The eco-affective health assessment protocol: a standardized multimodal instrument battery for measuring affective, cognitive, and functional responses to environmental change

1 Introduction

The study of how environmental change shapes human psychological experience has reached an important inflection point. Over the past two decades, a substantial and rapidly growing body of research has documented that climate-related exposures, including not only acute events such as floods, wildfires, and heatwaves but also the slower, cumulative deterioration of familiar environments, produce measurable effects on human affect, cognition, and behavior (Clayton, 2020; Pihkala, 2022; Cunsolo and Ellis, 2018). Constructs once confined to the margins of environmental psychology, such as solastalgia, eco-anxiety, and ecological grief, have moved into the mainstream of climate-health discourse (Albrecht, 2007), and there is now broad recognition that the psychological and cognitive dimensions of the human response to environmental change are not peripheral phenomena but central mechanisms through which climate impacts on health, behavior, and social functioning unfold (Brosch, 2021; Weber, 2016; Marques and Franco, 2025).

Yet this conceptual progress has not been matched by full methodological coherence, though it should be acknowledged that meaningful psychometric work has been conducted on several individual constructs within this field, including the development and validation of instruments such as the Hogg Eco-Anxiety Scale Hogg et al., (2021). The field of eco-affective health as a whole, however, remains characterized by a proliferation of instruments that measure related constructs through heterogeneous operationalizations, scoring conventions, and item formats, a pattern that complicates cross-study comparison even where individual instruments are well validated. Solastalgia alone has been measured using at least six distinct instruments across the published literature, with no consensus on item content, response format, or scoring approach (Galway et al., 2019). Eco-anxiety, while increasingly distinguished from climate anxiety in some conceptual frameworks, for example (Hogg et al. 2021), who define eco-anxiety as a broader multidimensional response to the environmental crisis rather than a synonym for climate-specific worry, continues to be operationalized heterogeneously across the literature, appearing variously as a subscale of general anxiety, a trait-level disposition, and a state-level response, with the Climate Anxiety Scale (Clayton and Karazsia, 2020), the Hogg Eco-Anxiety Scale (Hogg et al., 2021), and numerous ad hoc measures circulating simultaneously without systematic comparison. Environmental risk perception is measured in ways that conflate its affective and cognitive components, despite compelling evidence that these components predict different downstream outcomes through different mechanisms (Slovic, 2000; van der Linden, 2015). Adaptive decision-making under ecological stress, a theoretically central mechanism linking exposure to behavioral response, is rarely measured directly at all.

This fragmentation has concrete consequences. It prevents meta-analytic synthesis across studies. It makes cross-cultural and cross-contextual comparison unreliable. It produces conflicting findings about the relationships between constructs that may reflect measurement inconsistency rather than genuine empirical variation. And it impedes the translation of eco-affective research into policy-relevant surveillance systems, because decision-makers cannot act on a body of evidence organized around incompatible instruments. What the field requires is not another instrument for any single construct, but a shared methodological infrastructure: a protocol that organizes a battery of predominantly validated instruments, together with one adapted candidate measure pending independent validation (see Table 2), into a coherent, theoretically grounded, and practically implementable assessment framework.

This paper introduces the Eco-Affective Health Assessment Protocol (EAHAP) as that infrastructure. The EAHAP draws on the Eco-Affective Health framework (Marques, 2026a), which positions affect not as a downstream reaction to environmental conditions but as a primary regulatory mechanism through which humans perceive, interpret, and respond to the living world. Within this framework, eco-affective states, cognitive mediators, and functional outcomes are not independent variables to be measured in isolation but interdependent signal layers that together describe the human response to environmental change across its full range: from early affective sensing through cognitive processing and appraisal to behavioral regulation and functional impact. This affect-as-regulatory-mechanism premise is not proposed here for the first time: it is the organizing claim of a peer-reviewed theoretical account of Eco-Affective Health (Marques, 2026a) that derives, from the same premise, testable hypotheses regarding the ordering of affective and cognitive signal layers. The ordering adopted in the EAHAP architecture, in which eco-affective states are positioned as proximal to environmental exposure and cognitive mediators as positioned downstream, is not intended to displace appraisal-based models such as Lazarus and Folkman (1984); rather, appraisal processes are treated as continuously interacting with, and in part shaped by, prior affective states, consistent with evidence that affective responses to environmental information can precede and bias subsequent cognitive appraisal (Brosch, 2021; Weber, 2016). This is also why the EAHAP architecture already incorporates a feedback pathway, illustrated in Figure 1, whereby affective numbing attenuates cognitive risk perception, allowing for dynamic, bidirectional influence between the eco-affective and cognitive domains rather than a strictly unidirectional cascade. The EAHAP operationalizes each of these layers with validated instruments selected for their psychometric properties, brevity, cultural adaptability, and compatibility with both fundamental research and applied surveillance contexts.

