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Influence of accumulation of humidity under wound dressings and effects on transepidermal water loss (tewl) and skin hydration.

1. Introduction

2. materials and methods, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Britto, E.J.; Nezwek, T.A.; Popowicz, P.; Robins, M. Wound Dressings. In StatPearls ; StatPearls Publishing: Petersburg, FL, USA, 2023. Available online: http://www.ncbi.nlm.nih.gov/books/NBK470199/ (accessed on 24 August 2024).
• Lindholm, C.; Searle, R. Wound management for the 21st century: Combining effectiveness and efficiency. Int. Wound J. 2016 , 13 (Suppl. S2), 5–15. [ Google Scholar ] [ CrossRef ]
• Landriscina, A.; Rosen, J.; Friedman, A.J. Systematic Approach to Wound Dressings. J. Drugs Dermatol. 2015 , 14 , 740–744. [ Google Scholar ] [ PubMed ]
• Childs, D.R.; Murthy, A.S. Overview of Wound Healing and Management. Surg. Clin. N. Am. 2017 , 97 , 189–207. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Newton, H. Cost-effective wound management: A survey of 1717 nurses. Br. J. Nurs. 2017 , 26 , S44–S49. [ Google Scholar ] [ CrossRef ]
• Kujath, P.; Michelsen, A. Wounds—From Physiology to Wound Dressing. Dtsch. Ärzteblatt Int. 2008 , 105 , 239–248. [ Google Scholar ] [ CrossRef ]
• Baum, C.L.; Arpey, C.J. Normal cutaneous wound healing: Clinical correlation with cellular and molecular events. Dermatol. Surg. 2005 , 31 , 674–686, discussion 686. [ Google Scholar ] [ CrossRef ]
• Aumüller, G. Anatomie ; Georg Thieme Verlag: New York, NY, USA, 2014. [ Google Scholar ]
• Ulfig, N. Kurzlehrbuch Histologie ; Georg Thieme Verlag: New York, NY, USA, 2015. [ Google Scholar ]
• Röcken, M. Taschenatlas Dermatologie ; Georg Thieme Verlag: New York, NY, USA, 2010. [ Google Scholar ]
• Baroni, A.; Buommino, E.; De Gregorio, V.; Ruocco, E.; Ruocco, V.; Wolf, R. Structure and function of the epidermis related to barrier properties. Clin. Dermatol. 2012 , 30 , 257–262. [ Google Scholar ] [ CrossRef ]
• Verdier-Sévrain, S.; Bonté, F. Skin hydration: A review on its molecular mechanisms. J. Cosmet. Dermatol. 2007 , 6 , 75–82. [ Google Scholar ] [ CrossRef ]
• Amrani, G.; Peko, L.; Hoffer, O.; Ovadia-Blechman, Z.; Gefen, A. The microclimate under dressings applied to intact weight-bearing skin: Infrared thermography studies. Clin. Biomech. 2020 , 75 , 104994. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kottner, J.; Black, J.; Call, E.; Gefen, A.; Santamaria, N. Microclimate: A critical review in the context of pressure ulcer prevention. Clin. Biomech. 2018 , 59 , 62–70. [ Google Scholar ] [ CrossRef ]
• Yusuf, S.; Okuwa, M.; Shigeta, Y.; Dai, M.; Iuchi, T.; Rahman, S.; Usman, A.; Kasim, S.; Sugama, J.; Nakatani, T.; et al. Microclimate and development of pressure ulcers and superficial skin changes. Int. Wound J. 2013 , 12 , 40–46. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Borzdynski, C.; Miller, C.; Vicendese, D.; McGuiness, W. Brief intermittent pressure off-loading on skin microclimate in healthy adults—A descriptive-correlational pilot study. J. Tissue Viability 2021 , 30 , 379–394. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Denzinger, M.; Rothenberger, J.; Held, M.; Joss, L.; Ehnert, S.; Kolbenschlag, J.; Daigeler, A.; Krauss, S. A quantitative study of transepidermal water loss (TEWL) on conventional and microclimate management capable mattresses and hospital beds. J. Tissue Viability 2019 , 28 , 194–199. [ Google Scholar ] [ CrossRef ]
• Obagi, Z.; Damiani, G.; Grada, A.; Falanga, V. Principles of Wound Dressings: A Review. Surg. Technol. Int. 2019 , 35 , 50–57. [ Google Scholar ]
• Jeffcoate, W.J. Conflict over the need for evidence when choosing dressings for chronic wounds. J. Wound Care 2018 , 27 , 308–309. [ Google Scholar ] [ CrossRef ]
• Horkan, L.; Stansfield, G.; Miller, M. An analysis of systematic reviews undertaken on standard advanced wound dressings in the last 10 years. J. Wound Care 2009 , 18 , 298–304. [ Google Scholar ] [ CrossRef ]
• Duplan, H.; Nocera, T. Skin hydration and hydrating products. Ann. Dermatol. Venereol. 2018 , 145 , 376–384. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Camilion, J.V.; Khanna, S.; Anasseri, S.; Laney, C.; Mayrovitz, H.N. Physiological, Pathological, and Circadian Factors Impacting Skin Hydration. Cureus 2022 , 14 , e27666. [ Google Scholar ] [ CrossRef ]
• Liu, J.; Jin, Y.; Wei, Q.; Hu, Y.; Liu, L.; Feng, Y.; Jin, Y.; Jiang, Y. The relationship between aquaporins and skin diseases. Eur. J. Dermatol. 2023 , 33 , 350–359. [ Google Scholar ] [ CrossRef ]
• Lee, T.-Y.; Yoon, I.-J.; Han, S.-K.; Namgoong, S.; Jeong, S.-H.; Kim, D.-W.; Dhong, E.-S. Skin hydration level cutoff value to predict wound healing potential in diabetic foot ulcers. Diabetes Res. Clin. Pract. 2022 , 193 , 110122. [ Google Scholar ] [ CrossRef ]
• Pinnagoda, J.; Tupker, R.A.; Coenraads, P.J.; Nater, J.P. Transepidermal water loss with and without sweat gland inactivation. Contact Dermat. 1989 , 21 , 16–22. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Berardesca, E.; Maibach, H.I. Transepidermal water loss and skin surface hydration in the non invasive assessment of stratum corneum function. Dermatosen Beruf Umw. Occup. Environ. 1990 , 38 , 50–53. [ Google Scholar ]
• Green, M.; Feschuk, A.M.; Kashetsky, N.; Maibach, H.I. “Normal” TEWL-how can it be defined? A systematic review. Exp. Dermatol. 2022 , 31 , 1618–1631. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Agner, T.; Serup, J. Time course of occlusive effects on skin evaluated by measurement of transepidermal water loss (TEWL). Including patch tests with sodium lauryl sulphate and water. Contact Dermat. 1993 , 28 , 6–9. [ Google Scholar ] [ CrossRef ]
• Faergemann, J.; Aly, R.; Wilson, D.R.; Maibach, H.I. Skin occlusion: Effect on Pityrosporum orbiculare, skin PCO2, pH, transepidermal water loss, and water content. Arch. Dermatol. Res. 1983 , 275 , 383–387. [ Google Scholar ] [ CrossRef ]
• Aly, R.; Shirley, C.; Cunico, B.; Maibach, H.I. Effect of prolonged occlusion on the microbial flora, pH, carbon dioxide and transepidermal water loss on human skin. J. Investig. Dermatol. 1978 , 71 , 378–381. [ Google Scholar ] [ CrossRef ]
• Friebe, K.; Effendy, I.; Löffler, H. Effects of skin occlusion in patch testing with sodium lauryl sulphate. Br. J. Dermatol. 2003 , 148 , 65–69. [ Google Scholar ] [ CrossRef ]
• Shimada, K.; Yoshihara, T.; Yamamoto, M.; Konno, K.; Momoi, Y.; Nishifuji, K.; Iwasaki, T. Transepidermal water loss (TEWL) reflects skin barrier function of dog. J. Vet. Med. Sci. 2008 , 70 , 841–843. [ Google Scholar ] [ CrossRef ]
• Alexander, H.; Brown, S.; Danby, S.; Flohr, C. Research Techniques Made Simple: Transepidermal Water Loss Measurement as a Research Tool. J. Investig. Dermatol. 2018 , 138 , 2295–2300. [ Google Scholar ] [ CrossRef ]
• Jansen van Rensburg, S.; Franken, A.; Du Plessis, J.L. Measurement of transepidermal water loss, stratum corneum hydration and skin surface pH in occupational settings: A review. Skin Res. Technol. 2019 , 25 , 595–605. [ Google Scholar ] [ CrossRef ]
• Deutsch, C.J.; Edwards, D.M.; Myers, S. Wound dressings. Br. J. Hosp. Med. 2017 , 78 , C103–C109. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dini, V.; Bertone, M.; Romanelli, M. Prevention and management of pressure ulcers. Dermatol. Ther. 2006 , 19 , 356–364. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dai, C.; Shih, S.; Khachemoune, A. Skin substitutes for acute and chronic wound healing: An updated review. J. Dermatol. Treat. 2020 , 31 , 639–648. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Markiewicz-Gospodarek, A.; Kozioł, M.; Tobiasz, M.; Baj, J.; Radzikowska-Büchner, E.; Przekora, A. Burn Wound Healing: Clinical Complications, Medical Care, Treatment, and Dressing Types: The Current State of Knowledge for Clinical Practice. Int. J. Environ. Res. Public Health 2022 , 19 , 1338. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Widgerow, A.D.; Chait, L.A. Scar management practice and science: A comprehensive approach to controlling scar tissue and avoiding hypertrophic scarring. Adv. Skin Wound Care 2011 , 24 , 555–561. [ Google Scholar ] [ CrossRef ]
• Lee Peng, G.; Kerolus, J.L. Management of Surgical Scars. Facial Plast. Surg. Clin. N. Am. 2019 , 27 , 513–517. [ Google Scholar ] [ CrossRef ]
• Singh, B.; Maibach, H. Climate and skin function: An overview. Skin Res. Technol. 2013 , 19 , 207–212. [ Google Scholar ] [ CrossRef ]
• Ma, J.; Wu, C. Bioactive inorganic particles-based biomaterials for skin tissue engineering. Exploration 2022 , 2 , 20210083. [ Google Scholar ] [ CrossRef ]
• Ahadian, S.; Rahal, R.; Ramón-Azcón, J.; Obregón, R.; Hasan, A. Biomaterials in Tissue Engineering. In Tissue Engineering for Artificial Organs ; John Wiley & Sons, Ltd.: New York, NY, USA, 2017; pp. 35–83. ISBN 978-3-527-68993-4. [ Google Scholar ]
• Langer, R.; Vacanti, J. Advances in tissue engineering. J. Pediatr. Surg. 2016 , 51 , 8–12. [ Google Scholar ] [ CrossRef ]
• Cosgriff-Hernandez, E.; Timmins, L.H. Model-Directed Design of Tissue Engineering Scaffolds. ACS Biomater. Sci. Eng. 2022 , 8 , 4622–4624. [ Google Scholar ] [ CrossRef ]
• Choi, E.H. Aging of the skin barrier. Clin. Dermatol. 2019 , 37 , 336–345. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hertzog, M.A. Considerations in determining sample size for pilot studies. Res. Nurs. Health 2008 , 31 , 180–191. [ Google Scholar ] [ CrossRef ]
• Peer, R.P.; Burli, A.; Maibach, H.I. Unbearable transepidermal water loss (TEWL) experimental variability: Why? Arch. Dermatol. Res. 2022 , 314 , 99–119. Available online: https://pubmed.ncbi.nlm.nih.gov/33638033/ (accessed on 14 April 2024). [ CrossRef ] [ PubMed ]
• Green, M.; Kashetsky, N.; Feschuk, A.; Maibach, H.I. Transepidermal water loss (TEWL): Environment and pollution-A systematic review. Skin Health Dis. 2022 , 2 , e104. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Klotz, T.; Ibrahim, A.; Maddern, G.; Caplash, Y.; Wagstaff, M. Devices measuring transepidermal water loss: A systematic review of measurement properties. Skin Res. Technol. 2022 , 28 , 497–539. [ Google Scholar ] [ CrossRef ]
• Samadi, A.; Yazdanparast, T.; Shamsipour, M.; Hassanzadeh, H.; Hashemi Orimi, M.; Firooz, R.; Firooz, A. Stratum corneum hydration in healthy adult humans according to the skin area, age and sex: A systematic review and meta-analysis. J. Eur. Acad. Dermatol. Venereol. 2022 , 36 , 1713–1721. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cortés, H.; Mendoza-Muñoz, N.; Galván-Gil, F.A.; Magaña, J.J.; Lima, E.; González-Torres, M.; Leyva-Gómez, G. Comprehensive mapping of human body skin hydration: A pilot study. Skin Res. Technol. 2019 , 25 , 187–193. [ Google Scholar ] [ CrossRef ]
• Alanen, E.; Nuutinen, J.; Nicklén, K.; Lahtinen, T.; Mönkkönen, J. Measurement of hydration in the stratum corneum with the MoistureMeter and comparison with the Corneometer. Skin Res. Technol. 2004 , 10 , 32–37. [ Google Scholar ] [ CrossRef ]

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Rauscher, M.; Rauscher, A.; Hu, L.Y.; Schlitt, H.J.; Krauß, S.; Illg, C.; Reis Wolfertstetter, P.; Hofmann, A.; Knorr, C.; Denzinger, M. Influence of Accumulation of Humidity under Wound Dressings and Effects on Transepidermal Water Loss (TEWL) and Skin Hydration. Appl. Sci. 2024 , 14 , 7739. https://doi.org/10.3390/app14177739

Rauscher M, Rauscher A, Hu LY, Schlitt HJ, Krauß S, Illg C, Reis Wolfertstetter P, Hofmann A, Knorr C, Denzinger M. Influence of Accumulation of Humidity under Wound Dressings and Effects on Transepidermal Water Loss (TEWL) and Skin Hydration. Applied Sciences . 2024; 14(17):7739. https://doi.org/10.3390/app14177739

Rauscher, Marc, Andreas Rauscher, Linda Y. Hu, Hans J. Schlitt, Sabrina Krauß, Claudius Illg, Patricia Reis Wolfertstetter, Aybike Hofmann, Christian Knorr, and Markus Denzinger. 2024. "Influence of Accumulation of Humidity under Wound Dressings and Effects on Transepidermal Water Loss (TEWL) and Skin Hydration" Applied Sciences 14, no. 17: 7739. https://doi.org/10.3390/app14177739

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• Published: 02 September 2024

Green spaces provide substantial but unequal urban cooling globally

• Yuxiang Li 1 ,
• Jens-Christian Svenning   ORCID: orcid.org/0000-0002-3415-0862 2 ,
• Weiqi Zhou   ORCID: orcid.org/0000-0001-7323-4906 3 , 4 , 5 ,
• Kai Zhu   ORCID: orcid.org/0000-0003-1587-3317 6 ,
• Jesse F. Abrams   ORCID: orcid.org/0000-0003-0411-8519 7 ,
• Timothy M. Lenton   ORCID: orcid.org/0000-0002-6725-7498 7 ,
• William J. Ripple 8 ,
• Zhaowu Yu   ORCID: orcid.org/0000-0003-4576-4541 9 ,
• Shuqing N. Teng 1 ,
• Robert R. Dunn 10 &
• Chi Xu   ORCID: orcid.org/0000-0002-1841-9032 1  

Nature Communications volume  15 , Article number:  7108 ( 2024 ) Cite this article

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Metrics details

• Climate-change mitigation
• Urban ecology

Climate warming disproportionately impacts countries in the Global South by increasing extreme heat exposure. However, geographic disparities in adaptation capacity are unclear. Here, we assess global inequality in green spaces, which urban residents critically rely on to mitigate outdoor heat stress. We use remote sensing data to quantify daytime cooling by urban greenery in the warm seasons across the ~500 largest cities globally. We show a striking contrast, with Global South cities having ~70% of the cooling capacity of cities in the Global North (2.5 ± 1.0 °C vs. 3.6 ± 1.7 °C). A similar gap occurs for the cooling adaptation benefits received by an average resident in these cities (2.2 ± 0.9 °C vs. 3.4 ± 1.7 °C). This cooling adaptation inequality is due to discrepancies in green space quantity and quality between cities in the Global North and South, shaped by socioeconomic and natural factors. Our analyses further suggest a vast potential for enhancing cooling adaptation while reducing global inequality.

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Introduction.

