The scope of the Engineering Economics journal covers research that examines innovations-driven transformations in business, public, and financial domains, contributing to the qualitative evolution of economies. The journal seeks to explore the antecedents, processes, and outcomes of these transformations to inform managerial and policy efforts aimed at fostering sustainable economic and social development. The journal represents research from disciplines such as economics, finance, business and management, and public management, as long as creative destruction is evident through structural changes, market dynamics, institutional shifts, or innovation-driven economic adjustments. It particularly welcomes studies that analyze the role of organizational, regulatory, and strategic adaptations that reshape industries, financial markets, and public governance. The journal publishes research at macro, meso, and micro levels of analysis, embracing a wide range of empirical methods. However, it does not accept studies based solely on mathematical proofs of economic relationships. The Journal also does not publish papers that address issues primarily relevant to engineering sciences or related technical fields. Conceptual papers are not published unless they take the form of reviews employing quantitative bibliometric analysis methods.
Technological Innovation, AI, and ESG: A Comprehensive Study on the Firm Life Cycle in Chinese A-Share Firms
Amidst the ongoing artificial intelligence (AI) technology revolution, businesses are increasingly embracing AI and environmental social and governance principles. This integration leads to sustainable business operations, improved efficiency, and enduring value creation to address global challenges. Therefore, this study explores that how AI technology enhance firm ESG performance through firm life cycle stages. By analyzing a sample of Chinese A-share listed firms from 2010-2020, the study primary findings reveal that AI technology significantly improves firm ESG performance, emphasizing the importance of technological advancements in ESG initiatives. Furthermore, the study reveals that the impact of AI on ESG is more pronounced during the growth and mature stages of the firm cycle compared to the introduction, decline and shakeout stages. Additionally, the study investigates this impact through two channels: AI enhances green innovation and firm performance, which in turn enhance ESG performance. Moreover, heterogeneity analysis highlighting a more pronounced effect in non-SOEs compared to SOEs, and in low bank concentration and robustness analysis through 2SLS and PSM.
Copyright (c) 2026 Engineering Economics
Disruptive Technologies and the Accounting Profession: What Does the Scientific Literature Tell Us?
This article examines how disruptive digital technologies are reshaping the accounting profession by integrating bibliometric analysis with a critical, theory-informed synthesis. Using a structured review of Web of Science publications, the study maps the intellectual structure and thematic evolution of research on artificial intelligence, big data analytics, blockchain, cloud computing, and robotic process automation in accounting. The findings reveal a shift from infrastructure-oriented digitalization toward analytics-driven and intelligence-based applications, accompanied by growing attention to decision processes, control mechanisms, and organizational adaptation. Rather than producing uniform benefits, technological impacts are economically contingent, depending on complementary investments in human capital, governance structures, and institutional frameworks. The study advances prior literature by explicitly linking accounting digitalization to efficiency, transaction costs, productivity, and resource allocation. By combining bibliometric evidence with analytical interpretation, the article clarifies when and under what conditions digital transformation in accounting generates sustainable economic value for organizations and institutions across contemporary professional contexts.
Is the Flat Tax for Self-employed Persons in the Czech Republic Beneficial?
The article aims to evaluate for what amount of tax base or tax liability of a self-employed person the flat tax regime is advantageous. This evaluation is carried out in the legislative conditions of the Czech Republic valid in 2023. While the world's literature often understands the flat tax as the existence of a single tax rate, the concept of a flat tax in the Czech Republic is different. It combines payments for social security contributions and personal income tax as one payment. Since the flat tax amount is fixed, the research question is formulated, considering the literature, in the form of what tax base the entry into the flat tax regime is advantageous. Literature often mentions that taxpayers with a high tax base can benefit from the flat tax regime. However, it is a research question whether this will also apply in the conditions of the Czech Republic, where this payment also includes the aforementioned social security contributions. The different conceptual approaches to the flat tax in the Czech Republic create the broad potential that will be developed in this study.
The Role of Mineral Resource Usage, Energy Tariffs, Fintech, Green Bonds, Carbon Policy in Adopting Net-Zero Supply Chain in Energy System
With response towards the global responsibility in addressing the climate change, China has implemented a wider range of policy measures that aimed at limiting the emissions of growth and promotion of renewable energy consumption. These measures include the regulation of energy parameters, with increased funding for research and development, the digitalisation of financial services, and expansion of environmental taxation and energy efficiency policies. In this context, current study determines the impact of carbon policy, natural resource rents, financial technology (fintech), energy tariffs and green bonds on renewable energy consumption in China for the period of 2000-2023. With the use of Dynamic Auto-regressive Distributed Lag (Dynamic ARDL) approach, the outcomes indicate that carbon policy, green bonds, and energy tariffs exerts a significant and positive effect on renewable consumption of energy in long-run, where as fintech development and natural resource rents do not show statistically significant impact on renewable energy consumption, however carbon-policy, energy tariffs, natural resource rents and green bonds do not show significant effects. These outcomes offer significant policy implications for China, suggesting that strengthening green bond markets, enhancement of carbon policy frameworks, optimisation of energy tariff structures, and supporting fintech development could play a crucial role in promoting the renewable energy consumption.
