1. Introduction

2. Material and Methods

3. Results and Discussion

3.3. Research on Opportunities for a Circular Economy in West Kazakhstan Region

3.3.1. Analysis of the Generally Accepted Strategy and Practice of Its Implementation at KPO to Reduce Harmful Air Pollution into the Environment Within the Framework of ESG.

The potential for introducing and implementing the principles of a circular economy is manifested in those countries and regions in which operating industrial companies demonstrate their commitment to sustainable development goals and ESG principles (Yan Li,et al, 2023). To analyze the main factors determining emissions of pollutants into the atmosphere of the region, the main strategic directions for reducing emissions associated with KPO production activities were studied. The company was one of the first in Kazakhstan to formulate its vision of environmental policy as the initial stage of implementing the Environmental, Social, and Corporate Governance (ESG) system (https://www.kpo.kz/en/sustainability).An analysis of the strategy and practice of atmospheric emission reduction methods used at KPO showed that in achieving stated goals and indicators, in this industrial sphere that at the same time is ecologically impacting the environment, there are many factors and, as a result: risks in this industry(Figure 5). The construction of this matrix made it possible to identify important independent variables that influence the dependent criterion - emissions of main pollutants per unit of GDP and per living population, as an indicator affecting the circularity of the West Kazakhstan Region economy.

Figure 6 shows information from the KPO Sustainable Development Report for 10 years, characterizing atmosphere emissions, including greenhouse gases and generation of waste, which allows us to provide an additional assessment of the KPO Sustainable Development Strategy implementation.

Reducing the gas flaring volume, and consequently reducing atmosphere emissions, is one of KPO’s key obligations. As a number of measures result to implement the best available technologies, a significant reduction in pollutants emissions into the air was achieved. Thus, due to the ESG strategy implementation for the period 2012-2022, KPO’s atmospheric emissions per capita decreased by 2.2 times. Significant changes have been observed since 2017 when emissions fell by 25% compared to 2016. Moreover, the results of this period made it possible to achieve a 40% reduction in emissions by 2022 (https://www.kpo.kz/docs/sustainability_report_2023). It was the high-pressure gas reinjection technology introduction, which makes it possible to significantly reduce the gas flaring volume from the total volume of gas produced (0.82 tons per 1 thousand tons), that created these waste reduction results. Gradually, since 2013, the volume of flare gas combustion began to decrease, which correlates with a decrease in the volume of atmospheric emissions in the WKR (Figure 6a).

Technological modernization of oil and gas condensate production has allowed KPO to significantly reduce its environmental footprint. Analysis of the dynamics of the gas injection volume into the reservoir for 2012–2022 (Application A) showed that KPO, having provided a significant reduction in gas flaring, achieved a high level (99.95%) of gas utilization, thanks to the successful use of advanced solutions and technologies to reduce emissions. Thus, in 2023, the volume of gas reinjection to maintain reservoir pressure amounted to 12.650 billion m³, which approximately corresponds to 56.5% of the total volume of gas produced (https://www.kpo.kz/docs/sustainability_report_2023). As a result, in 2023, the specific indicator of greenhouse gas emissions in CO₂ equivalent at KPO amounted to 65 tons of CO₂ per thousand tons of hydrocarbon production, i.e., according to the International Association of Oil and Gas Producers (IOGP), greenhouse gas emissions per unit of hydrocarbon production at KPO are 14% lower than European indicators and 44% lower than international indicators.

As part of the implementation of strategy to reduce harmful atmospheric emissions, KPO also uses a set of management decisions (Figure 5). Thus, for energy saving and increasing energy efficiency, the company conducts a mandatory energy audit according to the ISO 50001 “Energy management system” every five years (https://www.bsigroup.com/). At the same time, KPO’s energy intensity indicator in 2022 was 1,18 GJ/ton of hydrocarbons, which is lower than the average energy intensity indicator of companies reporting to the International Association of Oil & Gas Producers (IOGP) -1.5 (https://www.kpo.kz/docs/sustainability_report_2023).

