Empirical research review on Solastalgia: Place, people and policy pathways for addressing environmental distress
Bibliographic record
Abstract
Abstract The growing impacts of climate change and environmental degradation have heightened concerns about human emotional and psychological well‐being, particularly regarding place loss and environmental changes. Solastalgia, defined as distress from environmental change while in one's ‘home’ environment, is an emerging concept for assessing loss and damage, influencing environmental evaluations, shaping legal cases and safeguarding health and well‐being. As solastalgia gains traction in academic, policy and practical work, understanding its conceptualization and measurement becomes paramount and timely. This scoping review examines 20 years of scholarly empirical literature on solastalgia, exploring its conceptualization and measurement, environmental triggers, susceptibility factors and responses of those affected and recommendations made by the studies. Studies primarily focus on environmental changes like climate change, urbanization and mining as key solastalgia triggers, with rural and coastal areas being the most frequently studied. Recent literature also expands its application to reflect its broader socio‐ecological relevance. Methodological trends show a growing increase in quantitative studies, with scales such as the Environmental Distress Scale and the Solastalgia Scale increasingly used alongside qualitative research. However, measurement inconsistencies persist, highlighting a need for refined assessment tools. Certain groups, including women, ethnic minorities, lower‐income populations and Indigenous communities, have been reported to be disproportionately susceptible to solastalgia due to heightened place attachment and systemic vulnerabilities. Slow‐onset environmental changes tend to result in prolonged distress compared to acute events. Adaptive strategies, ranging from community resilience‐building to political mobilization, have been key responses. Emotional responses vary widely across contexts, from grief and withdrawal to participating in activism and place‐based restoration efforts. Integrating solastalgia into mental health programmes, environmental impact assessments and community resilience planning is essential for mitigating its psychological effects. Policymakers and practitioners must recognize solastalgia as a critical component in climate adaptation strategies, promoting community‐driven approaches as well as individual resilience‐building initiatives to address environmental distress. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".