Decarbonization is crucial to combat climate change. However, some decarbonization strategies could profoundly impact the nitrogen cycle. In this Review, we explore the nitrogen requirements of five major decarbonization strategies to reveal the complex interconnections between the carbon and nitrogen cycles and identify opportunities to enhance their mutually sustainable management. Some decarbonization strategies require substantial new nitrogen production, potentially leading to increased nutrient pollution and exacerbation of eutrophication in aquatic systems. For example, the strategy of substituting 44% of fossil fuels used in marine shipping with ammonia-based fuels could reduce CO₂ emissions by up to 0.38 Gt CO₂-eq yr⁻¹ but would require a corresponding increase in new nitrogen synthesis of 212 Tg N yr⁻¹. Similarly, using biofuels to achieve 0.7 ± 0.3 Gt CO₂-eq yr⁻¹ mitigation would require new nitrogen inputs to croplands of 21–42 Tg N yr⁻¹. To avoid increasing nitrogen losses and exacerbating eutrophication, decarbonization efforts should be designed to provide carbon–nitrogen co-benefits. Reducing the use of carbon-intensive synthetic nitrogen fertilizer is one example that can simultaneously reduce both nitrogen inputs by 14 Tg N yr⁻¹ and CO₂ emissions by 0.04 (0.03–0.06) Gt CO₂-eq yr⁻¹. Future research should guide decarbonization efforts to mitigate eutrophication and enhance nitrogen use efficiency in agriculture, food and energy systems.

The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results-all of which are critically useful to pave the way for successful implementation of nature-based solutions.