This paper develops an integrated methodological framework for classifying and forecasting fiscal sustainability among the 27 Member States of the European Union, starting from a fundamental economic question: how can EU Member States’ fiscal sustainability be reliably assessed and predicted in a context marked by repeated macroeconomic shocks and institutional heterogeneity? An integrated model for forecasting fiscal sustainability in the European Union is proposed, built upon a well-defined economic hypothesis: fiscal sustainability can be captured by a composite index derived from statistically validated fiscal and macroeconomic indicators. Our approach addresses a major gap in the economic literature, where forecasting models often fail to integrate institutional constraints and policy relevance. By combining advanced statistical techniques and macroeconomic scenario simulation – a Composite Fiscal Sustainability Index (CFSI) is constructed and its predictive performance evaluated under three alternative economic scenarios. The model achieves an overall accuracy of 85.19%, demonstrating strong discriminatory power and the ability to provide early warning signals of fiscal profile transitions. Positioned at the intersection of research on fiscal rules, public debt dynamics, and fiscal stress indicators, this study contributes both a methodological innovation – through an integrated and reproducible forecasting framework – and policy-relevant insights for enhancing fiscal governance within the European Union.
This study investigates the adoption of augmented reality (AR) as a training tool for housekeeping staff in star‐classified hotels in India, an emerging economy. Building on the Technological‐Organisational-Environment (T-O-E) framework, this study extends the theory by exploring the interplay of behavioural antecedents influencing AR adoption under conditions of cost sensitivity and economic constraint. Using a hybrid SEM–ANN modelling approach with data from 300 hoteliers, the study identifies external support systems, organisational flexibility, and perceived competitive advantage as key facilitators of adoption, while cost perceptions and technological anxiety act as inhibitors. Crucially, technological selfefficacy is introduced as a moderating factor, strengthening the influence of technological environments on adoption intention. From an economic perspective, the results highlight how AR can reduce training costs, improve efficiency, and enhance competitiveness in low‐margin service industries. In practice, the study recommends that hotels invest in “train‐the‐trainer” programmes to build managerial self‐efficacy, thereby mitigating technological anxiety and promoting sustainable digital transformation. The research offers novel insights by applying a hybrid inference model to AR adoption in the hospitality sector, demonstrating its theoretical and practical relevance in emerging economies. Future research should expand the analysis to include more regions and test experimental interventions to validate causal relationships.
The purpose of this research is to identify and analyse the impact of socio-economic, technological and institutional factors on countries performance in terms of sustainable development. It presents a study of the structural factors that influence sustainable development using panel data from 165 countries between 2000 and 2023. The UN SDG Index Score has been modelled as a dependent variable, incorporating World Bank predictors related to access to information technology, the labour market, institutional quality, per capita income and economic inequality. Five progressive models have been estimated, applying fixed effects methodology and validating the basic assumptions of linear regression with panel data. The results are partial and subject to revision as new controls and methodological improvements are incorporated in later stages, but it has been observed that access to communication technologies and good institutional quality favour sustainable development goals, while a high level of inequality has a negative impact on these goals. First published online 5 August 2026
Poverty has long been defined by income, but Sen (1995) broadened it to include deprivation of basic capabilities that prevent a decent life. This perspective has reshaped poverty assessment, leading institutions such as the European Union’s Social Protection Committee and Eurostat to adopt household deprivation indicators to capture poverty and social exclusion. Such deprivation is usually measured by the lack of a certain quantity of essential goods or services, though further study is needed on how demanding decision-makers define poverty and social exclusion. Therefore, this work aims to define various scenarios of material deprivation, considering how demanding decision-makers are when measuring such deprivation. It employs three methods: (i) the Ordered Weighted Averaging (OWA) aggregation operator to simulate decision-makers’ attitudes, (ii) the 2-tuple linguistic model to improve the interpretability of material deprivation results without information loss, and (iii) hierarchical clustering to summarize material deprivation in Spanish households by region and degree of urbanization. Results show that households may be economically poor based on income, but still have access to basic goods and services necessary for a decent life. The proposed model offers a more comprehensive, dynamic measure of material deprivation, enabling clearer insights into regional disparities through multiple scenarios. First published online 3 August 2026
