Systematic Review
Frontiers in Climate ·
Volume 8 - 2026 | https://doi.org/10.3389/fclim.2026.1837284
Understanding climate emotions in relation to mental health and pro-environmental behavior: a systematic review and meta-analysis
- Thomas Treesa Alisha1
- John Romate1*corresponding author
- Eslavath Rajkumar2*corresponding author
- K. S. Sruthi3
- Jose Mariya Lipsa2
- Mahesh Manjima1
- 1Department of Psychology, Central University of Karnataka, Kalaburagi, India
- 2Department of Liberal Arts, Indian Institute of Technology Bhilai, Bhilai, Chhattisgarh, India
- 3School of Psychology and Behavioral Sciences, RV University, Mysuru, India
Abstract
Human behavior is fundamentally influenced by emotions, while in the anthropogenic era, climate emotions have been observed to function as a significant driver influencing mental health (MH) and pro-environmental behaviors. Yet existing reviews emphasize the segment of fear in climate emotions. This review assessed the relationship between the emotions in the climate emotion wheel (sadness, positivity, fear, anger) on mental health outcomes, as well as pro-environmental behavioral intentions (PEBI). A systematic search in Scopus, Science Direct, Web of Science, PubMed, APA PsycNet, Wiley yielded 3,313 records and after screening, 253 studies were chosen for narrative synthesis, with 56 included in the meta-analysis. The quality of the included studies is assessed using Joanna Briggs Institute (JBI) Critical appraisal checklists and Mixed Methods Appraisal Tool (MMAT). In our meta-analysis, climate emotions (hope, empathy, anxiety, worry, and guilt) were positively associated with PEBI. Specifically, climate anxiety (r = 0.25, 95% CI [0.15, 0.36], p < 0.001), climate worry (r = 0.48, 95% CI [0.40, 0.56], p < 0.001), and climate guilt (r = 0.21, 95% CI [0.06, 0.35], p = 0.01) predicted stronger PEBI, whereas emotions in the segment of fear resulted in poorer mental health outcomes, including climate anxiety (r = 0.39, 95% CI [0.30, 0.48], p < 0.001), highlighting the dual motivational and psychological burden of climate emotions. Future research should prioritize experimental and longitudinal designs in climate emotion and should emphasize on the underexplored constructs such as gratitude, betrayal, inspiration, apathy, outrage and loneliness. The prime concern is the paucity of exploration in lower middle-income countries and how positive emotions influence PEBI and mental health outcomes. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251145857.
Keywords
1 Introduction
Life on Earth has evolved since the beginning of time, and change has always been a constant, and yet we have continued to maintain an innate and deep bond with the natural environment throughout history (Barragan-Jason et al., 2025; Morgan et al., 2022; Whitburn et al., 2020). However, this relationship has been significantly affected by urban lifestyles, industrialization, and technological advancements (Aschenbrand, 2024; Richardson, 2025). This situation has led to an immense threat to our ecology, thereby significantly impacting psychological and physical health (Heinz, 2024; Watts et al., 2019). In this regard, the literature suggests that environmental variables such as pollution, extreme temperatures, and exposure to pollutants can trigger neuroinflammation (Jilanee et al., 2025; Zundel et al., 2022), which impairs the cognitive and emotional pathways that enable us to regulate our emotions, thereby leaving us at risk for stress, anxiety, and depression (Bollen et al., 2017; Calcia et al., 2016; Kim and Won, 2017; Sălcudean et al., 2025).
According to the evolutionary perspective, emotions such as fear and anxiety originally developed as useful psychological adaptations meant to warn people of environmental risks, and in this context, the current global climate crisis is triggering this survival instinct (Parmentier et al., 2024; Steimer, 2002). As a result, these ancestral responses have evolved into current “eco-emotions” or “climate emotions,” such as climate anxiety, ecological grief, and eco-hope (Pihkala, 2022b), indicating the presence of an evolutionary line that transitions from adaptive concern to potential psychological distress with the onset of environmental loss (Mento et al., 2023; Pihkala, 2022a). Unlike clinical anxiety, which denotes a diagnosable mental health condition, climate anxiety refers to distress and worry specifically elicited by the impacts and anticipated consequences of climate change (Gholamnia et al., 2025).
Understanding this emerging class of emotions is crucial, as they hold significant implications for mental well-being and pro-environmental behavioral intentions (PEBIs), which in turn are strongly associated with improved mental health (MH) outcomes (Kerse and Kerse, 2025; Pinho, 2025). The climate emotion wheel (Pihkala, 2025), a visual tool that aids individuals to better understand and interpret their own feelings associated with climate change, represents a wide array of climate emotions across four segments (anger, sadness, positivity, fear) along with some additional climate emotions. Existing reviews and meta-analyses primarily focus on climate anxiety, excluding emotions such as guilt, anger, sadness, hope, etc. (Baroni et al., 2025; Contreras et al., 2024; Kühner et al., 2025; Mathers-Jones and Todd, 2023; Mondal et al., 2024; Pihkala, 2022b). There is a crucial need to potentially expand studies beyond the negative set of climate emotions to understand their role as a psychological buffer or catalyst in terms of PEBI (Pihkala, 2022b; Sangervo et al., 2022; Schneider et al., 2021). Against this backdrop, we examine climate emotions represented in the wheel, and their association with MH outcomes and PEBIs. MH outcomes are operationalized as symptoms, functional impairment, and subjective psychological distress caused by anxiety and depression, which are studied through validated psychometric tools such as PHQ-4, BDI, and GAD-7 (Beck et al., 2011; Beck et al., 1996; Larionow and Mudło-Głagolska, 2023; Spitzer et al., 2006). On the other hand, PEBI includes intended actions and the self-reported frequency of engagement in conservation efforts, protective actions, sustainable behaviors, etc.
This current review aims to systematically map the complex landscape of affective responses through the lens of the climate emotion wheel (Pihkala, 2025) and their association with MH outcomes and PEBIs. Furthermore, we attempt to provide extensive empirical evidence by identifying the underexplored segments of climate emotions, filling conceptual gaps, and integrating quantitative data. The review findings may aid in better understanding the ever-changing MH dimension in the Anthropocene era and support future trajectories of research on sustainable behavior enhancement and well-being.
2 Methods
2.1 Search strategy
The study has been documented in the PROSPERO database (CRD420251145857). According to, the reporting follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) (Page et al., 2021). We conducted an extensive search of the literature, including all publications from their inception until September 2025, using titles, abstracts, and keywords in the databases PubMed, PsycINFO, Science Direct, Scopus, Wiley, and Web of Science databases. Search terms related to climate emotions (e.g., climate anxiety OR eco-anger OR eco-hope) were combined with terms associated with PEB and MH (e.g., behavioral engagement OR pro-environmental behavior AND depression OR anxiety) (refer to Supplementary Table S1).
2.2 Study selection and eligibility criteria
Strict eligibility criteria were enforced with the inclusion of only peer-reviewed empirical studies, covering quantitative, qualitative, and mixed methods, which explicitly focus on one or more emotions from the climate emotions wheel. If the studies were found to cover either MH or PEBs or both, they were further considered for meta-analysis. Screening was performed based on the availability of full-text articles that were published in English language from inception till September 2025. Additionally, there were no geographical and population-based restrictions in the eligibility criteria. Theoretical, Conceptual Review Papers and Grey literature were excluded. Title, abstract, and full-text screening were independently done by two reviewers manually for eligibility based on relevance to the scope of the current study. In case of disagreement, a third reviewer was consulted to reach a consensus. Only articles published in English were included. In order to eliminate duplicates and for reference management, the studies were transferred to Zotero. Subsequently, after the screening and extraction of data, a manual search was carried out in December to find any additional records that would meet the inclusion criteria before running the final analysis.
2.3 Data extraction and quality appraisal
After extracting all the data, two independent reviewers reached a consensus to settle any disagreements. Title, author(s), year of publication, study type (qualitative, quantitative, or mixed-methods), study design, country of data collection, target population, sample size, and participant age were all organized for each included study. The climate emotions wheel was used as a framework to categorize and synthesize the emotions reported in each study, as well as related MH outcomes (such as well-being, anxiety, and depression) and PEBs to meet the review objectives. Additionally, provided estimates were also recorded (e.g., correlation coefficients and β-values) if available.
The Barker et al. (2026) Critical Appraisal Checklists for cross-sectional (Table 1), cohort (Table 2), quasi-experimental (Table 3), RCT (Table 4), and qualitative (Table 5) research were used to evaluate the methodological quality and bias risk of the included research. Each of the items were assessed as yes, no, uncertain, or not applicable. The Mixed Methods Appraisal Tool was used to evaluate mixed-methods research. (Table 6).
Table 1. Study quality assessment of cross-sectional studies.
