Institute for Ecological Economy Research

Groundwater recharge is a key yet highly uncertain component of the hydrological cycle and is characterized by pronounced variability across temporal and spatial scales. Recharge processes are commonly represented using long-term averages and spatially aggregated concepts, by which the episodic, heterogeneous, and scale-dependent nature of recharge is often obscured. In this presentation, a multiscale perspective on groundwater recharge is presented, covering field observations, regional analyses, and global modeling approaches to illustrate how recharge occurs on timescales ranging from minutes to millennia and from local plots to the planetary scale.The temporal dimension of recharge is illustrated using examples from arid environments, with a focus on Saudi Arabia. Under current climatic conditions, diffuse groundwater recharge in these regions is typically very low. However, rapid and focused recharge can be generated within minutes during intense rainfall events in areas with exposed karst features, where surface runoff is efficiently channeled into the subsurface. But there are also important processes related to groundwater recharge on much longer timescales. In many arid regions, fossil groundwater is a vital resource that was replenished under past climatic conditions. These paleo-groundwater recharge events represent hydraulic impulses that continue to influence today's groundwater levels and flow patterns, resulting in non-stationary groundwater systems.The spatial variability of groundwater recharge is examined at local and global scales. Based on exemplary case studies from South Asia, recharge is found to be highly heterogeneous and is controlled by climatic gradients, geological conditions, and intensive human water use, including irrigation return flows. At the global level, groundwater recharge is estimated using neurol networks with eXplainable AI, identifying the dominant factors for groundwater recharge for different climate zones and showing that the control and sensitivity of predictors for groundwater recharge are highly region-specific.Building on these temporal and spatial perspectives, the implications of groundwater recharge for water quality are highlighted. Using the Hessian Ried (Germany) as an example, a control of recharge rates and residence times in the unsaturated zone on reactive solute transport to groundwater is demonstrated, thereby directly linking recharge dynamics to groundwater quality.Overall, groundwater recharge is shown to be inherently multiscale and dependent on specific environmental conditions, with important implications for the selection of recharge estimation methods, the temporal and spatial aggregation of groundwater models, and sustainable groundwater management under changing climatic and land-use conditions.

This study examines the impact of monetary policy on functional income distribution, which is defined here as the level of real wages and the size of the labor share of income. This topic has often been overshadowed by analyses of personal income inequality. Although prior research has presented mixed evidence while focusing on short-term effects, we investigate the medium- to long-term distributional consequences of monetary tightening. Using a panel vector autoregressive framework and a dataset encompassing 15 advanced economies from 1970 to 2019, we analyze how real wages and the labor share respond to contractionary monetary policy. Our findings reveal that contractionary monetary policies lead to persistent declines in both real wages and the labor share. By disentangling the underlying transmission mechanisms, we identify the labor market channel as the primary driver: the scarring effects on wages and labor force participation endure even after output and prices stabilize. These results underscore the importance of incorporating long-term distributional considerations into the formulation of monetary policy. • Monetary policy’s effect on income distribution is studied using a panel VAR method • Evidence supports the presence of monetary policy’s persistent real effects • Contractionary monetary policy adversely impacts real wage levels and labor share • Labor market dynamics drive long-term inequality from contractionary policy shocks • Policymakers should consider this evidence when designing monetary policy

The value of environmental goods to individuals often depends on spatial features such as distance. The most common approach of accounting for distance decay is to model utility as some function of distance. It has been suggested to instead model the value as a function of the quantity of an environmental good within a certain distance. We develop three novel quantity-within-distance models that may be more suited for evaluating quantity changes in an environmental good. We argue that these models could capture spatial patterns better than distance-based models when i) secondary benefits are a relevant source of welfare, ii) the environmental change is spatially scattered, iii) the distribution of the endowment, i.e. the present availability of the environmental good, matters. Using data from choice experiments on the extension of green space and trees in two urban areas, we compare required assumptions, model fit, and size and precision of aggregated welfare estimates. Our results indicate limited differences in model fit. However, the quantity-within-distance models consistently produce aggregate welfare estimates roughly half of common distance decay models and have narrower confidence intervals. While it is not possible to infer which is more accurate, the large differences can have considerable policy implications.

