CC BY
365. Bezena, I. M., Sychenko, V. V., Rybkina, S. О. (2020) Mechanisms of public administration democratization and the career development of officials of authorities: regional aspect. Public Administration and Customs Administration. Scientific and practical journal of public administration. № 2, 2020. С. 50-57. DOI https://doi.org/10.32836/2310-9653-2020-2.9
6. On approval of the Concept of reforming local self-government and territorial organization of power in Ukraine (2014). Order of the Cabinet of Ministers of Ukraine from 01.04.2014 р. №333-р. [Published in Ukrainian]. URL: https://za-kon.rada.gov.ua/laws/show/3 3 3 -2014-%D1%80#Text
7. On service in local self-government bodies (2001). Law of Ukraine. Information of the Verkhovna Rada of Ukraine, 2001, №33, ст.175. [Published in Ukrainian]. URL: https://za-kon.rada.gov.ua/laws/show/2493-14#Text
8. Habermas, Jürgen. (2003) Die Postnationale Konstellation. Politische Essays. Frankfurt am Main: Suhrkamp Verlag, 1998. S. 91-163). Логос, 2003, № 4-5 (39). URL: http://www.ruthenia.ru/logos/num-ber/39/09.pdf
9. Encyclopedic Dictionary of Public Administration / Ed. Y. Kovbasyuk, V. Troshchynsky, Y. Sur-min. Kiev, 2010. 820с. [Published in Ukrainian].
10. On Education. Law of Ukraine (2017). [Published in Ukrainian]. URL: https://za-kon.rada.gov.ua/laws/show/2145-19/print
11. Akimova L., Khomiuk N., Bezena I., Lytvyn-chuk I., Petrove O.. Planning of Socio-Economic Development of the Territories (Experience of European Union). International Journal of Management (IJM). Volume 11, Issue 4, April 2020, pp. 638-646.
12. On local self-government in Ukraine. Law of Ukraine (2020). [Published in Ukrainian]. URL: https://zakon.rada.gov.ua/laws/show/280/97-%D0%B2%D1%80
13. On approval of the Regulations on the system of professional training of civil servants, heads of local state administrations, their first deputies and deputies, local self-government officials and deputies of local councils. Resolution of the Cabinet of Ministers of Ukraine from 06.02.2019 №106. [Published in Ukrainian]. URL: https://zakon.rada.gov.ua/laws/show/106-2019-%D0%BF#Text
14. On approval of the Procedure for the organization of advanced training of civil servants, heads of local state administrations, their first deputies and deputies, local self-government officials. Order of the National Agency of Ukraine for Civil Service dated 26.11.2019 11211-19, registered in the Ministry of Justice of Ukraine on 02.01.2020 for №5/34288. [Published in Ukrainian]. URL: https://za-kon.rada.gov.ua/laws/show/z0005-20#Text
15. Decentralization provides opportunities -Dnipropetrovsk region: the formula for success (2020). The site of the Dnepropetrovsk CRMS. [Published in Ukrainian]. URL: https://decentralization.gov.ua/ar-eas/0562
16. State Statistics Service of Ukraine Information on local governments as of 2015. [Published in Ukrainian]. URL: http://www.ukrstat.gov.ua/
17. Bezena I. M. (2019) Democratization of public administration and info-media development of its officials' competences. State and regions. Series: Public Administration". № 2, 2019. C58-64. [Published in Ukrainian]. DOI https://doi.org/10.32840/1813-3401-2019-3-5
ECONOMIC AND ENVIRONMENTAL ANALYSIS OF KAZAKHSTAN'S CARBON DIOXIDE EMISSION REDUCTION BASED ON A COMPUTABLE GENERAL EQUILIBRIUM MODEL
Bolor-Erdene Turmunkh
School of Economics and International Trade, Hunan University
Changsha city, P.R. China
Abstract
Throughout the world have been looking for reducing significant ways for Global warming and greenhouse gas (GHG) emissions. Kazakhstan is located in Central Asia, has been managed the natural resource management badly, that is influenced by environmental degradation for many years. Kazakhstan is being faced with difficulty to stop carbon dioxide (CO2) emissions due to its growing power plant energy consumption. A detailing and accurate calculation of CO2 emissions in Kazakhstan in terms of production and consumption is the first step in further action. The efficient use of petroleum products plays an important role in reducing total CO2 emissions in the power plant energy sector. This study uses a simulation calculation and a Computable general equilibrium (CGE) model because it takes into account the interaction between power energy products and the economy. In 2005 Social Accounting Matrix (SAM) table of Kazakhstan was used for simulation. This study contains three scenarios of Business as Usual (BaU) and CO2 emissions that were developed and predicted by the production-based CO2 emission inventory in Kazakhstan from 2020 to 2035. An Input-Output (IO) table covering 18 industries in Kazakhstan which were including information on five power energy generating industries. Depend on the simulation results, policymakers emphasize that controlling the growing energy consumption and CO2 emissions in Kazakhstan, improving fuel efficiency is to be an important strategy. Implementing a coal resource tax policy is an appropriate measure to further reduce CO2 emissions, and there is an urgent need to develop renewable energy.
Keywords: Computable general equilibrium model; Social Accounting Matrix; Fossil fuel subsidy; Carbon emission; Kazakhstan
1. INTRODUCTION
The Kyoto Protocol (1997) represented the first historic agreement to control GHG emissions. Since the first commitment of the Kyoto Protocol expired in 2012, the world has been exploring more effective ways to reduce GHG emissions, with the active participation of developed and developing countries focusing on reducing CO2 emissions. This was followed by the Paris Agreement, signed during the 2015 United Nations Climate Change Conference (UNFCCC), which outlined a global response to climate change by 2020. According to the Paris Agreement, Kazakhstan will meet its unconditional target of a 15 percent reduction in greenhouse gas emissions by December 31, 2030 (compared to 1990) and conditional by December 31, 2030 (compared to 1990). committed to target and reduce greenhouse gas emissions by 25 percent (Kazakhstan 2015; UNFCCC, 2019).
Many researchers have been analyzing CO2, CH4, and N2O emissions from major countries around the world, and CO2 accounts for 60 percent of the world's total GHG emissions since the 1970s, economic growth has been increasing in recent years, energy demand has got up, and environmental pollution has become a major contributor to GHG emissions and global climate change (Tang et al., 2017). Due to the rapid pace of global industrialization, excessive energy consumption is a major cause of CO2 emissions.
Kazakhstan has been a steadily growing country in terms of population and gross domestic product (GDP) since 2000. Kazakhstan annually exports more than 100 billion tons of oil equivalent to three types of energy resources (coal, oil, and gas) to the world market (Wang et al., 2019). As Kazakhstan has an important energy export status, we will further analyze CO2 emissions from domestic and foreign markets using an environmentally friendly expanded output model. Research on energy and environmental issues in Kazakhstan began in 2010, and most of the research has been empirical (Amirov et al., 2018; Bolor-Erdene T. 2020; Diyar et al., 2014; Karatairi et al., 2018; Li et al., 2019; Russell et al., 2018; Nugumanova et al., 2017; Nurgaliyeva, 2016; Wang et al., 2019). For example, Bolor-Erdene T. (2020) examines the relationships between non-renewable and renewable energy consumption, CO2 emissions, and economic growth in Kazakhstan, using 1990 to 2014 data from the World Development Indicators. It performs tests to verify the existence of the long run relationships among the variables and examines short and long run causal relationships. It finds that increased fossil fuel use is the main cause of increased CO2 emissions.
