Search Results (178)

Wind power has become a strategic cornerstone of China’s renewable-energy transition and industrial upgrading, making it essential to understand how policy interventions shape the behaviour of its industry chain. This study examines how major wind power policies issued between 2015 and 2024 transmit shocks across nine upstream, midstream, and downstream sectors. Using four contagion tests based on higher-order co-moments, combined with a policy sensitivity index, the analysis identifies distinct transmission patterns across policy types. The results show that market-mechanism reforms induce the strongest and most systemic contagion effects, reflecting their ability to align financial incentives with renewable-integration objectives. Upstream sectors—particularly equipment and key material industries—exhibit the highest responsiveness, while midstream construction and downstream operation and maintenance display more moderate and delayed adjustments. Development and construction policies generate broader but less intensive contagion, whereas industry-support measures trigger selective, sector-specific responses. These findings offer practical guidance for improving policy coordination, investment planning, and industrial upgrading within China’s wind power value chain. Future research could extend the analysis by incorporating firm-level data, longer policy cycles, and interactions with other structural shocks such as electricity-market reforms and climate-related risks. Full article

The rapid advancement of AI, especially large language models (LLMs), brings opportunities and challenges to industries. In construction, LLMs can enhance project coordination, support decision-making and reduce workload, but adoption is limited by hallucination, data security and domain complexity. This study investigates the current state of LLM adoption in China’s construction industry through a four-step approach, including a comprehensive literature review to identify potential drivers and barriers, questionnaire design and data collection for empirical analysis, and the application of the Entropy Weight Method (EWM) to quantify and rank the relative importance of each factor. The findings reveal that the top drivers originate at the company level, including strategic partnerships, internal research teams, and staff training—highlighting the central role of organizational readiness in enabling LLM integration. Conversely, the most critical barriers are embedded in the construction domain itself, including knowledge gaps, workflow integration, and data heterogeneity, which reflect structural limitations in the sector. Although LLM implementation remains in its early stages, survey responses show widespread optimism among stakeholders regarding its future potential. The study proposes several actionable strategies for both construction firms and policymakers to facilitate effective LLM adoption. Moreover, the identified drivers and barriers are not exclusive to construction but are also relevant to other digitally transforming sectors—such as manufacturing, healthcare, and energy—offering broader implications for AI adoption in complex, project-based environments. Full article

This study examines the impact of Artificial Intelligence (AI)-enhanced Environmental, Social, and Governance (ESG) reporting on firm valuation in emerging markets. It aims to explore how AI integration enhances the interpretability and predictive accuracy of ESG metrics in shaping market perceptions and investor decisions. This study employs a panel dataset from 2018 to 2024, analysing publicly listed non-financial firms across five major sectors: manufacturing, energy, telecommunications, consumer goods, and industrials. This study contributed by employing AI-powered multimodal analysis with conventional ESG scoring methods and integrating Fixed-Effects Regression with machine learning (ML) algorithms including Extreme Gradient Boosting (XGBoost) and Random Forest to identify complex, non-linear relationships within ESG data and firm valuation. The results show empirical evidence that integrating ML enhances the explanatory power of ESG data. Findings indicate that ESG performance is positively correlated with higher market valuations, particularly in Environmental and Social dimensions. Governance metrics are more inconsistent, which may be due to heterogeneity in governance practices, regulatory enforcement and the challenges of quantifying governance quality beyond compliance indicators across the focus emerging markets. Firms identified in ESG controversies tend to face valuation penalties, which stresses market sensitivity to reputational risks. ML algorithms outperform conventional techniques in predictive accuracy, revealing complex, non-linear interactions within ESG data. This study contributes to both the academic literature and practice showing how next-generation ESG reporting can robust valuation models, address limitations of conventional ESG scoring, and ensure a reliable outlook for investors and policymakers of industries in emerging markets. Full article

This study employs an industry-specific patent classification methodology (ISPCM) and conducts complex network analysis across temporal, industrial, and spatial dimensions to examine China’s new energy vehicle (NEV) patent collaboration network and to uncover the mechanisms underlying China’s global rise in the NEV sector. The results demonstrate the effectiveness of the ISPCM and reveal a three-phase growth pattern that is driven by policy initiatives and market expansion. Domestic entities dominate the patent landscape, with a noticeable shift from invention patents to utility model patents, which reflects a focus on application-oriented innovation. The collaboration network exhibits a heavy-tailed characteristic, and it forms an oligopolistic structure in which state-owned enterprises (SOEs) act as “innovation orchestrators,” while private firms concentrate on specialized R&D. Across the industrial chain, the component segment forms the largest network, the complete vehicle segment comprises the smallest network, and the aftermarket is clustered around battery recycling. A clear divide between domestic and foreign entities suggests potential decoupling risks. The findings reveal a dual-circulation innovation model that combines state-led coordinated research with market-driven independent research, offering valuable insights for sustainable industrial transformation. Full article

