# Exposure and connectedness to natural environments: An examination of the measurement invariance of the Nature Exposure Scale (NES) and Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups

Viren Swami, Mathew P White, Martin Voracek, Ulrich S Tran, Toivo Aavik, Hamed Abdollahpour Ranjbar, Sulaiman Olanrewaju Adebayo, Reza Afhami, Oli Ahmed, Annie Aimé, et al.

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## To cite this version:

Viren Swami, Mathew P White, Martin Voracek, Ulrich S Tran, Toivo Aavik, et al. Exposure and connectedness to natural environments: An examination of the measurement invariance of the Nature Exposure Scale (NES) and Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups. Journal of Environmental Psychology, 2024, 99, 10.1016/j.jenvp.2024.102432. hal-04910387

HAL Id: hal-04910387

https://amu.hal.science/hal-04910387v1

Submitted on 24 Jan 2025

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<table><tr><td>Publikacja / Publication</td><td>Exposure and connectedness to natural environments: An examination of the measurement invariance of the Nature Exposure Scale (NES) and Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups, Swami Viren, White Mathew P., Voracek Martin, Tran Ulrich S., Aavik Toivo, Ranjbar Hamed Abdollahpour, Adebayo Sulaiman Olanrewaju, Afhami Reza, Ahmed Oli, Lipowska Małgorzata</td></tr><tr><td>DOI wersji wydawcy / Published version DOI</td><td>http://dx.doi.org/10.1016/j.jenvp.2024.102432</td></tr><tr><td>Adres publikacji w Repozytorium URL / Publication address in Repository</td><td>https://repozytorium.bg.ug.edu.pl/info/article/UOG0c22bef36362408d98ece232231558d4/</td></tr><tr><td>Data opublikowania w Repozytorium / Deposited in Repository on</td><td>Sep 25, 2024</td></tr><tr><td>Rodzaj licencji / Type of licence</td><td>Attribution (CC-BY 4.0) 🌐</td></tr></table>
Swami Viren, White Mathew P., Voracek Martin, Tran Ulrich S., Aavik Toivo, Ranjbar Hamed Abdollahpour, Adebayo Sulaiman Olanrewaju, Afhami Reza, Ahmed Oli, Aimé Annie, Akel Marwan, Halbusi Hussam Al, Alexias George, Ali Khawla F., Alp-Dal Nursel, Alsalhani Anas B., Álvarez-Solas Sara, Amaral Ana Carolina Soares, Andrianto Sonny, Aspden Trefor, Argyrides Marios, Aruta John Jamir Benzon R., Atkin Stephen, Ayandele Olusola, Baceviciene Migle, Bahbouh Radvan, Ballesio Andrea, Barron David, Bellard Ashleigh, Bender Sóley Sesselja, Beydağ Kerime Derya, Birovljević Gorana, Blackburn Marie-Ève, Borja-Alvarez Teresita, Borowiec Joanna, Bozogáňová Míroslava, Bratland-Sanda Solfríd, Browning Matthew H.E.M., Brytek-Matera Anna, Burakova Marina, Çakır-Koçak Yeliz, Camacho Pablo, Camilleri Vittorio Emanuele, Cazzato Valentina, Cerea Silvia, Chaiwutikornwanich Apitchaya, Chaleeraktrakoon Trawin, Chambers Tim, Chen Qing-Wei, Chen Xin, Chien Chin-Lung, Chobthamkit Phatthanakit, Choompunuch Bovornpot, Compte Emilio J., Corrigan Jennifer, Cosmas Getrude, Cowden Richard G., Czepczor-Bernat Kamila, Czub Marcin, da Silva Wanderson Roberto, Dadfar Mahboubeh, Dalley Simon E., Dany Lionel, Datu Jesus Alfonso D., de Carvalho Pedro Henrique Berbert, Coelho Gabriel Lins de Holanda, de Jesus Avila Odia S., Debbabi Sonia Harzallah, Dhakal Sandesh, di Bernardo Francesca, Dimitrova Donka D., Dion Jacinthe, Dixson Barnaby, Donofrio Stacey M., Drysch Marius, Du Hongfei, Dzhambov Angel M., El-Jor Claire, Enea Violeta, Eskin Mehmet, Farbod Farinaz, Farrugia Lorleen, Fian Leonie, Fisher Maryanne L., Folwarczny Michał, Frederick David A., Fuller-Tyszkiewicz Matthew, Furnham Adrian, García Antonio Alias, Geller Shulamit, Ghisi Marta, Ghorbani Alireza, Martinez Maria Angeles Gomez, Gradidge Sarah, Graf Sylvie, Grano Caterina, Gyene Gyöngyvér, Hallit Souheil, Hamdan Motasem, Handelzalts Jonathan E., Hanel Paul H.P., Hawks Steven R., Hekmati Issa, Helmy Mai, Hill Tetiana, Hina Farah, Holenweger Geraldine, Hřebičková Martina, Ijabadeniyi Olasupo Augustine, Imam Asma, İnce Başak, Irrazabal Natalia, Jankauskiene Rasa, Jiang Ding-Yu, Jiménez-Borja Micaela, Jiménez-Borja Verónica, Johnson Evan M., Jovanović Veljko, Jović Marija, Jović Marko, Junqueira Alessandra Costa Pereira, Kahle Lisa-Marie, Kantanista Adam, Karakiraz Ahmet, Karkin Ayşe Nur, Kasten Erich, Khatib Salam, Khieowan Nuannut, Kimong Patricia Joseph, Kiropoulos Litza, Knittel Joshua, Kohli Neena, Koprivnik Mirjam, Kospakov Aituar, Król-Zielińska Magdalena, Krug Isabel, Kuan Garry, Kueh Yee Cheng, Kujan Omar, Kukić Miljana, Kumar Sanjay, Kumar Vipul, Lamba Nishtha, Lauri Mary Anne, Laus Maria Fernanda, LeBlanc Liza April, Lee Hyejoo J., Lipowska Małgorzata, Lipowski Mariusz, Lombardo Caterina, Lukács Andrea, Maïano Christophe, Malik Sadia, Manjary Mandar, Baldó Lidia Márquez, Martinez-Banfi Martha, Massar Karlijn, Matera Camila, McAnirlin Olivia, Mebarak Moisés Roberto, Mechri Anwar, Meireles Juliana Fernandes Filgueiras, Mesko Norbert, Mills Jacqueline, Miyairi Maya, Modi Ritu, Modrzejewska Adriana, Modrzejewska Justyna, Mulgrew Kate E., Myers Taryn A., Namatame Hikari, Nassani Mohammad Zakaria, Nerini Amanda, Neto Félix, Neto Joana, Neves Angela Noguiera, Ng Siu-Kuen, Nithiya Devi, O Jiaqing, Obeid Sahar, Oda-Montecinos Camila, Olapegba Peter Olamakinde, Olonisakin Tosin Tunrayo, Omar Salma Samir, Örlygsdóttir Brynja, Özsoy Emrah, Otterbring Tobias, Pahl Sabine, Panasiti Maria Serena, Panasiti Maria Serena, Park Yonguk, Patwary Muhammad Mainuddin, Pethö Tatiana, Petrova Nadezhda, Pietschnig Jakob, Pourmahmoud Sadaf, Prabhu Vishnunarayan Girishan, Poštuvan Vita, Prokop Pavol, Winter Virginia L. Ramseyer, Razmus Magdalena, Vidal-Mollón Jose, Rupar Mirjana, Sahlan Reza N., Hassan Mohammad Salah, Šalov Andela, Sapkota Saphal, Sarfo Jacob Owusu, Sawamiya Yoko, Schaefer Katrin, Schulte-Mecklenbeck Michael, Seekis Veya, Selvi Kerim, Sharifi Mehdi, Shrivastava Anita, Siddique Rumana Ferdousi, Sigurdsson Valdimar, Silkane Vineta, Šimunić Ana, Singh Govind, Slezáčková Alena, Sundgot-Borgen Christine, Hoor Gill Ten, Tevichapong Passagorn, Tipandjan Arun, Todd Jennifer, Togas Constantinos, Tonini Fernando, Tovar-Castro Juan Camilo, Trangsrud Lise Katrine Jepsen, Tripathi Pankaj, Tudorel Otilia, Tylka Tracy L., Uyzbayeva Anar, Vally Zahir, Vanags Edmunds, Vega Luis Diego, Vicente-Arruebarrena Aitor, Vidal-Mollón Jose, Vilar Roosevelt, Villegas Hyxia, Vintilà Mona, Wallner Christoph, Whitebridge Simon, Windhager Sonja, Wong Kah Yan, Yau Eric Kenson, Yamamiya Yuko, Yeung Victoria Wai Lan, Zanetti Marcelo Callegari, Zawisza Magdalena, Zeeni Nadine, Zvariková Martina, Stieger Stefan: Exposure and connectedness to natural environments: An examination of the measurement invariance of the Nature Exposure Scale (NES) and Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups, Journal of Environmental Psychology, vol. 99, 2024, Article number: 102432, pp. 1-23, DOI:10.1016/j.jenvp.2024.102432
# Exposure and connectedness to natural environments: An examination of the measurement invariance of the Nature Exposure Scale (NES) and Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups

Viren Swami<sup>a,b,*</sup>, Mathew P. White<sup>c</sup>, Martin Voracek<sup>c</sup>, Ulrich S. Tran<sup>c</sup>, Toivo Aavik<sup>d</sup>, Hamed Abdollahpour Ranjbar<sup>e</sup>, Sulaiman Olanrewaju Adebayo<sup>f</sup>, Reza Afhami<sup>g</sup>, Oli Ahmed<sup>h</sup>, Annie Aimé<sup>i</sup>, Marwan Akel<sup>j,k</sup>, Hussam Al Halbusi<sup>l</sup>, George Alexias<sup>m</sup>, Khawla F. Ali<sup>n</sup>, Nursel Alp-Dal<sup>o</sup>, Anas B. Alsalhani<sup>p</sup>, Sara Álvarez-Solas<sup>q</sup>, Ana Carolina Soares Amaral<sup>r</sup>, Sonny Andrianto<sup>s</sup>, Trefor Aspden<sup>t</sup>, Marios Argyrides<sup>u</sup>, John Jamir Benzon R. Aruta<sup>v</sup>, Stephen Atkin<sup>n</sup>, Olusola Ayandele<sup>w,x</sup>, Migle Baceviciene<sup>y</sup>, Radvan Bahbouh<sup>z</sup>, Andrea Ballesio<sup>aa</sup>, David Barron<sup>ab</sup>, Ashleigh Bellard<sup>ac</sup>, SóleySesselja Bender<sup>ad</sup>, Kerime Derya Beydağ<sup>ae</sup>, Gorana Birovljević<sup>af</sup>, Marie-Ève Blackburn<sup>ag</sup>, Teresita Borja-Alvarez<sup>ah</sup>, Joanna Borowiec<sup>al</sup>, Miroslava Bozogáňová<sup>aj,ak</sup>, Solfrid Bratland-Sanda<sup>al</sup>, Matthew H.E.M. Browning<sup>am</sup>, Anna Brytek-Matera<sup>an</sup>, Marina Burakova<sup>ao</sup>, Yeliz Çakır-Koçak<sup>ap</sup>, Pablo Camacho<sup>aq</sup>, Vittorio Emanuele Camilleri<sup>ar</sup>, Valentina Cazzato<sup>ac,as</sup>, Silvia Cerea<sup>at,au</sup>, Apitchaya Chaiwutikornwanich<sup>av</sup>, Trawin Chaleeraktrakoon<sup>aw</sup>, Tim Chambers<sup>ax</sup>, Qing-Wei Chen<sup>ay,az</sup>, Xin Chen<sup>ba</sup>, Chin-Lung Chien<sup>bb</sup>, Phatthanakit Chobthamkit<sup>aw</sup>, Bovornpot Choompunuch<sup>bc</sup>, Emilio J. Compte<sup>bd,be</sup>, Jennifer Corrigan<sup>bf</sup>, Getrude Cosmas<sup>bg</sup>, Richard G. Cowden<sup>bh</sup>, Kamila Czepczor-Bernat<sup>bi</sup>, Marcin Czub<sup>an</sup>, Wanderson Roberto da Silva<sup>bj</sup>, Mahboubeh Dadfar<sup>bk</sup>, Simon E. Dalley<sup>bl</sup>, Lionel Dany<sup>ao</sup>, Jesus Alfonso D. Datu<sup>bm</sup>, Pedro Henrique Berbert de Carvalho<sup>bn,bo</sup>, Gabriel Lins de Holanda Coelho<sup>bf</sup>, Avila Odia S. De Jesus<sup>v</sup>, Sonia Harzallah Debbabi<sup>bp</sup>, Sandesh Dhakal<sup>bq</sup>, Francesca Di Bernardo<sup>br</sup>, Donka D. Dimitrova<sup>bs,bt</sup>, Jacinthe Dion<sup>bu</sup>, Barnaby Dixson<sup>bv</sup>, Stacey M. Donofrio<sup>bl</sup>, Marius Drysch<sup>bw</sup>, Hongfei Du<sup>bx</sup>, Angel M. Dzhambov<sup>bt,by</sup>, Claire El-Jor<sup>bz</sup>, Violeta Enea<sup>ca</sup>, Mehmet Eskin<sup>cb</sup>, Farinaz Farbod<sup>cc</sup>, Lorleen Farrugia<sup>ar</sup>, Leonie Fian<sup>c</sup>, Maryanne L. Fisher<sup>cd</sup>, Michał Folwarczny<sup>ce</sup>, David A. Frederick<sup>cf</sup>, Matthew Fuller-Tyszkiewicz<sup>ax</sup>, Adrian Furnham<sup>cg</sup>, Antonio Alías García<sup>ch</sup>, Shulamit Geller<sup>ci</sup>, Marta Ghisi<sup>at,cj</sup>, Alireza Ghorbani<sup>ck</sup>, Maria Angeles Gomez Martinez<sup>cl</sup>, Sarah Gradidge<sup>a</sup>, Sylvie Graf<sup>cm</sup>, Caterina Grano<sup>aa</sup>, Gyöngyvér Gyene<sup>cn</sup>, Souheil Hallit<sup>co,cp</sup>, Motasem Hamdan<sup>cq</sup>, Jonathan E. Handelzalts<sup>ci,cr</sup>, Paul H.P. Hanel<sup>cs</sup>, Steven R. Hawks<sup>ct</sup>, Issa Hekmati<sup>cu</sup>, Mai Helmy<sup>cv,cw</sup>, Tetiana Hill<sup>cx</sup>, Farah Hina<sup>cy</sup>, Geraldine Holenweger<sup>cz</sup>, Martina Hřebíčková<sup>cm</sup>, Olasupo Augustine Ijabadeniyi<sup>da</sup>, Asma Imam<sup>cq</sup>, Başak İnce<sup>db</sup>, Natalia Irrazabal<sup>dc</sup>,

<footnote>* Corresponding author. School of Psychology and Sport Science, Anglia Ruskin University, Cambridge, Cambridgeshire, CB1 1PT, United Kingdom
E-mail address: viren.swami@aru.ac.uk (V. Swami).</footnote>
Rasa Jankauskiene y, Ding-Yu Jiang dd, Micaela Jiménez-Borja ah, Verónica Jiménez-Borja de, Evan M. Johnson cf, Veljko Jovanović df, Marija Jović dg, Marko Jović dh, Alessandra Costa Pereira Junqueira di,dj, Lisa-Marie Kahle dk, Adam Kantanista ai, Ahmet Karakiraz dl, Ayşe Nur Karkin cb, Erich Kasten dm, Salam Khatib dn, Nuannut Khieowan do, Patricia Joseph Kimong bg, Litza Kiropoulos dp, Joshua Knittel cz, Neena Kohli dq, Mirjam Koprivnik dr, Aituar Kospakov ds,dt, Magdalena Król-Zielińska ai, Isabel Krug dp, Garry Kuan du, Yee Cheng Kueh dv, Omar Kujan dw, Miljana Kukić af, Sanjay Kumar dx, Vipul Kumar dy, Nishtha Lamba dz, Mary Anne Lauri ar, Maria Fernanda Laus di,dj, Liza April LeBlanc cd, Hyejoo J. Lee ea, Małgorzata Lipowska eb, Mariusz Lipowski ec, Caterina Lombardo aa, Andrea Lukács ed, Christophe Maïano i,ee, Sadia Malik ef, Mandar Manjary dx,eg, Lidia Márquez Baldó eh, Martha Martinez-Banfi ei,ej, Karlijn Massar ek, Camilla Matera el, Olivia McAnirlin am, Moisés Roberto Mebarak em, Anwar Mechri en, Juliana Fernandes Filgueiras Meireles eo, Norbert Mesko ep, Jacqueline Mills ax, Maya Miyairi eq, Ritu Modi dq, Adriana Modrzejewska er, Justyna Modrzejewska es, Kate E. Mulgrew bv, Taryn A. Myers et, Hikari Namatame eu, Mohammad Zakaria Nassani ev, Amanda Nerini el, Félix Neto ew, Joana Neto ex, Angela Nogueira Neves ey, Siu-Kuen Ng ez, Devi Nithiya fa, Jiaqing O t, Sahar Obeid fb, Camila Oda-Montecinos fc, Peter Olamakinde Olapegbaw, Tosin Tunrayo Olonisakin f, Salma Samir Omar fd, Brynja Örlygsdóttir ad, Emrah Özsoy dl, Tobias Otterbring fe, Sabine Pahl c, Maria Serena Panasiti aa,ff, Yonguk Park fg, Muhammad Mainuddin Patwary fh,fi, Tatiana Pethö aj, Nadezhda Petrova fj, Jakob Pietschnig fk, Sadaf Pourmahmoud g, Vishnunarayan Girishan Prabhu fl, Vita Poštuvan fm,fn, Pavol Prokop fo,fp, Virginia L. Ramseyer Winter fq, Magdalena Razmus fr, Taotao Ru ay,az, Mirjana Rupar cm, Reza N. Sahlan fs, Mohammad Salah Hassan ft, Anđela Šalov fu, Saphal Sapkota fv, Jacob Owusu Sarfo fw, Yoko Sawamiya et, Katrin Schaefer fx,fy, Michael Schulte-Mecklenbeck cz,fz, Veya Seekis ga, Kerim Selvi gb, Mehdi Sharifi gc, Anita Shrivastava dz, Rumana Ferdousi Siddique gd, Valdimar Sigurdsson ge, Vineta Silkane gf, Ana Šimunić fu, Govind Singh dq, Alena Slezáčková gg, Christine Sundgot-Borgen gh, Gill Ten Hoor ek, Passagorn Tevichapong gi, Arun Tipandjan gj, Jennifer Todd a,b, Constantinos Togas m, Fernando Tonini dc, Juan Camilo Tovar-Castro gk, Lise Katrine Jepsen Trangsrud al, Pankaj Tripathi dq, Otilia Tudorel gl, Tracy L. Tylka gm, Anar Uyzbayeva dt, Zahir Vally gn, Edmunds Vanags go, Luis Diego Vega gp, Aitor Vicente-Arruebarrena cl, Jose Vidal-Mollón eh, Roosevelt Vilar gq, Hyxia Villegas gp, Mona Vintilă gl, Christoph Wallner bw, Simon Whitebridge n, Sonja Windhager fx,fy, Kah Yan Wong gr, Eric Kenson Yau gs, Yuko Yamamiya gt, Victoria Wai Lan Yeung gs,gu, Marcelo Callegari Zanetti gv, Magdalena Zawisza a, Nadine Zeeni bz, Martina Zvaríková fo, Stefan Stieger gw

<footnote>a School of Psychology, Sport, and Sensory Sciences, Anglia Ruskin University, Cambridge, United Kingdom</footnote>

<footnote>b Centre for Psychological Medicine, Perdana University, Kuala Lumpur, Malaysia</footnote>

<footnote>c Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria</footnote>

<footnote>d Institute of Psychology, University of Tartu, Tartu, Estonia</footnote>

