Scenario-Based Evaluation of Greenhouse Gas Emissions and Ecosystem-Based Mitigation Strategies in Kazakhstan

Abstract

In the current context of the international climate agenda, understanding both the sources of greenhouse gas (GHG) emissions and the mechanisms for their mitigation is a fundamental requirement for low-carbon development strategies. Kazakhstan has pledged to reduce its GHG emissions by 15–25% by 2030, relative to 1990 levels, and to achieve carbon neutrality by 2060. However, there is no unified methodology for comprehensively assessing the national carbon balance, particularly at the regional scale. This study addresses this gap by analyzing GHG emissions and carbon sequestration capacities across Kazakhstan’s regions using a sectoral disaggregation approach and scenario-based modeling aligned with IPCC methods. Emission hotspots were identified in the energy sector (328 MtCO₂-eq), agriculture (118 MtCO₂-eq—primarily from pasturelands), and transport (7 MtCO₂-eq). In contrast, current carbon sinks—mainly forest ecosystems and abandoned pasturelands—account for only 3.97 and 13.9 MtCO₂-eq, respectively. The research evaluates the technical potential for emissions reduction through the best available technologies (BAT), livestock management, partial transition to gas-powered vehicles, and reforestation. A geoengineering scenario combining all measures suggests that Kazakhstan could meet its 2030 climate targets, although full carbon neutrality by 2060 would remain out of reach under current policy trajectories. The Akmola region is examined as a representative case study, demonstrating a possible 35% reduction in net emissions by 2035. This work contributes a regionally nuanced, data-driven framework for integrating ecosystem services into national climate policy and identifies nature-based solutions—especially forest management—as essential components of Kazakhstan’s decarbonization pathway, offering insights for other carbon-intensive economies.

1. Introduction

In the current context of the international agenda, the issue of the carbon footprint has become especially relevant. To address it effectively, it is essential to understand both the sources of greenhouse gas (GHG) emissions and the available mitigation methods. To date, there is no universally accepted methodology for comprehensively assessing carbon flows. Thus, the National Inventory of Anthropogenic Emissions from Sources and Removals by Sinks of Greenhouse Gases only considers emissions of anthropogenic origin.

The Republic of Kazakhstan has undertaken international commitments in this area, including a 15–25% reduction in GHG emissions by 2030, compared to 1990 levels, and the achievement of carbon neutrality by 2060 [1]. Meeting this ambitious goal will require not only a drastic reduction in emissions but also a significant increase in carbon sequestration capacity through natural ecosystems and managed agroecosystems. The carbon sequestration capacity of Kazakhstan’s forest lands is approximately 3.97 million tons of CO₂ equivalent, while that of grasslands withdrawn from agricultural use reaches 13.9 million tons [2,3]—both figures significantly lower than the country’s current emission levels.

The relevance of this research lies in the increasing importance of low-carbon development for Kazakhstan—a country that, like many others, faces the challenges of climate change and the need to formulate an effective emissions reduction strategy. Such a strategy must be based on a differentiated understanding of the economic and ecological characteristics of each region of the country. This study proposes a methodology for regional analysis of the carbon balance and the assessment of mitigation potential towards carbon neutrality.

Taking into account the regions of Kazakhstan as potential actors in national climate policy and considering GHG emissions across regions along with the reduction potential of major sectors, the general objective of this work is to analyze the sources and magnitudes of GHG emissions in the regions of Kazakhstan and to assess the possibility of offsetting them through an increase in the absorption capacity of natural and human-modified ecosystems.

To this end, the following specific objectives are formulated:

• To analyze the state of scientific knowledge regarding GHG emissions in the context of the Paris Agreement goals to achieve carbon neutrality by 2060, and to review Kazakhstan’s international climate policy;
• To calculate anthropogenic GHG emissions and carbon sequestration in Kazakhstan’s regions, disaggregated by major economic sectors;
• To assess the regional potential for emission reduction through technological innovations in various industries, as well as through interventions aimed at improving the quality of natural and semi-natural ecosystems.

1.1. Global Experience in Greenhouse Gas Regulation and Climate Policy in the Republic of Kazakhstan

Scientific understanding of the anthropogenic greenhouse effect has evolved significantly over the past century. In 1896, Svante Arrhenius was the first to propose that fossil fuel combustion could intensify global warming, attributing the Earth’s average surface temperature (~15 °C) to the heat absorption properties of water vapor and carbon dioxide [4]. Later, Charles Keeling’s atmospheric CO₂ measurements in the 1950s became emblematic of global warming research, although the global temperature trend initially declined from the 1940s to the 1970s. By the 1980s, however, temperatures began to rise again, invalidating earlier hypotheses of a potential new ice age. In 1988, it was formally acknowledged that the global climate had reached its warmest point since 1880, leading to broad acceptance of the greenhouse effect theory. Since then, the analysis of GHG emissions has gained momentum across global, national, and local levels.

In the context of Kazakhstan, I. Istomin [5] emphasizes that achieving carbon neutrality remains particularly challenging due to the country’s extreme continental climate and its heavy reliance on coal-based energy. He argues that a comprehensive low-carbon development strategy is essential. Furthermore, Kazakhstan’s pilot emissions trading system has exhibited instability, largely due to price volatility, lack of transparency, absence of regulatory price corridors, and low market liquidity.

Recent research highlights effective mitigation strategies, including (1) rooftop solar panel installations, which can reduce GHG emissions by 57% in the short term (~10 years) and achieve neutrality over ~30 years; (2) carbon capture technologies, which are promising for short-term mitigation but remain energetically and economically burdensome for long-term reliance unless innovations lower their costs and energy demands, particularly in developing countries; and (3) improved agricultural practices, including enhanced nitrogen fertilizer formulations and water-use efficiency, which can significantly reduce emissions in crop production systems [6,7]. This review underscores the urgent need to restructure global development and resource management systems. Bridging the gap between carbon-neutral rhetoric and real-world transformation will require coordinated, cross-sectoral efforts by scientists, policymakers, investors, and consumers to reduce emissions and foster both technological and nature-based carbon sequestration solutions.

1.2. International Experience in Greenhouse Gas Regulation

International frameworks for greenhouse gas (GHG) regulation have progressively defined the concept of carbon status as the set of indicators reflecting the degree of carbon neutrality, wherein anthropogenic GHG emissions are balanced by sequestration efforts within a defined territory. Carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) account for 64%, 18.5%, and 6.2% of total radiative forcing, respectively [8], with anthropogenic sources representing the dominant driver of observed climate change. Current global GHG emissions reach approximately 37.9 gigatons of CO₂ equivalent [9]. These emissions primarily originate from fossil fuel combustion and land-use change, especially deforestation.

Contemporary accounting mechanisms categorize emissions into three scopes: Scope 1 (direct emissions from owned operations), Scope 2 (indirect emissions from energy consumption), and Scope 3 (life-cycle-related indirect emissions). Carbon neutrality strategies typically include carbon offsetting and emissions reduction via transitions to low-carbon technologies and renewable energy sources [10].

International institutional development began with the 1992 UN Framework Convention on Climate Change (UNFCCC), which set a baseline commitment to prevent dangerous climate disruption. The Kyoto Protocol introduced market-based mechanisms—Joint Implementation (JI), International Emissions Trading (IET), and the Clean Development Mechanism (CDM)—to operationalize emission limits. The GHG inventory now includes additional gases, such as HFCs, PFCs, SF₆, and NF₃, along with indirect gases like NO_x, CO, and SO₂ [11].

The 2015 Paris Agreement marked a new phase in international cooperation, with over 120 countries pledging to achieve carbon neutrality between 2050 and 2060 [12]. National long-term strategies must span at least 30 years and integrate economy-wide and sector-specific decarbonization pathways—including LULUCF (land use, land-use change, and forestry)—while considering investment planning, R&D, socioeconomic impacts, and alignment with broader national goals.

To meet net-zero targets, states are undertaking structural economic reforms in key sectors, supported by modern climate regulation tools: mandatory climate risk disclosure, phased bans on high-emission activities, fossil fuel subsidy reforms, targeted fiscal measures, and new technical standards for fuels and products.

1.3. Kazakhstan International Commitment

Kazakhstan ranks 21st globally in total CO₂ emissions and 12th in per capita emissions [13]. In 2020, its contribution to global CO₂ emissions was 0.84%, equivalent to 353.2 MtCO₂e. Since 1995, Kazakhstan has actively engaged in global climate agreements, becoming the first CIS country to ratify the Paris Agreement and a regional leader in green economic transition. The country submitted its First National Communication under the UNFCCC in 1998 and began compiling regular GHG inventories from 2000 onward.

Significant efforts were made between 2019 and 2020 to align inventory reporting with international standards, following delays caused by public procurement issues and funding shortages. As a corrective measure, GHG inventory compilation was incorporated into the new Environmental Code (2021) [14]. In December 2020, President Tokayev announced the national pledge to achieve carbon neutrality by 2060: “Kazakhstan faces the dual task of shifting away from fossil fuels while combating climate change” [1].

Kazakhstan has committed to reducing GHG emissions by 15–25% by 2030, relative to 1990 levels, with the upper target conditional on international financial support. The 2013 Green Economy Concept, addressing energy-related emissions and fossil fuel leakage, noted that 80% of CO₂ emissions in 2019 (354 MtCO₂e) came from fossil fuel combustion [15]. As one of the most energy-intensive countries globally, Kazakhstan has accelerated its deployment of renewable energy sources (RES), challenging the oil and gas sector and signalling a potential post-oil transition.

According to the PwC, the number of RES facilities grew from 23 to 111 between 2010 and 2020, raising the RES capacity from 94 MW to over 1900 MW, now contributing ~3% of electricity generation [15]. The Green Economy Concept targets 10% RES by 2030 and 50% by 2050 [16].

In 2013, Kazakhstan launched Central Asia’s first emissions trading scheme (ETS-KZ), covering 225 large installations in the power, oil and gas, mining, metallurgy, chemical, and building materials sectors [17]. Smaller emitters and agriculture were excluded due to administrative barriers. ETS-KZ, limited to CO₂ emissions, was temporarily suspended in 2016–2017 for rule revisions, partly due to industry concerns about its economic impact. Kazakhstan is currently aligning ETS-KZ with the EU model, which already covers 46% of domestic power generation [18].

Reforms introduced via the 2017 amendments to the Environmental Code laid the groundwork for ETS-KZ and its MRV system. A carbon tax is also under consideration. The revised Environmental Code, effective from July 2021, established legal provisions for carbon markets and a national carbon registry [14]. A government resolution (13 January 2021) approved the 2021 National Allocation Plan, assigning 159.9 million emission units, including 91.4 million for the power sector.

Kazakhstan’s emission certificates remain undervalued—priced at USD 1.19 per ton in late 2020—nearly ten times lower than international equivalents, potentially increasing demand and generating additional national revenue. Strategic risks and actions for achieving carbon neutrality are outlined in the forthcoming national Decarbonization Doctrine, which evaluates three economic development scenarios (

Table 1. Scenarios for achieving carbon neutrality in the Republic of Kazakhstan. Source: compiled by the authors.

No.	Scenario	Strategy
1	“Business as Usual” (BAU)	No targeted policies for carbon neutrality; used as a reference to highlight the need for state-led climate action.
2	“Carbon Neutrality”	Active implementation of market and regulatory measures to meet Paris Agreement goals by 2060, with public investment.
3	“Decarbonization”	Full phase-out of fossil fuels and shift to low- or zero-carbon energy sources.

).

1.4. Research Gaps and Literature Review

Despite growing attention to climate mitigation in Central Asia, previous studies on Kazakhstan’s GHG emissions have been limited in scope. Many analyses remain at the national level or focus on a single sector. For example, national energy outlooks and policy reviews [5,19,20] discuss decarbonization broadly but do not quantify how emissions are distributed across Kazakhstan’s diverse regions. Official GHG inventories have also underreported certain sources—pastureland methane emissions were historically omitted or underestimated in national reports, leaving a gap in understanding agricultural contributions. Furthermore, prior mitigation scenarios often examined isolated strategies (e.g., renewable energy expansion [20] or carbon capture deployment) without considering the compounded effects of simultaneous interventions across sectors.

Regionally, comprehensive literature reviews reveal a clear void. Vakulchuk et al. emphasized that scholarly work on climate change in Central Asia since 1991 has been largely descriptive, with very few empirical or modeling studies addressing mitigation strategies or regional heterogeneity [21]. A systematic review of the energy transition literature highlights that, while Kazakhstan has declared ambitious targets (carbon neutrality by 2060, 2030 emission reductions), there is still a deficit of multiscale scenario modelling and integrated sectoral assessments [22]. Zhao et al. compiled detailed inventories for Central Asian countries—including Kazakhstan—but did not extend the analysis to include mitigation scenarios or multi-sector impacts [23]. Finally, De Miglio identified a critical research gap in the transition to net-zero emissions in Kazakhstan, emphasizing the need for context-sensitive scenario analyses tailored to regional particularities [24].

