The researchers examined informal religious representations, a category that has received less attention than formal religious imagery and practices. These representations can remain embedded in ordinary expressions, values and social norms rather than appearing as explicit statements of belief. By analyzing public discussion of a controversial advertisement, the study shows how such meanings may become visible in social-media language without being immediately labeled as religious.

The advertisement drew criticism in Japan because of perceptions that its treatment of objects conflicted with beliefs and values concerning respect for objects. The analysis covered news videos discussing the commercial and included more than 7,000 comments written in Japanese or English. To identify relevant language across different linguistic contexts, the researchers combined dictionary-based methods with topic modeling. This approach allowed them to examine how emotion- and religion-related terms clustered in discussions rather than treating individual words in isolation.

In the Japanese corpus, terms related to respect for objects appeared in topics linked to religion-related and negative emotional language. Emotional expressions were also prominent in the English-language comments, but the combined association between object-respect language, religious topics and negative emotion was not observed there. The distinction between the two corpora is observational: it shows a difference in the language patterns detected in the analyzed comments, not proof that the advertisement caused a particular religious response or that one language is inherently more religious.

The findings suggest that informal religious representations can surface through expressions such as “mottainai,” whose religious significance depends partly on context. Rather than requiring an overt religious declaration, a comment can connect criticism of an advertisement to ideas about how objects should be treated. The study therefore offers a technical way to detect subtle links among emotion, everyday values and religious meaning in digital discourse, while also demonstrating the importance of comparing linguistic communities rather than assuming that patterns found in one language will appear in another.

Subject of Research: Computational analysis of informal religious representations in YouTube comments responding to an Apple advertisement

Article Title: Making informal religious representations visible in Japanese YouTube comments on Apple’s Crush advertisement

Article References: Tsunematsu, A., Komiya, A., & Takano, R. (2026). Making informal religious representations visible in Japanese YouTube comments on Apple’s Crush advertisement. Scientific Reports. https://doi.org/10.1038/s41598-026-75884-4

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75884-4

Keywords: informal religion, YouTube comments, Japanese language, religious language, topic modeling, dictionary analysis, social media, emotion analysis, Apple advertisement, Making, informal, religious

The research, led by Xianyou Pan of Shanghai University of Electric Power together with colleagues from Basin Electric Power Cooperative and State Grid Zhoushan Electric Power Supply Company, tackles one of the most consequential puzzles in corporate climate finance. According to the 28th Conference of the Parties, global climate finance flows currently amount to only one-third of what is required to meet temperature control goals, creating what economists call a funding bottleneck. Annual investments of 2.4 trillion dollars are expected to be needed by 2030. Yet corporate willingness and efficiency in green investment remain generally low, because such projects often generate limited short-term economic returns and carry considerable uncertainty. Under resource constraints, rational entrepreneurs who favor certain returns are reluctant to proactively allocate funds to green projects, and each enterprise adopts a wait-and-see approach out of concern that it will bear the costs of the green transformation alone.

Pan and colleagues argue that this dilemma cannot be understood by treating each company as an isolated decision-maker. Corporate green investment, they contend, is embedded within a network of group decision-making comprising enterprises in the same industry, region, or supply chain. Their theoretical framework draws on collective intelligence theory—which holds that when most individuals within a group engage in coordinated action, they can generate substantial collective benefits—and organizational information processing theory, which examines how firms selectively interpret and respond to external signals. The key insight is that the investment behavior of peer enterprises constitutes an external information pool, and whether, how, and to what extent an enterprise utilizes this pool depends on its internal group decision-making mechanisms, including the board’s network for information acquisition, the information-processing structure of senior management, and the transparency of information disclosure.

To test these ideas empirically, the team assembled an unbalanced panel of 821 Chinese listed enterprises from 2010 to 2020, comprising 4,065 observations. Green investment data were manually collected from the annual reports of listed enterprises, screening expenditures directly related to environmental protection in the notes to construction in progress—projects for wastewater treatment, exhaust gas control, dust removal, energy conservation, desulfurization, and denitrification. Green investment efficiency was measured using the Richardson model, a capital allocation framework in which the absolute value of the regression residual captures the gap between actual investment and the economically optimal level conditional on enterprise fundamentals. The primary explanatory variable, Deviation, measures the absolute difference between a company’s green investment and the average green investment of its peer group, defined by industry classification according to the China Securities Regulatory Commission’s standards. The analysis employed fixed-effects models with individual and year fixed effects, alongside system GMM estimators to address dynamic endogeneity.

The benchmark results reveal a striking asymmetry. The coefficient on peer green investment deviation is significantly positive at the 1 percent level (0.0033), supporting the hypothesis that peer effects enhance corporate green investment efficiency. In economic terms, a one-standard-deviation increase in deviation leads to approximately a 0.0039 change in the green investment inefficiency index, corresponding to roughly 26 percent of the mean inefficiency during the sample period—indicating that peer convergence behavior can alleviate about one-quarter of the efficiency deviation. When the team decomposed the effect, they found that negative peer deviation is significantly positive (0.0034, p < 0.01), whereas positive peer deviation is statistically insignificant. In other words, efficiency losses occur only when peer enterprises’ green investment falls below that of the focal enterprise, suggesting the peer effect operates through a directional information-learning mechanism rather than simple overinvestment.