<footnote>e Department of Psychology, Koç University, Istanbul, Turkey</footnote>

<footnote>f Department of Psychology and Behavioural Studies, Ekiti State University, Ado-Ekiti, Nigeria</footnote>

<footnote>g Department of Art Studies, Tarbiat Modares University, Tehran, Iran</footnote>

<footnote>h Department of Psychology, University of Chittagong, Chattogram, Bangladesh</footnote>

<footnote>i Department of Psychoeducation and Psychology, Université Du Québec en Outaouais, Saint-Jérôme, Canada</footnote>

<footnote>j INSPECT-LB: National Institute of Public Health, Clinical Epidemiology, and Toxicology, Beirut, Lebanon</footnote>

<footnote>k School of Pharmacy, Lebanese International University, Beirut, Lebanon</footnote>

<footnote>l Department of Management, Ahmed Bin Mohammad Military College, Doha, Qatar</footnote>

<footnote>m Faculty of Psychology, Panteion University of Social and Political Sciences, Athens, Greece</footnote>

<footnote>n Royal College of Surgeons Ireland-Bahrain, Adliya, Bahrain</footnote>

<footnote>o Department of Midwifery, Faculty of Health Sciences, Munzur University, Tunceli, Turkey</footnote>

<footnote>p Department of Oral Medicine and Diagnostic Sciences, Vision College of Dentistry and Nursing, Vision Colleges, Riyadh, Saudi Arabia</footnote>

<footnote>q Facultad de Ciencias de La Vida, Universidad Regional Amazónica Ikiam, Muyuna, Ecuador</footnote>

<footnote>r Federal Institute of Education, Science, and Technology of Southeast of Minas Gerais, Barbacena, Brazil</footnote>

<footnote>s Department of Psychology, Universitas Islam Indonesia, Yogyakarta, Indonesia</footnote>

<footnote>t Department of Psychology, Aberystwyth University, Ceredigion, United Kingdom</footnote>
u Department of Psychology, Neapolis University Pafos, Paphos, Cyprus

v Department of Psychology, De La Salle University, Philippines

w Department of General Studies, The Polytechnic, Ibadan, Nigeria

x Department of Psychology, University of Ibadan, Ibadan, Nigeria

y Health Research and Innovation Science Centre, Faculty of Health Sciences, Klaipeda University, Klaipeda, Lithuania

z Department of Psychology, Faculty of Arts, Charles University, Prague, Czech Republic

aa Department of Psychology, Sapienza University of Rome, Rome, Italy

ab School of Social Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia

ac School of Psychology, Faculty of Health, Liverpool John Moores University, Liverpool, United Kingdom

ad Faculty of Nursing and Midwifery, University of Iceland, Reykjavik, Iceland

ae Department of Nursing, Faculty of Health Sciences, Yalova, University, Yalova, Turkey

af Department of Psychology, Faculty of Humanities and Social Sciences, University of Rijeka, Rijeka, Croatia

ag ÉCOBES-Research and Transfer, Cégep de Jonquière, Québec, Canada

ah Colegio de Ciencias Sociales y Humanidades, Universidad San Francisco de Quito USFQ, Quito, Ecuador

ai Department of Physical Education and Lifelong Sports, Poznań University of Physical Education, Poznań, Poland

ak Institute of Social Sciences of the Centre of Social and Psychological Sciences, Košice, Slovakia

ak Faculty of Humanities and Natural Sciences, Institute of Pedagogy, Andragogy, and Psychology, University of Prešov, Prešov, Slovakia

a Department of Sports, Physical Education and Outdoor Studies, University of South-Eastern Norway, Bø in Telemark, Norway

am Department of Parks, Recreation and Tourism Management, Clemson University, Clemson, SC, USA

an Institute of Psychology, University of Wroclaw, Wroclaw, Poland

a Laboratory of Social Psychology, Aix-Marseille University, Aix-en-Provence, France

ap Department of Midwifery, Faculty of Health Sciences, Bartın University, Bartın, Turkey

aq CentroSan Isidoro University Center, Seville, Spain

ar Department of Psychology, University of Malta, Msida, Malta

as Department of Cognitive Sciences, Psychology, Education, and Cultural Studies, University of Messina, Messina, Italy

at Department of General Psychology, University of Padova, Padova, Italy

au Department of Biomedical Sciences, University of Padova, Padova, Italy

av Faculty of Psychology, Chulalongkorn University, Bangkok, Thailand

aw Department of Psychology, Faculty of Liberal Arts, Thammasat University, Pathumthani, Thailand

ax School of Psychology, Deakin University, Geelong, Australia

ay Lab of Light and Physio-Psychological Health, National Center for International Research on Green Optoelectrics, South China Normal University, Guangzhou, China

az Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China

ba Department of Psychology, Graduate School of Arts and Science, New York University, New York, USA

bb Department of Psychology, Soochow University, Taipei, Taiwan

bc Department of Educational Psychology and Guidance, Faculty of Education, Mahasarakham University, Maha Sarakham, Thailand

bd School of Psychology, Universidad Adolfo Ibáñez, Penalolen, Chile

be Comenzar de Nuevo Treatment Center, Monterrey, Mexico

bf School of Applied Psychology, University College Cork, Cork, Ireland

bg Faculty of Psychology and Education, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

bh Human Flourishing Program, Harvard University, Cambridge, USA

bl Department of Pediatrics, Pediatric Obesity and Metabolic Bone Diseases, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland

b Graduate Program in Food, Nutrition, and Food Engineering, São Paulo State University, São Paulo, Brazil

bk Department of Addiction, School of Behavioral Sciences and Mental Health (Tehran Institute of Psychiatry), Iran University of Medical Sciences, Tehran, Iran

bl Department of Psychology, University of Groningen, Groningen, the Netherlands

bm Academic Unit of Human Communication, Learning, and Development, Faculty of Education. The University of Hong Kong, China

bn Body Image and Eating Disorders Research Group, Federal University of Juiz de Fora, Juiz de Fora, Brazil

bo Institute of Psychiatry, University of São Paulo, São Paulo, Brazil

bp Faculty of Medicine, University of Sousse, Sousse, Tunisia

bq Central Department of Psychology, Tribhuvan University, Kathmandu, Nepal

br Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy

bs Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria

bt Environmental Health Division, Research Institute at Medical University of Plovdiv, Medical University of Plovdiv, Plovdiv, Bulgaria

bu Department of Psychology, Université Du Québec à Trois-Rivières, Trois-Rivières, Canada

bv School of Health, University of the Sunshine Coast, Moreton Bay, Australia

bw Department of Plastic and Hand Surgery, BG University Hospital Bergmannsheil Bochum, Ruhr University Bochum, Bochum, Germany

bx Department of Psychology, Beijing Normal University at Zhuhai, Zhuhai, China

by Health and Quality of Life in a Green and Sustainable Environment Research Group, Strategic Research and Innovation Program, Medical University of Plovdiv, Plovdiv, Bulgaria

bz Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon

ca Department of Psychology, Alexandru Ioan Cuza University, Iași, Romania

cb Department of Psychology, Koc University, Istanbul, Turkey

cd Department of Textile and Fashion Design, Alzahra University, Tehran, Iran

ce Department of Psychology, Saint Mary's University, Halifax, Canada

cf Discipline of Marketing, J.E. Cairnes School of Business & Economics, University of Galway, Galway, Ireland

cg Crean College of Health and Behavioral Sciences, Chapman University, Orange, USA

ch Department of Leadership and Organizational Behaviour, Norwegian Business School, Oslo, Norway

ch Department of Education, University of Almería, Almería, Spain

ci School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel Aviv-Yafo, Israel

ci Unità Operativa Complessa (UOC) Hospital Psychology, Padova University Hospital, Padova, Italy

ck Department of Social Sciences, Payam Noor University, Tehran, Iran

cl Faculty of Psychology, Pontifical University of Salamanca, Salamanca, Spain

cm Institute of Psychology, Czech Academy of Sciences, Brno, Czech Republic

cn Doctoral School of Psychology, Ötvös Loránd University, Budapest, Hungary

co School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon

cp Applied Science Research Center, Applied Science Private University, Amman, Jordan

ct Faculty of Public Health, Al-Quds University, East Jerusalem, Palestine

ct Department of Psychiatry, University of Michigan, Ann Arbor, USA

cs Department of Psychology, University of Essex, Colchester, United Kingdom

ct Department of Kinesiology and Health Science, Utah State University, Logan, USA
cu Department of Psychology, Faculty of Human Science, University of Maragheh, Maragheh, Iran

cv Department of Psychology, College of Education, Sultan Qaboos University, Muscat, Oman

cw Department of Psychology, Faculty of Arts, Menoufia University, Shebin El Kom, Egypt

cx Hertfordshire Business School, University of Hertfordshire, Hatfield, United Kingdom

cy Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

cz Department of Consumer Behavior, University of Bern, Bern, Switzerland

da Department of Sociology and Social Justice, Afe Babalola University, Ado-Ekiti, Nigeria

db Centre for Research in Eating and Weight Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom

dc Faculty of Social Sciences, University of Palermo, Buenos Aires, Argentina

dd Department of Psychology, National Chung Cheng University, Chia Yi, Taiwan

de Colegio de Comunicación y Artes Contemporáneas, Universidad San Francisco de Quito USFQ, Quito, Ecuador

df Department of Psychology, Faculty of Philosophy, University of Novi Sad, Novi Sad, Serbia

dg Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia

dh Faculty of Medicine, University of Belgrade, Belgrade, Serbia

dl Department of Psychology, University of São Paulo, Ribeirão Preto, Brazil

dI Department of Nutrition, University of Ribeirão Preto, Ribeirão Preto, Brazil

dk Faculty of Life Sciences, Medical School Hamburg, Hamburg, Germany

dl Sakarya Business School, Sakarya University, Sakarya, Turkiye

dm Practice for Psychotherapy, Am Krautacker 25, Travemünde, Germany

dn Faculty of Health Professions, Al-Quds University, East Jerusalem, Palestine

do Asian Studies Department, Faculty of International Studies, Prince of Songkla University, Phuket Campus, Phuket, Thailand

dp School of Psychological Sciences, University of Melbourne, Melbourne, Australia

dq Department of Psychology, University of Allahabad, Prayagraj, India

dI Institute of Anton Martin Slomsek, Primary School Montessori, Maribor, Slovenia

ds Department of Sociology and Social Work, Al-Farabi Kazakh National University, Almaty, Kazakhstan

dI Department of General Education Disciplines, Astana IT University, Astana, Kazakhstan

du Exercise and Sport Sciences Programme, School of Health Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia

dv Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia

dw Oral Diagnostics and Surgical Sciences, UWA Dental School, The University of Western Australia, Nedlands, Australia

dx Department of Psychology, D.A.V. College, Muzaffarnagar, India

dy Department of Psychology, Kashi Naresh Government Post-Graduate College, Gyanpur, India

dz Department of Psychology, Middlesex University Dubai, Dubai, United Arab Emirates

ea Department of Counselling Psychology and Social Welfare, Handong Global University, Pohang, South Korea

eb Institute of Psychology, University of Gdańsk, Gdańsk, Poland

ec Faculty of Social and Humanities, University WSB Merito, Gdansk, Poland

ed Faculty of Health Sciences, University of Miskolc, Miskolc, Hungary

ee Cyberpsychology Laboratory, Department of Psychoeducation and Psychology, Université Du Québec en Outaouais (UQO), Saint-Jérôme, Canada

ef Department of Psychology, University of Sargodha, Sargodha, Pakistan

es M.M.D. Public School, Brahmpuri, Muzaffarnagar, India

eh Department of Research Methods and Diagnosis in Education, University of València, València, Spain

el Faculty of Legal and Social Sciences, Simón Bolívar University, Barranquilla, Colombia

el Life Science Research Centre, Simón Bolívar University, Barranquilla, Colombia

ek Department of Work and Social Psychology, Maastricht University, Maastricht, the Netherlands

el Department of Education, Languages, Intercultures, Literatures, and Psychology, University of Florence, Florence, Italy

em Department of Psychology, Universidad Del Norte, Barranquilla, Colombia

en Faculty of Medicine of Monastir, Eya Medical Centre, Monastir, Tunisia

eo Department of Family and Community Medicine, School of Community Medicine, University of Oklahoma, Tulsa, USA

ep Institute of Psychology, University of Pécs, Pécs, Hungary

eq Department of Health Sciences, DePaul University, Chicago, USA

er Department of Medical Anthropology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland

es Institute of Pedagogy, University of Bielsko-Biala, Bielsko-Biala, Poland

et Department of Psychology, Virginia Wesleyan University, Virginia Beach, USA

eu Faculty of Human Sciences, University of Tsukuba, Tsukuba, Japan

ev Department of Restorative and Prosthetic Dental Sciences, College of Dentistry, Dar Al Uloom University, Riyadh, Saudi Arabia

ew Faculty of Psychology and Educational Sciences, University of Porto, Porto, Portugal

ex REMIT - Research on Economics, Management and Information Technologies, Universidade Portucalense, Porto, Portugal

ey Division of Research, Physical Education College of the Brazilian Army, Rio de Janeiro, Brazil

ez Physical Education Unit, Chinese University of Hong Kong, China

fA Department of Physiology, Mahatma Gandhi Medical College and Research Institute, Puducherry, India

fB Social and Education Sciences Department, School of Arts and Sciences, Lebanese American University, Jbeil, Lebanon

fC Institute of Social Sciences, Universidad de O'Higgins, Rancagua, Chile

fD Department of Dermatology, Venereology, and Andrology, Alexandria University, Alexandria, Egypt

fe Department of Management, University of Agder, Kristiansand, Norway

fS Santa Lucia Foundation, Scientific Institute for Research and Healthcare, Rome, Italy

fB Department of Psychology, Kyungnam University, Changwon, South Korea

fC Environment and Sustainability Research Initiative, Khulna, Bangladesh

fI Environmental Science Discipline, Life Science School, Khulna University, Khulna, Bangladesh

fJ Department of Human Anatomy, Histology and Embryology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria

fH Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria

fI Industrial and Systems Engineering, University of North Carolina, Charlotte, USA

fm Slovene Centre for Suicide Research, Andrej Marusic Institute, University of Primorska, Koper, Slovenia

fP Department of Psychology FAMNIT, University of Primorska, Koper, Slovenia

fQ Department of Environmental Ecology and Landscape Management, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovakia

fp Institute of Zoology, Slovak Academy of Sciences, Bratislava, Slovakia

fS School of Social Work, University of Missouri, Columbia, USA

fT Institute of Psychology, Marie Curie-Sklodowska University, Lublin, Poland

fD Department of Counseling, School, and Educational Psychology, Graduate School of Education, University at Buffalo-SUNY, Buffalo, USA

ft Management Department, College of Business Administration (COBA), A'Sharqiyah University (ASU), Ibra, Oman

fu Department of Psychology, University of Zadar, Zadar, Croatia
fv KOSHISH-National Mental Health Self-Help Organization, Kusunti, Lalitpur, Nepal

fw Department of Health, Physical Education and Recreation, University of Cape Coast, Cape Coast, Ghana

g Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria

h Human Evolution and Archaeological Sciences, University of Vienna, Vienna, Austria

i Max Planck Institute for Human Development, Berlin, Germany

ja School of Applied Psychology, Griffith University, Gold Coast, Australia

kb Department of Psychology, Eskişehir Osmangazi University, Eskişehir, Turkiye

kc Department of Psychology, Islamic Azad University, Bandar Gaz, Iran

kd Department of Psychology, University of Dhaka, Dhaka, Bangladesh

ke Department of Business Administration, Reykjavik University, Reykjavik, Iceland

gl Faculty of Social Sciences, Vidzeme University of Applied Sciences, Valmiera, Latvia

g Department of Medical Psychology and Psychosomatics, Faculty of Medicine, Masaryk University, Brno, Czech Republic

gh Regional Department for Eating Disorders, Oslo University Hospital, Oslo, Norway

g Department of Psychology, Faculty of Humanities, Chiang Mai University, Chiang Mai, Thailand

h International Centre for Psychological Counselling and Social Research, Puducherry, India

k Department of Psychology, University of the Andes, Bogotá, Colombia

kl Department of Psychology, West University of Timisoara, Timișoara, Romania

lm Department of Psychology, The Ohio State University, Columbus, USA

m Department of Clinical Psychology, United Arab Emirates University, Al Ain, United Arab Emirates

n Faculty of Education, Psychology, and Art, University of Latvia, Riga, Latvia

p Vice-rectory for Teaching, Research, and Extension, Universida Latina de Costa Rica, San José, Costa Rica

q Department of Psychology, Massey University, Auckland, New Zealand

r School of Psychology, University of Nottingham Malaysia, Semenyih, Malaysia

s Department of Psychology, Lingnan University, China

t Department of Undergraduate Studies, Temple University, Japan Campus, Tokyo, Japan

u Wofoo Joseph Lee Consulting and Counselling Psychology Research Centre, Lingnan University, China

v Department of Physical Education, São Judas Tadeu University, São Paulo, Brazil

w Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria

ARTICLE INFO

Handling Editor: L. McCunn

Keywords:

Nature exposure scale

Connectedness to nature scale

Measurement invariance

Cross-cultural

Multi-group confirmatory factor analysis (MG-CFA)

A B S T R A C T

Detachment from nature is contributing to the environmental crisis and reversing this trend requires detailed monitoring and targeted interventions to reconnect people to nature. Most tools measuring nature exposure and attachment were developed in high-income countries and little is known about their robustness across national and linguistic groups. Therefore, we used data from the Body Image in Nature Survey to assess measurement invariance of the Nature Exposure Scale (NES) and the Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups (N = 56,968). While multi-group confirmatory factor analysis (MG-CFA) of the NES supported full scalar invariance across gender identities and age groups, only partial scalar invariance was supported across national and linguistic groups. MG-CFA of the CNS also supported full scalar invariance across gender identities and age groups, but only partial scalar invariance of a 7-item version of the CNS across national and linguistic groups. Nation-level associations between NES and CNS scores were negligible, likely reflecting a lack of conceptual clarity over what the NES is measuring. Individual-level associations between both measures and sociodemographic variables were weak. Findings suggest that the CNS-7 may be a useful tool to measure nature connectedness globally, but measures other than the NES may be needed to capture nature exposure cross-culturally.

1. Introduction

At the turn of the century, the United Nations Millennium Declarat-
tion recognised that insufficient respect for nature is a fundamental
challenge for international relations and global sustainable development
(United Nations General Assembly, 2000). Despite ongoing efforts,
anthropogenic-related climate change, biodiversity loss, and land,
water, and air pollution are accelerating (e.g., Goudie, 2019). Some
observers have suggested that this is, at least in part, a consequence of a
growing detachment from the natural world, especially among
increasingly urbanised populations (Beery et al., 2023; Soga & Gaston,
2016). Reversing these trends requires an understanding of the drivers
and barriers of ecological and pro-environmental behaviours, and using
this knowledge to promote widespread behaviour change (Grilli &
Curtis, 2021; Schultz & Kaiser, 2012).

One important avenue of research concerns people's physical contact with, and psychological connectedness to, the natural world. In terms of the former, the relationship between recreational nature exposure (e.g., leisure visits to parks, woodlands, and beaches) and a range of public and private pro-environmental behaviours has been shown to be consistently positive (De Ville et al., 2021; Martin et al., 2020). Regarding psychological connectedness, robust evidence shows that people who feel more connected to the natural world – independent of

their actual exposure – have more positive attitudes towards the natural
environment, ecological behaviours, and nature protection (for
meta-analyses, see Barragan-Jason et al., 2022; Whitburn et al., 2020).
They also exhibit better well-being and mental health (Capaldi et al.,
2014; White et al., 2021).