| Sl. no. | Author and year | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | % Yes | Risk of bias |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Böhm et al. (2023) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 2 | Rizeq and Ojala (2025) | Y | Y | U | Y | Y | Y | Y | Y | 87.5 | Low |
| 3 | Nicolau et al. (2025) | Y | Y | Y | Y | N | N | Y | Y | 75 | Low |
| 4 | Hajek and König, 2024 | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 5 | Heeren et al. (2022) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 6 | Próchniak et al. (2024) | Y | Y | Y | Y | Y | U | Y | Y | 87.5 | Low |
| 7 | Asal et al. (2026) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 8 | Parmentier et al. (2024) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 9 | Liga et al. (2024) | Y | Y | Y | Y | N | N | Y | Y | 75 | Low |
| 10 | Hajek and König (2022) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 11 | Thomson and Roach (2023) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 12 | Whitmarsh et al. (2022) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 13 | Heeren and Asmundson (2023) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 14 | Awad et al. (2025) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 15 | Reese et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 16 | Ramírez-López et al. (2023) | Y | Y | Y | Y | U | Y | Y | Y | 87.5 | Low |
| 17 | Plohl et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 18 | Chapman and Peters (2024) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 19 | Weimann and Opaliński (2024) | Y | Y | Y | Y | N | N | Y | Y | 75 | Low |
| 20 | Lau et al. (2025) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 21 | Magklara et al. (2025) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 22 | Kovács et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 23 | Akakpo et al. (2024) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 24 | Bakul et al. (2025) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 25 | Söder et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 26 | Vecina et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 27 | Kouarfaté et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 28 | Wullenkord et al. (2021) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 29 | Qin et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 30 | Fuertes-Prieto et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 31 | Soomro et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 32 | Hogg et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 33 | Stewart et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 34 | Marczak et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 35 | Wullenkord et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 36 | Cosh et al. (2024b) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 37 | Pitiruţ et al. (2022) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 38 | Carlson et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 39 | Zimmer-Gembeck et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 40 | Toros (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 41 | Ágoston et al. (2024) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 42 | Martin et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 43 | Atta et al. (2024a) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 44 | Abdu et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 45 | Baum et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 46 | Turcotte-Tremblay et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 47 | Ullah et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 48 | Stanley et al. (2021) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 49 | Lukacs et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 50 | Lykins et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 51 | Hickman et al. (2021) | Y | Y | U | U | N | N | U | Y | 37.5 | High |
| 52 | Verplanken and Roy (2013) | Y | Y | Y | Y | Y | U | Y | Y | 87.5 | Low |
| 53 | Ferrari et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 54 | Zimmermann et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 55 | Crandon et al. (2024) | Y | Y | U | N | Y | Y | U | Y | 62.5 | Moderate |
| 56 | Vercammen et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 57 | Reyes et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 58 | Larionow et al. (2022) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 59 | Maduneme (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 60 | Lenhard et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 61 | Tucholska et al. (2024) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 62 | Pitron et al. (2024) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 63 | Daeninck et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 64 | Clayton et al. (2023) | Y | Y | Y | Y | Y | U | Y | Y | 87.5 | Low |
| 65 | Sangervo et al. (2022) | Y | Y | Y | U | Y | Y | U | Y | 75 | Low |
| 66 | Hajek and König (2022) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 67 | Hajek and König (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 68 | Abdu et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 69 | Bahar et al. (2024) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 70 | Bahja and Hancer (2021) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 71 | Fernandez et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 72 | Sohrabi et al. (2025) | Y | Y | Y | Y | Y | N | Y | Y | 87.5 | Low |
| 73 | Tam (2019) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 74 | Aruta (2025) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 75 | Khalil et al. (2025) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 76 | Atta et al. (2024b) | Y | Y | Y | Y | U | Y | Y | Y | 87.5 | Low |
| 77 | Stefkovics et al. (2024) | U | Y | Y | Y | Y | Y | Y | Y | 87.5 | Low |
| 78 | Galway and Field (2023) | Y | Y | Y | U | N | N | Y | Y | 62.5 | Moderate |
| 79 | Zeier and Wessa (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 80 | Batool and Batool (2024) | Y | Y | Y | Y | U | N | Y | Y | 75 | Low |
| 81 | Gregersen et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 82 | Cao et al. (2022) | Y | Y | Y | Y | U | U | Y | Y | 75 | Low |
| 83 | Bdier et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 84 | Kabasakal Cetin (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 85 | Ogunbode et al. (2022) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 86 | Simon et al. (2025) | Y | Y | U | Y | Y | Y | Y | Y | 87.5 | Low |
| 87 | Innocenti et al. (2023b) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 88 | Heinzel et al. (2023) | U | Y | Y | Y | Y | Y | Y | Y | 87.5 | Low |
| 89 | Gökçeli and Akkaya (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 90 | Regnoli et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 91 | Duran and Kaynak (2024) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 92 | Fertelli (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 93 | Mathers-Jones and Todd (2023) | Y | Y | Y | Y | Y | U | Y | Y | 87.5 | Low |
| 94 | Larionow et al. (2024) | Y | Y | Y | Y | Y | N | Y | Y | 87.5 | Low |
| 95 | Cameron and Kagee (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 96 | Jylhä et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 97 | Hong and Jeon (2025) | Y | Y | U | Y | Y | Y | U | Y | 75 | Low |
| 98 | Mandić et al. (2024) | Y | Y | Y | Y | N | N | Y | Y | 75 | Low |
| 99 | Garfin et al. (2024) | Y | Y | Y | Y | Y | Y | U | Y | 87.5 | Low |
| 100 | Li and Monroe (2018) | Y | Y | Y | N | N/A | N/A | Y | Y | 62.5 | Low |
| 101 | Cheung (2024) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 102 | Pittaway et al. (2024) | Y | Y | Y | N | N | Y | Y | Y | 75 | Low |
| 103 | Lee et al. (2024) | Y | Y | Y | N | Y | Y | U | Y | 75 | Low |
| 104 | Kıvanç et al. (2022) | Y | Y | Y | N | N/A | N/A | Y | Y | 62.5 | Moderate |
| 105 | Lutz et al. (2023) | Y | Y | Y | N | N | N/A | Y | Y | 62.5 | Moderate |
| 106 | Erkan and Budak (2025) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 107 | Ediz and Yanik (2023) | Y | Y | Y | N | Y | N | Y | Y | 75 | Low |
| 108 | Fekih-Romdhane et al. (2024) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 109 | Pinho (2025) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 110 | Kurt and Akdur (2024) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 111 | Yesildere Saglam and Mizrak Sahin (2025) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 112 | Çimşir et al. (2024) | Y | Y | Y | N | U | N | Y | Y | 62.5 | Moderate |
| 113 | Jalin et al. (2024) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 114 | Abunyewah et al. (2023) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 115 | Sampaio et al. (2023) | Y | Y | Y | N | Y | Y | Y | Y | 87.5 | Low |
| 116 | Jang et al. (2023) | Y | Y | Y | N | U | N | Y | Y | 62.5 | Moderate |
| 117 | Abd-El-Aal and Evans (2025) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 118 | Chan et al. (2024) | Y | Y | Y | Y | N/A | Y | Y | Y | 87.5 | Low |
| 119 | Hogg et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 120 | Asgarizadeh et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 121 | Mohd Mahudin and Syed Lokhikmal Hakim (2023) | Y | Y | Y | Y | U | Y | Y | Y | 87.5 | Low |
| 122 | Donati et al. (2025) | Y | Y | Y | Y | N | N/A | Y | Y | 75 | Low |
| 123 | Leite et al. (2023) | U | Y | Y | Y | Y | Y | Y | Y | 87.5 | Low |
| 124 | Innocenti et al., 2021 | Y | Y | N/A | Y | Y | Y | Y | Y | 87.5 | Low |
| 125 | Wullenkord and Ojala (2023) | Y | Y | N/A | Y | Y | Y | Y | Y | 87.5 | Low |
| 126 | Galway and Beery (2022) | Y | Y | N/A | Y | Y | N | Y | Y | 75 | Low |
Table 2. Study quality assessment of cohort studies.
| Sl. no. | Author and year | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | % Yes | Risk of bias |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Vergunst et al. (2024a) | Y | Y | N | Y | Y | Y | Y | Y | N | Y | Y | 81.8181818 | Low |
| 2 | Becht et al. (2024) | Y | Y | Y | Y | Y | U | Y | Y | N | Y | Y | 81.8181818 | Low |
| 3 | Schuitema and Lacchia (2025) | Y | Y | Y | U | N | Y | Y | Y | Y | U | Y | 72.7272727 | Low |
| 4 | Li et al. (2024) | Y | NA | Y | Y | Y | Y | Y | Y | Y | Y | Y | 90.9090909 | Low |
| 5 | Anneser et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 6 | Williams et al. (2024) | NA | NA | U | Y | Y | NA | Y | Y | Y | Y | Y | 63.6363636 | Moderate |
| 7 | Hania et al. (2025) | NA | NA | Y | Y | Y | NA | U | Y | Y | Y | Y | 63.6363636 | Moderate |
| 8 | Garfin et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 9 | Contreras et al. (2024) | NA | Y | Y | Y | U | NA | U | Y | Y | U | Y | 54.5454545 | Moderate |
| 10 | Hamama-Raz and Shinan-Altman (2024) | Y | Y | U | Y | Y | N | Y | Y | Y | Y | Y | 81.8181818 | Low |
| 11 | Knez et al. (2018) | Y | Y | Y | N | N | NA | Y | Y | N | N | Y | 54.5454545 | Moderate |
| 12 | Kühner et al. (2024) | Y | Y | Y | Y | Y | N/A | Y | Y | N | Y | Y | 81.8181818 | Low |
| 13 | Fertelli (2023) | Y | Y | U | Y | Y | N/A | Y | Y | Y | Y | Y | 81.8181818 | Low |
| 14 | Bechtoldt and Schermelleh-Engel (2025) | Y | Y | Y | Y | Y | N | Y | Y | N | Y | Y | 81.8181818 | Low |
| 15 | Mohamed et al. (2025) | Y | Y | Y | Y | Y | N/A | Y | Y | U | U | Y | 72.7272727 | Low |
Table 3. Study quality assessment of quasi-experimental studies.
| Sl. no. | Author and year | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | % Yes | Risk of bias |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Coates et al. (2024) | Y | N | Y | Y | N | Y | Y | Y | Y | 77.77777778 | Low |
| 2 | Lu (2016) | Y | Y | Y | Y | N | Y | Y | Y | Y | 88.88888889 | Low |
| 3 | Gould et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 4 | von Gal et al. (2024) | Y | Y | Y | Y | U | Y | Y | Y | Y | 88.88888889 | Low |
| 5 | Wan et al. (2024) | Y | Y | Y | Y | N | Y | Y | NA | Y | 77.77777778 | Low |
| 6 | Goldwert et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 7 | Zaremba et al. (2025) | Y | Y | Y | Y | N | Y | Y | Y | Y | 88.88888889 | Low |
| 8 | Gregersen et al. (2023) | Y | Y | Y | Y | N | Y | Y | Y | Y | 88.88888889 | Low |
| 9 | Sabherwal et al. (2021) | Y | Y | Y | Y | N | Y | Y | Y | Y | 88.88888889 | Low |
| 10 | Balundė and Poškus (2025) | Y | N | Y | U | Y | Y | U | Y | Y | 66.66666667 | Moderate |
| 11 | Levesque and Rocque (2025) | Y | N | N/A | N/A | Y | Y | Y | Y | Y | 66.66666667 | Moderate |
Table 4. Study quality assessment of randomized controlled trial studies.
