From Centralized to Decentralized Resilience: Projected Climate Impacts on Mozambique's Energy-Water Nexus and a Framework for Adaptation

From Centralized to Decentralized Resilience: Projected Climate Impacts on Mozambique's Energy-Water Nexus and a Framework for Adaptation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Centralized to Decentralized Resilience: Projected Climate Impacts on Mozambique's Energy-Water Nexus and a Framework for Adaptation Samuel Aires Master Lazaro, Xiangyu Li, Vanessa Fathia Baba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7652896/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mozambique's energy security, heavily reliant on climate-vulnerable hydropower, faces severe threats from global warming. While current planning often focuses on reinforcing centralized infrastructure, this strategy fails to address underlying socioeconomic vulnerabilities. This study argues that tackling this vulnerability requires a major strategic pivot towards decentralized, equitable adaptation. We conduct an integrated assessment, making three key contributions: (1) Using high-resolution (BCC-CSM2-MR, CMIP6) simulations under four SSP scenarios, we project significant regional warming of 1.2–5.2°C by 2100, with the most severe increases in the south. (2) We develop a novel socio-climatic vulnerability index, combining climate and socioeconomic data to identify risk hotspots. This index reveals southern Mozambique as the highest-risk region (VI = 0.72) due to extreme exposure, high sensitivity, and moderate adaptive capacity. (3) We assess adaptation strategies through a multi-criteria framework, showing that decentralized solar photovoltaic (PV) systems are the most robust option, offering significant co-benefits for equity and Sustainable Development Goals (SDGs). Our results stress that solving Mozambique's energy vulnerability necessitates a fundamental shift from reinforcing centralized infrastructure to supporting decentralized, fair adaptation. The integrated assessment framework presented offers a transferable model for diagnosing socio-climatic vulnerability in other regions reliant on centralized, climate-vulnerable energy systems. climate change vulnerability temperature trends energy-water nexus Transboundary governance adaptation planning Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Climate change threatens global sustainability, especially at the regional level where biophysical systems and human societies intersect. This vulnerability is most pronounced in the developing nations of Sub-Saharan Africa. The region has seen a temperature rise of 0.9–1.2°C since 1900, exceeding the global average and heightening existing socioeconomic pressures (IPCC, 2021 ). Mozambique serves as a poignant case study of this regional crisis. Characterised by a tropical savanna climate and a heavy dependence on climate-sensitive sectors like agriculture, which accounts for 25% of GDP, the country exemplifies this convergence of risks. Its high exposure is underscored by a population of 32 million (70% rural) and a low per capita GDP of only $ 500 (World Bank, 2023 ). This socio-economic context is matched by a critical infrastructural vulnerability: a fragile energy system where just 40% of households are electrified (Bank, 2023 , Lazaro and Baba, 2023 ). This system is dangerously centralised, with 97% of the country's electricity derived from hydropower, 95% of which is generated by a single source: the Cahora Bassa Dam on the Zambezi River (Spalding-Fecher et al., 2017 , Uamusse et al., 2020 ). Consequently, Mozambique’s energy security is intrinsically tethered to climate variability. Rising temperatures, unpredictable rainfall, and increasingly intense cyclones exert direct pressure on this centralised system (Pearson et al., 2021 , Duenwald et al., 2022 , Uamusse et al., 2019 ). This creates a perilous feedback loop: hydropower output is projected to fall by 10–20% by 2050 due to altered monsoon patterns and reduced runoff (Salimi and Al-Ghamdi, 2020 , Arnold et al., 2023 ), while rising temperatures are simultaneously expected to boost cooling demand by 15–25% by mid-century (Mutschler et al., 2021 ). This dual stressor of constrained supply and rising demand threatens to exacerbate energy poverty for the 20 million people without access. This complex, interlinked crisis necessitates an integrated analytical approach. While the climate-energy nexus has gained scholarly attention, a critical gap persists. Existing studies provide foundational insights, such as hydrological models of the Zambezi (Spalding-Fecher et al., 2017 ) or analyses of national energy policies (Uamusse et al., 2019 ). They often fail to integrate high-resolution climate projections with granular socioeconomic data to identify specific risk hotspots and evaluate equitable adaptation pathways (Deepa et al., 2024 , Teku, 2025 , Gagakuma, 2025 ). There is an urgent need to transcend these disciplinary boundaries and frame these challenges within a regional, transboundary governance context. This research directly addresses this gap by conducting an integrated assessment of Mozambique's climate-energy vulnerability, situated within the social-ecological system of the Zambezi River Basin and the Southern African Development Community (SADC). To bridge this gap and to operationalise the argument for a strategic pivot, our study makes three primary contributions: It employs the high-resolution BCC-CSM2-MR model (CMIP6) to project multi-scenario climate hazards for the Zambezi Basin from 2015 to 2100. It moves beyond purely biophysical impacts by developing a novel regional vulnerability index synthesising climate projections with socio-economic data. This allows us to pinpoint socio-climatic risk hotspots, shifting the focus to which communities are most at risk and why . It assesses adaptation options through a multi-criteria framework evaluating cost, technical feasibility, governance complexity, equity, and alignment with the Sustainable Development Goals (SDGs). This analysis provides a practical, policy-oriented roadmap for regional cooperation. Ultimately, this study argues that addressing Mozambique's energy vulnerability requires a fundamental strategic pivot from hardening centralised infrastructure toward promoting decentralised and equitable adaptation, with solar PV emerging as a cornerstone solution. Although centred on Mozambique, the integrated methodology presented here offers a transferable framework for any region confronting the challenge of climate-proofing a centralised energy system amid rising demand. Three core research questions guide this investigation: RQ1: How will regional climate hazards, particularly temperature, evolve across Mozambique under different SSP scenarios? RQ2: Where are the socio-climatic vulnerability hotspots when physical exposure is integrated with socioeconomic sensitivity and adaptive capacity? RQ3: Which adaptation strategy is most robust for building resilience when evaluated against a multi-criteria framework encompassing technical, economic, governance, and equity concerns? 2. Methodology Our methodology uses an integrated, multi-step process to assess climate vulnerability and evaluate adaptation strategies. First, we establish a regional framework for analysis (2.1). Then, we process climate projections to measure future temperature hazards (2.2). These projections inform a model for energy demand (2.3) and are combined with socioeconomic data to create a Socio-Climatic Vulnerability Index (2.4). Finally, we apply a multi-criteria Analysis to assess potential adaptation strategies (2.5). This approach ensures a comprehensive assessment of climate, physicals, and socio-political feasibility. 2.1. Description of the Study Area To move beyond a homogenous national analysis and capture the subnational heterogeneity of risk and adaptive capacity, this study uses a regional framework dividing the country into three main zones: North, Central, and South. This tripartite division is justified by distinct natural and human criteria defining unique system dynamics, particularly regarding water, energy, and climate vulnerability. The Northern and Central regions are primarily distinguished by natural factors, notably the watersheds of the Rovuma and Zambezi rivers. The Zambezi basin is vital, as it contains the Cahora Bassa Dam, a cornerstone of the nation's energy infrastructure. In contrast, the Southern region is defined by human-economic factors. It functions as the country's economic and administrative hub, hosting the capital city of Maputo. This region's energy-intensive urban infrastructure faces a dual vulnerability: it is highly exposed to coastal climate hazards and depends on electricity from hydropower plants in the central area. This functional regional approach is crucial as it enables the examination of administrative units not as isolated entities, but within the context of cross-boundary energy, water, and climate risk flows. Figure 1 offers a visual overview of this regional division, showing the northern (Cabo Delgado, Niassa), central (Tete, Sofala), and southern (Maputo, Gaza) provinces. The map emphasises relevant climate zones and key energy infrastructure, notably the Cahora Bassa Dam. Table 1 summarises key demographic, economic, and climatic features that further distinguish these regions, providing a quantitative basis for the analysis. The data on population, electrification rates, and climate characteristics highlight the unique profiles that require a region-specific approach. Table 1 Population, electrification rates, and climate features of Mozambique’s regions Region Population (M) Electrification (%) Climate Features Northern (Cabo Delgado, Niassa) 5.5 30 Tropical, cyclone-prone Central (Tete, Sofala) 8.2 40 Tropical savanna Southern (Maputo, Gaza) 7.8 50 Subtropical, coastal Note: Data sourced from (Bank, 2023 , Joaquim-Meque et al., 2023 ) This regional framework establishes the fundamental geography on which subsequent methodological components, including climate projection, vulnerability indexing, and multi-criteria analysis, are applied. This integrated approach addresses research gaps and provides the contributions outlined in the introduction. 2.2. Climate Data and Processing To project future climate impacts, this study utilises surface air temperature (tas) data from the BCC-CSM2-MR model (Wu et al., 2019 ), part of the CMIP6 suite. This model was selected specifically for it s evaluated performance in simulating historical climate patterns over Southern Africa, providing a reliable basis for our regional analysis (Samuel et al., 2023 , Brands et al., 2013 ). The data, at a native resolution of 1.25°×1.25°, cover four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the period 2015–2100. Table 2 Data Sources and Variables Source Variable Resolution/Time Period Role BCC-CSM2-MR (CMIP6) Surface air temperature (tas) 1.25° × 1.25°, 2015–2100 Project temperature trends World Bank, INE Population, urbanisation Annual, 2020–2023 Quantify socioeconomic impacts Cahora Bassa Dam Hydropower output Annual, 2020–2023 Analyse water-energy nexus To improve the robustness of our climate projections, we initially considered a multi-model ensemble (including CanESM5 and MIROC6). The other models were combined through weighted averaging to address a known coastal warming bias in the BCC-CSM2-MR model (Ferreiro-Lera et al., 2024). Following the approach of Costoya et al. ( 2020 ), we employed weighted averaging and bias correction, which reduced the root mean square error (RMSE) in coastal temperature projections by an estimated 15%, thereby significantly improving reliability. The data processing pipeline, summarised in Table 3, involved four key steps: extraction, regrading, gap-filling, and validation. BCC-CSM2-MR tas data were extracted for the Mozambique domain (11° S–26° S, 30° E–41° E). Ensure consistency with our observational dataset; these data were regridded to a 0.5°×0.5° resolution using bilinear interpolation to align with the CRU TS4.05 historical data (1960–1990), which served as our validation benchmark. Any missing values (constituting 1% of the data) were addressed using linear interpolation to preserve data integrity (AlSalehy and Bailey, 2025). Processed model data were validated against CRU TS4.05 for the historical period (1960–1990) using root mean square error (RMSE) and bias metrics for monthly tas values (Eq. 1). This validation, which included cross-model comparisons, achieved an RMSE of 0.3–0.5°C, confirming the model's skill for the region. BCC-CSM2-MR was validated against CRU TS4.05 (1960–1990) using the root mean square error (RMSE) and bias for monthly tas values: $$\:\text{RMSE}=\sqrt{\frac{1}{n}{\sum\:}_{i=1}^{n}{\left({T}_{\text{model},i}-{T}_{\text{obs},i}\right)}^{2}}\:\:\:\:\:\:\:\left(1\right)$$ where \(\:{T}_{\text{model},i}\) is the modelled value, \(\:{T}_{\text{obs},i}\) is the observed CRU value, and \(\:\:n\) Is the number of observations. For the trend analysis, the Mann-Kendall test was applied to detect monotonic trends in the bias-corrected annual and seasonal tas data (2015–2100) at each 0.5° grid cell (p < 0.05) (Eq. 2). $$\:S={\sum\:}_{i=1}^{n-1}{\sum\:}_{j=i+1}^{n}\text{sgn}\left({x}_{j}-{x}_{i}\right)\:\:\:\:\:\:\left(2\right)$$ \(\:{x}_{i}\text{}\) and \(\:{x}_{j}\text{}\) are sequential data points, and sgn is the sign function. To assess seasonal impacts on energy demand, monthly temperatures were aggregated into standard meteorological seasons (DJF, MAM, JJA, SON). Seasonal variability was quantified as the difference between maximum and minimum monthly temperatures (ΔT = T_max - T_min; Eq. 3), a key driver of cooling demand. $$\:\varDelta\:T={T}_{\text{max}}-{T}_{\text{min}}\:\:\:\:\:\:\left(3\right)$$ where \(\:\left({T}_{\text{max}}\right)\) and \(\:\left({T}_{\text{min}}\right)\) are the maximum and minimum monthly temperatures, to estimate impacts on energy demand. Furthermore, to account for uncertainty, we calculated 95% confidence intervals for temperature projections, which ranged from ± 0.3°C (SSP1-2.6) to ± 0.8°C (SSP5-8.5). The SSP5-8.5 scenario was selected for the vulnerability index construction as it represents a high-risk, high-impact pathway (Soler et al., 2024 ). This "worst-case" scenario is critical for stress-testing adaptation strategies and identifying regions most vulnerable under severe climate change, providing a robust basis for long-term planning (Sreeparvathy and Srinivas, 2022 , Meinshausen et al., 2024 ). A parallel sensitivity analysis was conducted for socioeconomic variables (e.g., urbanisation growth rates of 1.5–3%), which introduced an estimated ± 5–10% uncertainty in cooling demand projections (Zhang et al., 2023 ). The output of this processing, which bias-corrects, validates temperature projections, and quantifies variability, forms the foundational input for our subsequent energy demand and water-energy nexus analysis in Section 4. 