Search Results (125)

This paper examines the effect of digital innovation on cost stickiness in manufacturing firms, focusing on the underlying mechanisms and contextual factors. Using data from Chinese A-share listed manufacturing firms from 2012 to 2023, we find that, first, for each one-unit increase in the level of digital technology, the cost stickiness index of enterprises decreases by an average of 0.4315 units, primarily through digital process innovation and digital business model innovation, whereas digital product innovation does not exhibit a statistically significant impact. Second, manufacturing servitization and the optimization of human capital structure are identified as key mediating mechanisms. Digital innovation promotes servitization by transitioning firms from product-centric to service-oriented business models, thereby reducing fixed costs and improving resource flexibility. It also optimizes human capital by increasing the proportion of high-skilled employees and reducing labor adjustment costs. Third, the effect of digital innovation on cost stickiness is found to be heterogeneous. Firms with high financing constraints benefit more from the cost-reducing effects of digital innovation due to improved resource allocation efficiency. Additionally, mid-tenure executives are more effective in leveraging digital innovation to mitigate cost stickiness, as they balance short-term performance pressures with long-term strategic investments. These findings contribute to the understanding of how digital transformation reshapes cost behavior in manufacturing and provide insights for policymakers and firms seeking to achieve sustainable development through digital innovation. Full article

This study examines the impact of free trade zone (FTZ) establishment on regional economic resilience (RER) in China, using provincial-level panel data spanning from 2010 to 2022 and a multi-period difference-in-differences (DID) approach. The empirical results indicate that FTZ implementation significantly enhances regional economic resilience by 3.46%, with the development of green finance acting as a key moderating mechanism that amplifies this positive effect. Heterogeneity analysis uncovers notable disparities across policy cohorts and geographical regions: the first wave of FTZs demonstrates the most pronounced resilience-enhancing impact, whereas later cohorts exhibit weaker or even adverse effects. Coastal regions experience substantial benefits from FTZ policies, in contrast to statistically insignificant outcomes observed in inland areas. These findings suggest that strategically expanding the FTZ network, when paired with tailored implementation mechanisms and the integration of green finance, could serve as a powerful policy tool for post-COVID economic recovery. Importantly, by strengthening economic resilience through institutional openness and green investment, this study offers valuable insights into balancing economic growth with environmental sustainability. It provides empirical evidence to support the optimization of FTZ spatial governance and institutional innovation pathways, thereby contributing to the pursuit of sustainable regional development. Full article

Blockchain technology is being increasingly deployed to store and process transactions and information in the global financial sector. Blockchain underpins cryptocurrencies such as Bitcoin and facilitates decentralized finance (DeFi), representing a paradigm shift in the global financial landscape, offering alternative solutions to traditional banking, and fostering financial inclusion. In developing economies such as Morocco, where a significant portion of the population remains unbanked, these digital financial innovations present both opportunities and challenges. This study examines the potential role of cryptocurrencies and DeFi in enhancing financial inclusion in Morocco, where cryptocurrencies have been banned since 2017. However, the public continues to use cryptocurrencies, circumventing restrictions, and the Moroccan Central Bank is now preparing to introduce new regulations to legalize their use within the country. In this context, this article analyses the potential of cryptocurrencies to mitigate barriers such as high transaction costs, restricted access to financial services in rural areas, and limited financial literacy in the country. The study pursues a mixed-methods approach, which combines a quantitative survey with qualitative expert interviews and adapts the Unified Theory of Acceptance and Use of Technology (UTAUT) model to the Moroccan context. The findings reveal that while cryptocurrencies offer cost-efficient financial transactions and improved accessibility, their adoption may be constrained by regulatory uncertainty, security risks, and technological limitations. The novelty of the article thus lies in its focus on the key mechanisms that influence the adoption of cryptocurrencies and their potential impact in a specific national context. In so doing, the study highlights the need for a structured regulatory framework, investment in digital infrastructure, and targeted financial literacy initiatives to optimize the potential role of cryptocurrencies in progressing financial inclusion in Morocco. This underscores the need for integrated models and guidelines for policymakers, financial institutions, and technology providers to ensure the responsible introduction of cryptocurrencies in developing world environments. Full article