| Sl. no. | Author and year | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | % Yes | Risk of bias |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Benoit et al. (2025) | Y | U | Y | N | Y | Y | Y | Y | Y | Y | Y | Y | Y | 84.61538462 | Low |
| 2 | Eweida et al. (2025) | Y | Y | Y | N | N | Y | U | Y | Y | Y | Y | Y | Y | 76.92307692 | Low |
| 3 | Gregersen et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | Y | NA | Y | Y | Y | 92.30769231 | Low |
| 4 | Comba and Starke (2024) | Y | Y | Y | Y | NA | Y | NA | Y | Y | Y | Y | Y | Y | 84.61538462 | Low |
| 5 | Li et al. (2024) | Y | Y | Y | Y | N | Y | Y | Y | Y | N | N/A | N | U | 61.53846154 | Moderate |
| 6 | Ng et al. (2024) | Y | U | U | N | N/A | N/A | N | N/A | Y | Y | Y | Y | Y | 46.1538462 | High |
| 7 | Lim and Hong (2022) | Y | U | U | N | N/A | N/A | N | N/A | Y | Y | Y | Y | Y | 46.1538462 | High |
| 8 | Rees et al. (2015) | Y | U | U | N | Y | Y | Y | N/A | Y | Y | Y | Y | Y | 69.2307692 | Moderate |
| 9 | MacKinnon et al. (2022) | Y | U | U | N | N/A | N/A | Y | N | U | Y | Y | Y | Y | 46.1538462 | High |
| 10 | Wang et al. (2023) | Y | U | Y | N | N/A | N/A | Y | N | Y | Y | Y | Y | Y | 61.5384615 | Moderate |
| 11 | Timmons et al. (2024) | Y | U | Y | N | N/A | N/A | Y | U | U | Y | Y | Y | Y | 53.8461538 | Moderate |
| 12 | Gago and Sá (2021) | Y | U | Y | N | Y | Y | Y | Y | N/A | Y | Y | Y | Y | 76.9230769 | Moderate |
| 13 | Neale et al. (2023) | Y | U | U | N | Y | Y | Y | N/A | N | Y | Y | Y | Y | 61.5384615 | Moderate |
| 14 | Chen et al. (2024) | Y | U | U | N | Y | Y | Y | N/A | N | Y | Y | Y | Y | 61.5384615 | Moderate |
| 15 | Spaiser et al. (2024) | Y | U | U | N | Y | Y | Y | N/A | Y | Y | Y | Y | Y | 69.2307692 | Moderate |
| 16 | Goldwert et al. (2024) | Y | U | U | N | Y | Y | Y | N/A | N | Y | Y | Y | Y | 61.5384615 | Moderate |
| 17 | Liu and Kuang (2024) | Y | U | U | N | Y | Y | Y | N/A | N | Y | Y | Y | Y | 61.5384615 | Moderate |
| 18 | Malecki et al. (2025) | Y | U | U | N | Y | Y | Y | N/A | Y | Y | U | Y | Y | 61.5384615 | Moderate |
Table 5. Study quality assessment of qualitative studies.
| Sl. no. | Author and year | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | % Yes | Risk of bias |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Marczak et al. (2023b) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 2 | Jones and Whitehouse (2021) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 3 | Cairns and Pihkala (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 4 | Friedman et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 5 | Tschakert et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 6 | Hamby et al. (2022) | U | Y | Y | Y | Y | Y | Y | Y | Y | Y | 90 | Low |
| 7 | Halstead et al. (2021) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 8 | Zaremba et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 9 | Jones and Lucas (2023) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 10 | Spiteri (2026) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 11 | Herrick et al. (2026) | Y | Y | Y | Y | Y | Y | Y | Y | U | Y | 90 | Low |
| 12 | Smith et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 13 | Silva (2026) | Y | Y | Y | Y | Y | U | N | Y | Y | Y | 80 | Low |
| 14 | Runkle et al. (2025) | Y | Y | Y | Y | Y | U | U | Y | Y | Y | 80 | Low |
| 15 | Lehtonen and Pihkala (2021) | Y | Y | Y | Y | Y | U | U | Y | U | Y | 70 | Low |
| 16 | Budziszewska and Jonsson (2021) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 17 | Léger-Goodes et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 18 | Elgaaied-Gambier and Mandler (2021) | Y | Y | Y | Y | Y | N | U | Y | N | Y | 70 | Low |
| 19 | Edwards et al. (2024) | Y | Y | Y | Y | Y | N | U | Y | Y | Y | 80 | Low |
| 20 | Zeyer and Roth (2013) | Y | Y | Y | Y | Y | N | U | Y | N | Y | 70 | Low |
| 21 | Budziszewska and Jonsson (2022) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 22 | Baker (2025) | Y | Y | Y | Y | Y | Y | Y | Y | U | Y | 90 | Low |
| 23 | Haugestad and Carlquist (2025) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 24 | Russell and Ashkanasy (2021) | Y | Y | Y | Y | Y | Y | U | Y | Y | Y | 90 | Low |
| 25 | Iqani (2025) | Y | Y | Y | Y | Y | Y | U | Y | Y | Y | 90 | Low |
| 26 | Chou et al. (2023) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 27 | Nielsen et al. (2024) | Y | Y | Y | Y | Y | U | U | Y | Y | Y | 80 | Low |
| 28 | Mkono and Hughes (2020) | Y | Y | Y | Y | Y | U | U | Y | U | Y | 70 | Low |
| 29 | Zurba et al. (2025) | Y | Y | Y | Y | Y | Y | U | Y | Y | Y | 90 | Low |
| 30 | Pihkala (2025) | Y | Y | Y | Y | Y | Y | Y | Y | N | Y | 90 | Low |
| 31 | Schor et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 32 | Hamilton (2022) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 33 | Ágoston et al. (2022a) | Y | Y | Y | Y | Y | U | U | Y | Y | Y | 80 | Low |
| 34 | Coppola and Pihkala (2023) | Y | Y | Y | Y | Y | Y | U | Y | Y | Y | 90 | Low |
| 35 | Verlie et al. (2021) | Y | Y | Y | Y | Y | Y | U | Y | Y | Y | 90 | Low |
| 36 | Davidson et al. (2023) | Y | Y | Y | Y | Y | U | N | Y | NA | Y | 70 | Low |
| 37 | Salgado and Bos (2026) | Y | Y | Y | Y | Y | U | N | Y | NA | Y | 70 | Low |
| 38 | Skilling et al. (2023) | Y | Y | Y | Y | Y | Y | Y | U | U | Y | 80 | Low |
| 39 | Olson et al. (2025) | Y | Y | Y | Y | Y | U | U | Y | U | Y | 70 | Low |
| 40 | Henry and Giannouli (2025) | Y | Y | Y | Y | Y | U | U | Y | Y | Y | 80 | Low |
| 41 | Vandenhole et al. (2024) | Y | Y | Y | Y | Y | Y | Y | Y | N | Y | 90 | Low |
| 42 | Alvey (2020) | Y | Y | Y | Y | Y | U | U | Y | U | Y | 70 | Low |
| 43 | Puig (2025) | U | Y | Y | Y | Y | N | U | Y | U | Y | 70 | Low |
| 44 | Lizarralde et al. (2025) | Y | Y | Y | Y | Y | N | Y | Y | Y | Y | 90 | Low |
| 45 | Dennis and Stock (2024) | U | Y | Y | Y | Y | N | Y | Y | Y | Y | 80 | Low |
| 46 | Beban and Korson (2025) | U | Y | Y | Y | Y | Y | Y | Y | Y | Y | 90 | Low |
| 47 | Ebhuoma et al. (2022) | U | Y | Y | Y | Y | N | Y | Y | Y | Y | 80 | Low |
| 48 | El Refaie and Thatcher (2025) | U | Y | Y | Y | Y | N | Y | Y | Y | Y | 80 | Low |
| 49 | Salmela et al. (2025) | U | Y | Y | Y | Y | N | U | Y | N | Y | 60 | Moderate |
| 50 | Sköld (2025) | U | Y | Y | Y | Y | Y | Y | Y | N | Y | 80 | Low |
| 51 | Zurba et al. (2024) | U | Y | Y | Y | Y | Y | U | Y | N | Y | 70 | Low |
| 52 | Vandaele and Stålhammar (2022) | U | Y | Y | Y | Y | N | U | Y | N | Y | 60 | Moderate |
| 53 | Roy and Ayalon (2024) | U | Y | Y | Y | Y | Y | U | Y | Y | Y | 80 | Low |
| 54 | Mahaffy et al. (2024) | U | Y | Y | Y | Y | N | Y | Y | N | Y | 70 | Low |
| 55 | White et al. (2025) | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | 100 | Low |
| 56 | Hockey (2024) | Y | Y | Y | Y | Y | U | U | Y | Y | Y | 80 | Low |
| 57 | Nairn et al. (2025) | U | Y | Y | Y | Y | Y | Y | Y | N | Y | 80 | Low |
| 58 | Vandenhole et al. (2025) | Y | Y | Y | Y | Y | U | N/A | Y | N/A | Y | 70 | Low |
Table 6. Study quality assessment of mixed-method studies.
