Volume 4, issue 6, 2025
131
FORECASTING DIRECTIONS OF O‘ZTRANSGAZ JSC'S ACTIVITIES FOR THE
PERIOD 2024–2027
Sindorov Davlatbek Abdumajid ugli
Tashkent State University of Economics
Assistant Lecturer, Department of Econometrics
ORCID: 0009-0003-3299-824X
E-mail:
Phone: +998 99 440-60-09
Murodov Sardor Nurali ugli
Tashkent State University of Economics
Assistant Lecturer, Department of Econometrics
ORCID: 0009-0001-1938-5567
E-mail:
Phone: +998 94 868-38-28
Otaboyeva Dildora Ilhomboy kizi
Tashkent State University of Economics
Assistant Lecturer, Department of Econometrics
E-mail:
otaboyevadildora0819@gmail.com
Phone: +998 94 256-45-66
Sarvar Mamasoliyev Fayzullo ugli
Tashkent State University of Economics
Assistant Lecturer, Department of Econometrics
ORCID: 0009-0003-1905-5108
E-mail:
Phone: +998 99 976-40-30
Abstract:
This article analyzes the activities of “Uztransgaz” JSC during the period of 2015–
2023 based on a multivariate regression-correlation analysis. Despite a decline in the volume of
natural gas supply during this period, the company succeeded in increasing its gross revenue by
1.8 times. This outcome is associated with modernization, infrastructure renewal, and the
implementation of an effective remuneration system. The study mathematically models the
impact of production volume, average wages, and incentive expenditures on revenue. Based on
the regression equation and correlation analysis, the main factors influencing revenue were
identified, and forecasting was carried out in three stages. In particular, the strengthening of
wages and incentives under the mobilization process emerged as a key factor in stable revenue
growth.
Keywords:
multivariate regression analysis, correlation analysis, natural gas supply, economic
efficiency, average wages, incentive expenditures, revenue forecast, strategic processes,
“Uztransgaz” JSC.
Volume 4, issue 6, 2025
132
"Uztransgaz" JSC holds a strategic position among the enterprises responsible for supplying
natural gas to the population in Uzbekistan. In particular, as a result of the measures aimed at
improving the company’s operations in 2023, the following positive outcomes were achieved:
As a result of modernization measures implemented at the “Sheykhoncha” gas
distribution station, which was commissioned by the company in 2018, an additional pipeline
was constructed for the “Aksa Enerji” power plant. With this addition, the total number of such
pipelines reached three, improving the natural gas supply for nearly 30,000 households and 17
industrial enterprises in Bukhara city and its districts.
The commissioning of 31 kilometers of the “Termiz-2” main gas pipeline, constructed in
parallel to the “Temiz” main pipeline (with a total planned length of 46.5 km), improved the
natural gas supply for six regions of Surkhandarya, namely: the city of Termiz, and the districts
of Termiz, Angor, Jarkurgan, Qiziriq, and Sherobod. Additionally, this initiative is expected to
enhance natural gas supply for over 62,000 households during the 2024–2025 autumn-winter
season.
A new gas pipeline constructed by “Uztransgaz” JSC — the “Olimkent Avto Reys” gas
distribution station (GDS) — connected 5,640 households and 147 wholesale consumers from
the neighborhoods of “Sultonobod,” “S. Tegizboev,” “S. Rahimov,” “Qo‘rg‘oncha,” “Birlik,”
and “Mustaqillik” in Oqqorgon district. As a result, the gas transmission distance in the area was
reduced by 16 km, and gas pressure was successfully doubled.
In order to improve natural gas supply indicators for the population of the Ferghana
Valley (Andijan, Namangan, and Ferghana regions), six new wells were drilled in 2023,
increasing daily natural gas production from 9,000 m³ to 12,000 m³.