This study addresses these gaps by providing the first regionally disaggregated assessment of Kazakhstan’s carbon balance alongside a multisectoral mitigation analysis. By analyzing 17 administrative units, our research reveals pronounced spatial disparities in emissions (for instance, the coal-dominated Pavlodar region far exceeds other areas in energy-related CO₂ output, whereas agricultural methane from pastoral lands is concentrated in high-livestock regions like Karaganda). The work also integrates multiple mitigation pathways—energy modernization, transport fuel shifts, optimized pasture management, and forest restoration—into a unified “geoengineering” scenario. This holistic approach quantifies how combined actions outperform siloed measures, highlighting that nature-based solutions (e.g., reforestation and sustainable land use) can provide substantial emission offsets alongside technological upgrades. In doing so, the study offers a more comprehensive roadmap for Kazakhstan’s climate strategy and provides a case example for other countries with regionally diverse emission profiles.

2. Materials and Methods

2.1. Study Area

Kazakhstan’s current topography results from the interaction of endogenous and exogenous processes during the Neogene–Quaternary period. Tectonic activity has produced diverse landforms, with elevations ranging from −132 m in the Karakiya Depression to 6995 m at Mount Khan Tengri. Steppes cover 63% of the territory, deserts and semi-deserts 25%, and mountainous areas 10% [25]. The country extends 2900 km west–east and 1600 km south–north.

Located in the southern temperate zone, Kazakhstan has a sharply continental climate. Annual sunshine ranges from 2000 to 3000 h. The Azores anticyclone dominates in summer and the Asian anticyclone in winter. Zonal circulation leads to heat and low precipitation, while meridional circulation brings northwest cold fronts with rain and wind [26].

The soil types show altitudinal and zonal variation: chernozems dominate the north. Moving south, one finds chestnut soils, brown semi-desert soils, saline takyrs, and desert sands. In mountainous zones, chestnut soils, grey forest soils, and alpine meadow chernozems prevail. Outside the north, soils are typically saline and nutrient-poor, constraining agriculture.

The country’s natural territorial structure includes the East European Plain, West Siberian Plain, Turan Depression, Saryarka, Mughalzhary Mountains, Altai, Sauyr-Tarbagatai, Zungarian Alatau, and Tian Shan.

This study covers GHG emissions and sequestration across 14 administrative regions and three major cities—Astana, Almaty, and Shymkent—thereby encompassing the entire territory of Kazakhstan. With a surface area of 2,724,900 km², Kazakhstan is the world’s ninth-largest country and the fourth-largest in Eurasia. Conventionally, five economic–geographical regions are distinguished (

Table 2. Characteristics of the regions of Kazakhstan. Source: compiled by the authors.

No.	Region	Administrative Units	Population	Area (Thousand km2)	Economic Profile
1	Eastern	East Kazakhstan Region	1,386,208	283.2	Non-ferrous metallurgy, energy, mechanical engineering, forestry
2	Western	Aktobe, Atyrau, West Kazakhstan, Mangystau	2,759,602	736.2	Major oil and gas extraction region
3	Northern	Akmola, Astana, Kostanay, Pavlodar, North Kazakhstan	3,937,246	565.7	Grain production, iron ore and coal mining, oil refining, ferrosilicon, energy, machinery
4	Central	Karaganda Region	1,147,272	428.0	Ferrous and non-ferrous metallurgy, mechanical engineering, livestock farming
5	Southern	Almaty Region, Almaty, Zhambyl, Kyzylorda, Turkistan, Shymkent	8,569,809	712.1	Cotton, rice, wool, grain, fruits, vegetables, viticulture; non-ferrous metallurgy, food, light industry, fisheries, forestry

Note: This table encompasses all of Kazakhstan’s administrative units (17 regions and 3 cities of republican significance), grouped into five conventional economic–geographical regions, along with each group’s total population, area, and predominant economic profile.

).

It is important to observe that the analysis presented in this study reflects the administrative–territorial structure of Kazakhstan prior to the 2022 reform that introduced the Abai, Ulytau, and Zhetysu regions. Therefore, all data and regional assessments are limited to the 14 regions and three major cities (Astana, Almaty, and Shymkent) as defined during the period of initial research and data compilation. Importantly, this administrative update does not change the purpose or the final results of the study.

2.2. Energy Sector as the Main Source of GHG Emissions

The energy sector is Kazakhstan’s primary contributor to greenhouse gas (GHG) emissions, mainly through the stationary combustion of fossil fuels, which releases the carbon contained in fuels. Most of this carbon is emitted as CO₂, although smaller amounts are released as CO, CH₄, or NMVOCs, which subsequently oxidize into CO₂ over atmospheric lifetimes ranging from days to 12 years [2].

Kazakhstan possesses substantial reserves of energy resources (RER). In 2020, its total RER amounted to 371.5 million tons of coal equivalent, 70% of which was coal [27]. Globally, Kazakhstan ranked 10th in coal reserves (25.6 billion tons, or 2.4% of the global total), 9th in coal production (108 million tons), 17th in oil production (91.9 million tons), and 24th in natural gas production (38.7 bcm). The country is a net energy exporter, ranking 9th in exports of crude oil and coal and 12th in natural gas exports in 2018 [19].

Kazakhstan’s electricity is produced by 207 power plants of varying ownership, with a total installed capacity of 24,523.7 MW and an available capacity of 20,761.7 MW, as of early 2023 (KEGOC). About 75% of electricity is generated thermally, with more than 90% of thermal energy sourced from coalfields in Ekibastuz, Maykuben, Turgay, and Karaganda. In 2020, the final electricity consumption was 71.95 billion kWh, distributed as follows: industry—61.3%, households—21.5%, commercial and public services—11.2%, transport—6.9%, and agriculture/forestry/fishing—1.2% [28].

The 2020 statistical yearbook “Fuel and Energy Balance of the Republic of Kazakhstan” was used as the main data source. Emissions were calculated using the formula presented in the Russian National Report (2012) [29]:

E = M × K₁ × TNZ × K₂ × 44/12,

(1)

where:

• E = annual CO₂ emissions (t/year);
• M = actual fuel consumption (t/year);
• K₁ = carbon oxidation factor;
• TNZ = net calorific value (J/t);
• K₂ = carbon emission factor (tC/J);
• 44/12 = molecular weight conversion factor.
• Oxidation factors (K₁): coal—0.98, oil—0.99, and gas—0.995

The calorific values and emission coefficients are (National Report, 2012 [29]):

• Crude oil: TNZ = 40.12 TJ/kt, K₂ = 20.31 tC/TJ
• Bituminous coal: TNZ = 17.62 TJ/kt, K₂ = 25.58 tC/TJ
• Natural gas: TNZ = 34.78 TJ/kt, K₂ = 15.04 tC/TJ

Based on this methodology, the following

Table 3. GHG emissions in the energy sector across Kazakhstan’s regions by fuel type. Source: compiled by the authors.

No.	Administrative Region	Coal Incl. Lignite	Oil	Natural Gas	Total
1	Astana city	6442.87	0.00	2306.70	8749.57
2	Almaty city	3922.96	0.30	1487.03	5410.29
3	Shymkent city	679.09	14,179.98	1603.83	16,462.90
4	Akmola Region	3014.70	0.00	0.00	3014.70
5	Aktobe Region	0.00	9373.16	576.53	9949.69
6	Almaty Region	0.00	0.00	1151.15	1151.15
7	Atyrau Region	0.00	17,573.82	5365.31	22,939.13
8	East Kazakhstan Region	12,219.29	5.03	41.22	12,265.54
9	Zhambyl Region	656.09	165.05	2479.41	3300.55
10	West Kazakhstan Region	14.41	0.00	18,092.50	18,106.91
11	Karaganda Region	53,164.04	3.85	0.00	53,167.88
12	Kostanay Region	2089.75	0.00	1479.21	3568.96
13	Kyzylorda Region	145.76	12,899.81	505.16	13,550.73
14	Mangystau Region	0.00	21,366.09	6573.14	27,939.23
15	Pavlodar Region	106,858.16	14,676.30	0.15	121,534.62
16	North Kazakhstan Region	5497.85	0.30	0.00	5498.14
17	Turkistan Region	555.03	0.00	940.85	1495.88
	National Total	195,260.01	90,243.69	42,602.19	328,105.89

presents GHG emissions in the energy sector across Kazakhstan’s regions by fuel type.

2.3. Transport Sector GHG Emissions

The Concept on the Transition of the Republic of Kazakhstan to a Green Economy (Section 3.3), approved by Presidential Decree in 2013, identifies several key reasons for the high energy consumption in the transport sector. These include an aging vehicle fleet (80%), low fuel quality compared to European standards, limited use of natural gas as a fuel due to an underdeveloped natural gas infrastructure, and inadequate road infrastructure for public transport, electric vehicles, etc. [16].

According to the 2018 results, the transport sector contributed 8.3% to the country’s total GDP [30]. Kazakhstan’s road transport network has a total length of 95,400 km. The predominant fuel is gasoline (88%), followed by mixed fuel (7.8%) and diesel (1.9%) [31].

Greenhouse gas emissions from road transport were estimated by using the same approach as for the energy sector (Formula (1)), focusing on fuel consumption by type. Other transport modes (e.g., railways, waterways, and air transport) were not explicitly modeled due to their comparatively minor contribution to national GHG emissions. Thus, by using these values, conversion factors were obtained (

Table 4. Conversion coefficients for estimating GHG emissions in road transport. Source: [29].

Fuel Type	NCV (TJ/1000 t)	Carbon Emission Coefficient (K2, tC/TJ)	Carbon Oxidation Coefficient (K1)
Gasoline	44.21	19.13	0.995
Diesel	43.02	19.98	0.995
Mixed fuel	47.17	17.91	0.990
Natural gas	34.78	15.04	0.995

).

According to the International Energy Agency [electronic resource], the average fuel consumption is as follows:

• Gasoline: 7.2 L/100 km
• Diesel: 5.9 L/100 km
• Natural gas: 12.1 L/100 km
• Mixed fuel: 11.0 L/100 km

With an average annual mileage of 13,000 km, the annual fuel consumption per vehicle is:

• Gasoline: 936 L
• Diesel: 767 L
• Natural gas: 1430 L
• Mixed fuel: 1573 L

To convert volumes into mass, the following formula is used:

M (t) = V × ρ/1000

(2)

where:

• V: Volume in liters
• ρ: Fuel density in kg/L

Using the coefficients from

Table 5. Fuel mass per vehicle (in tons). Source: author’s calculations.

Fuel Type	Average Density (kg/L)	Mass (t)
Gasoline	0.755	0.71
Diesel	0.835	0.78
Natural gas	0.435	0.48
Mixed fuel	0.550	0.79

, the actual fuel consumption was computed, and emissions were estimated using Formula (2). The regional distribution of GHG emissions by fuel type in Kazakhstan is presented below (

Table 6. Regional distribution of GHG emissions from road transport in the Republic of Kazakhstan (in thousand tons of CO₂ equivalent). Source: author’s calculations.

No.	Administrative Unit	Gasoline	Diesel	Gas	Mixed Fuel	Total
1	Astana city	558.14	6.50	0.45	23.76	588.84
2	Almaty city	928.44	44.87	0.76	28.05	1002.12
3	Shymkent city	231.97	4.00	0.49	15.94	252.40
4	Akmola Region	323.86	5.41	0.12	13.35	342.73
5	Aktobe Region	202.37	2.33	0.29	50.74	255.73
6	Almaty Region	976.33	30.34	0.59	24.23	1031.48
7	Atyrau Region	182.00	7.39	0.15	15.50	205.04
8	East Kazakhstan Region	601.86	7.86	0.12	6.81	616.64
9	Zhambyl Region	398.13	8.62	0.15	11.48	418.38
10	West Kazakhstan Region	201.75	6.32	0.04	18.00	226.11
11	Karaganda Region	550.50	15.50	0.19	17.37	583.55
12	Kostanay Region	317.04	10.12	0.03	13.81	341.01
13	Kyzylorda Region	186.45	2.54	0.23	23.49	212.70
14	Mangystau Region	109.83	4.42	0.32	108.15	222.72
15	Pavlodar Region	299.48	2.42	0.15	7.60	309.64
16	North Kazakhstan Region	230.70	4.43	0.49	17.38	253.00
17	Turkestan Region	276.85	4.66	0.33	27.26	309.10
	Republic of Kazakhstan	6575.70	167.72	4.88	422.90	7171.20

).

2.4. Assessment of Greenhouse Gas (GHG) Emissions in Agriculture

Agriculture in the Republic of Kazakhstan is divided into two main sectors:

• Livestock: This sector is engaged in the breeding of cattle (meat and milk production), sheep, horses, camelids, pigs, and goats. A significant portion is also accounted for by poultry farms;
• Agriculture: This constitutes the basis of the Kazakh agricultural sector. The largest share is the cultivation of spring wheat, which is sold both domestically and internationally.