Heat extremes are projected to be substantially intensified by global warming 1 , 2 , imposing a major threat to human mortality and morbidity in the coming decades 3 , 4 , 5 , 6 . This threat is particularly concerning as a majority of people now live in cities 7 , including those cities suffering some of the hottest climate extremes. Cities face two forms of warming: warming due to climate change and warming due to the urban heat island effect 8 , 9 , 10 . These two forms of warming have the potential to be additive, or even multiplicative. Climate change in itself is projected to result in rising maximum temperatures above 50 °C for a considerable fraction of the world if 2 °C global warming is exceeded 2 ; the urban heat island effect will cause up to >10 °C additional (surface) warming 11 . Exposures to temperatures above 35 °C with high humidity or above 40 °C with low humidity can lead to lethal heat stress for humans 12 . Even before such lethal temperatures are reached, worker productivity 13 and general health and well-being 14 can suffer. Heat extremes are especially risky for people living in the Global South 15 , 16 due to warmer climates at low latitudes. Climate models project that the lethal temperature thresholds will be exceeded with increasing frequencies and durations, and such extreme conditions will be concentrated in low-latitude regions 17 , 18 , 19 . These low-latitude regions overlap with the major parts of the Global South where population densities are already high and where population growth rates are also high. Consequently, the number of people exposed to extreme heat will likely increase even further, all things being equal 16 , 20 . That population growth will be accompanied by expanded urbanization and intensified urban heat island effects 21 , 22 , potentially exacerbating future Global North-Global South heat stress exposure inequalities.

Fortunately, we know that heat stress can be buffered, in part, by urban vegetation 23 . Urban green spaces, and especially urban forests, have proven an effective means through which to ameliorate heat stress through shading 24 , 25 and transpirational cooling 26 , 27 . The buffering effect of urban green spaces is influenced by their area (relative to the area of the city) and their spatial configuration 28 . In this context, green spaces become a kind of infrastructure that can and should be actively managed. At broad spatial scales, the effect of this urban green infrastructure is also mediated by differences among regions, whether in their background climate 29 , composition of green spaces 30 , or other factors 31 , 32 , 33 , 34 . The geographic patterns of the buffering effects of green spaces, whether due to geographic patterns in their areal extent or region-specific effects, have so far been poorly characterized.

On their own, the effects of climate change and urban heat islands on human health are likely to become severe. However, these effects will become even worse if they fall disproportionately in cities or countries with less economic ability to invest in green space 35 or in other forms of cooling 36 , 37 . A number of studies have now documented the so-called ‘luxury effect,’ wherein lower-income parts of cities tend to have less green space and, as a result, reduced biodiversity 38 , 39 . Where the luxury effect exists, green space and its benefits become, in essence, a luxury good 40 . If the luxury effect holds among cities, and lower-income cities also have smaller green spaces, the Global South may have the least potential to mitigate the combined effects of climate warming and urban heat islands, leading to exacerbated and rising inequalities in heat exposure 41 .

Here, we assess the global inequalities in the cooling capability of existing urban green infrastructure across urban areas worldwide. To this end, we use remotely sensed data to quantify three key variables, i.e., (1) cooling efficiency, (2) cooling capacity, and (3) cooling benefit of existing urban green infrastructure for ~500 major cities across the world. Urban green infrastructure and temperature are generally negatively and relatively linearly correlated at landscape scales, i.e., higher quantities of urban green infrastructure yield lower temperatures 42 , 43 . Cooling efficiency is widely used as a measure of the extent to which a given proportional increase in the area of urban green infrastructure leads to a decrease in temperature, i.e., the slope of the urban green infrastructure-temperature relationship 42 , 44 , 45 (see Methods for details). This simple metric allows quantifying the quality of urban green infrastructure in terms of ameliorating the urban heat island effect. Meanwhile, the extent to which existing urban green infrastructure cools down an entire city’s surface temperatures (compared to the non-vegetated built-up areas) is referred to as cooling capacity. Hence, cooling capacity is a function of the total quantity of urban green infrastructure and its cooling efficiency (see Methods).

As a third step, we account for the spatial distributions of urban green infrastructure and populations to quantify the benefit of cooling mitigation received by an average urban inhabitant in each city given their location. This cooling benefit is a more direct measure of the cooling realized by people, after accounting for the within-city geography of urban green infrastructure and population density. We focus on cooling capacity and cooling benefit as the measures of the cooling capability of individual cities for assessing their global inequalities. We are particularly interested in linking cooling adaptation inequality with income inequality 40 , 46 . While this can be achieved using existing income metrics for country classifications 47 , here we use the traditional Global North/South classification due to its historical ties to geography which is influential in climate research.

Results and discussion

Our analyses indicate that existing green infrastructure of an average city has a capability of cooling down surface temperatures by ~3 °C during warm seasons. However, a concerning disparity is evident; on average Global South cities have only two-thirds the cooling capacity and cooling benefit compared to Global North cities. This inequality is attributable to the differences in both quantity and quality of existing urban green infrastructure among cities. Importantly, we find that there exists considerable potential for many cities to enhance the cooling capability of their green infrastructure; achieving this potential could dramatically reduce global inequalities in adaptation to outdoor heat stress.

Quantifying cooling inequality

Our analyses showed that both the quantity and quality of the existing urban green infrastructure vary greatly among the world’s ~500 most populated cities (see Methods for details, and Fig.  1 for examples). The quantity of urban green infrastructure measured based on remotely sensed indicators of spectral greenness (Normalized Difference Vegetation Index, NDVI, see Methods) had a coefficient of variation (CV) of 35%. Similarly, the quality of urban green infrastructure in terms of cooling efficiency (daytime land surface temperatures during peak summer) had a CV of 37% (Supplementary Figs.  1 , 2 ). The global mean value of cooling capacity is 2.9 °C; existing urban green infrastructure ameliorates warm-season heat stress by 2.9 °C of surface temperature in an average city. In truth, however, the variation in cooling capacity was great (global CV in cooling capacity as large as ~50%), such that few cities were average. This variation is strongly geographically structured. Cities closer to the equator - tropical and subtropical cities - tend to have relatively weak cooling capacities (Fig.  2a, b ). As Global South countries are predominantly located at low latitudes, this pattern leads to a situation in which Global South cities, which tend to be hotter and relatively lower-income, have, on average, approximately two-thirds the cooling capacity of the Global North cities (2.5 ± 1.0 vs. 3.6 ± 1.7°C, Wilcoxon test, p  = 2.7e-12; Fig.  2c ). The cities that most need to rely on green infrastructure are, at present, those that are least able to do so.

a , e , i , m , q Los Angeles, US. b , f , j , n , r Paris, France. c , g , k , o , s Shanghai, China. d , h , l , p , t Cairo, Egypt. Local cooling efficiency is calculated for different local climate zone types to account for within-city heterogeneity. In densely populated parts of cities, local cooling capacity tends to be lower due to reduced green space area, whereas local cooling benefit (local cooling capacity multiplied by a weight term of local population density relative to city mean) tends to be higher as more urban residents can receive cooling amelioration.

a Global distribution of cooling capacity for the 468 major urbanized areas. b Latitudinal pattern of cooling capacity. c Cooling capacity difference between the Global North and South cities. The cooling capacity offered by urban green infrastructure evinces a latitudinal pattern wherein lower-latitude cities have weaker cooling capacity ( b , cubic-spline fitting of cooling capacity with 95% confidence interval is shown), representing a significant inequality between Global North and South countries: city-level cooling capacity for Global North cities are about 1.5-fold higher than in Global South cities ( c ). Data are presented as box plots, where median values (center black lines), 25th percentiles (box lower bounds), 75th percentiles (box upper bounds), whiskers extending to 1.5-fold of the interquartile range (IQR), and outliers are shown. The tails of the cooling capacity distributions are truncated at zero as all cities have positive values of cooling capacity. Notice that no cities in the Global South have a cooling capacity greater than 5.5 °C ( c ). This is because no cities in the Global South have proportional green space areas as great as those seen in the Global North (see also Fig.  4b ). A similar pattern is found for cooling benefit (Supplementary Fig.  3 ). The two-sided non-parametric Wilcoxon test was used for statistical comparisons.

When we account for the locations of urban green infrastructure relative to humans within cities, the cooling benefit of urban green infrastructure realized by an average urban resident generally becomes slightly lower than suggested by cooling capacity (see Methods; Supplementary Fig.  3 ). Urban residents tend to be densest in the parts of cities with less green infrastructure. As a result, the average urban resident experiences less cooling amelioration than expected. However, this heterogeneity has only a minor effect on global-scale inequality. As a result, the geographic trends in cooling capacity and cooling benefit are similar: mean cooling benefit for an average urban resident also presents a 1.5-fold gap between Global South and North cities (2.2 ± 0.9 vs. 3.4 ± 1.7 °C, Wilcoxon test, p  = 3.2e-13; Supplementary Fig.  3c ). Urban green infrastructure is a public good that has the potential to help even the most marginalized populations stay cool; unfortunately, this public benefit is least available in the Global South. When walking outdoors, the average person in an average Global South city receives only two-thirds the cooling amelioration from urban green infrastructure experienced by a person in an average Global North city. The high cooling amelioration capacity and benefit of the Global North cities is heavily influenced by North America (specifically, Canada and the US), which have both the highest cooling efficiency and the largest area of green infrastructure, followed by Europe (Supplementary Fig.  4 ).

One way to illustrate the global inequality of cooling capacity or benefit is to separately look at the cities that are most and least effective in ameliorating outdoor heat stress. Our results showed that ~85% of the 50 most effective cities (with highest cooling capacity or cooling benefit) are located in the Global North, while ~80% of the 50 least effective are Global South cities (Fig.  3 , Supplementary Fig.  5 ). This is true without taking into account the differences in the background temperatures and climate warming of these cities, which will exacerbate the effects on human health; cities in the Global South are likely to be closer to the limits of human thermal comfort and even, increasingly, the limits of the temperatures and humidities (wet-bulb temperatures) at which humans can safely work or even walk, such that the ineffectiveness of green spaces in those cities in cooling will lead to greater negative effects on human health 48 , work 14 , and gross domestic product (GDP) 49 . In addition, Global South cities commonly have higher population densities (Fig.  3 , Supplementary Fig.  5 ) and are projected to have faster population growth 50 . This situation will plausibly intensify the urban heat island effect because of the need of those populations for housing (and hence tensions between the need for buildings and the need for green spaces). It will also increase the number of people exposed to extreme urban heat island effects. Therefore, it is critical to increase cooling benefit via expanding urban green spaces, so that more people can receive the cooling mitigation from a given new neighboring green space if they live closer to each other. Doing so will require policies that incentivize urban green spaces as well as architectural innovations that make innovations such as plant-covered buildings easier and cheaper to implement.

The axes on the right are an order of magnitude greater than those on the left, such that the cooling capacity of Charlotte in the United States is about 37-fold greater than that of Mogadishu (Somalia) and 29-fold greater than that of Sana’a (Yemen). The cities presenting lowest cooling capacities are most associated with Global South cities at higher population densities.

Of course, cities differ even within the Global North or within the Global South. For example, some Global South cities have high green space areas (or relatively high cooling efficiency in combination with moderate green space areas) and hence high cooling capacity. These cities, such as Pune (India), will be important to study in more detail, to shed light on the mechanistic details of their cooling abilities as well as the sociopolitical and other factors that facilitated their high green area coverage and cooling capabilities (Supplementary Figs.  6 , 7 ).

We conducted our primary analyses using a spatial grain of 100-m grid cells and Landsat NDVI data for quantifying spectral greenness. Our results, however, were robust at the coarser spatial grain of 1 km. We find a slightly larger global cooling inequality (~2-fold gap between Global South and North cities) at the 1-km grain using MODIS data (see Methods and Supplementary Fig.  17 ). MODIS data have been frequently used for quantifying urban heat island effects and cooling mitigation 44 , 45 , 51 . Our results reinforce its robustness for comparing urban thermal environments between cities across broad scales.

Influencing factors

The global inequality of cooling amelioration could have a number of proximate causes. To understand their relative influence, we first separately examined the effects of quality (cooling efficiency) and quantity (NDVI as a proxy indicator of urban green space area) of urban green infrastructure. The simplest null model is one in which cooling capacity (at the city scale) and cooling benefit (at the human scale) are driven primarily by the proportional area in a city dedicated to green spaces. Indeed, we found that both cooling capacity and cooling benefit were strongly correlated with urban green space area (Fig.  4 , Supplementary Fig.  8 ). This finding is useful with regards to practical interventions. In general, cities that invest in saving or restoring more green spaces will receive more cooling benefits from those green spaces. By contrast, differences among cities in cooling efficiency played a more minor role in determining the cooling capacity and benefit of cities (Fig.  4 , Supplementary Fig.  8 ).

a Relationship between cooling efficiency and cooling capacity. b Relationship between green space area (measured by mean Landsat NDVI in the hottest month of 2018) and cooling capacity. Note that the highest level of urban green space area in the Global South cities is much lower than that in the Global North (dashed line in b ). Gray bands indicate 95% confidence intervals. Two-sided t-tests were conducted. c A piecewise structural equation model based on assumed direct and indirect (through influencing cooling efficiency and urban green space area) effects of essential natural and socioeconomic factors on cooling capacity. Mean annual temperature and precipitation, and topographic variation (elevation range) are selected to represent basic background natural conditions; GDP per capita is selected to represent basic socioeconomic conditions. The spatial extent of built-up areas is included to correct for city size. A bi-directional relationship (correlation) is fitted between mean annual temperature and precipitation. Red and blue solid arrows indicate significantly negative and positive coefficients with p  ≤ 0.05, respectively. Gray dashed arrows indicate p  > 0.05. The arrow width illustrates the effect size. Similar relationships are found for cooling benefits realized by an average urban resident (see Supplementary Fig.  8 ).

A further question is what shapes the quality and quantity of urban green infrastructure (which in turn are driving cooling capacity)? Many inter-correlated factors are possibly operating at multiple scales, making it difficult to disentangle their effects, especially since experiment-based causal inference is usually not feasible for large-scale urban systems. From a macroscopic perspective, we test the simple hypothesis that the background natural and socioeconomic conditions of cities jointly affect their cooling capacity and benefit in both direct and indirect ways. To this end, we constructed a minimal structural equation model including only the most essential variables reflecting background climate (mean annual temperature and precipitation), topographic variation (elevation range), as well as gross domestic product (GDP) per capita and city area (see Methods; Fig.  4c ).

We found that the quantity of green spaces in a city (again, in proportion to its size) was positively correlated with GDP per capita and city area; wealthier cities have more green spaces. It is well known that wealth and green spaces are positively correlated within cities (the luxury effect) 40 , 46 ; our analysis shows that a similar luxury effect occurs among them at a global scale. In addition, larger cities often have proportionally more green spaces, an effect that may be due to the tendency for large cities (particularly in the US and Canada) to have lower population densities. Cities that were hotter and had more topographic variation tended to have fewer green spaces and those that were more humid tended to have more green spaces. Given that temperature and humidity are highly correlated with the geography of the Global South and Global North, it is difficult to know whether these effects are due to the direct effects of temperature and precipitation, for example, on the growth rate of vegetation and hence the transition of abandoned lots into green spaces, or are associated with historical, cultural and political differences that via various mechanisms correlate to climate. Our structural equation model explained only a small fraction of variation among cities in their cooling efficiency, which is to say the quality of their green space. Cooling efficiency was modestly influenced by background temperature and precipitation—the warmer a city, the greater the cooling efficiency in that city; conversely, the more humid a city the less the cooling efficiency of that city.

Our analyses suggested that the lower cooling adaptation capabilities of Global South cities can be explained by their lower quantity of green infrastructure and, to a much lesser extent, their weaker cooling efficiency (quality; Supplementary Fig.  2 ). These patterns appear to be in part structured by GDP, but are also associated with climatic conditions 39 , and other factors. A key question, unresolved by our work, is whether the climatic correlates of the size of green spaces in cities are due to the effects of climate per se or if they, instead, reflect correlates between contemporary climate and the social, cultural, and political histories of cities in the Global South 52 . Since urban planning has much inertia, especially in big cities, those choices might be correlated with climate because of the climatic correlates of political histories. It is also possible that these dynamics relate, in part, to the ways in which climate influences vegetation structure. However, this seems less likely given that under non-urban conditions vegetation cover (and hence cooling capacity) is normally positively correlated with mean annual temperature across the globe, opposite to our observed negative relationships for urban systems (Supplementary Fig.  9g ). Still, it is possible that increased temperatures in cities due to the urban heat island effects may lead to temperature-vegetation cover-cooling capacity relationships that differ from those in natural environments 53 , 54 . Indeed, a recent study found that climate warming will put urban forests at risk, and the risk is disproportionately higher in the Global South 55 .