| Sl. no. | Author and year | 1 | 2 | 3 | 4 | 5 | Comment-1 | Comment-2 | Comment-3 | Comment-4 | Comment-5 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Pitt et al. (2024) | Y | Y | Y | C | C | Adequate rationale: Yes – study aimed to capture professional perspectives using both qualitative (thematic analysis) and quantitative (survey frequency analysis) methods to comprehensively explore climate anxiety interventions. | Integration: While both qualitative themes and quantitative survey findings are presented, integration across strands is somewhat limited; findings are mostly discussed side-by-side rather than fully synthesized. | Interpretation of integration: Outputs are interpreted, showing how quantitative prevalence supports qualitative themes. | Divergences: Not clearly addressed. For example, mixed results on nature connection (supportive for some, triggering for others) are reported but not deeply reconciled. | Quality of each component: Qualitative data (reflexive thematic analysis) is strong. Quantitative component is descriptive and limited (frequency counts only), making it weaker. |
| 2 | Schwartz et al. (2023) | Y | Y | Y | Y | Y | Clear rationale for using a mixed-methods design: quantitative survey assessed associations between climate change anxiety (CCA), depression (MDD), and anxiety (GAD), while qualitative responses deepened understanding of participants’ worries and actions. | Quantitative and qualitative components were effectively integrated – qualitative data explained and contextualized quantitative findings (e.g., buffering role of collective activism). | Outputs of integration were adequately interpreted, showing how activism moderated relationships and gave participants a sense of agency. | Divergences were acknowledged: individual action lowered GAD but did not buffer associations, while collective activism buffered depression—this was explicitly discussed. | Both components met quality standards: quantitative analysis used validated tools (PHQ-8, GAD-7, CCA scale) with acceptable reliability, and qualitative analysis followed COREQ guidelines with thematic rigor. |
| 3 | Haltinner et al. (2021) | Y | Y | Y | C | Y | Adequate rationale for mixed methods is clearly presented: combining quantitative (surveys) and qualitative (interviews) helped capture the nuanced emotional dimensions of skepticism. | Integration is evident: qualitative interviews explain and contextualize quantitative patterns (e.g., why skeptics feel worry/dread despite disbelief). | Outputs of integration are adequately interpreted—findings highlight how emotions correlate with environmental concern and policy support. | Divergences are mentioned (e.g., religious ideation producing contradictory emotional responses), but these inconsistencies are not always systematically addressed. | Both qualitative (semi-structured interviews, inductive analysis) and quantitative (representative survey, regression analysis) components meet methodological quality standards. |
| 4 | Khalaim and Budziszewska (2024) | Y | Y | Y | C | Y | Adequate rationale for using mixed methods (survey + interviews) is provided to map and understand climate anxiety experiences in students. | Integration between quantitative and qualitative strands is evident in the discussion, but full joint displays or deeper synthesis are limited. | Outputs of integration are adequately interpreted, with both strands supporting the conclusion that climate anxiety is multi-faceted, partly paralyzing and partly motivating. | Divergences/inconsistencies are not explicitly addressed (e.g., cases where anxiety was overwhelming vs. empowering are noted but not reconciled). | Both components adhere to methodological quality: survey analysis used appropriate statistics; qualitative interviews used reflexive thematic analysis with reflexivity and participant validation. Overall, quality is moderate to high. |
| 5 | Iniguez-Gallardo et al. (2021) | Y | Y | Y | Y | C | Adequate rationale: Yes. They justify using mixed methods because previous studies used only quantitative closed-ended measures, and they aimed to explore both frequency of emotions (quantitative) and causal reasons (qualitative). | Integration: Yes. The quantitative checklist results were combined with open-ended qualitative responses, enabling richer interpretation of causes behind emotions. | Interpretation: Yes. Outputs were adequately interpreted, showing how concern, guilt, powerlessness, anger, and confusion differ across demographics. | Divergences: Yes. They addressed some inconsistencies (e.g., guilt not varying by demographics in stats but differing in qualitative reasons between rural vs. urban) | Adherence to quality of each component: Cannot tell fully. Quantitative sampling was clear and statistically adequate (400 participants), but the qualitative analysis relied on manual coding with discarded disagreements instead of negotiated consensus, which may weaken qualitative rigor. |
| 6 | Wu et al. (2023) | Y | Y | Y | C | Y | Sampling strategy (Q1): Relevant – adolescents (15–18) recruited from BC schools via the YDI survey, aligned with the research aim to validate a youth measure. | Representativeness (Q2): Large, diverse sample (N = 2,306) covering multiple school districts, although limited to one Canadian province, which may affect generalizability. | Measurements (Q3): Appropriate – adaptation of the Climate Change Anxiety Scale, confirmatory and exploratory factor analyses, Cronbach’s alpha (0.95), convergent/discriminant validity tested. | Nonresponse bias (Q4): Cannot fully tell – participation rate = 61.9%, but limited discussion of differences between responders and non-responders. | Statistical analysis (Q5): Appropriate – EFA, CFA, correlations with related constructs, reliability indices reported, all analyses justified. |
| 7 | Ágoston et al. (2022b) | Y | Y | Y | Y | Y | Clear rationale for developing new scales to assess eco-anxiety, eco-guilt, and ecological grief (derived from literature + thematic analysis of interviews) | The qualitative and quantitative phases were logically linked: interviews informed item generation, which was tested in a large sample (N = 4,608). | Integration of components was adequate (qualitative data shaped items; statistical analyses validated them). | No divergences between qualitative and quantitative findings were reported (they were complementary). | Both the qualitative (semi-structured interviews, thematic analysis) and quantitative parts (factor analysis, reliability, validity) were conducted rigorously with clear reporting and appropriate statistics |
| 8 | Jones and Lucas (2023) | Y | Y | Y | Y | Y | The research questions were clearly stated: to explore how Australian youth (15–19) experience talking about climate-change emotions | The sampling strategy (non-probability via youth orgs + social media) was appropriate for exploratory aims, though not fully representative. | Measurements were suitable: large-scale online survey (N = 1,943) plus open-ended responses; statistical analysis and thematic analysis were appropriate. | Nonresponse bias risk is low but acknowledged as a limitation (self-selection toward climate-engaged youth). | Statistical and qualitative analyses were thorough: logistic regressions, chi-squares, thematic coding with clear themes (e.g., validation, shared feelings). Mixed-methods integration was limited: qualitative data were used to elaborate quantitative results, but the study mainly reports them in parallel; still, both parts are of high quality. |
| 9 | Marczak et al. (2023a) | Y | Y | Y | Y | Y | The measure development was based on qualitative exploration, literature review, and content validation to operationally define a spectrum of emotional responses that guided the formulation of the item pool | The initial qualitative work, including cognitive interviews with the target population, informed the final modifications to the wording of the items and instructions used in the subsequent quantitative studies | Based on theoretical and qualitative grounds, the indicators of climate sorrow were included for further evaluation in Study 2, even though they did not form a separate factor in the initial 7-factor EFA solution | Divergence regarding the lack of a “climate sorrow” factor in the EFA was addressed by including the indicators in the subsequent CFA based on strong theoretical and qualitative research identifying it as a core dimension | High psychometric quality was secured in the two quantitative studies using rigorous methods like EFA and CFA, and the initial item pool development included expert validation and cognitive interviews |
| 10 | Iñiguez-Gallardo et al. (2025) | Y | Y | Y | N/A | Y | The study aims to examine the qualitative and quantitative factors shaping emotional responses to climate change, utilizing both closed and open-ended questions in an online survey to capture emotional responses and the reasons for them | The study used open-ended questions to provide participants with the “agency to declare the reasons for experiencing the emotions selected,” and these qualitative responses were manually coded and organized for pattern detection and content analysis alongside the quantitative data | The qualitative factors (reasons for emotions) were presented in detail to complement the quantitative findings (prevalence of emotions), which showed that feelings like powerlessness and guilt were driven by factors like inability to halt change and self-assessed personal shortcomings | The study primarily used the qualitative data (open-ended responses) to explain and provide context for the quantitative prevalence of emotions, and the sources do not report or address any conflicts, contradictions, or divergences between the two sets of results | Rigorous analytical methods were used, including content analysis for qualitative results and multiple linear regression and decision tree models for quantitative results, to assess knowledge, stances, and emotions |
| 11 | Gunasiri et al. (2022) | Y | Y | Y | Y | N | An exploratory mixed-method approach was used to identify climate-related mental health promotion issues among young people aged 18–24 years in Australia. Criterion sampling of young people engaged in climate actions provided an important qualitative component to the themes generated in the survey | Both data sets were triangulated and interpreted in relation to existing literature from the fields of psychology and health promotion. The themes/results from the survey validated the findings from the interviews about mental health impacts of climate change on young people involved in climate change activities and the impact of social media on their mental health | The discussion triangulated and interpreted findings in relation to existing literature, noting that engaging in climate action can help young people manage their anxieties about the future and transform their feelings into optimism and determination. The overall interpretations showed the added value of conducting a mixed methods study by concluding that a duality of responses to social media mediated by climate reporting provides leverage points for public health practitioners | The duality of the climate change experience is explored; positive impacts of engaging in climate change action and negative impacts of climate change are identified. The study noted that young people view polarising climate information and highlighted the importance of creating a balanced feed. | A limitation is the small sample size. As the researchers did not have access to organisation or network databases, precise reach and participation rates of the online survey could not be obtained |
| 12 | Ngo (2025) | Y | U | Y | N/A | N | Employing a mixed-methods approach, the study found that stronger reported family bonds were significantly associated with higher levels of environmental awareness | The research uses a mixed-methods approach on a specific dataset, including anonymized questionnaire responses and children’s drawings, but the mechanism of integration between these data types is not detailed | The study supported that strong emotional bonds with caregivers might significantly decrease the levels of anxiety these children experience due to environmental concerns | The paper focused on confirming the positive and significant relationship between family-child bonding and environmental views, and no divergences between the components were reported | The primary limitation of this study stems from the use of non-standardized measures to assess complex themes such as climate anxiety and perceptions of government responses. |
| 13 | Clissold et al. (2022) | Y | Y | Y | Y | N | Bringing these data sets together allows us to explore a diversity of experiences, perspectives, and emotional responses to the Anthropocene from participants across Oceania. | Drawing on survey and interview data sets, as well as a body of Pacifika art, we hope to contribute to the first step of acknowledging and naming the emotions and grief in Oceania such that they can then be worked through. | It demonstrates a broad range of emotions, including fear, anxiety, exhaustion, grief, anger, and helplessness, as well as empowerment, through place-specific understandings of impacts and responses. | The productive potential of emotions emerged in our study among some Australians and Pacific Islanders, who draw on anger and anxiety to motivate pro-environmental behavior, call for change, and inspire hope. Emotions in Oceania, however, do not always inspire motivation, hope, and action, and can, as seen in other studies, result in a sense of hopelessness, denial, and disavowal. | The sample size of the study remains small and subjective in nature. The results from this study are, therefore, not necessarily transferable or representative of the Oceania experience. |
| 14 | Wang et al. (2018) | Y | Y | Y | Y | N | We propose that strong emotional responses to climate change result from perceiving one’s “objects of care” as threatened by climate change, which motivates caring about climate change itself, and in turn predicts behavior | We demonstrated a connection between objects of care, the emotions they evoke, and actions to address climate change | The emotion profile that most closely resembled scientists’ qualitative reports predicted the greatest levels of support for climate policies | While the Strong Negative Emotion group differed from the reported emotions of scientists – namely, a lack of hope – the group still maintained greater support for climate action | First, the scientists were given different instructions to the students; scientists were approached to write about their emotions as part of a project to engage lay people with climate change, and this introduced a few methodological differences |
| 15 | Angel (2025) | Y | Y | Y | U | U | The mixed-methods approach was selected to ensure a comprehensive and nuanced understanding of civil society’s contributions to climate change mitigation | The mixed-methods approach synergizes these qualitative and quantitative components, enhancing the study’s robustness | The conclusion brings together these diverse findings, highlighting civil society’s pivotal role as a catalyst for change | By triangulating data from multiple sources, the study aims to provide a more comprehensive picture of civil society’s contributions, impacts, and challenges in climate change mitigation | Quantitative data are also collected to analyze the impact of their activities on policy reforms, community participation, and climate resilience |
