Beatriz Mayor

Nature-based solutions (NBS) offer multiple solutions to urban challenges simultaneously, but realising funding for NBS remains a challenge. When the concept of NBS for societal challenges was first defined by the EC in 2017, financing was recognised as one of the major challenges to its mainstreaming. The complexity of NBS finance has its origin in the multiple benefits/stakeholders involved, which obscures the argument for both public and private sector investment. Since 2017, subsequent waves of EU research- and innovation-funded projects have substantially contributed to the knowledge base of funding and business models for NBS, particularly in the urban context. Collaborating and sharing knowledge through an EU Task Force, this first set of EU projects laid important knowledge foundations, reviewing existing literature, and compiling empirical evidence of different financing approaches and the business models that underpinned them. The second set of EU innovation actions advanced this knowledge base, developing and testing new implementation models, business model tools, and approaches. This paper presents the findings of these projects from a business model perspective to improve our understanding of the value propositions of NBS to support their mainstreaming.

Groundwater stored in aquifers experiences a wide variety of natural, induced and/or anthropogenic disturbances. Among them, groundwater extraction is the main disturbance that affects most of the aquifers in the world. Aquifer’s resilience, understood as the potential of the aquifer to sustain disturbances on the long term and to guarantee essential qualities and functions, provides a key tool when assessing sustainable groundwater management alternatives. The aim of this work is to illustrate an aquifer resilience framework that can support groundwater sustainable management. A theoretical framework is based on the identification of the key variables that parameterize the quantitative and qualitative responses of the groundwater flow system to pumping. An example from the literature based in Denmark is provided as an illustration of the proposed framework. The results show that long-term high quality data are essential to make a step further in aquifers dynamic responses. The quantitative understanding of the aquifer’s behavior before, during and after groundwater extraction provides a valuable source of information in order to identify thresholds of change (tipping points, transitions or regime shifts) which could permit pro-active groundwater management decisions. Moreover, a deeper understanding on the aquifer’s dynamics provides useful information in order to avert threats that may put the sustainability of the system at risk.

Nature-based solutions (NBS) are increasingly being promoted because they can solve different pursued aims together with providing an additional array of multiple ecosystem services or co-benefits. Nevertheless, their implementation is still being curbed by several barriers, for example, a lack of examples, a lack of finance, and a lack of business cases. Therefore, there is an urgent need to facilitate the construction of business models and business cases that identify the elements required to capture value. These are necessary to catalyze investments for the implementation of NBS. This article presents a tool called a Natural Assurance Schemes (NAS) canvas and explains how it can be applied to identify business models for NBS strategies providing climate adaptation services, showing an eye-shot summary of critical information to attract funding. The framework is applied in three case studies covering different contexts, scales, and climate-related risks (floods and droughts). Finally, a reflective analysis is done, comparing the tool with other similar approaches while highlighting the differential characteristics that define the usefulness, replicability, and flexibility of the tool for the target users, namely policymakers, developers, scientists, or entrepreneurs aiming to promote and implement NAS and NBS projects.

We have witnessed the great changes that hydrogeological systems are facing in the last decades: rivers that have dried up; wetlands that have disappeared, leaving their buckets converted into farmland; and aquifers that have been intensively exploited for years, among others. Humans have caused the most part of these results that can be worsened by climate change, with delayed effects on groundwater quantity and quality. The consequences are negatively impacting ecosystems and dependent societies. The concept of resilience has not been extensively used in the hydrogeological research, and it can be a very useful concept that can improve the understanding and management of these systems. The aim of this work is to briefly discuss the role of resilience in the context of freshwater systems affected by either climate or anthropic actions as a way to increase our understanding of how anticipating negative changes (transitions) may contribute to improving the management of the system and preserving the services that it provides. First, the article presents the basic concepts applied to hydrogeological systems from the ecosystem’s resilience approach. Second, the factors controlling for hydrogeological systems’ responses to different impacts are commented upon. Third, a case study is analyzed and discussed. Finally, the useful implications of the concept are discussed.

As a technology develops and matures, both economies of scale and the lessons learned through experience drive down the cost over time. This article analyzes and separates the effects of economies of scale and learning through experience on historical cost reductions for three mature desalination technologies: multi‐effect distillation (MED), multi‐stage flash (MSF) distillation, and reverse osmosis (RO). The analysis suggests that learning has been the dominant driver for cost reductions, with learning rates of 23%, 30%, and 12% for MED, MSF, and RO, respectively, when the effects of scale are removed. The highest influence of economies of scale is found for MED, with an exponential scale coefficient of 0.71 and the largest difference between a traditional or scale‐free estimation of the learning rate. MSF and RO showed smaller differences between the traditional and de‐scaled learning rates (only 3%), pointing at learning as the main factor driving their historical cost reductions. However, a trend break observed over the last 10 years mirrors an exhaustion of the potential for technical improvements, as well as an increasing complexity and nonlinearity of the factors influencing the systems' cost. The findings provide useful data and insights for integrated and economic modeling frameworks, while providing guidance to prevent overestimations of the learning effect due to the confounding influence of economies of scale effects associated to historical unit upscaling processes.