The most provocative finding concerns whom companies choose to imitate. The team classified enterprises into leaders (the top third) and followers (the bottom two-thirds) along two dimensions: the scale of green investment and corporate social responsibility scores. When followers simply replicated the visible green-investment scale of leaders, the coefficient was insignificant, suggesting that such simple replication does not improve efficiency. By contrast, when followers learned from CSR-oriented leaders, the effect was significantly positive (0.5660, p < 0.01), and this promoting effect even exceeded the peer effect within the CSR leader group itself (0.4520, p < 0.01). The interaction term test further indicated that the follower group exhibits significantly higher sensitivity to peer deviation than the leader group. The explanation, the authors argue, is that green-investment-scale leaders may be motivated by non-economic considerations such as policy compliance and image-building; followers who copy the visible scale without discerning the underlying rationale are prone to blind imitation, triggering a bandwagon phenomenon that crowds out genuinely efficient green investments. CSR leaders, however, reflect more robust long-term strategies and stakeholder relationships, and learning from them constitutes rational information learning through which followers can effectively optimize investment allocation.

The study also identifies the conditions under which peer effects translate into efficiency gains, examining three sequential stages of information learning. Information transparency was proxied by the KV index, a market-based measure of corporate disclosure quality in which a smaller value indicates more comprehensive disclosure. Information acquisition capability was measured by the betweenness centrality of independent directors within the director network, capturing the structural-hole positions through which directors access heterogeneous information. Information analysis capability was proxied by the number of executives with environmental backgrounds, identified by resume keywords such as ecology, low carbon, sustainability, energy conservation, and clean energy. All three interaction terms were significant in the expected direction: higher transparency, stronger acquisition capability, and greater analytical capability each amplified the positive impact of peer effects on green investment efficiency. Notably, the main effect of information analysis capability was positive and significant, while transparency and acquisition capability exerted only indirect influence—consistent with the theoretical expectation that environmentally versed executives can directly optimize green investment decisions while also strengthening the interpretation of peer signals.

The team conducted extensive robustness checks to distinguish the information-learning mechanism from regulatory imitation. The interaction term between industry deviation and the intensity of environmental regulation was significantly positive (4.739, p < 0.01), and remained so when regulation was lagged by one period (2.989, p < 0.01), ruling out reverse causality. The authors reason that if the peer effect were solely attributable to passive imitation by enterprises seeking to comply with regulatory requirements, environmental regulation should not reinforce the efficiency-enhancing effect of the peer effect. Cross-validation using a VRS-DEA output-oriented model that incorporates both environmental and economic performance dimensions confirmed the benchmark findings (0.0161, p < 0.05), although a pure green DEA model relying solely on a single environmental output dimension failed to achieve statistical significance, suggesting that a single environmental indicator is insufficient to capture the economic-environmental trade-offs inherent in green investment efficiency. After controlling for geographical-level peer effects, the industry deviation coefficient remained significant while the geographical coefficient did not, indicating that technological trajectories and institutional pressures among enterprises in the same industry are the dominant channels through which peer effects operate.

The findings carry pointed implications for both policymakers and corporate managers. From the government perspective, the authors recommend leveraging the guiding role of leading enterprises by establishing model and pilot enterprises to stimulate other companies in the same industry to actively participate in green investment, while strengthening oversight mechanisms involving the government and the public to prevent the compliant bandwagon phenomenon. From the corporate governance perspective, enterprises should enhance their information coordination capabilities through disclosure, acquisition, and analysis, fostering rational information learning behaviors and preventing blind following of trends. The authors also acknowledge limitations: the Deviation variable cannot fully distinguish whether convergence stems from active information learning or passive institutional imitation, the reflection problem and common-shock problem inherent in peer-effect research are difficult to fully resolve with existing methods, and peer relationships defined by industry classification fail to capture supply chain linkages and interlocking director networks. Future research leveraging exogenous policy shocks, such as environmental regulations or green finance pilot programs rolled out in batches, would help disentangle these mechanisms.

As climate finance flows remain far short of what is needed, the study offers a sobering message: the aggregate quality of green capital allocation may depend less on how much companies invest and more on how they learn from one another. In a group decision-making environment with excessive information noise, managers can easily form a false consensus and trigger the bandwagon phenomenon, leading to resource misallocation and overcapacity. But when enterprises possess the internal information-processing capabilities to observe, acquire, and analyze peer signals rationally, the collective intelligence of the market can compensate for the shortcomings of policy pressures and incentives, amplifying investment variability and steering green capital toward genuinely efficient uses. The difference, it turns out, is not imitation itself but the sophistication of the learner.

