The concept at the heart of the study is eco-anxiety: the chronic emotional distress that arises from concern about climate change and environmental degradation. Unlike a clinical phobia, eco-anxiety is generally understood as a rational response to a real threat, but its intensity varies enormously from person to person, and its relationship to behavior has proven stubbornly difficult to pin down. Earlier work, including the development of the Hogg eco-anxiety scale and the climate change anxiety measure by Susan Clayton and Bryan Karazsia, has established that eco-anxiety is multidimensional, encompassing affective symptoms, rumination, behavioral disruption and anxiety about one’s own ecological impact. What has remained unclear is how these feelings map onto the everyday, consumption-related choices that ultimately determine an individual’s carbon footprint.

To probe that connection, the Castilla-La Mancha team applied a deliberately multidimensional framework. They surveyed adolescents across the region and measured not only eco-anxiety but also a battery of socioeconomic and sociodemographic characteristics, psychosocial variables — most importantly, the degree to which respondents include nature in their sense of self — and pro-environmental behaviors defined as consumption-related practices with measurable climate change mitigation potential. The analytical toolkit combined multiple linear regression, which allowed the researchers to isolate the independent contribution of each factor to eco-anxiety scores, with correspondence analyses, a technique well suited to uncovering behavioral profiles and associations among categorical variables.

The regression results are striking in their clarity. Eco-anxiety was significantly associated with sociodemographic variables, most notably gender, echoing a broad international literature showing that girls and young women consistently report higher levels of climate concern and climate-related distress than their male peers. It was also strongly linked to connection with nature: adolescents who described themselves as psychologically intertwined with the natural world — a construct operationalized through measures of inclusion of nature in the self, following the connectedness-to-n tradition pioneered by F. Stephan Mayer and Cynthia McPherson Frantz — reported markedly higher eco-anxiety. In other words, the more a teenager sees nature as part of who they are, the more they suffer when that nature appears threatened.

But the study’s most consequential finding concerns behavior, and it cuts against the intuitive assumption that anxiety and action go hand in hand. The researchers found a sharp divergence between two facets of pro-environmental engagement. Adolescents who actually performed mitigation-related practices — the concrete, consumption-linked behaviors that carry genuine climate mitigation potential — reported lower levels of eco-anxiety. Adolescents who expressed a strong willingness to act but had not yet converted that intention into practice reported higher eco-anxiety. Actual mitigation, in short, appears to soothe; mere willingness appears to gnaw. The authors interpret this as evidence of a psychological gap between intention and action, in which unresolved intention becomes itself a source of distress, while completed behavior restores a sense of agency and coherence.

Using the correspondence analysis, the team identified four distinct behavioral profiles among the adolescents, and each tells a different story about environmental commitment in the climate crisis era. The first group, labeled “involved,” combines high eco-anxiety with strong behavioral engagement — young people who worry intensely and act accordingly. The second, “content,” consists of adolescents who appear satisfied with their current behaviors, achieving a kind of equilibrium between effort and reassurance. The third, “willing,” is the group that embodies the intention-action gap: high willingness, unresolved distress, limited actual practice. The fourth, “disengaged,” shows both low engagement and low eco-anxiety — teenagers who are neither acting nor particularly worried, a pattern the authors implicitly flag as fragile, because it may reflect psychological distancing from a problem that will not go away.

This four-profile typology matters because it demonstrates that pro-environmental commitment cannot be captured by any single indicator. A survey that asked only “how willing are you to act?” would conflate the involved and willing groups, treating a teenagers’ stated intentions as equivalent to their lived behavior. A survey that measured only current behavior would miss the distress brewing in the willing group. By examining patterns rather than points, the Castilla-La Mancha researchers reveal a heterogeneous landscape in which adolescents occupy qualitatively different relationships to the climate crisis — some anxious and active, some anxious and frozen, some calm and committed, some simply absent from the conversation.

The regional setting lends the findings particular weight. Castilla-La Mancha is one of Spain’s most depopulated regions, a territory of dispersed settlements and long travel distances whose per-capita carbon footprint has been the subject of sustained economic research by the same university community, including analyses of household emissions, carbon taxation vulnerability and the so-called “empty Spain.” Previous work by co-authors affiliated with the Universidad de Castilla-La Mancha has documented gender differences in household carbon footprints in Spain and quantified the mitigation potential of consumption options across European contexts. Placing adolescent eco-anxiety within this regional economic reality connects the psychological to the material: young people in rural and semi-rural Spain are forming their environmental identities in a place where low-carbon living is structurally harder, not easier.

