Targeting climate adaptation to safeguard and advance the Sustainable Development Goals

In 2015, governments committed to both the 169 SDG targets and to climate adaptation under the Paris Agreement. Despite calls to align these agendas, practical alignment has been limited due to siloed governance and an inadequate, actionable understanding of how climate impacts on sectors affect SDG target achievement. Most adaptation planning is organised by sector, whereas prior research has largely assessed climate–SDG linkages at broad scales and has not systematically captured how sectors mediate between climate risks and SDG outcomes or how interdependent sectoral effects cascade across targets. This paper addresses these gaps by proposing and globally applying a sector-scale framework that conceptualises bi-directional influences among 169 SDG targets, 12 acute and chronic climatic impact-drivers, and 22 ecosystem and socio-economic sectors. The purpose is to guide targeted adaptation that safeguards and advances SDG achievement by 2030, identifying where sectoral adaptation can reduce near-term climate risk and deliver SDG co-benefits.

The study builds on three strands of literature: (a) mappings of interlinkages between sectors and SDG targets; (b) evidence on influences of climatic impact-drivers (e.g., floods, warming) on sectors; and (c) analyses of interdependencies across SDG targets. Prior studies have been limited by separating SDG and adaptation domains, focusing on subsets of SDGs, sectors, or climate drivers, and neglecting interdependent sectoral effects on SDG targets. The authors integrate and expand these strands to include all 169 SDG targets, 12 climate impact-drivers, and 22 sectors, emphasizing direct, indirect, and interdependent (unique, cross-sectoral, substitutable) influences. This synthesis responds to calls for actionable frameworks that can inform national adaptation plans aligned with SDGs, incorporating insights from ecosystem services, infrastructure for sustainable development, and climate risk transmission across sectors.

The research followed a three-step approach. Step 1 (Aim and Concept): The authors conceptualised a framework using sectors as the intermediary between climate impacts and SDG targets, justified because national plans are typically sector-organised and sectors both provide essential services and are climate-exposed. They defined 22 sectors: ecosystems (based on updated USGS/IUCN/SEEA ecosystem types) and socio-economic sectors (ISIC Rev. 4). The analysis focused on sector service provision (goods and services), cataloguing 35 ecosystem services and 32 socio-economic services. Climatic impact-drivers were taken from IPCC AR5 (12 drivers classified as acute or chronic). Step 2 (Data Source and Selection): Two phases of evidence mapping were undertaken. Phase 1 mapped sector–SDG influences for each of 22 sectors and 169 SDG targets, classifying: (1) direct influences (target text explicitly describes services of a sector); (2) interdependent influences—unique (single sector service), cross-sectoral (multiple independent services), and substitutable (different sectors provide the same service); and (3) indirect influences where published evidence indicates that improving sector service quality/quantity enhances target achievement (even if not explicitly mentioned in the target text). Phase 2 mapped climate–sector influences for each of 12 climatic impact-drivers and each sector, recording negative/positive effects on service supply factors (land/natural resources, physical capital, labour) and on demand. Evidence was sourced in stages: Tier-1 journals and IPCC assessments/special reports (including IPCC AR5 and SR1.5), other peer-reviewed articles and preprints, and grey literature. Searches used Web of Science and Google Scholar, in English. Influences were screened against predefined inclusion/exclusion criteria; ambiguous cases were resolved by consensus among authors. One piece of evidence sufficed to register an influence. Step 3 (Analysis and Presentation): Sector–SDG influences were summarised using descriptive statistics at sector and SDG levels, including counts of direct, indirect, and interdependent influences, and aggregated by sector categories (ecosystems; utilities; primary/secondary; tertiary). Climate–sector influences were described by counts and direction of effects per impact-driver and sector, including the affected supply factors/demand. For magnitude of climate risk, IPCC AR5 Table TS.4 near-term (to the 2030s) sectoral risk rankings were applied to identify sectors at high global near-term risk from specific drivers. Using deductive reasoning, sector–SDG and climate–sector mappings were integrated to estimate which SDG targets are potentially affected by acute/chronic climate influences via sectors and by near-term sectoral risk. Results were synthesised globally to inform where adaptation can safeguard SDG targets.