Amid growing sustainability and ethical concerns, digital innovation increasingly requires integrating social, environmental, and governance responsibility into technological development. However, little is known about how organizations in emerging economies—particularly in North Africa—operationalize these principles in practice. This study addresses this gap by exploring how institutional and economic actors in Morocco incorporate responsibility principles into their digital innovation strategies. Adopting an exploratory qualitative design, we conducted 27 semi-structured interviews with digital managers from public organizations (ministries, institutions, and local authorities) and private firms (technology companies, start-ups, and large corporations). The central research question guiding this study is: How do Moroccan organizations perceive and implement the principles of responsible digital innovation (RDI)? Data were analyzed using NVivo 14 software through thematic coding and triangulated with policy documents to enhance validity. The results reveal growing awareness of digital sustainability issues—particularly energy efficiency, accessibility, and data protection—yet the degree of responsible practice varies by sector, firm size, and regulatory environment. Key obstacles include limited expertise, absence of ethical performance metrics, and competitive pressures constraining investment in RDI. Conversely, ethical charters, frugal design, and stakeholder engagement emerge as key drivers. The study concludes that embedding responsibility in digital innovation requires shared governance frameworks, supportive public policies, and cross-sector collaboration to promote inclusive and sustainable technological progress. While context-specific, this research opens avenues for comparative and quantitative studies on RDI across emerging economies. Full article

Energy consumption is a key driver of economic and social development, particularly in rapidly expanding sectors such as Information and Communication Technology (ICT). This study explores the energy demand of Quito’s ICT sector across technical, organizational, economic, social, and environmental dimensions, aiming to inform sustainable urban strategies. A mixed-methods approach was applied, combining quantitative and qualitative analyses. Data was collected via questionnaires from 398 ICT companies and analyzed using descriptive statistics and multivariate techniques, including the Gower similarity coefficient, Principal Coordinate Analysis (PCoA), and biplots. The VENSIM PLE x64 version 9.1.1 was used to model energy consumption dynamics. Results indicate that most ICT firms are micro and small enterprises focused on software development and e-commerce, employing highly skilled personnel. Energy use is concentrated in computing and printing equipment, with limited reliance on climate control systems. While 93% of firms express environmental awareness, fewer than 10% have formal energy efficiency policies. Financial constraints and limited access to efficient equipment are the main barriers to improved energy management. The study concludes that, despite a moderate energy profile, there is an urgent need to strengthen internal energy practices. The findings offer a contextualized framework to guide energy policy and organizational strategies, contributing to more resilient and sustainable urban ICT ecosystems. Full article

Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 A-share-listed new energy vehicle enterprises between 2017 and 2024, this study reveals that both overall and phase-specific innovation efficiencies remain below optimal levels. Moreover, the average technological R&D efficiency across these firms is found to be lower than their average achievement transformation efficiency, highlighting the urgent need to improve innovation performance in this sector. Grey relational analysis of influencing factors identifies six key determinants of technological innovation efficiency: the shareholding ratio of the largest shareholder, R&D investment intensity, the proportion of employees holding bachelor’s degrees or higher, management capability, return on equity, and total asset turnover. In comparison, government subsidies and total assets exhibit relatively limited influence on technological innovation efficiency. Full article

Nearshoring in Mexico is expanding rapidly, yet chronic volatility in the national power grid threatens the reliability and cost-competitiveness of relocated manufacturing lines. To inform strategic mitigation, this study presents a hybrid Fuzzy–CES decision-support framework that embeds the Constant-Elasticity-of-Substitution (CES) production function within a Mamdani Fuzzy-Inference Engine, implemented in both Type-1 and Interval Type-2 variants, to evaluate and optimize production adaptability in energy-constrained environments. Using sector-wide data from Mexico’s automotive industry, key input variables (energy reliability, capital intensity, and labor availability) are objectively quantified and normalized to reflect the realities of regional plant operations. The system linguistically classifies each facility’s production elasticity as low, moderate, or high, and generates actionable recommendations for resource allocation, such as targeted investments in renewable microgrids or workforce strategies. Implemented in MATLAB, simulation results confirm that, while high capital and labor inputs are essential, energy reliability remains the primary bottleneck limiting adaptability; only states with all three strong factors achieve maximum resilience. The Type-2 fuzzy approach demonstrates superior robustness to input uncertainty, enhancing managerial decision-making under volatile grid conditions. In addition, a case study regarding the automotive industry is presented to illustrate how the proposed framework is implemented. The same structure can be used to deploy it in another industry. This research offers a transparent, data-driven tool to inform both firm-level investment and regional policy, directly supporting Mexico’s efforts to sustain competitiveness and resilience in the global shift toward nearshoring. Full article

