Recently, there has been more and more research on the abundance of MPs (MPs) in oceans, seas, and rivers. A lot is still uncertain about the distribution of MPs, and whether they are mainly deposited in seas & oceans, or river sediments. As global models on MP transport through rivers have only used statistical methods, we present a global riverine MP transport model based on mechanical principles. The model incorporates particle advection, settling, entrainment, and input emissions from wastewater treatment plants. The model was run for a period of 5 years, on 8 MP mixes of 15 MPs each, with the same 24 uncertainty scenarios for each MP mix (totalling 192 runs). Exported (to seas and oceans) and sedimented MPs showed a linear increase over time, while MPs suspended in the river reached steady state, but showed heavy seasonal fluctuations. Under the modelled uncertainties, after 5 years of simulation time, 76% of MPs are exported to seas and oceans and 19 % of MPs are deposited in river sediment. 5% of MPs were suspended in the water column. Major contributing areas to global MP emissions are area’s with large population densities, like Europe, North America, China & South East Asia, and India. Our work contributes to the understanding of MP flows through rivers, and could be used as a starting point for a MP material flow analysis, or as the basis for MP impact assessments. Future iterations of the model should implement man-made barriers and reservoirs, which
were not considered in the current version of the model.

Hydropower is currently the largest renewable energy generation method worldwide, being the third overall after coal and natural gas, and providing 15% of global electricity. However, generation data regarding hydropower is scarce. If available, it mostly exists at national and annual level. Only limited generation data is available at plant scale. For research on power grid decarbonization, electricity grid expansion development and electricity grid optimization, more data on hydropower generation is wanted at plant scale. Hydropower generation differs significantly throughout the year following weather patterns, which means monthly generation data would be beneficial for research. To fill in data gaps, two models were created for the prediction of hydropower, using plant capacity, discharge, and reservoir area as predictor variables for the monthly model. For the yearly model, reservoir area was not included in the final model. A linear mixed-effects regression model and a mixed-effects random forest model were fitted and compared to the Hydro Plant Generation Estimation Model. The models were created using data from the United States (US) and used for predictions with hydropower plants from the US and the European Union (EU). The median KGE for the monthly LMER model was -0.08 in the US. For the monthly MERF model, the median KGE was 0.12 in the US. In the EU the models were evaluated at an annual time step due to data limitations, resulting in the LMER model scoring better (-0.16) than the MERF model (-0.68) on median KGE. The prediction errors of the annual US model were comparable to the Hydro Plant Generation Estimation Model. Discharge and plant capacity were found to be important predictor variables, followed by reservoir area for the monthly model. The models were able to predict at plant scale in data-scarce regions and at a monthly time step, although they can produce large outliers. A purpose for the model could be to not use it at plant scale but at a larger scale, as the median KGE scores were around zero, showing that predictions over multiple HPPs are usable.

Ecosystem services (ES), the benefits humans receive from nature, increasingly come under pressure in Stockholm, one of the fastest growing capitals in Europe. To enhance the amount of eco-efficient area, Stockholm developed the Green Space Index (GSI). It is not known which areas in Stockholm might be particularly vulnerable in terms of ES availability, or whether the GSI objectively improves ES availability.

The aim of this thesis was threefold. Firstly, to provide an overview of the current distribution of ES in the city; secondly, to test for a potential correlation between ES availability and socio-economic opportunity; thirdly, to test for differences in ES availability between an area where the GSI had been implemented, and areas without the GSI. Specifically, the ES of stormwater retention, annual water retention, heat mitigation, and nature access were assessed.

For quantifying ES, the Urban InVEST models from the Natural Capital Project were used. Results from the models were combined to form a heatmap of ES. A correlation between socio-economic opportunity and the availability of ES was assessed with a Spearman correlation test on the neighbourhood deprivation index (NDI) and the ES. The NDI measures income, education, population receiving social benefits, and employment. Differences between the areas with and without GSI were tested for with t-tests. All statistical analyses were conducted in RStudio.

The analysis revealed that the ES in Stockholm are available less in the city centre and increase towards the outskirts. Economic opportunity in a district is negatively correlated with stormwater retention, annual water retention, and nature access – the higher the socio-economic opportunity, the less of these services is available.

Where the GSI had been implemented was significantly different from all the areas it was compared to. Due to different types of land use both water retention services are lower in the GSI area compared with the situation prior to redevelopment, the residential area, and one of the three other areas that had been redeveloped without the GSI. For heat mitigation, the GSI area performed worse than two of the redeveloped areas.

While the outskirts seem to be better provided with ES, this does not mean that the demands are met everywhere. Large parts of the construction in the coming decades are planned to take place in these less well-off areas, potentially threatening the natural elements providing ES and affecting populations that are less well equipped to make up for the loss of ES. The GSI can be a powerful tool to combat this. However, the research uncovered some shortcomings that should be addressed: striving for a high GSI score is not sufficient, measures with which the GSI is achieved must be suitable for the needs of the location. A process focussed on uncovering these needs first, or splitting up the GSI into several assessment categories, could aide in this.

Future research should continue assessing the GSI, either in a scenario analysis, or by monitoring. With these findings, the present thesis hopefully contributes to an improved understanding of ES in Stockholm.