Analysis of reporting on KPO’s sustainable development, pro-sustainable news on events reflecting the company’s environmental, social and management responsibility, made it possible to collect and systematize data on KPO’s social impact indicators over a ten-year period (https://www.kpo.kz/en/sustainability, https://www.kpo.kz/ru/ustoichivoe-razvitie/socialnaja-otvetstvennost/podderzhka-socialnoi-infrastruktury). It should be highlighted that KPO systematically implemented the standards of Occupational Health and Safety Management Systems (OHSAS18001-2007): 403-1, 403-7, 403-8 (applied until 2017, and since 2018 - ISO45001-2018), GRI Management: 103-1, 103-2 (3-3), ISO14001:2018 and others (https://www.globalreporting.org/, http://www.aims.org., https://www.iso.org/ru, https://www.greenwgroup.com). This undoubtedly indicates to sustainable development commitment of the company; nevertheless, an influence of external factors corrects the overall quantitative results. Thus, the presented data (Figure 6b) indicate that, in general, the ESG assessment is not entirely unambiguous.

On one hand, the company provides information on costs of environmental protection (the average amount for the period is $20.7 million) and the financing of regional development projects (similarly, $19.3 million). On the other hand, unfortunately, both indicators decreased in 2022 by almost 4 times compared to the average values for the period under review. Basing on it, we are concerned about the continued level of energy consumption and energy intensity, which are directly related to greenhouse gas emissions (Figure 7), that also continue to remain high - 1851 thousand tons in 2022, which is even 9 thousand tons higher than the average value for the period.

The company highlights the results of its activities in restoring disturbed lands, although its indicators decreased by almost 2 times in 2022. The process of reducing foreign content in the KPO value chain looks more effective. Thus, KPO is carrying out comprehensive work to involve Kazakh suppliers and commodity producers in the development of the field in terms of works, goods and services purchase. As a result of systematic work, the share of local content in the company in 2023 reached USD 756.1 million (63.2%) (Figure 6b).

An analysis of the strategy and practice of waste management methods used at KPO showed that in the implementation of the stated goals and achievement of indicators, in this sphere, as an industrial one and one that has an environmental impact, there are many factors and, as a result – risks. The construction of this matrix (Application B) made it possible to identify important independent variables that influence the dependent criterion - ESG commitment as an indicator influencing the circularity of the West Kazakhstan Region economy.

It should be noted that the KPO sustainability report, along with quantitative indicators reflecting the circularity level of companies, contains data on the specific activities of company in green and circular economy, for example, in biodiversity conservation, prospects for the use of renewable energy sources, etc. that are of interest to a large number of stakeholders. Nevertheless, during this analysis, no data were found regarding the use of renewable energy sources. It is too early to talk about the use of renewable energy sources. Only in 2022, KPO gradually began developing a “green” KPO strategy, which in the future provides for the development of renewable energy sources and projects to reduce atmospheric emissions aimed at preserving natural ecosystems and biodiversity, we are talking about the use of the region’s wind potential (https://www.kpo.kz/en/sustainability).

During the research, it was found that KPO uses the subsidiary environmental responsibility principle, implementing measures to minimize the environmental footprint and impact as a business entity (from investment activities). Thus, within the framework of the terms of the contracts, the company transfers part of the waste for disposal to specialized contractors, who independently determine methods for further handling of waste received from KPO and quarterly report on their transfer to third parties. In accordance with the norms of the Environmental Code in Kazakhstan (Article 351) (Adilet.zan.kz, 2021), the company is working to organize the sorting and separate collection of industrial and household waste with the participation of contractors. This waste is then transferred to specialized enterprises for use as recyclable materials.

In addition, according to the restrictions introduced in Kazakhstan since January 2019, enterprises are prohibited from transporting to landfills anything that can be recycled: waste paper, plastic, scrap metal, glass. This limitation was reflected in reporting: business entities began to present detailed data (Application C). KPO, in turn, started transferring wastes for processing even earlier, but only since 2018 this information began to be reflected in details. In particular, according to 2017 data, the total volume of waste transferred for recycling amounted to 152 tons, while only plastic (23.18 tons) and waste paper (14.4 tons) were specified in detail.

