Page 128 - IJPS-11-4

International Journal of
Population Studies Environmental impact on Ukraine’s quality of life

rate facilitates comparisons across regions or countries, by many variables, including environmental conditions,
providing a consistent metric to assess how various factors economic status, and social determinants. Therefore,
influence life quality. While mortality rates are powerful applying multiple regression models offers a powerful
indicators, they may not capture all aspects of quality of approach to studying how these factors collectively impact
life, such as mental health, social well-being, or individual mortality rates.
satisfaction. However, given these data are fully available The ordinary least squares (OLS) method was
for the period of the study, it is appropriate to use them in chosen to estimate the model due to its simplicity,
our analysis.
efficiency, and widespread applicability in regression
2. Data and methods analysis. OLS minimizes the sum of squared residuals,
ensuring the best linear unbiased estimates under the
2.1. Data sources Gauss–Markov assumptions, which include linearity,
The study drew on socioeconomic and environmental homoscedasticity, no autocorrelation, and the absence of
data from 2001 to 2020 to examine mortality trends in perfect multicollinearity. OLS is particularly advantageous
the Carpathian region of Ukraine. All data were obtained because it provides interpretable coefficients, which
from the State Statistics Service of Ukraine, except for indicate the magnitude and direction of the impact of each
water pollution indicators, which were sourced from the independent variable on the dependent variable. In this
State Agency of Water Resources of Ukraine. Mortality study, OLS helps quantify how socioeconomic factors, such
rates, a central variable, were calculated as the median as GRP and FDI, alongside environmental indicators, such
rate across four oblasts – Zakarpattia, Ivano-Frankivsk, as pollutant emissions and public expenditures, influence
Lviv, and Chernivtsi. Socioeconomic indicators such mortality rates. Moreover, including differenced data and
as gross regional product (GRP) per capita and foreign lagged variables allows for capturing temporal effects and
direct investment (FDI) highlight economic conditions delayed responses, adding depth to the analysis. Using
and investment dynamics. Environmental factors include this method, the research gains insights into the intricate
emissions of air pollutants and discharge of contaminated dynamics between public health and its determinants,
water, both of which showed declining trends during supporting evidence-based policymaking for regional
the study period. In addition, government expenditures development.
on environmental protection and healthcare provide The basic model is as follows:
insight into policy-driven efforts to mitigate negative
environmental and health impacts. This combination Y = a + β GRP + β FDIR + β PolAir + β PolWater +
t−1
t
3
t
1
2
t
4
t
of data allows for a comprehensive understanding of the β Env + β Health +μ (I)
6
t
5
t
factors influencing mortality in the region. The dependent variable Y represents the mortality rate
t
2.2. Methods in the Carpathian region of Ukraine per thousand people
at time t during the study period (2001 – 2020). It was
To study the impact of environmental and economic calculated as the median mortality rate of the four oblasts
factors on the mortality rates in the Carpathian region of the Carpathian Region of Ukraine. Between 2001 and
as a whole, and in its constituent oblasts (Zakarpattia, 2020, a slight decrease in mortality was observed in the
Ivano-Frankivsk, Lviv, and Chernivtsi), we used regression region (Figure 2). The significant increase in 2020 can
analysis. The multiple regression and least squares methods be attributed to the impact of the COVID-19 pandemic.
are the standard mathematical and statistical instruments The data were obtained from the State Statistics Service of
for assessing the relationships between these factors (Chen Ukraine.
et al., 2021; Huang et al., 2020). • GRP represents the GRP per capita in the Carpathian
t
Multiple regression models are essential for analyzing region. It helps determine the volume of the internal
complex relationships between socioeconomic, regional market and indicates the level of welfare in
environmental, and health-related factors. They are the region. Its effect on the dependent variable was
particularly useful for extracting valuable insights expected to be negative.
from large datasets and mathematically modeling the • FDIR refers to the amount of FDI in the region, with
t−1
relationships between independent and dependent a 1-year lag. The lag indicator was used because some
variables. By understanding these relationships, time must pass between the moment of investment and
researchers can predict the value of the dependent variable the commencement of active operations in polluting
based on the known values of the independent variables. In industries. The impact of FDI on the dependent
the context of public health, mortality rates are influenced variable was expected to be positive.
Volume 11 Issue 4 (2025) 122 https://doi.org/10.36922/ijps.4487