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In the context of green development in China, the circumstance in which Environmental, Social and Governance (ESG) ratings function has changed. As an important external governance mechanism of sustainable development, ESG ratings can also be a two-edged sword for the implementation of carbon emission reduction. This research examines the connection of ESG ratings and corporate carbon emission reduction in the context of green development. This present study postulates that the impact of ESG ratings on carbon emission reduction performance in the context of green development is inverted U-shaped.

To obtain empirical evidence for the hypotheses proposed, this study makes an empirical test based on the two-way fixed effects model. The data is taken from listed Chinese manufacturing firms between 2012 and 2021.

The study reveals that there is a significant inverted U-shape relationship between ESG ratings and carbon emission reduction performance in the context of green development. Managerial myopic behaviour plays a positive moderating role in the above relationship. In addition, it makes the inflection point of inverted U-shaped curve move to left. Heterogeneity analyses show that the above inverted U-shaped relationship is more significant for firms that don’t hire CEO with environmental protection background or big four accounting firms.

In the background of green development, this study helps to understand dual influence of ESG ratings on corporate carbon emission reduction deeply. It is beneficial to guide enterprises to utilize ESG ratings mechanism reasonably, thus enhancing the effectiveness of carbon emission reduction. This study provides decision-making reference for government to accelerate low-carbon transformation in microcosmic field.

This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated.

Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ.

Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.

Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture.

This study aims to determine the best way to apply material flow cost accounting (MFCA) in an SME environment with the goal of visualizing negative product cost during the manufacturing process and pinpointing places where improvements can be made.

This study uses a case study approach to demonstrate the usefulness of the MFCA tool in an SME in India that produces aluminum energy products used in the electrical power sector through gravity die casting.

According to the results, the company’s gravity die casting has a negative product cost margin of 27.38% as a result of MFCA analysis. It is also determined that the negative material cost is Rs. 22,919, the negative system cost is Rs. 462 and the negative energy cost is Rs. 1,069 for processing 300 kg of raw material. The typical monthly raw material processing for this company is 45,000 kg.

This research shows that MFCA’s implementation will improve the company’s environmental consciousness and bottom line. To the best of the authors’ knowledge, this study is the first to implement MFCA in aluminum gravity die casting of electrical parts manufacturing.

Exploring the Lean and Green relationship goes back to the beginnings of Lean manufacturing. Most cases established that companies implementing Lean have Green results. However, there are Lean practices with a higher impact on Green, but others with less impact. Therefore, this paper presents research that explores the relationship between Lean and Green in manufacturing companies and aims to determine whether Lean practices have a higher association with Green aspects.

A survey was conducted amongst manufacturing firms to determine their Lean Index (LI). The internally related elements of the Lean construct determined each firm’s LI, whilst Cronbach alpha determined internal LI consistency. The survey also identified firms developing six Green aspects: International Organisation for Standardisation (ISO) 14001, ISO 50001, general Green aspects and the specific aspects of materials, energy and water. An individual sample t-test shows different LI levels of association for each Green aspect. Binomial logistic regression shows the LI element association for each Green aspect.

LI is higher at firms reporting the inclusion of Green aspects. More than half of LI components have a statistically relevant association with the six Green aspects. In general, Ishikawa diagrams had the highest association with Green aspects whilst the lowest was seen in workers as improvement initiators. By grouping the LI elements into their categories, the Lean practices related to controlling processes have a higher association, whilst the involvement of employees has the lowest.

Further research found in this paper identifies the possibilities for investigating the specificities of each Lean tool to develop Green aspects in companies.

Practitioners learn that Lean and Green are not separate issues in business. This article provides evidence that Lean practices in place at companies are already associated with Green aspects, so integration may already be happening.

This paper provides specifics on the relationship between each Lean practice and developing Green aspects. Thus, this paper specifies the Lean practices that contribute most to Green efficiency to support the joint development of both themes.