The CGE model has the unique advantage of simulating energy policy through a comprehensive analysis of general equilibrium compared to other methods. The CGE model is widely used in various energy taxes and environmental policies. In particular, there is some research on the centralization of coal resource taxes based on the CGE model. Targeted studies on the economy of Kazakhstan and CO2
emissions using the CGE model are very limited. In contrast, China's SAM table, CGE model, and provincial and city-level census of CO2 emission tax policy implementation have been relatively complete, and specialized studies have been conducted (Feng and Zhang, 2018; Li et al., 2020; Liu et al., 2015). These independent research articles are an effective addition to the general information of international agencies, as they are often focused on specific countries.
The purpose of this study is to prepare a SAM table based on the 2005 IO table in Kazakhstan, to develop a CGE model, and improve three scenarios of BaU scenario and CO2 emissions for 2020 to 2035, CO2 emissions forecasting, macroeconomic and commodities forecasting, and socio-economic calculations. This study is expected to make a significant contribution to literature in Kazakhstan and internationally. The article is divided into five sections. Following this introduction in Section 1, there is a review of related literature in Section 2. Section 3 describes the discusses the methodology. Section 4 discusses the data of sources. Section 5 provides the simulation results and discussion of the finding, while Section 6 concludes with some recommendations and suggestions for future research.
2. LITERATURE REVIEW
Many studies have been conducted on the economic and environmental analyses of different individual countries' energy subsidies, CO2 emissions, and economic growth using the CGE model. The empirical literature abounds with studies that investigate the environmental effects of energy use and economic growth for both developed and developing countries using the IO table and SAM, model specifications, methodologies, and functional forms.
Several studies focus on the impacts of economic policies on energy subsidies and CO2 emission. Fa-rajzadeh and Bakhshoodeh (2015) analyze the economic and environmental implications of the elimination of energy subsidies in Iran applying a CGE model. The subsidy reform was investigated under two scenarios namely, redistributing total subsidy revenue back to households and allocating it to households and producers proportionally. The results show that the elimination of energy subsidies via resource reallocation causes a fall in GDP relative to the initial equilibrium by at least 15 percent, while the general level of prices tends to increase by more than 10 percent compared to the initial level. Gelan (2018) examined the economic and environmental impacts of reducing electricity subsidies in Kuwait. A SAM was constructed together with energy consumption with CO2 emission were compiled, and then calibrated with a CGE model. The subsidy deducted from the electricity sector was allocated to users according to their share in the base year's total expenditure on electricity. The results indicated that such transfers would reduce the adverse economic effects, CO2 emissions fell by 0.5 percent. The GDP and household welfare effects were reversed, rising by 0.4% and 0.1% respectively. For Romania, Silviu
(2015) builds a CGE model capturing the mechanisms through which the availability of energy resources affects the economy. The analysis focuses on fossil energy resource depletion. The model is an open economy general equilibrium model with bilateral trade and a nested constant elasticity of substitution (CES) production function to capture the impacts of depletion. The effects on the main relevant economic indicators like GDP, sectorial production, household consumption, welfare is analyzed. Jendrzejewski (2020) integrates values of natural capital into Supply and Use Tables to illustrate depletion of forest due to natural disaster. It further applies the CGE model to demonstrate the economy-wide effects of a real event in which a hurricane felled almost 80 thousand hectares of trees in Polish forests in 2017. The model results correspond with the statistical data published after the mentioned event. Furthermore, they align with findings of previous studies, which applied different methodical approaches and show that without natural capital accounting the mac-roeconomic estimates provide misleading information about economic performance. Nong (2019) examine the economy-wide effects of this policy on the Australian economy by employing a national environmental and economic general equilibrium model so that Australians and the international audience would be aware of the impacts and how the reverse auction and carbon subsidy work as a policy to reduce emissions at large scales across all sectors in a country. Results show that Australia only experiences a relatively small impact on its economy. Real GDP only declines by 0.3% to 0.4% overall scenarios. Woldie and Siddig (2019) simulate the macroeconomic and distributional impacts of exchange rate devaluation in Ethiopia using a dynamic single country CGE model. We find that although devaluation helps exports to be more competitive in the short term, thereby increasing export earnings, over the long term the policy is found to have a contractionary and inflationary impact in a developing country like Ethiopia. For Malaysia, Pui and Othman (2017) investigate whether an environmental tax on petroleum products could induce more energy-saving and emission control. This research applies the CGE modeling for the simulation, as it takes into account the interaction between petroleum products and the economy as a whole. The simulation results found that fuel efficiency improvement could produce a double dividend effect with simultaneous benefits on the economy and environmental quality. Moreover, He et al., (2015) defines energy policies as the compilation of energy prices, taxes, and subsidy policies. Moreover, it establishes the optimization model of China's energy policy based on the dynamic CGE model, which maximizes the total social benefit, to explore the comprehensive influences of a carbon tax, the sales pricing mechanism, and the renewable energy fund policy.
In most of the panel analyses that examined in the literature to study the impacts of fossil-fuel subsidy reform has been the use of a computable general equilibrium model (Fujimori et al., 2014; Lee, 2020; Li and Masui, 2019; Nong, 2020; Zhang et al., 2018). For example, Fujimori et al., (2014) present how energy end-
use technologies are treated within the model and analyze the characteristics of the model's behavior. Energy service demand and end-use technologies are explicitly considered, and the share of technologies is determined by a discrete probabilistic function, namely a Logit function, to meet the energy service demand. Coupling with detailed technology information enables the CGE model to have a more realistic representation of energy consumption. Zhang et al., (2018) present the model structure and mathematical formulation of AIM/Transport and explains how it was integrated with the CGE model by an iterative algorithm, taking into consideration the feedback between AIM/Transport and AIM/ CGE. Li and Masui (2019) combine a data envelopment analysis, a dynamic CGE model with estimated secondary material flows for a circular economy, based on economist Joseph Schumpeter's macro-economic theory, to develop a novel soft-link model to determine the efficiency of forty-three dark-fermentative technologies of biohydrogen, and technology improvement impacts on biohydrogen output and supply price for six major emerging Asian countries. This study finds that the efficiency of continuous technology significantly exceeds that of batch technology. The models and results of this study provide guidelines and references for decision-makers in industry and government who are responsible for reforming future energy policy. Nong (2020) shows that incorporation of non-CO2 emissions in the model significantly alters the results of which the economy of South Africa experiences higher costs compared to the case that only has CO2 emissions. Results also show that South Africa only experiences small tradeoffs from introducing the carbon tax in all scenarios. Lee (2020) combines a data envelopment analysis, a dynamic CGE model with estimated secondary material flows for the circular economy, based on economist Joseph Schumpeter's macroeco-nomic theory, to develop a novel soft-link model to determine the efficiency of forty-three dark-fermentative technologies of biohydrogen, and technology improvement impacts on biohydrogen output and supply price for six major emerging Asian countries. This study finds that the efficiency of continuous technology significantly exceeds that of batch technology. The models and results of this study provide guidelines and references for decision-makers in industry and government who are responsible for reforming future energy policy.
