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This concept paper aims to shed light on the emergence of the first to the fifth industrial revolutions, their evolution, and their transformative steps towards Society 5.0. By explaining the nuances of the different phases of industrial revolutions and their positive and negative externalities, we found that the fifth industrial revolution can be considered a transformative step for the emergence or coevolution of Society 5.0. By examining how Society 5.0 affects various aspects of human society (e.g., advances in healthcare and improved life expectancy; business, the economy, growth, and industry; education and skills; privacy and cybersecurity; smart cities; labour and the workforce), we conclude that Society 5.0 should move forward by adhering to the harmonious integration of humans and technology to address the world’s pressing problems in the future.

This study is an attempt to comparatively analyze the impact of renewable energy sources on air quality represented by particulate matter 2.5 concentrations utilizing panel data of 60 countries which are divided into two sub-panels industrialized economies and emerging industrial economies over the period 2010–2019. The study adopts both demand- and supply-side approaches and hence renewable sources are handled in two different structures, i.e., renewable energy consumption and production. Empirical results from both demand- and supply-side regressions strongly confirm the positive impact of renewable sources on air quality in all country groups, meaning that higher renewable energy production and consumption bring about improvement in air quality. In addition, this positive impact of renewables on air quality turned out to be higher in emerging industrial economies than that in industrialized ones. To be more precise, as all control variables are considered, a 10% increase in the production of renewable energy sources brings about a 0.66% improvement in air quality in industrialized economies while its impact is a value of 1.33% in emerging industrial economies. On the other hand, a 10% increase in consumption of renewable energy sources leads to a 0.62% improvement in air quality in industrialized economies and a 1.97% improvement in emerging industrial economies. As for control variables, industrialization gives rise to an increase in air pollution in all country groups, whereas economic growth and trade openness function as favorable factors for air quality. Although population density improves air quality in industrialized economies, it is found as one of the main pollutant factors in emerging industrial economies. Overall results proved that renewable sources improve air quality by reducing particulate matter 2.5 concentrations. Therefore, these countries, especially emerging industrial economies, should replace primitive energy sources like fossil fuels with renewables to bring down environmental degradation up to a reasonable level and increasingly continue to invest in renewable energy domain to reach their environmental sustainability targets. The study also provides some additional policy implications.

Accelerated introduction of digital technology has recently become one of the key areas in development of Russian economy. The paper presents the approach to innovation intensity assessment by sector and economic activity. The method makes it possible to identify the growth intensity for an output of innovative products. This serves as an indicator of introduction of new technologies and a transition to a more high-tech conversion. The narration assumes that innovations in various sectors are unstable and uneven. There is an observation that there was the highest efficiency increase over the period under review in production and distribution of electrical power, gas, and water, and in other low-tech sectors (primarily, food production). There is a highly intensive character of innovations observed in high-tech and medium-tech sectors. There is another observation that the reasons for the unstable and multidirectional dynamics are as follows: high dependence of efficiency and intensity of innovations on external economic shocks, significant impact made by measures of state support on intensity of innovations, concentration of innovating at large-scale Russian and transnational companies. The results obtained led to the conclusion on a need in more stimuli for national demand from the part of Russian businesses for innovations, including digital technology.

The rapid rise in climate and ecological challenges have allowed policymakers to introduce stringent environmental policies. In addition, financial limitations may pose challenges for countries looking to green energy investments as energy transition is associated with geopolitical risks that could create uncertainty and dissuade green energy investments. The current study uses PTR and PSTR as econometric strategy to investigate how geopolitical risks and financial development indicators influence energy transition in selected industrial economies. Our findings indicate a non-linear DCPB-RE relationship with a threshold equal to 39.361 in PTR model and 35.605 and 122.35 in PSTR model. Additionally, when the threshold was estimated above, financial development indicators and geopolitical risk positively impacts renewable energy. This confirms that these economies operate within a geopolitical context, with the objective of investing more in clean energy. We report novel policy suggestion to encourage policymakers promoting energy transition and advance the sustainable financing development and ecological sustainability.

This paper employs directional distance function (DDF) and the global Malmquist–Luenberger (GML) productivity index to measure the green total factor productivity (GTFP) growth of China’s 36 industrial sectors from 2000 to 2014. Based on this, this paper ascertains the determinants of GTFP from the perspectives of institution, technology, and structure, and the determinant factors that affect GTFP are empirically tested by a dynamic panel data (DPD) model. The research shows that, considering energy consumption and environmental undesirable outputs, the industrial GTFP goes backwards by 0.02% per year on average, and the contributions of GTFP to output growth are far from the target value of 50% in all industrial sectors, which indicates that the growth of industrial economy sacrifices resources and environment to a certain degree. In terms of the determinant factors of GTFP, environmental regulation does improve the GTFP, while environmental regulation is difficult to promote GTFP by the route of technological innovation. Compared with technology importation, the driving effect of independent research and development on GTFP is obvious, especially promoting the GTFP of moderately and lightly polluting industries, while the driving effect in heavily polluting industries is poor. Endowment structure and property right structure play a positive role in improving GTFP, but the impacts of capital structure and energy structure on GTFP are poor.