| 16 | Jalin et al. (2025) | Y | Y | Y | Y | N | Research therefore followed the recommendations of Coffey et al. (2021), who demonstrated the relevance of using mixed methods to better understand the complexity of psychological reactions to climate change. | To fully reflect the diversity of its manifestations, the construction of the items was based on a qualitative study involving in-depth interviews with 18 eco-anxious people. | Factor analyses led us to retain a hierarchical model of eco-anxiety, confirming the existence of a general factor of eco-anxiety (Bacher, 2002). | The three subfactors of this model (i.e., anxiety-depressive manifestations, relational disturbances, and obsession with ecology) were strongly correlated. | The first limitation of this study was the profile of the respondents; half of them (49.2%) had a high education level, which is not representative of the French population and may indicate either increased interest among populations who are liable to be better informed about the climate issue, or else a bias in the recruitment process. |
| 17 | Hoppe et al. (2023) | Y | Y | Y | U | N | This study used the Knowledge, Attitude and Practices (KAP) survey design to collect both quantitative and qualitative information from MHPs licensed to practice in the State of Minnesota | Findings of this study will help educational institutions, state agencies, licensing boards, and other mental health organizations with developing curriculum and other guidance materials to support professionals in their approach to addressing climate-related mental health impacts with their clients | Results from this comprehensive survey underscore the need for interdisciplinary research and practice communities to design and implement assessment, intervention, and evaluation tools that address the broad impacts of CC on help-seeking clients | The self-administered survey was provided online using Verint analytics software (Verint EFM Version 15.3 FP3) through the Minnesota Department of Health (MDH) website. | A limitation of this approach is that the results of the survey are not representative of the general population, only those seeking care |
| 18 | Leino and Kulha (2023) | Y | Y | Y | U | N | The line of thought suggests that satisfaction with the participatory process, receiving information and developing more multi-faceted images of the future may promote positive emotions, which in turn could contribute to thinking of the future more often, reduced temporal discounting, enhanced civic engagement and lessened support for short-term, populist policies. | The analysis makes use of both survey and qualitative data, which originate from a Regional Citizens’ Assembly held in the region of Satakunta, Finland during the fall of 2020. | Empirical findings from the Satakunta Citizens’ Assembly lend some support to our assumptions, however. After discussing the Satakunta region’s future in the year 2050, participants reported heightened feelings of hopefulness and compassion, whereas negatively charged feelings were reduced. | The pessimistic approach of some participants does not mean that they felt that negative developments were completely inevitable. Even though interviewees were asked about feelings concerning the year 2050, identifying the “targets” of particular feelings can be complicated | The nature of our analysis as a case study with a relatively small number of observations causes problems regarding generalization |
| 19 | Verplanken et al. (2020) | Y | Y | Y | Y | N | The overall aim of the present studies is thus to investigate the constructive and unconstructive nature of habitual worry about global warming. | This paradigm decomposes habitual thinking into content and process | The three studies together suggest that while habitual global warming worry may be unconstructive and part of intrapersonal dysfunction for some individuals, for many others it is a constructive adaptive pro-environmental response | Taken together, the three studies produced signs of an unconstructive nature of global warming worry (i.e., correlation with pathological worry; negative emotions), as well as a constructive nature (i.e., no significant relation with pathology when controlled for other worries; correlations with pro-environmental views and positive emotions) | Firstly, the studies were conducted amongst convenience samples with high proportions of students and females |
| 20 | Spence et al. (2025) | Y | Y | Y | Y | Y | Design is therefore explanatory-sequential: applying mixed methods whereby qualitative data is used to provide insight into a parallel quantitative analysis | The analysis combined quantitative and qualitative methodologies to provide insights into the types of emotions that flood victims reported. and a depth of understanding as to the reasons that emotions may have been experienced | The General Discussion states that greater levels of anxiety, distress, and gratitude related to both personal environmental behavioral intentions and future environmental policy support | The study noted the inconsistency that significant relationships between emotions and behavior were found in the non-flooded group but not the flooded group, suggesting this may be due to other immediate demands that are placed on the flooded group | The quantitative outcome scale for intentions formed a reliable scale as assessed using Omega total: 0.76, and two researchers independently coded a subsample (N = 200) of the qualitative data |
| 21 | Johnson et al. (2024) | Y | Y | Y | N/A | Y | The paper draws on collaborative, culturally-sensitive mixed-methods ethnographic research | I applied a bespoke analytical framework combining thematic and intersectional styles of data analysis | The narratives underscore the vital importance of engaging social context when conceptualising and responding to L&D, support the move towards Indigenous-led, decolonised adaptation | The research focused on synthesizing qualitative (ethnographic narratives) and quantitative (contextual factors/themes) aspects consistently to build one coherent argument | Data was gathered between 2019–2020 using collaborative, culturally-sensitive mixed-methods ethnographic research comprising semi-structured and unstructured interviews, participant observation, and reflective journaling |
| 22 | Marks et al. (2023) | Y | Y | Y | N/A | Y | A 3-h workshop for delivery in schools was co-designed with young people, researchers, educators and clinicians, using principles of Youth Participatory Action Research (YPAR) | The workshop was recorded, transcribed and analysed using Thematic Analysis and sentiment analysis | This innovative pilot study suggests that a school-based youth participatory group could offer a novel way of helping young people to engage more with the climate and ecological crises in a way that supports their wellbeing | The workshop was analysed in three ways: thematic analysis, sentiment analysis, and content analysis of the post-workshop surveys; the findings were presented as supporting the overall concept | The workshop was analysed using reflexive, inductive thematic analysis (Braun and Clarke, 2006), and sentiment analysis was performed in R version 4.1.3 |
| 23 | Hogg et al. (2021) | Y | Y | Y | N/A | Y | We used a multi-study mixed-methods design to explore eco-anxiety in Australia and New Zealand, validating a new eco-anxiety scale | We used our findings from Study One to develop a more comprehensive eco-anxiety scale in Study Two | Our exploration of the dimensionality of eco-anxiety in Study One demonstrates that people’s anxieties. are interconnected and merge together to form an undifferentiated dimension | Qualitative responses indicate that eco-anxiety is also experienced as behavioral/social symptoms and ruminative cognitions, which is consistent with case studies | The subscales demonstrated excellent internal reliability at both time points and strong discriminant validity, with unique patterns emerging in the correlations between the subscales |
| 24 | Wamsler et al. (2023) | Y | Y | Y | N/A | Y | SenseMaker, a narrative-based methodology that includes quantitative and qualitative data collection and analyses that are facilitated by software. captures and assesses micro-narratives (i.e., personal stories) to ‘make sense’ of complex situations by identifying underlying patterns. | Qualitative and quantitative results were then compared, and linkages were explored to identify and make sense of individual and collective values, beliefs, emotions, paradigms, agency, political engagement, and future visions. | Results shed light on individual, collective, and structural capacities that must be supported to address climate change. | The combination of qualitative and quantitative analyses involved visualizing and identifying emergent patterns, then filtering based on demographics and questions of interest. | Quantitative data from triads, dyads, and multiple-choice questions were visualized and statistically analysed (comparative, correlation, and goodness-of-fit analyses). |
| 25 | Stanley et al. (2025) | Y | Y | Y | N/A | Y | Study aimed to catalogue the content of Australians’ anger about climate change. to contribute to understanding about the manifestations of anger that could propel action on climate change | Coded data were then merged into the SPSS file with participants’ quantitative responses on the measures detailed above | Correlations pointed to some differences in the behaviors and distress experienced by those angry for different reasons. | We did not identify a specific climate injustice category of anger as we had tentatively predicted. One possible interpretation of this finding is that injustice is implied by responses | We included two facets of the DASS-21. to record experiences of symptoms of depression (α = 0.94) and anxiety (α = 0.88). |
2.4 Synthesis method and statistical analysis
Based on the objectives, the findings of the studies were tabularized. Quantitative and qualitative data reporting climate emotions among the general population were considered for analysis. The characteristics of the included studies were reported using a descriptive analysis. To summarize the findings on climate emotions across numerous studies, we also listed the individual study results in the tables. Findings were synthesized through a narrative synthesis. Meta-analysis was conducted using the correlation coefficients, and in the case of studies reporting the standardized beta coefficients, these were converted into correlation coefficients (r) using the formula r = β + 0.05λ (where λ ≈ 1 for nonnegative values and 0 for negative values) and then transformed into the Fisher’s z scale for statistical processing within STATA software (Borenstein, 2009). Cohen’s recommendations were followed while interpreting effect size correlations, classifying them as extremely weak (r ≤ 0.10), weak (0.1 ≤ r < 0.3), moderate (0.3 ≤ r < 0.5), or strong (r ≥ 0.5), and presenting results within a 95% confidence interval (CI) determined by its lower limit (LL) and upper limit (UL) (Peterson and Brown, 2005; Rajkumar et al., 2024).
Subgroup analysis was performed for the subcomponents of MH (depression and anxiety) and PEBI (BE and PEB) with each of the climate emotions to examine whether the strength of the outcome varied across specific subdomains. The I2 statistic was used to examine heterogeneity. Greater heterogeneity between the studies corresponded to a higher I value/percentage. If I2 is 50%, a fixed effects model was used; if I2 > 50%, a random effects model was employed. Only results with p < 0.05 were considered statistically significant. Additionally, sensitivity analyses were carried out by the sequential exclusion of individual studies to test the robustness and reliability of the pooled effect size, and publication bias was evaluated using funnel plots and Egger’s test (refer to Supplementary material). All of the statistical analyses were performed using STATA 18.0 SE-Standard Edition software. In case studies of studies that were ineligible for meta-analysis, a narrative synthesis was considered.
3 Results
We identified a total of 3,313 studies from the following databases: Scopus (n = 2,965), Web of Science (n = 157), PubMed (n = 35), APA PsycNet (n = 11), ScienceDirect (n = 131), and Wiley Online Library (n = 14). Two thousand nine hundred eighty-one records were found eligible for title and abstract screening. After 332 duplicate records and one retracted article (found using Zotero) were eliminated. Of these, 2,699 items were disqualified owing to noncompliance with the eligibility criteria. After the remaining 341 articles were subjected to full-text screening, 90 studies were eliminated because their variables did not meet the inclusion criteria, that is, they either did not specifically address MH or pro-environmental behaviors (PEBs) or were theoretical, conceptual, or review articles. At this stage, 251 studies were retained, and in addition, two eligible studies were identified through manual searching. Consequently, we included a total of 253 studies in the final review (see Figure 1). Of these, 56 studies were selected for meta-analysis, in which 117 independent values were analyzed.
3.1 Description of included studies
Of the 253 included studies, 170 were quantitative works composed of cross-sectional studies (n = 126), cohort (n = 15), randomized controlled trial (n = 18), and quasi-experimental studies (n = 11) (Supplementary Table S2). Furthermore, 58 of the studies were qualitative (Supplementary Table S3), and 25 employed mixed methods (Supplementary Table S4). In the context of the review, we observed that the publication trend on climate emotions exhibits a clear and substantial increase in recent years. While early work surfaced as early as 2013, a majority of studies were conducted between 2021 and 2025, with 2024 emerging as the peak year, accounting for the greatest number of identified papers (Cunsolo Willox et al., 2013; Ojala, 2012; Stanley et al., 2021; Toros, 2025; Turcotte-Tremblay et al., 2024, https://www.zotero.org/google-docs/?broken=iuvnvW). However, most of this climate emotion research has mainly focused on youth, adolescents, and young adults (aged 15 to 35), who were often recruited from high school or university settings, with student samples and environmentally engaged groups extensively sampled (Crandon et al., 2024; Henry and Giannouli, 2025; Hickman et al., 2021). Geographically, most studies are concentrated in Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries (Ágoston et al., 2022b; Cairns and Pihkala, 2024; Erkan and Budak, 2025). Although some studies include professional and general adult populations, older adults, indigenous communities, and socioeconomically vulnerable groups are underrepresented (Cunsolo Willox et al., 2013; Johnson et al., 2024). While there is an increasing inclusion of the Global South and Asian contexts, such studies are still very limited (Che Rus et al., 2025; Iñiguez-Gallardo et al., 2025; Leite et al., 2023).
