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(iii) H3: Business environment has a positive impact on green economic
development.
According to Rosatom State Corporation (2018), business environment factors
include access to finance, human resources, infrastructure, and supportive institutional
conditions—facilitates enterprise development. Moreover, political stability and
governance quality positively influence investment decisions by creating a flexible
business environment that adapts to trends such as innovation, digital transformation,
and green transition, thereby supporting sustainable development.
(iv) H4: Human capital positively affects sustainable development.
Human capital is a critical determinant ensuring the efficiency of economic growth
(Voronin et al., 2020). High-quality human resources are essential for enabling access to
sustainable development rather than material wealth. Therefore, developing high-quality
human capital is essential not only for socio-economic development but also for accessing
the knowledge economy, digital economy, and green economy.
(v) H5: Economic growth has a positive/negative impact on sustainable
development. Economic growth, measured by GDP, helps firms, investors, and analysts
make decisions based on economic conditions. An increase in GDP indicates a well-
performing economy, encouraging investment in infrastructure and facilities that support
green development. However, rapid economic growth may also generate environmental
pressures and resource depletion due to the close linkage between economic expansion
and resource consumption.
3.3. Research methodology
Secondary data on digital economic development, green economy, business
environment, scientific research and innovation development, human capital, and
economic growth were collected during the period 2015–2024, with a total of 220
observations (10×22). The dependent variable in this study is the Sustainable
Development Index (SD), which is aggregated from indicators of average disposable
income per capita of urban and rural residents, GDP per capita, centralized wastewater
treatment rate, rate of non-hazardous household waste treatment, forest area, average
daily solid waste treatment volume, human development index, and percentage of
households with access to safe water sources. These indicators were obtained from the
General Statistics Office's database. To construct the SD index, the authors applied
Principal Component Analysis (PCA), which reduces a large number of correlated variables
into a new set of uncorrelated variables, where each new variable is a linear combination
of the original variables. Similarly, the independent variable DE, measuring the digital
economy, is aggregated from indicators such as the number of telephone subscribers, the
percentage of mobile subscribers in the total number of telephone subscribers, the
number of fixed broadband internet (ADSL) subscribers, and telecommunications revenue,
also through PCA. All variables in the model are organized according to a time series data
structure, serving the quantitative analysis of the relationship between the digital
economy, the green economy, and sustainable development.
* Descriptive statistics: The statistical indicators include Obs (number of
observations), Mean, Standard Deviation, Minimum, and Maximum values, which are
presented in the following table.

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