1-rasm
"Economic performance indicators of O‘ztransgaz JSC"
Years
Total
revenue of
the
enterprise,
million
UZS (y)
Goods
(works,
services),
million m³
(x1)
Average
salary,
million
UZS (x2)
Employee
incentives,
million UZS
(x3)
2015
8359680.4
38344.4
1.5
186566.9
2016
10125910.7
39562.5
1.9
164895.2
2017
11005757.3
37450
2.3
168859.1
2018
15750540.5
38882.2
2.5
112743.7
2019
25392640.6
38660.1
4.5
120007.9
2020
24367330.5
30906.9
6.8
79746.7
2021
23644758.3
23644.8
7.4
94547.3
2022
20509751.5
22978.2
8.5
138588.9
1
"O‘ztransgaz" JSC announced the results of its activities for 2023. January 24, 2024. [Online resource]. URL:
https://kun.uz/kr/news/2024/01/24/oztransgaz-aj-2023-yildagi-faoliyati-yakunlarini-elon-qildi
2
"O‘ztransgaz" JSC announced the results of its activities for 2023. January 24, 2024. [Online resource]. URL:
https://kun.uz/kr/news/2024/01/24/oztransgaz-aj-2023-yildagi-faoliyati-yakunlarini-elon-qildi
3
“O‘ztransgaz” AJ ma’lumotlari asosida tuzilgan
Volume 4, issue 6, 2025
133
2023
15165150.1
2298.4
9.2
160615.7
According to the data provided by “O‘ztransgaz” JSC, during the period from 2015 to 2023, the
company supplied the country’s population and real sector enterprises with a total of 292,727.5
million cubic meters of natural gas. Over the same period, the company's total revenue amounted
to approximately 15,165.2 billion UZS. Notably, the volume of natural gas produced by the
company decreased by 1.7 times or 41.8%, indicating a decline in the country’s natural gas
reserves.
At the same time, despite the downward trend in production volume, the company’s gross
revenue increased by 1.8 times, which signifies high economic efficiency. Additionally, during
the study period, the average salary at the company rose from 1.5 million UZS to 9.2 million
UZS, while employee incentive expenses decreased by 13.9% (Figure 1).
According to the company’s management, the reduction in incentive expenses over the analyzed
period was due to a shift towards uniform motivation through increased base salaries, instead of
selective incentives. Over time, the company has been gradually transitioning to a model where
only high-performing employees are rewarded, which reflects a focus on establishing an
effective employee incentive mechanism.
In the coming years, it is necessary to define strategic development directions by developing
forecast indicators based on employee productivity and economic efficiency trends. These
projections must be calculated using the data shown in Figure 1, provided by the company. For
this purpose, we have deemed it appropriate to conduct a multiple regression-correlation analysis.
In forecasting, since the company’s economic efficiency indicator is selected as the dependent
variable, it is necessary to evaluate how changes in production volume (x1), average salary (x2),
and employee incentive expenses (x3) affect the company’s revenue (y). Furthermore, the
internal correlation between these indicators must also be assessed.
To do this, we must first construct a multiple regression model. The regression equation for the
multiple regression model is calculated using the following function:
f y = a
0
+ a
1
x
1
+ a
2
x
2
+ a
3
x
3
+ ε
Here:
a
0
; a
1
; a
2
; a
3
– oefficients of the independent variables;
x
1
, x
2
, x
3
– independent variables (x
i
), i.e., the factors influencing the dependent variable (y).
The values of the independent variables and the dependent indicator of the multiple regression
function are presented in Figure 1. Based on these values, the vector coefficients of the
independent variables of the function are determined using the following formula:
s = (x
T
x)
−1
x
T
y
Based on the above formula, the matrix method is used to calculate the vector coefficients of the
independent variables in the multiple regression model. In this case, matrices composed of the
independent variables (x
i
) and the dependent variable (y) are constructed accordingly, and the
vector coefficients of the multiple regression model are calculated based on these corresponding
matrices.
Y(X
)
15432152
0
=
9a
0
272727,5a
1
44,6a
2
1226571,4a
3
=
3758478 a0
4,546E+1
2
272727,5a
0
9,492E+09
a
1
1091594,7
a
2
3,728E+10
a
3
398,9902
8
a
1
Volume 4, issue 6, 2025
134
86525655
4
44,6a
0
1091594,7a
1
295,54a
2
5701019,6a
3
2354957,
6
a
2
1,937E+1
3
1226571,4
a
0
3,728E+10
a
1
5701019,6
a
2
1,779E+11
a
3
-
76,10768
9
a
3
Based on the calculation results, the function of the multiple regression model was determined as
follows:
f y = 3758478,03 + 398,99x
1
+ 2354957,63x
2
− 76,1x
3
Based on the multiple regression model function, the following conclusions were drawn:
The economic efficiency of “O‘ztransgaz” JSC's operations tends to decline by a coefficient
of 3,758,478.03 if the influence of the selected independent variables is not considered.