3. METHODOLOGY
3.1. CGE model
The present article follows from this literature on CGE models. CGE models are complete mathematical representations of an economy, comprising the actions of households, producers, government, investors, and exporters. Each of these agents participates in markets for supplying and demanding commodities and factors and has an underlying behavior that determines their decisions. Previous studies like Peng et al., (2019), Latorre et al. (2018), Liu et al. (2017), Tan et al. (2019), Wang et al. (2020), and Zhang et al. (2018) for a comprehensive review of CGE models and their applications. The General Algebraic Modeling System (GAMS) is used to solve the CGE model. Figure 1 displays the general structure of the CGE model in this
study. The CGE model in this paper consists of four CGE model in this paper is derived from Huang et al., blocks: Production and Consumption, as well as Factor (2020). The framework and most of the equations of it and Commodity markets. The main structure of the are from Wang et al., (2017).
Factors
Labor
Capital
Resources
Emission
Investment f^-
Household
ConsumeA
I
Governmen
Domestic production activity
.._!-----1
p-1 Tax System |
__:r:_____
~ProäücerP, \ Commodities
Value added and Energy
Non-Energy intermediate
Capital and Energy
Labor
Input 1 .
Input n
Capital
Energy
Electricit
Clean
Thermal power
Fossil fuel
z
Coal
Hydropowe
Renewable energy
Coal
CO,
Non-solid
z
Ga
Oil
Wind power
Other power
Gas
VJZ
CO,
Oil
CO,
Domestic
Import
Export
Overseas
Figure 1: Structure of the Kazakhstan CGE model.
On the Production block of the model, first multilevel nested functions are adopted to describe the inputs and outputs that are generated during production activities. As shown in Figure 1, the technology of production is represented by a nested Leontief production function between the value-added and intermediates. This means that the value-added cannot be substituted by the intermediates and that the substitution between these two inputs is zero. The Leontief production function is expressed as the following equation:
Y =
INT,
1,i
INTLi
9
,
(1)
where, Yi is aggregate marketed quantity of domestic output of sector i; INT[i,..., INTjti are quantity of intermediate input from sector j to sector i; ^[Y, .■■,Vj'JT are the input requirement coefficients. The substitution relationship between labor and capital is described by a CES production function. At the next nested level, the capital input is decided by natural capital, which in this study refers to ores of rare earth, and monetary capital with a CES function. The CES function can be expressed as follows:
VAi=A(^aiF-
(2)
where, VAi is quantity of value added to sector i; Fi is the i-th input factor; A, a and p are the parameters. The electricity sector is further disaggregated into five different technologies, including coal power, gas electricity, nuclear power, hydroelectricity, and renewable
energies. The CO2 emission amount is calculated based on the consumption of different fossil fuel goods. The elasticities of substitution used in this paper are adopted from some classic CGE model studies (Wesseh et al., 2016 and He et al., 2015).
On the Consumption block of the model, this paper includes two representative consumers: government and households. For household consumption, the model includes both urban and rural households. Households' income mainly comes from labor wages, returns of capital, and transfers from the government and enterprises. The government's income comes from all kinds of taxes and its expenditures include the purchase of commodities, transfer payments, and savings. Government consumption of commodities and services is also determined by the extended Linear Expenditure System (ELES) function. The ELES function is expressed as follows:
Ci = PiXi+bi(YR-^PiXi
(3)
where Ci is the expenditure on the i-th commodity or service; Pi is the price of the i-th commodity or service; Xi is the basic demand for the i-th commodity or service; bi is the parameter that denotes; YR is disposable revenue. In addition, for investment, the neoclassical closure assumption is adopted. Investment is endogenous and equal to total savings from households, government, enterprises, and foreign agents.
Finally, in the commodity market and the factor market, the equilibrium price makes the optimal supply and demand equation, and the economic system reaches
n
i=1
1
n
p
=1
a stable equilibrium. In the dynamic mechanism, the model considers four driving factors: labor growth, capital accumulation, supply changes of natural resources, and improvement of production technologies. The CGE model solves the general equilibrium where all the factor markets and commodity markets clear and all the account's expenditure equals their income at the same time.
3.2. CO2 emission scenarios
Three scenarios of GHG emissions were developed to assess the impact of all policies and measures. All scenarios assume annual GDP growth of an average of 3.5% till 2020 and 3% after 2020. There are three scenarios of the development of Kazakhstan's energy system treated in this study, with one BaU scenario. The countermeasure (CM) scenarios are ones where there is an emission reduction target. The difference between those three scenarios is in the treatment of the mitigation actions for GHG emissions. In scenario CM1, a scenario without measures, this scenario reflects a possible change in CO2 emissions without any measures to reduce them. Further economic growth is enabled by cheap coal as a fuel for energy production. This scenario assumes that GHG emissions depend on the overall rate of GDP and population growth. This scenario is based on the following assumptions all processes leading to improved energy efficiency in the process of optimization based on the achievement of reduced costs are not taken into account. On the other hand, in a scenario CM2, the scenario with current measures, this scenario includes adopted and planned measures and policies aimed directly at reducing CO2
Data source
emissions or have an indirect impact on the reduction of CO2 emissions. This scenario includes adopted and planned measures and policies aimed directly at reducing greenhouse gas emissions or have an indirect impact on the reduction of GHGs all new existing or planned power plants that lead to a reduction in CO2 emissions, i.e. gas, renewable, and alternative energy sources are put into operation. Moreover, in scenario CM3, a scenario with current and additional measures, this scenario reflects a possible change in CO2 emissions without any measures to reduce them. This scenario includes possible measures and policies that are directly aimed at reducing CO2 emissions capacities to be installed by 2025 according to the renewable energy sector development targets are doubled by 2035 (50% in 2030 and the remaining 50%).
4. DATA OF SOURCES
4.1. Basic data
The data covers all the Kazakhstan's for the years between 2005 and 2019. The datasets used in this study include the Kazakhstan IO table, energy historical data, SAM table, Socioeconomic data, and CO2 emission data (Table 1). Socioeconomic data, such as population and GDP, were obtained from the shared Socioeconomic database. The data has a cross-section dimension of Kazakhstan and a time series dimension of 15 years. Hence, it is the most up-to-date and comprehensive dataset used in the literature. The IO table presents the sectoral production and consumption structure. Considering the data accessibility and completeness, this paper uses public data sources and chooses the year 2005 as the base year.
Table 1
of this study_
Data
Sources
Macroeconomic SAM data, IO table Socioeconomic data Energy historical data CO2 emission data
Kazakhstan National Statistical Agency, 2007 Kazakhstan statistical yearbook, 2019 Energy Outlook for Asia and the Pacific, 2013 World Bank data indicator, 2005-2019
2005 IO table of Kazakhstan released by the Kazakhstan National Statistical Agency. For this study, the input-output relationships among the sectors in 2007 are assumed to be stable. The classifications can be seen in Table 2.