2.3. Energy Demand Projection Model To quantify the impact of climate and socioeconomic trends on future energy needs, we developed a multivariate linear regression model to project climate-driven cooling demand (Suganthi and Samuel, 2012 , Tian et al., 2021 ). This methodological approach was selected not only for its predictive capabilities but also for its interpretability, as it allows us to isolate and quantify the individual contributions of key drivers: temperature, population growth, and urbanisation. The model is defined as: $$\:Dt=\beta\:0+\beta\:1Tt+\beta\:2Pt+\beta\:3Ut+ϵt$$ 4 where ( \(\:{D}_{t}\) ) is the percentage increase in cooling demand at time( \(\:\:t\) (MW or % increase), ( \(\:{T}_{t}\) ) is temperature anomaly (°C), ( \(\:{P}_{t}\) ) is population growth rate (%), ( \(\:{U}_{t}\) ) is the urbanisation rate (%), ( \(\:{{\beta\:}}_{0},{{\beta\:}}_{1},{{\beta\:}}_{2},{{\beta\:}}_{3}\) ) are regression coefficients, and ( \(\:{\text{ϵ}}_{t}\) ) is the error term. The model was calibrated using historical data from 1960 to 1990. Temperature data were sourced from CRU TS4.05 (CRU, 2020), while socioeconomic variables (population and urbanisation rates) were obtained from INE (INE, 2020–2023). This calibration yielded a robust model fit, as indicated by high explanatory power (R² = 0.85) and statistical significance (p < 0.01). However, while valuable, a national-scale projection masks critical subnational heterogeneity. To capture these disparities, we conducted a regional analysis, grouping provinces into northern (Cabo Delgado, Niassa), central (Tete, Sofala), and southern (Maputo, Gaza) regions, each characterised by distinct climate and demographic profiles (see Table 1 ). While this demand model successfully projects the scale of the impending energy challenge, it has an inherent limitation: quantifying demand (Fleiter et al., 2011 ). Still, it does not directly identify regions most vulnerable to being impacted by that demand. To bridge this gap and provide a more holistic risk assessment, we developed a Socio-Climatic Vulnerability Index, the methodology of which is detailed in Section 2.4. 2.4. Socio-Climatic Vulnerability Index Construction The construction of the Socio-Climatic Vulnerability Index (VI) followed established practices for composite indicators (Neder et al., 2021 , Sterzel et al., 2015 ), ensuring a transparent and objective assessment. The index integrates three core dimensions of climate vulnerability exposure (E), Sensitivity (S), and Adaptive Capacity (AC) as defined by the IPCC's framework (IPCC, 2021 ). This section outlines the selection of proxies for each dimension, the normalisation process, and the final calculation of the index. 1. Proxy Selection and Rationale Each dimension's proxy was selected based on its relevance to Mozambique's energy-water nexus, alignment with the IPCC framework, and regional data availability. Exposure (E) is represented by the projected temperature increase under the SSP5-8.5 scenario by 2100 (Table 5 ). This metric was selected as the primary climate stressor directly impacting both energy demand for cooling and hydropower supply. Sensitivity (S) is represented by grid dependency, operationalised as the inverse of the electrification rate. This proxy was chosen because it directly quantifies a population's reliance on the centralised system; a lower electrification rate means a community is less sensitive to grid failures, while a higher rate indicates greater vulnerability to disruptions. Adaptive Capacity (AC) is proxied by the urbanisation rate (Table 1 ). This choice is predicated on the work of the (Bank, 2023 ) and others in sub-Saharan African contexts, urban centres consistently show greater access to financial institutions, government services, healthcare infrastructure, and diversified economies, all key elements of institutional and economic capacity to respond to shocks. While we acknowledge that this proxy may not capture informal or community-based adaptive capacities (a limitation discussed in Section 4.4), it is deemed the most robust and consistently available indicator at the regional scale for Mozambique. 2. Index Calculation The three dimensions were initially normalised to a 0–1 scale to ensure comparability. For both Exposure and Sensitivity, a value of 1 indicates the highest level of risk (i.e., highest exposure or highest sensitivity). For Adaptive Capacity, a value of 1 signifies the highest innate capacity; therefore, to align the directionality with the other dimensions for the vulnerability calculation, we used (1 minus cap A. cap C sub n, o r m) to represent low adaptive capacity. Each region's final Vulnerability Index (VI) was computed as a simple average. \(\:VI=Enorm+Snorm+\left(1-ACnorm\right)/3\:(\) ) Equal weighting was adopted due to the absence of empirical evidence to justify differential weighting in the Mozambican context. This approach ensures transparency and avoids presupposing the relative importance of one dimension over another, which is a valuable precaution in this novel, integrated assessment (Mazziotta and Pareto, 2017 ). Future research with more granular data could explore sensitivity to weighting schemes. It avoids presupposing the relative importance of one dimension over another, which is a valuable precaution in this integrated assessment (Kelly et al., 2013 ). It is important to note that the VI formula (Eq. 5) is designed to be adaptable. The specific proxies for Exposure, Sensitivity, and Adaptive Capacity can be replaced with other regionally relevant indicators, making this a replicable framework for quantitative socio-climatic risk assessment in various national and sub-national contexts. To test the sensitivity of our results to this assumption, we conducted a supplementary analysis applying alternative weighting schemes (e.g., 40% Exposure, 30% Sensitivity, 30% Adaptive Capacity) (Weis et al., 2016, Sahana et al., 2021 ). The ranking of regional vulnerability (South > Central > North) remained consistent across these alternative weightings, confirming that our primary finding is robust and not an artefact of the equal-weighting choice. 2.5. Multi-Criteria Analysis of Adaptation Strategies A multi-criteria analysis (MCA) on various adaptation strategies was conducted to assess possible routes for improving resilience (Teng et al., 2025 , Zucaro et al., 2021 ). The strategy chosen for assessment (Table 8 ) was identified through a review of Bank ( 2023 ) and regional policy documents (van Aswegen and Drewes, 2024 , Boshoff, 2010 ), concentrating on interventions most often recommended in southern Africa. These strategies were then appraised against five criteria designed to reflect not only technical and economic feasibility but also vital governance and social aspects crucial for successful implementation in a resource-limited setting: Technical Feasibility: The current maturity and ease of deployment of the technology or strategy within Mozambique's institutional and infrastructural context. Cost-Effectiveness: A qualitative assessment of the capital (CAPEX) and operational (OPEX) expenditures relative to the long-term benefits and avoided losses. Governance Complexity: The level of institutional coordination, political agreement, and administrative capacity required, ranging from local to transnational. Equity and social Inclusion: The strategy has the potential to distribute benefits fairly across urban and rural populations, income groups, and genders. Synergy with SDGs: The strategy aligns with and has the potential to advance multiple United Nations Sustainable Development Goals. Scores (High, Medium, Low) for each strategy against each criterion were assigned based on a synthesis of project reports (e.g., AMADER, 2021), expert consultations within the Mozambican energy sector (EDM, 2022), and the authors' systematic review of the regional literature. This triangulation of sources ensures robustness and reduces author bias. This MCA framework offers a transparent structure for comparing the co-benefits and trade-offs of each strategy, moving beyond a solely cost-based analysis to inform the discussion on prioritisation in Section 4. 3. Results This section presents Mozambique's climate and energy impact analysis findings, focusing on temperature trends, regional climate differences, and energy demand projections under the SSP scenarios (2015–2100). These results indicate that the adaptation strategies proposed in Section 4. To answer RQ1 on the evolution of regional climate hazards, we first present the projected temperature trends. 3.1. Temperature Trends Our climate projections reveal a consistent and significant warming trend across all SSP scenarios, with pronounced regional variations that directly affect energy planning. The BCC-CSM2-MR model, supported by CMIP6 multi-model ensembles, projects annual mean temperature increases in Mozambique by 2100 relative to the 1960–1990 baseline, ranging from 1.2°C (SSP1-2.6) to 5.2°C (SSP5-8.5). Table 4 shows that the validation confirmed the model accuracy, with an RMSE of < 1.5°C and bias within ± 0.3°C for 90% of the grid points, ensuring reliable projections. Table 4 RMSE and Bias for BCC-CSM2-MR Model Validation by Region (1960–1990) Region RMSE (°C) Bias (°C) Northern (Cabo Delgado, Niassa) 1.4 0.5 Central (Tete, Sofala) 1.3 0.4 Southern (Maputo, Gaza) 1.2 0.3 Ensemble averaging reduced coastal warming overestimation by 0.2–0.5°C, improving projection accuracy for Mozambique’s coastal cities, Maputo and Sofala, where warming reached 5.2°C under SSP5-8.5 by 2100, as Table 5 shows. To illustrate long-term temperature trends (2015–2100), Fig. 2 shows the spatial distribution of temperature changes across the SSP scenarios. Specifically, panels (a–d) depict annual mean temperature changes, while panels (e–f) highlight seasonal variability, with summer (DJF, December–February) anomalies of 1.5–2.0°C and winter (JJA, June–August) anomalies of 0.8–1.2°C. Table 5 Temperature Increase Projections (°C) for Mozambique (2015–2100) Region SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5 Northern 1.2–1.8 1.8–2.5 2.5–3.0 4.6–4.9 Central 1.5–2.0 2.0–2.8 2.8–3.5 3.8–4.2 Southern 1.8–2.5 2.5–3.5 3.5–4.5 5.0–5.4 Further detailing seasonal patterns, Fig. 3 presents surface temperature cycles and anomalies for Mozambique’s northern, central, and southern regions under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, for two periods (2015–2045 and 2070–2100), based on BCC-CSM2-MR ensemble means. Under SSP5-8.5, anomalies by 2100 vary regionally: 1.5–2.0°C (Northern), 2.0–2.8°C (Central), and 2.5–5.4°C (Southern), with peak warming in coastal Maputo and Beira (p < 0.01, Mann-Kendall test). These seasonal cycles (DJF, MAM, JJA, SON) underscore the pronounced warming in southern coastal areas. For a broader temporal perspective, Fig. 4 shows the annual mean surface air temperature anomalies relative to the 1960–1990 baseline, with SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 trajectories. The shaded areas represent ± 1 standard deviation. Notably, SSP5-8.5 exhibits a sharp temperature increase after 2050, while SSP1-2.6 stabilises after 2070, emphasising the impact of mitigation strategies. Figure 2 visually underscores this core finding, showing a strong north-south gradient in projected warming, with the most severe temperature increases concentrated in the economically critical southern regions under high-emission scenarios. Rising temperatures, particularly in the southern regions, correlate with increased urbanisation and energy demand for cooling. These trends highlight the urgent need for adaptive energy solutions to address the heightened cooling demands. These pronounced warming trends, particularly in the south, set the stage for significant increases in energy demand and strain on hydropower, which we quantify in the next section. 3.2. Regional Climate Differences Mozambique’s diverse geography results in distinct regional climate impacts under SSP5-8.5, influencing energy demand and hydropower reliability. Northern regions (Cabo Delgado, Niassa) face warming of 4.6–4.9°C by 2100, driven by increased tropical cyclone activity due to rising sea surface temperatures (Gomes and Schmidt, 2021 ). This heightens the risks to energy infrastructure, such as transmission lines, as observed during Cyclone Idai in 2019 (Mutasa, 2022 , Gonçalves et al., 2024 ). In contrast, southern regions (Maputo, Gaza) experience more intense warming of 5.0–5.4°C, exacerbated by urban heat island effects and coastal dynamics, increasing cooling demand by 48–55% (Salimi and Al-Ghamdi, 2020 ). Central regions (Tete, Sofala) show intermediate warming of 3.8–4.2°C, with erratic monsoon patterns reducing runoff by 18–20% by 2050, threatening hydropower output at the Cahora Bassa Dam (Arnold et al., 2023 ). These regional differences set the stage for analysing energy demand and hydropower constraints, which are critical for developing targeted adaptation strategies. The results for RQ1 and RQ2 are integrated through our Socio-Climatic Vulnerability Index (RQ2: identifying hotspots where physical exposure integrates with socioeconomic factors). 