The EAHAP has three primary applications. First, it provides a standardized instrument battery for fundamental research on eco-affective and cognitive responses to environmental change, enabling cumulative knowledge and cross-study comparison. Second, it constitutes the human signal layer of the Eco-Affective Early Warning System (EAEWS), a multimodal surveillance framework that links spatially explicit environmental exposure indices to human eco-affective and cognitive signals at policy-relevant geographic scales (Marques, 2026b, a forthcoming book under contract and currently in preparation, cited here only for the description of the EAEWS surveillance architecture and not as theoretical support for the EAHAP's own domain structure, which rests instead on the peer-reviewed account in Marques, 2026a). Third, it provides a replicable assessment tool for evaluating interventions in climate adaptation, ecological restoration, and community resilience programs. Across all applications, the EAHAP is designed to be modular, allowing domain-specific implementation in resource-limited contexts, and fully integrated when epidemiological completeness is required.

2 Materials and equipment

2.1 Instrument battery

The EAHAP comprises 19 validated or adapted instruments organized across three domains. Table 1 presents the full taxonomic structure of the constructs measured by the protocol, including operational definitions, distinctions from adjacent constructs, and positioning within the EAHAP signal architecture. Table 2 provides the complete instrument matrix with item counts, estimated administration times, validation references, and cultural adaptation notes.

Table 1. Taxonomic structure of Eco-Affective Health Assessment Protocol (EAHAP) constructs, including operational definitions, distinctions from adjacent constructs, and positioning within the signal architecture.

Constructs are organized by domain (Eco-Affective, Cognitive, and Functional) and positioned within the EAHAP signal architecture.

Distinctions from adjacent constructs are provided to guide item selection and prevent conceptual overlap in participant briefings.

All constructs are treated as continuous dimensions rather than categorical states; clinical cut-offs are not applied in non-clinical research or surveillance contexts.

Table 2. Complete instrument matrix of the Eco-Affective Health Assessment Protocol (EAHAP), including instruments, item counts, estimated administration times, validation references, and cultural adaptation notes.

Item counts refer to the standard validated version of each instrument. Adaptations for ecological momentary assessment (EMA) use ultra-short versions of 4–6 items per construct. Administration times are estimated for self-completion formats; interviewer-administered formats may require additional time. Total estimated time for the full protocol is 50–65 min. Domain-specific modules can be administered in 15–25 min.

Instruments marked as adapted have been modified from original sources with construct definition and item content preserved; full adaptation procedures are described in section 4.5.

2.2 Infrastructure requirements

The EAHAP can be administered in paper-and-pencil or digital formats. For digital administration, compatible platforms include REDCap, Qualtrics, and equivalent survey systems capable of routing logic, skip patterns, and geocoding. For studies requiring spatial linkage with environmental exposure data, the platform must support indirect geocoding, in which participants self-identify their census tract, postal code, or equivalent administrative unit, without collecting or storing individual-level address data. The linkage between participant spatial units and the Environmental Exposure Index (EEI) is performed at the aggregate level after individual data are stripped of any identifying geographic information.

For ecological momentary assessment studies, participants require access to a smartphone with notification capability. Study teams require a platform capable of scheduling randomized or fixed-interval notifications, delivering ultra-short item sets, and storing time-stamped responses. For studies in populations with limited smartphone access, validated paper-based EMA alternatives using scheduled diary completion are acceptable.

All implementations require ethics committee approval, informed consent procedures compliant with national research governance frameworks, and a data management plan specifying procedures for separation of identifiable and analytical data, pseudonymization of longitudinal participant identifiers, and secure storage. Studies that collect geocoded data must include explicit consent items covering spatial data use and must specify the level of geographic precision collected.