Figure 7. Waste processing data at KPO. Source: compiled by the authors based on https://www.kpo.kz/en/sustainability.

Figure 7. Waste processing data at KPO. Source: compiled by the authors based on https://www.kpo.kz/en/sustainability.

Studying data on the waste management system at KPO allows us to note the use of various technologies for the materials reuse, treatment and reuse of wastewater, processing of liquid and solid waste. Figure 7 shows data on the volume of various waste processed at KPO facilities over 10 years, which shows that the share of processed waste is growing annually, while the overall volume of waste is decreasing.

Thus, the study showed that in order to reduce atmospheric emissions, KPO uses such practices as optimization of production processes using innovations and the best available technologies, development and application of closed production cycles, introduction of energy efficient technologies and energy management systems (Popkova,E., Sergi, B.,2023), creation of an effective waste management system, including sorting, recycling and disposal of waste, promoting responsible consumption and production. In this regard, KPO, as the flagship of the oil and gas industry in Kazakhstan, has great opportunities to prevent the negative consequences of environmental pollution in the region.

The conducted research allowed us to draw a conclusion about the possibility of a more in-depth study of the experience of KPO in solving issues of reducing emissions of pollutants into the atmosphere, as an economic entity implementing measures and introducing technologies to reduce harmful impacts on the environment, and creating conditions for the formation of closed-loop production.

3.5. Studying the Creation of a Circular Economy Meso-Model Based on KPO

Within the framework of this research, studying the situation in reducing harmful emissions into the atmosphere and reducing waste is considered as a key basis for creating conditions for the economy circularization. This is dictated by the fact that the following approach is taken as the basis for the method of organizing such an economy. The circular economy is considered as a development model that sets itself the complex task of minimizing waste and reducing emissions based on optimizing the use of resources (Circle Economy and Shifting Paradigms,2019, Circle Economy, 2020, PottingJ,et al,2017).

3.5.1. Dependence of Atmospheric Pollutants Emissions from “Pro-Sustainable” Measures at KPO for 2012-2022.

As part of the task set to find the main reducing factors of harmful atmospheric emissions, according to the results of the literature review and analysis of the main quantitative and qualitative indicators dynamics (Figure 1, Figure 2 and Figure 3 and Figure 6, Application A), the following indicators were identified and systematized. These indicators will be used as the initial metadata base for correlation and regression analysis (all indicators for KPO).

Dependent Variables: EMISSION1 level per unit of production (UPR), EMISSION2 per capita (CAP) and EMISSION3 that will be checked for sensitivity to independent variables, for multicollinearity, and as a result one dependent variable will be selected.

The following were selected as Independent Variables:

Independent Variables	Description	Independent Variables	Description
OIL GAS PROD	Oil and gas condensate production, million barrels	PRO WASTE	Volume of processed industrial waste, th.t
FLARED GAS	Volume of flared gas, million m3	ENV PROT COST	Total costs for environmental protection, million USD
ENV TECH	Use of environmentally friendly technologies - gas injection into the reservoir, billion m3	ENERGY_ CONS	Energy consumption, PJ

Table 1. Initial data for identifying the dependence of atmospheric emissions at KPO.

Table 1. Initial data for identifying the dependence of atmospheric emissions at KPO.

			2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
DV	EMISSION1	KPO emission level per unit of production, kg/t.	0,55	0,61	0,721	0,585	0,599	0,431	0,386	0,404	0,387	0,262	0,299
	EMISSION2	Volumes of pollutant emissions from KPO, per capita in the Burli district, kg/person.	199,34	213,3	261,9	208,8	205,2	154,1	138,17	133,9	132,49	82,95	89,57
	EMISSION3	Volumes of pollutant emissions from KPO into the atmosphere, tons	10472	11320	14005	11314	11421	8569	7759	7597	7591	4798	5236
IDV1	OIL GASPROD	Oil and gas condensate production, million barrels	139,5	136	142,5	141,7	139,7	145,8	147,5	137,9	143,9	134,1	128,5
IDV2	FLARED GAS	Volume of flared gas, million m3	23,5	38	35,5	26,8	29	16	12	17	15	11	13
Note: compiled based on sources (https://www.kpo.kz/en/sustainability, EEAR, 2022 Nyashina, G.,2018, TurbinaK and Yurgens Yu, red.,2022., Browder G. et al.,2018).