In China’s economic practice, the mismatch between investment and financing horizons is widespread, exerting complex effects on corporate performance. With the rise of the digital economy, enterprises facing this mismatch exhibit new dynamic characteristics in innovation activities and outputs. Therefore, this study employs a comprehensive approach using quadratic regression and panel threshold models, based on 33,802 firm-year observations. It also constructs a provincial digital economy index using the entropy weight method to further explore the relationship between investment-financing maturity mismatch and corporate performance. The findings reveal that under the current digital economy environment, the relationship between investment-financing maturity mismatch and corporate performance still follows an inverted U-shaped curve. Firm characteristics significantly influence this relationship: investment-financing maturity mismatch tends to affect technology-based firms earlier, though technology-based firms exhibit lower sensitivity to mismatch compared to non-technology-based firms. Furthermore, digital economic development flattens the inverted U-curve, shifting its inflection point to the left, with technology-based firms exhibiting this effect more pronounced than non-technologybased firms. This divergence in the inverted U-relationship among firms can be attributed to differences in internal governance and market competitive environments. First published online 31 July 2026
Li Ji, Shigui Tao, Tai-Yu Lin, Yanan Sun, Mingle Chen, Yung-ho Chiu, Wesley Hu, authors of the article “Efficiency and regional divergence in China’s poverty reduction (SDG 1) and decent work and economic growth (SDG 8)”, published in Technological and Economic Development of Economy, 32(2), 485–507, https://doi.org/10.3846/tede.2025.24894 inform that the following note below Table 5 in page 493 should be added. Note: Due to the unavailability of relevant data, the study sample does not include Xizang, Hong Kong, Macao, and Taiwan; therefore, the analysis focuses only on 30 provincial-level regions in China. The correction does not affect the data, empirical results, or conclusions of the article. The authors regret the errors.
Li Ji, Shigui Tao, Jiawei Liu, Yanan Sun, Jingjing Geng, Yung-ho Chiu, authors of the article “The efficiency of sustainable development goals 4 and 8 in China: impact of low fertility”, published in Technological and Economic Development of Economy, 32(1), 206–232, https://doi.org/10.3846/tede.2025.24427 inform that several details in Figures 5 and 11 need to be corrected. Figure 5 (p. 219): “No data” should be used to indicate that the white areas are not included in the study sample, and the inset map has been updated. The note has been added. Figure 11 (p. 229), the inset map has been updated. The corrections have no impact on the data, methodology, results, conclusions, or scientific findings of the paper. The authors regret the errors.
This study investigates the relationships among green entrepreneurial activities, Green Techno-Environmental Innovations (GTEI) and environmental quality. Economic development and renewable energy consumption (CRE) are used as control variables to explore the phenomenon more comprehensively. Data are collected from 40 Organization for Economic Cooperation and Development (OECD) economies and analyzed using ordinary least squares regression models. The findings indicate that green entrepreneurship, GTEI and CRE enhance climate sustainability, whereas economic development leads to a deterioration in environmental quality. This study replaces GTEI with green technologies related to energy generation and production and the disposal of greenhouse gases individually and finds similar results, except for clean technologies in the production of goods. Green innovations linked to production processes which do not show a significant impact on environmental quality. This result arises because production processes lead to harmful emissions, which, in turn, lead to a deterioration in climate quality. This study provides crucial insights and policy implications for enhancing green entrepreneurial activities within OECD economies.
