Over the past decade, water conflicts have risen, and cooperation has declined. Research highlights multiple factors driving this change, with climate change acting as a threat multiplier. Human activities, like dam construction and irrigation, and climate-induced hydro-climatic shifts, including extreme precipitation and prolonged droughts, contribute to the risk of increased water conflicts. To guide interventions and reverse this trend, our focus is on enhancing the understanding of factors that facilitate successful cooperation and mitigate water conflicts effectively. In this study, we investigate cooperation and conflict events worldwide in the last 70 years, together with climatic and socioeconomic factors, such as wealth, export dependency, demographics, water use, and hydro-climate trends. The dataset on cooperation and conflict events used is based on the Transboundary Freshwater Dispute Database and Water Conflict Chronology in combination with more current cooperation events extracted from media news reports. Relationships between investigated factors and cooperation are analyzed by combining panel data analysis and qualitative text content analysis of events. The results provide a deeper understanding of the factors behind why certain events are more successful in achieving conflict mitigation than others. We found that cooperation between countries struggling with water-related challenges can reduce expected conflicts over the next five years. The economic benefits of cooperation show a positive correlation between water-related cooperation and improved wealth (measured by GDP growth), particularly in countries with high export dependency. As such, economic collaboration can be an effective tool for enhancing resilience in high-water stress areas, where collaboration in these areas can contribute to a substantial reduction in future conflicts while simultaneously improving economic prosperity. Engaging in cooperation with other countries can therefore contribute to economic growth and resilience, as well as decreasing conflict risk. Understanding successful conflict mitigation factors can provide helpful insights to global policymakers and leaders in water management to avoid future conflict based on current and projected water availability. Keywords: water conflict; collaboration; conflict mitigation; mixed methods; socioeconomic factors

An ideal form of smart city planning would focus on the availability of urban amenities that can meet the basic needs of a resident’s material life, civil connections, and humanistic spirit. Previous studies have concentrated on analyzing the spatial distribution of urban services, with less attention on their contribution as local urban amenities. In this study, we propose a spatial dynamic modeling approach based on urban amenities using social media data from Google Place API to provide locational information on potential resident interactions. We use a representative region in Europe (Stockholm County, SE) to simulate and project urban development in the region until 2050. Our circular conceptual framework of spatial information and feedback supports decision-makers in testing possible urban planning scenarios that align with the vision of a smart city. Simulation results reveal the interplay between human-land interactions on a specific spatial-temporal scale, and we analyze scenario outcomes in relation to commercial and residential land uses. Overall, our study provides a new perspective on human-social behavior-driven urban development, through a smart, spatial dynamic model as a planning support system that can enhance realism, and ultimately help realize planned development objectives in the region.

Abstract We assessed the mitigation potential of nature-based solutions (NbS) within commonly overlooked pathways, including human behavioral interventions and resource savings, in addition to the well-understood carbon sequestration area. We found that general NbS implementation in the residential, transport, and industrial sectors of European cities can reduce urban carbon emissions by up to 25%. Based on spatial patterns of carbon emissions and the local context of each city, we then prioritized spatial allocation of different types of NbS implementations within 54 major EU cities, in order to maximize the carbon emissions reduction potential. We found that prioritized NbS could reduce human activity-related carbon emissions by on average 17.4% for all cities, with 8.1%, 14.0%, and 9.6% reduction in the residential, industrial, and transport sector, respectively, while 5.6% of the remaining carbon emissions could be captured by carbon sequestration. Projections to 2030 showed that prioritized NbS implementations on all available land parcels in the RCP 1.9 scenario would reduce total carbon emissions by on average 62.5% (95% CI: 47.9–66.7%) compared with the baseline scenario, with NbS capturing 22.0% marginal emissions and sequestration capturing 13.3%. Some pioneering cities climate action are projected to be very close to achieve carbon neutrality by 2030 while 3 cities can realize the goal. For carbon neutrality, cities therefore need to co-integrate indirect (human behaviors and resource saving) and direct (sequestration) contributions of NbS into aggressive climate action plans.