Herding or Anti-Herding? Understanding Investor Behavior in the Baltic States
This paper investigates market-wide herding behavior in the stock markets of Estonia, Latvia, and Lithuania, employing measures that assess the proximity of market returns to market consensus. Using a survivor-bias-free dataset of daily stock returns, we document significant anti-herding behavior among investors in Latvia and Lithuania throughout the sample period. Results for the Estonian market vary based on the herding measure chosen. Herding dynamics fluctuate over time, with the COVID-19 pandemic increasing the intensity of the effect in Estonia and Lithuania. Moreover, the deterioration of investor sentiment and stronger funding restrictions are found to amplify herding in these two markets. Estonia's market displays asymmetric behavior, with herding being more pronounced during negative market performance. Collectively, these findings are relevant to management practice since they suggest that it is difficult to extract the desired benefits of international diversification in the Baltic region, especially during market downturns.
Copyright (c) 2025 Engineering Economics
Digital Trade, Carbon Finance, and Sustainable Natural Resource Management: A Case Study of Thailand in the Globalized Economy
This research has examined the role of carbon finance, globalization, and digital trade for shaping sustainable management of total natural resources (TNR) in Thailand over the period of 2000 to 2022. For the analysis of these relationships and capturing potential asymmetries, the research employs a Non-linear Autoregressive Distributed Lag (NARDL) model, which allows both short-run and long-run effects to be determined across different quantiles. The empirical outcomes reveals that globalization has a negative and significant effect on TNR in both short and long run, which suggests an increased economic integration would worsen natural resource depletion. Digital trade also reveals generally negative relationship with TNR, although its long-run impact is significantly adverse, the short-run effects are statistically insignificant, highlighting complex temporal dynamics. On contrast, carbon finance shows a positive and significant impacts on natural resource management across time horizons, underscoring its potential as a sustainable financial mechanism. Moreover, the economic growth is found to have a positive relationship with TNR, that likely to reflect increased extraction and utilization of finite resources rather than sustainable use, signalling a potential trade-off which challenges long-term environmental objectives. The findings of study offer crucial insights for policy implications for Thailand, focusing the need to refine globalization and digital trade policies for mitigating their environmental impacts, while simultaneously leveraging carbon finance to promote sustainable resource management. These insights contribute towards development of integrated strategies for balancing economic development with environmental sustainability in context of Thailand’s evolving digital economy.
Natural Resource Efficiency: The role of Financial Development, Recycling and Eco-Innovation in ASEAN
Rising concerns over environmental degradation that have been intensified with a global focus on efficient management of natural resources. Within this context, factors such as financial development (DD) and eco-innovation (EI) have been received increasingly with scholarly attention. This research aims to examine the impact of FD and EI on natural resource efficiency (NRE) within ASEAN economies, while integration of artificial intelligence (AI), urbanization (URB), and economic growth (GDPG) as additional determinants. With utilization of secondary data, the analysis employs the Method of Moments Quantile Regression (MMQR) for generating robust and distribution-sensitive estimates. The empirical outcomes shows that FD positively contributes to NRE, whereas EI shows no significant relationship. Moreover, recycling activity (RECY) shows a negative and statistically significant relationship with NRE. Within this contrast AI, GDPG, and URB are found to have significant and positive relationship on NRE. The research has advanced the current body of knowledge by applying a rigorous methodological approach and integration of emerging variables relevant to sustainable resource management. Additionally, this offers significant practical implications for government, regulatory bodies and policy makers seeking to enhance natural resource efficiency across ASEAN region.