Table 2
Sectors and Commodities in the IO table
Sector/Commodity Category Sector/Commodity Category
S1 Agriculture Non-Energy S10 Other manufacturing Non-Energy
S2 Forestry Non-Energy S11 Electricity, gas and water Energy
S3 Fishery Non-Energy S12 Construction industry Non-Energy
S4 Coal, Lignite and Peat Energy S13 Trade Non-Energy
S5 Crude oil Extraction Energy S14 Hotels and Restaurants Non-Energy
S6 Other mining Energy S15 Transport Non-Energy
S7 Food, Clothing and Tobacco Non-Energy S16 Post and Communications Non-Energy
S8 Fuels and Chemicals Energy S17 Financial services Non-Energy
S9 Metals and metal products Non-Energy S18 Public and other services Non-Energy
4.2. Input-Output table
The input-output table is the data source for this CGE model. This paper has assessed the data of 18 production sectors and 13 commodities obtained from the
4.3. Social accounting matrix (SAM) A SAM is an invaluable tool in bringing together whatever data there are and in helping to fashion a quantitative description of the initial position in an economy (Pyatt and Round, 1985). SAM is the most important data source of the CGE model. The SAM of Kazakhstan in the present paper is established using
2005 as the base year. The data obtained from the 2005 IO tables of Kazakhstan and Kazakhstan National Statistical Agency yearbook of 2007. With the additional information of capital flow adopted from Hare and Naumov, (2008), the 2005 SAM is constructed as shown in Table 3.
Tabic 3
Macro SAM of Kazakhstan in the year 2005 (Unit: in billions of Kazakh Tenge)._
COM ACT CAP LAB HHD GOV TCON TEXP TCAP TINT TINC INV ROW TOTAL
COM 3925.5 2205.9 435.0 1101.6 7668.1
ACT 5841.4 1781.7 7623.1
CAP 1964.8 1964.8
LAB 1429.2 1429.2
HHD 1964.8 1429.2 122.2 3516.2
GOV 276.8 78.7 81.0 110.5 112.0 259.7 918.7
TCON 78.7 78.7
TEXP 81.0 81.0
TCAP 110.5 110.5
TINT 112.0 112.0
TINC 259.7 259.7
INV 680.8 313.5 107.2 1101.6
ROW 1748.0 93.0 47.9 1888.9
TOTAL 7668.1 7623.1 1964.8 1429.2 3516.2 918.7 78.7 81.0 110.5 112.0 259.7 1101.6 1888.9
Notes: COM: commodities or products, ACT: activities or industries, CAP: capital, LAB: Kazakhstan labor, Hill): households, GOV: government, TCON: taxes on final consumption, TEXP: taxes on export duties, TCAP: taxes on capital, TINT: taxes on intermediate consumption, TINC: taxes on income, INV: investment, ROW: rest of world, TOTAL: control totals of each account. Source: Kazakhstan National Statistical Agency (2007).
5. RESULT AND DISCUSSION
5.1. Macroeconomic and sectoral impact
The economic growth of Kazakhstan has been booming rapidly with coal, oil, and gas production since 2005. However, the world economy was grown gradually from 2012 to 2013, but the external and economic situation of Kazakhstan had not been very well since 2014, the world economy has been volatile, with China and Russia, the main trading partners, slowing and economic demand declining compared to 2012 and 2013 (Wang et al., 2019). However, the rapid growth of domestic investment, the rapid growth of the service sector, and the relatively high growth of the heavy industry and construction sectors have contributed to the significant development of Kazakhstan's economy.
Table 4 shows the simulation results of Kazakhstan's macroeconomic indicators, household consumption, government consumption, export, import, and sectors using the CGE model. This section of the study uses the SAM table to discuss Kazakhstan's macroeconomic and energy production in Coal, Lignite, and Peat (S4), Crude oil Extraction (S5), Other mining (S6), Fuels and Chemicals (S8) and Electricity, gas. and water (S11) sectors were modeled using the CGE model. Simulation results show that increased demand for the Crude oil Extraction (S5) sector is having a positive impact on Kazakhstan's economy and other sectors. An 18 percent increase in oil exports
boosted domestic production by 12.9 percent. The results show that 40 percent of real GDP growth since 2005 has been in the oil sector. A 1 percent increase in Fuels and Chemicals (S8) exports also saw a 1.7 percent increase in domestic production, with no significant change.
In other sectors, the construction industry (S12), financial services (S17) public, and other services (S18) are expected to expand. At the same time, similar industries, such as other Mining (S6) and competing with intermediate inputs, would grow faster if there was no demand from the oil industry. Some of the appreciation of the exchange rate resulting from increased export earnings has had a positive effect on cheap imports and increased domestic demand for high-consumption producers and end-users. Although some sectors benefited less than others, overall economic activity, measured by total output, grew by 3 percent as the oil sector expanded. Electricity, gas, and water (S11) rose 2.9 percent. There have been several improvements and reductions in this simulation. This progress in the oil sector is primarily stimulating the production and services of sectors that are strongly linked to the oil sector. On the other hand, sectors that are not particularly closely linked to the oil sector but compete with the same intermediate inputs and factors of production may face higher prices if they do not encourage cheaper imports.
Table 4
Simulation results: Impact on macro-variables, by sector
Final consumption Export Import Total demand for Total domestic commodities production Domestic production sold on the domestic market
S1 2.4 (0.4) 7.9 1.8 1.0 1.6
S2 3.0 (0.1) 5.8 1.8 1.3 1.4
S3 3.1 0.7 5.3 1.9 1.9 1.9
S4 1.4 (2.7) 9.5 0.4 (0.7) 0.2
S5 0.0 18.0 (3.5) 7.6 12.9 9.6
S6 2.5 (5.4) 1.7 (3.4) (3.7) (3.6)
S7 3.5 (0.4) 5.9 3.3 1.1 1.1
S8 4.0 1.0 4.5 3.2 1.7 1.9
S9 1.5 (5.8) 11.9 2.2 (4.1) (1.7)
S10 4.2 (3.6) 5.2 4.0 (2.0) (1.4)
S11 2.9 0.5 6.4 2.0 1.9 1.9
S12 3.5 2.8 9.6 5.5 4.4 4.4
S13 2.8 1.1 8.6 2.9 2.4 2.9
S14 3.1 0.0 0.0 3.5 3.5 3.5
S15 3.4 1.0 6.5 2.8 2.4 2.4
S16 3.0 1.1 8.2 3.2 2.7 2.8
S17 3.1 4.3 11.9 7.0 6.1 6.2
S18 2.5 2.6 12.2 5.0 4.8 4.9
Note: () - negative number. CGE model Simulation results for Kazakhstan. Source: Compiled by the author based on Macro SAM data of Kazakhstan in the year 2005.
The sector with the lowest growth in Agriculture (S1). Other mining (S6) and Other manufacturing (S10) are expected to shrink. The rapid growth of Crude Oil Extraction (S5) does not mean that Other Mining (S6) and Other Manufacturing (S10) are shrinking. It could be correctly explained that their output would have increased by the same percentage if Crude Oil Extraction (S5) had not developed rapidly. As shown in
Figure 2, the Coal, Lignite, and Peat (S4) sectors need to be looked at more simply to explain why some industries have shrunk as a result of the Crude Oil Extraction (S5) demand shock. When this model is in equilibrium, the supply of coal is equal to the relative price of the intermediate and the demand for the relative factor. Inward growth in oil exports means an increase in domestic oil output. This suggests that exports to
sectors with the strongest impact of the oil shock, such as the construction industry (S12) and Financial services (S17), may increase slightly. An increase in oil output will require an increase in intermediate inputs, capital, and labor, which will increase the price of these intermediate inputs and factors of production. Coal, Lignite, and Peat (S4) mines use capital products similar to those of oil refineries but provide almost no intermediate input. So it is unlikely that the demand for
14%
12% 10% 8% 6% 4% 2% 0% -2% -4% -6%
their products will change, but will face increasing factors and good intermediate prices, as well as a real appreciation of the exchange rate. Coal output should be reduced to restore the balance between demand and lower prices at relatively new prices. Expanding coal exports is expected to boost output. In particular, it is possible to export gaseous fuels instead of solid fuels for energy production, which is creating added value.
