Experts have been searching for ways to mitigate the impacts of climate change on resources since the early 20th century. In response, the World Economic Forum introduced the concept of a “nexus”, which involves the simultaneous, systematic collaboration of multiple individuals or sectors, such as water, energy, and food, in order to create an integrated approach to reducing resource scarcity through a multi-disciplinary framework. In contrast, a circular economy (CE) involves restructuring material flows from a linear economic system and closing the loop on resource exploitation. Both the nexus and CE have been developed to address the overexploitation of resources, but they also contribute to the Sustainable Development Goals (SDGs) and decouple carbon emissions from economic growth. This study explores the potential of combining the nexus and CE to pursue the SDGs on a global scale. Our findings reveal significant research gaps and policy implementation challenges in developing countries, as well as the potential consequences of adopting integrative scenarios. Finally, we propose a system dynamics model as a way to address the difficulties of coupling policies and to better understand the interdependencies between different parts of the economy.

The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5–2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data.