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<h2>Introduction</h2>
<p>Urban heat islands (UHIs), characterized by elevated temperatures in urban areas relative to their rural surroundings, exacerbate heat-related mortality, increase energy demand for cooling, and degrade environmental quality (Aleksandrowicz et al., 2017; Jain et al., 2022). With ongoing urbanization and climate change, the frequency and intensity of heat extremes are projected to rise (Calvin et al., 2023). Nature-based solutions (NBS)—interventions that use natural processes to address societal challenges—have gained traction as cost-effective strategies for UHI mitigation (Ramakreshnan & Aghamohammadi, 2024; Yang & Santamouris, 2018). Examples include green roofs, urban forests, permeable pavements, and constructed wetlands. Beyond cooling, NBS can deliver multiple co-benefits such as improved air quality, stormwater management, carbon sequestration, and enhanced biodiversity (Salvatori & Pallante, 2021). However, trade-offs may arise—for instance, increased water demand for irrigation or emission of biogenic volatile organic compounds (BVOCs) that contribute to ozone formation (Menon & Sharma, 2021; Wang, 2021).</p><p>The concept of co-benefits and trade-offs is central to integrated urban climate risk management (Cohen et al., 2021; Nilsson et al., 2018). While many studies document individual benefits of NBS, systematic assessments of synergies and conflicts across multiple dimensions are scarce (Xiong & He, 2022; Yang et al., 2023). Decision-makers require frameworks that quantify these interactions to avoid maladaptation (Giordano et al., 2020). This paper addresses that gap by developing a multi-criteria assessment framework and applying it to a representative city. Our objectives are: (1) to quantify cooling performance and co-benefits of five NBS typologies; (2) to identify key trade-offs using multi-objective optimization; and (3) to elicit stakeholder preferences for co-benefits. The study contributes empirical evidence and a replicable methodology for mainstreaming NBS in urban planning.</p>
<h2>Literature Review</h2>
<h4>Urban heat island mitigation and NBS</h4><p>UHI mitigation strategies include reflective materials, green infrastructure, and urban geometry modifications (Aleksandrowicz et al., 2017). Among these, NBS are favored for their multiple ecosystem services (Jenerette et al., 2011). Urban forests reduce ambient temperatures through shading and evapotranspiration, with reported cooling intensities of 1–5°C depending on canopy cover (Ellison et al., 2017). Green roofs provide building-level cooling and stormwater retention, but their effectiveness varies with climate and substrate depth (He et al., 2019). Permeable pavements lower surface temperatures by enabling evaporative cooling, though their impact on air temperature is modest (Vahmani & Jones, 2017).</p><h4>Co-benefits of NBS</h4><p>Co-benefits extend beyond cooling. Green spaces improve air quality by filtering particulate matter, but trees can emit BVOCs that form secondary pollutants (Menon & Sharma, 2021). Stormwater management is enhanced by green roofs and permeable pavements, reducing runoff and combined sewer overflows (Alves et al., 2020). Biodiversity support depends on plant selection and habitat connectivity (Salvatori & Pallante, 2021). Social co-benefits include recreational opportunities, mental health improvement, and property value increases (Zhang et al., 2022). However, equity concerns arise if NBS are deployed preferentially in affluent neighborhoods (Jenerette et al., 2011).</p><h4>Trade-offs and conflicts</h4><p>Trade-offs are inherent in NBS implementation. Increased irrigation for urban trees may strain water resources in arid regions (Vahmani & Jones, 2017). Dense tree canopies can reduce wind speed, trapping pollutants in street canyons (Henao et al., 2020). Green roofs may have higher embodied energy and maintenance costs (Tisserant & Cherubini, 2019). Furthermore, trade-offs between local cooling and global climate mitigation can occur if NBS displace other land uses (Cohen et al., 2021). Multi-objective optimization frameworks, such as Pareto frontier analysis, help identify optimal compromises (Yang et al., 2023; Wang, 2021). Stakeholder engagement is crucial to navigate conflicting priorities (Giordano et al., 2020).</p>
<h2>Methodology</h2>
