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”.
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.
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