The mechanisms underpinning these relationships are thought to reflect a combination of genetic inheritance (Kellert & Wilson, 1993), personal experience and associative learning (e.g., Yannick & de Block, 2011), and salient sociocultural norms (Bourassa, 1990). In addition, nature exposure and nature connectedness are likely to be mutually reinforcing. Positive contact with the natural world can increase feelings of connectedness to nature (e.g., Fränkel et al., 2019; Lengieza & Swim, 2021; Martin et al., 2020; Swami, Barron, et al., 2020), with a meta-analysis of experimental manipulations and field interventions reporting a moderate positive mean effect of nature contact on nature connectedness (g = .44, 95% CI = .31, .58; Sheffield et al., 2022). Conversely, greater nature connectedness can encourage people to seek more nature exposure (Martin et al., 2020; Stehl et al., 2024). Positive experiences may then strengthen nature connectedness over time.

Although improving our understanding of these processes remains
important, our objective here was to consider how physical contact and
psychological connectedness with nature are measured and, in partic-
ular, how generalisable existing measures are across linguistic and
national contexts. Given that the environmental crisis is a global phenomenon (Goudie, 2019), adequate measurement and monitoring across contexts requires instruments that can be deployed reliably in multiple settings to make robust comparisons. Crucial to this issue is the concept of *measurement invariance*, the notion that a measurement tool should measure the same underlying construct in the same way across different groups (Swami & Barron, 2019; Vandenberg & Lance, 2000), which in turn ensures that measurement biases leading to artefactual, inaccurate, or irreplicable results are avoided (Fischer et al., 2023). Measurement invariance can be determined at different levels, with scalar or partial scalar invariance typically considered a minimum threshold for comparison of latent means (Chen, 2007).

To date, however, determining measurement invariance of key na-
ture exposure and nature connectedness instruments has been hampered
because most research – including the development of instruments to
measure these constructs – has been conducted in a small handful of
countries in the Global North (Tirri et al., 2021; Zhang et al., 2020).
Here, we aimed to address this shortcoming. Specifically, using data
from 65 countries, we aimed to explore the measurement invariance of
two well-known instruments across multiple linguistic and national
contexts. The first was a measure of nature exposure – the Nature
Exposure Scale (NES; Kamitsis & Francis, 2013) – while the second was a
measure of nature connectedness, the Connectedness to Nature Scale
(CNS; Mayer & Frantz, 2004). We briefly review what is currently
known about these instruments.

1.1. *Nature exposure*

Many definitions of nature exposure, or nature contact, exist (Holland et al., 2021). Much of the research linking nature exposure to health and well-being uses remote sensing data to estimate the percentage of vegetation around the home using various radial buffers (e.g., Browning & Lee, 2017) or distance to local green and blue spaces (e.g., Geary et al., 2023). Others consider vegetation around other core locations, such as work/school (e.g., Dadvand et al., 2015), or explore more deliberative, intentional nature contact, such as leisure visits to natural settings (e.g., Garrett et al., 2023). Finally, many of the beneficial effects of nature contact on health and pro-environmentalism may depend on psychological awareness of exposure, or a certain degree of mindfulness of this contact with nature (e.g., Macaulay et al., 2022; Richardson, Hamlin, Butler, et al., 2022).

One self-report measure that attempts to address all three aspects – that is, everyday nature around the home/work, recreational visits, and nature awareness – is the 4-item Nature Exposure Scale (NES, *Kamitsis & Francis*, 2013; see Appendix 1 for items). Although the instrument has been utilised in diverse national groups (e.g., Arroz et al., 2022; Baceviciene et al., 2021; Picanço et al., 2024; Stieger et al., 2022), its factorial validity has been infrequently assessed. Studies with adults from the United States (Swami et al., 2016), Portugal (Arroz et al., 2022; Picanço et al., 2024), and Lithuania (Matukyniene et al., 2021) suggest scores are unidimensional, whereas a study with an online sample (nationality unreported) found that it was necessary to drop one item (Item #1) to achieve unidimensionality (Wood et al., 2019).

The equivocal nature of findings *vis-à-vis* the factorial validity of the NES may reflect the fact that a single instrument is trying to measure very different types of exposure using different response outcomes. Thus, while some authors have suggested that overall scores on the 4-item NES demonstrate adequate indices of face validity (e.g., *Picanço et al., 2024; Swami et al., 2019*), others have implied that it is only the two items that assess direct contact with nature that truly assesses nature exposure (*Goh et al., 2023*). As scholars increasingly seek brief self-report measures of nature exposure, a fuller understanding of the factorial validity of the NES, including item behaviour, is vital (*Swami, 2024*). In the same vein, more can be done to understand the psychometric properties of this instrument beyond singular national groups, including in terms of gendered identities, age groups, and languages.

This is particularly important as some work has suggested that de-
mographic factors affect responding on the NES (Picanço et al., 2024).

1.2. *Connectedness to nature*

Although many measures of nature connectedness exist (Martin & Czellar, 2016; Richardson et al., 2019), one of the most widely used is the Connectedness to Nature Scale (CNS; Mayer & Frantz, 2004; cited more than 3500 times based on Google Scholar citations up to August 2024). In the original study reporting on the development of the 14-item CNS (see Appendix 1 for items), Mayer and Frantz (2004) reported – based on exploratory factor analyses (EFAs) – that scores were unidimensional in two community and three college samples from the United States. The unidimensional model of the 14-item CNS scores has also been supported in other national and linguistic contexts, such as Brazil (Pessoa et al., 2016), China (Li & Wu, 2016), Italy (Lovati et al., 2023), and Spain (Mattas-Terrón & Elósegui-Bandera, 2012).

However, not all studies have demonstrated adequate fit of the unidimensional model of CNS scores, and in some national contexts, a unidimensional model was only supported once several items were eliminated: one item in Spain (Olivos et al., 2013), three items in France (Navarro et al., 2017) and Australia (Pearce et al., 2022), four each in Kenya (Marczak & Sorokowski, 2018) and South Korea (Gim et al., 2019), and seven items in Poland (Strzelecka et al., 2023). Likewise, Andić and Šuperina (2021) reported difficulties translating four CNS items into Croatian, resulting in a 10-item, single factor, instrument. In Turkey, the CNS reduced to two dimensions reflecting integration with nature (two items) and feeling part of nature (six items; Bektaş et al., 2017).

These equivocal findings may reflect the fact items on the CNS contain two verbal structures: items that include the word “feel” as an emotional component and other items that more closely reflect a cognitive belief in one’s connection to nature (Lee & Oh, 2021; Perrin & Benassi, 2009). Alternatively, it is possible that some CNS items either function poorly or are redundant in some national contexts. Based on Item Response Theory, Pasca et al. (2017) suggested that seven CNS items (Items #1, 3, 4, 8, 12, 13, and 14) were either redundant or lacked adequate fit in a sample of Spanish adults. In subsequent analyses, the same authors also suggested that a truncated, 7-item version of the CNS (i.e., the CNS-7) had adequate composite reliability, although factorial validity was not assessed. In a more recent study with Brazilian university students, Rosa et al. (2022) reported that scores on the CNS-7 were unidimensional and that the CNS-7 had slightly improved fit indices compared to the full version.

To date, however, assessments of the measurement invariance of the CNS across national groups in the same study, conducted at the same time, remain rare. One study using samples from seven nations (Spain, the Netherlands, Turkey, Portugal, Germany, France, and Hungary) utilising the CNS-7 supported metric, but not scalar invariance, once the loading associated with Item #7 was relaxed (Navarro et al., 2022). In terms of the 14-item CNS, Pasca et al. (2018) examined measurement invariance across samples from Spain and the United States (the latter representing data from Mayer & Frantz, 2004). Their analyses indicated support for configural, but not metric, invariance. Based on Item Response Theory, Pasca et al. (2018) further noted that seven of the CNS items showed differential functioning across groups. These studies suggest that the latent connectedness to nature construct is not equivalent across national groups (Navarro et al., 2022).

Beyond invariance across national groups, very little work has assessed invariance of the CNS across other sociodemographic characteristics. For instance, only two studies have examined the measurement invariance of the CNS across gender identities. In samples of Italian adults, Di Fabio and Rosen (2019) and Lovati et al. (2023), respectively, reported that the CNS achieved full scalar invariance across women and men. However, it remains possible that gendered experiences – particularly across national or cultural groups – shape one’s understanding
and manifestations of connectedness to nature. Drawing on gender socialisation theories, for instance, McCright (2010) suggested that women, more so than men, are expected to demonstrate an ethic of care for the natural environment and exhibit both greater environmental concern (e.g., Xiao & McCright, 2015) and dispositional empathy with nature (Tam, 2013).

As such, there is a need to more carefully and comprehensively assess the extent to which the CNS is invariant across gender identities in multiple cultural contexts. Likewise, we are unaware of any previous work that has examined invariance of the CNS across age groups. Existing studies have reported equivocal results in terms of the association between CNS scores and age, with some studies reporting that CNS scores increase with older age (Swami et al., 2016) and other studies reporting no significant association (e.g., Swami et al., 2016). Other work has suggested that connectedness to nature dips in adolescence before returning to pre-adolescent levels in early adulthood (Richardson et al., 2019), where it then remains relatively stable (Anderson & Krettenauer, 2021). More work is needed to explore these patterns across different cultural contexts.

1.3. *The present study*

Large, multinational studies offer the best opportunity to deal with
many of the issues noted above, particularly given that research on
nature exposure and connectedness to nature often centres the experi-
ences of respondents in the Global North (Barragan-Jason et al., 2023;
Soga & Gaston, 2023). Thus, in the present study, we utilised data from
the Body Image in Nature Survey (BINS; Swami, Tran et al., 2022), a
collaborative, 253 researcher-crowdsourced project that gathered CNS
and NES data between 2020 and 2022 from participants in 65 nations
across 40 language groups with variance across gender identities and
age groups. In terms of the NES, we considered whether a unidimen-
sional model with all four items, as well as multidimensional models,
would offer optimal fit. Given that there are few assessments of the
factorial validity of this instrument and the limits of cross-sectional data
for establishing the dimensionality of measures, we do not advance any
specific hypotheses here. In terms of the CNS, we adopted an exploratory
framework, considering the extent to which either the full 14-item CNS,
or the truncated CNS-7, would balance item retention and measurement
invariance across groups. As a preliminary hypothesis, we expected that
the CNS-7 would demonstrate superior fit compared to the full CNS and
would also evidence scalar or partial scalar invariance across groups.

A second objective was to assess whether, and the extent to which,
ature exposure is significantly associated with connectedness to nature
across nations. Our expectation, based on previous work, was of a small,
positive correlation in the r ~ .30 range (Sheffield et al., 2022; Swami
et al., 2016; Swami, Barron, et al., 2020). Finally, we also assessed the
extent to which sociodemographic variables included in the BINS (i.e.,
financial security, urbanicity, educational qualifications, marital status,
and racialised status) were associated with both nature exposure and
connectedness to nature. Although this aspect of our study was more
exploratory, based on the available evidence, we expected that greater
nature exposure and connectedness to nature would be significantly
associated with greater financial security, rural residence (e.g., Carrus
et al., 2020; Richardson et al., 2019), higher educational qualifications
(e.g., Nesbitt et al., 2019), being married/in a committed relationship
(Pasanen et al., 2023), and racialised majority status (e.g., Murdock,
2019).

2. Materials and methods

2.1. Overview of the Body Image in Nature Survey

Full details of the Body Image in Nature Survey (BINS) are published
elsewhere (Swami, Tran et al., 2022). Data were collected between
November 2020 and February 2022 with community sampling, with the

majority of recruitment taking place online. The overall project received
ethics approval from the School Research Ethics Panel at the first au-
thor's institution (approval code: PSY-S19-015) and, unless exempt by
national laws, all collaborating teams additionally obtained ethics
approval from local institutional ethics committees or review boards. A
list of nations, associated sample sizes, data collection methods, ethics
approvals, and survey languages is presented in Supplementary Table
S1. Sample sizes ranged from 204 in the United Arab Emirates (Arabic
to 3275 in Thailand.

## 2.2. Participants

The BINS dataset consists of 56,968 respondents, of whom 58.9%
were women, 40.5% were men, and 0.6% reported another gender
identity. Ages ranged from 18 to 99 years (M = 33.10, SD = 13.79). In
terms of financial security compared to others of their age in their
country, 25.5% and 49.6% felt more or equally secure, respectively,
with 24.9% feeling less secure. Most (84.5%) lived in an urban rather
than a rural (15.5%) area, and the majority reported at least completing
secondary education (72.6%). In total, 53.0% were in a committed
relationship including marriage. The majority (74.2%) self-identified as
being part of a racialised majority in their country, whereas 11.3%
identified as part of a racialised minority group (13.5% were uncertain
and race data were not collected in France due to prohibition of the
collection and storage of race-related data). Table 1 presents detailed
sample description data for all individual nations. In six countries, data
was collected in either two (Canada, Iceland, India, the Philippines, the
United Arab Emirates [UAE]) or three (China) languages.

2.3. *Measures*

2.3.1. *Nature exposure*

The 4-item self-reported Nature Exposure Scale (NES; Kamitsis & Francis, 2013) covers perceptions about everyday nature exposure, frequency of more distal visits (“nature exposure”; Items #1 and #3), and attention paid to nature in both settings (“noticing nature”; Items #2 and #4; see Appendix 1 for English wording). Response anchors varied depending on the item, but all used 5-point scales. The NES was translated for use in the present project using the back-translation procedure (Brislin, 1986; for further information, see Swami, Tran et al., 2022) unless it was presented in English or a validated, localised version was available for use. A list of the 40 languages in which the BINS survey package was presented is reported in Supplementary Table S1 and all translations are available from the first author.

2.3.2. *Connectedness to nature*

The 14-item Connectedness to Nature Scale (CNS; Mayer & Frantz, 2004; items in English are presented in Appendix 1) uses a 5-point response scale: 1 (*strongly disagree*) to 5 (*strongly agree*). Unless presented in English, or where a previously validated translation was not available, the CNS was also translated for use in the BINS using the parallel back-translation procedure (see Supplementary Table S1).

2.3.3. *Urbanicity*

To assess urbanicity, participants were asked about their current
place of residence, with response options adapted from Pedersen and
Mortensen (2001) as follows: *capital city*, *capital city suburbs*, *provincial city* (*more than 100,000 residents*), *provincial town* (*more than 10,000 residents*), and *rural areas*. Response options were assigned values 1 to 5
(in the above order) for statistical analysis and collapsed into *urban* versus *rural* for descriptive purposes. This measure of urbanicity has
been used in previous cross-national work (Swami et al., 2020).