Analysing the Effects of Natural Resource Utilization, Biodiversity, and Globalization: The Role of Fintech in Sustainable Financing
This research attempted to provide insights regarding the role of Fintech, globalization, natural resource utilization and biodiversity in sustainable finance in the context of Thailand. For this purpose, data for Thailand spanning over 2000 to 2024 period is analysed using Quantile Autoregressive Distributed Lag (QARDL) technique. The outcomes indicated that in the long run, none of the selected determinants is significantly associated with sustainable finance. But, in the short run, Fintech is significantly and positively associated with sustainable finance. Similar to this outcome, biodiversity is also significantly linked with carbon finance and it was demonstrated that rising levels of biodiversity increased carbon finance. Likewise, globalization and natural resource utilization are significantly and positively associated with sustainable finance in the long-run. Lastly, the short-run analysis confirmed the convergence to equilibrium with a negative and significant error correction term. The outcomes presented in the study demand policy response in Thailand to enhance sustainable financing. Along with policy recommendations, the study offers insights and recommendations that can be utilized by future researchers to advance the research in Thailand.
Natural Resource Efficiency, Financial Development, and Digital Finance: A Pathway to Sustainable Natural Resource Utilization in Thailand and China
Sustainable utilization of natural resources would remain a basic prerequisite for the achieving long-term economic growth while ensuring environmental preservation specifically within emerging economies such as Thailand and China. This research determines the dynamic relationship between financial development, resource efficiency, and digital finance in promotion of sustainable natural resource utilization within the manufacturing sectors of these two nations. Grounded with Resource-Based View, Knowledge-Based View Dynamic Capabilities Theory, and Ecosystem Theory, the study has developed an integrative framework for explaining how digital maturity and intelligent innovation would facilitate the transition towards circular economy practices and resource sustainability. Aligned with framework of United Nations Sustainable Development Goals specifically SDG-9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action) this research has addresses a crucial gap for existing literature by exploring combined impact of digital transformation and entrepreneurial eco-systems on sustainable resource management. With utilization of secondary data from 2000 to 2024 and employing Driscoll-Kraay Standard Error (DKSE) estimation technique, the empirical analysis of study shows that both financial development and resource efficiency strengthens sustainable resource utilization, whereas the role of digital finance remains comparatively limited. The outcomes further reveal that Knowledge Integration Capability (KIC) serves as the most impactful predictor and serves as the key mediating mechanism which links the core constructs to Circular Economy Adoption (CEA), while Digital Circular Economy Maturity (DCEM) acts as a moderator which strengthens positive relationship between KIC and CEA to enhance the overall impact of core drivers. Study also offers theoretical contributions by advancement of integrated perspective on digital and financial drivers of sustainability, while providing practical implications for policy-makers and industry-leaders who seek to enhance digital capabilities, fostering innovation and aligning national industrial strategies with global sustainability objectives.
Sustainable Natural Resource Utilization
The Role of Natural Resource, Carbon Finance, FinTech, Financial Inclusion in Advancing Energy Efficiency: Does the Synergy Promise a Greener Future?
There has been a substantial body of research examining the relationship between resource growth and resource finance; limited attention has been devoted to the resource-energy interface. This gap has motivated the current study, which examines the relationship between resources, finance, and energy in the context of China. However, China has undertaken extensive efforts to diversify its energy portfolio; coal remains a dominant source of energy to meet national demand. China has been actively pursuing alternative pathways for achieving its target of carbon neutrality by 2060. Drawing attention towards an extensive literature review, this research has posited that natural resources, eco-financing, fintech, and financial inclusion are key determinants of energy efficiency. Using annual data spanning 2000 to 2023, the study employs the Quantile Autoregressive Distributed Lag (QARDL) model to examine the heterogeneous impacts of these determinants across the distribution of energy efficiency. Empirical findings reveal that fintech consistently enhances energy efficiency in both the short- and long-run across multiple quantiles. On the other hand, financial inclusion and natural resource availability negatively affect energy efficiency, suggesting the presence of structural and behavioural inefficiencies. Moreover, carbon finance is found to have no statistically significant effect on energy efficiency across all quantiles and time horizons. Based on these insights, the study proposes targeted policy recommendations to advance China’s transition toward a more sustainable, energy-efficient economy.
The Impact of Natural Resource Utilisation, Green Energy, Carbon Finance, Artificial Intelligence, and Sustainable Innovation on the Environment: Evidence from Asia
With escalating global urgency surrounding environmental sustainability, Asian economies have intensified their efforts to improve environmental quality, particularly through reductions in carbon emissions. These have also been supported by the production of advanced technological solutions and the expansion of financial mechanisms dedicated towards environmental initiatives. Within this context, the present research empirically determines the impact of carbon finance, artificial intelligence, green energy, total natural resources, and sustainable innovation on environmental quality. A panel dataset covering three Asian countries is analysed by using advanced econometric techniques to ensure methodological robustness. The empirical outcomes show that carbon finance, green energy utilisation, and the integration of artificial intelligence, which exerts a positive and statistically significant effect on environmental quality, largely through reductions in CO2 emissions. In contrast, reliance on natural resources and higher economic growth are found to deteriorate environmental quality, whereas sustainable innovations show no significant relationship. These outcomes extend the existing literature by validating key determinants of environmental quality in Asian economies. Drawing on these insights, the research outlines policy implications to strengthen environmental performance in the countries under study.