In the company's operations, a one-unit change in production volume increases the income
by an additional coefficient of 398.99, positively impacting the economic efficiency of the
company.
The company has shifted to a practice of motivating all employees equally by increasing the
average salary. As a result, the economic efficiency indicator has improved, with each unit
increase in average salary corresponding to an increase of 2,354,957.63 in gross income.
Employee incentive expenses currently in place have had a negative effect on the company’s
economic efficiency. For this reason, measures have been taken to improve the employee
motivation system. According to the analysis, for every one-unit increase in incentive expenses,
the economic efficiency indicator of the company decreased by a coefficient of -76.1.
Based on the developed multiple regression model, it can be concluded that shifting employee
motivation practices to include all employees within the system will allow “O‘ztransgaz” JSC to
achieve significantly higher efficiency.
In the next stage, a multiple correlation analysis based on the developed hypothesis should be
conducted. According to this hypothesis, a matrix consisting of the dependent variable (y) and
the defined independent variables (xi) will be constructed (see Appendix 3). Using this matrix,
the correlation coefficient—which reflects the internal relationship between the indicators—will
be calculated using the following formula:
r
xy
=
x ∗ y − x ∗ y
s x ∗ s(y)
Based on the formula mentioned above, the internal relationship between the dependent indicator
(y) and the specified independent variables (xi) is determined in accordance with the hypothesis.
The relationship between the main outcome and the influencing factors is examined using the
formula for the pairwise (simple) correlation coefficient.
Table 2.
Table 2
. The Relationship Between the Company’s Income and Factors (Correlation Coefficient)
Total revenue
of the
enterprise,
million UZS
(y)
Goods
(works,
services),
million m³
(x1)
Average
salary,
million UZS
(x2)
Employee
incentives,
million UZS
(x3)
Total revenue
of the
enterprise,
1
Volume 4, issue 6, 2025
135
million UZS
(y)
Goods
(works,
services),
million m³
(x1)
-
0,201156028
1
Average
salary,
million UZS
(x2)
0,627957024
-
0,859375373
1
Employee
incentives,
million UZS
(x3)
-
0,865092286
0,03014534
-
0,421313396
1
Based on the established hypothesis, the internal relationship between the defined independent
variables (xi) was assessed through calculations, leading to the following conclusions:
The correlation coefficient between the company’s income and the volume of goods (works,
services) is -0.201156028, indicating a weak inverse relationship. This suggests that as the
volume of goods or services increases, income tends to slightly decrease. However, the change is
not significant, and other influencing factors may be at play.
The correlation coefficient between the company’s income and average salary is 0.627957,
indicating a moderate positive relationship. This means that as employee salaries increase, the
company's total income also tends to grow.
The correlation coefficient between company income and employee incentives is -
0.865092286, indicating a strong inverse relationship. This shows that as incentive expenses
increase, the company's income tends to decrease, or vice versa.
The correlation coefficient between goods (works, services) and average salary is also -
0.865092286, showing a strong inverse relationship. As salaries increase, the volume of goods or
services tends to decrease, or vice versa.
The correlation coefficient between goods (works, services) and employee incentives is
0.03014534, indicating almost no relationship. That is, the level of incentives does not
significantly affect the volume of goods or services.
The correlation coefficient between average salary and employee incentives is -0.421313396,
showing a moderate inverse relationship. This suggests that as salaries increase, the amount of
incentives tends to decrease.
Using the partial correlation coefficient formula, the partial correlation between goods (works,
services) and the company’s total income, excluding the effects of other variables (salary and
incentives), is 0.968580343, indicating a very strong positive relationship. This means that the
volume of goods directly and strongly impacts the company’s income when other factors are
held constant.
Volume 4, issue 6, 2025
136
Based on the results of the multivariate regression-correlation analysis conducted, forecast
indicators were developed for the increase in the company’s income due to improved employee
performance motivation within the operations of “O‘ztransgaz” JSC.