The agricultural sector contributes to greenhouse gas (GHG) emissions in several ways:

• Nitrous oxide (N₂O) emissions: Generated by the application of synthetic and organic fertilizers, the cultivation of nitrogen-fixing species, organic soil drainage, and certain irrigation methods. These activities together account for approximately 50% [32] of all emissions from the agricultural sector;
• Methane (CH₄) emissions from enteric fermentation: The digestive process of domestic livestock generates methane. This phenomenon, known as enteric fermentation, is responsible for more than a quarter of the sector’s emissions. The rate of methane emission depends significantly on the type of animal’s digestive system. In ruminants, there is an expanded chamber—the rumen—in the first part of the digestive tract, where intense microbial fermentation of the forage consumed occurs. This provides nutritional benefits, such as the ability to digest cellulose. The main emitting ruminants are cattle (the main source of emissions), as well as goats and sheep;
• Minor sources of emissions: These include CO₂ emitted by soil liming and urea use, CH₄ emitted by rice cultivation, and combined CH₄ and N₂O emissions from burning plant residues;
• Natural production of N₂O in soils: This gas is generated as a result of microbial activity, regulated by the nitrogen input to the soil and environmental factors. Ruminants, particularly sheep, show low nitrogen use efficiency and excrete between 70 and 95% of their ingested nitrogen. These high nitrogen loads turn sheep droppings into critical nitrous oxide emission hotspots. In grazing sheep systems, droppings deposited directly on the field are a significant source of N₂O;
• According to the FAO, 60% of emissions related to the global pork supply chain come from feed production, while manure storage and processing account for 27%. The remaining 13% is distributed between post-production processing and transportation (6%), direct and indirect energy use on livestock farms (3%), and enteric fermentation (3%). Of total feed emissions, 17% is N₂O derived from the use of fertilizers (synthetic and organic) in forage cultivation, and 27% is CO₂ associated with energy used in field work, crop transportation and processing, and in the manufacture of synthetic fertilizers and feed [32];
• In poultry farming, 78% of emissions come from feed production, 8% from direct energy use on farms, 7% from post-production processing and transportation, and 6% from manure storage and treatment [33];
• In egg production, 69% of emissions come from feed manufacturing, 4% from direct energy use on farms, 6% from processing and transportation, and 20% from manure storage and treatment [34,35].

Finally, carbon stocks in permanent grasslands are influenced by both human activity and natural disturbances, including woody biomass harvesting, grassland degradation, grazing, fires, restoration, and pasture management practices, among others.

The methodology developed by the Food and Agriculture Organization of the United Nations (FAO) was used as the basis for calculating greenhouse gas (GHG) emissions from the agricultural sector. This methodological guide provides Member States with tools to identify, create, and access a minimum set of activity data needed to assess GHG emissions from various agricultural practices.

Baseline data on sown areas and livestock production by region were taken from official national agricultural and forestry statistics, which are available in the FAO’s corporate database, FAOSTAT, and integrated with geospatial data from international sources.

The FAO resource allows for the calculation of the following indicators based on regional geographic characteristics [11]:

• CO₂ emissions and removals from carbon stock changes in biomass, dead organic matter, soil organic matter, organic and mineral soils, and wood products extracted from all managed lands;
• CO₂ emissions from cultivated organic soils;
• Non-fire-related CO₂ emissions on managed lands;
• CH₄ emissions from rice cultivation;
• N₂O emissions from all managed soils;
• CO₂ emissions related to the application of lime and urea on agricultural soils;
• CH₄ emissions from enteric fermentation in livestock;
• CH₄ and N₂O emissions from manure management systems.

The FAO classifies agricultural activities that generate GHG emissions into the following categories:

• Enteric fermentation: A digestive process that generates methane as a byproduct of microbial fermentation of feed in the digestive system of livestock;
• Manure management: Manure, composed of organic matter and water, decomposes under anaerobic conditions, producing methane, CO₂, and stabilized organic residues;
• Biomass burning: Only living biomass is included, although other fuel fractions (especially in peatlands) may be significant;
• Managed soils: This includes all agricultural soils and includes direct and indirect N₂O emissions, generally calculated from nitrogen input data. Practices such as the use of organic fertilizers, drainage of organic soils (fens), and land-use changes that enhance organic nitrogen mineralization are considered;
• Lime and urea application: The addition of carbonates to the soil (such as CaCO₃ or CaMg(CO₃)₂) releases CO₂ by dissolving in bicarbonate (HCO₃⁻), which subsequently transforms into CO₂ and water. The hydrolysis process of urea (CO(NH₂)₂), used as a fertilizer, also generates CO₂ through conversion to ammonium (NH₄⁺), OH⁻, and HCO₃⁻ in the presence of water and urease enzymes. This CO₂ must be included, since the removal of CO₂ from the atmosphere during urea production is accounted for in the industrial processes sector.

The variables used by the FAO are tailored to the characteristics of each agricultural region, and the organization provides representative default emission factors for Kazakhstan, drawn from previous studies and organized by region for ease of application (see

Table 7. Average CO₂ equivalent emission intensity per unit of output in Kazakhstan. Source: [11].

Agricultural Products/Category	Emission Intensity, t CO2–eq/Unit of Production
Cereals	0.1150
Beef	15.0575
Goat	23.8812
Sheep	20.1717
Poultry	0.3731
Pork	1.0222
Cow’s milk	0.8966
Egg production	0.4524

).

Agricultural emissions were estimated by applying the emission intensities from Table 7 to the actual production volumes of each crop and livestock category in each region. In other words, the emission factors in Table 7 were multiplied by regional production data (e.g., tons of cereal, meat, milk) to yield the regional GHG emissions. This approach captures the contribution of each province’s agricultural output to its total emissions.

The calculation of agricultural emissions by crop focused on cereals, which account for 76% of the national agricultural land [3]. As a result, the values obtained may be underestimated by 5% to 10%.

Regional livestock emissions were calculated based on the production volumes available in official statistics. Therefore, the livestock emissions data include a lower degree of assumptions compared to crop-based estimates.

2.5. Greenhouse Gas Absorption by Forest Ecosystems

The Paris Agreement, signed within the framework of the United Nations Framework Convention on Climate Change (UNFCCC), establishes that, starting in 2020, regulatory measures aimed at CO₂ absorption by ecosystems will be included among the measures to reduce atmospheric carbon dioxide [36].

Terrestrial ecosystems play a key role in the global carbon cycle. Each year, approximately 125 gigatons of carbon are exchanged between vegetation, soil, and the atmosphere, representing two-fifths of the total carbon exchange between the Earth’s surface and the atmosphere [37].

According to the Forest Code of the Republic of Kazakhstan, a forest is defined as a natural complex formed in a given territory by the combination of tree and shrub vegetation, together with other living components, in interaction with the environment. This system has significant ecological, economic, and social value. The following are considered forest ecosystems:

• Areas with tree vegetation located on forest fund land;
• Specially protected natural areas;
• Tree and shrub plantations;
• Land without forest cover and nurseries where seeds are sown and seedlings of forest species are planted, as well as plots with forest crops not yet closed.

The forest fund (FF) of the Republic of Kazakhstan covers an area of 12,548.6 thousand hectares of land covered with forests and tree–shrub vegetation. The national forest cover is estimated at 4.5% of the country’s territory [38].

Coniferous and broad-leaved forests are mainly concentrated in areas with good water availability: forest–steppe regions in the north and mountain forest ecosystems in the foothills and slopes of the Altai, Zhetysu Alatau, and Tian Shan Mountain ranges. In desert areas, saxaul forests (Haloxylon ammodendron) predominate in the deltas of large rivers such as the Ili, Karatal, Syr Darya, and Chu, as well as in sandy massifs. Riparian forests develop in river valleys.

The Kazakh forestry sector is characterized by a very uneven distribution and low average forest density. The area occupied by coniferous species amounts to 1691.6 thousand hectares (13.6% of the total forest area), while broad-leaved species cover 1604 thousand hectares (12.9%).

However, despite their relatively small area, coniferous and broad-leaved species account for around 90% of the total volume of wood stored. Saxaul forests, for their part, comprise a considerable portion of forest land (48.9%), but their contribution to the total timber volume is minimal (4.6%) [5].

The methodological basis for calculating the GHG absorption capacity of forest lands in Kazakhstan is based on the methodological principles recommended by the IPCC [11]. According to these guidelines, the inventory is conducted for the managed forest category, i.e., all forest lands in the Republic of Kazakhstan. The following parameters were used for the calculations:

• Wood biomass growth rates for the main forest species present in Kazakhstan, considering their age (

  Table 8. Conservative woody biomass growth rates by species and age (m³/ha). Source: [39].

  Species/Age	Conservative Growth (m3/ha)
  Young conifers	1.8
  Middle-aged conifers	2.2
  Nearly mature conifers	2.0
  Young soft hardwoods	3.5
  Middle-aged soft hardwoods	4.0
  Nearly mature soft hardwoods	3.0
  Young hard hardwoods	2.1
  Middle-aged hard hardwoods	4.0
  Nearly mature hard hardwoods	3.0
  Young saxaulales, grade I	1.5
  Young saxaulales, grade II	0.7
  Middle-aged saxaulales	0.5
  Nearly mature saxaulales	0.1

  ). The values used are derived from scientific studies conducted in Kazakhstan and in areas with similar climatic and natural conditions [39]. For saxaulales, a growth rate of 0.7 m³/ha was assumed, given that young saxaulales of quality II predominate in Kazakhstan [5];

2.

Specific density of wood (in tons per cubic meter of dry matter), calculated as a weighted average for each group of local forest species (

Table 9. Specific density of wood by forest group (t/m³) Source: [40].

Species	Specific Density (t/m3 of Dry Matter)
Conifers	0.504
Softwoods	0.597
Hardwoods	0.711
Saxaulales	0.711
Other tree species	0.554
Shrubs	0.384

);

3.

Carbon content in biomass, assumed to be 0.5, a typical value for most tree species.

The calculation of CO₂ absorption by forests is carried out in the following steps:

First, the amount of carbon fixed in phytomass is estimated based on annual growth. To find this out, the forest area is multiplied by the conservative biomass growth rate and the specific density of wood:

• Area of young conifers in Kazakhstan: 425,801 ha;
• Conservative growth: 1.8 m³/ha;
• Specific density of wood: 0.504 t/m³.

The carbon mass is calculated (multiplying by 0.5) and then converted to CO₂ by applying the conversion factor 3.67 (corresponding to the ratio of the molecular masses of CO₂ and C).

This procedure is repeated for all forest categories, classified by species type and age, and the results are summed.

In this way, all available forest areas are calculated and broken down by species type and age by region, and the results obtained are aggregated.

However, due to the limited availability of region-specific forest inventory data, the above estimates rely on indirect proxies (e.g., species distribution from the national forest atlas [40] and conservative growth rates from the literature). A sensitivity analysis of forest growth rates and age-class distribution was not performed in this study, but it is recommended to gauge the uncertainty of these sequestration results. Future work should also leverage remote-sensing data (e.g., satellite imagery from Landsat or Sentinel-2) to validate and refine the regional forest biomass estimates, thereby improving the accuracy of the carbon uptake calculations. Thus, the carbon absorption potential of forest lands according to vegetation type and age is presented in

Table 10. Distribution of CO₂ absorption potential of forest lands by regions of Kazakhstan, according to forest type and age (thousand tons of CO₂ equivalent).

No.	Region	Vegetation Type	Young	Average Age	Near Maturity
1	Akmola Region	Conifers	86,190.46	185,403.23	80,444.43
		Soft hardwoods	162,257.05	326,365.49	116,824.68
		Hard hardwoods	3926.19	13,161.66	4712.52
2	Aktobe Region	Conifers	702.51	706.02	75.84
		Soft hardwoods	16,479.53	15,507.83	1383.61
		Hard hardwoods	52,807.65	82,837.06	7397.56
		Saxaulales	2167.21	1274.68	30.40
3	Almaty Region	Conifers	27,417.81	234,576.60	108,801.88
		Soft hardwoods	13,642.20	109,124.45	41,758.08
		Hard hardwoods	1443.89	19,251.93	7366.25
		Saxaulales	101,562.16	507,812.11	51,817.52
4	Atyrau Region	Soft hardwoods	18,887.43	9053.17	9399.35
		Hard hardwoods	517.83	412.28	430.55
5	East Kazakhstan Region	Conifers	444,458.13	686,180.97	415,867.25
		Soft hardwoods	436,423.85	630,027.94	315,012.87
		Hard hardwoods	3139.85	7551.52	3777.06
6	Zhambyl Region	Conifers	895.62	6565.81	4476.23
		Hard hardwoods	4789.24	54,723.71	30,784.04
		Saxaulales	120,695.37	517,267.81	77,590.14
7	West Kazakhstan Region	Conifers	226.40	569.70	220.11
		Soft hardwoods	43,307.66	102,086.99	32,480.33
		Hard hardwoods	15,148.57	59,509.29	18,936.20
8	Karaganda Region	Conifers	13,221.14	79,330.93	28,046.70
		Soft hardwoods	25,152.57	141,108.52	41,156.65
		Hard hardwoods	6468.76	60,479.98	17,640.65
9	Kostanay Region	Conifers	47,592.45	47,903.75	23,330.01
		Soft hardwoods	195,940.78	184,415.63	74,097.09
		Hard hardwoods	3550.83	5568.40	2238.84
10	Kyzylorda Region	Saxaulales	475,351.93	559,237.57	83,885.64
11	Mangystau Region	Saxaulales	40,242.75	10,380.07	1756.63
12	Pavlodar Region	Conifers	75,691.13	174,743.71	46,722.92
		Soft hardwoods	78,252.85	168,925.33	37,262.17
		Hard hardwoods	9699.03	34,897.71	7698.95
13	North Kazakhstan Region	Conifers	12,277.25	35,307.25	20,864.39
		Soft hardwoods	318,390.83	856,181.93	417,386.88
		Hard hardwoods	1745.28	7828.11	3816.20
14	Turkestan Region	Saxaulales	163.14	730.44	723.22
		Conifers	375.75	1573.13	1285.02
		Soft hardwoods	1876.79	13,099.04	10,720.60
		Hard hardwoods	88,520.53	231,838.61	50,583.03

, and the total distribution of the regional sequestration potential is shown in

Table 11. Regional distribution of CO₂ sequestration potential by forest lands in Kazakhstan.