3 Methods

3.1 Objectives and validation of the method

The primary objective of the EAHAP is to enable standardized, replicable, and cross-contextually comparable measurement of the affective, cognitive, and functional dimensions of the human response to environmental change. Each instrument included in the protocol satisfies four selection criteria: demonstrated construct validity in peer-reviewed publications, sufficient brevity for field implementation, sensitivity to subclinical variation below clinical thresholds, and established or establishable cross-cultural adaptability. Instruments failing any of these criteria were excluded even when they measured theoretically relevant constructs, on the grounds that methodological rigor cannot be compromised for conceptual coverage. In practice, these criteria were applied sequentially rather than as a simultaneous checklist: construct validity was screened first, against the peer-reviewed psychometric literature for each construct, to generate a shortlist of eligible instruments; brevity and subclinical sensitivity were then used to select among eligible instruments where more than one satisfied the validity criterion, prioritizing the shortest validated version reporting adequate performance below clinical cut-offs; cross-cultural adaptability was assessed last, based on whether an instrument had already been adapted and validated in more than one linguistic or cultural context, or, where this was not yet available, whether its item content was judged conceptually portable across contexts pending the adaptation procedure specified in section 3.5. Instruments for which no version satisfied all four criteria simultaneously, most notably ecological grief, is flagged explicitly in Table 2 as an adapted measure requiring further validation, rather than presented as a fully validated instrument.

The integration architecture of the EAHAP is validated by its correspondence to the theoretical pathway model of the Eco-Affective Health framework. In this model, environmental exposures generate eco-affective states through processes of appraisal and meaning-making; these states are modulated by cognitive mediators including risk perception, attentional processing, and coping appraisal; and the combined effect of affective states and cognitive processing produces functional outcomes that are measurable, economically relevant, and predictive of longer-term health and behavioral trajectories (Marques, 2026a). The three-domain structure of the EAHAP is not an arbitrary classification but a reflection of this theoretical architecture, and the expected empirical relationships between domains, described in section 5, provide the primary basis for concurrent validity assessment across implementation sites.

3.2 Step-by-step procedures

The EAHAP implementation is organized into three phases: preparation, data collection, and processing. Table 3 provides a complete step-by-step overview with timing and pause points. The sections below describe each step in detail.

Table 3. Step-by-step Eco-Affective Health Assessment Protocol (EAHAP) implementation procedures, including timing and pause points with decision rules.

Timing estimates are approximate and will vary with sample size, platform configuration, and local administrative requirements. Pause points indicate mandatory decision gates at which implementation must stop pending resolution of the specified condition before proceeding. EMA: ecological momentary assessment.

EEI, Environmental Exposure Index.

K6, Kessler Psychological Distress Scale.

It is worth making explicit, beyond Table 3, the level of participant and staff commitment implied by Phase 2, Step 6: re-administration of the EAHAP at Days 30, 60, and 90 corresponds to one full protocol administration per month for 3 consecutive months, in addition to any ongoing EMA sampling. Study teams planning field implementation should budget for this recurring monthly commitment explicitly in participant retention planning and staffing schedules, rather than treating Step 6 as a single follow-up event.

3.3 Module selection

The EAHAP is designed as a modular system. Not all studies require the full protocol, and domain-specific implementations are methodologically valid for studies with appropriately scoped research questions. Table 4 provides a module selection matrix specifying recommended configurations for seven common study types. The decision to use domain-specific rather than full protocol administration should be made before data collection begins and documented in the study protocol, as post-hoc module selection introduces analytic flexibility that undermines replicability.

Table 4. EAHAP module selection matrix for common study types, specifying required, recommended, and optional domain administrations.

Required modules must be included for the study results to be interpretable within the EAHAP framework. Recommended modules substantially increase analytic power and interpretability and should be included unless resource constraints prohibit. Optional modules add value in specific analytic contexts but do not compromise the primary research question if omitted. EAEWS: Eco-Affective Early Warning System.

K6: Kessler Psychological Distress Scale.

3.4 Administration order and participant briefing

The standardized administration order is: (1) eco-affective domain, (2) cognitive domain, (3) functional domain. This order reflects the theoretical pathway structure of the Eco-Affective Health framework, in which affective states precede and shape cognitive processing, and both upstream processes produce functional outcomes. Administering the functional domain before the eco-affective domain risks priming participants with clinical symptom framing, which can inflate responses on eco-affective scales that are designed to capture non-pathological adaptive responses to environmental conditions.

