In 2015, the world committed to achieving 169 Sustainable Development Goals (SDGs) by 2030 and to implementing climate adaptation measures under the Paris Agreement. However, aligning these commitments remains challenging due to siloed governance and an incomplete understanding of the interplay between SDGs and climate adaptation. Existing research often focuses on broad scales, lacking actionable frameworks for integrating sector-level considerations. This paper addresses this gap by proposing a sector-scale framework to analyze the relationship between climate change impacts, adaptation strategies, and SDG target achievement. The framework considers the mediating role of ecosystems and socio-economic sectors in the complex relationship between climate impacts and SDGs. It aims to provide a practical tool for aligning national adaptation plans with SDG targets, ensuring that adaptation efforts contribute to, rather than detract from, sustainable development. The inadequacy of current approaches, wherein only a small fraction of National Adaptation Plans explicitly consider SDG targets, highlights the need for a comprehensive and practical framework.

The paper builds upon previous studies that mapped the relationships between sectors and SDG targets, climate impacts and sectors, and interdependencies across SDG targets. However, these studies often suffer from limitations such as focusing on a limited set of SDGs, sectors, or climate impact drivers, and neglecting interdependent effects. This framework aims to overcome these limitations by integrating and expanding on previous findings, providing a more holistic and comprehensive view of the complex interplay between climate change, adaptation, and sustainable development.

The researchers developed a three-step methodology. Step 1 involved conceptualizing the framework, defining the scope of analysis, and selecting relevant sectors and climate impact drivers. A holistic set of 22 ecosystems and socio-economic sectors were chosen based on international classifications (USGS, IUCN, ISIC), ensuring mappability and global applicability. The analysis focused on the services provided by these sectors. Step 2 involved comprehensive evidence mapping using content analysis and literature review. They examined sector-SDG influences (direct, indirect, interdependent – unique, cross-sectoral, substitutable) and climate-sector influences (impact on supply and demand), differentiating between acute and chronic climate impacts. Step 3 involved analyzing the mapped evidence using descriptive statistics to quantify sector-SDG and climate-sector influences. IPCC AR5 data on near-term sectoral risk was integrated to assess the potential impact of climate change on SDG target achievement. Deductive reasoning combined sector-SDG and climate-sector influences to determine the potential effects of climate change on SDG targets, using IPCC-derived global data on near-term sectoral risk.

The framework revealed that all 169 SDG targets are threatened by near-term climate risks. Ecosystem adaptation can safeguard 62% of targets, utility infrastructure adaptation 81%, primary/secondary sector adaptation 40%, and tertiary sector adaptation all targets (if well-planned and governed). Integrating ecosystems into adaptation is essential to complement socio-economic sectors in achieving targets across 13 of 17 SDGs. All acute climatic impact drivers potentially directly undermine 86% of SDG targets, with chronic drivers affecting even more. Considering direct and indirect influences, all SDG targets are potentially threatened. The analysis identified seven sectors (rivers & lakes, wetlands & peatlands, cropland, electricity, water & waste, construction, and housing & real estate) at highest near-term risk, potentially impacting 68% of SDG targets by 2030. The study differentiated among types of sector-SDG influences (unique, cross-sectoral, substitutable, indirect) and adaptation approaches (hazard-based, exposure-based, vulnerability-based) to inform tailored adaptation strategies.

The findings highlight the interconnectedness between climate change, adaptation, and the SDGs. A sector-scale approach provides a more nuanced understanding of climate change impacts compared to broader analyses. The framework allows working backward from SDGs to identify specific adaptation needs and options. Tailoring adaptation to unique, cross-sectoral, substitutable, or indirect influences is essential for maximizing SDG co-benefits. Hazard-based, exposure-based, and vulnerability-based adaptation options are discussed, along with the potential role of Nature-Based Solutions (NbS) in protecting multiple sectors and advancing SDG co-benefits. The study emphasizes the importance of considering non-substitutable ecosystem services and the need to account for spatial and temporal dynamics in adaptation planning. The results underscore the urgency of targeted adaptation action to safeguard SDG progress.

This research provides a novel framework for aligning climate adaptation with the SDGs. The findings demonstrate the widespread threat to SDG targets from climate change and the crucial role of sector-specific adaptation. The framework facilitates tailored adaptation strategies, maximizing SDG co-benefits and minimizing risks. Future research should focus on refining the framework through more comprehensive data, incorporating more detailed sector classifications, and enhancing the evaluation of interconnected risks and trade-offs. Furthermore, future applications need to explicitly engage with local communities to ensure equitable and just outcomes and avoid maladaptation.

The study acknowledges limitations regarding sector definitions and scope, climatic impact-driver definitions, SDG target definitions, and the reliance on evidence mapping. The sector classification, while aiming for global applicability, might not fully capture nuanced national contexts. The reliance on existing literature may not fully capture all potential influences. Global application relied on IPCC AR5 data, which might not encompass all sectors and interactions comprehensively. The absence of an influence in the analysis doesn't necessarily imply the absence of a link. Future research needs to address these limitations through improved data and more comprehensive analysis, including quantitative causal inferences and dynamic evidence mapping.