Small island developing states (SIDS) face a dual constraint of “environmental vulnerability and energy dependence” in the context of climate change. How to achieve just energy transitions has become a core proposition for SIDS to address. This paper focuses on how SIDS can advance Sustainable Development Goal (SDG) 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action) through UNCLOS–Paris Agreement integration in energy transitions. Grounded in the theoretical framework of the Multidimensional Vulnerability Index (MVI), this research aims to construct a comprehensive analytical system that systematically examines the energy transition challenges facing SIDS and provide multi-level energy transition solutions spanning from international to domestic contexts for climate-vulnerable SIDS. The research findings reveal that SIDS face a structural predicament of “high vulnerability–low resilience” and the triple challenge of “energy–climate–development”. International climate finance is severely mismatched with the degree of vulnerability in SIDS; the United Nations Convention on the Law of the Sea (UNCLOS) and the Paris Agreement lack institutional synergy and fail to adequately support marine renewable energy development in SIDS. In response to these challenges, this study proposes multi-level solutions to promote the synergistic achievement of SDG 7 and SDG 13: at the international level, improve climate finance rules, innovate financing mechanisms, strengthen technological cooperation, and integrate relevant international legal framework; at the domestic level, optimize the layout of marine renewable energy development, construct sustainable investment ecosystems, and strengthen environmental scientific research and local data governance. Full article

This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices. Full article

As ESG practices become increasingly embedded in global capital markets, their impact on firm financing costs remains an open question in emerging economies, where regulatory divergence and rating inconsistency complicate investor perceptions, particularly in China’s rapidly evolving financial environment. This study examines the impact of Environmental, Social, and Governance (ESG) performance on financing costs among Chinese non-financial listed firms, with a focus on the moderating roles of financial regulation and ESG rating divergence. Using a panel dataset of 4493 firms across 33,773 firm–year observations from 2011 to 2022, we employ a two-way fixed effects model, along with Propensity Score Matching and Difference-in-Differences (PSM-DID) techniques, to address endogeneity concerns and enhance causal inference. The findings reveal that improvements in ESG performance significantly reduce financing costs, substantially affecting debt relative to equity. Moreover, the cost-saving benefits of ESG are amplified in industries with stronger regulatory oversight, while high ESG rating divergence undermines these benefits by increasing uncertainty. These results highlight the importance of standardizing ESG rating systems and enhancing regulatory consistency. Such efforts can lower capital costs and improve financial access for firms, particularly in capital-intensive and environmentally sensitive sectors, offering actionable guidance for policymakers shaping disclosure frameworks and corporate managers optimizing ESG investment strategies. Full article

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. To tackle these issues, a dual-mode cost–benefit analysis framework was developed, integrating two distinct investment models: self-invested construction (SIC), where farmers independently finance and manage the system, and energy performance contracting (EPC), where third-party investors fund infrastructure through shared energy-saving or revenue agreements. Then, an integrated photovoltaic-storage agricultural greenhouse (PSAG) microgrid optimization model is established, synergizing renewable energy generation, battery storage, and demand-side management while incorporating operational mode selection. The proposed model is validated through a real-world case study of a village agricultural greenhouse in Gannan, China, characterized by typical rural energy profiles and climatic conditions. Simulation results demonstrate that the optimal system configuration requires 27.91 kWh energy storage capacity and 18.67 kW peak output, with annualized post-depreciation costs of 81,083.69 yuan (SIC) and 74,216.22 yuan (EPC). The key findings reveal that energy storage integration reduces operational costs by 8.5% compared to non-storage scenarios, with the EPC model achieving 9.3% greater cost-effectiveness than SIC through shared-investment mechanisms. The findings suggest that incorporating an energy storage system reduces costs for farmers, with the EPC model offering greater cost savings. Full article

The integration of non-conventional renewable energy sources (NCRES) plays a critical role in achieving sustainable and decentralized power systems. However, accurately assessing the economic feasibility of NCRES projects requires methodologies that account for policy-driven incentives and financing mechanisms. To support the shift towards NCRES, evaluating their financial viability while considering public policies and funding options is important. This study presents an improved version of the Levelized Cost of Electricity (LCOE) that includes government incentives such as tax credits, accelerated depreciation, and green bonds. We apply a flexible investment model that helps to find the most cost-effective financing strategies for different renewable technologies. To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. The model is tested in a case study in Colombia covering battery storage, large- and small-scale solar power, and wind energy. Results show that combining smart financing with policy support can significantly lower electricity costs, especially for technologies with high upfront investments. We also explore how changes in interest rates affect the results. This framework can help policymakers and investors design more affordable and financially sound renewable energy projects. Full article