This research is driven by the absence of a unified consensus regarding the relationship between artificial intelligence, energy consumption, and economic growth and their impact on CO2 emissions. Not clear whether AI increases or decreases CO2. The novelty – identification of the two-way causal link between the implementation of AI and carbon emissions, a dynamic not previously confirmed in the literature. The originality – the model tested on a scale of Japan as developed country which still around 90% depending on fossil fuels. Data period: 1995–2024. An ARDL-based econometric approach to analyze the long-term and short-term impacts and several diagnostics to improve the precision and reliability of the study results. Tests used: the Breusch-Godfrey Serial Correlation, Ramsey RESET, Breusch-Pagan-Godfrey, CUSUMSQ and CUSUM. The study outputs reveal that in Japan, AI and energy consumption are associated with an increase in carbon emissions, while exports – with decrease in emission levels. In developed economies governments recommended to lower CO2 emissions by speeding up the shift toward renewable energy sources and investing in environmentally friendly AI technologies. Policymakers should adopt an integrated approach that links AI, energy, environmental, and economic policies, supported by regulatory reforms to promote sustainability and achieve carbon neutrality. First published online 8 June 2026
Amid global carbon-neutrality pledges and sustainable development agendas, balancing green transformation with manufacturing competitiveness has become a core challenge for the world economy. However, whether Manufacturing Agglomeration (MA) promotes or inhibits Green Development (GD) remains debated. Using panel data from 287 Chinese prefecture-level cities (2011–2023), this study employs econometric models to explore the overall, mediating, spatial, and threshold effects of MA on GD. Findings show that China’s urban GD index rose from 0.118 to 0.232, with a spatial pattern of “higher in the east and south, lower in the west and north.” Overall, MA significantly suppresses GD, a result robust to multiple tests. Mechanism analysis reveals that MA inhibits GD through the mediating effect of artificial intelligence industry agglomeration, while green technological innovation partly offsets this negative impact. Moreover, MA produces negative spatial spillovers, as environmental pressure and low-end lock-in spread through factor flows and supply-chain linkages. Threshold effects indicate the inhibition is strongest at moderate MA but weakens at higher levels, while heterogeneity analysis shows stronger suppression in the east, within urban clusters, and in higher-tier cities. This study enriches understanding of the MA-GD nexus and offers policy insights for advancing industrial green transformation and sustainable development. First published online 9 June 2026
In the context of accelerated digital transformation, understanding the factors that stimulate entrepreneurship at the European level has become a strategic priority. This study investigates the impact of digital infrastructure and human capital on new business density in the European Union, testing the hypothesis that these determinants operate differently depending on countries’ innovation stage. The analysis is grounded in a balanced panel dataset covering 26 EU member states over the period 2010–2023, grouped into four innovation clusters according to the European Innovation Scoreboard. Empirical analysis adopts a dual econometric approach. Static relationships are estimated using a Panel EGLS framework with cross-section SUR weights to capture short-run correlations and cross-country interdependence. Dynamic relationships are examined through a Panel Autoregressive Distributed Lag (ARDL) model estimated via the Pooled Mean Group (PMG) method, allowing for the identification of long-run equilibrium effects and adjustment dynamics while accounting for structural heterogeneity. Results show substantial cross-cluster heterogeneity. For Moderate Innovators, primarily located in Southern and Central Europe, digital infrastructure acts as a clear catalyst for entrepreneurship, exerting positive effects on new business density in both the short and long run. In contrast, for Strong Innovators, digitalization displays a more complex temporal pattern, with short-run effects dominated by implementation and adjustment costs, while long-run benefits materialize as productivity gains. The analysis shows that tertiary education supports entrepreneurial entry in the short run, while its long-run effect becomes negative, reflecting rising opportunity costs and the increasing absorption of skilled labor into corporate employment. While emerging economies benefit directly from investments in digital infrastructure, mature economies require structural interventions to reduce market rigidities and better align human capital with entrepreneurial activity.