Across the reviewed literature, anxiety, worry, guilt, and anger in particular were observed to exhibit a strong correlation with negative MH outcomes, such as psychological distress, depressive symptoms, and functional impairment (Hickman et al., 2021; Hogg et al., 2021; Lukacs et al., 2023; Vergunst et al., 2024a, 2024b). These emotions simultaneously served as adaptive motivators, with moderate levels of anxiety and moral emotions such as eco-guilt and anger consistently predicting stronger PEBIs, activism, and policy support (Freiling and Matthes, 2023; Stanley et al., 2021; Verplanken et al., 2020). Consequently, the synthesized findings correspond to a non-linear, often inverted U-shaped relationship in which moderate emotional distress promotes engagement while excessive anxiety can lead to “eco-paralysis,” avoidance, or heightened distress without corresponding behavioral gains (Coates et al., 2024; Goldwert et al., 2024; Lukacs et al., 2023). Importantly, constructive hope and perceived efficacy emerge as significant factors that hinder negative MH outcomes and in maintaining PEBs overtime (Kovács et al., 2024; Sangervo et al., 2022).
3.2 Meta-analysis of climate emotions and mental health (MH)
3.2.1 Studies on climate anxiety (CA)
The meta-analysis, which investigated the association between CA and MH outcomes, included 19 studies. The pooled correlation revealed a significance at the p > 0.001 level with an overall moderate positive effect size (r = 0.39, 95% confidence interval (CI) [0.30, 0.48]). However, heterogeneity among studies was extremely high, indicating considerable variability across the included studies (Figure 2).
Our analysis of the funnel plot (Supplementary Figure S2) revealed possible publishing bias. However, Egger’s regression test revealed no significant small-study effect (Supplementary Figure S3), suggesting that the observed asymmetry may be due to language bias, as only English-language publications were included. The robustness of the results was assessed using sensitivity analysis (Supplementary Figure S4).
A subgroup analysis was carried out to investigate the relationship between CA and particular MH outcomes, including anxiety, depression, and overall MH (Supplementary Figure S1 refer to Supplementary material). For anxiety (9 studies), the pooled effect size was r = 0.38 (95% CI [0.28, 0.47]), reflecting a moderate and statistically significant positive correlation (p < 0.001), with substantial heterogeneity (τ2 = 0.03, I2 = 94.79%, H2 = 19.18). For depression (8 studies), we observed an effect size of r = 0.30 (95% CI [0.19, 0.40]), representing a moderate, significant association (p < 0.001), with high heterogeneity (τ2 = 0.02, I2 = 93.63%, H2 = 15.71). The pooled effect size for MH in the two studies was r = 0.70 (95% CI [0.66, 0.73]), indicating a strong, statistically significant positive relationship (p < 0.001) with no apparent heterogeneity (τ2 = 0.00, I2 = 0.00%, H2 = 1.00). Despite substantial heterogeneity in the overall MH aspect (I2 = 97.19%), effect estimates varied significantly between studies, potentially owing to changes in study design, demographic characteristics, or measuring tools.
3.2.2 Studies on the climate worry (CW)
The association between CW and MH outcomes was investigated in 10 studies. Higher levels of CW were linked to poorer MH outcomes (Figure 3). However, heterogeneity was substantial (τ2 = 0.03, I2 = 94.47%, H2 = 18.09, Q(9) = 158.65, p < 0.001), indicating considerable variability among studies. Our funnel plot (Supplementary Figure S6) indicated a dispersed pattern, but an Egger’s regression test (Supplementary Figure S7) revealed no significant small-study impact (z = −1.09, p = 0.27), indicating that the observed asymmetry is most likely not because of publication bias (see Supplementary material).
To investigate relationships between CW and the particular MH domains of anxiety and depression, a subgroup analysis was carried out (Supplementary Figure S5 in Supplementary material). For anxiety (5 studies), the pooled effect size was r = 0.19 (95% CI [0.03, 0.34], z = 2.37, p = 0.02), indicating a small but significant positive relationship, with high heterogeneity (τ2 = 0.03, I2 = 92.32%, H2 = 13.02, Q(4) = 40.02, p < 0.001). Similarly, for depression (5 studies), the pooled correlation was r = 0.19 (95% CI [0.02, 0.34], z = 2.20, p = 0.03), also reflecting a small but significant association, accompanied by very high heterogeneity. The group differences test revealed no significant change across subgroups (Qb(1) = 0.00, p = 0.99), implying that the degree of the association between CW and MH did not differ significantly among anxiety and depression outcomes. These findings indicate that climate concern is modestly but consistently associated with poorer MH, particularly anxiety and depression; however, the observed significant heterogeneity suggests that contextual and methodological differences between studies may have influenced the observed associations. The robustness of the findings was tested using a sensitivity analysis (Supplementary Figure S8).
3.2.3 Studies on cognitive-emotional impairment (CEI), a subdimension of climate change anxiety (CA)
A total of 12 studies investigated the link between CA, specifically the cognitive-emotional impairment (CEI) subdimension, and MH concerns. Stronger CEI associated with CA has been correlated to impaired MH, according to the pooled analysis, which showed a small-to-moderate, statistically significant positive association (r = 0.25, 95% CI [0.17, 0.33], z = 6.06, p < 0.001) (Figure 4). However, our analysis also revealed substantial heterogeneity (τ2 = 0.02, I2 = 89.61%, H2 = 9.63, Q(11) = 73.18, p < 0.001), indicating notable variation across studies. The corresponding funnel plot (Supplementary Figure S10) showed a dispersed pattern, and the Egger’s regression test (Supplementary Figure S11) revealed no significant small-study impact (z = −0.92, p = 0.35), indicating that publication bias was not the cause of the apparent asymmetry, and sensitivity analysis revealed the robustness of the findings (Supplementary Figure S12) (Supplementary material).
To investigate the relationship between the CEI dimension and anxiety and depression independently, we next performed a subgroup analysis (Supplementary Figure S9). The pooled correlation for anxiety (7 studies) was r = 0.26 (95% CI [0.16, 0.35], z = 5.13, p < 0.001), suggesting a statistically significant small-to-moderate connection with high heterogeneity (τ2 = 0.02, I2 = 87.09%, H2 = 7.74, Q(6) = 36.53, p < 0.001). For depression (5 studies), the pooled effect was r = 0.24 (95% CI [0.09, 0.38], p < 0.001), also representing a small-to-moderate, significant relationship, with substantial heterogeneity. The test of group differences indicated no significant difference between the anxiety and depression subgroups (Qb(1) = 0.04, p = 0.83), suggesting that the strength of association between CEI and MH outcomes was comparable across the two domains.
3.2.4 Studies on functional impairment (FI) subdimension of climate change anxiety (CA)
Functional impairment (FI) was analysed in relation to MH issues across 12 studies. The pooled analysis demonstrated a small but statistically significant positive association between FI and MH outcomes (r = 0.19, 95% CI [0.10, 0.28], z = 3.92, p < 0.001), indicating that higher levels of FI were related to poorer MH outcomes (Figure 5). However, the analysis revealed high heterogeneity (τ2 = 0.03, I2 = 92.39%, H2 = 13.14, Q (11) = 169.12, p < 0.001), suggesting substantial variability across studies. The Egger’s regression test (Supplementary Figure S15) showed no evidence of small-study effects (z = −0.18, p = 0.85), suggesting that publication bias was unlikely despite the funnel plot’s (Supplementary Figure S14) apparent wide dispersion (see Supplementary Figure S16 for the sensitivity analysis in Supplementary material).
A subgroup analysis examined the association of FI with anxiety and depression (Supplementary Figure S13). For anxiety (7 studies), the pooled correlation was r = 0.22 (95% CI [0.13, 0.32], z = 4.72, p < 0.001), reflecting a small but significant positive relationship, with substantial heterogeneity (τ2 = 0.01, I2 = 84.69%, H2 = 6.53, Q(6) = 34.52, p < 0.001). For depression (5 studies), the pooled effect was r = 0.15 (95% CI [−0.04, 0.33], z = 1.53, p = 0.13), which was not statistically significant, though heterogeneity remained high (τ2 = 0.05, I2 = 94.81%, H2 = 19.28, Q(4) = 99.53, p < 0.001).
A test of group differences showed no significant difference between the anxiety and depression subgroups (Qb(1) = 0.42, p = 0.52), suggesting comparable associations across these outcomes. The consistently high heterogeneity implies that contextual, demographic, or methodological factors may contribute to the observed variability among studies.
3.3 Meta-analysis of climate emotions and pro-environmental behavioral intentions
3.3.1 Studies on CA and PEBI
A total of 21 studies examined the association between CA and PEBIs or behavioral engagement (BE). The pooled correlation showed a small to moderate positive relationship (r = 0.25, 95% CI [0.15, 0.36], z = 4.50, p < 0.001), indicating that higher levels of CA are associated with stronger PEBs and BE (Figure 6). However, the meta-analysis revealed extremely high heterogeneity (τ2 = 0.07, I2 = 98.10%, H2 = 52.57, Q(20) = 767.37, p < 0.001), suggesting substantial variability among studies. Our funnel plot (Supplementary Figure S18) revealed an asymmetry suggestive of publication bias, which was supported by Egger’s regression test (Supplementary Figure S19), detecting a significant small-study effect (z = 2.48, p = 0.013). This finding suggests that smaller studies may have reported stronger associations between CA and PEB/BE [see Supplementary material for sensitivity analysis (Supplementary Figure S20)].
A subgroup analysis was performed to separately assess associations between CA and BE, and between CA and PEB (Supplementary Figure S17). For BE (7 studies), the pooled correlation was r = 0.21 (95% CI [−0.01, 0.42], z = 1.89, p = 0.06), indicating a small, non-significant positive relationship, accompanied by very high heterogeneity (τ2 = 0.08, I2 = 98.07%, H2 = 51.71, Q(6) = 115.17, p < 0.001). In contrast, for PEBs (14 studies), the pooled effect was r = 0.28 (95% CI [0.15, 0.40], z = 4.13, p < 0.001), representing a moderate and statistically significant positive association, again with extremely high heterogeneity (τ2 = 0.06, I2 = 98.09%, H2 = 52.28, Q(13) = 651.85, p < 0.001). Additionally, a test of subgroup differences revealed no significant difference between the BE and PEB groups (Qb(1) = 0.38, p = 0.54), suggesting that the magnitude of association between CA and behavioral outcomes was generally similar across subtypes (refer to the Supplementary material).