Nature-based solutions (NBS) are essential for carbon-neutral cities, yet how to effectively allocate them remains a question. Carbon neutrality requires city-led climate action plans that incorporate both indirect and direct contributions of NBS. Here we assessed the carbon emissions mitigation potential of NBS in European cities, focusing particularly on commonly overlooked indirect pathways, for example, human behavioural interventions and resource savings. Assuming maximum theoretical implementation, NBS in the residential, transport and industrial sectors could reduce urban carbon emissions by up to 25%. Spatially prioritizing different types of NBS in 54 major European Union cities could reduce anthropogenic carbon emissions by on average 17.4%. Coupling NBS with other existing measures in Representative Concentration Pathway scenarios could reduce total carbon emissions by 57.3% in 2030, with both indirect pathways and sequestration. Our results indicate that carbon neutrality will be near for some pioneering cities by 2030, while three can achieve it completely.

Climate change poses a threat to cities. Geospatial information and communication technology (Geo-ICT) assisted planning is increasingly being utilised to foster urban sustainability and adaptability to climate change. To fill the theoretical and practical gaps of urban adaptive planning and Geo-ICT implementation, this article presents an urban ecosystem vulnerability assessment approach using integrated socio-ecological modelling. The application of the Geo-ICT method is demonstrated in a specific case study of climate-resilient city development in Nanjing (China), aiming at helping city decision-makers understand the general geographic data processing and policy revision processes in response to hypothetical future disruptions and pressures on urban social, economic, and environmental systems. Ideally, the conceptual framework of the climate-resilient city transition proposed in this study effectively integrates the geographic data analysis, policy modification, and participatory planning. In the process of model building, we put forward the index system of urban ecosystem vulnerability assessment and use the assessment result as input data for the socio-ecological model. As a result, the model reveals the interaction processes of local land use, economy, and environment, further generating an evolving state of future land use in the studied city. The findings of this study demonstrate that socio-ecological modelling can provide guidance in adjusting the human-land interaction and climate-resilient city development from the perspective of macro policy. The decision support using urban ecosystem vulnerability assessment and quantitative system modelling can be useful for urban development under a variety of environmental change scenarios.

In the context of accelerated urbanization, ecological and agricultural lands are continuously sacrificed for urban construction, which may severely affect the urban ecological environment and the health of citizens in cities in the long-term. To explore the sustainable development of cities, it is of considerable significance to study the complex and non-linear coupling relationship between urban expansion and the ecological environment. Different from static quantitative analysis, this paper will establish a spatial dynamic modeling approach couples the urban land-use change and ecosystem services. The spatial dynamic modeling approach combines a network-based analysis method with accurate environmental assessments, which includes a causal change mechanism that simplifies the complex interaction between the urban system and the surrounding environment. Because the model can use a pre-determined cell transformation rules to simulate the conversion probability of land cells at a specific point in time, it provides the opportunity to test the impact of changes in different policy scenarios. In the phase of the environmental impact assessment, the change probability will be converted into an environmental impact based on the calculation of the ecosystem services values under different development scenarios. Taking Nanjing, a rapidly developing city in China as an example, this paper will set up a variety of sustainable development policy scenarios based on the feedback relationship of local land use driving factors. We will test and evaluate the “what-if” consequences through a comparative study to help design the optimal environmental regulation scheme. Planning and decision support will be made to further guide the rational allocation of land use parcel and land development intensity towards a sustainable development future. As a result, this study can support policy decision makings on urban land-use planning and achieve ecological and agricultural land preservation strategies.