2.3.4. Financial security

Following previous cross-national work (Swami et al., 2012, 2020),
participants were asked to self-report how financially secure they felt
<table><caption>**Table 1**
Sample Descriptions of Data from the Body Image in Nature Survey (BINS).</caption><thead><tr><th>Nation</th><th>Sample size</th><th>Mean age (SD)</th><th>% Women</th><th>Mean financial security (SD)</th><th>%Urban residence</th><th>%Secondary/tertiary education</th><th>%In committed relationship or married</th><th>%Racialised minority</th></tr></thead><tbody><tr><td>Argentina</td><td>670</td><td>35.36 (13.6)</td><td>57</td><td>2.13 (.7)</td><td>98</td><td>81</td><td>50</td><td>9</td></tr><tr><td>Australia</td><td>1038</td><td>35.23 (13.1)</td><td>71</td><td>1.90 (.8)</td><td>93</td><td>77</td><td>55</td><td>18</td></tr><tr><td>Austria</td><td>1279</td><td>41.99 (16.5)</td><td>54</td><td>2.08 (.7)</td><td>67</td><td>62</td><td>63</td><td>9</td></tr><tr><td>Bahrain</td><td>441</td><td>30.47 (9.8)</td><td>74</td><td>1.98 (.6)</td><td>98</td><td>87</td><td>51</td><td>8</td></tr><tr><td>Bangladesh</td><td>460</td><td>29.30 (8.6)</td><td>42</td><td>1.78 (.8)</td><td>88</td><td>80</td><td>51</td><td>13</td></tr><tr><td>Bosnia & Herzegovina</td><td>406</td><td>43.93 (10.9)</td><td>64</td><td>2.15 (.7)</td><td>87</td><td>90</td><td>70</td><td>16</td></tr><tr><td>Brazil</td><td>1462</td><td>36.77 (12.0)</td><td>58</td><td>2.21 (.7)</td><td>99</td><td>86</td><td>66</td><td>12</td></tr><tr><td>Bulgaria</td><td>248</td><td>33.52 (14.1)</td><td>62</td><td>2.16 (.6)</td><td>92</td><td>54</td><td>52</td><td>4</td></tr><tr><td>Canada (English)</td><td>336</td><td>24.61 (10.0)</td><td>83</td><td>2.10 (.7)</td><td>82</td><td>36</td><td>48</td><td>14</td></tr><tr><td>Canada (French)</td><td>806</td><td>38.22 (12.8)</td><td>88</td><td>2.29 (.7)</td><td>78</td><td>95</td><td>72</td><td>7</td></tr><tr><td>Chile</td><td>422</td><td>36.14 (13.6)</td><td>79</td><td>2.28 (.8)</td><td>94</td><td>73</td><td>41</td><td>8</td></tr><tr><td>China (Cantonese)</td><td>409</td><td>20.50 (5.9)</td><td>58</td><td>2.18 (.7)</td><td>100</td><td>96</td><td>2</td><td>2</td></tr><tr><td>China (English)</td><td>349</td><td>21.93 (5.3)</td><td>65</td><td>1.79 (.7)</td><td>97</td><td>62</td><td>26</td><td>6</td></tr><tr><td>China (Mandarin)</td><td>1231</td><td>35.00 (7.3)</td><td>69</td><td>1.82 (.6)</td><td>95</td><td>92</td><td>86</td><td>4</td></tr><tr><td>Colombia</td><td>793</td><td>27.15 (11.5)</td><td>60</td><td>2.01 (.8)</td><td>96</td><td>57</td><td>22</td><td>7</td></tr><tr><td>Croatia</td><td>898</td><td>39.10 (12.1)</td><td>59</td><td>2.08 (.7)</td><td>71</td><td>91</td><td>69</td><td>2</td></tr><tr><td>Cyprus</td><td>363</td><td>34.31 (9.6)</td><td>65</td><td>2.09 (.7)</td><td>87</td><td>69</td><td>64</td><td>4</td></tr><tr><td>Czechia</td><td>700</td><td>38.10 (17.0)</td><td>66</td><td>2.29 (.6)</td><td>82</td><td>75</td><td>62</td><td>2</td></tr><tr><td>Ecuador</td><td>863</td><td>30.97 (12.3)</td><td>53</td><td>1.81 (.8)</td><td>86</td><td>65</td><td>33</td><td>11</td></tr><tr><td>Egypt</td><td>1627</td><td>23.62 (8.7)</td><td>72</td><td>2.06 (.6)</td><td>98</td><td>86</td><td>27</td><td>6</td></tr><tr><td>Estonia</td><td>449</td><td>38.93 (14.1)</td><td>63</td><td>2.10 (.7)</td><td>80</td><td>64</td><td>58</td><td>2</td></tr><tr><td>France</td><td>562</td><td>36.01 (14.2)</td><td>76</td><td>2.08 (.7)</td><td>64</td><td>67</td><td>47</td><td>NA</td></tr><tr><td>Germany</td><td>620</td><td>31.01 (11.9)</td><td>62</td><td>2.18 (.8)</td><td>83</td><td>64</td><td>58</td><td>12</td></tr><tr><td>Ghana</td><td>434</td><td>21.97 (4.5)</td><td>41</td><td>2.08 (.8)</td><td>84</td><td>72</td><td>32</td><td>26</td></tr><tr><td>Greece</td><td>556</td><td>31.49 (11.8)</td><td>65</td><td>2.03 (.7)</td><td>91</td><td>63</td><td>55</td><td>5</td></tr><tr><td>Hungary</td><td>654</td><td>32.80 (13.4)</td><td>69</td><td>2.07 (.6)</td><td>72</td><td>69</td><td>63</td><td>2</td></tr><tr><td>Iceland (English)</td><td>1149</td><td>38.50 (17.5)</td><td>50</td><td>2.27 (.7)</td><td>92</td><td>61</td><td>65</td><td>11</td></tr><tr><td>Iceland (Icelandic)</td><td>432</td><td>54.91 (15.5)</td><td>54</td><td>2.05 (.6)</td><td>75</td><td>81</td><td>78</td><td>3</td></tr><tr><td>India (Hindi)</td><td>1664</td><td>32.07 (11.8)</td><td>45</td><td>2.14 (.8)</td><td>73</td><td>78</td><td>45</td><td>13</td></tr><tr><td>India (Tamil)</td><td>376</td><td>36.78 (12.1)</td><td>52</td><td>1.71 (.6)</td><td>57</td><td>65</td><td>70</td><td>37</td></tr><tr><td>Indonesia</td><td>292</td><td>19.79 (3.2)</td><td>72</td><td>1.76 (.5)</td><td>87</td><td>43</td><td>14</td><td>3</td></tr><tr><td>Iran</td><td>1318</td><td>33.46 (11.3)</td><td>60</td><td>1.99 (.6)</td><td>95</td><td>82</td><td>61</td><td>29</td></tr><tr><td>Iraq</td><td>405</td><td>34.13 (12.1)</td><td>33</td><td>1.49 (.5)</td><td>100</td><td>97</td><td>45</td><td>53</td></tr><tr><td>Ireland</td><td>351</td><td>33.73 (12.4)</td><td>50</td><td>2.11 (.8)</td><td>76</td><td>80</td><td>62</td><td>5</td></tr><tr><td>Israel</td><td>493</td><td>30.77 (11.6)</td><td>62</td><td>2.13 (.7)</td><td>87</td><td>67</td><td>32</td><td>7</td></tr><tr><td>Italy</td><td>2307</td><td>33.17 (14.0)</td><td>62</td><td>1.95 (.6)</td><td>81</td><td>67</td><td>61</td><td>6</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 1 (continued)</caption><thead><tr><th>Nation</th><th>Sample size</th><th>Mean age (SD)</th><th>% Women</th><th>Mean financial security (SD)</th><th>%Urban residence</th><th>%Secondary/tertiary education</th><th>%In committed relationship or married</th><th>%Racialised minority</th></tr></thead><tbody><tr><td>Japan</td><td>360</td><td>49.44 (16.6)</td><td>100</td><td>1.79 (.6)</td><td>90</td><td>81</td><td>61</td><td>8</td></tr><tr><td>Kazakhstan</td><td>380</td><td>30.07 (11.3)</td><td>53</td><td>2.04 (.6)</td><td>94</td><td>76</td><td>48</td><td>11</td></tr><tr><td>Latvia</td><td>827</td><td>41.04 (12.8)</td><td>66</td><td>2.02 (.7)</td><td>74</td><td>82</td><td>69</td><td>4</td></tr><tr><td>Lebanon</td><td>1295</td><td>25.74 (12.3)</td><td>67</td><td>1.93 (.7)</td><td>70</td><td>63</td><td>33</td><td>16</td></tr><tr><td>Lithuania</td><td>491</td><td>40.34 (12.8)</td><td>51</td><td>2.05 (.6)</td><td>72</td><td>84</td><td>74</td><td>3</td></tr><tr><td>Malaysia</td><td>1193</td><td>27.81 (8.7)</td><td>69</td><td>1.74 (.6)</td><td>76</td><td>84</td><td>29</td><td>30</td></tr><tr><td>Malta</td><td>347</td><td>35.52 (15.4)</td><td>72</td><td>2.10 (.7)</td><td>78</td><td>71</td><td>60</td><td>7</td></tr><tr><td>Nepal</td><td>353</td><td>25.78 (6.0)</td><td>50</td><td>1.77 (.7)</td><td>82</td><td>98</td><td>28</td><td>5</td></tr><tr><td>Netherlands</td><td>1004</td><td>46.81 (16.3)</td><td>53</td><td>2.05 (.6)</td><td>61</td><td>98</td><td>69</td><td>9</td></tr><tr><td>Nigeria</td><td>1274</td><td>31.64 (9.2)</td><td>34</td><td>1.85 (.8)</td><td>93</td><td>64</td><td>63</td><td>14</td></tr><tr><td>Norway</td><td>360</td><td>41.24 (11.6)</td><td>77</td><td>2.17 (.7)</td><td>78</td><td>92</td><td>77</td><td>4</td></tr><tr><td>Pakistan</td><td>267</td><td>20.59 (2.7)</td><td>28</td><td>2.16 (.9)</td><td>100</td><td>47</td><td>83</td><td>49</td></tr><tr><td>Palestine</td><td>401</td><td>27.64 (9.5)</td><td>25</td><td>2.01 (.6)</td><td>81</td><td>90</td><td>42</td><td>7</td></tr><tr><td>Philippines (English)</td><td>350</td><td>24.87 (11.2)</td><td>0</td><td>2.03 (.7)</td><td>97</td><td>56</td><td>24</td><td>13</td></tr><tr><td>Philippines (Tagalog)</td><td>504</td><td>37.43 (11.9)</td><td>73</td><td>1.83 (.7)</td><td>97</td><td>89</td><td>65</td><td>16</td></tr><tr><td>Poland</td><td>1954</td><td>30.51 (11.9)</td><td>62</td><td>1.99 (.7)</td><td>74</td><td>63</td><td>56</td><td>3</td></tr><tr><td>Portugal</td><td>363</td><td>36.53 (17.9)</td><td>68</td><td>2.05 (.7)</td><td>85</td><td>81</td><td>37</td><td>5</td></tr><tr><td>Romania</td><td>1819</td><td>26.94 (10.8)</td><td>53</td><td>2.05 (.7)</td><td>80</td><td>49</td><td>60</td><td>5</td></tr><tr><td>Russia</td><td>206</td><td>39.94 (11.8)</td><td>71</td><td>1.84 (.5)</td><td>97</td><td>84</td><td>67</td><td>8</td></tr><tr><td>Saudi Arabia</td><td>380</td><td>28.02 (9.7)</td><td>55</td><td>2.03 (.7)</td><td>94</td><td>83</td><td>33</td><td>20</td></tr><tr><td>Serbia</td><td>650</td><td>30.72 (11.3)</td><td>56</td><td>2.20 (.7)</td><td>95</td><td>65</td><td>65</td><td>10</td></tr><tr><td>Slovakia</td><td>814</td><td>37.79 (14.7)</td><td>54</td><td>1.92 (.6)</td><td>65</td><td>75</td><td>67</td><td>4</td></tr><tr><td>Slovenia</td><td>452</td><td>36.84 (14.9)</td><td>59</td><td>2.16 (.7)</td><td>49</td><td>87</td><td>66</td><td>2</td></tr><tr><td>South Africa</td><td>318</td><td>35.15 (16.1)</td><td>53</td><td>1.74 (.8)</td><td>78</td><td>73</td><td>45</td><td>31</td></tr><tr><td>South Korea</td><td>381</td><td>27.60 (9.7)</td><td>48</td><td>1.89 (.6)</td><td>98</td><td>54</td><td>43</td><td>52</td></tr><tr><td>Spain</td><td>1266</td><td>34.54 (16.3)</td><td>52</td><td>2.17 (.8)</td><td>88</td><td>82</td><td>43</td><td>5</td></tr><tr><td>Switzerland</td><td>377</td><td>46.48 (15.2)</td><td>52</td><td>1.98 (.7)</td><td>62</td><td>51</td><td>66</td><td>5</td></tr><tr><td>Taiwan</td><td>529</td><td>41.36 (13.6)</td><td>60</td><td>2.48 (.7)</td><td>90</td><td>92</td><td>67</td><td>7</td></tr><tr><td>Thailand</td><td>3275</td><td>25.85 (10.8)</td><td>62</td><td>1.76 (.6)</td><td>87</td><td>45</td><td>23</td><td>6</td></tr><tr><td>Tunisia</td><td>374</td><td>41.62 (15.2)</td><td>55</td><td>2.10 (.6)</td><td>96</td><td>90</td><td>63</td><td>0</td></tr><tr><td>Türkiye</td><td>2518</td><td>31.63 (11.5)</td><td>57</td><td>1.98 (.8)</td><td>97</td><td>61</td><td>57</td><td>14</td></tr><tr><td>Ukraine</td><td>141</td><td>39.00 (11.7)</td><td>59</td><td>1.74 (.6)</td><td>95</td><td>87</td><td>71</td><td>9</td></tr><tr><td>United Arab Emirates (Arabic)</td><td>204</td><td>26.37 (6.7)</td><td>73</td><td>2.07 (.4)</td><td>99</td><td>35</td><td>39</td><td>10</td></tr><tr><td>United Arab Emirates (English)</td><td>904</td><td>27.50 (11.8)</td><td>36</td><td>2.13 (.8)</td><td>98</td><td>73</td><td>43</td><td>31</td></tr><tr><td>United Kingdom</td><td>1243</td><td>37.99 (13.9)</td><td>54</td><td>2.03 (.7)</td><td>84</td><td>87</td><td>68</td><td>23</td></tr><tr><td>United States of America</td><td>2531</td><td>35.35 (12.7)</td><td>62</td><td>1.93 (.7)</td><td>85</td><td>82</td><td>61</td><td>20</td></tr></tbody></table>

Note. SD = standard deviation.
relative to others of their own age in their country of residence (1 = less secure, 2 = same, 3 = more secure).

2.3.5. Demographics

Highest educational qualification was assessed as follows: 1 = no formal education, 2 = primary education, 3 = secondary education, 4 = still in full-time education, 5 = undergraduate degree, 6 = postgraduate degree, 7 = other; marital status was assessed as: 1 = single, 2 = single but in a committed relationship, 3 = married, 4 = other; and racialised status relative to their country of residence was assessed as: 1 = ethnic/racial majority, 2 = ethnic/racial minority, 3 = not sure. The latter item provides a common metric of categorising ethnicity/race across diverse nations (Swami, Barron, et al., 2020). For descriptive purposes at the national level and for analyses, response options for highest educational qualification were collapsed into secondary/tertiary (secondary education, undergraduate degree, postgraduate degree) versus other (all remaining categories) and response options of racialised status were collapsed into racialised minority (racial minority) versus other (all remaining categories).

2.4. Procedures, ethics, and data sharing

Full procedural information about the BINS is provided in Swami et al. (2022). The BINS project was conducted in accordance with the principles of the Declaration of Helsinki and following all local institutional guidelines. In brief, once local ethics approval had been obtained or collaborators confirmed that approval was not required as per national laws (see Supplementary Table S1), researchers recruited participants from the community in their respective nations between November 2020 and February 2022. Inclusion criteria were being ≥ 18 years of age, a resident and citizen of the particular nation in which recruitment took place, and being able to complete a survey in the language in which it was presented. In all but nine locales (see Supplementary Table S1), data collection was conducted online. All participants were presented with a standardised information sheet and provided (digital or written) informed consent before completing an anonymous version of the BINS package. Upon completion, participants received debriefing information, which included contact information for the first author as well as a local researcher. The BINS data and our analytic codes are available on the Open Science Framework at https://osf.io/rfhwe/?view_only=dc87d4d3088b4f62922177fbbe06e8b6.

2.5. Analytic strategy

The general analytic plan, including structural and measurement
invariance analyses of the key variables of the BINS (including the NES
and CNS) is described in the BINS study protocol (Swami et al., 2022).
Further analyses not covered in the study protocol were not preregis-
tered separately.

The analysis proceeded in four steps, in a similar fashion for both the
NES and CNS: first, CFA models were fitted to the total sample to
determine the structure of the NES and test both the full CNS (henceforth
“CNS-14”) and the CNS-7 for unidimensionality. For the NES, unidi-
mensional and 2-factor models were fitted, testing for the possible scale
multidimensionality (nature exposure in everyday life and environ-
ments: Items #1 and #2; nature exposure outside everyday environ-
ments: Items #3 and #4). As the items further allude to a conditional
structure (with Items #1 and #3 assessing levels of “nature exposure” in
each domain and Items #2 and #4 assessing “noticing nature” in each
domain), a unidimensional model with correlated error variances be-
tween Items #1 and #2, and Items #3 and #4, respectively was also
fitted on the data (an analogous 2-factor model could not be tested, as it
was under-identified with only 4 items). The best-fitting model was then
used for further analysis. In the unidimensional model of the CNS, error
variances of Items #5 and #7, and Items #5 and #10 were allowed to
correlate (following Rosa et al., 2022). We expected a better model fit

for the CNS-7 than the CNS-14, but followed this strategy to determine
which version of the instrument should be used in further analysis.

Second, in the six countries where data was collected in multiple
languages, measurement invariance of the NES and CNS was tested with
multi-group CFAs (MG-CFAs) for cross-language survey presentation.
Data from the same nations were merged in the subsequent measure-
ment invariance analysis of nations only if scalar measurement invari-
ance held within nations (i.e., all linguistic versions of the scales showed
satisfactory invariance). This was only true for China. Where this was
not the case, data for the two different language versions of the survey
were kept separate in further analysis (i.e., Canada, Iceland, India, the
Philippines, and the UAE). We therefore use the term “national groups”
rather than “countries” where we are reporting results that include
multiple languages within countries.

Third, measurement invariance of the CNS and NES was tested with MG-CFAs for: national groups, languages, gender identities (women vs. men vs. other gender identities), and age groups based on Arnett (2000) and Erikson (1968), namely young adulthood (18–24 years), middle adulthood (25–44 years) and older adulthood (≥ 45 years). Note that testing measurement invariance of languages implied mixing different national groups with the same language in analysis (e.g., English was the survey language in the UK, the USA, but, *inter alia*, also in one of the Chinese samples). Assuming scalar or at least partial scalar measurement invariance, we then examined latent means in these groups and, assuming at least metric invariance, we examined associations between the NES and CNS across national groups, using factor scores.

Fourth, assuming scalar or at least partial scalar measurement
invariance, sociodemographic correlates of the NES and CNS factor
scores were investigated with multilevel models (MLMs) across national
groups. Predictors were financial security, urbanicity (urban vs. rural)
education (secondary/tertiary vs. other), marital status (committed/
married vs. other), and racialised identity (racialised minority vs. other).

Mplus 8.8 (Muthén & Muthén, 2022) was used for the CFAs, MG-CFAs, and MLMs. Significance was set to *p* < .05. For the structural analyses, the weighted mean- and variance-adjusted weighted least squares estimator (WLSMV) was used to account for the ordered-categorical item response formats of the NES and CNS. WLSMV estimates one loading parameter for each item, but \#response options – 1 threshold parameters (one for each transition of one response option to the next) instead of a single intercept parameter per item. To account for missing data (0.4% in the NES, 1.4% in the CNS-14), full information maximum likelihood was used.

Measurement invariance analyses tested for configural invariance (i. e., same loading patterns across groups), metric invariance (i.e., same unstandardised loadings across groups), and scalar invariance (i.e., same unstandardised loadings and threshold parameters across groups). Based on the configural invariance models, reliability estimates (ω total) are presented for all groups. If full scalar measurement invariance could not be assumed, we aimed for partial scalar measurement invariance instead (i.e., equal item parameters across some groups and items, but not all). For this, the alignment method (e.g., Asparouhov & Muthén, 2023) was used for guidance. Alignment does not require exact measurement invariance, but instead seeks a solution that minimises the differences in loadings and threshold parameters across groups, while still retaining identical fit to the configural invariance model. The method provides for each item parameter information on the groups for which invariance holds and an R² measure that indicates the amount of invariance (typically, the more invariant, the higher the R²; however, in some special cases, R² is a poor measure of invariance; Asparouhov & Muthén, 2023). This information was used to (a) select two items per scale (anchor items) for which invariance was assumed (two anchor items being sufficient for the comparison of latent means; Pokropek et al., 2019) and to (b) exclude groups that violated invariance the most. Additionally, we looked at the reliability of the scales in each group and the contributions of each group to the overall χ² values of the MG-CFAs. If composite reliabilities were low and/or the contributions to the χ² value were high,
compared to sample size, groups were excluded from the final partial scalar measurement models.

For the assessment of model fit, commonly used fit indices were consulted: the comparative fit index (CFI; good/acceptable fit: ≥ .95/.90), the Tucker-Lewis index (TLI; ≥ .95/.90), the root-mean square error of approximation (RMSEA; good fit: ≤ .06) and its 90% confidence interval, and the standardised root mean square residual (SRMR; good fit ≤ .08; Hu & Bentler, 1999). The cut-off for the RMSEA was set to .15 for MG-CFAs with more than 10 groups (Rutkowski & Svetina, 2014). For the measurement invariance analyses, we present ΔCFI and ΔRMSEA values, and Δχ² tests, but primarily interpreted the former two as they were not affected by sample size. For the comparison of metric to configural, and scalar to metric invariance models, the cut-offs ΔCFI ≤ .020/.010 and ΔRMSEA ≤ .030/.015 were used to indicate good fit of the respective stricter model (Rutkowski & Svetina, 2014).

For the MLMs, Bayesian estimation (using diffuse priors as specified in Mplus default settings) was used. This allowed obtaining correctly standardised (cf. van Assen et al., 2022) parameter estimates that were interpreted as measures of effect size (comparable to Pearson's *r*). For the dichotomous predictors, these estimates were further transformed into the metric of Cohen's *d* as well. The predictors of financial security, urbanicity, education, marital status, and racialised identity were groupmean-centred on Level 1, and their cluster-level means were further used as Level-2 predictors. Thereby, associations on the individual level (Level 1) and on the cluster-level (national groups; Level 2) could be optimally distinguished. However, to avoid overfitting on Level 2, we only kept significant Level-2 predictors in the final models.

# 3. Results

## 3.1. Nature exposure scale

### 3.1.1. Structural analysis in the total sample

The unidimensional model had poor to borderline fit to the data, judging by its CFI value (and disregarding TLI and RMSEA values, because of the small degrees of freedom of this model; Table 2). Items #1

and #3 had lower standardised loadings (.54 and .67) than Items #2 and #4 (.84 and .72; all ps < .001). Including correlated errors between Items #1 and #2, and #3 and #4, respectively, to accommodate the model for their conditional structure improved the model fit considerably (to keep this model identified, the strength of the residual association between the two items needed to be constrained to equality across pairs). The standardised items loadings in this model were .44, .86, .60, and .67, with residual correlations between Items #1 and #2, and #3 and #4, of .35 and .27 (all ps < .001).

In contrast, a correlated 2-factor model (setting equality constraints for the similar loadings in each pair of Items #1 and #3, and #2 and #4 to obtain admissible parameter estimates of the residual variances) resulted in a poorer model fit (Table 2). In this model, Items #1 and #2 loaded on one factor (nature exposure in everyday life and environments; standardised loadings = .62 and .87) and Items #3 and #4 on another (nature exposure outside everyday environments; .63 and .89). The latent factor intercorrelation was .76 (all ps < .001).

Thus, even though there was some indication of potential multidimensionality in the NES, a unidimensional model that accommodated the conditional structure of the items fitted the data best and was, therefore, used in all subsequent analyses. However, according to their threshold parameters, some response options were relatively uninformative, because item categories were so close to one another (which was also reflected in the relative sparseness of response option endorsement). Thus, response options 1 and 2, and 4 and 5 were each combined for Items #1 and #3, and response options 1 and 2 for Item #4. Even though this measure only slightly increased some model fit indices and slightly decreased others (Table 2), it ensured that sparseness of data did not further complicate the subsequent multigroup analyses. Thus, response options were also combined in all multigroup analyses.

### 3.1.2. Invariance of the cross-language results in the six countries with multiple survey languages

The MG-CFA invariance test results are presented in Supplementary Table S2. Configural and metric invariance was found for all six countries, but scalar invariance only for China. Thus, only the data from

<caption>**Table 2**</caption>

Analyses of the NES in the total sample and invariance of the NES concerning national groups, language, gender identity, and age.