A standardized participant briefing must precede administration of any EAHAP module. The briefing has three mandatory components: (a) a clear statement that the study is measuring psychological responses to environmental conditions, not diagnosing clinical disorders; (b) a brief explanation of the distinction between eco-affective experiences and mental health diagnoses, using accessible language; and (c) information about how to access support if participation evokes distressing memories or feelings. The exact wording of the briefing is not prescribed, to allow cultural adaptation, but all three components must be present and the briefing must be reviewed by at least one member of the local research team with training in environmental psychology or related fields.

3.5 Cultural adaptation procedure

The original validation samples for the instruments in Table 2 are concentrated in a small number of national and linguistic contexts: predominantly the United States (the Climate Anxiety Scale, K6, GAD-7, PHQ-9, and Pittsburgh Sleep Quality Index, and Slovic's contribution to the Environmental Risk Perception Scale), Canada (the conceptual basis for the Ecological Grief Scale, the Place Attachment Scale, and the Ecological Behavior Intention and Fatalism Scale), Australia (the Brief Solastalgia Scale), and the United Kingdom and Netherlands (van der Linden's contribution to the Environmental Risk Perception Scale), with the WHO Disability Assessment Schedule underlying the functional impairment items constituting the principal exception as a genuinely multi-country validated instrument. No instrument in the core battery was originally validated in a Latin American, Sub-Saharan African, or South or Southeast Asian sample. This concentration is not unusual for the eco-affective and environmental psychology literature, but it means the psychometric properties anticipated in section 4.1 should be read as expectations anchored in these specific validation contexts rather than as claims of universal measurement invariance; it is the specific reason the cultural adaptation procedure below is mandatory, rather than optional, for any implementation outside these original contexts. Cultural adaptation is a mandatory component of all EAHAP implementations outside the original validation context of each instrument. The adaptation procedure follows a structured sequence that balances fidelity to the original construct definition with responsiveness to local linguistic and cultural context. The sequence comprises five steps: (1) forward translation of all items by a bilingual specialist with expertise in psychology and environmental science; (2) independent back-translation by a second bilingual specialist with no access to the original; (3) expert review by a committee of at least three members including a local environmental psychologist, a community health researcher, and a bilingual language specialist, with attention to semantic equivalence, cultural resonance, and construct relevance; (4) cognitive interviewing with 5–10 members of the target population using think-aloud protocols to identify comprehension difficulties and culturally incongruent items; (5) psychometric pilot with 30-50 participants to assess internal consistency and factor structure of adapted instruments before full-scale implementation. This five-step sequence is designed to satisfy the requirements of the three principal international frameworks governing test adaptation and evaluation: the International Test Commission's ITC Guidelines for Translating and Adapting Tests (International Test Commission, 2017), which structure steps (1) through (4); the Standards for Educational and Psychological Testing (American Educational Research Association, 2014), whose validity and fairness criteria inform the expert review and cognitive interviewing stages; and the EFPA Review Model for the Description and Evaluation of Psychological Tests (European Federation of Psychologists Associations, 2013), which structures the psychometric pilot in step (5) and the reporting format used in Table 2. Where a validated translation of a given instrument already exists in the target language, for example published adaptations of the Climate Anxiety Scale, the GAD-7, or the PHQ-9 across numerous languages, adapters should locate and evaluate that existing translation against local cultural referents before undertaking fresh forward-translation from the original English source, and should document in study materials why an existing validated translation was or was not adopted. The Hogg Eco-Anxiety Scale is a useful illustration of how quickly this landscape can change: independently validated language versions already exist in German (Heinzel et al., 2023), French (Mathé et al., 2025), Italian (Spano et al., 2025), and Persian (Hasani and Jafari, 2026), in addition to the original English version (Hogg et al., 2021). Adapters working in any of these languages should evaluate these existing validated versions before undertaking fresh translation, and, because new validated translations of eco-affective instruments continue to appear, adapters working in any language should check for recently published versions at the time of implementation rather than assuming the absence of a prior translation. Forward-translation from the original English source should be treated as a fallback for constructs and languages lacking a prior validated version, which at the time of writing includes the Ecological Grief Scale (adapted), for which no prior translated or independently validated version exists in any language.