The results of the indicators dynamics’ analysis and the conclusions interpretation made it possible to establish that the use of a relative indicator - KPO emission level per unit of production, kg/t (EMISSION1) as a dependent variable will not be entirely correct, since the calculation base for it was the volume of oil and gas condensate production in natural volumes. While other indicators by level of pollutant emissions in the country and WKR were calculated based on GDP in monetary terms.

The results of assessing the correlation dependence of EMISSION1 on independent variables showed that there is practically no connection with three indicators - OIL GAS PROD, PRO WASTE, ENV PROT COST, which indicates the need to check the next dependent variable EMISSION2. At this stage, we have obtained a low dependence and interrelation between EMISSION2 and the PRO WASTE indicator, however, in general, this dependence can be considered for further research. Considering that the EMISSION2 indicator is a qualitative criterion obtained by calculation, the process of econometric calculations for the EMISSION3 indicator was continued (Table 2).

An assessment of the correlation dependence for the dependent variable EMISSION3 (volume of pollutant emissions into the atmosphere at KPO) showed that, just like for the EMISSION2 indicator, here we observe a weak relation with the PRO WASTE indicator. Obviously, this is explained by small values of PRO WASTE, i.e. those wastes that are processed at KPO itself, and the impact of non-recyclable waste incinerated in the furnace, which can significantly reduce their volume (by 89%), is obviously reflected in an increase in ENV PROT COST energy consumption, which requires further study. At this stage of the study, we excluded the independent variable PRO WASTE - the volume of processed KPO industrial waste, as a weakly influencing indicator. At the same time, the interrelation with other indicators turned out to be higher. In this regard, under the influence of identified factors, it was decided to continue econometric calculations for the dependent variable EMISSION3 and select it as the main performance indicator for modeling forecast elections.

Table 3. Correlation dependence (accepted result for EMISSION3).

Table 3. Correlation dependence (accepted result for EMISSION3).

	EMISSION3	OIL GAS PROD	FLARED GAS	ENV TECH	ENV PROT COST	ENERGY_ CONS
EMISSION3	1
OIL GAS PROD	0,381727	1
FLARED GAS	0,904194	0,029387	1
ENV PROT COST	-0,7176	-0,45938	-0,5966	1
ENERGY_ CONS	0,303915	0,365131	0,340601	-0,34042	1
ENV PROT COST	-0,55713	-0,06261	-0,55508	0,731779	-0,20968	1

Output of regression analysis results using the R program

Before building a model, we will form a correlation matrix to determine multicolinearity

It clearly shows the correlation between each pair of variables. Analyzing the heat map, we can see that multicolinearity between the regressors is not observed. Based on this, we include all parameters in the model and build an equation.

Residual standard error: 714.8 on 5 degrees of freedom

Multiple R-squared: 0.9685, Adjusted R-squared: 0.9371

F-statistic: 30.79 on 5 and 5 DF, p-value: 0.0009212

By removing the remaining predictors from the equation, except for the first two, we improved the model. We also checked the significance of the regressors using the Student criterion, which showed that the calculated t-statistics are greater than the critical one and confirmed the significance of the factors. Since the regression showed no significant relationship between EMISSION3 and ENV TECH, PRO WASTE, ENV PROT COST and ENERGY CONS we removed them from the model and conducted further research on the relationship between EMISSION3 ~ OIL GAS PROD + FLARED GAS.

Based on this, the forecast will be more accurate.

Let's build a 3D visualization of the relationship model <- lm(EMISSION3 ~ OIL GAS PROD + FLARED GAS, data = data)

Figure 8. 3D visualization of the relationship model.

Figure 8. 3D visualization of the relationship model.