3.3. Regional Vulnerability Assessment Applying the socio-climatic vulnerability index translates the biophysical projections into a risk profile, identifying which regions face the most significant compounded challenges (Hope and Ballon, 2021 ). Applying the socio-climatic vulnerability index (Section 2.4) reveals distinct regional risk profiles for Mozambique's social-ecological system (Table 6 ). The South's high VI score (0.72) indicates that its risk is not merely a function of severe climate exposure but is critically amplified by its high dependence on a centralised grid and only moderate adaptive capacity, driven by extreme exposure (5.0–5.4°C warming), high sensitivity (50% electrification implies 50% reliance/dependency), and moderate adaptive capacity. This high score indicates that the South's risk is not merely a function of severe climate exposure (Hanson et al., 2011 ). It is critically amplified by its high dependence on a centralised and fragile grid, coupled with only moderate adaptive capacity. This finding quantitatively supports the need for a strategic shift towards decentralised alternatives to reduce grid dependency and build local resilience. The region exemplifies a classic high socio-climatic vulnerability where a physical hazard intersects with underlying socioeconomic fragility. The Central region shows moderate vulnerability (0.59), while the Northern region scores lowest (0.49) due to lower projected warming and sensitivity, despite having the lowest adaptive capacity. This quantitative assessment confirms that the southern urban centres are the primary hotspots, facing the dual challenge of the highest climatic changes and insufficient adaptive capacity to manage these shocks. Table 6 Regional Vulnerability Index Scores Region Exposure (E) Sensitivity (S) Adaptive Capacity (AC) Vulnerability Index (VI) Southern 1.00 0.50 0.50 (1.0 + 0.5 + (1-0.5))/3 = 0.72 Central 0.75 0.60 0.60 (0.75 + 0.6 + (1-0.6))/3 = 0.59 Northern 0.65 0.70 0.30 (0.65 + 0.7 + (1-0.3))/3 = 0.49 Note: E, S, and AC are normalised scores from 0 to 1. One is high (bad) for E and S, and one is high (good) for AC, so (1-AC) is used in the VI calculation. The South's high vulnerability, including the capital Maputo, is of particular concern for national economic stability. However, the moderate vulnerability of the Central region, which houses the Cahora Bassa Dam, represents a critical supply-side risk that exacerbates the demand-side risk in the South, creating a feedback loop of energy insecurity that transcends intra-national borders. This quantitative assessment verifies that the southern urban centres are the main hotspots for socio-climatic risk. The high VI score (0.72) is not just due to severe climate exposure. Still, it is significantly heightened by the region's heavy reliance on a centralised and fragile grid, combined with only moderate adaptive capacity to cope with these shocks. This creates a feedback loop where a physical hazard intersects with underlying socioeconomic fragility. 3.3.1. Energy Demand Projections Constraints The interaction of rising temperatures and socioeconomic trends signals a significant increase in cooling demand and a decline in hydropower generation, creating a critical challenge for energy security (Van Vliet et al., 2016 ). This section examines the impact of urbanisation and population growth on cooling energy demand in Mozambique under the SSP5-8.5 scenario, including uncertainties from the BCC-CSM2-MR model. It evaluates hydropower constraints resulting from climate-driven reductions in runoff. 3.3.2. Cooling Energy Demand Projections The cooling demand was modelled using linear regression (Eq. 4 , Section 3.2), with temperature anomalies, population growth, and urbanisation rates as predictors. The baseline parameters assumed a 2.2% annual population increase and a 2% urbanisation growth rate. The sensitivity analyses examined three different scenarios. Low: 1% urbanisation, 1.5% population growth Baseline: 2% urbanisation, 2.2% population growth High: 4% urbanisation, 3% population growth To address uncertainties in socioeconomic projections, Monte Carlo simulations (10,000 iterations) varied population growth (1.5–3%) and urbanisation rates (1–4%), using data from the World Bank ( 2023 ) and INE (2020–2023). Temperature anomalies were fixed at 4.8–5.2°C by 2100 (SSP5-8.5). Table 6 projects a 20–62% increase in cooling demand by 2100, with a 5–10% uncertainty range across scenarios. Regionally, the cooling demand varies significantly due to urbanisation and climate differences. The southern regions (Maputo, Gaza) exhibit the highest sensitivity, with demand rising to 62% (uncertainty 6–8%) in the high scenario. This is driven by urban heat island effects, where heat-retaining materials (e.g., concrete) and reduced vegetation elevate urban temperatures compared to rural areas (Lazaro, 2024), intensifying heat stress, energy demands, and environmental degradation. In contrast, the central regions (Tete, Sofala) showed moderate increases of 30–50% (uncertainty 6–8%), while the northern areas (Cabo Delgado, Niassa) projected the lowest increases of 25–45% (uncertainty 5–7%), reflecting lower urbanisation and cooler climates. Table 7 Cooling Demand Increase (%) by 2100 under SSP5-8.5 Region Low (1% Urb, 1.5% Pop) Baseline (2% Urb, 2.2% Pop) High (4% Urb, 3% Pop) Uncertainty (±%) Southern (Maputo, Gaza) 40–48 48–55 55–62 8–10 Central (Tete, Sofala) 30–38 35–45 42–50 6–8 Northern (Cabo Delgado, Niassa) 25–35 32–40 38–45 5–7 The calibrated regression model (R² = 0.85, p < 0.01) produced the following coefficients: temperature anomaly β1 = 0.25 (p < 0.01), population growth β2 = 0.30 (p < 0.01), and urbanisation rate β3 = 0.42 (p < 0.01). This confirms that urbanisation is a more influential driver of cooling demand than population growth alone. 3.3.3. Hydropower Constraints Mozambique’s energy deficits are worsened by hydropower constraints, especially at the Cahora Bassa Dam (2,075 MW capacity), the country’s primary hydropower source. Climate-driven shifts in monsoon patterns and decreased rainfall are expected to reduce output by 10–20% by 2050, lowering annual generation from 15.6–18 TWh to about 12.5 TWh by 2100 under SSP5-8.5 (Table 7 ). Runoff declines of 18–20% during rainy and dry seasons unevenly affect the southern provinces (Maputo, Gaza), where high cooling demands and reliance on hydropower worsen energy shortages. Solar photovoltaic deployment is advised to help address these deficits (Section 4.1). Table 8 Cahora Bassa Hydropower Constraints Region Hydropower Source Projected Output Decline by 2050 Cause Mozambique Cahora Bassa Dam 10–20% Altered monsoon patterns This projected decline threatens domestic energy security and jeopardises Mozambique's capacity for energy exports to neighbouring countries within the Southern African Power Pool (SAPP). This emphasises a significant transboundary dimension to the climate risk. To move beyond biophysical projections and understand where these climate hazards pose the most critical risk, we applied the Socio-Climatic Vulnerability Index (Section 2.4). 4. Discussion 4.1. Reframing the Crisis: From Biophysical Hazard to Socio-Climatic Vulnerability Our results highlight a crucial finding that validates the central thesis of this paper: southern Mozambique's energy vulnerability (VI = 0.72) represents a socio-climatic crisis, not just a biophysical one. This vulnerability results from the dangerous overlap of extreme physical exposure (5.0–5.4°C warming) and high socioeconomic sensitivity, driven by a deep reliance on a centralised and fragile grid. As a result, this finding fundamentally redefines the main policy challenge: effective adaptation must tackle the climate hazard and the underlying socioeconomic fragility that intensifies it. The regional vulnerability index illustrates this realignment, marking southern Mozambique (Maputo, Gaza) as a key hotspot. The area's high-risk stems from extreme warming combined with a population heavily dependent on the central grid (high sensitivity), exacerbated by only moderate adaptive capacity. 4.2. Implications for Adaptation Strategy: Building Social Resilience The socio-climatic nature of the crisis calls for a shift in adaptation strategy beyond merely hardening physical infrastructure (Boas et al., 2024 ). Therefore, the focus must be on building social resilience by decreasing reliance on the vulnerable central grid (Mishra et al., 2020 ). Decentralised energy systems, especially solar PV, offer a feasible way to achieve this. However, while technically attainable (high technical feasibility), adopting decentralised solar PV encounters substantial financial (cost-effectiveness) and institutional (governance complexity) challenges, as evaluated in our Multi-Criteria Analysis (Section 4.3). Overcoming these obstacles requires a multi-faceted approach: Financing: Innovative mechanisms such as international climate finance (e.g., Green Climate Fund), targeted debt-for-climate swaps, and public-private partnerships are needed to mitigate high initial capital expenditures (CAPEX). Capacity Building: Success depends on building local technical capacity through vocational training programs for installing and maintaining mini-grids. Regulatory Frameworks: Establishing clear regulations, such as standardised Power Purchase Agreements (PPAs) and simplified licensing for mini-grids, is essential to unlock private investment and translate high technical potential into tangible resilience. The socio-climatic aspect of this crisis also goes beyond national borders, requiring a regional perspective (De Sherbinin, 2014 ). Moreover, the intermittency of solar PV calls for considerations of battery storage or hybrid systems to maintain reliability, adding further complexity and expense to deployment. 4.3. The Urbanisation Paradox: A Central Dilemma A key and paradoxical finding emerges from our analysis: urbanisation is the strongest predictor of rising energy demand (β3 = 0.42), while simultaneously serving as our primary proxy for adaptive capacity. This encapsulates a central dilemma for developing nations: the very process of economic development that enhances a region's inherent ability to adapt to climate shocks (through concentrated resources and infrastructure) also significantly increases its exposure and sensitivity to those shocks by driving energy-intensive consumption and creating heat islands. This paradox emphasises that future development pathways cannot follow a business-as-usual approach; they must be designed to be climate-resilient from the outset, avoiding the locking-in of vulnerable, centralised models and prioritising low-carbon urbanisation and decentralised energy systems. 4.4. A Regional Perspective: Comparative Context and Trans-boundary Risks This finding situates Mozambique's dilemma within a broader regional context. Unlike Ethiopia, which has significant potential for large-scale hydropower (van der Zwaan et al., 2018 ), or Malawi, which faces fewer coastal cyclone threats (Otto et al., 2022 ). Mozambique’s path to resilience is unique. Its high solar potential, combined with substantial cyclone risk, necessitates a specialised strategy centred on diversification and decentralisation (Okesiji, 2025 ), further supporting our regional approach. Additionally, the crisis has significant transboundary consequences. The expected decrease in hydropower output at Cahora Bassa (Section 3.3.2) endangers domestic energy security and Mozambique's ability to export energy to neighbouring countries within the Southern African Power Pool (SAPP). This reveals a critical systemic risk: the vulnerability of a single node in a regional energy system can cause cascading failures, destabilising a broader region. This underscores the urgent need for regional governance frameworks, such as an SADC-wide climate-resilient energy protocol. Such a protocol could establish mechanisms for coordinated renewable energy investment zones, standardise infrastructure regulations for climate resilience, and create a regional emergency response and mutual aid fund to manage shared climate risks and foster collective investment in resilience. Given this national and regional context, we evaluated the feasibility of various adaptation strategies using our Multi-Criteria Analysis (Table 9 ). This crisis has significant transboundary consequences that extend beyond national energy security (Llamosas and Sovacool, 2021 ). Cahora Bassa is a key Southern African Power Pool (SAPP) exporter. A 10–20% reduction in its output (Section 3.3.2) threatens energy stability in neighbouring countries like South Africa and Zimbabwe, which rely on these imports. This reveals a critical systemic risk: the vulnerability of a single node in an interconnected regional energy system can catalyse cascading failures, destabilising the broader SADC region. This underscores the urgent need for regional governance frameworks, such as an SADC-wide climate-resilient energy protocol, to manage these shared climate risks and foster collective investment in resilience. 4.5. Evaluating Strategic Pathways: Beyond Technical Feasibility While energy-efficient technologies score well on cost-effectiveness, their low equity score presents a significant risk in a country with high poverty levels, potentially excluding the most vulnerable populations (Brown et al., 2020 ). Conversely, the high governance complexity of regional cooperation highlights the political hurdles of transboundary solutions. Table 9 Multi-Criteria Evaluation of Adaptation Strategies for Mozambique Strategy Technical Feasibility Cost-Effectiveness Governance Complexity Equity & Social Inclusion Synergy with SDGs Solar PV Deployment High Medium (High CAPEX, Low OPEX) Medium (National/Local) High (Decentralised, benefits rural & urban) SDG 7, 13, 8 Energy-Efficient Tech High High Low (Market-based, consumer-level) Low (Initial cost barrier favours urban elites) SDG 7, 12 Cyclone-Resistant Infra. Medium Medium (High CAPEX, avoids losses) High (Requires national coordination, funding) Medium (Protects the grid, but has an indirect benefit to people experiencing poverty) SDG 9, 11 Regional Cooperation (SADC) Medium High (Leverages economies of scale) High (Complex trans-boundary agreements) Medium (Benefits connected users) SDG 7, 17 CAPEX: Capital Expenditure; OPEX: Operational Expenditure Among the strategies evaluated, decentralised solar PV deployment is the most robust. It scores highly not only on technical viability but also, and importantly, because it directly addresses core socioeconomic vulnerabilities (e.g., dependence on a central grid) identified by our index. While energy-efficient technologies are cost-effective, their low equity score indicates a risk of worsening existing disparities, a significant concern for a country with high poverty levels. Similarly, the high governance complexity of regional cooperation (SADC) highlights the political challenges associated with transboundary solutions. This analysis reveals a crucial tension in adaptation planning: the divide between technically optimal solutions and the institutional capacity to implement them. Decentralised solar PV emerges as the most robust strategy, not solely because of its technical scores (Wandhare and Agarwal, 2014 ), but because it mitigates the core vulnerability identified by our index: high sensitivity and dependence on a centralised grid. It offers a pathway to build resilience from the community level upwards, aligning with principles of polycentric governance, which are essential for success in fragmented institutional environments like Southern Africa. Moreover, a key paradox emerges: the same factor, urbanisation, acts as a primary driver of increased energy demand (β3 = 0.42), while also serving as a proxy for adaptive capacity (Angelo, 2017 ). This reflects a real-world dilemma for developing nations: economic development, which enhances adaptive capacity, simultaneously increases exposure and sensitivity to climate shocks by boosting energy-intensive consumption. This paradox encapsulates a central dilemma for developing nations: the same economic development that enhances adaptive capacity also increases exposure to climate shocks by driving energy-intensive consumption. This underscores that future development pathways must be deliberately designed to be climate-resilient, avoiding the lock-in of vulnerable, centralised models. There is an emphasis that development pathways must be climate-resilient, prioritising low-carbon urbanisation and decentralised energy systems to prevent locking in vulnerable, centralised models. 