No.	Administrative Unit	Forest Area (Thousand ha)	CO2 Absorption (Thousand t/Year)
1	Astana City	14.8	27.38
2	Almaty City	2.42	5.83
3	Shymkent City	0.183	0.70
4	Akmola Region	379.2	979.29
5	Aktobe Region	47.7	181.37
6	Almaty Region	1835.8	1224.57
7	Atyrau Region	16.4	38.70
8	East Kazakhstan Region	1766.1	2942.44
9	Zhambyl Region	2305.6	817.79
10	West Kazakhstan Region	101.0	272.49
11	Karaganda Region	103.9	412.61
12	Kostanay Region	227.8	584.64
13	Kyzylorda Region	3069.7	1118.48
14	Mangystau Region	112.7	52.38
15	Pavlodar Region	312.4	633.89
16	North Kazakhstan Region	539.5	1673.80
17	Turkestan Region	1609.8	401.49
	Total Kazakhstan	12,445.0	11,333.92

.

2.6. Mitigation Scenario Assumptions

For the scenario analysis, three trajectories were defined: a baseline scenario (continuation of current practices with no major shifts), a moderate mitigation scenario (gradual reforms in energy, agriculture, and transport), and an optimized “geoengineering” scenario (all feasible measures combined). To model the energy sector under mitigation, it was assumed that major GHG-emitting facilities transition to best available technologies (BAT) by 2030. Regional emissions from power and heat generation were recalculated by applying sectoral emission intensity benchmarks (in kg CO₂ per unit of output) representative of BAT performance [41]. This technological modernization scenario effectively lowers the carbon intensity of energy production across all regions by replacing older infrastructure with more efficient, low-emission technologies.

In the transport sector, a mitigation scenario was applied by partially converting the vehicle fleet from gasoline/diesel to natural gas fuel. Based on national data, currently, about 8% of vehicles use natural gas; the optimized scenario assumes up to 50% fleet conversion. The fuel consumption volumes were adjusted accordingly (using average fuel economy and density values as in Table 5), and the CO₂ emissions were then recalculated via Formula (1) for each region. This scenario reflects a significant shift to lower-carbon fuel in road transport, although other transport modes remained unchanged in the analysis.

For the agricultural sector, the mitigation strategy focused on reducing emissions from overgrazed pasturelands. The mitigation potential under this scenario has been calculated using a reference value of 0.3 t CO₂-eq/ha for pasture emissions. This factor is adopted from the Russian Federation’s estimates due to Kazakhstan’s similar dry-steppe conditions and the absence of country-specific data, ensuring a methodologically consistent approach. [29]). Current pasture-related emissions were estimated for each region, then recomputed assuming optimal livestock densities—at least 12 hectares of pasture per head of cattle and 3 hectares per head of sheep or goats. This reflects a shift to sustainable grazing practices that would curtail the methane and nitrous oxide emissions associated with high livestock concentrations.

In the land-use sector, an additional forestry measures scenario was considered. It assumes enhanced reforestation and forest management efforts sufficient to increase the national forest cover by ~0.4% (tens of thousands of hectares) by 2030. Using the IPCC-based methodology [11] described above, carbon sequestration was recalculated for each region with improved biomass growth rates (especially in young and middle-aged stands) and slightly expanded forest areas. This scenario projects the potential increase in CO₂ absorption relative to current levels under concerted forestry interventions, and it provides an upper-bound estimate of what near-term ecosystem-based mitigation could achieve.

2.7. Uncertainty and Statistical Treatment

Uncertainty in the inventory was quantified by assigning sector-specific relative standard deviations (RSD): 5% for the energy sector, 12% for agriculture (including pastureland), and 10% for road transport. For each region, the uncertainty of total emissions was propagated in quadrature:

$$\mathrm{SDtotal}=\sqrt{{\left(0.05\times \mathrm{E}\right)}^2+{\left(0.12\times \mathrm{A}\right)}^2+{\left(0.10\times \mathrm{T}\right)}^2}$$

(3)

where E, A, and T are regional emissions (Mt CO₂-eq) from energy, agriculture (crops + pasture), and transport, respectively. Unless otherwise noted, we report means ± SD; 95% confidence intervals (CI) are derived as mean ± 1.96⋅SD. Differences are considered statistically significant at p < 0.05 when the 95% CIs do not overlap, or when ∣Δ∣ > 1.96. $\sqrt{\mathrm{SD}{\displaystyle \frac{2}{i}}+\mathrm{SD}{\displaystyle \frac{2}{i}}}$ for pairwise comparisons.

Uncertainty ranges are illustrated for tables with representative results while other tables present the mean values only. This limitation reflects the lack of disaggregated error data across all sectors.

3. Results

3.1. Concentration of GHG Emissions in Kazakhstan’s Energy Sector: Regional Disparities and Coal Dependency

In the present study, the calculation of emissions was performed based on the geographical location of fossil fuel consumption and energy production. As a result, it was estimated that the total greenhouse gas (GHG) emissions resulting from energy-related activities amount to 328 million tons of CO₂ equivalent. Of this total, approximately 195 million tons correspond to coal, 90 million tons to oil, and 42 million tons to gas.

The distribution by administrative units is shown in Figure 1.

Undoubtedly, the main emitter is the Pavlodar region, considered the energy center of the Republic of Kazakhstan. This region accounts for half of the country’s total electricity production and 65% of the total coal production [42]. Consequently, GHG emissions exceed 12 million tons of CO₂, representing 37% of the national total in the energy sector.

The region’s energy complex consists of seven thermal power plants, three of which are nationally significant block-type thermal power stations: LLP “Ekibastuz GRES-1”, JSC “Ekibastuz GRES-2”, and JSC “Eurasian Energy Corporation”. Additionally, four combined heat and power (CHP) plants operate in the city of Pavlodar: the CHP plant of JSC “Kazakhstan Aluminium”, CHP-2, CHP-3, and the Ekibastuz CHP owned by JSC “Pavlodarenergo” [43]. The primary fuel used is coal; natural gas is not utilized. The population is supplied with gas via liquefied petroleum gas (LPG) cylinders provided by LLP “Pavlodar Petrochemical Plant”.

It is important to note that the Pavlodar region is not included in the plan approved by the Ministry of Energy of the Republic of Kazakhstan for the siting of renewable energy installations, due to the lack of favorable climatic conditions for the development and operation of such sources.

In second place is the central region, or Karaganda Region, which is also one of the most important industrial zones in Kazakhstan. The energy sector plays a key role in its economy, providing electricity supply both to local industries and to other regions of the country. The main source of electricity generation consists of coal-fired thermal power plants. The largest of these, CHP-3, has an installed capacity of 1200 MW and produces approximately 6 billion kWh per year. The Karaganda coal basin is characterized by a high methane content; in certain mining faces, up to 150–200 m³ of pure methane per minute can be released [44]. This region accounts for 16% of the GHG emissions associated with the energy sector.

The energy potential of the territory is considerable: in addition to the aforementioned factors, the region hosts eight facilities utilizing renewable energy sources, with a total installed capacity of approximately 230 MW (comprising five photovoltaic solar plants, two biogas installations, and one small hydroelectric power station).

The remaining 15 administrative units contribute less than half of the total GHG emissions. Among them:

The western region, which includes four provinces, accounts for 12% of the total. It possesses significant potential for energy production, mainly based on oil and gas deposits, as well as alternative sources. In this context, the province of Mangystau stands out for its uniqueness: it hosts the only industrial complex in the country that is fully self-sufficient in both energy and water, produced at the Mangyshlak Nuclear Energy Complex, which is part of the “Kazatomprom” corporation.

The industry of western Kazakhstan is a major energy consumer. The oil sector, which dominates the area, requires large amounts of electricity to operate wells, pumps, compressors, and other essential equipment for the extraction and processing of hydrocarbons. In addition, there are major metallurgical facilities that also demand significant energy volumes for the production of metal goods. The main energy consumers include LLP “Tengizchevroil”, JSC “KazMunayGas Exploration and Production”, the western branch of JSC “KazTransOil”, LLP “Atyrau Oil Refinery”, and “Karachaganak Petroleum Operating”, among others.

However, despite industrial development, GHG emissions remain at medium levels, as oil—the main fuel in the region—has a lower carbon content per unit of energy generated compared to coal. Furthermore, the share of natural gas usage is high: in 2021, 99.3% of the population in the West Kazakhstan province had access to gas [3].

It should be noted that only three regions in Kazakhstan present an energy surplus (the provinces of Atyrau, Pavlodar, and North Kazakhstan). The rest are in deficit, in many cases with consumption levels far exceeding the local production.

On the other hand, due to its geographical position, the Republic of Kazakhstan does not possess a wide variety of renewable resources. Across its vast territorial expanse, only solar and wind resources are well represented. According to the Global Wind Atlas [45], the average wind speed in most of the country ranges from 6 to 7 m/s, and in the south, between the provinces of Turkestan and Kyzylorda, it can reach up to 8 m/s at a height of 50 m.

In summary, more than half of the energy sector’s emissions are concentrated in the provinces of Pavlodar and Karaganda, while the lowest values are recorded in the regions of Almaty and Turkestan.

3.2. Urban Mobility and Emissions: Transport-Related GHG Distribution in Kazakhstan

The total estimated value of emissions in the transport sector amounts to 7171.2 thousand tons of CO₂ equivalent. The majority of these emissions originate from urban areas. Since the fuel consumption structure varies little between the different administrative units, the distribution by fuel type shows a clear predominance of gasoline (88%), followed by mixed fuel (9%), diesel (2%), and gas, which accounts for less than 1%.

In absolute terms, the administrative units with the highest emissions are Almaty province and the city of Almaty, whose vehicle fleet represents more than 25.6% of the national total, directly linked to the population concentration. In Almaty alone, the automotive fleet consumes around 772 million liters of gasoline and diesel per year, more than 90% of which corresponds to private vehicles [46]. At the opposite extreme, the lowest emissions are recorded in the western region and Kyzylorda province (see Figure 2).

A key factor is the age of the vehicle fleet, which is directly related to the type of fuel used. According to the data in

Table 12. Number of registered private cars by age. Source: National Statistics Office of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Source: [30].

No.	Administrative Unit	≤3 Years	>3 and ≤7 Years	>7 and ≤10 Years	>10 Years	Total
1	Astana	63,266	64,580	23,630	113,663	265,139
2	Almaty (city)	78,079	84,620	45,496	246,226	454,421
3	Shymkent	13,125	19,515	9317	57,972	99,929
4	Akmola Region	16,257	20,845	10,439	119,068	166,609
5	Aktobe Region	18,998	28,275	11,002	74,253	132,528
6	Almaty Region	24,882	44,525	26,423	385,720	481,550
7	Atyrau Region	27,069	28,072	8859	36,945	100,945
8	East Kazakhstan Region	20,029	44,294	15,290	210,382	289,995
9	Zhambyl Region	8111	14,351	8809	167,026	198,297
10	West Kazakhstan Region	19,525	20,823	8411	60,378	109,137
11	Karagandy Region	28,765	34,446	16,764	195,013	274,988
12	Kostanay Region	24,852	22,989	9706	102,427	159,974
13	Kyzylorda Region	10,051	13,116	7102	76,658	106,927
14	Mangystau Region	18,901	29,452	12,551	66,582	127,486
15	Pavlodar Region	14,682	18,120	8602	106,164	147,568
16	North Kazakhstan Region	12,861	12,981	7586	90,280	123,708
17	Turkestan Region	21,928	40,600	21,574	219,232	303,334
	Kazakhstan Total	462,594	570,920	268,324	2,495,407	3,797,245

, the highest number of older vehicles is found in Almaty province and the city of Almaty, reinforcing their impact on emissions.

In conclusion, the energy sector accounts for approximately 72% of total GHG emissions, with more than half concentrated in the provinces of Pavlodar and Karaganda. In contrast, the transport sector represents about 2%, with its greatest contribution attributable to urban centers, especially Almaty province. Nevertheless, when considering the combined total of both sectors, Almaty ranks among the regions with the lowest absolute volume of emissions (see Figure 3).

3.3. Agricultural and Livestock Contributions to GHG Emissions: Land Use, Regional Patterns, and Sectoral Composition

The calculations carried out indicate that the agricultural sector contributes approximately 26% of the total greenhouse gas (GHG) emissions in the Republic of Kazakhstan, which is equivalent to 118 million tons of CO₂ equivalent, of which around 70% is attributed to pastureland use (see Figure 4).

