• Energy Research

The intensification of ecological issues provokes to search for the appropriate mechanism and resources to solve them without declining the economic growth. This requires moving from resources oriented to green economic development. It could be realised through two goals: achieving macroeconomic stability – core driver of economic growth; declining environmental degradation and increasing efficiency of resources using – core requirements for green development. The paper aims to check the hypothesis on macroeconomic stability's impact on the green development of the countries. The object of investigation is European Union countries from 2000 to 2020. The study applied the following methods: the Global Malmquist-Luenberger productivity index – to estimate the green development of the countries; Macroeconomic Stabilisation Pentagon model – to estimate macroeconomic stability; Kernel density estimation and Tobit model – to check the macroeconomic stability impact on the green development of the countries. The empirical findings show that Malta from the ‘Green Group’ and Estonia from the ‘Yellow group’ have the highest value of green development, and Sweden and Greece have the highest value of macroeconomic stability. Besides, the findings allow confirming the research hypothesis. Thus, the growth of external dimensions of macroeconomic stability by 1 point led to the growth of green economic development by 0.085 (among ‘Green group’) and 0.195 (among ‘Yellow group’). It confirms that harmonising macroeconomic stability among all EU members allows for achieving the synergy effect.

AbstractThis paper aims to estimate the spatial dynamic evolution of renewable energy development efficiency and justify the dimensions that impact renewable energy development efficiency. The study applies the following methods: the ultraefficient slack‐based model (SBM) (to measure the efficiency of renewable energy development); the Dagum‐Gini coefficient decomposition process (to measure the interregional differences in the development of renewable energy efficiency); nuclear density estimation (to measure the dynamic distribution); the Markov model (to forecast renewable energy development efficiency); and the Tobit model (to justify the influencing factors of renewable energy development efficiency). The empirical findings confirm that the overall regional gaps in renewable energy development efficiency in China are widening year by year. The average value of renewable energy development efficiency increased from 0.932 in 2006 to 1.078 in 2020. The mean Gini coefficient increased gradually from 0.028 in 2006 to 0.174, with mean differences exceeding the average growth trend after 2011 and slowly decreasing post‐2016. There is polarization in the eastern region, while there is no polarization in the northeast. The overall level of renewable energy development efficiency in the middle and western areas is improving and showing a trend of absolute difference narrowing. In addition, economic development, green finance, technological progress, urbanization rate, and economic openness are conducive to renewable energy development efficiency, and renewable energy development efficiency is in a rapid development trend. Considering the findings, China should implement targeted regional development strategies, enhance green finance mechanisms, promote technological innovation, and align urbanization policies with renewable energy goals to reduce regional disparities and accelerate sustainable renewable energy development.

The unexpected pandemic has provoked changes in all economic sectors worldwide. COVID-19 has had a direct and indirect effect on countries’ development. Thus, the pandemic limits the movements of labour forces among countries, restricting migrants’ remittances. In addition, it provokes the reorientation of consumer behaviour and changes in household expenditure. For developing countries, migrant remittances are one of the core drivers for improving household wellbeing. Therefore, the paper aims to analyse how the COVID-19 pandemic has affected household expenditure in Ukraine, as being representative of a developing country. For this purpose, the data series were compiled for 2010 to the second quarter of 2021. The data sources were as follows: Ministry of Finance of Ukraine, The World Bank, and the State Statistics Service of Ukraine. The core variables were as follows: migrants’ remittances and expenditure of households by the types. The following methods were applied to achieve the paper’s aims: the Dickey–Fuller Test Unit Root and the ARIMA model. The findings confirmed that COVID-19 has changed the structure of household expenditure in Ukraine. Considering the forecast of household expenditure until 2026, it was shown that due to changes in migrants’ remittances, household expenditure in all categories tends to increase. The forecasted findings concluded that household expenditure on transport had the most significant growth due to changing migrants’ remittances.

Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government’s strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency.