The study also carries practical implications for educators, clinicians and policymakers. If willingness without action is itself a driver of distress, then environmental education that stops at raising awareness may inadvertently intensify eco-anxiety rather than relieve it. The findings suggest that interventions should be designed to help adolescents close the intention-action gap — providing concrete, achievable mitigation practices that convert worry into efficacy. Conversely, the low-anxiety, low-engagement disengaged profile warns against complacency: silence about climate is not the same as resilience. The authors note that their survey data contain sensitive information and can be shared only under well-founded request and after anonymization, underscoring the care required when researching minors’ mental health. The work was funded by the Regional Government of Castilla-La Mancha and approved by the university’s Social Research Ethics Committee.

As climate change accelerates, the inner lives of adolescents are becoming a legitimate barometer of the crisis. This study shows that barometer is reading high in some quarters, low in others, and that the relationship between what young people feel and what they do is neither simple nor stable. Eco-anxiety, the research suggests, is not a fixed pathology but a signal — amplified by connection to nature and by unfulfilled intentions, dampened by real action — and understanding its behavioral geography among the young may prove essential to sustaining both their mental health and the environmental commitment the coming decades will demand of them.

Subject of Research: Eco-anxiety and pro-environmental behavior among adolescents in Castilla-La Mancha, Spain

Article Title: Adolescents in the climate crisis: Eco-anxiety and the fragility of environmental commitment in Castilla-La Mancha

Article References: Soria, C., Olivos-Jara, P., Gómez, N., & Sánchez-Serrano, M. (2026). Adolescents in the climate crisis: Eco-anxiety and the fragility of environmental commitment in Castilla-La Mancha. Regional Environmental Change, 26(4), Article 191. https://doi.org/10.1007/s10113-026-02678-9

Image Credits: AI Generated

DOI: 10.1007/s10113-026-02678-9

Keywords: eco-anxiety, adolescents, climate change, pro-environmental behavior, Castilla-La Mancha, connection with nature, mitigation, environmental psychology, behavioral profiles, carbon footprint, gender differences, Spain

The problem, the researchers argue, is that most existing similarity and difference analysis techniques rely on relatively single analysis criteria. Traditional methods typically consider only the dependency relations between activities in a process model. That works reasonably well for general business workflows, but manufacturing processes have a distinctive feature that such approaches ignore: the execution of every operation is accompanied by the consumption of a series of components and changes in their states. Two processes may look behaviorally identical on paper while consuming entirely different parts, or use identical parts while ordering their operations in critically different ways. When screening large process libraries for similar models, these blind spots cause numerous processes to be mistakenly flagged as highly similar, inflating review workloads and obscuring the real differences that matter for quality control.

To address this, the team first reformulated manufacturing processes as Petri net models, a formalism in which places represent products such as raw materials, items in progress or finished goods, transitions represent operations, and arcs capture the flow between them. Crucially, the model includes a function that assigns sets of components to each operation. Building on this representation, the authors introduce the concept of an operation profile, which characterizes how pairs of operations relate when a process executes. Five relations are distinguished: a strict relation in which one operation always precedes another, an interleaving relation that splits into circulation and parallel subtypes depending on whether the operations loop back to each other, and an exclusive relation in which two operations can never occur in the same execution.

A motivating example in the paper shows why a single criterion fails. When only operation profiles are compared, two processes with identical ordering relations score a similarity of exactly 1.0, implying equivalence, even though the components attached to their operations are not the same. Conversely, two processes sharing the same operation items but differing in whether two operations run strictly in sequence or in parallel are also hard to separate under a parts-only view. The lesson is that neither operation items nor operation profiles alone can deliver reliable judgments; both must be weighed together.

From this insight the researchers derive three similarity measurement methods. The operation items, or OI-based, method scores the similarity of corresponding operations by the Jaccard coefficient of their component sets, capturing how much the parts consumed by matching operations overlap. The operation profile, or OP-based, method instead counts how many operation pairs share the same occurrence relation, normalized by the number of possible pairs. The centerpiece, however, is the operation-aware profile, or COP-based, method, which fuses the two perspectives: for every operation pair it multiplies the pair’s component similarity by a binary indicator of whether the occurrence relation matches, then aggregates across the whole process. In a worked example, two processes that scored a perfect 1.0 under profile-only analysis drop to 0.87 once component differences are counted, and processes differing in both dimensions fall to 0.84, exactly the kind of downward correction that prevents false positives in process screening.

