Side effects of climate mitigation and adaptation to sustainable development related to water and food (2024)

How would progress towards the Sustainable Development Goals (SDGs) be affected if climate mitigation and adaptation measures in one sector proceeded without coordination with other interconnected sectors? A more comprehensive and quantitative answer to this question becomes increasingly important as the global temperature target set by the Paris Agreement, which is closely linked to Goal 13 (Climate Action), requires the implementation of stringent climate mitigation measures and transformative changes in human systems for emissions reduction and adaptation.

Human actions for climate mitigation and/or adaptation (together referred to as climate action) in one sector unintentionally affect the SDGs of other sectors. The main effects of climate actions manifest as emission reductions or more climate-resilient human systems. A side effect of climate action that leads to a preferable outcome for SDGs can be defined by a significant and positive association, or synergy, between a pair of indicators; climate action and other SDGs (Pradhan et al 2017). However, a side effect that leads to a less optimal or significantly negative association to other SDGs is a tradeoff of climate action. A neutral interaction is one in which a significant association is not present.

Synergies and trade-offs involving climate action have attracted considerable attention (IPCC 2022) and have been studied using a variety of integrated assessment models (IAMs) (Jakob et al 2016, von Stechow et al 2016, Soergel et al 2021). These IAMs can be used to outline pathways for achieving climate mitigation goals under given scenarios of emission reductions and socioeconomic conditions. However, IAMs have several limitations. Although IAM-based assessments do not need to cover all 169 Targets in 17 Goals set by SDGs, many more climate-sensitive SDG indicators than are currently typically included can be considered for comprehensiveness. For example, food prices (von Stechow et al 2016), malnourished population (Soergel et al 2021), population at risk of hunger (Hasegawa et al 2018, Fujimori et al 2020), and percentage of area used for bioenergy production (Jakob and Steckel 2016) can be used to explore the side effects of climate mitigation on Goal 2 (Zero Hunger), but another Target in Goal 2, i.e. the productivity and income of small-scale food producers (FAO 2021), is not considered. Furthermore, IAMs are limited in terms adaptation measures (van Maanen et al 2023) and their side effects (table 1). Climate impact models (CIMs), such as hydrological and crop models that operate at daily scales, have been used to quantify the physical/biophysical impacts of climate change, including those from extreme weather and climate events, as well as the effects of different adaptation measures at regional and finer scales. Although relatively few CIM-based global assessments of side-effects have been undertaken to date, CIMs could potentially provide information that supplements the outputs of IAMs and contribute to the diversity of scientific inputs to policymakers.

Here, we present four lessons learned from the authors' recent collaborative research on quantifying the side effects of climate action on SDG indicators related to water using global hydrological models (see table S1). The indicators investigated include some of those employed in Goal 1 (No Poverty) and Goal 11 (Sustainable Cities and Communities) for reducing disaster risks (e.g. flood risk) in vulnerable communities and cities, as well as in Goal 6 (Clean Water and Sanitation) for improving the quantity and quality of water resources. Goal 2, which is closely related to the livelihood of small-scale food producers, is also considered using outputs from a process-based global gridded crop model. Since agriculture is the largest user of water, efforts focusing on Goal 2 will influence the water-related SDG indicators. Consequently, climate actions in any sectors that consume large amounts of water would compromise Goal 2.

The first lesson is that CIMs can be used to estimate indicators that are sector-specific, finer in terms of their spatiotemporal resolution, and currently not calculated by IAMs. The indicators calculated using CIMs include populations exposed to flood risk (Goal 1 and Goal 11), the amount of water resources affected by bioenergy with carbon capture and storage (BECCS) (Goal 6), and small-scale farmer agricultural output (Goal 2) (table 1). These indicators were estimated based on the outputs of hydrological and/or crop models. For example, the side effects of BECCS on water resources were estimated using a global hydrological model that dynamically simulates climate change impacts on river discharge, water uptake for irrigation and bioenergy crop transpiration and biomass. It is estimated that, under a sustainable irrigation scenario, BECCS would limit the increase in water withdrawal for bioenergy crops by up to 298 km³ yr⁻¹ and achieve a carbon removal of 2.09 GtC yr⁻¹ without compromising food crop production in current cropland and municipal and industrial water uses (Ai et al 2021, figure 1). This rate of carbon removal would prevent global decadal mean surface warming by 0.005 °C yr⁻¹. Carbon removal equivalent to 1.6–4.1 GtC yr⁻¹ is required through negative emissions technology, including BECCS, to limit global warming by 2 °C or 1.5 °C. Therefore, the level of BECCS derived from the sustainable irrigation scenario (2.09 GtC yr⁻¹) may not be sufficient to achieve the temperature targets. However, further use of water for bioenergy crops is likely to pose a significant increase in the competition for water resources among various water users.