European Tourism Between Economic Prosperity and Green Pressures: Multidimensional Analysis of Resilience and Sustainability
This paper analyses tourism influence on growth and environment in the EU27 for the period 2009-2024, integrating tourism-specific factors (seasonality, tourist revenues, tourist expenditures per trip, international arrivals, the share of tourism in total exports) along with macroeconomic and institutional variables (economic freedom, state investments in education, unemployment rate). We have approached tourism from two angles: economic (in terms of resilience) and ecological (in terms of environmental sustainability), applying multiple linear regression (MLR) and structural equation models (SEM), based on Pearson correlations. Tourism simultaneously and antagonistically influences the environment's resilience and sustainability. Tourist expenditure per trip, revenues from international tourism, the marginal effect of tourist flows, or tourist attractiveness, the market's freedom supports resilience, but not the share of tourism in total exports. Environmental sustainability is positively supported by market freedom and negatively endorsed by the tourist expenditure per trip. Seasonality appears as a reliable solution for both environmental sustainability and resilience. The results indicate no statistically significant effects of unemployment and education, and reveal that tourism dependency entails risks to resilient growth. Nevertheless, tourism remains essential for European economic prosperity. Reducing green pressure involves a series of concessions on seasonality and directing tourism revenues toward technologies and practices that reduce the carbon footprint. The novelty lies in the integrated approach to resilience and sustainability, highlighting the ambivalent impact of tourism, and showing that seasonality is rather a structural policy tool than a constraint in the simultaneous achievement of environmental and resilience objectives, offering insights with direct implications for European tourism development policies.
Effects of Female Entrepreneurship on Sustainable Development Goals: The Moderating Role of Gender Gap
Female Entrepreneurship (FE) is discussed and examined for its potential to support Sustainable Development Goals (SDGs). Recent economic literature has recognized that the contribution of FE to the achievement of the 2030 Agenda is a domain still scarcely explored. The aim of this study is to analyze if FE has a positive impact on Social, Economic and Environmental Development Goals. Our premise is that these impacts are negatively moderated by Gender Gap. To achieve this objective, the research conducts an empirical cross-country analysis with the Structural Equations Model (SEM) for 76 countries worldwide using different databases such as the SDG Index, FE Index, and Global Gender Gap Index, all of which are open access. Our findings show positive and significant effects of FE on social, economic and environmental development. Moreover, the Gender Gap plays a negative and significant moderating role in Environmental Development Goals. Although other studies have proven the relationship between female entrepreneurship and economic, social or environmental sustainability, the integration of all of them into a single model gives rigor to our results. Moreover, it is the first to include the moderating role of the gender gap in this relationship. Policymakers, business leaders and communities should work together to create an environment encouraging and supporting FE, and reducing the gender gap, for the positive consequences that this can have in the form of greater impact of FE on environmental sustainable development.
The Evolution of Niche Fitness in China's Regional Digital Innovation Ecosystems: An Evaluation and Prediction Framework
With the rapid expansion of digital innovation ecosystems (DIEs), the quantitative assessment of system adaptability and collaborative evolutionary capacity, as well as the forward-looking prediction of future development trajectories, has become increasingly important. Nevertheless, existing studies still lack an integrated analytical framework capable of simultaneously capturing multidimensional ecological elements and supporting reliable trend forecasting. To address this limitation, this study develops an ecology-inspired evaluation and prediction framework specifically designed for DIEs. The framework conceptualizes niche structure across four dimensions, including digital innovation communities, resources, environment, and demand, and applies the method based on the removal effects of criteria (MEREC) for objective weighting. This approach is combined with an improved measurement of alternatives and ranking according to compromise solution (MARCOS) method using the Heronian mean (HM) operator to derive composite measures of niche fitness. In addition, enhanced grey prediction models are introduced to strengthen the framework’s capacity to forecast evolutionary trends under weak-information conditions. The framework is empirically applied to provincial-level DIEs data from China covering the period from 2015 to 2021. The results indicate that overall niche fitness exhibits a fluctuating upward trend, accompanied by pronounced regional differentiation. Persistent imbalances are observed across niche dimensions, with resources, communities, and demand niches emerging as the primary constraints on the evolutionary process of DIEs. Forecasting results further suggest that niche fitness is likely to continue improving in the coming years, although structural misalignments may remain difficult to fully resolve in the short term. Overall, the proposed evaluation and prediction framework enables the systematic identification of structural constraints and latent development potential within DIEs, while also facilitating the analysis of their future evolutionary trajectories. As such, it provides a generalizable and scalable methodological tool for comparative analysis, dynamic monitoring, and cross-regional research on DIEs.