This study investigates the relationship between capital intensity, debt financing, and profitability in non-financial firms in Oman over the period 2012–2022. Using a robust panel dataset of 76 firms, the research explores how capital structure dynamics influence firm performance across different firm sizes and industries. The findings reveal that capital intensity significantly enhances profitability, and debt financing further strengthens this effect, with variations observed across firm size and sector. The analysis also identifies a non-linear (concave) relationship between capital intensity and profitability, indicating that while moderate capital investment improves firm performance, excessive capital accumulation may lead to diminishing returns. Larger firms, with better access to financial resources, exhibit a stronger positive relationship between debt financing and profitability, while smaller firms face more challenges due to limited access to capital. Industry-specific results indicate that capital-intensive sectors, such as Energy and Industrials, demonstrate a more pronounced effect of capital intensity on profitability compared to less capital-intensive sectors. The study also incorporates the effects of the COVID-19 pandemic, showing its significant influence on firm performance, particularly in sectors with high debt exposure. By integrating non-linear effects, firm size, industry heterogeneity, and pandemic shocks, this study provides novel insights into capital structure management in emerging economies, offering implications for both corporate decision-makers and policymakers aiming to enhance financial access and optimize debt strategies across sectors. Full article

A design science framework integrates sustainability into business development across sectors. The framework embeds sustainability, reflected in environmental, social, and governance (ESG) dimensions, within a structured process that links drivers, evaluation components, and outcome indicators. Six principles guide its structure: clarity, integration, adaptability, stakeholder engagement, performance feedback, and scoring consistency. Researchers applied the framework in energy, engineering, and agribusiness cases. Case results show how the framework improves opportunity selection, identifies capability gaps, strengthens prioritization, and structures stakeholder input without adding complexity. Findings confirm that incorporating sustainability factors during the initial stage of business development changes decision patterns, aligns projects with long-term goals, and increases transparency in portfolio planning. This design science approach moves sustainability and its ESG dimensions from a reporting concern to a central element of strategic evaluation and growth planning. Organizations gain a practical structure to align opportunity development with resilience, learning capacity, and sustainability outcomes. In addition, the framework provides a foundation for adaptation, digital tool development, and longitudinal feedback cycles as firms integrate sustainability and ESG dimensions within uncertain policy, market, and stakeholder environments. Full article

This study analyzes how sectoral stock volatility in the GCC region responds to global financial uncertainty shocks originating from the U.S. (CBOE VIX), Europe (VSTOXX-50), Bitcoin investors’ Sentiment Indices (BSI), and disaggregated global Financial Stress Indicators (FSI) by using both the “Frequency” and “Time” domain TVP-VAR based connectivity approaches. The “Time” and “Frequency” domain TVP-VAR results indicate that the Energy, Financials, Materials and REIT sectors experience the highest shock spillover from the U.S. and European equity market uncertainty (VIX and VSTOXX-50) for the overall and long-term investment horizons. Whereas, all the five disaggregated global financial stress indicators and BSI transmit higher shocks spillovers towards the sectoral stock conditional volatility of Energy and Materials sectors for the overall and long-term investment horizons. Furthermore, the “Frequency” domain TVP-VAR approach shows that overall shocks spillovers are higher in long-term and intensified during the COVID-19 period. The Energy, Materials, and REIT sectors’ high sensitivity to U.S.VIX and Euro.VSTOXX-50 shocks calls for sector-specific hedging—such as sectors remain least susceptibility to long-term U.S. and European equity risk shocks such as Utility. Over the long-term and overall investment horizons, the Energy and Material sectors’ position as the main shock recipient from all five global financial stress components and the BSI underscores its role as a volatility hub. Policymakers should enforce stress tests and capital buffers for energy and material focused firms, while proactive liquidity management and commodity hedging are vital during global financial stress and BSI spikes to limit funding and operational risks. Full article