<table><thead><tr><th rowspan="2">Grouping variable and type of model</th><th rowspan="2">χ²(df)</th><th rowspan="2">CFI</th><th rowspan="2">TLI</th><th rowspan="2">RMSEA</th><th rowspan="2">90% CI</th><th rowspan="2">SRMR</th><th colspan="4">Model comparisons</th></tr><tr><th>ΔCFI</th><th>ΔRMSEA</th><th>Configural</th><th>Metric</th></tr></thead><tbody><tr><td>Total sample</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1F</td><td>6369.45(2)</td><td>.944</td><td>.831</td><td>.236</td><td>[.232, .241]</td><td>.043</td><td></td><td></td><td></td><td></td></tr><tr><td>1FCE</td><td>1754.73(1)</td><td>.984</td><td>.907</td><td>.175</td><td>[.169, .182]</td><td>.021</td><td></td><td></td><td></td><td></td></tr><tr><td>1FCE + CRO</td><td>1493.96(1)</td><td>.984</td><td>.902</td><td>.162</td><td>[.155, .169]</td><td>.027</td><td></td><td></td><td></td><td></td></tr><tr><td>2FConL</td><td>4434.23(2)</td><td>.961</td><td>.882</td><td>.197</td><td>[.192, .202]</td><td>.042</td><td></td><td></td><td></td><td></td></tr><tr><td>2FConL + CRO</td><td>3367.73(2)</td><td>.963</td><td>.889</td><td>.172</td><td>[.167, .177]</td><td>.048</td><td></td><td></td><td></td><td></td></tr><tr><td>National groups</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Configural invariance</td><td>1423.31(139)</td><td>.987</td><td>.961</td><td>.107</td><td>[.102, .112]</td><td>.034</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>6951.05(346)</td><td>.934</td><td>.920</td><td>.153</td><td>[.150, .156]</td><td>.066</td><td>.053</td><td>.046</td><td>5300.73(207)</td><td></td></tr><tr><td>Scalar invariance</td><td>19559.39(691)</td><td>.812</td><td>.886</td><td>.183</td><td>[.181, .185]</td><td>.084</td><td>.124</td><td>.030</td><td>17980.50(552)</td><td>13946.75(345)</td></tr><tr><td>Language</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Configural invariance</td><td>1579.37(79)</td><td>.985</td><td>.954</td><td>.115</td><td>[.111, .120]</td><td>.032</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>5970.23(196)</td><td>.942</td><td>.929</td><td>.144</td><td>[.141, .147]</td><td>.055</td><td>.043</td><td>.029</td><td>4266.41(117)</td><td></td></tr><tr><td>Scalar invariance</td><td>17242.02(430)</td><td>.831</td><td>.906</td><td>.166</td><td>[.164, .168]</td><td>.072</td><td>.023</td><td>.022</td><td>15544.82(351)</td><td>12088.40(234)</td></tr><tr><td>Gender identity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Configural invariance</td><td>1052.28(5)</td><td>.989</td><td>.959</td><td>.105</td><td>[.100, .110]</td><td>.027</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>863.35(11)</td><td>.991</td><td>.985</td><td>.064</td><td>[.060, .068]</td><td>.027</td><td>-.002</td><td>-.041</td><td>23.19(6)</td><td></td></tr><tr><td>Scalar invariance</td><td>989.69(23)</td><td>.989</td><td>.992</td><td>.047</td><td>[.045, .050]</td><td>.028</td><td>.002</td><td>-.017</td><td>134.03(18)</td><td>120.26(12)</td></tr><tr><td>Age</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Configural invariance</td><td>1206.13(5)</td><td>.987</td><td>.952</td><td>.112</td><td>[.107, .118]</td><td>.027</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>1048.65(11)</td><td>.989</td><td>.981</td><td>.070</td><td>[.067, .074]</td><td>.028</td><td>-.002</td><td>-.042</td><td>150.04(6)</td><td></td></tr><tr><td>Scalar invariance</td><td>1473.44(23)</td><td>.984</td><td>.987</td><td>.058</td><td>[.055, .060]</td><td>.031</td><td>.005</td><td>-.012</td><td>536.33(18)</td><td>414.80(12)</td></tr></tbody></table>

Note. 1F = 1-factor model; 1FCE = 1-factor model with correlated errors between Items #1 and #2, and #3 and #4; 1FCE + CRO = 1FCE model with combined response options in Items #1, #3, and #4 (see main text for details); 2FConL = correlated 2-factors model with equality constraints on the loadings of Items #1 and #3, and #2 and #4; 2FConL + CRO = 2FConL model with combined response options in Items #1, #3, and #4 (see main text for details). All ps of χ² and Δχ² tests (comparisons of the multigroup models) were <.001. Gender identity compared groups of women, men, and other gender identity, age compared groups of participants with 18–24 years, 25–44 years, ≥45 years of age.
China (i.e., Mandarin, Cantonese, and English versions) were pooled for the analysis of national groups. This analysis therefore included 70 “national groups” from 65 countries.

### 3.1.3. Invariance across national groups, languages, gender identities, and age groups

#### 3.1.3.1. Overall findings.
The results of the MG-CFA analyses are presented in Table 2. The NES showed configural, but neither metric nor scalar invariance, across the 70 national groups and 40 languages.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Ordering and Magnitude of Standardised Latent Mean Differences (Cohen's d) in the NES Between National Groups (as Compared to the UK; Left) and Languages (as Compared to English; Right).</figcaption></figure>

Note. Data from UAE (Arabic version), Ecuador, Iran, Iraq, and Spain were excluded either due to poor scale reliability or poor fit in the partial measurement model (see main text).
However, it showed configural, metric, and scalar invariance across gender identities and age groups. Scale reliability was not strong. Median scale reliability (ω total) across all national groups was .65, ranging from just .16 (UAE [Arabic version] though only reliability below .44) to .87 (Saudi Arabia; P<.05, P<.74). Nevertheless, with only 4 items and two correlated errors, scale reliability was judged to be sufficient in all national groups (except the UAE Arabic version) for further analyses

for current purposes. Data from the UAE (Arabic version) were excluded in all further analyses of national groups and in the partial measurement model for languages.

3.1.3.2. National groups. The alignment method (Table S3) suggested the following descending ordering of the four NES items concerning their invariance: #2, #4, #3, #1. Item #3 had a higher summed R² value

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Ordering and Magnitude of Standardised Latent Mean Differences (Cohen's d) in the CNS-7 Between National Groups (as Compared to the UK; Left) and Languages (as Compared to English; Right).</figcaption></figure>

Note. Data from Iraq and Bosnia and Herzegovina were excluded due to the poor scale reliabilities in these countries.
than Item #4; however, Item #4 had a higher loading and exhibited
invariance in its parameters in a larger number of national groups than
Item #3. Items #2 and #4 were, therefore, used as item anchors in a
partial scalar measurement model. Note that this indicated that the two
"nature noticing" items (#2 and #4) were more invariant across na-
tional groups than the two "nature exposure" items (#1 and #3). In
addition to the UAE (Arabic version), data from Ecuador, Iran, Iraq, and
Spain were also subsequently excluded, as they disproportionately
decreased the fit of the partial scalar measurement model, relative to
their sample sizes.

For the remaining 65 national groups (including 61 single language
countries and four multi-language countries), the partial scalar mea-
surement model (using items #2 and #4 as anchors) had acceptable fit,
χ² = 7722.24, df = 449, CFI = .913, TLI = .924, RMSEA = .141 (.138,
.144), SRMR = .051. Compared to the UK as a reference category (for
purely nominal reasons [the first author is based there]), the largest
positive differences in self-reported nature exposure, as reflected by the
latent means on the NES, were for (in descending order) Bosnia and
Herzegovina, Croatia, and Lithuania, and were in the range of d = .98 to
1.18 (see Fig. 2, left panel; for individual Cohen's d values, see Table S4).
The largest negative differences compared to the UK were observed for
Lebanon, South Korea, and, Brazil and were in the range of d = -.97 to
-2.21.

3.1.3.3. *Languages.* Using Items #2 and #4 again as anchor items, the partial scalar measurement model had an acceptable fit to the data of the 40 language groups, $\chi^2 = 6959.85$, *df* = 274, *CFI* = .926, *TLI* = .935, *RMSEA* = .131 (.128, .134), *SRMR* = .048. Here, the largest positive differences in country-level latent means on the NES, compared to English (the original scale language), were observed for (in descending order) Bosnian, Lithuanian, and Croatian and were in the range of *d* = 1.04 to 1.18 (see Fig. 2, right panel; for individual Cohen's *d* values, see Table S5). The largest negative differences were observed for Cantonese, Korean, and Portuguese and were in the range of *d* = -.87 to -1.68. These rankings mostly matched the rankings of the analysis of national groups (see above; but note that languages could contain a mix of different national groups, if the language in which the scale was presented there was the same; this specifically applied to English).

3.1.3.4. *Gender identities and age groups.* Men reported lower nature exposure (i.e., had lower NES latent means) than women (Cohen's *d* = -.14, *p* < .001), as did those with other gender identities (*d* = -.25, *p* < .001). Reported nature exposure also increased with age: age groups 25-44 years vs. 18-24 years differed by *d* = .14 (*p* < .001), whereas age groups ≥ 45 years vs. 18-24 years by *d* = .48 (*p* < .001).

3.2. Connectedness to nature scale

3.2.1. *Structural analysis of the CNS-14 and CNS-7 in the total sample*

A unidimensional model had a poor fit on the CNS-14, $\chi^2 = 56589.17$, *df* = 75, *CFI* = .921, *TLI* = .904, *RMSEA* = .115 (.114, .116), *SRMR* = .048, compared to the CNS-7, $\chi^2 = 5583.20$, *df* = 12, *CFI* = .987, *TLI* = .978, *RMSEA* = .090 (.088, .092), *SRMR* = .017. Additionally, the standardised loadings of Items #12 and #13, and of the negatively worded Items #4 and #14, were low ($\le$ .36). Hence, the CNS-7 was used in all subsequent analyses.

3.2.2. *Invariance of the cross-language results in the six countries with multiple survey languages*

The MG-CFA invariance test results of the cross-language survey
presentation of the CNS-7 in Canada, China, Iceland, India, the
Philippines, and the UAE are presented in Supplementary Table S6. Full
scalar invariance could be assumed for all countries, except Canada and
Iceland, for which metric invariance was upheld. In all countries, except
Canada and Iceland, the available data were pooled for the analysis of

national groups. Thus, this analysis included 67 national groups from 65
nations.

3.2.3. *Invariance across national groups, languages, gender identities, and age*

3.2.3.1. *Overall findings.* The results of the MG-CFA analyses are presented in Table 3. The CNS-7 showed configural, metric, and scalar invariance across gender identities and age groups, but only configural and metric invariance for the 67 national groups and 40 languages. Median scale reliability (ω total) across all national groups was considerably higher than the NES (.92), ranging from .64 (Iraq) to .97 (Spain; P25 = .89, P75 = .93). Bosnia and Herzegovina (.73) was the only other country below .80.

3.2.3.2. *National groups.* Using the alignment method, we obtained information on items and item parameters that were most invariant among the 67 national groups (Table S7). According to the summed $R^2$ values across the loading and all threshold parameters per item (to get an indication of overall item invariance), Items #7, #5, and #2 (in this order) appeared to be the most invariant. However, it was Items #2 and #7 whose loadings were also invariant amongst the largest number of national groups (Item #5 had the lowest number here). Hence, we opted for using Items #2 and #7 as anchor items in a partial scalar measurement model. Considering the low scale reliabilities in the Iraq and Bosnia and Herzegovina data, we excluded these national groups from this analysis. Data of these two countries were also excluded in all further analyses of national groups and in the partial measurement model of languages.

The partial scalar measurement model had a good fit to the data, $\chi^2 = 10264.83$, *df* = 1164, *CFI* = .982, *TLI* = .979, *RMSEA* = .095 (.094, .097), *SRMR* = .026. Comparing all other national groups to the UK, the ordering and magnitude of standardised latent mean differences (Cohen's *d*) are provided in Fig. 2 (individual Cohen's *d* values are provided in Table S8). The largest positive differences were observed for (in descending order) Nepal, Iran, and South Africa and were in the range of *d* = 1.20 to 1.39 (see Fig. 1, left panel), suggesting that participants from these nations reported higher connectedness to nature compared to the United Kingdom. The largest negative differences were observed for Israel, Japan, and Spain and were in the range of *d* = -.30 to -.61.

3.2.3.3. *Languages.* Using Items #2 and #7 as anchor items again, the partial scalar measurement model had a good fit on the data of the 40 language groups, $\chi^2 = 8666.51$, *df* = 716, *CFI* = .985, *TLI* = .983, *RMSEA* = .088 (.086, .090), *SRMR* = .023. Comparing all other languages to English (again the original scale language), the ordering and magnitude of standardised latent mean differences (Cohen's *d*) are provided in Fig. 2 (individual Cohen's *d* values are provided in Table S9). The largest positive differences were observed for (in descending order) Nepali, Bangla, and Farsi and were in the range of *d* = .63 to .88 (see Fig. 2, right panel). The largest negative differences were observed for Dutch, Hebrew, and Japanese and were in the range of *d* = -.40 to -.69. These rankings mostly matched the rankings of the analysis of national groups (see above).

3.2.3.4. *Gender identities and age groups.* Men reported slightly lower connectedness to nature (CNS-7 latent means) than women (Cohen's *d* = -.08, *p* < .001). Other gender identities reported similar connectedness to women (*d* = .02, *p* = .79). Connectedness to nature also increased with age: age groups 25-44 years vs. 18-24 years differed by *d* = .17 (*p* < .001), whereas age groups ≥ 45 years vs. 18-24 years by *d* = .36 (*p* < .001).
<table><caption>**Table 3**
Invariance of the CNS-7 concerning national groups, language, gender identity, and age.</caption><thead><tr><th rowspan="2">Grouping variable</th><th rowspan="2">χ<sup>2</sup>(df)</th><th rowspan="2">CFI</th><th rowspan="2">TLI</th><th rowspan="2">RMSEA</th><th rowspan="2">90% CI</th><th rowspan="2">SRMR</th><th colspan="4">Model comparisons</th></tr><tr><th>ΔCFI</th><th>ΔRMSEA</th><th>Configural</th><th>Metric</th></tr></thead><tbody><tr><td colspan="11"><strong>National groups</strong></td></tr><tr><td>Configural invariance</td><td>8001.09(804)</td><td>.986</td><td>.976</td><td>.103</td><td>[.101, .105]</td><td>.024</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>9076.08(1200)</td><td>.985</td><td>.982</td><td>.088</td><td>[.086, .090]</td><td>.028</td><td>.001</td><td>-.015</td><td>2679.88(396)</td><td></td></tr><tr><td>Scalar invariance</td><td>33207.52(2520)</td><td>.941</td><td>.967</td><td>.120</td><td>[.119, .121]</td><td>.044</td><td>.044</td><td>.032</td><td>25335.58(1716)</td><td>25786.21(1320)</td></tr><tr><td colspan="11"><strong>Language</strong></td></tr><tr><td>Configural invariance</td><td>6625.49(480)</td><td>.988</td><td>.980</td><td>.095</td><td>[.093, .097]</td><td>.020</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>7376.45(714)</td><td>.987</td><td>.985</td><td>.081</td><td>[.079, .083]</td><td>.024</td><td>-.005</td><td>-.014</td><td>2167.62(234)</td><td></td></tr><tr><td>Scalar invariance</td><td>27691.05(1494)</td><td>.951</td><td>.972</td><td>.111</td><td>[.110, .112]</td><td>.038</td><td>.036</td><td>.020</td><td>21099.45(1014)</td><td>21599.26(780)</td></tr><tr><td colspan="11"><strong>Gender identity</strong></td></tr><tr><td>Configural invariance</td><td>5361.43(36)</td><td>.988</td><td>.979</td><td>.088</td><td>[.086, .090]</td><td>.017</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>3851.56(48)</td><td>.992</td><td>.989</td><td>.065</td><td>[.063, .066]</td><td>.017</td><td>-.004</td><td>-.023</td><td>120.35(12)</td><td></td></tr><tr><td>Scalar invariance</td><td>4026.67(88)</td><td>.991</td><td>.994</td><td>.049</td><td>[.047, .050]</td><td>.018</td><td>.001</td><td>-.016</td><td>766.62(52)</td><td>710.21(40)</td></tr><tr><td colspan="11"><strong>Age</strong></td></tr><tr><td>Configural invariance</td><td>5784.07(36)</td><td>.987</td><td>.977</td><td>.092</td><td>[.090, .094]</td><td>.017</td><td></td><td></td><td></td><td></td></tr><tr><td>Metric invariance</td><td>3981.35(48)</td><td>.991</td><td>.988</td><td>.066</td><td>[.064, .067]</td><td>.017</td><td>-.004</td><td>-.026</td><td>150.24(12)</td><td></td></tr><tr><td>Scalar invariance</td><td>4236.02(88)</td><td>.991</td><td>.993</td><td>.050</td><td>[.049, .051]</td><td>.018</td><td>.000</td><td>-.016</td><td>837.15(52)</td><td>757.37(40)</td></tr></tbody></table>

Note. All ps of χ² and Δχ² tests (model comparisons) were <.001. Gender identity compared groups of women, men, and other gender identity, age compared groups of participants with 18–24 years, 25–44 years, ≥45 years of age.

### 3.3. Associations between NES and CNS-7 factor scores within national groups

In the 65 national groups for which both CNS-7 and NES factor scores could be computed, the association between nature exposure and connectedness to nature ranged from $r = -.13$ (Cyprus) to .22 (Malta), with $r = .03$ in median. That is, the median correlation between these two constructs across the 65 national groups was practically nil.

### 3.4. Sociodemographic correlates of NES and CNS-7 factor scores

At the individual (Level-1) level, higher financial security, living in rural (vs. urban) settings, secondary/tertiary (vs. other) educational qualification, and being in a committed relationship or married (vs. other) were all associated with both greater nature exposure and connectedness to nature (see Table 4). Being a member of a racialised minority (vs. other) was associated with lower nature exposure. All associations at this individual level were, however, small and statistically explained only small amounts of the Level-1 variance. Expressed as Cohen's *ds*, the effect sizes for the dichotomous predictors of nature exposure and connectedness to nature, respectively, were as follows: urban vs. rural living setting, -.25/-.10; secondary/tertiary vs. other educational qualification, .02/.05; and committed/married vs. other, .07/.11. For racial minority (vs. other), Cohen's *d* was -.04 for nature exposure and non-significant for nature connectedness.

At the national group cluster-level (Level 2), connectedness to nature was not associated with any of the sociodemographic variables.

However, higher cluster-level means of nature exposure were associated with lower cluster-level means (i.e., lower prevalence rates) of living in urban (vs. rural) settings and of being a member of a racial minority (vs. other), and thus included in the final model. In other words, countries (including those with multiple languages) with more rural respondents had higher nature exposure and those with more racial minority respondents had lower overall nature exposure, echoing the Level-1 results. These cluster-level associations were sizeable and, combined, explained 26% of the Level-2 variance.