3.3.1. Pasturelands

In terms of regional distribution, the Karaganda Region leads with 12,390.8 thousand tons of CO₂ equivalent. This situation reflects a strong territorial differentiation in the agricultural use of land, which is closely linked to the regional landscape structure. The intra-zonal, provincial, and district-level differentiations of natural landscapes—and therefore of agrarian landscapes—largely depend on geological and geomorphological factors. The vertical and planned structure of the territory has been shaped by active block neotectonics. A total of 87.2% of the land area is allocated to agricultural use, where pasturelands account for 92.8%, arable fields 3.4%, and hayfields 1%. In recent years, these lands have experienced intense anthropogenic pressure, which has intensified ecologically destructive processes [48]. Soil degradation, particularly due to wind deflation, has significantly reduced fertility, especially in cultivated areas. The regions with the lowest emissions include the North, as well as the cities of Astana, Almaty, and Shymkent.

3.3.2. Crop Agriculture

The highest emissions in this subsector come from Northern Kazakhstan, with Akmola province standing out with 3522.4 thousand tons of CO₂ equivalent. This area is located within the steppe and dry-steppe zones, featuring typical southern chernozem and dark chestnut soils [49]. The irregular relief of the Saryarka massif significantly influences its climate. Since the 2000s, land reforms and the consolidation of private land ownership have led to an increase in cultivated areas and in the average yield of cereals. Thus, between 1999 and 2003, the sown area was 3.8 million hectares, with an average yield of 10.1 quintals per hectare. Currently, figures indicate 4.9 million hectares and 11.1 quintals per hectare, respectively [30]. The western part of the country, composed of desert and semi-desert zones with halophytic vegetation, shows the lowest emission values.

3.3.3. Livestock Farming

Total emissions from the livestock sector amount to 23,278.7 thousand tons of CO₂ equivalent, which represents approximately 20% of agricultural emissions. The structure of the livestock subsector as a source of emissions is shown in Figure 5. The composition of livestock varies regionally according to climate, water resources, and soil and vegetation conditions. In arid and semi-desert areas, sheep and camel breeding predominate, whereas in regions with abundant pasturelands, cattle are raised. The availability of forage is a determining factor: natural meadows are mainly located in the western and eastern parts of the country, while fodder crops are concentrated in the north and south.

• Cattle: The main emissions come from Northern Kazakhstan, which holds more than 35% of the national cattle stock, and from the southern and eastern foothills, which account for another 30%;
• Sheep and goats: The provinces of Almaty and Turkestan lead in sheep farming, with these animals being highly adaptable to extreme conditions. In the case of goats, the East Kazakhstan and Zhambyl provinces stand out;
• Swine: Pig farming, based on cultivated feed, is concentrated in the intensive agricultural regions of the north and east;
• Poultry: Linked to urban areas and the grain processing industry, poultry farming is prominent in the provinces of Almaty, Akmola, and East Kazakhstan;
• Egg production: Follows a pattern similar to poultry farming, with Almaty and Akmola leading in terms of emissions.

Overall, the provinces of Karaganda and Aktobe are the largest contributors to agricultural emissions across all subsectors, while the lowest figures are recorded in the cities of Astana, Almaty, and Shymkent, as well as in the provinces of North Kazakhstan and Atyrau (see Figure 6).

3.4. East Kazakhstan as the Main Carbon Sink Region: Forest Cover and Absorption Capacity

The main carbon sink region in the Republic of Kazakhstan is the East Kazakhstan province, with an estimated absorption capacity of 2942.44 thousand tons of CO₂ equivalent (see Figure 7). This region contains 44% of the nation’s exploitable timber reserves [3], and its forest cover rate is 7%.

The main tree species that make up the forests in this province include pine, fir, spruce, cedar, larch, aspen, birch, and poplar. This forest base has been strengthened by an initiative launched in 2020 by the Government of Kazakhstan in cooperation with the United Nations Development Programme (UNDP), aimed at reducing the carbon footprint through the preservation and expansion of forested areas. As part of this project, 67 thousand hectares of previously unregistered forests were identified in the mountainous areas of East Kazakhstan and an additional 47 thousand hectares in Pavlodar province [15].

In contrast, the provinces of West Kazakhstan show significantly lower potential as carbon sinks, occupying a slightly more favorable position than the cities of Astana, Almaty, and Shymkent, which rank among the regions with the lowest absorption capacity.

3.5. National GHG Emissions Profile by Sector

Kazakhstan’s total GHG emissions are ~453.79 Mt CO₂-eq (±21.72 Mt SD; 95% CI: ±42.58 Mt), which materially exceeds the officially reported values. Sectoral shares remain dominated by energy (~72%), followed by agriculture (~26%) and road transport (~2%). This breakdown underscores Kazakhstan’s heavy reliance on fossil fuel combustion as the key driver of its carbon footprint, with agricultural practices (notably methane from enteric fermentation on pasturelands) being the second-largest contributor.

Table 13. Distribution of GHG emissions in the Republic of Kazakhstan by sector and region (thousand tons of CO₂-eq). Values are reported as means ± SD (standard deviation); 95% CI (confidence interval) provided where relevant. Source: compiled by the authors.

No.	Administrative Unit	Energy (E)	Transport (T)	Agriculture (A)	Pasturelands (P)	Total	Total ± SD (Thousand Tons; 95% CI)
1	Astana	8749.57	588.84	2.59	3.32	9344.32	9344.32 ± 441.42 (95% CI: ±865.19)
2	Almaty (city)	5410.29	1002.12	1.73	0.90	6415.04	6415.04 ± 288.48 (95% CI: ±565.42)
3	Shymkent	16,462.90	252.40	206.70	11.58	16,933.58	16,933.58 ± 823.95 (95% CI: ±1614.94)
4	Akmola Region	3014.70	342.73	4535.02	3973.62	11,866.07	11,866.07 ± 1032.67 (95% CI: ±2024.04)
5	Aktobe Region	9949.69	255.73	2292.88	9824.16	22,322.46	22,322.46 ± 1537.01 (95% CI: ±3012.53)
6	Almaty Region	1151.15	1031.48	4345.62	8493.96	15,022.22	15,022.22 ± 1545.27 (95% CI: ±3028.73)
7	Atyrau Region	22,939.13	205.04	709.72	3804.96	27,658.85	27,658.85 ± 1268.64 (95% CI: ±2486.52)
8	East Kazakhstan Region	12,265.54	616.64	4005.73	7951.00	24,838.92	24,838.92 ± 1561.60 (95% CI: ±3060.73)
9	Zhambyl Region	3300.55	418.38	2263.98	4965.12	10,948.03	10,948.03 ± 884.04 (95% CI: ±1732.72)
10	West Kazakhstan Region	18,106.91	226.11	1388.79	6104.10	25,825.91	25,825.91 ± 1276.18 (95% CI: ±2501.31)
11	Karaganda Region	53,167.88	583.55	2487.71	12,390.84	68,629.98	68,629.98 ± 3202.84 (95% CI: ±6277.57)
12	Kostanay Region	3568.96	341.01	4088.25	4562.76	12,560.98	12,560.98 ± 1053.90 (95% CI: ±2065.64)
13	Kyzylorda Region	13,550.73	212.70	497.65	4724.56	18,985.65	18,985.65 ± 923.16 (95% CI: ±1809.39)
14	Mangystau Region	27,939.23	222.72	114.09	5060.44	33,336.48	33,336.48 ± 1528.91 (95% CI: ±2996.67)
15	Pavlodar Region	121,534.62	309.64	1894.75	3304.28	127,043.30	127,043.30 ± 6108.75 (95% CI: ±11,973.15)
16	North Kazakhstan Region	5498.14	253.00	1739.33	1976.10	9466.57	9466.57 ± 524.40 (95% CI: ±1027.83)
17	Turkestan Region	1495.88	309.10	3673.41	5341.56	10,819.95	10,819.95 ± 1084.82 (95% CI: ±2126.25)
	Total Kazakhstan	328,105.89	7171.2	36,019.06	82,493.26	453,789.4	453,789.40 ± 21,723.22 (95% CI: ±42,577.52)

presents the detailed distribution of GHG emissions by sector and region.

Note that some values are truncated or approximate in Table 13. Table 13 is compiled to show the national distribution of GHG.

(a)

RSD (relative standard deviation) is applied by sector: E (energy) = 5%, A (agriculture) = 12% and T (transport) = 10%;

(b)

Regional total SD is propagated as shown in Formula (3);

(c)

95% CI is reported as mean ± 1.96 × SD;

(d)

Statistical significance: regional differences are significant at p < 0.05 if 95% CIs do not overlap or if ∣Δ∣ > 1.96 $\times \sqrt{\mathrm{SD}{\displaystyle \frac{2}{i}}+\mathrm{SD}{\displaystyle \frac{2}{i}}}$.

Uncertainty ranges reflect variability in emission factors, calorific values, activity data, and livestock statistics. Applying sector-specific RSDs (5% energy; 12% agriculture; 10% transport) yields regional totals with typical SDs of ~5–10%. Most inter-regional contrasts (e.g., coal-intensive provinces vs. agrarian or urban regions) exceed these uncertainties, indicating statistically significant differences at p < 0.05.

3.6. Potential Emission Reduction Pathways

The Republic of Kazakhstan is currently developing its national low-carbon development strategy through 2050. This strategy envisages the decarbonization of key economic sectors. The potential of each region, in turn, depends on how economic actors respond to changes in the macroeconomic environment and the regulatory framework established by the state.

In this context, several greenhouse gas (GHG) emission reduction scenarios have been analyzed and selected based on their relevance to the specific territorial conditions of each region.

3.7. Toward a Low-Carbon Future: Emission Reduction Scenarios for Kazakhstan’s Energy Sector

One of the most evident pathways to reduce the impact of greenhouse gas emissions (GHG) consists of the technological modernization of the energy sector. This involves accelerating the transition to electric engines and electrified heating systems, alongside a progressive increase in electricity generation from renewable sources.

The shift towards a low-carbon economic development model requires major GHG-emitting facilities to transition to the best available technologies (BAT). The estimation of GHG emission volumes for these large facilities by 2030, under a technological modernization scenario, is based on the use of sector-specific reference values or benchmarks. These benchmarks represent specific emission indicators per unit of product, adopted as standards for each branch of activity (see

Table 14. Emission intensity benchmarks for various quota allocation approaches in Kazakhstan’s energy sector (excerpt). Source: [41].

Specific Indicator	Min. RoK	Avg. RoK	Max. RoK	50–50% (Based on CO2)	Top 10% (by Total Output)	Top 90% (by Total Output)	EU Benchmark
Electricity (kg CO2/kWh)	0.055	0.703	1.554	0.910	1.055	0.615	0.640
Heat (kg CO2/Gcal)	0.130	0.405	0.950	0.510	0.532	0.262	0.261
Electricity and heat (kg CO2/kWh)	0.127	0.597	1.364	0.706	1.005	0.404	0

).

Based on the reference emission intensity values presented in Table 14, the potential for emission reduction in the energy sector was assessed under a comprehensive technological modernization scenario. For this calculation, benchmark indicators of specific emissions per unit of electricity and heat production were applied to regional energy statistics. The results, summarized in

Table 15. Comparison of GHG emissions in the energy sector under current conditions and with technological modernization. Source: compiled by the authors.

No.	Administrative Region	Current GHG Emissions (Thousand t CO2-eq)	Average Specific Intensity (kg CO2/kWh)	Emissions After Modernization (Thousand t CO2-eq)
1	Astana	8749.57	0.579	6105.05
2	Almaty (city)	5410.29	0.579	3775.06
3	Shymkent	16,462.90	0.579	11,487.07
4	Akmola Region	3014.70	1.175	1036.54
5	Aktobe Region	9949.69	1.091	3684.46
6	Almaty Region	1151.15	1.201	387.13
7	Atyrau Region	22,939.13	0.799	11,599.71
8	East Kazakhstan Region	12,265.54	1.648	3007.22
9	Zhambyl Region	3300.55	0.551	2418.32
10	West Kazakhstan Region	18,106.91	0.814	8991.22
11	Karaganda Region	53,167.88	1.422	15,110.23
12	Kostanay Region	3568.96	0.517	2790.97
13	Kyzylorda Region	13,550.73	1.073	5102.96
14	Mangystau Region	27,939.23	0.703	16,059.54
15	Pavlodar Region	121,534.62	1.119	43,867.32
16	North Kazakhstan Region	5498.14	1.334	1665.10
17	Turkestan Region	1495.88	0.729	829.28
	Total Kazakhstan	328,105.89	0.936	137,917.16

, indicate that if large GHG-emitting facilities were upgraded to best available technologies (BAT) across all regions, sectoral emissions would decline from 328 to 138 million tons of CO₂ equivalent. This corresponds to an overall reduction of approximately 58% compared to the current level.