Second, multi-sector coverage is important; however, one CIM does not necessarily need to cover multiple sectors. A combined analysis of outputs from different CIMs for different sectors can identify connections between indicators. Since each of the authors' research groups has its own CIM, each group first studied the side effects to the indicators that could be calculated by their own CIM, and then efforts were made to elucidate potential connections between the various CIM-derived indicators.

As one example, clarifying the implications of BECCS side effects on water resources and how this would affect small-scale farmer agricultural output was examined. Fewer than 37% of small-scale farms have irrigation in water scarce regions of low- and middle-income countries, compared with 42% of non-small-scale farms (Ricciardi et al 2020). It has been estimated that closing the irrigation gap would result in a 10%–11% increase in annual agricultural output (i.e. USD per household) generated by cereal production for small-scale farmers (Nozaki et al 2023, figure 1). If BECCS were deployed under a full irrigation scenario, the additional water withdrawal for irrigated bioenergy crops would be 13 times higher in Central and South America and 21 times higher in Africa compared to that under a sustainable irrigation scenario (figure S1). The full irrigation scenario assumed no consideration for the adverse consequences on biodiversity, food crop production, and land degradation, while the sustainable irrigation scenario assumed that the competition for water by BECCS and other water users is minimized. The anticipated increase in water withdrawal under the full irrigation scenario would likely hinder efforts for improving agricultural output and adaptation by hindering closure of the existing gap in irrigation use.

Third, while time-consuming, incorporating additional processes into a CIM is sometimes necessary in order to quantify the side effects of climate action. For example, quantifying the positive side effects of soil organic carbon (SOC) accumulation on water quality. SOC build-up in cropland is a land-based climate mitigation measure, but at the same time it moderates drought damage to crops (Iizumi and Wagai 2019, Renwick et al 2021) and is considered to be an adaptation measure that can be implemented by farmers at a local scale. An increase in SOC plays a crucial role in increasing crop yields, particularly in arid and semiarid regions of the world where current SOC levels are typically low. In addition, increases in SOC can mitigate against the need for inorganic nitrogen (N) fertilizer inputs. By applying machine learning techniques to global crop yield, climate, soil and agronomic management datasets, it is estimated that over the next 50–75 years up to 12.78 GtC of additional SOC stock would increase annual crop production by 38.2 Mt globally. Such an increase equates to what could be achieved by an annual inorganic N input of 5.82 MtN and would contribute towards preventing global decadal mean surface temperature warming by 0.030 °C. 17% of the food crop production increase and 19% of the inorganic N input would occur in croplands where small-scale farmers are dominant (Iizumi et al 2021, figure 1). Such a reduction in N fertilizer use would have the added benefit of protecting drinking water and fisheries resources by avoiding eutrophication of waters. Efforts have been made to incorporate physical/chemical processes into a river routing model to simulate river water temperatures and riverine dissolved inorganic N concentrations (Huang et al 2021). Implementing these processes into the CIM will enable quantification of the positive side effect of agricultural SOC build-up on water quality in the future research.

Finally, CIMs can be used to explicitly evaluate the effects of adaptation measures under different socioeconomic conditions and levels of climate change (table 1). For example, a global river hydrodynamics model with an output resolution of 0.25° in longitude and latitude can calculate the population exposed to flood risk with different protection levels. It has been estimated that the population exposed to flood risk would decrease by 27.1 million people globally if flood protection was optimized. The protection level was set to maximize the net benefit of adaptation calculated by subtracting the adaptation cost from avoided loss of climate change (Tanoue et al 2021, figure 1). These findings indicate that at least some parts of Goal 1 and Goal 11, i.e. the aspects related to disaster risk management, could benefit directly from adaptation. Further, the reduction in flood risk through adaptation would likely benefit crop production. These benefits could be significant as the total cost of flood damage to maize, rice, soybean and wheat for the period 1982–2016 is estimated to be 5.5 billion USD globally (Kim et al 2023). However, as in the third lesson, understanding the various physical/eco-physiological processes that are related to crop damage associated with inundation is needed in order to estimate the positive side effect of improved flood protection level on food and bioenergy crop production.

The authors' experiences reported above shed light on the characteristics of CIMs, relative to IAMs, in quantifying the side effects of climate action on SDGs. CIM-derived indicators can supplement the findings of IAM-based assessments and, as has recently been done for estimates of the total cost of climate change (Oda et al 2023), enable policymakers to obtain a more comprehensive view of the side effects of climate action. Regression/correlation analyses can be used to infer connections between indicators (Pradhan et al 2017, Rimba and Hirabayashi 2023) and to present researchers with a starting point for assessing which pairs of indicators are worth studying using CIMs. Although the improvement of CIMs will require continuous efforts to better understand the processes, data collection, model validation, and collaborative research between interconnected sectors, we advocate undertaking CIM-based research for a synthesis with IAM-based assessments in the future.