Our model serves as a starting point for unraveling the mechanisms underlying global cooling inequality. We cannot rule out the possibility that other unconsidered factors correlated with the studied variables play important roles. We invite systematic studies incorporating detailed sociocultural and ecological variables to address this question across scales.

Potential of enhancing cooling and reducing inequality

Can we reduce the inequality in cooling capacity and benefits that we have discovered among the world’s largest cities? Nuanced assessments of the potential to improve cooling mitigation require comprehensive considerations of socioeconomic, cultural, and technological aspects of urban management and policy. It is likely that cities differ greatly in their capacity to implement cooling through green infrastructure, whether as a function of culture, governance, policy or some mix thereof. However, any practical attempts to achieve greater cooling will occur in the context of the realities of climate and existing land use. To understand these realities, we modeled the maximum additional cooling capacity that is possible in cities, given existing constraints. We assume that this capacity depends on the quality (cooling efficiency) and quantity of urban green infrastructure. Our approach provides a straightforward metric of the cooling that could be achieved if all parts of a city’s green infrastructure were to be enhanced systematically.

The positive outlook is that our analyses suggest a considerable potential of improving cooling capacity by optimizing urban green infrastructure. An obvious way is through increases in urban green infrastructure quantity. We employ an approach in which we consider each local climate zone 56 to have a maximum NDVI and cooling efficiency (see Methods). For a given local climate zone, the city with the largest NDVI values or cooling efficiency sets the regional upper bounds for urban green infrastructure quantities or quality that can be achieved. Notably, these maxima are below the maxima for forests or other non-urban spaces for the simple reason that, as currently imagined, cities must contain gray (non-green) spaces in the form of roads and buildings. In this context, we conduct a thought experiment. What if we could systematically increase NDVI of all grid cells in each city, per local climate zone type, to a level corresponding to the median NDVI of grid cells in that upper bound city while keeping cooling efficiency unchanged (see Methods). If we were able to achieve this goal, the cooling capacity of cities would increase by ~2.4 °C worldwide. The increase would be even greater, ~3.8°C, if the 90th percentile (within the reference maximum city) was reached (Fig.  5a ). The potential for cooling benefit to the average urban resident is similar to that of cooling capacity (Supplementary Fig.  10a ). There is also potential to reduce urban temperatures if we can enhance cooling efficiency. However, the benefits of increases in cooling efficiency are modest (~1.5 °C increases at the 90th percentile of regional upper bounds) when holding urban green infrastructure quantity constant. In theory, if we could maximize both quantity and cooling efficiency of urban green infrastructure (to 90th percentiles of their regional upper bounds respectively), we would yield increases in cooling capacity and benefit up to ~10 °C, much higher than enhancing green space area or cooling efficiency alone (Fig.  5a , Supplementary Fig.  10a ). Notably, such co-maximization of green space area and cooling efficiency would substantially reduce global inequality to Gini <0.1 (Fig.  5b , Supplementary Fig.  10b ). Our analyses thus provide an important suggestion that enhancing both green space quantity and quality can yield a synergistic effect leading to much larger gains than any single aspect alone.

a The potential of enhancing cooling capacity via either enhancing urban green infrastructure quality (i.e., cooling efficiency) while holding quantity (i.e., green space area) fixed (yellow), or enhancing quantity while holding quality fixed (blue) is much lower than that of enhancing both quantity and quality (green). The x-axis indicates the targets of enhancing urban green infrastructure quantity and/or quality relative to the 50–90th percentiles of NDVI or cooling efficiency, see Methods). The dashed horizontal lines indicate the median cooling capacity of current cities. Data are presented as median values with the colored bands corresponding to 25–75th percentiles. b The potential of reducing cooling capacity inequality is also higher when enhancing both urban green infrastructure quantity and quality. The Gini index weighted by population density is used to measure inequality. Similar results were found for cooling benefit (Supplementary Fig.  10 ).

Different estimates of cooling capacity potential may be reached based on varying estimates and assumptions regarding the maximum possible quantity and quality of urban green infrastructure. There is no single, simple way to make these estimates, especially considering the huge between-city differences in society, culture, and structure across the globe. Our example case (above) begins from the upper bound city’s median NDVI, taking into account different local climate zone types and background climate regions (regional upper bounds). This is based on the assumption that for cities within the same climate regions, their average green space quantity may serve as an attainable target. Still, urban planning is often made at the level of individual cities, often only implemented to a limited extent and made with limited consideration of cities in other regions and countries. A potentially more realistic reference may be taken from the existing green infrastructure (again, per local climate zone type) within each particular city itself (see Methods): if a city’s sparsely vegetated areas was systematically elevated to the levels of 50–90th percentiles of NDVI within their corresponding local climate zones within the city, cooling capacity would still increase, but only by 0.5–1.5 °C and with only slightly reduced inequalities among cities (Supplementary Fig.  11 ). This highlights that ambitious policies, inspired by the greener cities worldwide, are necessary to realize the large cooling potential in urban green infrastructure.

In summary, our results demonstrate clear inequality in the extent to which urban green infrastructure cools cities and their denizens between the Global North and South. Much attention has been paid to the global inequality of indoor heat adaptation arising from the inequality of resources (e.g., less affordable air conditioning and more frequent power shortages in the Global South) 36 , 57 , 58 , 59 . Our results suggest that the inequality in outdoor adaptation is particularly concerning, especially as urban populations in the Global South are growing rapidly and are likely to face the most severe future temperature extremes 60 .

Previous studies have been focusing on characterizing urban heat island effects, urban vegetation patterns, resident exposure, and cooling effects in particular cities 26 , 28 , 34 , 61 , regions 22 , 25 , 62 , or continents 32 , 44 , 63 . Recent studies start looking at global patterns with respect to cooling efficiency or green space exposure 35 , 45 , 64 , 65 . Our approach is one drawn from the fields of large-scale ecology and macroecology. This approach is complementary to and, indeed, can, in the future, be combined with (1) mechanism driven biophysical models 66 , 67 to predict the influence of the composition and climate of green spaces on their cooling efficiency, (2) social theory aimed at understanding the factors that govern the amount of green space in cities as well as the disparity among cities 68 , (3) economic models of the effects of policy changes on the amount of greenspace and even (4) artist-driven projects that seek to understand the ways in which we might reimagine future cities 69 . Our simple explanatory model is, ultimately, one lens on a complex, global phenomenon.

Our results convey some positive outlook in that there is considerable potential to strengthen the cooling capability of cities and to reduce inequalities in cooling capacities at the same time. Realizing this nature-based solution, however, will be challenging. First, enhancing urban green infrastructure requires massive investments, which are more difficult to achieve in Global South cities. Second, it also requires smart planning strategies and advanced urban design and greening technologies 37 , 70 , 71 , 72 . Spatial planning of urban green spaces needs to consider not only the cooling amelioration effect, but also their multifunctional aspects that involve multiple ecosystem services, mental health benefits, accessibility, and security 73 . In theory, a city can maximize its cooling while also maximizing density through the combination of high-density living, ground-level green spaces, and vertical and rooftop gardens (or even forests). In practice, the current cities with the most green spaces tend to be lower-density cities 74 (Supplementary Fig.  12 ). Still, innovation and implementation of new technologies that allow green spaces and high-density living to be combined have the potential to reduce or disconnect the negative relationship between green space area and population density 71 , 75 . However, this development has yet to be realized. Another dimension of green spaces that deserves more attention is the geography of green spaces relative to where people are concentrated within cities. A critical question is how best should we distribute green spaces within cities to maximize cooling efficiency 76 and minimize within-city cooling inequality towards social equity 77 ? Last but not least, it is crucial to design and manage urban green spaces to be as resilient as possible to future climate stress 78 . For many cities, green infrastructure is likely to remain the primary means people will have to rely on to mitigate the escalating urban outdoor heat stress in the coming decades 79 .

We used the world population data from the World’s Cities in 2018 Data Booklet 80 to select 502 major cities with population over 1 million people (see Supplementary Data  1 for the complete list of the studied cities). Cities are divided into the Global North and Global South based on the Human Development Index (HDI) from the Human Development Report 2019 81 . For each selected city, we used the 2018 Global Artificial Impervious Area (GAIA) data at 30 m resolution 82 to determine its geographic extent. The derived urban boundary polygons thus encompass a majority of the built-up areas and urban residents. In using this approach, rather than urban administrative boundaries, we can focus on the relatively densely populated areas where cooling mitigation is most needed, and exclude areas dominated by (semi) natural landscapes that may bias the subsequent quantifications of the cooling effect. Our analyses on the cooling effect were conducted at the 100 m spatial resolution using Landsat data and WorldPop Global Project Population Data of 2018 83 . In order to test for the robustness of the results to coarser spatial scales, we also repeated the analyses at 1 km resolution using MODIS data, which have been extensively used for quantifying urban heat island effects and cooling mitigation 44 , 45 , 51 . We discarded the five cities with sizes <30 km 2 as they were too small for us to estimate their cooling efficiency based on linear regression (see section below for details). We combined closely located cities that form contiguous urban areas or urban agglomerations, if their urban boundary polygons from GAIA merged (e.g., Phoenix and Mesa in the United States were combined). Our approach yielded 468 polygons, each representing a major urbanized area that were the basis for all subsequent analyses. Because large water bodies can exert substantial and confounding cooling effects, we excluded permanent water bodies including lakes, reservoirs, rivers, and oceans using the Copernicus Global Land Service (CGLS) Land Cover data for 2018 at 10 m resolution 84 .

Quantifying the cooling effect

As a first step, we calculated cooling efficiency for each studied city within the GAIA-derived urban boundary. Cooling efficiency quantifies the extent to which a given area of green spaces in a city can reduce temperatures. It is a measure of the effectiveness (quality) of urban green spaces in terms of heat amelioration. Cooling efficiency is typically measured by calculating the slope of the relationship between remotely-sensed land surface temperature (LST) and vegetation cover through ordinary least square regression 42 , 44 , 45 . It is known that cooling efficiency varies between cities. Influencing factors might include background climate 29 , species composition 30 , 85 , landscape configuration 28 , topography 86 , proximity to large water bodies 33 , 87 , urban morphology 88 , and city management practices 31 . However, the mechanism underlying the global pattern of cooling efficiency remains unclear.

We used Landsat satellite data provided by the United States Geological Survey (USGS) to calculate the cooling efficiency of each studied city. We used the cloud-free Landsat 8 Level 2 LST and NDVI data. For each city we calculated the mean LST in each month of 2018 to identify the hottest month, and then derived the hottest month LST; we used the cloud-free Landsat 8 data to calculate the mean NDVI for the hottest month correspondingly.

We quantified cooling efficiency for different local climate zones 56 separately for each city, to account for within-city variability of thermal environments. To this end, we used the Copernicus Global Land Service data (CGLS) 84 and Global Human Settlement Layers (GHSL) Built-up height data 89 of 2018 at the 100 m resolution to identify five types of local climate zones: non-tree vegetation (shrubs, herbaceous vegetation, and cultivated vegetation according to the CGLS classification system), low-rise buildings (built up and bare according to the CGLS classification system, with building heights ≤10 m according to the GHSL data), medium-high-rise buildings (built up and bare areas with building heights >10 m), open tree cover (open forest with tree cover 15–70% according to the CGLS system), and closed tree cover (closed forest with tree cover >70%).

For each local climate zone type in each city, we constructed a regression model with NDVI as the predictor variable and LST as the response variable (using the ordinary least square method). We took into account the potential confounding factors including topographic elevation (derived from MERIT DEM dataset 90 ), building height (derived from the GHSL dataset 89 ), and distance to water bodies (derived from the GSHHG dataset 91 ), the model thus became: LST ~ NDVI + topography + building height + distance to water. Cooling efficiency was calculated as the absolute value of the regression coefficient of NDVI, after correcting for those confounding factors. To account for the multi-collinearity issue, we conducted variable selection based on the variance inflation factor (VIF) to achieve VIF < 5. Before the analysis, we discarded low-quality Landsat pixels, and filtered out the pixels with NDVI < 0 (normally less than 1% in a single city). Cooling efficiency is known to be influenced by within-city heterogeneity 92 , 93 , and, as a result, might sometimes better fit non-linear relationships at local scales 65 , 76 . However, our central aim is to assess global cooling inequality based on generalized relationships that fit the majority of global cities. Previous studies have shown that linear relationships can do this job 42 , 44 , 45 , therefore, here we used linear models to assess cooling efficiency.

As a second step, we calculated the cooling capacity of each city. Cooling capacity is a positive function of the magnitude of cooling efficiency and the proportional area of green spaces in a city and is calculated based on NDVI and the derived cooling efficiency (Eq.  1 , Supplementary Fig.  13 ):

where CC lcz and CE lcz are the cooling capacity and cooling efficiency for a given local climate zone type in a city, respectively; NDVI i is the mean NDVI for 100-m grid cell i ; NDVI min is the minimum NDVI across the city; and n is the total number of grid cells within the local climate zone. Local cooling capacity for each grid cell i (Fig.  1 , Supplementary Fig.  7 ) can be derived in this way as well (Supplementary Fig.  13 ). For a particular city, cooling capacity may be dependent on the spatial configuration of its land use/cover 28 , 94 , but here we condensed cooling capacity to city average (Eq.  2 ), thus did not take into account these local-scale factors.

where CC is the average cooling capacity of a city; n lcz is the number of grid cells of the local climate zone; m is the total number of grid cells within the whole city.

As a third step, we calculated the cooling benefit realized by an average urban resident (cooling benefit in short) in each city. Cooling benefit depends not only on the cooling capacity of a city, but also on where people live within a city relative to greener or grayer areas of the city. For example, cooling benefits in a city might be low even if the cooling capacity is high if the green parts and the dense-population parts of a city are inversely correlated. Here, we are calculating these averages while aware that in any particular city the exposure of a particular person will depend on the distribution of green spaces in a city, and the occupation, movement trajectories of a person, etc. On the scale of a city, we calculated cooling benefit following a previous study 35 , that is, simply adding a weight term of population size per 100-m grid cell into cooling capacity in Eq. ( 1 ):

Where CB lcz is the cooling benefit of a given local climate zone type in a specific city, pop i is the number of people within grid cell i , \(\overline{{pop}}\) is the mean population of the city.

Where CB is the average cooling benefit of a city. The population data were obtained from the 100-m resolution WorldPop Global Project Population Data of 2018 83 . Local cooling benefit for a given grid cell i can be calculated in a similar way, i.e., local cooling capacity multiplied by a weight term of local population density relative to mean population density. Local cooling benefits were mapped for example cities for the purpose of illustrating the effect of population spatial distribution (Fig.  1 , Supplementary Fig.  7 ), but their patterns were not examined here.

Based on the aforementioned three key variables quantified at 100 m grid cells, we conducted multivariate analyses to examine if and to what extent cooling efficiency and cooling benefit are shaped by essential natural and socioeconomic factors, including background climate (mean annual temperature from ECMWF ERA5 dataset 95 and precipitation from TerraClimate dataset 96 ), topography (elevation range 90 ), and GDP per capita 97 , with city size (geographic extent) corrected for. We did not include humidity because it is strongly correlated with temperature and precipitation, causing serious multi-collinearity problems. We used piecewise structural equation modeling to test the direct effects of these factors and indirect effects via influencing cooling efficiency and vegetation cover (Fig.  4c , Supplementary Fig.  8c ). To account for the potential influence of spatial autocorrelation, we used spatially autoregressive models (SAR) to test for the robustness of the observed effects of natural and socioeconomic factors on cooling capacity and benefit (Supplementary Fig.  14 ).

Testing for robustness

We conducted the following additional analyses to test for robustness. We obtained consistent results from these robustness analyses.

(1) We looked at the mean hottest-month LST and NDVI within 3 years (2017-2019) to check the consistency between the results based on relatively short (1 year) vs. long (3-year average) time periods (Supplementary Fig.  15 ).

