Recent trends prove that energy production is shifting from traditional fossil fuel combustion technologies to renewable energy-based technologies. To estimate the economic efficiency of renewable energy technology implementation, the data for the EU-27 member states during the 2012–2021 period were collected; additionally, technological efficiency was analyzed based on a critical literature review. Breusch and Pagan Lagrangian multiplier tests were employed to select the most suitable econometric model. The results suggest that an increase in the share of renewable energy sources by one percentage point (1) decreased CO₂ emissions by 0.137 metric tons per capita (technological efficiency) and (2) decreased greenhouse gases by 13 g per EUR, in terms of GDP (economic efficiency). Regarding the Kyoto Protocol implementation, it was found for EU-27 that an increase in the share of renewable energy sources by one percentage point was related to a decrease of one percentage point in the greenhouse gases index. GDP per capita appeared to be an insignificant driver for reductions in per capita CO₂ emissions, while it proved to be important for economic efficiency models. Thus, increasing GDP per capita by 1000 USD reduces greenhouse gases by 7.1 g per EUR of GDP in EU-27. This paper also confirmed that a unit of electricity (1 kWh) generated by traditional energy plants is seven to nineteen times more environmentally costly than renewable energy generation. This paper thus concludes that digital transformations and additive manufacturing brought about the significant dematerialization of industrial production and the promotion of renewable energy on industrial and household levels.

The transition to sustainability is a complex process that requires a clear understanding of its drivers and barriers. The paper explores the impact of different social and economic factors on sustainable development as a holistic process. The research involved data from 27 EU member states during 2012–2020. Hausman specification and Breusch and Pagan Lagrangian multiplier test were used to select the proper econometric model, which led to the use of generalized least squares regression with random effects to estimate the sustainable development drivers in the EU. The results suggested that corruption has no statistically significant impact on sustainability, whereas economic freedom increases Sustainable Development Goals (SDG) Index. Our empirical results demonstrated that GDP per capita inhibits sustainability transition, which could be a case of the environmental Kuznets curve hypothesis. Unemployment has a negative impact on sustainable development; however, employment in science and research is its driver. It was unfolded that median income per capita and life expectancy have a statistically significant positive impact on the SDG Index. Following these findings, a wide range of policy recommendations was suggested. They include but are not limited to: ensuring economic freedom, human capital development, digitalization of public services, and lifelong education promotion.

Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required production companies to adapt production and business models according to the needs. The demand for real-time decision support systems adapted to these raising business needs is continuously growing. Nevertheless, businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to 96% when combining economic and demand information, optimal forecasting period from 10 months to five years, more frequent use of soft indicators in forecasting, the relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and demand-oriented, (III) industry-specific, and (IV) media information indicators, were separated. The analysis forms a suggestion for decision-makers to pay more attention to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling. AcknowledgmentThis work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.

The rapid development of digital technologies, digital transformation, business innovation, and the emergence of such technologies as artificial intelligence, blockchain, the Internet of Things provides significant potential for transforming business processes and creating new innovative models. The trend analysis conducted in this article (using the Google Trends tool) around the world for 2009-2023 showed that public interest in “business innovation” was almost constant until 2021 (according to the GT Scale, it ranged from 0.25 to 0.5). After 2021, it increased (according to the GT Scale, it was more than 0.5). At the same time, public interest in “digital transformation” has significantly increased since 2014, reaching almost maximum values in 2020-2023 (0.75 – 1.00 on the GT Scale). This confirms a growing need in society to acquire new knowledge related to the ability of businesses to adapt to digital transformation, including through creating innovative developments. Unlike trend analysis, bibliometric and comparative analyses identify trends in developing interest in this topic not among ordinary citizens – users of the Google search platform ‒ but among scientists who publish the results of their research either in monographs or in authoritative peer-reviewed scientific journals indexed by leading scientometric databases. The comparative analysis conducted in the article using the Google Books Ngram Viewer (Google) tool, which displays the frequency of references to relevant terms in English-language books for 2009-2019 worldwide, showed similar trends: the persistence of scholars’ attention to business innovation, while their interest in digital transformation is growing: in 2009-2013 – a slight increase, in 2013-2019 ‒ a significant increase. In the bibliometric analysis, a five-level mechanism for limiting the research sample was applied: only English-language and most recent (for 2018-2022) scientific articles indexed by the Scopus database in the subject area of Business, Management and Accounting were subject to analysis. Based on these limitations, the research sample for the keyword “digital transformation” was reduced from 12131 to 1700 papers, and for “business innovation” from 865 to 70. The analysis of the published articles, their views and citations, carried out using the SciVal tool, fully confirmed the conclusions drawn from the trend and comparative analysis results. In terms of digital transformation, the leaders in terms of the number of papers and the number of views are Germany, Italy, the UK and the US; in terms of citations – the UK, Germany, the US, and in terms of individual scientists – Brazil and Germany. As for the topic of business innovation, the United States is the undisputed leader in all these parameters, followed by China and the United Kingdom. Some scientists from Germany and Spain also attract the attention of the academic community (in terms of views and citations). Changes in trends and research priorities in each of the studied topics were identified and described using SciVal (analysis of the reference dynamics to the top 50 keywords in articles for 2018-2023). For example, in 2023, compared to 2022, scientific interest in issues related to “value creation” (+61.5%), “incumbents” (+66.7%), “service economy” (+83.3%), “digital marketing” (+75.0%), and “design thinking” (+150.0%) increased the most. VOSviewer software made it possible to cluster the scientific work by thematic areas. The largest cluster on digital transformation relates this concept to small and medium-sized businesses and technologies, and the largest cluster on business innovation relates it to the development of small and medium-sized enterprises and the protection of property rights.

This study aims to identify whether achieving sustainable development goals influences SMEs’ development and assess its degree. The dataset on SMEs’ development indicators and SDGs 2, 8, 9, 12, and 13 for the panel of EU-27 countries in 2011–2020 was collected using Eurostat and OECD datasets. Breusch and Pagan Lagrangian multiplier test for pooled OLS/panel data random effects and Hausman test for fixed/random effects were utilized. The results were in favor of random effect GLS regression for SDG2 models, SDG9 models, and SDG12-13 (Model 1) and fixed effect GLS regression for SDG8 models and SDG12-13 (Model 2), respectively. Based on bibliometric analyses using VOSViewer 14 and a comprehensive literature review, 19 independent variables have been selected from the “Sustainable development indicators” catalog covering five sustainable development goals; SMEs’ turnover and SMEs’ employees employed are used as the dependent variables to reflect SMEs’ development. The empirical evidence suggests a significant relationship between individual sustainable development and SMEs’ development indicators. It was found that all seven sustainable development indicators of SDG 2 (Zero hunger) and SDG 12 (Responsible consumption and production) have a significant relationship with the indicators of SMEs’ development. Instead, only a part (8 out of 13) of the sustainable development indicators of SDG 8 (Decent work and economic growth), SDG 9 (Industry, innovation and infrastructure), and SDG 13 (Climate action) have a significant relationship with two or one of the SMEs’ development indicators. Therefore, achieving sustainability goals stimulates the development of SMEs itself. AcknowledgmentThis study is supported by the British Academy’s Researchers at Risk Fellowships Program (Award Reference: RaR\100673).