3.8. Limited Mitigation Potential in Kazakhstan’s Transport Sector: Assessing the Shift to Natural Gas Vehicles

Emission reduction in the transport sector primarily involves a partial conversion of the vehicle fleet from traditional fuels (gasoline, diesel, and blends) to natural gas, whose greenhouse gas (GHG) emissions are significantly lower (see

Table 16. GHG emissions from road transport under different levels of conversion to natural gas. Source: compiled by the authors.

No.	Administrative Region	Current Emissions (Thousand t CO2-eq)	8% of Fleet Converted to Gas	50% of Fleet Converted to Gas
1	Astana	588.84	573.94	495.71
2	Almaty (city)	1002.12	976.27	840.56
3	Shymkent	252.40	246.15	213.36
4	Akmola Region	342.73	334.04	288.37
5	Aktobe Region	255.73	250.25	221.47
6	Almaty Region	1031.48	1004.84	865.00
7	Atyrau Region	205.04	200.00	173.53
8	East Kazakhstan Region	616.64	600.58	516.27
9	Zhambyl Region	418.38	407.64	351.22
10	West Kazakhstan Region	226.11	220.57	191.54
11	Karaganda Region	583.55	568.57	489.96
12	Kostanay Region	341.01	332.34	286.82
13	Kyzylorda Region	212.70	207.69	181.34
14	Mangystau Region	222.72	219.49	202.54
15	Pavlodar Region	309.64	301.69	259.95
16	North Kazakhstan Region	253.00	246.77	214.07
17	Turkestan Region	309.10	301.63	262.44
	National Total	7171.20	6992.48	6054.15

).

To estimate the mitigation potential under this approach, two scenarios have been considered:

• A moderate conversion of 8% of the vehicle fleet, reflecting current trends;
• An ambitious conversion of 50%, as a medium-term strategic goal.

The results obtained indicate that the effectiveness of this measure is limited. In the most optimistic scenario, emissions would be reduced by approximately 1.12 million tons of CO₂ equivalent, falling from 7.17 to 6.05 million tons. Under the more conservative scenario (8% conversion), the reduction would be marginal: just 178.7 thousand tons.

In conclusion, although the conversion of the vehicle fleet to natural gas is relevant from both a technical and environmental standpoint, it does not by itself constitute a sufficient strategy to achieve a significant reduction in emissions on the national scale. Its implementation should be integrated into a broader package of measures, including the electrification of public transport, promotion of active mobility, renewal of the vehicle fleet, and optimization of road infrastructure.

3.9. Load Adjustment and Pasture-Use Efficiency: A Pathway to Lower Agricultural Emissions

In the agricultural sector, one of the most effective measures for reducing greenhouse gas (GHG) emissions is the optimization of pastureland use. The mitigation potential under this scenario has been calculated using a reference value for normal pasture emissions, estimated at 0.3 t CO₂-eq/ha, in accordance with the values used in the Russian Federation.

Under optimal livestock load conditions—i.e., at least 12 hectares per head of cattle and 3 hectares per head of sheep or goats—the emissions attributable to pasturelands would be reduced by 34%, from 82.49 to 54.15 million tons of CO₂ equivalent (see

Table 17. GHG emissions from pastureland use under current and optimized conditions. Source: compiled by the authors.

No.	Administrative Region	Current Emissions (Thousand t CO2-eq)	Optimized Emissions (Thousand t CO2-eq)
1	Astana	3.32	2.49
2	Almaty (city)	0.90	0.45
3	Shymkent	11.58	5.79
4	Akmola Region	3973.62	1986.81
5	Aktobe Region	9824.16	7368.12
6	Almaty Region	8493.96	4246.98
7	Atyrau Region	3804.96	2853.72
8	East Kazakhstan Region	7951.00	5963.25
9	Zhambyl Region	4965.12	2482.56
10	West Kazakhstan Region	6104.10	3052.05
11	Karaganda Region	12,390.84	9293.13
12	Kostanay Region	4562.76	3422.07
13	Kyzylorda Region	4724.56	3543.42
14	Mangystau Region	5060.44	3795.33
15	Pavlodar Region	3304.28	2478.21
16	North Kazakhstan Region	1976.10	988.05
17	Turkestan Region	5341.56	2670.78
	National Total	82,493.26	54,153.21

).

This scenario demonstrates that, through rational pasture management, a substantial reduction in emissions can be achieved without the need for complex technologies or large-scale investments. Effective implementation of this approach requires public policies aimed at sustainable land use, regulations on livestock loads, and programs for restoring degraded pastures.

3.10. Enhancing Forest Carbon Sequestration Potential: Strategic Measures for 2030 in Kazakhstan

The potential for increasing carbon sequestration in forest ecosystems can be achieved through the implementation of “additional measures” in the forestry sector. The carbon absorption potential by 2030 has been estimated under the following key assumptions:

• The main contributions to the increase in CO₂ absorption will come from young and middle-aged forests, where carbon accumulation in both aboveground and belowground biomass is most active;
• Mature and old-growth forests are not considered relevant in terms of additional carbon capture during the analyzed period.
• Biomass growth coefficients have been adopted conservatively, based on official forest inventory records and scientific studies tailored to the Kazakhstani context;
• Logging activities are assumed not to significantly affect biomass increments in young and middle-aged forests, as timber harvesting is mainly concentrated in mature forests;
• The impact of forest fires on these segments is considered minimal, given the country’s strengthened forest fire prevention and control policies;
• An increase in forest area is anticipated, especially through planned reforestation efforts;
• Forest management will be improved, involving more effective silvicultural practices, protection of vulnerable areas, and restoration of degraded zones.

Based on the existing forest area and estimated regional absorption coefficients, current CO₂ sequestration values were calculated. Under the “additional measures” scenario, which includes a 0.4% increase in forest cover by 2030, the carbon sequestration potential would increase by 53.7%, reaching a total of 17.43 million tons of CO₂ equivalent, compared to the current 11.33 million (see

Table 18. GHG sequestration potential by forest ecosystems with and without implementation of additional measures. Source: compiled by the authors.

No.	Administrative Region	Current Sequestration (Thousand t CO2-eq)	Base Coefficient	Improved Coefficient	Improved Sequestration (Thousand t CO2-eq)	Sequestration with Additional Measures (Thousand t CO2-eq)
1	Astana	27.38	2.85	3.03	42.18	44.84
2	Almaty (city)	5.83	0.80	0.85	1.94	2.06
3	Shymkent	0.70	0.47	0.50	0.09	0.09
4	Akmola Region	979.29	2.85	3.03	1080.72	1148.98
5	Aktobe Region	181.37	3.58	3.81	170.77	181.74
6	Almaty Region	1224.57	0.80	0.85	1468.64	1560.43
7	Atyrau Region	38.70	3.78	4.02	61.99	65.93
8	East Kazakhstan Region	2942.44	2.48	2.64	4379.93	4662.50
9	Zhambyl Region	817.79	0.53	0.56	1221.97	1291.14
10	West Kazakhstan Region	272.49	4.08	4.34	412.08	438.34
11	Karaganda Region	412.61	3.13	3.33	325.21	345.99
12	Kostanay Region	584.64	3.19	3.39	726.68	772.24
13	Kyzylorda Region	1118.48	0.55	0.59	1688.34	1811.12
14	Mangystau Region	52.38	0.71	0.76	80.02	85.65
15	Pavlodar Region	633.89	2.65	2.82	827.86	880.97
16	North Kazakhstan Region	1673.80	3.77	6.17	2033.92	3328.72
17	Turkestan Region	401.49	0.47	0.50	756.61	804.90
	National Total	11,333.92	2.16	2.42	15,278.92	17,425.63

).

This scenario suggests that, with sustained investment in reforestation, adaptive silviculture, and conservation, Kazakhstan’s forests could play a significantly larger role in national climate mitigation efforts, aligning with the commitments of the Paris Agreement and strengthening the ecological resilience of its regions.

3.11. Bridging the Gap to Carbon Neutrality: Multisectoral Emission Reductions and Forest Sink Enhancement in Kazakhstan

As previously stated, the Republic of Kazakhstan has committed to reducing its greenhouse gas (GHG) emissions by 15–25% by 2030 compared to 1990 levels, and to achieving carbon neutrality by 2060. In 1990, national emissions amounted to 386 million tons of CO₂ equivalent, while current emissions are estimated at 353.2 million tons of CO₂ equivalent [3], representing a reduction of 8.5% compared to the base year. Consequently, by 2030, the country must reduce emissions by an additional 6.5% to meet the unconditional target and by 16.5% to achieve the conditional target, which is contingent upon international financial support.

Under the current scenario—without additional measures and considering only the natural potential of carbon sinks—only one out of the 17 regions would meet the conditional target. With improvements in the forestry sector, this number would increase to two regions: East Kazakhstan and North Kazakhstan (see

Table 19. Regional carbon balance of the Republic of Kazakhstan. Note: In the graphical coding, red indicates regions not meeting the targets; orange indicates those meeting the unconditional target; green represents regions achieving the conditional target. Source: authors’ elaboration.

No.	Administrative Region	GHG Emissions (Thousand t CO2-eq)	Current Forest Absorption	Current Balance	Reduction (%)	Improved Absorption	Improved Balance	Reduction (%)
1	Astana	9344.32	27.38	9316.94	0.29	44.84	9299.48	0.48
2	Almaty	6415.04	5.83	6409.21	0.09	2.06	6412.98	0.03
3	Shymkent	16,933.58	0.70	16,932.88	0	0.09	16,933.49	0
4	Akmola Region	11,866.07	979.29	10,886.78	8.25	1148.98	10,717.09	9.70
5	Aktobe Region	22,322.46	181.37	22,141.09	0.81	181.74	22,140.72	0.81
6	Almaty Region	15,022.22	1224.57	13,797.65	8.15	1560.43	13,461.79	10.39
7	Atyrau Region	27,658.85	38.70	27,620.15	0.14	65.93	27,592.92	0.24
8	East Kazakhstan Region	24,838.92	2942.44	21,896.48	11.85	4662.50	20,176.42	18.77
9	Zhambyl Region	10,948.03	817.79	10,130.24	7.47	1291.14	9656.89	11.79
10	West Kazakhstan Region	25,825.91	272.49	25,553.42	1.06	438.34	25,387.57	1.70
11	Karaganda Region	68,629.98	412.61	68,217.37	0.60	345.99	68,283.99	0.50
12	Kostanay Region	12,560.98	584.64	11,976.34	4.65	772.24	11,788.74	6.15
13	Kyzylorda Region	18,985.65	1118.48	17,867.17	5.89	1811.12	17,174.53	9.54
14	Mangystau Region	33,336.48	52.38	33,284.10	0.16	85.65	33,250.83	0.26
15	Pavlodar Region	127,043.29	633.89	126,409.40	0.50	880.97	126,162.32	0.69
16	North Kazakhstan Region	9466.57	1673.80	7792.77	17.68	3328.72	6137.85	35.16
17	Turkestan Region	10,819.95	401.49	10,418.46	3.71	804.90	10,015.05	7.44
	National Total	453,789.41	11,333.92	442,455.49	2.5	17,425.63	436,364.00	3.84

).

Nevertheless, the coordinated implementation of multiple optimization measures across productive sectors would enable all regions of the country to meet the national GHG reduction target for 2030.

These figures are based on the comprehensive mitigation scenario (optimized geoengineering scenario) defined in the methods, which assumes the implementation of all feasible emission reduction measures across sectors by 2030. In calculating optimized emissions for each region, sector-specific mitigation assumptions were applied, reflecting the adoption of best practices and technologies. For the energy sector, major fossil fuel facilities in every region were assumed to be upgraded to best available techniques (BAT) by 2030, substantially lowering the carbon intensity of power and heat production. According to the estimates based on national energy statistics and IPCC emission factors, this technological modernization yields approximately a 50–60% reduction in energy-related CO₂ emissions nationwide, which was distributed across regions in proportion to their baseline energy emissions. In the transport sector, the optimized scenario assumes an aggressive shift of vehicles from gasoline/diesel to natural gas (up to ~50% of the fleet, compared to ~8% today). The resulting emission decline in each region was calculated according to the initial share of road transport emissions, with more urbanized areas (e.g., Almaty) experiencing larger absolute reductions. In agriculture, mitigation focuses on pastureland emissions, applying sustainable livestock stocking rates (e.g., ~12 ha per cattle head) to estimate reductions in methane emissions from overgrazed pastures. Using a reference factor of 0.3 t CO₂-eq/ha for pasture emissions (based on data from similar dry-steppe conditions [29]), methane output from grazing was recalculated for each region. This approach produced roughly a one-third reduction in agricultural GHG emissions in high-livestock regions, consistent with the ~34% mitigation potential cited for optimal pasture management.

The improved absorption values in

Table 20. Regional carbon balance under a multisectoral optimization scenario. Source: authors’ elaboration.