Particular attention is required for constructs whose referents are culturally variable (Table 5). Solastalgia items must specify the relevant environmental context explicitly, as the sources of place-based distress vary markedly across urban, rural, agricultural, coastal, and Indigenous contexts. Place attachment items require a stable reference environment and must be adapted for populations with disrupted place histories, including displaced persons, refugees, and communities experiencing rapid land-use change. Ecological grief items must allow participants to specify the nature of the loss, as the objects of ecological mourning, whether species, seasonal patterns, water bodies, or agricultural landscapes, vary significantly across cultural contexts and must be locally grounded to capture genuine ecological experience rather than abstract concern.

Table 5. Common pitfalls in EAHAP implementation, with likely causes, prevention strategies, and troubleshooting measures.

Pitfalls are organized by implementation phase. Prevention strategies should be applied during protocol design and pilot stages; troubleshooting measures apply when a pitfall is identified after full-scale data collection has begun. EMA: ecological momentary assessment.

EEI, Environmental Exposure Index.

3.6 Scoring procedures

Each instrument is scored according to the specifications of its original validated version, with the following protocol-level conventions. All scores are treated as continuous rather than categorical; clinical cut-offs are not applied in non-clinical or surveillance contexts, where the goal is to detect subclinical variation rather than classify disorder. Missing data are handled according to a uniform rule: for any individual instrument, item-mean imputation is applied for up to 20% of missing items; above this threshold, the instrument score is treated as invalid for that participant and excluded from analyses using that instrument. Participants with more than 30% missing data across the full protocol are flagged for exclusion from primary analyses and examined for systematic patterns that may indicate problems with instrument comprehension or administration. Item-mean imputation and equal-weighted composites are adopted here as operational defaults chosen for field simplicity and cross-site consistency in low-resource implementation contexts, not because they are presented as statistically superior to alternatives. Multiple imputation and full-information maximum likelihood methods are now the recommended standard for handling missing data in psychometric research (Schafer and Graham, 2002) and generally outperform single-value imputation methods such as item-mean substitution, particularly when data are not missing completely at random. Study teams with the statistical infrastructure and software access to implement multiple imputation are encouraged to do so as the primary analytic approach, using the item-mean imputation rule specified here as a documented, replicable fallback for field contexts where this is not feasible, and as a sensitivity comparison in all contexts. Similarly, equal weighting of instruments within domain composites is adopted as a transparent, assumption-light default; it is not intended as a claim that all instruments within a domain contribute equally to the underlying construct, and studies with adequate sample sizes are encouraged to report impact-weighted composites as a sensitivity analysis, as already noted below.

For domain-level composite scores, individual instrument scores within each domain are first standardized using z-scores within the study sample. Domain composites are then computed as the mean of standardized instrument scores within the domain. Equal weighting of instruments within domains is the default; impact-weighted composites using meta-analytic effect size estimates as weights are permitted as sensitivity analyses but should not replace the equal-weighted composite as the primary outcome. The three domain composites, eco-affective, cognitive, and functional, constitute the primary analytic outputs of the full EAHAP and are used in dose-response modeling, early warning detection, and intervention evaluation applications.

3.7 Spatial linkage with environmental exposure data

For studies using the EAHAP as the human signal layer of the EAEWS or other spatial exposure-health frameworks, the linkage of EAHAP scores to environmental exposure indices requires careful procedural attention. The primary spatial unit of analysis is the census tract or equivalent administrative unit, which provides geographic specificity sufficient for subnational policy decisions while preserving participant privacy through spatial aggregation. Individual participant data are never linked to environmental data at the individual level; all linkage occurs at the aggregate level after individual spatial assignment is complete and individual identifiers are removed.

Temporal alignment of EAHAP collection with environmental exposure windows is a critical design decision. For studies examining cumulative chronic exposure, the recommended approach is to align the EAHAP collection date with the endpoint of the exposure window, such that a 30-day exposure window captures the 30 days immediately preceding participant assessment. For studies examining acute event impacts, the EAHAP is administered at a fixed interval after the event, with the interval specified in the study protocol. Multiple exposure windows, at 30, 60, and 90 days, can be examined in sensitivity analyses to assess whether effect estimates are stable across exposure window specifications, which constitutes evidence of dose-response robustness.

