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?

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

INSPIRE Data

• 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

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