In the context of accelerated urbanization, socioeconomic development, and population growth, as well as the rapid advancement of information and communication technology (ICT), urban land is rapidly expanding worldwide. Unplanned urban growth has led to the low utilization efficiency of land resources. Also, ecological and agricultural lands are continuously sacrificed for urban construction, which in the long-term may severely impact the health of citizens in cities. A thorough understanding of the mechanisms and driving forces of a city’s urban land use changes, including the influence of ICT development, is therefore crucial to the formation of optimal and feasible urban planning in the new era. Taking Nanjing as a study case, this article attempts to explore the measurable “smart” driving indicators of urban land use change and analyze the tapestry of the relationship between these and urban land use change. Different from the traditional linear regression analysis method of driving force of urban land use change, this study focuses on the interaction relationship and the underlying causal relationship among various “smart” driving factors, so it adopts a fuzzy statistical method, namely the grey relational analysis (GRA). Through the integration of literature research and known effective data, five categories of “smart” indicators have been taken as the primary driving factors: industry and economy, transportation, humanities and science, ICT systems, and environmental management. The results show that these indicators have different impacts on driving urban built-up land growth. Accordingly, optimization possibilities and recommendations for development strategies are proposed to realize a “smarter” development direction in Nanjing. This article confirms the effectiveness of GRA for studies on the driving mechanisms of urban land use change and provides a theoretical basis for the development goals of a smart city.

The preservation of open spaces is treated as an important policy in recent years as urbanization level is increasing higher in the world (Geoghegan, 2002). There are multiple positive effects associated with open spaces, including recreation, aesthetic and environment values (Geoghegan, 2002). The positive effects of open space as a nature-based solution on urban social, economic and environmental factors have been explored by a number of previous papers, such as housing price (Lutzenhiser & Netusil, 2001; Bolitzer & Netusil, 2000), spatial pattern (Lewis et al., 2009; Irwin & Bockstael, 2004), human health (Groenewegen et al., 2006; Irvine et al., 2013) and social safety (Groenewegen et al., 2006; Fischer et al., 2004). However, relatively less papers have predicted the open spaces’ influences on socio-economic development. This paper will firstly verify the open space influences on economic factor (housing sale prices) and social factor (sense of safety, residential agglomeration) using a linear regression model. We consider the housing attributes, urban form attributes (eg. population density, block size, road density), driving and walking accessibility to different types of public open spaces, and accessibility to other amenities (eg. hospitals and schools) as influential features. Then, we test several machine learning algorithms in predicting the housing price and sense of safety change based on future open space planning scenarios, and choose the most suitable machine learning algorithm. City of Chicago, Illinois, US is chosen to be study area since data availability, sufficient open space types and long-term open space preservation strategies. This study can quantify the values of the open spaces in influencing socio-economic developments and provide a way to test the open space scenarios. It has potential to work as a tool for local planners to make better nature-based solutions in open space designs and plans.

Adapting successes of policy transition from one city to another has been more difficult than single case of successful sustainability-driven projects and developments. A thorough understanding of local biophysical and socio-economic conditions is essential in formulating effective development plans and policies. Here, we propose the use of a social-ecological model as a comparative tool to help understand these critical components in order to inform sustainability-driven strategic interventions and best practice learning. We use the cities of Chicago and Stockholm as our comparison cases, and explore the spatial relationships between development patterns and accessibility attractors such as employment, transportation, and recreational opportunities. Potential environmental impacts are evaluated for comparison using ecosystem service value and Normalized Difference Vegetation Index (NDVI). The results indicate that although each city exhibits distinctive patterns of development, there are commonalities to build on for potential adaption strategies. For example, to mitigate the high ecosystem service and NDVI losses of Chicago from urban development, what can be learned from Stockholm are: 1) promoting infill for future residential development; and 2) stronger restrictions on both commercial and residential developments on buffer zones of valuable ecosystem services, especially waterways. These findings help us to understand the driving forces of different patterns of urban growth and to give suggestions on city-specific sustainability policies.

Although urban scenario planning is widely applied for exploring various directions of urban development, it often has high requirements on the medium of quantitative information analysis and transformation. Thus, this study establishes a method of combining scenario planning with a spatial dynamic planning support system to predict urban growth. Specifically, a scenario-based spatial dynamic modelling method is integrated with the information module of planning policy for better decision support. The integrated modelling method is applied for an actual urban land use planning case of Nanjing, an evolving city in China. The spatial forms of future urban land use are simulated under four different pre-set policy scenarios. The differences in simulated results under multi-criteria restrictions reveal the effectiveness and practical value of the integration approach. The findings of this study provide policymakers with a process-based approach to test and evaluate ‘what-if’ consequences and help stakeholders reach consensus.