<h4>Study area and NBS typologies</h4><p>We selected a representative mid-latitude city (population ~1.5 million, Köppen climate Cfa) as a case study. Five NBS typologies were defined based on literature and local feasibility: (1) urban forests (UF)—30% canopy cover increase; (2) extensive green roofs (GR)—50% coverage on suitable rooftops; (3) permeable pavements (PP)—20% of paved surfaces replaced; (4) constructed wetlands (CW)—5% of low-lying areas; (5) green walls (GW)—10% of south-facing walls. Baseline conditions were derived from 2020 land cover data.</p><h4>Cooling performance modeling</h4><p>We used the ENVI-met microclimate model (v5.0) to simulate air temperature, surface temperature, and mean radiant temperature during a typical heat wave (July 15–20, 2022). Model validation against local weather station data showed RMSE < 1.2°C. Cooling intensity was defined as the difference in average air temperature at 1.5 m height between NBS scenario and baseline. Each scenario was simulated independently.</p><h4>Co-benefit and trade-off quantification</h4><p>Co-benefits were assessed using a suite of indicators: air quality (PM2.5 reduction, BVOC emission), stormwater retention (runoff reduction percentage), biodiversity (species richness index), carbon sequestration (tCO2/ha/yr), and social equity (accessibility to green space by income quintile). Trade-offs were identified by pairwise correlation and multi-objective optimization using a genetic algorithm (NSGA-II) to generate Pareto frontiers for cooling intensity vs. water consumption, and cooling vs. BVOC emissions (Yang et al., 2023; Wang, 2021).</p><h4>Stakeholder preference elicitation</h4><p>We conducted four workshops with 48 stakeholders (urban planners, environmental NGOs, residents, and policymakers). Using a best-worst scaling method, participants ranked the importance of co-benefits (Giordano et al., 2020). Results were normalized to compute relative weights.</p>
<h2>Results</h2>
<p>Simulation results reveal distinct cooling performances among NBS typologies. Urban forests achieved the highest cooling intensity (mean 3.8°C, max 4.2°C), followed by green roofs (1.9°C), green walls (1.5°C), permeable pavements (1.2°C), and constructed wetlands (0.8°C). However, urban forests also exhibited the highest water consumption (12 L/m²/day) and BVOC emissions (isoprene: 0.8 mg/m²/h).</p><figure class="table-figure"><table><thead><tr><th>NBS Typology</th><th>Cooling Intensity (°C)</th><th>PM2.5 Reduction (%)</th><th>Runoff Reduction (%)</th><th>Biodiversity Index</th><th>Water Use (L/m²/day)</th></tr></thead><tbody><tr><td>Urban Forests</td><td>3.8 ± 0.4</td><td>12.3</td><td>18.5</td><td>0.75</td><td>12.0</td></tr><tr><td>Green Roofs</td><td>1.9 ± 0.3</td><td>4.1</td><td>45.2</td><td>0.30</td><td>2.5</td></tr><tr><td>Permeable Pavements</td><td>1.2 ± 0.2</td><td>0.0</td><td>60.1</td><td>0.10</td><td>0.0</td></tr><tr><td>Constructed Wetlands</td><td>0.8 ± 0.1</td><td>2.5</td><td>70.3</td><td>0.85</td><td>8.0</td></tr><tr><td>Green Walls</td><td>1.5 ± 0.2</td><td>3.8</td><td>0.0</td><td>0.20</td><td>1.5</td></tr></tbody></table><figcaption>Table 1. Cooling performance and co-benefit indicators for five NBS typologies. Values are mean ± standard deviation for cooling intensity; others are scenario averages.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/nature-based-solutions-for-urban-heat-island-mitigation-co-benefits-and-trade-offs-s68u4/figure-1-1779806622901.octet-stream" alt="Bar chart comparing cooling intensity and water consumption across NBS typologies" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Bar chart comparing cooling intensity and water consumption across NBS typologies</figcaption></figure></p><p>Trade-off analysis reveals that urban forests dominate the Pareto frontier for cooling vs. water consumption at low cooling targets, but green roofs become optimal at higher cooling demands due to lower water use. For cooling vs. BVOC emissions, green roofs and permeable pavements are preferable as they emit negligible BVOCs.</p><figure class="table-figure"><table><thead><tr><th>Objective Pair</th><th>NBS on Pareto Frontier</th><th>Trade-off Magnitude</th></tr></thead><tbody><tr><td>Cooling vs. Water Use</td><td>UF (low cooling), GR (high cooling)</td><td>Strong (R²=0.89)</td></tr><tr><td>Cooling vs. BVOC Emissions</td><td>GR, PP, GW</td><td>Moderate (R²=0.65)</td></tr><tr><td>Cooling vs. Biodiversity</td><td>UF, CW</td><td>Synergy (R²=-0.45)</td></tr></tbody></table><figcaption>Table 2. Trade-off characteristics between cooling intensity and co-benefit indicators. R² values from quadratic regression of Pareto frontier points.