Sustainable mobility in university environments presents both a challenge and an opportunity to reduce environmental impact and promote energy efficiency. This study assesses the feasibility of implementing electric buses in the internal transportation system of the Federal University of Paraíba (UFPB), considering environmental, economic, and operational aspects. The analysis demonstrates that transitioning to this model can lead to a significant reduction in greenhouse gas (GHG) emissions, noise pollution mitigation, and optimization of operational costs throughout the vehicle’s life cycle. The study examines technical, structural, and financial factors, emphasizing the necessary infrastructure, academic community acceptance, and the economic viability of the project, as well as the strategic advantage of integrating the electric fleet with photovoltaic energy generation. The key highlights of this research include: (i) Sustainability and energy efficiency, emphasizing a reduction of up to 52.52% in CO₂ emissions when vehicles are powered by photovoltaic energy in an LCA context, alongside improvements in air quality and noise pollution mitigation. (ii) Economic feasibility analysis, comparing operational and maintenance costs between electric and conventional diesel buses, evaluating the financial viability and potential return on investment. (iii) Infrastructure and implementation challenges, addressing the need for charging stations, adaptation of UFPB’s infrastructure, and financing models, including government subsidies and strategic partnerships. (iv) Impact on the academic community, analyzing student and staff perceptions and acceptance of fleet electrification and the promotion of sustainable practices. (v) Future projections and replicability, exploring trends in the sustainable transportation sector, as well as the potential expansion of the electric fleet and its integration with energy storage systems. The results indicate that adopting electric buses at UFPB can position the institution as a benchmark in sustainable mobility, serving as a replicable model for other universities and contributing to carbon emission reduction and modernization of university transportation. Full article

In the sustainable evolution of the entrepreneurial ecosystem, the efficiency of early-stage capital allocation directly affects the intergenerational transmission capacity of innovation resources. The financing barriers caused by information asymmetry urgently require institutional solutions. This study, based on tracking data from 19,463 startups in China’s information technology sector (2016–2019), analyzes how government-certified incubators (GCIs) optimize the sustainability of the entrepreneurial ecosystem through signaling mechanisms. The empirical results show that collaboration with a GCI can significantly increase the likelihood of IT startups securing venture capital by approximately 25%. This effect is not only due to the strict screening and resource support provided by GCIs, but also due to their role in amplifying internal signals from startups, such as the experience of founders and intellectual property. Notably, in the IT sector, the impact of GCIs is more significant for startups traditionally disadvantaged, particularly those led by female founders. Our research demonstrates that GCIs drive the sustainable development of the entrepreneurial ecosystem through three signaling mechanisms: (1) institutional certification screening, which optimizes the intergenerational allocation efficiency of ecosystem resources; (2) the signaling validation–amplification mechanism, which enhances the value of intellectual property and founder experience, alleviating investors’ challenges in quantifying startup potential; (3) inclusive signal rebalancing, where GCI certification significantly improves the funding success rate of female founders, breaking traditional market biases in screening disadvantaged groups and supporting the inclusive and sustainable development of the entrepreneurial ecosystem. These findings provide a new pathway for emerging economies to optimize the resilience of their entrepreneurial ecosystems through policy tools: for governments, GCIs achieve sustainable development goals at low institutional cost; for investors, the signal integration mechanism reduces investment information friction; and for entrepreneurs, certification endorsements accelerate market validation of sustainable business models. Full article

Amid escalating global climate challenges, the interplay between corporate climate risk disclosure and green technological innovation has become a pivotal scholarly focus in sustainability research. This study empirically examines the impact of climate risk disclosure on corporate green technology innovation and its underlying mechanisms using data from China’s A-share listed companies spanning 2004 to 2022. Key findings reveal that climate risk information disclosure significantly enhances green innovation capabilities through dual pathways: elevating media attention and reducing agency costs. Specifically, media scrutiny exerts external pressure via reputational incentives and public oversight, driving firms to accelerate green technology deployment. Concurrently, reduced agency costs mitigate information asymmetry between shareholders and management, enabling optimized resource allocation for long-term innovation investments. Heterogeneity analysis indicates that this catalytic effect is more pronounced in larger firms and those facing lower financing constraints. The research theoretically and practically elucidates the dual mechanisms through which climate disclosure propels green innovation, providing empirical support for refining corporate sustainability reporting systems and recalibrating regulatory frameworks. Policy recommendations include adopting differentiated climate disclosure standards, strengthening media and investor oversight, and incentivizing green innovation through executive performance metrics to facilitate low-carbon economic transition. Full article