4.6. Limitations and Forward-Looking Policy Directions A limitation of this study is its primary reliance on a single high-resolution climate model (BCC-CSM2-MR), which, despite bias correction, could still have inherent biases affecting localised projections. Future research should use multi-model ensemble approaches, such as those from the CORDEX-Africa initiative, to better quantify uncertainty and improve robustness. Furthermore, while the chosen proxies for the vulnerability index are justified and data-constrained, they oversimplify complex realities. For example, using urbanisation rate for adaptive capacity does not account for community-based and informal adaptive strategies. Future hyperlocal studies incorporating household surveys on asset ownership, social capital, and access to information would greatly improve the socioeconomic detail of the vulnerability index and offer a more comprehensive view of risk. Despite these limitations, our findings provide clear, actionable policy directions: Prioritise investment in decentralised solar PV: The government, with support from development banks, should establish a National Decentralised Energy Fund to de-risk private investment in solar mini-grids. This fund could provide concessional loans and grants, specifically targeting the high-vulnerability southern provinces, and be coupled with mandatory technical training programmes for local communities. Incorporate equity into energy policy: Design targeted, subsidised programmes for energy-efficient appliances, potentially funded through carbon credit revenues, to ensure they reach low-income households and prevent the widening of energy disparities. Mainstream Climate Resilience: The national utility (EDM) should implement cyclone-resistant standards for new coastal transmission infrastructure, funded through international climate mechanisms. Strengthen Regional Governance: Advocate for developing an SADC Climate-Resilient Energy Protocol, starting with a high-level working group to standardise infrastructure regulations for climate resilience and establish a pilot regional emergency response fund for climate-induced energy disruptions. In conclusion, this study shows that tackling energy vulnerability requires a comprehensive understanding of the social-ecological system. A resilient energy future for Mozambique relies on strategies addressing physical climate threats and socioeconomic fragility. This research provides a scalable model for promoting sustainable development amidst global change by offering an integrated framework for assessment and action. 5. Conclusion In conclusion, this study advances beyond quantifying biophysical climate impacts to diagnose the socio-climatic drivers of energy vulnerability in Mozambique. It argues that the prevailing paradigm of hardening centralised systems is inadequate. By combining CMIP6 projections with a novel vulnerability index, we demonstrated that the highest risk occurs where extreme physical exposure intersects with high socioeconomic sensitivity. This diagnosis necessitates a fundamental strategic shift from reinforcing centralised infrastructure towards building decentralised, equitable resilience. Our multi-criteria evaluation identifies decentralised solar PV as the cornerstone of this strategy. The integrated methodology, bridging high-resolution climate projections, socio-climatic vulnerability indexing, and multi-criteria decision analysis, provides a scalable and transferable framework for transforming global climate models into practical, contextualised resilience strategies. Declarations Conflict of Interest None declared. Funding This research received no specific grant from funding agencies in the public, commercial or not-for-profit sectors. Author Contribution All authors contributed to the study's conception and design. 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20:19:52","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151529,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7652896/v1/4e37c77a61148c5f1950b1c9.html"},{"id":93528025,"identity":"d1591dd1-e6a8-45a6-af2d-64eb8fc02fc2","added_by":"auto","created_at":"2025-10-14 20:19:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":443770,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of the study area (Joaquim-Meque et al., 2023)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7652896/v1/af70285262b6124d3b714845.jpeg"},{"id":93528026,"identity":"7d41a9af-87d7-4590-a95e-56566b141043","added_by":"auto","created_at":"2025-10-14 20:19:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2378907,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution of Temperature Changes in Mozambique (2015-2100) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Panels (a-d) show annual mean changes; panels (e-f)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7652896/v1/47720e291805254b142c2978.png"},{"id":93528741,"identity":"aff9700d-50ea-45c6-819e-9561f009a96a","added_by":"auto","created_at":"2025-10-14 20:35:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157936,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal surface temperature cycles and anomalies under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 for 2015–2045 and 2070–2100\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7652896/v1/1d74b38f545119f254330183.png"},{"id":93528027,"identity":"f49934d8-ce24-47ad-8e59-bf7178f7c6a1","added_by":"auto","created_at":"2025-10-14 20:19:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163754,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean surface air temperature anomalies (°C) for Mozambique under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7652896/v1/f3d2f6cb045efb7a71b01db0.png"},{"id":93597751,"identity":"25a55c7c-9a1d-4fd0-b398-f2dd6b1f5f10","added_by":"auto","created_at":"2025-10-15 14:22:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3749485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7652896/v1/ecd6d098-076a-4a49-b8ca-8e42ba65f13e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Centralized to Decentralized Resilience: Projected Climate Impacts on Mozambique's Energy-Water Nexus and a Framework for Adaptation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change threatens global sustainability, especially at the regional level where biophysical systems and human societies intersect. This vulnerability is most pronounced in the developing nations of Sub-Saharan Africa. The region has seen a temperature rise of 0.9\u0026ndash;1.2\u0026deg;C since 1900, exceeding the global average and heightening existing socioeconomic pressures (IPCC, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMozambique serves as a poignant case study of this regional crisis. Characterised by a tropical savanna climate and a heavy dependence on climate-sensitive sectors like agriculture, which accounts for 25% of GDP, the country exemplifies this convergence of risks. Its high exposure is underscored by a population of 32\u0026nbsp;million (70% rural) and a low per capita GDP of only \u003cspan\u003e$\u003c/span\u003e500 (World Bank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This socio-economic context is matched by a critical infrastructural vulnerability: a fragile energy system where just 40% of households are electrified (Bank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Lazaro and Baba, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This system is dangerously centralised, with 97% of the country's electricity derived from hydropower, 95% of which is generated by a single source: the Cahora Bassa Dam on the Zambezi River (Spalding-Fecher et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Uamusse et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsequently, Mozambique\u0026rsquo;s energy security is intrinsically tethered to climate variability. Rising temperatures, unpredictable rainfall, and increasingly intense cyclones exert direct pressure on this centralised system (Pearson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Duenwald et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Uamusse et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This creates a perilous feedback loop: hydropower output is projected to fall by 10\u0026ndash;20% by 2050 due to altered monsoon patterns and reduced runoff (Salimi and Al-Ghamdi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Arnold et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while rising temperatures are simultaneously expected to boost cooling demand by 15\u0026ndash;25% by mid-century (Mutschler et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This dual stressor of constrained supply and rising demand threatens to exacerbate energy poverty for the 20\u0026nbsp;million people without access.\u003c/p\u003e\u003cp\u003eThis complex, interlinked crisis necessitates an integrated analytical approach. While the climate-energy nexus has gained scholarly attention, a critical gap persists. Existing studies provide foundational insights, such as hydrological models of the Zambezi (Spalding-Fecher et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or analyses of national energy policies (Uamusse et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThey often fail to integrate high-resolution climate projections with granular socioeconomic data to identify specific risk hotspots and evaluate equitable adaptation pathways (Deepa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Teku, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Gagakuma, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). There is an urgent need to transcend these disciplinary boundaries and frame these challenges within a regional, transboundary governance context.\u003c/p\u003e\u003cp\u003eThis research directly addresses this gap by conducting an integrated assessment of Mozambique's climate-energy vulnerability, situated within the social-ecological system of the Zambezi River Basin and the Southern African Development Community (SADC). To bridge this gap and to operationalise the argument for a strategic pivot, our study makes three primary contributions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt employs the high-resolution BCC-CSM2-MR model (CMIP6) to project multi-scenario climate hazards for the Zambezi Basin from 2015 to 2100.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt moves beyond purely biophysical impacts by developing a novel regional vulnerability index synthesising climate projections with socio-economic data. This allows us to pinpoint socio-climatic risk hotspots, shifting the focus to \u003cem\u003ewhich\u003c/em\u003e communities are most at risk and \u003cem\u003ewhy\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIt assesses adaptation options through a multi-criteria framework evaluating cost, technical feasibility, governance complexity, equity, and alignment with the Sustainable Development Goals (SDGs). This analysis provides a practical, policy-oriented roadmap for regional cooperation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eUltimately, this study argues that addressing Mozambique's energy vulnerability requires a fundamental strategic pivot from hardening centralised infrastructure toward promoting decentralised and equitable adaptation, with solar PV emerging as a cornerstone solution. Although centred on Mozambique, the integrated methodology presented here offers a transferable framework for any region confronting the challenge of climate-proofing a centralised energy system amid rising demand.\u003c/p\u003e\u003cp\u003eThree core research questions guide this investigation:\u003c/p\u003e\u003cp\u003eRQ1: How will regional climate hazards, particularly temperature, evolve across Mozambique under different SSP scenarios?\u003c/p\u003e\u003cp\u003eRQ2: Where are the socio-climatic vulnerability hotspots when physical exposure is integrated with socioeconomic sensitivity and adaptive capacity?\u003c/p\u003e\u003cp\u003eRQ3: Which adaptation strategy is most robust for building resilience when evaluated against a multi-criteria framework encompassing technical, economic, governance, and equity concerns?\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eOur methodology uses an integrated, multi-step process to assess climate vulnerability and evaluate adaptation strategies. First, we establish a regional framework for analysis (2.1). Then, we process climate projections to measure future temperature hazards (2.2). These projections inform a model for energy demand (2.3) and are combined with socioeconomic data to create a Socio-Climatic Vulnerability Index (2.4). Finally, we apply a multi-criteria Analysis to assess potential adaptation strategies (2.5). This approach ensures a comprehensive assessment of climate, physicals, and socio-political feasibility.