(2) We carried out the approach at a coarser spatial scale of 1 km, using MODIS-derived NDVI and LST, as well as the population data 83 in the hottest month of 2018. In line with our finer-scale analysis of Landsat data, we selected the hottest month and excluded low-quality grids affected by cloud cover and water bodies 98 (water cover > 20% in 1 × 1 km 2 grid cells) of MODIS LST, and calculated the mean NDVI for the hottest month. We ultimately obtained 441 cities (or urban agglomerations) for analysis. At the 1 km resolution, some local climate zone types would yield insufficient samples for constructing cooling efficiency models. Therefore, instead of identifying local climate zone explicitly, we took an indirect approach to account for local climate confounding factors, that is, we constructed a multiple regression model for a whole city incorporating the hottest-month local temperature 95 , precipitation 96 , and humidity (based on NASA FLDAS dataset 99 ), albedo (derived from the MODIS MCD43A3 product 100 ), aerosol loading (derived from the MODIS MCD19A2 product 101 ), wind speed (based on TerraClimate dataset 96 ), topography elevation 90 , distance to water 91 , urban morphology (building height 102 ), and human activity intensity (VIIRS nighttime light data as a proxy indicator 103 ). We used the absolute value of the linear regression coefficient of NDVI as the cooling efficiency of the whole city (model: LST ~ NDVI + temperature + precipitation + humidity + distance to water + topography + building height + albedo + aerosol + wind speed + nighttime light), and calculated cooling capacity and cooling benefit based on the same method. Variable selection was conducted using the criterion of VIF < 5.

Our results indicated that MODIS-based cooling capacity and cooling benefit are significantly correlated with the Landsat-based counterparts (Supplementary Fig.  16 ); importantly, the gap between the Global South and North cities is around two-fold, close to the result from the Landsat-based result (Supplementary Fig.  17 ).

(3) For the calculation of cooling benefit, we considered different spatial scales of human accessibility to green spaces: assuming the population in each 100 × 100 m 2 grid cell could access to green spaces within neighborhoods of certain extents, we calculated cooling benefit by replacing NDVI i in Eq. ( 3 ) with mean NDVI within the 300 × 300 m 2 and 500 × 500 m 2 extents centered at the focal grid cell (Supplementary Fig.  18 ).

(4) Considering cities may vary in minimum NDVI, we assessed if this variation could affect resulting cooling capacity patterns. To this end, we calculated the cooling capacity for each studied city using NDVI = 0 as the reference (i.e., using NDVI = 0 instead of minimum NDVI in Supplementary Fig.  13b ), and correlated it with that using minimum NDVI as the reference (Supplementary Fig.  19 ).

Quantifying between-city inequality

Inequalities in access to the benefits of green spaces in cities exist within cities, as is increasingly well-documented 104 . Here, we focus instead on the inequalities among cities. We used the Gini coefficient to measure the inequality in cooling capacity and cooling benefit between all studied cities across the globe as well as between Global North or South cities. We calculated Gini using the population-density weighted method (Fig.  5b ), as well as the unweighted and population-size weighted methods (Supplementary Fig.  20 ).

Estimating the potential for more effective and equal cooling amelioration

We estimated the potential of enhancing cooling amelioration based on the assumptions that urban green space quality (cooling efficiency) and quantity (NDVI) can be increased to different levels, and that relative spatial distributions of green spaces and population can be idealized (so that their spatial matches can maximize cooling benefit). We assumed that macro-climate conditions act as the constraints of vegetation cover and cooling efficiency. We calculated the 50th, 60th, 70th, 80th, and 90th percentiles of NDVI within each type of local climate zone of each city. For a given local climate zone type, we obtained the city with the highest NDVI per percentile value as the regional upper bounds of urban green infrastructure quantity. The regional upper bounds of cooling efficiency are derived in a similar way. For each local climate zone in a city, we generated a potential NDVI distribution where all grid cells reach the regional upper bound values for the 50th, 60th, 70th, 80th, or 90th percentile of urban green space quantity or quality, respectively. NDVI values below these percentiles were increased, whereas those above these percentiles remained unchanged. The potential estimates are essentially dependent on the references, i.e., the optimal cooling efficiency and NDVI that a given city can reach. However, such references are obviously difficult to determine, because complex natural and socioeconomic conditions could play important roles in determining those cooling optima, and the dominant factors are unknown at a global scale. We employed the simplifying assumption that background climate could act as an essential constraint according to our results. We therefore used the Köppen climate classification system 105 to determine the reference separately in each climate region (tropical, arid, temperate, and continental climate regions were involved for all studied cities).

We calculated potential cooling capacity and cooling benefit based on these potential NDVI maps (Fixed cooling efficiency in Fig.  5 ). We then calculated the potentials if cooling efficiency of each city can be enhanced to 50–90th percentile across all urban local climate zones within the corresponding biogeographic region (Fixed green space area in Fig.  5 ). We also calculated the potentials if both NDVI and cooling efficiency were enhanced (Enhancing both in Fig.  5) to a certain corresponding level (i.e., i th percentile NDVI +  i th percentile cooling efficiency). We examined if there are additional effects of idealizing relative spatial distributions of urban green spaces and humans on cooling benefits. To this end, the pixel values of NDVI or population amount remained unchanged, but their one-to-one correspondences were based on their ranking: the largest population corresponds to the highest NDVI, and so forth. Under each scenario, we calculated cooling capacity and cooling benefit for each city, and the between-city inequality was measured by the Gini coefficient.

We used the Google Earth Engine to process the spatial data. The statistical analyses were conducted using R v4.3.3 106 , with car v3.1-2 107 , piecewiseSEM v2.1.2 108 , and ineq v0.2-13 109 packages. The global maps of cooling were created using the ArcGIS v10.3 software.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

City population statistics data is collected from the Population Division of the Department of Economic and Social Affairs of the United Nations ( https://www.un.org/development/desa/pd/content/worlds-cities-2018-data-booklet ). Global North-South division is based on Human Development Report 2019 which from United Nations Development Programme ( https://hdr.undp.org/content/human-development-report-2019 ). Global urban boundaries from GAIA data are available from Star Cloud Data Service Platform ( https://data-starcloud.pcl.ac.cn/resource/14 ) . Global water data is derived from 2018 Copernicus Global Land Service (CGLS 100-m) data ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global ), European Space Agency (ESA) WorldCover 10 m 2020 product ( https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100 ), and GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database) at https://www.soest.hawaii.edu/pwessel/gshhg/ . Landsat 8 LST and NDVI data with 30 m resolution are available at  https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 . Land surface temperature (LST) data with 1 km from MODIS Aqua product (MYD11A1) is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 . NDVI (1 km) dataset from MYD13A2 is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2 . Population data (100 m) is derived from WorldPop ( https://developers.google.com/earth-engine/datasets/catalog/WorldPop_GP_100m_pop ). Local climate zones are also based on 2018 CGLS data ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global ), and built-up height data is available from Global Human Settlement Layers (GHSL, 100 m) ( https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2023A_GHS_BUILT_H ). Temperature data is calculated from ERA5-Land Monthly Aggregated dataset ( https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR ). Precipitation and wind data are calculated from TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces, University of Idaho) ( https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE ). Humidity data is calculated from Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System ( https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 ). Topography data from MERIT DEM (Multi-Error-Removed Improved-Terrain DEM) product is available at https://developers.google.com/earth-engine/datasets/catalog/MERIT_DEM_v1_0_3 . GDP from Gross Domestic Product and Human Development Index dataset is available at https://doi.org/10.5061/dryad.dk1j0 . VIIRS nighttime light data is available at https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG . City building volume data from Global 3D Building Structure (1 km) is available at https://doi.org/10.34894/4QAGYL . Albedo data is derived from the MODIS MCD43A3 product ( https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 ), and aerosol data is derived from the MODIS MCD19A2 product ( https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES ). All data used for generating the results are publicly available at https://doi.org/10.6084/m9.figshare.26340592.v1 .

Code availability

The codes used for data collection and analyses are publicly available at https://doi.org/10.6084/m9.figshare.26340592.v1 .

Dosio, A., Mentaschi, L., Fischer, E. M. & Wyser, K. Extreme heat waves under 1.5 °C and 2 °C global warming. Environ. Res. Lett. 13 , 054006 (2018).

Article   ADS   Google Scholar  

Suarez-Gutierrez, L., Müller, W. A., Li, C. & Marotzke, J. Hotspots of extreme heat under global warming. Clim. Dyn. 55 , 429–447 (2020).

Article   Google Scholar  

Guo, Y. et al. Global variation in the effects of ambient temperature on mortality: a systematic evaluation. Epidemiology 25 , 781–789 (2014).

Article   PubMed   PubMed Central   Google Scholar  

Mora, C. et al. Global risk of deadly heat. Nat. Clim. Chang. 7 , 501–506 (2017).

Ebi, K. L. et al. Hot weather and heat extremes: health risks. Lancet 398 , 698–708 (2021).

Article   PubMed   Google Scholar  

Lüthi, S. et al. Rapid increase in the risk of heat-related mortality. Nat. Commun. 14 , 4894 (2023).

Article   ADS   PubMed   PubMed Central   Google Scholar  

United Nations Department of Economic Social Affairs, Population Division. in World Population Prospects 2022: Summary of Results (United Nations Fund for Population Activities, 2022).

Sachindra, D., Ng, A., Muthukumaran, S. & Perera, B. Impact of climate change on urban heat island effect and extreme temperatures: a case‐study. Q. J. R. Meteorol. Soc. 142 , 172–186 (2016).

Guo, L. et al. Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria. Sci. Rep. 12 , 14168 (2022).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Liu, Z. et al. Surface warming in global cities is substantially more rapid than in rural background areas. Commun. Earth Environ. 3 , 219 (2022).

Mentaschi, L. et al. Global long-term mapping of surface temperature shows intensified intra-city urban heat island extremes. Glob. Environ. Change 72 , 102441 (2022).

Asseng, S., Spänkuch, D., Hernandez-Ochoa, I. M. & Laporta, J. The upper temperature thresholds of life. Lancet Planet. Health 5 , e378–e385 (2021).

Zander, K. K., Botzen, W. J., Oppermann, E., Kjellstrom, T. & Garnett, S. T. Heat stress causes substantial labour productivity loss in Australia. Nat. Clim. Chang. 5 , 647–651 (2015).

Flouris, A. D. et al. Workers’ health and productivity under occupational heat strain: a systematic review and meta-analysis. Lancet Planet. Health 2 , e521–e531 (2018).

Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C. & Scheffer, M. Future of the human climate niche. Proc. Natl Acad. Sci. USA 117 , 11350–11355 (2020).

Lenton, T. M. et al. Quantifying the human cost of global warming. Nat. Sustain. 6 , 1237–1247 (2023).

Harrington, L. J. et al. Poorest countries experience earlier anthropogenic emergence of daily temperature extremes. Environ. Res. Lett. 11 , 055007 (2016).

Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 4 , eaar5809 (2018).

Alizadeh, M. R. et al. Increasing heat‐stress inequality in a warming climate. Earth Future 10 , e2021EF002488 (2022).

Tuholske, C. et al. Global urban population exposure to extreme heat. Proc. Natl Acad. Sci. USA 118 , e2024792118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Manoli, G. et al. Magnitude of urban heat islands largely explained by climate and population. Nature 573 , 55–60 (2019).

Article   ADS   CAS   PubMed   Google Scholar  

Wang, J. et al. Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities. Nat. Clim. Chang. 11 , 1084–1089 (2021).

Article   ADS   CAS   Google Scholar  

Bowler, D. E., Buyung-Ali, L., Knight, T. M. & Pullin, A. S. Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc. Urban Plan. 97 , 147–155 (2010).

Armson, D., Stringer, P. & Ennos, A. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 11 , 245–255 (2012).

Wang, C., Wang, Z. H. & Yang, J. Cooling effect of urban trees on the built environment of contiguous United States. Earth Future 6 , 1066–1081 (2018).

Pataki, D. E., McCarthy, H. R., Litvak, E. & Pincetl, S. Transpiration of urban forests in the Los Angeles metropolitan area. Ecol. Appl. 21 , 661–677 (2011).

Konarska, J. et al. Transpiration of urban trees and its cooling effect in a high latitude city. Int. J. Biometeorol. 60 , 159–172 (2016).

Article   ADS   PubMed   Google Scholar  

Li, X., Zhou, W., Ouyang, Z., Xu, W. & Zheng, H. Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landsc. Ecol. 27 , 887–898 (2012).

Yu, Z., Xu, S., Zhang, Y., Jørgensen, G. & Vejre, H. Strong contributions of local background climate to the cooling effect of urban green vegetation. Sci. Rep. 8 , 6798 (2018).

Richards, D. R., Fung, T. K., Belcher, R. & Edwards, P. J. Differential air temperature cooling performance of urban vegetation types in the tropics. Urban For. Urban Green. 50 , 126651 (2020).

Winbourne, J. B. et al. Tree transpiration and urban temperatures: current understanding, implications, and future research directions. BioScience 70 , 576–588 (2020).

Schwaab, J. et al. The role of urban trees in reducing land surface temperatures in European cities. Nat. Commun. 12 , 6763 (2021).

Vo, T. T. & Hu, L. Diurnal evolution of urban tree temperature at a city scale. Sci. Rep. 11 , 10491 (2021).

Wang, J. et al. Comparing relationships between urban heat exposure, ecological structure, and socio-economic patterns in Beijing and New York City. Landsc. Urban Plan. 235 , 104750 (2023).

Chen, B. et al. Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nat. Commun. 13 , 4636 (2022).

Pavanello, F. et al. Air-conditioning and the adaptation cooling deficit in emerging economies. Nat. Commun. 12 , 6460 (2021).

Turner, V. K., Middel, A. & Vanos, J. K. Shade is an essential solution for hotter cities. Nature 619 , 694–697 (2023).

Hope, D. et al. Socioeconomics drive urban plant diversity. Proc. Natl Acad. Sci. USA 100 , 8788–8792 (2003).

Leong, M., Dunn, R. R. & Trautwein, M. D. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14 , 20180082 (2018).

Schwarz, K. et al. Trees grow on money: urban tree canopy cover and environmental justice. PloS ONE 10 , e0122051 (2015).

Chakraborty, T., Hsu, A., Manya, D. & Sheriff, G. Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective. Environ. Res. Lett. 14 , 105003 (2019).

Wang, J. et al. Significant effects of ecological context on urban trees’ cooling efficiency. ISPRS J. Photogramm. Remote Sens. 159 , 78–89 (2020).

Marando, F. et al. Urban heat island mitigation by green infrastructure in European Functional Urban Areas. Sust. Cities Soc. 77 , 103564 (2022).

Cheng, X., Peng, J., Dong, J., Liu, Y. & Wang, Y. Non-linear effects of meteorological variables on cooling efficiency of African urban trees. Environ. Int. 169 , 107489 (2022).

Yang, Q. et al. Global assessment of urban trees’ cooling efficiency based on satellite observations. Environ. Res. Lett. 17 , 034029 (2022).

Yin, Y., He, L., Wennberg, P. O. & Frankenberg, C. Unequal exposure to heatwaves in Los Angeles: Impact of uneven green spaces. Sci. Adv. 9 , eade8501 (2023).

Fantom N., Serajuddin U. The World Bank’s Classification of Countries by Income (The World Bank, 2016).

Iungman, T. et al. Cooling cities through urban green infrastructure: a health impact assessment of European cities. Lancet 401 , 577–589 (2023).

He, C. et al. The inequality labor loss risk from future urban warming and adaptation strategies. Nat. Commun. 13 , 3847 (2022).

Kii, M. Projecting future populations of urban agglomerations around the world and through the 21st century. npj Urban Sustain 1 , 10 (2021).

Paschalis, A., Chakraborty, T., Fatichi, S., Meili, N. & Manoli, G. Urban forests as main regulator of the evaporative cooling effect in cities. AGU Adv. 2 , e2020AV000303 (2021).

Hunte, N., Roopsind, A., Ansari, A. A. & Caughlin, T. T. Colonial history impacts urban tree species distribution in a tropical city. Urban For. Urban Green. 41 , 313–322 (2019).

Kabano, P., Harris, A. & Lindley, S. Sensitivity of canopy phenology to local urban environmental characteristics in a tropical city. Ecosystems 24 , 1110–1124 (2021).

Frank, S. D. & Backe, K. M. Effects of urban heat islands on temperate forest trees and arthropods. Curr. Rep. 9 , 48–57 (2023).

Esperon-Rodriguez, M. et al. Climate change increases global risk to urban forests. Nat. Clim. Chang. 12 , 950–955 (2022).

Stewart, I. D. & Oke, T. R. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 93 , 1879–1900 (2012).

Biardeau, L. T., Davis, L. W., Gertler, P. & Wolfram, C. Heat exposure and global air conditioning. Nat. Sustain. 3 , 25–28 (2020).

Davis, L., Gertler, P., Jarvis, S. & Wolfram, C. Air conditioning and global inequality. Glob. Environ. Change 69 , 102299 (2021).

Colelli, F. P., Wing, I. S. & Cian, E. D. Air-conditioning adoption and electricity demand highlight climate change mitigation–adaptation tradeoffs. Sci. Rep. 13 , 4413 (2023).