GPT (Generative Pre-trained Transformer) is a groundbreaking generative model that has facilitated substantial progress in natural language processing (NLP). As the GPT-n series has continued to evolve, its applications have garnered considerable attention across various industries, particularly in finance. In contrast, traditional financial research has primarily focused on analyzing structured data such as stock prices. However, recent trends highlight the growing importance of natural language techniques that address unstructured factors like investor sentiment and the impact of news. Positive or negative information about specific companies, industries, or the overall economy found in news or social media can influence investor behavior and market volatility, highlighting the critical need for robust sentiment analysis. In this context, we utilize the state-of-the-art language model GPT and the finance-specific sentiment analysis model FinBERT to perform sentiment and time-series analyses on financial news data, comparing the performance of the two models to demonstrate the potential of GPT. Furthermore, by examining the relationship between sentiment shifts in financial markets and news events, we aim to provide actionable insights for investment decision-making, emphasizing both the performance and interpretability of the models. To enhance the performance of GPT-4o, we employed a systematic approach to prompt design and optimization. This process involved iterative refinement, guided by insights derived from a labeled dataset. This approach emphasized the pivotal importance of prompt design in improving model accuracy, resulting in GPT-4o achieving higher performance than FinBERT. During the experiment phase, sentiment scores were generated from New York Times news data and visualized through time-series graphs for both models. Although both models exhibited similar trends, significant differences arose depending on news content characteristics across categories. According to the results, the performance of GPT-4o, optimized through prompt engineering, outperformed that of FinBERT by up to 10% depending on the sector. These findings emphasize the importance of prompt engineering and demonstrate GPT-4o’s potential to improve sentiment analysis. Furthermore, the categorized news data approach suggests potential applications in predicting the outlook of categorized financial products. Full article

This study examines the mutual relationships among ESG performance, total factor productivity (TFP), and energy efficiency (EE) in a sample of Chinese A-share listed firms from 2010 to 2022. This study shows that ESG has a significant promotional effect on TFP. Reducing financing constraints and inefficient investment are among the mediating mechanisms, and the latter plays a greater role. Heterogeneity analyses suggest that state-owned enterprises (SOEs) and heavy-polluting enterprises (HPEs) should be consistently committed to ESG responsibility fulfillment. Formal environmental regulation (FER) can be complementary to ESG, but informal environmental regulation (IER) has the opposite effect. TFP was instead suppressed by the triple combined effect of ESG with these two. The results of the threshold effects of ESG and EE indicate that the positive impact on EE becomes more pronounced as ESG performance improves. However, ESG performance varies across subdimensions. As green technology research and development efficiency (GRDE) and green technology transformation efficiency (GTTE) improve, stronger ESG promotes EE. This threshold effect also exhibits heterogeneity with respect to the ownership structure. Moreover, there is bidirectional causality between EE and TFP, and EE has a stronger positive effect on TFP. These findings reveal the optimal paths and potential risks for moving toward sustainability for firms. Full article

In the context of escalating climate change and mounting environmental challenges, green finance has emerged as a crucial mechanism for fostering sustainable development. This paper presents an experimental analysis that illustrates how the integration of blockchain technology into financial technology (fintech) strategies can significantly enhance the efficacy of green investments. Our proposed framework facilitates the optimization of these strategies by improving transparency and fund traceability in environmentally focused projects. Through rigorous testing and data-driven insights, we demonstrate the potential of blockchain to streamline financing processes, mitigate risks associated with fraudulent practices, and promote accountability among stakeholders. By establishing a synergistic relationship between fintech and ecological responsibility, this research provides a novel approach that contributes to both academic discourse and practical applications in green finance. The proposed approach showcases experimental originality by integrating blockchain technology with green finance, setting a precedent for future research in this interdisciplinary field. Our findings reveal that blockchain can significantly enhance the efficiency of financing processes, reducing transactional delays and fostering transparency that mitigates risks related to fraud. Moreover, this study highlights the potential of this synergistic model to cultivate a robust framework for accountability among stakeholders, ultimately guiding investment toward environmentally sustainable initiatives and bolstering the integrity of green financial practices. Full article

Government investment is one of the main sources of financing for large-scale infrastructure development. These government-invested infrastructure construction projects are often characterized by large investments, long construction periods, and relatively high investment risks. In this study, the process of infrastructure projects was divided into three stages, namely pre-decision, construction, and post-evaluation. Research methods of literature review and expert interviews were adopted to determine the factors that influenced the investment risk of infrastructure construction projects. A corresponding evaluation index system was established. By using the Fuzzy Analytic Hierarchy Process (FAHP) and survey questionnaire research methods, the weights for each risk factor were derived, and an investment risk evaluation model for infrastructure projects was constructed. The reliability and effectiveness of the model in project investment risk evaluation are verified by combining a practical case study and the relevant sensitivity analysis of indices. The reliable assessment model for the investment risk of government-invested infrastructure construction projects was established, and the optimizing management of the project investment was proposed to entirely improve the construction quality. Full article