4 Anticipated results

Everything that follows in this section, including specific numerical thresholds for internal consistency, fit indices, inter-instrument correlation patterns, and the shapes of dose-response curves, is an a priori theoretical prediction derived from the EAHAP's conceptual architecture and existing literature on its component constructs. No original data were collected for this manuscript, and none of the values reported below should be read as empirical findings. These predictions are offered as falsifiable, pre-registerable benchmarks against which future multi-site validation studies can be evaluated, and their specificity is intended to support that falsifiability rather than to imply that they have already been observed.

4.1 Expected psychometric properties

Each instrument in the EAHAP has established psychometric properties in its original validation context. The primary anticipated result of the EAHAP psychometric evaluation at each new implementation site is replication of acceptable internal consistency (Cronbach's alpha or McDonald's omega above 0.70) for each instrument after cultural adaptation. Factor structures corresponding to published validation studies are expected to replicate in confirmatory factor analyses, with acceptable fit indices (comparative fit index above 0.90, root mean square error of approximation below 0.08). Where instruments are adapted for new contexts, some deviation from published factor solutions is anticipated and should be evaluated through exploratory factor analysis before confirmatory testing.

At the domain level, moderate positive correlations among instruments within each domain are expected, reflecting genuine construct overlap while confirming that instruments are not redundant. The eco-affective domain is expected to show the strongest inter-instrument correlations, given the theoretical proximity of solastalgia, eco-anxiety, and ecological grief as distinct but related responses to environmental loss and threat. The cognitive domain is expected to show somewhat lower inter-instrument correlations, reflecting the greater conceptual heterogeneity of risk perception, attentional processing, appraisal, and decision-making as constructs. Across domains, moderate positive correlations between the eco-affective and functional domains are expected, with the cognitive domain showing intermediate correlations with both (Figure 2).

4.2 Expected dose-response relationships

In studies linking EAHAP scores to environmental exposure indices, a positive dose-response relationship between Environmental Exposure Index scores and eco-affective domain composite scores is the primary expected finding. Based on existing literature on solastalgia, eco-anxiety, and related constructs, the relationship is expected to be non-linear, with a steeper increase in eco-affective burden at higher exposure levels, consistent with a threshold effect above which cumulative environmental stress produces disproportionate affective burden. This non-linearity is one of the theoretically central predictions of the Eco-Affective Health framework and should be examined using flexible modeling approaches such as generalized additive models rather than assumed linear in primary analyses.

The cognitive domain composite is expected to show a more complex relationship with environmental exposure than the eco-affective domain. This added complexity follows directly from the framework's premise, elaborated in section 3.1, that cognitive processing is shaped by prior affective states rather than developing independently of them: across the exposure range, cognitive scores are the net result of two competing processes rather than a single mechanism. At low to moderate exposure levels, risk perception and threat appraisal scores are expected to increase with EEI, reflecting genuine environmental threat awareness. At high exposure levels, particularly in contexts of chronic cumulative exposure, a potential plateau or even decline in risk perception scores may be observed, reflecting the onset of affective numbing and cognitive avoidance as regulatory responses to overwhelming threat. In terms of the pathway shown in Figure 1, this pattern is the empirical signature of the feedback arrow linking affective numbing to risk perception: at low and moderate exposure, that feedback pathway is largely inactive because numbing has not yet accumulated, so risk perception tracks exposure directly; at high, chronic exposure, the feedback pathway becomes active as numbing accumulates, and its attenuating effect on risk perception increasingly offsets, and at sufficiently high exposure may outweigh, the direct threat-detection effect, producing the plateau or reversal. This is conceptually analogous to the affective tipping point dynamics described for related eco-affective systems, in which the same exposure level can produce divergent cognitive and behavioral trajectories depending on whether protective affective resources have already been depleted (Pihkala, 2020; Stanley et al., 2021; Marques, 2026a). This inverted-U or plateau pattern in the cognitive domain at high exposure levels is a theoretically novel prediction of the Eco-Affective Health framework that distinguishes it from linear exposure-response models and has direct implications for early warning system design. These curves represent expected population-average relationships between exposure and each domain; the framework additionally predicts substantial heterogeneity around this average, driven by collective efficacy and social support, such that identical exposure levels may yield divergent cognitive trajectories across subpopulations, with high-collective-efficacy populations expected to show a shallower or absent plateau and low-collective-efficacy populations expected to show an earlier and steeper decline (Marques, 2026a). This moderation should be examined empirically by stratifying or interacting collective efficacy measures with EEI scores in dose-response models, rather than assuming a single population-average curve applies uniformly across contexts. Collective efficacy is not itself included as a core EAHAP instrument in Table 2 or the module selection matrix in Table 4: it is a theorized moderator external to the battery as currently defined, and studies wishing to test this moderation hypothesis must supplement the EAHAP with a validated collective efficacy measure of their own choosing (e.g., a neighborhood or community collective efficacy scale appropriate to the study context), selected and adapted following the same cultural-adaptation procedure specified in section 3.5 for core EAHAP instruments.