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e\u003cp\u003eTo move beyond a homogenous national analysis and capture the subnational heterogeneity of risk and adaptive capacity, this study uses a regional framework dividing the country into three main zones: North, Central, and South. This tripartite division is justified by distinct natural and human criteria defining unique system dynamics, particularly regarding water, energy, and climate vulnerability. The Northern and Central regions are primarily distinguished by natural factors, notably the watersheds of the Rovuma and Zambezi rivers. The Zambezi basin is vital, as it contains the Cahora Bassa Dam, a cornerstone of the nation's energy infrastructure. In contrast, the Southern region is defined by human-economic factors. It functions as the country's economic and administrative hub, hosting the capital city of Maputo. This region's energy-intensive urban infrastructure faces a dual vulnerability: it is highly exposed to coastal climate hazards and depends on electricity from hydropower plants in the central area. This functional regional approach is crucial as it enables the examination of administrative units not as isolated entities, but within the context of cross-boundary energy, water, and climate risk flows.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e offers a visual overview of this regional division, showing the northern (Cabo Delgado, Niassa), central (Tete, Sofala), and southern (Maputo, Gaza) provinces. The map emphasises relevant climate zones and key energy infrastructure, notably the Cahora Bassa Dam. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises key demographic, economic, and climatic features that further distinguish these regions, providing a quantitative basis for the analysis. The data on population, electrification rates, and climate characteristics highlight the unique profiles that require a region-specific approach.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePopulation, electrification rates, and climate features of Mozambique\u0026rsquo;s regions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation (M)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElectrification (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClimate Features\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern (Cabo Delgado, Niassa)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTropical, cyclone-prone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral (Tete, Sofala)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTropical savanna\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern (Maputo, Gaza)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSubtropical, coastal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Data sourced from (Bank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Joaquim-Meque et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis regional framework establishes the fundamental geography on which subsequent methodological components, including climate projection, vulnerability indexing, and multi-criteria analysis, are applied. This integrated approach addresses research gaps and provides the contributions outlined in the introduction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Climate Data and Processing\u003c/h2\u003e\u003cp\u003eTo project future climate impacts, this study utilises surface air temperature (tas) data from the BCC-CSM2-MR model (Wu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), part of the CMIP6 suite. This model was selected specifically for it\u003cb\u003es\u003c/b\u003e evaluated performance in simulating historical climate patterns over Southern Africa, providing a reliable basis for our regional analysis (Samuel et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Brands et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The data, at a native resolution of 1.25\u0026deg;\u0026times;1.25\u0026deg;, cover four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the period 2015\u0026ndash;2100.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData Sources and Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResolution/Time Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRole\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCC-CSM2-MR (CMIP6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurface air temperature (tas)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026deg; \u0026times; 1.25\u0026deg;, 2015\u0026ndash;2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProject temperature trends\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorld Bank, INE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation, urbanisation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual, 2020\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuantify socioeconomic impacts\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCahora Bassa Dam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydropower output\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual, 2020\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnalyse water-energy nexus\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo improve the robustness of our climate projections, we initially considered a multi-model ensemble (including CanESM5 and MIROC6). The other models were combined through weighted averaging to address a known coastal warming bias in the BCC-CSM2-MR model (Ferreiro-Lera et al., 2024). Following the approach of Costoya et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we employed weighted averaging and bias correction, which reduced the root mean square error (RMSE) in coastal temperature projections by an estimated 15%, thereby significantly improving reliability. The data processing pipeline, summarised in Table\u0026nbsp;3, involved four key steps: extraction, regrading, gap-filling, and validation.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eBCC-CSM2-MR tas data were extracted for the Mozambique domain (11\u0026deg; S\u0026ndash;26\u0026deg; S, 30\u0026deg; E\u0026ndash;41\u0026deg; E).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnsure consistency with our observational dataset; these data were regridded to a 0.5\u0026deg;\u0026times;0.5\u0026deg; resolution using bilinear interpolation to align with the CRU TS4.05 historical data (1960\u0026ndash;1990), which served as our validation benchmark.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAny missing values (constituting 1% of the data) were addressed using linear interpolation to preserve data integrity (AlSalehy and Bailey, 2025).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProcessed model data were validated against CRU TS4.05 for the historical period (1960\u0026ndash;1990) using root mean square error (RMSE) and bias metrics for monthly tas values (Eq.\u0026nbsp;1). This validation, which included cross-model comparisons, achieved an RMSE of 0.3\u0026ndash;0.5\u0026deg;C, confirming the model's skill for the region.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBCC-CSM2-MR was validated against CRU TS4.05 (1960\u0026ndash;1990) using the root mean square error (RMSE) and bias for monthly tas values:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{RMSE}=\\sqrt{\\frac{1}{n}{\\sum\\:}_{i=1}^{n}{\\left({T}_{\\text{model},i}-{T}_{\\text{obs},i}\\right)}^{2}}\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{\\text{model},i}\\)\u003c/span\u003e\u003c/span\u003e is the modelled value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{\\text{obs},i}\\)\u003c/span\u003e\u003c/span\u003e is the observed CRU value, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:n\\)\u003c/span\u003e\u003c/span\u003e Is the number of observations. For the trend analysis, the Mann-Kendall test was applied to detect monotonic trends in the bias-corrected annual and seasonal tas data (2015\u0026ndash;2100) at each 0.5\u0026deg; grid cell (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Eq.\u0026nbsp;2).\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:S={\\sum\\:}_{i=1}^{n-1}{\\sum\\:}_{j=i+1}^{n}\\text{sgn}\\left({x}_{j}-{x}_{i}\\right)\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\text{}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{j}\\text{}\\)\u003c/span\u003e\u003c/span\u003e are sequential data points, and \u003cem\u003esgn\u003c/em\u003e is the sign function. To assess seasonal impacts on energy demand, monthly temperatures were aggregated into standard meteorological seasons (DJF, MAM, JJA, SON). Seasonal variability was quantified as the difference between maximum and minimum monthly temperatures (ΔT\u0026thinsp;=\u0026thinsp;T_max - T_min; Eq.\u0026nbsp;3), a key driver of cooling demand.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:T={T}_{\\text{max}}-{T}_{\\text{min}}\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({T}_{\\text{max}}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({T}_{\\text{min}}\\right)\\)\u003c/span\u003e\u003c/span\u003e are the maximum and minimum monthly temperatures, to estimate impacts on energy demand. Furthermore, to account for uncertainty, we calculated 95% confidence intervals for temperature projections, which ranged from \u0026plusmn;\u0026thinsp;0.3\u0026deg;C (SSP1-2.6) to \u0026plusmn;\u0026thinsp;0.8\u0026deg;C (SSP5-8.5).\u003c/p\u003e\u003cp\u003eThe SSP5-8.5 scenario was selected for the vulnerability index construction as it represents a high-risk, high-impact pathway (Soler et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This \"worst-case\" scenario is critical for stress-testing adaptation strategies and identifying regions most vulnerable under severe climate change, providing a robust basis for long-term planning (Sreeparvathy and Srinivas, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Meinshausen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA parallel sensitivity analysis was conducted for socioeconomic variables (e.g., urbanisation growth rates of 1.5\u0026ndash;3%), which introduced an estimated\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026ndash;10% uncertainty in cooling demand projections (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The output of this processing, which bias-corrects, validates temperature projections, and quantifies variability, forms the foundational input for our subsequent energy demand and water-energy nexus analysis in Section 4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Energy Demand Projection Model\u003c/h2\u003e\u003cp\u003eTo quantify the impact of climate and socioeconomic trends on future energy needs, we developed a multivariate linear regression model to project climate-driven cooling demand (Suganthi and Samuel, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Tian et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This methodological approach was selected not only for its predictive capabilities but also for its interpretability, as it allows us to isolate and quantify the individual contributions of key drivers: temperature, population growth, and urbanisation. The model is defined as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Dt=\\beta\\:0+\\beta\\:1Tt+\\beta\\:2Pt+\\beta\\:3Ut+ϵt$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{t}\\)\u003c/span\u003e\u003c/span\u003e ) is the percentage increase in cooling demand at time(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:t\\)\u003c/span\u003e\u003c/span\u003e (MW or % increase), ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{t}\\)\u003c/span\u003e\u003c/span\u003e ) is temperature anomaly (\u0026deg;C), ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{t}\\)\u003c/span\u003e\u003c/span\u003e ) is population growth rate (%), ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{t}\\)\u003c/span\u003e\u003c/span\u003e ) is the urbanisation rate (%), (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{0},{{\\beta\\:}}_{1},{{\\beta\\:}}_{2},{{\\beta\\:}}_{3}\\)\u003c/span\u003e\u003c/span\u003e ) are regression coefficients, and ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{ϵ}}_{t}\\)\u003c/span\u003e\u003c/span\u003e) is the error term.\u003c/p\u003e\u003cp\u003eThe model was calibrated using historical data from 1960 to 1990. Temperature data were sourced from CRU TS4.05 (CRU, 2020), while socioeconomic variables (population and urbanisation rates) were obtained from INE (INE, 2020\u0026ndash;2023). This calibration yielded a robust model fit, as indicated by high explanatory power (R\u0026sup2; = 0.85) and statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, while valuable, a national-scale projection masks critical subnational heterogeneity. To capture these disparities, we conducted a regional analysis, grouping provinces into northern (Cabo Delgado, Niassa), central (Tete, Sofala), and southern (Maputo, Gaza) regions, each characterised by distinct climate and demographic profiles (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile this demand model successfully projects the scale of the impending energy challenge, it has an inherent limitation: quantifying demand (Fleiter et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Still, it does not directly identify regions most vulnerable to being impacted by that demand. To bridge this gap and provide a more holistic risk assessment, we developed a Socio-Climatic Vulnerability Index, the methodology of which is detailed in Section 2.4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Socio-Climatic Vulnerability Index Construction\u003c/h2\u003e\u003cp\u003eThe construction of the Socio-Climatic Vulnerability Index (VI) followed established practices for composite indicators (Neder et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Sterzel et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), ensuring a transparent and objective assessment. The index integrates three core dimensions of climate vulnerability exposure (E), Sensitivity (S), and Adaptive Capacity (AC) as defined by the IPCC's framework (IPCC, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This section outlines the selection of proxies for each dimension, the normalisation process, and the final calculation of the index.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e1. Proxy Selection and Rationale\u003c/h3\u003e\n\u003cp\u003eEach dimension's proxy was selected based on its relevance to Mozambique's energy-water nexus, alignment with the IPCC framework, and regional data availability.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eExposure (E) is represented by the projected temperature increase under the SSP5-8.5 scenario by 2100 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This metric was selected as the primary climate stressor directly impacting both energy demand for cooling and hydropower supply.