## 4. Discussion

Here, we used the BINS dataset – with data from 56,968 respondents across 65 nations and 40 languages – to conduct the most comprehensive assessment of the factorial validity and measurement invariance of the NES and CNS. In terms of the NES, reliability across countries was highly variable with many countries falling below the usual thresholds of acceptability. A unidimensional model of the NES did, however, show full scalar invariance across gender identities and age groups; partial scalar invariance was also found for all languages and all but five national groups. In terms of the CNS, our results are consistent with previous work suggesting that the 14-item, unidimensional model of this instrument has poor factorial validity (Pasca et al., 2017; Rosa et al., 2022). Conversely, the CNS-7 showed full scalar invariance across gender identities and age groups, and partial scalar invariance across all languages and all but two national groups. Associations between nature exposure and connectedness to nature across nations were negligible,

<table><caption>**Table 4**
Sociodemographic correlates of nature exposure and connectedness to nature.</caption><thead><tr><th rowspan="2">Predictor</th><th colspan="3">Nature exposure</th><th colspan="3">Connectedness to nature</th></tr><tr><th>Estimate (posterior SD)</th><th>95% credibility interval</th><th>p (one-tailed)</th><th>Estimate (posterior SD)</th><th>95% credibility interval</th><th>p (one-tailed)</th></tr></thead><tbody><tr><td colspan="7"><strong>Level 1: Individual level</strong></td></tr><tr><td>Financial security</td><td>0.07 (0.004)</td><td>[0.06, 0.08]</td><td>&lt; 0.001</td><td>0.02 (0.005)</td><td>[0.01, 0.03]</td><td>&lt; 0.001</td></tr><tr><td>Urbanicity</td><td>-0.12 (0.004)</td><td>[-0.13, -0.12]</td><td>&lt; 0.001</td><td>-0.05 (0.004)</td><td>[-0.06, -0.04]</td><td>&lt; 0.001</td></tr><tr><td>Education</td><td>0.01 (0.004)</td><td>[0.005, 0.02]</td><td>&lt; 0.001</td><td>0.03 (0.004)</td><td>[0.02, 0.04]</td><td>&lt; 0.001</td></tr><tr><td>Marital status</td><td>0.04 (0.005)</td><td>[0.03, 0.05]</td><td>&lt; 0.001</td><td>0.05 (0.004)</td><td>[0.05, 0.06]</td><td>&lt; 0.001</td></tr><tr><td>Racialised status</td><td>-0.02 (0.005)</td><td>[-0.03, -0.01]</td><td>&lt; 0.001</td><td>.002 (0.004)</td><td>[-.005, .01]</td><td>.29</td></tr><tr><td colspan="7"><strong>Level 2: Cluster-level means</strong></td></tr><tr><td>Urbanicity</td><td>-0.35 (0.098)</td><td>[-0.51, -0.14]</td><td>&lt; 0.001</td><td></td><td></td><td></td></tr><tr><td>Racialised status</td><td>-0.30 (0.103)</td><td>[-0.46, -0.05]</td><td>&lt; 0.001</td><td></td><td></td><td></td></tr><tr><td colspan="7"><strong>Random Effects</strong></td></tr><tr><td>Intercept residual variance</td><td>.74 (.09)</td><td>[.56, .92]</td><td>&lt;.001</td><td>1.00 (.00)</td><td>NA</td><td>&lt;.001</td></tr><tr><td>Explained variance (Level 1/Level 2)</td><td>2%/26%</td><td></td><td></td><td>.7%/0%</td><td></td><td></td></tr></tbody></table>

Note. Estimates are on a standardised scale. SD = standard deviation. Significant (one-sided *p* < .025) estimates are highlighted in boldface.
although we did find significant (albeit weak) individual level associations between both constructs and several sociodemographic factors.

## 4.1. Nature exposure

We tested both a unidimensional model of the NES with all four items and a correlated 2-factor model, with equality constraints set for the two items that most closely assess “nature exposure” (#1 and #3) and “noticing nature” (#2 and #4), respectively. In the total sample, the 2-factor model had poor fit to the data, with item loadings reflecting “nature exposure” in everyday environments (#1 and #2) and outside everyday environments (#3 and #4), rather than an “exposure-noticing” split. In contrast, a unidimensional model had adequate fit to the data, although we did find that the “nature exposure” items had lower standardised loadings than the “noticing nature” items. Including correlated errors between two item pairs (#1 and #2, and #3 and #4, respectively) substantially improved fit of the unidimensional model. In short, although the NES does present some indication of possible statistical multidimensionality, this does not obviously align with face validity of the items, and a unidimensional model presented the best fit to the data anyway.

Although this unidimensional model of the NES showed configural invariance, it did not show metric or scalar invariance across national groups and languages. This suggests that, while there may be a near-universally plausible basic organisation of the nature exposure construct – as measured using the NES – each item does not contribute to the latent construct in the same way across nations or languages, making it a problematic measure to use in cross-cultural research. It was nonetheless possible to achieve partial scalar invariance across all but five of the national groups (i.e., UAE [in Arabic], Ecuador, Iran, Iraq, and Spain) represented in the analyses and all 40 languages, suggesting that it may tap a common latent construct across national groups and languages, albeit with some variation in meaning.

There were also large inter-nation and inter-language differences in NES scores. These results may reflect actual cross-national differences in exposure to natural environments, as evidenced by the large variations seen in Fig. 1. Content-wise, these differences appeared to be particularly driven by differences in “noticing nature” (Items #2 and #4 were the primary contributors to latent NES scores) but may have also been affected by differences in the meaning of “exposure to nature” across national groups (McPhie & Clarke, 2020). Here, we highlight Brazilian participants as an outlier in terms of their low latent NES scores (see Fig. 1). Reasons for this are unclear but may reflect difficulties that Brazilian participants experience in noticing natural environments, despite their proximity to such environments (see Profice et al., 2023). Alternatively, it may reflect low levels of nature exposure that many urban dwellers in low- and lower-middle income nations have due to a lack of time, money, and nearby natural environments (Awoyemi et al., 2024) or a perceived lack of safety in natural environments (e.g., due to social unrest).

Conversely, there was evidence that the unidimensional model of the NES achieved full scalar invariance across gender identities and age groups. In terms of gender identities, women reported significantly greater nature exposure than men and individuals who identified their gender in another way, although effect sizes were small. This is a curious finding, particularly as studies in Western nations have generally identified a gender gap in nature contact, with women visiting natural environments less frequently than men (e.g., Boyd et al., 2018), possibly because of experiences of fear and vulnerability in natural environments or due to societal and gendered norms that mean women often feel a lack of entitlement to leisure time in general (Day, 2000). It is possible that this discrepancy across studies is due to the inclusion of “noticing nature” in the NES and possibly women having a greater tendency to “notice nature” because they may be more sensitive to particular settings that may evoke fear, anxiety, and negative emotions (van den Berg & ter Heijne, 2005). In terms of age, self-reported nature exposure using the

NES generally increased from emerging to older adulthood. It is likely that the latter group has greater time and opportunities to engage with nature (Freeman et al., 2019) and may also have a fuller understanding of the natural world, which in turn may enhance their abilities to “notice” the natural environment (Ojala, 2009).

## 4.2. Connectedness to nature

The CNS results indicated that a unidimensional model including all 14 items had poor fit. Although the 14-item model demonstrated adequate fit in some previous research (e.g., Li & Wu, 2016; Mayer & Frantz, 2004; Pessoa et al., 2016), our findings corroborate more recent difficulties replicating this model (e.g., Navarro et al., 2017; Olivos et al., 2013; Pearce et al., 2022). As Pasca et al. (2017) have suggested, it is highly likely that some CNS items either perform poorly – for linguistic, conceptual, or semantic reasons – or are redundant in some national contexts. Adopting an Item Response Theory approach also suggested that some items may inadequately discriminate between individuals who vary in their degree of connectedness to nature (Pasca et al., 2017). For these reasons, Pasca et al. (2017) recommended using a truncated version of the CNS that includes only seven of the 14 original items, which has been found to have a unidimensional factor structure in previous work (Rosa et al., 2022).

Using this CNS-7, our results suggested that configural and metric invariance was supported across nations, languages, gender identities, and age groups. In terms of national groups and languages, the lack of full scalar invariance is consistent with previous work in seven European nations, where only configural and metric invariance was found (Navarro et al., 2022). One possible reason for the lack of scalar invariance of the CNS-7 across national groups and languages is that this instrument – and indeed the construct of connectedness to nature itself – is steeped in a boundary distinction between humans and the natural environment (i.e., that one can be distinct or disconnected from nature). As Fletcher (2017, p. 228) has suggested, however, this view is grounded in a “culturally specific ... conceptual dichotomy between opposing realms of ‘nature’ and ‘culture’” that is characteristic of wealthy, Western nations. In other words, the suggestion that humans can be separate from nature – or, indeed that humans are not nature – may be a cultural worldview with limited global application, which in turn may explain the lack of scalar invariance in relation to the CNS-7 in the present study.

Having said this, full scalar invariance is often an unrealistic goal for datasets with a large number of groups (Marsh et al., 2018). Importantly, our results also suggested that it was possible to achieve partial scalar invariance of the CNS-7 across all but two national groups (i.e., Iraq and Bosnia and Herzegovina, respectively) and all 40 languages represented in the BINS. This is important theoretically because it suggests that the CNS-7 can be used to measure a latent construct of connectedness to nature that may have near-universal applicability, albeit with some possible loss of meaning. From a practical point-of-view, achieving partial scalar invariance means that we were able to compare latent CNS-7 means across national groups and languages, with our results showing large cross-national and cross-language differences in CNS-7 latent means. Understanding why such differences exist is, however, more difficult and only preliminary explanations can be put forward based on the present data.

Richardson, Hamlin, Elliott, and White (2022), for instance, suggested that lower country-level nature connectedness may reflect historical interactions with, and attitudes towards, the natural world. Observing that all six of the countries with the lowest nature connectedness scores in an 18-country study by White et al. (2021) were at some time subject to British rule, Richardson, Hamlin, Elliott, and White (2022) wondered whether early industrialisation and urbanisation of the UK, and resource extraction and exploitation of its colonies, encouraged seeing nature as external and separate to human lives. However, although the UK again had very low levels of CNS in the
present research, as did some of its former colonies (e.g., Australia, Canada [English], Ireland, and the USA), other former British colonies, notably on the African continent, including South Africa, Nigeria, and Ghana, were among the countries with the highest connectedness to nature. An alternative suggestion, therefore, is that it is the English language version that results in lower CNS scores, an idea supported by the English versus French/Icelandic data from Canada and Iceland data, but undermined by the fact that these three African countries also used the English language version.

More broadly, therefore, it appears that the variation in connected-
ness to nature across nations and languages may reflect differences in
the ways that connectedness to nature is constructed, negotiated, and
experienced by different communities (see McPhie & Clarke, 2020).
That is, what it means to be “connected to nature” or experience a sense
of oneness with nature likely varies across nations and/or cultures,
which results in the type of variation that can be seen in Fig. 2 when
individuals are asked to self-report their experiences. It is also possible
that cultural practices and traditions that foster both connectedness to
nature, as well as the ability to articulate that connectedness, varies
across national groups (Keaulana et al., 2021). For instance, there is
some evidence to suggest that individuals in Nepal and Bangladesh – two
nations that had very high latent CNS-7 scores in the present study – live
in ways that are intimately connected to the natural world (Widdop
Quinton & Khatun, 2020). Further work is clearly needed to explore
these issues in greater depth.

In terms of gender identities, we found that the CNS-7 showed full
scalar invariance across women, men, and individuals who identified
their gender in another way. The CNS-7 taps a common underlying
construct of connectedness to nature that is not differentially affected by
gender identities. Women reported higher nature connectedness than
men, consistent with previous work showing that women have greater
environmental concern (e.g., Xiao & McCright, 2015) and dispositional
empathy with nature (Tam, 2013), though effect sizes were relatively
small. Additionally, we found that the CNS-7 was fully invariant across
age groups, with connectedness to nature generally increasing from
emerging adulthood to older adulthood, though again group differences
were relatively small.

4.3. Correlates of nature exposure and connectedness

Contrary to our hypothesis, the median correlation between nature
exposure and nature connectedness, as measured using the NES and
CNS, across nations was practically nil. This finding stands in contrast to
previous cross-sectional (e.g., Fränkel et al., 2019; Martin et al., 2020)
and experimental studies (Sheffield et al., 2022) that have shown these
constructs to be weakly-to-moderately correlated. Moreover, where
studies have assessed these constructs using the NES and CNS, respec-
tively, reported associations have tended to be moderate-to-large
(Baceviciene et al., 2021; Picanço et al., 2024; Swami et al., 2016;
Swami, Barron, et al., 2020). In these studies, however, the significant
associations may have been inflated through the use of ecological cor-
relations in singular nations.

Instead, the present results suggest that the true variation in the as-
sociation between nature exposure (measured using the NES) and
connectedness to nature (measured using the CNS-7) across nations may
be relatively wide. This raises questions about what exactly is being
measured by the NES, and how. While we acknowledge the rationale for
the scale to want to incorporate the three types of exposure explored in
the literature (local, active visits, and awareness) into a single measure,
the way this is operationalised is logically problematic because there is
no longer any single underlying latent construct. While Items #2 and #4
both tap into "nature-noticing" using the same response options, Items
#1 and #3, by design, are interested in different types of "nature-
exposure" and use different response options. It is perhaps not surpris-
ing, then, that the reliability across many countries was low.

Although there was some statistical evidence that the items could be

collapsed into a unidimensional scale representing a single underlying
factor, it was perhaps not surprising that Items #2 and #4 were the
strongest since they were the only two measuring the same thing in the
same way. By contrast, the 4-item scale lacks clear conceptual or face
validity. So, if the NES is not measuring nature exposure, what is it
measuring? One possibility is that it is heuristically measuring a general
attitude towards the natural world with people generally interested in
nature answering relatively positively to the four items and vice versa. If
this were the case, however, one might still expect stronger associations
between NES scores and connectedness to nature. Another possibility is
that the NES is measuring different types of "exposure" in a single
metric, and which form of exposure is most salient or relevant may
depend on the national or linguistic context.

In terms of relations of the two instruments to selected demographics, both greater nature exposure and connectedness to nature were significantly (but weakly) associated with greater financial security, rural residence (versus urban residence), higher education, being in a committed relationship (versus being single), and being in a racial majority in a specific country. Broadly speaking, the significant associations with financial security, higher education, and racial majority status reflect known socioeconomic inequities in terms of the distribution of, and outcomes of exposure to, natural environments (e.g., Gerrish & Watkins, 2018; Murdock, 2019). Likewise, the significant association with rurality is perhaps to be expected and consistent with previous findings (e.g., Carrus et al., 2020; Martin & Czellar, 2017). The finding that those in committed relationships were more likely to report greater connectedness to nature and nature exposure than those who were single is also consistent with existing research (e.g., Teixeira et al., 2023) and may reflect the beneficial impact of social support in nature engagement. Racialised minority individuals may also experience natural environments differently to majority groups (e.g., in terms of perceived safety, comfort, a sense of belonging) due to historic and contemporary inequities stemming from structural racism (Collier, 2022; Roberts et al., 2023), which impacts ongoing nature exposure.

4.4. Constraints on generalisability and directions for future research

Although the present work provides one of the largest cross-national
databases on nature exposure and connectedness to nature, our findings
should be considered in light of a number of constraints on their gen-
eralisability (Simons et al., 2017). First, our sampling strategy was
opportunistic in most cases and, as such, the individual samples should
not be considered representative of a particular nation. This may reduce
the generalisability of our findings, particularly when making compar-
isons across nations or linguistic groups. Relatedly, although one of the
strengths of the BINS dataset is the focus on operational equivalence
across research sites (Swami et al., 2022), we cannot entirely rule out
small differences in recruitment and survey completion (e.g., in terms of
online versus offline completion, participant remuneration, specific
recruitment methods). Also related to recruitment, because the BINS
dataset was researcher-crowdsourced, our data was under-represented
in several world regions (e.g., Africa, Central Asia, the Caribbean,
Central America), though this is a common limitation of many
large-scale, cross-national studies (Krys et al., 2024).

Another constraint on generalisability was that specific conditions
during the period of data collection – which extended over 15 months
and took place in the shadow of the COVID-19 pandemic – may have
varied substantially across nations. This is particularly important when
considering that pandemic-related policies may have directly impacted
one’s ability to spend time in nature (e.g., due to periods of lockdown;
Stieger et al., 2021). These varying conditions make it difficult to know
to what extent our data are temporally reliable and whether specific
pandemic-related experiences (e.g., being in lockdown, severity of the
pandemic, national and international responses to the pandemic, none
of which were measured in our survey) may have affected our findings.
Still, given the consistency of factor structures across groups, any biases
in results are likely to be reflected in latent group differences.

Finally, because the BINS dataset consists of self-reported data, we
cannot rule out common-method biases. On this note, there was some
evidence of response-scale spareness in terms of the NES (i.e., some
response options were rarely utilised), which may have affected our
findings. Yet, even though some response options had to be combined to
make the multigroup analyses feasible, this measure is not needed when
utilising the instrument. We still recommend using its 5-point response
scale, but expect that some response options will only be seldomly used.
Also, there was evidence of insufficient fit of a unidimensional model for
the NES in a number of countries, which may further limit its
applicability.

Our findings also raise several important questions that could be
more fully answered in future research. For instance, it is unclear at
present why instrument composite reliabilities were less-than-adequate
in some linguistic or national groups (e.g., the Arabic NES in the United
Arab Emirates or the CNS in Iraq and Bosnia and Herzegovina). Like-
wise, while our work provides a useful starting point, much more can be
done to interrogate and understand latent national and linguistic dif-
ferences in nature exposure and connectedness to nature. More gener-
ally, it remains important to explore the diverse ways that “nature” and
“nature connectedness” are conceptualised, understood, and lived in
diverse cultural contexts, as well as the ways in which nuances in un-
derstandings of “nature” affect human behaviour (Droz et al., 2022).
Doing so will help researchers and practitioners ensure that voices from
diverse populations worldwide are not rendered invisible or muffled in
the scientific literature.

4.5. Conclusion

These constraints on generalisability notwithstanding, the present
work suggests that the CNS-7 is a useful tool for assessing a latent
connectedness to nature construct across national and linguistic groups,
gender identities, and adult age groups. While our statistical analyses
also pointed to the potential use of the NES to generate a univariate
score representing nature exposure, important questions remain around
this instrument’s face and nomological validity. While we conclude that
the CNS-7 can be used in cross-national and cross-linguistic research
with no substantive loss of meaning, we also caution that our work was
not set up to investigate and understand localised, cultural meanings
and experiences of connectedness to nature. Further work is needed to
robustly capture nature exposure. One may flip our methodological
design and utilise more emic approaches in the future to fully under-
stand geographic, cultural, and linguistic variations in how nature is
experienced and how connectedness to nature manifests (e.g., Sedawi
et al., 2021).