The first set of forecast indicators was calculated based on the assumption that the motivational
strategies implemented in recent years would continue in the future. The inertial scenario
forecast indicators were calculated using the following formula:
f y = 3758478,03 + 398,99x
1
+ 2354957,63x
2
− 76,1x
3
Based on the calculations of the inertial scenario forecast indicators, it is projected that if the
measures aimed at motivating employee productivity implemented in recent years by
“O‘ztransgaz” JSC continue to follow the same trends, the company’s gross income by the year
2030 will increase 1.7 times compared to 2023 (Figure 3).
According to the calculations performed using the multiple regression model, the company’s
income by the year 2027 was estimated to increase by 2.47 times compared to 2023, with a high
degree of probability (Figure 3).
Figure 3. Forecast Indicators of Achieving Economic Efficiency Through Employee Productivity
Motivation in the Activities of “O‘ztransgaz” JSC for 2024–2027, in million UZS
The third forecast indicators were calculated based on mobilization scenarios. In this case,
priority was given to increasing the average salary, which has a positive effect on improving the
company’s economic efficiency through employee productivity motivation. The forecast
indicators were determined using the following formula:
Based on calculations using the low probability model, it was found that in the coming years,
there is a possibility to increase the company’s revenue volume by 0.33 times by 2027 compared
to 2023 due to the rise in average wages.
According to the multiple econometric regression model, the company’s real revenues during
2023–2026 were significantly higher than the forecasted (trend) values. This situation indicates
4
Muallif tomonidan hisoblangan
Volume 4, issue 6, 2025
137
that the company’s economic activities have been developing steadily, with increased production
and service volumes, and that internal factors (such as effective management, incentive systems,
workforce productivity, and rising demand) have yielded positive results.
In particular, in 2023, revenues increased by more than 10 billion UZS compared to the forecast,
marking the peak of positive growth. Similarly, in 2024 and 2025, a positive difference was
maintained, confirming the sustained growth of the company.
However, in 2027, a sharp decline was observed, with revenues falling over 6 billion UZS below
the forecasted value. This indicates the emergence of possible external economic factors, internal
financial issues, or a drop in demand.
Conclusion:
In general, the analysis shows that 2023–2026 was a period of growth for the company, while
2027 saw a negative shift. This highlights the need for risk assessment, strategic planning, and
ensuring financial stability going forward.
The analysis of “O‘ztransgaz” JSC’s activities, conducted on the basis of a multiple regression-
correlation model, identified the main factors affecting the company’s economic efficiency. The
results showed that despite a decrease in the volume of natural gas production over 2015–2023,
the company’s total revenues increased by 1.8 times. This is primarily attributed to infrastructure
modernization, wage increases, and improvements in the compensation system.
According to the multiple regression analysis, both average wages and production volume have a
positive impact on revenue, while incentive expenditures have a negative impact. Notably,
revenue increased significantly with the rise in average wages, confirming the success of
transitioning to a generalized incentive system and ensuring efficiency through fair
compensation for labor.
Correlation analysis results also confirmed a significant relationship between these factors.
Specifically, a moderate positive correlation was found between average wages and revenue,
while a strong negative correlation was observed between incentive expenditures and revenue.
Based on these results, it can be concluded that in the future, strategic decision-making should
focus on increasing labor productivity, fair wage system design, and further improvement of
incentive mechanisms. This will contribute to the long-term economic stability of “O‘ztransgaz”
JSC and strengthen its role in the national energy system.
References:
1.
G‘ulomov S.S., Abdurahmonov Q.X., Xudoyberdiyev A.X. Macroeconomics. —
Tashkent: “IQTISOD-MOLIYA”, 2021.
2.
Ahmadjonov A.A. Fundamentals of Regression and Correlation Analysis. — Tashkent:
“Fan”, 2017.
3.
Tashkent State University of Economics. Practical Guide on Econometrics. — TSUE,
2021.
4.
Presidential Decree of the Republic of Uzbekistan, No. PQ–4422, August 9, 2019, “On
Measures for Reforming the Gas Pipeline System”.
5.
Khodjayev U. Theory and Practice of Statistics. — Tashkent: “Iqtisodiyot”, 2019.