S1 S2 S3 S4 S5
S11 S12 S13 S14 S15 S16 S17 S18
Figure 2: Impact on total domestic production (% change). Source: CGE model for Kazakhstan.
Another essential point of CGE modeling is the from the Kazakhstan statistical yearbook from 2005 to
macroeconomic assumption used in the model. Table 1 2019, and the projection from 2020 to 2035 was calcu-
shows the Projection of Population and GDP in this pa- lated (Table 5). per. For the population and GDP, we used actual values
Table 5
Projection of Population and GDP, 2005-2035 (million people)._
Population (million) Population Growth rate (%) GDP (Billion US$)
2005 15.1 0.83% 109.4
2010 16.3 1.31% 148.1
2015 17.5 1.15% 186.3
2020 18.9 0.89% 165.5
2025 19.7 0.65% 202.6
2030 20.6 0.52% 237.9
2035 21.4 0.49% 280.2
Sources: Compiled by the author based on World Bank data (2005-2020).
The GDP projection from 2020 to 2035 was calculated by assuming that the GDP growth during this period was around 5%. This figure reflects GDP growth corrected by the National Bank and Ministry of Finance of the Republic of Kazakhstan to fit the new economic growth assumption, with a range of around 5%. Kazakhstan's government decided to slow economic growth in line with global economic trends. To make sure the model worked consistently, we compared the GDP from the statistics and the GDP expected with the
GDP calculated by the model in the BaU case (Figure 3). Driven mainly by the exports of mineral resources, Kazakhstan's GDP is projected to reach $280.2 billion (constant 2010 $) in 2035, increasing from $109.4 billion in 2010 at an annual rate of 8.6%. The population will increase moderately at 0.8% per year through 2035, reaching 21.4 million in 2035 from 15.1 million in 2005. As a result, Kazakhstan's GDP per capita will reach $13,093, more than double the 2005 level of $7,245.
300 250 200 150 100 50 0
2005
2035
25
20
15
10
2010 2015 2020 2025 2030
-GDP (2010 $ billion) -Population (million)
Figure 3: Projection of Population and GDP Source: Author's calculations.
As a result of the deployment of advanced technologies for energy savings, Kazakhstan's primary energy demand in the alternative case will increase moderately at 1.0% through 2035, reaching 95.2 Mtoe in
120
2035. This means that Kazakhstan has the potential to save about 12.9 Mtoe (or 13%) in 2035 compared with the primary energy demand in the BaU case in 2035 (Figure 4).
5
0
100
80 60 40 20 0
2005 2010 2015 2020 2030 2035
BaU Alternative History
Figure 4:
Comparison of Primary Energy Demand, Mtoe = million tons of oil equivalent. Source: Author's calculations.
In the BaU case, as shown in Table 6. The primary annual rate of 1.4%. With such steady growth, Kazakh-energy demand of Kazakhstan is projected to increase stan's per capita energy demand will increase from 3.65 from 55.2 Mtoe in 2005 to 108.1 Mtoe in 2035 at an Mtoe per person in 2005 to 5.05 Mtoe per person in
2035.
Table 6
Historical data
Historical data
Mtoe Share (%) AAGR (%)
2005 2010 2015 2005 2010 2015 2005-2015 2010-2015
TOTAL 55.2 75.0 72.4 100 100 100 1.4 (0.2)
Coal 27.1 34.5 35.3 49.1 46.0 48.8 1.3 0.1
Oil 12.7 17.1 13.8 23.0 22.8 19.1 0.4 (1.1)
Natural gas 14.6 22.6 22.5 26.4 30.1 31.1 2.2 0.0
Hydro 0.6 0.7 0.7 1.1 0.9 1.0 0.8 0.0
Others 0.2 0.1 0.1 0.4 0.1 0.1 (3.5) 0.0
Business-As-Usual Case
Mtoe Share (%) AAGR (%)
2020 2030 2035 2020 2030 2035 2020-2035 2030-2035
TOTAL 87.8 89.0 108.1 100 100 100 1.0 1.0
Coal 39.1 41.0 51.2 44.5 46.1 47.4 1.3 1.1
Oil 20.5 17.5 23.5 23.3 19.7 21.7 0.7 1.5
Natural gas 27.1 29.7 32.3 30.9 33.4 29.9 0.9 0.4
Hydro 0.9 0.7 0.9 1.0 0.8 0.8 0.0 1.3
Others 0.2 0.1 0.2 0.2 0.1 0.2 0.0 3.5
Alternative Case
Mtoe Share (%) AAGR (%)
2020 2030 2035 2020 2030 2035 2020-2035 2030-2035
TOTAL 82.9 89.8 95.2 100 100 100 0.7 0.3
Coal 35.2 38.4 41.5 42.5 42.8 43.6 0.8 0.4
Oil 20.1 21.2 22.3 24.2 23.6 23.4 0.5 0.3
Natural gas 26.5 28.4 30.3 32 31.6 31.8 0.7 0.3
Hydro 0.9 0.9 0.9 1.1 1.0 0.9 0.0 0.0
Others 0.2 0.9 0.2 0.2 1.0 0.2 0.0 (7.5)
Note: () - negative number, AAGR - average annual growth rate, Mtoe - million tons of oil equivalent, Other -include geothermal, solar, wind, and renewables. Sources: Compiled by the Author based on Asian Development Bank estimates.
Energy resources directly affect the structure of energy supply and supply, which is a major contributor to air and environmental pollution. Fossil fuel combustion is a major source of CO2 emissions in Kazakhstan, and the structure of fuel production and consumption of solid fuels is intensifying CO2 emissions. Coal alone accounts for 40 percent of total primary energy consumption, while oil and natural gas account for 30 percent. This shows that coal dominates Kazakhstan's energy consumption, and countries with similar energy consumption often have difficulty reducing air pollution. To change Kazakhstan's energy consumption trends, the country's economic development must also be taken into account.
5.2. Environmental impact
This section examines how fossil fuels are harmful to the environment and CO2 emissions, and how CO2 emissions can be reduced through energy. Kazakhstan's current energy structure is not improving significantly, and huge amounts of traditional energy resources, such as coal, are still being used for economic growth. Kazakhstan has many large fossil coal mines, and CO2 emissions from coal-fired power plants are much higher than in other countries. The most polluting energy product is a fossil fuel. Natural gas and coal are used by all households. The use of raw coal and natural gas as energy is a source of CO2 emissions.
CM1, CM2, and CM3 scenarios are based on technical and economic modeling of processes related to fuel combustion and CO2 emissions. Figure 4 shows the three scenarios of CO2 emissions (2020-2035) with real data on CO2 emissions in from 2005 to 2020 and other colors in the yellow line. CM1 scenario is not effective enough to reduce CO2 emissions. Emission levels in BaU and CM1 scenarios did not differ significantly. A similar explanation is given for emissions and activities under CM2 and CM3 scenarios. Although the emission rates of these two scenarios do not differ significantly in numerical values, the CO2 emissions for the CM3 scenario are relatively low compared to the CM2 scenario.