Sun, L., Chen, J., Li, Q. & Huang, D. Dramatic uneven urbanization of large cities throughout the world in recent decades. Nat. Commun. 11 , 5366 (2020).

Liu, D., Kwan, M.-P. & Kan, Z. Analysis of urban green space accessibility and distribution inequity in the City of Chicago. Urban For. Urban Green. 59 , 127029 (2021).

Hsu, A., Sheriff, G., Chakraborty, T. & Manya, D. Disproportionate exposure to urban heat island intensity across major US cities. Nat. Commun. 12 , 2721 (2021).

Zhao, L., Lee, X., Smith, R. B. & Oleson, K. Strong contributions of local background climate to urban heat islands. Nature 511 , 216–219 (2014).

Wu, S., Chen, B., Webster, C., Xu, B. & Gong, P. Improved human greenspace exposure equality during 21st century urbanization. Nat. Commun. 14 , 6460 (2023).

Zhao, J., Zhao, X., Wu, D., Meili, N. & Fatichi, S. Satellite-based evidence highlights a considerable increase of urban tree cooling benefits from 2000 to 2015. Glob. Chang. Biol. 29 , 3085–3097 (2023).

Article   CAS   PubMed   Google Scholar  

Nice, K. A., Coutts, A. M. & Tapper, N. J. Development of the VTUF-3D v1. 0 urban micro-climate model to support assessment of urban vegetation influences on human thermal comfort. Urban Clim. 24 , 1052–1076 (2018).

Meili, N. et al. An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1. 0). Geosci. Model Dev. 13 , 335–362 (2020).

Nesbitt, L., Meitner, M. J., Sheppard, S. R. & Girling, C. The dimensions of urban green equity: a framework for analysis. Urban For. Urban Green. 34 , 240–248 (2018).

Hedblom, M., Prévot, A.-C. & Grégoire, A. Science fiction blockbuster movies—a problem or a path to urban greenery? Urban For. Urban Green. 74 , 127661 (2022).

Norton, B. A. et al. Planning for cooler cities: a framework to prioritise green infrastructure to mitigate high temperatures in urban landscapes. Landsc. Urban Plan 134 , 127–138 (2015).

Medl, A., Stangl, R. & Florineth, F. Vertical greening systems—a review on recent technologies and research advancement. Build. Environ. 125 , 227–239 (2017).

Chen, B., Lin, C., Gong, P. & An, J. Optimize urban shade using digital twins of cities. Nature 622 , 242–242 (2023).

Pamukcu-Albers, P. et al. Building green infrastructure to enhance urban resilience to climate change and pandemics. Landsc. Ecol. 36 , 665–673 (2021).

Haaland, C. & van Den Bosch, C. K. Challenges and strategies for urban green-space planning in cities undergoing densification: a review. Urban For. Urban Green. 14 , 760–771 (2015).

Shafique, M., Kim, R. & Rafiq, M. Green roof benefits, opportunities and challenges—a review. Renew. Sust. Energ. Rev. 90 , 757–773 (2018).

Wang, J., Zhou, W. & Jiao, M. Location matters: planting urban trees in the right places improves cooling. Front. Ecol. Environ. 20 , 147–151 (2022).

Lan, T., Liu, Y., Huang, G., Corcoran, J. & Peng, J. Urban green space and cooling services: opposing changes of integrated accessibility and social equity along with urbanization. Sust. Cities Soc. 84 , 104005 (2022).

Wood, S. & Dupras, J. Increasing functional diversity of the urban canopy for climate resilience: Potential tradeoffs with ecosystem services? Urban For. Urban Green. 58 , 126972 (2021).

Wong, N. H., Tan, C. L., Kolokotsa, D. D. & Takebayashi, H. Greenery as a mitigation and adaptation strategy to urban heat. Nat. Rev. Earth Environ. 2 , 166–181 (2021).

United Nations. Department of economic and social affairs, population division. in The World’s Cities in 2018—Data Booklet (UN, 2018).

United Nations Development Programme (UNDP). Human Development Report 2019: Beyond Income, Beyond Averages, Beyond Today: Inequalities in Human Development in the 21st Century (United Nations Development Programme (UNDP), 2019)

Li, X. et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environ. Res. Lett. 15 , 094044 (2020).

Stevens, F. R., Gaughan, A. E., Linard, C. & Tatem, A. J. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS ONE 10 , e0107042 (2015).

Buchhorn, M. et al. Copernicus global land cover layers—collection 2. Remote Sens 12 , 1044 (2020).

Gillerot, L. et al. Forest structure and composition alleviate human thermal stress. Glob. Change Biol. 28 , 7340–7352 (2022).

Article   CAS   Google Scholar  

Hamada, S., Tanaka, T. & Ohta, T. Impacts of land use and topography on the cooling effect of green areas on surrounding urban areas. Urban For. Urban Green. 12 , 426–434 (2013).

Sun, X. et al. Quantifying landscape-metrics impacts on urban green-spaces and water-bodies cooling effect: the study of Nanjing, China. Urban For . Urban Green. 55 , 126838 (2020).

Zhang, Q., Zhou, D., Xu, D. & Rogora, A. Correlation between cooling effect of green space and surrounding urban spatial form: Evidence from 36 urban green spaces. Build. Environ. 222 , 109375 (2022).

Pesaresi, M., Politis, P. GHS-BUILT-H R2023A - GHS building height, derived from AW3D30, SRTM30, and Sentinel2 composite (2018) . European Commission, Joint Research Centre (JRC) https://doi.org/10.2905/85005901-3A49-48DD-9D19-6261354F56FE (2023).

Yamazaki, D. et al. A high‐accuracy map of global terrain elevations. Geophys. Res. Lett. 44 , 5844–5853 (2017).

Wessel, P. & Smith, W. H. A global, self‐consistent, hierarchical, high‐resolution shoreline database. J. Geophys. Res. Solid Earth 101 , 8741–8743 (1996).

Ren et al. climatic map studies: a review. Int. J. Climatol. 31 , 2213–2233 (2011).

Zhou, X. et al. Evaluation of urban heat islands using local climate zones and the influence of sea-land breeze. Sust. Cities Soc. 55 , 102060 (2020).

Zhou, W., Huang, G. & Cadenasso, M. L. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc. Urban Plan 102 , 54–63 (2011).

Muñoz Sabater, J. ERA5-Land monthly averaged data from 1981 to present . Copernicus Climate Change Service (C3S) Climate Data Store (CDS) https://doi.org/10.24381/cds.68d2bb30 (2019).

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5 , 1–12 (2018).

Kummu, M., Taka, M. & Guillaume, J. H. Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. Sci. Data 5 , 1–15 (2018).

Zanaga, D. et al. ESA WorldCover 10 m 2020 v100. https://doi.org/10.5281/zenodo.5571936 (2021).

McNally, A. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. Data 4 , 1–19 (2017).

Schaaf C., & Wang Z. MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V061 . NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD43A3.061 (2021).

Lyapustin A., & Wang Y. MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V061 . NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD19A2.061 (2022).

Li, M., Wang, Y., Rosier, J. F., Verburg, P. H. & Vliet, J. V. Global maps of 3D built-up patterns for urban morphological analysis. Int. J. Appl. Earth Obs. Geoinf. 114 , 103048 (2022).

Google Scholar  

Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C. & Ghosh, T. VIIRS night-time lights. Int. J. Remote Sens. 38 , 5860–5879 (2017).

Zhou, W. et al. Urban tree canopy has greater cooling effects in socially vulnerable communities in the US. One Earth 4 , 1764–1775 (2021).

Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5 , 1–12 (2018).

R. Core Team. R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2023).

Fox J., & Weisberg S. An R Companion to Applied Regression 3rd edn (Sage, 2019). https://socialsciences.mcmaster.ca/jfox/Books/Companion/ .

Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7 , 573–579 (2016).

Zeileis, A. _ineq: Measuring Inequality, Concentration, and Poverty_ . R package version 0.2-13. https://CRAN.R-project.org/package=ineq (2014).

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Acknowledgements

We thank all the data providers. We thank Marten Scheffer for valuable discussion. C.X. is supported by the National Natural Science Foundation of China (Grant No. 32061143014). J.-C.S. was supported by Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173), and his VILLUM Investigator project “Biodiversity Dynamics in a Changing World”, funded by VILLUM FONDEN (grant 16549). W.Z. was supported by the National Science Foundation of China through Grant No. 42225104. T.M.L. and J.F.A. are supported by the Open Society Foundations (OR2021-82956). W.J.R. is supported by the funding received from Roger Worthington.

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Y.L., S.N.T., R.R.D., and C.X. designed the study. Y.L. collected the data, generated the code, performed the analyses, and produced the figures with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., Z.Y., S.N.T., R.R.D. and C.X. Y.L., S.N.T., R.R.D. and C.X. wrote the first draft with inputs from J.-C.S., W.Z., K.Z., J.F.A., T.M.L., W.J.R., and Z.Y. All coauthors interpreted the results and revised the manuscript.

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Li, Y., Svenning, JC., Zhou, W. et al. Green spaces provide substantial but unequal urban cooling globally. Nat Commun 15 , 7108 (2024). https://doi.org/10.1038/s41467-024-51355-0

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Asch & Variables for Conformity ( AQA A Level Psychology )

Revision note.

Head of Humanities & Social Sciences

Asch & Variables for Conformity

Variables affecting conformity include group size, unanimity and task difficulty as investigated by Asch.

Asch 1951:  A classic study of conformity

Asch wanted to investigate whether people would conform to the majority in situations where an answer was obvious

• Participants were tested in groups of 6 to 8 
• Each group was presented with a standard line and three comparison lines
• Participants had to say aloud which comparison line matched the standard line in length
• In each group there was only one genuine (naive) participant the remaining were confederates
• The genuine participant was seated second to last and did not know the other participants were fake participants
• The fake confederate participants all gave the same incorrect answer
• Confederates were told to give the incorrect answer on 12 out of 18 trails
• Genuine participants conformed a third of the time
• 75% of the sample conformed to the majority on at least one trial
• 25% of participants never gave a wrong answer, which shows there were individual differences

Artificial situation and task 

• One limitation of Asch's reach is that it is artificial in both task and situation
• Participants may have gone along with what was expected as they knew they were in a research study (Demand Characteristics) 
• The task was trivial and did not impact the participants in their 'real life', which means there was no reason not to conform
• Findings do not generalise to real-world situations, especially where there could be important consequences to conformity

Limited application 

• Another limitation, Asch's participants were all men from the USA 
• Other research has suggested that women may be more conformist due to their concern with social relationships 
• The USA is an individualist culture (where people are concerned with themselves as the individual more so than in collectivist cultures where they are concerned with their social groups)
• Findings tell us little about how women or those from other cultures may confirm

Research support 

• One strength of Asch's research is it has been supported by other studies
• Lucas et al (2006) asked participants to solve easy and hard maths problems and found participants conformed to the wrong answer more often when the problems were hard
• This supports Asch's claim that task difficulty is one variable that effects conformity
• However, Lucas et al (2006) also found that conformity is more complex than suggested by Asch
• They found individual-level factors can influence conformity and those who were confident in their maths skills were less likely to conform
• Asch did not research the roles of individual factors 

Ethical issues

• The genuine (naive) participants were deceived as they thought the confederates were also participants
• However, it can be argued that this ethical cost does not outweigh the findings of the research

On a 16-mark question, you will be expected to describe Asch's study before evaluating it.

Keep this clear and concise to allow for the development of the evaluation.

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Methodology

• Confounding Variables | Definition, Examples & Controls

Confounding Variables | Definition, Examples & Controls

Published on May 29, 2020 by Lauren Thomas . Revised on June 22, 2023.

In research that investigates a potential cause-and-effect relationship, a confounding variable is an unmeasured third variable that influences both the supposed cause and the supposed effect.

It’s important to consider potential confounding variables and account for them in your research design to ensure your results are valid . Left unchecked, confoudning variables can introduce many research biases to your work, causing you to misinterpret your results.

Table of contents

What is a confounding variable, why confounding variables matter, how to reduce the impact of confounding variables, other interesting articles, frequently asked questions about confounding variables.

Confounding variables (a.k.a. confounders or confounding factors) are a type of extraneous variable that are related to a study’s independent and dependent variables . A variable must meet two conditions to be a confounder:

• It must be correlated with the independent variable. This may be a causal relationship, but it does not have to be.
• It must be causally related to the dependent variable.

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To ensure the internal validity of your research, you must account for confounding variables. If you fail to do so, your results may not reflect the actual relationship between the variables that you are interested in, biasing your results.

For instance, you may find a cause-and-effect relationship that does not actually exist, because the effect you measure is caused by the confounding variable (and not by your independent variable). This can lead to omitted variable bias or placebo effects , among other biases.

Even if you correctly identify a cause-and-effect relationship, confounding variables can result in over- or underestimating the impact of your independent variable on your dependent variable.

There are several methods of accounting for confounding variables. You can use the following methods when studying any type of subjects— humans, animals, plants, chemicals, etc. Each method has its own advantages and disadvantages.

Restriction

In this method, you restrict your treatment group by only including subjects with the same values of potential confounding factors.

Since these values do not differ among the subjects of your study, they cannot correlate with your independent variable and thus cannot confound the cause-and-effect relationship you are studying.

• Relatively easy to implement
• Restricts your sample a great deal
• You might fail to consider other potential confounders

In this method, you select a comparison group that matches with the treatment group. Each member of the comparison group should have a counterpart in the treatment group with the same values of potential confounders, but different independent variable values.

This allows you to eliminate the possibility that differences in confounding variables cause the variation in outcomes between the treatment and comparison group. If you have accounted for any potential confounders, you can thus conclude that the difference in the independent variable must be the cause of the variation in the dependent variable.

• Allows you to include more subjects than restriction
• Can prove difficult to implement since you need pairs of subjects that match on every potential confounding variable
• Other variables that you cannot match on might also be confounding variables

Statistical control

If you have already collected the data, you can include the possible confounders as control variables in your regression models ; in this way, you will control for the impact of the confounding variable.

Any effect that the potential confounding variable has on the dependent variable will show up in the results of the regression and allow you to separate the impact of the independent variable.

• Easy to implement
• Can be performed after data collection
• You can only control for variables that you observe directly, but other confounding variables you have not accounted for might remain

Randomization

Another way to minimize the impact of confounding variables is to randomize the values of your independent variable. For instance, if some of your participants are assigned to a treatment group while others are in a control group , you can randomly assign participants to each group.

Randomization ensures that with a sufficiently large sample, all potential confounding variables—even those you cannot directly observe in your study—will have the same average value between different groups. Since these variables do not differ by group assignment, they cannot correlate with your independent variable and thus cannot confound your study.

Since this method allows you to account for all potential confounding variables, which is nearly impossible to do otherwise, it is often considered to be the best way to reduce the impact of confounding variables.

• Allows you to account for all possible confounding variables, including ones that you may not observe directly
• Considered the best method for minimizing the impact of confounding variables
• Most difficult to carry out
• Must be implemented prior to beginning data collection
• You must ensure that only those in the treatment (and not control) group receive the treatment

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

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Conformity - Asch (1951)

Last updated 6 Sept 2022

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Asch (1951) conducted one of the most famous laboratory experiments examining conformity. He wanted to examine the extent to which social pressure from a majority, could affect a person to conform.

Asch’s sample consisted of 50 male students from Swarthmore College in America, who believed they were taking part in a vision test. Asch used a line judgement task, where he placed on real naïve participants in a room with seven confederates (actors), who had agreed their answers in advance. The real participant was deceived and was led to believe that the other seven people were also real participants. The real participant always sat second to last.

In turn, each person had to say out loud which line (A, B or C) was most like the target line in length.

Unlike Jenness’ experiment , the correct answer was always obvious. Each participant completed 18 trials and the confederates gave the same incorrect answer on 12 trials, called critical trials. Asch wanted to see if the real participant would conform to the majority view, even when the answer was clearly incorrect.

Asch measured the number of times each participant conformed to the majority view. On average, the real participants conformed to the incorrect answers on 32% of the critical trials. 74% of the participants conformed on at least one critical trial and 26% of the participants never conformed. Asch also used a control group, in which one real participant completed the same experiment without any confederates. He found that less than 1% of the participants gave an incorrect answer.

Asch interviewed his participants after the experiment to find out why they conformed. Most of the participants said that they knew their answers were incorrect, but they went along with the group in order to fit in, or because they thought they would be ridiculed. This confirms that participants conformed due to normative social influence and the desire to fit in.