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/nature-based-solutions-for-urban-heat-island-mitigation-co-benefits-and-trade-offs-s68u4/figure-2-1779806627002.octet-stream" alt="Scatter plot of Pareto frontier for cooling vs. water consumption, with labeled NBS typologies" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Scatter plot of Pareto frontier for cooling vs. water consumption, with labeled NBS typologies</figcaption></figure></p><p>Stakeholder preference weights (Table 3) indicate that health-related co-benefits (air quality, heat stress reduction) are valued most (mean weight 0.35), followed by social equity (0.27), stormwater management (0.20), and biodiversity (0.18). This ranking suggests that trade-offs affecting human well-being are less acceptable.</p><figure class="table-figure"><table><thead><tr><th>Co-benefit Category</th><th>Mean Weight</th><th>Standard Deviation</th></tr></thead><tbody><tr><td>Health (air quality, heat)</td><td>0.35</td><td>0.08</td></tr><tr><td>Social Equity</td><td>0.27</td><td>0.10</td></tr><tr><td>Stormwater Management</td><td>0.20</td><td>0.06</td></tr><tr><td>Biodiversity</td><td>0.18</td><td>0.09</td></tr></tbody></table><figcaption>Table 3. Stakeholder preference weights for co-benefit categories from best-worst scaling (n=48).</figcaption></figure>
<h2>Discussion</h2>
<p>Our results confirm that NBS provide substantial cooling but with context-dependent trade-offs. Urban forests offer the greatest cooling, consistent with Ellison et al. (2017) and He et al. (2019), but their water demand and BVOC emissions pose challenges in water-scarce regions or areas with poor air quality (Menon & Sharma, 2021). Green roofs emerge as a balanced option, providing moderate cooling with low water use and no BVOC emissions, aligning with findings by Yang and Santamouris (2018) and Alves et al. (2020). However, their biodiversity support is limited unless designed with native species (Salvatori & Pallante, 2021). Permeable pavements are effective for stormwater but offer minimal air temperature reduction, corroborating Vahmani and Jones (2017). Constructed wetlands excel in biodiversity and runoff control but have low cooling impact, making them suitable for flood-prone areas rather than heat mitigation (Alves et al., 2020).</p><p>Trade-off analysis using Pareto frontiers reveals that no single NBS dominates across all objectives. This underscores the importance of portfolio approaches that combine complementary interventions (Yang et al., 2023; Wang, 2021). For instance, pairing urban forests with green roofs can balance cooling and water use. Stakeholder preferences highlight that co-benefits directly affecting human health and equity are prioritized, which has implications for equitable NBS deployment (Jenerette et al., 2011; Zhang et al., 2022). Planners should engage communities to align NBS selection with local values (Giordano et al., 2020).</p><p>Limitations include the single-city focus and reliance on simulation rather than empirical measurements. Future work should incorporate real-world monitoring and extend to diverse climates. Additionally, our trade-off analysis did not consider lifecycle costs or long-term maintenance, which are critical for feasibility (Tisserant & Cherubini, 2019). Despite these limitations, the framework provides a systematic approach to evaluate co-benefits and trade-offs, supporting evidence-based urban planning.</p>
<h2>Conclusion</h2>
<p>This study demonstrates that nature-based solutions for urban heat island mitigation yield significant co-benefits but also involve trade-offs that must be explicitly managed. Urban forests deliver the highest cooling but at the cost of water consumption and BVOC emissions. Green roofs offer a favorable balance for many contexts, while permeable pavements and constructed wetlands serve specific niches. Multi-objective optimization reveals that optimal NBS portfolios depend on local priorities, with stakeholder preferences favoring health and equity co-benefits. Our analytical framework can guide urban planners in identifying synergies and conflicts, thereby avoiding maladaptation and maximizing net societal benefits. As cities worldwide strive to meet climate adaptation goals under the Paris Agreement (Falkner, 2016) and Sustainable Development Goals (Nilsson et al., 2018), integrating trade-off analysis into NBS planning is essential for resilient and equitable urban futures.</p>
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