By removing non-significant predictors from the model, we found that even (Intercept) became significant at the p value of 0.00626 **

The significance of the model increased F-statistic: from 30.79 up to 67.19

model <- lm(EMISSION3 ~ OIL GAS PROD + FLARED GAS , data = data)

Residuals:

Min 1Q Median 3Q Max

-1458.07 -356.51 38.36 463.90 894.01

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -22400.09 6094.87 -3.675 0.00626 **

OIL GAS PROD 184.55 43.53 4.240 0.00284 **

FLARED GAS 265.24 24.88 10.660 5.26e-06 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 755.3 on 8 degrees of freedom

Multiple R-squared: 0.9438, Adjusted R-squared: 0.9298

F-statistic: 67.19 on 2 and 8 DF, p-value: 9.968e-06

By removing the remaining predictors from the equation, except for the first two, we improved the model. We also checked the significance of the regressors using the Student criterion, which showed that the calculated t-statistics are greater than the critical one and confirmed the significance of the factors.

print(future_predictions (Figure 9))

Year Forecast_Value

1 4592.582

2 3841.618

3 3090.655

4 2339.691

5 1588.727

Based on the provided results of the linear regression model metrics, we will conduct a deep testing of the model for its accuracy from the standpoint of errors.

The result of calculations of forecast reliability metrics is made in the R program:

Mean absolute error (MAE): 526.8903

> cat("Mean square error (MSE):", mse, "\n")

Mean square error (MSE): 414844.4

> cat("Root of the root mean square error (RMSE):", rmse, "\n")

Root of the root mean square error (RMSE): 644.0842

> cat("Determination coefficient (R²):", r_squared, "\n")

Determination coefficient (R²): 0.9438106

> mape <- mean(abs((data$Y3 - predictions) / data$Y3)) * 100

> cat("Mean absolute percentage error (MAPE):", mape, "%\n")

Interpreting the output of the model error check in R

Mean Absolute Percentage Error (MAPE): 5.923575 %

(MAE): the error of 526.89 can be considered small, since Y3 varies in thousands (which we can see from the data), so the model shows a good level of accuracy.

(MSE): 414,844.4 (MSE measures the average squared deviation between forecasts and actual values, the metric is especially sensitive to large deviations. MSE is more sensitive to outliers than MAE). In this case, it does not exceed MAE (526.89), which indicates the absence of large outliers.

(RMSE): 644.08 The difference between MAE and RMSE indicates the absence of significant outliers, the absence of outliers means there are no anomalies in the original data itself.

(R²): 0.9438 - R² indicates that the model explains 94.38% of the variations in the dependent variable (EMISSION3), indicating that the model explains the relationship between the independent variables (OIL GAS PROD, FLARED GAS) and the dependent variable (EMISSION3) well. This means that most of the variations in EMISSION3 are explained by the model.

(MAPE): 5.92% indicates that on average, the model's forecasts deviate from the actual values ​​by 5.92%, indicating high forecast accuracy.

Metrics Inference: All metrics including MAE, RMSE, R², and MAPE indicate that the model is highly accurate and does a good job of forecasting. Small deviations revealed by RMSE may be insignificant and indicate the absence of significant outliers.

The construction of this regression model made it possible to confirm the positive relationship between emissions and production volume and the volume of flared gas, which proves the correctness of our chosen independent variables and the presence of mathematically proven connections between these indicators. At the same time, testing the model showed the absence of a statistically significant relationship between emissions and environmental protection costs, the volume of gas injected into the reservoir and the volume of energy consumed. Thus, a forecast was built based on the relationship EMISSION3 ~ OIL GAS PROD + FLARED GAS, also guided by the results of error checking

The data obtained as a result of the multiple regression analysis on the forecasted indicators of emissions of pollutants into the atmosphere, both through modeling in the R program and in the Excel program, made it possible to establish that while maintaining certain dynamics of independent indicators development, the level of emissions at KPO may be reduced 2 times by 2028. However, forecasting a high probability of events, as well as predicting a low probability of events, determined the possibility of establishing a possible corridor for the probability of deviations, along which the level of emissions by 2028 could rise to the level of 2019 in case of a deterioration in the indicators selected as factor predictors of atmospheric emissions.