This paper combines a Granger causality test and a VAR model to investigate the relationships among oil price shocks, global economic policy uncertainty (GEPU), and China's industrial economic growth. Based on monthly data from 2000 to 2017, we reveal that GEPU and world oil prices jointly Granger cause China's industrial economic growth; world oil prices have a positive effect on China's industrial economic growth, while GEPU has a negative effect. Further analyses investigate the asymmetry effect of oil prices and find that the negative component shows a more significant impact on China's industrial economic growth. The results are robust to different oil price and EPU proxies.

Constructing a new development pattern and developing high-quality productivity cannot be separated from the innovative development of various industrial economies. What factors affect the development of industrial economy, according to which industrial economic development strategies can be summarized, is the focus of attention in this paper. Ten secondary indicators corresponding to the level of economic development, the level of urbanization, the standard of living of the population, the labor force and the role of the government are selected as variables and incorporated into the ordered Logit model for regression analysis, and innovative development strategies are proposed based on the results of the analysis. The significance p>0.05 of variables such as urban land utilization rate, number of people employed in the industrial economy, and cost of living index, and the regression coefficients of variables such as GDP, GDP per capita, proportion of the urban population, disposable income per capita, number of college graduates, and government financial expenditures and efficiencies were all found to be positive, which suggests that in order to realize the innovative development of the industrial economy, we should implement the following measures: strengthening the economy, expanding the industrial clusters, promoting consumption, attracting talents, increasing government support and other strategies should be implemented to realize the innovative development of industrial economy.

Artificial intelligence (AI) has rapidly expanded across multiple industries and technologies, driving economic growth and offering innovative solutions to structural challenges. However, its environmental impact remains contested. While firms investing in AI aim to lower its carbon footprint, its widespread use continues to generate significant emissions. This study examines the environmental effects of AI investments, particularly on carbon emissions, while also accounting for human and economic development indicators. The analysis employs the Panel ARDL-PMG approach using data from 2012–2023 for nine technologically advanced economies characterized by extensive use of robotics (South Korea, Japan, Germany, the United States, China, Singapore, Sweden, Italy, and France). The findings reveal the existence of a stable long-run equilibrium among the variables. The negative and significant ECT indicates that about 32% of short-term imbalances are corrected each year, suggesting that the system steadily moves toward its long-run equilibrium. In the long run, per capita GDP and renewable energy consumption reduce carbon emissions, whereas AI investments (AIINV), Foreign Direct Investment (FDI), and the Human Development Index (HDI) increase them. The results show that AIINV and FDI do not contribute to reducing carbon emissions. In this context, the findings suggest that investments in the energy sector are not directed toward encouraging the transformation of energy sources. These results highlight the environmental risks posed by the growing prevalence of AI. However, AIINV and FDI have the potential to help reduce carbon emissions if they are aligned with the transformation of energy sources. Thus, aligning AI with green innovation and sustainable environmental policies is essential. This study emphasizes the importance of enabling the energy transition to reduce carbon emissions arising from AIINV and FDI in the sector. Promoting eco-efficient technologies and sustainable innovation processes can help mitigate the carbon-intensive effects of digital transformation.

Global efforts to build sustainable e-commerce ecosystems through various prediction tools have suffered due to uncertainty in politics, the economy, and the environment. This paper proposes a new integrative prediction model to track the sustainable development of e-commerce. Using US e-commerce data, this study explores the prediction accuracy of the mixed data sampling (MIDAS) model in combination with the attention mechanism (AM) approach, analyzing the performance differences between the model’s before and after improvements. More so, the paper evaluates the performance of the new prediction approach against other competing models using the prediction accuracy metric, the probability interval test, and the Diebold and Mariann (DM) test methods. The results indicate that the introduction of the MIDAS and the AM approaches allows the prediction model to fully utilize the effective information of the mixed-frequency data while simultaneously capturing the differences in the importance of the variables in terms of their time series and the non-linear relationship of the learning variables, thereby positively influencing the economic prediction of the e-commerce industry. Second, the proposed prediction model combines the ability of long-term and short-term high-precision prediction and performs multi-step probability prediction on the development of the e-commerce industry. It can better track abnormal changes in macroeconomic indicators and fit their fluctuation trends. Third, based on the results of the three evaluation indicators, the MIDAS–AM–Deep autoregressive recurrent neural network (DeepAR) model achieves optimal prediction accuracy, allowing it to provide more timely, accurate, and comprehensive predictions for e-commerce management decisions when macroeconomic conditions are undergoing significant transformations.

In industrial economy, the main factors influencing the urban construction and development of social economy is linked to the high-speed rail site accessibility, and this has its influence on the regional economy as a whole, and as an example, this can be explained through the study using a multiple linear regression equation model, built and compared before and after 2 years of data and information. Based on the weighted average travel time and economic potential, it is clear that the construction of urban expressway can help accelerate the aggregation and flow of production factors within the region, to guide the integrated development of local society and economy.