Evaluation of Asch

Asch used a biased sample of 50 male students from Swarthmore College in America. Therefore, we cannot generalise the results to other populations, for example female students, and we are unable to conclude if female students would have conformed in a similar way to male students. As a result Asch’s sample lacks population validity and further research is required to determine whether males and females conform differently

Furthermore, it could be argued that Asch’s experiment has low levels of ecological validity . Asch’s test of conformity, a line judgement task, is an artificial task, which does not reflect conformity in everyday life. Consequently, we are unable to generalise the results of Asch to other real life situations, such as why people may start smoking or drinking around friends, and therefore these results are limited in their application to everyday life.

Finally, Asch’s research is ethically questionable. He broke several ethical guidelines , including: deception and protection from harm . Asch deliberately deceived his participants, saying that they were taking part in a vision test and not an experiment on conformity. Although it is seen as unethical to deceive participants, Asch’s experiment required deception in order to achieve valid results. If the participants were aware of the true aim they would have displayed demand characteristics and acted differently. In addition, Asch’s participants were not protected from psychological harm and many of the participants reporting feeling stressed when they disagreed with the majority. However, Asch interviewed all of his participants following the experiment to overcome this issue.

• Normative Social Influence
• Task Difficulty

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The Asch Conformity Experiments

What These Experiments Say About Group Behavior

What Is Conformity?

Factors that influence conformity.

The Asch conformity experiments were a series of psychological experiments conducted by Solomon Asch in the 1950s. The experiments revealed the degree to which a person's own opinions are influenced by those of a group . Asch found that people were willing to ignore reality and give an incorrect answer in order to conform to the rest of the group.

At a Glance

The Asch conformity experiments are among the most famous in psychology's history and have inspired a wealth of additional research on conformity and group behavior. This research has provided important insight into how, why, and when people conform and the effects of social pressure on behavior.

Do you think of yourself as a conformist or a non-conformist? Most people believe that they are non-conformist enough to stand up to a group when they know they are right, but conformist enough to blend in with the rest of their peers.

Research suggests that people are often much more prone to conform than they believe they might be.

Imagine yourself in this situation: You've signed up to participate in a psychology experiment in which you are asked to complete a vision test.

Seated in a room with the other participants, you are shown a line segment and then asked to choose the matching line from a group of three segments of different lengths.

The experimenter asks each participant individually to select the matching line segment. On some occasions, everyone in the group chooses the correct line, but occasionally, the other participants unanimously declare that a different line is actually the correct match.

So what do you do when the experimenter asks you which line is the right match? Do you go with your initial response, or do you choose to conform to the rest of the group?

Conformity in Psychology

In psychological terms, conformity refers to an individual's tendency to follow the unspoken rules or behaviors of the social group to which they belong. Researchers have long been been curious about the degree to which people follow or rebel against social norms.

Asch was interested in looking at how pressure from a group could lead people to conform, even when they knew that the rest of the group was wrong. The purpose of the Asch conformity experiment was to demonstrate the power of conformity in groups.

Methodology of Asch's Experiments

Asch's experiments involved having people who were in on the experiment pretend to be regular participants alongside those who were actual, unaware subjects of the study. Those that were in on the experiment would behave in certain ways to see if their actions had an influence on the actual experimental participants.

In each experiment, a naive student participant was placed in a room with several other confederates who were in on the experiment. The subjects were told that they were taking part in a "vision test." All told, a total of 50 students were part of Asch’s experimental condition.

The confederates were all told what their responses would be when the line task was presented. The naive participant, however, had no inkling that the other students were not real participants. After the line task was presented, each student verbally announced which line (either 1, 2, or 3) matched the target line.

Critical Trials

There were 18 different trials in the experimental condition , and the confederates gave incorrect responses in 12 of them, which Asch referred to as the "critical trials." The purpose of these critical trials was to see if the participants would change their answer in order to conform to how the others in the group responded.

During the first part of the procedure, the confederates answered the questions correctly. However, they eventually began providing incorrect answers based on how they had been instructed by the experimenters.

Control Condition

The study also included 37 participants in a control condition . In order to ensure that the average person could accurately gauge the length of the lines, the control group was asked to individually write down the correct match. According to these results, participants were very accurate in their line judgments, choosing the correct answer 99% of the time.

Results of the Asch Conformity Experiments

Nearly 75% of the participants in the conformity experiments went along with the rest of the group at least one time.

After combining the trials, the results indicated that participants conformed to the incorrect group answer approximately one-third of the time.

The experiments also looked at the effect that the number of people present in the group had on conformity. When just one confederate was present, there was virtually no impact on participants' answers. The presence of two confederates had only a tiny effect. The level of conformity seen with three or more confederates was far more significant.

Asch also found that having one of the confederates give the correct answer while the rest of the confederates gave the incorrect answer dramatically lowered conformity. In this situation, just 5% to 10% of the participants conformed to the rest of the group (depending on how often the ally answered correctly). Later studies have also supported this finding, suggesting that having social support is an important tool in combating conformity.

At the conclusion of the Asch experiments, participants were asked why they had gone along with the rest of the group. In most cases, the students stated that while they knew the rest of the group was wrong, they did not want to risk facing ridicule. A few of the participants suggested that they actually believed the other members of the group were correct in their answers.

These results suggest that conformity can be influenced both by a need to fit in and a belief that other people are smarter or better informed.

Given the level of conformity seen in Asch's experiments, conformity can be even stronger in real-life situations where stimuli are more ambiguous or more difficult to judge.

Asch went on to conduct further experiments in order to determine which factors influenced how and when people conform. He found that:

• Conformity tends to increase when more people are present . However, there is little change once the group size goes beyond four or five people.
• Conformity also increases when the task becomes more difficult . In the face of uncertainty, people turn to others for information about how to respond.
• Conformity increases when other members of the group are of a higher social status . When people view the others in the group as more powerful, influential, or knowledgeable than themselves, they are more likely to go along with the group.
• Conformity tends to decrease, however, when people are able to respond privately . Research has also shown that conformity decreases if people have support from at least one other individual in a group.

Criticisms of the Asch Conformity Experiments

One of the major criticisms of Asch's conformity experiments centers on the reasons why participants choose to conform. According to some critics, individuals may have actually been motivated to avoid conflict, rather than an actual desire to conform to the rest of the group.

Another criticism is that the results of the experiment in the lab may not generalize to real-world situations.

Many social psychology experts believe that while real-world situations may not be as clear-cut as they are in the lab, the actual social pressure to conform is probably much greater, which can dramatically increase conformist behaviors.

Asch SE. Studies of independence and conformity: I. A minority of one against a unanimous majority . Psychological Monographs: General and Applied . 1956;70(9):1-70. doi:10.1037/h0093718

Morgan TJH, Laland KN, Harris PL. The development of adaptive conformity in young children: effects of uncertainty and consensus . Dev Sci. 2015;18(4):511-524. doi:10.1111/desc.12231

Asch SE. Effects of group pressure upon the modification and distortion of judgments . In: Guetzkow H, ed.  Groups, Leadership and Men; Research in Human Relations. Carnegie Press. 1951:177–190.

Britt MA. Psych Experiments: From Pavlov's Dogs to Rorschach's Inkblots . Adams Media. 

Myers DG. Exploring Psychology (9th ed.). Worth Publishers.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• Psychology , Psychology Experiments

The Asch Conformity Experiments: The Line Between Independence and Conformity

Your school is having students take their annual vision test, but to save time, they’re having multiple students go at once. In each group of four, students will go down a line and verbally give their answers. You’re at the end of the line, which means you give your answer last. The vision test is fairly uneventful for the most part as you all answer what letter is currently being shown. Then as you’re shown what’s clearly the letter “O,” something strange happens: the first student labels it a “Q.” Then the second says, “Q” as well. So does the third. It’s now your turn: what letter do you call out?

Do you stick to your answer, declaring your independence, or do you yield to group conformity? If you yield, do you truly believe that the rest of the students are right, or do you just not want to stand out? Dr. Solomon Asch found answers to such queries in what would later be called the Asch Conformity Experiments.

The Asch Conformity Experiments 

The Asch Conformity Experiments were instrumental in discovering much of what we know today about the pressures of group conformity. Asch and his colleagues studied if and how individuals give into or remain strong against group majority and the effects of the majority on beliefs and opinions. Many variations of his experiments have been conducted since, examining the effects of task importance, gender, race, age, and culture on the results. Thus, it can be argued that Asch inspired much of the research conducted on conformity and independence.

The Experiment

In 1951 at Swarthmore College, Dr. Solomon Asch conducted his first conformity experiment using white male college students. Groups of eight students would be shown a large card with a line on it, along with another card with lines labeled A, B, and C. Participants were asked to verbally answer which one of these lines matched the example line in length. No optical illusions were in play here. If participants were asked to complete the task all alone, they correctly answered practically every time.

Only one member of each group was an actual test subject. The rest were actors. Groups were asked to complete 18 trials of this “perception task.” For the first two trials, the actors would give the clearly correct answer, but for the remaining 12 trials, the actors would unanimously vote for a wrong answer. 

While a majority of test subjects’ responses remained correct in the actor condition, a significant minority of over one third conformed to the actors’ wrong answers. Further investigation found that only 25% of subjects always defied majority opinion, 5% were always swayed by the group, and the remaining 70% conformed on some trials. 

Interviews with the test subjects revealed that all of them had significant doubts on the legitimacy of the group’s answers, regardless of whether they yielded to them or not. Participants who conformed on one or more trials did so out of either informational conformity, i.e. they began to believe that the group must be right because so many of them were in agreement, or normative conformity, i.e. they still believed their own assessments were right but went along with the group so as to not stand out.

Applying It

Despite the fact that only a minority of the total responses were wrong, a majority of subjects gave into group pressure at some point during the experiment. In these trials, participants could clearly see what the correct answer was, yet almost all of them felt uncomfortable, nervous, and doubtful about going against the group. Imagine how much harder it must be to go against the majority on a less clear-cut issue, like who to vote for in an election or how to solve infrastructure problems. Furthermore, the actors making up the majority weren’t trusted officials, close friends, or family members. Sticking to a minority opinion when the group consists of loved ones or respected and trusted authorities is no easy feat. Even in groups with only four students, three people unanimously agreeing generated the same amount of pressure for conformity. Majorities, no matter their size or makeup, are persuasive.

Now, it might not seem particularly dangerous to give into majority opinion so long as you are only displaying normative conformity. After all, you still know you’re right. Yet what good are beliefs if they’re not acted upon? Bad decisions don’t cease to be wrong just because you recognize them as such. If you’re going to vote for a popular yet corrupt official, go along with group bullying, or steal because your friends insist you should, you’re still committing immoral acts. Your reasoning for doing so doesn’t absolve your guilt. Conforming to an incorrect majority still makes you incorrect, regardless of why you decide to conform.

Every participant, whether they conformed or not, doubted the accuracy of the group’s judgment. If you really think you’re right, stick to your initial judgement. It won’t be easy, but making a decision you yourself are proud of is more important. Who knows, maybe you’ll inspire others to join your side.

Think Further

• Why do we value conformity so much?
• When do you find yourself yielding to group pressure most often?
• Do you think a silent majority holds as much power as a vocal majority does? Explain your answer.

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Chapter 65 . Conformity: The Asch Experiment

Learning objectives.

Describe the results of the Solomon Asch experiment on conformity.

Identify social situations that create strong pressure to conform to a group standard.

Select the NEXT button to continue with the Review.

1. One of the most important principles in psychology is the power of social influence. The behavior of other people affects our behavior, and we feel pressure to make our actions match those of the people around us. This is called conformity —adjusting our thinking or behavior to coincide with a group standard.

2. The pressure to conform is very strong in ambiguous situations. If you don't know what to do, the best advice is to watch what others are doing and then imitate them. For example, if you don’t know which fork to use for each course of a banquet, you would probably conform to the behavior of the people around you.

3. But what about unambiguous situations? Research by social psychologist Solomon Asch was the first to demonstrate that people will conform to a group's judgment even if that judgment is clearly wrong. In Asch’s famous experiment , groups of six or seven people were given a visual perception test. Each participant was asked to decide which of three comparison lines was the same length as a standard line, and then call out that judgment for the experimenter to record.

4. All but one of the participants in each testing group was paid by the experimenter to give the wrong answer on some trials, such as claiming that line 3 is the correct answer in this example. Surprisingly, more than two-thirds of the “real” participants gave in to group pressure on at least some of the trials. They denied the evidence from their own eyes, and conformed by agreeing with the group’s incorrect answer.

5. Asch found that the pressure to conform was greatest when the behavior was public and the group was unanimous. If even one other person defied the group, the "real" participant was empowered to give the correct answer rather than go along with the group's incorrect answer. If the “real” participant could answer privately, that person almost always gave the correct answer.

Practice 1: Simulating the Asch Experiment

Select the PLAY button to view an animation of the first two trials of the experiment.

Here are the six participants in Asch's study. The experimenter has explained the instructions, and the participants are ready to begin the experiment . The person with the brown shirt (in position 5) is the only real participant—all the others are accomplices of the experimenter who have been instructed to give the correct answer for the first two trials, and the wrong answer for all remaining trials.

Practice 2: The Critical Trials

Select the PLAY button to view an animation of the next four trials of the experiment.

How would you respond when the other people in the group begin giving the wrong answer? Would you stand firm and ignore the others, or would you cave in to group pressure and conform to the group's judgment? For Asch, the next several trials were critical for finding the answer to these questions.

Practice 3: The Results

Select the NEXT button and move to Quiz 1.

Asch found that, when the accomplices unanimously agreed on an obviously incorrect judgment, the real participants conformed to the group decision and reported the group's wrong answer more than one-third of the time. Apparently, the desire to be "like everybody else" was more powerful than the desire to be accurate.

The real participants had an error rate of 37 percent in this group setting, compared with an error rate of less than 1 percent when other participants were tested individually on this same task.

Even more striking, in the group setting, 74 percent of the real participants conformed on at least one of the critical trials!

Select a button to indicate whether each statement is True or False . When responses have been placed for all the statements, select the CHECK ANSWER button.

Answer the question. Then, select the CHECK ANSWER button.

The following experiment scenarios are variations on the original Asch experiment. There is only one real participant. The others are accomplices paid to give a specified answer. All participants answered in order (1 through 6).

Experiment 1: The real participant was seated in position 5. Everything was identical to the original Asch experiment, except that the person in position 2 always gave the correct answer.

Experiment 2: The real participant was seated in position 1. Everything else was identical to the original Asch experiment.

Experiment 3: The real participant was seated in position 5. Everything was identical to the original Asch experiment, except that the experiment had already started by the time the real participant arrived, so the real participant was allowed to write the answers on paper rather than call them out.

Experiment 4: The real participant was seated in position 6. Everything else was identical to the original Asch experiment.

Asch Conformity Experiments: Line Study

Categories Social Psychology

Will people conform to the group’s opinions, even if they disagree? That was the question behind one of the most famous experiments in psychology history. The Asch conformity experiments were a series of studies by social psychologist Solomon Asch during the 1950s. In the studies, Asch sought to learn more about how social pressure could lead to conformity .

In the studies, people were asked to choose a line that matched the length of another line. When the others in the group chose the incorrect line, participants would often conform to the rest of the group, even though they were clearly wrong.

The experiments are classic studies in social psychology, offering important insights into when and why people conform to group norms and pressures.

Table of Contents

The Asch Experiments

In the main version of the experiment, Asch told the participants that they were taking part in a vision test. Each participant was then placed in a group of people who were actually confederates in the study. In other words, they were actors who were involved in the experiment.

The group was shown a line on a card and then another card with several lines of varying lengths. They were asked to pick the line that matched the first line.

It was a simple task. When asked on their own, almost all participants were able to easily perform the task correctly. When they were in the group, and the confederates gave the wrong answers, the participants were often go along with the group.

Results of the Asch Conformity Experiments

The results of the Asch conformity experiments were startling. They revealed that a staggering 75% of the participants conformed to the group at least once. Even more surprising, about 25% never conformed, while 5% conformed every single time.

For the control group, where people faced no social pressure, incorrect responses were given less than 1% of the time.

Explanations for the Results

What explains the high rates of conformity in Asch’s experiments? There are several important psychological factors at work. The reasons people went along with the group even when they knew the others were wrong come down to several reasons:

Normative Social Influence

People have a desire for social acceptance. They want to fit in with the group and prefer not to stand out. By agreeing with the rest of the group, they increase the likelihood of being liked and accepted by others.

The fear of embarrassment can also play a role. Being the only one to voice a different answer comes with the risk of appearing foolish or being ridiculed. Even if people knew they were right, fear of social disapproval caused them to conform.

Informational Social Influence

When making decisions under uncertainty, people often look to other people as a source of information. If other people say one thing is correct, people often assume that others know something they don’t, which is why they conform.