Thus, based on studying the atmospheric pollution reduction based on a circular economy model formation in the WKR, we can conclude that two poles can be distinguished in the region: one is the industrial north, where the main emissions into the atmosphere occur; the other is predominantly agricultural, which accounts for the remaining emissions. The analysis showed that the introduction of the circular economy principles is possible at the mezo level - of an individual company, in this case KPO, to create an effective air quality management system.

3.5.2. Matrix of Industrial-Agrarian Potential of West Kazakhstan Region

The following directions, consolidated into a matrix of regional capabilities, have been chosen as the key approaches for systematizing accumulated experience and technological innovations as the basis for the circularization of the region. The construction of a matrix for the industrial sector in “oil and gas production” category made it possible to identify the main conditions, factors, expected results from the circularization measures implementation and identify the risks accompanying this process at the beginning of the period under study (2012). A similar approach was applied to other categories of the industrial sector, but in this case for the current moment - the construction industry, energy, transport and logistics, waste processing and the agro-industrial sector and its categories (crop production and livestock farming). According to the calculations of foreign experts in Kazakhstan, 46% of the used arable land (11.5 million hectares) and 70% of pastures in terms of ecological condition are suitable for organic crop and livestock production (https://www.fao.org/agroecology/database/detail/ru). It should be noted that the use of this approach was dictated by different resource capabilities and technological features of these industries. In other words, what KPO has done over the period from 2012 to 2022 in the field of reducing pollutants emissions into the atmosphere, introducing the best available technologies to reduce emissions and recycle waste, other enterprises in other industries are just starting to implement, or their experience is negligible. For this reason, the remaining sectors were assessed according to their current state of development in the area. For each of them, only those aspects that are directly related to the terms for the circular economy formation were specified.

The matrix, built basing on the materials, reflecting main conditions and factors determining the need to organize a circular economy in the WKR, as well as taking into account all previously obtained results, allows us to make the following conclusions (Figure 10).

Regional zoning has shown that the significant concentration of emissions in the northern part of the region determines the need to concentrate circularization efforts, first of all, among those companies operating in this part of the region. In addition, this requires concentrated efforts to create the necessary infrastructure for monitoring and control of atmospheric emissions and wastes in this part of the region and surrounding areas. At the same time, the study shows that companies in other industries also demonstrate a desire to implement circular management principles. Thus, by determining the conditions/factors that exist or which ensure the implementation of the circularity of technological, production and labor relations – regional authorities can direct efforts to these components when developing Roadmaps, strategies or programs for the development of the region.

The correlation and regression analysis, revealing close relationship between the volume of atmospheric emissions and indicators reflecting the results of KPO’s measures to implement the best available technologies, spending funds on environmental protection, the volume of gas burned and others, showed the effectiveness of KPO’s emission reduction activities. From other side, this analysis made it possible to argue for the correctness of the approaches and their applicability in constructing a matrix of the industrial-agrarian potential of the WKR to create conditions for the circularization of the region’s economy. By connecting the matrix of “Oil refining, 2012” category with the results of econometric modeling, we were able to assess the measures efficiency and argue conclusions regarding the standard selection of KPO as an example for building managerial, technological and economic decisions to form conditions for the WKR’s economy circularization. This, in turn, made it possible to model a matrix for KPO, reflecting the consequences of the “circularity” of production at the enterprise, achieved as a result of the implementation of measures to reduce emissions and recycle wastes (Application D).

An assessment of the matrix of other industries in the region, which are just launching technologies that will form conditions for the regional economy circularization, showed the effectiveness of the systematic approach used in the study and emphasized the achievability of the goal set in the study. Thus, the matrix allows us to see the potential for circularity between industries in the agro-industrial sector and the energy sector, transport industry, logistics, waste management and construction. This matrix allows us to identify the region’s potential to implement the integration of all production, technological, and logistics processes using the key principle - a focus on reducing polluting emissions into the atmosphere and recycling waste.

