A financing gap is seen as a crucial barrier, hampering the further uptake and upscaling of Nature-based Solutions (NbS). However, it is not always clear what is meant by this, nor is it clear why this barrier exists and persists. The aim of this paper is to generate an enhanced understanding of financial barriers to NbS. This is accomplished through first conceptually and theoretically clarifying the difference between funding and financing and then exploring these through an integrative literature review. We expose three different dimensions of financial barriers in NbS projects, namely the occurrence of multiple types of funding gaps, the occurrence of multiple types of financing gaps, and the particular and complex cost structures of NbS. NbS funding gaps can be broken down into public funding gaps, private funding gaps, and funding gaps specific for lifecycle phases, activities, and cost types. Bridging the funding gap is a necessary (although not sufficient) condition for bridging the finance gap and financing alone cannot solve a funding problem. We further find that these different dimensions of financial barriers can be explained by the misalignment between the characteristics of NbS and the characteristics of our existing institutions. These misalignments occur through different institutional mechanism, including (i) Funders’ preferences, (ii) Revenue generation enablers, (iii) Justification requirements, (iv) Funders’ regimes, (v) Financiers’ preferences and (vi) Finance application processes. All mechanisms influence the occurrence of public and private funding and financing gaps and they influence the cost structure of NbS, in particular transaction costs. The results of this analysis suggest that overcoming NbS funding and financing challenges requires a systemic, multi-level approach as the barriers to project implementation are not all located within a project's sphere of influence or control.@en

Transportation plays a pivotal role in society in the accessibility of socio-economic functions, such as education and health services. At the same time these transport networks are put under pressure due to increasing demands and the often-increasing occurrence of climate-induced events. To increase resilience of the transportation network to disruptions, network criticality has been used to prioritise segments of the network for interventions. Here we present how equity principles can be applied in the context of decision making for resilient infrastructure. This is done for both a data-rich (The Hague, The Netherlands) and data-poor (Pontianak, Indonesia) environment. The results show that depending on the underlying equity principle different intervention locations are prioritized and changes the impact for different socio-economic groups and the general population.

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The energy required for space heating amounts to approximately 70% of the total energy demand of existing buildings in Europe. Decarbonization of the heat demand of existing buildings is one of the grand challenges in the energy transition and sustainable district heating is a promising solution in many neighborhoods. The construction cost of district heating networks (DHNs) in an existing neighborhood is associated with large uncertainties and therefore investment risk. We have hypothesized that the CAPEX of DHNs is determined by spatial parameters that characterize the subsurface space scarcity. We have investigated this hypothesis by collecting construction cost data of 66 recently finished projects in close collaboration with Dutch district heating companies. Furthermore, we have collected construction cost data on replacement of 68 water distribution and 52 gas distribution networks. The replacement costs of these critical infrastructures might be affected by similar spatial parameters. The analysis of the district heating data reveals that the unit construction costs (€/m routing) are affected by the presence of water bodies, the road width, the zip code and the age of the neighborhood. The combined analysis with data from three infrastructures reveals that the unit construction costs (€/m routing) are affected by the presence of water bodies, soil contamination, the age of the neighborhood, the relative footprint of buildings and the zip code. The relevance of the zip code in this analysis is an indication that local regulations may have a significant impact on construction costs. Given the large number of drivers for the DHN construction costs, the datasets are too small for a statistically reliable construction cost model. We conclude that it is practically impossible to develop a project database of recently executed DH construction works with sufficient variation on all relevant characteristics. Future work will focus on automated generation of an activity-based construction cost model, using multiple GIS-layers. The improved construction cost model will be validated against traditional construction costing methods by 4 or 5 contractors.@en