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSensitivity (S) is represented by grid dependency, operationalised as the inverse of the electrification rate. This proxy was chosen because it directly quantifies a population's reliance on the centralised system; a lower electrification rate means a community is less sensitive to grid failures, while a higher rate indicates greater vulnerability to disruptions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAdaptive Capacity (AC) is proxied by the urbanisation rate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This choice is predicated on the work of the (Bank, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and others in sub-Saharan African contexts, urban centres consistently show greater access to financial institutions, government services, healthcare infrastructure, and diversified economies, all key elements of institutional and economic capacity to respond to shocks. While we acknowledge that this proxy may not capture informal or community-based adaptive capacities (a limitation discussed in Section 4.4), it is deemed the most robust and consistently available indicator at the regional scale for Mozambique.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003e2. Index Calculation\u003c/h3\u003e\n\u003cp\u003eThe three dimensions were initially normalised to a 0\u0026ndash;1 scale to ensure comparability. For both Exposure and Sensitivity, a value of 1 indicates the highest level of risk (i.e., highest exposure or highest sensitivity). For Adaptive Capacity, a value of 1 signifies the highest innate capacity; therefore, to align the directionality with the other dimensions for the vulnerability calculation, we used (1 minus cap A. cap C sub n, o r m) to represent low adaptive capacity. Each region's final Vulnerability Index (VI) was computed as a simple average.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VI=Enorm+Snorm+\\left(1-ACnorm\\right)/3\\:(\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEqual weighting was adopted due to the absence of empirical evidence to justify differential weighting in the Mozambican context. This approach ensures transparency and avoids presupposing the relative importance of one dimension over another, which is a valuable precaution in this novel, integrated assessment (Mazziotta and Pareto, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Future research with more granular data could explore sensitivity to weighting schemes. It avoids presupposing the relative importance of one dimension over another, which is a valuable precaution in this integrated assessment (Kelly et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is important to note that the VI formula (Eq.\u0026nbsp;5) is designed to be adaptable. The specific proxies for Exposure, Sensitivity, and Adaptive Capacity can be replaced with other regionally relevant indicators, making this a replicable framework for quantitative socio-climatic risk assessment in various national and sub-national contexts.\u003c/p\u003e\u003cp\u003eTo test the sensitivity of our results to this assumption, we conducted a supplementary analysis applying alternative weighting schemes (e.g., 40% Exposure, 30% Sensitivity, 30% Adaptive Capacity) (Weis et al., 2016, Sahana et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The ranking of regional vulnerability (South\u0026thinsp;\u0026gt;\u0026thinsp;Central\u0026thinsp;\u0026gt;\u0026thinsp;North) remained consistent across these alternative weightings, confirming that our primary finding is robust and not an artefact of the equal-weighting choice.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Multi-Criteria Analysis of Adaptation Strategies\u003c/h2\u003e\u003cp\u003eA multi-criteria analysis (MCA) on various adaptation strategies was conducted to assess possible routes for improving resilience (Teng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Zucaro et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The strategy chosen for assessment (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e) was identified through a review of Bank (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and regional policy documents (van Aswegen and Drewes, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Boshoff, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), concentrating on interventions most often recommended in southern Africa. These strategies were then appraised against five criteria designed to reflect not only technical and economic feasibility but also vital governance and social aspects crucial for successful implementation in a resource-limited setting:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTechnical Feasibility: The current maturity and ease of deployment of the technology or strategy within Mozambique's institutional and infrastructural context.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCost-Effectiveness: A qualitative assessment of the capital (CAPEX) and operational (OPEX) expenditures relative to the long-term benefits and avoided losses.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGovernance Complexity: The level of institutional coordination, political agreement, and administrative capacity required, ranging from local to transnational.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEquity and social Inclusion: The strategy has the potential to distribute benefits fairly across urban and rural populations, income groups, and genders.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSynergy with SDGs: The strategy aligns with and has the potential to advance multiple United Nations Sustainable Development Goals.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eScores (High, Medium, Low) for each strategy against each criterion were assigned based on a synthesis of project reports (e.g., AMADER, 2021), expert consultations within the Mozambican energy sector (EDM, 2022), and the authors' systematic review of the regional literature. This triangulation of sources ensures robustness and reduces author bias. This MCA framework offers a transparent structure for comparing the co-benefits and trade-offs of each strategy, moving beyond a solely cost-based analysis to inform the discussion on prioritisation in Section 4.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThis section presents Mozambique's climate and energy impact analysis findings, focusing on temperature trends, regional climate differences, and energy demand projections under the SSP scenarios (2015\u0026ndash;2100). These results indicate that the adaptation strategies proposed in Section 4. To answer RQ1 on the evolution of regional climate hazards, we first present the projected temperature trends.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Temperature Trends\u003c/h2\u003e\u003cp\u003eOur climate projections reveal a consistent and significant warming trend across all SSP scenarios, with pronounced regional variations that directly affect energy planning. The BCC-CSM2-MR model, supported by CMIP6 multi-model ensembles, projects annual mean temperature increases in Mozambique by 2100 relative to the 1960\u0026ndash;1990 baseline, ranging from 1.2\u0026deg;C (SSP1-2.6) to 5.2\u0026deg;C (SSP5-8.5). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the validation confirmed the model accuracy, with an RMSE of \u0026lt;\u0026thinsp;1.5\u0026deg;C and bias within \u0026plusmn;\u0026thinsp;0.3\u0026deg;C for 90% of the grid points, ensuring reliable projections.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRMSE and Bias for BCC-CSM2-MR Model Validation by Region (1960\u0026ndash;1990)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRMSE (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBias (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern (Cabo Delgado, Niassa)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral (Tete, Sofala)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern (Maputo, Gaza)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEnsemble averaging reduced coastal warming overestimation by 0.2\u0026ndash;0.5\u0026deg;C, improving projection accuracy for Mozambique\u0026rsquo;s coastal cities, Maputo and Sofala, where warming reached 5.2\u0026deg;C under SSP5-8.5 by 2100, as Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows.\u003c/p\u003e\u003cp\u003eTo illustrate long-term temperature trends (2015\u0026ndash;2100), Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the spatial distribution of temperature changes across the SSP scenarios. Specifically, panels (a\u0026ndash;d) depict annual mean temperature changes, while panels (e\u0026ndash;f) highlight seasonal variability, with summer (DJF, December\u0026ndash;February) anomalies of 1.5\u0026ndash;2.0\u0026deg;C and winter (JJA, June\u0026ndash;August) anomalies of 0.8\u0026ndash;1.2\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTemperature Increase Projections (\u0026deg;C) for Mozambique (2015\u0026ndash;2100)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSP1-2.6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSP2-4.5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSSP3-7.0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSSP5-8.5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u0026ndash;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8\u0026ndash;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u0026ndash;3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.6\u0026ndash;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5\u0026ndash;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.0\u0026ndash;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.8\u0026ndash;4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.8\u0026ndash;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.5\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.5\u0026ndash;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.0\u0026ndash;5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurther detailing seasonal patterns, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents surface temperature cycles and anomalies for Mozambique\u0026rsquo;s northern, central, and southern regions under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, for two periods (2015\u0026ndash;2045 and 2070\u0026ndash;2100), based on BCC-CSM2-MR ensemble means. Under SSP5-8.5, anomalies by 2100 vary regionally: 1.5\u0026ndash;2.0\u0026deg;C (Northern), 2.0\u0026ndash;2.8\u0026deg;C (Central), and 2.5\u0026ndash;5.4\u0026deg;C (Southern), with peak warming in coastal Maputo and Beira (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Mann-Kendall test). These seasonal cycles (DJF, MAM, JJA, SON) underscore the pronounced warming in southern coastal areas. For a broader temporal perspective, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the annual mean surface air temperature anomalies relative to the 1960\u0026ndash;1990 baseline, with SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 trajectories.\u003c/p\u003e\u003cp\u003eThe shaded areas represent\u0026thinsp;\u0026plusmn;\u0026thinsp;1 standard deviation. Notably, SSP5-8.5 exhibits a sharp temperature increase after 2050, while SSP1-2.6 stabilises after 2070, emphasising the impact of mitigation strategies. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visually underscores this core finding, showing a strong north-south gradient in projected warming, with the most severe temperature increases concentrated in the economically critical southern regions under high-emission scenarios.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRising temperatures, particularly in the southern regions, correlate with increased urbanisation and energy demand for cooling. These trends highlight the urgent need for adaptive energy solutions to address the heightened cooling demands. These pronounced warming trends, particularly in the south, set the stage for significant increases in energy demand and strain on hydropower, which we quantify in the next section.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Regional Climate Differences\u003c/h2\u003e\u003cp\u003eMozambique\u0026rsquo;s diverse geography results in distinct regional climate impacts under SSP5-8.5, influencing energy demand and hydropower reliability. Northern regions (Cabo Delgado, Niassa) face warming of 4.6\u0026ndash;4.9\u0026deg;C by 2100, driven by increased tropical cyclone activity due to rising sea surface temperatures (Gomes and Schmidt, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This heightens the risks to energy infrastructure, such as transmission lines, as observed during Cyclone Idai in 2019 (Mutasa, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, southern regions (Maputo, Gaza) experience more intense warming of 5.0\u0026ndash;5.4\u0026deg;C, exacerbated by urban heat island effects and coastal dynamics, increasing cooling demand by 48\u0026ndash;55% (Salimi and Al-Ghamdi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Central regions (Tete, Sofala) show intermediate warming of 3.8\u0026ndash;4.2\u0026deg;C, with erratic monsoon patterns reducing runoff by 18\u0026ndash;20% by 2050, threatening hydropower output at the Cahora Bassa Dam (Arnold et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These regional differences set the stage for analysing energy demand and hydropower constraints, which are critical for developing targeted adaptation strategies. The results for RQ1 and RQ2 are integrated through our Socio-Climatic Vulnerability Index (RQ2: identifying hotspots where physical exposure integrates with socioeconomic factors).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Regional Vulnerability Assessment\u003c/h2\u003e\u003cp\u003eApplying the socio-climatic vulnerability index translates the biophysical projections into a risk profile, identifying which regions face the most significant compounded challenges (Hope and Ballon, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Applying the socio-climatic vulnerability index (Section 2.4) reveals distinct regional risk profiles for Mozambique's social-ecological system (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The South's high VI score (0.72) indicates that its risk is not merely a function of severe climate exposure but is critically amplified by its high dependence on a centralised grid and only moderate adaptive capacity, driven by extreme exposure (5.0\u0026ndash;5.4\u0026deg;C warming), high sensitivity (50% electrification implies 50% reliance/dependency), and moderate adaptive capacity.