Along with it, this matrix was supplemented with other conditions ensuring the circularization process. First of all, the development of IT infrastructure through the creation of an automated information system (AIS) that will form an information database for industries that helps ensure uninterrupted supply chains, organizes an effective sales system for finished products (high need in the agro-industrial sector), and maintains up-to-date data on pollution, emissions and waste (environmental sustainability of the region). In addition, a major contribution to the circularity of the region’s economy can be made through the development of IT technologies in crop production (introduction of “smart” greenhouses, “smart” irrigation), in livestock farming (“smart” pasture), in construction (introduction of digital technologies as in construction of residential buildings, as well as industrial and office premises) and others (Popkova,E., Sergi,B.,Eds.,2021).

An important condition for stimulating the conditions creation for the circularity of the economy is intended to be a system of preferential taxation and a targeted system of payments, the sale of quotas, which can be implemented within a given region, and initiated generally throughout the country. Launching a circulation system within even one region will require a qualitative update of the region’s labor resources. In this case, the support of regional authorities is key factor. This can be expressed in the organization of programs/platforms for retraining personnel, training a new generation of personnel, and even attracting specialists from other regions. The main risks in the matrix of regional potential are increased costs, rising prices for products and services for the end consumer, and lack of investment.

Thus, the set goal was achieved and the hypothesis put forward was confirmed during the research. The results of the analysis made it possible to identify a high level of scientific attractiveness of reducing atmospheric emissions based on the formation of a circular economy in the WKR. The study of various scientific works on the research problem made it possible to substantiate the argumentation of the factors determining the reduction of atmospheric emissions of pollutants. As a result of the study, using the example of KPO, a circular economy model was developed, a close connection was revealed between the volume of atmospheric emissions and indicators reflecting the results of KPO’s activities to implement the best available technologies, energy saving, and spending funds on environmental protection. It has been established that while maintaining independent indicators’ certain dynamic of development according to the forecast model for the period 2024-2028, the level of atmospheric emissions at KPO can be reduced by 2 times.

The established and proven potential of KPO as a company that successfully implements measures to reduce atmospheric emissions and implement an ESG sustainable development strategy allows regional authorities to take into account their experience when creating conditions for the formation of a circular economy in the region, when developing Roadmaps, strategies or programs for the development of the region. The developed matrix of industrial and agricultural potential of the West Kazakhstan region made it possible to identify the region's potential for the implementation of the integration of all production, technological, and logistics processes, using the key principle - focus on reducing polluting emissions into the atmosphere and recycling waste. Thus, we can recommend key measures to reduce atmospheric emissions: technological modernization of production based on the best available technologies and innovations; forming an reliable waste management system, including sorting, recycling and disposal of waste, promoting responsible consumption and production; energy efficiency stimulation and renewable energy sources use; development of IT infrastructure (creation of AIS/information databases by industry; introduction of “smart” digital technologies); development of economic incentive instruments (tax and payment benefits). The system of taxes on industrial pollution could be transformed into taxes on major pollutants to encourage the use of cleaner and more efficient fuels and technologies. In this regard, attracting investments in the form of issuing green regional bonds, international grants and loans is a necessary resource for circularization in the region.

Limitations and prospects for further research. When developing the model, only those variables were chosen that showed a close and moderate relationship with the aggregate indicator: in particular, we observed a weak connection with the X4 indicator - the volume of recycled industrial waste, due to their small values. At the same time, as part of further research, it will be possible to consider all types of companies formed both at KPO and in the region as a whole. The meteorological factors of the region, due to their diversity and complex nature of impact, were not considered in this study and require further research, the results of which will make it possible to create a modern information database of pollutants using GIS technologies to predict atmospheric air quality.