- Sector–SDG influences: - Ecosystems directly influence 24% of the 169 SDG targets; beyond SDG14 and SDG15, 11 additional goals explicitly reference ecosystem services. - Utility sectors (electricity, transport, water) directly influence 17% of targets; primary/secondary sectors (manufacturing, mining, construction) 8%; tertiary sectors (e.g., public administration, education, healthcare) 74%. - Public administration directly influences 50% of SDG targets via governance services. - Interdependencies: 43% of targets have unique sector influences; 33% cross-sectoral; 11% substitutable contributions. Ecosystems complement or substitute socio-economic sectors across targets spanning 13 of 17 SDGs. - Indirect influences are, on average, five times more numerous than direct ones; highest indirect/direct ratios occur for SDG8, SDG5, SDG4, and SDG16, and for sectors such as digital communications, mining, manufacturing, and transport. - Climate–sector influences: - Acute climatic impact-drivers threaten all 22 sectors via impacts on supply or demand; chronic drivers predominantly impose negative effects via land/natural resources with some regional positives (e.g., potential yield increases in limited regions under warming). - IPCC AR5 near-term high-risk sectors include: rivers & lakes, wetlands & peatlands, cropland, electricity, water & waste, construction, and housing & real estate. - SDG targets influenced by climate change: - Acute drivers can potentially undermine achievement of 146/169 (86%) SDG targets directly via sectoral effects; combined direct and indirect pathways suggest all 169 targets are potentially threatened by acute or chronic drivers. - Chronic drivers threaten 37% more SDG targets than they support through opportunities (regional positives). - Adaptation potential: when planned and governed well, adapting ecosystems can help safeguard 62% of targets; utilities 81%; primary/secondary sectors 40%; tertiary sectors can help safeguard all SDG targets. About 21% of climate-sensitive targets require adaptation across both ecosystems and socio-economic sectors. - Focusing on the seven sectors at highest near-term risk: their risk can directly hamper 36% of SDG targets; considering direct and indirect influences, near-term risk to these sectors can affect 68% of targets across all 17 goals. SDG2, SDG6, SDG7, SDG9, SDG11, SDG12, and SDG14 have at least half of their targets directly influenced by one or more of these seven sectors. - Strategic adaptation insights: - Unique influences suggest sector-specific adaptation (e.g., public administration facilities) can uniquely safeguard the largest share of targets absent location-specific risk data. - Cross-sectoral influences indicate the need for coordinated adaptation across multiple climate-sensitive sectors (notably public administration and ecosystems) to safeguard many targets. - Substitutable influences reveal opportunities for ecosystem-based substitution (e.g., rivers & lakes, wetlands & peatlands, forests) to safeguard 8–9% of targets each by replacing or complementing climate-sensitive socio-economic services.

The framework addresses the core question of how to target adaptation so that it safeguards and advances SDG targets by explicitly modelling sectors as mediators between climate impacts and development outcomes. Findings demonstrate that climate risks propagate through sectors to affect nearly all SDG targets, implying that sector-focused adaptation planning is necessary but must be aligned with SDG objectives. The study shows how adaptation can be tailored using the typology of sector–SDG influences: unique influences warrant sector-specific measures; cross-sectoral influences call for coordinated, multi-sector adaptation; and substitutable influences enable strategic choices, such as deploying ecosystem-based services to complement or replace vulnerable socio-economic services. Operationally, adaptation should be designed across the components of risk: (1) hazard-focused interventions (e.g., nature-based solutions like wetland restoration to reduce flood hazards) that can yield extensive indirect SDG co-benefits; (2) exposure-focused measures (e.g., land-use and working-hour policies to reduce exposure of assets and labour); and (3) vulnerability-focused strategies targeting populations and sectors with low adaptive capacity, especially where services are non-substitutable or exposure cannot be feasibly reduced. Nature-based Solutions can deliver multi-hazard protection and SDG co-benefits if they respect cultural and ecological rights and support biodiversity. The results highlight priority domains where adaptation is most urgent (e.g., SDG2, 6, 7, 9, 11, 12, 14) due to high near-term sectoral risk and extensive direct/indirect linkages. They also underline the non-substitutability of certain ecosystem services (e.g., air purification, natural and cultural heritage) relevant to SDG11 and SDG14, reinforcing the need for conservation and management to prevent irreversible losses. The framework is adaptable for climate-first, development-first, or sector-first applications, enabling stakeholders to quantify climate impacts on SDGs, ground adaptation in national SDG visions, or tailor sector-level actions to maximize SDG co-benefits and minimize trade-offs.

This paper introduces and globally applies an actionable sector-scale framework that links climatic impact-drivers, sector service provision, and SDG target achievement. It demonstrates that all SDG targets are potentially threatened by climate change through sectoral pathways, while identifying where targeted adaptation—particularly in ecosystems, utilities, and governance—can safeguard a large share of targets and deliver co-benefits. By clarifying unique, cross-sectoral, and substitutable sector–SDG influences and integrating near-term sectoral risk, the framework enables alignment of National Adaptation Plans with SDG priorities so that adaptation advances, rather than detracts from, sustainable development. Future work should expand sectoral scope and evidence bases (including AR6 updates), improve quantification of influence magnitudes and cascading interdependencies, leverage machine learning for dynamic literature mapping, integrate input–output models for cascading effects, and incorporate equity, justice, and local/indigenous perspectives to ensure inclusive, context-specific adaptation pathways.

Key limitations include: (1) Sector definition/scope: The internationally based land-cover and economic classifications (USGS/SEEA; ISIC) provide transferability but may mask cultural or nation-specific sector categorizations and the full diversity of ecosystem services across spatial/temporal scales. Income was not treated as a sector service to avoid pervasive indirect links. (2) Climatic impact-driver scope: Based on IPCC AR5; some drivers (e.g., wildfire) are not explicit and drought classification straddles acute/chronic. (3) SDG target dependence: Direct influence identification relies on target wording, a negotiated political text rather than a purely scientific specification, potentially affecting counts. (4) Evidence mapping approach: The study catalogs potential influences without a full meta-analysis; evidence quality/quantity and geographic coverage were not systematically rated, and some true influences may be missing. (5) Global risk application: Near-term sectoral risk relies on AR5 synthesis with uncertain sector coverage (e.g., governance/public administration may be underrepresented). Comparative magnitudes of risk across sectors, interactions with non-climatic stressors, and systemic feedbacks were not quantified. (6) Trade-offs: Negative sector–SDG influences and interdependent trade-offs were not comprehensively assessed; more work is needed on cascading, compound risks and SDG–SDG interactions. These limitations point to future research needs for more comprehensive, quantitative, and context-specific applications.