2. Methodology

2.1 Long list of international directives, strategies, goals and obligations

A review of the Nordic countries commitment to international directives, strategies, goals and obligations was undertaken by identifying different levels of policy instruments that the Nordic countries have committed to. The analysis was divided into four categories:

Level 1: international
Level 2: national
Level 3: regional
Level 4: local/municipal

Based on the four levels, a systematic review of the Nordic countries commitments was undertaken. The systematic review analysed the policy instruments and divided them into multiple categories to clearly identify what type of policy instruments the Nordic countries have committed to; what societal challenges, ecosystems and pollutants the policy instruments cover. A database, policy overview, in Microsoft excel was created to systematically present the relevant international and national commitments the Nordic countries have committed to that addresses emissions of NH₃, NO_X, N₂O and CH₄ from agriculture. The full database can be viewed in appendix 1 – Mapping policy overview.

Based on the policy overview a database with quantified goals was created with the international and national obligations the Nordic countries have committed to. The database was divided into four different categories with international goals and obligations:

Air – NO_x and NH₃
Climate – CH₄ and N₂O
Water – NO₃
Health – NO_X, NH₃ and CH₄

The database provides the Nordic countries a quick systematic overview of which international goals and obligations, with quantified measures, can be connected to emissions from the agricultural sector. It can be found in appendix 2 – Mapping Nordic countries commitments and obligations.

2.2 Inventory of measures to mitigate ammonia, methane and nitrogen emissions

A comprehensive literature review on measures related to CH₄, NH₃ and Nr was conducted to create a database (in Microsoft excel) of relevant measures to abate NH₃, Nr and CH₄ from the agricultural sector in the Nordic countries and beyond. This database, measures to mitigate ANM, includes descriptions of the measures, their areas of application and their potential environmental impact and is available in appendix 3 – Mapping measures to mitigate ANM. The review involved gathering compilations of mitigation measures from various Nordic government bodies, agricultural agencies, and relevant institutions. These were further supplemented by research from European and international sources to provide a broader perspective of measures to reduce emissions from the agricultural sector. The aim of the literature review was to provide a thorough overview of emission reduction strategies, combining national-level reports with scientific literature. To achieve this, we opted to find literature reviews from each Nordic country that compile and summarise data and research from multiple sources. This approach provided a more comprehensive overview of the existing research. While the database does not claim to be fully exhaustive, it presents a wide range of measures in use or under consideration in the Nordic countries.

For each identified measure, the primary pollutants were identified, although for Nr, we opted to include the speciation of different N sources to pinpoint the effectiveness of the measure towards a specific form of N, while the release of dinitrogen (N₂) itself does not directly cause pollution it signifies a loss of valuable Nr, which lowers the N use efficiency. Consequently, additional inputs of Nr, such as fertilisers and biologically fixed N, become necessary, ultimately leading to an increase in N pollution. The following speciation was used for the inventory:

Ammonia (NH₃)
Nitrogen oxide (NO₂)
Nitrous oxide (N₂O)
Nitrate (NO₃^-)
Dinitrogen (N₂)

The impact of each measure on CH₄, and Nr emissions has been classified as positive, negative, not effective or unknown. If the measure has positive or negative effect on multiple pollutants, they have been classified accordingly. Note that this classification in the database is derived from a single source for each measure, which may not encompass all the potential effects. Therefore, there could be additional positive or negative impacts not addressed in the specific literature. This can however be addressed by comparing measures from different sources using the filtering function Measure Type described below.

The database is structured in a systematic manner and encompasses a wide range of different mitigation measures implemented in the Nordic countries and beyond. These measures were categorised under two main classifications:

Measure type
Measure category

The category measure type is a first classification of the individual measures reported in the literature. Some individual measures appear multiple times in the database, as they are reported across various sources or from different countries. To address this, the database employs a filtering function within the measure type classification. This classification helps manage the occurrence of similar or repeated measures across different literature sources or countries by grouping them under a single type, regardless of their origin. The classification measure type covers all the different measures identified in the database and has been divided into approximately 150 different measure types that cover measures for CH₄, NH₃ and Nr. For instance, the measures are classified as cooling of slurry, biogas production and precision farming. This allows the end user to narrow down the measure to a more specific interest and compare results from different literature sources and countries.

The second classification, Measure Category, was designed to group the mitigation measures from the literature into broader categories of different agricultural practices. These categories allow for an assessment of measures across different sources and countries within the same area of practice. This allows users to easily navigate, group and compare strategies and to compare how different strategies address emissions within the various agricultural sectors. In total, six categories were identified:

Manure storage and processing: This category includes measures such as slurry cooling, acidification of manure, and the use of slurry covers, all of which target multiple types of emissions.
Field application of manure and/or fertilisers: Measures in this category focus on optimising nutrient use efficiency and minimising emissions during field application. Examples include rapid incorporation of manure, band spreading and the reduction of fertiliser doses.
Crop production and crop rotation: This category encompasses strategies aimed at enhancing soil health and crop productivity while mitigating emissions. Key measures include the use of cover crops, precision farming techniques, and adjustments in crop rotation.
Livestock production, housing, and diet: Measures in this category focus on modifying livestock diets, improving housing conditions, and managing waste within animal production systems.
Land use and landscape management: This category includes a variety of strategies to optimise land use for environmental benefits. For example, implementation of agroforestry, permanent fallow and perennial crops.
Energy and nutrient recovery: Measures in this category focuses on capturing energy and nutrients from waste streams to reduce emissions and enhance resource efficiency. Key examples include biogas production from manure and other organic residues.