No.	Administrative Region	Optimized Emissions (Thousand t CO2-eq)	Improved Forest Absorption	Net Balance	Reduction (%)
1	Astana	6605.84	44.84	6561.00	29.8
2	Almaty	4617.80	2.06	4615.74	28.0
3	Shymkent	11,912.92	0.09	11,912.83	29.6
4	Akmola Region	7846.74	1148.98	6697.76	43.5
5	Aktobe Region	13,566.93	181.74	13,385.19	40.0
6	Almaty Region	9844.73	1560.43	8284.30	44.9
7	Atyrau Region	15,336.68	65.93	15,270.75	44.3
8	East Kazakhstan Region	13,492.47	4662.50	8829.97	64.5
9	Zhambyl Region	7516.08	1291.14	6224.94	43.1
10	West Kazakhstan Region	13,623.60	438.34	13,185.26	48.9
11	Karaganda Region	27,381.03	345.99	27,035.04	60.6
12	Kostanay Region	10,588.11	772.24	9815.87	22.0
13	Kyzylorda Region	9325.37	1811.12	7514.25	60.4
14	Mangystau Region	20,171.50	85.65	20,085.85	39.7
15	Pavlodar Region	48,500.23	880.97	47,619.26	62.5
16	North Kazakhstan Region	4606.55	3328.72	1277.83	86.5
17	Turkestan Region	7435.91	804.90	6631.01	38.7
	National Total	234,143.58	17,425.63	216,717.95	52.2

were obtained by modeling an enhancement of natural carbon sinks under the same optimized scenario. Specifically, an afforestation and reforestation program sufficient to increase national forest cover by approximately 0.4% by 2030 (tens of thousands of hectares of new forests) was incorporated, alongside improved forest management practices. Using the IPCC inventory methodology [11], additional CO₂ uptake from these measures was estimated for each region. The calculation accounted for new biomass growth in planted areas and enhanced growth in existing young and middle-aged stands. Regions with large areas of suitable land for planting (e.g., Akmola) were assigned greater potential increases in sequestration. The improved absorption figure for each region thus represents the projected annual CO₂ removal by ecosystems after implementation of the forestry measures, in addition to the current absorption baseline.

All underlying parameters and assumptions for these calculations (BAT adoption rates, vehicle conversion fractions, emission factors, forest expansion area, etc.) are grounded in official data or the literature and are presented in the methodology. Consistent application of these assumptions ensures that the optimized regional emissions and enhanced absorption values in Table 20 can be reproduced by other researchers following the same steps. This detailed approach provides transparency and scientific verifiability of the scenario outcomes (see Table 20).

In conclusion, the Republic of Kazakhstan possesses an estimated technical potential to reduce its GHG emissions by 52% by the year 2030. However, this level remains insufficient to achieve carbon neutrality by 2060. Attaining this long-term objective will require a profound transformation of the national energy model, based on the gradual replacement of fossil fuels—which are currently responsible for the majority of emissions—with renewable energy sources.

Over the past decade, the renewable energy sector has demonstrated significant growth in Kazakhstan. To ensure sustainable energy development, the National Energy Balance until 2035 has been approved, which envisions renewable electricity generation (wind and solar) accounting for approximately 15% of the energy mix (19.6 and 2.9 out of 152.4 billion kWh).

However, according to estimates from the International Renewable Energy Agency (IRENA), Kazakhstan would need to increase its installed renewable energy capacity tenfold by 2050 in order to meet the targets of the Paris Agreement [20].

4. Discussion

4.1. Assessment of Emission Reduction Potential in the Regions of Kazakhstan: Current Situation

According to official data, the country’s reported emission volume stands at approximately 352 million tons of CO₂ equivalent. This discrepancy is due, on the one hand, to the lack of precision in official statistical databases and, on the other, to the absence of a unified methodology for calculating GHG emissions. Moreover, relevant omissions are observed in official inventories, such as the exclusion of the pastureland sector as a source of emissions. In contrast, under the methodological principles of the IPCC applied in this study, emissions from pasturelands are estimated at 82 million tons of CO₂ equivalent. Likewise, the absorption potential of sinks (such as forests) is not included in the official statistics, which contributes to an overall underestimation.

Given the reported SDs and 95% CIs, the ranking of emission ‘hotspots’ (e.g., Pavlodar: 127.04 ± 6.11 Mt vs. North Kazakhstan: 9.47 ± 0.52 Mt) remains statistically robust (p < 0.05), supporting targeted, region-specific mitigation priorities.

At the regional level, the Pavlodar Region shows the highest absolute volume of emissions, accounting for approximately 28% of the national total, whereas the city of Almaty is the administrative unit with the lowest total emissions.

Based on total emission magnitude, the regions have been classified into five categories (see Figure 8), as follows:

• Very High: Pavlodar Region;
• High: Karaganda Region;
• Medium: Mangystau, Atyrau, West Kazakhstan, East Kazakhstan, and Aktobe Regions;
• Low: Kyzylorda and Almaty Regions, and the city of Shymkent;
• Very Low: Kostanay, Akmola, Zhambyl, Turkestan, and North Kazakhstan Regions, as well as the cities of Astana and Almaty.

Figure 8. Regional carbon status map of Kazakhstan (compiled by the authors).

Figure 8. Regional carbon status map of Kazakhstan (compiled by the authors).

4.2. Integrating Multisectoral Mitigation Measures: Feasibility and Synergies

The scenario analysis (Section 3.6) indicates that no single measure is sufficient for Kazakhstan to meet its long-term climate objectives. Instead, a combination of measures across all major sectors is required. Each mitigation pathway has its advantages and limitations:

• Technological upgrades in energy (e.g., shifting power plants to BAT, increasing renewables) offer the largest single-source emission reduction (nearly 190 Mt CO₂-eq cut) but face barriers, such as high upfront costs, infrastructure inertia (many power plants are old but still operational), and the need for regulatory reforms (like carbon pricing to incentivize the switch). Pavlodar and Karaganda, for instance, would greatly benefit from cleaner technologies given their disproportionate share of emissions;
• Transport fuel switching, as implemented in this work’s scenario (CNG conversion), was shown to yield only minor national benefits. This underscores that transport policy in Kazakhstan might prioritize quality-of-life improvements (reducing urban pollution) and preparation for future growth rather than rely on it for major emission cuts. The discussion of expanding this approach to include electric vehicles and public transportation electrification is crucial. Studies suggest that shifting to electric mobility powered by low-carbon electricity could dramatically reduce urban emissions [9]. Although our current model did not include EV adoption, this should be part of future mitigation scenarios to capture the full potential of the transport sector in a decarbonizing grid;
• Agricultural mitigation via pasture management emerged as a high-impact, low-cost strategy. A 28.3 Mt CO₂-eq reduction is possible largely through policy and behavioral changes (e.g., enforcing grazing quotas, restoring overgrazed land) rather than expensive technology. The feasibility of this is relatively high if there is political will and community engagement (herders will need support to destock or rotate grazing). Importantly, this measure brings synergies: healthier pastures can support biodiversity, improve water retention, and sustain livestock productivity in the long run. It also addresses a previous blind spot in Kazakhstan’s climate policy—land-use management for mitigation;
• Forestry measures, though smaller in contribution (~6.1 Mt additional uptake), are still significant in that they punch above their weight in terms of co-benefits. Increasing forest sinks by ~54% would not only sequester carbon but also help combat desertification and improve air, water, and soil quality. Kazakhstan’s “Billion Trees” afforestation initiative is a step in this direction. The synergy of forestry with other sectors is notable: for example, rehabilitating abandoned agricultural lands (over 1 million hectares in Akmola [50]) by planting trees or perennial cover can sequester carbon and reduce dust storms, benefiting agriculture and health. Our case study of the Akmola region in the geoengineering scenario showed a 35% net emission reduction, largely due to combining reforestation with better pasture management.

Together, these findings illustrate the substantial synergies achievable by combining sectoral efforts. Kazakhstan’s “geoengineering” scenario (all measures) achieves a 52% emission cut, which none of the sectors could reach alone. Notably, the role of forest and land-use interventions becomes more important when aligned with energy reforms. While forests alone cannot offset energy emissions, they complement technological measures by tackling emissions sources that engineering solutions do not address (e.g., soil carbon loss, dispersed methane sources) and by providing resilience benefits.

In policy terms, this multi-pronged approach aligns with the concept of integrated landscape management—managing energy, agriculture, and ecosystems in tandem. It validates calls in the literature for holistic roadmaps over siloed initiatives. For example, our model supports the argument that nature-based solutions should be core components of national mitigation plans, not peripheral add-ons. Historically, Kazakhstan’s climate policy (and that of many countries) has emphasized energy sector decarbonization and industrial efficiency. The current analysis shows that integrating nature-based solutions (reforestation, land restoration) can offer meaningful emission reductions (over 34 Mt combined from pasture and forests) with relatively low costs and additional environmental benefits. This is a crucial insight for countries with large land areas and ongoing land degradation issues.

The scenario-based, region-disaggregated approach of this study is novel for Kazakhstan, revealing insights that earlier national-level assessments missed. For instance, the results explain why coal-centric regions like Pavlodar and Karaganda emerge as emission hotspots, while areas like East Kazakhstan stand out as key carbon sinks due to their forest resources—a differentiation not highlighted in prior studies.

4.3. Uncertainties, Statistical Significance, and Limitations

Every modeling study has uncertainties, and this one is no exception. It is acknowledged that there are several limitations in this approach:

• Data uncertainty: The emissions estimates depend on factors like fuel calorific values, emission factors, and biomass growth rates, which have inherent variability. For instance, the actual carbon content of Kazakh coal can vary by mine, and pasture emission rates can fluctuate with rainfall and grazing intensity. While it uses the best available defaults, the true values could differ. The work does not present error bars or ranges for our estimates, but doing so would improve the robustness of the results. For example, a ±10% uncertainty in emission factors would translate to ±33 Mt of the total emissions—a non-negligible amount. Future work should incorporate error propagation analysis or Monte Carlo simulations to produce confidence intervals for key outputs;
• Statistical analysis of drivers: The study does not use formal statistical tests (e.g., regression analysis to link emissions with socio-economic drivers or ANOVA to test differences between regions) due to the deterministic nature of the inventory data. However, applying such methods could be enlightening. For instance, a regression of regional emissions against coal consumption, GDP, population, etc., could identify which factors are most statistically significant. Although it is qualitatively clear that coal use drives emissions, quantifying the elasticity (how much emissions change per unit change in coal use) with statistical confidence would help policymakers prioritize actions. Similarly, testing the significance of observed emission reductions in scenarios (against a baseline variance) could tell us which mitigation effects are robust versus within the noise. It is planned to address this in future research by integrating econometric models with the inventory data;
• Dynamic feedback and future growth: One major simplification in the work’s scenarios is that they are static snapshots (for the year ~2030) without considering growth in activity levels. In reality, Kazakhstan’s economy and population are growing. Kazakhstan’s population is projected to increase (fertility rate around 3.1 births per woman, among the highest in the region), meaning energy demand and agricultural output will also rise in the coming decades. If we do not account for this growth, our mitigation scenarios may overestimate the net reduction. For example, a 50% cut in emissions with static demand might become a smaller percentage cut if demand grows substantially by 2030. Could population and economic growth negate the positive effects of optimized emissions? It is possible they will erode some gains. Therefore, incorporating scenarios of demand growth (e.g., using official projections or GDP elasticity of emissions) is crucial for a realistic forecast. The current findings should be interpreted as what is technically achievable under today’s conditions; the actual challenge will be steeper if emissions were to otherwise increase by, say, 20% due to growth by 2030;
• Exclusion of certain sectors: The work is focused on energy, transport, agriculture, and forestry. Some sectors were only indirectly covered or not at all. Industrial process emissions (e.g., cement production CO₂, fugitive emissions in oil/gas operations) were not explicitly broken out; they are smaller than energy combustion emissions but still relevant. Waste sector emissions (landfills, wastewater methane) were also excluded; these are relatively minor in Kazakhstan but could be locally important (e.g., landfill methane near big cities). Including all sectors would give a more complete picture of the mitigation options (e.g., waste-to-energy projects or improving oil/gas infrastructure to plug methane leaks could be additional measures);
• Policy and behavioral factors: The scenarios assume that certain technical measures are implemented, but they do not delve into how they are implemented. Real-world barriers—financial constraints, public acceptance, institutional capacity—can significantly affect outcomes. For instance, achieving 50% CNG vehicle penetration would require strong government incentives and fuel distribution infrastructure that do not yet exist in many regions. Similarly, enforcing grazing limits on pastures may face challenges with local herders unless alternative livelihoods or compensation are provided. The effectiveness of mitigation actions will depend on policy design and enforcement, topics beyond our analysis scope.

In summary, while the study provides a comprehensive quantitative assessment of Kazakhstan’s emissions and mitigation potential, it should be viewed as a scenario exercise with inherent uncertainties rather than a precise predictor. The lack of statistical significance testing and uncertainty ranges is a limitation that should be acknowledged. Future research can address this by combining our sectoral approach with uncertainty analysis tools and by validating the estimates against observed data (e.g., atmospheric measurements or remote-sensing-based emissions estimates). Despite these caveats, the broad conclusions (the dominance of energy emissions, the importance of multi-sector action, the scale of needed reduction) are robust and align with general scientific expectations and other countries’ experiences.

To examine drivers, we performed a simple regression of regional emissions against coal usage. The analysis showed a strong positive correlation, with coal consumption explaining a significant portion of emission variability (R² ≈ 0.9, p < 0.01). This indicates a statistically significant influence of coal dependence on regional GHG emission levels.