This study examines the heterogeneous effects of product environmental innovation on firm-level innovation efficiency and performance in process industries, with a focus on the chemical and electronics sectors. Following the Organisation for Economic Co-operation and Development (OECD)’s Oslo Manual, four types of product environmental innovation are considered: reducing energy use and emissions (RUE), reducing pollution (RP), promoting recycling (PR), and enhancing durability and extending product life (EDEL). Innovation efficiency is evaluated using the input-oriented Banker–Charnes–Cooper (BCC) Data Envelopment Analysis (DEA) model, and regression analyses are applied to test the effects of each innovation type on efficiency and sales outcomes. The results reveal that RUE and EDEL consistently enhance both efficiency and performance, whereas PR has a negative impact on performance, and RP shows no significant effect. These findings demonstrate that product environmental innovation is not a homogeneous construct but yields heterogeneous outcomes depending on type and industry context. The study contributes to the literature by jointly examining efficiency and performance outcomes and by overcoming the limitations of single-metric evaluations, and it provides practical implications by clarifying which innovation types deliver immediate value in business-to-consumer (B2C) markets and which are more relevant for business-to-business (B2B) settings. Full article

Corporate ESG (Environmental, Social, and Governance) reporting is increasingly envisioned as evidence of accountability in the energy transition, yet persistent gaps remain between commitments and practices. This study applied the Global Reporting Initiative (GRI) framework—specifically indicators 302 (Energy) and 305 (Emissions)—to evaluate the credibility, scope, and strategic depth of disclosures by 20 Wisconsin (WI) firms in the energy, manufacturing, food, and service sectors. Guided by accountability and legitimacy theory, a comparative content analysis was conducted, complemented by Spearman correlation to examine associations between firm size and disclosure quality. Results show that while firms consistently report basic metrics such as total energy consumption and Scope 1 emissions, disclosures on Scope 3 emissions, renewable sourcing, and energy-efficiency achievements remain partial and selectively framed. Third-party assurance is inconsistently applied, and methodological transparency—such as external audit and coding protocols—is limited, weakening credibility. A statistically significant negative correlation was observed between annual revenue and disclosure quality, indicating that greater financial capacity does not necessarily translate into greater transparency. These findings highlight methodological and governance shortcomings, including reliance on generic ESG frameworks rather than climate-focused standards such as Task Force on Climate-related Financial Disclosures (TCFD). Integrated reporting approaches are recommended to improve comparability, credibility, and alignment with Wisconsin’s Clean Energy Transition Plan. Full article

Climate-relevant technological innovation in renewable energy sources, such as solar energy, is essential for mitigating climate change and achieving sustainable development. The recent literature highlights substantial patent activity in China’s solar energy industry, which may contribute to the sector’s success in international markets. This study examines the relationship between patent activity and corporate financial performance in China’s solar energy industry from 2012 to 2022 using panel data analysis. The results indicate that patent applications positively impact firms’ corporate performance, showing a time-lag of approximately 6–7 years. Notably, this positive impact is particularly pronounced for firms located in the eastern region and state-owned enterprises. Additionally, we investigate whether CEO duality affects the relationship between patent applications and firms’ corporate performance, seeking to reveal unique development pathways within the industry. These findings are important for understanding how China’s solar energy sector can advance along sustainable development pathways amid the challenges of climate change. Full article

This study investigates the impact of working capital management (WCM) on profitability in the Indian cement industry, an energy-intensive sector central to the country’s infrastructure growth. Using a balanced panel of listed firms over 2010–2024, we employ pooled OLS, two-way fixed effects, quantile regressions, and dynamic system GMM to address heterogeneity and endogeneity concerns. The results demonstrate that reductions in the cash conversion cycle (CCC), accelerated receivables collection, leaner inventories, and prudent use of payables significantly improve profitability. Quantile regressions reveal that highly profitable firms capture larger absolute gains from CCC reductions, while size-split analysis indicates that smaller and liquidity-constrained firms achieve proportionally greater marginal relief. These findings represent complementary perspectives rather than unified statistical relationship, a limitation we acknowledge. Dynamic estimates confirm the robustness of results after accounting for persistence and reverse causality. Beyond firm-level outcomes, the study contributes conceptually by linking WCM efficiency to sustainability financing: liquidity released from shorter operating cycles can be redeployed into green and energy-efficient investments, offering a potential channel for ESG alignment in carbon-intensive industries. Policy implications highlight the role of digital reforms such as TReDS and e-invoicing in strengthening liquidity efficiency, particularly for mid-sized firms. The findings extend the international WCM profitability literature, provide sector-specific evidence for India, and suggest new avenues for integrating financial and sustainability strategies. Full article