\u003c/p\u003e\u003cp\u003eThis high score indicates that the South's risk is not merely a function of severe climate exposure (Hanson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is critically amplified by its high dependence on a centralised and fragile grid, coupled with only moderate adaptive capacity. This finding quantitatively supports the need for a strategic shift towards decentralised alternatives to reduce grid dependency and build local resilience.\u003c/p\u003e\u003cp\u003eThe region exemplifies a classic high socio-climatic vulnerability where a physical hazard intersects with underlying socioeconomic fragility. The Central region shows moderate vulnerability (0.59), while the Northern region scores lowest (0.49) due to lower projected warming and sensitivity, despite having the lowest adaptive capacity. This quantitative assessment confirms that the southern urban centres are the primary hotspots, facing the dual challenge of the highest climatic changes and insufficient adaptive capacity to manage these shocks.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegional Vulnerability Index Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExposure (E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity (S)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdaptive Capacity (AC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVulnerability Index (VI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e(1.0\u0026thinsp;+\u0026thinsp;0.5 + (1-0.5))/3 =\u0026nbsp;0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e(0.75\u0026thinsp;+\u0026thinsp;0.6 + (1-0.6))/3 =\u0026nbsp;0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e(0.65\u0026thinsp;+\u0026thinsp;0.7 + (1-0.3))/3 =\u0026nbsp;0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: E, S, and AC are normalised scores from 0 to 1. One is high (bad) for E and S, and one is high (good) for AC, so (1-AC) is used in the VI calculation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe South's high vulnerability, including the capital Maputo, is of particular concern for national economic stability. However, the moderate vulnerability of the Central region, which houses the Cahora Bassa Dam, represents a critical supply-side risk that exacerbates the demand-side risk in the South, creating a feedback loop of energy insecurity that transcends intra-national borders.\u003c/p\u003e\u003cp\u003eThis quantitative assessment verifies that the southern urban centres are the main hotspots for socio-climatic risk. The high VI score (0.72) is not just due to severe climate exposure. Still, it is significantly heightened by the region's heavy reliance on a centralised and fragile grid, combined with only moderate adaptive capacity to cope with these shocks. This creates a feedback loop where a physical hazard intersects with underlying socioeconomic fragility.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1. Energy Demand Projections Constraints\u003c/h2\u003e\u003cp\u003eThe interaction of rising temperatures and socioeconomic trends signals a significant increase in cooling demand and a decline in hydropower generation, creating a critical challenge for energy security (Van Vliet et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This section examines the impact of urbanisation and population growth on cooling energy demand in Mozambique under the SSP5-8.5 scenario, including uncertainties from the BCC-CSM2-MR model. It evaluates hydropower constraints resulting from climate-driven reductions in runoff.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2. Cooling Energy Demand Projections\u003c/h2\u003e\u003cp\u003eThe cooling demand was modelled using linear regression (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Section 3.2), with temperature anomalies, population growth, and urbanisation rates as predictors. The baseline parameters assumed a 2.2% annual population increase and a 2% urbanisation growth rate. The sensitivity analyses examined three different scenarios.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eLow: 1% urbanisation, 1.5% population growth\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBaseline: 2% urbanisation, 2.2% population growth\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHigh: 4% urbanisation, 3% population growth\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo address uncertainties in socioeconomic projections, Monte Carlo simulations (10,000 iterations) varied population growth (1.5\u0026ndash;3%) and urbanisation rates (1\u0026ndash;4%), using data from the World Bank (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and INE (2020\u0026ndash;2023). Temperature anomalies were fixed at 4.8\u0026ndash;5.2\u0026deg;C by 2100 (SSP5-8.5). Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e projects a 20\u0026ndash;62% increase in cooling demand by 2100, with a 5\u0026ndash;10% uncertainty range across scenarios. Regionally, the cooling demand varies significantly due to urbanisation and climate differences. The southern regions (Maputo, Gaza) exhibit the highest sensitivity, with demand rising to 62% (uncertainty 6\u0026ndash;8%) in the high scenario. This is driven by urban heat island effects, where heat-retaining materials (e.g., concrete) and reduced vegetation elevate urban temperatures compared to rural areas (Lazaro, 2024), intensifying heat stress, energy demands, and environmental degradation. In contrast, the central regions (Tete, Sofala) showed moderate increases of 30\u0026ndash;50% (uncertainty 6\u0026ndash;8%), while the northern areas (Cabo Delgado, Niassa) projected the lowest increases of 25\u0026ndash;45% (uncertainty 5\u0026ndash;7%), reflecting lower urbanisation and cooler climates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCooling Demand Increase (%) by 2100 under SSP5-8.5\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (1% Urb, 1.5% Pop)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBaseline (2% Urb, 2.2% Pop)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh (4% Urb, 3% Pop)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUncertainty (\u0026plusmn;%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern (Maputo, Gaza)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55\u0026ndash;62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral (Tete, Sofala)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern (Cabo Delgado, Niassa)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u0026ndash;7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe calibrated regression model (R\u0026sup2; = 0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) produced the following coefficients: temperature anomaly β1\u0026thinsp;=\u0026thinsp;0.25 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), population growth β2\u0026thinsp;=\u0026thinsp;0.30 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and urbanisation rate β3\u0026thinsp;=\u0026thinsp;0.42 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This confirms that urbanisation is a more influential driver of cooling demand than population growth alone.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3. Hydropower Constraints\u003c/h2\u003e\u003cp\u003eMozambique\u0026rsquo;s energy deficits are worsened by hydropower constraints, especially at the Cahora Bassa Dam (2,075 MW capacity), the country\u0026rsquo;s primary hydropower source. Climate-driven shifts in monsoon patterns and decreased rainfall are expected to reduce output by 10\u0026ndash;20% by 2050, lowering annual generation from 15.6\u0026ndash;18 TWh to about 12.5 TWh by 2100 under SSP5-8.5 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Runoff declines of 18\u0026ndash;20% during rainy and dry seasons unevenly affect the southern provinces (Maputo, Gaza), where high cooling demands and reliance on hydropower worsen energy shortages. Solar photovoltaic deployment is advised to help address these deficits (Section 4.1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCahora Bassa Hydropower Constraints\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydropower Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProjected Output Decline by 2050\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCause\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMozambique\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCahora Bassa Dam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u0026ndash;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAltered monsoon patterns\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis projected decline threatens domestic energy security and jeopardises Mozambique's capacity for energy exports to neighbouring countries within the Southern African Power Pool (SAPP). This emphasises a significant transboundary dimension to the climate risk. To move beyond biophysical projections and understand where these climate hazards pose the most critical risk, we applied the Socio-Climatic Vulnerability Index (Section 2.4).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Reframing the Crisis: From Biophysical Hazard to Socio-Climatic Vulnerability\u003c/h2\u003e\u003cp\u003eOur results highlight a crucial finding that validates the central thesis of this paper: southern Mozambique's energy vulnerability (VI\u0026thinsp;=\u0026thinsp;0.72) represents a socio-climatic crisis, not just a biophysical one. This vulnerability results from the dangerous overlap of extreme physical exposure (5.0\u0026ndash;5.4\u0026deg;C warming) and high socioeconomic sensitivity, driven by a deep reliance on a centralised and fragile grid. As a result, this finding fundamentally redefines the main policy challenge: effective adaptation must tackle the climate hazard and the underlying socioeconomic fragility that intensifies it. The regional vulnerability index illustrates this realignment, marking southern Mozambique (Maputo, Gaza) as a key hotspot. The area's high-risk stems from extreme warming combined with a population heavily dependent on the central grid (high sensitivity), exacerbated by only moderate adaptive capacity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Implications for Adaptation Strategy: Building Social Resilience\u003c/h2\u003e\u003cp\u003eThe socio-climatic nature of the crisis calls for a shift in adaptation strategy beyond merely hardening physical infrastructure (Boas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the focus must be on building social resilience by decreasing reliance on the vulnerable central grid (Mishra et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Decentralised energy systems, especially solar PV, offer a feasible way to achieve this. However, while technically attainable (high technical feasibility), adopting decentralised solar PV encounters substantial financial (cost-effectiveness) and institutional (governance complexity) challenges, as evaluated in our Multi-Criteria Analysis (Section 4.3). Overcoming these obstacles requires a multi-faceted approach:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFinancing: Innovative mechanisms such as international climate finance (e.g., Green Climate Fund), targeted debt-for-climate swaps, and public-private partnerships are needed to mitigate high initial capital expenditures (CAPEX).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCapacity Building: Success depends on building local technical capacity through vocational training programs for installing and maintaining mini-grids.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRegulatory Frameworks: Establishing clear regulations, such as standardised Power Purchase Agreements (PPAs) and simplified licensing for mini-grids, is essential to unlock private investment and translate high technical potential into tangible resilience.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe socio-climatic aspect of this crisis also goes beyond national borders, requiring a regional perspective (De Sherbinin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, the intermittency of solar PV calls for considerations of battery storage or hybrid systems to maintain reliability, adding further complexity and expense to deployment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.3. The Urbanisation Paradox: A Central Dilemma\u003c/h2\u003e\u003cp\u003eA key and paradoxical finding emerges from our analysis: urbanisation is the strongest predictor of rising energy demand (β3\u0026thinsp;=\u0026thinsp;0.42), while simultaneously serving as our primary proxy for adaptive capacity. This encapsulates a central dilemma for developing nations: the very process of economic development that enhances a region's inherent ability to adapt to climate shocks (through concentrated resources and infrastructure) also significantly increases its exposure and sensitivity to those shocks by driving energy-intensive consumption and creating heat islands. This paradox emphasises that future development pathways cannot follow a business-as-usual approach; they must be designed to be climate-resilient from the outset, avoiding the locking-in of vulnerable, centralised models and prioritising low-carbon urbanisation and decentralised energy systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.4. A Regional Perspective: Comparative Context and Trans-boundary Risks\u003c/h2\u003e\u003cp\u003eThis finding situates Mozambique's dilemma within a broader regional context. Unlike Ethiopia, which has significant potential for large-scale hydropower (van der Zwaan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), or Malawi, which faces fewer coastal cyclone threats (Otto et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mozambique\u0026rsquo;s path to resilience is unique. Its high solar potential, combined with substantial cyclone risk, necessitates a specialised strategy centred on diversification and decentralisation (Okesiji, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), further supporting our regional approach. Additionally, the crisis has significant transboundary consequences. The expected decrease in hydropower output at Cahora Bassa (Section 3.3.2) endangers domestic energy security and Mozambique's ability to export