In other words, the higher the emissions from operations, the more efficient the energy sector must be to achieve its goal of reducing CO2 emissions. If the CM1 scenario is established, improving energy efficiency and reducing emissions are needed to achieve the goal. Improving energy efficiency means that the economy must produce the same or more products with less or the same energy input. According to this study, if productivity is improved, the reduction in CO2 emissions in 2035 will be around 359.2 Mtoe CO2 eq under CM1, compared to only 318.5 Mtoe CO2 eq (equivalent) and 289.1 Mton CO2 eq for CM2 and CM3 (Figure 5).
50 0
2005 2010 2015 2020 2025 2030 2035
-CM1 -CM2 -CM3 -History
Figure 5:
Historical data and forecast of CO2 emissions, Mtoe - million tons of equivalent. Source: Author's calculations.
In the future, CO2 emissions from power plants are expected to decrease the most. It should be noted that the CM2 scenario results in a relative reduction in CO2 emissions compared to the CM1 scenario concerning the overall economic changes, which are related to GDP growth. This highlights the potential economic and environmental degradation and policy conflicts that may arise from the reform of the electricity tax. The potential emissions of the Kazakh government from the energy sector can be compared with the assumption that the CO2 emissions will fluctuate around 289.1-359.2
Mtoe CO2 eq. Based on this hypothesis, CM2 and CM3 scenarios approximate the results.
Therefore, it can be concluded that reforestation and land protection are as important as achieving energy efficiency. As Kazakhstan is a developing country, it is very difficult to achieve high energy efficiency, which is a very long process. These results show that if we focus only on intensification, without taking into account the level of savings and reduction of environmental damage in other sectors of Kazakhstan, it will be a significant burden on the energy sector.
Table 7
_History data and Scenario of CO2 emissions_
2005 2010 2015 2020 2025 2030 2035
CM1 172.4 220.3 239.7 258.7 296.2 331.2 359.2
CM2 172.4 220.3 239.7 258.7 287.9 304.1 318.5
CM3_172.4_220.3_239.7_258.7 282.2_288.4_289.1
Note: Million tons CO2 - equivalent, Source: Compiled by the author based on World Bank data (2005-2019).
Table 7 presents the results of the scenarios without measures (CM1), with current measures (CM2), and with current and additional measures (CM3) from the processes associated with fuel combustion, as well as inventory results. The impact of current and planned policies on greenhouse gas emissions varies from year to year. For example, the main possibility of a reduction in greenhouse gas emissions by 2020 is due to the conversion of electricity and heat generating companies to gas. The following factors can have the greatest impact on GHG emissions between 2025 and 2030 in a significant order. These include increasing the efficiency of existing energy technologies and introducing new technologies. Use of low-emission natural gas for electricity and heat generation. Targeted renewable energy sectors. These will enable emissions reductions, which could reduce CO2 emissions per ton in all sectors.
Figure 6 shows Kazakhstan's GDP and CO2 emissions data for 2005-2020 and the growth forecasts for GDP and CO2 emissions for 2020-2035, averaging the CM1, CM2, and CM3 scenarios. Kazakhstan's GDP declined in 2015, while CO2 emissions continued to rise. GDP growth has been shown to have an indirect effect on CO2 emissions. However, it is unlikely that CO2 emissions will be reduced before 2030. By 2035, Kazakhstan's carbon emissions are likely to reach a maximum of 359.2 Mtoe, which is 2.08 times higher than in 2005. Kazakhstan's energy sector and some heavy and light industries need to be reformed, and the share of renewable energy will significantly reduce the role of coal in the country's economic development and increase its benefits. With the development of wind and solar energy, CO2 emissions will continue to decline.
400 350 300 250 200 150 100 50 0
300 250 200 150 100 50 0
2005 2010 2015 2020 2025 2030 -CO2 (Mtoe) -GDP (Billion US$)
2035
Figure 6: Historical data and forecast of GDP (Billion US$) and CO2 emissions, Mtoe - million tons of equivalent. Source: Author's calculations.
The gap between the economic and environmental effects of electricity tax reform is determined by the mix of energy available in Kazakhstan's economy. Kazakhstan's economy as a whole is heavily dependent on fossil fuels, and power plants are largely dependent on coal, oil, and gas to generate electricity (Figure 7). Kazakhstan is developing renewable energy sources such as wind and solar energy but has not yet reached all consumers. Improving fuel efficiency is effective in reducing CO2 emissions, especially in energy-intensive industries, by promoting more efficient energy use. This approach is particularly effective in supporting oil
refining and land transportation to reduce energy consumption in the short term.
Research and development investments in biofuels and the redistribution of fuel taxes are leading the refinery to reduce energy consumption. The land transport sector reduces energy consumption by about 4 percent over time (Pui and Othman, 2017). Such investments will lead to a significant increase in energy consumption in other energy-intensive sectors. Controlling CO2 emissions in energy-intensive industries could help Kazakhstan save energy and reduce emissions.
Figure 7: Kazakhstan's Primary Energy Demand. Source: Author's calculations.
The industrial sector is divided into energy production, heavy industry, light industry, and other sectors. Energy production emits 70 percent of the total CO2 emissions from coal combustion, while other industries emit 30 percent (Wang et al., 2019). Except for 2015, a year of economic flexibility in Kazakhstan, the industrial sector has always played an important role in CO2 emissions. For a developing economy with energy production, energy consumption will be the
main driver of the country's economic development. On the one hand, the reform of the coal resource tax will significantly reduce all the pollutants considered, which will help Kazakhstan to improve the environment. Kazakhstan is similar to other Central Asian countries, such as Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistanin terms of economic structure and CO2 emissions, but is quite rich economically.
Kazakhstan is facing serious environmental problems. It is estimated that the removal of all fossil fuel subsidies worldwide could reduce CO2 emissions by 6.9 percent by 2020 through energy production and use (Wesseh et al., 2016). The coal resource tax continues to be effective in reducing CO2 emissions over time by directing energy-intensive industries to use fewer energy raw materials. Conversely, lower energy taxes increase most CO2 emissions from production processes. More than 85 percent of industrial CO2 emissions are due to lower energy taxes. More than 44 percent of CO2 emissions come from industrial-based products and the rest from agriculture (Farajzadeh and Bakhshoodeh, 2015). In terms of emissions reductions, energy tax reductions are limited to reducing emissions from energy consumption, while some pollutants tend to increase emissions from production processes and final consumption.
6. CONCLUSION AND
RECOMMENDATIONS
According to the 2005 Input-Output table in Kazakhstan, the study developed a Computable general equilibrium (CGE) model using the Business as Usual (BaU) scenario and three carbon dioxide (CO2) emission scenarios to make a forecast for 2035. Improving the efficiency of Kazakhstan's main energy products, such as coal, natural gas, and petroleum products, has been studied to determine energy efficiency, CO2 emission control, and further emissions in a growing economic environment. The study developed a CGE model that provides detailed information on Kazakhstan's economy and CO2 emissions, as well as the electricity and energy sectors. This simulation can be used in economic activities as a result of the implementation of environmental protection policies, which may reduce the estimates due to good environmental policies. A regional energy excise tax scheme has been proposed to control the total amount of energy by reducing the use of energy resources. These results suggest that from a production perspective, even the supply of coal is more dependent on imports, indicating that coal-related fuels are a major contributor to CO2 emissions. Accordingly, energy production and heavy industry are the main CO2 emitters. As Kazakhstan remains a growing economy, cleaners are more expensive, and the public tends to choose cheaper energy, but carbon emissions are expected to increase by 2030.