Subject of Research: Enhancement mechanism of corporate green investment efficiency from the perspective of group decision making

Article Title: Enhancement mechanism of corporate green investment efficiency from the perspective of group decision making

Article References: Pan, X., Wu, X., Ma, J., Lu, H., & Liu, B. (2026). Enhancement mechanism of corporate green investment efficiency from the perspective of group decision making. Frontiers in Environmental Economics, 5, Article 1876957. https://doi.org/10.3389/frevc.2026.1876957

Image Credits: AI Generated

DOI: 10.3389/frevc.2026.1876957

Keywords: Enhancement, mechanism, corporate, green, investment, efficiency, perspective, group, decision, making, scientific research

Funded by the European Commission and coordinated by Radboud University, the cornerstone project NatureRestoreR: guiding nature restoration through integrated ecological modelling was officially launched on 1 September 2026. The project carries grant number 101287178 and will be implemented over a four-year period, concluding on 1 September 2030. The primary objective of NatureRestoreR is to provide publicly available ecological modelling tools and guidelines. These resources are intended to enable national and regional authorities to design restoration plans based on a scientifically sound understanding of how species, habitats, and ecosystem services respond to restoration measures in different places and under evolving climate dynamics.

A critical challenge in guiding ecosystem restoration efforts is that there is no single approach to restoring nature. Species and habitats may respond differently to a given restoration measure depending on local environmental conditions. Furthermore, climate change can affect the long-term outcomes of restoration efforts. Consequently, authorities need robust scientific evidence to understand how species and ecosystems are likely to respond to restoration under specific circumstances. They also need to identify where interventions can create synergies or trade-offs. NatureRestoreR seeks to address this challenge by convening existing data, scientific knowledge, and modelling approaches to develop an innovative suite of adaptive and easy-to-use ecological modelling tools.

These modelling tools will enable evidence-based prospective assessment of restoration measures. Specifically, they will model the potential impacts on species’ habitats, the extent and condition of habitats protected under the EU Nature Directives, and the provision of ecosystem services. By providing these insights, the project aims to help decision-makers make more informed choices about where and how to allocate restoration resources. This approach is designed to ensure that restoration efforts are not only ecologically sound but also practically feasible for national and regional authorities.

The development of these modelling tools will be a collaborative effort, co-developed and tested through a co-creation process involving researchers and implementation partners. The project will use information from the initial and final revised National Restoration Plans of five EU Member States: Austria, Italy, France, Estonia, and the Netherlands. This collaborative approach will enable the project partners to assess how these plans contribute to the objective of the EU Biodiversity Strategy and the Nature Restoration Regulation. By working with these specific national plans, the project ensures that the tools are relevant to real-world policy contexts and can be adapted to different national circumstances.

Reflecting on the project’s long-term vision, Aafke Schipper, a member of the coordination team at Radboud University, emphasized the importance of the partnership structure. Schipper noted that NatureRestoreR relies on a unique co-creation partnership between ecological researchers and national authorities responsible for or involved in the implementation of the Nature Restoration Regulation. Through this partnership, the project ensures that it meets the needs of decision-makers and practitioners who are implementing and monitoring the regulation. This focus on user needs is central to the project’s design, aiming to create tools that are not only scientifically rigorous but also practical for those on the ground.

NatureRestoreR’s transdisciplinary partnership brings together ecological modelling experts and national agencies with deep knowledge of the EU Nature Directives and National Restoration Plans. Working together, these partners aim to strengthen the relation between ecological knowledge and the decisions shaping Europe’s landscape. The project recognizes that effective restoration requires a close link between scientific understanding and policy implementation. By integrating these two areas, NatureRestoreR hopes to create a more cohesive approach to ecosystem restoration across the continent. This integration is seen as essential for achieving the ambitious targets set by the EU.

Throughout the project, NatureRestoreR will engage with researchers, practitioners, policymakers, and other stakeholders interested in advancing effective, evidence-informed ecosystem restoration across Europe. This broad engagement is intended to ensure that the project’s outputs are widely accessible and useful to a diverse range of actors involved in nature restoration. The project team encourages stakeholders to follow NatureRestoreR on LinkedIn to stay up to date with the project’s latest news, activities, and results. This ongoing communication is part of the project’s strategy to build a community of practice around evidence-based restoration.

The launch of NatureRestoreR represents a significant step forward in the effort to make nature restoration smarter and more effective in Europe. By providing the tools and evidence needed to design and refine restoration plans, the project aims to support the implementation of the EU Nature Restoration Regulation. The focus on co-creation and transdisciplinary collaboration is expected to ensure that the project’s outputs are relevant and useful to those responsible for implementing restoration measures. As the project progresses over the next four years, it will provide valuable insights into how ecological modelling can be used to guide restoration efforts in a changing climate. The ultimate goal is to contribute to the restoration of Europe’s ecosystems and the achievement of the EU’s biodiversity targets.

Subject of Research: Environmental Policy and Ecology

Article Title: NatureRestoreR launches to support Europe in making nature restoration smarter and more effective

Article References: NatureRestoreR launches to support Europe in making nature restoration smarter and more effective. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Nature Restoration, EU Policy, Ecological Modelling, Biodiversity, Climate Change, Radboud University, European Commission, NatureRestoreR, launches, support, Europe, making