energy to neighbouring countries within the Southern African Power Pool (SAPP). This reveals a critical systemic risk: the vulnerability of a single node in a regional energy system can cause cascading failures, destabilising a broader region. This underscores the urgent need for regional governance frameworks, such as an SADC-wide climate-resilient energy protocol. Such a protocol could establish mechanisms for coordinated renewable energy investment zones, standardise infrastructure regulations for climate resilience, and create a regional emergency response and mutual aid fund to manage shared climate risks and foster collective investment in resilience. Given this national and regional context, we evaluated the feasibility of various adaptation strategies using our Multi-Criteria Analysis (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis crisis has significant transboundary consequences that extend beyond national energy security (Llamosas and Sovacool, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cahora Bassa is a key Southern African Power Pool (SAPP) exporter. A 10\u0026ndash;20% reduction in its output (Section 3.3.2) threatens energy stability in neighbouring countries like South Africa and Zimbabwe, which rely on these imports. This reveals a critical systemic risk: the vulnerability of a single node in an interconnected regional energy system can catalyse cascading failures, destabilising the broader SADC region. This underscores the urgent need for regional governance frameworks, such as an SADC-wide climate-resilient energy protocol, to manage these shared climate risks and foster collective investment in resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Evaluating Strategic Pathways: Beyond Technical Feasibility\u003c/h2\u003e\u003cp\u003eWhile energy-efficient technologies score well on cost-effectiveness, their low equity score presents a significant risk in a country with high poverty levels, potentially excluding the most vulnerable populations (Brown et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, the high governance complexity of regional cooperation highlights the political hurdles of transboundary solutions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-Criteria Evaluation of Adaptation Strategies for Mozambique\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical Feasibility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCost-Effectiveness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGovernance Complexity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEquity \u0026amp; Social Inclusion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSynergy with SDGs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolar PV Deployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium (High CAPEX, Low OPEX)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedium (National/Local)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u0026nbsp;(Decentralised, benefits rural \u0026amp; urban)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSDG 7, 13, 8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy-Efficient Tech\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow (Market-based, consumer-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow (Initial cost barrier favours urban elites)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSDG 7, 12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyclone-Resistant Infra.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium (High CAPEX, avoids losses)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u0026nbsp;(Requires national coordination, funding)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedium (Protects the grid, but has an indirect benefit to people experiencing poverty)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSDG 9, 11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional Cooperation (SADC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u0026nbsp;(Leverages economies of scale)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u0026nbsp;(Complex trans-boundary agreements)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedium (Benefits connected users)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSDG 7, 17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eCAPEX: Capital Expenditure; OPEX: Operational Expenditure\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong the strategies evaluated, decentralised solar PV deployment is the most robust. It scores highly not only on technical viability but also, and importantly, because it directly addresses core socioeconomic vulnerabilities (e.g., dependence on a central grid) identified by our index. While energy-efficient technologies are cost-effective, their low equity score indicates a risk of worsening existing disparities, a significant concern for a country with high poverty levels. Similarly, the high governance complexity of regional cooperation (SADC) highlights the political challenges associated with transboundary solutions.\u003c/p\u003e\u003cp\u003eThis analysis reveals a crucial tension in adaptation planning: the divide between technically optimal solutions and the institutional capacity to implement them. Decentralised solar PV emerges as the most robust strategy, not solely because of its technical scores (Wandhare and Agarwal, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), but because it mitigates the core vulnerability identified by our index: high sensitivity and dependence on a centralised grid. It offers a pathway to build resilience from the community level upwards, aligning with principles of polycentric governance, which are essential for success in fragmented institutional environments like Southern Africa. Moreover, a key paradox emerges: the same factor, urbanisation, acts as a primary driver of increased energy demand (β3\u0026thinsp;=\u0026thinsp;0.42), while also serving as a proxy for adaptive capacity (Angelo, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This reflects a real-world dilemma for developing nations: economic development, which enhances adaptive capacity, simultaneously increases exposure and sensitivity to climate shocks by boosting energy-intensive consumption. This paradox encapsulates a central dilemma for developing nations: the same economic development that enhances adaptive capacity also increases exposure to climate shocks by driving energy-intensive consumption. This underscores that future development pathways must be deliberately designed to be climate-resilient, avoiding the lock-in of vulnerable, centralised models. There is an emphasis that development pathways must be climate-resilient, prioritising low-carbon urbanisation and decentralised energy systems to prevent locking in vulnerable, centralised models.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Limitations and Forward-Looking Policy Directions\u003c/h2\u003e\u003cp\u003eA limitation of this study is its primary reliance on a single high-resolution climate model (BCC-CSM2-MR), which, despite bias correction, could still have inherent biases affecting localised projections. Future research should use multi-model ensemble approaches, such as those from the CORDEX-Africa initiative, to better quantify uncertainty and improve robustness. Furthermore, while the chosen proxies for the vulnerability index are justified and data-constrained, they oversimplify complex realities. For example, using urbanisation rate for adaptive capacity does not account for community-based and informal adaptive strategies. Future hyperlocal studies incorporating household surveys on asset ownership, social capital, and access to information would greatly improve the socioeconomic detail of the vulnerability index and offer a more comprehensive view of risk.\u003c/p\u003e\u003cp\u003eDespite these limitations, our findings provide clear, actionable policy directions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePrioritise investment in decentralised solar PV: The government, with support from development banks, should establish a National Decentralised Energy Fund to de-risk private investment in solar mini-grids. This fund could provide concessional loans and grants, specifically targeting the high-vulnerability southern provinces, and be coupled with mandatory technical training programmes for local communities.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIncorporate equity into energy policy: Design targeted, subsidised programmes for energy-efficient appliances, potentially funded through carbon credit revenues, to ensure they reach low-income households and prevent the widening of energy disparities.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMainstream Climate Resilience: The national utility (EDM) should implement cyclone-resistant standards for new coastal transmission infrastructure, funded through international climate mechanisms.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStrengthen Regional Governance: Advocate for developing an SADC Climate-Resilient Energy Protocol, starting with a high-level working group to standardise infrastructure regulations for climate resilience and establish a pilot regional emergency response fund for climate-induced energy disruptions.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn conclusion, this study shows that tackling energy vulnerability requires a comprehensive understanding of the social-ecological system. A resilient energy future for Mozambique relies on strategies addressing physical climate threats and socioeconomic fragility. This research provides a scalable model for promoting sustainable development amidst global change by offering an integrated framework for assessment and action.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study advances beyond quantifying biophysical climate impacts to diagnose the socio-climatic drivers of energy vulnerability in Mozambique. It argues that the prevailing paradigm of hardening centralised systems is inadequate. By combining CMIP6 projections with a novel vulnerability index, we demonstrated that the highest risk occurs where extreme physical exposure intersects with high socioeconomic sensitivity. This diagnosis necessitates a fundamental strategic shift from reinforcing centralised infrastructure towards building decentralised, equitable resilience. Our multi-criteria evaluation identifies decentralised solar PV as the cornerstone of this strategy. The integrated methodology, bridging high-resolution climate projections, socio-climatic vulnerability indexing, and multi-criteria decision analysis, provides a scalable and transferable framework for transforming global climate models into practical, contextualised resilience strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eNone declared.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study's conception and design. S.A.M.L, L.X and V.F.B prepared material, collected data, and analysed. S.A.M.L and V.F.B wrote the first draft of the manuscript, and L.X revised the final draft. All authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData can be made available upon request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eANGELO H (2017) From the city lens toward urbanisation as a way of seeing: Country/city binaries on an urbanising planet. Urban Stud 54:158\u0026ndash;178\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eARNOLD W, CARLINO SALAZARJZ, GIULIANI A, M., CASTELLETTI A (2023) Operations eclipse sequencing in multipurpose dam planning. \u003cem\u003eEarth's Future\u003c/em\u003e, 11, e2022EF003186\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBANK W (2023) Unlocking Efficiency. 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Bio-based Appl Econ 10:109\u0026ndash;122\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"climate change, vulnerability, temperature trends, energy-water nexus, Transboundary governance, adaptation planning","lastPublishedDoi":"10.21203/rs.3.rs-7652896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7652896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMozambique's energy security, heavily reliant on climate-vulnerable hydropower, faces severe threats from global warming. While current planning often focuses on reinforcing centralized infrastructure, this strategy fails to address underlying socioeconomic vulnerabilities. This study argues that tackling this vulnerability requires a major strategic pivot towards decentralized, equitable adaptation. We conduct an integrated assessment, making three key contributions: (1) Using high-resolution (BCC-CSM2-MR, CMIP6) simulations under four SSP scenarios, we project significant regional warming of 1.2\u0026ndash;5.2\u0026deg;C by 2100, with the most severe increases in the south. (2) We develop a novel socio-climatic vulnerability index, combining climate and socioeconomic data to identify risk hotspots. This index reveals southern Mozambique as the highest-risk region (VI\u0026thinsp;=\u0026thinsp;0.72) due to extreme exposure, high sensitivity, and moderate adaptive capacity. (3) We assess adaptation strategies through a multi-criteria framework, showing that decentralized solar photovoltaic (PV) systems are the most robust option, offering significant co-benefits for equity and Sustainable Development Goals (SDGs). Our results stress that solving Mozambique's energy vulnerability necessitates a fundamental shift from reinforcing centralized infrastructure to supporting decentralized, fair adaptation. The integrated assessment framework presented offers a transferable model for diagnosing socio-climatic vulnerability in other regions reliant on centralized, climate-vulnerable energy systems.\u003c/p\u003e","manuscriptTitle":"From Centralized to Decentralized Resilience: Projected Climate Impacts on Mozambique's Energy-Water Nexus and a Framework for Adaptation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 20:19:47","doi":"10.21203/rs.3.rs-7652896/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dda6507d-b560-422d-a436-fe553877a2ea","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:36:04+00:00","index":16,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T20:19:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 20:19:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7652896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7652896","identity":"rs-7652896","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}