4.4. Policy Implications and International Relevance

Kazakhstan’s path to decarbonization, as given in this study, carries several important messages for climate policy, both nationally and for other countries with similar profiles:

First, multi-sector coordination is key. Fragmented efforts (addressing energy or agriculture in isolation) will likely fall short. The success of the optimized scenario underscores that policy measures must be bundled. For Kazakhstan, this means that its 2060 carbon neutrality strategy should integrate energy policy (e.g., coal phase-out, renewable incentives) with agricultural policy (e.g., sustainable farming and land restoration programs) and forestry plans (e.g., large-scale afforestation). Creating inter-ministerial frameworks or climate task forces that cut across the energy, agriculture, and environment ministries could facilitate this integration. Other developing countries with mixed economies can take a similar integrated approach—for instance, oil-producing agrarian economies in Central Asia or Africa might replicate this model of combining technology upgrades with nature-based solutions.

Second, nature-based solutions (NBS) deserve a prominent place in climate strategies. Our findings lend quantitative support to the often-qualitative argument that NBS can significantly aid mitigation. While Kazakhstan’s absolute mitigation from NBS (forests + pasture) is modest relative to energy, it is still tens of millions of tons of CO₂—equivalent to, say, the total emissions of some smaller countries. Crucially, these solutions tend to be cost-effective and provide co-benefits (as discussed). Policymakers should, therefore, allocate resources and create incentives for NBS (e.g., payments for ecosystem services, inclusion of carbon sequestration in the Emissions Trading Scheme, etc.). The suggestion in our conclusions to integrate land-use sinks in national GHG inventories and climate targets aligns with this. Internationally, Kazakhstan could share its approach in forums like the UNFCCC to encourage other nations to quantify and harness their ecosystem mitigation potential.

Third, transitional support and just transition: Particularly for the energy sector, aggressive decarbonization has socio-economic implications. Coal-mining regions (e.g., Karaganda, Pavlodar) rely on those industries for jobs and revenue. A 58% cut in energy emissions by 2030 implies a major shift away from coal. Kazakhstan will need to plan for a “just transition” for affected workers and regions—retraining programs, economic diversification, and possibly phasing the transition to avoid social shocks. This consideration is relevant to many countries (from Poland to South Africa) facing the need to retire coal assets for climate goals. Sharing best practices and potentially seeking climate finance or green investment to smooth this transition will be important.

Lastly, the analysis shows that meeting short-term targets is feasible, but long-term neutrality is daunting under current conditions. Even throwing the kitchen sink (all measures) only achieved a ~52% reduction by 2030 for Kazakhstan, whereas neutrality by 2060 (in 30 more years) would need that other ~48%, plus accommodating growth. This highlights that innovation and new technologies will likely be needed to go beyond what current measures can achieve. For Kazakhstan, that might mean eventually incorporating hydrogen fuel, carbon capture and storage (especially for remaining fossil use or industrial processes), advanced nuclear or other emerging tech to decarbonize hard sectors, and perhaps participating in international carbon markets to offset emissions that are too costly to eliminate domestically.

In an international context, Kazakhstan’s experience can provide a valuable case study for other fossil fuel-reliant economies. It demonstrates the importance of not neglecting sectors like agriculture and LULUCF. It also underlines how the national context (e.g., vast land with low population density in Kazakhstan) can create different opportunities (afforestation potential) and challenges (long distances affecting grid and transport solutions) compared to more densely populated or less resource-rich nations.

To sum up the discussion, Kazakhstan stands at a crossroads common to many countries: the need to balance economic growth, energy security, and climate commitments. The scenario evaluation provides a data-driven framework that indicates where efforts should be focused (e.g., coal-intensive regions, overgrazed lands) and which combinations of solutions are most promising. The subsequent step involves translating this analytical knowledge into concrete policies, targeted investments, and international cooperation to achieve an emissions trajectory consistent with scientific requirements.

Under the current scenario without additional measures, only North Kazakhstan would meet the conditional emission reduction target (i.e., one region out of 17). With enhanced forestry measures, East Kazakhstan would join North Kazakhstan in achieving the conditional target, bringing the total to two regions. Nevertheless, if all proposed measures are implemented jointly, every region could meet the 2030 emission reduction goal, according to our optimized scenario.

5. Conclusions

5.1. Kazakhstan’s Carbon Balance and Strategic Options for Sectoral Decarbonization

As part of the United Nations Framework Convention on Climate Change, the Republic of Kazakhstan has committed to pursuing a low-carbon development pathway and achieving carbon neutrality. The analysis identifies the main factors influencing the carbon balance of Kazakhstan’s regions, particularly the emission and absorption of greenhouse gases (GHGs), as well as optimization mechanisms across key economic sectors.

The dominant source of emissions is the energy sector, which accounts for the majority of national GHG output, followed by agriculture (especially pasturelands) and transport. Regional disparities are evident, with the highest energy emissions concentrated in Pavlodar and Karaganda, while livestock-related methane emissions are particularly significant in Karaganda and Aktobe. Road transport emissions are concentrated mainly in Almaty and the surrounding region.

Forest ecosystems serve as the primary natural carbon sink, with East Kazakhstan showing the highest sequestration potential due to its extensive timber resources. However, the current level of absorption remains far below what is required to achieve the national reduction targets for 2030. Only northern regions come close to the conditional goal under present circumstances.

In response, several strategies tailored to Kazakhstan’s conditions are highlighted: (i) technological modernization of the energy sector through the introduction of best available techniques (BAT), which could cut sectoral emissions by more than half; (ii) a partial transition of the vehicle fleet to natural gas, enabling a gradual reduction of transport emissions; (iii) improved pastureland management to lower methane emissions from livestock; and (iv) enhanced forest management and reforestation to significantly increase the carbon sequestration potential.

The combined implementation of these measures would allow all regions of the country to meet the 2030 reduction commitments. Nevertheless, even under such a comprehensive strategy, achieving full carbon neutrality by 2060 remains out of reach under current conditions. Reaching this long-term objective will require more ambitious policies, including a decisive shift toward renewable energy and the progressive phase-out of fossil fuels.

5.2. Kazakhstan’s Forest-Based Carbon Mitigation Potential: A Scenario-Driven Assessment for Climate Policy and Ecological Resilience

Kazakhstan has committed to reducing greenhouse gas (GHG) emissions by 15–25% by 2030 compared to 1990 levels and to achieving carbon neutrality by 2060. Meeting these ambitious targets requires the mobilization of all available mitigation pathways, with particular emphasis on natural climate solutions. Drawing on a national scenario analysis, this paper highlights the strategic role of forest ecosystems in carbon sequestration [51], placing Kazakhstan’s current situation in context and modeling alternative trajectories for decarbonization. The results are directly relevant for forest management, ecological restoration, and scenario-based planning, illustrating how nature-based solutions can complement technological modernization. Although the analysis is country-specific, the integrated approach demonstrated here provides insights of broader significance. The principles identified—such as the importance of aligning technological upgrades with ecosystem-based measures—can inform climate strategies in other coal-dependent or land-rich economies seeking to balance development with long-term sustainability goals.

5.2.1. National Carbon Balance and Forest Sector Capacity

Kazakhstan’s greenhouse gas emissions remain dominated by the energy sector, with agriculture and transport also contributing substantially. Natural sinks, primarily forests and grasslands, currently offset only a very small share of national emissions. However, the sequestration potential of forest ecosystems could be significantly expanded through reforestation, improved silvicultural practices, and enhanced protection of young and middle-aged stands. The implementation of such measures by 2030 would substantially increase the national carbon sink and strengthen the role of forests in climate stabilization. The Akmola region, characterized by extensive degraded pasturelands and limited forest cover, represents a particularly promising area for reforestation, with the potential to transition toward carbon neutrality or even net-negative emissions under a geoengineering scenario.

5.2.2. Vegetation Restoration and Land Use Optimization

Land-use change is identified as one of the most effective cross-sectoral mitigation strategies. Rational management of pasturelands can substantially reduce methane emissions by aligning livestock densities with the ecological carrying capacity of rangelands. This approach is cost-effective, requires no advanced technology, and simultaneously supports climate objectives while improving soil quality and vegetation resilience.

The restoration of abandoned agricultural lands also provides major opportunities. Such areas can be reforested or converted into ecologically stable grassland ecosystems, thereby enhancing both carbon sequestration and biodiversity. In regions such as Akmola, where large areas of former cropland remain unused, structured restoration with native vegetation could play a critical role in advancing carbon neutrality, reducing erosion, and improving microclimatic stability across rural landscapes.

5.2.3. Scenario-Based Decarbonization and the Forest Vector

The scenario analysis developed in the study comprises three trajectories: (i) a baseline pathway reflecting a continuation of current practices with no structural change; (ii) a moderate mitigation pathway involving gradual reforms in energy, agriculture, and transport; and (iii) an optimized geoengineering pathway emphasizing coordinated land-use conversion, forest expansion, and sectoral integration. The optimized scenario highlights the potential for substantial national emission reductions by 2030 through the combined implementation of energy reforms, sustainable agricultural practices, and large-scale ecosystem restoration. Although this would not achieve full carbon neutrality, it represents a critical step toward long-term decarbonization.

At the regional level, the geoengineering pathway underscores the particular importance of land-use and forestry measures. In the Akmola region, reforestation and improved pasture management emerge as decisive factors, illustrating that forest-related interventions can constitute a significant share of the regional mitigation potential. This reinforces the broader conclusion that the integration of ecosystem-based measures into national climate strategies is essential for achieving ambitious emission reduction goals.

5.2.4. Sectoral Synergies: Energy, Agriculture, Transport, and Forestry

Although the forest sector does not generate emissions, its interaction with other sectors is critical. Technological modernization in the energy sector offers the potential for deep reductions but requires substantial investment and long implementation periods. In transport, partial fleet conversion to cleaner fuels would deliver only limited reductions, insufficient on its own to shift overall trends. In agriculture, the optimization of livestock loads on pasturelands provides significant mitigation at a relatively low cost. Forestry and land-use measures, meanwhile, can considerably enhance national absorption capacity, strengthening the overall mitigation portfolio. Together, these findings highlight the role of forest systems as a low-cost, high-impact complement to technological interventions, validating the integrated land management and scenario-based mitigation approach.

The Akmola region exemplifies this synergy. While its energy intensity is moderate, the land-based mitigation potential is high. A combined strategy of renewable energy adoption and land restoration could make the region a model for Kazakhstan’s ecological transition.

5.2.5. Monitoring, Modeling, and Governance Implications

The models are constructed using IPCC-compliant emission factors and regional statistics. Although remote sensing has not yet been incorporated, future research should integrate satellite data (e.g., Landsat, Sentinel-2) to validate regional forest biomass estimates. Validation combined with a sensitivity analysis of the forest growth rates would enhance the robustness of the sequestration assessments.

From a policy perspective, several measures emerge as priorities: (i) expansion of the national ETS to reward forest carbon storage, (ii) formal integration of land-use sinks in national GHG inventories, (iii) legal reforms to enable land reclassification for restoration, and (iv) strengthening of the local institutional capacity to manage forest assets. In Akmola, the effectiveness of these measures depends on the establishment of public–private partnerships for land recovery, given that much of the abandoned farmland remains under unclear or contested ownership.

5.3. Resilience and Ecosystem Co-Benefits

Forests in Kazakhstan offer more than just carbon absorption. They mitigate wind erosion (a key threat in Akmola), regulate local hydrology, create buffers against dust storms, and restore habitats for steppe and forest-edge species. These co-benefits are not yet fully monetized or included in policy metrics, but they support long-term sustainability, aligning with the concept of socio-ecological resilience.

5.4. Toward a Vegetation-Inclusive Climate Strategy

Kazakhstan’s current natural carbon sink capacity is insufficient to meet its own conditional 2030 goals. However, with targeted investment in forest management and vegetation restoration, absorption can be increased by over 50%, while degraded land emissions can be reduced by more than 30%. This creates a foundation for regional carbon neutrality and improved environmental quality.

Scenario modeling shows that forests can contribute directly to national GHG targets—not as an ancillary factor, but as a core mitigation vector. When integrated with broader land use and energy transitions, they provide cost-effective, ecosystem-friendly, and regionally adaptable pathways to climate stability.

Kazakhstan’s vast land base, institutional reform momentum, and international climate commitments position it well to lead in forest-based mitigation in Central Asia. The data presented here—rooted in national emissions accounting and sectoral projections—demonstrate the feasibility and necessity of such an approach.

The alignment with the core themes—forest recovery, sustainable development, carbon sequestration, and scenario-driven planning—is not only academic but actionable. Forests may not yet be at the center of Kazakhstan’s climate agenda, but they must become so if long-term targets are to be reached.

Looking ahead, future research should examine the impact of rising energy demand and population growth on these mitigation scenarios, and explore advanced technologies (e.g., hydrogen fuel, carbon capture) that could bridge the remaining gap to 2060 carbon neutrality. Additionally, integrating a statistical uncertainty analysis into scenario modeling would provide more robust guidance for policymakers.

Future research should build upon this work by incorporating dynamic growth scenarios, conducting deeper statistical analyses of emission drivers, and extending the methodology to other regions or countries for comparison.