Method Based on Removal Effects of Criteria (MEREC)
Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS)
The Effect of External Environment Factors and Founders’ Characteristics on Digital Platform Start-up Performance. Dynamic Competition Perspectiveive
Digital platform start-ups rely on technological innovations to compete with other start-ups and incumbents. Competition by innovation, so-called dynamic competition, radically changes how start-ups leverage the external environment and founders’ characteristics to create superior performance. Although scholars unveiled the significant role of dynamic competition in entrepreneurial ecosystems, it is little known about how external environment factors and founders' characteristics interact under dynamic competition. This study focuses on the healthcare industry, which is a highly innovative sector where digital platform start-ups face unique challenges and opportunities. Using a sample of 235 digital platform start-ups, this study quantifies the impact of external environment factors and analyses the role of founders’ characteristics through managerial effectiveness. The findings reveal that economic freedom and governance quality may negatively affect performance, while founder characteristics enhance managerial effectiveness and significantly support start-up success. This study contributes to the literature by linking digital entrepreneurship with dynamic competition and offers insights into how start-ups can strategically navigate competitive environments to achieve superior performance.
Unpacking Competitive Performance in 5* Hotels: The Joint Effect of Dynamic Managerial Capabilities and Digital Transformation
Digital transformation in the hospitality sector was accelerated by the COVID-19 pandemic. Strategy scholars discussed dynamic managerial capabilities as capabilities needed to adapt to rapid change and navigate digital transformation to gain a competitive advantage in the hospitality sector. It suggests that by leveraging digital technologies, fostering a culture of innovation, and building agile business ecosystems, hospitality organizations can enhance their competitive position and ensure long-term sustainability in an increasingly digitalized marketplace. The research question of our study is how dynamic managerial capabilities and the adoption of digital technologies affect the competitive advantage of the hospitality sector. We address the research question by deploying a multiple case study research design and collecting data from 5* Hotels in Lithuania. The study finds that the synergy between dynamic managerial capabilities such as data-driven decision-making, connectivity to internal and external networks, also digitally driven operational efficiency, and sustainability-oriented attitudes significantly enhances the competitive performance of 5-star hotels by fostering innovation, strategic alignment, and superior guest value. We also provide insights for 5* hotel owners and managers.
Leveraging Financial Inclusion to Drive Public-Private Partnership Investments in Energy: Role of Financial Institutions in ASEAN Nations
Amid rising concerns over energy security and the growing demand for sustainable infrastructure, mobilizing public-private partnership (PPP) investments in the energy sector has become a critical policy objective for ASEAN nations. This study contributes to the literature by uncovering the pivotal role of financial inclusion (FIC) in enhancing such investments. By improving access to financial services, FIC fosters a more conducive environment for energy-related PPPs, particularly in emerging economies where capital mobilization is constrained. Using panel data from 1999 to 2023 for ASEAN countries, the analysis confirms that FIC exerts a positive and statistically significant impact on PPP investments in energy (PPI), even after controlling for government spending, FDI, economic growth, and aid. These findings position financial inclusion not only as a tool for economic empowerment but also as a strategic lever for infrastructure development. The study further strengthens its conclusions through robust empirical techniques. Overall, this research offers novel insights into how inclusive financial systems can unlock private investment in critical energy infrastructure, supporting broader sustainable development goals in the region.
SME Bankruptcy Prediction Using Convolutional Neural Networks
Failure to repay obligations to creditors, whether credit institutions or business partners, causes serious economic problems not only for the debtor but also for its stakeholders. Preventing this problem requires identifying the potential threat. This paper explores the potential use of Convolutional Neural Networks (CNN) in identifying businesses at risk of bankruptcy. It is based on a graphical representation of differences in company performance and selected macroeconomic indicators. In our research, we used the GoogLeNet neural network architecture. The approach used allowed to display the financial situation of a company so that the generated CNN could identify active companies and companies at risk of bankruptcy with high accuracy. The procedure was applied to data of companies operating in the construction industry in the Czech Republic. The accuracy of the model was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). The use of CNN has yielded high forecast accuracy, demonstrating the ability to efficiently process graphical displays of financial data and capture differences between healthy and risky companies. The indicators identified in the constructed model can be used as input variables in an early warning system for financial distress.