4.3 Advantages

The principal advantage of the EAHAP over existing approaches is the provision of a theoretically coherent and empirically grounded integrated framework that covers the full range from early affective sensing to functional impact within a single assessment protocol. No existing instrument or protocol covers this range with the combination of theoretical integration, predominantly validated instruments supplemented by one explicitly flagged candidate measure pending validation, and implementable procedures that the EAHAP provides. The modular structure allows researchers with domain-specific questions to use the relevant subset without sacrificing the ability to position their findings within the broader EAHAP framework. The standardized scoring and composite construction procedures enable cross-study synthesis in ways that the current fragmented landscape of eco-affective measurement does not.

A second advantage is the explicit integration of cultural adaptation as a mandatory protocol component rather than an optional supplement. By specifying the adaptation procedure in detail and identifying the constructs most sensitive to cultural variation, the EAHAP enables genuinely cross-cultural research rather than the superficial translation of instruments developed in Western high-income contexts. The compatibility of the EAHAP with spatial exposure data linkage, through its geocoding and temporal alignment procedures, positions it as the only available instrument battery designed from the outset for use in environmental epidemiology and early warning contexts, rather than being adapted for these purposes post hoc.

4.4 Limitations

The EAHAP in its standard form relies entirely on self-report, which introduces well-documented vulnerabilities to social desirability bias, retrospective distortion, and limitations of introspective access to affective and cognitive states. The ecological validity of self-report instruments is particularly uncertain for attentional processing constructs, where behavioral task performance has been shown to diverge from self-reported attentional tendencies in laboratory studies of environmental threat processing. Researchers requiring high-precision measurement of attentional mechanisms should supplement EAHAP cognitive domain instruments with objective attentional tasks, and the protocol provides guidance on appropriate tasks for laboratory implementations.

The concurrent validity of the EAHAP, specifically the empirical relationships among domains and between domains and environmental exposure, has not yet been established through multi-site longitudinal studies using the full protocol. The anticipated results described above are grounded in the existing literature on individual constructs and the theoretical architecture of the Eco-Affective Health framework, but they constitute predictions rather than established findings. Multi-site validation of the complete EAHAP is the most urgent next step for the field and should be treated as a priority research agenda item by groups adopting the protocol.

Finally, some constructs in the EAHAP, particularly ecological grief and solastalgia, may have limited applicability in populations with very low ecological embeddedness, including urban populations with minimal contact with natural environments and limited place attachment to local ecological features. In these populations, eco-anxiety and adaptive decision-making constructs are likely to show more variance and stronger relationships with environmental exposure than place-based constructs, and module selection should reflect this population characteristic.

5 Discussion

The Eco-Affective Health Assessment Protocol addresses a specific and consequential gap in the scientific infrastructure of a rapidly growing field. The proliferation of incompatible instruments measuring the same constructs is not merely an aesthetic problem of scientific tidiness. It is a structural barrier to cumulative knowledge, a source of apparent empirical inconsistency that may reflect measurement differences rather than genuine variation in the phenomena being studied, and a practical obstacle to the translation of eco-affective research into the surveillance systems and policy instruments that the urgency of accelerating environmental change demands. The EAHAP does not solve this problem by introducing yet another set of instruments. It solves it by providing the organizing framework within which existing instruments, the large majority already independently validated and one explicitly flagged as a candidate measure pending validation, can be deployed coherently, compared systematically, and synthesized cumulatively.