CRediT authorship contribution statement

Viren Swami: Writing – original draft, Project administration, Methodology, Investigation, Data curation, Conceptualization. Mathew P. White: Writing – original draft, Investigation. Martin Voracek: Writing – review & editing, Validation, Methodology, Investigation, Data curation, Conceptualization. Ulrich S. Tran: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Toivo Aavik: Writing – review & editing, Investigation, Data curation. Hamed Abdollahpour Ranjbar: Writing – review & editing, Investigation, Data curation. Sulaiman Olanrewaju Adebayo: Writing – review & editing, Investigation, Data curation. Reza Afhami: Writing – review & editing, Investigation, Data curation. Oli Ahmed: Writing – review & editing, Investigation, Data curation. Annie Aimé: Writing – review & editing, Investigation, Data curation. Marwan Akel: Writing – review & editing, Investigation, Data curation. Hussam Al Halbusi: Writing – review & editing, Investigation, Data curation. George Alexias: Writing – review & editing, Investigation, Data curation. Khawla F. Ali: Writing – review & editing,

Investigation, Data curation. **Nursel Alp-Dal:** Writing – review & editing, Investigation, Data curation. **Anas B. Alsalhani:** Writing – review & editing, Investigation, Data curation. **Sara Álvarez-Solas:** Writing – review & editing, Investigation, Data curation. **Ana Carolina Soares Amaral:** Writing – review & editing, Investigation, Data curation. **Sonny Andrianto:** Writing – review & editing, Investigation, Data curation. **Trefor Aspden:** Writing – review & editing, Investigation, Data curation. **Marios Argyrides:** Writing – review & editing, Investigation, Data curation. **John Jamir Benzon R. Aruta:** Writing – review & editing, Investigation, Data curation. **Stephen Atkin:** Writing – review & editing, Investigation, Data curation. **Olusola Ayandele:** Writing – review & editing, Investigation, Data curation. **Migle Baceviciene:** Writing – review & editing, Investigation, Data curation. **Radvan Bahbouh:** Writing – review & editing, Investigation, Data curation. **Andrea Ballesio:** Writing – review & editing, Investigation, Data curation. **David Barron:** Writing – review & editing, Investigation, Data curation. **Ashleigh Bellard:** Writing – review & editing, Investigation, Data curation, Writing – review & editing, Investigation, Data curation. **SóleySesselja Bender:** Writing – review & editing, Investigation, Data curation. **Kerime Derya Beydağ:** Writing – review & editing, Investigation, Data curation. **Gorana Birovljević:** Writing – review & editing, Investigation, Data curation. **Marie-Ève Blackburn:** Writing – review & editing, Investigation, Data curation. **Teresita Borja-Alvarez:** Writing – review & editing, Investigation, Data curation. **Joanna Borowiec:** Writing – review & editing, Data curation. **Miroslava Bozogánová:** Writing – review & editing, Investigation, Data curation. **Solfrid Bratland-Sanda:** Writing – review & editing, Investigation, Data curation. **MatthewH.E.M. Browning:** Writing – review & editing, Investigation, Data curation. **Marina Burakova:** Writing – review & editing, Investigation, Data curation. **Yeliz Çakır-Koçak:** Writing – review & editing, Investigation, Data curation. **Pablo Camacho:** Writing – review & editing, Investigation, Data curation. **Vittorio Emanuele Camilleri:** Writing – review & editing, Investigation, Data curation. **Valentina Cazzato:** Writing – review & editing, Investigation, Data curation. **Silvia Cerea:** Writing – review & editing, Investigation, Data curation. **Apitchaya Chaiwutikornwanich:** Writing – review & editing, Investigation, Data curation. **Trawin Chaleeraktrakoon:** Writing – review & editing, Investigation, Data curation. **Tim Chambers:** Writing – review & editing, Investigation, Data curation. **Qing-Wei Chen:** Writing – review & editing, Investigation, Data curation. **Xin Chen:** Writing – review & editing, Investigation, Data curation. **Chin-Lung Chien:** Writing – review & editing, Investigation, Data curation. **Phatthanakit Chobthamkit:** Writing – review & editing, Investigation, Data curation. **Bovornpot Choompunuch:** Writing – review & editing, Investigation, Data curation. **Emilio J. Compte:** Writing – review & editing, Investigation, Data curation. **Jennifer Corrigan:** Writing – review & editing, Investigation, Data curation. **Getrude Cosmas:** Writing – review & editing, Investigation, Data curation. **Richard G. Cowden:** Writing – review & editing, Investigation, Data curation. **Kamila Czepczor-Bernat:** Writing – review & editing, Investigation, Data curation. **Marcin Czub:** Writing – review & editing, Investigation, Data curation. **Wanderon Roberto da Silva:** Writing – review & editing, Investigation, Data curation. **Mahboubeh Dadfar:** Writing – review & editing, Investigation, Data curation. **Simon E. Dalley:** Writing – review & editing, Investigation, Data curation. **Lionel Dany:** Writing – review & editing, Investigation, Data curation. **Jesus Alfonso D. Datu:** Writing – review & editing, Investigation, Data curation. **Pedro Henrique Bert de Carvalho:** Writing – review & editing, Investigation, Data curation. **Gabriel Lins de Holanda Coelho:** Writing – review & editing, Investigation, Data curation. **Avila Odia S. De Jesus:** Writing – review & editing, Investigation, Data curation. **Sonia Harzallah Debbabi:** Writing – review & editing, Investigation, Data curation. **Sandesh Dhakal:** Writing – review & editing, Investigation, Data curation. **Francesca Di Bernardo:** Writing – review & editing, Investigation, Data curation. **Donka D. Dimitrova:** Writing – review & editing, Investigation, Data curation. **Jacinthe Dion:** Writing – review & editing,
Investigation, Data curation. **Barnaby Dixon:** Writing – review & editing, Investigation, Data curation. **Stacey M. Donofrio:** Writing – review & editing, Investigation, Data curation. **Marius Drysch:** Writing – review & editing, Investigation, Data curation. **Hongfei Du:** Writing – review & editing, Investigation, Data curation. **Angel M. Dzhambo:** Writing – review & editing, Investigation, Data curation. **Claire El-Jor:** Writing – review & editing, Investigation, Data curation. **Violeta Enea:** Writing – review & editing, Investigation, Data curation. **Mehmet Eskin:** Writing – review & editing, Investigation, Data curation. **Farinaz Farbod:** Writing – review & editing, Investigation, Data curation. **Lorleen Farrugia:** Writing – review & editing, Investigation, Conceptualization. **Leonie Fian:** Writing – review & editing, Investigation, Data curation. **Maryanne L. Fisher:** Writing – review & editing, Investigation, Data curation. **Michał Folwarczny:** Writing – review & editing, Investigation, Data curation. **Writing – review & editing, Investigation, Data curation.** **David A. Frederick:** Writing – review & editing, Investigation, Data curation. **Adrian Furnham:** Writing – review & editing, Investigation, Data curation. **Antonio Alías García:** Writing – review & editing, Investigation, Data curation. **Shulamit Geller:** Writing – review & editing, Investigation, Data curation. **Marta Ghisi:** Writing – review & editing, Investigation, Data curation. **Alireza Ghorbani:** Writing – review & editing, Investigation, Conceptualization. **Maria Angeles Gomez Martinez:** Writing – review & editing, Investigation, Data curation. **Sarah Gradidge:** Writing – review & editing, Investigation, Data curation. **Sylvie Graf:** Writing – review & editing, Investigation, Data curation. **Caterina Grano:** Writing – review & editing, Investigation, Data curation. **Gyöngyvér Gyene:** Writing – review & editing, Investigation, Data curation. **Souheil Hallit:** Writing – review & editing, Investigation, Data curation. **Motasem Hamdan:** Writing – review & editing, Investigation, Data curation. **Jonathan E. Handelzalts:** Writing – review & editing, Investigation, Data curation. **PaulH.P. Hanel:** Writing – review & editing, Investigation, Data curation. **Steven R. Hawks:** Writing – review & editing, Investigation, Data curation. **Issa Hekmati:** Writing – review & editing, Investigation, Data curation. **Mai Helmy:** Writing – review & editing, Investigation, Data curation. **Tetiana Hill:** Writing – review & editing, Investigation, Data curation. **Farah Hina:** Writing – review & editing, Investigation, Data curation. **Geraldine Holenweger:** Writing – review & editing, Investigation, Data curation. **Martina Hrebícková:** Writing – review & editing, Investigation, Data curation. **Olasupo Augustine Ijabadeniyi:** Writing – review & editing, Investigation, Data curation. **Asma Imam:** Writing – review & editing, Investigation, Data curation. **Başak İnce:** Writing – review & editing, Investigation, Data curation. **Natalia Irrazabal:** Writing – review & editing, Investigation, Data curation. **Rasa Jankauskiene:** Writing – review & editing, Investigation, Data curation. **Ding-Yu Jiang:** Writing – review & editing, Investigation, Data curation. **Evan M. Johnson:** Writing – review & editing, Investigation, Data curation. **Veljko Jovanović:** Writing – review & editing, Investigation, Data curation. **Writing – review & editing, Investigation, Data curation.** **Marija Jović:** Writing – review & editing, Investigation, Data curation. **Marko Jović:** Writing – review & editing, Investigation, Data curation. **Writing – review & editing, Investigation, Data curation.** **Alessandra Costa Pereira Junqueira:** Writing – review & editing, Investigation, Data curation. **Lisa-Marie Kahle:** Writing – review & editing, Investigation, Data curation. **Adam Kantanista:** Writing – review & editing, Investigation, Data curation. **Ahmet Karakiraz:** Writing – review & editing, Investigation, Data curation. **Ayşe Nur Karkin:** Writing – review & editing, Investigation, Data curation. **Salam Khatib:** Writing – review & editing, Investigation, Data curation. **Nuannut Khieowan:** Writing – review & editing, Investigation, Data curation. **Patricia Joseph Kimong:** Writing – review & editing, Investigation, Data curation. **Litza Kiropoulos:** Writing – review & editing, Investigation, Data curation. **Joshua Knittel:** Writing – review & editing, Investigation, Data curation. **Neena Kohli:** Writing – review & editing, Investigation, Data curation. **Mirjam Koprivnik:** Writing – review & editing,

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curation. Stefan Stieger: Writing – review & editing, Investigation, Data curation.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvp.2024.102432.

## Appendix 1

### The Nature Exposure Scale (NES)

Two of the four NES items ask about nature exposure in one's everyday life and environments: Item #1 "In your everyday home, travel, and work environments and activities, please rate your level of exposure to 'natural environments' with options ranging from 'High (most of my everyday environment is natural)' (5)', to 'Low (very little of my everyday environment is natural)' (1), and Item #2 "How much do you notice the natural environments in your everyday life?" with options ranging from 'A great deal' (5), to 'Not much' (1). Two further items ask about nature exposure during excursions outside of one's everyday environments: Item #3 "Please rate the frequency (how often) of exposure to nature-rich environments outside of your everyday environment" with options ranging from 'High (once a month or more often)' (5), to 'Low (once a year or less)' (1), and Item #4 "How much notice would you take of nature in these environments?" with the same response options as item 2.

### The Connectedness to Nature Scale (CNS)

The 14 items of the CNS are as follows, with response options ranging from 1 (*strongly disagree*) to 5 (*strongly agree*): #1 "I often feel a sense of oneness with the natural world around me", #2 "I think of the natural world as a community to which I belong", #3 "I recognize and appreciate the intelligence of other living organisms", #4 "I often feel disconnected from nature", #5 "When I think of my life, I imagine myself to be part of a larger cyclical process of living", #6 "I often feel a kinship with animals and plants", #7 "I feel as though I belong to the Earth as equally as it belongs to me", #8 "I have a deep understanding of how my actions affect the natural world", #9 "I often feel part of the web of life", #10 "I feel that all inhabitants of Earth, human, and nonhuman, share a common 'life force'", #11 "Like a tree can be part of a forest, I feel embedded within the broader natural world", #12 "When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature", #13 "I often feel like I am only a small part of the natural world around me, and that I am no more important than the grass on the ground or the birds in the trees", and #14 "My personal welfare is independent of the welfare of the natural world".

## References

Andić, D., & Superina, L. (2021). How important is future teachers' "connectedness to nature"? Adaptation and validation of the connectedness to nature scale. *Education Sciences, 11*(5), 250. https://doi.org/10.3390/educsci11050250

Anderson, D. J., & Krettenauer, T. (2021). Connectedness to nature and pro-environmental behaviour from early adolescence to adulthood: A comparison of urban and rural Canada. *Sustainability, 13*(7), 3655. https://doi.org/10.3390/su13073655

Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. *American Psychologist, 55*(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469

Arroz, A. M., Picanço, A., Silva, A. R., Amorim, I. R., & Gabriel, R. (2022). Nature exposure scale: Psychometric properties, reliability and validity evidence from azores (Portugal). In *PSICAMB – XVI Congresso de Psicologia Ambiental: Pessoas e lugares num mundo em mudança – Ambientes Naturais*. Universidade do Algarve. https://doi.org/10.34623/mt6n-ef36

Asparouhov, T., & Muthén, B. (2023). Multiple group alignment for exploratory and structural equation models. *Structural Equation Modeling, 30*(2), 169–191. https://doi.org/10.1080/10705511.2022.2127100
Awoyemi, A. G., Ibáñez-Rueda, N., Guardiola, J., & Ibáñez-Alamo, D. (2024). Human-nature interactions in the Afrotropics: Ecological and cognitive connections among urban residents in southern Nigeria. *Ecological Economics*, **218**, Article 108105.
https://doi.org/10.1016/j.ecolecon.2024.108105

Baceviciene, M., Jankauskiene, R., & Swami, V. (2021). Nature exposure and positive body image: Examining the mediating roles of physical activity, autonomous motivation, connectedness to nature, and perceived restorativeness. *International Journal of Environmental Research and Public Health*, **18**(22), Article 12246. doi.org/10.3390.ijerph18221246.

Barragan-Jason, G., de Mazancourt, C., Parmesan, C., Singer, M. C., & Loreau, M. (2022). Human-nature connectedness as a pathway to sustainability: A global meta-analysis. *Conservation Letters*, **15**(1), Article e12852. https://doi.org/10.1111/conl.12852

Barragan-Jason, G., Loreau, M., de Mazancourt, C., Singer, M. C., & Parsesan, C. (2023). Psychological and physical connections with nature improve both human well-being and nature conservation: A systematic review of meta-analyses. *Biological Conservation*, **277**, Article 109842. https://doi.org/10.1016/j.biocon.2022.109842

Beery, T., Stahl Olafsson, A., Gentin, S., Maurer, M., Stålhammar, S., Albert, C., Bieling, C., Buijs, A., Fagerholm, N., Garcia-Martin, M., Plieninger, T., ... Raymond, C. M. (2023). Disconnection from nature: Expanding our understanding of human-nature relations. *People and Nature*, 5(2), 470–488. https://doi.org/10.1002/pan.10451

Bektaş, F., Kural, B., & Orçan, F. (2017). Turkish version of the connectedness to the nature. *Reliability and Validity. Paper presented at the 5th international science, culture, and sports congress (13-15 april) (Turkestan).*

Bourassa, S. C. (1990). A paradigm for landscape aesthetics. *Environment and Behaviour*, **22**(6), 787–812. https://doi.org/10.1177/0013916590226004

Boyd, F., White, M. P., Bell, S. L., & Burt, J. (2018). Who doesn't visit natural environments for recreation and why: A population representative analysis of spatial, individual and temporal factors among adults in gis. *Landscape and Urban Planning*, **175**, 102–113. https://doi.org/10.1016/j.landurbplan.2018.03.016

Brislin, R. W. (1986). The wording and translation of research instruments. In W. Lonner, & J. Berry (Eds.), *Field methods in cross-cultural research* (pp. 137–164). Sage.

Browning, M., & Lee, K. (2017). Within what distance does not "greenness" best predict physical health? A systematic review of articles withgis buffer analyses across the lifespan. *International Journal of Environmental Research and Public Health*, **14**(7), 675. https://doi.org/10.3390/ijperh.14070675

Capaldi, A., Dopko, R. L., & Zelenski, J. M. (2014). The relationship between nature connectedness and happiness: A meta-analysis. *Frontiers in Psychology*, **5**, 976. https://doi.org/10.3389/fpsyg.2014.00976

Carrus, G., Panno, A., Aragonés, J. I., Marchetti, M., Motta, R., Tonon, G., & Sanesi, G. (2020). Public perceptions of forests from Italy: An exploratory national survey. *Forest: Biogeosciences and Forestry*, **13**(4), 323–328. doi.org/10.3832/ifor3394-013.

Chen, F. F. (2007). Sensitivity of goodness of fit indices to lack of measurement invariance. *Structural Equation Modeling*, **14**(3), 464–504. https://doi.org/10.1080/10705510701301834

Collier, B. (2022). Not my green space? White attitudes towards black presence in UK green spaces. An auto-ethnography. In B. Plüschke-Altof, & H. Sooväli-Sepping (Eds.), *Whose green city? (Sustainable development goals series)* (pp. 41–58). Springer. https://doi.org/10.1007/978-3-031-04636-0-3.

Dadvand, P., Nieuwenhujsen, M. J., Esnaola, M., Forns, J., Basagana, X., Alvarez-Pedrerol, M., Rivas, I., López-Vicente, M., De Castro Pascual, M., Su, J., Jerrett, M., Querol, X., & Sunyer, J. (2015). Green spaces and cognitive development in primary schoolchildren. *Proceedings of the National Academy of Sciences*, **112**(26), 7937–7942. https://doi.org/10.1073/pnas.150340211

Day, K. (2000). The ethic of care and women's experiences of public space. *Journal of Environmental Psychology*, **20**(2), 103–124. https://doi.org/10.1006/jenvp.1999.0152

Droz, L., Chen, H.-M., Chu, H.-T., Fajrini, R., Imbong, J., Jannel, R., Komatsubara, O., Lagasca-Hiloma, C. M. A., Meas, C., Nguyen, D. H., Sherpa, T. O., Tun, S., & Undrakh, B. (2022). Exploring the diversity of conceptualization of nature in east and South-east Asia. *Humanities and Social Sciences Communications*, 9, 186. https://doi.org/10.1057/s41599-022-01186-5

Erikson, E. H. (1968). *Identity: Youth, and crisis*. W. W. Norton.

Fischer, R., Karl, J. A., Fontaine, J. R. J., & Poortinga, Y. H. (2023). Evidence of validity does not rule out systematic bias: A commentary on nomological noise and cross-cultural invariance. *Sociological Methods & Research*, **52**(3), 1420–1437. https://doi.org/10.1177/00491241221091756

Fletcher, R. (2017). Connection with nature is an oxymoron: A political ecology of "nature-deficit disorder". *The Journal of Environmental Education*, **48**(4), 226–233. https://doi.org/10.1080/00958964.2016.1139534

Fränkel, S., Sellmann-Risse, D., & Basten, M. (2019). Fourth graders' connectedness to nature - does cultural background matter? *Journal of Environmental Psychology*, **66**, Article 101347. https://doi.org/10.1016/j.jenvp.2019.101347

Freeman, C., Waters, D. L., Buttery, Y., & van Heezik, Y. (2019). The impacts of ageing on connection to nature: The varied responses of older adults. *Health & Place*, **56**, 24–33. https://doi.org/10.1016/j.healthplace.2019.01.010

Garrett, J. K., Rowney, F. M., White, M. P., Lovell, R., Fry, R. J., Akbari, A., Geary, R., Lyons, R. A., Mizen, A., Nieuwenhujsen, M., Parker, C., Song, J., Stratton, G., Thompson, D. A., Watkins, A., White, J., Williams, S. A., Rodgers, S. E., & Wheeler, B. W. (2023). Visiting nature is associated with lower socioeconomic inequalities in well-being in Wales. *Scientific Reports*, **13**, 9684. https://doi.org/10.1038/s41598-023-35427-7

Geary, R., Thompson, D. A., Mizen, A., Akbaroi, A., Garrett, J. K., & Rodgers, S. E. (2023). Ambient greenness, access to local green spaces, and subsequent mental health: A 10-year longitudinal dynamic panel study of 2.3 million adults in wales. *The Lancet Planetary Health*, **7**, e809–e818. https://doi.org/10.1016/S2542-5196 (23)00212-7

Gerrish, E., & Watkins, S. L. (2018). The relationship between urban forests and income: A meta-analysis. *Landscape and Urban Planning*, **170**, 293–308. https://doi.org/10.1016/j.landurbplan.2017.09.005

Gim, W.-S., Lee, M.-H., & Kim, K.-H. (2019). A validation study of the Korean version of the Connectedness to Nature Scale. *Journal of People, Plants, and Environment*, **22**(6), 621–628. https://doi.org/10.11628/ksppe.2019.22.6.621

Goh, A. Y. H., Chia, S. M., Majeed, N. M., Chen, N. R. Y., & Hartanto, A. (2023). Untangling the additive and multiplicative relations between nature scenery exposure and human-animal interaction on affective well-being: Evidence from daily diary studies. *Sustainability*, **15**(4), 2910. https://doi.org/10.3390/su15042910

Goudie, A. S. (2019). *Human impact on the natural environment: Past, present and future*. Wiley.