Does Greenwashing Hinder the Contribution of Fintech to Environmental Sustainability? Evidence from BRICS Countries
The greenwashing problem arises when companies provide misleading information about environmental sensitivity or exaggerate the sensitivity in question. At this point, it is claimed that financial technologies are seen as a solution to greenwashing. However, it is not known how environmentally friendly fintech companies are, in other words, whether fintech activities are a greenwashing problem. In this regard, this study investigates the effects of fintech on the novel load capacity factor to examine whether fintech creates a greenwashing problem for BRICS countries. In doing this, the period of 1992-2021 is analyzed with the quantile-on-quantile technique. According to empirical findings, financial technology activities harm environmental quality. Therefore, it is concluded that companies carrying out fintech activities for BRICS countries cause the greenwashing so the problem of greenwashing hinders fintech's potential contributions to the environment. Based on the findings, policy recommendations are made to audit companies conducting R&D studies on fintech and to penalize companies that make fake reports.
Corporate Social Responsibility Strategy, Behavior and Financial Performance: Evidence from Inconsistent Phenomena in China
In recent years, with the increasing awareness of sustainable development, corporate social reposibility (CSR) has garnered widespread attention from investors and has had a sustained impact on long-term business operations. In developed countries, CSR enriches societal well-being and promotes a positive brand image for companies, eventually enhancing their corporate financial performance (governance effect). However, some Chinese enterprises reluctance to undertake CSR or only greenwashing behavior (window-dressing effect). This study attempts to investigate the inconsistent influence of conventional CSR on corporate financial performance within the Chinese context on the basis of strategic management, stakeholder, and reputation capital theories. The sample used in this study was China's listed companies from 2011-2019 via the stepwise generalized method of moments (GMM) and bootstrap tests. The results revealed that CSR strategy and behavior are insignificant with accounting performance (no governance effect) and significant with market performance (window-dressing effect) within the Chinese context. This study also revealed that corporate reputation does not mediate the relationship among CSR strategy, behavior and accounting performance and partially mediates the relationship with market performance in China. These findings fill gaps in the previous literature, and also partially explain the reasons for the greenwashing behavior of Chinese companies, which has management implications.
Data-Driven Empowerment: How Government Data Governance Facilitates Firm Upgrading
Promoting deep industrial transformation and upgrading serves as the micro-level foundation for improving the institutional framework of new quality productive forces and advancing a high-standard socialist market economy. Enhancing government data governance mechanisms represents a critical pathway to achieving this objective. Drawing on panel data from 2011 to 2021 concerning the establishment of government big data administration agencies and Chinese A-share listed firms, this paper employs a multi-period difference-in-differences (DID) approach to systematically examine the impact of government data governance on firm upgrading, along with its underlying mechanisms. The empirical results reveal that government data governance significantly facilitates firm upgrading, and this effect remains robust across a series of sensitivity checks. Heterogeneity analyses indicate that the positive effects are more pronounced among firms in heavily regulated industries, high-tech sectors, enterprises with higher levels of data utilization, and regions with higher administrative levels of data governance institutions. Mechanism tests further suggest that government data governance improves the quality of public service delivery and empowers corporate digital governance, both of which play key intermediary roles in promoting firm upgrading. These findings confirm the enabling role of government data governance in advancing firm upgrading and offer policy implications for stimulating innovation among market entities and strengthening the micro-foundations of China’s economic recovery and growth momentum.
Do Financial Inclusion, Regulatory Quality and Government Effectiveness Matter for Green Development? Evidence from New EU Countries
Nowadays, the world is facing climate change and environmental problems due to the increasing use of energy resources and environmental emissions. In order to promote green development, financial inclusion is an important factor in the development process. Therefore, this paper examines the effects of financial inclusion, regulatory quality, government effectiveness and economic growth on green development in the new EU countries for the period from 2004 to 2021. In order to obtain empirical results, the pooled mean group and mean group estimator have been employed to check the short-run and long-run effects on green development. The results show a strong relationship between the variables and CO2 emissions in the long run. The findings support that enhancing the level of financial inclusion and regulatory quality by 1% increases carbon emissions by around 0.3% and 0.4% in the long run. However, government effectiveness and economic growth have a negative effect on CO2 emissions in the long run, while in the short run economic growth has a positive effect on CO2 emissions. Based on the empirical results, policymakers should improve financial inclusion and reinforce stringent regulations to enhance green development.