The green and digital transitions are essential priorities in the current context of the European Union’s commitment to achieve climate neutrality by 2050 and multiple financial, pandemic, energy crises and international conflicts. The study assesses the relationships between economic, social and environmental variables, using data from 2010–2022 and applying econometric models to analyze the relationships between net greenhouse gas emissions, real Gross Domestic Product (GDP) per capita and factors such as access to digital technology, share of renewable energy, green tax revenues, municipal waste generation and long-term unemployment rate. The results underline the persistent disparities between Member States and the vulnerability of the European Union to external shocks. The novelty of this paper lies in its integrated approach to the relationships between economic, social and environmental variables in the context of the EU’s green and digital transition, using econometric models to analyze the influences between greenhouse gas emissions, real GDP per capita and other determinants. The research contributes to the literature by highlighting essential directions for formulating sustainable public policies capable of meeting both the environmental objectives and the economic needs of the European Union in an increasingly uncertain global landscape. First published online 8 June 2026
With the acceleration of aging and the development of robot, it affects the economic and trade development of countries around the world. However, few papers have provided insight into whether and how industrial robot input affects export in an aging real economy. This study examines the relationship between aging, industrial robot input and export performance from both theoretical and empirical perspectives. Theoretically, it integrates aging and industrial robot into a model of heterogeneous firms for analysis. Empirically, the study utilizes industrial firm data, customs data and census data in China from 2002 to 2016, established a fixed-effect regression model, complemented by a series of robustness tests and analyses of heterogeneity. The findings indicate that, although aging results in increased labour costs for firms and constrains research and development expenditure by both local governments and businesses, the negative impact of aging can be somewhat mitigated by the substitution, creation, and productivity effects of industrial robot inputs. While we may be unable to control the inevitability of aging and the rapid advancement of artificial intelligence, we can attain a deeper understanding of their theoretical mechanisms and transmission pathways. By adapting to these trends, we can offer valuable policy recommendations and theoretical foundations to support the economic development of countries. First published online 8 June 2026
The widening gap in social development across regions poses a critical challenge to achieving inclusive growth in emerging economies. This study investigates whether and how the digital economy can act as an equalizing force, promoting shared prosperity. Using a balanced panel of 268 Chinese cities from 2003 to 2020, we construct a multidimensional digital economy index through the entropy method and apply an Instrumental-Variable (IV) approach based on historical postal network density to address endogeneity. The results show that digitalization significantly reduces regional social disparities – particularly in western cities – by strengthening fintech inclusion, improving rural living conditions, and enhancing urban innovation capacity. Robustness tests with alternative indicators, subsample analyses, and policy-control variables confirm the stability of the findings. Methodologically, this paper adds value by integrating entropy-weighted digital measurement with IV-based causal inference; conceptually, it advances the understanding of digital transformation as both a driver of productivity and a social equalizer. Policy-wise, the findings suggest that targeted investment in digital infrastructure, inclusive finance, and human-capital development is essential for aligning digital strategies with sustainable and regionally balanced growth. First published online 8 June 2026
This study establishes a Digital Buffer Enhancement framework based on the buffer-stock model to investigate how digital finance affects consumption levels of migrant worker households, with implications for inclusive growth and sustainable development. Utilizing microdata from the 2019 China Household Finance Survey (CHFS) and applying Endogenous Switching Regression (ESR), Instrumental Variable (IV) approach, and quantile regression analysis, the research reveals three key findings. First, digital finance adopters exhibit 13.74% higher consumption levels, while non-adopters could increase consumption by 5.62% via adoption. Intriguingly, the Digital Buffer Enhancement framework identifies three complementary mechanisms: transaction convenience leapfrogging, liquidity constraint mitigation, and income uncertainty reduction, which together reduce precautionary savings thresholds. Second, enhanced marginal effects emerge among elderly households, eastern regions, and low-consumption quantile groups. Third, analysis of consumption structure heterogeneity indicates that developmental consumption elasticity substantially exceeds hedonic and subsistence consumption, with credit functions demonstrating the strongest impact among digital finance types, followed by payment and wealth management services. These findings advance theoretical understanding of technology-driven consumption transformation and provide practical guidance for developing inclusive digital finance policies in emerging economies. First published online 5 June 2026
