Urban areas face an increasing urgency to adapt to climate change, yet adaptation efforts remain insufficient. Addressing this adaptation gap requires an understanding of the psychological mechanisms and contextual influences shaping climate adaptation behaviour. Whereas behavioural scientists have explored citizens’ climate adaptation behaviours, the decision-making of policymakers is often overlooked despite its importance in closing the adaptation gap. To address this, we conducted a behavioural systems analysis to uncover behavioural dynamics that shape policymakers' decision-making, based on 32 semi-struct ured interviews and a workshop with Dutch urban climate adaptation policymakers. Combing thematic and content analysis with behavioural system mapping, our results highlight the importance of an integrated, dynamic system approach to understand psychological and contextual influences on policymakers' decision-making. We identified nine central themes reflecting key behavioural dynamics: reliance on precedents; fragmented roles and responsibilities; habitual thinking based on longstanding processes; policy stringency, clarity and process; conflicting political priorities; importance of individuals; externally-motivated action; illusion of local actor engagement; moving from awareness creation to mainstreaming. Furthermore, analysis of behavioural influences using the Theoretical Domains Framework identified policymakers’ beliefs about consequences, environmental context and resources, and goals as most influential determinants of behaviour. Behavioural system mapping revealed three impactful levers for interventions, namely supporting precedent identification, stimulating information gathering, and boosting mainstreaming processes, to improve decision-making in urban climate adaptation. By integrating behavioural insights and systems analysis, this work proposes a novel approach to analyse contextual, dynamic influences and determinants of behaviour that shape adaptation policymaking. Therefore, it aligns with recent calls from behavioural scientists highlighting the need for systemic approaches in behavioural science.

Although causal evidence synthesis is critical for the policy sciences—whether it be analysis for policy or analysis of policy—its repeatable, systematic, and transparent execution remains challenging due to the growing volume, variety, and velocity of policy-relevant evidence generation as well as the complex web of relationships within which policies are usually situated. To address these shortcomings, we develop a novel, semi-automated approach to synthesizing causal evidence from policy-relevant documents. Specifically, we propose the use of natural language processing (NLP) for the extraction of causal evidence and subsequent homogenization of the text; causal mapping for the collation, visualization, and summarization of complex interdependencies within the policy system; and graph analytics for further investigation of the structure and dynamics of the causal map. We illustrate this approach by applying it to a collection of 28 articles on the emissions trading scheme (ETS), a policy instrument of increasing importance for climate change mitigation. In all, we find 300 variables and 284 cause-effect pairs in our input dataset (consisting of 4524 sentences), which are reduced to 70 unique variables and 119 cause-effect pairs after homogenization. We create a causal map depicting these relationships and analyze it to demonstrate the perspectives and policy-relevant insights that can be obtained. We compare these with select manually conducted, previous meta-reviews of the policy instrument, and find them to be not only broadly consistent but also complementary. We conclude that, despite remaining limitations, this approach can help synthesize causal evidence for policy analysis, policy making, and policy research.

We explore the dynamics of centralised and decentralised wastewater infrastructure across various scenarios and introduce novel insights into their performance regarding structural vulnerability, hydraulic capacity, and costs. This study determines circumstances under which infrastructure hybridisation outperforms traditional centralised infrastructure paradigms. We combined system analysis to map out the modelling problem with the model-based exploration of the transition space using the novel TURN-Sewers model. System diagramming was used to identify the parameters or combinations of parameters that significantly influence the performance indicators being assessed. This allowed the creation of relevant simulation scenarios to identify circumstances where a decentralised sewer system could outperform a centralised one. TURN-Sewers was applied to model the infrastructure maintenance and generation of new infrastructure over 20 years for a municipality on the Swiss Plateau, considering a population growth rate of 0.03 a⁻¹. Results show that decentralisation in expansion areas with higher densification can outperform the hydraulic performance and structural vulnerability of expanding centralised sanitary wastewater infrastructure. Decentralised systems can also offer economic advantages when capital expenditure costs for small-scale wastewater treatment plants are significantly reduced compared to current costs, particularly at higher discount rates, e.g. reaping effects of economies of scale. The findings of this study emphasise the potential of transition pathways towards decentralisation in urban water infrastructures and the value of models that allow the exploration of this transition space.

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.

There is an urgent need for urban environments to be flood resilient due to increasing urbanization and climate change. This can be addressed by adopting sustainable drainage solutions (SuDS) in households. However, lack of knowledge and awareness among urban residents is a barrier. In this paper, we present an educational serious game called SuDSbury to overcome this barrier and a pre-/post-game survey-based evaluation to study whether the game can educate citizens (and to what degree). An exploratory study with 14 players across three game sessions suggests that playing SuDSbury induced changes in knowledge, comprehension, and personal norms regarding SuDS. However, comprehension of concepts related to urban drainage can be improved by increasing game realism. The game should be further tested with a larger sample and a diverse demographic of urban residents. The participants further found that SuDSbury is fun and engaging to play, making it suitable for broader public interventions.

Freshwater resources in coastal areas are under intense pressure from excessive groundwater extraction, which amplifies saltwater intrusion (SWI) into coastal freshwater aquifers, such as in the Mekong Delta. Studies that combine socioenvironmental data and households' psychological factors next to salinity measurement data to design groundwater conservation strategies are rare. In this study, these aspects are combined to explore their influence on the public willingness to conserve groundwater using a Bayesian belief network model. We analyzed 313 household survey data spread over three districts in the coastal province of Tra Vinh, located in the Vietnamese Mekong Delta. The level of salinity is significantly correlated with the willingness to conserve groundwater. The top three socioenvironmental characteristics that influence willingness are the level of salinity, type of employment - i.e., being a farmer - and frequency of being exposed to groundwater or SWI promotional activities. Social norm, i.e., perceived social pressure, is the most influential psychological factor that determines willingness. This study reveals an urgency for the local government to intervene and create social pressure regarding the issue.

Environmental decisions are complex as they are multi-dimensional, highly interdisciplinary and not only involve multiple stakeholders with conflicting objectives, but also many possible alternatives with uncertain consequences. The difficulty lies in making trade-offs between tough value trade-offs on the one hand while appreciating uncertain impacts of alternatives on the other. To support decisions tackling such problems, a combination of multi-criteria decision analysis (MCDA) and environmental models is promising yet limited by the available MCDA software. Here, we present Decisi-o-rama, an open-source Python MCDA library for single and sets (portfolios) of alternatives in the context of multi-attribute value/utility theory (MAUT/MAVT). Its development was driven by four aspirations that are crucial for usability in the context of environmental decision-making: (1) interoperability, (2) uncertainty-awareness, (3) computational efficiency, and (4) integration with portfolio decisions. The results indicate that these aspirations are met, thus facilitating the adoption of MCDA methods by environmental researchers and practitioners.

Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include nongrid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to coevolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should pursue a transdisciplinary research approach to generating evidence through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experiences from these projects in diverse socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.