Does Enterprise Digitalization Promote the Green and Low-carbon Transformation and Upgrading? Evidence from A-share Listed Companies in China
As the world's top emitter of greenhouse gases, China's acceleration of emission mitigation is critical for global climate governance. Enterprises, as core economic actors, play a pivotal role in achieving sustainable development through green and low-carbon transformation (GLCT), a cornerstone of China's carbon peaking and neutrality objectives. This study examines whether enterprise digitalization—a key driver of digital-real economy integration—facilitates reductions in corporate greenhouse gas and pollutant emissions, thereby advancing GLCT. Utilizing 2008-2020 data from Chinese listed firms, we investigate the mechanisms underlying this relationship and heterogeneity across enterprises. Results reveal that digitalization significantly lowers emission intensity via pathways such as technological innovation, efficiency gains, operational environment optimization, and structural adjustments. Heterogeneity analysis highlights stronger effects in state-owned enterprises and competitive industries, with no regional regulatory disparities observed. This study demonstrates that digitalization significantly contributes to corporate green and low-carbon transition (GLCT) by reducing carbon and pollutant emission intensities. Empirical evidence reveals four critical mechanisms driving this transformation: technological innovation enhancement, operational efficiency optimization, managerial environment improvement, and production structure reorganization. Heterogeneity analysis highlights pronounced GLCT effects in state-owned enterprises and firms within hypercompetitive industries, while environmental regulation intensity shows negligible regional differentiation. The research systematically examines enterprise pathways for emission reduction in digital contexts, establishing a theoretical framework for achieving carbon neutrality goals. The findings inform policymakers in developing tailored digital strategies aligned with enterprise capabilities and sectoral attributes.
Determinants of the Laser Industry Export Development: The Case of Lithuania
The Lithuanian laser industry is considered one of the country’s most appreciated manufacturing sectors due to its high value-added output, growing potential significance, and the wide range of future applications of laser technologies. Despite considerable global potential and increasing export volumes, the laser industry faces challenges such as intensifying global competition, short product life cycles, high production costs, demand for highly skilled employees, dependence on global supply chains, standards, and regulations. The core aim of the research is to identify the main economic and non-economic determinants of laser export development in Lithuania in the context of rapid technological progress and geopolitical uncertainty. To empirically assess the impact of selected factors on the export results of the Lithuanian laser industry, correlation analysis, the Granger causality test, regression model, and the autoregressive distributed lag (ARDL) model were used. The results indicate that the export performance of the Lithuanian laser industry is positively affected by investment in patents and licenses. However, after controlling for other selected factors, interest rates, the number of military conflicts, and R&D expenditure were found to have a negative effect. Strategic support for intellectual property acquisition and mitigation of external economic risks are key insights for policymakers and stakeholders.
Improving Natural Resource Efficiency: The Role of Eco-Innovation, Recycling, Waste Management, Youth Education and Digital Finance to Promote Sustainability
This research has addressed the crucial significance of natural resource efficiency for achieving sustainable development, specifically between contemporary environmental challenges. However, previous research has highlighted the relevance of eco-innovation, recycling of waste management, youth education, and digital finance for sustainability, their combined impact on natural resource efficiency remains undiscovered. The study focused on China and Korea over the period of 2000 to 2022, employing advanced panel data econometric techniques, which include Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), for empirically testing these variables on natural resource efficiency. The outcomes shows that eco-innovation, recycling, youth education, and digital finance have significantly contributed towards enhancing the efficient use of natural resources. The outcome underscores the synergistic role of technological innovation, educational advancement, sustainable waste practices, and digital financial platforms for promoting sustainable developments. The policy implications focus on the significance of integrating these factors for fostering responsible resource sustainability in resource-abundant economies.
Specific Risk Modelling Approach Under Permacrisis Conditions: Some Empirical Evidence
The subject of the research is testing, analyzing, and considering the possibility of applying different risk modeling approaches to optimize the effects of investment activities. In order to have a realistic research basis, concrete data from certain markets were used to cover the studied phenomena as efficiently as possible. The aim is to obtain reliable, timely information about the accomplishment of the specific risk modeling approach under permacrisis conditions. Specific results and their significance for science and practice underscore the focus on distinct crisis periods. The findings confirm the practical relevance and advantages of applying EVT and CEVT-based Value-at-Risk models. This is achieved using different confidence levels (90% and 99%) and a 250-day moving window. The novelty of the research lies in the comparative assessment of investment risk in both developed and developing markets under permacrisis conditions, with the strengths and weaknesses of the tested models clearly identified. The study thereby contributes to creating an optimal market environment that supports informed investment decisions regarding expected returns. The limitations of the research reflect the distinct characteristics of developed and developing markets, with a particular challenge being the identification of systemic risk under the prevailing conditions.