Furthermore, the literature used in the study has been classified on the scale of the measure and what societal challenge and ecosystem it encompasses.

2.3 Quantification of measures and synergies

To identify synergies from measures affecting both air quality and climate, identified measures in the long list were prioritised based on the classification positive, not effective or unknown. Measures classified as positive for reducing NO_x or NH₃and at the same time being classified as positive for reducing CH₄ or N₂O were considered as measures having a synergy effect on air quality as well as on climate.

The magnitude of the effect from the measures considered having desired synergy effects was then quantified through two methods. The first method included an extensive literature review of primarily Nordic literature on emission control from agriculture. The second method included comparison of emission scenarios for the Nordic countries where the scenarios differed with respect to measures being implemented.

The Nordic countries do not consistently report emission projections and alternative scenarios for the pollutants and greenhouse gases studied in this work. Therefore, to estimate emission reduction potentials for the Nordic countries we have used the ECLIPSE version 6b emission scenarios as developed by IIASA.

IIASA, 2021

Höglund-Isaksson, Gomez-Sanabria, Klimont, Rafaj, & Schöpp, 2020

The ECLIPSE scenarios presents technologically and economically consistent scenarios of emissions until 2050 for all countries in the world. For the air pollutants, CH₄, and N₂O considered in this report, the emission scenarios are calculated with the GAINS model

Amann, et al., 2011

Kiesewetter, Schöpp, Heyes, & Amann, 2015

which is commonly used as decision support to the European Commission and to the Air Convention. Sector- and pollutant-specific GAINS model documentation is found at the IIASA web page.

GAINS Model

For the Nordic countries, there are two scenarios of interest for this study, the ECLIPSE 6b CLE and the ECLIPSE 6b MTFR. CLE stands for ‘current legislation’, and the CLE scenario correspondingly illustrates current and future emissions as a function of existing and decided emission control legislation. MTFR stands for ‘maximum feasible reduction’ and the emissions in the MTFR scenario are a function of a maximised implementation of all emission control technologies represented in the GAINS model (given vintage-specific constraints on the rate of possible implementation). The CLE and the MTFR scenarios give future emission trajectories from agricultural sources (excl. non-road mobile machinery) as in figure 2.

Figure 2. ECLIPSE v. 6b current legislation (CLE) and maximum feasible reduction (MTFR) emission scenarios for the Nordic countries and the pollutants N₂O, NO_x, NH₃, and CH₄.

Even though future emissions are inherently uncertain, it is still interesting to compare the emission trajectories for the ECLIPSE v.6b scenarios with the corresponding national emission projections (Nat.) as reported to the EU, CLRTAP and UNFCCC by national experts in the respective countries. Unfortunately, the national emission projections include emissions from non-road mobile machinery used in the agricultural sector, so the comparison is most valid with respect to direction of the emissions, figure 3.

Figure 3. Comparison of nationally reported emission projections (Nat.) and the current legislation (CLE) emission scenario for the five Nordic countries and N₂O, NO_x, NH₃, and CH₄.

The comparison shows that the nationally reported emission projections assume faster emission reductions than in the CLE scenario for almost all emissions. Most problematic for this study is the large difference in absolute levels of Icelandic CH₄ emissions and the fact that emission projections for Swedish CH₄ and NH₃ diverge. The differences are presumably caused by differences in assumptions on future sizes of livestock numbers.

2.4 Calculation and assessment of effect size

To further quantify the reduction potential for the prioritised measures on the four pollutants NO_x,NH_3,CH₄and N₂O, the measure categories and types identified in this work were matched with defined sectors, activities, and control measures in the GAINS model. Based on the matching, scenario-specific emissions from specific measure categories and types were identified for the year 2040. Those measures representing the highest emission reduction potential between CLE and MTFR were prioritised into a short-list. The national CLE implementation rate of the measures might however differ. The matching was made as aggregations over categories and types since the measures identified in this work aren’t identical to the measures in the GAINS model. Furthermore, the effect of the measures on specifically NH3 emissions depend on whether one measure is implemented in isolation or if several measures are implemented together. There is a cascade effect that needs to be considered. Correspondingly, the emission reduction potential identified includes possible joint implementation of several measures within the same measure type.

Some measures had no clear match to a GAINS measure. These were thereby prioritised based on an extensive literature search and added to the shortlist.

By combining the method of prioritising and quantifying estimated effects from literature reviews with the approach of estimating potential effects, based on the CLE and MTFR scenario comparisons, it was possible to estimate emission reduction potentials for the synergy measures. Due to project constraints, the final shortlist of synergy measures contains up to five of the most effective measures for each of the actual pollutants.

The ECLIPSE scenarios also enable an estimate of the costs associated with some of these synergy measure types. Due to some imprecision induced by the measure matching, the costs are approximations, just as the emission reduction potential estimates. The GAINS model cost calculations present annual costs associated with scenario-specific technology investments and maintenance, as well as potential profits from, for example, utilisation of products from the technology. Furthermore, the annual costs are calculated based on the technical lifetime of an investment, and a four percent interest rate is used. The costs of emission reductions presented in this report corresponds to the additional costs of shifting technologies from the technologies assumed in the CLE scenario into the technologies assumed in the MTFR scenario.