The rapid expansion of low-carbon energy technologies across European regions has been argued to be essential in tackling climate change, necessitating the transition of energy systems towards renewable energy technologies, including wind energy. To accelerate wind energy developments, policies have been put in place to speed up planning and implementation in several European countries in recent years. However, the impacts of these policies remain uncertain, seeing that historical developments have been characterized by accelerations and slowdowns, including actions against wind energy that point to diverse justice issues. Research examining the relations between acceleration and justice in low-carbon transitions is limited, with some studies highlighting the trade-off between rapid developments and just processes and outcomes. Drawing on qualitative case studies from Brandenburg (Germany) and North Holland (Netherlands), this paper develops a heuristic of spatial and temporal aspects to investigate how accelerations in wind energy developments have played out and how relations with notions of justice have emerged and/or been sustained over time. It underscores the importance of creating nuanced understandings of acceleration and justice while accounting for temporal and spatial aspects to be able to govern towards more just and accelerated energy transitions. • Examines the relations between acceleration and justice in wind energy development • Introduces a focus on temporal and spatial aspect of wind energy development • Rapid expansions and slowdowns are linked to processes of periphery creation • Past injustices make a shifting of narratives and actions against wind energy difficult • Slowdowns are linked to a misalignment between governance levels and their resources

Participatory practices and social innovations are recognised as important for sustainable renewable energy projects. However, information on the significance of diverse participatory practices and social innovations for advancing inclusive and sustainable wind energy developments remains limited. To address this gap, this study aims to understand how diverse participatory practices in wind energy developments across the globe influence energy justice differently. It does so, by analysing how a fairer participation of communities is interconnected to the models of participation while investigating how participatory practices interrelate with social innovations. Drawing on the ‘ecologies of participation’ approach, the work compiles a dataset of 312 case studies of collective participatory practices in wind energy projects, of which 209 are classified as social innovation projects. Findings indicate that not all participatory practices and social innovations are equal in ensuring just processes in wind energy governance. This requires careful consideration of different aspects of participation (e.g., opportunities provided, continuity of the process) and adherence to principles of justice in wind energy governance (e.g., inclusivity, capacity to influence, transparency). Conclusions highlight that fairer participation practices are interconnected to both the models of participation and to the wider spaces of participation in which they are embedded.

The adoption of the Paris Agreement fundamentally altered the foundation of climate protection concepts, programs, and targets around the world in all countries and communities. Not only at the national level, but in many cities and communities, ambitious ways to achieve climate neutrality have been sought since then. Thus, even in the big city and metropolis of Berlin, at the same time a federal state and the capital of the Federal Republic of Germany, new ways were developed and political instruments discussed. In this chapter, we use the example of Berlin to show how a fossil-fuelled city can move towards climate neutrality in all sectors. Restriction-based scenarios are used to address conflicting goals and identify possible solutions. From this, recommendations for urban measures for climate neutrality are derived, which must be supplemented by appropriate political climate governance.

<h3>Abstract</h3> Stated preference studies increasingly elicit respondents' perceptions about survey consequentiality to mitigate hypothetical bias concerns and enhance validity of value estimates. A typical practice is to ask about these perceptions after preferences. We examine the sensitivity of the perceptions, willingness-to-pay estimates, and the relationship between them to the perception elicitation location in a discrete choice experiment survey. Our empirical results suggest that the location matters: the perceptions and willingness-to-pay values are affected. In our data, the self-reported consequentiality is stronger when elicited before, rather than after, the preferences. We discuss implications of the findings for elicitation of perceived consequentiality.

Numerical weather prediction models have systematic errors and biases, which can be reduced through statistical correction methods. The most intuitive approach to correcting weather forecasts is to establish a statistical relationship between the forecast and the corresponding observations. Once the relationship is established, it can be used to correct future forecasts. These approaches, also known as statistical adaptation, are used on a daily basis to improve the quality of operational forecasts. Many methods have been proposed in the literature to calibrate ensemble forecasts, and these can be divided into two main groups: parametric (that has a hypothesis on the underlying distribution) and non-parametric methods. One of the most widespread and used methods of the parametric group is the so-called Ensemble Model Output Statistics (EMOS). We show the performance of EMOS for the post-processing of probabilistic temperature forecasts at 2m. This method assumes that the underlying distribution is Gaussian (in the case of 2m T), and the parameters to be estimated are therefore the mean and the standard deviation. First results were obtained using the ECMWF operational forecast ensemble against SYNOP observations. Further studies were carried out to investigate the reliability of the estimated parameters using not only the raw ensemble data, but also the ECMWF deterministic operational forecast HRES and the control member. The results showed an improvement, especially for the shorter lead-times. The improvement was measured using weather scores for forecast verification and performance: bias, CRPS (Continuous Ranked Probability Score) and CRPSS (Continuous Ranked Probability Skill Score). These post-processed forecasts will serve as a reference for future studies.