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

This study aims to explore the emerging trend of business-wide Sustainability Third-Party Labels (STPLs), exemplified by entities like B-Corp. These labels are awarded to organizations committed to a distinctive approach to business, typically embracing the triple-bottom-line (TBL) framework, prioritizing not only financial performance but also social and environmental impact. The research investigates whether these labels enhance trust and influence perceptions of sustainability information quality among young consumers in Spain.

A factorial experiment has been conducted among a convenience sample of individuals belonging to the Z-generation (n = 126). The experiment involved randomly exposing the participants to different versions of an informational brochure from a fictional company in the agricultural sector (with and without label). Following the experiment, a focus group with 15 participants was conducted to assist in interpreting the results.

The results of this study suggest that the use of a nonsector specific label across various sectors with distinct sustainability challenges can lead to confusion among Z-generation consumers. Especially within sectors grappling with environmental concerns, such labels may be susceptible to being perceived as manifestations of greenwashing. Additionally, the study adds supporting evidence to the existing body of literature asserting gender differences in the interpretation of sustainability signals, including labels.

As far as this research is concerned, to the best of the authors’ knowledge, this is the first research that studies the perception of Z-generation members regarding business-wide STPLs. Focusing on studying, the attitudes toward sustainability of younger generations and how they respond to signals like business-wide STPLs are relevant, as they not only possess the longevity to drive substantial change but are also more susceptible to behavioral shifts, thereby holding significant potential in shaping a sustainable future. The study combines both qualitative and quantitative perspective and provides critical insights, relevant to stakeholders within business-wide STPL ecosystems, emphasizing the need for strategic coherence and transparency in label implementation.

This paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time?

Com_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions.

Experiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes.

Com_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics.

Com_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.

This paper explores whether fintech paves the way for the transition to carbon neutrality in the context of China’s climate policy uncertainty (CCPU) and the influence of the ocean carbon sink market.

We apply a novel wavelet analysis technique to investigate the time-frequency dependence between the CCPU index, the CSI (China Securities Index) Fintech Theme Index (CFTI) and the Carbon Neutral Concept Index (CNCI).

The empirical results show that CCPU and CFTI have a detrimental effect on CNCI in high-frequency bands. Furthermore, in low-frequency domains, the development of CFTI can effectively promote the realization of carbon neutrality.

Our findings show that information from the CCPU and CFTI can be utilized to forecast the movement of CNCI. Therefore, the government should strike a balance between fintech development and environmental regulation and, hence, promote the use of renewable energy to reduce carbon emissions, facilitating the orderly and regular development of the ocean carbon sink market.

The development of high-quality fintech and positive climate policy reforms are crucial for achieving carbon neutrality targets and promoting the growth of the marine carbon sink market.

Purpose: The primary purpose is to discuss the productivity and digitalisation interaction at the theoretical level, analyse the productivity and digitalisation differences between the European Union (EU)-14 and EU-13 countries, and evaluate the digitalisation impact on the manufacturing sector labour productivity of the EU countries.

Need for study: The average added value created per capita in new EU countries (EU-13) is one-third lower than in old EU countries (EU-14). To increase productivity, manufacturing companies must adapt to modern trends and take advantage of industrial digitisation opportunities. Digitisation can improve production efficiency, reduce costs, and improve product quality, allowing continuous monitoring and analysis of production data, enabling informed decisions and faster problem-solving.

Methodology: Analysis of scientific literature, comparing viewpoints, insights, and conclusions. The empirical study includes calculating rates of change of indicators, differences between EU-14 and EU-13, and structural analysis. The impact of digitisation on the productivity of EU countries is studied by creating a correlation matrix and using regression analysis: ordinary least square models.

Findings: EU-13 countries are behind EU-14 in labour productivity and manufacturing digitalisation. Digitalisation positively impacts productivity per employee. A faster increase in digitisation, industrial robot use, and e-commerce sales could significantly increase productivity in EU-13, reducing productivity differences between countries.

Practical implications: This study highlights the need for policy promoting digitisation innovation, particularly in EU-13 countries, to be implemented by both national and EU-based economic development and regional and cohesion institutions.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.