3.3.2 Studies on guilt and PEBI
The relationship between environment-related guilt and PEBI was investigated in eight different studies. Our meta-analysis found a small but statistically significant positive correlation (r = 0.21, 95% CI [0.06, 0.35], z = 2.79, p = 0.01), indicating that higher levels of guilt about environmental issues were weakly associated with stronger intentions to engage in PEBs (Figure 7). However, there was significant heterogeneity among studies (τ2 = 0.04, I2 = 95.42%, H2 = 21.84, Q(7) = 124.06, p < 0.001), indicating wide variance in study outcomes, presumably owing to differences in sample characteristics, cultural contexts, or tools.
The potential for publishing bias (Supplementary Figure S21) was illustrated by the corresponding funnel plot’s clear asymmetry. This result was validated by an Egger’s regression test (Supplementary Figure S22), which showed a significantly small-study impact (z = 2.65, p = 0.008). According to this finding, smaller studies tend to report stronger correlations between guilt and PEBIs. This could be because of sampling variability, as smaller studies are frequently less precise, and publication bias, where studies with significant or larger effects are more likely to be published [see Supplementary material for the sensitivity analysis (Supplementary Figure S23)]. These results indicate that environmental guilt is positively and significantly associated with PEBIs, although the high heterogeneity and evidence of small study effects suggest that these findings should be interpreted with caution.
3.3.3 Studies on climate worry (CW) and PEBI
A total of 14 independent values from individual studies were included to examine the association between climate worry (CW) and PEBI, including both PEB and BE. The pooled analysis showed a moderate and statistically significant positive correlation (r = 0.48, 95% CI [0.40, 0.56], z = 9.72, p < 0.001), indicating that higher CW levels were associated with stronger intentions to engage in PEBs (Figure 8). This implies that intentions to be ecologically responsible may be motivated by emotional concern about climate change.
There was significant heterogeneity among the considered studies (τ2 = 0.04, I2 = 94.63%, H2 = 18.61, Q(13) = 253.73, p < 0.001), indicating significant diversity in the magnitude of association reported, despite the consistent pattern of findings. A visual analysis of the funnel plot (Supplementary Figure S24) revealed that the studies were widely distributed, indicating the need for additional research on publication bias. However, the Egger’s regression test (Supplementary Figure S25) found no indication of small-study effects (z = −0.41, p = 0.68), indicating no small study effects [see Supplementary material for the sensitivity analysis (Supplementary Figure S26)]. These results suggest that affective concern about climate change may be a significant psychological driver since they show a moderate and consistent association between CCW and PEBIs.
3.3.4 Studies on hope and PEB
The association between hope and PEB was explored using eight coefficients from separate studies. Our meta-analysis found a minor but statistically significant positive connection between higher levels of hope and increased engagement in PEBs (r = 0.21, 95% CI [0.09, 0.32], z = 3.32, p < 0.001) (Figure 9).
The meta-analysis found significant heterogeneity among studies (τ2 = 0.03, I2 = 94.87%, H2 = 19.48, Q(7) = 214.03). This disparity in reported effect sizes could be due to differences among study populations, measurement tools utilized, or cultural contexts. The funnel plot (Supplementary Figure S27) revealed a dispersion of studies, encouraging further investigation for publishing bias. A significant small-study effect (z = −2.06, p = 0.04) was found using the Egger’s regression test (Supplementary Figure S28), indicating that smaller studies tended to report stronger relationships between hope and PEB. This could be due to sample variability or publication bias [see Supplementary material for the sensitivity analysis (Supplementary Figure S29)]. These results suggest that hope is positively associated with PEBs, though the small effect size, high heterogeneity, and evidence of small-study effects indicate that this relationship should be interpreted with caution.
3.3.5 Studies on sadness and PEBI
Two distinct studies examined the connection between sadness and PEBI. Although the evidence is inconclusive, the pooled correlation revealed a small-to-moderate positive association that was not statistically significant (r = 0.32, 95% CI [−0.01, 0.59], z = 1.91, p = 0.06), implying that higher levels of sadness may be weakly associated with PEBs (Figure 10).
Our analysis revealed considerable heterogeneity (τ2 = 0.06, I2 = 92.41%, H2 = 13.17, Q(1) = 13.17, p < 0.001), showing substantial variability across the two studies, most likely due to differences in sample characteristics, cultural contexts, or measurement techniques. Although the effect size points to a possible positive correlation between depression and PEB, the small number of studies, significant variability, and lack of statistical significance suggest that the association should be evaluated cautiously [refer to Supplementary material for sensitivity analysis (Supplementary Figure S30)].
3.3.6 Studies on anger and PEBIs
A total of five studies examined the association between anger and PEBIs. The pooled correlation indicated a small, non-significant association with overall effect size r = 0.15 (95% CI [−0.03, 0.31], z = 1.67, p = 0.10), suggesting that anger showed a weak and inconsistent relationship with PEBI (Figure 11). Heterogeneity was very high across studies (τ2 = 0.03, I2 = 96.93%, H2 = 32.59, Q(4) = 49.14, p < 0.001), indicating substantial variability in the reported associations. Our funnel plot (Supplementary Figure S32) revealed a dispersed pattern, but the Egger’s regression test (Supplementary Figure S33) found no indication of small-study effects (z = 0.92, p = 0.36), implying that publication bias is unlikely (see Supplementary material, Supplementary Figure S34 for sensitivity analysis).
We performed a subgroup analysis to examine the relationship between anger and BEs and PEB separately (Supplementary Figure S31). For BEs (2 studies), the pooled correlation was r = 0.33 (95% CI [−0.04, 0.69], z = 1.75, p = 0.08), indicating a moderate but non-significant positive association, with high heterogeneity (τ2 = 0.07, I2 = 93.22%, H2 = 14.74, Q(1) = 14.74, p < 0.001). For PEB (3 studies), the pooled correlation was r = 0.02 (95% CI [−0.00, 0.05], z = 1.61, p = 0.11), suggesting no meaningful association, and the heterogeneity was negligible (τ2 = 0.00, I2 = 0.01%, H2 = 1.00, Q(2) = 0.15, p = 0.93). There was no significant difference between BE and PEBs, according to the test of subgroup differences (Qb(1) = 2.66, p = 0.10), indicating that the impact of anger on pro-environmental outcomes did not significantly differ across these behavioral domains. Overall, these results imply that anger is not always linked to pro-environmental actions or intentions, and that the relationship may vary depending on the context of the research or approach (refer to Supplementary material).
3.3.7 Studies on empathy and PEB
The association between empathy and PEB was studied using six independent coefficients from different studies. Our pooled analysis revealed a significant positive connection between empathy and PEB (r = 0.55, 95% CI [0.39, 0.67], z = 6.08, p < 0.001) (Figure 12).
Although the association was strong, there was significant heterogeneity (τ2 = 0.05, I2 = 90.36%, H2 = 10.37, Q(5) = 46.18, p < 0.001), attributable to differences in research demographics, measurement tools, or contextual variables. Additionally, a dispersed pattern was evident in the funnel plot (Supplementary Figure S35), prompting further analysis for publication bias. A significantly small-study effect (z = 2.92, p = 0.0035) was found upon applying an Egger’s regression test (Supplementary Figure S36), indicating that smaller studies may have reported stronger associations between empathy and PEB. This could be due to sampling variability (see Supplementary material, Supplementary Figure S37, for the sensitivity analysis). These findings indicate that empathy is a robust and reliable predictor of PEBs; however, the observed high heterogeneity and small-study effects indicate caution when interpreting the association.
4 Discussion
The evidence from the included studies revealed the affective aspect of climate change. Our systematic review and meta-analysis examined the relationship between different emotions in the climate emotions wheel and PEB, as well as MH outcomes. The complex interplay between climate emotions, human behavior, and well-being often reveals complementary pathways, where discomfort is associated with both psychological consequences and an urge for climate action. Though they are viewed as adaptive and rational responses to an existential threat, climate emotions, which range from anxiety and fear to anger, grief, and hope, can lead to severe psychological distress (Gago and Sá, 2021). The literature on how certain emotions influence PEBs and MH outcomes has shown mixed results, especially depending on the measurement tools utilized and whether the emotional experience is either positive or negative (Pitt et al., 2023).
Our findings suggest that climate anxiety (CA), along with its subdimensions, cognitive-emotional impairment (CEI) and functional impairment (FI), are positively associated with poorer MH outcomes like depression and anxiety. The same can be applied in the context of climate worry (CW). These findings supported prior research demonstrating that anxiety and worry about climate change can cause emotional distress, rumination, and functional impairment, leading to trouble in focusing, sleeping, and interference with social functioning (Cosh et al., 2024a, 2024b; Pitt et al., 2023; Reyes et al., 2023; Schwartz et al., 2023; Verplanken and Roy, 2013; Wullenkord et al., 2021). However, the strong correlations between CA and CW on PEBIs show that these distress-based emotions are equally important for mobilizing climate action (Clayton, 2020; Garfin and Wong-Parodi, 2024; Garfin et al., 2024; Heeren et al., 2023; Innocenti et al., 2023a, 2023b; Sangervo et al., 2022). The dual role of these emotions in triggering distress and climate action has been well explained by psychological appraisal frameworks, which propose that emotions triggered by threat evaluations can evoke both threat emotions and engagement reactions, depending on perceived efficacy and controllability (Lazarus, 1991; Yang and Chu, 2018). While reasonable levels of worry or anxiety can effectively boost motivation towards PEBIs, the discomfort that becomes excessive or lacks appropriate channels can instead result in disengagement or eco-paralysis (Doherty and Clayton, 2011; Mathers-Jones and Todd, 2023). Thus, the association between climate-related distress and PEB appears to follow a curvilinear or threshold pattern, depending on cognitive appraisal and contextual environmental factors (Csonka, 2020; Cunsolo and Ellis, 2018; Parmentier et al., 2024).
The findings from the moral emotions implied that eco-guilt exhibits a positive association with PEBI. Guilt is commonly conceptualized as a moral emotion arising from perceiving personal responsibility for violating environmental norms or contributing to pollution, and is associated with motivating reparative, prosocial action (Ágoston et al., 2022a; Harth et al., 2013; Mallett, 2012; Moore and Yang, 2020; Pasca, 2022; Shipley and Van Riper, 2022; Tam, 2019). Studies indicate that eco-guilt positively correlates with PEBs and BE, thus suggesting that it functions adaptively by motivating compensatory behavior to alleviate psychological discomfort, particularly when coupled with the low perceived environmental impact of one’s PEBs (Chen et al., 2024; Jaśkiewicz et al., 2023; Pasca, 2022; Shipley and Van Riper, 2022; Tam, 2019). In contrast, the link between guilt and MH suggests high levels may contribute to distress, as evidenced by its correlation with the negative consequences of anxiety (Ágoston et al., 2022b).