The omnichannel approach to forming marketing strategies for the development of the green competitiveness of enterprises is seen as a process for the inseparable interaction of marketing-mix elements that are aimed at promoting green competitiveness. This approach integrates traditional and digital marketing communication channels and provides consideration for stakeholder interests. The effectiveness of applying the omnichannel approach to the formation of marketing strategies to develop the green competitiveness of enterprises depends on a set of marketing communication channels, which, in various combinations, can increase or decrease the level of companies’ green competitiveness. For that purpose, this paper proposes a scientific approach to identifying the quality parameters of marketing communication channels, which involves testing the hypothesis that statistically significant relationships exist between their quality parameters and the levels of green competitiveness. The objects analyzed in the paper comprise large Ukrainian production companies that are part of the agro-industrial, mechanical engineering, and food industries, and that work in both the local and international markets. According to the results of the calculations, four relevant parameters were identified for determining the quality of the marketing communication channels: the speed of loading pages, the failure rate, image, and remarketing activities.

A green economy refers to a modern form of harmony between the environment and the economy. China showing the fastest economic growth in the world has entered into a new phase of advance, facing a critical industrial transformation and progression. The paper aims to analyse China's green economic development considering the differences in development of regions. The study applied the ultra-efficient slacks-based measure model to scrutinize China's green economic development efficiency. Dagum Gini coefficient and Kernel density methods are used to estimate spatial characteristics, local adjustments, and dynamic evolution trends. The analysis is based on an annual dataset of 30 Chinese provinces from 2010 to 2019. The findings did not confirm extensive China's green economic development. In contrast, the development efficacy reveals an influential drive over the years. Regional green development is detected as unstable and diverges due to interregional differences. The findings showed that environmental regulation, government investment, industrial structure, education development were 0.0648, 0.00154, 0.0035 and 0.118 (significant at 5% and 1%), respectively. Besides, they stimulate the green economic development in the analysed regions. However, urbanization and openness of economy had the negative value. It confirmed their restriction impact on the green economic development. In addition, the findings showed that ongoing Chines policy on management of environmental development is the priority direction and provoke the declining the environmental pollution. Besides, the modernization and optimization of the Chinese industry structure stimulate the further green economic progress.

Digitalization has become a key driver of business innovation in recent years. It provides businesses with new opportunities to innovate and create value. Digital technologies, such as cloud computing, big data analytics, and artificial intelligence, have helped businesses boost the development of new products and services, optimize their operations, and improve customer engagement. This study aimed to analyze the impact of digitalization on business performance within business innovation. This study applied an ordinary least square regression model and an intermediary to explore relationship in the chain of digital capability–business model innovation–company performance. The object of investigation was 1663 listed A-share companies Shanghai and Shenzhen in the software and information technology service sectors. The results showed that digital capabilities could be divided into three dimensions according to the hierarchical relationship: (1) basic digital capabilities, (2) digital operation capabilities, and (3) digital integration capabilities, all of which significantly positively affected enterprise performance. Furthermore, while business model innovation significantly positively affected corporate performance, it was also driven by the preceding variables of digital capabilities. Business model innovation enhanced the positive impact of basic digital capabilities, digital operation capabilities, and digital integration capabilities on company’s performance. Considering the empirical results, this study underlines that the government should promote digital skills development, create supportive regulatory environments, promote access to funding for innovations, foster partnerships between businesses and technology providers, and promote collaboration between businesses, which are conducive to extending digitalization within the business innovation model and improving business performance.

Ongoing environmental issues and degradation provoke the implementation of relevant incentives to overcome them without restrictions of economic growth. Considering the Chinese sustainable development policy, each province should provide the effective ecological regulations that consider the dynamic changes of the economic and ecological indicators of the province’s development. In this case, the paper aims to analyze the relationship between economic growth and environmental quality. The object of the investigation is the Henan provinces of China from 1994 to 2020. The study applied a vector autoregression model between the one-way and two-way relationship analysis, Granger causality test, cointegration test, and impulse response function. The findings confirm that GDP growth causes exhaust gas production and that SO2 will also influence wastewater. The results of the co-consolidation analysis showed that if the production of industrial solid waste gas and SO2 volume increased by 1% each, GDP per capita would increase by 0.22% and 0.35%, respectively. The findings of the variance decomposition of the GDP per capita in the first phase are all due to their perturbation term. The other influencing factors have no influence. Over time, GDP per capita is less and less affected and significantly enhanced by wastewater, exhaust gas, and SO2.