives, not only CO2 budgets

Air quality data

  • Directive 2008/50/EC on ambient air quality and cleaner air for Europe
    • Specify monitoring and reporting obligations related to air quality
    • What to monitor (SO2, NOx, CO, PM, …), selection of sampling points, thresholds etc.
    • CO2 not included in this directive
  • Directive 2004/107/EC relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air
  • Directive (EU) 2016/2284 on the reduction of national emissions of certain atmospheric pollutants

EEA air pollution map



  • Directive 2007/2/EC establishing an infrastructure for spatial information in the European community (INSPIRE)
    • Aiming to provide interoperable spatial data of Europe
    • Structured into 34 data themes
  • Air quality data can be found in the following themes
    • Atmospheric conditions
    • Environmental monitoring facilities
    • Meteorological geographical features


INSPIRE data grouped after reporting obligation

For Sweden, air quality data according to OGC-SOS is provided

ICOS Carbon Portal


ICOS measurements Norunda

Copernicus Climate Change Service

Downloading C3S data

  • Geometric Resolution: TANSO - around 10 km x 10 km
  • Temporal coverage: 2003 – 2019
  • Temporal resolution: According to satellite orbits
  • File format: NetCDF4 (Can be read by QGIS as mesh)
  • Reference system: Lat-Long Grid

CO2 at Nyköping, 2019-12-01

From SDI to business ecosystems

Source: DG CNECT

Forecasting and trends

  • Autoregressive models is a simple way of forecasting, or to be more precise, extrapolation of trends
  • The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term
  • In this case, we will use a third order AR model for predicting future values
  • We will use different AR models for each sector (agriculture, industry, private cars etc)
  • Each AR model can be replaced by a better forecast, if we have such forecasts available

Environmental dashboards

Slide show ends