Self-doubt in these situations can also play a role. Once others started choosing the wrong answer, the participants may have started to question their response and wondered if they had overlooked something.

Other Factors That Can Influence Conformity

There are also a number of other factors that can affect the likelihood that people with conform like they did Asch conformity experiments.

These include:

• Group size : Conformity usually increases with group size, at least up to a certain point. When 3 to 5 people are present, there is a lot of pressure to conform. When the number of people exceeds that, conformity typically starts to decline.
• Status : People are more likely to conform if the others in the group are seen as having a higher status, more authority, or greater expertise.
• Privacy of responses : People are more inclined to conform if their responses are public. When responses are private, conformity rates drop.
• Uncertainty and difficulty : If the task is ambiguous or difficult, people are less likely to trust their own judgment. They will often look to others for information and assurance, which increases conformity.
• Group unity : Conformity is higher in very cohesive groups. The stronger the bonds between group members, the more likely people are to conform.

In a 2023 replication of Asch’s conformity experiment, researchers found an error rate of 33%, similar to the one in Asch’s original study. They found that offering monetary incentives helped reduce errors but didn’t eliminate the effects of social influence. The study also found that social influence impacted political opinions, leading to a conformity rate of 38% (Franzen & Mader, 2023).

The study also examined how Big Five personality factors might be linked to conformity. While openness was associated with susceptibility to group pressure, other personality traits were not significantly connected.

One 2018 experiment found that the social delivery of information caused 33% of participants to change their political opinions (Mallinson & Hatemi, 2018).

Critiques of the Asch Conformity Experiments

While influential, the Asch experiments were not without criticism. Some of the main criticisms hinge on the following:

• The impact of demand characteristics : Some critics suggest that some participants may have suspected the study’s real intentions and behaved to meet the experimenter’s expectations.
• Lack of relevance in the real world : Critics also suggest that the experimental setup needed to be more contrived and accurately reflect real-world situations where conformity might occur.
• Cultural factors : The time and place of the experiments (the United States and during the 1950s) may also have contributed to the high conformity rates. During that time, conformity to American norms and values was highly valued. Such characteristics may not be universal to other places and periods.
• Simplified approach: While Asch’s experiments demonstrate one aspect of conformity (normative social influence), they don’t address the many other factors that can contribute to this behavior in real-world settings.

Impact and Contributions of the Asch Conformity Experiments

Asch’s conformity experiments had a major impact on the field of psychology. They helped inspire further research on conformity, compliance, and obedience.

The studies demonstrated that conformity is not just about fear of punishment ; it often comes from a deep psychological need for acceptance and group harmony.

These findings have influenced a wide range of fields, from understanding peer pressure and decision-making in groups to exploring the dynamics of social behavior in various cultural and political contexts. Asch’s experiments remain a cornerstone in social psychology , shaping how we think about the relationships between individual judgment and group influence.

Related reading:

• Classic Psychological Experiments
• The Robbers Cave Experiment
• Kohlberg’s Stages of Moral Development
• What Is the Ingroup Bias?

Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority . Psychological Monographs: General and Applied , 70(9), 1–70. https://doi.org/10.1037/h0093718

Franzen, A., & Mader, S. (2023). The power of social influence: A replication and extension of the Asch experiment . PloS one , 18 (11), e0294325. https://doi.org/10.1371/journal.pone.0294325

Levine J. M. (1999). Solomon Asch’s legacy for group research . Personality and Social Psychology Review : An Official Journal of the Society for Personality and Social Psychology, Inc , 3 (4), 358–364. https://doi.org/10.1207/s15327957pspr0304_5

Mallinson, D. J., & Hatemi, P. K. (2018). The effects of information and social conformity on opinion change . PloS One , 13 (5), e0196600. https://doi.org/10.1371/journal.pone.0196600

A Wealth of Free Psychology!

Asch (1955) – opinions and social pressure – conformity experiment, asch, s. e. (1955) ‘opinions and social pressure’,  scientific american  193 (5), 31-5..

This is the second study we will be looking at from the ‘reaching a verdict’ section of ‘reaching a verdict’ , as part of your OCR A2 Forensic Psychology course. It is further categorised into ‘ Majority Influence ‘

In this classic social psychology experiment Solomon Asch looked at conformity: particularly the influence of the majority on the minority.

This is one of the most influential and well-known studies in Psychology. It looks at conformity, which of course means that this study was conducted from a Social Psychological perspective. However, as this is from the Forensic Section of your OCR A2 Psychology exam, then we need to consider it from a forensic perspective. In this case how to do juries come to either a unanimous or majority decision.

“Exactly what is the effect of the opinions of others on our own?”

– Asch (1955)

Asch’s aim was to investigate the effects of conformity to a majority when the task is unambiguous.

Method and Design

A laboratory experiment. The participants were told that this would be a ‘vision test.’

Participants

50 male participants from Swarthmore College in the USA.

Asch (1955) tested single participants in a room full of confederates. In some cases all the confederates would answer with a unanimous wrong answer, for example in the picture above all the confederates would answer B, as opposed to the correct answer C.

Asch (1955) had arranged for the naïve participants to be asked the question: ‘which of the three lines, A, B or C, matches the stimulus line X?’  In all the conditions the confederates answered the question first and gave clearly wrong answers.

Asch (1955) found that individuals conformed on one out of three occasions. This finding of approximately 32% conformity is a robust one until just one stooge in the group is instructed disrupt this conformity when it falls to about 5%. Another finding is that majorities bigger than three make very little difference to the conformity effect, which may be because three is enough to create a group norm, while two would be insufficient.

Asch   (1955) Evaluation

– Androcentrism and ethnocentrism – as the study comprised of only 50 male students from the United States, it is difficult to generalise the results to other populations. If we think of a similar experiment: Milgram (1963) used a very similar sample, but later when the study was replicated in other areas, it was found that many other places had differing rates of obedience, with Australia having the lowest rate of obedience.

+ Validity – the independent variable was clearly manipulated with few extraneous and confounding variables, therefore we can argue that the experiment is high in internal validity.

+ Predictive Validity – the predictive validity of this study is high.

– Generalisability – the results of this study may be hard to generalise to a forensic environment.  Remember in this experiment there was no consequences for responding with the wrong answer, but this is not the case for juries.

Audio Podcast

Asch, S. E. (1995) ‘Opinions and Social Pressure’, Scientific American 193 (5), 31-5.

Further Reading

Decoding Conformity: Alternatives and Consequences in Asch’s Experiments

Table of Contents

Have you ever agreed with a group despite your own differing opinion, simply to avoid standing out? This phenomenon is at the heart of Asch’s conformity experiments, a classic study in social psychology . Solomon Asch ’s experiments from the 1950s still resonate today, revealing the powerful influence of social pressure on our decisions. In this exploration, we’ll unravel the intricate dance between conformity and independence, and the consequences that come with the choices we make under the watchful eye of society.

The Asch Conformity Experiments Explained

Solomon Asch’s experiments placed participants in a group setting where they were asked to compare the length of lines. Unbeknownst to the subject, the group was comprised of actors instructed to give incorrect answers. The true test was whether the subject would conform to the group’s wrong consensus or trust their own perception. The results were startling, with a significant number of participants choosing to conform to the incorrect majority.

The Conflict of Choice: Conformity vs. Independence

Participants in Asch’s study faced a dilemma: conform to the majority and avoid the discomfort of being different, or uphold their independence by trusting their senses despite potential ostracism. This conflict mirrors everyday situations where social influence is at play. Whether it’s following fashion trends or adhering to group ideologies, the tension between fitting in and standing out is a fundamental aspect of human behavior.

Conformity: The Path of Least Resistance

• Fear of Rejection : The discomfort of potential social rejection often leads individuals to conform.
• Desire for Harmony : Aiming for group harmony, individuals may silence their dissenting opinions.
• Uncertainty : In ambiguous situations, looking to others can provide a sense of guidance.

Independence: The Road Less Traveled

• Trust in Perception : Relying on one’s senses can be empowering, affirming self-confidence and judgment.
• Social Costs : Standing against the group may lead to isolation or marginalization.
• Long-term Benefits : Upholding personal integrity can build resilience and foster leadership qualities.

Insights into Human Behavior

The Asch experiments are more than a psychological curiosity; they offer profound insights into human social behavior. Conformity can be seen as a social survival tactic , while independent thought often drives innovation and progress. Understanding this dynamic helps us navigate complex social landscape s, from the workplace to social movements .

Social Pressure in Action

Social pressure is omnipresent, shaping decisions in subtle and overt ways. It can influence voting behavior, consumer choices, and even moral judgments. Recognizing the mechanisms of social influence is the first step toward mindful decision-making .

The Value of Independent Thought

While conformity often gets a bad rap, independent thought is celebrated as a hallmark of progress. It’s the force behind challenging the status quo , fostering critical thinking , and leading societal change. Encouraging independence can cultivate creativity and innovation in various domains.

Navigating the Social Maze: Strategies for Balancing Conformity and Independence

Finding the balance between conforming for social cohesion and asserting independence for personal integrity is a delicate act. Here are some strategies to navigate this social maze:

Cultivating Self-Awareness

• Reflection : Regularly reflect on decisions to determine if they’re a result of personal belief or social pressure.
• Self-Confidence : Building self-confidence can reduce the need for external validation.

Developing Critical Thinking

• Questioning Norms : Learn to question social norms and consider the rationale behind them.
• Seeking Information : Gather diverse opinions and information to make well-informed decisions.

Choosing Your Battles

• Prioritization : Not all situations require taking a stand. Choose the moments when independence is most valuable.
• Impact Assessment : Consider the potential impact of conformity versus independence on personal and social levels.

The legacy of Asch’s conformity experiments endures because it shines a light on the human condition. Our choices, whether swayed by the group or guided by our convictions, define us and the society we live in. By understanding the dynamics of conformity and independence, we can better navigate the social pressures that shape our lives and make decisions that align with our values and beliefs.

What do you think? Have you ever found yourself conforming to a group against your better judgment? How do you strike a balance between fitting in and standing out? Share your experiences and thoughts on the fine line between conformity and independence.

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Social Psychology

1 Definition, Concept and Research Methods in Social Psychology

• Definition and Concept of Social Psychology
• Research Methods in Social Psychology
• Experimental Methods
• Non-Experimental Methods
• Other Research Methods
• Research Ethics

2 Historical Perspective of Social Psychology, Social Psychology and Other Related Disciplines

• Historical Perspective
• Landmarks in the History of Social Psychology
• Social Psychology and Other Related Disciplines
• Significance of Social Psychology Today

3 Social and Person Perception – Definition, Description and Functional Factors

• Social Cognition – Description and Nature
• Social Perception – Definition
• Understanding Temporary States
• Understanding of the Most Permanent or Lasting Characteristics – Attributions
• Impression Formation
• Implicit Personality Theory
• Person Perception
• Social Categorisation

4 Cognitive Basis and Dynamics of Social Perception and Person Perception

• Cognitive and Motivational Basis of Social and Person Perception
• Bias in Attribution
• Role of Emotions and Motivation in Information Processing
• Motivated Person Perception
• Effect of Cognitive and Emotional States

5 Definition, Concept, Description, Characteristic of Attitude

• Defining Attitudes
• Attitudes, Values, and Beliefs
• Formation of Attitudes
• Functions of Attitudes

6 Components of Attitude

• ABCs of Attitudes
• Properties of Attitudes

7 Predicting Behaviour from Attitude

• Relationship between Attitude and Behaviour
• Attitudes Predict Behaviour
• Attitudes Determine Behaviour?
• Behaviour Determine Attitudes

8 Effecting Attitudinal Change and Cognitive Dissonance Theory, Compliance of Self-perception Theory, Self-affirmation

• Self Presentation
• Cognitive Dissonance
• Self Perception
• Self Affirmation

9 Introduction to Groups- Definition, Characteristics and Types of Groups

• Groups-Definition Meaning and Concepts
• Characteristics Features of Group
• Types of Group
• The Role of Groups

10 Group Process- Social Facilitation, Social Loafing, Group Interaction, Group Polarization and Group Mind

• Social Facilitation
• Social Loafing
• Group Interaction
• Group Polarization

11 Group Behaviour- Influence of Norms, Status and Roles; Introduction to Crowd Behavioural Theory, Crowd Psychology (Classical and Convergence Theories)

• Human Behaviour in Groups
• Influence of Norms Status and Roles
• Crowd Behavioural Theory
• Crowd Psychology

12 Crowd Psychology- Collective Consciousness and Collective Hysteria

• Crowd: Definition and Characteristics
• Crowd Psychology: Definition and Characteristics
• Collective Behaviour
• Collective Hysteria

13 Definition of Norms, Social Norms, Need and Characteristics Features of Norms

• Meaning of Norms
• Types of Norms
• Violation of Social Norms
• Need and Importance of Social Norms
• Characteristic Features of Social Norms

14 Norm Formation, Factors Influencing Norms, Enforcement of Norms, Norm Formation and Social Conformity

• Norm Formation
• Factors Influencing Norm Formation
• Enforcement of Norms
• Social Conformity

15 Autokinetic Experiment in Norm Formation

• Autokinetic Effect
• Sherif’s Experiment
• Salient Features of Sherif’s Autokinetic Experiments
• Critical Appraisal
• Related Latest Research on Norm Formation

16 Norms and Conformity- Asch’s Line of Length Experiments

• Solomon E. Asch – A Leading Social Psychologist
• Line and Length Experiments
• Alternatives Available with Probable Consequences
• Explanation of the Yielding Behaviour
• Variants in Asch’s Experiments
• Salient Features
• Related Research on Asch’s Findings

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The Asch Line Study (+3 Conformity Experiments)

Sheep. Complicit. Doormat. There are a lot of negative connotations associated with conformity, especially in the United States. Individualist societies push back on "going along with what everyone is doing." And yet, we conform more than we think. According to studies like the Asch Line Study, humans have a natural tendency to conform.

The Asch Line Study is one of the most well-known experiments in modern psychology, but it's not without its faults. Keep reading to learn about how the Asch Line Study worked, its criticisms, and similar experiments!

How Did the Asch Line Study Work?

In his famous “Line Experiment”, Asch showed his subjects a picture of a vertical line followed by three lines of different lengths, one of which was obviously the same length as the first one. He then asked subjects to identify which line was the same length as the first line.

Solomon Asch used 123 male college students as his subjects, and told them that his experiment was simply a ‘vision test’. For his control group, Asch just had his subjects go through his 18 questions on their own.

However, for his experimental group, he had his subjects answer each of the same 18 questions in a group of around a dozen people, where the first 11 people intentionally said obviously incorrect answers one after another, with the final respondee being the actual subject of the experiment.

Who was Solomon Asch?

Solomon E. Asch was a pioneer in social psychology. He was born in Poland in 1907 and moved to the United States in 1920. Asch received his Ph.D. from Columbia University in 1932 and went on to perform some famous psychological experiments about conformity in the 1950s.

One of these studies is known as the “Asch Line Experiment”, where he found evidence supporting the idea that humans will conform to and accept the ideas of others around them, even if those ideas are obviously false. This study is one of the most influential studies in social psychology .

Findings of Asch's Conformity Study

​ Asch found that his subjects indeed were more likely to give a false response after the other members of their group (the actors) gave false responses. As shown in this ‘Table 1’ from his experiment, during 18 trials, the ‘Majority Error’ column shows no error when the group response was the correct response, such as in Trial #1.

However, when the entire group intentionally gave a false answer (these situations are designated with an * under the “Group Response” column), the ‘Majority Error’ did exist and was slanted toward the opinion of the group.

For example, if the group answered with a line that was too long, such as in Trial #3, the ‘Majority Error’ column shows that the subjects generally estimated the line to be longer than it really was (denoted with a ‘+’), and vice-versa for when the group answered with a line that was too short, such as in Trial #4.

As for his control group, Asch found that people generally said the correct answer when they did not have a group of actors saying answers before them.

Interviewing the Participants

​ After the experiment, Asch revealed the true experiment to his subjects and interviewed them. Some subjects had become very agitated during the experiment, wondering why they kept disagreeing with the group. When the group pressed one particular subject on why he thought that he was correct and the entire group was wrong, he replied defiantly, exclaiming: “You're probably right, but you may be wrong!”

Other subjects admitted during the interview that they changed their answers after hearing others in their group reply differently. One was recorded saying, “If I’d been the first I probably would have responded differently.” Another subject admitted, “...at times I had the feeling: 'to heck with it, I'll go along with the rest.' "

Conclusions from the Asch Line Study

​ Asch found that his subjects often changed their answers when they heard the rest of the group unanimously giving a different response.