4. Conclusion

• This study is the first presentation of the possibility of reducing air pollution based on the circular economy formation in Kazakhstan’s industrial-agrarian region. Despite the fact that efforts are being made in Kazakhstan to build a circular economy, in comparison with world developed countries, the field of circular economy is still at the beginning of its journey.
• In this work, based on an analysis of pollutant emissions from each sector of the West Kazakhstan region economy, based on international experience, the task was to consider possible approaches to a circular economy formation that would ensure an atmospheric emissions reduction. The construction of the industrial-agrarian potential matrix of the West Kazakhstan region made it possible to obtain results aimed at updating approaches in managing regional development in order to circularize all processes.
• The study of atmospheric emissions in the West Kazakhstan region made it possible to zonally identify two sectors: the industrial north - 95.8%, (Uralsk - 34.1%, Burli -26.7% and Baiterek - 35%), and – agricultural south, 4.2% from all atmospheric pollutants emissions in the West Kazakhstan region. Taking this factor into account allows you to adjust the strategy for developing decisions. In the north of the region, in the industrial center, it is necessary to install a coordinated system of automated monitoring and control of atmospheric air quality to promptly respond to changes, allowing adjustment of measures taken on the basis of the geoportal created in the region.
• It is shown that the international company KPO has the greatest economic (taxes in 2022 - $3 billion), environmental (68,5% of atmospheric emissions from the Burli region) and social impact (share of local content -63.2% in KPO work) on the regional sustainable development. Within the framework of the KPO strategy, implemented in the period 2012-2022 measures to reduce emissions and waste made it possible to note the effectiveness of these measures and the achievement of a reduction in KPO atmospheric emissions per capita by 2.2 times, the gas utilization rate was 99.95%.
• The analysis showed that at the moment, the introduction of the circular economy principles is possible only at the mezo level - of an individual international company, in particular KPO. The possibility of using circularity indicators in the company's work was assessed and a circular economy model was proposed. Correlation and regression analysis revealed a close relationship between the volume of atmospheric emissions and indicators reflecting the results of KPO’s activities to implement the best available technologies, energy saving, and spending funds on environmental protection. According to the forecast model for the period 2024-2028, it was determined that while maintaining independent indicators’ certain dynamics of development, the atmospheric emissions level at KPO can be reduced by 2 times.
• The scientific work results contain proven arguments for the prospects of further research into the circular economy models formation in the regions of Kazakhstan, aimed at protecting the environment in general, and in particular reducing air pollution.
• Experience of KPO company which was studied in the work on the formation of a circular economy in the region is relevant and can be applied in other Central Asian countries with the same commodity economy with significant influence of international companies. The governments of Central Asia countries during regulation of the operation of international companies need to form close cooperation with those companies within the framework of corporate social responsibility of business in order to implement sustainable development practices.

Application A. Environmental Friendliness of Gas Production at KPO for 2012-2022

Figure A1. Environmental friendliness of gas production at KPO for 2012-2022. Note: compiled by the authors based on https://www.kpo.kz/.

Figure A1. Environmental friendliness of gas production at KPO for 2012-2022. Note: compiled by the authors based on https://www.kpo.kz/.

Application B. Compliance Matrix for Waste Management Strategy and Tactics at KPO

Figure A2. Compliance Matrix for Waste Management Strategy and Tactics at KPO. Note: compiled by the authors based on sources (Circle Economy and Shifting Paradigms,2019, https://www.kpo.kz/en/sustainability, http://waste-nn.ru/sistema-upravleniya-othodami-v-stranah-es).

Figure A2. Compliance Matrix for Waste Management Strategy and Tactics at KPO. Note: compiled by the authors based on sources (Circle Economy and Shifting Paradigms,2019, https://www.kpo.kz/en/sustainability, http://waste-nn.ru/sistema-upravleniya-othodami-v-stranah-es).

Application C. Transfer of KPO Waste for Processing to Third Parties for 2012-2022

Figure A3. Transfer of KPO waste for processing to third parties for 2012-2022. Source: compiled by the authors based on https://www.kpo.kz/en/sustainability.

Figure A3. Transfer of KPO waste for processing to third parties for 2012-2022. Source: compiled by the authors based on https://www.kpo.kz/en/sustainability.

Application D. KPO Matrix for the Forecast Period of the Study (2024-2028)

Figure A4. KPO Matrix for the forecast period of the study (2024-2028).

Figure A4. KPO Matrix for the forecast period of the study (2024-2028).

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