Based on the above analysis, this study recommends the following recommendations. Kazakhstan needs to focus on reducing CO2 emissions in each sector and implement the latest advanced technologies to reduce CO2 emissions as much as possible, taking into account the country's economic potential. To develop a low-carbon dioxide economy, the government of Kazakhstan needs to implement a coal resource tax reform based on statistics. A coal resource tax is useful for saving energy at the expense of having a small negative impact on the economy. This will accelerate the green transition of the energy sector by gradually shifting environmental taxes, carbon taxes, coal, and natural gas taxes to environmentally-friendly electricity generation technologies. This tax policy was able to limit the recovery of energy resources to some extent. This study
suggests that improving the interaction between economic growth, energy consumption, and CO2 emissions will require policies that are beneficial to both the environment and the economy. It is expected that shortly, at least until the coal and oil sectors are disconnected from energy sources and technological innovations are made, economic growth will lead to an increase in the consumption of petroleum products. Therefore, the most important policy is to develop a renewable energy system that will gradually replace coal. Kazakhstan has been developing renewable energy since the beginning of this century, but the production of renewable energy, such as coal, is still not well developed. To achieve low CO2 emissions, Kazakhstan needs a comprehensive strategy to support the development of renewable energy. First of all, the government needs to increase financial support for renewable energy. There is also a need for low-carbon, renewable energy economic growth. It would be more effective if Kazakhstan could explore new opportunities for economic growth, such as new jobs in renewable energy, new industries, and supply chains. Finally, there is a need for international cooperation in the field of green economy and renewable energy.
References
1. Amirov A., Kozhukhova, M., Koshebaeva, G., Biryukov, V. and Zhiyenbayev M., 2018. Economic and energy security of the Republic of Kazakhstan. International Journal of Energy Economics and Policy, 8(6), 16-21. https://doi.org/10.32479/ijeep.6935
2. Bolor-Erdene Turmunkh. 2020. Renewable and Non-Renewable Energy Consumption, CO2 Emissions and Economic Growth in a Developing Country: Empirical Evidence from Kazakhstan. Asian Development Perspectives, 11(2), 1-16. DOI: https://doi.org/10.22681/ADP.2020.1L2.106
3. Diyar, S., Akparova, A., Toktabayev, A. and Tyutunnikova, M., 2014. Green Economy-Innovation-based Development of Kazakhstan. Procedia-Social and Behavioral Sciences, 140, 695-699. https://doi.org/10.1016Zj.sbspro.2014.04.497
4. Farajzadeh, Z. and Bakhshoodeh, M., 2015. Economic and environmental analyses of Iranian energy subsidy reform using Computable General Equilibrium (CGE) model. Energy for Sustainable Development, 27, 147-154. https://doi.org/10.1016/j.esd.2015.06.002
5. Feng, S. and Zhang, K., 2018. Fuel-factor nesting structures in CGE models of China. Energy Economics, 75, 274-284. https://doi.org/10.1016/j.en-eco.2018.08.030
6. Fujimori, S., Masui, T. and Matsuoka, Y., 2014. Development of a global computable general equilibrium model coupled with detailed energy end-use technology. Applied Energy, 128, 296-306. https://doi.org/10.1016/j.apenergy.2014.04.074
7. Gelan, A., 2018. Economic and environmental impacts of electricity subsidy reform in Kuwait: A general equilibrium analysis. Energy Policy, 112, 381-398. https://doi.org/10.1016/j.enpol.2017.10.032
8. He, Y.X., Liu, Y.Y., Du, M., Zhang, J.X. and Pang, Y.X., 2015. Comprehensive optimization of
China's energy prices, taxes and subsidy policies based on the dynamic computable general equilibrium model. Energy Conversion and Management, 98, 518-532. https://doi.org/10.1016Zj.enconman.2015.03.010
9. Huang, H., Roland-Holst, D., Wang, C. and Cai, W., 2020. China's income gap and inequality under clean energy transformation: A CGE model assessment. Journal of Cleaner Production, 251, 119626. https://doi.org/10.1016Zj .jclepro .2019.119626
10. Jendrzejewski, B., 2020. Bioeconomic model-ling-An application of environmentally adjusted economic accounts and the computable general equilibrium model. Land Use Policy, 92, 104431. https://doi.org/10.1016/j.landusepol.2019.104431
11. Karatairi, E., Rojas-Solorzano, L.R. and Kerimray, A., 2018. Renewable energy in Kazakhstan rises in the shadow of fossil fuels. MRS Bulletin, 43(9), 656-658. https://doi.org/10.1557/mrs.2018.215
12. Kazakhstan, 2015. In: Intended Nationally Determined Contribution - Submission of the Republic of Kazakhstan. UNFCCC. http://ex-twprlegs1.fao.org/docs/pdf/kaz190349.pdf
13. Kyoto Protocol, 1997. In United Nations framework convention on climate change. Kyoto Protocol, Kyoto, 19, 497. https://unfccc.int/re-source/docs/convkp/kpeng.pdf
14. Latorre, M.C., Yonezawa, H. and Zhou, J., 2018. A general equilibrium analysis of FDI growth in Chinese services sectors. China Economic Review, 47, 172-188. https://doi.org/10.1016/jxhieco.2017.09.002
15. Lee, D.H., 2020. Efficiency and economic benefit of dark-fermentative biohydrogen production in Asian circular economies: Evaluation using soft-link methodology with data envelopment analysis (DEA) and computable general equilibrium model (CGE). International Journal of Hydrogen Energy, 45(6), 36883698. https://doi.org/10.1016/j.ijhydene.2019.08.250
16. Li, G. and Masui, T., 2019. Assessing the impacts of China's environmental tax using a dynamic computable general equilibrium model. Journal of cleaner production, 208, 316-324. https://doi.org/10.1016/jjclepro.2018.10.016
17. Li, H., Semeyutin, A., Lau, C.K.M. and Gozgor, G., 2019. The relationship between oil and financial markets in emerging economies: The significant role of Kazakhstan as the oil exporting country. Finance Research Letters. https://doi.org/10.1016/jirl.2019.04.028
18. Li, X., Yao, X., Guo, Z. and Li, J., 2020. Employing the CGE model to analyze the impact of carbon tax revenue recycling schemes on employment in coal resource-based areas: Evidence from Shanxi. Science of The Total Environment, 720, 137192. https://doi.org/10.1016/j. scitotenv.2020.137192
19. Liu, J.Y., Lin, S.M., Xia, Y., Fan, Y. and Wu, J., 2015. A financial CGE model analysis: Oil price shocks and monetary policy responses in China. Economic Modelling, 51, 534-543. https://doi.org/10.1016/j.econmod.2015.08.025
20. Liu, J.Y., Xia, Y., Fan, Y., Lin, S.M. and Wu, J., 2017. Assessment of a green credit policy aimed at
energy-intensive industries in China based on a financial CGE model. Journal of Cleaner Production, 163, 293-302. https://doi.org/10.1016/jjclepro.2015.10.111