After the interviews, Asch concluded in his study that his subjects conformed to the opinions of the group for three different reasons:

Distortion of perception due to the stress of group pressure: This group of subjects always agreed with the group and said during the interview that they wholeheartedly believed that their obviously incorrect answers were correct. Asch concluded that the stress of group pressure had distorted their perception.

Distortion of judgment: This was the most common outcome, where subjects assumed that their individual answers were incorrect after seeing the rest of the group answer differently, so they changed their answer to align with the group.

Distortion of action: These subjects never doubted that they were correct and the group was wrong, but out of fear of being perceived as different, they suppressed their opinions and intentionally lied when it was their turn to give an answer.

Asch Line Study vs. Milgram Experiment

Both the Asch Line Study and the Milgram Experiment look at conformity, obedience, and the negative effects of going along with the majority opinion. Those negative effects are slightly awkward, like in the Asch Line Study, or dangerous, like in the Milgram Experiment. Both experiments were conducted in the Post-WWII world as a response to the conformity that was required for Nazi Germany to gain power. The premise of Asch's study was not nearly as dramatic. Milgram's was. 

To test conformity, Milgram and his researchers instructed participants to press a button. Participants believed that the buttons would shock another "participant" in a chair, who was really an actor. (No one was shocked.) The study continued as long as participants continued to shock the participant at increasingly dangerous levels. The participants knew that they could cause serious harm to the person in the chair. Yet, many obeyed.

Further Experiments and Variations

Solomon Asch didn't just conduct one experiment and move on. He replicated his experiment with new factors, including:

• Changing the size of the actor group
• Switching to a non-unanimous actor group
• Having a unanimous actor group, except for one actor who sticks to the correct response no matter what the group or subject says
• Instructing the one actor who gives the correct response come in late
• Having one actor decide to change their answer from the group’s answer to the subject’s answer

There are also many reproductions and replications of this study online. Not all of them come to the same conclusions! Read through the following texts to get a sense of how other psychologists approached this subject:

• Mori K, Arai M. No need to fake it: reproduction of the Asch experiment without confederates. Int J Psychol. 2010 Oct 1;45(5):390-7. doi: 10.1080/00207591003774485. PMID: 22044061.

Why Is The Asch Line Study Ethnocentric? And Other Criticisms

​ One big issue with the Asch line study is that the subjects were all white male college students between the ages of 17 and 25, with a mean age of 20. Since the experiment only shows results for this small and specific group of people, it alone cannot be applied to other groups such as women or older men.

Experimenter Bias in the Asch Line Study

Only choosing subjects from one demographic is a form of Experimenter Bias . Of course, researchers can use one demographic if they are specifically studying that demographic. But Asch was not just looking at young, white men. If he had expanded his research to include more participants, he may have produced different answers.

We assume Asch did not go about his study with the intention of being biased. That's the tricky thing about biases. They sneak up on us! Even the way that we share information about psychology research is the result of bias. Reporter bias is the tendency to highlight certain studies due to their results. The Asch Line Study produced fascinating results. Therefore, psychology professors, reporters, and students find it fascinating and continue to share this concept. They don't always share the full story, though.

Did you know that 95% of the participants actually defied the majority at least once during the experiment? Most textbooks don't report that. Nor did they report that the interviewees knew that they were right all along! Leaving out this key information is not Asch's fault. But it should give you, a psychology student, some pause. One thing that we should take away from this study is that we have a natural tendency to conform. This tendency also takes place when we draw conclusions from famous studies! Be critical as you learn about these famous studies and look to the source if possible.

Further Criticisms of the Asch Line Study

Does the Asch Line Study stand the test of time? Not exactly. If we look at what was happening in 1950s society, we can see why Asch got his results. Young white men in the early 1950s may have responded differently to this experiment than young white men would today. In the United States, which is where this experiment was performed, the mid-1950s was a historic turning point in terms of rejecting conformity. Youth were pushing toward a more free-thinking society. This experiment was performed right around the time that the movement was just starting to blossom, so the subjects had not grown up in the middle of this new anti-conformist movement. Had Asch performed this experiment a decade later with youth who more highly valued free-thinking, he may have come across very different results.

Another thing to note is that, at least in the United States, education has evolved with this movement of encouraging free-thinking. Teachers today tell students to question everything, and many schools reject ideas of conformity. This could once again mean that, if done again today, Asch would have found very different results with this experiment.

Another problem with this experiment is that, since subjects were not told it was a psychological experiment until after it was over, subjects may have gone through emotional and psychological pain during what they thought was just a simple ‘vision test’.

Finally, it’s good to remember that the ‘Asch Line Experiment’ is just that: an experiment where people looked at lines. This can be hard to apply to other situations because humans in group settings are rarely faced with questions that have one such obvious and clear answer, as was the case in this experiment.

Related posts:

• Solomon Asch (Psychologist Biography)
• 40+ Famous Psychologists (Images + Biographies)
• Stanley Milgram (Psychologist Biography)
• Experimenter Bias (Definition + Examples)
• The Monster Study (Summary, Results, and Ethical Issues)

Reference this article:

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Confounding Variables in Psychology: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A confounding variable is an unmeasured third variable that influences, or “confounds,” the relationship between an independent and a dependent variable by suggesting the presence of a spurious correlation.

Confounding Variables in Research

Due to the presence of confounding variables in research, we should never assume that a correlation between two variables implies causation.

When an extraneous variable has not been properly controlled and interferes with the dependent variable (i.e., results), it is called a confounding variable.

For example, if there is an association between an independent variable (IV) and a dependent variable (DV), but that association is due to the fact that the two variables are both affected by a third variable (C). The association between IV and DV is extraneous.

Variable C would be considered the confounding variable in this example. We would say that the IV and DV are confounded by C whenever C causally influences both the IV and the DV.

In order to accurately estimate the effect of the IV on the DV, the researcher must reduce the effects of C.

If you identify a causal relationship between the independent variable and the dependent variable, that relationship might not actually exist because it could be affected by the presence of a confounding variable.

Even if the cause-and-effect relationship does exist, the confounding variable still might overestimate or underestimate the impact of the independent variable on the dependent variable.

Reducing Confounding Variables

It is important to identify all possible confounding variables and consider their impact of them on your research design in order to ensure the internal validity of your results.

Here are some techniques to reduce the effects of these confounding variables:

• Random allocation : randomization will help eliminate the impact of confounding variables. You can randomly assign half of your subjects to a treatment group and the other half to a control group. This will ensure that confounders have the same effect on both groups, so they cannot correlate with your independent variable.
• Control variables : This involves restricting the treatment group only to include subjects with the same potential for confounding factors. For example, you can restrict your subject pool by age, sex, demographic, level of education, or weight (etc.) to ensure that these variables are the same among all subjects and thus cannot confound the cause-and-effect relationship at hand.
• Within-subjects design : In a within-subjects design, all participants participate in every condition.
• Case-control studies : Case-control studies assign confounders to both groups (the experimental group and the control group) equally.

Suppose we wanted to measure the effects of caloric intake (IV) on weight (DV). We would have to try to ensure that confounding variables did not affect the results. These variables could include the following:

• Metabolic rate : If you have a faster metabolism, you tend to burn calories more quickly.
• Age : Age can affect weight gain differently, as younger individuals tend to burn calories quicker than older individuals.
• Physical Activity : Those who exercise or are more active will burn more calories and could weigh less, even if they consume more.
• Height : Taller individuals tend to need to consume more calories in order to gain weight.
• Sex : Men and women have different caloric needs to maintain a certain weight.

Frequently asked questions

1. what is a confounding variable in psychology.

A confounding variable in psychology is an extraneous factor that interferes with the relationship between an experiment’s independent and dependent variables . It’s not the variable of interest but can influence the outcome, leading to inaccurate conclusions about the relationship being studied.

For instance, if studying the impact of studying time on test scores, a confounding variable might be a student’s inherent aptitude or previous knowledge.

2. What is the difference between an extraneous variable and a confounding variable?

A confounding variable is a type of extraneous variable . Confounding variables affect both the independent and dependent variables. They influence the dependent variable directly and either correlate with or causally affect the independent variable.

An extraneous variable is any variable that you are not investigating that can influence the dependent variable.

3. What is Confounding Bias?

Confounding bias is a bias that is the result of having confounding variables in your study design. If the observed association overestimates the effect of the independent variable on the dependent variable, this is known as a positive confounding bias.

If the observed association underestimates the effect of the independent variable on the dependent variable, this is known as a negative confounding bias.

Glen, Stephanie. Confounding Variable: Simple Definition and Example. Retrieved from StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/experimental-design/confounding-variable/

Thomas, L. (2021). Understanding confounding variables. Scribbr. Retrieved from https://www.scribbr.com/methodology/confounding-variables/

University of Michigan. (n.d.). Confounding Variables. ICPSR. Retrieved from https://www.icpsr.umich.edu/web/pages/instructors/setups2012/exercises/notes/confounding-variable.html

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• Caveata: Notes On This Book
• Part I: Contemporary Psychology
• Chapter 1: The Holy Art of Skepticism
• The Purpose of this Book
• The Biggest Problem is You
• Jonathan Haidt
• Elephant and Rider: Dual Process Theory
• Moral Dumbfounding
• Moral Foundations Theory: The Righteous Mind
• But "They" Can be a Problem Too
• The Four Cognitive Artifices: Biases, Fallacies, Devices, and Distortions.
• Highlights: Cognitive Artifices You Should Know
• Confabulation
• The Dunning-Kreuger Effect
• The Fallacy Fallacy
• Approaches to Psychology
• Confirmation Bias
• Hume's Guillotine and the Positive/Normative Distinction
• KUDOs and Brainwashing, Reprise
• Chapter 2: Tools of the Scientist
• Oh Say, What Is Truth?
• Experimental Design and Experimental Psych
• Correlation and Causation
• The Placebo Effect
• Treatment, Control, and Random Assignment

Asch Conformity Experiment

• Peer Review
• Milgram Shock Experiment
• Stanford Prison Experiment
• Tools of the Scientist: The Statistics of Psychology
• Week 2.1 Core Content
• Week 2.1 Supplemental Content
• Week 2.2 Core Content
• Week 2.2 Supplemental Content
• Chapter 3: Becoming a Scholar - Practitioner
• Evidentiary Standards
• Major Research Methodologies
• Evidence-Based Practices
• Collective Teacher Efficacy
• Common Neuromyths of Education
• John Hattie and Visible Learning
• Science of Reading
• Instructional Hierarchy
• Week 3.1 Core Content
• Week 3.1 Supplemental Content
• Week 3.2 Core Content
• Week 3.2 Supplemental Content
• Behaviorism, Pavlov's Dogs, Token Economies
• Dual-coding theory
• Constructivism, Assimilation, and Accommodation
• Evidence Hierarchy in Academic Research
• The Replication Crisis
• The Great Inquiry Learning Debate
• Chapter 4: Toward a Better Psychology
• Social Media Impacts on Students/Children
• Social Media Impact on Mental Health
• Diagnosis Crisis
• Audience Capture
• Mental Health Crisis
• Individual Preventative Factors for Mental Illness
• Screeners vs. diagnostics
• Anxiety: Etiology, Diagnosis, Treatment
• ADHD: Etiology, Diagnosis, Treatment
• Depression: Etiology, Diagnosis, and Treatment
• Perfectionism: Etiology, Diagnosis, Treatment
• Scrupulosity: Etiology, Diagnosis, Treatment
• Efficacy: Pharmacology and Therapy
• Positive Psychology
• Part II: Developmental Psychology
• Attachment Styles
• Week 4.1 Supplemental Content
• Week 4.2 Supplemental Content
• Chapter 5: Child Development and Parenting
• Parenting Styles
• Critical and Sensitive Periods
• Dangers of Permissive Parenting
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Overview of Concepts

"the tendency to conformity in our society is so strong that reasonably intelligent and well-meaning young people are willing to call white black. this is a matter of concern. it raises questions about our ways of education and about the values that guide our conduct." , solomon asch.

“Solomon Eliot Asch was born September 14, 1907, in Warsaw, Poland.”  (Biography, Par. 3)

“Solomon Asch was born in Warsaw but emigrated to the United States in 1920  at the age of 13.” (Biography, Par. 5)

Solomon “attended the College of the City of New York and graduated with his  bachelor's degree in 1928.” (Biography, Par.6) He then went to Columbia  University, where he earned his master's degree in 1930 and a PhD in 1932. ( Biography, Par. 6)

“Solomon Asch began studying the impact of propaganda and indoctrination  while he was a professor at Brooklyn College's psychology department” during  WWII and Hitler’s reign. (Biography, Par. 7)

In 1951, Solomon Asch observed the extent to which the social pressure of the majority group would affect an individual. Asch was interested in observing how pressure from a group could lead people to conform. For his experiment, Asch asked seven of the eight participants in each group to answer the questions wrong to see if the other participant’s answers changed to match theirs.

He used used “50 male students from Swarthmore College and told them they were participating in a vision test.” (Experiments, Par. 3)

He put one participant in a line with seven other participants, who already knew what their answer would be when given the line test. The participant who was being tested sat at the end of the line.

There were 18 trials and the other 7 participants gave the incorrect answer 12 times.  (Experiments, Par. 5)

The Line Test  

Significance of the Asch Conformity Experiment

References  .

Asch Conformity Experiment. (2012, February 20). YouTube . https://youtu.be/NyDDyT1lDhA?si=S7HEEVVWycAomhfY

Cherry, K. (2023, November 14). Meet the social psychologist behind the conformity experiments. Verywell Mind . https://www.verywellmind.com/solomon-asch-biography-2795519

Cherry, K. (2023a, November 13). Asch’s seminal experiments showed the power of conformity. Verywell Mind . https://www.verywellmind.com/the-asch-conformity-experiments-2794996#:~:text=Asch%20was%20interested%20in%20looking,power%20of%20conformity%20in%20groups .

Modern Therapy. (2023, November 28). Asch Conformity Experiment explained. https://moderntherapy.online/blog-2/asch-conformity-experiment-explained

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This content is provided to you freely by BYU-I Books.

Access it online or download it at https://books.byui.edu/development_motivati/asch_conformity_experiment .

The Asch Conformity Experiments

What Solomon Asch Demonstrated About Social Pressure

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The Asch Conformity Experiments, conducted by psychologist Solomon Asch in the 1950s, demonstrated the power of conformity in groups and showed that even simple objective facts cannot withstand the distorting pressure of group influence.

The Experiment

In the experiments, groups of male university students were asked to participate in a perception test. In reality, all but one of the participants were "confederates" (collaborators with the experimenter who only pretended to be participants). The study was about how the remaining student would react to the behavior of the other "participants."

The participants of the experiment (the subject as well as the confederates) were seated in a classroom and were presented with a card with a simple vertical black line drawn on it. Then, they were given a second card with three lines of varying length labeled "A," "B," and "C." One line on the second card was the same length as that on the first, and the other two lines were obviously longer and shorter.

Participants were asked to state out loud in front of each other which line, A, B, or C, matched the length of the line on the first card. In each experimental case, the confederates answered first, and the real participant was seated so that he would answer last. In some cases, the confederates answered correctly, while in others, the answered incorrectly.

Asch's goal was to see if the real participant would be pressured to answer incorrectly in the instances when the Confederates did so, or whether their belief in their own perception and correctness would outweigh the social pressure provided by the responses of the other group members.

Asch found that one-third of real participants gave the same wrong answers as the Confederates at least half the time. Forty percent gave some wrong answers, and only one-fourth gave correct answers in defiance of the pressure to conform to the wrong answers provided by the group.

In interviews he conducted following the trials, Asch found that those that answered incorrectly, in conformance with the group, believed that the answers given by the Confederates were correct, some thought that they were suffering a lapse in perception for originally thinking an answer that differed from the group, while others admitted that they knew that they had the correct answer, but conformed to the incorrect answer because they didn't want to break from the majority.

The Asch experiments have been repeated many times over the years with students and non-students, old and young, and in groups of different sizes and different settings. The results are consistently the same with one-third to one-half of the participants making a judgment contrary to fact, yet in conformity with the group, demonstrating the strong power of social influences.

Connection to Sociology

The results of Asch's experiment resonate with what we know to be true about the nature of social forces and norms in our lives. The behavior and expectations of others shape how we think and act on a daily basis because what we observe among others teaches us what is normal , and expected of us. The results of the study also raise interesting questions and concerns about how knowledge is constructed and disseminated, and how we can address social problems that stem from conformity, among others.

Updated  by Nicki Lisa Cole, Ph.D.

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