The Innovation Paradox of AI-Driven Development: Resource Allocation Distortion and Corporate R&D Motivation Loss
As artificial intelligence technology rapidly develops, enterprises pursuing intelligent transformation may face innovation resource allocation dilemmas. On one hand, the construction and maintenance of AI systems require substantial financial investment, and this high-cost pressure may crowd out resources traditionally allocated to innovation activities. On the other hand, excessive dependence on AI may lead enterprises to neglect talent cultivation and equipment updates, resulting in technological path lock-in. Based on resource allocation theory, principal-agent theory, and path dependence theory, this paper uses Chinese A-share listed companies from 2013-2023 as research samples and employs text analysis methods to construct an enterprise AI application intensity index to explore the impact of AI on enterprise innovation behavior and its mechanisms. The research finds that: (1) AI application has a significant crowding-out effect on enterprise innovation; (2) this crowding-out effect is primarily realized through two channels: reducing innovation talent investment intensity and cutting professional technical equipment configuration; (3) heterogeneity analysis shows that characteristics such as state ownership, high market competition, low financing constraints, high technology intensity, and high management shareholding can effectively mitigate AI's inhibitory effect on innovation. This research not only deepens theoretical understanding of the relationship between AI and enterprise innovation but also provides practical guidance for optimizing innovation resource allocation during enterprise digital transformation.
Economic Growth in the Digital Era: Innovation, Cultural Dimensions, and Environmental Policy Challenges
This research provides an analysis of the effects of innovation, digitalization, and environmental taxation on economic growth, as well as the moderating effect of national cultural traits within the context of the European Union's member states. It addresses a need that arises as the significance of technological transformation has increased within the context of multiple nations with diverse cultures. The study analyzed a balanced panel dataset covering a time period of 2013 to 2022 for 27 EU member states, with a total of 270 observations. We employed a mixture of econometric analyses, including ordinary least squares (OLS) estimates, fixed-effects panel regressions, and a form of lagged variable modeling. Environmental tax revenues are found to have a negative effect on GDP per capita in the short run. Scientific output from research and digitalization engagement levels (with a measurement variable based on online purchasing) both show positive impacts on the rate of economic growth. To account for the potential effects of culture on the outcomes, countries are grouped into 3 clusters based upon Hofstede’s cultural dimensions. Results show that cultural differences play an important role in determining the effect of innovation, environmental policy, and environmental taxation on economic outcomes. In short, EU member state countries with improved rates of growth were observed in the context of low levels of uncertainty avoidance and high levels of individualism. The findings of this study, as discussed in the empirical section of this paper, provide several contributions to research and future policy considerations. For theoretical contributions, the study expanded upon macroeconomic models of empirical strategies by adding cultural elements, following the work of Hofstede. As for future policies, the results suggest that customizing innovation and environmental policy to the context of the national culture may leverage better performance of these outcomes.
Ability to Borrow Modeling Techniques for Small and Medium-Sized Enterprises
This study comprehensively evaluates the ability to borrow machine learning modeling techniques for SMEs, utilizing a sample of the Baltic States with many variables. The study aims to assess the applicability of access to credit modeling techniques for SMEs. This is the first study in which a large–scale assessment has been carried out in the Baltic States sample, covering five years of credit applications from SMEs to a depository institution. The results showed that Gradient Boosting produces the most accurate results. Gradient Boosting demonstrated better results than the benchmark Logistic Regression as well as other advanced machine learning models, including Random Forests and Multilayer Perceptron models. The method showed the highest accuracy of the overall receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) (ROC–AUC) and Average Precision values, as well as other discriminatory threshold values, compared to alternative methods.
The Impact of M&A on Financial Performance from the Perspective of Life Cycle
Based on the theory of enterprise life cycle and the theory of M&A synergy, this paper selects the successful cases of M&A of A-share listed companies in Shanghai and Shenzhen from 2000 to 2020 as the research object, and takes their financial data from 2000 to 2023 as the research sample, and empirically studies the impact of M&A on the financial performance of enterprises in the growth, maturity and recession stages. The results show that M&A in the growth stage can help to enhance the market position and competitiveness of enterprises, that is, to enhance the development ability. The implementation of mergers and acquisitions by mature enterprises can significantly improve the profitability, operation and debt repayment ability of enterprises, but cannot significantly improve the development ability. During the recession period, enterprises can improve their solvency and stabilize their financial status through asset restructuring and optimization. Therefore, it is necessary to accurately identify the life cycle of the enterprise before implementing M&A activities, and continuously monitor and evaluate it in order to seize the opportunity to grow rapidly in the wave of M&A.