21. Nong, D., 2019. A general equilibrium impact study of the Emissions Reduction Fund in Australia by using a national environmental and economic model. Journal of cleaner production, 216, 422-434. https://doi.org/10.1016/jjclepro.2019.01.193
22. Nong, D., 2020. Development of the electricity-environmental policy CGE model (GTAP-E-PowerS): A case of the carbon tax in South Africa. Energy Policy, 140, 111375. https://doi.org/10.1016/j.en-pol.2020.111375
23. Nugumanova, L., Frey, M., Yemelina, N. and Yugay, S., 2017. Environmental problems and policies in Kazakhstan: Air pollution, waste and water (No. 366). IOS Working Papers. http://hdl.handle.net/10419/162149
24. Nurgaliyeva, L., 2016. Kazakhstan's economic soft balancing policy vis-à-vis Russia: From the Eurasian Union to the economic cooperation with Turkey. Journal of Eurasian Studies, 7(1), 92-105. https://doi.org/10.1016/j.euras.2015.10.008
25. Hare, P. and Naumov, A., 2008. August. A study of changing income distribution in Kazakhstan using a new social accounting matrix and household survey data. In 30th General Conference of The International Association for Research in Income and Wealth, Portoroz, Slovenia, August, 24-30. https ://ideas.repec.org/e/pha140. html
26. Peng, J.T., Wang, Y., Zhang, X., He, Y., Taketani, M., Shi, R. and Zhu, X.D., 2019. Economic and welfare influences of an energy excise tax in Jiangsu province of China: A computable general equilibrium approach. Journal of cleaner production, 211, 403-1411. https://doi.org/10.1016/jjcle-pro.2018.11.267
27. Pui, K.L. and Othman, J., 2017. Economics and environmental implications of fuel efficiency improvement in Malaysia: A computable general equilibrium approach. Journal of cleaner production, 156, 459469. https://doi.org/10.1016/jjclepro.2017.04.067
28. Pyatt G., and Round, J. I., (eds), 1985. Social Accounting Matrices: A Basis for Planning, The World Bank, Washington D C. https://www.un.org/en/devel-opment/desa/policy/mdg_workshops/eclac_train-ing_mdgs/round_2003_sams_chapter14.pdf
29. Russell, A., Ghalaieny, M., Gazdiyeva, B., Zhumabayeva, S., Kurmanbayeva, A., Akhmetov, K.K., Mukanov, Y., McCann, M., Ali, M., Tucker, A. and Vitolo, C., 2018. A spatial survey of environmental indicators for Kazakhstan: an examination of current conditions and future needs. International Journal of Environmental Research, 12(5), 735-748. https://doi.org/10.1007/s41742-018-0134-7
30. Silviu, D.A., 2015. Simulating the economic impact of resources depletion using a computable general equilibrium model for Romania. Procedia Economics and Finance, 22, 618-626. https://doi.org/10.1016/S2212-5671(15)00272-5
31. Tan, L., Wu, X., Xu, Z. and Li, L., 2019. Comprehensive economic loss assessment of disaster based on CGE model and IO model—A case study on Beijing
"7.21 Rainstorm". International Journal of Disaster Risk Reduction, 39, 101246.
https://doi.org/10.1016/j.ijdrr.2019.101246
32. Tang, L., Shi, J., Yu, L. and Bao, Q., 2017. Economic and environmental influences of coal resource tax in China: A dynamic computable general equilibrium approach. Resources, Conservation and Recycling, 117, 34-44. https://doi.org/10.1016/j.resconrec.2015.08.016
33. UNFCCC, 2019. Report on the Individual Review of the Annual Submission of Kazakhstan. https://unfccc.int/sites/default/files/re-source/arr2019_KAZ.pdf
34. Wang, X., Ge, J., Li, J. and Han, A., 2017. Market impacts of environmental regulations on the production of rare earths: A computable general equilibrium analysis for China. Journal of Cleaner Production, 154, 614-620. https://doi.org/10.1016/jjcle-pro.2017.03.200
35. Wang, X., Zheng, H., Wang, Z., Shan, Y., Meng, J., Liang, X., Feng, K. and Guan, D., 2019. Kazakhstan's CO2 emissions in the post-Kyoto Protocol era: Production-and consumption-based analysis. Journal of environmental management, 249, 109393. https://doi.org/10.1016/jjenvman.2019.109393
36. Wang, Y., Hu, J., Pan, H. and Failler, P., 2020. Ecosystem-based fisheries management in the Pearl
River Delta: Applying a computable general equilibrium model. Marine Policy, 112, 103784. https://doi.org/10.1016/j.marpol.2019.103784
37. Wesseh Jr, P.K., Lin, B. and Atsagli, P., 2016. Environmental and welfare assessment of fossil-fuels subsidies removal: A computable general equilibrium analysis for Ghana. Energy, 116, 1172-1179. https://doi.org/10.1016/j.energy.2016.10.053
38. Woldie, G.A. and Siddig, K., 2019. Macroe-conomic and distributional impacts of exchange rate devaluation in Ethiopia: A computable general equilibrium approach. Heliyon, 5(12), e02984. https://doi.org/10.1016/j.heliyon.2019.e02984
39. Zhang, L., Li, Y. and Jia, Z., 2018. Impact of carbon allowance allocation on power industry in China's carbon trading market: Computable general equilibrium-based analysis. Applied energy, 229, 814827. https://doi.org/10.1016/j.apenergy.2018.08.055
40. Zhang, R., Fujimori, S., Dai, H. and Hanaoka, T., 2018. Contribution of the transport sector to climate change mitigation: insights from a global passenger transport model coupled with a computable general equilibrium model. Applied energy, 211, 76-88. https://doi.org/10.1016/j.apenergy.2017.10.103
BASIC ASPECTS OF THE USE OF INTELLECTUAL SYSTEMS IN THE ACTIVITIES OF A
HIGHER EDUCATIONAL INSTITUTION
Pilipenko E.F.
Senior Lecturer Tashkent State University of Economics Uzbekistan, Tashkent, Islam Karimov Avenue 49
ОСНОВНЫЕ АСПЕКТЫ ИСПОЛЬЗОВАНИЯ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ В ДЕЯТЕЛЬНОСТИ ВЫСШЕГО УЧЕБНОГО ЗАВЕДЕНИЯ
Пилипенко Е.Ф.
Старший преподаватель Ташкентский государственный Экономический университет Узбекистан, Ташкент Проспект Ислама Каримова, 49
Abstract
This article shows that in the modern period digital technologies, including artificial intelligence, have an impact on the functioning of economic objects. Digital technologies have gained a great influence in the organization of the educational process and the management of a higher educational institution. The author presents the main aspects of the influence of intelligent systems on the educational and management activities of a higher educational institution, which contributes to the training of highly qualified specialists for the national economy.
Аннотация
В настоящей статье показано, что в современный период цифровые технологии, в том числе и искусственный интеллект оказывают воздействие на функционирование объектов экономики. Большое влияние цифровые технологии получили в организации учебного процесса и управлении высшим учебным заведением. Автором представлены основные аспекты влияния интеллектуальных систем на образовательную и управленческую деятельность высшего учебного заведения, что способствует подготовке высококвалифицированных специалистов для национальной экономики.