Ecological concerns encompassed within the sadness segment of the climate emotion wheel, with ecological grief as a subdimension, evoke emotional responses to actual or anticipated losses of species, ecosystems, and landscapes (Ágoston et al., 2022a, 2022b; Cunsolo and Ellis, 2018; Pihkala, 2022b; Stanley et al., 2021; Comtesse et al., 2021). Evidence regarding the relationship between eco-sadness and pro-environmental behavior (PEB) remains mixed. While some studies suggest that the segment of sadness in climate emotions can positively predict PEBs and collective climate action, reflecting a constructive motivational role (Ágoston et al., 2022b), others have found no significant association with collective action (Stanley et al., 2021), indicating that the behavioral effects of sadness may be more complex and context-dependent. Consistent with this complexity, our findings indicate that sadness is associated with lower engagement in PEBs, a pattern supported by the review by Arias et al. (2020), which linked sadness to behavioral disengagement. This relationship may be explained by the Hopelessness Theory of Depression, which posits that perceiving adverse events as stable, global, and uncontrollable can foster helplessness and diminish motivation to act (Liu et al., 2015). Within this dimension, ecological grief appears to have particularly important implications for mental health, as our findings show that it is associated with poorer mental health outcomes, including symptoms of depression and anxiety, consistent with previous evidence (Stanley et al., 2021). Collectively, these findings suggest that although sadness-related climate emotions may motivate environmental action under certain conditions, prolonged or intense experiences of ecological grief may instead contribute to psychological distress and reduce sustained pro-environmental engagement.
The study findings also suggest that positive emotions, particularly empathy and hope, demonstrate significant and robust relationships with PEBs, which aligns with the existing literature, which suggests that the positivity segment of emotions often corresponds with the strongest and most consistent associations with environmental engagement (Geiger et al., 2023; Brown et al., 2019). Empathy is considered foundational for prosocial and environmental action, fostering the emotional affinity required to care about climate issues (Berenguer, 2007). As it strengthens the emotional connection between nature and its beings, which aids people in identifying environmental concerns by facilitating a connection with the environment (Chen et al., 2024). Although the analysis demonstrated a significant positive association between empathy and PEB, substantial heterogeneity and publication bias was observed, suggesting that the strength of this relationship may vary across studies. This variability is likely attributable to differences in participant populations, cultural and contextual factors, and the measurement instruments used to assess empathy and PEB. Hope also emerges as a significant predictor of BE (Geiger et al., 2023; Ojala, 2012). Importantly, hope based on efficacy and agency, defined as recognizing potential paths towards achievement, is favorably connected to climate involvement (Ojala, 2012, 2023a, 2023b; Rizeq and Ojala, 2025). Specifically, personal efforts exhibit a very strong link with engagement, surpassing hope about climate change (Chen et al., 2024; Ojala, 2023b). Additionally, hope serves as a vital psychological buffer, enabling people to actively manage unpleasant feelings such as climate anxiety and providing sustained strength to address the issue. This adaptive link shows that social support for dealing with distress caused by climate change can encourage long-term PEBI (Nieminen et al., 2025).
Eco-anger (or climate anger), defined as anger that is directed specifically at climate change, exhibits a complex and inconsistent relationship with PEBs and MH outcomes (Baroni et al., 2025; Contreras et al., 2024; Li et al., 2022; Pihkala, 2022b; Stanley et al., 2021; Bringle et al., 2018). Consistent with this mixed evidence, our findings indicated a non-significant association between eco-anger and PEBI. This finding can be interpreted through appraisal-based theories of emotion, which propose that emotions are more likely to translate into behavior when individuals perceive sufficient control and response efficacy (Moors, 2020; Gregersen et al., 2023; Panno et al., 2021). Accordingly, eco-anger may be more likely to motivate collective environmental engagement than individual PEBs when directed toward perceived societal or governmental inaction (Stanley et al., 2021). Although previous studies suggest that anger may stimulate pro-environmental intentions, these may not translate into actual behavior (Rees et al., 2015; Spence et al., 2025). This distinction explains the non-significant association of eco-anger and PEBI. Nevertheless, our review has identified eco-anger as both a moral emotion and a powerful psychological mechanism for mobilizing collective action, predicting a greater involvement in ecologically responsible behaviors, climate activism, and policy support (Baroni et al., 2025; Kühner et al., 2025; Li et al., 2022). For instance, a linear relationship has been demonstrated between anger and willingness to take climate action in the context of readiness to change (Baroni et al., 2025). However, the substantial heterogeneity observed in our results suggests that the relationship between eco-anger and PEBI might be due to the limited number of studies along with variations in population and measurement tools. Holistically, the influence of eco-anger on individual PEB remains inconsistent and may be weaker than positive emotions, such as hope and empathy, that sustain long-term engagement (Geiger et al., 2023; Stanley et al., 2021). Hence there is a clear need for further investigation on this particular segment of emotion to identify the behavioral and mental health implications.
Despite growing interest, the field remains methodologically limited. Most studies employ cross-sectional designs and rely on WEIRD samples, particularly students and young adults, which constrains causal inference and limits cross-cultural generalizability. Cross-cultural research further indicates that emotional responses to climate change are shaped by local histories, cultural framings, environmental exposures, and socioeconomic contexts (Greenaway et al., 2018; Niedenthal et al., 2019), underscoring the importance of contextualizing emotion research across diverse ecological and cultural settings. Evidence also highlights that existing climate emotion measures have been validated predominantly in convenience samples with relatively little research involving groups expected to experience greater climate distress such as individuals living in climate vulnerable regions, farmers, and communities directly exposed to environmental change thereby limiting confidence in their applicability across diverse populations. A major challenge is the lack of standardized and validated measurement tools, with considerable variability in how climate emotions are conceptualized and operationalized. There is a crucial need to study the segment of anger and positivity in the climate emotion wheel and its effect on MH and PEB. While distinct emotions such as eco-anger, eco-guilt, eco-grief, eco-empathy, eco-sadness, eco-depression and eco-hope are inconsistently defined and measured. Addressing this issue should be a priority for future research through the development and standardization of multidimensional climate emotion scales that clearly differentiate discrete emotional constructs while capturing their cognitive, affective, physiological, and behavioral dimensions. Such measures should also demonstrate cross-cultural validity and measurement invariance to enable meaningful comparisons across populations. Furthermore, future studies should adopt longitudinal and experimental designs to establish temporal and causal relationships between climate emotions, PEBs, and MH outcomes, while incorporating repeated assessments to capture the dynamic nature of emotional responses over time. Expanding research beyond WEIRD populations will also be essential for developing a more comprehensive understanding of global climate emotional ecologies and improving the generalizability of findings.
The major limitations of our current review and meta-analysis include the following: the review does not account for additional emotions mentioned in the climate emotions wheel, such as apathy/numbness, confusion, and horror, as this was beyond the current scope. Studies examining other MH outcomes, such as well-being and stress, were not included in the review. Although a comprehensive systematic search was conducted, the number of eligible studies for several climate emotions and their subdimensions was limited. Consequently, planned subgroup analyses could not be conducted for some outcomes because the number of available primary studies did not meet the minimum threshold required for reliable meta-analysis. Specifically, separate sub-group analyses for the climate change anxiety (CCA) subdimensions cognitive emotional impairment (CEI) and functional impairment (FI) in relation to PEBI, as well as for eco-guilt, eco-worry, eco-hope, and eco-empathy in relation to PEB and BE, were not feasible due to the limited number of eligible studies. As a result, our ability to examine potential heterogeneity for these constructs was constrained. Furthermore, the included studies exhibit significant heterogeneity in their research design, population studies, measurement tools, which places substantial limitations on making decisive inferences especially for the results of empathy with small study effect. The generalizability of our findings may have been constrained given the focus on published research that was mainly available in English, potentially excluding relevant evidence from non-English speaking settings.
5 Conclusion
Our systematic review and meta-analysis shed light on the existing literature on climate emotions represented in the climate emotion wheel. The findings revealed that distress-related emotions like anxiety and worry are positively associated with poorer MH outcomes, that is, elevated levels of general anxiety, depression, etc. The findings affirm that emotions such as climate anxiety and related constructs, such as worry, and moral emotions, such as guilt, are positively linked to PEBIs, suggesting these emotions function as activating responses to the climate crisis. Environmental empathy is strongly linked to PEBIs and action, as this positivity segment of the climate emotions wheel motivates individuals to help and support others, increase endorsement of climate-change mitigation policies, and serve as a constructive sign of deep engagement in environmental behaviors. However, measured broadly, hope often shows a slightly significant association with climate action. While anger is often characterized in the literature as an activating emotion that drives PEBs, and sadness is largely understood as a distress emotion leading to disengagement (Grau, 2025; Stanley et al., 2021; Verplanken et al., 2020).
Our findings suggested a non-significant relation between eco-anger and eco-sadness with PEBI, which suggests the need for a more detailed exploration of how specific environmental emotions influence particular segments of behaviors. This highlights the need to bridge the gap that exists in experimental exploration of climate emotions especially there is a pressing need for developing scales in the positivity and anger segment of climate emotions wheel. Future studies should look into studying climate emotions in lower middle-income countries.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fclim.2026.1837284/full#supplementary-material
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Footnotes
Edited by: Maria Josefina Figueroa, Copenhagen Business School, Denmark
Reviewed by: Vanessa Weihgold, University of Tübingen, Germany
Peyman Mamsharifi, Allameh Tabataba’i University, Iran
Statements
- Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Funding
The author(s) declared that financial support was not received for this work and/or its publication.
- Data availability
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
- Author contributions
TA: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing, Investigation. JR: Formal analysis, Supervision, Writing – review & editing, Validation. ER: Formal analysis, Supervision, Writing – review & editing, Validation. KS: Data curation, Methodology, Writing – original draft, Visualization. JL: Data curation, Formal analysis, Writing – original draft. MM: Data curation, Writing – review & editing.
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Citation
Alisha, T. T., et al. (2026). Understanding climate emotions in relation to mental health and pro-environmental behavior: a systematic review and meta-analysis. Frontiers in Climate, Volume 8. https://doi.org/
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- Accepted
- Published
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- 8 - 2026
Copyright
Copyright © 2026 Alisha, Romate, Rajkumar, Sruthi, Lipsa and Manjima.
License: https://creativecommons.org/licenses/by/4.0/
*Correspondence: John Romate; Eslavath Rajkumar
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