The paper refers to the twelve principles of fiscal decentralization, proposed by Roy Bahl (Bahl’s Rules for Fiscal Decentralization – BRfd), with special emphasis on the first Bahl’s rule (regarding the comprehensiveness of the fiscal decentralization, CSfd). The purpose of the research was to estimate the level of CSfd in OECD countries. Thus, the objective was to construct a synthetic measure of the first Bahl’s rule (CSfd) which constitutes the value added of this research. The goal was also to rank countries as the main idea was to advance empirical assessment in the field of fiscal federalism and to provide practical guidance for shaping public finance policy including policies on the collection of public revenues and the allocation of public expenditure. This directly corresponds to the research problem of the very limited number of tools available for measuring fiscal decentralization. Based on Hellwig’s method of linear ordering two rankings were developed using the Euclidean metric and the Mahalanobis metric. The results indicate that the most comprehensive fiscal decentralisation systems in 2022 were found in the Slovak Republic, Switzerland and Canada (ranking based on the Mahalanobis metric). When the Euclidean metric is used, Switzerland emerges as the leader, followed by Spain and Austria. First published online 4 June 2026
Although natural resource’s negative impacts on economic growth have been extensively explored, its curse on energy carbon performance may remain ignored. Combining theoretical analysis, this paper further examines the curse of mineral resource dependence on energy carbon performance and the specific mechanism. In 2019, if optimal production is achieved in all sample cities, China would reduce electricity consumption by 42.941% and carbon emissions by 43.958%. Compared to non-resource-based cities, the potential ratios of energy savings and carbon reduction in resource-based cities are 5.403% and 6.059% higher, respectively. When the mineral resource dependence is less than 0.042%, it contributes to energy carbon performance, but when the mineral resource dependence exceeds 0.042%, higher mineral resource dependence implies more serious energy carbon performance curse, which explains the coexistence of resource blessing and resource curse. Economic restructuring stickiness caused by mineral resource dependence serves as a key mechanism for energy carbon performance curse, and energy, factor and industry restructuring stickiness are specific channels for this mechanism. First published online 4 June 2026
Dynamic development of digital technologies and the Internet in the 1990s. and the beginning of the 21st century significantly influenced the directions of social, economic and cultural development of societies in all countries of the world. A negative consequence of this process is the phenomenon of digital exclusion. The aim of this work is to measure this phenomenon in EU countries in 2023 and assess it in the context of the sustainable development goals. For this purpose, synthetic taxonomic measures were constructed using the TOPSIS method. The source of data was the Eurostat database, from which selected information from the ICT (Information and Communication Technologies) study and indicators describing the level of sustainable development. The research results show that EU countries were clearly differentiated in terms of the level of digital exclusion. This also had an impact on their implementation of selected sustainable development goals. First published online 4 June 2026
Highway projects that harness digital technologies during operation (known as digital highway projects; DHPs) can stimulate economic growth, but limited efforts have been made to fully unravel this mechanism. To address this void, this study examined the impact of DHPs on the economic growth. Specifically, under the auspices of the regional competitiveness theory, the development level of DHPs, transportation demand, new factor endowments, and related and supporting industries were identified and measured first, and their impact on economic growth was then unearthed using data from 11 operational DHPs and a partial least squares structural equation modeling (PLS-SEM) framework. It was observed, from the perspective of stakeholders of our DHPs, that DHPs increase transportation demand, which in turn has a positive effect on a new factor endowment (i.e., data flow) and the development of related and supporting industries, with the former (beta = 0.859, p < 0.001) being impacted more than the latter (beta = 0.363, p < 0.001). In addition, new factor endowment has a statistically significant impact (beta = 0.666, p < 0.001) on the economy, while the development of related and supporting industries is insignificant (beta = 0.095, p > 0.05). Finally, although DHPs promote economic growth, this path can be mediated by increased transportation demand and subsequently by the new factor endowment including data flow. As such, this study further develops the regional competitiveness theory in the context of DHPs and provides new empirical evidence on the 'transportation induced demand' effect and the growth theory. Practically, this study arms policymakers with a better understanding of how DHPs influence the regional economy, and offers effective and targeted recommendations for managing these projects.
This study employs a mediating effect model and the Generalized Method of Moments (GMM) approach to examine the direct effect of ICT on the economy and the mediating role of data flow in the ICT – economic growth nexus. The results indicate that ICT significantly enhances data flow intensity, which in turn promotes economic growth. Moreover, both the direct effect of ICT and the mediating role of data flow are more pronounced in developed regions compared to underdeveloped areas. Further analysis shows that with the implementation of the policy, the mediating effect of data flow shifted from being insignificant (2006–2010) to significant (2011–2019). This study contributes to the understanding of the digital divide, highlighting potential drivers such as disparities in ICT infrastructure and data flow inequality. The government should develop tailored ICT development policies based on the region’s economic level to fully harness the benefits of digitalization. First published online 18 May 2026