The same machinery extends to difference analysis. Given a newly created process, the method first retrieves all processes in the library whose COP similarity exceeds a threshold, set at 0.8 in the study. The operation profile matrices of these similar variants are aggregated into a single weighted matrix whose entries record the frequency of each occurrence relation across the variant set. Two quantitative indicators then drive an iterative clustering procedure. A separation value, computed with a weighted cosine measure, identifies which operations are most suitable to merge together, while a deviation value, measured against benchmark vectors for each relation type, determines which occurrence relation best characterizes the merged pair. Because different relations carry different constraint strength, from the weakly constrained interleaving relation to the strongly constrained exclusive relation, the weights are calibrated by combining domain rules with data-driven optimization rather than manual assignment. The result is a candidate operation profile matrix that summarizes the typical behavior of the whole variant family, against which the new process is compared entry by entry to expose every structural difference.

To test the approach, the team assembled a dataset of low-cost rapid manufacturing temperature sensors, establishing 12 reference processes with 4 variants each, drawn largely from the published literature and converted into Petri net form. A benchmark for true similarity was set with the help of eight experienced modelers, and the methods were evaluated on precision, recall, F1-score and accuracy. The COP method achieved a perfect precision of 1.0 in every case where similar variants existed, correctly rejecting dissimilar processes that the single-criterion methods accepted. The OP method, by contrast, reached recall of 1.0 in most cases, but its broad matching strategy came at the cost of substantial noise, with accuracy in some cases falling as low as 0.25. The COP method attained an average accuracy of 0.83 while the older OTV approach, which considers only part of the profile information, trailed behind in precision.

Statistical validation reinforced the picture. Paired t-tests at a significance level of 0.05 showed that the COP method’s improvements over the OI and OP methods were significant across both precision and accuracy, with all p-values below 0.05. Against the advanced OTV method, COP retained a statistically significant advantage in precision, while the difference in accuracy was not significant, with a p-value of approximately 0.1039, indicating at least comparable overall performance. The authors note that COP nonetheless offers greater theoretical completeness and interpretability, since it fully integrates both operation-component relations and the complete set of occurrence relations rather than a subset.

Perhaps the most practically significant finding concerns the method’s strictness toward small deviations. Rapid manufacturing processes are sensitive to environmental factors, and minor non-standard changes in operation sequence or execution logic frequently arise in production. The t-test results confirmed that when such substandard structural changes occur, the COP method assigns markedly lower similarity scores, flagging potential quality risks that looser methods gloss over. The authors therefore position the OI method for production resource arrangement and equipment scheduling, the OP method for macroscopic process planning and template construction, and the COP method for compliance verification and production quality control, where catching hidden deviations matters most.

In terms of difference mining, simulation experiments on grouped process models demonstrated that the iterative aggregation and clustering procedure correctly recovers candidate operation profiles, even when one process variant dominates the population with a 70 percent share, and that comparing candidate operation items against a new process pinpoints exactly which components deviate from established practice. The work was supported by the National Natural Science Foundation of China and several Anhui provincial programs. The authors acknowledge that, constrained by the availability of real industrial data and privacy limits, the evaluation relied on benchmark datasets from the literature; future work will apply the model to actual production logs with noise in real manufacturing workshops. If those trials succeed, factories could gain a rigorous, quantitative tool for managing sprawling process libraries, promoting reuse, and standardizing production on a scale that manual review has never been able to match.

Subject of Research: A Petri net-based method for measuring similarity and detecting differences between manufacturing processes by jointly analyzing operation profiles and operation-component relations.

Article Title: Analysis of similarity and difference in manufacturing processes based on petri nets

Article References: Analysis of similarity and difference in manufacturing processes based on petri nets. (n.d.). https://doi.org/10.1007/s10586-026-06494-y

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06494-y

Keywords: Petri nets, manufacturing processes, similarity measurement, operation profile, process mining, difference analysis, behavioral profiles, process optimization, Cluster Computing, t-test, rapid manufacturing, precision and recall