The theoretical positioning of the EAHAP within the Eco-Affective Health framework has implications that extend beyond the choice of instruments. By treating affect as a primary regulatory mechanism rather than a downstream outcome, the framework generates specific and testable predictions about the structure of relationships among domains, including the non-linear dose-response patterns described in section 4.2, the potential plateau and reversal of cognitive domain scores at high exposure levels, and the delayed emergence of functional domain effects relative to eco-affective and cognitive signals. These predictions are not merely theoretical elaborations; they are the basis on which the EAHAP can be used to detect early warning signatures of rising environmental mental health burden before functional and clinical thresholds are crossed. The early warning logic of the protocol, formalized in the EAEWS framework, depends precisely on this temporal and causal ordering of the signal layers. This logic is directly analogous to the use of early-warning signals for critical transitions in complex systems more broadly, where measurable changes in system behavior, such as rising variance and slowing recovery from perturbation, precede large-scale regime shifts (Scheffer et al., 2009); the EAHAP applies this same early-warning logic to eco-affective and cognitive signal layers rather than to ecological state variables.

The cognitive domain of the EAHAP deserves particular comment in the context of eco-affective cognition research. The four constructs measured in this domain, environmental risk perception, attentional processing, environmental appraisal, and adaptive decision-making, are not simply add-ons to a primarily affective framework. They are the mechanisms through which ecological belonging or disconnection is actively constructed and sustained in real time. Risk perception determines which environmental signals reach awareness and which are filtered out. Attentional scanning and avoidance determine whether threat information is approached or deflected. Primary appraisal determines how severe the threat is judged to be; secondary appraisal determines whether anything meaningful can be done about it. And adaptive decision-making determines whether the affective and cognitive processing that precedes it results in engagement, avoidance, or fatalistic withdrawal. Without measuring these cognitive mechanisms, eco-affective research can document that people feel distressed about environmental change without being able to explain why some feel distressed and act, while others feel distressed and disengage, and still others appear to feel nothing and neither act nor disengage but simply carry on in a state of functional but affectively impoverished accommodation to degraded conditions.

The modular design of the EAHAP is a practical necessity but also a theoretical commitment. Not all research questions require measurement of all three domains, and the scientific cost of imposing unnecessary measurement burden on participants, in the form of longer protocols that generate higher dropout and lower data quality, outweighs the abstract benefit of comprehensiveness. The module selection matrix in Table 4 is designed to guide researchers toward the minimum viable protocol for their question, while making transparent the tradeoffs involved in domain-specific implementation. Studies that use only the eco-affective domain will be unable to explain cognitive variability in response to comparable exposure. Studies that use only the cognitive domain will be unable to detect the affective early warning signals that often precede measurable cognitive changes. And studies that use only the functional domain will be measuring downstream consequences without access to the upstream processes that generate them. These are not methodological failures but deliberate analytic choices, and they should be acknowledged as such in the discussion of any study using domain-specific rather than full EAHAP implementation.

The cultural adaptation requirements of the EAHAP reflect a genuine epistemological commitment rather than a procedural formality. Eco-affective constructs are not culturally neutral. The experience of solastalgia is shaped by the specific ecological relationships, land-use histories, and place narratives that characterize different cultural contexts, and these vary profoundly across urban and rural environments, Indigenous and non-Indigenous communities, Global North and Global South settings, and societies with different relationships between human identity and ecological belonging. Adapting the EAHAP for use in a new cultural context is not primarily a translation exercise; it is an interpretive process that requires engagement with local environmental psychologists, community members, and researchers who understand how ecological experience is constructed and expressed in that context. The five-step adaptation procedure specified in section 3.5 is the minimum necessary for this interpretive work to produce an instrument that is both structurally comparable to the original and genuinely valid in the new setting.

The most important next step for the field is the empirical validation of the complete EAHAP across multiple sites and cultural contexts. The psychometric properties and inter-domain relationships described in section 4 are predictions grounded in existing literature and theoretical logic, but they require prospective empirical confirmation in studies that administer the full protocol, collect environmental exposure data, and follow participants longitudinally. Such multi-site studies would simultaneously validate the instrument battery, test the theoretical pathway model, and calibrate the dose-response and threshold parameters needed for the EAHAP to function as a genuine early warning system. The development of an international collaborative network of research groups committed to shared EAHAP implementation is, in the view of this paper, the scientific infrastructure investment most likely to accelerate the field of eco-affective health toward the cumulative, cross-contextual, and policy-relevant knowledge base that the scale of the environmental crisis demands.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Footnotes

Edited by: Andrea Bosco, University of Bari Aldo Moro, Italy

Reviewed by: Silvia Jessica Guarderas, Central University of Ecuador, Ecuador

Alice Stocco, Ca' Foscari University of Venice, Italy