Grilli, G., & Curtis, J. (2021). Encouraging pro-environmental behaviours: A review of methods and approaches. *Renewable and Sustainable Energy Reviews*, **135**, Article 110039. https://doi.org/10.1016/j.rser.2020.110039

Holland, I., DeVille, N. V., Browning, M. H. E. M., Buehler, R. M., Hart, J. E., Hipp, J. A., Mitchell, R., Rakow, D. A., Schiff, J. E., White, M. P., Yin, J., & James, P. (2021). Measuring nature contact: A narrative review. *International Journal of Environmental Research and Public Health*, **18**(8), 4092. https://doi.org/10.3390/ijerph18084092

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. *Structural Equation Modeling*, **6**(1), 1–55. https://doi.org/10.1080/10705519909540118

Kamitsis, I., & Francis, A. J. P. (2013). Spirituality mediates the relationship between engagement with nature and psychological wellbeing. *Journal of Environmental Psychology*, **36**, 136–143. https://doi.org/10.1016/j.envp.2013.07.013

Keaulana, S., Kahili-Heede, M., Riley, L., Park, M. L. N., Makua, K. L., Kahaulahilahi Vegas, J., & Antonio, M. C. K. (2021). A scoping review of nature, land, and environmental connectedness and relatedness. *International Journal of Environmental Research and Public Health*, **18**(11), 5987. https://doi.org/10.3390/ijerph18115897

Kellert, S. R., & Wilson, E. O. (Eds.). (1993). *The biophilia hypothesis*. Island Press.

Krys, K., de Almeida, I., Wasiel, A., & Vignoles, V. L. (2024). WEIRD–Confucian comparisons: Ongoing cultural biases in psychology’s evidence base and some recommendations for improving global representation. *American Psychologist*. https://doi.org/10.1037/amp0001298. Advance online publication.

Lee, J. S.-H., & Oh, C.-O. (2021). Effects of the perceived trustworthiness of the government and connectedness to nature on payments for ecosystem services: A case study of national parks in South Korea. *Journal of Outdoor Recreation and Tourism*, **36**, Article 100446. https://doi.org/10.1016/j.jort.2021.100446

Lengieza, M. L., & Swim, J. K. (2021). The paths to connectedness: A review of the antecedents of connectedness to nature. *Frontiers in Psychology*, **12**, Article 763231. https://doi.org/10.3389/fpsyg.2021.763231

Li, N., & Wu, J. P. (2016). Revise of the connectedness to nature scale and its reliability and validity. *China Journal of Health Psychology*, **24**(9), 1347–1350.

Lavici, C., Manzi, F., Di Dio, C., Gilli, G., & Marchetti, A. (2023). Feeling connected to nature: Validation of the connectedness to nature scale in the Italian context. *Frontiers in Psychology*, **14**, Article 1242699. https://doi.org/10.3389/fpsyg.2023.1242699

Macaulay, R., Lee, K., Johnson, K., & Williams, K. (2022). Mindful engagement, psychological restoration, and connection with nature in constrained nature experiences. *Landscape and Urban Planning*, **217**, Article 104263. https://doi.org/10.1016/j.landurbplan.2021.104263

Marczak, M., & Sorokowski, P. (2018). Emotional connectedness to nature is meaningfully related to modernization: Evidence from the Meru of Kenya. *Frontiers in Psychology*, **9**, 1789. https://doi.org/10.3389/fpsyg.2018.01789

Marsh, H. W., Guo, J., Parker, P. D., Nagengast, B., Asparouhov, T., Muthén, B., & Dicke, T. (2018). What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups. *Psychological Methods*, **23**(3), 524–545. https://doi.org/10.1037/met0000113

Martin, C., & Czellar, S. (2016). The extended inclusion of nature in self scale. *Journal of Environmental Psychology*, **47**, 181–194. https://doi.org/10.1016/j.jenvp.2016.05.006

Martin, C., & Czellar, S. (2017). Where do biospheric values comes from? A connectivity to nature perspective. *Journal of Environmental Psychology*, **52**, 56–68. https://doi.org/10.1016/j.jenvp.2017.04.009

Martin, L., White, M. P., Hunt, A., Richardson, M., Pahl, S., & Burt, J. (2020). Nature contact, nature connectedness and associations with health, wellbeing and pro-environmental behaviors. *Journal of Environmental Psychology*, **68**, Article 101389. https://doi.org/10.1016/j.jenvp.2020.101389

Mattas-Terrón, A., & Elósegui-Bandera, E. (2012). Psychometric properties of the Connectedness to Nature Scale tested on a sample of university students. *PsyEcology*, **3**(1), 101–111. https://doi.org/10.1174/217119712799240297

Matukyniene, K., Baceviciene, M., & Jankauskiene, R. (2021). Prieiga priežaliųjų plotų, rysus su gamta, atkūramasis gamtos poveikis ir psichologinė gerovė tirianti Lietuvos gyventojų intym [Associations between nature exposure, nature connectedness, perceived restorativeness and psychological well-being in Lithuanian inhabitants]. *Visuom. Sveik*, 3, 23–32.

Mayer, F. S., & Frantz, C. M. (2004). The connectedness to nature scale: A measure of individuals' feeling in community with nature. *Journal of Environmental Psychology*, **24**(4), 503–515. https://doi.org/10.1016/j.jenvp.2004.10.001

McCright, A. M. (2010). The effects of climate change knowledge and concern in the American public. *Population and Environment*, **32**(1), 66–87.

McPhie, J., & Clarke, D. A. (2020). Nature matters: Diffracting a keystone concept of environmental education research – just for kicks. *Journal of Environmental Education Research*, **26**(9–10), 1509–1526. https://doi.org/10.1080/13504622.2018.1531387
Murdock, E. G. (2019). Nature where you're not: Rethinking environmental spaces and racism. In S. M. Meagher, S. Noll, & J. S. Biehl (Eds.), *The Routledge handbook of philosophy of the city* (pp. 301–313). Routledge.

Muthén, L. K., & Muthén, B. (2022). *Mplus user's guide*. Muthén & Muthén.

Navarro, O., Galharret, J.-M., Olivos, P., Loureiro, A., Wittenberg, I., Lemée, C., & Fleury-Bahi, G. (2022). The brief version of the “Connectedness to Nature Scale”: Factorial structure and invariance study across seven European cities. *Ecopsychology, 14*(3), 190–199. https://doi.org/10.1089/eco.2021.0058

Navarro, O., Olivos, P., & Fleury-Bahi, G. (2017). “Connectedness to nature scale”: Validity and reliability in the French context. *Frontiers in Psychology, 8*, 1280. https://doi.org/10.3389/fpsyg.2017.02180

Ojala, A. (2009). The interaction between emotional connectedness to nature and leisure activities in predicting ecological worldview. *Umweltpsychologie, 13*(2), 10–22.

Olivos, P., Aragonés, J. I., & Amérigo, M. (2013). The Connectedness to Nature Scale and its relationship with environmental beliefs and identity. In C. García, V. Corral-Verdugo, & D. Moreno (Eds.), *Recent Hispanic research on sustainable behavior and interbehavioral psychology* (pp. 1–15). Nova Science Publishers.

Pasanen, T. P., White, M. P., Elliott, L. R., van den Bosch, M., Bratman, G. N., Ojala, A., Korpela, K., & Fleming, L. E. (2023). Urban green space and mental health among people living alone: The mediating roles of relational and collective restoration in an 18-country sample. *Environmental Research, 232*, Article 116324. https://doi.org/10.1016/j.envres.2023.116324

Pasca, L., Aragonés, J. I., & Coello, M. T. (2017). An analysis of the connectedness to nature scale based on item response theory. *Frontiers in Psychology, 8*, 1330. https://doi.org/10.3389/fpsyg.2017.01330

Pasca, L., Coello, M. T., Aragonés, J. I., & Frantz, C. M. (2018). The equivalence of measures on the connectedness to nature scale: A comparison between the ordinal methods of dif detection. *PLoS One, 13*(11), Article e0207739. doi.org/10.1371/journal.pone.0207739

Pearce, J., Huang, S., Dowling, R. K., & Smith, A. J. (2022). Effects of social and personal norms, and connectedness to nature, on pro-environmental behavior: A study of western Australian protected area visitors. *Tourism Management Perspectives, 42*, Article 100966. https://doi.org/10.1016/j.tmp.2022.100966

Pedersen, C. B., & Mortensen, P. B. (2001). Evidence of a dose-response relationship between urbanicity during upbringing and schizophrenia risk. *Archives of General Psychiatry, 58*(11), 1039–1046. https://doi.org/10.1001/archpsyc.58.11.1039

Perrin, J. L., & Benassi, V. A. (2009). The connectedness to nature scale: A measure of emotional connection to nature. *Journal of Environmental Psychology, 29*(4), 434–440. https://doi.org/10.1016/j.jenvp.2009.03.003

Pessoa, V. S., Gouveia, V. V., Soares, A. K. S., Vilar, R., & Freires, I. A. (2016). Escala de conexão com a natureza: Evidências psicométricas no contexto brasileiro [*Connectedness to Nature Scale: Psychometric evidence in Brazil*. *Estudos de Psicologia, 33*(2), 271–282. https://doi.org/10.1590/1982-0275016000200009

Picanço, A., Arroz, A. M., Tsafack, N., Sánchez, A. F., Pereira, E., & Benedict, M., Amorim, I. R., da Luz Melo, M., & Gabriel, R. (2024). Assessing nature exposure: A study on the reliability and validity of a Portuguese version of the nature exposure scale. *Frontiers in Psychology, 15*, Article 1290066. https://doi.org/10.3389/fpsyg.2024.1290066

Pokropek, A., Davidov, E., & Schmidt, P. (2019). A Monte Carlo simulation study to assess the appropriateness of traditional and newer approaches to test for measurement invariance. *Structural Equation Modeling, 26*(5), 724–744. https://doi.org/10.1080/10705511.2018.1561293

Profice, C. C., Grenno, F. E., Fandi, A. C., Menezes, S. M., Semina, C. I., & Cassano, C. R. (2023). Leaning in the forest: Environmental perception of Brazilian teenagers. *Frontiers in Psychology, 14*, Article 1046405. https://doi.org/10.3389/fpsyg.2023.1046405

Richardson, M., Hamlin, I., Butler, C. W., Thomas, R., & Hunt, A. (2022). Actively noticing nature (not just time in nature) helps promote nature connectedness. *Ecopsychology, 14*(1), 8–16. https://doi.org/10.1089/eco.2021.0023

Richardson, M., Hamlin, I., Elliott, L. R., & White, M. P. (2022b). Country-level factors in a failing relationship with nature: Nature connectedness as a key metric for a sustainable future. *Ambio, 51*, 2201–2213. https://doi.org/10.1007/s13280-022-01744-w

Richardson, M., Hunt, A., Hinds, J., Bragg, R., Fido, D., Petronzi, D., Barbett, L., Clithrow, T., & White, M. P. (2019). An affective measure of nature connectedness for children and adults: Validation, performance and insights. *Sustainability, 11*, 3250. https://doi.org/10.3390/su11123250

Roberts, J. D., Omidvar Tehrani, S., & Bratman, G. N. (2023). Black spaces and green spaces: Remembering the eminence of nature during a pandemic. In D. L. Andrews, H. Thorpe, & J. I. Newman (Eds.), *Sport and physical culture in global pandemic times (Global culture and sport series)* (pp. 213–240). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-14387-8_9

Rosa, C. D., Collado, S., Profice, C. C., & Pires, P. P. (2022). The 7-items version of the connectedness to nature scale: A study of its validity and reliability with Brazilians. *Current Psychology, 41*, 5105–5110. https://doi.org/10.1007/s12144-020-01026-2

Rutkowski, L., & Svetina, D. (2014). Assessing the hypothesis of measurement invariance in the context of large-scale international surveys. *Educational and Psychological Measurement, 74*, 31–57. https://doi.org/10.1177/0013164413984257

Schultz, P. W., & Kaiser, F. G. (2012). Promoting pre-environmental behavior. In S. D. Clayton (Ed.), *The Oxford handbook of environmental and conservation psychology* (pp. 556–580). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199733026.013.0029.

Sedawi, W., Ben Zvi Assaraf, O., & Reiss, M. J. (2021). Challenges in measuring “connectedness to nature” among indigenous children: Lessons from the Negev Bedouin. *Cultural Studies of Science Education, 16*, 193–229. https://doi.org/10.1007/s11422-020-09995-3

Sheffield, D., Butler, C. W., & Richardson, M. (2022). Improving nature connectedness in adults: A meta-analysis, review and agenda. *Sustainability, 14*(19), Article 12494. https://doi.org/10.3390/su141912494

Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on generality (cog): A proposed addition to all empirical papers. *Perspectives on Psychological Science, 12*(6), 1123–1128. https://doi.org/10.1177/174569161770863

Soga, M., & Gaston, K. J. (2016). Extinction of experience: The loss of human-nature interactions. *Frontiers in Ecology and the Environment, 14*(2), 94–101. https://doi.org/10.1002/fee.1225

Soga, M., & Gaston, K. J. (2023). Global synthesis reveals heterogeneous changes in connection of humans to nature. *One Earth, 6*(2), 131–138. https://doi.org/10.1016/j.oneear.2023.01.007

Stehl, P., White, M. P., Vitale, V., Pahl, S., Elliot, L. R., Fian, L., & van den Bosch, M. (2024). From childhood blue space exposure to adult environmentalism: The role of nature connectedness and nature contact. *Journal of Environmental Psychology, 93*, Article 102225. https://doi.org/10.1016/j.jenvop.2023.102225

Stieger, S., Aichinger, I., & Swami, V. (2022). The impact of nature exposure on body image and happiness: An experience sampling study. *International Journal of Environmental Health Research, 32*(4), 870–884. https://doi.org/10.1080/09603123.2020.1803805

Stieger, S., Lewetz, D., & Swami, V. (2021). Psychological well-being under conditions of lockdown: An experience sampling study in Austria during the COVID-19 pandemic. *Journal of Happiness Studies, 22*, 2703–2720. https://doi.org/10.1007/s10902-020-00337-2

Strzelecka, M., Prience, S., & Boley, B. B. (2023). Resident connection to nature and attitudes towards tourism: Findings from three different rural nature tourism destinations in Poland. *Journal of Sustainable Tourism, 31*(3), 664–687. https://doi.org/10.1080/09669582.2021.1995399

Swami, V. (2024). Associations between nature exposure and body image: A critical, narrative review of the evidence. *Acta Psychologica, 248*, Article 104355. https://doi.org/10.1016/j.actpsy.2024.104355

Swami, V., & Barron, D. (2019). Translation and validation of body image instruments: Challenges, good practice guidelines, and reporting recommendations for test adaptation. *Body Image, 31*, 204–220. https://doi.org/10.1016/j bodyim.2018.08.014

Swami, V., Barron, D., Hari, R., Grover, S., Smith, L., & Furnham, A. (2019). The nature of positive body image: Examining associations between nature exposure, self-compassion, functionality appreciation, and body appreciation. *Ecopsychology, 11*(4), 243–253. https://doi.org/10.1089/eco.2019.0019

Swami, V., Barron, D., Todd, J., Horne, G., & Furnham, A. (2020). Nature exposure and positive body image: (Re-)examining the mediating roles of connectedness to nature and trait mindfulness. *Body Image, 34*, 201–208. https://doi.org/10.1016/j bodyim.2020.06.004

Swami, V., Barron, D., Weis, L., & Furnham, A. (2016). Bodies in nature: Associations between exposure to nature, connectedness to nature, and body image in U.S. adults. *Body Image, 18*, 153–161. https://doi.org/10.1016/j.bodyim.2016.07.002

Swami, V., Kannan, K., & Furnham, A. (2012). Positive body image: Inter-ethnic and rural-urban differences among an indigenous sample from Malaysian Borneo. *International Journal of Social Psychiatry, 58*(6), 568–576. https://doi.org/10.1177/0020764011415208

Swami, V., Tran, U. S., Barron, D., Afhami, R., Aimé, A., Almenara, C. A., Alp Dal, N., Amaral, A. C. S., Andrianto, S., Anjum, G., Argyrides, M., Atari, M., Aziz, M., Banai, B., Borowiec, J., Brewis, A., Cakir Kocak, Y., Campos, J. A. D. B., Carmona, C., ... Voracek, M. (2020). The Breast Size Satisfaction Survey: Breast size dissatisfaction and its antecedents and outcomes in women from 40 countries. *Body Image, 32*, 199–217. https://doi.org/10.1016/j.bodyim.2020.01.006

Swami, V., Tran, U. S., Stieger, S., & Voracek, M. (2022). Developing a model linking self-reported nature exposure and positive body image: A study protocol for the body image in nature survey (BINS). *Body Image, 40*, 50–57. https://doi.org/10.1016/j bodyim.2021.11.002

Swami, V., von Nordheim, L., & Barron, D. (2016). Self-esteem mediates the relationship between connectedness to nature and body appreciation in women, but not men. *Body Image, 16*, 41–44. https://doi.org/10.1016/j.bodyim.2015.11.001

Tam, K. (2013). Dispositional empathy with nature. *Journal of Environmental Psychology, 35*, 92–104. https://doi.org/10.1016/j.jenvp.2013.05.004

Teixeira, A., Gabriel, R., Martinho, J., Santos, M., Faria, A., Oliveira, I., & Moreira, H. (2023). Pro-environmental behaviors: Relationship with nature visits, connectedness to nature and physical activity. *American Journal of Health Promotion, 37*(1), 12–29. https://doi.org/10.1177/0890171212119089

Tirri, C., Swanson, H., & Meenar, M. (2021). Finding the “heart” in the green: Conducting a bibliometric analysis to emphasize the need for connecting emotions with biophilic urban planning. *International Journal of Environmental Research and Public Health, 18* (18), 9435. https://doi.org/10.3390/ijerph18189435

United Nations General Assembly. (2000). *United nations Millennium declaration*. United Nations General Assembly. https://www.refworld.org/docid/3b00f4ea3.html.

van Assen, M. A., Stoevenbelt, A. H., & van Aert, R. C. (2022). The end justifies all means: Questionable conversion of different effect sizes to a common effect size measure. *Religion, Brain & Behavior, 13*(3), 345–347. https://doi.org/10.1080/2153599X.2022.7020749

van den Berg, A. E., & ter Heijne, M. (2005). Fear versus fascination: An exploration of emotional responses to natural threats. *Journal of Environmental Psychology, 25*(3), 261–272. https://doi.org/10.1016/j.jenvp.2005.08.004

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. *Organizational Research Methods, 31*(1), 4–70. https://doi.org/10.1177/109442810031002
Whitburn, J., Linklater, W., & Abrahamse, W. (2020). Meta-analysis of human connection to nature and proenvironmental behavior. *Conservation Biology, 34*(1), 180–193. https://doi.org/10.1111/cobi.13381

White, M. P., Elliott, L. R., Grellier, J., Economou, T., Bell, S., Bratman, G. N., Criach, M., Gascon, M., Ojala, A., Roiko, A., Lima, M. L., Lohmus, M., Nieuwenhuijsen, M., Schultz, P. W., van den Bosch, M. A., & Fleming, L. E. (2021). Associations between green/blue spaces and mental health across 18 countries. *Scientific Reports, 11*, 8903. https://doi.org/10.1038/s41598-021-87675-0

Widdop Quinton, H., & Khatun, F. (2020). Childhoodnature alternatives: Adolescents in India, Nepal, and Bangladesh explore their nature connectedness. In A. Cutter-Mackenzie-Knowles, K. Malone, & E. Barratt Hacking (Eds.), *Research handbook on childhoodnature* (pp. 1043–1074). Springer. https://doi.org/10.1007/978-3-319-67286-1_57.

Wood, C., Barron, D., & Smyth, N. (2019). The current and retrospective intentional nature exposure scales: Development and factorial validity. *International Journal of Environmental Research and Public Health, 16*(22), 4443. https://doi.org/10.3390/ijerph16224443

Xiao, C., & McCright, A. M. (2015). Gender differences in environmental concern: Revisiting the institutional trust hypothesis in the USA. *Environment and Behavior, 47*(1), 17–37. https://doi.org/10.1177/0013916513491571

Yannick, J., & de Block, A. (2011). ‘Nature and I are two’: A critical examination of the biophilia hypothesis. *Environmental Values, 20*(2), 189–215. https://doi.org/10.3197/096327111X12997574391724

Zhang, J., Yu, Z., Zhao, B., Sun, R., & Vejre, H. (2020). Links between green space and public health: A bibliometric review of global research trends and future prospects from 1901 to 2019. *Environmental Research Letters, 15*(6), Article 063001. https://doi.org/10.1088/1748-9326/ab7f64